Electric Energy Economics (E3) Group

Last Updated: 19 October 2009

Site Maintained By:
Leigh Tesfatsion
Professor of Economics, Mathematics,
  & Electrical and Computer Engineering
Department of Economics
Iowa State University
Ames, IA 50011-1070
http://www.econ.iastate.edu/tesfatsi/
tesfatsi AT iastate.edu

Table of Contents:

Regular Meeting Time/Place for E3 Group:

Fall 2009 Meetings:   Mondays, 3:10-5:00pm, Coover 2222 (second floor)

General E3 Group Information

E3 Mailing List (includes main participants listed below):

E3Group AT iastate.edu

Core E3 Group Participants:

Other Participants:

Current Project Support

PNNL Project (Wholesale/Retail Power Market Integration):
Sponsor: Pacific Northwest National Laboratory/Battelle
Title: "A Test Bed for the Integrated Experimental Study of Retail and Wholesale Power Market Designs"
Principal Investigator: Leigh Tesfatsion
Date of Award: Funding Start Date - June 2009
Duration of Award: Three years (conditional on successful end-of-year progress reviews)

EPRC Project 3 (ISO/GenCo Risk Management):
Sponsor: Electric Power Research Center (EPRC)
Title: "Financial and Operational Risk Management for Restructured Wholesale Power Markets"
Principle Investigator: Leigh Tesfatsion
Date of Award: Funding Start Date - August 2009
Duration of Award: Three years (conditional on successful end-of-year progress reviews)

EPRC Project 2 (ISO/GenCo Forecasting):
Sponsor: Electric Power Research Center (EPRC)
Title: "Forecasting Grid Congestion and Prices for Transmission Grid Operation and Investment"
Principle Investigators: Chen-Ching Liu and Leigh Tesfatsion
Date of Award: Funding Start Date - August 2007
Duration of Award: Three years (conditional on successful end-of-year progress reviews)
Project Homepage: http://econ.iastate.edu/tesfatsi/EPRCForecastGroup.htm

EPRC Project 1 (MISO Market Performance):
Sponsor: Electric Power Research Center (EPRC)
Title: "Testing the Efficiency and Reliability Impacts of MISO's Midwest Market Initiative"
Principle Investigators: Leigh Tesfatsion (Lead PI) and Herman C. Quirmbach
Date of Award: Funding Start Date - August 2006
Duration of Award: Three years (conditional on successful end-of-year progress reviews)
Project Homepage: http://econ.iastate.edu/tesfatsi/MISOEnergyGroup.htm

NSF Project (U.S. Bulk Energy Transportation Networks):
Sponsor: National Science Foundation (NSF)
Title: "Decision Models for Bulk Energy Transportation Networks"
Program: Human and Social Dynamics Competition (Decision Making, Risk, and Uncertainty)
Principal Investigators:
Jim McCalley (Lead PI), Sarah Ryan, Stephen Sapp, & Leigh Tesfatsion
Date of Award: September 2005
Duration of Award: Three years (with Continuation)
Project Homepage: http://econ.iastate.edu/tesfatsi/NSFEnergy2005.htm

Project Interim Reports

Project Publications and Working Papers

Hongyan Li and Leigh Tesfatsion, "ISO Net Surplus Extraction in Restructured Wholesale Power Markets" (download site), ISU Economics Working Paper No. 09015, August 2009.
Abstract: This study uses dynamic 5-bus and 30-bus test cases to explore the social efficiency implications of the net surplus (congestion rents) collected and redistributed by ISOs in restructured wholesale power markets with grid congestion managed by locational marginal pricing (LMP). Demand price sensitivity and generator learning capabilities are taken as treatment factors. A key finding is that ISO net surplus substantially increases as the price-sensitivity of demand is reduced and the learning capabilities of generators are increased, conditions resulting in greater economic capacity withholding and a possible wastage of resources. A practical implication is that a more transparent public oversight of all net surplus extractions and uses in wholesale power markets operating under LMP would be publicly prudent because these extractions are not structurally well-aligned with social efficiency objectives.

