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Monte Carlo Simulation (MCS) in Supply & Demand Forecasting and Financial Modeling

January 17th, 2015 No Comments   Posted in Monte Carlo Simulation

Monte Carlo Simulation (MCS) in Supply & Demand Forecasting and Financial & Process Modeling

The conventional or usual approach to forecasting supply & demand of power, energy and goods and services is to use deterministic modeling technique which uses fixed or static data – quantity, price and growth rate.

The same is true with financial & process modeling – fixed or static data is used in estimating plant output (production rate, output, power to GDP elasticity ratio), revenues (growth rate, price escalation), capital and operating costs, financing costs (equity returns, debt interest, commitment fees, front-end fees) and capital structure (% equity, % debt).

However, at best, static data used in deterministic models, provides only static results for equity returns (NPV, IRR, PAYBACK), project returns (NPV, IRR, PAYBACK), net present value of income after tax, discounting rate pre-tax WACC, and the unit tariff (selling price, electricity price, energy price).

Thus, static inputs and outputs from deterministic models provide very little insight into the risk profile of a given project being analyzed for possible investment decisions – go or no go.

This is where stochastic (probabilistic) modeling, instead of static or deterministic modeling, is the better if not superior approach in estimating the most probable value of the statistic being forecasted (mean and its standard deviation, and resulting min and max values of the statistic).

Thus, the use of Monte Carlo Simulation (MCS), which started in 1964, has gained wide prominence in the power generation industry to provide a better estimate of the risk profile of supply & demand forecasting and in determining the technical and economic feasibility of proposed projects, especially of capital-intensive power generation project using conventional, fossil, nuclear and renewable energy technologies.

In supply forecasting, the deterministic model is converted to an stochastic model using the following transformation:

 

     Capacity (sto) = Capacity (det) x [ min% + (max% - min%) x rand() ]

     Total Capacity = sum (of all capacity from each power generation technology or power plant)

     where min% = 60%, max% = 100% and rand() = random function of MS EXCEL (gives value from 0.0 to 1.0).

 

In demand forecasting, the deterministic model is likewise converted to an stochastic model using the formula:

 

     AGR(sto) = AGR(det) x  [ min% + (max% - min%) x rand() ]

     Demand(t) = Demand(t-1) x [ 1 + AGR(sto) ]

     where min% = 90% , max% = 110%; which simulates the annual growth rate (AGR) from its 100% value

     t = time period, year

 

 Hence, the net capacity surplus/(deficit) is given by:

 

     Net Capacity Surplus(t) = Total Capacity(t) – Demand(t)

 

In power plant project finance modeling, the main determinants (variables that have greatest impact on the equity IRR) are listed below.

In a coal-fired power plant, a 20% swing (-10% to +10%) provides the following changes (delta) on the equity IRR, from the largest positive determinant – tariff (6.33%), followed by capacity factor (4.67%), efficiency (1.68%), debt ratio or % debt (1.29%), installed capacity (0.24%).

On the other hand, the largest negative determinant is capital cost or Capex (-4.27%), fuels & chemical costs (-1.66%), debt interest or loan interest (-1.13%) and O&M or Opex costs (-0.45%).

 

6.33% Tariff
4.67% Capacity Factor
1.68% Efficiency
1.29% Debt Ratio
0.24% Capacity
-0.45% Opex
-1.13% Debt Interest
-1.66% Fuels
-4.27% Capex

 

As an example, the Tariff and other main determinants, including foreign exchange rate, are modeled as follows:

 

Tariff  = (5.500 PhP/kWh net)  x [ 90% + (110% - 90%) x rand() ]

Net Capacity Factor = (85% of installed capacity) x [ 90% + (110% - 90%) x rand() ]

Power Plant Thermal Efficiency = (42.00% of GHV) x  [ 90% + (110% - 90%) x rand() ]

Debt Ratio = % Debt = (70% of all-in capital cost) x  [ 90% + (110% - 90%) x rand() ]

Installed Capacity = (135 MW gross per unit) x  [ 90% + (110% - 90%) x rand() ]

Var O&M Costs = (Var Opex, PhP/kWh net) x  [ 90% + (110% - 90%) x rand() ]

Fix O&M Costs = (Fix Opex, PhP/kW/year) x  [ 90% + (110% - 90%) x rand() ]

Debt Interest = Loan Interest = (7.00% p.a.) x  [ 90% + (110% - 90%) x rand() ]

Fuel Cost = Coal Cost = ($85.00 per metric ton) x  [ 90% + (110% - 90%) x rand() ]

Overnight Capital Cost = (Capex, $/kW gross) x  [ 90% + (110% - 90%) x rand() ]

Forex Rate = (44.00 PhP/US$) x  [ 90% + (110% - 90%) x rand() ]

 

where net = gross – own use & losses (applies to both MW capacity and MWh generation).

