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Energy Trading as a Multiplayer Game
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Energy Trading as a Multiplayer Game Brandon R. Sutherland1,* Growing installations of distributed energy resources present an untapped opportunity for electricity trading. Recently in Applied Energy, Bhatti and Broadwater presented a new approach based on game theory to model future energy markets. Their proposal to factor historical reputation of individual buyers brings such economic frameworks closer to real-world relevance.
Evolution yielded species of sufficient intelligence to comprehend happiness, and from this came efforts to distill amusement from the passage of time. Primates in the pre-history era were presumably clashing among themselves to unearth the largest rock in the tribe. In current times there is a ubiquitous love for sports, and now competitive videogame tournaments have teams and individuals battling for more than US$30 million in prize money (see Dota 2 The International and Fortnite World Cup). Gaming, in one form or another, is central to the rational enjoyment of life. Games are defined by a set of rules, goals, and players. With a definition that broad, almost anything can be a game. The game of life could be thought of as a semi-cooperative contest to maximize one’s happiness using the finite resources of the universe. The branch of applied mathematics that uses the generalized concept of gaming to address complex problems in society and economics is aptly termed ‘‘game theory.’’ The mathematical basis for game theory was developed by HungarianAmerican scientist John von Neumann in the late 1920s.1 His seminal work examined a two-player zero-sum game with perfect information—where gains and losses are exactly balanced and
the complete historical actions of all parties are known and shared by each participant. In this work, he proved that in such scenarios there exists an equilibrium state where each player is acting in their own best interest to minimize their maximum losses, termed the minimax state. In 1950, mathematician John Forbes Nash Jr. reported a further generalized theorem stating: ‘‘every finite game has a mixed strategy equilibrium.’’2. The foundation of game theory concerns solving for such a state (often called the Nash equilibrium) to identify the simultaneous set of optimal strategies to be used by each player to maximize their benefits. An emerging application of game theory that has garnered growing interest is the trading of electricity as a market resource by balancing its supply and demand among producers and consumers. The modern paradigm of transmitting electricity long distances from the point of generation to consumers dates to the early iterations of the electric grid in the late 1800s. Here, Thomas Edison and George Westinghouse— famously engaged in a ‘‘war of the currents’’—worked to develop early pilot demonstrations of centralized generators delivering power throughout the US northeast through transmission lines. By the 20th century, such electric grids became the global standard for the developed world.
In the case of centralized power plants providing energy to customers, energy trading consists of a small number of sellers (utilities and retailers) and a fleet of buyers (any end consumer of electricity). For simple centralized models such as this, classical economic approaches were sufficient to keep consumer costs low and producer profits high. Now, things are beginning to change. Driven by new technological opportunities, the multifaceted needs of consumers, and a transition away from traditional fossil-fuel-emitting energy sources to environmentally friendly renewable power, the global energy infrastructure is evolving. Today, the options for energy supply go beyond simply plugging into the grid and can include on-site solar, wind, or gas microturbines, often coupled with battery storage. The rise in such distributed energy resources (DERs) presents an opportunity for connecting residential, industrial, and commercial consumers generating off-grid power with each other and with power plants. This enables trading of energy not just unilaterally from power plant to consumer but between consumers themselves and from consumers back to power plants. With this increased trading complexity, there is a need for more sophisticated solutions to regulate a wholesale energy market.3 This brings energy economists back to emerging concepts such as game theory.4–6 Recently in Applied Energy, Bhatti and Broadwater reported a non-cooperative multiplayer game model for energy trading with distributed resources, factoring into account real-world imperfections such as the reputation of each player.7 In the proposed framework,
1Joule,
Cell Press, 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, USA *Correspondence: [email protected] https://doi.org/10.1016/j.joule.2019.08.003
Joule 3, 1810–1821, August 21, 2019 ª 2019 Published by Elsevier Inc. 1817
player game continues to provide compelling mathematical evidence for a manageable opportunity-rich market. Meanwhile, the distributed generation market is predicted to reach US$5.5 billion by 2024.8 If individuals can make a profit for adopting renewable distributed energy generation technology, they will be more likely to purchase such installations, thereby accelerating the adoption of low-carbon energy production to help mitigate anthropogenic climate change. Providing a robust economic platform for tomorrow’s increasingly distributed energy infrastructure will require further efforts to expand models to mimic real-world conditions and engage policymakers and industry to initiate small-scale demonstrations of feasibility. Figure 1. A Model Community with Distributed Energy Resources in the Form of Home Microgrids Operating alongside Utilities and Retailers (All Players in the Energy Trading Game) and Governed by a Market Operator Depending on predicted load and generation forecasting, buying and selling of electricity at optimized prices agreed upon with the market operator yield maximum profits for all players.
