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Testing Institutional Arrangements via Agent-Based Modeling: A U.S. Electricity Market Leigh Tesfatsion

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    Leigh TesfatsionProfessor of Econ, Math, and Electrical and Computer EngineeringIowa State University, Ames, Iowahttp://www.econ.iastate.edu/tesfatsi/tesfatsi@iastate.edu

    Presentation Slides: Last Revised 5/14/2010 www.econ.iastate.edu/tesfatsi/TestInstViaABM.Waterloo2010.pdf

    Testing Institutional Arrangements via Agent-Based Modeling Illustrative Findings for Electric Power Markets

  • *Presentation Outline Complexity of large-scale institutions

    Agent-based test beds for institutional design

    Illustration: An ABM test bed for studying efficiency and welfare implications of North American electric power markets operating under new market designs

    Sample findings (incentive misalignments) Incentives for price manipulation Incentives for congestion inducement

  • *Modern societies depend strongly on large-scale institutions for production & distribution of critical goods and services (e.g., energy, finance, health care, )

    Institutional outcomes depend in complicated ways on Physical constraints restricting feasible actions Rules governing participation, operation & oversight Behavioral dispositions of participants Interaction patterns of participants

    To be useful and informative, institutional studies need to take proper account of all four elements.Complexity of Large-Scale Institutions

  • *Mathematical Modeling of Institutional Systems:Classical vs. Agent-Based Modeling ApproachesClassical Approach (Top Down): Model the system by means of parameterized differential equations

    Example: Archimedes, a large-scale system of ODEs modeling pathways of disease spread under alternative possible health care response systems

    ABM Approach (Bottom Up): Model the system as a collection of interacting agents

    Each agent is an autonomous software program encapsulating data (attributes) and methods

    Agents can contain other agents as member data (permits hierarchical constructions)

  • *Meaning of Agent in ABM Agent = Encapsulated bundle of data and methods acting within a computationally constructed world.

    Agents can represent: Individuals (consumers, traders, entrepreneurs,) Social groupings (households, communities,) Institutions (markets, corporations, govt agencies,) Biological entities (crops, livestock, forests,) Physical entities (weather, landscape, electric grids,)

  • *Meaning of Agent in ABM Cognitive agents are capable (in various degrees) of

    Behavioral adaptation

    Goal-directed learning

    Social communication (talking with each other!)

    Endogenous formation of interaction networks

    Autonomy: Self-activation and self-determination based on private internal data and methods as well as on external data streams (including from real world)

  • *Illustration: UML diagram with is a and has a agent relations for an economic ABM

  • *ABM vs. Object-Oriented Programming Key distinction is autonomy of ABM CogAgents

    ABMs characterized by distributed control, not simply by distributed action.

    Conventional OOP objects encapsulate data and methods but do not permit self-activation and local action choice.

  • *Importance of Agent EncapsulationIn the real world, all calculations must be done by entities actually residing in the world.

    ABM forces modelers to respect this constraint.

    Procedures encapsulated into the methods of a particular agent can only be implemented using the particular resources available to that agent.

    This encapsulation achieves a more transparent and realistic representation of real-world systems composed of interacting distributed entities with limited information and computational capabilities.

  • *Constructive Replacement In principle, as a result of agent encapsulation:

    Any cognitive agent interacting with an ABM through a particular input-output public interface can be replaced by a person that interacts with the ABM through this same public interface.

    Since method implementations by the cognitive agent and its human replacement need not be the same, the resulting outcomes under replacement could differ.

    The only claim here is the feasibility of replacement due to the imposition of agent boundaries in ABMs.

  • *Role of Equations Any agent in an ABM can have data and/or methods involving equations.

    These equations can be the basis in part or in whole for the agents actions.

    ABM world events are driven solely by the actions undertaken by the ABM agents within their world.

    ABM world events are not driven by equations existing outside of the data and methods of agents. For example, sky hook equilibrium conditions are not permitted.

  • *ABM and Institutional DesignKey Issues: Will a proposed or actual design promote efficient, fair, and orderly social outcomes over time? Will the design give rise to unintended consequences?ABM Culture-Dish Approach:Develop a computational world embodying the design, physical constraints, strategic participants,

    Set initial world conditions (agent states).

