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Systems Analysis Advisory Committee (SAAC) Thursday, December 19, 2002 Michael Schilmoeller John Fazio 1 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) 2 Northwest Power Planning Council 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 3 Northwest Power Planning Council 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 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 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 6 Northwest Power Planning Council Review 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 7 Northwest Power Planning Council Review 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 8 Northwest Power Planning Council Review 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) 9 Northwest Power Planning Council Review wholesale electricity market “Real Options” minimum restart period minimum shut-down period evalulation phase aluminum-elec price spread expected price trend evalulation phase time 10 Northwest Power Planning Council Review 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 11 Northwest Power Planning Council Review 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 12 Northwest Power Planning Council Review 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 13 Northwest Power Planning Council 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 14 Northwest Power Planning Council Transmission Reliability 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. 15 Northwest Power Planning Council Transmission Reliability 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 Native Net * * Net Native * Native Net 16 Northwest Power Planning Council Transmission Reliability Unconstrained Case • Given resource stacks in each area, the flows and prices are determined by total native loads, and all prices are the same. Native Net * * Net Native * Native Net 17 Northwest Power Planning Council Transmission Reliability 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 Native Net * * Net Native * Native Net 18 Northwest Power Planning Council Transmission Reliability 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. Native Net * * Net Native * Native Net 19 Northwest Power Planning Council Transmission Reliability 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. 20 Northwest Power Planning Council Transmission Reliability 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. 21 Northwest Power Planning Council Transmission Reliability 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. Native Net * * Net Native * Native Net 22 Northwest Power Planning Council Transmission Reliability 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. 23 Northwest Power Planning Council Transmission Reliability 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 24 Northwest Power Planning Council 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 25 Northwest Power Planning Council Resource Diversity 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) 26 Northwest Power Planning Council Resource Diversity Ensemble Forced Outage Rate • At the user’s discretion, unit availability may be modelled using a binomial distribution (assumed independent of other stochastic variables) Probability density 1.2 Hour’s Load 1 density 0.8 0.6 LOLP 0.4 0.2 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Hour's Load 27 Northwest Power Planning Council Resource Diversity 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 28 Northwest Power Planning Council Resource Diversity 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 29 Northwest Power Planning Council 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 Transmission Congestion Rescource Outages Hydro Generation Reserve Margin Market Prices for Electricity DSI Loads Aluminum Prices Fuel Prices Non-DSI Loads Temperature 30 Northwest Power Planning Council Influence Diagram Rescource Outages Transmission Congestion Hydro Generation Reserve Margin Market Prices for Electricity DSI Loads Aluminum Prices Fuel Prices Non-DSI Loads Temperature 31 Northwest Power Planning Council Influence Diagram 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 32 Northwest Power Planning Council Statistics • Statistics – – – – Historical Dailie Distributions within the month, year Reasons for variation over time Correlation among electricity, load, temperature, aluminum prices, hydro, natural gas prices 33 Northwest Power Planning Council Statistics 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 34 Northwest Power Planning Council Statistics 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 35 Northwest Power Planning Council Background Slides • These are intended primarily to answer questions that come up 36 Northwest Power Planning Council Representation of dispatchables • Oil price forecast 35 ? 30 25 20 History Low 15 Medlo Medium 10 Medhi ? High EIA02-R 5 EIA02-H EIA902-L 0 8 Others 1990 1995 2000 2005 2010 2015 2020 37 Northwest Power Planning Council Representation of dispatchables • NG price forecast ? 4.5 4 3.5 History Low 2000$/MMBtu 3 Medlo Medium 2.5 Medhi High 2 EIA-Ref ? 1.5 EIA-Low EIA-High 1 DRI-WEFA GRI 0.5 CEC ICF 0 1995 2000 2005 2010 2015 2020 2025 38 Northwest Power Planning Council Electricity Markets • By its nature, distinct markets for electricity exists for different locations and times • Variation vs Volatility • The prices in the figure at the right have NO volatility 39 Northwest Power Planning Council Terms and Concepts Mid-Columbia price forecast Average annual w/comparisons ? $55 $50 Price (2000$/MWh) $45 $40 $35 $30 $25 $20 ? Current Trends Hi Shape (092702) $15 5th Plan corrected transfer (062402). $10 Adequacy & Reliability Study (Feb 2000) $5 $0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 40 Northwest Power Planning Council DSI Loads $3,500 $3,250 $3,000 $2,750 $2,500 $2,250 $2,000 $1,750 $1,500 $1,250 $1,000 Cash 15-Month Real Cash Linear (Real Cash) $0.80 $0.70 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 US$/Tonne LME Cash Aluminum Prices: Daily 1989-2002 41 Northwest Power Planning Council Non-DSI Loads Total Sales Non-DSI 45000 97.5% Average Megawatts 40000 35000 30000 25000 20000 97.5% 15000 23 20 20 20 17 20 14 20 11 20 08 20 05 20 02 20 99 19 96 19 93 19 90 19 87 19 84 19 19 81 10000 42 Northwest Power Planning Council 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 43 Northwest Power Planning Council 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) 44 Northwest Power Planning Council 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) 45 Northwest Power Planning Council