Power Generation and Fuel Cycle Technologies – a mini-lecture series with power point presentation and excel project finance models

July 4th, 2017 No Comments   Posted in power generation

Power Generation and Fuel Cycle Technologies – a mini-lecture series with power point presentation and excel project finance models

Your energy technology selection expert is beginning a lecture series on power generation and fuel cycle technologies. This will involve a discussion on the principles of the technology, its history, capital and operating costs, benefits and risks.

Objectives

1) To provide the participants a basic understanding of the following commercially available:

– fuel cycle technologies

– power generation technologies, and

– energy storage technologies

2) To know the basic principles, costs, environmental impact, risks and applicability of each of these technologies, and

3) To present the technology roadmap of each of these technologies to guide us in the near term (next 20 years – up to 2020) and in the long term (next 50 years – up to 2050)

The Past

o Introduction – what-is-electricity

o How is Electricity Generated – generation-of-electricity

o History of Power Generation – history-of-power-generation

o The Complete Electric Power System (base load, intermediate & peaking loads)

The Present

o Commercially Available Fuel Technologies, Power Generation Technologies, and Energy Storage Technologies –

commercially-available-fuel-cycle-technologies

o Primary Energy Sources – primary-energy-sources

o Fuel Properties – fuel-properties

o For the commercially available technologies:

– Basic Principles

– Costs

– Environmental Impact

– Associated Risks

– Applicability

COMMERCIALLY AVAILABLE POWER GENERATION TECHNOLOGIES:

 The Future

o The Technology Roadmap: Vision, Portfolio, Approach, Global Drivers of Change, Cost of Not Yet Commercially Available Technology –

technology-roadmap

o The Near Term Fuel, Power Generation, and Energy Storage Technologies (up to 2020) –

near-term-energy-sources

o The Long Term Fuel, Power Generation, and Energy Storage Technologies (up to 2050) –

long-term-energy-sources

=========

Email me for the power point presentations (in pdf format):

mars_ocampo@yahoo.com

energydataexpert@gmail.com

=========

SUMMARY OF MODEL INPUTS:

Installed capacity:

Unit capacity, MW/unit = 50.00

No. of units = 1

Total installed capacity = 50.00 x 1 = 50.00 MW

Net capacity factor (NCF):

Availability, % of time or days down = 97.08% or 11 days off-line

Load Factor, % of gross capacity = 95.00%

Own Use, % of gross capacity = 10.00%

Net capacity factor target, % = 97.08% x 95.00% x (1 – 10.00%) = 83.00%

Gross generation = 50.00 x (24 x 365) x (97.08% x 95.00%) = 403,933 MWh/year

Net Generation = 50.00 x (24 x 365) x 83.00% = 363,540 MWh/year

All-in Capital and Operating & Maintenance (O&M) costs:

All-in capital cost target, USD/kW = 4,114 (or absolute USD = 4,114 x 50.00 x 1,000)

Fixed O&M cost target, USD/kW/year = 105.63

Variable O&M cost target, USD/MWh = 5.26

G&A cost target, ‘000 USD/year = 10.00

Balance Sheet accounts:

Salvage value = 5% of original value

Days receivable, days = 30

Days payable, days = 30

Days inventory (fuel, lubes, supplies) = 60

Depreciation period (straight line), years = 20

Refurbishment cost (% of EPC as overhaul cost) = 10%

Timing of Refurbishment (year from COD) = 10

Local Component (LC) and Foreign Components (FC):

Target local cost (LC), % of all-in capital cost = 59.2%

Target foreign cost (FC), % of all-in capital cost = 1 – 59.2% = 40.8%

Note: local CAPEX to be funded by local debt

foreign CAPEX to be funded by foreign debt

Local and Foreign Debt:

Local and foreign debt upfront legal & financing fees = 2.00%

Local and foreign commitment fees = 0.50 p.a.

Local and Foreign Grace Period from COD, months = 6

Local and Foreign debt Service Reserve (DSR), months = 6

Local Debt All-in Interest Rate excluding tax =10.00% p.a.

Local Debt Payment Period (from end of GP), years = 10

Foreign Debt All-in Interest Rate excluding tax =10.00% p.a.

Foreign Debt Payment Period (from end of GP), years = 10

Capital structure and target IRR:

Debt ratio target, % of total capital = 70%

Equity ratio target, % of total capital = 1 – 70% = 30%

Target IRR = 16.44% p.a.

Tax Regime:

Income tax holiday (ITH) = 7 years (pay income tax on 8th year)

Income tax rate (after ITH) = 10% of taxable income

Property tax rate (from COD) = 1.5%

Property tax valuation rate (% of NBV) = 80%

Local business tax (% of revenue) = 1.0%

Government share for RE (from COD) = 1.0% of revenues – cost of goods sold

ER 1-94 contribution, PHP/kWh sold = 0.01 (to DOE)

Withholding Tax on Interest (Foreign Currency) – WHT = 10%

Gross Receipts Tax on Interest (Local Currency) – GRT = 5%

Documentary Stamps Tax (DST) = 0.5% (not used)

PEZA incentives (income tax rate from COD) = 5% (if used)

Royalty = 1.5% (if used in mini-hydro)

VAT on importation = 12%

VAT recovery rate = 70%

Timing of VAT recovery (years after COD) = 5

Customs duty = 0%

Flags (Switches):

Biomass Fuel switch (1 = yes, 0 = no) = 1

Type of incentives (1 = NO, 2 = BOI, 3 = PEZA) = 2

Value added tax (0 = NO, 1 VAT) = 0 for renewable energy (RE)

Timing:

Construction period (from FC), months = 24

Operating period (from COD) = 20 years (maximum 30)

Years from base year CPI for CAPEX estimates = 1 (usually zero)

Years from base year CPI for OPEX estimates = 1 (usually zero)

Exchange Rate and Inflation:

Base foreign exchange rate, PHP/USD = 50.00

Forward foreign exchange rate, PHP/USD = 50.00

OPEX inflation (CPI): to model real vs. nominal analysis

Local inflation (CPI) = 0.0% p.a. (real analysis)

Foreign inflation (CPI) = 0.0% p.a. (real analysis)

CAPEX inflation (CPI): to model construction delay

Local inflation (CPI) = 4.0% p.a. (escalation of local CAPEX)

Foreign inflation (CPI) = 2.0% p.a. (escalation of foreign CAPEX)

Power plant footprint:

Plant footprint, hectares = 50.00

Price of land (purchased), PHP/m2 = 28.65 (land is purchased)

Land area (lease), m2 = 500,000

Land lease rate , PHP/m2/year = 0.00 (no land lease)

Fuel properties and cost:

Density of solid fuel, kg/MT = 1,000 (for solid biomass)

Density of liquid fuel, kg/L = 0.966 (for liquid fuel oil or bunker)

Cost of bagasse = 1,988 PHP/MT (at 2,275 kcal/kg) at 30% blend

Cost of rice hull = 1,000 PHP/MT (at 3,150 kcal/kg) at 70% blend

Average cost of solid fuel = 1,299 PHP/MT (biomass)

Average cost of liquid fuel = 34.84 PHP/L (fuel oil)

Average cost of gaseous fuel = 8.628 $/GJ (natural gas)

Average heating value of solid fuel, Btu/lb = 5,198 (biomass)

Average heating value of liquid fuel, Btu/lb = 19,500 (fuel oil)

Average heating value of gaseous fuel, Btu/lb = 22,129 (natural gas)

Power plant thermal efficiency or plant heat rate:

Plant heat rate (at 100% efficiency) = 3,600/1.05506 = 3,412 Btu/kWh

Plant heat rate (Btu of GHV per kWh gross) = 12,186

Target Thermal efficiency = 3,412/12,186 = 28.00%

============

The project finance models are available in Philippine Pesos (PHP) and United States Dollar (USD) and in two versions: deterministic (fixed inputs) and stochastic (random inputs using Monte Carlo Simulation).

Before you can run the MCS model, you need to download first the Monte Carlo Simulation add-in and run it before running the MCS model:

MonteCarlito_v1_10

Here is the complete list of deterministic and stochastic project finance models.

RENEWABLE ENERGY

1) process heat (steam) and power (cogeneration)

ADV Biomass Cogeneration Model3 (demo)

ADV Biomass Cogeneration Model3 (demo) (USD)

ADV Biomass Cogeneration Model3_MCS (demo)

ADV Biomass Cogeneration Model3_MCS (demo) (USD)

2) bagasse, rice husk or wood waste fired boiler steam turbine generator

ADV Biomass Direct Combustion Model3 (demo)

ADV Biomass Direct Combustion Model3 (demo) (USD)

ADV Biomass Direct Combustion Model3_MCS (demo)

ADV Biomass Direct Combustion Model3_MCS (demo) (USD)

3) gasification (thermal conversion in high temperature without oxygen or air

ADV Biomass Gasification Model3 (demo)

ADV Biomass Gasification Model3 (demo) (USD)

ADV Biomass Gasification Model3_MCS (demo)

ADV Biomass Gasification Model3_MCS (demo) (USD)

4) integrated gasification combined cycle (IGCC) technology

ADV Biomass IGCC Model3 (demo)

ADV Biomass IGCC Model3 (demo) (USD)

ADV Biomass IGCC Model3_MCS (demo)

ADV Biomass IGCC Model3_MCS (demo) (USD)

5) waste-to-energy (WTE) technology for municipal solid waste (MSW) disposal and treatment

ADV Biomass WTE Model3 (demo)

ADV Biomass WTE Model3 (demo) (USD)

ADV Biomass WTE Model3_MCS (demo)

ADV Biomass WTE Model3_MCS (demo) (USD)

6) waste-to-energy (WTE) pyrolysis technology

ADV Biomass WTE Model3 – pyrolysis (demo)

ADV Biomass WTE Model3 – pyrolysis (demo) (USD)

ADV Biomass WTE Model3 – pyrolysis_MCS (demo)

ADV Biomass WTE Model3 – pyrolysis_MCS (demo) (USD)

7) run-of-river (mini-hydro) power plant

ADV Mini-Hydro Model3_NIA (demo)

ADV Mini-Hydro Model3_NIA (demo) (USD)

ADV Mini-Hydro Model3_NIA_MCS (demo)

ADV Mini-Hydro Model3_NIA_MCS (demo) (USD)

8) concentrating solar power (CSP) 400 MW

ADV Concentrating Solar Power (CSP) Model3 (demo)

ADV Concentrating Solar Power (CSP) Model3 (demo) (USD)

ADV Concentrating Solar Power (CSP) Model3_MCS (demo)

ADV Concentrating Solar Power (CSP) Model3_MCS (demo) (USD)

9) solar PV technology 1 MW Chinese (roof top BIPV)

ADV Solar PV 1 mw Model3 (demo)

ADV Solar PV 1 mw Model3 (demo) (USD)

ADV Solar PV 1 mw Model3_MCS (demo)

ADV Solar PV 1 mw Model3_MCS (demo) (USD)

10) solar PV technology 25 MW European and Non-Chinese (Korean, Japanese, US) (solar PV farm)

ADV Solar PV 25 mw Model3 (demo)

ADV Solar PV 25 mw Model3 (demo) (USD)

ADV Solar PV 25 mw Model3_MCS (demo)

ADV Solar PV 25 mw Model3_MCS (demo) (USD)

11) includes 81 wind turbine power curves from onshore WTG manufacturers (onshore wind farm)

ADV Wind Onshore Model3 (demo)

ADV Wind Onshore Model3 (demo) (USD)

ADV Wind Onshore Model3_MCS (demo)

ADV Wind Onshore Model3_MCS (demo) (USD)

12) includes 81 wind turbine power curves from offshore WTG manufacturers (offshore wind farm)

ADV Wind Offshore Model3 (demo)

ADV Wind Offshore Model3 (demo) (USD)

ADV Wind Offshore Model3_MCS (demo)

ADV Wind Offshore Model3_MCS (demo) (USD)

13) ocean thermal energy conversion (OTEC) technology 10 MW

ADV Ocean Thermal Model3_10 MW (demo)

ADV Ocean Thermal Model3_10 MW (demo) (USD)

ADV Ocean Thermal Model3_10 MW_MCS (demo)

ADV Ocean Thermal Model3_10 MW_MCS (demo) (USD)

14) ocean thermal energy conversion (OTEC) technology 50 MW

ADV Ocean Thermal Model3_50 MW (demo)

ADV Ocean Thermal Model3_50 MW (demo) (USD)

ADV Ocean Thermal Model3_50 MW_MCS (demo)

ADV Ocean Thermal Model3_50 MW_MCS (demo) (USD)

14) ocean current and tidal technology (30 MW) – this is a similar to an air wind turbine but under water with a turbine propeller (Taiwan has an operating prototype in Kuroshio and PNOC-EC is venturing into ocean current at the Tablas Strait).

ADV Tidal Current Model3_30 MW (demo)

ADV Tidal Current Model3_30 MW (demo) (USD)

ADV Tidal Current Model3_30 MW_MCS (demo)

ADV Tidal Current Model3_30 MW_MCS (demo) (USD)

CONVENTIONAL, FOSSIL AND NUCLEAR ENERGY

1) geothermal power plant 100 MW

ADV Geo Thermal Model3 (demo)

ADV Geo Thermal Model3 (demo) (USD)

ADV Geo Thermal Model3_MCS (demo)

ADV Geo Thermal Model3_MCS (demo) (USD)

2) large hydro power plant 500 MW

ADV Large Hydro Model3 (demo)

ADV Large Hydro Model3 (demo) (USD)

ADV Large Hydro Model3_MCS (demo)

ADV Large Hydro Model3_MCS (demo) (USD)

3) subcritical circulating fluidized bed (CFB) technology 50 MW

ADV Coal-Fired CFB Thermal Model3_50 MW (demo)

ADV Coal-Fired CFB Thermal Model3_50 MW (demo) (USD)

ADV Coal-Fired CFB Thermal Model3_50 MW_MCS (demo)

ADV Coal-Fired CFB Thermal Model3_50 MW_MCS (demo) (USD)

4) subcritical circulating fluidized bed (CFB) technology 135 MW

ADV Coal-Fired CFB Thermal Model3_135 MW (demo)

ADV Coal-Fired CFB Thermal Model3_135 MW (demo) (USD)

ADV Coal-Fired CFB Thermal Model3_135 MW_MCS (demo)

ADV Coal-Fired CFB Thermal Model3_135 MW_MCS (demo) (USD)

5) subcritical pulverized coal (PC) technology 400 MW

ADV Coal-Fired PC Subcritical Thermal Model3 (demo)

ADV Coal-Fired PC Subcritical Thermal Model3 (demo) (USD)

ADV Coal-Fired PC Subcritical Thermal Model3_MCS (demo)

ADV Coal-Fired PC Subcritical Thermal Model3_MCS (demo) (USD)

6) supercritical pulverized coal (PC) technology 500 MW

ADV Coal-Fired PC Supercritical Thermal Model3 (demo)

ADV Coal-Fired PC Supercritical Thermal Model3 (demo) (USD)

ADV Coal-Fired PC Supercritical Thermal Model3_MCS (demo)

ADV Coal-Fired PC Supercritical Thermal Model3_MCS (demo) (USD)

7) ultra-supercritical pulverized coal (PC) technology 650 MW

ADV Coal-Fired PC Ultrasupercritical Thermal Model3 (demo)

ADV Coal-Fired PC Ultrasupercritical Thermal Model3 (demo) (USD)

ADV Coal-Fired PC Ultrasupercritical Thermal Model3_MCS (demo)

ADV Coal-Fired PC Ultrasupercritical Thermal Model3_MCS (demo) (USD)

8) diesel-fueled genset (compression ignition engine) technology 50 MW

ADV Diesel Genset Model3 (demo)

ADV Diesel Genset Model3 (demo) (USD)

ADV Diesel Genset Model3_MCS (demo)

ADV Diesel Genset Model3_MCS (demo) (USD)

9) fuel oil (bunker oil) fired genset (compression ignition engine) technology 100 MW

ADV Fuel Oil Genset Model3 (demo)

ADV Fuel Oil Genset Model3 (demo) (USD)

ADV Fuel Oil Genset Model3_MCS (demo)

ADV Fuel Oil Genset Model3_MCS (demo) (USD)

10) fuel oil (bunker oil) fired oil thermal technology 600 MW

ADV Fuel Oil Thermal Model3 (demo)

ADV Fuel Oil Thermal Model3 (demo) (USD)

ADV Fuel Oil Thermal Model3_MCS (demo)

ADV Fuel Oil Thermal Model3_MCS (demo) (USD)

11) natural gas combined cycle gas turbine (CCGT) 500 MW

ADV Natgas Combined Cycle Model3 (demo)

ADV Natgas Combined Cycle Model3 (demo) (USD)

ADV Natgas Combined Cycle Model3_MCS (demo)

ADV Natgas Combined Cycle Model3_MCS (demo) (USD)

12) natural gas simple cycle (open cycle) gas turbine (OCGT) 70 MW

ADV Natgas Simple Cycle Model3 (demo)

ADV Natgas Simple Cycle Model3 (demo) (USD)

ADV Natgas Simple Cycle Model3_MCS (demo)

ADV Natgas Simple Cycle Model3_MCS (demo) (USD)

13) natural gas thermal 200 MW

ADV Natgas Thermal Model3 (demo)

ADV Natgas Thermal Model3 (demo) (USD)

ADV Natgas Thermal Model3_MCS (demo)

ADV Natgas Thermal Model3_MCS (demo) (USD)

14) petroleum coke (petcoke) fired subcritical thermal 220 MW

ADV Petcoke-Fired PC Subcritical Thermal Model3 (demo)

ADV Petcoke-Fired PC Subcritical Thermal Model3 (demo) (USD)

ADV Petcoke-Fired PC Subcritical Thermal Model3_MCS (demo)

ADV Petcoke-Fired PC Subcritical Thermal Model3_MCS (demo) (USD)

15) nuclear (uranium) pressurized heavy water reactor (PHWR) technology 1330 MW

ADV Nuclear PHWR Model3 (demo)

ADV Nuclear PHWR Model3 (demo) (USD)

ADV Nuclear PHWR Model3_MCS (demo)

ADV Nuclear PHWR Model3_MCS (demo) (USD)

 

WASTE HEAT RECOVERY BOILER (DIESEL genset; GASOLINE genset; PROPANE, LPG or NATURAL GAS simple cycle)

1) combined heat and power (CHP) circulating fluidized bed (CFB) technology 50 MW

ADV Coal-Fired CFB Thermal Model3_50 MW CHP (demo)

ADV Coal-Fired CFB Thermal Model3_50 MW CHP (demo) (USD)

2) diesel genset (diesel, gas oil) and waste heat recovery boiler 3 MW

ADV Diesel Genset and Waste Heat Boiler Model3 (demo)

ADV Diesel Genset and Waste Heat Boiler Model3 (demo) (USD)

3) fuel oil (bunker) genset and waste heat recovery boiler 3 MW

ADV Fuel Oil Genset and Waste Heat Boiler Model3 (demo)

ADV Fuel Oil Genset and Waste Heat Boiler Model3 (demo) (USD)

4) gasoline genset (gasoline, land fill gas) and waste heat recovery boiler 3 MW

ADV Gasoline Genset and Waste Heat Boiler Model3 (demo)

ADV Gasoline Genset and Waste Heat Boiler Model3 (demo) (USD)

5) simple cycle GT (propane, LPG) and waste heat recovery boiler 3 MW (e.g. Capstone)

ADV Propane Simple Cycle and Waste Heat Boiler Model3 (demo)

ADV Propane Simple Cycle and Waste Heat Boiler Model3 (demo) (USD)

6) simple cycle GT (natural gas, land fill gas) and waste heat recovery boiler 3 MW (e.g. Capstone)

ADV Simple Cycle and Waste Heat Boiler Model3 (demo)

ADV Simple Cycle and Waste Heat Boiler Model3 (demo) (USD)

A simple user manual on how to use the deterministic and stochastic project finance models and user license information are found in the files below:

_How to run the Advanced Project Finance Models of OMT (ver 3)

_DISCLAIMER, CONTACT INFORMATION, PAYMENT DETAILS and NON-DISCLOSURE

Our company (OMT Energy Enterprises) can also provide customization services to provide you with power plant project finance models with fixed inputs (deterministic models) as well as random inputs (stochastic models).

If you have an existing model which you want to be audited or upgraded to have stochastic modeling capability, you may also avail of our services at an hourly rate of USD200 per hour for a maximum of 5 hours of charge for customization services.

Use the deterministic model to determine project feasibility, e.g. given first year tariff, determine the equity and project returns (NPV, IRR, PAYBACK), or given the equity or project target returns, determine the first year tariff.

Use the stochastic model to determine project risks during the project development stage. By varying the estimation error on the independent variable (+10% and -10%) and conducting 1,000 random trials, this model will show the upper limit of the estimation error so that the dependent variables will converge to a real value (no error).

A pre-feasibility study has a +/- 15-20% estimation error on the independent variables using rule-of-thumb values.

A detailed feasibility study has a +/- 10-15% estimation error on the independent variables using reasonable estimates guided by internet research on suppliers of equipment.

A final bankable feasibility study has a +/- 5-10% estimation error on the independent variables using EPC contractor and OEM supplier bids.

In the case of fuel oil (bunker) genset, for instance, the estimation error on the independent variables should be less than +3% and -3% so that the dependent variables will converge to a real value.

The model inputs consist of the fixed inputs (independent variables) plus a random component as shown below (based on +/- 10% range, which you can edit in the Sensitivity worksheet):

1) Plant availability factor (% of time) = 94.52% x ( 90% + (110% – 90%) * RAND() )

2) Fuel heating value (GHV) = 5,198 Btu/lb x ( 90% + (110% – 90%) * RAND() )

3) Plant capacity per unit = 12.00 MW/unit x ( 90% + (110% – 90%) * RAND() )

4) Variable O&M cost (at 5.26 $/MWh) = 30.05 $000/MW/year x ( 90% + (110% – 90%) * RAND() )

5) Fixed O&M cost (at 105.63 $/kW/year) = 1,227.64 $000/unit/year x ( 90% + (110% – 90%) * RAND() )

6) Fixed G&A cost = 10.00 $000/year x ( 90% + (110% – 90%) * RAND() )

7) Cost of fuel = 1.299 PHP/kg x ( 90% + (110% – 90%) * RAND() )

8) Plant heat rate = 12,186 Btu/kWh x ( 90% + (110% – 90%) * RAND() )

9) Exchange rate = 43.00 PHP/USD x ( 90% + (110% – 90%) * RAND() )

10) Capital cost = 1,935 $/kW x ( 90% + (110% – 90%) * RAND() )

The dependent variables that will be simulated using Monte Carlo Simulation and which a distribution curve (when you make bold font the number of random trials) may be generated are as follows:

1) Equity Returns (NPV, IRR, PAYBACK) at 30% equity, 70% debt

2) Project Returns (NPV, IRR, PAYBACK) at 100% equity, 0% debt

3) Net Profit After Tax

4) Pre-Tax WACC

5) Electricity Tariff (Feed-in-Tariff)

The models are in Philippine Pesos (PHP) and may be converted to any foreign currency by inputting the appropriate exchange rate (e.g. 1 USD = 1.0000 USD; 1 USD = 50.000 PHP, 1 USD = 3.800 MYR, etc.). Then do a global replacement in all worksheets of ‘PHP’ with ‘XXX’, where ‘XXX’ is the foreign currency of the model.