 

 

Sample results of the Monte Carlo Simulation after 1,000 trials in a project finance modeling exercise are shown below:

 

 

Stochastic Model      
                              Equity Returns (30% Equity, 70% Debt)
press ctrl + W to run NPV IRR PAYBACK
1,000 264,230 16.56% 7.72
Mean 80,363 16.26% 8.14
Standard error 41,555 0.10% 0.06
Median -5,224 15.99% 8.17
Standard deviation 1,314,093 3.15% 1.96
Variance 1,726,840,158,606 0.10% 3.85
Skewness 0.307 0.384 -0.046
Kurtosis 2.540 2.606 2.009
       
Expected value = 80,363 16.26% 8.14
The standard deviation*1.96 = 2,575,622 6.18% 3.84
95% of all outcomes, max = 2,655,985 22.43% 11.99
95% of all outcomes, min = -2,495,259 10.08% 4.30

 

 

Stochastic Model      
                               Project Returns (100% Equity, 0% Debt)
press ctrl + W to run NPV IRR PAYBACK
1,000 (2,793,242) 13.00% 6.33
Mean -2,727,555 12.76% 6.55
Standard error 48,386 0.06% 0.03
Median -2,790,486 12.68% 6.49
Standard deviation 1,530,092 1.77% 0.87
Variance 2,341,181,104,103 0.03% 0.77
Skewness 0.106 0.204 0.276
Kurtosis 2.494 2.491 2.519
       
Expected value = -2,727,555 12.76% 6.55
The standard deviation*1.96 = 2,998,980 3.48% 1.71
95% of all outcomes, max = 271,425 16.24% 8.26
95% of all outcomes, min = -5,726,535 9.28% 4.83

 

Stochastic Model Net Profit pre-Tax Electricity
  After Tax WACC Tariff
press ctrl + W to run Million PhP % PhP/kWh
1,000 12,015 13.06% 5.752
Mean 10,480 12.92% 5.447
Standard error 48 0.04% 0.010
Median 10,479 12.81% 5.459
Standard deviation 1,523 1.35% 0.312
Variance 2,318,825 0.02% 0.098
Skewness 0.090 0.382 -0.057
Kurtosis 2.637 2.606 1.801
       
Expected value = 10,480 12.92% 5.447
The standard deviation*1.96 = 2,985 2.65% 0.612
95% of all outcomes, max = 13,465 15.57% 6.059
95% of all outcomes, min = 7,496 10.28% 4.835

 

Deterministic models as well as Deterministic + Stochastic models are available for the following power generation technologies:

 

Biomass cogeneration (power + steam + hot water/air + chilled water/refrigeration/air conditioning)

Biomass direct combustion (boiler steam + hot water/air)

Biomass gasification (pyrolysis)

Biomass IGCC (integrated gasification combined cycle)

Biomass WTE (waste to energy)

Coal-fired CFB (circulating fluidized bed)

Coal-fired PC (pulverized coal) subcritical

Coal-fired PC (pulverized coal) supercritical

Coal-fired PC (pulverized coal) ultrasupercritical

Diesel Engine Genset (diesel, gas oil, bunker, fuel oil)

Oil Thermal (bunker, fuel oil)

Gas Thermal (natural gas)

Simple Cycle (Open Cycle) Gas Turbine (Natural Gas, Oil such as diesel, gas oil, kerosene, naptha)

Combined Cycle Gas Turbine (Natural Gas, Oil such as diesel, gas oil, kerosene, naptha)

Geothermal (flash, binary)

Nuclear (PHWR)

Ocean Thermal Energy Conversion (OTEC)

Solar PV

Wind (Onshore, Offshore)

 Large Hydro

Pumped Hydro

Mini-Hydro

 

 The reader is advised to search the internet for the definition of the above statistical terms (mean, standard error, median, standard deviation, variance, skewness and Kurtosis).

 

Please email energydataexpert@gmail.com should you have any questions.

