Capacity Optimization of a Centralized Charging Station in Joint Operation with a Wind Farm

Zhe Jiang, Xueshan Han, Zhimin Li, Mingqiang Wang, Guodong Liu, Mengxia Wang, Wenbo Li and Thomas B. Ollis
Additional contact information
Zhe Jiang: Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
Xueshan Han: Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
Zhimin Li: Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
Mingqiang Wang: Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
Guodong Liu: Power and Energy Systems Group, Oak Ridge National Laboratory, One Bethel Valley Road, P.O. Box 2008 MS6007, Oak Ridge, TN 37831, USA
Mengxia Wang: Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
Wenbo Li: Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250003, China
Thomas B. Ollis: Power and Energy Systems Group, Oak Ridge National Laboratory, One Bethel Valley Road, P.O. Box 2008 MS6007, Oak Ridge, TN 37831, USA

Energies, 2018, vol. 11, issue 5, 1-18

Abstract: In the context of large-scale wind power integration and rapid development of electric vehicles (EVs), a joint operation pattern was proposed to use a centralized charging station (CCS) to address high uncertainties incurred by wind power integration. This would directly remove the significant indeterminacy of wind power. Because the CCS is adjacent to a wind power gathering station, it could work jointly with wind farms to operate in power system as an independent enterprise. By combining the actual operational characteristics of the wind farm and the CCS, a multidimensional operating index evaluation system was created for the joint system. Based on the wind farm’s known capacity, a multi-target capacity planning model was established for CCS to maximize the probability of realizing diverse indices of the system based on dependent-chance goal programming. In this model, planning and dispatching are combined to improve the feasibility of results in the operational stage. This approach takes the effects of randomness into account for wind power and battery swapping. The model was solved through combining Monte Carlo simulation and a genetic algorithm (GA) based on segmented encoding. As shown in the simulation results, the proposed model could comprehensively include factors such as initial investment, wind power price, battery life, etc., to optimize CCS capacity and to ultimately improve the operating indices of the joint system.

Keywords: power system; wind farm; centralized charging station; dependent chance programming; capacity optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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