含分布式储能的主动配电网鲁棒优化经济调度方法

Robust economic dispatch of active distribution networks with distributed energy storage systems

为克服风电输出的不确定性,基于鲁棒优化中的worst-best理论,提出一种主动配电网的鲁棒优化经济调度模型.该模型采用拉丁超立方采样方法生成风电场景集表征风电预测的不确定性.在建模过程中,引入分布式储能的等效运维成本,同时以微型燃气轮机、分布式储能和主动配电网购电/售电调度成本最低为目标函数,采用基于随机变异的粒子群优化算法对模型进行求解,可得到极端场景下运行成本最小的调度方案.仿真结果验证了该模型及求解算法的有效性,得到的调度方案能在风电预测误差范围内满足系统所有约束条件,并给出经济性最优的调度方案.与确定性经济调度方案进行对比可知,本文所提出的调度方案具有更强的鲁棒性.

To address the impacts of uncertainty of wind outputs on the power system operation,this paper proposes a robust optimization economic dispatch method in the active distribution network.The model is built on a worst-best theory that integrates Latin hyper cube sampling method for generating wind output scene set to represent uncertainty of wind outputs.The aims of the model is to minimize the operation costs for distributed energy storage and micro turbine,and purchase costs from the upper grid.The particle swarm optimization algorithm based on random mutation is used to solve the model,and the optimized results with the least operating cost in extreme scenarios can be obtained.The proposed approach is implemented on a 33-bus distribution system.Our results demonstrate that optimized dispatch can satisfy all of the constraints within forecast error of wind outputs.The comparison of the determined economic dispatch method demonstrate that the proposed method is more robust.