To enhance the market-oriented operation capability of integrated energy retailers and improve the synergy and economic efficiency of complex microgrids, this paper constructs a bi-level optimization model of “upper-level price optimization, lower-level multi-energy flow scheduling” under the background of multi-energy coupling of electricity, heat, gas, and hydrogen. The upper level optimizes electricity and heat price signals using the APSO and IGWO algorithms, while the lower level realizes coordinated multi-energy flow scheduling based on these signals. The operational performance of the two algorithms is compared across four scenarios. The results show that the scenario with multi-energy storage (Scenario 3) is the optimal adaptive scenario: the charge–discharge regulation of energy storage interacts with price guidance, and the peak-shaving and valley-filling characteristics significantly improve the system’s energy utilization efficiency. This scenario can fully unlock the value of bi-level optimization and meet the operational requirements of complex multi-energy coupling. In the algorithm comparison, the APSO algorithm presents distinct advantages, outperforming the IGWO algorithm in the precise regulation of upper-level electricity and heat prices, lower-level multi-energy flow balance, total operation cost control, and convergence stability. It provides an effective technical solution for the economic and stable operation of integrated energy systems.
Shen et al. (Mon,) studied this question.