Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
| Autor: | Duncan S. Callaway, Oscar Sondermeijer, Roel Dobbe, Daniel Arnold, David Fridovich-Keil, Claire J. Tomlin |
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| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Distribution networks Computer science business.industry 020209 energy Distributed computing 020208 electrical & electronic engineering Control (management) 02 engineering and technology 7. Clean energy Decentralised system Distribution system Power flow Distributed generation 0202 electrical engineering electronic engineering information engineering Rate distortion business Voltage |
| Zdroj: | IEEE Transactions on Smart Grid. 11:1296-1306 |
| ISSN: | 1949-3061 1949-3053 |
| DOI: | 10.1109/tsg.2019.2935711 |
| Popis: | The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework enables the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. The methodology provides a natural extension to decide what nodes a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators, focusing on DERs that do not exhibit intertemporal dependencies. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks. |
| Databáze: | OpenAIRE |
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