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MASCEM: A Multiagent System That Simulates Competitive Electricity Markets Isabel Praça, Carlos Ramos, and Zita Vale, Polytechnic Institute of Porto Manuel Cordeiro, University of Trás-os-Montes e Alto Douro
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round the world, the electricity industry, which has long been dominated by vertically integrated utilities, is experiencing major changes in the structure
of its markets and regulations. Owing to new regulations, it’s evolving into a distributed industry in which market forces drive electricity’s price. The industry is becoming
MASCEM, a multiagent simulator system, can be a valuable framework for evaluating new rules, new behavior, and new participants in the numerous electricity markets that are moving toward liberalization and competition.
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competitive; a market environment is replacing the traditional centralized-operation approach. This transformation is often called the deregulation of the electricity market. Many electricity companies have split into several companies that specialize in energy generation, transmission, or distribution. These companies need to adopt new management approaches to survive in this market. The industry needs new software tools—such as price-based unit commitment and price-bidding tools—to support new market activities. Also, it needs new modeling approaches that simulate how electric power markets might evolve over time and how market participants might react to the changing economic, financial, and regulatory environment in which they operate. To study electricity market behavior and evolution, we developed MASCEM,1 a multiagent simulator system where agents represent market entities such as generators, consumers, and market operators, and new participants such as traders. Agents can establish their own objectives and decision rules. Moreover, they can adapt their strategies as the simulation progresses on the basis of previous efforts’ success or failure. The simulator probes the effects of market rules and conditions by simulating the participants’ strategic behavior. As a decision support tool, our simulator includes several types of negotiation mechanisms found in electricity markets to let the user test them and learn the best way to negotiate in each one. The simulator
is flexible; the user completely defines the model he or she wants to simulate, including the number of agents, each agent’s type and strategies, and the market type. (See the “Related Work” sidebar for other approaches to simulation.) Electric companies as well as regulator entities can use MASCEM to analyze the impact of their potential decisions.
Market structure An electricity market’s main objectives are to ensure the system’s secure and efficient operation and to decrease the cost of electricity through competition. Several market structure models exist that could help achieve this goal.2 The market environment typically consists of a pool, as well as a floor for bilateral contracts. A pool, or power exchange, is a marketplace where electricity-generating companies submit production bids and their corresponding market prices, and consumer companies submit consumption bids (see the “Pool Mechanisms” sidebar). A market operator regulates the pool. The market operator uses a market-clearing tool to set the market price every hour; this results in a market-clearing price and a set of accepted production and consumption bids. In pools, an appropriate market-clearing tool is an auction mechanism—the most common being a standard uniform auction. Bilateral contracts are negotiable agreements between sellers and buyers (or traders) about power supply and receipt. The bilateral-contract model is
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Related Work Some other approaches to agent-based simulation applications for competitive electricity markets are more targeted or limited than MASCEM. PowerWeb (http://stealth.ee.cornell.edu/powerweb) considers single uniform auctions with fixed demand. The Auction Agents for the Electric Power Industry project (www.agentbuilder.com/Documentation/EPRI) implements a Dutch auction, which is a specific type of auction mechanism. SEPIA (Simulator for Electric Power Industry Agents) studies just bilateral contracts (www.htc.honeywell.com/projects/sepia). The works of Derek Bunn and Fernardo Oliveira;1 FrançoisRégis Monclar and Richard Quatrain;2 and James Nicolaisen, Valentin Petrov, and Leigh Tesfatsion3 are relevant to our research in agent-based simulation; however, they discuss only the market in England and Wales. Jorge Villar and Hugh Rudnick present another important simulation application,4 focusing particularly on hydroelectric power station parameters. Javier Contreras and his colleagues present an interesting lab experience that shows the practical utility of electricity market simulators.5 The Electricity Market Complex Adaptive System (EMCAS)6 is a relevant and interesting e-laboratory for testing regulatory structures where agents have strategies based on learning and adaptation.
flexible; negotiating parties can specify their own contract terms. The hybrid model combines features of pools and bilateral contracts. In this model, a pool isn’t mandatory, and customers can either negotiate a power supply agreement directly with suppliers or accept power at the pool market price. In this model, participants can negotiate in the pool and also through bilateral contracts. This model therefore offers customer choice.