 

How to keep flying safe again – learn the basics of flying without sophisticated instruments

December 30th, 2014 No Comments   Posted in Air Safety

How to keep flying safe again – learn the basics of flying without sophisticated instruments

After going through the various news reports and expert analysis on why airplane get lost in the air, fall down from the sky, stall, get its pitot tubes blocked by air crystals, planes not transmitting real-time data on exact location, engine performance, plane integrity – it is about time that a new paradigm in air safety is made:

1) Pilots learn to fly again the airplane manually in a turbulence, and not rely on autopilot that lowers engine speed and tilts the airplane nose upward when it senses it is “over speeding” as a result of erroneous plane speed readings from blocked pitot tubes due to ice formation at high altitude.

2) The pilots must prepare simple flying charts showing flap position, throttle opening, plane air speed at various elevations. In this way, if he suspects icing on the pitot tubes, he can easily open the throttle and over-ride the auto-pilot to avoid flying too slow.

3) The pilot must hang a simple pendulum blob inside the cockpit to show the level position of the aircraft if it is too dark to see the horizon, so that the plane is flying a the right tilt going up or going down. He will also know if the aircraft is flying inverted.

4) The pilot must constantly update flight control towers of his condition, especially of an impending emergency, must not hesitate to return to airbase or nearest airport in the event that weather conditions are so bad. Never mind the added expense of fuel and hotel accommodations.

5) Owners of the plane must now equip all their aircraft with engine monitors and global positioning system that are in constant communications with a ground recording system so that in the event of engine failure, or diversion of aircraft, their location is always known.

These are simple steps.

And when I fly the plane next time, as a passenger, I will be always observant of what the pilot is doing – is he flying too steep, falling down, flying level, flying too fast or too slow that the plane might go into a stall and fall down from the sky for lack of lift at the wings.

If you just sleep in the flight, you might just wake up someday upside down at the bottom of the ocean, simply because the pilot could not relate his air speed with the throttle opening to maintain a minimum safe speed above stalling and falling down from the sky.

Oh my…..

 

Capital Cost, Maintenance Cost, Levelized Tariff and Levelized Cost of Nuclear Power Plant – advanced project finance model

October 6th, 2014 No Comments   Posted in cost of power generation

Capital Cost, Maintenance Cost, Levelized Tariff and Levelized Cost of Nuclear Power Plant – advanced project finance model

This latest update will present the following:

1) The summary inputs and outputs from runs using the advanced project finance models for conventional, fossil, nuclear and renewable energy power generation technologies.

2) The worksheets or tabs of the advanced project finance model More »

Advanced (ADV) Project Finance Models for Conventional, Fossil, Nuclear and Renewable Energy Power Generation Technologies – Price List and Specs

September 28th, 2014 No Comments   Posted in cost of power generation

Advanced (ADV) Project Finance Models for Conventional, Fossil, Nuclear and Renewable Energy Power Generation Technologies – Price List and Specs (offer up to Sep 30, 2014 only)

Your power generation technology selection expert is pleased to make a final call to all project finance and power plant modelers to purchase the Advanced (ADV) Project Finance Models for Conventional, Fossil, Nuclear and Renewable Energy Power Generation Technologies.

The model consists of the following worksheets/tabs: More »

Why the Philippines is Lacking in Power Supply Always and is Expensive Compared to its Asian Neighbors

September 24th, 2014 No Comments   Posted in cost of power generation

Why the Philippines is Lacking in Power Supply Always and is Expensive Compared to its Asian Neighbors

Following is the outline of my power point presentation on “Why the Philippines is Lacking in Power Supply Always” and  why the Philippines has one of the highest power rate in Asia and the World.

If you need the pdf version, please email me so I could respond to your request.

 “Why the Philippines is Lacking in Power Supply Always”

By: Marcial T. Ocampo

        Energy Technology Selection and Optimization Consultant at

        OMT Energy Enterprises More »

Summary of inputs and results for project finance models for various power generation technologies – up to Sep 30, 2014 only

September 14th, 2014 No Comments   Posted in cost of power generation

Summary of inputs and results for project finance models for various power generation technologies – up to Sep 30, 2014 only

Dear Friends,

You only have up to September 30, 2014 to purchase the advanced project finance models for conventional, fossil, nuclear and renewable energy power generation technologies.

Beginning Oct 1, 2014, I will be working full-time with a major IPP in the country and I will take a leave in providing project finance models and Feasibility Study and Market Study preparations for a while.

So don’t dilly dally. Order now before I shut down this website for selling such models.

Cheers,

Energy Technology Selection Expert More »

Starting your Lending Investor Business – Use this package of services and softwares

July 25th, 2014 1 Comment   Posted in lending investors

Dear Friends,

 To jump-start immediately your Lending Investor Business, please use this package of services and softwards to ensure you started on the right foot.

