The AMES Wholesale Power Market Test Bed
An Open-Source Computational Laboratory
- Last Updated: 16 January 2023
- Site Maintained By:
- Research Professor & Professor Emerita of Economics
- Courtesy Research Professor of
Electrical & Computer Engineering
- Heady Hall 260
- Iowa State University
- Ames, Iowa 50011-1054
tesfatsi AT iastate.edu
Integrated T&D System Project
Electricity Market Open-Source Software
Agent-Based Electricity Research
Agent-Based Computational Economics (ACE)
Software Release Disclaimer:
- The AMES Market Package is the software implementation, in Java/Python, of the AMES Wholesale Power Market Test Bed. This software, provided below, is unsupported and provided as-is, without warranty of any kind.
Table of Contents:
AMES Software Overview
The wholesale power market design proposed by the U.S. Federal Energy Regulatory Commission (FERC) in an April 2003 white paper
has the following core features:
- a wholesale power market operating over a high-voltage transmission grid;
- central management by an independent system operator;
- a two-settlement system consisting of a daily day-ahead market operating in tandem with a collection of
shorter-horizon real-time markets
to ensure continual balancing of supply and demand for power
across the grid;
- management of grid congestion by means of locational marginal pricing (LMP), i.e., the pricing of power by the location and timing of its injection into, or withdrawal from, the transmission grid;
- market power oversight and mitigation.
Versions of FERC's wholesale power market design have been implemented in seven U.S. energy regions: the Midwest (MISO), New England (ISO-NE), New York (NYISO), the mid-Atlantic states (PJMB), California (CAISO), the southwest (SPP), and Texas (ERCOT).
One key problem for researchers wishing to study these markets is a lack of full transparency regarding market operations. Due in great part to the complexity of the market design in its various actual implementations, the business practices manuals and other public documents released by market operators are daunting to read and difficult to comprehend. A second key problem is that market outcomes are typically posted in a partial and masked form, with a significant time delay. A third key problem is that structural aspects such as grid topology
are not publicly released for security reasons. The result is that it is difficult for outsiders (e.g., university researchers) to subject the operation of these markets to systematic performance testing in a compelling manner.
The AMES Wholesale Power Market Test Bed has been developed in response to these concerns. Its purpose is to provide a "simple but not too simple" computational laboratory for the systematic experimental study of wholesale power markets restructured in accordance with FERC's market design. AMES is an acronym for Agent-based Modeling of Electricity Systems.
More precisely, AMES has been designed to facilitate research, teaching, and training, not commercial-grade applications. The open source release of AMES is intended to encourage the cumulative development of this test bed by others (as well as ourselves) in directions appropriate for their specific needs.
It is also intended to encourage continual dialog with market stakeholders and regulators leading to successive refinements and improvements of the test bed.
To further these purposes, AMES has been constructed (in Java/Python) to have an extensible modular architecture and an easily-navigated graphical user interface (GUI).
The latest AMES release (8/7/2020) is AMES V5.0. Documentation, source code, and test case materials are maintained for AMES V5.0 at a GitHub repository, and are maintained for earlier AMES releases V3.0 and V4.0 at separate developer repositories. Links to all of these sites are provided below.
This AMES homepage provides a detailed description for AMES V2.06, the basic foundation for all subsequent AMES version releases. While AMES V2.06 lacks the sophisticated features of later releases, its simpler form could provide a useful starting point for power market researchers and educators.
Detailed descriptions and downloads for all AMES versions released to date, with summary explanations and comparisons of their features, can be found at the
AMES Version Release History Site.
AMES Software Downloads and Supporting Materials
International users should be aware that AMES uses U.S. formatting (points) for decimal separators, not commas; e.g., 30000.00 rather than 30000,00. Use of commas instead of points for decimals will result in incorrect outcomes and possibly also in error messages indicating "out of bound" numbers. As noted in Step 14 in the
Basic AMES Instructions Manual (pdf,632KB),
to avoid this problem some international users have reported they found it necessary to include one of the following instructions in the "VMOptions" tab:
“-Duser.language=en –Duser.region=US” (Win OS); or
“-Duser.language=en” (Mac OS).
