Low-Carbon Community Adaptive Energy Management Optimization Toward Smart Services

Abstract

With the rapid development of society and the economy and the increasing seriousness of environmental problems, renewable energy and high-quality energy services in low-carbon communities have become popular research topics. However, a large number of volatile distributed generation power systems in the community are connected to the grid. It is difficult to stabilize and efficiently interact with fragmented and isolated energy management systems, and it is difficult to meet energy management needs in terms of low-carbon emissions, stability, and intelligence. Therefore, by considering operation costs, pollution control costs, energy stability, and plug-in hybrid electric vehicles, this article proposes a regional energy supply model called community energy Internet and builds a low-carbon community energy adaptive management model for smart services. Then, to address energy supply instability, an adaptive feedback control mechanism developed based on model predictive control is introduced to adapt to the changing environment. Finally, a long short-term memory-recurrent neural network-based Tabu search is introduced to prevent the multiobjective particle swarm optimization algorithm from easily falling into a local optimum. The simulation results show that the proposed model can effectively realize the optimal allocation of energy, which solves the problem of fragmented energy islands caused by distributed power access. This method has quality of service benefits for users, such as cost, time, and stability, and realizes wide interconnections, high intelligence, and low-carbon efficiency of community energy management.