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MATREM: An Agent-Based Simulation Tool for Electricity Markets

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Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-based Simulation, and Emerging Designs

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 144))

Abstract

This chapter presents the key features of an agent-based simulation tool, called MATREM (for Multi-Agent TRading in Electricity Markets). The tool allows the user to conduct a wide range of simulations regarding the behavior and outcomes of electricity markets (EMs), including markets with large penetrations of renewable energy. In each simulation, different autonomous software agents are used to capture the heterogeneity of EMs, notably generating companies (GenCos), retailers (RetailCos), aggregators, consumers, market operators (MOs) and system operators (SOs). The agents are essentially computer systems capable of flexible, autonomous action and able to interact, when appropriate, with other agents to meet their design objectives. They are able to generate plans and execute actions according to a well-known practical reasoning model—the belief-desire-intention (BDI) model. MATREM supports two centralized markets (a day-ahead market and an intra-day market), a bilateral market for trading standardized future contracts (a futures market), and a marketplace for negotiating the terms and conditions of two types of tailored (or customized) long-term bilateral contracts: forward contracts and contracts for difference. The tool is currently being developed using both JADE—the JAVA Agent DEvelopment framework—and Jadex—the BDI reasoning engine that runs over JADE, enabling the development of BDI agents. A graphical interface allows the user to specify, monitor and steer all simulations. The human-computer interaction paradigm is based on a creative integration of direct manipulation interface techniques with intelligent assistant agents. The target platform for the system is a 32/64-bit computer running Microsoft Windows.

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Notes

  1. 1.

    Other markets are expected to be available soon. In particular, a market for trading standardized option contracts is currently under development. Also, future work aims at extending the simulation tool by incorporating a market to match the imbalances caused by the variability and uncertainty present in power systems.

  2. 2.

    To date, our focus has been on four key types of market participants. One important area for future work is to consider other types of traditional power industry agents, including transmission company agents (TransCos) and distribution company agents (DistCos).

  3. 3.

    The gate closure time of the DAM is a simulation parameter. Thus, the tool allows the user to specify market simulations involving different gate closures (e.g., 10 a.m. or 6 p.m. of the day before the day of operation).

  4. 4.

    The number of intra-day market sessions is also a simulation parameter, meaning that the tool allows the user to phase the intra-day market into different sessions (e.g., only two sessions a day, or twenty-four sessions a day, one for each hour).

  5. 5.

    The operation of the intra-day market is essentially identical to that of the day-ahead market, and is therefore omitted.

  6. 6.

    The simulation tool allows the user to omit this step if necessary or desired. Specifically, the user may assume that demand is (highly) inelastic and set according to a load forecast. The demand curve is then a vertical line defined by simply considering the value of the load forecast.

  7. 7.

    Future work aims at extending the tool by incorporating a detailed invoicing and settlement process. The user will then be able to simulate forward payments from buyers to sellers following the delivery of energy. GenCos will be paid the market-clearing price for every megawatt-hour that they will produce, whereas RetailCos and large consumers will pay this price for every megawatt-hour that they will consume.

  8. 8.

    Under LMP, the user needs to specify the location of the various GenCos and RetailCos at the various nodes of the transmission grid.

  9. 9.

    The supply bids of the GenCo agents are modeled as linear functions, relating money and power. Although other functions are discussed in the literature on energy markets (e.g., quadratic functions), we note that the implications of the supply bid format for the operation of EMs is an important topic that requires further research (but see Chap. 2).

  10. 10.

    For simplicity, the tool allows the user to model RetailCos as non-strategic agents servicing price-insensitive loads only—that is, the demand serviced by RetailCos may exhibit a negligible price sensitivity and thus the price-sensitive demand part of the offers to buy energy may be omitted. Alternatively, the user may omit the fixed demand part of the offers.

  11. 11.

    Financial future contracts involve the notional supply of electrical energy—that is, the delivery is purely financial based on a reference price. On the other hand, physical future contracts involve the real supply of electricity at constant power (e.g., 1 MW) during all the hours of the delivery period.