AMES Market Package--Version 5.0 (Released: 8/7/2020)
AMES V5.0 was developed by Swathi Battula and Leigh Tesfatsion at Iowa State University, with support from Pacific Northwest National Laboratory researchers (Tom McDermott, Mitch Pelton, Qiuhua Huang, and Sarmad Hanif). Documentation, source code, and test case materials for AMES V5.0 can be found at the
AMES V5.0 GitHub Repository
maintained by Swathi Battula.
ERCOT Test System (Released 8/7/2020): Implemented in part by AMES V5.0
The ERCOT Test System, based on operating characteristics and data for the Electric Reliability Council of Texas (ERCOT) energy region, was developed by Swathi Battula and Leigh Tesfatsion at Iowa State University and Thomas E. McDermott at the Pacific Northwest National Laboratory. Documentation, source code, and test case materials for the ERCOT Test System can be found at the
ERCOT Test System GitHub Repository.
AMES Market Package--Version 4.0 (Released 4/13/2017)
AMES V4.0 was developed by Dheepak Krishnamurthy, Sean L. Mooney, Auswin George Thomas, Wanning Li, and Leigh Tesfatsion at Iowa State University.
Documentation, source code, and test case materials for AMES V4.0 can be found at the
AMES V4.0 GitHub Repository
maintained by Dheepak Krishnamurthy.
Eight-Zone ISO-NE Test System (Released 11/30/2015): Implemented in part by AMES V4.0
The 8-Zone ISO-NE Test System, based on operating characteristics and data for the ISO New England energy region, was developed by Dheepak Krishnamurthy, Wanning Li, and Leigh Tesfatsion at Iowa State University. Documentation, source code, and test case materials for the 8-Bus ISO-NE Test System can be found at the
8-Zone ISO-NE Test System Repository
maintained by Dheepak Krishnamurthy.
AMES Market Package--Version 3.0 (Released 12/16/2017)
AMES 3.0 was developed by Auswin George Thomas and Leigh Tesfatsion, at Iowa State University. Documentation and source code for AMES V3.0 can be found at the
AMES V3.0 Homepage,
maintained by Auswin George Thomas.
AMES Market Package--Version 2.06 (Released: 5/22/2013)
AMES V2.06 was developed by Hongyan Li, Leigh Tesfatsion, and Sean L. Mooney at Iowa State University.
AMES V2.06 Software Developers
AMES V2.06 Software Downloads
- Zip file
(i) an src folder containing full compilable source code; (ii) a lib folder containing all required libraries for building, testing, and generating documentation;
(iii) a test folder containing basic unit tests;
(iv) a DATA folder containing a 2-bus test case, a 5-bus test case, and a 30-bus test case (in both regular and batched modes);
(v) a netbeans project folder nbproject; and
(vi) a build file (build.xml) to facilitate command line use.
- Zip file
that includes a directly runnable version of AMES V2.06 in the form of a compiled jar, runtime libraries/jars, and a data folder
AMES V2.06 Development Software
The following free open-source software was used to support the development of AMES V2.06.
- Java platform: For Java SE Development Kit (JDKB) 6 update 1 (6u1) or higher, visit the
Java SE Downloads Page.
You can download JDK 6u1 (or higher) either alone or in combination with the NetBeans Integrated Development Environment (IDE) 6.0 (or higher).
- Java IDE: For NetBeans IDE 6.0 (or higher), visit the
You can also download the Java SE Development Kit (JDKB) 6u1 (or higher) with the NetBeans IDE from the linked download page. The NetBeans IDE is a powerful open-source cross-platform tool for Java programming.
IMPORTANT NOTE: JAR (Java ARchive) files for the following support software for AMES V2.06 are included in the library directory as part of the AMES download and do not need to be separately downloaded in order to compile and run AMES V2.06.
- Java Chart Library: JFreeChart
- Repast J: A Java Agent-Based Toolkit, Version 3.1
For an on-line self-study guide for Repast J and Java, visit
- JReLM (Java REinforcement Learning Module): Open-source software for use with Repast J developed by Charles Gieseler:
Source Code (zip,110KB);
M.S. Thesis (pdf,1.1MB);
Thesis Slide Presentation (pdf,1.1MB).