References 1. D. Bunn and F.S. Oliveira, “Agent-Based Simulation—An Application to the New Electricity Trading Arrangements of England and Wales,” IEEE Trans. Evolutionary Computation, vol. 5, no. 5, Oct. 2001, pp. 493–503. 2. F.R. Monclar and R. Quatrain, “Simulation of Electricity Markets: A MultiAgent Approach,” Proc. 2001 Int’l Conf. Intelligent System Application to Power Systems (ISAP 01), 2001, pp. 207–212. 3. J. Nicolaisen, V. Petrov, and L. Tesfatsion, “Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing,” IEEE Trans. Evolutionary Computation, vol. 5, no. 5, Oct. 2001, pp. 504–523. 4. J. Villar and H. Rudnick, “Hydrothermal Market Simulator Using Game Theory: Assessment of Market Power,” IEEE Trans. Power Systems, vol. 18, no. 1, Feb. 2003, pp. 91–98. 5. J. Contreras et al., “Power Engineering Lab: Electricity Market Simulator,” IEEE Trans. Power Systems, vol. 17, no. 2, May 2002, pp. 223–228. 6. M. North et al., “E-Laboratories: Agent-Based Modeling of Electricity Markets,” Proc. Am. Power Conf., PennWell Corp., 2002; www.dis.anl. gov/msv/msv_cas.html.
Market facilitator
Buyers
Sellers Market operator Trader Pool
~ Network operator
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Overview of MASCEM Our market simulator is related to the electricity day-ahead market, with 24 negotiation periods each day. It includes these types of agents: a market facilitator, sellers, buyers, traders, a market operator, and a network operator (see Figure 1). The market facilitator coordinates the simulated market and ensures that it functions correctly. It knows the identities of all agents in the market, regulates negotiation, and ensures the market operates according to established rules. Before entering the market, agents must first register with the market facilitator, specifying their role and services. Sellers and buyers are the two key players in the market, so we devote special attention to them—particularly to their objectives and strategies to reach them. Sellers represent entities able to sell electricity in the market, such as generating companies holding elecNOVEMBER/DECEMBER 2003
~
Bilateral contracts
~ Figure 1. Multiple agents in MASCEM.
tricity production units. Buyers represent electricity consumers and electricity distribution companies. The user completely defines the number of sellers and buyers in each scenario and must specify their intrinsic and strategic characteristics. “Intrinsic characteristics” refers to the agent’s individual knowledge related to limit price, preferred prices, and available capacity (or consumption needs if it’s a buyer). “Strategic characteristics” refers to the strategies the agent will www.computer.org/intelligent
use to reach the objective of selling the available capacity at the best price (if a seller) or buying the needed power (if a buyer). Seller agents will compete with each other because they’re all interested in selling their available capacity at the highest-possible market quote. On the other hand, seller agents will cooperate with buyer agents to establish an agreement that’s profitable for both. This is a rich domain for which it’s possible to develop and test several algorithms and negotiation mech55
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Pool Mechanisms In pools, the most common type of negoAsymmetric market Symmetric market tiation is a standard uniform auction.1,2 If Kilowatts Kilowatts only suppliers can compete in the pool, it’s called an asymmetric market. If both suppliBuyers' Sellers' bids ers and buyers can compete, it’s a symmetric bids Demand Sellers' bids market (similar to a double auction in aucD tion theory). Figure A shows both markets. In an asymmetric market, the suppliers present their bids, and the market operator (the entity responsible for the pool’s nondiscriminatory functioning) organizes them Cents per Cents per Market price Market price kilowatt-hour kilowatt-hour starting with the lowest price and moving (1) (2) up. The consumers reveal their needs to set up the demand. Once the market operator knows the demand, it accepts the suppliers’ Figure A. Two types of markets: (1) asymmetric; (2) symmetric. bids starting from the lowest and accepts as many as are necessary to fill the demand. ket price and every consumer offering prices higher than the The market price—to be paid to all accepted suppliers—is that market price will be accepted. of the last accepted bid (the one with the highest price). In a symmetric market, suppliers and consumers both submit References bids. The market operator orders the selling and demand offers: selling bids start with the lowest price and move up, and demand 1. G. Sheblé, Computational Auction Mechanisms for Restructured bids start with the highest price and move down. Then, the proPower Industry Operation, Kluwer Academic Publishers, 1999. posed bids form the supply and demand step curves, and the point at which both curves intersect determines the market 2. P. Klemperer, “Auction Theory: A Guide to the Literature,” J. Economic Surveys, vol. 13, no. 3, July 1999, pp. 227–286; www.nuff. price, paid to all accepted supplier and consumers. The bids of ox.ac.uk/economics/people/klemperer.htm. every supplier offering prices lower than the established mar-
