【Applied Energy最新原创论文】通过可解释性人工智能技术实现光伏系统的无功功率控制

原文信息：

Reactive power control in photovoltaic systems through (explainable) artificial intelligence

原文链接：

https://www./science/article/pii/S0306261922012612

Highlights

· Machine learning and Explainable AI (XAI) for reactive power control in PV systems.

· ANN learns approximate mapping of optimal reactive power settings from ACOPF.

· SHAP (XAI) identifies relevant grid state measurements for each PV system.

· Centralized and decentralized ANN minimize energy consumed; prevent grid problems.

· Decentralized ANN promotes data privacy and reduces overall computational burden.

摘要

       在世界各地，为了支持能源转型和遏制气候变化，过去十年安装的可再生分布式发电机（DG）数量大幅增加，其中光伏（PV）系统是发展最快的技术。然而，众所周知，光伏在电网中的高渗透会导致许多运行问题，如电压波动和线路阻塞，这些问题可以通过利用光伏系统的无功功率容量来缓解。为此，作者建议使用人工神经网络（ANN）来预测光伏系统中的最优无功功率分配，以集中式或分散式的方式学习交流潮流最优（ACOPF）解的近似输入-输出映射。在分散控制的情况下，作者利用SHAP（Shapley Additive Explations），一种可解释性人工智能（XAI）技术，来识别显著影响每个独立系统的最优调度的非本地电网状态测量量。通过基于CIGRE中压配电网的案例研究，对基于ANN的集中式和分散式控制器进行了评估，并与基线控制策略进行了比较。结果表明，与恒定功率因数控制相比，两种基于ANN的控制器都表现出优异的性能，能够避免基线策略所遇到的电压问题和线路阻塞，同时节能0.44%。通过利用ANN和SHAP，所提出的用于无功功率控制的分散控制器能够实现ACOPF级别的性能，同时提高数据私密性并减少计算负担。

更多关于"Explainable artificial intelligence"的研究请见：              

https://www./search?qs=Explainable%20artificial%20intelligence&pub=Applied%20Energy&cid=271429

Abstract

Across the world, efforts to support the energy transition and halt climate change have resulted in significant growth of the number of renewable distributed generators (DGs) installed over the last decade, among which photovoltaic (PV) systems are the fastest growing technology. However, high PV penetration in the electricity grid is known to lead to numerous operational problems such as voltage fluctuations and line congestions, which could be eased by utilizing the reactive power capability of PV systems. To this end, we propose to use artificial neural network (ANN) to predict optimal reactive power dispatch in PV systems by learning approximate input–output mappings from AC optimal power flow (ACOPF) solutions in either a centralized or a decentralized manner. In the case of decentralized control, we leverage Shapley Additive Explanations (SHAP), an explainable artificial intelligence (XAI) technique, to identify non-local grid state measurements which significantly influence the optimal dispatch of each individual system. Both centralized and decentralized ANN-based controllers are evaluated through a case study based on the CIGRE medium-voltage distribution grid and compared to baseline control strategies. Results show that both ANN-based controllers exhibit superior performance, hindering voltage problems and line congestions which are encountered with baseline strategies while recording an energy saving of 0.44% compared to fixed power factor control. By leveraging ANN and SHAP, the proposed decentralized controllers for reactive power control are able to achieve ACOPF-level performance while promoting data privacy and reducing computational burden.

Keywords

Reactive power

Photovoltaic

Optimal power flow

Machine learning

Explainable artificial intelligence

Graphics

Fig. 1. Schematic of CIGRE MV distribution grid.

Fig. 2. Proposed ANN structure for reactive power control.

Fig. 5. Distribution of loading values in percentage for lines (12) and (23).

Fig. 9. SHAP summary plot for optimal reactive power dispatch at node 11.

Fig. 10. Voltage profile at node 3 for the decentralized controllers.

关于Applied Energy

本期小编：王琼    审核人：赵林川

《Applied Energy》是世界能源领域著名学术期刊，在全球出版巨头爱思唯尔 (Elsevier) 旗下，1975年创刊，影响因子11.446，CiteScore 20.4，高被引论文ESI全球工程期刊排名第4，谷歌学术全球学术期刊第50，本刊旨在为清洁能源转换技术、能源过程和系统优化、能源效率、智慧能源、环境污染物及温室气体减排、能源与其他学科交叉融合、以及能源可持续发展等领域提供交流分享和合作的平台。开源（Open Access）姊妹新刊《Advances in Applied Energy》现已正式上线。在《Applied Energy》的成功经验基础上，致力于发表应用能源领域顶尖科研成果，并为广大科研人员提供一个快速权威的学术交流和发表平台，欢迎关注！

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