Here is the price list of our services and softwares.

We can discuss this further if you wish.

Best regards,

Marcial More »

Why the Philippines is Lacking in Power Supply Always

May 16th, 2014 No Comments   Posted in Energy Supply and Demand

Why the Philippines is Lacking in Power Supply Always

This paper will answer the question of “Why the Philippines is lacking in power supply always and have one of the highest power rates in the world.”

Firstly, the country has a poor mix of baseload capacity (must-run renewables such as solar PV, wind, mini-hydro, biomass; hydro for irrigation and water supply; geothermal; coal), limited mid-merit capacity (natural gas-fired open cycle gas turbine and combined cycle gas turbine), and expensive peak load capacity (stored hydro, diesel genset, oil-fired thermal, large hydro, oil-fired open cycle gas turbine). More »

Coal-Fired Power Plant: How to Design and Calculate Plant Footprint, Fuel, Limestone, Hauling Trucks and Storage Areas for Coal and Ash

May 16th, 2014 No Comments   Posted in clean coal technologies

Coal-Fired Power Plant: How to Design and Calculate Plant Footprint, Fuel, Limestone, Hauling Trucks and Storage Areas for Coal and Ash

Yes, your favourite energy technology expert has prepared a simple but easy-to-use power plant model to augment your project finance model to calculate the following:

1) Coal quality and quantity of coal reserves (measured, indicative, inferred, total in-situ reserves)

2) Average specification of coal reserve (heating value, ash, volatile combustible matter, fixed carbon, sulfur, moisture)

3) Ultimate analysis of coal reserve (Carbon, Hydrogen, Nitrogen, Oxygen, Sulfur) More »

Economics of a 135 MW (net) coal-fired Circulating Fluidized Bed (CFB) Thermal Power Plant

May 14th, 2014 No Comments   Posted in clean coal technologies

Economics of a 135 MW (net) coal-fired Circulating Fluidized Bed (CFB) Thermal Power Plant

Following is an annual construction model (3 years or 36 months) and a 25-year operating project finance model (30% equity, 70% debt) with a 16% p.a. equity IRR and coal cost of US$85 per tonne (metric ton or MT) with a gross heating value (GHV) of 10,000 Btu/lb,  36 months construction, 25 years commercial operation) using average annual drawdown (1/3 in year 1, 1/3 in year 2, 1/3 in year 3 construction drawdown). The CFB has an overall fuel to electricity thermal efficiency of 37.39% (92.5% boiler efficiency, 42.0% steam turbine efficiency and 96.25% mechanical clutch & electric generator efficiency). The results are as follows: More »

Cogeneration (CHP) Biomass Power Project Finance Model

May 8th, 2014 No Comments   Posted in cogeneration

Cogeneration (CHP) Biomass Power Project Finance Model

A new and powerful tool for analysing the technical and economic viability of cogeneration (combined heat & power) using biomass fuel such as wood chips, agri-wastes, bagasse, wood-wastes is now available in the market to calculate your feed-in-tariff (FIT) rates or IRR and payback periods.

Your technology expert is pleased to announce the availability of a new cogeneration (combined heat & power or CHP) biomass power project finance model.

Be the first to use this powerful tool in analysing your biomass resource potential and convert it to useful energy and power.

The main input and output summary sheet is shown below:

======

Project Site Name & Location Cogeneration (CHP)
Renewable Energy Source Biomass
Hours per Year 8760
Timing  
  Construction Period (from FC) (months) 24
  Operating Period (Yrs from COD) 30
  Yrs from base year CPI & Forex (2010) for FIT 4
  Yrs from base year CPI (2012) for CAPEX 1
  Yrs from base year CPI (2012) for OPEX 3
   
Construction Sources and Uses of Funds, $000  
  Uses of Fund:  
    Land $118
    EPC (Equipment, Balance of Plant, Transport, Access Roads) $21,444
    Transmission Line Interconnection Facility $42
    Sub-Station Facility $817
    Development & Other Costs $3,450
    Construction Contingency $1,032
    Value Added Tax $1,905
    Financing Costs $2,680
    Initial Working Capital $2,391
  Total Uses of Fund $33,880
   
  Sources of Fund:  
    Debt $23,716
    Equity $10,164
  Total Sources of Fund $33,880
   
  Construction Unit Costs (US$/kW):  
    EPC Cost $7,148
    Plant Cost (Excluding VAT, Financing, Working Capital) $8,928
    All-in Project Cost $11,293
   
