Energy Management for Multi-agent Integrated Energy Systems based on Bi-level Optimization

Introduction: Integrated energy systems face inherent conflicts of interest among various entities, which complicate efficient energy management. Understanding and resolving these conflicts are essential for optimizing the performance and economics of multi-agent integrated energy systems. This paper aims to analyze energy management strategies in multi-agent integrated energy systems and propose an innovative energy scheduling strategy to enhance system-wide economic performance and operational efficiency. Methods: An in-depth analysis of the operational characteristics of individual microgrids (MG) must be conducted and a demand response model that integrates both electrical and thermal loads should be developed. Ahierarchical master-slave game optimization framework with the system operator as the upper-level leader and microgrid load aggregators, energy storage stations, and wind farms as lower-level followers should be established. Power interaction channels among entities to enable coordinated energy exchange should be designed. At the upper level, an adaptive particle swarm optimization algorithm to determine optimal energy pricing and demand response compensation has to applied. At the lower level, overall operational costs using energy scheduling strategies should be minimized. Results: To validate the proposed method, four scenarios were simulated using the control variable method. Taking Scenario 3 as an example, where energy exchanges between parks were eliminated and each park operated independently, the total profit decreased by 14387 CNY (Chinese Yuan) compared to the proposed master-slave game optimization strategy. This demonstrates the critical role of coordinated energy exchanges in improving system efficiency and highlights the effectiveness of the proposed method. Discussion: The bi-level game framework in this study demonstrates that coordinated energy exchange and integrated demand response could significantly enhance system-wide economic efficiency. The approach effectively balances collective benefits with individual agent interests. However, the results rely on a deterministic model, highlighting the need for future research to address system uncertainties and more complex network constraints. Conclusion: The proposed strategy not only addresses the inherent conflicts in multi-agent integrated energy systems but also enhances system-wide economic and operational efficiency through a robust optimization framework.