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

• URL: http://arxiv.org/abs/2208.12777v1
• Date: Fri, 26 Aug 2022 16:45:40 GMT
• Title: Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing
• Authors: Ashutosh Timilsina, Simone Silvestri
• Abstract summary: In this paper, we design an automated P2P energy market that takes user perception into account. We introduce a risk-sensitive Q-learning mechanism named Q-b Pricing and Risk-sensitivity (PQR), which learns the optimal price for sellers considering their perceived utility. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approach achieves a 26% higher perceived value for buyers.
• Score: 2.2463154358632473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread adoption of distributed energy resources, and the advent of smart grid technologies, have allowed traditionally passive power system users to become actively involved in energy trading. Recognizing the fact that the traditional centralized grid-driven energy markets offer minimal profitability to these users, recent research has shifted focus towards decentralized peer-to-peer (P2P) energy markets. In these markets, users trade energy with each other, with higher benefits than buying or selling to the grid. However, most researches in P2P energy trading largely overlook the user perception in the trading process, assuming constant availability, participation, and full compliance. As a result, these approaches may result in negative attitudes and reduced engagement over time. In this paper, we design an automated P2P energy market that takes user perception into account. We employ prospect theory to model the user perception and formulate an optimization framework to maximize the buyer's perception while matching demand and production. Given the non-linear and non-convex nature of the optimization problem, we propose Differential Evolution-based Algorithm for Trading Energy called DEbATE. Additionally, we introduce a risk-sensitive Q-learning algorithm, named Pricing mechanism with Q-learning and Risk-sensitivity (PQR), which learns the optimal price for sellers considering their perceived utility. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approach achieves a 26% higher perceived value for buyers and generates 7% more reward for sellers, compared to a recent state of the art approach.

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