- DCOPFJ (DC-OPF Solver in Java): A free open-source solver for DC optimal power flow
problems. For software downloads and manual materials, visit
AMES V2.06 Set-Up Illustration for the NetBeans Integrated Development Environment (IDE)
IMPORTANT NOTE: Similar set-up instructions apply for other Java IDEs such as Eclipse.
- The first step is to install JDK 6u1 (or higher) either separately or in combination with the NetBeans IDE (6.0 or higher). Please note that JDK 6u1 (or higher) is required for the AMES V2.06 code to run correctly. Error messages will be generated if you attempt to compile the AMES V2.06 code with any earlier JDK release.
- The second step is to install the NetBeans IDE (6.0 or higher) if you have not already done so in step one.
The final step is then to use the NetBeans IDE to create a standard NetBeans project using the contents of the data ("DATA") directory, required library ("lib") directory, and source code ("src") directory extracted from the above AMES V2.06 source code zip file. All Java archive ("jar") files in the lib directory extracted from
the AMES V2.06 source code zip file must be included in the required library for your AMES V2.06 project in order for the AMES V2.06 code to run correctly.
Note in particular that a jar file for Repast J 3.1 (repast.jar) is included in the lib directory extracted from
the AMES V2.06 zip file. Consequently, Repast J 3.1 does not have to be separately downloaded and installed unless you are planning to undertake code development for parts of the
AMES V2.06 code involving Repast J and you would like to have access to RePast J debugging facilities.
- Detailed step-by-step instructions for setting up and running AMES V2.06 as a standard NetBeans project using the NetBeans IDE (6.0 or higher) are provided in the following
Basic AMES Instructions Manual (pdf,632KB).
After your AMES V2.06 project compiles, you can use entries in appropriate AMES GUI screens to load and run the provided 5-bus and 30-bus test cases, to experiment with changes in the parameter settings for these test cases, and/or to develop and run new cases. Event handlers are now in place to handle problematic parameter settings resulting in "inadequacy events" (supply less than demand) for some hours of the day-ahead market. Nevertheless, care must still be taken because empirically implausible data entries will result in empirically implausible outcomes ("garbage in, garbage out").
AMES V2.06 Manuals and Tutorials
Basic AMES V2.06 Instruction Manual
Topics covered in this basic manual include: Basic Project Set-Up Info; Loading and Viewing AMES Test Cases; Development of New AMES Test Cases; AMES Source Code Modification; Running AMES Experiments in Batch Mode.
- "The AMES Wholesale Power Market Test Bed: Project Description"
This slide presentation giving a summary overview of the AMES Wholesale Power Market Test Bed together with illustrative experimental results.
- "The AMES Wholesale Power Market Test Bed: Package Class Diagrams"
These slides display class diagrams for (1) the basic AMES classes and (2) the AMES graphical user interface (GUI).
- 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.
Note: This article is an abridged version of ISU Economics Working Paper No. 06025
Junjie Sun and Leigh Tesfatsion, "DC Optimal Power Flow Formulation and Testing Using QuadProgJ"
ISU Economics Working Paper No. 06014, Department of Economics, Iowa State University. Last Revised: March 2010.
This paper describes in detail the open source DCOPFJ solver incorporated into AMES V2.06 for the solution of security-constrained economic dispatch (SCED) problems for day-ahead and real-time markets via bid/offer-based DC optimal power flow (DCOPF) optimizations with fixed (non-price-sensitive) demand bids for load-serving entities (LSEs). DCOPFJ is a Java solver consisting of two parts: (i) QuadProgJ, a quadratic programming solver; and (ii) an outer shell that accepts DC-OPF inputs in Standard International (SI) form, formulates a DC-OPF optimization problem in pu form, calls QuadProgJ to solve this DC-OPF problem in pu form, and then converts outputs back into SI units.