anisms for cooperation and competition. The increased competition creates opportunities for new players or agents to enter the market, such as traders. The introduction of traders promotes liberalization and competition and simplifies sellers’ and buyers’ dealings with each other and with the market operator. Traders are intermediaries between consumers and suppliers. They can act as retailers, brokers, aggregators, or marketers. Other simulators don’t include this agent type. The network operator is specific to the application domain of electricity markets. It is responsible for the system’s security and all involved technical constraints. One network operator is present in every simulation and must be informed of every contract established—either through bilateral contracts or through the pool. It analyzes the contract’s technical viability from the power system viewpoint (for example, the feasibility of power flow to meet all needs) and manages to solve congestion situations. The market operator manages the pool mechanism. This agent is present only in pool or hybrid market simulations. We’ll talk more about this in the next section. 56
The pool The market operator starts the negotiation by sending a request for proposals at the beginning of each negotiation period. All interested agents—sellers, buyers, and traders—reply by sending bids to the pool. Then the market operator organizes the bids received, determines the market price, and determines the accepted and rejected bids. It performs bid matching according to the network operator’s technical viability analysis. After the market operator processes the bids and establishes the market price, it communicates the results to each pool participant. Bilateral contracts Bilateral contracts are established through requests for proposals distributed by buyers or traders—the demand agents. If a demand agent chooses to participate in the bilateral market, it will first send a request for electricity with its price expectations to all the sellers in the simulated market. In response, a seller analyzes its own capabilities, current availability, and past experience. The seller must be sure that it’s feasible to deliver energy to the buyer’s location. So, it must get the network operator’s feedback before www.computer.org/intelligent
reaching agreement with the demand agent. If the seller can make an offer to the requested parameters, it formulates a proposal and sends a message to the demand agent. The demand agent evaluates the proposals and accepts or rejects the offers. On the basis of the results from a negotiation period, sellers, buyers, and traders revise their strategies for the next period. Hybrid market In hybrid markets, agents must decide whether to establish a bilateral contract either before trying the pool or right after they learn pool results if their bids weren’t accepted. To make this decision, agents use past experiences and market strategies. We’ve given the details elsewhere about how the agents handle messages in the described market types.3
Defining bids Developing a strategic, highly profitable offer is a fundamental issue for market participants, so the policies that each agent implements must be analyzed carefully. Agents take into account their past experiences and expectations about market evolution. Sellers, buyers, and traders all have IEEE INTELLIGENT SYSTEMS
dynamic strategies to define the price at which they’re willing to sell or buy in each negotiation period. These agents can also use a scenario analysis algorithm, which we describe in detail later. This algorithm offers more complex support for developing and implementing dynamic pricing strategies. In terms of strategic behavior, sellers and demand agents (buyers or traders) have similar structures and somewhat symmetrical behavior owing to their opposite objectives. We’ll explain the strategies from a seller’s viewpoint. However, we’ll point out the differences when necessary.
