Optimal Allocation Model of Virtual Power Plant Capacity considering Electric Vehicles

Author:

Geng Shiping1,Tan Caixia1ORCID,Niu Dongxiao1,Guo Xiaopeng1

Affiliation:

1. North China Electric Power University, Beijing 102206, China

Abstract

To push forward the development of electric vehicles while improving the economy and environment of virtual power plants (VPPs), research on the optimization of VPP capacity considering electric vehicles is carried out. In this paper, based on this, this paper first analyzes the framework of the VPP with electric vehicles and models each unit of the VPP. Secondly, the typical scenarios of wind power, photovoltaic, electric vehicle charging and discharging, and load are formed by the Monte Carlo method to reduce the output deviation of each unit. Then, taking the maximization of the net income and clean energy consumption of the VPP as the objective function, the capacity optimal allocation model of the VPP considering multiobjective is constructed, and the conditional value-at-risk (CVaR) is introduced to represent the investment uncertainty faced by the VPP. Finally, a VPP in a certain area of Shanxi Province is used to analyze a calculation example and solve it with CPLEX. The results of the calculation example show that, on the one hand, reasonable selection of the optimal scale of EV connected to the VPP is able to improve the economy and environment of the VPP. On the other hand, the introduction of CVaR is available for the improvement of the scientific nature of VPP capacity allocation decisions.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Reference43 articles.

1. A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles

2. Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties

3. Multi-agent joint investment microgrid source-storage multi-strategy bounded rational decision evolution game capacity planning;N. T. Huang;Proceedings of the CSEE,2020

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