Nanpeng Yu, Chen-Ching Liu, and James Price, “Evaluation of Market Rules Using a Multi-Agent Platform,”, IEEE Transactions on Power Systems, 2009, to appear.

Nanpeng Yu and Chen-Ching Liu, “Multi-agent system applications in power systems,” , in Volume III: Advanced Techniques and Technologies: Facts and A.I. Part Two: Artificial Intelligence Techniques, to appear.

Hongyan Li and Leigh Tesfatsion, "Development of Open Source Software for Power Market Research: The AMES Test Bed" (pdf preprint,601K), Journal of Energy Markets, Vol. 2, No. 2, Summer 2009, 111-128.
Abstract: This study discusses potential benefits and drawbacks of developing open-source software for power market research, using the AMES Wholesale Power Market Test Bed for concrete illustration.

Leigh Tesfatsion, "Auction Basics for Wholesale Power Markets: Objectives and Pricing Rules" (pdf,504K), IEEE Proceedings, Power and Energy Society General Meeting, Calgary, Alberta, CA, July 26-30, 2009.
Abstract: Power systems have distinctive features that greatly complicate the development of auction designs. This study reviews the theory and practice of auction design as it relates specifically to U.S. restructured wholesale power markets, i.e., centrally-administered wholesale power markets with congestion managed by locational marginal prices. Basic auction concepts such as reservation value, net seller surplus, net buyer surplus, competitive market clearing, market efficiency, market pricing rules, supply offers, demand bids, strategic capacity withholding, and market power are explained and illustrated. Complicating factors specific to wholesale power markets are clarified, and recent advances in computational tools designed to address these complications are briefly noted.

Hongyan Li and Leigh Tesfatsion, "The AMES Wholesale Power Market Test Bed: A Computational Laboratory for Research, Teaching, and Training" (pdf,930K), IEEE Proceedings, Power and Energy Society General Meeting, Calgary, Alberta, CA, July 26-30, 2009.
Abstract: Wholesale power markets around the world are currently undergoing a controversial restructuring of their architecture and rules of operation. Some commentators have argued that restructuring has not produced the intended improvements in market efficiency while at the same time it has complicated efforts to ensure reliability and fairness of operations. This situation suggests the desirability of having publicly available test beds suitable for the objective study of this restructuring process. This study reports on the AMES Wholesale Power Market Test Bed. AMES is an open-source agent-based computational laboratory designed for the systematic study of restructured wholesale power markets operating over AC transmission grids subject to congestion. The AMES traders have learning capabilities permitting them to evolve their trading strategies over time. The potential usefulness of AMES for research, teaching, and training purposes is discussed and illustrated.

Hongyan Li, Junjie Sun, and Leigh Tesfatsion, "Separation and Volatility of Locational Marginal Prices in Restructured Wholesale Power Markets" (download site), ISU Economics Working Paper #09009, June 2009.
Abstract: This study uses the AMES test bed to investigate separation and volatility of locational marginal prices (LMPs) in an ISO-managed restructured wholesale power market operating over an AC transmission grid. Particular attention is focused on the dynamic and cross-sectional response of LMPs to systematic changes in demand-bid price sensitivities and supply-offer price cap levels under varied learning specifications for the generation companies. Also explored is the extent to which the supply offers of the marginal (price-determining) generation companies induce correlations among neighboring LMPs.

Haifeng Liu, Leigh Tesfatsion, and A. A. Chowdhury, "Derivation of Locational Marginal Prices for Restructured Wholesale Power Markets", Journal of Energy Markets, Vol. 2, No. 1, Spring 2009, 3-27.
Note: An abridged version of this paper was presented at the IEEE Power and Energy Society General Meeting, Calgary, CA, July 26-30, 2009.
Abstract: Although Locational Marginal Pricing (LMP) plays an important role in many restructured wholesale power markets, the detailed derivation of LMPs as actually used in industry practice is not readily available. This lack of transparency greatly hinders the efforts of researchers to evaluate the performance of these markets. In this paper, different AC and DC optimal power flow (OPF) models are presented to help understand the derivation of LMPs. As a byproduct of this analysis, we are able to provide a rigorous explanation of the basic LMP and LMP-decomposition formulas (neglecting real power losses) presented without derivation in the business practice manuals of the U.S. Midwest Independent System Operator (MISO).