   
Model Check:  
Balance Sheet OK
Foreign Debt Amortization OK
Local Debt Amortization OK
Depreciation OK
Sources and Uses of Funds  OK
Debt-to-Equity Ratio  OK
   
Base Years  
  Base year CPI & Forex for FiT 2010
  Base year CPI for CAPEX 2012
  Base year CPI for OPEX 2012
   
Commercial Operating Date 2014
   

 

Technical and EPC Assumptions Power Heat (net)
Unit Capacity of Plant (MW power/unit) 3.00 12.00
No. of Units (unit), total net 1.0 1.0
Gross Installed Capacity (MW) 3.00 12.00
Net Installed Capacity (MW) 2.50 12.00
Plant Availability Factor, % 93.18%  
Guaranteed Efficiency Factor, % 98.00% 8,000
Allowance for losses & own use, % 16.66% Utilization Ratio
Net Capacity Factor after losses & own use, % 76.10% 105.6%
Net Electrical Output (MWh in 1st Year), heat output 20,000 101,376
Plant Degration, % p.a. (1-20 Yrs) 0.20% % Local Content
Land cost ($000) $113.64 100.0%
Equipment Cost ex BOP, Transport ($000/MW) – pellet $1,013.00  
Equipment Cost ex BOP, Transport ($000/MW) – power $1,500.00 11.4%
Insurance, Ocean Freight, Local Transport – power 4.5% 100.0%
Balance of Plant (BOP), % of Equipment Cost – power 21.0% 100.0%
Transmission Line Distance (km) 1.00  
T/L Cost per km, 69 kV ($000/km) $40.00 100.0%
Switchyard & Transformers ($000) $786.00 100.0%
Access Roads ($000/km) $182.00 100.0%
Distance of Access Road (km) 1.00  
Dev’t & Other Costs (land, permits, etc) (% of EPC) 15.0% 100.0%
VAT on importation (70% recoverable) 12% 100.0%
Initial Working Capital (% of EPC) 11.00% 100.0%
Contingency (% of Total Cost) 4.0% 50.0%
SET NPV (G28) TO ZERO BY VARYING FIT (F30) – ctrl +e Heat cost adj. 0.00
Operating Assumptions 0.94 Equity IRR
  Feed-in-Tariff (PhP/kWh) 6.813 20.0%
  Duration of FiT (Yrs) 20 Project IRR
  Tariff post FiT period (PhP/kWh) 5.000 14.5%
  FiT using Asset Base Methodology (PhP/kWh) 8.532  
  Annual CER Volume (tCO2e/year) and $/tCO2e                      -   $5.00
  Pellet production rate (mt/hr) and selling price (PhP/mt) 10.00           4,000.00
  O&M Cost ($000/unit/year) – power, pellet $1,150.00 $2,642.01
  Spare Parts, Tools & Equipment ($000/MW/yr) – power, pellet $10.00 $22.97
  O&M + Spares as % of EPC, T/L, S/S 5.29%  
  Refurbishment Cost (% of EPC) 30%  
  Timing of Refurbishment (Year from COD) 10  
  G&A ($000/year) – power, pellet $20.00 $45.95
  Fuel Cost (switch for Biomass: 1=yes, 0=no) 1  
    Average Fuel Cost (PhP/mt)            5,000.00  
    Fuel Rate (kWh/mt)               700.00  
    Average Fuel Consumption (mt/year)               34,882  
    Average Unit Cost of Fuel (PhP/kWh)                 5.228  
   
  Days Receivable & Payable 30 30
  VAT Recovery 70%  
  Timing of VAT recovery (Yrs after COD) 5  
     

 

   
Debt and Equity Assumptions  
Local/Foreign Capital Mix:  
  Local Capital 49.61%
  Foreign Capital 50.39%
   
Debt:  
  Local & Foreign Upfront & Financing Fees 2.00%
  Local & Foreign Commitment Fees 0.50%
  Local All-in Interest Rate excluding tax 10.00%
  Local Debt Payment Period (from end of GP) (Yrs) 10
  Foreign All-in Interest Rate excluding tax 8.00%
  Foreign Debt Payment Period (from end of GP) (Yrs) 10
  Local and Foreign Grace Period from COD (months) 6
  Local and Foreign debt Service Reserve (months) 6
  Debt Ratio 70.00%
  Total Local Debt ($000) – 30% $6,645
  Total Foreign Debt ($000) – 70% $17,071
    Total Debt Amount ($000) $23,716
   