To purchase, email me at:

energydataexpert@gmail.com

You may pay using PayPal:

energydataexpert@gmail.com

or via bank/wire transfer:

====================

1) Name of Bank Branch & Address:

The Bank of the Philippine Islands (BPI)

Pasig Ortigas Branch

G/F Benpres Building, Exchange Road corner Meralco Avenue

Ortigas Center, PASIG CITY 1605

METRO MANILA, PHILIPPINES

2) Account Name:

Marcial T. Ocampo

3) Account Number:

Current Account = 0205-5062-41

4) SWIFT ID Number = BOPIPHMM

====================

Once I confirm with PayPal or with my BPI current account that the payment has been made, I will then email you the real (un-locked) model to replace the demo model you have downloaded.

Hurry and order now, this offer is only good until January 31, 2018.

Regards,

Your Energy Technology Selection and Project Finance Expert

 

Complete List of Project Finance Models with Tornado Chart (for Sensitivity Analysis)

February 4th, 2018 No Comments   Posted in financial models

Complete List of Project Finance Models with Tornado Chart (for Sensitivity Analysis)

NOTE:

To download this article to view the charts and tables, please click the link below:

Complete List of Project Finance Models with Tornado Chart (Sensitivity Analysis)

The latest project finance modeling tools from you Energy Technology Selection and Project Finance Modeling Expert now includes Sensitivity Analysis using the Tornado Chart (also known as Spider Chart), as shown below:

The above Tornado Chart (graph) was prepared from the data computed by the Tornado Project Finance Model:

Plant Variable (50 MW) Change in IRR per 20% change % Change 16.44%
-10% 0 10% Base value
Electricity Tariff 9.65% 11.80% 16.44% 21.45% 7.364
Plant Availability Factor 7.55% 12.76% 16.44% 20.31% 97.08%
Fuel Heating Value 2.15% 15.27% 16.44% 17.42% 5,198
Debt Ratio 0.72% 16.17% 16.44% 16.89% 70%
Plant Capacity per Unit 1.00% 15.89% 16.44% 16.89% 50.00
O&M Cost (Opex) – variable O&M -0.25% 16.57% 16.44% 16.32% 27.27
O&M Cost (Opex) – fixed O&M -0.94% 16.91% 16.44% 15.97% 5,132.70
O&M Cost (Opex) – fixed G&A 0.00% 16.44% 16.44% 16.44% 10.00
Cost of Fuel -2.13% 17.52% 16.44% 15.39% 1.299
Plant Heat Rate -2.13% 17.52% 16.44% 15.39% 12,186
Exchange Rate -3.64% 18.46% 16.44% 14.82% 50.00
Capital Cost (Capex) -6.40% 20.02% 16.44% 13.62% 1,964.36

 

As shown in the table above, the biomass cogeneration technology is most positively sensitive for electricity tariff at 9.65% change per 20% change in value (from -10% to +10%) when the inputs are changed one at a time as follows. The equity IRR changes from 11.80% to 21.45% when the base case electricity tariff of 7.364 PHP/kWh is changed by -10% to +10%.

The top 4 positively sensitive variables are electricity tariff (9.65%), plant availability factor (7.55%), fuel heating value (2.15%), and plant capacity (1.00%) when such variable is changed from -10% to +10%.

On the other hand, the capital cost is the most negatively sensitive independent variable at -6.40% for a 20% change from the base capital cost (FOB value from OEM) of 1,964.36 USD/kW as it is changed from -10% to +10%.

The top 4 negatively sensitive variables are capital cost (-6.40%), exchange rate (-3.64%), plant heat rate (-2.13%) which is the inverse of plant thermal efficiency, and cost of fuel (-2.13%) when such variable is changed from -10% to +10%.

Plant Variable (50 MW) Spider Chart (press ctrl + p) (0)
-10% 0% 10% Used Current Value
Electricity Tariff 0.9 1 1.1 1 7.364
Plant Availability Factor 0.9 1 1.1 1 97.08%
Fuel Heating Value 0.9 1 1.1 1 5,198
Debt Ratio 0.9 1 1.1 1 70%
Plant Capacity per Unit 0.9 1 1.1 1 50
O&M Cost (Opex) – variable O&M 0.9 1 1.1 1 27.27
O&M Cost (Opex) – fixed O&M 0.9 1 1.1 1 5,132.70
O&M Cost (Opex) – fixed G&A 0.9 1 1.1 1 10.00
Cost of Fuel 0.9 1 1.1 1 1.299
Plant Heat Rate 0.9 1 1.1 1 12,186
Exchange Rate 0.9 1 1.1 1 50.00
Capital Cost (Capex) 0.9 1 1.1 1 1,964.36

 

SUMMARY OF INPUTS:

Installed capacity:

Unit capacity, MW/unit = 50.00

No. of units = 1

Total installed capacity = 50.00 x 1 = 50.00 MW

Net capacity factor (NCF):

Availability, % of time or days down = 97.08% or 11 days off-line

Load Factor, % of gross capacity = 95.00%

Own Use, % of gross capacity = 10.00%

Net capacity factor target, % = 97.08% x 95.00% x (1 – 10.00%) = 83.00%

Gross generation = 50.00 x (24 x 365) x (97.08% x 95.00%) = 403,933 MWh/year

Net Generation = 50.00 x (24 x 365) x 83.00% = 363,540 MWh/year

All-in Capital and Operating & Maintenance (O&M) costs:

All-in capital cost target, USD/kW = 4,114 (or absolute USD = 4,114 x 50.00 x 1,000)

Fixed O&M cost target, USD/kW/year = 105.63

Variable O&M cost target, USD/MWh = 5.26

G&A cost target, ‘000 USD/year = 10.00

Balance Sheet accounts:

Salvage value = 5% of original value

Days receivable, days = 30

Days payable, days = 30

Days inventory (fuel, lubes, supplies) = 60

Depreciation period (straight line), years = 20

Refurbishment cost (% of EPC as overhaul cost) = 10%

Timing of Refurbishment (year from COD) = 10

Local Component (LC) and Foreign Components (FC):

Target local cost (LC), % of all-in capital cost = 59.2%

Target foreign cost (FC), % of all-in capital cost = 1 – 59.2% = 40.8%

Note: local CAPEX to be funded by local debt

foreign CAPEX to be funded by foreign debt

Local and Foreign Debt:

Local and foreign debt upfront legal & financing fees = 2.00%

Local and foreign commitment fees = 0.50 p.a.

Local and Foreign Grace Period from COD, months = 6

Local and Foreign debt Service Reserve (DSR), months = 6

Local Debt All-in Interest Rate excluding tax =10.00% p.a.

Local Debt Payment Period (from end of GP), years = 10

Foreign Debt All-in Interest Rate excluding tax =10.00% p.a.

Foreign Debt Payment Period (from end of GP), years = 10

Capital structure and target IRR:

Debt ratio target, % of total capital = 70%

Equity ratio target, % of total capital = 1 – 70% = 30%

Target IRR = 16.44% p.a.

Tax Regime:

Income tax holiday (ITH) = 7 years (pay income tax on 8th year)

Income tax rate (after ITH) = 10% of taxable income

Property tax rate (from COD) = 1.5%

Property tax valuation rate (% of NBV) = 80%

Local business tax (% of revenue) = 1.0%

Government share for RE (from COD) = 1.0% of revenues – cost of goods sold

ER 1-94 contribution, PHP/kWh sold = 0.01 (to DOE)

Withholding Tax on Interest (Foreign Currency) – WHT = 10%

Gross Receipts Tax on Interest (Local Currency) – GRT = 5%

Documentary Stamps Tax (DST) = 0.5% (not used)

PEZA incentives (income tax rate from COD) = 5% (if used)

Royalty = 1.5% (if used in mini-hydro)

VAT on importation = 12%

VAT recovery rate = 70%

Timing of VAT recovery (years after COD) = 5

Customs duty = 0%

Flags (Switches):

Biomass Fuel switch (1 = yes, 0 = no) = 1

Type of incentives (1 = NO, 2 = BOI, 3 = PEZA) = 2

Value added tax (0 = NO, 1 VAT) = 0 for renewable energy (RE)

Timing:

Construction period (from FC), months = 24

Operating period (from COD) = 20 years (maximum 30)

Years from base year CPI for CAPEX estimates = 1 (usually zero)

Years from base year CPI for OPEX estimates = 1 (usually zero)

Exchange Rate and Inflation:

Base foreign exchange rate, PHP/USD = 50.00

Forward foreign exchange rate, PHP/USD = 50.00

OPEX inflation (CPI): to model real vs. nominal analysis

Local inflation (CPI) = 0.0% p.a. (real analysis)

Foreign inflation (CPI) = 0.0% p.a. (real analysis)

CAPEX inflation (CPI): to model construction delay

Local inflation (CPI) = 4.0% p.a. (escalation of local CAPEX)

Foreign inflation (CPI) = 2.0% p.a. (escalation of foreign CAPEX)

Power plant footprint:

Plant footprint, hectares = 50.00

Price of land (purchased), PHP/m2 = 28.65 (land is purchased)

Land area (lease), m2 = 500,000

Land lease rate , PHP/m2/year = 0.00 (no land lease)

Fuel properties and cost:

Density of solid fuel, kg/MT = 1,000 (for solid biomass)

Density of liquid fuel, kg/L = 0.966 (for liquid fuel oil or bunker)

Cost of bagasse = 1,988 PHP/MT (at 2,275 kcal/kg) at 30% blend

Cost of rice hull = 1,000 PHP/MT (at 3,150 kcal/kg) at 70% blend

Average cost of solid fuel = 1,299 PHP/MT (biomass)

Average cost of liquid fuel = 34.84 PHP/L (fuel oil)

Average cost of gaseous fuel = 8.628 $/GJ (natural gas)

Average heating value of solid fuel, Btu/lb = 5,198 (biomass)

Average heating value of liquid fuel, Btu/lb = 19,500 (fuel oil)

Average heating value of gaseous fuel, Btu/lb = 22,129 (natural gas)

Power plant thermal efficiency or plant heat rate:

Plant heat rate (at 100% efficiency) = 3,600/1.05506 = 3,412 Btu/kWh

Plant heat rate (Btu of GHV per kWh gross) = 12,186

Target Thermal efficiency = 3,412/12,186 = 28.00%

=============================================

Your energy technology selection expert is pleased to announce that deterministic (fixed inputs) and stochastic (random inputs from Monte Carlo Simulation) are now available for all power generation technologies (renewable energy such as biomass, solar PV and CSP, wind, mini-hydro, ocean thermal and ocean tidal/current, and conventional energy such as large hydro, geothermal, and fossil energy such as oil diesel and oil thermal, natural gas simple cycle and combined cycle, coal thermal and clean coal technologies, nuclear energy, and energy storage and waste heat recovery and combined heat and power technologies).

You may download the following samples to try the advanced features of using fixed inputs and random inputs in order to manage your project risks:

Deterministic (fixed inputs) model: (USD 700):

Tornado Chart (-10% to +10% sensitivity on inputs) model: (USD 500):

Stochastic (random inputs from Monte Carlo Simulation) model (USD 1400):

Before you can run the MCS model, you need to download first the Monte Carlo Simulation add-in and run it before running the MCS model:

MonteCarlito_v1_10

Here is the complete list of deterministic and stochastic project finance models.

RENEWABLE ENERGY

1) process heat (steam) and power (cogeneration)

ADV Biomass Cogeneration Model3 (demo)

ADV Biomass Cogeneration Model3 (spider)

ADV Biomass Cogeneration Model3_MCS (demo)

2) bagasse, rice husk or wood waste fired boiler steam turbine generator

ADV Biomass Direct Combustion Model3 (demo)

ADV Biomass Direct Combustion Model3 (spider)

ADV Biomass Direct Combustion Model3_MCS (demo)

3) gasification (thermal conversion in high temperature without oxygen or air

ADV Biomass Gasification Model3 (demo)

ADV Biomass Gasification Model3 (spider)

ADV Biomass Gasification Model3_MCS (demo)

4) integrated gasification combined cycle (IGCC) technology

ADV Biomass IGCC Model3 (demo)

ADV Biomass IGCC Model3 (spider)

ADV Biomass IGCC Model3_MCS (demo)

5) waste-to-energy (WTE) technology for municipal solid waste (MSW) disposal and treatment

ADV Biomass WTE Model3 (demo)

ADV Biomass WTE Model3 (spider)

ADV Biomass WTE Model3_MCS (demo)

6) waste-to-energy (WTE) pyrolysis technology

ADV Biomass WTE Model3 – pyrolysis (demo)

ADV Biomass WTE Model3 – pyrolysis (spider)

ADV Biomass WTE Model3 – pyrolysis_MCS (demo)

7) run-of-river (mini-hydro) power plant

ADV Mini-Hydro Model3_NIA (demo)

ADV Mini-Hydro Model3_NIA (spider)

ADV Mini-Hydro Model3_NIA_MCS (demo)

8) concentrating solar power (CSP) 400 MW

ADV Concentrating Solar Power (CSP) Model3 (demo)

ADV Concentrating Solar Power (CSP) Model3 (spider)

ADV Concentrating Solar Power (CSP) Model3_MCS (demo)

9) solar PV technology 1 MW Chinese (roof top BIPV)

ADV Solar PV 1 mw Model3 (demo)

ADV Solar PV 1 mw Model3 (spider)

ADV Solar PV 1 mw Model3_MCS (demo)

10) solar PV technology 25 MW European and Non-Chinese (Korean, Japanese, US) (solar PV farm)

ADV Solar PV 25 mw Model3 (demo)

ADV Solar PV 25 mw Model3 (spider)

ADV Solar PV 25 mw Model3_MCS (demo)

11) includes 81 wind turbine power curves from onshore WTG manufacturers (onshore wind farm)

ADV Wind Onshore Model3 (demo)

ADV Wind Onshore Model3 (spider)

ADV Wind Onshore Model3_MCS (demo)

12) includes 81 wind turbine power curves from offshore WTG manufacturers (offshore wind farm)

ADV Wind Offshore Model3 (demo)

ADV Wind Offshore Model3 (spider)

ADV Wind Offshore Model3_MCS (demo)

13) ocean thermal energy conversion (OTEC) technology 10 MW

ADV Ocean Thermal Model3_10 MW (demo)

ADV Ocean Thermal Model3_10 MW (spider)

ADV Ocean Thermal Model3_10 MW_MCS (demo)

14) ocean thermal energy conversion (OTEC) technology 50 MW

ADV Ocean Thermal Model3_50 MW (demo)

ADV Ocean Thermal Model3_50 MW (spider)

ADV Ocean Thermal Model3_50 MW_MCS (demo)

14) ocean current and tidal technology (30 MW) – this is a similar to an air wind turbine but under water with a turbine propeller (Taiwan has an operating prototype in Kuroshio and PNOC-EC is venturing into ocean current at the Tablas Strait).

ADV Tidal Current Model3_30 MW (demo)

ADV Tidal Current Model3_30 MW (spider)

ADV Tidal Current Model3_30 MW_MCS (demo)

 

CONVENTIONAL, FOSSIL AND NUCLEAR ENERGY

1) geothermal power plant 100 MW

ADV Geo Thermal Model3 (demo)

ADV Geo Thermal Model3 (spider)

ADV Geo Thermal Model3_MCS (demo)

2) large hydro power plant 500 MW

ADV Large Hydro Model3 (demo)

ADV Large Hydro Model3 (spider)

ADV Large Hydro Model3_MCS (demo)

3) subcritical circulating fluidized bed (CFB) technology 50 MW

ADV Coal-Fired CFB Thermal Model3_50 MW (demo)

ADV Coal-Fired CFB Thermal Model3_50 MW (spider)

ADV Coal-Fired CFB Thermal Model3_50 MW_MCS (demo)

4) subcritical circulating fluidized bed (CFB) technology 135 MW

ADV Coal-Fired CFB Thermal Model3_135 MW (demo)

ADV Coal-Fired CFB Thermal Model3_135 MW (spider)

ADV Coal-Fired CFB Thermal Model3_135 MW_MCS (demo)

5) subcritical pulverized coal (PC) technology 400 MW

ADV Coal-Fired PC Subcritical Thermal Model3 (demo)

ADV Coal-Fired PC Subcritical Thermal Model3 (spider)

ADV Coal-Fired PC Subcritical Thermal Model3_MCS (demo)

6) supercritical pulverized coal (PC) technology 500 MW

ADV Coal-Fired PC Supercritical Thermal Model3 (demo)

ADV Coal-Fired PC Supercritical Thermal Model3 (spider)

ADV Coal-Fired PC Supercritical Thermal Model3_MCS (demo)

7) ultra-supercritical pulverized coal (PC) technology 650 MW

ADV Coal-Fired PC Ultrasupercritical Thermal Model3 (demo)

ADV Coal-Fired PC Ultrasupercritical Thermal Model3 (spider)

ADV Coal-Fired PC Ultrasupercritical Thermal Model3_MCS (demo)

8) diesel-fueled genset (compression ignition engine) technology 50 MW

ADV Diesel Genset Model3 (demo)

ADV Diesel Genset Model3 (spider)

ADV Diesel Genset Model3_MCS (demo)

9) fuel oil (bunker oil) fired genset (compression ignition engine) technology 100 MW

ADV Fuel Oil Genset Model3 (demo)

ADV Fuel Oil Genset Model3 (spider)

ADV Fuel Oil Genset Model3_MCS (demo)

10) fuel oil (bunker oil) fired oil thermal technology 600 MW

ADV Fuel Oil Thermal Model3 (demo)

ADV Fuel Oil Thermal Model3 (spider)

ADV Fuel Oil Thermal Model3_MCS (demo)

11) natural gas combined cycle gas turbine (CCGT) 500 MW

ADV Natgas Combined Cycle Model3 (demo)

ADV Natgas Combined Cycle Model3 (spider)

ADV Natgas Combined Cycle Model3_MCS (demo)

12) natural gas simple cycle (open cycle) gas turbine (OCGT) 70 MW

ADV Natgas Simple Cycle Model3 (demo)

ADV Natgas Simple Cycle Model3 (spider)

ADV Natgas Simple Cycle Model3_MCS (demo)

13) natural gas thermal 200 MW

ADV Natgas Thermal Model3 (demo)

ADV Natgas Thermal Model3 (spider)

ADV Natgas Thermal Model3_MCS (demo)

14) petroleum coke (petcoke) fired subcritical thermal 220 MW

ADV Petcoke-Fired PC Subcritical Thermal Model3 (demo)

ADV Petcoke-Fired PC Subcritical Thermal Model3 (spider)

ADV Petcoke-Fired PC Subcritical Thermal Model3_MCS (demo)

15) nuclear (uranium) pressurized heavy water reactor (PHWR) technology 1330 MW

ADV Nuclear PHWR Model3 (demo)

ADV Nuclear PHWR Model3 (spider)

ADV Nuclear PHWR Model3_MCS (demo)

WASTE HEAT RECOVERY BOILER (DIESEL genset; GASOLINE genset; PROPANE, LPG or NATURAL GAS simple cycle)

1) combined heat and power (CHP) circulating fluidized bed (CFB) technology 50 MW

ADV Coal-Fired CFB Thermal Model3_50 MW CHP (demo)

(Tornado Chart model to follow – please order via email)

2) diesel genset (diesel, gas oil) and waste heat recovery boiler 3 MW

ADV Diesel Genset and Waste Heat Boiler Model3 (demo)

(Tornado Chart model to follow – please order via email)

3) fuel oil (bunker) genset and waste heat recovery boiler 3 MW

ADV Fuel Oil Genset and Waste Heat Boiler Model3 (demo)

(Tornado Chart model to follow – please order via email)

4) gasoline genset (gasoline, land fill gas) and waste heat recovery boiler 3 MW

ADV Gasoline Genset and Waste Heat Boiler Model3 (demo)

(Tornado Chart model to follow – please order via email)

5) simple cycle GT (propane, LPG) and waste heat recovery boiler 3 MW (e.g. Capstone)

ADV Propane Simple Cycle and Waste Heat Boiler Model3 (demo)

(Tornado Chart model to follow – please order via email)

6) simple cycle GT (natural gas, land fill gas) and waste heat recovery boiler 3 MW (e.g. Capstone)

ADV Simple Cycle and Waste Heat Boiler Model3 (demo)

(Tornado Chart model to follow – please order via email)

A simple user manual on how to use the deterministic and stochastic project finance models and user license information are found in the files below:

_How to run the Advanced Project Finance Models of OMT (ver 3)

_DISCLAIMER, CONTACT INFORMATION, PAYMENT DETAILS and NON-DISCLOSURE

Our company (OMT Energy Enterprises) can also provide customization services to provide you with power plant project finance models with fixed inputs (deterministic models) as well as random inputs (stochastic models).

If you have an existing model which you want to be audited or upgraded to have stochastic modeling capability, you may also avail of our services at an hourly rate of USD200 per hour for a maximum of 5 hours of charge for customization services.

Use the deterministic model to determine project feasibility, e.g. given first year tariff, determine the equity and project returns (NPV, IRR, PAYBACK), or given the equity or project target returns, determine the first year tariff.

Use the Tornado Chart model to conduct your sensitivity analysis by varying each independent variable one at a time from -10% to +10% and plot the results like in a Tornado Chart or also known as Spider Chart.

Use the stochastic model to determine project risks during the project development stage. By varying the estimation error on the independent variable (+10% and -10%) and conducting 1,000 random trials, this model will show the upper limit of the estimation error so that the dependent variables will converge to a real value (no error).

A pre-feasibility study has a +/- 15-20% estimation error on the independent variables using rule-of-thumb values.

A detailed feasibility study has a +/- 10-15% estimation error on the independent variables using reasonable estimates guided by internet research on suppliers of equipment.

A final bankable feasibility study has a +/- 5-10% estimation error on the independent variables using EPC contractor and OEM supplier bids.

In the case of fuel oil (bunker) genset, for instance, the estimation error on the independent variables should be less than +3% and -3% so that the dependent variables will converge to a real value.

