Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity trading
The maximisation of renewable energy generation is critical for net-zero aspiring countries around the globe. Local energy markets facilitate the seamless incorporation of energy from distributed renewable energy resources into the electricity network, serving as platforms for trading locally-genera...
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Elsevier
2025-09-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000758 |
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| author | Ioanna Kalospyrou Timothy Hutty Robert Milton Solomon Brown |
| author_facet | Ioanna Kalospyrou Timothy Hutty Robert Milton Solomon Brown |
| author_sort | Ioanna Kalospyrou |
| collection | DOAJ |
| description | The maximisation of renewable energy generation is critical for net-zero aspiring countries around the globe. Local energy markets facilitate the seamless incorporation of energy from distributed renewable energy resources into the electricity network, serving as platforms for trading locally-generated renewable energy between prosumers in residential communities. However, local energy markets’ essential role in distributed energy resource integration is not enough to encourage participation. Prosumers are more likely to join a local energy market if financial incentives are offered. To address this, we present the Flexible Aggressiveness Probabilistic Optimisation (FAPO) bidding strategy for trading electricity within a local energy market aimed at maximising participation incentives. This is formulated as an optimisation problem targeting the maximisation of prosumers’ individual utilities. The FAPO methodology is applied in a simplified local energy market simulation environment, and its results are compared to two other well-established bidding strategies: Zero Intelligence-Constrained and Adaptive Aggressiveness. The results indicate that FAPO achieved a wider range of clearing prices than both Adaptive Aggressiveness and Zero Intelligence-Constrained, incentivising greater prosumer participation. Specifically, FAPO enabled the trading of 1.48 MWh of electricity, compared to 1.34 MWh with Adaptive Aggressiveness and 1.37 MWh with Zero Intelligence-Constrained. Furthermore, FAPO cleared 100% of all asks and 98% of all bids, while the other two strategies cleared approximately 90% of submitted orders. Consequently, FAPO is proven to be an engaging bidding methodology likely to attract more prosumers to local energy markets. This is critical for the successful acceptance, uptake, and widespread application of this financial market type, which is key for smooth distributed energy resource integration into the network. |
| format | Article |
| id | doaj-art-8b07e51a72a5400f84854fd25d8e006b |
| institution | Matheson Library |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
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| series | Energy and AI |
| spelling | doaj-art-8b07e51a72a5400f84854fd25d8e006b2025-07-04T04:47:02ZengElsevierEnergy and AI2666-54682025-09-0121100543Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity tradingIoanna Kalospyrou0Timothy Hutty1Robert Milton2Solomon Brown3School of Chemical, Materials and Biological Engineering, University of Sheffield, Sir Frederick Mappin Bldg, Mappin St, Sheffield City Centre, Sheffield, S1 3JD, South Yorkshire, United KingdomSchool of Chemical, Materials and Biological Engineering, University of Sheffield, Sir Frederick Mappin Bldg, Mappin St, Sheffield City Centre, Sheffield, S1 3JD, South Yorkshire, United KingdomSchool of Chemical, Materials and Biological Engineering, University of Sheffield, Sir Frederick Mappin Bldg, Mappin St, Sheffield City Centre, Sheffield, S1 3JD, South Yorkshire, United KingdomCorresponding author.; School of Chemical, Materials and Biological Engineering, University of Sheffield, Sir Frederick Mappin Bldg, Mappin St, Sheffield City Centre, Sheffield, S1 3JD, South Yorkshire, United KingdomThe maximisation of renewable energy generation is critical for net-zero aspiring countries around the globe. Local energy markets facilitate the seamless incorporation of energy from distributed renewable energy resources into the electricity network, serving as platforms for trading locally-generated renewable energy between prosumers in residential communities. However, local energy markets’ essential role in distributed energy resource integration is not enough to encourage participation. Prosumers are more likely to join a local energy market if financial incentives are offered. To address this, we present the Flexible Aggressiveness Probabilistic Optimisation (FAPO) bidding strategy for trading electricity within a local energy market aimed at maximising participation incentives. This is formulated as an optimisation problem targeting the maximisation of prosumers’ individual utilities. The FAPO methodology is applied in a simplified local energy market simulation environment, and its results are compared to two other well-established bidding strategies: Zero Intelligence-Constrained and Adaptive Aggressiveness. The results indicate that FAPO achieved a wider range of clearing prices than both Adaptive Aggressiveness and Zero Intelligence-Constrained, incentivising greater prosumer participation. Specifically, FAPO enabled the trading of 1.48 MWh of electricity, compared to 1.34 MWh with Adaptive Aggressiveness and 1.37 MWh with Zero Intelligence-Constrained. Furthermore, FAPO cleared 100% of all asks and 98% of all bids, while the other two strategies cleared approximately 90% of submitted orders. Consequently, FAPO is proven to be an engaging bidding methodology likely to attract more prosumers to local energy markets. This is critical for the successful acceptance, uptake, and widespread application of this financial market type, which is key for smooth distributed energy resource integration into the network.http://www.sciencedirect.com/science/article/pii/S2666546825000758P2P tradingIntelligent agentsOptimisationFlexibilityElectrificationMicrogrid |
| spellingShingle | Ioanna Kalospyrou Timothy Hutty Robert Milton Solomon Brown Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity trading Energy and AI P2P trading Intelligent agents Optimisation Flexibility Electrification Microgrid |
| title | Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity trading |
| title_full | Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity trading |
| title_fullStr | Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity trading |
| title_full_unstemmed | Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity trading |
| title_short | Flexible aggressiveness probabilistic optimisation (FAPO) bidding for peer-to-peer electricity trading |
| title_sort | flexible aggressiveness probabilistic optimisation fapo bidding for peer to peer electricity trading |
| topic | P2P trading Intelligent agents Optimisation Flexibility Electrification Microgrid |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000758 |
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