Equity:  
  Equity Ratio 30.00%
  Equity Investment $10,164
  Cost of Equity (Onshore Equity IRR) – Nominal 20.00%
  Cost of Equity (Onshore Equity IRR) – Real 15.38%
   
WACC pre-tax 12.66%
WACC after-tax 11.39%
   
Tax Assumptions  
  Income Tax Holiday (Yrs) 7
  Income Tax Rate % (after ITH) 10.00%
  Property tax (from COD) 1.50%
  Property tax valuation rate (% of NBV) 80.00%
  Local Business Tax 1.00%
  Government Share (from COD) 0.00%
  ER 1-94 Contribution (PhP/kWh) 0.01
  Withholding Tax on Interest (Foreign Currency) – WHT 10.00%
  Gross Receipts Tax on Interest (Local Currency) – GRT 5.00%
  Euro to US$
Economic Assumptions 1.3500
  Base Foreign Exchange Rate (PhP/US$) – 2010 47.8125
  Forward Fixed Exchange Rate (2012) 44.0000
  Base Local CPI – 2009 160.00
  Annual Local CPI 4.00%
  Annual US CPI 2.00%
  Annual Peso Depreciation Rate 1.96%
   
   

 

Gross Heating Value 4,000 Btu/lb
  2.2046 lb/kg
  1000 kg/mt
  8,818,400 Btu/mt
Efficiency, % 27.09% of GHV
Plant Heat Rate 3600 kJ/kWh
  1.05506 kJ/Btu
  3,412 Btu/kWh
  12,598 Btu/kWh
Fuel Rate 700 kWh/mt
Cost of Fuel (no CPI and FOREX adjustment)            5,000.00 PhP/mt
  700 kWh/mt
                  7.143 PhP/kWh
Cost of heat (no CPI and FOREX adjustment)                 6.410 PhP/kWh
     
mt/h pellet 10.00  
h/yr 8,000  
mt/yr pellet 80,000  
Btu/mt pellet 8,818,400  
Btu/yr from pellet 705,472,000,000  
Btu/kWh @ 100% efficiency 3,412  
kWh/yr from pellet 206,754,247  
MWh/yr from pellet 206,754  
     
MW power (net) 2.50  
MW heat (net) 12.00  
MW power + heat (net) 14.50  
cogeneration (CHP) efficiency 72.0%  
MW power + heat (gross) 20.14  
h/yr 8,000  
MWh/yr (power + heat) 161,112  
     
MWh/yr from pellet 206,754  
MWh/yr (power + heat) 161,112  
MWh/yr surplus (pellet to export) 45,642  
Btu/kWh @ 100% efficiency 3,412  
Btu/yr 155,737,071,139  
Btu/mt pellet 8,818,400  
mt/yr surplus (pellet to export) 17,660  
     
MWh/yr surplus (pellet to export) 161,112  
MW power + heat (net) 3,412  
Btu/yr 549,734,928,861  
MW power (net) 8,818,400  
mt/yr pellet fuel (power and heat) 62,340  
     

 

======

If you are interested, email me to confirm order at:

energydataexpert@gmail.com

Then I will provide my current account bank details so you could remit payment via bank / wire transfer.

Thanks,

Your energy technology expert

 

Philippine Energy Data Analytics Service Provider – from your energy technology expert

April 25th, 2014 No Comments   Posted in energy data analytics

Philippine Energy Data Analytics Service Provider  - from your energy technology expert

You might be interested to look into the latest power supply and demand outlook (forecast 2014-2020) from the DOE.

It includes the existing 2011 installed capacity, plus constructed 2012-2013, plus committed 2014-2016 projects, then forecast peak demand, total reserves, total supply available.

Aside from the committed, there is also a list of indicative projects as well as future capacity additions for base load, mid-merit and peak load from the power development plan of DOE (from their optimized expansion planning modelling exercise).

This could be added to the supply curve to anticipate future supply/demand imbalances or capacity deficits.

Normally, the private sector will address the future capacity additions proposed by the DOE power development plan and implement them thru committed projects as well as indicative projects in the drawing  board.

Adding the indicative projects to the committed projects (with financial close and starting with permitting, construction, site preparation, procurement, etc.) will be a forecast of future supply availability which is then compared to peak demand plus reserve requirement (total supply required for a secure power supply that can withstand power plant breakdown of the largest unit). Reserve requirement is 4% of peak demand + largest capacity for spinning reserve and back-up power.