  12. 12.

    Contracts are traded in a continuous mode. Future work aims at extending the tool to support auctions during the trading period to achieve more flexibility.

  13. 13.

    The trading platform supports anonymous operations on contracts only. The underlying anonymity model is widely used and often considered very useful (see, e.g., [27]). Market participants can formulate expectations relative to price variations based on specific strategies without discriminating between different agents, creating conditions for determining fair energy prices. Also, since all bids and offers are public, participants may exploit eventual disparities resulting from the “gap” between the supply and demand of electricity.

  14. 14.

    Orders specifying a price out of the price variation limits are not accepted by the platform.

  15. 15.

    Price is the main negotiable element of the standardized future contracts. However, if desirable or even necessary, the parties may increase or reduce the energy quantity.

  16. 16.

    The financial settlement based on a DSV value applies exclusively to existing positions on daily, weekly and monthly financial future contracts.

  17. 17.

    Arguably, most real-world long-term contracts are forward contracts between retailers and end-use customers. Standardized long-term contracts for differences (CFDs) have recently started to be used as a mechanism to support renewable generation (see, e.g., [34]). This work aims at going one step beyond by considering tailored long-term CFDs. Accordingly, the current version of the tool allows market participants (e.g., GenCos, RetailCos and large customers) to negotiate any CFD terms that are deemed appropriate. Tailored CFDs allow market participants to take part in the centralized day-ahead market, while insulating them from the market-clearing prices (i.e., they provide a hedge against price volatility in the DAM).

  18. 18.

    The term “common screen layout” refers to the primary windows displayed on the screen when the system starts running. Each window is associated with a specific area (or part) of the screen.

  19. 19.

    The current version of the system allows the user to indicate the agents participating in a particular market by using the participants menu—that is, the user selects the agents sequentially, one at time, and confirms their bids/offers. Future work will focus on developing a graphical editor for constructing and/or modifying electric power industry scenarios. The editor will allow the user to specify market agents graphically and to define relationships between them (i.e., interconnecting market agents by links representing the power grid, ownership and money flow).

  20. 20.

    An noted earlier, one important area for future work is to develop TransCo and DistCo agents responsible for operating the transmission and distribution systems, respectively.

  21. 21.

    The information displayed in the middle window is essentially complementary to that provided by the Sniffer agent or the Java Sniffer application (see [21] for details of these two tools).

  22. 22.

    The simulation tool supports not only market agents (e.g., GenCos and RetailCos), but also a special type of agent referred to as assistant agent (but see below).

  23. 23.

    Software assistants are computer programs that provide assistance to users dealing with computer-based applications [44]. Autonomous interface agents are agents capable of operating the interface—or at least part of the interface—in an autonomous way and also act in parallel with the user [45].

  24. 24.

    Currently, the simulation tool includes a specialized interface for each simulated market. An earlier design placed the interactions related to all markets within a common (general-purpose) interface, but it was proven to be not adequate nor effective, causing (test) users to be confused about tasks associated with different markets and/or pricing mechanisms (e.g., the bid submission process involving either the system marginal pricing or the locational marginal pricing).

  25. 25.

    The simulation tool has been designed to provide the user with a high degree of positive control over system behavior, although it retains a strong measure of autonomy. Subsequently to the assignment of tasks by the user, he/she and the agents address their individual responsibilities in a fairly independent manner, initiating interactions with one another as needed.

  26. 26.

    For retailer agents participating in the day-ahead market, the offer submission process is essentially identical to that of GenCo agents, and is therefore omitted.

  27. 27.

    To date, the simulation tool can “monitor” the Iberian Electricity Market (MIBEL). In particular, the (light-blue) assistant agents can interact with MIBEL (www.omie.es) to get the daily market-clearing prices of both Portugal and Spain. Future work will focus on monitoring other markets, notably the Nordic power market.

  28. 28.

