大规模风电接入微电网的两阶段分布式鲁棒储能容量优化方法

Two-stage Distributed Robust Energy Storage Capacity Optimization Method for Large-scale Wind Power Access to Microgrid

  • 摘要: 针对可再生能源出力的不确定性和随机性,利用KL散度(Kullback-Leibler divergence,KL)模拟风电不确定性,建立两阶段分布式鲁棒优化模型,第一阶段确定储能容量投资成本,第二阶段计算微电网最小运行成本,相对于传统鲁棒优化在保证鲁棒性的同时提高经济性。模型中考虑了储能、分布式可控电源、需求响应负荷的协调控制,并提出相应调度策略,且可通过调节KL散度阈值控制方案的鲁棒程度。将椭圆模糊集转化为盒式不确定集后通过CC&G算法(Column and constraint generation algorithm,CC&G)和强对偶理论将原问题分解成形式为混合整数线性规划的主问题和子问题后进行求解,最终得到最优解。仿真结果证明所提方法具有良好的经济性、鲁棒性和灵活性,并且能对现实决策提供参考。

     

    Abstract: For the uncertainty and randomness of renewable energy output, KL divergence is used to simulate the uncertainty of wind power, and a two-stage distributed robust optimization model is established, the first stage determines the investment cost of energy storage capacity, and the second stage calculates the minimum operating cost of the microgrid, which improves the economy while ensuring robustness compared with traditional robust optimization. In the model, the coordinated control of energy storage, distributed controllable power supply and demand response load is considered, and a response scheduling strategy is proposed, and the robustness of the KL divergence threshold control scheme can be adjusted. After transforming the elliptic fuzzy set into a box uncertain set, the original problem is decomposed into the main problem and sub-problem in the form of mixed integer linear programming by CC&G algorithm and strong duality theory, and finally the optimal solution is obtained. The simulation results show that the proposed method has good economy, robustness and flexibility, and can provide reference for practical decisions.

     

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