A novel multi-objective Stackelberg game model for multi-energy dynamic pricing and flexible scheduling in distributed multi-energy system

Amidst global energy and environmental challenges, distributed multi-energy system (DMES) offers a promising solution. To optimize resource allocation and foster energy market development, this paper proposes a novel multi-energy management and pricing framework based on a multi-objective Stackelberg game. The DMES operator (DMEO) serves as the leader, optimizing DMES scheduling and pricing to balance revenue, energy consumption, and carbon emissions objectives. Users adjust their energy demand based on pricing to maximize consumption surplus. To enable the low-carbon and flexible operation of DMES, carbon trading and demand response mechanisms are introduced. The model is solved using the NSGA-III algorithm and Gurobi solver, with a robust optimization model with adjustable robust measure is established to address renewable energy uncertainty. The validity of the proposed model is verified through a case study of a data center. Compared with the traditional Stackelberg game model, the proposed model reduces carbon emissions by 1.64 % and energy consumption by 1.59 %. Under this optimization framework, the exergy efficiency and energy efficiency of DMES are 0.535 and 0.798, while the share of renewable energy is 34.6 %. Increased renewable energy uncertainty results in diminished DMEO revenue, heightened carbon emissions and energy consumption, while significantly reducing energy and exergy efficiency.