The model inputs consist of the fixed inputs (independent variables) plus a random component as shown below (based on +/- 10% range, which you can edit in the Sensitivity worksheet):

1) Plant availability factor (% of time) = 94.52% x ( 90% + (110% – 90%) * RAND() )

2) Fuel heating value (GHV) = 5,198 Btu/lb x ( 90% + (110% – 90%) * RAND() )

3) Plant capacity per unit = 12.00 MW/unit x ( 90% + (110% – 90%) * RAND() )

4) Variable O&M cost (at 5.26 $/MWh) = 30.05 $000/MW/year x ( 90% + (110% – 90%) * RAND() )

5) Fixed O&M cost (at 105.63 $/kW/year) = 1,227.64 $000/unit/year x ( 90% + (110% – 90%) * RAND() )

6) Fixed G&A cost = 10.00 $000/year x ( 90% + (110% – 90%) * RAND() )

7) Cost of fuel = 1.299 PHP/kg x ( 90% + (110% – 90%) * RAND() )

8) Plant heat rate = 12,186 Btu/kWh x ( 90% + (110% – 90%) * RAND() )

9) Exchange rate = 43.00 PHP/USD x ( 90% + (110% – 90%) * RAND() )

10) Capital cost = 1,935 $/kW x ( 90% + (110% – 90%) * RAND() )

The dependent variables that will be simulated using Monte Carlo Simulation and which a distribution curve (when you make bold font the number of random trials) may be generated are as follows:

1) Equity Returns (NPV, IRR, PAYBACK) at 30% equity, 70% debt

2) Project Returns (NPV, IRR, PAYBACK) at 100% equity, 0% debt

3) Net Profit After Tax

4) Pre-Tax WACC

5) Electricity Tariff (Feed-in-Tariff)

The models are in Philippine Pesos (PHP) and may be converted to any foreign currency by inputting the appropriate exchange rate (e.g. 1 USD = 1.0000 USD; 1 USD = 50.000 PHP, 1 USD = 3.800 MYR, etc.). Then do a global replacement in all worksheets of ‘PHP’ with ‘XXX’, where ‘XXX’ is the foreign currency of the model.

To purchase, email me at:

energydataexpert@gmail.com

You may pay using PayPal:

energydataexpert@gmail.com

or via bank/wire transfer:

====================

1) Name of Bank Branch & Address:

The Bank of the Philippine Islands (BPI)

Pasig Ortigas Branch

G/F Benpres Building, Exchange Road corner Meralco Avenue

Ortigas Center, PASIG CITY 1605

METRO MANILA, PHILIPPINES

2) Account Name:

Marcial T. Ocampo

3) Account Number:

Current Account = 0205-5062-41

4) SWIFT ID Number = BOPIPHMM

====================

Once I confirm with PayPal or with my BPI current account that the payment has been made, I will then email you the real (un-locked) model to replace the demo model you have downloaded.

Hurry and order now, this offer is only good until January 31, 2018.

Regards,

Your Energy Technology Selection and Project Finance Expert

 

Special Sale on Power Plant Project Finance Models (Deterministic and Stochastic) – Renewable, Conventional, Fossil, Nuclear and Waste Heat Recovery Technologies

January 7th, 2018 No Comments   Posted in financial models

Special Sale on Power Plant Project Finance Models (Deterministic and Stochastic) – Renewable, Conventional, Fossil, Nuclear and Waste Heat Recovery

=============================================

NEWS FLASH JUST NOW.

YOU CAN NOW ORDER AND PURCHASE DETERMINISTIC AND STOCHASTIC (MCS) PROJECT FINANCE MODELS IN UNITED STATES DOLLAR (USD).

HERE ARE SOME EXAMPLE DEMO (LOCKED) MODELS:

ADV Biomass Cogeneration Model3 (demo)

ADV Biomass Cogeneration Model3 (demo) (USD)

ADV Biomass Cogeneration Model3_MCS (demo)

ADV Biomass Cogeneration Model3_MCS (demo) (USD)

ADV Biomass Direct Combustion Model3 (demo)

ADV Biomass Direct Combustion Model3 (demo) (USD)

ADV Biomass Direct Combustion Model3_MCS (demo)

ADV Biomass Direct Combustion Model3_MCS (demo) (USD)

FOR OTHER POWER GENERATION TECHNOLOGIES, YOU MAY ORDER AND PURCHASE BY EMAIL AT:

energydataexpert@gmail.com

AND SPECIFY YOUR TYPE OF MODEL. YOU MAY ALSO INCLUDE IN YOUR EMAIL YOUR SAMPLE INPUTS SO I CAN IMMEDIATELY CUSTOMIZE YOUR MODEL FOR FREE.

Installed capacity:

Unit capacity, MW/unit = 50.00

No. of units = 1

Total installed capacity = 50.00 x 1 = 50.00 MW

Net capacity factor (NCF):

Availability, % of time or days down = 97.08% or 11 days off-line

Load Factor, % of gross capacity = 95.00%

Own Use, % of gross capacity = 10.00%

Net capacity factor target, % = 97.08% x 95.00% x (1 – 10.00%) = 83.00%

Gross generation = 50.00 x (24 x 365) x (97.08% x 95.00%) = 403,933 MWh/year

Net Generation = 50.00 x (24 x 365) x 83.00% = 363,540 MWh/year

All-in Capital and Operating & Maintenance (O&M) costs:

All-in capital cost target, USD/kW = 4,114 (or absolute USD = 4,114 x 50.00 x 1,000)

Fixed O&M cost target, USD/kW/year = 105.63

Variable O&M cost target, USD/MWh = 5.26

G&A cost target, ‘000 USD/year = 10.00

Balance Sheet accounts:

Salvage value = 5% of original value

Days receivable, days = 30

Days payable, days = 30

Days inventory (fuel, lubes, supplies) = 60

Depreciation period (straight line), years = 20

Refurbishment cost (% of EPC as overhaul cost) = 10%

Timing of Refurbishment (year from COD) = 10

Local Component (LC) and Foreign Components (FC):

Target local cost (LC), % of all-in capital cost = 59.2%

Target foreign cost (FC), % of all-in capital cost = 1 – 59.2% = 40.8%

Note: local CAPEX to be funded by local debt

foreign CAPEX to be funded by foreign debt

Local and Foreign Debt:

Local and foreign debt upfront legal & financing fees = 2.00%

Local and foreign commitment fees = 0.50 p.a.

Local and Foreign Grace Period from COD, months = 6

Local and Foreign debt Service Reserve (DSR), months = 6

Local Debt All-in Interest Rate excluding tax =10.00% p.a.

Local Debt Payment Period (from end of GP), years = 10

Foreign Debt All-in Interest Rate excluding tax =10.00% p.a.

Foreign Debt Payment Period (from end of GP), years = 10

Capital structure and target IRR:

Debt ratio target, % of total capital = 70%

Equity ratio target, % of total capital = 1 – 70% = 30%

Target IRR = 16.44% p.a.

Tax Regime:

Income tax holiday (ITH) = 7 years (pay income tax on 8th year)

Income tax rate (after ITH) = 10% of taxable income

Property tax rate (from COD) = 1.5%

Property tax valuation rate (% of NBV) = 80%

Local business tax (% of revenue) = 1.0%

Government share for RE (from COD) = 1.0% of revenues – cost of goods sold

ER 1-94 contribution, PHP/kWh sold = 0.01 (to DOE)

Withholding Tax on Interest (Foreign Currency) – WHT = 10%

Gross Receipts Tax on Interest (Local Currency) – GRT = 5%

Documentary Stamps Tax (DST) = 0.5% (not used)

PEZA incentives (income tax rate from COD) = 5% (if used)

Royalty = 1.5% (if used in mini-hydro)

VAT on importation = 12%

VAT recovery rate = 70%

Timing of VAT recovery (years after COD) = 5

Customs duty = 0%

Flags (Switches):

Biomass Fuel switch (1 = yes, 0 = no) = 1

Type of incentives (1 = NO, 2 = BOI, 3 = PEZA) = 2

Value added tax (0 = NO, 1 VAT) = 0 for renewable energy (RE)

Timing:

Construction period (from FC), months = 24

Operating period (from COD) = 20 years (maximum 30)

Years from base year CPI for CAPEX estimates = 1 (usually zero)

Years from base year CPI for OPEX estimates = 1 (usually zero)

Exchange Rate and Inflation:

Base foreign exchange rate, PHP/USD = 50.00

Forward foreign exchange rate, PHP/USD = 50.00

OPEX inflation (CPI): to model real vs. nominal analysis

Local inflation (CPI) = 0.0% p.a. (real analysis)

Foreign inflation (CPI) = 0.0% p.a. (real analysis)

CAPEX inflation (CPI): to model construction delay

Local inflation (CPI) = 4.0% p.a. (escalation of local CAPEX)

Foreign inflation (CPI) = 2.0% p.a. (escalation of foreign CAPEX)

Power plant footprint:

Plant footprint, hectares = 50.00

Price of land (purchased), PHP/m2 = 28.65 (land is purchased)

Land area (lease), m2 = 500,000

Land lease rate , PHP/m2/year = 0.00 (no land lease)

Fuel properties and cost:

Density of solid fuel, kg/MT = 1,000 (for solid biomass)

Density of liquid fuel, kg/L = 0.966 (for liquid fuel oil or bunker)

Cost of bagasse = 1,988 PHP/MT (at 2,275 kcal/kg) at 30% blend

Cost of rice hull = 1,000 PHP/MT (at 3,150 kcal/kg) at 70% blend

Average cost of solid fuel = 1,299 PHP/MT (biomass)

Average cost of liquid fuel = 34.84 PHP/L (fuel oil)

Average cost of gaseous fuel = 8.628 $/GJ (natural gas)

Average heating value of solid fuel, Btu/lb = 5,198 (biomass)

Average heating value of liquid fuel, Btu/lb = 19,500 (fuel oil)

Average heating value of gaseous fuel, Btu/lb = 22,129 (natural gas)

Power plant thermal efficiency or plant heat rate:

Plant heat rate (at 100% efficiency) = 3,600/1.05506 = 3,412 Btu/kWh

Plant heat rate (Btu of GHV per kWh gross) = 12,186

Target Thermal efficiency = 3,412/12,186 = 28.00%

=============================================

This is a special offer for the entire year of 2018. For the price of a deterministic model, you get a free copy of a stochastic model.

Our company (OMT Energy Enterprises) can also provide customization services to provide you with power plant project finance models with fixed inputs (deterministic models) as well as random inputs (stochastic models).

If you have an existing model which you want to be audited or upgraded to have stochastic modeling capability, you may also avail of our services at an hourly rate of USD200 per hour for a maximum of 5 hours of charge for customization services.

Use the deterministic model to determine project feasibility, e.g. given first year tariff, determine the equity and project returns (NPV, IRR, PAYBACK), or given the equity or project target returns, determine the first year tariff.

Use the stochastic model to determine project risks during the project development stage. By varying the estimation error on the independent variable (+10% and -10%) and conducting 1,000 random trials, this model will show the upper limit of the estimation error so that the dependent variables will converge to a real value (no error).

A pre-feasibility study has a +/- 15-20% estimation error on the independent variables using rule-of-thumb values.

A detailed feasibility study has a +/- 10-15% estimation error on the independent variables using reasonable estimates guided by internet research on suppliers of equipment.

A final bankable feasibility study has a +/- 5-10% estimation error on the independent variables using EPC contractor and OEM supplier bids.

In the case of fuel oil (bunker) genset, for instance, the estimation error on the independent variables should be less than +3% and -3% so that the dependent variables will converge to a real value.

The model inputs consist of the fixed inputs (independent variables) plus a random component as shown below (based on +/- 10% range, which you can edit in the Sensitivity worksheet):

1) Plant availability factor (% of time) = 94.52% x ( 90% + (110% – 90%) * RAND() )

2) Fuel heating value (GHV) = 5,198 Btu/lb x ( 90% + (110% – 90%) * RAND() )

3) Plant capacity per unit = 12.00 MW/unit x ( 90% + (110% – 90%) * RAND() )

4) Variable O&M cost (at 5.26 $/MWh) = 30.05 $000/MW/year x ( 90% + (110% – 90%) * RAND() )

5) Fixed O&M cost (at 105.63 $/kW/year) = 1,227.64 $000/unit/year x ( 90% + (110% – 90%) * RAND() )

6) Fixed G&A cost = 10.00 $000/year x ( 90% + (110% – 90%) * RAND() )

7) Cost of fuel = 1.299 PHP/kg x ( 90% + (110% – 90%) * RAND() )

8) Plant heat rate = 12,186 Btu/kWh x ( 90% + (110% – 90%) * RAND() )

9) Exchange rate = 43.00 PHP/USD x ( 90% + (110% – 90%) * RAND() )

10) Capital cost = 1,935 $/kW x ( 90% + (110% – 90%) * RAND() )

The dependent variables that will be simulated using Monte Carlo Simulation and which a distribution curve (when you make bold font the number of random trials) may be generated are as follows:

1) Equity Returns (NPV, IRR, PAYBACK) at 30% equity, 70% debt

2) Project Returns (NPV, IRR, PAYBACK) at 100% equity, 0% debt

3) Net Profit After Tax

4) Pre-Tax WACC

5) Electricity Tariff (Feed-in-Tariff)

The following deterministic (fixed inputs) and stochastic (random inputs using Monte Carlo Simulation) models may be downloaded for only USD1,400.

Before you can run the MCS model, you need to download first the Monte Carlo Simulation add-in and run it before running the MCS model:

MonteCarlito_v1_10

The models for renewable, conventional, fossil, nuclear, energy storage, and combined heat and power (CHP) project finance models are based on a single template so that you can prioritize which power generation technology to apply in a given application for more detailed design and economic study.

The models below are in Philippine Pesos (PHP) and may be converted to any foreign currency by inputting the appropriate exchange rate (e.g. 1 USD = 1.0000 USD; 1 USD = 50.000 PHP, 1 USD = 3.800 MYR, etc.). Then do a global replacement in all worksheets of ‘PHP’ with ‘XXX’, where ‘XXX’ is the foreign currency of the model.

RENEWABLE ENERGY

process heat (steam) and power

http://energydataexpert.com/shop/power-generation-technologies/advanced-biomass-cogeneration-project-finance-model-ver-3/

bagasse, rice husk or wood waste fired boiler steam turbine generator

http://energydataexpert.com/shop/power-generation-technologies/advanced-biomass-direct-combustion-project-finance-model-ver-3/

gasification (thermal conversion in high temperature without oxygen or air)

http://energydataexpert.com/shop/power-generation-technologies/advanced-biomass-gasification-project-finance-model-ver-3/

integrated gasification combined cycle (IGCC) technology

http://energydataexpert.com/shop/power-generation-technologies/advanced-biomass-igcc-project-finance-model-ver-3/

waste-to-energy (WTE) technology for municipal solid waste (MSW) disposal and treatment

http://energydataexpert.com/shop/power-generation-technologies/advanced-biomass-waste-to-energy-wte-project-finance-model-ver-3-2/

waste-to-energy (WTE) pyrolysis technology

http://energydataexpert.com/shop/power-generation-technologies/advanced-biomass-waste-to-energy-wte-pyrolysis-project-finance-model-ver-3/

run-of-river (mini-hydro) power plant

http://energydataexpert.com/shop/power-generation-technologies/advanced-mini-hydro-run-of-river-project-finance-model-ver-3/

concentrating solar power (CSP) 400 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-concentrating-solar-power-csp-project-finance-model-ver-3/

solar PV technology 1 MW Chinese

http://energydataexpert.com/shop/power-generation-technologies/advanced-solar-photo-voltaic-pv-project-finance-model-ver-3-1-mw/

solar PV technology 25 MW European and Non-Chinese (Korean, Japanese, US)

http://energydataexpert.com/shop/power-generation-technologies/advanced-solar-photo-voltaic-pv-project-finance-model-ver-3-25-mw/

includes 81 wind turbine power curves from onshore WTG manufacturers

http://energydataexpert.com/shop/power-generation-technologies/advanced-onshore-wind-energy-project-finance-model-ver-3-copy/

includes 81 wind turbine power curves from offshore WTG manufacturers

http://energydataexpert.com/shop/power-generation-technologies/advanced-offshore-wind-project-finance-model-ver-3/

ocean thermal energy conversion (OTEC) technology 10 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-ocean-thermal-energy-conversion-otec-10-mw-project-finance-model-ver-3/

ocean thermal energy conversion (OTEC) technology 50 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-ocean-thermal-energy-conversion-otec-project-finance-model-ver-3-50-mw/

CONVENTIONAL, FOSSIL AND NUCLEAR ENERGY

geothermal power plant 100 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-geo-thermal-project-finance-model-ver-3/

large hydro power plant 500 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-large-hydro-impoundment-project-finance-model-ver-3/

subcritical circulating fluidized bed (CFB) technology 50 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-coal-fired-circulating-fluidized-cfb-project-finance-model-ver-3-50-mw/

subcritical circulating fluidized bed (CFB) technology 135 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-coal-fired-circulating-fluidized-bed-cfb-project-finance-model-ver-3-135-mw/

subcritical pulverized coal (PC) technology 400 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-pulverized-coal-pc-subcritical-project-finance-model-ver-3/

supercritical pulverized coal (PC) technology 500 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-pulverized-coal-pc-supercritical-project-finance-model-ver-3/

ultra-supercritical pulverized coal (PC) technology 650 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-pulverized-coal-pc-ultrasupercritical-project-finance-model-ver-3/

diesel-fueled genset (compression ignition engine) technology 50 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-diesel-genset-project-finance-model-ver-3-copy/

fuel oil (bunker oil) fired genset (compression ignition engine) technology 100 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-fuel-oil-genset-project-finance-model-ver-3-copy-2/

fuel oil (bunker oil) fired oil thermal technology 600 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-fuel-oil-thermal-project-finance-model-ver-3/

natural gas combined cycle gas turbine (CCGT) 500 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-natgas-fired-combined-cycle-gas-turbine-ccgt-project-finance-model-ver-3/

natural gas simple cycle (open cycle) gas turbine (OCGT) 70 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-natgas-fired-open-cycle-gas-turbine-ocgt-project-finance-model-ver-3/

natural gas thermal 200 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-natgas-fired-thermal-project-finance-model-ver-3/

petroleum coke (petcoke) fired subcritical thermal 220 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-petcoke-thermal-power-plant-project-finance-model-ver-3/

nuclear (uranium) pressurized heavy water reactor (PHWR) technology 1330 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-nuclear-power-phwr-project-finance-model-ver-3/

WASTE HEAT RECOVERY BOILER (DIESEL genset; GASOLINE genset; PROPANE, LPG or NATURAL GAS simple cycle)

combined heat and power (CHP) circulating fluidized bed (CFB) technology 50 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-coal-fired-cfb-combined-heat-and-power-chp-project-finance-model-ver-3/

diesel genset (diesel, gas oil) and waste heat recovery boiler 3 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-diesel-fired-genset-combined-heat-and-power-chp-project-finance-model-ver-3/

fuel oil (bunker) genset and waste heat recovery boiler 3 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-bunker-fired-genset-combined-heat-and-power-chp-project-finance-model-ver-3/

gasoline genset (gasoline, land fill gas) and waste heat recovery boiler 3 MW

http://energydataexpert.com/shop/power-generation-technologies/advanced-gasoline-fired-genset-combined-heat-and-power-chp-project-finance-model-ver-3/

simple cycle GT (propane, LPG) and waste heat recovery boiler 3 MW (e.g. Capstone)

http://energydataexpert.com/shop/power-generation-technologies/advanced-lpg-fired-genset-combined-heat-and-power-chp-project-finance-model-ver-3/

simple cycle GT (natural gas, land fill gas) and waste heat recovery boiler 3 MW (e.g. Capstone)

http://energydataexpert.com/shop/power-generation-technologies/advanced-natgas-fired-genset-combined-heat-and-power-chp-project-finance-model-ver-3/

Cheers,

Your energy technology selection and project finance modeling expert

Complete List of Deterministic and Stochastic Project Finance Models

January 5th, 2018 No Comments   Posted in financial models

Complete List of Deterministic (fixed inputs) and Stochastic (random inputs) Project Finance Models

=============================================

NEWS FLASH JUST NOW.

YOU CAN NOW ORDER AND PURCHASE DETERMINISTIC AND STOCHASTIC (MCS) PROJECT FINANCE MODELS IN UNITED STATES DOLLAR (USD).

HERE ARE SOME EXAMPLE DEMO (LOCKED) MODELS:

ADV Biomass Cogeneration Model3_MCS (demo)

ADV Biomass Cogeneration Model3_MCS (demo) (USD)

ADV Biomass Cogeneration Model3 (demo)

ADV Biomass Cogeneration Model3 (demo) (USD)

ADV Biomass Direct Combustion Model3_MCS (demo)

ADV Biomass Direct Combustion Model3_MCS (demo) (USD)

ADV Biomass Direct Combustion Model3 (demo)

ADV Biomass Direct Combustion Model3 (demo) (USD)

FOR OTHER POWER GENERATION TECHNOLOGIES, YOU MAY ORDER AND PURCHASE BY EMAIL AT:

energydataexpert@gmail.com

AND SPECIFY YOUR TYPE OF MODEL. YOU MAY ALSO INCLUDE IN YOUR EMAIL YOUR SAMPLE INPUTS SO I CAN IMMEDIATELY CUSTOMIZE YOUR MODEL FOR FREE.

Installed capacity:

Unit capacity, MW/unit = 50.00

No. of units = 1

Total installed capacity = 50.00 x 1 = 50.00 MW

Net capacity factor (NCF):

Availability, % of time or days down = 97.08% or 11 days off-line

Load Factor, % of gross capacity = 95.00%

Own Use, % of gross capacity = 10.00%

Net capacity factor target, % = 97.08% x 95.00% x (1 – 10.00%) = 83.00%

Gross generation = 50.00 x (24 x 365) x (97.08% x 95.00%) = 403,933 MWh/year

Net Generation = 50.00 x (24 x 365) x 83.00% = 363,540 MWh/year

All-in Capital and Operating & Maintenance (O&M) costs:

All-in capital cost target, USD/kW = 4,114 (or absolute USD = 4,114 x 50.00 x 1,000)

Fixed O&M cost target, USD/kW/year = 105.63

Variable O&M cost target, USD/MWh = 5.26

G&A cost target, ‘000 USD/year = 10.00

Balance Sheet accounts:

Salvage value = 5% of original value

Days receivable, days = 30

Days payable, days = 30

Days inventory (fuel, lubes, supplies) = 60

Depreciation period (straight line), years = 20

Refurbishment cost (% of EPC as overhaul cost) = 10%

Timing of Refurbishment (year from COD) = 10

Local Component (LC) and Foreign Components (FC):

Target local cost (LC), % of all-in capital cost = 59.2%

Target foreign cost (FC), % of all-in capital cost = 1 – 59.2% = 40.8%

Note: local CAPEX to be funded by local debt

foreign CAPEX to be funded by foreign debt

Local and Foreign Debt:

Local and foreign debt upfront legal & financing fees = 2.00%

Local and foreign commitment fees = 0.50 p.a.

Local and Foreign Grace Period from COD, months = 6

Local and Foreign debt Service Reserve (DSR), months = 6

Local Debt All-in Interest Rate excluding tax =10.00% p.a.

Local Debt Payment Period (from end of GP), years = 10

Foreign Debt All-in Interest Rate excluding tax =10.00% p.a.

Foreign Debt Payment Period (from end of GP), years = 10

Capital structure and target IRR:

Debt ratio target, % of total capital = 70%

Equity ratio target, % of total capital = 1 – 70% = 30%

Target IRR = 16.44% p.a.

