1. 1 Systems Analysis Advisory Committee (SAAC) Thursday, December 19, 2002 Michael Schilmoeller John Fazio

2. Northwest Power Planning Council 2 Last Agenda Approval of the Oct 24 meeting minutes Review and questions from the last meeting –Representation of dispatchable resources in the portfolio model –Metrics Representations in the portfolio model –Price responsive demand –Renewables and conservation Hydro (worksheet function, sustained peaking) Loads (HELM model, Terry Morlan’s DSI model) Natural gas prices (description of processes)

3. Northwest Power Planning Council 3 Today’s Agenda Approval of the Nov 22 meeting minutes Review and questions from the last meeting –Dispatchable plants (Beaver) –Price responsive demand –Renewables and conservation –Hydro –Loads Representation of Transmission Reliability Representation of Resource Diversity Influence Diagram of Effects Statistical Results for –Natural gas prices, electricity prices, load, temperature, aluminum prices, hydro, transmission congestion –Correlations among these

4. Northwest Power Planning Council 4 Today’s Agenda Approval of the Nov 22 meeting minutes Review and questions from the last meeting –Dispatchable plants (Beaver) –Price responsive demand –Renewables and conservation –Hydro –Loads Representation of Transmission Reliability Representation of Resource Diversity Influence Diagram of Effects Statistical Results for –Natural gas prices, electricity prices, load, temperature, aluminum prices, hydro, transmission congestion –Correlations among these

5. Northwest Power Planning Council 5 Today’s Agenda Approval of the Nov 22 meeting minutes Review and questions from the last meeting –Dispatchable plants (Beaver) –Price responsive demand –Renewables and conservation –Hydro –Loads Representation of Transmission Reliability Representation of Resource Diversity Influence Diagram of Effects Statistical Results for –Natural gas prices, electricity prices, load, temperature, aluminum prices, hydro, transmission congestion –Correlations among these

6. Northwest Power Planning Council 6 Plan Issues incentives for generation capacity price responsiveness of demand sustained investment in efficiency information for markets fish operations and power transmission and reliability resource diversity role of BPA global change Review

7. Northwest Power Planning Council 7 Representation of dispatchables The monthly spread option model gives a reasonable representation of expected capacity factors (and hence value) of resource options Given that the uncertainty in hourly prices exceeds the expected variation, the detailed information about hourly prices from any one scenario tells us little about the expected capacity factor and value of resource options Review

8. Northwest Power Planning Council 8 Price responsive demand Intended to represent short-term (1 day to 1 month) load reduction, on- and off-peak, if the price is right Does not address longer term DSI load curtailment (which is addressed later) Described by a supply curve Energy available represented as special continuous function of price –Zero variable cost, but some fixed cost Supply curve developed by Ken Corum Review

9. Northwest Power Planning Council 9 Conservation & Renewables Represent as non-dispatchable energy Supply curve for conservation developed by Tom Eckman Renewables cost and operating characteristics assembled by Jeff King Credit and availability advantages can be valued by adding these uncertainties to alternatives, such as contracts Modularity benefits require a new approach Example of Sustained Orderly Development (SOD) Review

10. Northwest Power Planning Council 10 “Real Options” minimum shut-down period evalulation phase time wholesale electricity market aluminum-elec price spread expected price trend minimum restart period evalulation phase Review

11. Northwest Power Planning Council 11 Loads Non-DSI Loads –Calibrate with data from NWPP –Short-term uncertainty driven by random temperatures (HELM) –Long term uncertainty from Terry Morlan’s work DSI Loads –Terry Morlan’s aluminum industry model Review

12. Northwest Power Planning Council 12 Hydrogeneration Excel Add-in has 50-year record Demonstrate: –Parameters to pull out different data –Use as random draw & correlation with other assumptions –Use of function to pull out specific year Reflects 10-hour sustained peaking capability from the trapazoidal approximation studies Review

13. Northwest Power Planning Council 13 Today’s Agenda Approval of the Nov 22 meeting minutes Review and questions from the last meeting –Dispatchable plants (Beaver) –Price responsive demand –Renewables and conservation –Hydro –Loads Representation of Transmission Reliability Representation of Resource Diversity Influence Diagram of Effects Statistical Results for –Natural gas prices, electricity prices, load, temperature, aluminum prices, hydro, transmission congestion –Correlations among these

14. Northwest Power Planning Council 14 Transmission Reliability To Show: The economic consequences of transmission congestion can be captured with the portfolio model The likelihood of congestion is related to other variables we are considering in the model Transmission Reliability

15. Northwest Power Planning Council 15 Transmission Reliability The portfolio model is not a reliability model or a transmission flow model We will rely on Genesys and primary data to provide insight into conditions when congestion is likely to occur For transmission flow information, we will rely on expert opinion. The Council currently has no transmission flow model. Transmission Reliability

