基于改进QPSO算法的BIPV建筑削峰填谷双层优化配置策略

Bi-Level Optimal Configuration Strategy for Peak Load Shifting of BIPV Buildings Based on Improved QPSO Algorithm

针对BIPV建筑负荷削峰填谷和接入配电网后考虑网损、电压偏差的选址优化问题,提出了一种BIPV建筑双层能源系统优化模型。模型下层在考虑DG成本和环境成本的前提下,利用储能对负荷进行削峰填谷,模型上层在考虑配电网网损、电压偏差的前提下对BIPV和储能的选址方案进行优化。在求解该双层模型时利用量子理论中的量子行为和概率表达特性设计了一种改进的多目标量子粒子群算法,在解的处理上引入了一种动态ε不可行度约束支配函数。以IEEE30节点系统为参考系统,算例优化结果表明:BIPV建筑负荷峰谷差、配电网网损和电压偏差得到了有效降低,证明了所提配置优化策略的可行性;同时也表明,所提算法在全局寻优能力和种群多样性方面得到了明显提升。

For the location optimization of the peak load cutting and valley filling of the BIPV building,considering network loss and voltage deviation after connecting to the distribution network,an optimization model of the BIPV building double-layer energy system is proposed in this paper.Considering the DG cost and the environment cost,the lower layer of the model uses energy storage to cut the peak load and fill the valley load,while the upper layer of the model optimizes the location scheme of the BIPV and energy storage under the premise of considering distribution network loss and voltage deviation.An improved multi-objective quantum particle swarm optimization algorithm is designed using the quantum behavior and probability expression characteristics of quantum theory when solving the double-layer model.A dynamicεinfeasibility constraint dominating function is introduced to deal with the solution.Finally,taking the IEEE30 bus system as a reference system,the optimization results show that the load peak-valley difference of the BIPV building,the distribution network loss and the voltage deviation are effectively reduced,which proves that the proposed configuration optimization strategy is feasible.At the same time,the results also show that the global optimization ability and population diversity of the proposed algorithm are significantly improved.