Tax Regime:

Income tax holiday (ITH) = 7 years (pay income tax on 8th year)

Income tax rate (after ITH) = 10% of taxable income

Property tax rate (from COD) = 1.5%

Property tax valuation rate (% of NBV) = 80%

Local business tax (% of revenue) = 1.0%

Government share for RE (from COD) = 1.0% of revenues – cost of goods sold

ER 1-94 contribution, PHP/kWh sold = 0.01 (to DOE)

Withholding Tax on Interest (Foreign Currency) – WHT = 10%

Gross Receipts Tax on Interest (Local Currency) – GRT = 5%

Documentary Stamps Tax (DST) = 0.5% (not used)

PEZA incentives (income tax rate from COD) = 5% (if used)

Royalty = 1.5% (if used in mini-hydro)

VAT on importation = 12%

VAT recovery rate = 70%

Timing of VAT recovery (years after COD) = 5

Customs duty = 0%

Flags (Switches):

Biomass Fuel switch (1 = yes, 0 = no) = 1

Type of incentives (1 = NO, 2 = BOI, 3 = PEZA) = 2

Value added tax (0 = NO, 1 VAT) = 0 for renewable energy (RE)

Timing:

Construction period (from FC), months = 24

Operating period (from COD) = 20 years (maximum 30)

Years from base year CPI for CAPEX estimates = 1 (usually zero)

Years from base year CPI for OPEX estimates = 1 (usually zero)

Exchange Rate and Inflation:

Base foreign exchange rate, PHP/USD = 50.00

Forward foreign exchange rate, PHP/USD = 50.00

OPEX inflation (CPI): to model real vs. nominal analysis

Local inflation (CPI) = 0.0% p.a. (real analysis)

Foreign inflation (CPI) = 0.0% p.a. (real analysis)

CAPEX inflation (CPI): to model construction delay

Local inflation (CPI) = 4.0% p.a. (escalation of local CAPEX)

Foreign inflation (CPI) = 2.0% p.a. (escalation of foreign CAPEX)

Power plant footprint:

Plant footprint, hectares = 50.00

Price of land (purchased), PHP/m2 = 28.65 (land is purchased)

Land area (lease), m2 = 500,000

Land lease rate , PHP/m2/year = 0.00 (no land lease)

Fuel properties and cost:

Density of solid fuel, kg/MT = 1,000 (for solid biomass)

Density of liquid fuel, kg/L = 0.966 (for liquid fuel oil or bunker)

Cost of bagasse = 1,988 PHP/MT (at 2,275 kcal/kg) at 30% blend

Cost of rice hull = 1,000 PHP/MT (at 3,150 kcal/kg) at 70% blend

Average cost of solid fuel = 1,299 PHP/MT (biomass)

Average cost of liquid fuel = 34.84 PHP/L (fuel oil)

Average cost of gaseous fuel = 8.628 $/GJ (natural gas)

Average heating value of solid fuel, Btu/lb = 5,198 (biomass)

Average heating value of liquid fuel, Btu/lb = 19,500 (fuel oil)

Average heating value of gaseous fuel, Btu/lb = 22,129 (natural gas)

Power plant thermal efficiency or plant heat rate:

Plant heat rate (at 100% efficiency) = 3,600/1.05506 = 3,412 Btu/kWh

Plant heat rate (Btu of GHV per kWh gross) = 12,186

Target Thermal efficiency = 3,412/12,186 = 28.00%

=============================================

Your energy technology selection expert is pleased to announce that deterministic (fixed inputs) and stochastic (random inputs from Monte Carlo Simulation) are now available for all power generation technologies (renewable energy such as biomass, solar PV and CSP, wind, mini-hydro, ocean thermal and ocean tidal/current, and conventional energy such as large hydro, geothermal, and fossil energy such as oil diesel and oil thermal, natural gas simple cycle and combined cycle, coal thermal and clean coal technologies, nuclear energy, and energy storage and waste heat recovery and combined heat and power technologies).

You may download the following samples to try the advanced features of using fixed inputs and random inputs in order to manage your project risks:

Deterministic (fixed inputs) model: (USD 700):

Stochastic (random inputs from Monte Carlo Simulation) model (USD 1400):

Before you can run the MCS model, you need to download first the Monte Carlo Simulation add-in and run it before running the MCS model:

MonteCarlito_v1_10

Here is the complete list of deterministic and stochastic project finance models.

RENEWABLE ENERGY

1) process heat (steam) and power (cogeneration)

ADV Biomass Cogeneration Model3 (demo)

ADV Biomass Cogeneration Model3 (demo) (USD)

ADV Biomass Cogeneration Model3_MCS (demo)

ADV Biomass Cogeneration Model3_MCS (demo) (USD)

2) bagasse, rice husk or wood waste fired boiler steam turbine generator

ADV Biomass Direct Combustion Model3 (demo)

ADV Biomass Direct Combustion Model3 (demo) (USD)

ADV Biomass Direct Combustion Model3_MCS (demo)

ADV Biomass Direct Combustion Model3_MCS (demo) (USD)

3) gasification (thermal conversion in high temperature without oxygen or air

ADV Biomass Gasification Model3 (demo)

ADV Biomass Gasification Model3 (demo) (USD)

ADV Biomass Gasification Model3_MCS (demo)

ADV Biomass Gasification Model3_MCS (demo) (USD)

4) integrated gasification combined cycle (IGCC) technology

ADV Biomass IGCC Model3 (demo)

ADV Biomass IGCC Model3 (demo) (USD)

ADV Biomass IGCC Model3_MCS (demo)

ADV Biomass IGCC Model3_MCS (demo) (USD)

5) waste-to-energy (WTE) technology for municipal solid waste (MSW) disposal and treatment

ADV Biomass WTE Model3 (demo)

ADV Biomass WTE Model3 (demo) (USD)

ADV Biomass WTE Model3_MCS (demo)

ADV Biomass WTE Model3_MCS (demo) (USD)

6) waste-to-energy (WTE) pyrolysis technology

ADV Biomass WTE Model3 – pyrolysis (demo)

ADV Biomass WTE Model3 – pyrolysis (demo) (USD)

ADV Biomass WTE Model3 – pyrolysis_MCS (demo)

ADV Biomass WTE Model3 – pyrolysis_MCS (demo) (USD)

7) run-of-river (mini-hydro) power plant

ADV Mini-Hydro Model3_NIA (demo)

ADV Mini-Hydro Model3_NIA (demo) (USD)

ADV Mini-Hydro Model3_NIA_MCS (demo)

ADV Mini-Hydro Model3_NIA_MCS (demo) (USD)

8) concentrating solar power (CSP) 400 MW

ADV Concentrating Solar Power (CSP) Model3 (demo)

ADV Concentrating Solar Power (CSP) Model3 (demo) (USD)

ADV Concentrating Solar Power (CSP) Model3_MCS (demo)

ADV Concentrating Solar Power (CSP) Model3_MCS (demo) (USD)

9) solar PV technology 1 MW Chinese (roof top BIPV)

ADV Solar PV 1 mw Model3 (demo)

ADV Solar PV 1 mw Model3 (demo) (USD)

ADV Solar PV 1 mw Model3_MCS (demo)

ADV Solar PV 1 mw Model3_MCS (demo) (USD)

10) solar PV technology 25 MW European and Non-Chinese (Korean, Japanese, US) (solar PV farm)

ADV Solar PV 25 mw Model3 (demo)

ADV Solar PV 25 mw Model3 (demo) (USD)

ADV Solar PV 25 mw Model3_MCS (demo)

ADV Solar PV 25 mw Model3_MCS (demo) (USD)

11) includes 81 wind turbine power curves from onshore WTG manufacturers (onshore wind farm)

ADV Wind Onshore Model3 (demo)

ADV Wind Onshore Model3 (demo) (USD)

ADV Wind Onshore Model3_MCS (demo)

ADV Wind Onshore Model3_MCS (demo) (USD)

12) includes 81 wind turbine power curves from offshore WTG manufacturers (offshore wind farm)

ADV Wind Offshore Model3 (demo)

ADV Wind Offshore Model3 (demo) (USD)

ADV Wind Offshore Model3_MCS (demo)

ADV Wind Offshore Model3_MCS (demo) (USD)

13) ocean thermal energy conversion (OTEC) technology 10 MW

ADV Ocean Thermal Model3_10 MW (demo)

ADV Ocean Thermal Model3_10 MW (demo) (USD)

ADV Ocean Thermal Model3_10 MW_MCS (demo)

ADV Ocean Thermal Model3_10 MW_MCS (demo) (USD)

14) ocean thermal energy conversion (OTEC) technology 50 MW

ADV Ocean Thermal Model3_50 MW (demo)

ADV Ocean Thermal Model3_50 MW (demo) (USD)

ADV Ocean Thermal Model3_50 MW_MCS (demo)

ADV Ocean Thermal Model3_50 MW_MCS (demo) (USD)

14) ocean current and tidal technology (30 MW) – this is a similar to an air wind turbine but under water with a turbine propeller (Taiwan has an operating prototype in Kuroshio and PNOC-EC is venturing into ocean current at the Tablas Strait).

ADV Tidal Current Model3_30 MW (demo)

ADV Tidal Current Model3_30 MW (demo) (USD)

ADV Tidal Current Model3_30 MW_MCS (demo)

ADV Tidal Current Model3_30 MW_MCS (demo) (USD)

CONVENTIONAL, FOSSIL AND NUCLEAR ENERGY

1) geothermal power plant 100 MW

ADV Geo Thermal Model3 (demo)

ADV Geo Thermal Model3 (demo) (USD)

ADV Geo Thermal Model3_MCS (demo)

ADV Geo Thermal Model3_MCS (demo) (USD)

2) large hydro power plant 500 MW

ADV Large Hydro Model3 (demo)

ADV Large Hydro Model3 (demo) (USD)

ADV Large Hydro Model3_MCS (demo)

ADV Large Hydro Model3_MCS (demo) (USD

3) subcritical circulating fluidized bed (CFB) technology 50 MW

ADV Coal-Fired CFB Thermal Model3_50 MW (demo)

ADV Coal-Fired CFB Thermal Model3_50 MW (demo) (USD)

ADV Coal-Fired CFB Thermal Model3_50 MW_MCS (demo)

ADV Coal-Fired CFB Thermal Model3_50 MW_MCS (demo) (USD)

4) subcritical circulating fluidized bed (CFB) technology 135 MW

ADV Coal-Fired CFB Thermal Model3_135 MW (demo)

ADV Coal-Fired CFB Thermal Model3_135 MW (demo) (USD)

ADV Coal-Fired CFB Thermal Model3_135 MW_MCS (demo)

ADV Coal-Fired CFB Thermal Model3_135 MW_MCS (demo) (USD)

5) subcritical pulverized coal (PC) technology 400 MW

ADV Coal-Fired PC Subcritical Thermal Model3 (demo)

ADV Coal-Fired PC Subcritical Thermal Model3 (demo) (USD)

ADV Coal-Fired PC Subcritical Thermal Model3_MCS (demo)

ADV Coal-Fired PC Subcritical Thermal Model3_MCS (demo) (USD)

6) supercritical pulverized coal (PC) technology 500 MW

ADV Coal-Fired PC Supercritical Thermal Model3 (demo)

ADV Coal-Fired PC Supercritical Thermal Model3 (demo) (USD)

ADV Coal-Fired PC Supercritical Thermal Model3_MCS (demo)

ADV Coal-Fired PC Supercritical Thermal Model3_MCS (demo) (USD)

7) ultra-supercritical pulverized coal (PC) technology 650 MW

ADV Coal-Fired PC Ultrasupercritical Thermal Model3 (demo)

ADV Coal-Fired PC Ultrasupercritical Thermal Model3 (demo) (USD)

ADV Coal-Fired PC Ultrasupercritical Thermal Model3_MCS (demo)

ADV Coal-Fired PC Ultrasupercritical Thermal Model3_MCS (demo) (USD)

8) diesel-fueled genset (compression ignition engine) technology 50 MW

ADV Diesel Genset Model3 (demo)

ADV Diesel Genset Model3 (demo) (USD)

ADV Diesel Genset Model3_MCS (demo)

ADV Diesel Genset Model3_MCS (demo) (USD)

9) fuel oil (bunker oil) fired genset (compression ignition engine) technology 100 MW

ADV Fuel Oil Genset Model3 (demo)

ADV Fuel Oil Genset Model3 (demo) (USD)

ADV Fuel Oil Genset Model3_MCS (demo)

ADV Fuel Oil Genset Model3_MCS (demo) (USD)

10) fuel oil (bunker oil) fired oil thermal technology 600 MW

ADV Fuel Oil Thermal Model3 (demo)

ADV Fuel Oil Thermal Model3 (demo) (USD)

ADV Fuel Oil Thermal Model3_MCS (demo)

ADV Fuel Oil Thermal Model3_MCS (demo) (USD)

11) natural gas combined cycle gas turbine (CCGT) 500 MW

ADV Natgas Combined Cycle Model3 (demo)

ADV Natgas Combined Cycle Model3 (demo) (USD)

ADV Natgas Combined Cycle Model3_MCS (demo)

ADV Natgas Combined Cycle Model3_MCS (demo) (USD)

12) natural gas simple cycle (open cycle) gas turbine (OCGT) 70 MW

ADV Natgas Simple Cycle Model3 (demo)

ADV Natgas Simple Cycle Model3 (demo) (USD)

ADV Natgas Simple Cycle Model3_MCS (demo)

ADV Natgas Simple Cycle Model3_MCS (demo) (USD)

13) natural gas thermal 200 MW

ADV Natgas Thermal Model3 (demo)

ADV Natgas Thermal Model3 (demo) (USD)

ADV Natgas Thermal Model3_MCS (demo)

ADV Natgas Thermal Model3_MCS (demo) (USD)

14) petroleum coke (petcoke) fired subcritical thermal 220 MW

ADV Petcoke-Fired PC Subcritical Thermal Model3 (demo)

ADV Petcoke-Fired PC Subcritical Thermal Model3 (demo) (USD)

ADV Petcoke-Fired PC Subcritical Thermal Model3_MCS (demo)

ADV Petcoke-Fired PC Subcritical Thermal Model3_MCS (demo) (USD)

15) nuclear (uranium) pressurized heavy water reactor (PHWR) technology 1330 MW

ADV Nuclear PHWR Model3 (demo)

ADV Nuclear PHWR Model3 (demo) (USD)

ADV Nuclear PHWR Model3_MCS (demo)

ADV Nuclear PHWR Model3_MCS (demo) (USD)

 

WASTE HEAT RECOVERY BOILER (DIESEL genset; GASOLINE genset; PROPANE, LPG or NATURAL GAS simple cycle)

1) combined heat and power (CHP) circulating fluidized bed (CFB) technology 50 MW

ADV Coal-Fired CFB Thermal Model3_50 MW CHP (demo)

ADV Coal-Fired CFB Thermal Model3_50 MW CHP (demo) (USD)

 2) diesel genset (diesel, gas oil) and waste heat recovery boiler 3 MW

ADV Diesel Genset and Waste Heat Boiler Model3 (demo)

ADV Diesel Genset and Waste Heat Boiler Model3 (demo) (USD)

 3) fuel oil (bunker) genset and waste heat recovery boiler 3 MW

ADV Fuel Oil Genset and Waste Heat Boiler Model3 (demo)

ADV Fuel Oil Genset and Waste Heat Boiler Model3 (demo) (USD)

 4) gasoline genset (gasoline, land fill gas) and waste heat recovery boiler 3 MW

ADV Gasoline Genset and Waste Heat Boiler Model3 (demo)

ADV Gasoline Genset and Waste Heat Boiler Model3 (demo) (USD)

 5) simple cycle GT (propane, LPG) and waste heat recovery boiler 3 MW (e.g. Capstone)

ADV Propane Simple Cycle and Waste Heat Boiler Model3 (demo)

ADV Propane Simple Cycle and Waste Heat Boiler Model3 (demo) (USD)

 6) simple cycle GT (natural gas, land fill gas) and waste heat recovery boiler 3 MW (e.g. Capstone)

ADV Simple Cycle and Waste Heat Boiler Model3 (demo)

ADV Simple Cycle and Waste Heat Boiler Model3 (demo) (USD)

A simple user manual on how to use the deterministic and stochastic project finance models and user license information are found in the files below:

_How to run the Advanced Project Finance Models of OMT (ver 3)

_DISCLAIMER, CONTACT INFORMATION, PAYMENT DETAILS and NON-DISCLOSURE

Our company (OMT Energy Enterprises) can also provide customization services to provide you with power plant project finance models with fixed inputs (deterministic models) as well as random inputs (stochastic models).

If you have an existing model which you want to be audited or upgraded to have stochastic modeling capability, you may also avail of our services at an hourly rate of USD200 per hour for a maximum of 5 hours of charge for customization services.

Use the deterministic model to determine project feasibility, e.g. given first year tariff, determine the equity and project returns (NPV, IRR, PAYBACK), or given the equity or project target returns, determine the first year tariff.

Use the stochastic model to determine project risks during the project development stage. By varying the estimation error on the independent variable (+10% and -10%) and conducting 1,000 random trials, this model will show the upper limit of the estimation error so that the dependent variables will converge to a real value (no error).

A pre-feasibility study has a +/- 15-20% estimation error on the independent variables using rule-of-thumb values.

A detailed feasibility study has a +/- 10-15% estimation error on the independent variables using reasonable estimates guided by internet research on suppliers of equipment.

A final bankable feasibility study has a +/- 5-10% estimation error on the independent variables using EPC contractor and OEM supplier bids.

In the case of fuel oil (bunker) genset, for instance, the estimation error on the independent variables should be less than +3% and -3% so that the dependent variables will converge to a real value.

The model inputs consist of the fixed inputs (independent variables) plus a random component as shown below (based on +/- 10% range, which you can edit in the Sensitivity worksheet):

1) Plant availability factor (% of time) = 94.52% x ( 90% + (110% – 90%) * RAND() )

2) Fuel heating value (GHV) = 5,198 Btu/lb x ( 90% + (110% – 90%) * RAND() )

3) Plant capacity per unit = 12.00 MW/unit x ( 90% + (110% – 90%) * RAND() )

4) Variable O&M cost (at 5.26 $/MWh) = 30.05 $000/MW/year x ( 90% + (110% – 90%) * RAND() )

5) Fixed O&M cost (at 105.63 $/kW/year) = 1,227.64 $000/unit/year x ( 90% + (110% – 90%) * RAND() )

6) Fixed G&A cost = 10.00 $000/year x ( 90% + (110% – 90%) * RAND() )

7) Cost of fuel = 1.299 PHP/kg x ( 90% + (110% – 90%) * RAND() )

8) Plant heat rate = 12,186 Btu/kWh x ( 90% + (110% – 90%) * RAND() )

9) Exchange rate = 43.00 PHP/USD x ( 90% + (110% – 90%) * RAND() )

10) Capital cost = 1,935 $/kW x ( 90% + (110% – 90%) * RAND() )

The dependent variables that will be simulated using Monte Carlo Simulation and which a distribution curve (when you make bold font the number of random trials) may be generated are as follows:

1) Equity Returns (NPV, IRR, PAYBACK) at 30% equity, 70% debt

2) Project Returns (NPV, IRR, PAYBACK) at 100% equity, 0% debt

3) Net Profit After Tax

4) Pre-Tax WACC

5) Electricity Tariff (Feed-in-Tariff)

The models are in Philippine Pesos (PHP) and may be converted to any foreign currency by inputting the appropriate exchange rate (e.g. 1 USD = 1.0000 USD; 1 USD = 50.000 PHP, 1 USD = 3.800 MYR, etc.). Then do a global replacement in all worksheets of ‘PHP’ with ‘XXX’, where ‘XXX’ is the foreign currency of the model.

 

To purchase, email me at:

energydataexpert@gmail.com

 

You may pay using PayPal:

energydataexpert@gmail.com

or via bank/wire transfer:

====================

1) Name of Bank Branch & Address:

The Bank of the Philippine Islands (BPI)

Pasig Ortigas Branch

G/F Benpres Building, Exchange Road corner Meralco Avenue

Ortigas Center, PASIG CITY 1605

METRO MANILA, PHILIPPINES

2) Account Name:

Marcial T. Ocampo

3) Account Number:

Current Account = 0205-5062-41

4) SWIFT ID Number = BOPIPHMM

====================

Once I confirm with PayPal or with my BPI current account that the payment has been made, I will then email you the real (un-locked) model to replace the demo model you have downloaded.

Hurry and order now, this offer is only good until January 31, 2018.

Regards,

Your Energy Technology Selection and Project Finance Expert

 

Biomass Direct Combustion (steam boiler + turbine) Project Finance Models (Deterministic and Stochastic)

January 4th, 2018 No Comments   Posted in financial models

Biomass Direct Combustion (steam boiler + turbine) Project Finance Models (Deterministic and Stochastic)

Your energy technology selection expert is pleased to announce that deterministic (fixed inputs) and stochastic (random inputs from Monte Carlo Simulation) are now available for all power generation technologies (renewable energy such as biomass, solar PV and CSP, wind, mini-hydro, ocean thermal and ocean tidal/current, and conventional energy such as large hydro, geothermal, and fossil energy such as oil diesel and oil thermal, natural gas simple cycle and combined cycle, coal thermal and clean coal technologies, nuclear energy, and energy storage and waste heat recovery and combined heat and power technologies).

In the case of biomass direct combustion (steam boiler + turbine), the following samples may be purchased at 50% discount.

You may download the following samples to try the advanced features of using fixed inputs and random inputs in order to manage your project risks:

Deterministic (fixed inputs) model: (USD 700):

ADV Biomass Direct Combustion Model3 (demo)  – in PHP

ADV Biomass Direct Combustion Model3 (demo) (USD)

Stochastic (random inputs from Monte Carlo Simulation) model (USD 1400):

ADV Biomass Direct Combustion Model3_MCS (demo) – in PHP

ADV Biomass Direct Combustion Model3_MCS (demo) (USD)

Before you can run the MCS model, you need to download first the Monte Carlo Simulation add-in and run it before running the above MCS model:

MonteCarlito_v1_10

The model inputs consist of the fixed inputs (independent variables) plus a random component as shown below (based on +/- 10% range, which you can edit in the Sensitivity worksheet):

1) Plant availability factor (% of time) = 94.52% x ( 90% + (110% – 90%) * RAND() )

2) Fuel heating value (GHV) = 5,198 Btu/lb x ( 90% + (110% – 90%) * RAND() )

3) Plant capacity per unit = 12.00 MW/unit x ( 90% + (110% – 90%) * RAND() )

4) Variable O&M cost (at 5.26 $/MWh) = 30.05 $000/MW/year x ( 90% + (110% – 90%) * RAND() )

5) Fixed O&M cost (at 105.63 $/kW/year) = 1,227.64 $000/unit/year x ( 90% + (110% – 90%) * RAND() )

6) Fixed G&A cost = 10.00 $000/year x ( 90% + (110% – 90%) * RAND() )

7) Cost of fuel = 1.299 PHP/kg x ( 90% + (110% – 90%) * RAND() )

8) Plant heat rate = 12,186 Btu/kWh x ( 90% + (110% – 90%) * RAND() )

9) Exchange rate = 43.00 PHP/USD x ( 90% + (110% – 90%) * RAND() )

10) Capital cost = 1,935 $/kW x ( 90% + (110% – 90%) * RAND() )

The dependent variables that will be simulated using Monte Carlo Simulation and which a distribution curve (when you make bold font the number of random trials) may be generated are as follows:

1) Equity Returns (NPV, IRR, PAYBACK) at 30% equity, 70% debt

2) Project Returns (NPV, IRR, PAYBACK) at 100% equity, 0% debt

3) Net Profit After Tax

4) Pre-Tax WACC

5) Electricity Tariff (Feed-in-Tariff)

The models are in Philippine Pesos (PHP) and may be converted to any foreign currency by inputting the appropriate exchange rate (e.g. 1 USD = 1.0000 USD; 1 USD = 50.000 PHP, 1 USD = 3.800 MYR, etc.). Then do a global replacement in all worksheets of ‘PHP’ with ‘XXX’, where ‘XXX’ is the foreign currency of the model.

