Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background

【Author】 Lu, Changchang; Chen, Sijie

【Source】COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

【影响因子】3.120

【Abstract】Large-scale and widely dispersed distributed energy resource (DER) can be gathered by a virtual power plant (VPP) in a given area, and its parameters can be combined into a single external operation pro<^>le. Each distributed energy source in the VPP has a complete backup of the critical information for the entire network because it is a node of blockchain. The distribution network can be accessed by DER freely and adaptable under the scientific management of the VPP, and it can oSer the system high-reliability, high-quality, and high-security power services. An energy blockchain network model based on particle swarm optimization (PSO) to optimise the neural network is proposed in this paper as a solution to the issues with the current VPP models. This will enable distributed dispatching of the VPP and reasonable load distribution among units. According to the simulation results, this algorithm's error is minimal and its accuracy can reach 94.98 percent. This model can more accurately capture demand-side realtime information, which bene<^>ts VPP's stable scheduling with a welcoming environment and transparent information. It also enhances the system's data security and storage security. This system can successfully address the issues of subject-to-subject mistrust and high information interaction costs in the VPP.

【Keywords】

【发表时间】2022 13-Sep

【收录时间】2022-12-18

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-能源领域