price change produced less revenue per unit, the seller makes a different price change. Demand agents also use both strategies, but with different parameters depending on their objective of buying all the needed power at the lower price. For both strategies, the next period’s offer price will be the previous period’s price adjusted by an amount that will be more or less than the previous price, depending on the strategy used. The price adjustment is determined by the same calculation: pricei+1 = pricei ± amounti+1 amounti+1 = pricei * β +
Dynamic strategies Agents use time-dependent strategies to change their price during a negotiation period. These strategies differ depending on both the point in time when the agent starts to modify the price and the amount it changes. Determined agents will maintain constant prices during the negotiation period. Anxious agents will start modifying prices early in the negotiation period by a small amount. Moderate agents will start changing prices in the middle of the period by a small amount. Gluttonous agents will start changing prices at the end of the period by a large amount. Agents use behavior-dependent strategies to adjust their price between negotiation periods. The composed goal-directed strategy is based on two consecutive objectives— for example, selling (or buying) all the available capacity (consumption needs) and then increasing the profit (or reducing the payoff). Following this strategy, sellers will lower their price if, in the previous period, they didn’t completely sell the available capacity. They’ll raise the price if they sold all the available capacity in the previous period. Similarly, demand agents will offer a higher price if they didn’t meet their consumption needs in the previous period and offer less if they succeeded in meeting their needs. The adapted derivative-following strategy is based on a derivative-following strategy proposed by Amy Greenwald, Jeffrey Kephart, and Gerald Tesauro.4 The working principle is the same; however, we’ve adapted the calculations to electricity markets. Adapted derivative following considers the revenue the seller earned last period as a result of the price change made between that period and the one before it. If that price change led to more revenue per unit, the seller makes a similar price change. If that NOVEMBER/DECEMBER 2003
∆i capacity_availablei * α
∆ = capacity_available – energy_soldi
Time-dependent strategies differ depending on the point in time when the agent starts to modify the price and the amount it changes. Instead of adjusting the price each day by a fixed percentage, the formula scales the change by a ratio to sell all the available capacity. The amount of change increases with the difference between the amount of energy the seller wanted to sell and the amount it actually sold. β and α are scaling factors. We implemented these strategies and have already obtained some results.5 The following example illustrates some differences in how the two behavior-dependent strategies performed. Consider a simple scenario with few buyers and sellers. One seller is more competitive than the others, and two sellers have similar prices. So, we can analyze the strategies’ behavior in a monopoly-like situation in periods of lower demand when only the competitive seller can sell. We can also consider how the strategies work when the market is competitive—in periods of higher demand. We simulated 24 negotiation periods to analyze the effect of demand fluctuations during one day. The adapted derivative-following strategy www.computer.org/intelligent
obtained higher market prices than the composed goal-directed strategy (see Figure 2). After carefully analyzing the results, we conclude that when the demand is less than the most competitive seller’s available capacity, the seller will lose money when using the composed goal-directed strategy. The seller will decrease the price and try to sell more, which won’t be possible because of insufficient demand. However, the composed goaldirected strategy can be valuable, particularly to increase market share when two or more sellers are competing directly because of similar proposed prices. It would be interesting to develop another strategy that combines the two described strategies. This way, an agent could select the most suitable strategy for each period on the basis of market conditions. For example, if a seller concludes that the demand is lower than its available capacity, it will use adapted derivative following. If it concludes that a competitor has similar prices, it will use the composed goal-directed strategy. Scenario analysis algorithm This algorithm is particularly suitable in pool or hybrid markets, to support not only agents’ decisions for proposing bids to the pool but also their decisions to accept or reject bilateral agreements (see Figure 3). Agents can use the algorithm to analyze different scenarios and evaluate the expected returns. Seller and demand agents are like players in a game, and this algorithm analyzes the possible scenarios resulting from other agents’ past and likely future reactions. Then, the algorithm will apply a decision method to decide what to bid in the pool and whether to accept a bilateral contract. Every period includes the same steps, but the results of the analyses from each period update the agent’s market knowledge. Scenarios and bid definition. Agents have historical information about the other agents in their market knowledge module. They can build a profile of other agents with the expected proposed prices, limit prices, and capacities. With this information, the agent constructs several scenarios and analyzes them to determine the best way to deal with competitors and how to bid to get a good— or the most reliable—payoff. To get warrantable data, each agent uses techniques based on statistical analysis and knowledge discovery tools, which search historical data. Usually, after a confidential 57
Market price (cents per kilowatt-hour)
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Figure 2. The composed goal-directed and adapted derivative-following strategies’ behavior in (a) an asymmetric pool and (b) a symmetric pool.
binations possible considering the two prices for each agent, is 2n, where n is the number of other agents in the model. It’s necessary to define which bids or moves an agent or player should analyze. The agent should analyze the incomes that result from bidding its limit, desired prices, and competitive prices—those that are just slightly lower (or higher, in the demand agent’s case) than its competitors’ prices. Consider how a seller applies the algorithm. Let j be the seller agent doing the analysis, capj its available capacity, limit_ pricej its minimum acceptable price, and desired_ pricej its expected desired price. (A demand agent applies the algorithm similarly, except the limit price is a maximum price instead of a minimum and the objective is to buy all the energy it needs at the lowest price instead of to sell at the highest price.) Let P denote the set of all players—sellers and demand agents—in the market. Let ε be the smallest positive number allowed as a bidding increment. The bids that agent j must analyze are bid (limit_ price j , cap j ) bid (desired_ price j , cap j ) , ∀ i ∈P , i ≠ j , bid (limit_ price i − ε , cap j ) bid (expected_ price − ε , cap ) i j
subject to User
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limit_ pricei − ε > limit_ pricej and
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expected_ pricei − ε > limit_ pricej . Bids definition Next period
Plays analysis Decision method
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Figure 3. The scenario analysis algorithm.