To purchase, email me at:

energydataexpert@gmail.com

You may pay using PayPal:

energydataexpert@gmail.com

or via bank/wire transfer:

====================

1) Name of Bank Branch & Address:

The Bank of the Philippine Islands (BPI)

Pasig Ortigas Branch

G/F Benpres Building, Exchange Road corner Meralco Avenue

Ortigas Center, PASIG CITY 1605

METRO MANILA, PHILIPPINES

2) Account Name:

Marcial T. Ocampo

3) Account Number:

Current Account = 0205-5062-41

4) SWIFT ID Number = BOPIPHMM

====================

Once I confirm with PayPal or with my BPI current account that the payment has been made, I will then email you the real (un-locked) model to replace the demo model you have downloaded.

Hurry and order now, this offer is only good until January 31, 2018.

Regards,

Your Energy Technology Selection and Project Finance Expert

 

Marcial Ocampo and his Major Achievements in Life

December 30th, 2017 No Comments   Posted in energy technology expert

Marcial Ocampo and his Major Achievements in Life

Marcial obtained his elementary education and graduated as the Grade 6 Valedictorian and continued his high school education at San Sebastian College in Manila and finished Year 4 Salutatorian.

Marcial studied at the University of the Philippines in Diliman Quezon City and finished his B.S. and M.S. Chemical Engineering degrees. He also took the Chemical Engineering Licensure Exam in August 1973 and passed as 2nd Placer with an 87.75% rating. He became a British Council scholar at the University of Leeds, UK, where he finished his M.S. Combustion & Energy and thesis in just one year.

Marcial joined the Department of Energy as a PNOC-PETRON-hire seconded as Section Chief of the Transport, Buildings & Machinery Section under the Conservation Division of DOE and conducted various energy audits of major industries throughout the country. Later on, when the DOE was abolished and replaced by the Ministry of Energy (MOE), Marcial transferred to the Petron Bataan Refinery (PBR) as Computer Systems Group head and Linear Programming (LP) model custodian. He retired from PETRON and then went on to work for PETRONAS Energy Philippines Inc. (PEPI) as EDP & Budget Manager and Executive Director of 50+ staff PCIERD-DOST upon the invitation of the DOST Secretary.

Marcial went into business providing computer hardware and General Ledger (GL) Accounting System that provided real-time transactions, month-to-date and year-to-date Income & Expense Statement, Balance Sheet Statement and Trial Balances which automated the preparation of Financial Reports for submission to SEC and BIR, as well as shareholders of any company. The GL was utilized in a number of lending investor companies that benefited from having a real-time accounting system.

Marcial was a Senior Technical Services Manager at First Gen Corporation where he was introduced to power plant modeling and simulation, and later, into project finance modeling that determines the economic feasibility of power plant projects and alternatives, and to value the privatization price of an asset of NPC for bidding to interested buyers.

He prepared a compendium of all power generation technologies (renewable, conventional, fossil, nuclear, energy storage) in power point presentation format and developed a template project finance model to calculate the first year tariff (or feed-in-tariff in the case of renewable energy), equity and project returns (IRR, NPV, PAYBACK), debt service cover ratio (DSCR), benefit-to-cost ratio (B/C), and other financial ratios to assess financial risks of the project during the planning stage of the project cycle. In addition to this deterministic (fixed) template, he prepared a version with stochastic (probabilistic) analysis using Monte Carlo Simulation (MCS).

The MCS model varied by +/- 10% the independent inputs in a random manner such as electricity tariff, availability factor, fuel heating value, debt ratio, plant capacity, all-in (overnight) capital cost, variable O&M cost, fixed O&M cost, cost of fuel, efficiency or plant heat rate and exchange rate. The MCS dependent output consists of a probabilistic distribution curve of equity and project returns (IRR, NPV, PAYBACK), net profit after tax, pre-tax WACC and electricity tariff (or feed-in-tariff for renewable energy). The shape of the distribution curve and relative position of the average value of the dependent variable is indicative of project risk.

He also prepared a manual on “How to Design a Mini-hydro Power Plant” and developed a model to “Optimize Penstock Diameter given its Thickness, Strength, Diameter, Capital and Operating Costs, Cost of Electricity and Friction Loses”.

Marcial is civic mined and patriotic, and helped the government thru the DOE in the “Crude Oil Price Hike to USD100 per barrel Impact Study in 2008” and the “Oil Price Review Study of 2012” where he developed the Oil Pump Price Calculation Excel Model to predict changes to pump price or absolute pump price given changes in FOB or MOPS import cost, ocean freight and insurance costs, exchange rate, gov’t excise taxes and port charges, brokerage and arrastre charges, VAT on importation activities, oil company margin, pumping and transshipment costs, hauling costs, dealer margin, and VAT on local activities. The pump price model can be downloaded from the DOE Website.

Later on, he was called upon by the DOE to assist in the 2012 Independent Oil Price Committee (IOPRC) review of the reasonableness of oil pump price (absolute and adjustments). His Oil Pump Price Calculation (OPPC) Model was adopted and posted in the DOE website which was later used very recently in the 2016 Oil Price Impact Study of an oil industry sector position paper submitted to the Philippine Congress.

He assisted a foreign consultant prepare a historical analysis of the short-run marginal cost (SRMC = variable O&M cost + fuel cost) and long-run marginal cost (LRMC = annualized capital cost + fixed O&M cost + regulatory cost + SRMC) for all power plants in the country in order to assist a client prepare his competitive bid offers in the Wholesale Electric Spot Market (WESM) as well as prepare their capacity expansion plans.

Marcial also assisted the Philippine Congressional Committee on Dam Safety in improving the Dam Water Release Protocol by providing Dam Water Release Simulation Model to predict dam height (meters) and volumetric release rate (cubic meters per second) every hour of the simulation given the initial dam height and volume, power generation and water discharge, dam strapping table (volume vs. height), rainfall data (mm per hour) and area of the dam watershed and upstream drainage area with rainfall data or equivalent upstream dam release rate. This model answered the question: “How many hours and rate of pre-emptive discharge is necessary to increase a dam’s storage capacity in order to have sufficient space to absorb an incoming storm and thus avoid a catastrophic dam spill that will inundate downstream low land areas”. The model accurately predicted the volumetric release rate at the height of the storm when the dam spilling level was breached. It also recommended how many days and rate of pre-emptive discharge is needed to avoid the dam spill during the height of Typhoon “Ondoy” and “Peping” that inundated the provinces of Pangasinan and Tarlac resulting in PhP 40 billion of damage and lost properties and lives.

He also assisted the economic team that studied the proposed excise tax increase in gasoline, diesel, kerosene, LPG, fuel oil, lubes & greases, and other petroleum products such as waxes & petrolatums to predict the price disturbance to be inputted into the input-output matrix of the Philippine economy to predict impact on GDP, inflation and employment.

Marcial also developed a Delivered Coal Price Calculation (DCPC) Model to calculate the impact of increasing the excise tax on coal from the current level of 10.00 PHP/MT to 300.00 PHP/MT, and to use his template project finance model for coal-fired power plants (PC, CFB, subcritical, super-critical and ultra-super-critical) to determine the impact of the excise tax increase in equity and project returns (IRR, NPV, PAYBACK) or determine the first year tariff to meet target equity and project IRR.

Marcial continued to develop his overall skills in energy & power and became an International Consultant at the United Nations Development Programme (UNDP) and travelled to Jakarta, Beijing, Shanghai, New Delhi and Chennai conducting mid-term and full-term project evaluation of wind diesel hybrid, 3rd generation fuel cell bus, biomass energy and India tea manufacturing.

Later, Marcial applied his energy & power expertise to join Sinclair Knight Mertz (SKM) as Senior Power Generation Engineer as part of the “Energy City”, an On-shore LNG Refrigerated Terminal and Re-gassing Facility project team at Limay, Bataan proposed by Atlantic Gulf & Pacific Company (AG&P). This project has been revived recently by the Araneta energy group.

Marcial then joined the SMC GLOBAL POWER HOLDINGS CORPORATION as Energy & Power Consultant and finished a number of feasibility studies for an industrial park, coal-fired power plant using clean coal technology (CFB) and a coal mine project where he converted the coal-mine production plan into a project finance model to determine the cost of delivered coal to another SMC power plant in Mindanao. He provided in-house financial modeling expertise on solar PV, wind, mini-hydro, large hydro, natural gas-fired CCGT and coal-fired clean coal technology (CFB).

Currently, Marcial is finishing the terminal (final) project evaluation of a UNDP-funded project being implemented by the Philippine Climate Change Commission (CCC) on “Low Emission Capacity Building (LECB) Project for the Philippines”.

Marcial is also developing a 50 MW Grid-Connected Solar PV Power and Energy Storage Project in a 50ha project site in Central Luzon in a vast titled estate to provide alternative income to the land owner. He will be soliciting shortly proposals from interested industrial partners to co-develop the project and said partner will provide the technical and financial know-how that will provide beneficial interest to the land owner by way of land rental and share of net income after tax.

Marcial is now available for new endeavors this coming New Year – January 1, 2018.

Contact Details:

Marcial T. Ocampo

+63-9156067949 (GLOBE mobile)

+63-2-9313713 (PLDT home landline)

mars_ocampo@yahoo.com (email)

energydataexpert@gmail.com (email)

 

Delivered Coal Price Calculation (DCPC) Model – an excel model

December 20th, 2017 No Comments   Posted in coal pricing formula

Delivered Coal Price Calculation (DCPC) Model – an excel model

A logical extension to the oil pump price calculation (OPPC) model is the delivered coal price calculation (DCPC) model developed again by Marcial Ocampo.

The need for such calculation model is to enable the user of the DCPC model to estimate the impact of the revised excise tax on coal (local and imported) which is levied currently at PHP 10.00 per metric ton (MT), and will be progressively increased to PHP 50, then 150 and finally 300 per MT under the new tax reform law (TRAIN) recently passed by both houses of Congress and Senate and signed into law by President DU30.

The excel model was developed by Marcial from his DOE and power industry experience and his excel modeling expertise in doing oil, coal, gas, geothermal, hydro, renewable energy pricing calculations and project finance modeling.

Once the model determines the delivered price of coal, this coal price is then used in the project finance model for a coal-fired power plant using various technologies such as traditional sub-critical pulverized coal (PC) and the cleaner coal technologies such as circulating fluidized bed (CFB), integrated gasification and combined cycle (IGCC). As boiler steam pressure has been raised to increase thermal efficiency, the use of super-critical and ultra-super-critical steam generation technologies have further raised the thermal efficiency of pulverized coal, CFB and IGCC technologies.

Since the excise tax is levied on a metric ton of coal (which includes the pure coal and attendant moisture (water) and ash), the use of local coal with lower heat content (BTU) and higher moisture and ash content as compared with imported coal with higher heat content and lower moisture and ash content will invariably raise the cost of generated electricity because of the effect of lower heat content and lower thermal efficiency when using low BTU coals.

The DCPC model is developed as follows:

Imported Coal Cost:

Description:                                          Indonesian / Baramulti Steam Coal

Tonnage:                                                  22,468.60 MT

Bill of Lading / Airway Bill No.:          113/BM/BJM-PHIL/VIII/2004

Estimated / Actual Date of Arrival:     9/20/2004

Port of Entry:                                      BCFTPP Port

CUSTOMS DUTY:

FOB Value = FOB$ = 60.00 $/MT * 22,468.60 MT = 1,348,116.58 USD

Freight = FRT$ = 15.00 $/MT * 22,468.60 MT = 337,029.15 USD

Insurance = INS$ = 0.50% of FOB = 0.50% * 1,348,116.58 = 6,740.58 USD

Other Charges = OTH$ = 0.00 USD

Dutiable Value in US$ = FOB$ + FRT$ + INS$ + OTH$ = 1,691,886.31 USD

Dutiable Value in PHP = 1,691,886.31 USD * 50.00 PHP/USD = 84,594,315.53 PHP

Customs Duty = 84,594,315.53 PHP * 3.00% = 2,537,829.47 PHP

TAXABLE VALUE:

Dutiable Value (DV) = 84,594,315.53 PHP

Bank Charges (BC) =  0.0585% of Dutiable Value = 49,487.67 PHP

Customs Duty (CD) = 2,537,829.47 PHP

Brokerage Fee (BF) = 0.1408% of Dutiable Value = 124,127.20 PHP

Arrastre Charges (AC) = 66.00 PHP/MT * 22,468.60 MT = 1,482,928.24 PHP

Wharfage Fee (WF) =  40.00 PHP/MT * 22,468.60 MT =  898,744.39 PHP

Customs Docs Stamps (CDS) = 2,120.00 PHP

Import Processing Fee (IPF) = 8,000.00 PHP

Excise Tax (ET) = 10.00 PHP/MT * 22,468.60 MT = 224,686.10 PHP

LANDED COST (LC) = DV + BC + CD + BF + AC + WF+ CDS + IPF + ET

=  89,922,238.59 PHP

VALUE ADDED TAX (VAT) = 12% of LC = 12% * 89,922,238.59 PHP

= 10,790,668.63 PHP

Tax Paid Landed Cost (TPLC) = 89,922,238.59 PHP+ 10,790,668.63 PHP

100,712,907.22 PHP

TPLC (PHP/MT) = 100,712,907.22 PHP / 22,468.60 MT = 4,482.38 PHP/MT

TPLC (USD/MT) = 4,482.38 PHP/MT / 50.00 PHP/MT = 89.65 USD/MT

SUMMARY

Customs Duty (CD) = 2,537,829.47 PHP

Excise Tax (ET) = 224,686.10 PHP

Customs Docs Stamps (CDS) = 2,120.00 PHP

Import Processing Fee (IPF) = 8,000.00 PHP

TOTAL DUTIES & TAXES =  CD + ET + CDS + IPF = 2,772,635.56 PHP

Delivered Price of Coal = 4,482.38 PHP/MT = 89.65 USD/MT

The next step is to correct for variance with agreed heating value, moisture, ash and other important parameters that will affect the coal-fired power generating plant. These price adjustments are included in the coal supply contract to adjust the delivered price of coal and convert it into a PARITY PRICE of a reference coal of given heating value, moisture, ash and other important specs.

You may download the delivered coal price calculation (DPCC) model by clicking this link:

Coal Parity Pricing and Specifications

CONCLUSION:

Since the excise tax is levied on the weight of the whole coal irrespective of its heating value or energy content and other specs such as moisture and ash, the revised excise tax which will reach a maximum rate of 300 PHP/MT will be highly punitive of lower rank coal such as lignite (low BTU, high moisture, high ash) and sub-bituminous coals with moderate BTU, moisture and ash compared to imported coal.

The resulting impact on the price of generated electricity from lower quality domestic coal will be significant compared to that of the better quality imported coal (higher BTU, low moisture, low ash).

Further, using lower quality domestic coal also has a further disadvantage as it has lower thermal efficiency because of its lower heat content and higher moisture and ash, which magnifies further the impact.

The next step is to run the project finance models for each technology (PC, CFB, IGCC using sub-critical, super-critical and ultra-super-critical pressures) to determine the cost of electricity to achieve a given desired equity or project returns (IRR, NPV, PAYBACK).

By comparing the base scenario of existing price of coal with current excise tax rate of 10.00 PHP/MT with the proposed coal tax of 50, 150 and 300 PHP/MT, we can predict the electricity price increase impact for each of the coal-fired power generation technologies currently in use and proposed in the future, and arrive at the average grid price due to higher coal-generated electricity price, and thus the over-all electricity price impact of the excise tax adjustment on domestic and imported coal supplies.

You may run the demo models for subcritical PC, CFB, IGCC and higher pressures (supercritical, ultra-super-critical):

ADV Coal-Fired CFB Thermal Model3_50 MW – demo5b

ADV Coal-Fired CFB Thermal Model3_135 MW – demo5b

ADV Coal-Fired PC Subcritical Thermal Model3 – demo5b

ADV Coal-Fired PC Supercritical Thermal Model3 – demo5b

ADV Coal-Fired PC Ultrasupercritical Thermal Model3 – demo5b

Email me:

energydataexpert@gmail.com

 

Oil Pump Price Calculation (OPPC) Model – an Excel Model

December 17th, 2017 No Comments   Posted in Oil Pricing Formula

Oil Pump Price Calculation (OPPC) Model – an Excel Model

  • Calibrate Model by Calculating % Gross Margin (%GM) from Pump Price Less All Costs:
  • %GM = {[PP – OPSF – TPLC * (1 – % biofuel)] / (1 + VAT2) – [(TS + PL + DE) * (1 – % biofuel) + BF + HF + DM]} / {TPLC * (1 – % biofuel)}
  • Calculate Pump Price (PP) using the % Gross Margin and Other Cost Inputs:
  • PP = TPLC * (1 – % biofuel) + [TPLC * (1 – % biofuel) * %GM + (TS + PL + DE) * (1 – % biofuel) + BF + HF + DM] * (1 + %VAT2) + OPSF

To download excel model and IOPRC 2012 report, click the following link of DOE:

  • Calculation of TPLC and PP
  • MOPS$ = Mean of Platts Singapore (imported cost of fuel)
  • FOB$ = Freight on Board in US$ = MOPS * 300,000
  • FRT$ = Ocean Freight in US$ = FOB$ * 2.00%
  • INS$ = Ocean Insurance in US$ = FOB$ * 4.00%
  • CIF$ = Cargo, Insurance & Freight in US$ = FOB$ + FRT$ + INS$
  • CIF = CIF in Pesos = CIF$ * (FOREX, P/$)
  • CD = Customs Duty = CIF * 3.00% (now zero due to ASEAN AFTA)
  • BF= Brokerage Fee = 5,300 + (CIF – 200,000) * 0.00125
  • BC = Bank Charges = CIF * 0.00125
  • AC = Arrastre Charge (gasoline) = 122 * (0.75 * 158.9868 / 1000) * 300,000
  • AC = Arrastre Charge (diesel) = 122 * (0.80 * 158.9868 / 1000) * 300,000
  • WC = Wharfage Charge (gasoline) = 36.65 * (0.75 * 158.9868 / 1000) * 300,000
  • WC = Wharfage Charge (diesel) = 36.65 * (0.80 * 158.9868 / 1000) * 300,000
  • IPF = Import Processing Fee = 1,000 per import entry
  • CDS = Customs Documentary Stamp = 256 per import entry
  • ET = Excise Tax (gasoline) = 4.35 * 158.9868 * 300,000
  • ET = Excise Tax (diesel) = 1.63 * 158.9868 * 300,000
  • LC = Landed Cost = CIF + CD + BF + BC + AC + WC + IPF + CDS + ET
  • VAT1 (on import) = 10% * Landed Cost (Nov 2005 – Jan 2000)
  •                             = 12% * Landed Cost (Feb 2006 – present)
  • TPLC (Tax Paid Landed Cost) = LC + VAT1 (imports) = LC * (1 + %VAT1)
  • TPLC (P/L) = TPLC / (300,000 * 158.9868)
  • Summary to BOC = CD + IPF + CDS + ET + VAT1
  • Summary to BOC (P/L) = Summary to BOC / (300,000 * 158.9868)
  • OCGM = Oil Company Gross Margin (P/L) = TPLC * (1 – % biofuel) * % gross margin
  • OOCC = Other Oil Company Costs (P/L) = (TS + PL + DE) * (1 – % biofuel) + BF
  • TS = Transshipment = 0.38 P/L (for oil tanker ships and barges)
  • PL = Pipeline = 0.000 P/L (for FPIC)
  • DE = depot = 0.27 P/L (gasoline)
  •                    = 0.28 P/L (diesel)
  • BF = Biofuels = 10% * (P/L of ETHANOL) = 2.63 P/L (gasoline)
  •                        =  2% * (P/L of CME Biodiesel) = 1.28 P/L (diesel)
  • HF = Hauler’s Fee (P/L) = 0.21 P/L (gasoline and diesel)
  • DM = Dealer’s Margin (P/L) = 1.72 (gasoline)
  •                                                 = 1.47 (diesel)
  • TLC = Total Local Costs (P/L) = OCGM + OOCC + HF + DM
  • VAT2 (local costs) = 10% * Total Local Cost (Nov 2005 – Jan 2006)
  •                                = 12% * Total Local Cost (Feb 2006 – present)
  • PP = TPLC * (1 – % biofuel) + [TPLC * (1 – % biofuel) * %GM + (TS + PL + DE) * (1 – % biofuel) + BF + HF + DM] * (1 + %VAT2) + OPSF

The pump price (PP) component called the oil company gross margin is given by:

  • OCGM (oil company gross margin) = TPLC * (1 – % biofuel) * %GM
  •  = fixed O&M + variable O&M + marketing expense + depreciation + profit margin

The OCGM is used to cover the fixed and variable costs of the oil company plus the marketing expenses and depreciation cost of its invested capital assets and provide profit margin that recovers its capital investments and thus determine the IRR of the investment made by the oil company:

  • (Capital Investment) = sum ( profit margin(t) * sales volume(t) / (1 + IRR)^t )
  • Thank You !!!
  • Prepared by:
  • Marcial T. Ocampo
  • TWG Member, IOPRC 2012

 

An Integrated Strategy for Asset Valuation and Disposal of Surplus and Redundant Power Generation Equipment

An Integrated Strategy for Asset Valuation and Disposal of Surplus and Redundant Power Generation Equipment

Mike Craigie

Managing Director

Craigie Engineering Sales & Services Ltd.

SYNOPSIS

This paper outlines the recommended strategy for the valuation, marketing and disposal of surplus power plant.

In addition to assessing the overall extent and varied sources of such available equipment, the paper also looks closely at the various options which a utility can adopt when disposing of such plant, and also looks at the merits and potential difficulties to be considered when investigating the feasibility of adopting all or part of such equipment or plant into a new power project development.