This could help your company/investor prioritize which grid (Luzon, Visayas or Mindanao) should your company propose to invest in new power generation capacity and what type (baseload, mid-merit, peaking) and technology (conventional, fossil, renewable) and fuel/energy type (oil, coal, natural gas, hydro, geothermal, biomass, solar, wind, ocean thermal), as well as configuration and capacity (e.g. 2 x 100, 200, 2 x 150, 300, 2 x 300, 600, etc.) is optimal for the given power demand profile, plant capital and operating cost, technology efficiency and fuel cost and fuel supply, environmental and social engineering issues.

Right now, I have the following information on the Philippine energy, oil, coal, gas and power industry, energy conservation and energy efficiency, fuel substitution, power plant emission and GHG emission among others:

Annex A:  ANNUAL TARGETS

Energy Demand and Supply Outlook:

A.1.1              Overall Energy Balance

A.1.2               Oil Demand by Sector

A.1.3a             Oil Demand by Product (Reference Case)

A.1.3b             Oil Demand by Product (Low carbon Scenario)

A.1.4               Natural Gas Demand Outlook

A.1.5               Coal Demand Outlook

A.1.6               Biodiesel Demand

A.1.7               Bioethanol Demand

A.1.8               Compressed Natural Gas (CNG) Bus Target

A.1.9               Compressed Natural Gas (CNG) Taxi Target

A.1.10             Auto-LPG Taxi Target

A.1.11             E-Vehicle Target

Energy Resource Development Outlook:

A.1.12             Oil and Gas Sector Targets

A.1.13             Coal Sector Targets

A.1.14             Geothermal Sector Targets

A.1.15             Hydropower Sector Targets

A.1.16             Wind Sector Targets

A.1.17             Biomass Sector Targets

A.1.18             Solar Sector Targets

A.1.19             List of Potential Renewable Energy (RE) Resources

Energy Efficiency Outlook:

A.1.20 Potential Cumulative Savings from Energy Efficiency Programs

Power Demand and Supply Outlook:

A.1.21             Cumulative Installed Capacity (in MW)

A.1.22             System Peak Demand, Main Grid (in MW)

A.1.23             System Peak Demand, Small Island Grids (in MW)

A.1.24             Electricity Sales Forecast (in GWh)

A.1.25             Committed Power Projects

A.1.26             Indicative Power Projects

A.1.27             Capacity Additions, Main Grid (in MW)

A.1.28             Capacity Additions, Small Island Grids (in MW)

A.1.29             Power Generation Forecast (in GWh)

A.1.30             TDP Projects – ERC Approved

A.1.31             TDP Projects – New Projects Third Regulatory Period (2011-2015)

A.1.32             TDP Projects – Projects Fourth Regulatory Period (2016-2020)

Carbon Dioxide Emissions and Avoidance:

A.1.33             Carbon Dioxide (CO2) Emissions from Energy (In Million Metric Tons)

Investment Requirements:

A.1.34 Annual Investment Requirements by Sector

A.1.35 Oil and Gas Investment Requirements

A.1.36 Geothermal Investment Requirements

A.1.37 Hydropower Investment Requirements

A.1.38 Biomass Investment Requirements

A.1.39 Wind Investment Requirements

A.1.40 Solar Investment Requirements

A.1.41 Ocean Energy Investment Requirements

A.1.42 CNG Investment Requirements

A.1.43 Auto-LPG Investment Requirements

A.1.44 E-Vehicles Investment Requirements

A.1.45 Bioethanol Investment Requirements

A.1.46 Biodiesel Investment Requirements

A.1.47 Power Generation Investment Requirements

A.1.48 Natural Gas Industry Investment Requirements

Annex B:  HISTORICAL PERFORMANCE

B.1      Energy Mix (In Million Barrels of Fuel Oil Equivalent, MMBFOE and MTOE)

B.2      Oil and Gas Sector Highlights

B.3      Geothermal Sector Highlights

B.4      Coal Sector Highlights

B.5      Hydropower Sector Highlights

B.6      Petroleum Products Consumption (In Thousand Barrels, MB)

B.7a    Oil Demand by Sector (In Thousand Barrels, MB)

B.7b    Oil Demand by Product (In Thousand Barrels, MB)

B.8      Coal Consumption by Sector (In Metric Tons, MT)

B.9      Coal Importation by Source (In Metric Tons, MT)

B.10    Installed Generating Capacity (In Megawatt, MW)

B.11    Power Generation by Source (In Gigawatt-Hour, GWh)

B.12    Electrification Profile

B.13    Energy Economy Indicators

 

Also, I have the copy of the Philippine Energy Plan (PEP 2012-2030).

