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London Economics International LLC Dr. Serkan Bah çeci Contact: serkan @londoneconomics

A Comparative Analysis of Actual LMPs and Estimated SRMCs in the PJM Market prepared for Assessing Restructured Electricity Markets: An APPA Symposium Washington, DC February 5, 2007. London Economics International LLC Dr. Serkan Bah çeci Contact: serkan @londoneconomics.com.

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London Economics International LLC Dr. Serkan Bah çeci Contact: serkan @londoneconomics

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  1. A Comparative Analysis of Actual LMPs and Estimated SRMCs in the PJM Market prepared for Assessing Restructured Electricity Markets: An APPA Symposium Washington, DC February 5, 2007 London Economics International LLC Dr. Serkan Bahçeci Contact: serkan@londoneconomics.com

  2. Plan of presentation Introduction Basics – prices, timeframe, market SRMC Modeling Methodology Results of the Study

  3. LEI has an extensive regulatory advisory background • Focus on applied economics with quantitatively rigorous analysis to policy and regulatory issues in the sector • Evolved as a result of the infrastructure restructuring in the UK – now headquartered in US, a global economic, financial, and strategic advisory professional services firm specializing in energy and infrastructure • LEI has a number of complementary and cross-disciplinary practice areas: • Regulatory economics and market design • Asset valuation, price forecasting and market analysis • Expert testimony and litigation consulting • Strategy advisory • LEI’s advisory experiences are complemented by a unique set of tools include network-based simulation models, cost-benefit frameworks, models of strategic bidding behavior, and real options analysis • Clients in energy industry include financial institutions, law firms, market institutions, utilities and IPPs

  4. Our assignment is to assess the relationship between energy prices and marginal costs of production • Aim to provide insight to the efficiency of market mechanism • Selected timeframe is from January 2003 to July 2006 • PJM Classic only (PJM as of January 2003) • Collate actual historical LMPs • Aggregates of the load zone LMPs – Day Ahead Energy Market • Simulate the market-clearing price that would have resulted if generators bid their short-run marginal costs (SRMC) • Compare actual LMPs to simulated SRMC-based prices • Re-created a price-cost markup index for a sample of discrete, hourly trading intervals over the timeframe based on simulated marginal-cost based bidding

  5. Plan of presentation Introduction Basics – prices, timeframe, market SRMC Modeling Methodology Results of the study

  6. We have focused on the Day-Ahead LMPs in the PJM market, as that is one of longest-lived nodal markets in the US • Day-ahead LMPs are financially binding and typically cover 60% of the system load in PJM (2005 average) • Day-ahead and real time LMPs are actually converging • In PJM, LMP is composed of generation marginal cost + transmission congestion costs (other markets have also included cost of marginal losses, which PJM is also introducing) • LMPs are equal, when transmission system is unconstrained (ignoring loss component) • LMPs vary by location, when transmission system is constrained • LMP is based on how energy flows – not contract paths • generators get paid at generation bus LMP • load zone LMPs used for load settlement • In order to encompass many actual conditions, our modeling focused on ‘LMP areas’ rather than individual nodes

  7. Selected timeframe is long enough to analyze trends and the selected sample is intended to be representative of each year • We sampled days (rather than hours) to capture fluctuations within days • The sample size is 55% • 200 days for each full year • 120 days for 2006 • Sampling is done using Latin hypercube sampling method, that allows multiple categories and makes sure each category is equally represented in the sample • months • days of the week • load level • daily load variation 2005 daily load –365 days 2005 selected sample – 200 days

  8. PJM Classic has 10 transmission zones, based on the service territories of the electric distribution companies

  9. Primary Fuel Type of Major Generating Assets Map also shows state and county borders and location of the 10 load zones While coal is concentrated in the west, Eastern PJM is dominated by gas-fired resources Source: Energy Velocity

  10. … and population density (proxy for load) is higher in the east compared to the west