Qun Zhou, Leigh Tesfatsion, and Chen-Ching Liu, "Scenario Generation for Price Forecasting in Restructured Wholesale Power Markets" (pdf,176K), IEEE Proceedings, Power Systems & Exposition Conference, Seattle, WA, March 15-18, 2009.
Abstract: In current restructured wholesale power markets, the short length of time series for prices makes it difficult to use empirical price data to test existing price forecasting tools and to develop new price forecasting tools. This study therefore proposes a two-stage approach for generating simulated price scenarios based on the available price data. The first stage consists of an Autoregressive Moving Average (ARMA) model for determining scenarios of cleared demands and scheduled generator outages (D&O), and a moment-matching method for reducing the number of D&O scenarios to a practical scale. In the second stage, polynomials are fitted between D&O and wholesale power prices in order to obtain price scenarios for a specified time frame. Time series data from the Midwest ISO (MISO) are used as a test system to validate the proposed approach. The simulation results indicate that the proposed approach is able to generate price scenarios for distinct seasons with empirically realistic characteristics.

Hongyan Li and Leigh Tesfatsion, "Capacity Withholding in Restructured Wholesale Power Markets: An Agent-Based Test Bed Study" (pdf,2.3M), IEEE Proceedings, Power Systems & Exposition Conference, Seattle, WA, March 15-18, 2009.
Abstract: This study uses a dynamic 5-bus test case implemented via the AMES Wholesale Power Market Test Bed to investigate strategic capacity withholding by generation companies (GenCos) in restructured wholesale power markets under systematically varied demand conditions. The strategic behaviors of the GenCos are simulated by means of a stochastic reinforcement learning algorithm motivated by human-subject laboratory experiments. The learning GenCos attempt to improve their earnings over time by strategic selection of their reported supply offers. This strategic selection can involve both physical capacity withholding (reporting of lower-than-true maximum operating capacity) and economic capacity withholding (reporting of higher-than-true marginal costs). We explore the ability of demand conditions to mitigate incentives for capacity withholding by letting demand bids vary from 100% fixed demand to 100% price-sensitive demand.

Abhishek Somani and Leigh Tesfatsion, "An Agent-Based Test Bed Study of Wholesale Power Market Performance Measures" (pdf,2.8M), IEEE Computational Intelligence Magazine, Vol. 3, No. 4, November 2008, 56-72.
Abstract: Wholesale power markets operating over transmission grids subject to congestion have distinctive features that complicate the detection of market power and operational inefficiency. This study uses a wholesale power market test bed with strategically learning traders to experimentally test the extent to which market performance measures commonly used for other industries are informative for the dynamic operation of restructured wholesale power markets. Examined measures include the Herfindahl-Hirschman Index (HHI), the Lerner Index, the Residual Supply Index, the Relative Market Advantage Index, and the Operational Efficiency Index. It is also shown that the objective function commonly used to manage these markets deviates systematically from the standard economic measure of market efficiency when grid congestion is present.

Hongyan Li, Junjie Sun, and Leigh Tesfatsion, "Dynamic LMP Response Under Alternative Price-Cap and Price-Sensitive Demand Scenarios" (pdf,465K), IEEE Proceedings, Power and Energy Society General Meeting, Carnegie-Mellon University, Pittsburgh, July 20-24, 2008.
Abstract: This study investigates the complicated nonlinear effects of demand-bid price sensitivity and supply-offer price caps on Locational Marginal Prices (LMPs) for bulk electric power when profit-seeking generators can learn over time how to strategize their supply offers. Systematic computational experiments are conducted using AMES, an open-source agent-based test bed developed by the authors. AMES models a restructured wholesale power market operating through time over an AC transmission grid subject to line constraints, generation capacity constraints, and strategic trader behaviors.

