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Decentralized control in active distribution grids via supervised and reinforcement learning

Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou gabriela hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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Personalized Federated Learning for Individual Consumer Load Forecasting

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 326-330页

作者： Yi Wang Ning Gao gabriela hug Department of Electrical and Electronic Engineering The University of Hong KongHong KongChinaare also with ETH ZurichZurichSwitzerland Power Systems Laboratory ETH Zurich8092 ZurichSwitzerland

Installation of smart meters enables electricity retailers or consumers to implement individual load forecasting for demand *** individual load forecasting model can be trained either on each consumer’s own smart meter data or the smart meter data of multiple *** former practice may suffer from overfitting if a complex model is trained because the dataset is limited;the latter practice cannot protect the privacy of individual *** paper tackles the dilemma by proposing a personalized federated approach for individual consumer load ***,a group of consumers first jointly train a federated forecasting model on the shared smart meter data pool,and then each consumer personalizes the federated forecasting model on their own *** case studies are conducted on an open dataset of 100 *** verify the proposed method can enhance forecasting accuracy by making full use of data from other consumers with privacy protection.

关键词： Federated learning load forecasting personalization privacy-preserving smart meter

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