Deep reinforcement learning approaches for the hydro-thermal economic dispatch problem considering the uncertainties of the context
article
Hydro-thermal economic dispatch is a widely analyzed energy optimization problem, which seeks to make the best use of available energy resources to meet demand at minimum cost. This problem has great complexity in its solution due to the uncertainty of multiple parameters. In this paper, we view hydro-thermal economic dispatch as a multistage decision-making problem, and propose several Deep Reinforcement Learning approaches to solve it due to their abilities to handle uncertainty and sequential decisions. We test our approaches considering several hydrological scenarios, especially the cases of hydrological uncertainty due to the high dependence on hydroelectric plants, and the unpredictability of energy demand. The policy performance of our algorithms is compared with a classic deterministic method. The main advantage is that our methods can learn a robust policy to deal with different inflow and load demand scenarios, and particularly, the uncertainties of the environment such as hydrological and energy demand, something that the deterministic approach cannot do.
Topics
Deep reinforcement learningEnergy marketHydro-thermal economic dispatchOptimization problemDecision makingDeep learningElectric load dispatchingEnergy managementEnergy resourcesOptimizationPower marketsUncertainty analysisDeep reinforcement learningEconomic DispatchEnergy demandsEnergy marketsHydro-thermal economic dispatchOptimization problemsReinforcement learning approachReinforcement learningsThermalUncertaintyReinforcement learning
TNO Identifier
987853
ISSN
23524677
Source
Sustainable Energy, Grids and Networks, 35
Publisher
Elsevier Ltd
Article nr.
101109
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