home microgrids (homes and small communities with on-site DERs) engage with each other through a market operator to identify optimal trading of electricity. Utilities and retailers continue to operate the grid and supply electricity to consumers as needed. This system is depicted in Figure 1. The strategy in this game is to optimize the selling price of the maximum amount of power each player wishes to trade every market interval to maximize profits. Home microgrids communicate through a smart energy management system with a market operator that manages the process. Using a new extremum seeking approach, the Nash equilibrium state is calculated at a defined market interval to determine the quantity and price of energy trading to optimize supply and demand. The market closes at the end of each interval, transactions are processed, and the market operator examines the difference between committed resources
1818 Joule 3, 1810–1821, August 21, 2019
and actual traded resources. The outcome of this comparison yields a market reputation index factor, one of the unique metrics defined in this particular study. Rewarding sellers that deliver agreed amounts of energy mimics real-world considerations in any distributed service. Sellers on eBay with higher ratings receive more purchasing offers. With distributed energy microgrids using intermittent energy resources such as solar and wind that are governed by weather conditions, predicted optimal values will not always match what sellers are able to provide. This incentivizes sellers in the energy market game to invest in smarter systems for predicting supply and demand. The long-standing aphorism of statistics (commonly attributed to George Box) states that ‘‘all models are wrong, but some are useful.’’ The expanding literature on energy trading as a multi-
1. von Neumann, J. (1928). Zur Theorie der Gesellschaftsspiele. Mathematische Annalen 100, 295–320. 2. Nash, J.F. (1950). Equilibrium points in n-person games. Proc. Natl. Acad. Sci. USA 36, 48–49. 3. Khorasany, M., Mishra, Y., and Ledwich, G. (2018). Market framework for local energy trading: a review of potential designs and market clearing approaches. IET Gener. Transm. Distrib. 12, 5899–5908. 4. Long, C., Zhou, Y., and Wu, J. (2019). A game theoretic approach for peer to peer energy trading. Energy Procedia 159, 454–459. 5. Marzband, M., Javadi, M., Pourmousavi, S.A., and Lightbody, G. (2018). An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory. Electr. Power Syst. Res. 157, 187–199. 6. Tushar, W., Yuen, C., Mohsenian-Rad, H., Saha, T., Poor, H.V., and Wood, K.L. (2018). Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches. IEEE Signal Process. Mag. 35, 90–111. 7. Bhatti, B.A., and Broadwater, R. (2019). Energy trading in the distribution system using a nonmodel based game theoretic approach. Appl. Energy 253, 113532. 8. Distributed Generation Market by Technology (Solar PV, Wind, Reciprocating Engines, Microturbines, Fuel Cells, Gas Turbines), Application (On-Grid, Off-Grid), End-User (Industrial, Commercial, Residential), By Region Analysis - Global Forecast 2018-2024 (2018). https://www.marketresearchengine. com/distributed-generation-market.
Energy Trading as a Multiplayer Game Brandon R. Sutherland1,* Growing installations of distributed energy resources present an untapped opportunity for electricity trading. Recently in Applied Energy, Bhatti and Broadwater presented a new approach based on game theory to model future energy markets. Their proposal to factor historical reputation of individual buyers brings such economic frameworks closer to real-world relevance.
Evolution yielded species of sufficient intelligence to comprehend happiness, and from this came efforts to distill amusement from the passage of time. Primates in the pre-history era were presumably clashing among themselves to unearth the largest rock in the tribe. In current times there is a ubiquitous love for sports, and now competitive videogame tournaments have teams and individuals battling for more than US$30 million in prize money (see Dota 2 The International and Fortnite World Cup). Gaming, in one form or another, is central to the rational enjoyment of life. Games are defined by a set of rules, goals, and players. With a definition that broad, almost anything can be a game. The game of life could be thought of as a semi-cooperative contest to maximize one’s happiness using the finite resources of the universe. The branch of applied mathematics that uses the generalized concept of gaming to address complex problems in society and economics is aptly termed ‘‘game theory.’’ The mathematical basis for game theory was developed by HungarianAmerican scientist John von Neumann in the late 1920s.1 His seminal work examined a two-player zero-sum game with perfect information—where gains and losses are exactly balanced and
the complete historical actions of all parties are known and shared by each participant. In this work, he proved that in such scenarios there exists an equilibrium state where each player is acting in their own best interest to minimize their maximum losses, termed the minimax state. In 1950, mathematician John Forbes Nash Jr. reported a further generalized theorem stating: ‘‘every finite game has a mixed strategy equilibrium.’’2. The foundation of game theory concerns solving for such a state (often called the Nash equilibrium) to identify the simultaneous set of optimal strategies to be used by each player to maximize their benefits. An emerging application of game theory that has garnered growing interest is the trading of electricity as a market resource by balancing its supply and demand among producers and consumers. The modern paradigm of transmitting electricity long distances from the point of generation to consumers dates to the early iterations of the electric grid in the late 1800s. Here, Thomas Edison and George Westinghouse— famously engaged in a ‘‘war of the currents’’—worked to develop early pilot demonstrations of centralized generators delivering power throughout the US northeast through transmission lines. By the 20th century, such electric grids became the global standard for the developed world.