A preliminary equipment/asset valuation guide is also included for discussion. The paper also takes a look at the industry’s changing attitude to the use of such plants, from the point of view of clients, OEM’s, owners and asset disposal managers.

SURPLUS EQUIPMENT:

The availability of ‘surplus’, canceled order, or ‘advanced order’ equipment at attractive cost and immediate delivery, is a worldwide phenomenon which has surprisingly few restrictions on capacity.

From our experiences over the past 20 years or so (while investigating the availability of such equipment), it is rare in fact to enter into discussions with any OEM, utility, major oil company, or large industrial group, and not find someone who does not have, or has had, ‘surplus’ unused equipment available from some project which was canceled, frustrated, or built ‘on spec’ and never found a buyer.

The term “surplus” equipment is most frequently used to avoid the pre-conceptions of some clients (and OEM’s) that what we are offering is basically someone else’s scrap:

Traditionally, up until the past few years at least, most of the leading manufacturers (OEM’s) would only consider offering refurbished equipment of their own manufacture, and even then only when their client could not afford the capital cost of new plant, or they could not convince the client that new equipment was a better option.

Most manufacturers have now dramatically changed their attitude to surplus equipment, with many more OEM’s now even purchasing, refurbishing and selling/renting other OEM’s equipment.  This trend is witnessed by GE’s strategic acquisition of GTS (Greenwich Turbine Services) and UNC-Metcalf, and Stewart & Stevenson (with Pratt & Whitney, Rolls Royce, Solar and now EGT/Ruston overhaul experience/capabilities).

Having now seen the successful implementation of several projects using surplus equipment, even the hardest of attitudes among clients (e.g. in the oil industry and with IPP developers) has changed remarkably and the general market perception is a move toward recycling and re-use wherever possible/practicable.

SURPLUS EQUIPMENT AVAILABILITY

The reasons for such equipment becoming available are varied:

  1. Political or Environmental:
  1. The 2 x 350MW oil-fired units from the ‘Shimaal’ project which were canceled due to Iraq’s excursion into Kuwait.
  2. Many aborted nuclear plants in Germany, Italy, Puerto Rico, Philippines, etc.
  3. 300MW CCGT Power Barges for Pakistan cancelled by new government.
  4. 2 x 110MW hydro/pumped storage plants for Northern Ireland cancelled due to security concerns for the site.
  5. 8 x 1250MW nuclear plant cancelled by TVA/US Government in mid 1980’s

Total estimate:                    20,000MW

  1. Availability of Fuel/Grid Constraints:
  1. The 4 x 660MW coal-fired units canceled by ENEL when their government made a policy decision not to increase the country’s dependence on imported coal.
  2. The 2 x 300MW units in Northern Ireland which have been unused due to their oil-fired design and reduced electrical demand.
  3. The 2 x Frame 9E gas turbines from cancelled re-powering project.
  4. 2 X 9MW diesels built as speculative/’back door’ IPP, with no PPA (Power Purchase Agreement).
  5. 2 x 150MW V94.2 gas turbines which can’t be run due to severe grid constraints.
  6. Several CCGT plants in India (6FA and 9FA) which do not have access to gas

Total estimate:                    20,000MW

  1. Overestimated Load Growth or Demand:
  2. 250MW Marsden B oil-fired power plant in New Zealand, mothballed since 1980.
  3. The 5 x 100MW coal-fired units in RSA which have seen little use due to large nuclear plant and larger coal-fired units running on base load.
  4. Many similar large coal and orimulsion power plants in UK now available as not competitive (under power bid process) with nuclear and cogen/CCGT plants.
  5. The 25MW backpressure steam turbine generator in Eastern Europe never installed due to cheaper power coming on line from adjacent large coal-fired station.
  6. The 400MW coal-fired unit at Salt River in USA on which construction was terminated due to reduced load growth.
  7. The 230MW combined cycle/cogen plant in Wisconsin which was cancelled by WEPCO when their load growth was covered by alternative power sources.
  8. Many thousands of MW of CCGT and open cycle GT plants in Italy, UK, Netherlands, Germany, etc which are now redundant due to reduces energy consumption and move to wind energy.

Total Estimate:   30,000MW

  1. Industrial/IPP’s with Financial Problems
  2. The 3 x 4 MW Centaur gas turbines in chp/cogen application for ceramics factory in Indonesia,
  3. 6FA cogen/CCGT extraction unit in Italy which had steam to paper mill which has now shut down.
  4. 64 MW condensing turbine generator in Eastern Europe from canceled project.
  5. 4 x 12 MW HFO-fired diesel engines from cancelled shipbuilding project
  6. Many paper mill cogeneration applications in UK, Finland, France, Italy, which shut down due to paper mills not being competitive with Far East

Total Estimate:   10,000MW

ADVANTAGES OF SURPLUS PLANT

Availability / Delivery:

This is not only a major factor favoring the use of cancelled-order, advance-order and unused equipment, but in many cases the available used equipment may already be overhauled or removed into storage ready for overhaul and rapid delivery, well in advance of corresponding delivery schedules for equivalent new equipment.

Cost / Economics:

The greatest advantage of utilizing ‘surplus’ equipment is of course usually the capital cost, but this option can not only be most financially advantageous, but also means that the equipment can be commissioned and ‘on line’ generating power (and steam/heat) within a very short period of time, leading to considerable savings in a number of areas:

  1. Construction cost is reduced due to lower overheads during the shorter period,
  2. Interest during construction (IDC) is reduced in direct proportion, and
  3. The developing company’s overheads in an IPP situation are also minimized to the extent that “up-front” profit can be increased by inflating the cost of the installed plant in line with the maximum installed cost which will satisfy the lead financing agency.
  4. In addition to these is the considerable benefit of early revenue.

For example, if one was to place an order on a 4MW cogen plant and wait 12 months for delivery with 6 months to deliver and install, a client purchasing a similar surplus unit with foundation designs and wiring diagrams modified easily to suit their site conditions could have the unit installed and commissioned in 3 – 4 months.

During this advantageous 14 month difference, that same plant could generate power alone worth over US$ 1 Million, (excluding the extra profit from steam sales) at 2.5 cents/kWh, and this is only a 4 Mw plant.

Imagine then the comparative savings in having a 300MW CCGT plant on line 14 months or more ahead of schedule. (US$ 75 Million in earned revenue using the same 2.5 cents/kWh)

Note:  Most modern turbine packages (e.g. Frame 6, Taurus or W251) are either 50 or 60 Hz machines with only a gearbox alteration required.  In fact the 60 Hz alternators at 13,800 V (1800 or 3600 RPM) are the same as used in the 50 Hz machines and re-adjusted on the voltage regulators to give 11,000 V at the relevant 50 Hz speeds (1500 or 3000 RPM)

Retained Equity

The other significant, and possibly the most important feature of utilizing such immediately available and ‘surplus equipment’ is that the owners will often be willing to retain part equity in any viable IPP development, thereby making overall project finance more accessible.

It is of course more attractive from their point of view to take a steady return on a retained equity/investment on the plant over several years, rather than continue to absorb the often substantial costs of storing the completed equipment at the OEM’s (original equipment manufacturers) factory and see its residual or resale value diminish at an even more alarming rate.

Valuation of the available plant:

At this stage it may be worth making a brief study of the likely cost or value of such surplus equipment. – Refer to Graph A

Firstly, let’s look at a typical depreciation in any type of power plant (diesel, gas or steam turbine) and the value of regular major overhauls and “zero hour” overhauls – Graph A.

Secondly, if we make the assumption (as most accountants would do) that straight-line depreciation of power plant takes place over 10, 15 or even 25 years.

From our own past experience and our ongoing involvement in the valuation, marketing, and sourcing of suitable surplus equipment, we have found it best (i.e. closest match), in the case of gas turbines particularly, to assume the designed 20 year life span of the equipment.

“Negative Equity” – Refer to Graph B

Obviously, the recent and substantial reductions in the delivered cost of new equipment have had a significant impact on the inherent value of both used and unused power plant. (e.g. Frame 6 units sold for US$ 10-11 Million 7-8 years ago, then dropped to US$ 7-8 M with over-supply 3-4 years ago, and now are listed (GTW Handbook 2001-2) at around US$ 13 Million.

This has given rise to the most unlikely scenario about 4 years ago, where the equipment value (in book terms), which an owner believed his equipment was worth, was substantially more than the real cost of similar/identical replacement units.

Aero-derivative Gas Turbines – Graph C

With this in mind we would note the anticipated selling price (FOB) for a 15 year old Centaur T4000, in operating condition, with basic/operational spare parts and full maintenance history, recent overhaul, and all ancillary equipment (coolers, inlet/exhaust, etc.), of around US$ 550,000.

Industrial Gas Turbines – Refer to Graph D

Here we have chosen to highlight the estimated cost for a 10 year old GE Frame 6 (38 Mw), again delivered FOB, with operational spares, auxiliaries, recent overhaul, and full maintenance history, at around US$ 6.5 M.

Proven Reliability/Availability

With most equipment, which has already been installed and operated, a full maintenance and operational history is usually available.

Technical Service Bulletins will also be available, highlighting the changes in maintenance and operating procedures, which have been recommended over the years for best performance; based on operating experiences within not only the existing plant but all other similar plants worldwide.  User symposiums will also have identified specific areas for concern and a wealth of historical documentation can usually be easily accessed.

Insurability

New equipment manufacturers (OEM’s) continue to drive forward at a relentless pace to achieve that extra 0.5% increased efficiency and/or that 1% reduction in emissions, which also employing new combustion techniques, such as dry low NOx combustion.

These efforts often lead to reduced flame instability and less margin for error in T1 and T2 temperatures, giving cause for concern, particularly now by the insurers of such plants.

Overhaul & Maintenance Facilities/Support:

Another major benefit of surplus equipment, which has been installed within the market for several years, is that there will be many sources of supply, not only for spare parts and overhaul but also for upgrade and experienced Operation & Maintenance (O & M) contractors.

There will also usually be a wealth of supporting services available for replacement blades, coatings, upgrade/replacement of control systems, vibration monitoring equipment, etc.

Valuation & Disposal Strategy

We typically recommend that surplus plant owners give themselves the maximum period of marketing prior to final decommissioning or dismantling. This then gives them a longer and more realistic period of finding the ‘right buyer with the appropriate project application.

With most owners preferring to sell such plant on an as-is, where-is basis, the frequently onerous cost of decommissioning and dismantling can be avoided, as this would then typically be borne by the purchaser, further saving the owner substantial costs.

Prior to entering into the marketing phase the most important criteria for successful disposal is to set realistic and attainable recovery/selling prices which match other surplus and new equipment in terms of price, scope and availability, with reasonable balancing of residual and elapsed lifetime. Allowance has also to be made for performance, spare part availability, terms of purchase, location and accessibility of site, etc.

Many brokers or marketing agents will attempt to secure lucrative contracts, which often require burdensome provision of project and on-site managers, advertising costs, with little or no margin for success-based incentives.

CESS usually recommend, and prefer to enter into, contracts which allow recovery of some or all of the hard costs, but with all of the profit-based elements of the contract linked directly to the success in finding the right end-user, willing to purchase at the best terms and highest recoverable cost to the owner.

Summary & Conclusions:

Unused and used but serviceable or overhauled power plants are available from the smaller 1 – 2MW gas and steam turbine units, right up to 1200MW, and the availability of such equipment is rarely a reflection of the lack of demand or unsuitability of the equipment, but can more commonly be linked to a lack of market knowledge of what is available.

 

How do we stabilize the grid with higher penetration of renewables?

November 3rd, 2017 No Comments   Posted in Energy Supply

How Do We Stabilize The Grid With Higher Penetration Of Renewables?

Chris James

The energy industry is in the process of understanding the full scope of renewable energy on the grid.

As more renewables are added onto the grid, the stability of the grid is generally decreasing. This is because the continuously rotating mass connected to the grid (turbines and generators on the production end) inherently stabilizes grid frequency. When those systems are taken offline and replaced by renewable energy systems, frequency stabilization becomes an increasing challenge.

Coal-fired power plants and gas turbines are examples. These systems have a lot of mass, and when they are rotating, they store energy. In the past, these systems have been beneficial for the grid because they rotate continuously and are difficult to slow down. If a large load makes a demand on the grid, say an industrial plant turns on a large device that pulls a lot of power, it still takes time to slow down these big machines so they may be able to, at least for short periods of time, source extra power into the grid.

This presents a challenge with clean alternatives. Normally, a solar panel system can’t generate more than what it’s already producing; the system is designed to always run at its maximum capacity. Wind turbines are similar. It would seem that there’s a lot of rotating mass in a wind turbine, but compared to a fast, massive traditional turbine, the wind turbine rotates slowly and doesn’t actually have that much energy in its rotating mass. Also, the clean energy systems being interconnected to the grid must synchronize with the existing grid frequency rather than drive the grid frequency. If you draw a lot of power for a short period of time, or overload the grid, the grid frequency starts lowering, and current clean energy systems can’t compensate for that. This is where ultracapacitors, also called supercapacitors, can be implemented to help compensate for high power transient loads.

The majority of events which destabilize the grid are fairly short. Studies have shown that a majority of grid disruptions are less than a few seconds long. That’s an indicator that destabilization events that are happening on the grid can be stabilized with ultracapacitors, which specialize in short-term, very high power, lower energy content storage.

If one measures the grid frequency very precisely, an ultracapacitor paired with a very large power inverter could push power back into the grid or pull power depending on the grid frequency swings, creating a “virtual rotating mass.” It also may be that a centralized approach will be used where operation centers for the grid dispatch energy storage as needed for stabilization.

The grid is made up of different segments, and there are some that locally have an abundance of power and some need power to be sourced from afar, as power has to be provided where the loads are. In some cases, centralized operation centers may best be able to deal with a power deficit or overabundance by commanding storage systems to come online to compensate for a grid event. On the other hand, since some control decisions have to happen very quickly to be effective, some storage systems may run themselves by self-monitoring a grid segment and reacting to changes. It’s likely that ultracapacitor-based stabilization systems will need to be autonomous like this, because they must react very fast to be effective. I imagine we will need to employ a variety of energy storage systems to meet our needs. This is a new area for the industry, so different approaches are still under exploration.

The traditional grid is self-stabilizing to a high degree. As clean energy sources that are variable continue to be added to the grid, it will be necessary to provide additional stabilization such as adding large-scale energy storage. It’s general industry knowledge that the lowest cost energy storage available is pumped hydroelectric storage. One problem with pumped hydroelectric storage is it can’t be turned on and off immediately. Time is required for spinning up/down these systems, and it seems that they also will need to be coupled with some sort of rapid stabilization.

Let’s say you’re using energy flowing directly from the wind and sun, and the turbines are off. What happens when you have another load? You will have to spin your turbines up. You need a short-term energy storage to ride through the increase in demand while you bring up the sources. It may be that you have battery systems that can achieve that. I think that ultracapacitors are poised to serve this application best in the long-term: If your lowest cost energy storage system doesn’t always source energy immediately, then you need something to bridge the gap, and ultracapacitors are in a good position to do just that.

The grid stability problem is going to stick around. It’s possible that the grid will need large ultracapacitor farms or other means to stabilize it. If stabilizing a grid fed by renewables is the goal, microcycling batteries may prove inefficient. Ultracapacitors, on the other hand, are designed for high cycle applications that require long life and are a viable option for stabilizing a renewables grid. I believe ultracapacitors will provide a very effective buffering solution as we increase the amount of clean energy technology that we employ.

This post was originally published by Maxwell Technologies and was reposted with permission.

 

How to develop a consistent lotto winning strategy – trapping and wheeling

November 1st, 2017 No Comments   Posted in lotto winning strategy

How to develop a consistent lotto winning strategy – trapping and wheeling

To download the complete article below with the tables, please click the link below:

How to develop a consistent lotto winning strategy

The fundamental formula for gambling (FFG) provides the number of consecutive draws needed to repeat an outcome, and thus predict when an event will repeat at a given confidence level. For the lotto games played in the Philippines where 6 numbered lotto balls are picked by a lotto drawing machine, the number of consecutive draws (N) at 95% confidence level is shown below:

N = log (1 – DC) / log (1 – p)

The result is then rounded upwards (Nr) by adding 0.5 and rounded-off to zero decimal point to have a whole integer number:

The number of consecutive draws to monitor when a lotto number is expected to come out is shown below:

FUNDAMENTAL FORMULA OF GAMBLING (FFG)
N = log(1 – DC)/log(1 – p)
DC 6/42 6/45 6/49 6/55 6/58 Number
Draws 0.1429 0.1333 0.1224 0.1091 0.1034 of Wins
50% 5 5 6 7 7 3.00
67% 8 8 9 10 11 4.00
75% 9 10 11 13 13 4.50
83% 12 13 14 16 17 5.00
90% 15 17 18 20 22 5.40
93% 18 19 21 24 25 5.58
95% 20 21 23 26 28 5.70

 

The next step is to get the historical draws and count the number of times the lotto number came out and divide this historical appearances with the N values above.

This will give the expected draws the lotto number will come out in the consecutive Nr draws. And you know what, the resulting expected draws is 3.0 draws for all lotto games (6/42, 6/45, 6/49, 6/55, and 6/58) after having 20, 21, 23, 26, and 28 consecutive draws for the lotto games, respectively.

By dividing the total historical draws for the lotto game by the number of jackpot wins to-date, this ratio provides the frequency of a lotto wins:

6/42 = 1508 draws / 423 jackpot wins = 3.6 draws between jackpot wins

6/45 = 2473 draws / 502 jackpot wins = 4.9 draws between jackpot wins

6/49 = 2063 draws / 301 jackpot wins = 6.9 draws between jackpot wins

6/55 = 1176 draws / 70 jackpot wins = 16.8 draws between jackpot wins

6/58 = 309 draws / 10 jackpot wins = 30.9 draws between jackpot wins

From the above draws needed to have a jackpot hit, it shows that 6/42, 6/45 and 6/49 have the shortest interval between jackpot hits while 6/55 and 6/58 have much longer intervals between jackpot hits, thought their jackpot winning price are much higher at 6M, 9M, 16M, 30M and 50M, respectively.

Also, it can be noted that most jackpot hits have drawn numbers between 1 and 31 – the calendar days in one month. This shows that the betting population in the Philippines use divine prayers that the birthdays of their family and love ones will give them luck. When the lotto draw results in low numbers between 1 and 31, and with a lot of bettors using the birthdays to select their bets, the chance of those bets hitting the draw results is indeed high.

The following data shows the number of draws with numbers between 1 and 31:

6/42 = 1508 draws / 223 jackpot wins = 6.8 draws between jackpot wins

6/45 = 2473 draws / 248 jackpot wins = 10.0 draws between jackpot wins

6/49 = 2063 draws / 118 jackpot wins = 17.5 draws between jackpot wins

6/55 = 1176 draws / 36 jackpot wins = 32.7 draws between jackpot wins

6/58 = 309 draws / 8 jackpot wins = 38.6 draws between jackpot wins

When monitoring the number of consecutive draws N and comparing with the number of actual hits within the range of N, this will provide which numbers is most likely to emerge in the next draw.

In the case of 6/42 lotto game, lotto numbers with 1-3 appearance within N = 20 prior draws have 3.5, 1.7, 0.6 for a total of 5.9 hits out of 6 winning lotto numbers, thus giving a higher chance of having 3, 4 and 5 winning numbers, and ultimately 6 winning jackpot numbers.

0 0 0 0.0
1 3363 59 3.5
2 1656 29 1.7
3 532 9 0.6
4 122 2 0.1
5 10 0
6 2 0
7 0 0
5685 100 5.9

In the case of 6/45 lotto game, lotto numbers with 1-3 appearance within N = 21 prior draws have 3.2, 1.6, 0.6 for a total of 5.9 hits out of 6 winning lotto numbers, thus giving a higher chance of having 3, 4 and 5 winning numbers, and ultimately 6 winning jackpot numbers.

0 769 8 0.5
1 5274 54 3.2
2 2605 27 1.6
3 900 9 0.6
4 169 2 0.1
5 26 0
6 0 0
7 0 0
9743 100 5.9

 

In the case of 6/49 lotto game, lotto numbers with 0-3 appearance within N = 23 prior draws have 0.5, 3.3, 1.6, 0.5 for a total of 5.9 hits out of 6 winning lotto numbers, thus giving a higher chance of having 3, 4 and 5 winning numbers, and ultimately 6 winning jackpot numbers.

0 607 8 0.5
1 4388 55 3.3
2 2156 27 1.6
3 720 9 0.5
4 149 2
5 22 0
6 1 0
7 0 0
8043 100 5.9

In the case of 6/55 lotto game, lotto numbers with 1-3 appearance within N = 26 prior draws have 0.5, 3.3, 1.6, 0.6 for a total of 5.9 hits out of 6 winning lotto numbers, thus giving a higher chance of having 3, 4 and 5 winning numbers, and ultimately 6 winning jackpot numbers.

0 353 8 0.5
1 2470 54 3.3
2 1203 26 1.6
3 428 9 0.6
4 78 2 0.1
5 9 0
6 1 0
7 0 0
4542 100 5.9

In the case of 6/58 lotto game, lotto numbers with 0-3 appearance within N = 28 prior draws have 0.4, 3.3, 1.6, 0.5 for a total of 6.0 hits out of 6 winning lotto numbers, thus giving a higher chance of having 3, 4 and 5 winning numbers, and ultimately 6 winning jackpot numbers.

0 79 7 0.4
1 625 56 3.3
2 298 27 1.6
3 93 8 0.5
4 25 2 0.1
5 1 0
6 0 0
7
1121 100 6.0

Aside from tracking the appearance within the N prior draws, it is important to observe that the total of the 6 numbers lie within a desired range based on historical performance and that there is a good balance or spread among odd (1, 3, 5, …) and even (2, 4, 6, …) lotto numbers and also low (1-21) and high (22-42) lotto numbers in the case of 6/42 lotto game. For other lotto games, the mid-point between 1 and 45, 1 and 49, 1 and 55 and 1 and 58 determines the low and high lotto numbers.

The desired sum of the 6 lotto numbers that constitute 80% of the winning draws are as follows:

Lotto Min Ave Max
6/42 100 129 158
6/45 106 138 170
6/49 115 150 185
6/55 129 168 207
6/58 136 177 218

Finally, once you have trapped or selected the numbers using the above criteria of number of appearance in N prior draws, total historical hits of that number since the start of the lotto game, the sum of the 6 lotto numbers selected, and the balance between odd and even and low and high lotto numbers, the next step is to use lotto wheels that are available in the internet that are free or available for sale.

Among the most popular lotto wheels are:

12 Number Plan – Guaranteed 4/4

18 Numbers In 42 Combinations

22 Numbers In 67 Combinations

8 Number Plan – Guaranteed 4/4

8 Number Plan – Guaranteed 5/5

9 Number Plan – Guaranteed 4/4

10 Number Plan – Guaranteed 4/4

10 Number Plan

20 Number Plan – Guaranteed 3/6

However, based on experience and cost effectiveness (expected winnings from hitting 3, 4, 5 and 6 numbers divided by cost of the lotto tickets), the most cost-effective wheeling strategy is the “12 Number Plan – Guaranteed 4/4” which is a scaled down and cheaper version of betting “System 12”.

