The AMES Wholesale Power Market Test Bed
A Free Open-Source Computational Laboratory
Software Release Disclaimer:
- The AMES Market Package is our 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
encompasses the following core features:
- central management by an independent market operator;
- a two-settlement system consisting of a day-ahead market supported by a parallel real-time market to ensure continual balancing of supply and demand for power;
- 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 (or scheduled for implementation) in U.S. energy regions in the Midwest (MISO), New England (ISO-NE), New York (NYISO), the mid-Atlantic states (PJMB), California (CAISO), the southwest (SPP), and Texas (ERCOT). Nevertheless, strong criticism of the design persists. Part of this criticism stems from the concerns of non-adopters about the suitability of the design for their regions due to distinct local conditions (e.g., hydroelectric power in the northwest). Even in regions adopting the design, however, criticisms continue to be raised about market performance.
One key problem for participants in wholesale power markets restructured in accordance with FERC's design 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. Moreover, in some energy regions, data is only posted in partial and masked form with a significant time delay. The result is that many participants are wary regarding the efficiency, reliability, and fairness of market protocols (e.g., settlement practices and market power mitigation rules). Moreover, outsiders (e.g., university researchers) are hindered from subjecting the design to systematic testing in an open and impartial manner.
As elaborated in
Sun and Tesfatsion (2007a),
Sun and Tesfatsion (2007b), and
Li, Sun, and Tesfatsion (2009),
the AMES Wholesale Power Market Test Bed is being developed as 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.
Our objective is the facilitation of research, teaching, and training, not commercial-grade application. 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 V2.06 was constructed (in Java) to have an extensible modular architecture and an easily-navigated graphical user interface (GUI). Detailed materials about AMES V2.06 are provided below.
Subsequent major extensions of AMES V2.06 - namely, AMES V3.0 (Java) and AMES V4.0 (Java/Python) - have been released by the developers of these extensions at independent websites. Links to these independent websites are provided below. Descriptions of the new features in these AMES extensions can be found at the
AMES Version Release History Site.
AMES Software Downloads and Supporting Materials
Important Note 1:
Explanations of the modifications incorporated into successive AMES versions released to date can be obtained at the
AMES Version Release History Site.
Important Note 2:
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 4.0 (Released 4/13/2017)
- Documentation, source code, and an 8-bus test case for AMES V4.0 can be found at the
AMES V4.0 Homepage,
maintained by Dheepak Krishnamurthy.
Eight-Zone ISO-NE Test System (Released 11/30/2015)
The 8-Zone ISO-NE Test System, based on ISO New England data and structural characteristics, is implemented by means of Version 4.0 of the AMES Market package.
is a computational platform (Java/Python) permitting the small-scale study of U.S. ISO-managed wholesale power markets operating over AC transmission grids with congestion handled by locational marginal pricing.
A detailed description of the 8-Zone ISO-NE Test System, together with an illustrative test case comparing stochastic versus deterministic DAM SCUC implementations, can be found in the following article:
Dheepak Krishnamurthy, Wanning Li, and Leigh Tesfatsion, "An 8-Zone Test System based on ISO New England Data: Development and Application"
IEEE Transactions on Power Systems, Vol. 31, Issue 1, January 2016, 234-246.
This study develops an open-source 8-zone test system (Java/Python) for teaching, training, and research purposes that is based on ISO New England structural attributes and data. The test system models an ISO-managed wholesale power market populated by a mix of generating companies and load-serving entities that operates through time over an 8-zone AC transmission grid. The modular extensible architecture of the test system permits a wide range of sensitivity studies to be conducted. To illustrate the capabilities of the test system, we report energy cost-savings outcomes for a comparative study of stochastic versus deterministic DAM Security Constrained Unit Commitment (SCUC) formulations under systematically varied reserve requirement levels for the deterministic formulation.
- Source code and documentation for the 8-Zone ISO-NE Test System, plus data for the illustrative test case comparing stochastic and deterministic DAM SCUC formulations, can be found at the following
code/data repository site
maintained by Dheepak Krishnamurthy.
The 8-Zone ISO-NE Test System is extended to include wind power in the following study:
Wanning Li and Leigh Tesfatsion, "An 8-Zone ISO-NE Test System with Physically-Based Wind Power,"
Economics Working Paper No. 17017, Department of Economics, Iowa State University, January 2017.
