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针对分时电价下的用户侧负荷转移困难、响应效果差的问题，提出一种计及分时电价的柔性负荷优化方法。首先采用卷积神经网络对用户数据进行扩充，然后通过制定负荷转出、中断、转入经济成本约束来建立优化模型，对需求侧负荷进行补偿优化，最后结合实际数据对季节性分时电价场景和不同合同容量补偿价格场景进行算例分析。实验结果表明，该模型可以有效减少负荷聚合商购电成本，且能针对不同场景进行优化策略的调整，效果明显。
Abstract：In order to address user-side load shifting difficulties and poor response effects under time-of-use electricity prices, a flexible load optimization method considering time-of-use electricity price is proposed, which firstly adopts convolutional neural network to expand user data, and then establishes an optimization model by formulating the constraints of economic cost of load transfer out, interruption, and transfer in to optimize the compensation of demand-side loads. Finally, the seasonal time-of-use electricity price scenarios and different contract capacity compensation price scenarios are analyzed by combining the actual data. The experimental results show that the model can effectively reduce the cost of electricity purchase for load aggregators, and adjust the optimization strategy for different scenarios with obvious effects.
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基本信息:
DOI：10.19929/j.cnki.nmgdljs.2025.0056
引用信息:
[1]李彬1,李若松1,张雨蒙1,等.一种考虑季节性分时电价的柔性负荷优化方法[J],2025,43(5):16-22.DOI:10.19929/j.cnki.nmgdljs.2025.0056.
基金信息:
国家电网公司科技项目“支撑重过载台区治理的区域供用电综合预测与智能预警技术研究与应用”（5108-202218280A-2-379-XG）