In the case of centralized power plants providing energy to customers, energy trading consists of a small number of sellers (utilities and retailers) and a fleet of buyers (any end consumer of electricity). For simple centralized models such as this, classical economic approaches were sufficient to keep consumer costs low and producer profits high. Now, things are beginning to change. Driven by new technological opportunities, the multifaceted needs of consumers, and a transition away from traditional fossil-fuel-emitting energy sources to environmentally friendly renewable power, the global energy infrastructure is evolving. Today, the options for energy supply go beyond simply plugging into the grid and can include on-site solar, wind, or gas microturbines, often coupled with battery storage. The rise in such distributed energy resources (DERs) presents an opportunity for connecting residential, industrial, and commercial consumers generating off-grid power with each other and with power plants. This enables trading of energy not just unilaterally from power plant to consumer but between consumers themselves and from consumers back to power plants. With this increased trading complexity, there is a need for more sophisticated solutions to regulate a wholesale energy market.3 This brings energy economists back to emerging concepts such as game theory.4–6 Recently in Applied Energy, Bhatti and Broadwater reported a non-cooperative multiplayer game model for energy trading with distributed resources, factoring into account real-world imperfections such as the reputation of each player.7 In the proposed framework,
1Joule,
Cell Press, 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, USA *Correspondence: [email protected] https://doi.org/10.1016/j.joule.2019.08.003
Joule 3, 1810–1821, August 21, 2019 ª 2019 Published by Elsevier Inc. 1817
player game continues to provide compelling mathematical evidence for a manageable opportunity-rich market. Meanwhile, the distributed generation market is predicted to reach US$5.5 billion by 2024.8 If individuals can make a profit for adopting renewable distributed energy generation technology, they will be more likely to purchase such installations, thereby accelerating the adoption of low-carbon energy production to help mitigate anthropogenic climate change. Providing a robust economic platform for tomorrow’s increasingly distributed energy infrastructure will require further efforts to expand models to mimic real-world conditions and engage policymakers and industry to initiate small-scale demonstrations of feasibility. Figure 1. A Model Community with Distributed Energy Resources in the Form of Home Microgrids Operating alongside Utilities and Retailers (All Players in the Energy Trading Game) and Governed by a Market Operator Depending on predicted load and generation forecasting, buying and selling of electricity at optimized prices agreed upon with the market operator yield maximum profits for all players.
home microgrids (homes and small communities with on-site DERs) engage with each other through a market operator to identify optimal trading of electricity. Utilities and retailers continue to operate the grid and supply electricity to consumers as needed. This system is depicted in Figure 1. The strategy in this game is to optimize the selling price of the maximum amount of power each player wishes to trade every market interval to maximize profits. Home microgrids communicate through a smart energy management system with a market operator that manages the process. Using a new extremum seeking approach, the Nash equilibrium state is calculated at a defined market interval to determine the quantity and price of energy trading to optimize supply and demand. The market closes at the end of each interval, transactions are processed, and the market operator examines the difference between committed resources
1818 Joule 3, 1810–1821, August 21, 2019
and actual traded resources. The outcome of this comparison yields a market reputation index factor, one of the unique metrics defined in this particular study. Rewarding sellers that deliver agreed amounts of energy mimics real-world considerations in any distributed service. Sellers on eBay with higher ratings receive more purchasing offers. With distributed energy microgrids using intermittent energy resources such as solar and wind that are governed by weather conditions, predicted optimal values will not always match what sellers are able to provide. This incentivizes sellers in the energy market game to invest in smarter systems for predicting supply and demand. The long-standing aphorism of statistics (commonly attributed to George Box) states that ‘‘all models are wrong, but some are useful.’’ The expanding literature on energy trading as a multi-
1. von Neumann, J. (1928). Zur Theorie der Gesellschaftsspiele. Mathematische Annalen 100, 295–320. 2. Nash, J.F. (1950). Equilibrium points in n-person games. Proc. Natl. Acad. Sci. USA 36, 48–49. 3. Khorasany, M., Mishra, Y., and Ledwich, G. (2018). Market framework for local energy trading: a review of potential designs and market clearing approaches. IET Gener. Transm. Distrib. 12, 5899–5908. 4. Long, C., Zhou, Y., and Wu, J. (2019). A game theoretic approach for peer to peer energy trading. Energy Procedia 159, 454–459. 5. Marzband, M., Javadi, M., Pourmousavi, S.A., and Lightbody, G. (2018). An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory. Electr. Power Syst. Res. 157, 187–199. 6. Tushar, W., Yuen, C., Mohsenian-Rad, H., Saha, T., Poor, H.V., and Wood, K.L. (2018). Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches. IEEE Signal Process. Mag. 35, 90–111. 7. Bhatti, B.A., and Broadwater, R. (2019). Energy trading in the distribution system using a nonmodel based game theoretic approach. Appl. Energy 253, 113532. 8. Distributed Generation Market by Technology (Solar PV, Wind, Reciprocating Engines, Microturbines, Fuel Cells, Gas Turbines), Application (On-Grid, Off-Grid), End-User (Industrial, Commercial, Residential), By Region Analysis - Global Forecast 2018-2024 (2018). https://www.marketresearchengine. com/distributed-generation-market.