Two-Stage Optimization Dispatching Strategy for Distribution Network Considering Dispatchable Potential of Charging Station
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摘要:
针对电动汽车与风电光伏大规模接入对配电网安全经济运行带来的挑战，以及传统单一长时间尺度优化调度方法准确度低的问题，提出一种计及充电场站可调度潜力的多时间尺度协调优化调度策略. 首先，基于闵可夫斯基和提出充电场站可调度容量评估方法，通过对场站内电动汽车集群进行聚合，将充电场站视为广义储能系统，计算各充电场站的实时可调度潜力. 然后，构建基于模型预测控制的配电网日前-日内两阶段优化调度策略，在日前调度阶段，以配电网一日运行成本最小为目标，统筹优化购售电计划及可控分布式电源、储能系统的出力计划，实现全局资源优化配置；在日内调度阶段，基于日前计划与实时状态，采取滚动优化与反馈校正机制，对可控设备的日前出力计划进行动态修正，以应对风光荷出力波动，保障系统状态量在扰动下的稳定运行. 最后，分析所提策略在不确定扰动下对日前调度指令的完成情况. 仿真结果表明：通过挖掘充电场站的可调度潜力，配电网日运行成本较不考虑车入网能力时下降10.86%；运行成本相比传统日前优化调度策略下降13.66%，且与日前联络线功率的偏差仅为1.77%. 所提策略实现了配电网分布式优化调度，提升不确定扰动下运行的经济性与鲁棒性.
Abstract:To address the challenges posed to the safe and economic operation of distribution networks by the large-scale integration of electric vehicles (EVs) and wind and solar power, as well as the issue of low accuracy in traditional single timescale optimization dispatching methods, a multi-timescale coordinated distribution network optimization dispatching strategy that considered the dispatchable potential of charging stations was proposed. Firstly, an assessment method for the dispatchable capacity of charging stations based on the Minkowski sum was proposed. By aggregating EV clusters within the stations and treating them as generalized energy storage systems, the real-time dispatchable potential of each station was accurately calculated. Subsequently, a day-ahead and intra-day two-stage optimization dispatching strategy for the distribution network based on model predictive control was introduced. In the day-ahead dispatching stage, with the objective of minimizing the daily operating cost of the distribution network, the electricity purchase and sales plans, along with the output schedules of controllable distributed generators and energy storage systems, were coordinately optimized to achieve optimal allocation of global resources. In the intra-day dispatching stage, by leveraging rolling optimization and feedback correction mechanisms based on the day-ahead plan and real-time states, the day-ahead output schedules of controllable devices were dynamically corrected to cope with intra-day fluctuations in wind power, photovoltaic power, and load, ensuring the stable operation of system state variables under disturbances. Finally, the performance of the proposed strategy in fulfilling day-ahead dispatching instructions under uncertain disturbances was analyzed. Simulation results demonstrate that by exploiting the dispatchable potential of charging stations, the proposed strategy reduces the daily operating cost of the distribution network by 10.86% compared to scenarios without V2G capability. Furthermore, it achieves an additional 13.66% cost reduction compared to day-ahead optimization strategies, while limiting the deviation from the day-ahead tie-line power to merely 1.77%. This approach realizes distributed optimization dispatching for the distribution network, significantly enhancing its economic efficiency and robustness amidst uncertainties.
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图 2 风电、光伏、负荷的日前-日内出力预测值
Figure 2. Day-ahead and intra-day output forecasts for wind power, photovoltaic power, and load
图 3 各区域充电场站的日前优化调度结果
Figure 3. Results of day-ahead optimization dispatching of charging stations by region
图 6 充电场站的实时可用容量跟踪控制效果
Figure 6. Effect of real-time available capacity tracking control for charging stations
图 7 各种可调度设备的日前与日内出力结果对比
Figure 7. Comparison of day-ahead and intra-day output results for various dispatchable equipment
表 1 仿真参数
Table 1. Simulation parameters
参数名称 取值 参数名称 取值 单充电桩最大充电功率/kW 10 风电短期预测的不确定性阈值 0.3 EV电池最大放电功率/kW 10 光伏短期预测的不确定性阈值 0.2 EV最大电池容量(kW•h) 80 负荷短期预测的不确定性阈值 0.4 EV离网时期望容量Ees N（0.8,0.05） 储能系统的最大容量(MW•h) 3.5 EV并网时的容量Eini N（0.6,0.05） 储能系统的最大充电功率/kW 400 单个充电站内的充电桩数/个 100 储能系统的最大放电功率/kW 400 
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表 2 不同策略下的配电网日运行成本对比
Table 2. Comparison of daily operating costs of distribution networks under different strategies
采取策略 配电网日运行成本/元 考虑充电场站的V2G调度能力 31872.62 不考虑充电场站的V2G调度能力 35753.86
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表 3 不同策略下的系统运行成本与联络线功率跟踪误差
Table 3. System operating cost and tie-line power tracking error under different strategies
策略 运行成本/元 联络线功率跟踪误差/% 日前优化调度策略 42657.31 100.00 单断面优化调度策略 38453.94 16.62 两阶段优化调度策略 36829.56 1.77
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