16. Northwest Power Planning Council 16 Transmission Reliability Begin with a common representation of the uncongested economic transport of energy (e.g., Aurora, Henwood’s Prosym) Simple model: no losses or variable wheeling charges Transmission Reliability Net Native Net Native Net Native * * *

17. Northwest Power Planning Council 17 Unconstrained Case Given resource stacks in each area, the flows and prices are determined by total native loads, and all prices are the same. Transmission Reliability Net Native Net Native Net Native * * *

18. Northwest Power Planning Council 18 Constrained Case To get some constraint, we assume the native load in one region increases a lot Transmission lines are filled to their maximum capacity Higher native load means higher net load Prices disconnect Transmission Reliability Net Native Net Native * * * Net Native

19. Northwest Power Planning Council 19 Constrained Case In the constrained case, the marginal value of transmission is the difference in price between areas of price difference. Equivalently, the cumulative value of transmission is the difference in costs to meet load, with and without the congestion. Transmission Reliability Net Native Net Native * * * Net Native

20. Northwest Power Planning Council 20 Statistical Representation In these situations, cause and effect is clear. The native loads drive prices. When the system is uncongested, prices in all regions are the same. When the system is congested, higher demand in some areas will result in the dispatch of more costly resources, resulting in higher prices for the high demand area than for other areas. Statistics does not care about causality. It deals with relationships among the values. Transmission Reliability

21. Northwest Power Planning Council 21 Statistical Representation If the prices are all equal, the transmission system is uncongested. We know exactly which resources are on the margin in each area and consequently what each net load is. (If we know the native load, we can also calculate the transmission flows.) If the prices are not equal, the transmission system is congested. We know exactly which resources are on the margin in each area and consequently what each net load is. Since the transmission flows are at their maximum capability, we know what native loads are. Transmission Reliability

22. Northwest Power Planning Council 22 Constrained Case The same information can be obtained either with information about the native loads or the native prices. The value of improved transmission reliability, of course, will also be the same. Transmission Reliability Net Native Net Native * * * Net Native

23. Northwest Power Planning Council 23 Conclusions about Transmission Reliability Market prices in regions, in particular the differences among prices, will give a “dual” representation of the state of the system. To predict when prices between regions are likely to be different (when congestion is likely to occur), we need statistical information relating congestion to other variables, such as temperatures or loads. Transmission congestion can then be modelled using a distribution of price differences that are correlated with the other variables. Transmission Reliability

24. Northwest Power Planning Council 24 Today’s Agenda Approval of the Nov 22 meeting minutes Review and questions from the last meeting –Dispatchable plants (Beaver) –Price responsive demand –Renewables and conservation –Hydro –Loads Representation of Transmission Reliability Representation of Resource Diversity Influence Diagram of Effects Statistical Results for –Natural gas prices, electricity prices, load, temperature, aluminum prices, hydro, transmission congestion –Correlations among these

25. Northwest Power Planning Council 25 Resource Diversity Benefits of resource diversity –Enhanced reliability ensemble forced outage rate not susceptible to transmission congestion –Depending on the technology, some diversification away from other, more dominant technologies –Distribution system advantages Voltage support Lower losses Delayed distribution system expansion costs Resource Diversity

26. Northwest Power Planning Council 26 Benefits of resource diversity Some benefits may be addressed as fixed cost adjustments –Voltage support (displacement of turbine capacity or reactive power elements) –Delayed distribution system expansion costs (displacement of comparable quantities of transforms) Lower losses benefit may be modelled by systematic modification to loads or an adjustment to the unit’s capacity The reliability enhancement related to having resources closer to the load may be modelled in the same way that we model local versus remote resources, i.e., by the price the resource sees The technology diversification is handled automatically Enhanced reliability due to ensemble forced outage rate may be represented using a different binomial distribution for availability (continued) Resource Diversity

27. Northwest Power Planning Council 27 Ensemble Forced Outage Rate At the user’s discretion, unit availability may be modelled using a binomial distribution (assumed independent of other stochastic variables) Resource Diversity LOLP Hour’s Load

28. Northwest Power Planning Council 28 Conclusions of Resource Diversity Representation We can expect that the economic advantages of resource diversity can be captured with the portfolio model We will rely on the Council’s Jeff King for the operational and economic detailed attributes of these technologies Resource Diversity

29. Northwest Power Planning Council 29 Today’s Agenda Approval of the Nov 22 meeting minutes Review and questions from the last meeting –Dispatchable plants (Beaver) –Price responsive demand –Renewables and conservation –Hydro –Loads Representation of Transmission Reliability Representation of Resource Diversity Influence Diagram of Effects Statistical Results for –Natural gas prices, electricity prices, load, temperature, aluminum prices, hydro, transmission congestion –Correlations among these

30. Northwest Power Planning Council 30 Influence Diagram of Effects Objectives: –To share views and develop some consensus on the significance and relationships among variable. –To construct a roadmap for finding relationships among variables Influence Diagram

