With the deepening of the “source-load-storage” interaction and the development of demand response technology, the emergence of prosumers has led to new vitality and potential for the optimal operation of microgrids. By implementing a demand response mechanism for prosumers, peak shaving and valley filling are realized, and load fluctuations are balanced. However, the high costs of investing and operating energy storage system (ESS) restrict their ability to participate in the scheduling of microgrids. In this paper, for the objectives of obtaining the lowest comprehensive costs and the smallest load fluctuations, an INSGA-II (Improved Fast Nondominated Sorting Genetic Algorithm) algorithm is proposed for the multiobjective configuration optimization model of a prosumer's ESS. To ensure the diversity of the population and improve the search ability of the algorithm in space, based on the original NSGA-II algorithm, the proportion factor set in the selection strategy is improved. The normal distribution crossover operator is introduced in the crossover process, and the local chaotic search strategy is added after the formation of the next generation of the population. An example of a science and technology park with five users is simulated and analyzed. Upon comparison with various typical intelligent algorithms, the results show that the performance of the improved NSGA-II algorithm is the best. At the same time, multiple calculation results show that the improved NSGA-II algorithm has strong algorithmic stability.

The charge/discharge operation of the prosumer’s energy storage and the energy interaction between prosumers and MGs are chaotic from the overall point of the MG’s operation. It causes considerable resource waste and reduces the overall benefits of the MG with multi-prosumers. Therefore, a game theory-based optimal scheduling strategy for the MG with multi-prosumers combined into a PRCO is proposed in this paper. According to the prosumers’ complementary characteristics of ES utilization and energy production, prosumers can be integrated into the PRCO to obtain energy reciprocity by sharing ES with an ordered charge–discharge operation. Meanwhile, to improve the collaboration of prosumers and the overall efficiency of the MG, a game scheduling model is established with the MG as the leader and the PRCO as the follower. The ToU price incentive policy is implemented in the MG to maximize the operational benefits and reduce the difference between the valley and peak load. Meanwhile, the PRCO responds to the price policy and implements an ordered charge–discharge strategy of ES to optimize each member’s energy scheduling strategy and minimize the total costs. The PRCO revenues are distributed to prosumers based on the Shapley value method. The uniqueness and existence of Stackelberg equilibrium in the game model are proved. The simulations of a community MG show that the ordered charge–discharge operation of ES is achieved and the overall benefits of the system are improved.

With the deepening integration and interaction of the prosumer's “source-load-storage,” for a microgrid (MG) with prosumers, the comprehensive demand-side response (CDSR) of the prosumer's multiresources is conducive to improving the system's new energy consumption，economic benefits, and prosumer's comprehensive electricity consumption satisfaction (CECS) of multiresources usage. Consequently, an optimal scheduling strategy for the MG with prosumers is proposed in this article, considering the CDSR and the CECS of the prosumer. In addition, a master–slave game model with the MG as the leader and the prosumer the follower is established, to stimulate the activity of prosumers participating in the scheduling by bargaining the price of the MG. Taking the maximum operation benefit as the optimization objective, the MG formulates the electricity price incentive policy to encourage prosumers to participate in the MG's peak adjustment dispatching; responding to the MG's price policy, the prosumer develops the scheduling strategy to minimize its total cost and maximize the CECS. After multiple rounds of iterative optimization, the Stackelberg equilibrium is obtained, while two parties are unable to obtain greater benefits by adjusting their own strategies. The simulation results show that the prosumer with the CDSR and the CECS may obtain the best scheduling performance, and the interests of the whole system are promoted with the proposed strategy.