Incentive-based demand response for smart grid with reinforcement learning and deep neural network

Renzhi Lu and Seung Ho Hong

Applied Energy, 2019, vol. 236, issue C, 937-949

Abstract: Balancing electricity generation and consumption is essential for smoothing the power grids. Any mismatch between energy supply and demand would increase costs to both the service provider and customers and may even cripple the entire grid. This paper proposes a novel real-time incentive-based demand response algorithm for smart grid systems with reinforcement learning and deep neural network, aiming to help the service provider to purchase energy resources from its subscribed customers to balance energy fluctuations and enhance grid reliability. In particular, to overcome the future uncertainties, deep neural network is used to predict the unknown prices and energy demands. After that, reinforcement learning is adopted to obtain the optimal incentive rates for different customers considering the profits of both service provider and customers. Simulation results show that this proposed incentive-based demand response algorithm induces demand side participation, promotes service provider and customers profitabilities, and improves system reliability by balancing energy resources, which can be regarded as a win-win strategy for both service provider and customers.

Keywords: Artificial intelligence; Reinforcement learning; Deep neural network; Incentive-based demand response; Smart grid (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (101)

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DOI: 10.1016/j.apenergy.2018.12.061

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