The paper starts by deriving a DC OPF optimization problem (in both SI and pu forms) from an AC OPF optimization problem under standard simplifying assumptions. It then augments this standard DC OPF optimization problem with physically meaningful restrictions, enabling solution values to be directly obtained for voltage angles, locational marginal prices, real power injections, and branch flows. It is then explained how the resulting augmented DCOPF optimization can be solved using DCOPFJ. Detailed DCOPF representations and DCOPFJ solutions are reported for 3-node and 5-node test cases taken from power systems texts and ISO-NE/PJM training manuals.
- Junjie Sun and Leigh Tesfation, "DC-OPF Formulation with Price-Sensitive Demand Bids"
Working Paper, Economics Department, Iowa State University, March 2008.
- Abstract: This paper describes in detail an extended DCOPFJ solver incorporated into AMES V2.06 for the solution of security-constrained economic dispatch (SCED) problems for day-ahead markets via bid/offer-based DC optimal power flow (DCOPF) optimizations when the demand functions of load-serving entities (LSEs) have both price-sensitive and fixed (non-price-sensitive) components. A detailed DCOPF representation and DCOPFJ solution are reported for a 5-node test case with price-sensitive demands.
- Leigh Tesfatsion, "Introductory Notes on Agent-Based Modeling, Agent-Oriented Programming, and the AMES Wholesale Power Market Test Bed"
- Leigh Tesfatsion, "Introductory Notes on DC-OPF Dispatch and LMP Solutions in the AMES Wholesale Power Market Test Bed"
- Hongyan Li and Leigh Tesfatsion, "The AMES Wholesale Power Market Test Bed: A Computational Laboratory for Research, Teaching, and Training"
Proceedings of the IEEE Power and Energy Society General Meeting, Calgary, Alberta, CA, July 26-30, 2009.
- Hongyan Li, Junjie Sun, and Leigh Tesfatsion, "Separation and Volatility of Locational Marginal Prices in Restructured Wholesale Power Markets"
ISU Economics Working Paper #09009, Latest Revision March 2010.
This study uses the default dynamic 5-bus test case downloaded with AMES V2.06 as the benchmark starting point for an investigation of separation and volatility of locational marginal prices (LMPs) in an ISO-managed restructured day-ahead wholesale power market. 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.
- Leigh Tesfatsion, "The AMES Wholesale Power Market
Test Bed as a Stochastic Dynamic State-Space Game"
Working Paper, Economics Department, Iowa State University, July 2008.
- Abstract: These notes show how the AMES Wholesale Power Market Test Bed can be recast in more standard state-space equation form. The result is a highly nonlinear and highly coupled system of first-order stochastic difference equations. The AMES state-space equation representation is used to explain how AMES constitutes an open-ended dynamic game among multiple strategically-learning players. It is also used to explain how AMES permits the development and experimental study of a wide variety of test cases.
AMES V2.06 Software Features
- AMES V2.06 incorporates, in simplified form, core features of the two-settlement design proposed by the U.S. Federal Energy Regulatory Commission (FERC) for U.S. wholesale power markets in a series of reports and notices (2001-2003). Over 60% of U.S. generating capacity is now operating under variants of this design within seven U.S. energy regions (CAISO, ERCOT, ISO-NE, MISO, NYISO, PJM, SPP). Below is a summary description of the logical flow of events in AMES V2.06:
The AMES V2.06 wholesale power market operates over a user-specified AC transmission grid starting on day 1
and continuing through a user-specified maximum day (unless terminated earlier
in accordance with a user-specified stopping rule). Each day D consists of 24 successive hours
H = 00,01, ...,23.
The AMES V2.06 wholesale power market includes an Independent System Operator (ISO) and a collection of energy traders consisting of Load-Serving Entities (LSEs) and Generation Companies (GenCos) distributed across the buses of the transmission grid. Each of these entities is implemented as a software program encapsulating both methods and data.
The objective of the ISO is the reliable attainment of operational efficiency for the wholesale power market subject to generation and transmission constraints. In an attempt to attain this objective, the ISO undertakes the daily operation of a day-ahead market settled by means of locational marginal pricing (LMP), i.e., the determination of prices for electric power in accordance with both the locating and timing of its injection into, or withdrawal from, the transmission grid.
