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

Abstract

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.

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

“…The penalty coefficient α for the joint system's power deviation was defined to be 0.3. capacity of the wind farm is 99 MW, and predicted values selected for the wind power on a typical day are presented in Figure 6. As assumed, relative predictive errors of wind power and battery swapping demands conform to normal distribution of N(0, 0.1 2 ) [11]. The grid purchase price of wind power is known, and the peak-valley price was adopted as given in Figure 7.…”

“…With the rapid development of electric vehicles (EVs), scholars have begun investigating ways to use EVs to store energy to help control the random fluctuations of wind power and to coordinate scheduling between wind power and EVs [8][9][10]. Dramatic uncertainties resulting from EV users' driving behavior and subjective demands have given rise to strong indeterminacy of EVs as flexible load or energy storage devices, which further affects their coordination with wind power [11]. The battery swapping pattern of centralized charging and unified distribution realizes geographical decoupling between EV battery replacement and charging [12], making it possible to collaborate between a centralized charging station (CCS) and a wind farm.…”

Joint Operation of Renewable Energy and Battery Switch Station Considering the Benefits of Different Subjects

“…In the interest of the RE and BSS, the integrated mode of them was proposed [15][16][17][18][19]. If built together with a wind turbine (WT) and photovoltaic (PV) generation unit, the BSS can use the RE to charge electric vehicle batteries directly, which can not only cut the charging cost, but also reduce the carbon emissions of electric vehicles.…”

“…Liu et al further researched the charging strategy of the PV-based BSS considering the self-consumption of PV energy in [17]. References [18,19] suggested that wind farms and BSS should be combined into a joint system and operate as an independent enterprise in the power system. With a unified coordinated control of wind farms and BSS, the whole benefit of the joint system is maximized.…”