Two-Stage Robust Scheduling Optimization of AC/DC Hybrid Microgrid with Electric Vehicles
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摘要:
随着电动汽车（electric vehicles，EVs）技术的快速发展，EVs数量激增，将其接入微电网中参与充放电调度成为了降低大规模EVs对电网负面影响的有效途径. 为此，将EVs接入交直流混合微电网的直流侧，考虑EVs的源荷双重特性，针对微电网系统中微源出力及负荷的不确定性，搭建了计及EVs充放电的交直流混合微电网两阶段鲁棒调度模型，以寻求系统在极端场景下的经济最优方案. 该模型采用盒式不确定集描述不确定性，通过不确定性预算灵活调节模型保守性；基于系统各单元运行约束条件，建立最小成本目标函数，并通过强对偶理论和BIG-M法将模型转化为混合整数线性规划模型；最后通过列约束生成算法对模型进行迭代求得最优解，结合算例进行了仿真. 结果发现：合理运用EVs的源荷特性能够有效降低微电网的日运行成本，其中，当50辆EVs并网运行时，无序充电模式下的运行成本较有序充放电模式下的成本高出1069.7元；在换流功率的限制下，随着EVs接入数量的增加，运行成本呈现先下降后上升的趋势；考虑实时调整成本，鲁棒调度模型的经济性更佳.
Abstract:Due to the rapid development of electric vehicles (EVs), the number of EVs has surged. Connecting EVs to micro-grid in charge-discharge scheduling become an effective way to reduce the negative impact of large-scale EVs on the power grid. Thus, given the source-charge characteristics of the EVs, with the access of EVs to DC side in micro AC/DC hybrid power grid, the two-phase robust scheduling model is built to settle the uncertainty in micro-source output and load of the microgrid system and find most economical solution in extreme situations. The model uses a box uncertainty set to describe the uncertainty, and flexibly adjusts the conservatism of the model through uncertain budget. Based on the operation constraints of each unit in the system, the objective function of minimum cost is established, and the model is transformed into a mixed integer linear programming model by the strong duality theory and BIG-M method. Finally, the model is iterated to obtain the optimal solution through the column and constraint generation algorithm. The simulation results of an example simulation show that reasonably utilizing the source-load characteristics of EVs can effectively reduce the microgrid daily operation cost. In the case of 50 EVs in parallel operation, the operation cost in the randomly charging mode is higher than that in the orderly charging and discharging mode by 1069.7 Yuan. In addition, under the restriction of power commutation, with the increase in the number of EV access, the operation cost also shows a trend of declining and then rising. In terms of real-time adjustment costs, the robust scheduling model has favorable economic benefits.
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Key words:
- uncertainty /
- AC/DC hybrid microgrid /
- electric vehicle /
- two-stage robust model /
- uncertainty budget
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图 6 不同电动汽车辆数下的调度运行成本
Figure 6. Operation costs for different numbers of electric vehicles
图 8 不同不确定预算下微电网综合运行成本
Figure 8. Integrated operation cost of microgrid under different uncertain budgets
表 1 基本参数设置
Table 1. Basic parameter setting
参数 数值 参数 数值 mPV、mWT/
（元/（kW•h）−1）0.01 $P_{{\rm{MT}}}^{\max }{\text{、}}P_{{\rm{MT}}}^{\min }$/kW 30 mMT、mBC/
（元/（kW•h）−1）0.1 $P_{{\rm{ES}}}^{{\rm{ch}},\max }$、$P_{{\rm{ES}}}^{{\rm{dis}},\max }$/kW 500 aMT/（元/（kW•h）−1） 0.79 $P_{{\rm{BC}}}^{{\rm{ad,max}}}$、$P_{{\rm{BC}}}^{{\rm{da,max}}}$/kW 500 mES/（元/（kW•h）−1） 0.35 $P_{{\rm{grid}}}^{{\rm{buy,max}}}$、$P_{{\rm{grid}}}^{{\rm{sell,max}}}$/kW 500 mEV/（元/（kW•h）−1） 0.8125 $P_{{\rm{EV}}i}^{{\rm{ch,max}}}$、$P_{{\rm{EV}}i}^{{\rm{dis,max}}}$/kW 3.6 $E_{{\rm{ES}}}^{\max }{\text{、}}E_{{\rm{ES}}}^{\min }$/(kW•h) 2400、500 $\eta _{{\rm{EV}}}^{{\rm{ch}}}{\text{、}}\eta _{{\rm{EV}}}^{{\rm{dis}}}$ 0.9 $E_{{\rm{EV}}i}^{{\rm{max}}}{\text{、}}E_{{\rm{EV}}i}^{{\rm{min}}}$/(kW•h) 30、6 $\eta _{{\rm{BC}}}^{{\rm{ad}}}{\text{、}}\eta _{{\rm{BC}}}^{{\rm{da}}}$ 0.95 $E_{{\rm{ES}},0}$/(kW•h) 800 $R_{{\rm{BC}}}^{{\rm{up}}}$、$R_{{\rm{BC}}}^{{\rm{down}}}$/kW 1000 $E_{{\rm{EV}}i,0}$/(kW•h) 9.6 $\eta _{{\rm{ES}}}^{{\rm{ch}}}{\text{、}}\eta _{{\rm{ES}}}^{{\rm{dis}}}$ 0.95 ΓWT、ΓPV 6 ΓLA、ΓLD 12 
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表 2 配电网分时电价
Table 2. Time-of-use prices for distribution network
时段类型 时段 电价/元 峰时 09：00—12：00 1.3 谷时 23：00—24：00 及 00：00—08：00 0.5 平时 其余时段 0.9
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表 3 电动汽车随机充电与有序充放电仿真结果对比
Table 3. Simulation results of randomly charging and orderly charging and discharging for electric vehicles
项目 电动汽车
补贴/元日运行
成本/元净购电
量/kW随机充电 9421.9 2128.3 有序充放电 1099.9 8352.2 1735.2
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