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计及工况预测误差的主动配电网日前无功优化调度策略

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为解决工况预测误差较大时,日前无功优化调度方案优化效果不佳的问题,提出了计及工况预测误差的主动配电网日前无功优化调度策略.首先,使用轻量级梯度提升机算法建立日前工况功率预测模型;其次,考虑大规模高比例分布式电源接入主动配电网,以调度时段内所有时间断面的多目标加权累加和为目标函数建立日前无功优化调度模型;最后,设计了一种变寻优粒子空间的改进引力搜索算法对日前无功优化调度模型进行求解,该算法根据历史工况预测误差评价指标调整寻优粒子空间各维度的上下限矩阵,从而抑制了当无功区域内工况预测误差较大时可控设备调度异常的缺陷.最后采用拓展的IEEE 33节点系统算例进行有效性验证.
Day-ahead Optimal Reactive Power Dispatch Strategy in Active Distribution Network Considering the Forecast Error of Working Conditions
In order to solve the problem that the optimization effect of the day-ahead optimal reactive power dispatch strategy is not good when the prediction error of the working conditions is large,we proposed a day-ahead optimal reac-tive power dispatch strategy for the active distribution network that takes into account the prediction error of the operat-ing conditions.Firstly,we used a light gradient boosting machine algorithm to establish a prediction model for day-a-head operating conditions;secondly,considering that large-scale and high-proportion distributed power sources are con-nected to the active distribution network,we established a day-ahead optimal reactive power dispatch model with the multi-objective weighted cumulative sum of all time sections in the dispatch period as the objective function;finally,we designed an improved gravitational search algorithm with variable optimization particle space to solve the optimal day-a-head reactive power dispatch model.The upper and lower limit matrices of the dimension are used to suppress the de-fect of abnormal dispatch of controllable equipment when the prediction error of working conditions in the reactive power area is large.Finally,an extended IEEE 33 node system example was used to verify the validity.

active distribution networkday-ahead optimal reactive power dispatchworking condition forecastdis-tributed generationlight gradient boosting machineimproved gravitational search algorithm

张旭、刘伯文、王怡

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华北电力大学电气与电子工程学院,北京 102206

主动配电网 日前无功优化调度 工况预测 分布式电源 轻量级梯度提升机 改进引力搜索算法

国家自然科学基金资助项目

51877079

2024

10.3969/j.ISSN.1007-2691.2024.02.04

华北电力大学学报(自然科学版)
华北电力大学

华北电力大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.868
ISSN：1007-2691
年,卷(期)：2024.51(2)
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