The objective of each LSE is to secure power for its downstream (retail) customers. During the morning of each day D, each LSE reports a demand bid to the ISO for the day-ahead market for day D+1. Each demand bid consists of two parts: a fixed demand bid (i.e., a 24-hour load profile); and 24 price-sensitive demand bids (one for each hour), each consisting of a linear demand function defined over a purchase capacity interval. LSEs have no learning capabilities; LSE demand bids are user-specified at the beginning of each simulation run.
The objective of each GenCo i is to secure for itself the highest possible net earnings each day. During the morning of each day D, each GenCo i uses its current action choice probabilities to choose a supply offer from its action domain ADi to report to the ISO for use in all 24 hours of the day-ahead market for day D+1. Each supply offer in ADi consists of a linear marginal cost function defined over an operating capacity interval. GenCo i's ability to vary its choice of a supply offer from ADi permits it to adjust the ordinate/slope of its reported marginal cost function and/or the upper limit of its reported operating capacity interval in an attempt to increase its daily net earnings.
After receiving demand bids from LSEs and supply offers from GenCos during the morning of day D, the ISO determines and publicly reports hourly power supply commitments and LMPs for the day-ahead market for day D+1 as the solution to hourly bid/offer-based DC optimal power flow (DC-OPF) problems. Transmission grid congestion is managed by the inclusion of congestion cost components in LMPs.
At the end of each day D, the ISO settles all of the commitments for the day-ahead market for day D+1 on the basis of the LMPs for the day-ahead market for day D+1.
At the end of each day D, each GenCo i uses stochastic reinforcement learning to update the action choice probabilities currently assigned to the supply offers in its action domain ADi, taking into account its day-D settlement payment.
In AMES V2.06 there are no system disturbances (e.g., weather changes) or shocks (e.g., forced generation outages or line outages). Consequently, the binding financial contracts determined in the day-ahead market are carried out as planned and traders have no need to engage in real-time (spot) market trading.
Each LSE and GenCo has an initial holding of money that changes over time as it accumulates earnings and losses.
There is no entry of traders into, or exit of traders from, the wholesale power market. LSEs and GenCos are currently allowed to go into debt (negative money holdings) without penalty or forced exit.
The essential idea of stochastic reinforcement learning is that the probability of choosing an action should be increased (reinforced) if the corresponding reward is relatively good and decreased if the corresponding reward is relatively poor. Each AMES GenCo determines its supply offer choices for the day-ahead market by means of VRE reinforcement learning, a variant of a stochastic reinforcement learning algorithm developed by Alvin Roth and Ido Erev on the basis of human subject experiments. The user can select either "daily profit" (revenues minus total cost) or
"daily net earnings" (revenues minus avoidable cost)
as the measure for each GenCo's daily "reward." The user can also
tailor the settings of each GenCo's learning parameter values to its situation, in particular to its cost attributes, its operating capacity limits, and its anticipated net earnings.
Each GenCo's VRE learning is implemented by means of a free open-source Java Reinforcement Learning Module (JReLMB) developed by
At the beginning of each run with learning GenCos, a competitive equilibrium benchmark is first calculated off line in which the GenCos' true cost and capacity attributes are used to solve for LMPs and power commitments. Comparing subsequent market outcomes under learning with competitive equilibrium benchmark outcomes permits the calculation of standard market performance measures such as market efficiency and market power.
The ISO determines hourly power-supply commitments and LMPs for the day-ahead market by solving hourly bid/offer-based DC optimal power flow (DC-OPF) problems that approximate underlying AC-OPF problems. The ISO solves its DC-OPF problems by invoking an accurate and efficient DC-OPF solver, DCOPFJ,
incorporated into AMES. Developed in Java by
Sun and Tesfatsion (2007c),
is free open-source software that can be used either as part of a Java application or as a stand-alone DC-OPF solver.
Specifically, AMES incorporates
as the ISO's solver for DC optimal power flow problems. DCOPFJ V2.0 is a generalization of DCOPFJ V1.0 that permits LSEs to submit price-sensitive as well as fixed demands to the ISO for the day-ahead market.