An example of the “12 Number Plan – Guaranteed 4/4” wheel is shown below:

1 2 3 4 5 6 7 8 9 10 11 12
11 30 23 28 36 9 5 8 24 25 38 42
1 1 1 1 1 1 1
2 1 1 1 1 1 1
3 1 1 1 1 1 1
4 1 1 1 1 1 1
5 1 1 1 1 1 1
6 1 1 1 1 1 1
7 1 1 1 1 1 1
8 1 1 1 1 1 1
9 1 1 1 1 1 1
10 1 1 1 1 1 1
11 1 1 1 1 1 1
12 1 1 1 1 1 1
13 1 1 1 1 1 1
14 1 1 1 1 1 1
15 1 1 1 1 1 1
16 1 1 1 1 1 1
17 1 1 1 1 1 1
18 1 1 1 1 1 1
19 1 1 1 1 1 1
20 1 1 1 1 1 1
21 1 1 1 1 1 1
22 1 1 1 1 1 1
23 1 1 1 1 1 1
24 1 1 1 1 1 1
25 1 1 1 1 1 1
26 1 1 1 1 1 1
27 1 1 1 1 1 1
28 1 1 1 1 1 1
29 1 1 1 1 1 1
30 1 1 1 1 1 1
31 1 1 1 1 1 1
32 1 1 1 1 1 1
33 1 1 1 1 1 1
34 1 1 1 1 1 1
35 1 1 1 1 1 1
36 1 1 1 1 1 1
37 1 1 1 1 1 1
38 1 1 1 1 1 1
39 1 1 1 1 1 1
40 1 1 1 1 1 1
41 1 1 1 1 1 1
42 1 1 1 1 1 1
0.50 21 21 21 21 21 21 21 21 21 21 21 21

And the tickets to be purchased are shown below with suggestion as to whether to bet based on the total of the 6 numbers and the balance of odd and even and low and high lotto numbers:

1 2 3 4 5 6 sum O E L H Bet
11 23 36 5 25 42 142 4 2 4 2 1
1 11 30 23 28 36 9 137 3 3 4 2 1
2 11 30 23 5 8 25 102 4 2 3 3 1
3 11 30 23 24 38 42 168 2 4 5 1 0
4 11 30 28 5 8 24 106 2 4 3 3 1
5 11 30 28 25 38 42 174 2 4 5 1 0
6 11 30 36 5 8 38 128 2 4 3 3 1
7 11 30 36 24 25 42 168 2 4 5 1 0
8 11 30 9 5 8 42 105 3 3 2 4 1
9 11 30 9 24 25 38 137 3 3 4 2 1
10 11 23 28 5 38 42 147 3 3 4 2 1
11 11 23 28 8 24 25 119 3 3 4 2 1
12 11 23 36 5 24 42 141 3 3 4 2 1
13 11 23 36 8 25 38 141 3 3 4 2 1
14 11 23 9 5 24 25 97 5 1 3 3 0
15 11 23 9 8 25 42 118 4 2 3 3 1
16 11 28 36 5 25 42 147 3 3 4 2 1
17 11 28 36 8 24 38 145 1 5 4 2 0
18 11 28 9 5 25 38 116 4 2 3 3 1
19 11 28 9 8 24 42 122 2 4 3 3 1
20 11 36 9 5 24 25 110 4 2 3 3 1
21 11 36 9 8 38 42 144 2 4 3 3 1
22 30 23 28 5 24 25 135 3 3 5 1 0
23 30 23 28 8 38 42 169 1 5 5 1 0
24 30 23 36 5 25 38 157 3 3 5 1 0
25 30 23 36 8 24 42 163 1 5 5 1 0
26 30 23 9 5 25 38 130 4 2 4 2 1
27 30 23 9 8 24 38 132 2 4 4 2 1
28 30 28 36 5 24 38 161 1 5 5 1 0
29 30 28 36 8 25 42 169 1 5 5 1 0
30 30 28 9 5 24 42 138 2 4 4 2 1
31 30 28 9 8 25 38 138 2 4 4 2 1
32 30 36 9 5 38 42 160 2 4 4 2 0
33 30 36 9 8 24 25 132 2 4 4 2 1
34 23 28 36 5 8 42 142 2 4 4 2 1
35 23 28 36 24 25 38 174 2 4 6 0 0
36 23 28 9 5 8 38 111 3 3 3 3 1
37 23 28 9 24 25 42 151 3 3 5 1 0
38 23 36 9 5 8 24 105 3 3 3 3 1
39 23 36 9 25 38 42 173 3 3 5 1 0
40 28 36 9 5 8 25 111 3 3 3 3 1
41 28 36 9 24 38 42 177 1 5 5 1 0
42 5 8 24 25 38 42 142 2 4 4 2 1
0.50 840 27
14.3% 540

And the expected winnings if the tickets hit 3, 4, 5 and 6 numbers are shown below:

    1 2 3 4 5 6
  Bet Hits 0 0 20 1,000 25,000 6,000,000
1 1 3 1
2 1 4 1
3 0 3 1
4 1 2 1
5 0 3 1
6 1 3 1
7 0 4 1
8 1 3 1
9 1 2 1
10 1 4 1
11 1 3 1
12 1 5 1
13 1 4 1
14 0 4 1
15 1 4 1
16 1 5 1
17 0 2 1
18 1 3 1
19 1 2 1
20 1 4 1
21 1 3 1
22 0 3 1
23 0 2 1
24 0 4 1
25 0 3 1
26 1 3 1
27 1 1 1
28 0 2 1
29 0 3 1
30 1 2 1
31 1 1 1
32 0 3 1
33 1 2 1
34 1 4 1
35 0 3 1
36 1 2 1
37 0 3 1
38 1 3 1
39 0 4 1
40 1 3 1
41 0 2 1
42 1 3 1
27 2 10 18 10 2 0 Wins
540 0 0 360 10,000 50,000 0 60,360
Cost
840
W / C Ratio
71.86

The above strategy of trapping the numbers from their appearance in N prior draws, historical hits, total of the 6 numbers and balance between odd and even and low and high numbers (to remove extreme and unlikely combinations) results in a cost-effective betting strategy that allows you to have a higher chance of hitting 3, 4, 5 and 6 numbers with the least cost as unlikely combinations are eliminated to avoid un-necessary costs.

Good Luck to You.

If you need the Excel Lotto Model with the Historical Hits of 6/42, 6/45, 6/49, 6/55 and 6/58 lotto games in the Philippines, as well the statistical analysis of total hits, appearances in N prior draws, trapping of the probable lotto numbers to bet on, and the lotto wheel to come up with the lotto tickets to prepare and bet, cleaned up for unlikely combinations, please email me:

mars_ocampo@yahoo.com

or

energydataexpert@gmail.com

A cash donation of PhP2,000 for each lotto game (6/42, 6/45, 6/49, 6/55 and 6/58) for a total of PhP10,000 for all Philippine lotto games is highly appreciated.

You may pay via PayPal:

=====

energydataexpert@gmail.com

=====

or remit payment via bank/wire transfer:

=====

1) Name of Bank Branch & Address:

The Bank of the Philippine Islands (BPI)

Pasig Ortigas Branch

G/F Benpres Building, Exchange Road corner Meralco Avenue

Ortigas Center, PASIG CITY 1605

METRO MANILA, PHILIPPINES

2) Account Name:

Marcial T. Ocampo

3) Account Number:

Current Account = 0205-5062-41

4) SWIFT ID Number = BOPIPHMM

=====

For other countries and territories, I can customize a system that includes all of the above features (database on historical draws, statistical analysis, macros for predicting probable numbers to bet – trapping, then wheeling the numbers to bet and applying criteria to remove extreme combinations and reduce cost, email me again and let us discuss your specific needs so I can prepare a job cost estimate.

Great and Let Us Start Winning

The Lotto Expert

63-915-6067949 (GLOBE Mobile)

 

The Thematic Resume/CV of Marcial Ocampo – the Energy Technology Expert

October 30th, 2017 No Comments   Posted in energy expert

The Thematic Resume/CV of Marcial Ocampo – the Energy Technology Expert

To download the thematic resume/CV of Marcial Ocampo, kindly click on the link below:

 

_Marcial Ocampo_CV_October 2017

 

Hope I can be of help and contribute to the growth of your company.

Regards,

Marcial Ocampo

63-915-6067949 (GLOBE mobile)

 

OMT ENERGY ENTERPRISES -Now Open for Business

October 3rd, 2017 No Comments   Posted in energy expert

OMT ENERGY ENTERPRISES – Now Open for Business

Yes, we are pleased to announce that OMT Energy Enterprises is now open for business.

OMT ENERGY can conduct in-house seminars, workshops and one-on-one training on power generation technology (description, history, capital and operating cost, power plant modeling, and economic and financial analysis to determine the feasibility of each technology).

Later on, OMT ENERGY will assist investors set up energy and power companies, register and secure permits, licenses and incentives from relevant authorities.

The cost of conducting in-house seminar, customizing project finance models, preparing power demand, energy demand, GDP and price forecasts, and feasibility studies  can be negotiated by contacting Marcial Ocampo at:

mars_ocampo@yahoo.com

or

energydataexpert@gmail.com

or

63-915-6067949 (GLOBE mobile)

Marcial can also be the chief executive officer (CEO, President), chief financial officer (CFO), chief operating officer (COO) or chief technical officer (CTO) or head of any major department in your company.

 

Following are services offered by OMT ENERGY:

Project Finance Modeling and Feasibility Study of any business enterprise

Supply/Demand/Price Forecasting with Monte Carlo Simulation (MCS)

Deterministic and Stochastic Project Finance Modeling with MCS

Integrated Wind Speed, Power Curves, Capacity Factor and Project Finance with MCS

Conventional and Renewable Energy Statistics (historical, forecast)

Renewable and Conventional Energy Supply/Demand and Tariff Studies

Renewable Energy Resource Assessment (wind, solar, mini-hydro) and Optimal Configuration

Clean Coal and Conventional Coal Project Finance and Feasibility Studies

Petroleum Supply/Demand and Pump Price Studies

LNG Market Study and Fuel Substitution Studies

Biomass Power Barrier Removal

Mini-hydro Power Design, Costing, Modeling and Feasibility Studies

Tri-Generation (Power, Heat, Cooling) Optimization & Financial Modeling

Mid-Term and Final Term Review of WB, UNDP and ADB Projects

Energy & Business Development

Oil, Energy & Electricity Pricing

Feed-In Tariff Calculation for Renewable Energy/Electricity

Refinery, Utilities, Distribution & Transportation Optimization

Refinery & Petrochem Process Modeling & Optimization

Optimal Power & Load Dispatch

Project Finance, Power Plant Modeling & Financial Modeling

Market, Technical & Economic Feasibility Studies

Dam Simulation Modeling & Studies

General Ledger Accounting System

Loans Processing System

Business Modeling & Corporate Planning

Oil Industry Retail & Distribution Expansion Studies

Small Scale Project Finance Models (diesel, hydro, biomass, wind, solar, cogeneration, hybrid-RE)

Large Scale Project Finance Models (oil, coal, geothermal, gas turbines, combined cycles, nuclear)

 

OMT Energy Enterprises

OMT Energy Enterprises is owned and headed by Marcial Ocampo.

Marcial has prepared the levelized cost of electricity (LCOE) of all power generation technologies and existing power plants in the country so that a merit order load dispatch schedule (least expensive to most expensive) is prepared to determine the marginal power plant and clearing price for WESM.

Marcial was engaged full-time by SMC GLOBAL POWER HOLDINGS from Oct 1, 2014 to Sep 30, 2017 to provide energy consultancy services in energy & power, financial modeling, optimization for least cost capacity expansion planning, optimal load dispatch, and Monte Carlo Simulation (MCS) of supply and demand studies, forecasting WESM clearing prices, and MCS of project finance models to determine distribution of NPV, IRR, and PAYBACK of equity and project returns, net present value of income after tax discounted with pre-tax WACC, the pre-tax WACC, electricity tariff, annual generation and average capacity factor.

He is an Energy & Power Generation Technology Selection and Business Development Consultant for oil, gas, coal, geothermal, hydro, and renewable energy technologies such as biomass, solar, wind, mini-hydro, ocean thermal and ocean wave, energy storage and clean energy technologies. He conducts power and energy market studies, supply & demand studies, energy forecasts & projections, pre-feasibility studies, power plant modeling, project finance modeling and feasibility studies. He also optimizes load dispatch, least cost capacity expansion planning using linear programming (LP) models.

Marcial provides optimization and LP models for maximizing refinery value (product sales less crude cost, refining cost, refinery fuel, power and utilities, and other costs), transportation optimization such as petroleum product transshipment, product formulation such as least cost feed-mix component blending, and optimizing manufacturing processes.

From your Energy Technology Expert

Marcial Ocampo

Solar + Energy Storage = Future of Mankind

September 10th, 2017 No Comments   Posted in solar energy storage

Solar + Energy Storage = Future of Mankind

I am sharing this earth-shaking article from ENERGY CENTRAL.

The Saharan Desert is poised to provide limitless power to the whole of EUROPE.

Here in the Philippines, the local pioneer is SOLAR PHILIPPINES headed by the young and energetic Mr. Leandro Leviste.

Likewise, electric vehicles (EVs) will dominate the global market by 2030-2040 as more global car manufacturers shift completely from petrol to petrol-electric hybrid to pure electric vehicles with grid electricity coming from renewable energy and off-peak solar photo voltaic (solar PV) and concentrated solar power (CSP) that will provide base-load generation thru large scale storage batteries (lithium ion, vanadium). Electric vehicles can now travel from 200-400 km per charge and is expected to rise as battery technology improves further.

It looks now that solar energy is poised to replace the sunset fossil oil and coal-fired power generation in many places of the world such as USA, China and Europe.

http://www.energycentral.com/c/pip/solar-storage-future-both-industries

The growth trend in both the energy storage market and the solar market puts solar-plus-storage in a market sweet spot. IMS Research indicates that the market for storing power from solar panels will grow to $19 billion by the end of this year.

Energy storage installation is expected to expand rapidly from 6 gigawatts in 2017 to more than 40 gigawatts by 2022 according to the Energy Storage Association, and the industry is expected to be worth nearly $11 billion by 2022. The solar industry has also experienced a boom as the United States solar market added 2,044 megawatts of new capacity in the first quarter of 2017.

More and more solar-plus-storage projects are starting all over the U.S., largely due to the fact that lithium-ion prices are dropping and customers now feel more comfortable with the technology. Thus, according to GreenTech Media, energy storage has become the “Darling of the Solar Industry.” The main benefit of solar-plus-storage is its ability to maximize the benefits of intermittent resources such as solar and wind power.

Residential + Solar + Storage

Homeowners benefit from solar-plus-storage because it saves them more money than either system can by themselves, and it reduces their carbon footprint that much more as well. As prices drop, more residential customers will install solar-plus-storage systems in their homes to take advantage of these benefits. Residential energy storage is expected to growth exponentially from 95 megawatts in 2016 to 3,773 megawatts by 2025.

The installed price of residential solar-plus-storage systems has already dropped 25 to 30 percent over the last two to three years, according to Ravi Manghani, director of energy storage for GTM Research. In addition, he says that consumers can realize additional cost reductions when they take advantage of state and federal incentives.

Utilities + Solar + Storage

Solar-plus-storage can make utilities more productive and help them maximize revenues. For example, demand for electricity can increase when consumers utilize solar-plus-storage technology. This demand reduces the need for new fossil fuel facilities, leading to an environmental benefit as well.

In addition, utilities can contract with their customers to draw power from their batteries when the grid needs it, thus lowering energy costs for all stakeholders and protecting against the environmental consequences of burning more fossil fuels to generate energy. More utilities will start to take advantage of solar-plus-storage as prices for utility-scale systems decrease. In fact, one manufacturer says that solar paired with energy storage can be supplied to utilities at a cost of 10 cents per kilowatt-hour.

Visit WillCoEnergy.com for more information.

Kevin Williams’s picture

Kevin Williams

Kevin Williams is a native of Kansas City, MO with a history of entrepreneurship. He has been a principal in several start-ups and consulted with business owners at many levels.

 

Career History of Marcial T. Ocampo

September 8th, 2017 No Comments   Posted in career history

Career History of Marcial T. Ocampo

Areas of Interest:

Energy & Power Generation

Linear Programming Optimization (Real and Mixed Integer LP)

Monte Carlo Simulation and Project Risk

Energy, Power and Fuel Supply & Demand Forecasting

Project Finance and Financial Modeling

Econometric Modeling (GDP, Price, Inflation, Employment)

Technical, Economic and Financial Feasibility Studies

Power Plant Management, Planning, Finance, Operations, Technical Services

WB and UNDP Renewable Energy, Barrier Removal and Project Evaluation

Education:

Elementary – Grade 6 – Valedictorian

High School – Year 4 – Salutatorian

College – B.S. Chemical Engineering, University of the Philippines

2nd Place – Chemical Engineering Board Exam – 87.75%

Masters – M.S. Chemical Engineering, University of the Philippines

Masters – M.S. Combustion & Energy, Leeds University, United Kingdom

Work Experience:

Jun 2014 – Present

Independent Advisor (see above expertise)

Jun 2014 – Present

Energy Technology Selection Expert, Project Finance Modeling, Optimization, Monte  Carlo Simulation at OMT Energy Enterprises

Oct 2014 – Present

Energy and Power Consultant at SMC GLOBAL POWER HOLDINGS CORPORATION

Mar 2013 – Sep 2017

Senior Power Generation Engineer at Sinclair Knight Mertz (SKM)

Sep 2012 – Nov 2012

Comprehensive Feasibility Study for Coal-Fired CFB Power Plant Project at Test Consultants, Inc.

Aug 2012 – Sep 2012

International Energy Consultant for Final Review of ENERGY CONSERVATION at UNDP-India

Feb 2012 – Sep 2012

Technical Working Group (TWG) Member, Independent Oil Industry Pricing Review Committee (IOPRC) at Philippine Department of Energy (Pump Price Calculation Model)

Feb 2012 – Jul 2012

CDM Consultancy to Wind Energy Farms of PhilCarbon at PhilCarbon Inc.

Jan 2012 – Jan 2012

External Evaluation of ESMAP 2007-2011 at Baastel

Dec 2011 – Dec 2011

International Energy Consultant / Expert Evaluator at UNDP-China

Sep 2011 – Oct 2011

Project Finance & Financial Modeling Consultant at Hitachi Asia Ltd

May 2011 – Jul 2011

Technical, Market, Economic and Feasibility Study Consultant at PNOC-EC

Apr 2011 – May 2011

Biomass Power Project Mid-Term Review Consultant at UNDP-India

Mar 2011 – Apr 2011

Natural Gas and LNG Market Study Consultant at Confidential Company

Jan 2011 – Mar 2011

Wind Energy Resource Assessment and Feasibility Study of 2 Sites at Constellation Energy Corporation

Nov 2010 – Nov 2010

Fuel Cell Hybrid Bus Demonstration at UNDP-China

Aug 2010 – Sep 2010

Wind-Diesel Hybrid Power Generation at UNDP Indonesia

Jan 2010 – Jan 2010

Presentor of Feed-In Tariff Calculation Procedure at DOE-NREB

Dec 2009 – Dec 2009

Seminar Lecturer & Consultant – Biomass Feed-In Tariff at Biomass Alliance & Phil. Sugar Mfg. Ass. (PSMA)

Dec 2009 – Dec 2009

Seminar Speaker, Feed-in Tariff Calculation at Energy Practitioners Association of the Philippines

Nov 2009 – Dec 2009

Expert on Dam Operation & Safety at House of Representatives of the Philippines (Pre-emptive discharge and dam water release simulation to avoid dam spill before incoming storm)

Jul 2009 – Oct 2009

Consultant for Greenfield Natural Gas CCGT Power Plant at PNOC Exploration Corporation

Jun 2009 – Jun 2009

Consultant for Lignite Coal Fired CFB Power Plant at PNOC Exploration Corporation

Oct 2008 – Nov 2008

CME Biodiesel Technical & Economic Consultant at Rapco CME Biodiesel

Jun 2008 – Jun 2008

Oil Pricing Expert & Consultant at Philippine Department of Energy

Apr 2008 – Apr 2008

Clean Coal Technology Consultant at E-Power

Jun 2007 – Dec 2007

Qualified Third Party (QTP) Consultant for Rural Electrification at World Bank & Philippine Department of Energy (Biomass-Diesel Hybrid Power Generation and Electricity Tariff Setting)

May 2007 – Dec 2007

Liquid Fuels & Additive Consultant at Octagon Chem Oil Corporation

Aug 2007 – Sep 2007

Financial Modeling Consultant at Harty Philippines, Inc.

Feb 2001 – Nov 2006

Senior Technical Services Manager at First Gen Corporation (Combined Cycle Gas Turbine, Pulverized Coal, and Large Dam power generation)

Sep 1999 – Jan 2001

Executive Director at Philippine Council for Industry & Energy Research & Development (PCIERD) of the Department of Science & Technology (DOST)

Jun 1997 – Jan 1998

EDP, Budget & Planning Manager at Petronas Energy Philippines, Inc.

Jun 1993 – May 1997

President & General Manager at Real Time Management Systems (Crude Oil Refinery Operation and Finished Product Distribution optimization with Linear Programming)

Nov 1990 – May 1993

Petron MIS Coordinator at PNOC-Petron Corporation (Nationwide computerization)

Jun 1983 – Nov 1990

Head, Computer Systems Group at PNOC-Petron Bataan Refinery (Refinery computerization and custodian of the Refinery Linear Programming model)

Apr 1978 – Jun 1986

Section Chief for Transport, Building & Machineries at Bureau of Energy Utilization, Philippine Department of Energy

Jun 1974 – Mar 1978

Lecturer at College of Engineering, University of the Philippines

=======

If you are interested in his services, email him quickly as he will be available by October 1, 2017:

mars_ocampo@yahoo.com

energydataexpert@gmail.com

or call:

63-915-6067949 (GLOBE mobile)

 

Nano machines that drill into cancer cells killing them in just 60 seconds developed by scientists

September 2nd, 2017 No Comments   Posted in cancer treatment

Nano machines that drill into cancer cells killing them in just 60 seconds developed by scientists

© Provided by The Telegraph

Nanomachines which can drill into cancer cells, killing them in just 60 seconds, have been developed by scientists.

The tiny spinning molecules are driven by light, and spin so quickly that they can burrow their way through cell linings when activated.

In one test conducted at Durham University the nanomachines took between one and three minutes to break through the outer membrane of prostate cancer cell, killing it instantly.

The ‘motor’ is a rotor-like chain of atoms that can be prompted to move in one direction, causing the molecule to rotate at high speed.

© Provided by The Telegraph

Dr Robert Pal of Durham University said: “We are moving towards realising our ambition to be able to use light-activated nanomachines to target cancer cells such as those in breast tumours and skin melanomas, including those that are resistant to existing chemotherapy.