  11. Network topology depends on transmission interfaces, which are not perfectly aligned with the boundaries of the load zones Map is showing: • Transmission lines greater than 200 kV • Generation units greater than 100 MW • Transmission interfaces recreated from PJM document titled “GIS Applied to PJM Transmission System”, October 18, 2005. Eastern Interface Western Interface Central Interface Source: Energy Velocity

  12. Our five region topology is consistent with the differences in zonal LMPs

  13. LMPs are consistently lower in the western zones during the study timeframe Source: PJM

  14. Plan of presentation Introduction Basics – prices, timeframe, market SRMC Modeling Methodology Results of the Study

  15. Short run marginal cost includes fuel costs, environmental costs and variable O&M costs • Environmental costs cover SO2 and NOx allowances • Variable O&M costs include • operations supervision • coolants and water expenditures; • pumped storage expenses • equipment expenses • steam expenses from other sources • expenses of transferred steam • electric expenses • miscellaneous steam power expenses • maintenance supervision and engineering • structures maintenance • boiler maintenance • maintenance of reservoirs • maintenance of electric plant • miscellaneous maintenance

  16. Fuel is the biggest component in SRMC calculations

  17. Average monthly fuel costs have rising trends through the timeframe

  18. Price-cost markup index is unit free, allowing comparisons across regions and time • Price-cost markup (in dollar terms) is informative but cannot be used for binary comparisons • Price-cost markup index (markup over price) is used by PJM as well as other ISOs, and measures the percentage of the price-cost markup of the actual LMP

  19. We simulated the market using POOLMod, our proprietary market simulation software • For each hour we used actual historical • load • transmission flows • imports and exports • Daily fuel prices are used for thermal plants • For gas and oil fired units the closest NG or oil hub (spot) price is used • Coal prices take long-term contracts and transportation costs into account • The availability of each thermal plant is determined using EPA’s CEMS database and other sources • CEMS cover more than 90% of the nameplate capacity • For hydro plants we used monthly production data as submitted in EIA Form 906

  20. (MW) (MW) Demand duration curve plus reserves for a typical day POOLMod simulates the actual market conditions to calculate SRMC-based market clearing prices Stage 1 Stage 1 Stage 1 Stage 1 - - - - Commitment (daily) Commitment Commitment Commitment Stage 2 Stage 2 Stage 2 Stage 2 - - - - Dispatch Dispatch Dispatch Dispatch (hourly) Is plant Is plant Is plant Is plant Resources dispatched based on offer price subject to transmission limits, as well as plant-specific technical constraints No No No No available? available? available? available? Competitive Competitive Competitive SRMC Not committed Not committed Not committed Not committed Yes for dispatch for dispatch for dispatch for dispatch bidding bidding bidding bidding assumed assumed assumed assumed Review technical Review technical Review technical Review technical capabilities of capabilities of capabilities of capabilities of units and offers units units units LMPs set equal to the bid of the most expensive dispatched resource Schedule hydro Schedule hydro Schedule hydro Schedule hydro based on optimal based on optimal based on optimal based on optimal duration of operation duration of operation duration of operation duration of operation Hourly (MW) (MW) demand

  21. Plan of presentation Introduction Basics – prices, timeframe, market SRMC Modeling Methodology Results of the Study

  22. Monthly averages of price-cost markup indices show variation across regions Peak markup index, %

  23. … and through time Markup index, % Load-weighted monthly peak and off-peak price-cost markup indices

  24. Monthly price-cost markup indices and monthly averages of regional load are not correlated

  25. Region P (Penelec) has relatively higher markup indices compared to the other regions

  26. Region E (AECO, JCPL, PECO, PSEG) had high markups through 2004 to 2005

  27. Region M (PPL and portion of METED) has many statistically insignificant off-peak indices

  28. Region B (BGE, PEPCO and remaining METED) has a smaller difference between peak and off-peak indices

  29. Region D (Delmarva peninsula) has the highest markup index values in late 2004

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