This study extends the agent-based 8-Zone ISO-NE
Test System to include wind turbine agents, each characterized
by location, physical type, and an output curve mapping local
wind speed into wind power output. Increases in wind power
penetration (WPP) are modeled as build-outs of investment
queues for planned wind turbine installations. The extended
system is used to study the effects of increasing WPP under
both stochastic and deterministic day-ahead market (DAM)
formulations for security-constrained unit commitment (SCUC).
For each tested WPP, the expected cost saving resulting from a
switch from deterministic to stochastic DAM SCUC is found
to display a U-shaped variation as the reserve requirement
(RR) for deterministic DAM SCUC is successively increased.
Moreover, the RR level resulting in the lowest expected cost
saving systematically increases with increases in WPP.
AMES Market Package--Version 3.0 (Released 12/16/2017)
- 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 Download
AMES Market Package--Version 2.06 (Released: 5/22/2013)
- Hongyan Li, Junjie Sun, Leigh Tesfatsion, and Sean L. Mooney, AMES Market Package--Version 2.06:
Release Date: 22 May 2013.
- 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 V2.06 will encourage the cumulative development of future versions with enhanced features critical for determining the performance of real-world restructured electricity 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 fully operational two-settlement system consisting of: (i) an ISO-managed Day-Ahead Market (DAM) with a SCED optimization that determines a generation dispatch schedule and locational marginal prices (LMPs) for next-day 24-hour operations; and (ii) a Real-Time Market (RTM) with a SCED optimization that determines a generation dispatch schedule and LMPs one hour in advance of real-time operations. (AMES V3.0).
An enhanced modeling of the ISO-managed DAM Security-Constrained Unit Commitment (SCUC) optimization that takes into account UC costs such as start-up costs, no-load costs, and shut-down costs, reserve requirement constraints, and standard 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 Optimization; AMES V5.0 - Standard Two-Settlement DAM/RTM System with SCUC/SCED Optimization)
Inclusion of zonal and system-wide reserve requirements in 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 work-in-progress on the use of AMES as a key component within an Integrated Transmission and Distribution (ITD) 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 (e.g., N-1) 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.
Upstream fuel markets permitting more empirically-based derivations 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 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 rights (FTR) agreements.
AMES Licensing Terms
Code and data for AMES V4.0, an extended and modified version of AMES V2.06, is licensed by the copyright holders (Dheepak Krishnamurthy, Sean L. Mooney, Auswin George Thomas, Wanning Li, and Leigh Tesfatsion) as free open-source software under the terms of the
Modified BSD License.
A licensee of BSD-licensed software can: (i) use, copy and distribute the unmodified source or binary forms of the licensed program; and (ii) use, copy and distribute modified source or binary forms of the licensed program provided that all distributed copies are accompanied by the license and the names of the previous contributors are not used to promote any modified versions without their written consent.
Code and data for the Eight-Zone Test System Based on ISO-NE Data, developed using AMES V4.0, is licensed by the copyright holders (Dheepak Krishnamurthy, Wanning Li, and Leigh Tesfatsion) as free open-source software under the terms of the
Modified BSD License.
AMES V2.06 is licensed by the copyright holders (Hongyan Li, Junjie Sun, 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
- 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.
- John Lally (2002), "Financial Transmission Rights: Auction Example," in Financial Transmission Rights Draft 01-10-02, m-06 ed., ISO New England, Inc., January.
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, "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, 2012, 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.
Mohammed Shahidehpour, Hatim Yamin, and Zuyi Li (2002), Market Operations in Electric Power Systems, IEEE, Wiley-Interscience.
Abhishek Somani and Leigh Tesfatsion, "An Agent-Based Test Bed Study of Wholesale Power Market Performance Measures"
IEEE Computational Intelligence Magazine, Volume 3, Number 4, November 2008, 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, to appear.
- Steven Widergren, Junjie Sun, and Leigh Tesfatsion (2006), "Market Design Test Environments"
Proceedings of the IEEE Power and Energy Society General
Meeting, Montreal, June 2006.
- The work reported at this site has been supported in part by Grant NSF-0527460 awarded by the National Science Foundation, and by grants and contracts awarded by the ISU Electric Power Research Center, the Advanced Research Projects Agency-Energy (ARPA-E) of the Department of Energy, the Pacific Northwest National Laboratory, and Sandia National Laboratories.