基于净能力及二阶锥规划的分布式光储多场景协同优化策略

针对现有配电网中分布式光储调度模型存在资源协同不足、求解复杂等问题，提出了一种基于净能力及二阶锥规划的分布式光储多场景协同优化调度策略。通过引入储能接入配电网后的功率转移分布因子，提出一种基于系统净能力的储能最优选址计算方法；综合考虑储能的运行特性和分布式光伏的出力不确定性，建立以系统日综合成本和削峰填谷为目标的分布式光储多场景协同优化调度模型；利用二阶锥松弛和Big-M法对潮流约束、储能运行约束进行处理，将原规划模型转化为混合整数二阶锥规划问题。以IEEE 33节点系统和西北某实际系统为算例进行仿真分析，结果表明所提方法能在降低负荷峰谷差和日综合成本、平抑负荷波动的同时，显著提高对分布式光伏的消纳能力，验证了所提方法的有效性和可行性。

Multi-scenario collaborative optimization strategy of distributed photovoltaic and energy storage based on net ability and second-order cone programming

In order to solve the problems of insufficient resource cooperation and complex solution in existing distributed photovoltaic and energy storage scheduling models, a multi-scenario collaborative optimization scheduling strategy of distributed photovoltaic and energy storage based on net ability and second-order cone programming is proposed. By introducing the power transfer distribution factor after the connection of energy storage into the distribution network, a calculation method for the optimal site selection of energy storage based on the net ability of the system is proposed. Considering the operation characteristics of energy storage and the output uncertainty of distributed photovoltaic, a multi-scenario collaborative optimization scheduling model of distributed photovoltaic and energy storage is established with the goal of system daily comprehensive cost and peak shaving. The second-order cone relaxation and Big-M method are used to deal with the power flow constraints and the energy storage operation constraints, and the original programming model is transformed into a mixed-integer second-order cone programming problem. Taking the IEEE 33-bus system and a practical system in Northwest China as examples, the simulative results show that the proposed method can significantly improve the consumption ability of distributed photovoltaic while reducing the load peak-valley difference and the daily comprehensive cost and smoothing the load fluctuation, which verifies the effectiveness and feasibility of the proposed method.