Incentive-based demand response for smart grid with reinforcement learning and deep neural network. (15th February 2019)

• Record Type: Journal Article
• Authors: Lu, Renzhi; Hong, Seung Ho
• Is Part Of: Applied energy. Volume 236(2019)
• Page Start: 937
• Page End: 949
• Publication Date: 2019-02-15
• Subjects: Artificial intelligence -- Reinforcement learning -- Deep neural network -- Incentive-based demand response -- Smart grid; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.12.061
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Propose an incentive-based demand response algorithm with artificial intelligence. Reinforcement learning is employed to obtain the optimal incentive rates. Achieve real-time performance with the aid of deep neural network. Customer diversity is taken into account by provision of different incentive rates. Service provider payment under cases without and with demand response is compared. 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.