Trading Arrangements in Power Pools Model Structure & Data

Transcription

1 Trading Arrangements in Power Pools Model Structure & Data Brian H. Bowen F.T. Sparrow Geoff Granum Power Pool Development Group Purdue University, U.S.A South Asia Regional Initiative in Energy Training Program July 19-23, 2003, Dhaka Bangladesh 07/08/2003 1

2 Electricity Trade Modeling Inputs Long Term Model Capital Costs Fuel Costs Heat Rates Line Losses Generation Capacities Outputs Cost Savings Optimal Expansions Trade Tariffs Wheeling Effects Reserve Margin Planning 07/08/2003 Purdue University 2

3 Short-Term and Long-Term Modeling Short-term (ST) modeling (fixed generation capacity) can be for almost any length of time less than 12 months. It can be a period of hours, days, weeks, or months. Longterm (LT) modeling (capacity expansions) is normally referring to several years. LT models are typically anywhere between 5 years and 20 years. 07/08/2003 Purdue University 3

4 Electricity Forecasting Policy Across the United States and in the industrialized nationals generally a growth rate of about 2% is typical. In the developing economies the growth rates are often quoted as being double or triple this 2% growth rate and even more. Enormous planning differences occur over a 20 year planning horizon with different rates of 4%, 8%, and 12% are used: = 1.48, = 2.19, = 4.66, = /08/2003 Purdue University 4

5 Electricity Trading Commodities The Purdue long-term electricity trade model (PLTETM) trades in two commodities: a.) Megawatt reserve power (MW) b.) Megawatt hour energy (MWh) 07/08/2003 Purdue University 5

6 Supply, Demand and Shipment (Existing and Proposed) The Purdue electricity and gas trade models optimize the minimum cost to meet the demands for electricity and natural gas within one region over a long-term horizon (e.g., 20 years). The region consists of several or more countries (indexed as z or zp). Normally each country is modeled as one node. Free trade is permitted to take place between all of the countries in the specified region. 07/08/2003 Purdue University 6

7 Figure 4.1 Training Model with Peak Demand (D) & Existing Generation (PG, CC, H) for Each Country Country 4 D = 1000 PG(4A) = 500 PG(4B) = (CC(4C) = GT(4D) = 300) Country 3 D = 300 PG(3A) = 260 (GT(3B) = 600) Country 5 D = 2000 PG(5A) = 2400 (CC(5B) = ) Country 2 D = 500 PG(2A) = 550 Country 6 D = 300 H(6A) = 600 (NH(6B) = ) Country 1 D = 3000 PG(1A) = 1200 PG(1B) = (NH(1C) = NH(1D) = 600 GT(1E) = 800) Country7D = 400 H(7A) = 450 (NH(7B) = ) Boundary of region for power pool All electricity annual demand growth rates are set at 4% for each country (Italicized values are proposed capacity expansions Key (all values in MW): D = Electricity Demand PG = Old thermal/oil generation CC= Old Combined Cycle generation H = Old hydropower generation

8 Country Country 1 Station Name PG(1A) PG(1B) NH(1C) NH(1D) GT(1E) Details of Station Existing thermal station, 1200MW. Fuel $68?MWh Existing thermal station, 1600MW (expansion is possible up to 2500MW, costing $0.5m/MW). Fuel $44/MWh Proposed new hydro station of 900MW with fixed cost $600m for the first 300MW and then a variable cost of $0.9m/MW Proposed new hydro station of 600MW with a fixed cost of $850m Proposed new gas turbine station capable of expansion up to 600MW with a variable cost of $0.3m/MW. Fuel $6/10 6 Btu

9 Continued Country Country 2 Country 3 Station Name PG(2A) PG(3A) GT(3B) Details of Station Existing thermal station, 550MW. Fuel $80/MWh Existing thermal station, 260MW. Fuel $25/MWh Proposed new gas turbine stations capable of expansion up to 600MW with a variable cost of $0.31m/MW. Fuel $7/10 6 Btu

10 Continued Country Station Name Details of Station Country4 PG(4A) PG(4B) Existing thermal station, 500MW. Fuel $59/MWh Existing combined cycle station, 1200MW, with option of expansion up to 2600MW, with a variable cost of $0.6m/MW. Fuel $30/MWh CC(4C) GT(4D) Proposed new combined cycle station, 300MW, with fixed cost of $175m and then the option of expansion up to 2100MW with a variable cost of $0.55m/MW. Fuel $3.8/10 6 Btu Proposed new gas turbine station, 300MW, with a variable cost of $0.325m/MW. Fuel $5.5/10 6 Btu

