 A parallel multi-scenario learning method for near-real-time power dispatch optimizationJinyu Guan, Hao Tang, Ke Wang, Jianguo Yao and Shengchun Yang Energy, 2020, vol. 202, issue C Abstract: Power dispatch problems become more complex when the weight of uncertain renewable resources in the power system gradually increases in recent years. To make use of renewable energy, such as wind energy, more adequately, wisely and intelligently, higher requirements are placed on the level of inter-region power dispatch coordination. In the context, solving the problem of power dispatch on a large scale in near-real-time (5 min in this paper) becomes more important. In this paper, the power dispatch was treated as a sequential decision-making problem and Deep Reinforcement Learning (DRL) with continuous control was introduced to offer a smarter solution. In this way, we designed a novel interactive learning environment based on the economic power dispatch model for the DRL algorithm and we proposed two feasible implementations to handle the different application scenarios. As a result, DRL with a continuous control method has a great performance in our proposed implementations. Moreover, we found that dispatching data richness has a significant influence on the generalization of the learned policy. Keywords: Power dispatch; Deep reinforcement learning; Near-real-time; Policy network; Parallel environments (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:202:y:2020:i:c:s036054422030815x DOI: 10.1016/j.energy.2020.117708 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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