    To date, our focus for personalization has been on endowing (light-blue) assistant agents with knowledge about different users playing the roles of typical market participants (e.g., generating companies, retailers and consumers). One important area for future work is to consider machine learning techniques to allow the agents to learn the users’ goals and preferences regarding the application domain. Furthermore, other area for future work is to analyze personalization from the point of view of the interaction between the user and the simulation tool (i.e., the assistant agents): discovering how the user wants to be assisted, learning when (and if) to interrupt the user, and learning his/her reactions towards different assistance actions (such as suggestions and warnings).

  29. 29.

    For convenience, and also simplicity in exposition, the previous section considered the terms “light-green interface agents” and “light-blue assistant agents” to refer to interface managers and intelligent assistants, respectively.

  30. 30.

    In this work, we adopt the graphical representation from the Unified Modelling Language [54]. Accordingly, classes are represented as rectangles with three compartments. The top compartment indicates the name of a class. The second and the third compartments list the variables and the methods of a class, respectively. For the sake of clarity and simplicity, the variables and methods of the various classes are not shown in Fig. 8.4. The generalization relationship is denoted by a solid directed line with a large open arrowhead, pointing to a superclass (or parent). More-specialized classes (subclasses) inherit the variables and methods of their parents, albeit they may have their own variables and methods.

  31. 31.

    Researchers working in some areas (e.g., philosophy, cognitive psychology and linguistics) may find the distinction between cognitive and communicative actions dubious. After all, an axiom of speech act theory is that agents requesting or informing are performing actions just like any other actions. This distinction is, however, rather natural and we believe suitable for the purposes of this work.

  32. 32.

    The earlier versions of the tool incorporated a number of software agents equipped with an operational model resulting from the implementation of the “traditional” deliberative model (or abstract architecture). Although limited in reasoning and decision-making, such agents proved to be relatively satisfactory, since the tool suited the interests and needs of several untrained (test) users.

  33. 33.

    The term “BDI model” has been coined by researchers working in closely related areas to describe slightly different types of models. Here, we use the term to describe any model of practical reasoning that makes use of the folk-psychology concepts of belief, desire and intention. In this way, a BDI model may or may not center on claims originally propounded by Bratman [55] about the role of intentions in focusing practical reasoning.

  34. 34.

    Practical reasoning involves two main processes [1]: deliberation and means-end reasoning. Deliberation is a complex process and consists mainly in defining a consistent set of goals from a (possibly inconsistent) set of desires and selecting (some) goals to commit to. For the sake of simplicity, the ongoing developments in the simulation tool consider that the agents have a consistent set of goals to achieve, but not a set of desires nor a deliberation process. Future work will focus on designing and implementing a (simple) deliberation process.

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Acknowledgements

For the most part, the work described in this chapter was performed under the project MAN-REM (FCOMP-01-0124-FEDER-020397), supported by both FEDER Funds, through the program COMPETE (“Programa Operacional Temático Factores de Competividade”), and National Funds, through FCT (“Fundação para a Ciência e a Tecnologia”). Some parts of the work, notably the improvements in the day-ahead market and the current developments in the real-time market, were performed under the project IRPWind: Integrated Research Programme on Wind Energy, funded by the European Union’s seventh programme for research, technological development and demonstration, under grant agreement 609795. The author also wishes to acknowledge the significant contributions made by a number of Ph.D. and M.Sc. students to the agent-based simulation tool, notably students from the NOVA University of Lisbon, University of Lisbon, University Institute of Lisbon (ISCTE) and Polytechnic Institute of Lisbon (ISEL).

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  1. LNEG–National Laboratory of Energy and Geology, Estrada do Paço do Lumiar 22, 1649-038, Lisbon, Portugal

    Fernando Lopes

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    Fernando Lopes

  2. University of Lisbon, Lisbon, Portugal

    Helder Coelho

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Lopes, F. (2018). MATREM: An Agent-Based Simulation Tool for Electricity Markets. In: Lopes, F., Coelho, H. (eds) Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-based Simulation, and Emerging Designs. Studies in Systems, Decision and Control, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-74263-2_8

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