The ISO is concerned about loss of operational efficiency due to the possible exercise of "market power" by GenCos through strategic reporting of supply offers. Specifically, a GenCo has market power if the GenCo can increase its net earnings (relative to the competitive benchmarKB) either by reporting a higher-than-true marginal cost function or by reporting a less-than-true upper operating capacity limit. As one possible approach to GenCo market power mitigation, the ISO can impose a supply-offer price cap (PCap). Under such a policy, the maximum sale price reported by any GenCo cannot exceed PCap.
The AMES V2.06 user can control the length of each simulation run by choosing to set (or not) any combination of the following five stopping rules: (1) stop when a specified maximum day is reached; (2) stop when each GenCo is choosing a single supply offer with a probability that exceeds a user-specified threshold probability; (3) stop when the probability distribution used by each GenCo to select its supply offers has stabilized to within a user-specified threshold for a user-specified number of days; (4) stop when the supply offer selected by each GenCo has stabilized to within a user-specified threshold for a user-specified number of days; and/or (5) stop when the net earnings of each GenCo have stabilized to within a user-specified threshold for a user-specified number of days. When multiple stopping rules are flagged, the simulation run terminates as soon as any one of the flagged stopping rules is satisfied.
AMES V2.06 has a graphical user interface (GUI) with separate screens for carrying out the following functions: (a) creation, modification, analysis and storage of case studies in either single-run and batched-run mode; (b) initialization and editing of the attributes of the transmission grid; (c) individualized initialization and editing of the attributes of Load-Serving Entities (LSEs) and GenCos; (d) individualized specification of parameter values for the learning method of each GenCo; (e) activation (or not) of a user-specified value for an ISO-imposed supply-offer price cap; (f) specification of simulation controls (e.g., stopping rules); and (f) customizable output reports in the form of both table and chart displays.
Finally, AMES V2.06 includes three test cases that can be used as templates for new test-case studies. The first test case is a simple two-bus system. The second test case is a dynamic extension of a static 5-bus example by
John Lally (2002, Section 6)
now used extensively in ISO-NE/PJM training manuals. The third test case is a dynamic extension of the static IEEE 30-bus system presented in an appendix (Section D.4, pp. 477-478) of
Shahidehpour et al. (2002).
It is hoped that the free open-source release of AMES will encourage the cumulative development of future versions with enhanced features critical for determining the performance of real-world centrally-managed wholesale power markets. Some of these enhanced features have already been developed in AMES releases subsequent to V2.06, or are in progress for future AMES releases.
Examples of such enhanced features include:
A two-settlement system implemented as: (i) a daily ISO-managed Day-Ahead Market (DAM) consisting of a SCED optimization, subject to system constraints, that determines a generation dispatch schedule and locational marginal prices (LMPs) for next-day operations; and (ii) a daily Real-Time Market (RTM) consisting of hourly SCED optimizations, subject to system constraints, that determine generation dispatch schedules and LMPs one hour in advance of each operating hour (AMES V3.0).
A more fully operational two-settlement system implemented as: (i) a daily ISO-managed DAM consisting of a combined SCUC/SCED optimization, subject to extensive system constraints, that determines unit commitments, a generation dispatch schedule, reserve, and LMPs for next-day operations; and (ii) a daily
RTM consisting of multiple SCED optimizations with user-set look-ahead horizons, subject to extensive system constraints, that determine generation dispatch schedules and LMPs in advance of real-time operations (AMES V5.0).
An enhanced implementation of the daily ISO-managed DAM SCUC/SCED optimization that takes into account: unit commitment costs such as start-up costs, no-load costs, and shut-down costs; and standard physical system constraints such as power balance constraints, line capacity limits, generation capacity limits, ramping limits, and minimum down-time/up-time constraints. (AMES V4.0 - Stochastic SCUC/SCED Optimization; AMES V5.0 - Standard Two-Settlement DAM/RTM System with SCUC/SCED Optimization)
Inclusion of zonal and system-wide reserve requirements in the system constraints for DAM/RTM SCUC/SCED optimizations. (AMES V5.0)
Incorporation of distribution systems linked to AMES transmission buses permitting more empirically-based derivations of Load Serving Entity (LSE) demand bids and actual real-time load profiles. For research entailing the use of AMES V5.0 as the transmission component within an Integrated Transmission and Distribution (ITD) software platform, see the
ITD Project Homepage.