11 Continued Country Country5 Country6 Country7 Station Name PG(5A) CC(5B) H(6A) NH(6B) H(7A) NH(7B) Details of Station Existing combined cycle plant, 2400MW. Fuel $65/MWh Proposed new combined cycle station, 350MW, with fixed cost $ 405m and then the option of expansion up to 2800MW with a variable cost of $0.63m/MW. Fuel $3.2/10 6 Btu Existing hydropower station, 600MW Proposed new hydropower station, 150MW, with fixed cost of $220m and then the option of expansion up to 900MW with a variable cost of $1.1/MW Existing hydropower station, 450MW Proposed new hydropower station, 200MW, with fixed cost of $270m, with the option of expansion up to 600MW at a variable cost of $1.3m/MW

12 Power Trading Short-Term The Purdue models can be used as a short-term model by limiting the length of the planning horizon. Typically it is used as a 10 year model (5 time periods with each period being 2 years long or 10 periods with each period being 1 year long). The amount of trading taking place (using a cost minimization objective) will be subject to a demand constraint: Generation + Imports + Distributed Generation = Demand Exports Total cost = Operational Costs (Fuel & maintenance) + Penalty Costs of unmet demand/power 07/08/2003 Purdue University 12

13 Tariff Setting The present default arrangement with the Purdue model is such that a trade tariff of 6 cents/kwh will take place when the marginal cost of the exporting country is 2 cents/kwh and the marginal cost of the importing country is 10 cents/kwh. Trade Tariff = Marginal cost*{(exporter cost + Importer cost)/2} The importing country makes a cost saving and the exporting country earns a revenue. 07/08/2003 Purdue University 13

14 Electricity Exporters & Importers Based on the cost minimization the model indicates which countries are net exporters and which are net importers. In the generic model it can be seen from the user-friendly Windows tm interface that the net importing countries would be countries 1, 2 & 3, and the net exporting countries are 4, 5, 6 & 7. Following are the examples: 07/08/2003 Purdue University 14

15 Country 1 Net Importer

16 Country 2 Net Importer

17 Country 3 Net Importer

18 Country 4 Net Exporter

19 Country 5 Net Exporter

20 Country 6 Net Exporter

21 Country 7 Net Exporter

22 Capacity Expansion Planning The model strategically expands generation and transmission capacities for a cost minimization objective. In the generic 7-node model, with free trade, a cost saving of 24% is made over the scenario where there is no trade. The trading in MW reserves provides increased reliability and significantly decreased costs. 07/08/2003 Purdue University 22

23 Capacity Expansion Planning 07/08/2003 Total regional cost ($bn) Operational Costs ($bn) Capacity capital costs ($bn) Unserved Energy, MWh ($bn) Unmet Reserve Margin, MW ($bn) Old Thermal (MW) New Combined Cycle (MW) New Hydropower (MW) New Gas Turbines (MW) Old Transmission (MW) New Transmission MW) Purdue University Free Trade 10 years 4% growth ,185 2, ,617 1,994 No Trade 10 years 4% growth ,110 1,

24 Note that total costs do not include revenues or cost savings from trade.

25 Objective Function Short-Term min c(i,z)pg(i,z,t) + DGcostDG(z,t) + UMcostUM(z) t i z c(i,z) = Fuel Cost/MW at i in z ($) PG(i,z,t) = Power Generation at i in z during t (MW) DGcost = Cost/MW of distributed generation demand ($) DG(z,t) = Distributed Generation in z during t (MW) UMcost = Cost/MW of unmet reserves ($) UM(z) = Unmet reserve requirement in z (MW) 07/08/2003 Purdue University 25

26 Objective Function Long-Term min Y y=1 i z t Y Y y= 1 τ = y ( ) ( ) + ( ) + ( ) c i,z PG i,z,t,y UEcost UE z,t,y UMcost UM z,y ( 1+disc) ( ) ( ) crf expcost i,z PGexp i,z,y ( ) 1+disc τ y + Costs of capital is now incorporated into the structure of the model, crf represents the capital recovery factor. 07/08/2003 Purdue University 26

27 Regional Integration - Transmission Energy Trade MWh Country A Country B Reserve Trade MW Each country/node has existing thermal and high power generation. In the LT model generation capacity expansion takes place. Transmission (existing and proposed) connects A to B, and expansions on the lines also take place. The need for trading requires extra load carrying capability. 07/08/2003 Purdue University 27

