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

arXiv: https://arxiv.org/abs/2208.12777 · HTML: https://mlsanity.com/paper/2208.12777/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Risk-sensitive Reinforcement Learning](https://mlsanity.com/paper/1311.2097/) (2013)
- [Peer-to-Peer Trading in Electricity Networks: An Overview](https://mlsanity.com/paper/2001.06882/) (2020)
- [Peer-to-Peer Energy Trading with Sustainable User Participation: A Game Theoretic Approach](https://mlsanity.com/paper/1809.11034/) (2018)
- [Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach](https://mlsanity.com/paper/1709.06071/) (2017)
- [Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches](https://mlsanity.com/paper/1804.00962/) (2018)
- [Towards a Consumer-Centric Grid: A Behavioral Perspective](https://mlsanity.com/paper/1511.01065/) (2015)
- [Grid Influenced Peer-to-Peer Energy Trading](https://mlsanity.com/paper/1908.09449/) (2019)
- [Feasibility Study of Financial P2P Energy Trading in a Grid-tied Power Network](https://mlsanity.com/paper/1909.04763/) (2019)

## Built on by (most cited first, 1 of 1)

- [textite-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing](https://mlsanity.com/paper/2304.04753/) (2023)

## Abstract (arXiv)

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.