31. Northwest Power Planning Council 31 Influence Diagram Rescource Outages Hydro Generation DSI Loads Non - DSI Loads Market Prices for Electricity Aluminum Prices Reserve Margin Temperature Fuel Prices Transmission Congestion

32. Northwest Power Planning Council 32 Today’s Agenda Approval of the Nov 22 meeting minutes Review and questions from the last meeting –Dispatchable plants (Beaver) –Price responsive demand –Renewables and conservation –Hydro –Loads Representation of Transmission Reliability Representation of Resource Diversity Influence Diagram of Effects Statistical Results for –Natural gas prices, electricity prices, load, temperature, aluminum prices, hydro, transmission congestion –Correlations among these

33. Northwest Power Planning Council 33 Statistics –Historical Dailie –Distributions within the month, year –Reasons for variation over time –Correlation among electricity, load, temperature, aluminum prices, hydro, natural gas prices Statistics

34. Northwest Power Planning Council 34 Statistics Daily correlation between gas prices and electricity prices Daily correlation between hydro generation and electricity prices Weaker correlation between gas prices and California temperatures Statistics

35. Northwest Power Planning Council 35 Next Meeting Remaining work on statistics Issue: Incentives for new generation Review of risk management problems of 2000-2001 –What worked and what did not Initial optimization for Region, using all mechanisms

36. Northwest Power Planning Council 36 Background Slides These are intended primarily to answer questions that come up

37. Northwest Power Planning Council 37 Representation of dispatchables Oil price forecast ? ?

38. Northwest Power Planning Council 38 Representation of dispatchables NG price forecast ? ?

39. Northwest Power Planning Council 39 Electricity Markets By its nature, distinct markets for electricity exists for different locations and times Terms and Concepts Variation vs Volatility The prices in the figure at the right have NO volatility

40. Northwest Power Planning Council 40 Mid-Columbia price forecast Average annual w/comparisons ? ?

41. Northwest Power Planning Council 41 DSI Loads

42. Northwest Power Planning Council 42 Non-DSI Loads Non-DSI 97.5%

43. Northwest Power Planning Council 43 Natural Gas Prices Data from Gas Daily Statistics? –Historical Dailies –Price processes –Distributions within the month, year –Future uncertainties (Terry) –Reasons for variation over time –Correlation with electricity, load, temperature, aluminum prices, hydro

44. Northwest Power Planning Council 44 Natural Gas Prices 1. Mean Reversion - Vasicek Model P(t+dt) - P(t) = Beta*(Alpha - P(t))*dt + Sigma*sqrt(dt)*N(0,1) 2. Mean reversion - CIR Model P(t+dt) - P(t) = Beta*(Alpha - P(t))*dt + Sigma*Sqrt(P(t))*sqrt(dt)*N(0,1) 3. Geometric Brownian Motion - GBM P(t+dt) - P(t) = Drift*P(t)*dt + Sigma*P(t)*sqrt(dt)*N(0,1) 4. Mean reversion - unrestricted P(t+dt) - P(t) = Beta*(Alpha - P(t))*dt + Sigma*P(t)^Gamma*sqrt(dt)*N(0,1) 5. Jump-diffusion (Use the same time step for estimation and simulation - h doesn't scale!!) P(t+dt) = P(t)exp( Drift*dt + Sigma*sqrt(dt)*N(0,1)+Y*N(Drift_j,Sigma_j)) Y=1 with probability h and Y=0 with probability (1-h) 6. Brennan and Schwartz Model P(t+dt) - P(t) = Beta*(Alpha - P(t))*dt + Sigma*P(t)*sqrt(dt)*N(0,1) 7. Mean reversion with jump-diffusion, Vasicek type diffusion P(t+dt) - P(t) = Beta*(Alpha - P(t))*dt + Sigma*sqrt(dt)*N(0,1)+Y*N(Drift_j,Sigma_j) Y=1 with probability h and Y=0 with probability (1-h)

45. Northwest Power Planning Council 45 Natural Gas Prices 8. Mean reversion with jump-diffusion, CIR type diffusion P(t+dt) - P(t) = Beta*(Alpha - P(t))*dt + P(t)^0.5*(Sigma*sqrt(dt)*N(0,1)+Y*N(Drift_j,Sigma_j)) Y=1 with probability h and Y=0 with probability (1-h) 9. Mean reversion with jump-diffusion, Brennan-Shcwartz type diffusion P(t+dt) - P(t) = Beta*(Alpha - P(t))*dt + P(t)*(Sigma*sqrt(dt)*N(0,1)+Y*N(Drift_j,Sigma_j)) Y=1 with probability h and Y=0 with probability (1-h) 10. Mean reversion with jump-diffusion, "Unrestricted" type diffusion P(t+dt) - P(t) = Beta*(Alpha - P(t))*dt + P(t)^gamma*(Sigma*sqrt(dt)*N(0,1)+Y*N(Drift_j,Sigma_j)) Y=1 with probability h and Y=0 with probability (1-h)