Nanpeng Yu, Chen-Ching Liu, and Leigh Tesfatsion, "Modeling of Suppliers’ Learning Behaviors in an Electricity Market Environment" (pdf,277K), International Journal of Engineering Intelligent Systems, vol. 15, no. 2, pp. 115-121, 2007.
Abstract: The Day-Ahead electricity market is modeled as a multi-agent system with interacting agents including supplier agents, Load Serving Entities and a Market Operator. Simulation of the market clearing results under the scenario in which agents have learning capabilities is compared with the scenario where agents report true marginal costs. It is shown that, with Q-Learning, electricity suppliers are making more profits compared to the scenario without learning due to strategic gaming. As a result, the LMP at each bus is substantially higher.

Junjie Sun and Leigh Tesfatsion, "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework", Computational Economics, Volume 30, Number 3, 2007, pp. 291-327. This article is an abridged version of ISU Economics Working Paper No. 06025 (pdf,2.2MB).
Abstract: In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design - the Wholesale Power Market Platform (WPMP) - for common adoption by all U.S. wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, and the Southwest, and adopted for implementation in California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid subject to congestion effects. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration.

Junjie Sun and Leigh Tesfatsion, "An Agent-Based Computational Laboratory for Wholesale Power Market Design" (pdf,724K), IEEE Proceedings, Power and Energy Society General Meeting, Tampa, Florida, June 2007.
Abstract: This proceedings paper is a brief summary of a Computational Economics article (see above). It reports on the model development and open-source implementation (in Java) of an agent-based computational wholesale power market organized in accordance with core FERC-recommended design features and operating over a realistically rendered transmission grid subject to congestion effects.

Junjie Sun and Leigh Tesfatsion, "Open-Source Software for Power Industry Research, Teaching, and Training: A DC-OPF Illustration" (pdf,115K), IEEE Proceedings, Power and Energy Society General Meeting, Tampa, Florida, June 2007.
Abstract: This proceedings paper is a brief summary of ISU Economics Working Paper No. 06014 (see below). It reports on the development and implementation of a stand-alone open-source Java solver for DC optimal power flow (DC-OPF) problems suitable for research, teaching, and training purposes. The DC-OPF solver is shown to match or exceed the accuracy of BPMPD (a proprietary third-party QP solver highly recommended by MatPower) when tested on a public repository of small to medium-sized QP problems. The capabilities of the DC-OPF solver are illustrated for a 5-node DC-OPF test case commonly used for training purposes.

Junjie Sun and Leigh Tesfatsion, "DC Optimal Power Flow Formulation and Testing Using QuadProgJ" (pdf,526K), ISU Economics Working Paper No. 06014, Department of Economics, Iowa State University, Revised July 2007.
Abstract: Under a set of simplifying assumptions, a nonlinear AC Optimal Power Flow (OPF) problem can be approximated by a linearized DC OPF problem to solve for power quantities and locational marginal prices in restructured electric wholesale power markets. We first establish that a commonly used DC OPF approximation in per unit form can be represented as a strictly convex quadratic programming (SCQP) problem subject to mixed equality and inequality constraints, given a physically meaningful Lagrangian augmentation. We then show how this SCQP problem can be solved using QuadProgJ, a Java SCQP solver newly developed by the authors that implements the well-known dual active-set SCQP algorithm by Goldfarb and Idnani (1983). QuadProgJ appears to be the first open-source SCQP solver developed completely in Java. QuadProgJ is specifically designed for the fast and efficient desktop solution of SCQP problems for research and training purposes with a maximum count of about 1500 decision variables plus constraints. Several numerical examples are provided to illustrate the accuracy of QuadProgJ, including 3-node and 5-node DC OPF case studies taken from power systems texts and ISO-NE/PJM training manuals.