period, markets disclose information about past transactions. For example, such information from the Spanish electricity market (OMEL) is available online at www.omel.es. Each market player has two prices. 58
limit_ price is the minimum price if the player is a seller and maximum price if the player is a demand agent. expected_ price is the previewed bid price. The number of scenarios, which result from the different comwww.computer.org/intelligent
The number of bids to analyze is 2 × n + 2. This number hits the maximum when limit_ price j is smaller than the other agents expected or the limit prices. We will call a bid–scenario pairing a play; then, the total number of plays to analyze is number_of_bids × number_of_scenarios, and the maximum value it can achieve is (2 × n + 2)2n. So far we’ve considered only when agents bid their limit or expected prices. However, an agent might bid between its limit and the expected price, or even above it. So, if we say that each agent might bid np prices, the number of scenarios becomes npn, and the number of plays to analyze is (np × n + 2)npn. Even in a model with few players, the number of plays can be high. For example, IEEE INTELLIGENT SYSTEMS
if an agent is analyzing a scenario containing four other players and three different prices for each, the agent must analyze a maximum of 1,134 plays! Furthermore, because the market is organized in several periods, an agent could increase, decrease, or maintain its bid—three possible actions—after each negotiation period, increasing the number of scenarios to analyze. So, after k periods, considering the possible bid updates, the number of plays to analyze becomes (np × n + 2)npn × 3(k − 1)n. Considering the same example, after three negotiation periods, an agent must analyze 7,440,174 plays, which is a huge number, even for a distributed execution of the model. However, must agents analyze every possible scenario? Because our simulator is a decision support tool, the user should have the flexibility to decide which and how many scenarios to analyze. To do so, the user must define the scenarios to simulate by specifying the price that agents will propose: pricei = λ × expected_ pricei + ϕ × limit_ pricei , where λ and ϕ are scaling factors that can differ for each agent. Suppose that the user selects λ = 0 and ϕ = 1 for every seller and λ = 1 and ϕ = 0 for every demand agent. This means he or she is interested in analyzing a pessimistic scenario (from the seller’s viewpoint). But, if the user selects λ = 1 and ϕ = 0 for every agent, he or she is interested in analyzing the most probable scenario. With this formula, the user can define each agent’s proposed prices for every scenario he or she wants to consider. If the user defines nc scenarios, the number of plays to analyze is (nc × n + 2)nc. After the agents analyze all the plays, the algorithm will construct a matrix, obtain the results, and apply a decision method to decide which bid to propose. Decision method. The matrix analysis with the simulated plays’ results is inspired by the game theory6,7 concepts for a pure-strategy two-player game, assuming each player seeks to minimize the maximum possible loss or maximize the minimum possible gain. A seller—like an offensive player—will try to maximize the minimum possible gain by using the MaxiMin decision method. A NOVEMBER/DECEMBER 2003
demand agent—like a defensive player—will select the strategy with the smallest maximum payoff by using the MiniMax decision method. In demand agents’ matrix analyses, they select only situations in which they can fulfil all their consumption needs. They avoid situations in which agents will accept reduced payoff but can’t satisfy their consumption needs completely. After applying the decision method, the agent selects one bid that it will propose on the pool, unless it reaches an agreement for a bilateral contract that’s more profitable than the previewed pool results. This analysis not only provides the agent with decision support about the bid to propose in a pool but also helps improve the
The multiagent technology allied to an object-oriented implementation enables easy future improvements and model enlargement.
period, if it couldn’t sell and is already bidding its limit price, it will most likely keep its bid at the limit price. The following are some sample rules to update demand agents’ behavior: • If a demand agent bid is lower than the market price and lower than its limit price, the agent will increase its bid. • If a demand agent bid is lower than the market price and equal to its limit price, the agent will maintain its bid. Because a demand agent probably won’t decrease its bid if it couldn’t buy in the previous period, if it couldn’t buy but is already bidding its limit price, it will most likely keep its bid at the limit price. The knowledge rules update agents’ bids in each scenario, but the number of scenarios remains the same. If at the end of a negotiation period the agent concludes—by analyzing market results—that it incorrectly evaluated other agents’ behavior, it will fix other agents’ profiles on the basis of the calculated deviation from the real results.
