基于深度学习和智能在线场景匹配的配电网源网荷储无功协调优化策略
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引用本文:秦帅1,邹晴1,李超然1,柳楠1,袁洲茂2,窦晓波2.基于深度学习和智能在线场景匹配的配电网源网荷储无功协调优化策略[J].电网与清洁能源,2022,38(5):25~35
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作者单位
秦帅1 1. 国网北京大兴供电公司 
邹晴1 1. 国网北京大兴供电公司 
李超然1 1. 国网北京大兴供电公司 
柳楠1 1. 国网北京大兴供电公司 
袁洲茂2 2. 东南大学 电气工程学院 
窦晓波2 2. 东南大学 电气工程学院 
基金项目:国网北京市电力公司科技项目资助(52021220007D);含动态控制区域的有源配电网区域协同控制(51777031)
中文摘要:配电网源网荷储协调优化是消纳可再生能源的重要手段,其中,无功优化可保障系统安全可靠和经济运行。该文提出了一种基于深度学习和智能在线场景匹配的配电网源网荷储无功协调优化方法,它充分考虑到运行场景的特性,直接利用配网运行大数据离线生成历史场景库并基于多目标粒子群优化算法离线构建历史策略库,再基于不同优化目标和K-means聚类算法对历史场景库和实时待优化场景进行两次分类,然后以历史场景库为训练集,以实时待优化场景为测试集,基于深度神经网络实现智能在线场景匹配,通过匹配效果评估匹配历史策略或在线优化分配无功优化方案。最后,在IEEE 30节点仿真模型接入分布式光伏、储能和电动汽车充电站等随机负荷进行算例验证。结果表明,方法可灵活、有效地对系统进行协调无功优化,不依赖于模型和参数,极大地提高了决策效率。
中文关键词:配电网源网荷储协调优化  无功优化  K-means聚类  深度学习  在线场景匹配  大数据
 
Strategy on Reactive Power Coordination Optimization in Distribution Network “Source-Network-Load-Storage” System Based on Deep Learning and Intelligence Online Scene Matching
Abstract:The coordinated optimization of “source-network-load-storage” in distribution network is an important means to absorb renewable energy, in which reactive power optimization can ensure the safe, reliable and economic operation of the system. This paper proposes a reactive power coordination method for “source-network-load-storage” system of distribution network based on deep learning and intelligent online scene matching. It fully considers the characteristics of operation scenes, directly uses big data of distribution network operation to generate the offline historical scene library, and constructs the offline historical strategy library based on multi-objective particle swarm optimization algorithm. Then, the historical scene library and real-time scenes to be optimized are classified twice based on different optimization objectives and K-means clustering algorithm. Next, the historical scene library is taken as the training set, and the real-time scenes to be optimized are taken as the test set. Intelligent online scene matching is realized based on the deep neural network technology. The paper determines the success or failure of the matching according to the matching evaluation index and allocates the reactive power optimization scheme by matching the historical strategy or online optimization. Finally, random loads such as distributed photovoltaics, energy storage devices and electric vehicle charging stations are added into IEEE 30-bus simulation model to verify the calculation example. The results show that the method is flexible and effective for coordinated reactive power optimization, independent of models and parameters, and greatly improves the decision-making efficiency.
keywords:source-network-load-storage coordination optimization in distribution network  reactive power optimization  K-means clustering  deep learning  online scene matching  big data
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