Supporting virtual power plants decision-making in complex urban environments using reinforcement learning

Author:

Liu Chengyang,Yang Rebecca Jing,Yu XinghuoORCID,Sun ChaynORCID,Rosengarten Gary,Liebman ArielORCID,Wakefield Ron,Wong Peter SP,Wang Kaige

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference44 articles.

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2. Socio-technical evolution of decentralized energy systems: a critical review and implications for urban planning and policy;Adil;Renewable and Sustainable Energy Reviews,2016

3. AEMO NEM Data dashboard. 2020 Nov 15th [cited 2020 May 22nd]; Available from: Https://www.aemo.com.au/Energy-systems/Electricity/National-Electricity-Market-NEM/Data-NEM/Data-Dashboard-NEM.

4. AEMO Virtual Power Plant Demonstration Knowledge Sharing Report. 2021, Austrlian Energy Market Operator.

5. Robustness and performance of deep reinforcement learning;Al-Nima;Applied Soft Computing,2021

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