Incorporation of demand-bid learning capabilities for LSEs.
Implementation of more sophisticated types of learning methods for market participants (e.g. anticipatory learning).
Enhanced transmission grid features.
Incorporation of an AC OPF solver to permit DC vs. AC OPF error comparisons.
Additional security constraints incorporated into SCUC/SCED optimization formulations as a hedge against system disturbances.
Incorporation of emission constraints and other types of proposed or mandated environmental protection measures.
A more careful consideration of upstream fuel markets to permit a more empirically-based derivation of generator cost functions.
Inclusion of bankruptcy rules to handle situations in which one or more market participants exhaust all of their liquid assets.
A financial transmission rights (FTR) market to permit hedging against transmission congestion costs.
Extension of the AMES scope to consider more carefully how energy traders engage in privately-negotiated physically covered bilateral contracts whose power outcomes must then be self-scheduled in a DAM/RTM to secure needed transmission. To assure privately negotiated prices for both traders, regardless of DAM/RTM LMP outcomes, this bilateral contracting process must typically include appropriate side agreements consisting of contract-for-difference (CFD) and financial transmission right (FTR) agreements.
AMES V2.06 Licensing Terms
AMES V2.06 is licensed by the developers
(Hongyan Li, Leigh Tesfatsion, and Sean Mooney) as free open-source software under the terms of the
GNU General Public License (GPL).
Anyone who is interested is allowed to view,
modify, and/or improve upon the code used to produce this package, but any software generated using all or part of this code must be released as free open-source
software in turn. The GNU GPL can be viewed in its entirety
AMES Publications and References
Swathi Battula, Leigh Tesfatsion, and Zhaoyu Wang (2020), "A Customer-Centric Approach to Bid-Based Transactive Energy System Design"
IEEE Transactions on Smart Grid, Vol. 11, No. 6,
4996-5008. DOI: 10.1109/TSG.2020.3008611
Swathi Battula, Leigh Tesfatsion, and Thomas E. McDermott (2020), "An ERCOT Test System for Market Design Studies"
Applied Energy, Volume 275, October, 115182. DOI:10.1016/j.apenergy.2020.115182
- Charles Gieseler (2005), "A Java Reinforcement Learning Module for the Repast Toolkit: Facilitating Study and Implementation with Reinforcement Learning in Social Science Multi-Agent Simulations"
Department of Computer Science, Iowa State University, M.S. Thesis.
- Deddy Koesrindartoto and Leigh Tesfatsion (2004), "Testing the Reliability of FERC's Wholesale Power Market Platform: An
Agent-Based Computational Economics Approach"
Energy, Environment, and Economics in a New Era, Proceedings of the
24th USAEE/IAEE North American Conference, Washington, D.C., July 8-10.
- Deddy Koesrindartoto, Junie Sun, and Leigh Tesfatsion (2005), "An Agent-Based Computational Laboratory for Testing the Economic Reliability of Wholesale Power Market Designs"
Proceedings of the IEEE Power and Energy Society General Meeting, San Francisco, California, June 12-16, pp. 931-936.
Dheepak Krishnamurthy, Wanning Li, and Leigh Tesfatsion (2016), "An 8-Zone Test System based on ISO New England Data: Development and Application"
IEEE Transactions on Power Systems, Vol. 31, Issue 1, 2016, 234-246.
Hongyan Li and Leigh Tesfatsion (2011), "ISO Net Surplus Collection and Allocation in Wholesale Power Markets Under Locational Marginal Pricing"
(Working Paper Version,pdf,819KB),
IEEE Transactions on Power Systems,
Vol. 26, No. 2, 627-641.
Hongyan Li and Leigh Tesfatsion (2012), "Co-Learning Patterns as Emergent Market Phenomena: An Electricity Market Illustration"
(WP pdf, 1.5M),
Journal of Economic Behavior and Organization, Volume 82, Issue 2-3, 395-419.
