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Optim."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p><jats:italic>Peer-to-peer (P2P) energy trading<\/jats:italic>is a decentralized energy market where local energy<jats:italic>prosumers<\/jats:italic>act as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling and assume users\u2019 sustained active participation and full compliance in the decision-making process. To overcome these unrealistic assumptions, and their deleterious consequences, in this article, we propose an<jats:italic>automated<\/jats:italic>P2P energy-trading framework that specifically considers the users\u2019 perception by exploiting<jats:italic>prospect theory<\/jats:italic>. We formalize an optimization problem that maximizes the buyers\u2019 perceived utility while matching energy production and demand. We prove that the problem is NP-hard and we propose a Differential Evolution-based Algorithm\u00a0for Trading Energy (<jats:italic>DEbATE<\/jats:italic>) heuristic. Additionally, we propose two automated pricing solutions to improve the sellers\u2019 profit based on reinforcement learning. The first solution, named Pricing mechanism with Q-learning and Risk-sensitivity (<jats:italic>PQR<\/jats:italic>), is based on Q-learning. Additionally, given the scalability issues of<jats:italic>PQR<\/jats:italic>, we propose a Deep Q-Network-based algorithm called<jats:italic>ProDQN<\/jats:italic>that exploits deep learning and a novel loss function rooted in prospect theory. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approaches achieve 26% higher perceived value for buyers and generate 7% more reward for sellers, compared to recent state-of-the-art approaches.<\/jats:p>","DOI":"10.1145\/3603148","type":"journal-article","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T10:10:21Z","timestamp":1686391821000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["P2P Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3674-7857","authenticated-orcid":false,"given":"Ashutosh","family":"Timilsina","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Kentucky, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2357-3429","authenticated-orcid":false,"given":"Simone","family":"Silvestri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Kentucky, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"1","volume-title":"IEEE International Conference on Communications (ICC\u201920)","author":"Agate Vincenzo","year":"2020","unstructured":"Vincenzo Agate, Atieh R. 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