I have some information on local Refinery expansion plans.

I have some information on local refineries and import terminals.

I have the refinery linear programming (LP) model for
optimizing crude oil/product imports, refinery operation, production
of oil products as well as national tankage by product by oil company
(storage capacity of depots) by region/location.

I have a simple LP model of a hydroskiming refinery.

I also have sales/demand of petroleum products by oil company by region/location.

I have the recent cost structure of importing crude oil and importing
finished products since I have been monitoring and developing the oil
pump price calculation formula.

I may be reached at energydataexpert@gmail.com for more information on how to get the raw data and perform the necessary data analytics to determine energy and power growth rates, percentile distribution, fuel substitution trends and remaining lifetimes of various fossil fuels.

Thanks for availing of our Philippine energy data analytics service.

 

HOW TO PLAN AND OPTIMIZE THE ENERGY, OIL, GAS, POWER AND TRANSMISSION INFRASTRUCTURE OF THE PHILIPPINES

HOW TO PLAN AND OPTIMIZE THE ENERGY, OIL, GAS, POWER AND TRANSMISSION INFRASTRUCTURE OF THE PHILIPPINES

My sincerest thanks to the readers, government officials, private investors, power developers, funding institution and non-government organizations that will respond positively to this conversation that I started recently as part of my functions as Senior Power Generation Engineer at SKM.

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August 21st, 2013 3 Comments   Posted in Dam water release

HOW TO MINIMIZE FLOODING IN CENTRAL LUZON AND THE MARIKINA VALLEY AND METRO MANILA

The San Roque Dam was designed for a 50-year return flood frequency. Since Typhoon Ondoy and Peping were of the order to a 75-100 year flood, it is thus imperative to lower the dam rule curve by 4-5 meters which is the equivalent of 1 major storm. Please note that it took 6 typhoons to fill up the San Roque Dam to its operating level to provide both power and irrigation during summer. More »

The Boeing 787 is Doomed, Unless it Gets out of the Battery

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BusinessWorld
http://www.bworldonline.com/content.php?section=Opinion&title=How-to-compute-prices-of-gasoline-and-diesel&id=73169
Thursday, July 11, 2013 | MANILA, PHILIPPINES

How to compute prices of gasoline and diesel

Strategic   Perspective René B. Azurin

SINCE PUMP prices of oil products are again rising, prompting the usual round of demonstrations against “overpricing,” I thought it would be useful to bring to everyone’s attention the pricing model developed by the Independent Oil Price Review Committee (IOPRC) which was tasked last year to study the reasonableness of retail prices of gasoline and diesel. More »

Why the Asiana Airline Boing 777 Crashed – No one cared about safety inside the plane and on the SF airport

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Why the Asiana Airline Boing 777 Crashed – No one cared about safety inside the plane and on the SF airport

The plane crashed because the other pilots were busy drinking coffee while the pilot in training was playing at the yoke.

Seriously, it crashed because of a series of simple events that no one noticed was leading to a dangerous situation: More »

Expertise of Marcial Ocampo- now available for engagement (projects and consultancy or fixed employment)

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Expertise of Marcial Ocampo – now available for engagement (projects and consultancy or fixed employment)

Conventional and Renewable Energy Statistics (historical, forecast)

Renewable Energy Supply/Demand and Tariff Studies

Renewable Energy Resource Assessment (wind, solar, mini-hydro) and Optimal Configuration Studies

Clean Coal and Conventional Coal Project Finance and Feasibility Studies

Electricity Supply/Demand and Tariff Studies (Luzon, Visayas, Mindanao, island grids) More »

Short-Term and Long-Term Solution to the Mindanao Power Crisis

March 29th, 2013 1 Comment   Posted in optimal load dispatch

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Mindanao needs temporarily for the next 5 years turbo-charged high-efficiency diesel genset units (4 x 50mw) in a power barge configuration. While it may appear expensive on a per kwh basis, it is operated only when there is a power deficiency, and its cost will be due to capacity fees (to recover capital costs for its 5-year deployment), operating & maintenance fees (to recover manpower, spare parts and admin fees) and fuel costs (pass thru fuel conversion costs). More »

Face-saving Measures to End Sabah Stand-off Needed

March 9th, 2013 2 Comments   Posted in Sabah Claim

Face-saving Measures to End Sabah Stand-off Needed

The problem with Sultan Kiram is he is acting alone, then expects the Philippine gov’t to bail him out of this problem, holding everyone hostage, to make the face-saving measures.

At this point, the only viable face-saving activities are: More »

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