考虑碳排放与合作博弈的多能互补系统双层优化OA

A bi-level optimization model integrating a stepped carbon trading mechanism and cooperative game theory is proposed to address the challenge of balancing low-carbon objectives with multi-agent economic coordination in the capacity configuration and operation scheduling of multi-energy complementary systems.In terms of system architecture,wind and solar power generation are integrated into a clean energy unit,thermal power serves as a traditional energy unit,and multiple energy storage devices are consolidated into an energy storage unit.A hierarchical control strategy is adopted to enhance system operational efficiency and stability.The upper-level model aims to minimize the equivalent annual cost over the system's full life cycle by introducing a stepped carbon trading cost to optimize the capacity configuration of each device.The lower-level model seeks to maximize the system's operational revenue during the scheduling period by employing a cooperative game approach to form a multi-agent alliance among the three subsystems.The Shapley value method is used for revenue allocation to optimize system operation strategies,and the model is solved using a multi-objective grey wolf optimizer combined with the CPLEX solver.Case study results demonstrate that the proposed model effectively reduces the configured capacity of thermal power and carbon emission costs,achieving an operational revenue of 924 700 yuan during the scheduling period,and a wind and solar power accommodation rate of 99.4%.These findings validate the synergistic effects of carbon trading and cooperative game theory in optimizing multi-energy complementary systems.