    Let the world evolve with no further intervention, and observe and evaluate the resulting outcomes.

  • *Agent-Based Test Bed Development viaIterative Participatory Modeling

    Stakeholders and researchers from multiple disciplines join together in a repeated looping through four stages of analysis: Field work and data collection Role-playing games/human-subject experiments Incorporate findings into agent-based test bed Generate hypotheses through intensive computational experiments.

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    Project Directors: Leigh Tesfatsion (Prof. of Econ, Math, & ECpE, ISU) Dionysios Aliprantis (Asst Prof. of ECpE, ISU) David Chassin (Staff Scientist, PNNL/DOE)Research Assocs: Dr. Junjie Sun (Fin. Econ, OCC, U.S. Treasury, Wash, D.C.) Dr. Hongyan Li (Consulting Eng., ABB Inc., Raleigh, NC)

    Research Assistants: Huan Zhao (ISU Econ PhD Candidate); Chengrui Cai (ISU ECpE PhD Candidate); Pedram Jahangiri & Auswin Thomas (ISU ECpE grad students) * Supported by grants from DOE/PNNL (Pacific Northwest National Laboratory, and the ISU EPRC (Electric Power Research Center)

    Project: Integrated Wholesale/Retail Power System Operation with Smart-Grid Functionality

  • *Retail & Wholesale Power System OperationsSource: http://www.nerc.com/page.php?cid=1|15

  • *Our Retail/Wholesale Test Bed Platform Based on Texas (ERCOT) Retail/Wholesale Structure Wholesale Test bed (AMES)developed by ISU Team Retail Test bed (GridLAB-D) developed by DOE/PNNLSeaming in ProgressxxBilateral Contracts

  • *AMES/GridLAB-D Seaming: Timing Details

  • * Meaning of Smart Grid Functionality? For our project purposes:

    Smart-grid functionality = Service-oriented grid enhancements permitting more responsiveness to needs and preferences of retail customers.

    Examples: Introduction of advanced metering and other technologies to support flexible retail contracting between suppliers and retail consumers embedding and use of distributed energy resources

  • *Project Context: North American restructuring of wholesale power markets In April 2003 the U.S. Federal Energy Regulatory Commission (FERC) proposed adoption of a wholesale power market design with particular core features.

    Over 50% of North American generation now operates under some variant of the FERC design.

    Adopters to Date: New York (NY-ISO), mid-Atlantic states (PJM), New England (ISO-NE), Midwest/Manitoba (MISO), Texas (ERCOT), Southwest (SPP), and California (CAISO)

    Note: Ontario (IESO), Alberta (AESO) , and other Canadian provinces have not fully adopted FERC design

  • *Regions Operating Under Some Version of FERC Design http://www.ferc.gov/industries/electric/indus-act/rto/rto-map.asp

  • *Core Features of FERCs Market Design

    Market to be managed by an independent system operator (ISO) having no ownership stake

    Two-settlement system: Concurrent operation of day-ahead (forward) & real-time (spot) markets

    Transmission grid congestion managed via Locational Marginal Prices (LMPs), where LMP at bus k = least cost of servicing 1 additional MW of power at bus k

    Oversight & market power mitigation by outside agency

    Has led in practice to complicated systems difficult to analyze by standard analytical & statistical tools !

  • *Example: Complex MISO Market Organization Business Practices Manual 001-r1 (1/6/09)Two-Settlement Power Market System under LMP Core of FERC design SsAMES project to dateXxXx

  • *Actual Electricity Prices in Midwest ISO (MISO) April 25, 2006, at 19:55Note this price,$156.35

  • *Five Minutes Later73% drop in price in 5 minutes!

  • *Actual Electricity Prices in Midwest ISO (MISO) September 5, 2006, 14:30 Note this price, $226.25

  • *Five Minutes Later79% drop in price in 5 minutes!

  • *Project Work to Date: Wholesale LevelDevelopment and open-source release of AMES (Agent-based Modeling of Electricity Systems)

    AMES = ABM test bed with core FERC design features

    Used to test performance under FERC design

    Used to test performance under modifications of design

    AMES Homepage (code/manual/publications): http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm

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    AMES (V2.0

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