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

Ashutosh Timilsina, Simone Silvestri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publication2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
Number of pages6
ISBN (Electronic)9781665435406
StatePublished - 2022
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: Dec 4 2022Dec 8 2022

Publication series

Name2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings


Conference2022 IEEE Global Communications Conference, GLOBECOM 2022
CityVirtual, Online

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work is supported by the NSF grant EPCN-1936131 and NSF CAREER grant CPS-1943035.

Publisher Copyright:
© 2022 IEEE.


  • differential evolution
  • dynamic pricing
  • Peer-to-peer energy trading
  • prospect theory
  • prosumer
  • Q-learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality


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