Junjie Sun and Wenzhuo Shang, "Evaluating the Performance of Financial Transmission Rights Auctions: Evidence from the U.S. Midwest Energy Region" (pdf), Dissertation Chapter, November 2006.
Abstract: This paper applies empirical methods to analyze performance of financial transmission rights (FTRs) auction markets in the Midwest energy region (MISO). The data we used are monthly FTR auction clearing prices and associated congestion revenues for the period April 2005 - March 2006. Based on the preliminary statistical analysis, we summarize and present the stylized facts about the MISO FTR auction market. Moreover, we fit the data with linear regressions and nonparametric kernel regressions, and carry out a bootstrap-based goodness-of-fit test on the linear versus kernel fits. The revenue sufficiency results suggest that the MISO FTR market is systematically losing money, which suggests that the market participants exhibit some degree of risk affection. More data are needed in order to obtain meaningful economic analysis such as estimating the impact of an agent's risk preference on his willingness to pay for the premium of FTR in this complex market. It would be especially helpful to acquire the actual bidding and asking prices of market participants in the MISO FTR auctions over time.

Steven Widergren, Junjie Sun, and Leigh Tesfatsion, "Market Design Test Environments" (pdf preprint,136K), IEEE Proceedings, Power and Energy Society General Meeting, Montreal, June 2006.
Abstract: Power industry restructuring continues to evolve at multiple levels of system operations. At the bulk electricity level, several organizations charged with regional system operation are implementing versions of a Wholesale Power Market Platform (WPMP) in response to U.S. Federal Energy Regulatory Commission initiatives. Recently the Energy Policy Act of 2005 and several regional initiatives have been pressing the integration of demand response as a resource for system operations. These policy and regulatory pressures are driving the exploration of new market designs at the wholesale and retail levels. The complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance demand a flexible computational environment where designs can be tested and sensitivities to power system and market rule changes can be explored. This paper discusses the use of agent-based computational methods for the study of electricity markets at the wholesale and retail levels, and explores distinctions in problem formulation between these levels.

Open Source Software Releases

AMES Wholesale Power Market Test Bed (Java): A Free Open Source Computational Laboratory for the Agent-Based Modeling of Electricity Systems

The AMES Wholesale Power Market Test Bed, developed entirely in Java by Hongyan Li, Junjie Sun, and Leigh Tesfatsion, is an extensible and modular agent-based computational laboratory for studying the dynamic efficiency and reliability of wholesale power markets restructured in accordance with guidelines issued by the U.S. Federal Energy Regulatory Commission. AMES models strategically-learning traders interacting over time in an ISO-managed wholesale power market operating over a transmission grid subject to congestion effects. Congestion on the grid is managed by means of locational marginal prices derived from optimal power flow solutions.

AMES is a free open source tool suitable for research, teaching, and training applications. It is designed for the intensive experimental study of small to medium-sized systems. A graphical user interface permits the creation, modification, analysis and storage of scenarios, parameter initialization and editing, specification of behavioral rules (e.g. learning methods) for market participants, and output reports through table and chart displays. AMES is an acronym for Agent-based Modeling of Electricity Systems.

DCOPFJ (Java): A Free Open Source Solver for DC Optimal Power Flow Problems

The DCOPFJ Package, developed entirely in Java by Junjie Sun and Leigh Tesfatsion, is a free open source stand-alone solver for small to medium-sized DC optimal power flow problems having a strictly convex quadratic programming (SCQP) formulation.

The DCOPFJ package incorporates an SCQP solver (QuadProgJ) wrapped in an outer SI-to-PU data processing shell. QuadProgJ implements the well-known dual active-set SCQP algorithm developed by Goldfarb and Idnani (1983). QuadProgJ has been shown to match or exceed the accuracy of the proprietary C-language QP solver BPMPD (highly recommended by MATPOWER) when tested on a public repository of small to medium-sized SCQP problems.

The DCOPFJ package has been successfully run on DC-OPF test cases commonly used for training purposes.

Project-Related Presentations to External Groups (Ordered by Date of Presentation)

Background Readings

Online Resource Materials

Miscellaneous