基于改进NSGA III-PSO的含风光柴储配电网优化调度方法研究

范展滔; 刘敬诚

范展滔,刘敬诚.基于改进NSGA III-PSO的含风光柴储配电网优化调度方法研究[J].电测与仪表,2025,62(11):167-175.
FAN Zhantao,LIU Jingcheng.Research on optimization scheduling method for WT-PV-DE-BES distribution network based on improved NSGA III-PSO[J].Electrical Measurement & Instrumentation,2025,62(11):167-175.

基于改进NSGA III-PSO的含风光柴储配电网优化调度方法研究

Research on optimization scheduling method for WT-PV-DE-BES distribution network based on improved NSGA III-PSO

DOI：10.19753/j.issn1001-1390.2025.11.019

中文关键词: 配电网微电网双层模型风光柴储第三代非支配排序遗传算法粒子群算法

英文关键词:Distribution network, Microgrid Double layer model, WT-PV-DE-BES, Non-dominated sorting genetic algorithm-Ⅲ, Particle Swarm Optimization Algorithm

基金项目:南方电网科技项目（ZDKJXM20220002）

作者中文名	作者英文名	单位
范展滔	FAN Zhantao	中国南方电网有限责任公司，广州 510600
刘敬诚	LIU Jingcheng	中国南方电网有限责任公司，广州 510600

摘要点击次数: 19

中文摘要:

微电网的不断接入使配电网结构日趋复杂、设备种类不断增多且运行方式趋于多样,为其运行调度的安全性和经济性带来了新的挑战。针对微电网多样设备接入所带来的调度模型复杂度增加和调度策略计算复杂度提高等问题,文中提出了一种考虑风光柴储的配电网安全和经济调度双层模型,其中上层在考虑潮流及与微电网交互等约束的条件下构建了总运行成本最低、网损和电压稳定性指数最小的多目标调度模型,通过改进的第三代非支配排序遗传算法对模型进行求解。下层则在储能和分布式电源等运行约束下构建了以微电网自身运行成本最低为目标的调度模型,通过改进粒子群算法对模型进行求解。结果表明,所提方法可以有效的兼顾经济性和安全性,通过两层模型的深入协调配合,实现多主体利益共赢,总成本为69574.81元,电压稳定性指数为0.0539,求解时间为425.12s。相比于常规方法,总成本降低大于3.00%,电压稳定性指数降低大于1.50%,总求解时间降低大于2.50%,具有一定的实用价值。

英文摘要:

The continuous integration of microgrids has made the distribution network structure increasingly complex, with an increasing number of equipment types and diverse operating modes, posing new challenges to the safety and economy of its operation and scheduling. In response to the issues of high model dimensionality, difficult solution, and low computational accuracy in existing intelligent scheduling methods for microgrid distribution network, this paper proposes a dual layer model for distribution network safety and economic scheduling that considers wind, solar, and diesel storage (WT-PV-DE-BES). The upper layer constructs a multi-objective scheduling model with the lowest total operating cost, minimum network loss, and voltage stability index considering constraints such as power flow and microgrid interaction. The model is solved using an improved third-generation non dominated sorting genetic algorithm (NSGA III). The lower layer constructs a scheduling model with the goal of minimizing the operating cost of the microgrid itself under the constraints of energy storage and distributed power generation, and solves the model through an improved particle swarm optimization (PSO) algorithm. The results indicate that, the proposed method can effectively balance economy and safety, and achieve a win-win situation for multiple parties through in-depth coordination and cooperation of the two-layer model. Compared with conventional methods, the total cost is reduced by more than 3.00%, the voltage stability index is reduced by more than 1.50%, and the total solution time is reduced by more than 2.50%, which has certain practical value.

查看全文查看/发表评论下载PDF阅读器

关闭