Optimal scheduling of regional integrated energy system based on cloud energy storage

In the context of smart grid, the penetration rate of distributed energy resources is increasing, but the disadvantages of high energy storage cost and low energy utilization rate are revealed. As a new energy use method, cloud energy storage has attracted the attention of researchers due to its high energy efficiency and excellent economic performance. This paper applies the cloud energy storage model to the integrated energy system. Firstly, we propose the overall system framework, and analyze the energy storage and energy consumption behaviors of each participant in the framework. Secondly, from the perspective of users, the interior point method is used to solve the energy consumption scheme that minimizes the energy consumption cost of each user, from the perspective of cloud energy storage aggregators, PSO(Particle Swarm optimization) is used to solve the scheduling scheme that maximizes the benefits of the aggregators. Finally, through case analysis, it is verified that the cloud energy storage model can be applied to the regional electric heating integrated energy system to achieve the purpose of reducing energy costs, and the participating entities can achieve mutual benefit and win-win results, which verifies the feasibility and superiority of the cloud energy storage model.