Implementation negotiation mechanism for establishing bilateral contracts. With this information, the agent can evaluate a bilateral contract’s potential benefits, compare them to the benefits expected in a pool, and make counterproposals. Scenario actualization. As we stated earlier, the analysis of each period’s results will update the agent’s market knowledge and the scenarios to study. After each negotiation period, instead of considering how other agents might increase, decrease, or maintain their bid, agents use knowledge rules that restrict modifications on the basis of other agents’ expected behavior. The following are some sample rules to update sellers’ behavior: • If a seller bid is higher than the market price and higher than its limit price, the agent will decrease its bid. • If a seller bid is higher than the market price and equal to its limit price, the agent will maintain its bid. Because a seller probably won’t increase its bid if it couldn’t sell in the previous www.computer.org/intelligent
We developed MASCEM in the Open Agent Architecture (www.ai.sri.com/~oaa) and Java. OAA, developed at SRI International, is a framework for integrating a community of heterogeneous software agents in a distributed environment. OAA is structured to minimize the effort of creating new agents written in various languages and operating platforms, to encourage the reuse of existing agents, and to allow the creation of dynamic, flexible agent communities. The OAA’s Interagent Communication Language is the interface and communication language that all agents share, no matter which machine they run on or language they’re programmed in. Because the OAA framework isn’t specifically devoted to developing simulations, we made it suitable by extensions—for example, we included a clock to introduce the simulation’s time evolution mechanism. We implemented each agent in Java as a Java thread. The model can be distributed over a computer network, which makes it easier to increase the number of simulation runs for scenarios with many agents. Figure 4 shows the prototype interface for a simple scenario with three seller agents, two buyer agents, and a trader. 59
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A u t h o r s Isabel Praça is an assis-
tant professor of computer engineering at the Polytechnic Institute of Porto’s Institute of Engineering and is preparing her PhD thesis on multiagent simulation of competitive electricity markets. Her research interests are in multiagent simulation and decision support systems. She received her MSc in electrical and computer engineering from the University of Porto. Contact her at Inst. Superior de Engenharia do Porto, Dept. de Engenharia Informática, Rua Dr. António Bernardino de Almeida, 4200072 Porto, Portugal;
[email protected]. Carlos Ramos is a coordinator professor of computer engineering and director of the Knowledge Engineering and Decision Support Research Group at the Polytechnic Institute of Porto’s Institute of Engineering. His research interests are artificial intelligence and decision support systems. He received his PhD in electrical engineering from the University of Porto. Contact him at Inst. Superior de Engenharia do Porto, Dept. de Engenharia Informática, Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal;
[email protected].
Zita Vale is a coordinator professor of electrical engineering at the Polytechnic Institute of Porto’s Institute of Engineering. Her research interests are decision support and artificial intelligence. She received her PhD in electrical engineering from the University of Porto. She is a member of the IEEE, IEE, and ACM. Contact her at Inst. Superior de Engenharia do Porto, Dept. de Engenharia Electrotécnica, Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal;
[email protected]. Manuel Cordeiro is a full professor at the University of Trás-osMontes and Alto Douro. His research interests are rational energy use, grounding systems, and power systems applications. He received his PhD in electrical engineering from the University of Trás-os-Montes and Alto Douro. Contact him at Univ. de Trás-os-Montes e Alto Douro, Engenharias II, 5000-911 Vila Real, Portugal;
[email protected].
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Figure 4. The prototype MASCEM interface.