“Once developed, this approach could provide a potential step change in non-invasive cancer treatment and greatly improve survival rates and patient welfare globally.”

Motorised molecules that target diseased cells may deliver drugs or kill the cells by drilling into the cell membranes.Credit: Tour Group/Rice University

The scientists, whose work is reported in the journal Nature, created several different light-activated motorised molecules designed to home in on specific cells.

They found that the nanomachines needed to spin at two to three million times per second to overcome nearby obstacles and outpace natural Brownian motion, the erratic movement of microscopic particles suspended in fluid.

The molecules could be used either to tunnel into cells carrying therapeutic agents, or to act as killer weapons that blast open tumour membranes.

© Provided by The Telegraph

Without an ultraviolet trigger, the motor molecules located target cells but then remained harmlessly on their surfaces.

The prostate cancer cells start to ‘bleb’ or disintegrate after just 60 seconds, as seen in the bottom image

When triggered, the molecules rapidly drilled through the cell membranes.

© Provided by The Telegraph

Dr James Tour, a member of the international team from Rice University in Houston, US, said: “These nanomachines are so small that we could park 50,000 of them across the diameter of a human hair, yet they have the targeting and actuating components combined in that diminutive package to make molecular machines a reality for treating disease.

“In this study we have shown that we can drill into cells, animal cells, human cells using these nanomachines, they will attach to the surface and then a light will be shone upon them and they will drill right into the cell.

“For many years I never had envisioned the nanomachines being used medically, I though they were way too small, because they are much much smaller than a cell, but now this work has really changed my thoughts on this and I think therapeutically this will be a whole new way to treat patients, it’s going to be an excellent application for cancer treatment, not just for killing of cells but for the treatment of cells, interacting with the human body using molecular machines.”

The researchers are already proceeding with experiments in microorganisms and small fish and hope to move to rodents soon, ahead of clinical trials in humans if animal testing is successful.

http://www.msn.com/en-ph/news/technology/nanomachines-that-drill-into-cancer-cells-killing-them-in-just-60-seconds-developed-by-scientists/ar-AAr30IZ?li=BBr8zL6&ocid=TSHDHP

 

When Nuclear Energy is not viable or applicable

June 28th, 2017 No Comments   Posted in power generation

When Nuclear Energy is not viable or applicable

The alternative to large-scale nuclear power is to use ocean energy – from waves, thermal gradients and ocean currents – and tidal currents due to changes in sea elevation resulting from gravitational forces of the moon and sun on the earth’s surface. Ocean and tidal currents are predictable unlike intermittent renewable solar PV, solar CSP, wind and to some extent mini-hydro which depends on rainfall. Stored biomass and waste-to-energy systems (gasification, pyrolysis) may provide dispatcheable power to act as baseload, together with predictable ocean and tidal currents – is the key to a reliable and stable electricity grid in the future.

But we still need other conventional and fossil energy sources such as oil, coal, natural gas, geothermal, hydro, simple and combined cycle gas turbines running on liquid and gaseous fuels to provide additional base load and mid-merit load, as well as high-speed peaking load plants to stabilize the electrical network.

I will soon start a mini-series on power generation technologies and present the description, theory, history, capital cost and operating cost, emissions, environmental impacts, benefits and risks of each technology.

From this information, I will then present a template project finance model for each technology to illustrate its economic viability and how it could compete in the electricity grid and thus dispatched to meet its revenue requirements to repay both equity and debt investors.

By using these template models to compute the short run marginal cost (SRMC = variable O&M cost + fuel cost + lube oil cost) and long run marginal cost (LRMC = annualized capital cost + fixed O&M + regulatory cost + SRMC), the energy & power planner can stack up the dependable power generation capacities from the cheapest to the most expensive SRMC or LRMC. The power technologies or power plants in the stack up to the power demand of the grid then gets dispatched and this is how we can ensure that dispatched power is the cheapest cost possible while meeting power demand.

Cheers

Email me to register to this mini-series. First come first serve.

energydataexpert@gmail.com

 

A Generic Strategy for Reducing Electricity Cost, Environmental Impact, and Promote Inclusive Economic Growth in Communities Hosting Energy & Power Industries

June 21st, 2017 No Comments   Posted in power generation

A Generic Strategy for Reducing Electricity Cost, Environmental Impact, and Promoting Inclusive Economic Growth in Communities Hosting Energy & Power Industries

Marcial Ocampo has a lifetime dream and advocacy: to help the country (Philippines) reduce its energy & power costs and consumption by optimizing the capacity and generation mix, reduce oil and energy imports by promoting indigenous resources, reduce the environmental impact footprint of power plants, and promote inclusive economic growth especially for the marginalized communities hosting the power plants and sources of fuels or energy.

Among the generic measures he proposes that can be applied to any country, especially countries with renewable energy sources, are as follows:

1) Use of advanced mixed integer linear programming (MILP) optimization software to process existing power plant data on capacity, efficiency or heat rate, availability and reliability, capital & operating costs, fuel costs & heating value, ramp-up and ramp-down rates and environmental emissions to optimize short-term and long-term capacity and generation mix, in order to achieve cheapest short-run generation cost (SRMC) and least cost long-run capacity expansion (LRMC).

2) Improve the quality of power generation (reliability, availability, frequency, load-following, backup reserves) in the country by having an optimal mix that balances the need for intermittent renewable energy for sustainable growth that also requires high-speed fossil generation to backup such intermittent technologies such as when the sun and wind becomes unavailable momentarily and stabilize the electrical network.

3) Make use of all municipal solid wastes (MSW), liquid and gaseous wastes (bio-gas and land-fill gas) to provide distributed power generation and process heat throughout the country in order to address waste collection, treatment, storage, sanitation and disposal problems. Not all cities and municipalities have access to geologic sites like gullies that can support environmentally sanitary landfills, so it is important that groups of cities and municipalities pool their resources to have a common and centrally located waste-to-energy system (gasification, pyrolysis) power plant utilizing MSW and biological wastes in order to reduce the size of MSW and its treatment residues.

4) Make use of all indigenous energy and fuel resources in the country in order to conserve precious foreign exchange (to purchase petroleum fuels, coal), utilize local coal and natural gas reserves, use carbon-neutral biomass from trees and shrubs to provide fuel pellets to co-fire boilers using oil and coal and thus initiate a gradual shift from fossil to renewable biomass power generation. I believe that anti-coal environmental advocates should take a second and favorable look into indigenous coal since later on, as the world runs out of fossil fuel, the country needs them for power and fuel security. Coal is a transition fuel as the world converts from oil products to renewable energy and delays the depletion of crude oil. It would be a crime in the future to burn oil products as fuel since scarce oil is more needed for lubrication of industrial and transport machineries and manufacture of pharmaceuticals and other chemicals.

5) Make use of available renewable energy such as biomass, waste-to-energy, solar PV, solar CSP, wind, mini-hydro and ocean energy provided by waves, thermal gradients, ocean currents and tidal flows due to the gravitational effects of the moon and sun on the earth’s surface that give rise to ocean currents or tidal currents in the vast oceans of the world. Estimates of 1.0 – 2.5 meters per second of ocean and tidal currents are found in the coastal vicinities of Japan, Taiwan, Vietnam and Philippines. Ocean currents are predictable and nearly constant as against intermittent solar and wind.

6) To utilize off-peak renewable energy to store energy in elevated dams or barriers, for future release using water turbines when peak energy and power is required. Energy may be stored as potential energy or as chemical energy in the form of Hydrogen gas from electrolysis of water using off-peak electricity and extracted in thermal plants or in fuel cells.

7) Let us integrate renewable energy in the design of our civil and transport infrastructures like putting solar PV and small-scale wind turbines in long-span bridges and dams, or putting ocean and tidal current water turbines under bridges or barrages that connects islands between straits, or when lakes or large marsh lands are surrounded with elevated highways that serves as flood control structures and provided with low-head water turbines to capture the energy of the released flood waters, just like in conventional large impoundment dams. This is one way of reducing the cost of the renewable energy by integrating them in the design and construction of public infrastructures. Building Integrated Photo Voltaic (BIPV) solar panels and rooftop-mounted solar heaters are now used in commercial buildings like malls, hotels and residential buildings to provide electricity and hot water.

8) Lastly, to reduce power costs drastically, adopt mine-mouth clean coal power generation technology (e.g. CFB). By using the low-BTU lignite coal reserves spread throughout the Philippine archipelago, which is economical only to use in mine-mouth configuration due to its low BTU, high moisture, high ash content, but low in sulfur and the mine adjacent to nearby limestone deposits, we can bring down further the electricity cost from base-load coal-fired power plants as it saves on the cost of logistics – hauling coal and barging or shipping costs – which are significant cost items. By integrating mine-mouth coal power plant with co-firing with biomass wood pellets coming from mature rubber trees and other fast-growing trees, the country can provide cheaper power without harming the environment and provide local job opportunities to coal miners and workers of tree plantations near the mine-mouth coal power plant. Planting rubber trees provide an immediate income stream to support the rural tree farm workers during the early life of the tree and once it become old and un-productive, it can be sold as wood pellets to the mine-mouth coal-fired power plant. Once the coal reserves are depleted or uneconomical to extract, the power plant becomes a renewable biomass wood chips and pellet power plant.

I am available for new endeavors this coming August 1, 2017.

I am hoping you would find time to communicate with me and discuss my ideas further.

Yours truly,

Marcial T. Ocampo

+63-9156067949 (GLOBE mobile)

+63-2-9313713 (PLDT home landline)

mars_ocampo@yahoo.com (email)

energydataexpert@gmail.com (email)

 

Marcial Ocampo and his Major Achievements in Life and Career Advancement

June 21st, 2017 No Comments   Posted in energy expert

Marcial Ocampo and his Major Achievements in Life and Career Advancement

Marcial obtained his elementary education and graduated as the Grade 6 Valedictorian and continued his high school education at San Sebastian College in Manila and finished Year 4 Salutatorian.

Marcial studied at the University of the Philippines in Diliman Quezon City, Philippines and finished his B.S. and M.S. Chemical Engineering degrees and worked part-time as personal driver of a college professor and College of Engineering Instructor. He also took the Chemical Engineering Licensure Exam in August 1973 and passed as 2nd Placer with an 87.75% rating. He became a British Council scholar at the University of Leeds, United Kingdom, where he finished his M.S. Combustion & Energy and thesis in just one year. More »

A short biography of the Energy Technology Selection Expert & Project Finance Modeling Expert

June 4th, 2017 No Comments   Posted in energy expert

A short biography of the Energy Technology Selection Expert & Project Finance Modeling Expert

Engineer Marcial Ocampo has humble beginnings as an adopted 3-year old boy of a young aunt Tining Ramos married to a young Doctor Potenciano Tawatao educated in America and having just one girl teen daughter as the older sister died during the height of WW2.

Marcial is the 2nd son of WW2 veteran Jose Ocampo and Pacita Lopez Tawatao (a war orphan and adopted by the young aunt). We were 9 children in all (5 boys and 4 girls).

This young family of 4 went to Musuan, Bukidnon in the island of Mindanao to join the faculty of Musuan Agricultural College (now Central Mindanao University). Marcial obtained his elementary education and graduated as the Grade 6 Valedictorian. When the only daughter transferred to Manila to study Medicine at UST in Manila, the young Marcial and his adoptive mama followed also and continued his high school education at San Sebastian College in Manila and finished Year 4 Salutatorian.

Marcial went on to study at the premiere University of the Philippines in Diliman Quezon City and finished his B.S. and M.S. Chemical Engineering degrees and worked part-time as personal driver of a college professor and College of Engineering Instructor. He also took the Chemical Engineering Licensure Exam in August 1973 and passed as 2nd Placer with an 87.75% rating. He became a British Council scholar at the University of Leeds, UK, where he finished his M.S. Combustion & Energy and thesis in just one year.

In 1979, Marcial married Veronilyn Palacio, daughter of Engineer and Bureau of Mines Mindanao Regional Director Demetrio Palacio and Veronica Pecaoco. The fruit of their love are 4 boys and 1 girl (Mark, Eric, Patrick, Francis and Catherine) and have now 3 granddaughters (Riona, Chissa and Briana). The couple also have provided education to 4 relatives (Gilbert, Justine, Kim and Julius). The couple are also active members of the Couples for Christ and has served as household member, Unit Head, Kids for Christ coordinator, and presently a household member.

After a number of starting jobs, Marcial joined the Department of Energy as a PNOC-PETRON-hire seconded as Section Chief of the Transport, Buildings & Machinery Section under the Conservation Division of DOE and conducted various energy audits of major industries throughout the country. Later on, when the DOE was abolished and replaced by the Ministry of Energy (MOE), Marcial transferred to the Petron Bataan Refinery (PBR) as Computer Systems Group head and Linear Programming (LP) model custodian. He retired from PETRON and then went on to work for PETRONAS Energy Philippines Inc. (PEPI) as EDP & Budget Manager and Executive Director of 50+ staff PCIERD-DOST.

Marcial later joined First Gen Corporation as Senior Technical Services Manager where he was introduced to power plant modeling and simulation, and later, into project finance modeling to determine the feasibility of power plant projects and alternatives, and to value the privatization price of an asset of NPC for bidding to interested buyers.

Armed with his accumulated expertise and knowledge of world energy resources, reserves, extraction rate, years to deplete, power generation technologies and its description, theory, history, capital and operating costs, availability and reliability, construction period, economic life, efficiency or plant heat rate, cost of fuel and its heating value, environmental impacts, benefits and risks, and commerciality, Marcial then prepared a compendium of all power generation technologies (renewable, conventional, fossil, nuclear, energy storage) in power point presentation format and developed a template project finance model to calculate the first year tariff (or feed-in-tariff in the case of renewable energy), equity and project returns (IRR, NPV, PAYBACK), debt service cover ratio (DSCR), benefit-to-cost ratio (B/C), and other financial ratios to assess financial risks of the project during the planning stage of the project cycle. In addition to this deterministic (fixed) template, he prepared a version with stochastic (probabilistic) analysis using Monte Carlo Simulation (MCS).

The MCS model varied by +/- 10% the independent inputs in a random manner such as electricity tariff, availability factor, fuel heating value, debt ratio, plant capacity, all-in (overnight) capital cost, variable O&M cost, fixed O&M cost, cost of fuel, efficiency or plant heat rate and exchange rate. The MCS dependent output consists of a probabilistic distribution curve of equity and project returns (IRR, NPV, PAYBACK), net profit after tax, pre-tax WACC and electricity tariff (or feed-in-tariff for renewable energy). The shape of the distribution curve and relative position of the average value of the dependent variable is indicative of project risk.

He also prepared a manual on “How to Design a Mini-hydro Power Plant” and developed a model to “Optimize Penstock Diameter given its Thickness, Strength, Diameter, Capital and Operating Costs, Cost of Electricity and Friction Loses”.

Marcial is civic mined and patriotic, and helped the government thru the DOE in the “Crude Oil Price Hike to USD100 per barrel Impact Study in 2008” and the “Oil Price Review Study of 2012” where he developed the Oil Pump Price Calculation Excel Model to predict changes to pump price or absolute pump price given changes in FOB or MOPS import cost, ocean freight and insurance costs, exchange rate, gov’t excise taxes and port charges, brokerage and arrastre charges, VAT on importation activities, oil company margin, pumping and transshipment costs, hauling costs, dealer margin, and VAT on local activities. The pump price model can be downloaded from the DOE Website.

He also assisted a foreign consultant prepare a historical analysis of the short-run marginal cost (SRMC = variable O&M cost + fuel cost) and long-run marginal cost (LRMC = annualized capital cost + fixed O&M cost + regulatory cost + SRMC) for all power plants in the country in order to assist a client prepare his competitive bid offers in the Wholesale Electric Spot Market (WESM) as well as prepare their capacity expansion plans.

He also assisted the Philippine Congressional Committee on Dam Safety in improving the Dam Water Release Protocol by providing Dam Water Release Simulation Model to predict dam height (meters) and volumetric release rate (cubic meters per second) every hour of the simulation given the initial dam height and volume, power generation and water discharge, dam strapping table (volume vs. height), rainfall data (mm per hour) and area of the dam watershed and upstream drainage area with rainfall data or equivalent upstream dam release rate. This model answered the question: “How many hours and rate of pre-emptive discharge is necessary to increase a dam’s storage capacity in order to have sufficient space to absorb an incoming storm and thus avoid a catastrophic dam spill that will inundate downstream low land areas”.  The model accurately predicted the volumetric release rate at the height of the storm when the dam spilling level was breached. It also recommended how many days and rate of pre-emptive discharge is needed to avoid the dam spill during the height of Typhoon “Ondoy” and “Peping” that inundated the provinces of Pangasinan and Tarlac resulting in PhP 40 billion of damage and lost properties and lives.

He also assisted the economic team that studied the proposed excise tax increase in gasoline, diesel, kerosene, LPG, fuel oil, lubes & greases, and other petroleum products such as waxes & petrolatums to predict the price disturbance to be inputted into the input-output matrix of the Philippine economy to predict impact on GDP, inflation and employment.

Marcial continued to develop his overall skills in energy & power and became an International Consultant at UNDP and travelled to Jakarta, Beijing, Shanghai, New Delhi and Chennai working on wind diesel hybrid, 3rd generation fuel cell bus, biomass energy and India tea manufacturing.

Later, Marcial applied his energy & power expertise to join Sinclair Knight Mertz (SKM) as Senior Power Generation Engineer as part of the On-shore LNG Refrigerated Terminal and Re-gassing Facility project team at Limay, Bataan, a proposed project of Atlantic Gulf & Pacific Company (AG&P).

Marcial then joined the SMC GLOBAL POWER HOLDINGS CORPORATION as Energy & Power Consultant and finished a number of feasibility studies for an industrial park, coal-fired power plant using clean coal technology (CFB) and a coal mine project where he converted the coal-mine production plan into a project finance model to determine the cost of delivered coal to another SMC power plant in Mindanao. He provided in-house financial modeling expertise on solar PV, wind, mini-hydro, large hydro, natural gas-fired CCGT and coal-fired clean coal technology (CFB).

Marcial’s lifetime dream and advocacy is to help the country reduce its energy & power costs and consumption by optimizing the capacity and generation mix, reduce oil and energy imports by promoting indigenous resources, reduce the environmental impact footprint of power plants, and promote inclusive economic growth especially for the marginalized communities hosting the power plants and sources of fuels or energy.

Among the measures he proposes are as follows:

1) Use of advanced mixed integer linear programming (MILP) optimization software to process existing power plant data on capacity, efficiency or heat rate, availability and reliability, capital & operating costs, fuel costs & heating value, ramp-up and ramp-down rates and environmental emissions to optimize short-term and long-term capacity and generation mix, in order to achieve cheapest short-run generation cost (SRMC) and least cost long-run capacity expansion (LRMC).

2) Improve the quality of power generation (reliability, availability, frequency, load-following, backup reserves) in the country by having an optimal mix that balances the need for intermittent renewable energy for sustainable growth that also requires high-speed fossil generation to backup such intermittent technologies such as when the sun and wind becomes unavailable momentarily and stabilize the electrical network.

3) Make use of all municipal solid wastes (MSW), liquid and gaseous wastes (bio-gas and land-fill gas) to provide distributed power generation and process heat throughout the country in order to address waste collection, treatment, storage, sanitation and disposal problems.

4) Make use of all indigenous energy and fuel resources in the country in order to conserve precious foreign exchange (to purchase petroleum fuels, coal), utilize local coal and natural gas reserves, use carbon-neutral biomass from trees and shrubs to provide fuel pellets to co-fire boilers using oil and coal and thus initiate a gradual shift from fossil to renewable biomass power generation. I believe that anti-coal environmental advocates should take a second and favorable look into indigenous coal since later on, as the world runs out of fossil fuel, the country needs them for power and fuel security. Coal is a transition fuel as the world converts from oil products to renewable energy and delays the depletion of crude oil. It would be a crime in the future to burn oil products as fuel since scarce oil is more needed for lubrication of industrial and transport machineries and manufacture of pharmaceuticals and other chemicals.

5) Make use of available renewable energy such as biomass, waste-to-energy, solar PV, solar CSP, wind, mini-hydro and ocean energy provided by waves, thermal gradients, ocean currents and tidal flows due to the gravitational effects of the moon and sun on the earth’s surface that give rise to ocean currents or tidal currents in the vast oceans of the world. Estimates of 1.0 – 2.5 meters per second of ocean and tidal currents are found in the coastal vicinities of Japan, Taiwan, Vietnam and Philippines. Ocean currents are predictable and nearly constant as against intermittent solar and wind.

6) To utilize off-peak renewable energy to store energy in elevated dams or barriers, for future release using water turbines when peak energy and power is required. Energy may be stored as potential energy or as chemical energy in the form of Hydrogen gas from electrolysis of water using off-peak electricity and extracted in thermal plants or in fuel cells.

7) Let us integrate renewable energy in the design of our civil and transport infrastructures like putting solar PV and small-scale wind turbines in long-span bridges and dams, or putting ocean and tidal current water turbines under bridges or barrages that connects islands between straits, or when lakes or large marsh lands are surrounded with elevated highways that serves as flood control structures and provided with low-head water turbines to capture the energy of the released flood waters, just like in conventional large impoundment dams. This is one way of reducing the cost of the renewable energy by integrating them in the design and construction of public infrastructures. Building Integrated Photo Voltaic (BIPV) solar panels and rooftop-mounted solar heaters are now used in commercial buildings like malls, hotels and residential buildings to provide electricity and hot water.

8) Lastly, to reduce power costs drastically, adopt mine-mouth clean coal power generation technology (e.g. CFB). By using the low-BTU lignite coal reserves spread throughout the Philippine archipelago, which is economical only to use in mine-mouth configuration due to its low BTU, high moisture, high ash content, but low in sulfur and the mine adjacent to nearby limestone deposits, we can bring down further the electricity cost from base-load coal-fired power plants as it saves on the cost of logistics – hauling coal and barging or shipping costs – which are significant cost items. By integrating mine-mouth coal power plant with co-firing with biomass wood pellets coming from mature rubber trees and other fast-growing trees, the country can provide cheaper power without harming the environment and provide local job opportunities to coal miners and workers of tree plantations near the mine-mouth coal power plant. Planting rubber trees provide an immediate income stream to support the rural tree farm workers during the early life of the tree and once it become old and un-productive, it can be sold as wood pellets to the mine-mouth coal-fired power plant. Once the coal reserves are depleted or uneconomical to extract, the power plant becomes a renewable biomass wood chips and pellet power plant.

It you feel that my ideas are worth pursuing, I am available for new endeavors this coming August 1, 2017.

I am hoping you would find time to communicate with me and discuss my ideas further.

Yours truly,

Marcial Ocampo

+63-9156067949 (GLOBE mobile)

+63-2-9313713 (PLDT home landline)

mars_ocampo@yahoo.com (email)

energydataexpert@gmail.com (email)

 

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