28 Figure 4.2 Training Model with Existing International Transmission Lines and Proposed New Lines Boundary of region for power pool Key (all line values in MW): Existing Line Proposed Line Italicized values are proposed new line expansions (MW) All lines can expand up to 2000MW

29 Generic Model, Free Trade, Existing Transmission Expansions

30 Generic Model, Free Trade, New Transmission Projects

31 Generic Model, Free Trade, New Transmission Expansions

32 International Electricity Trading Policy With international electricity imports and exports between utilities, a decision must be taken at some stage regarding the level of dependency that there is to be on the amount of purchases that are to take place. Considerable attention will be given to this policy as it will affect the planning for new capacity and the type of trading contract that is agreed upon between the buyer and seller. 07/08/2003 Purdue University 32

33 Continued The Purdue model represents this important trade policy issue with two parameters that are called autonomy factors. There is an autonomy factor for trading reserves and another one for trading energy: Reserves trading of MW, autonomy factor AF Energy trading of MWh, autonomy factor ENAF 07/08/2003 Purdue University 33

34 Continued. The autonomy factors are implemented such that: Generation Capacity > (Autonomy Factor AF * Peak Demand) Generation Production > (Autonomy Factor ENAF * Hourly Demand) If an autonomy factor is set at 100% independence (AF=1.0) then this means a policy requirement exists such that the country, at all times, will meet all of its own electricity demand. 07/08/2003 Purdue University 34

35 Wheeling Tariffs Where a third party country is involved for wheeling electricity from Country A to Country C then a wheeling policy will be needed. The present model demonstrates where this takes place, and currently allocates gains from trading. Country A Marginal cost: $.02 /kwh $.04/kWh Trade Tariff Country B Marginal cost: $0.06/kWh $.08/kWh Trade Tariff Country C Marginal cost: $0.10/kWh 07/08/2003 Purdue University 35

36 Wheeling Continued The general rule is that gains from trade between two countries are shared on a basis. Trade between A & B is $.04 per kwh, country A has revenue of $.02 and B saves $.02 per kwh Trade between B & C is $.08 per kwh, B has revenue of $.02 per kwh and C has cost saving of $.02 per kwh. Note that country B saves $.04 per kwh, but A and C only save $.02 per kwh. Beware how wheelers can control trade! Fair wheeling policy is a crucial issue in power pools 07/08/2003 Purdue University 36

37 Generation Plans & Technology Options The economic benefits of various generation technologies are included in the Purdue LT Model. Depending upon the type of technology, the capital fixed costs, operational costs - including fuel costs - and heat-rate parameters and others, all vary. How do we choose the most suitable technology? 07/08/2003 Purdue University 37

38 Least-Cost Cost Combined Cycle Capacity Expansions,

39 Least-Cost Hydropower Capacity Expansions,

40 Model Data Collection & Management The large electricity trade model requires extensive and accurate data collection. The methodology for the data collection requires collaborative and well coordinated trained personnel. The reliability of the data collection will determine the reliability of the model output. The model is very sensitive to the data inputs; Garbage in, garbage out. Examples follow of standardized data input sheets. 07/08/2003 Purdue University 40

41 Data Input Selection

42 Electricity Demand Data Inputs

43 Electricity Load Forecast Country:.. A : Yearly Data A1: Annual Peak Demand (MW) A2: Annual Energy Use (GWh) Projected by year, Projected by year, MW GWh Country annual demand growth rate for 2004: [(GWh in 2004 GWh in 2003) / (GWh in 2003)] * 100% 07/08/2003 Purdue University 43

44 Electricity Load Forecast B 1 Weekly peak load (MW) for the most recent year Year: C Electricity Load Forecast Hourly Data (MW) for a Representative Week, in the most recent year (24 x 7 = 168 values) Year:, Week Number: DAY & MW load each hour Hour Sun Mon Tues Weds Thurs Fri Sat 1 07/08/ Purdue University 44

