RESEARCH PAPER
Dynamic Modeling and Optimization of Energy Storage in Peer-to-Peer Energy Trading Systems
1
College of E-commerce & Logistics,, Henan Polytechnic, China
2
College of Statistics and Applied Mathematics, Anhui University of Finance & Economics, China
Submission date: 2024-06-08
Final revision date: 2024-09-26
Acceptance date: 2024-11-09
Online publication date: 2024-11-14
Publication date: 2024-11-14
Corresponding author
Chunzhong Li
College of Statistics and Applied Mathematics, Anhui University of Finance & Economics, China
College of Statistics and Applied Mathematics, Anhui University of Finance & Economics, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):195761
HIGHLIGHTS
- Two-stage optimization manages P2P energy sharing, ensuring fair benefit distribution.
- Nash bargaining theory ensures fairness in energy trading between prosumers and consumers.
- Real-time pricing adapts to demand, improving economic efficiency and energy management.
- Consumer-side storage optimizes trading and storage based on load and renewable energy.
- System reduces external power use by optimizing local renewable energy during peak times.
KEYWORDS
social welfare maximizationPeer-to-peer energy sharingcooperative energy tradingcommunity supply networksdistributed optimization algorithms
TOPICS
ABSTRACT
The power system landscape has evolved from isolated end-users to interactive communities due to advances in information and communication technologies. This paper explores peer-to-peer energy (P2PE) trading and sharing within a community, where customer incentives for energy exchange enhance collective profits. A two-stage optimization (TSO) framework is proposed: the first stage determines customer participation in P2PE, balancing individual and collective benefits, while the second stage optimizes economic aspects of P2P trading using a payment bargaining model. A case study demonstrates significant cost reductions and improved renewable energy utilization, with notable profit increments for participants. The study highlights the effectiveness of Nash bargaining theory and privacy-preserving algorithms in optimizing social welfare and economic interactions. Limitations include a focus on wind energy and simplified assumptions about energy storage. Future research should incorporate diverse renewable sources, dynamic modeling, and multi-community interactions.
FUNDING
This work was supported by the Anhui Provincial University Natural Science Foundation (No. KJ2021A0481).
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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