Samende
Multi-Agent Deep Deterministic Policy Gradient Algorithm for Peer-to-Peer Energy Trading Considering Distribution Network Constraints
Samende; Fan
Authors
Fan
Abstract
In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading. Due to (i) uncertainties caused by renewable energy generation and consumption, (ii) difficulties in developing an accurate and efficient energy trading model, and (iii) the need to satisfy distribution network constraints, it is challenging for prosumers to obtain optimal energy trading decisions that minimize their individual energy costs. To address the challenge, we first formulate the above problem as a Markov decision process and propose a multi-agent deep deterministic policy gradient algorithm to learn optimal energy trading decisions. To satisfy the distribution network constraints, we propose distribution network tariffs which we incorporate in the algorithm as incentives to incentivize energy trading decisions that help to satisfy the constraints and penalize the decisions that violate them. The proposed algorithm is model-free and allows the agents to learn the optimal energy trading decisions without having prior information about other agents in the network. Simulation results based on real-world datasets show the effectiveness and robustness of the proposed algorithm.
Acceptance Date | Apr 12, 2022 |
---|---|
Publication Date | Jul 1, 2022 |
Publicly Available Date | Mar 29, 2024 |
Journal | Applied Energy |
Print ISSN | 0306-2619 |
Publisher | Elsevier |
DOI | https://doi.org/10.1016/j.apenergy.2022.119123 |
Keywords | Multi-agent; Deep deterministic policy gradient; Peer-to-peer energy trading; Renewable generation; Markov decision process |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S0306261922005025?via%3Dihub#! |
Files
1-s2.0-S0306261922005025-main.pdf
(2.7 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/
2108.09053v1.pdf
(10.2 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/
You might also like
The role of ‘living laboratories’ in accelerating the energy system decarbonization
(2022)
Journal Article
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning
(2022)
Journal Article
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
(2021)
Presentation / Conference
Downloadable Citations
About Keele Repository
Administrator e-mail: research.openaccess@keele.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search