45 Existing Thermal Generation Data Input

46 Existing Thermal Generation Data Input Comment 1. Current net effective (dependable) sent out capacity (MW) 2. Expansion costs dollar per MW of old plants ($/MW) 3. Expansion step size for old thermo plants units (MW) 4. Max possible MW addition to existing thermo plants (MW) 5. Force outage rate for existing thermo units (fraction) 6. Unforced outage rate for existing thermo plants (fraction) 7. Capital recovery factor for existing thermals (fraction/year) 8. Variable O&M for old thermal plants ($/MWh) 9. Heat rate of old thermo plant set equal to one 10. Fuel cost of Existing thermo plant ($/MWh) 11. Escalation rate of fuel costs of old thermo plants (fraction/year) 12. Decay rate of old thermo plants (fraction/year) 13. Old thermal minimum usage in MWh per year 14. Forced Decommissioning AT period ty Value Parameter PGOinit Oexpcost PGOexpstep PGOmax FORPGO UFORPGO crfi VarOMoh HRO fpo fpesco decaypgo PGmin Fdecom

47 Existing Hydropower Data Input

48 Existing Hydropower Data Input Comment 1. Initial capacity of an existing hydro station (MW) 2. Capital cost of additional capacity for existing hydro ($/MW) 3. Expansion step for existing hydro (MW) 4. Maximum MW expansion that can be added (MW) 5. Annual MWh allowed at an existing dam (normal conditions) (MWh/yr) 7. Forced outage rate for existing hydro plant (fraction/year) 8. Capital recovery factor for an existing hydro plant (fraction/year) 9. Variable O&M cost for old hydro ($/MWh) 10. Decay rate of old hydro plants (fraction/year) 11. Reserve margin for hydro plants (fraction) 12. Old hydro minimum usage in MWh per year 13. Forced decommissioning AT period ty Value Parameter Hoinit HOVcost Hoexpstep HOVmax HOLF FORoh Crfih VarOMoh DecayHO Reshyd MinH FdecomH

49 Proposed Combined Cycle Data Input

50 Proposed Combined Cycle Data Input Comment 1. Fixed costs, site purchase preparation & infrastructure ($) 2. Expansion costs of new combined cycle plants ($/MW) 4. Expansion step size for combined cycle plants (MW) 5. Initial capacity of new combined cycle plants (MW) 6. Maximum expansion for a combined cycle plant (MW) 7. Forced outage rate for combined cycle plants (fraction) 8. Unforced outage rate for combined cycle plants (fraction) 9. Capital recovery factor for new thermal (fraction/year) 10. Variable O&M cost for combined cycle plants ($/MWh) 11. Fixed O&M cost for combined cycle plants ($/MW/year) 12. Heat rate of new combined cycle plants BTU s/mwh 13. Fuel costs of new combined cycle plants $/ BTU s 14. Escalation rate of fuel cost of new combined cycle plants (fraction/year) 15. Decay rate of combined cycle plants (fraction/year) 16. Combined cycle built AT period ty 17. Combined cycle built BEFORE or AT period ty 18. Combined cycle NOT built BEFORE or AT period ty 19. Combined cycle minimum usage in MWh per year Value Parameter FGCC NCCexpcost NCCexpstep PGNCCinit PGNCCmax FORNCC UFORNCC Crfni OMCC FixOMCC HRNCC FpNCC FpescNCC DecayNCC AtCC BefCC AftCC MinCC

51 Proposed Transmission Line Data Input

52 Proposed Transmission Line Data Input Comment 1. Initial tie lines capacity for new line (MW) 5. Capital recovery factor for transmission lines (fraction/year) 6. New tie line fixed cost, Engineering, procurement & construction (mill US $) 7. Cost of additional capacity on new line (wire cost) (mill $/MW) 8. Maximum MW expansions that can be added to a new tie line (MW) 9. Transmission loss factor on new lines (%) 12. Annual forced outage rate for new transmission line (%) 15. Decay rate of new lines (fraction/year) 16. Minimum power flow on a new line (MW) 17. Line built AT period ty 18. Line NOT built BEFORE or AT period ty 19. Line built BEFORE or AT period ty Value Parameter PFNinit Crf PFNFc PFNVc PFNVmax PFNloss FORICN DecayPFN MinPFN Atlines Aftlines Beflines

53 Regional Data

54 Country Data

55 Data Collection at Nodes The shipping capacity (of electricity and natural gas) between any two nodes/countries has to be known. Data for the existing and potentially new supply points are all needed. The existing demand at each node and the forecast for electricity growth in demand is required. The fuel types to be supplied to each node, for electricity generation, is part of the data. More than one node for each country can be created if shown to be necessary. 07/08/2003 Purdue University 55

56 In Summary: Power Pool models provide: Quantitative decision support tools Estimates of gains from more flexible trading contracts Support for coordinated planning Demonstration of the economies of scale 07/08/2003 Purdue University 56