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The widespread adoption of distributed energy resources, and the advent ofsmart grid technologies, have allowed traditionally passive power system usersto become actively involved in energy trading. Recognizing the fact that thetraditional centralized grid-driven energy markets offer minimal profitabilityto these users, recent research has shifted focus towards decentralizedpeer-to-peer (P2P) energy markets. In these markets, users trade energy witheach other, with higher benefits than buying or selling to the grid. However,most researches in P2P energy trading largely overlook the user perception inthe trading process, assuming constant availability, participation, and fullcompliance. As a result, these approaches may result in negative attitudes andreduced engagement over time. In this paper, we design an automated P2P energymarket that takes user perception into account. We employ prospect theory tomodel the user perception and formulate an optimization framework to maximizethe buyer's perception while matching demand and production. Given thenon-linear and non-convex nature of the optimization problem, we proposeDifferential Evolution-based Algorithm for Trading Energy called DEbATE.Additionally, we introduce a risk-sensitive Q-learning algorithm, named Pricingmechanism with Q-learning and Risk-sensitivity (PQR), which learns the optimalprice for sellers considering their perceived utility. Results based on realtraces of energy consumption and production, as well as realistic prospecttheory functions, show that our approach achieves a 26% higher perceived valuefor buyers and generates 7% more reward for sellers, compared to a recent stateof the art approach.

Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing