标题:Stochastic Distribution Network Planning with Uncertain Renewable Energy Based on Credibility Assessment
作者:Zhou, Yuyong ;Sun, Ke ;Yang, Xuan ;Zhang, Quanming ;Zheng, Chaoming ;Zhang, Luliang ;Chen, Jia-Jia ;Jiao, Pihua
通讯作者:Chen, JiaJia
作者机构:[Zhou, Yuyong ;Yang, Xuan ] State Grid Hangzhou Power Supply Company, Hangzhou; 310000, China;[Zhang, Luliang ] School of Electric Power Engineering, 更多
会议名称:2018 International Conference on Advanced Technologies in Energy, Environmental and Electrical Engineering, AT3E 2018
会议日期:October 26, 2018 - October 28, 2018
来源:IOP Conference Series: Earth and Environmental Science
出版年:2019
卷:223
期:1
DOI:10.1088/1755-1315/223/1/012010
摘要:Due to the uncertainty of renewable energy and complex construction of distribution network, the wind-solar power integrated distribution network is one of the most difficult optimization problems in operational planning of power system. This paper proposes a risk-based credibility assessment (RbCA) model for stochastic distribution network planning (SDNP) with wind-solar power penetration to obtain the optimal planning from the perspective of risk aversion. In the proposed model, the uncertain wind power and solar power are designed as credibility interval variables, and the operation risk of SDNP is measured by the credibility assessment of wind-solar power. In addition, in consideration that the SDNP is a constrained non-convex and multi-modal optimization problem, and the particle swarm optimization (PSO) is easily trapped into premature convergence in dealing with this kind of problems, a dynamic PSO with escaping prey (DPSOEP) has been developed to increase the diversity of the population and then obtain the optimal planning of distribution network. Simulation results obtained based on the modified IEEE 33 nodes distribution system show that the proposed model and algorithm are reasonable and effectiveness in dealing with the SDNP with uncertain wind-solar power penetration, considering the integration of various inequality and equality constraints.
© 2019 Published under licence by IOP Publishing Ltd.
收录类别:EI
资源类型:会议论文
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