Abstract
The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles’ charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (Ivse). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method.
Highlights
With the deepening of research, there have been studies on wind and photovoltaic capacity selection in distribution networks, mainly taking classical particle swarm optimization and genetic algorithm as examples [1]
The results show that the kernel extreme learning machine-based capacity configuration model can give reasonable and effective distributed generation capacity configuration results under the premise of considering electric vehicle charging characteristics
In view of the fundamental changes in the planning and operation characteristics of distribution networks caused by the large-scale access of wind and photovoltaic power generations and electric vehicles, the kernel extreme learning machine is used to build the capacity selection model
Summary
With the deepening of research, there have been studies on wind and photovoltaic capacity selection in distribution networks, mainly taking classical particle swarm optimization and genetic algorithm as examples [1]. Reference [8] established a two-layer optimization model for electric vehicle charging based on node blocking electricity prices, which maximized the economic benefits for the power grid and users. By introducing a kernel extreme learning machine, the original capacity configuration problem is transformed into an optimization problem that does not need to pay attention to the input and output weights and the number of hidden layer nodes, and has faster training speed and better generalization performance than the support vector machine. The results show that the kernel extreme learning machine-based capacity configuration model can give reasonable and effective distributed generation capacity configuration results under the premise of considering electric vehicle charging characteristics
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