Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station

Zhe Jiang, Xueshan Han, Zhimin Li, Wenbo Li, Mengxia Wang and Mingqiang Wang
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
Wenbo Li: Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250003, China
Mengxia Wang: Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
Mingqiang Wang: Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China

Energies, 2016, vol. 9, issue 11, 1-17

Abstract: In order to deal with the uncertainties of wind power, wind farm and electric vehicle (EV) battery switch station (BSS) were proposed to work together as an integrated system. In this paper, the collaborative scheduling problems of such a system were studied. Considering the features of the integrated system, three indices, which include battery swapping demand curtailment of BSS, wind curtailment of wind farm, and generation schedule tracking of the integrated system are proposed. In addition, a two-stage multi-objective collaborative scheduling model was designed. In the first stage, a day-ahead model was built based on the theory of dependent chance programming. With the aim of maximizing the realization probabilities of these three operating indices, random fluctuations of wind power and battery switch demand were taken into account simultaneously. In order to explore the capability of BSS as reserve, the readjustment process of the BSS within each hour was considered in this stage. In addition, the stored energy rather than the charging/discharging power of BSS during each period was optimized, which will provide basis for hour-ahead further correction of BSS. In the second stage, an hour-ahead model was established. In order to cope with the randomness of wind power and battery swapping demand, the proposed hour-ahead model utilized ultra-short term prediction of the wind power and the battery switch demand to schedule the charging/discharging power of BSS in a rolling manner. Finally, the effectiveness of the proposed models was validated by case studies. The simulation results indicated that the proposed model could realize complement between wind farm and BSS, reduce the dependence on power grid, and facilitate the accommodation of wind power.

Keywords: power system; wind farm; electric vehicle; battery switch station; dependent chance programming (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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