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• W4313854877 abstract "Utility companies recognize the importance and necessity of demand response (DR) programs for reducing the increased production costs associated with rising energy demand. The advent of smart information and communication systems has made DR programs on the basis of cost a viable option to control load in smart microgrids. Small domestic consumers are rapidly using stochastic renewable energy resources such as photovoltaic (PV). The study examines an integrated layout for residential load scheduling or load commitment problems (LCP) with renewable energy resources no matter what kind of tariff is applied. Uncertainty-based decision-making problems are effectively solved using reinforcement learning (RL). The paper proposes an RL-enabled solution to the LCP in smart microgrids. An innovative aspect of the study is the development of an integrated layout containing an implementable solution that takes into account user satisfaction, stochastic renewable power, and tariffs. In simulation tests, the suggested layout is tested for its effectiveness and flexibility. An analysis of the algorithm's efficiency using a household user with schedule-able and non-schedulable devices, together with a PV resource, has been presented." @default.
• W4313854877 created "2023-01-10" @default.
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• W4313854877 date "2023-01-01" @default.
• W4313854877 modified "2023-10-16" @default.
• W4313854877 title "Digital Twin-Based economic assessment of solar energy in smart microgrids using reinforcement learning technique" @default.
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• W4313854877 doi "https://doi.org/10.1016/j.solener.2022.12.031" @default.
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