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ecause of the growing number of entities and the redefined rules in newly competitive electricity markets, MASCEM seems to be a valuable framework for studying market evolution. The multiagent technology allied to an object-oriented implementation enables easy future improvements and model enlargement. This is important, considering markets are still very much in the evolutionary process. Market participants’ strategic behavior is very significant in the context of competition. Although we implemented some valuable, promising strategies, we must enlarge the portfolio of agents’ strategies and behaviors. For example, we’ll implement and test strategies based on learning algorithms, such as Q-learning. Developing targeted studies to analyze specific markets, such as the emerging market between Portugal and Spain known as the Iberian Market, will also be an important step toward validating the profitability of using tools such as MASCEM. www.computer.org/intelligent
References 1. I. Praça et al., “Multi-Agent Simulation of Electricity Markets,” Proc. 2nd Workshop Agent-Based Simulation, Soc. for Computer Simulation, 2001, pp. 89–94. 2. M. Shahidehpour, H. Yamin, and Z. Li, Market Operations in Electric Power Systems: Forecasting, Scheduling and Risk Management, John Wiley & Sons, 2002. 3. I. Praça et al., “Intelligent Agents for Negotiation and Game-Based Decision Support in Electricity Markets,” Proc. 12th Intelligent System Application to Power Systems Conf. (ISAP 03), 2003. 4. A. Greenwald, J. Kephart, and G. Tesauro, “Strategic Pricebots Dynamics,” Proc. 1st ACM Conf. Electronic Commerce, ACM Press, 1999, pp. 58–67. 5. I. Praça et al., “Agent Strategies in MultiPeriod Negotiations for Electricity Markets,” Proc. 2nd IASTED Int’l Conf. Artificial Intelligence and Applications, ACTA Press, 2002, pp. 411–416. 6. J. Von Neumann and O. Morgenstern, Theory of Games and Economic Behavior, Princeton Univ. Press, 1947. 7. D. Fudenberg and J. Tirole, Game Theory, MIT Press, 1991. IEEE INTELLIGENT SYSTEMS
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holds about 100 conferences each year and sponsors many educational activities, including computing science accreditation.
C O M M I T T E E VP, Standards Activities: JAMES W. MOORE† VP, Technical Activities: YERVANT ZORIAN† Secretary: OSCAR N. GARCIA* Treasurer: WOLFGANG K. GILOI* (2ND VP) 2002–2003 IEEE Division V Director: GUYLAINE M. POLLOCK†
COMPUTER SOCIETY O F F I C E S
IEEE
2003–2004 IEEE Division VIII Director: JAMES D. ISAAK† 2003 IEEE Division V Director-Elect: GENE H. HOFFNAGLE Computer Editor in Chief: DORIS L. CARVER† Executive Director: DAVID W. HENNAGE† * voting member of the Board of Governors † nonvoting member of the Board of Governors
OFFICERS
Headquarters Office 1730 Massachusetts Ave. NW Washington, DC 20036-1992 Phone: +1 202 371 0101 • Fax: +1 202 728 9614 E-mail:
[email protected]
President: MICHAEL S. ADLER
Publications Office 10662 Los Vaqueros Cir., PO Box 3014 Los Alamitos, CA 90720-1314 Phone:+1 714 821 8380 E-mail:
[email protected] Membership and Publication Orders: Phone: +1 800 272 6657 Fax: +1 714 821 4641 E-mail:
[email protected]
Executive Director: DANIEL J. SENESE
STAFF
Executive Director: DAVID W. HENNAGE Assoc. Executive Director: ANNE MARIE KELLY Publisher: ANGELA BURGESS Assistant Publisher: DICK PRICE Director, Finance & Administration: VIOLET S. DOAN Director, Information Technology & Services: ROBERT CARE Manager, Research & Planning: JOHN C. KEATON
magazines and 10 research transactions. Refer to membership application or request information as noted at left.
Asia/Pacific Office Watanabe Building 1-4-2 Minami-Aoyama,Minato-ku, Tokyo107-0062, Japan Phone: +81 3 3408 3118 • Fax: +81 3 3408 3553 E-mail:
[email protected]
President-Elect: ARTHUR W. WINSTON Past President: RAYMOND D. FINDLAY
Secretary: LEVENT ONURAL Treasurer: PEDRO A. RAY VP, Educational Activities: JAMES M. TIEN VP, Publications Activities: MICHAEL R. LIGHTNER VP, Regional Activities: W. CLEON ANDERSON VP, Standards Association: GERALD H. PETERSON VP, Technical Activities: LEAH JAMIESON IEEE Division VIII Director JAMES D. ISAAK President, IEEE-USA: JAMES V. LEONARD
