| 引用本文: | 崔婧琪,冀浩然,李 鹏,等.考虑数据隐私保护的分布式电源集群自适应可信协同决策方法[J].电力系统保护与控制,2025,53(24):111-120.[点击复制] |
| CUI Jingqi,JI Haoran,LI Peng,et al.Adaptive and trustworthy collaborative decision-making for distributed generator clusters considering data privacy protection[J].Power System Protection and Control,2025,53(24):111-120[点击复制] |
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| 摘要: |
| 规模化分布式电源的快速波动，容易导致配电网电压越限、波动剧烈等一系列问题，进而影响配电网安全稳定运行。此外，配电网各集群可能归属于不同利益主体，集群间隐私保护需求也日趋重要。针对多利益主体参与下配电网分布式电源集群控制的问题，提出了一种考虑数据隐私保护的分布式电源集群自适应可信协同决策方法。首先，基于拆分联邦学习方法构建配电网多集群可信协同框架，并建立面向配电网电压决策的深度学习模型，在考虑配电网利益主体间隐私保护的前提下实现了多集群之间的数据融合与协同。然后，在深度学习模型中增添奖励机制对量测数据进行优劣识别，实现了深度学习模型的自适应更新功能。最后，在广州蕉门算例上验证了所提方法的可行性与有效性。结果表明，所提方法具有较强的隐私保护性能，并且能提高配电网的电压质量，有效解决了配电网的电压越限问题。 |
| 关键词: 配电网 集群控制 分布式电源 自适应控制 隐私保护 |
| DOI：10.19783/j.cnki.pspc.250113 |
| 投稿时间：2025-01-28修订日期：2025-04-16 |
| 基金项目:国家自然科学基金项目资助(U22B20114)；南方电网公司科技项目资助(ZBKJXM20232291) |
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| Adaptive and trustworthy collaborative decision-making for distributed generator clusters considering data privacy protection |
| CUI Jingqi1,JI Haoran1,LI Peng1,YU Lei1,DUAN Shuyin2,LEI Yiyong3 |
| (1. State Key Laboratory of Intelligent Power Distribution Equipment and System, Tianjin University, Tianjin 300072, China;
2. China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510663, China;
3. China Southern Power Grid Company Limited, Guangzhou 510663, China) |
| Abstract: |
| Rapid fluctuations of large-scale distributed generation (DG) can easily cause issues such as voltage violation in distribution networks, affecting their safe and stable operation. Additionally, distribution clusters may belong to different stakeholders, making inter-cluster data privacy increasingly important. To address the problem of DG cluster control under multiple stakeholders, an adaptive and trustworthy collaborative decision-making method that considers data privacy protection is proposed. First, a multi-cluster trusted collaborative framework for distribution networks is constructed based on split federated learning approach, and a deep learning model is established for voltage decision-making. This framework enables data fusion and collaboration among multiple clusters with privacy protection between stakeholders. Then, a reward mechanism is incorporated into the deep learning model to evaluate the quality of measurement data, allowing for adaptive model updates. Finally, the feasibility and effectiveness of the proposed method are verified using a case study of the Jiaomen distribution network in Guangzhou. The results demonstrate that the proposed method offers strong privacy protection capabilities, improves voltage quality, and effectively mitigates voltage violation issues in the distribution network. |
| Key words: distribution network cluster control distributed generation adaptive control privacy protection |
