Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system

With the increasing uncertainty of energy supply side output, fully encouraging users to participate in demand response through different types of demand response incentive mechanisms has become one of the effective ways to deal with the uncertainty of integrated energy system operation and improve the overall energy efficiency. However, in existing studies, the coordination of uncertainty handling, optimization of demand response incentive strategies, and demand response measures at different time scales have not been adequately considered in the operation of integrated energy systems. Based on these considerations, this paper proposes a multi time-scale game optimization scheduling model for Park-level Integrated Energy System considering multiple types of demand response models. In the day-ahead stage, a Park-level Integrated Energy System optimization game scheduling model based on the demand response comprehensive incentive mechanism is established, and the uncertainty of the predicted value of distributed renewable energy and multi-type energy load was characterized based on the fuzzy chance-constrained programming method. In the intraday and real-time stages, a rolling optimization scheduling model is established with the minimum cost of Park-level Integrated Energy System operator scheduling. For the proposed model, an improved particle swarm optimization algorithm and an iterative solution strategy of CPLEX solver are introduced. Finally, the simulation results of an actual case show that the proposed model can effectively improve the Park-level Integrated Energy System operator and user economy while ensuring reliability.