Optimal Energy Sharing Strategy in Multi-Integrated Energy Systems Considering Asymmetric Nash Bargaining
Open Access
Abstract
Integrated energy systems (IESs) are increasingly being deployed and expanded, which integrate various energy infrastructures to enable flexible conversion and utilization among different energy forms. To facilitate collaboration among operators of varying scales and fully leverage the economic and environmental benefits of multi-integrated energy systems (MIESs), this study develops a peer-to-peer (P2P) energy sharing framework for MIES based on asymmetric Nash bargaining. First, an IoT-based P2P energy sharing architecture for MIES is proposed, which incorporates coordinated electricity–heat–gas multi-energy synergy within IES models. Carbon capture systems (CCS) and power-to-gas (P2G) units are integrated with carbon trading mechanisms to reduce carbon emissions. Then, an MIES energy sharing operational model is established using Nash bargaining theory, subsequently decoupled into two subproblems: alliance benefit maximization and individual IES benefit distribution optimization. For subproblem 2, an asymmetric bargaining method employing natural exponential functions quantifies participant contributions, enabling fair distribution of cooperative benefits. Finally, the alternating direction method of multipliers (ADMM) is employed to solve both subproblems distributively, effectively preserving participant privacy. The effectiveness of the proposed method is verified by case simulation, demonstrating reduced operational costs across all IESs alongside equitable benefit allocation proportional to energy-sharing contributions. Carbon emission amounts are simultaneously reduced.Keywords
Funding Information
- State Grid Integrated Energy Service Group Co., Ltd. (527899220006)
This publication has 24 references indexed in Scilit:
- A Distributionally robust game approach for integrated energy systems with prosumer interactive transactionsApplied Energy, 2025
- Enhancing cyber-resilience in integrated energy system scheduling with demand response using deep reinforcement learningApplied Energy, 2024
- Optimization of renewable energy-based integrated energy systems: A three-stage stochastic robust modelApplied Energy, 2024
- Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approachApplied Energy, 2024
- Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approachApplied Energy, 2023
- Cooperative operation of industrial/commercial/residential integrated energy system with hydrogen energy based on Nash bargaining theoryEnergy, 2023
- Multi-timescale rolling optimization dispatch method for integrated energy system with hybrid energy storage systemEnergy, 2023
- Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theoryEnergy, 2023
- Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responsesEnergy, 2023
- Bargaining-based cooperative energy trading for distribution company and demand responseApplied Energy, 2018