Hongyan Li, Junjie Sun, and Leigh Tesfatsion (2011),
"Testing Institutional Arrangements via Agent-Based Modeling: A U.S. Electricity Market Application"
(WP pdf, 2.2MB),
pp. 135-158 in H. Dawid and W. Semmler (Eds.), Computational Methods in Economic Dynamics, Dynamic Modeling and Econometrics in Economics and Finance 13, Springer.
- Hongyan Li and Leigh Tesfatsion (2009), "Development of Open Source Software for Power Market Research: The AMES Test
Journal of Energy Markets, Vol. 2, No. 2, 111-128.
Hongyan Li and Leigh Tesfatsion (2009), "Capacity Withholding in Restructured Wholesale Power Markets: An Agent-Based Test Bed Study"
Proceedings of the IEEE Power Systems Conference & Exposition (PSCE), Seattle, WA, March 15-18, 2009.
- Hongyan Li, Junjie Sun, and Leigh Tesfatsion (2009),Hongyan Li, Junjie Sun, and Leigh Tesfatsion, "Separation and Volatility of Locational Marginal Prices in Restructured Wholesale Power Markets"
ISU Economics Working Paper #09009, Latest Revision March 2010.
- Hongyan Li, Junjie Sun, and Leigh Tesfatsion (2008), "Dynamic LMP Response Under Alternative Price-Cap and Price-Sensitive Demand Scenarios"
Proceedings of the IEEE Power and Energy Society General
Meeting, Carnegie-Mellon University, Pittsburgh, July 20-24.
Wanning Li and Leigh Tesfatsion (2017), "An 8-Zone ISO-NE Test System with Physically-Based Wind Power,"
Economics Working Paper No. 17017, Department of Economics, Iowa State University, January.
Hieu T. Nguyen, Swathi Battula, Rohit Reddy Takkala, Zhaoyu Wang, and Leigh Tesfatsion (2019), "An Integrated Transmission and Distribution Test System for Evaluation of Transactive Energy Designs"
Applied Energy, Vol. 240, 2019, 666-679.
Abhishek Somani and Leigh Tesfatsion (2008), "An Agent-Based Test Bed Study of Wholesale Power Market Performance Measures"
IEEE Computational Intelligence Magazine, Volume 3, Number 4, November, pages 56-72.
- Junjie Sun and Leigh Tesfatsion (2007a), "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework", Computational Economics, Volume 30, Number 3, pp. 291-327.
- Note: This article is an abridged version of ISU Economics Working Paper No. 06025
July 2007. The working paper provides a detailed description of the AMES Wholesale Power Market Test Bed V1.0 together with illustrative experimental findings.
- Junjie Sun and Leigh Tesfatsion (2007b), "An Agent-Based Computational Laboratory for Wholesale Power Market Design"
Proceedings of the IEEE Power and Energy Society General Meeting, Tampa, Florida, June 2007.
- Junjie Sun and Leigh Tesfatsion (2007c), "DC Optimal Power Flow Formulation and Testing Using QuadProgJ"
ISU Economics Working Paper No. 06014, Department of Economics, Iowa State University, 2007.
- Junjie Sun and Leigh Tesfatsion (2007d), "Open-Source Software for Power Industry Research, Teaching, and Training: A DC-OPF Illustration"
Proceedings of the IEEE Power and Energy Society General Meeting, Tampa, Florida, June 2007.
Auswin G. Thomas and Leigh Tesfatsion (2018), "Braided Cobwebs: Cautionary Tales for Dynamic Pricing in Retail Electric Power Markets"
IEEE Transactions on Power Systems, Volume 33, Issue 6, 6870-6882.
- Steven Widergren, Junjie Sun, and Leigh Tesfatsion (2006), "Market Design Test Environments"
Proceedings of the IEEE Power and Energy Society General
Meeting, Montreal, June.
- The work reported at this site has been supported in part by grants and contracts from
the DOE Office of Electricity (OE),
the DOE Advanced Research Projects Agency-Energy (ARPA-E),
the ISU Electric Power Research Center,
the Los Alamos National Laboratory,
the National Science Foundation,
the Pacific Northwest National Laboratory (PNNL), and
Sandia National Laboratories.