Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 10, Issue 6, Pages 6629-6639Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2019.2909266
Keywords
Artificial intelligence; reinforcement learning; artificial neural network; demand response; home energy management
Categories
Funding
- National Research Foundation of Korea through the Framework of International Cooperation Programs (Korea-China) [NRF-2016K2A9A2A11938310, NRF-2018K1A3A1A61026320]
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Ever-changing variables in the electricity market require energy management systems (EMSs) to make optimal real-time decisions adaptively. Demand response (DR) is the latest approach being used to accelerate the efficiency and stability of power systems. This paper proposes an hour-ahead DR algorithm for home EMSs. To deal with the uncertainty in future prices, a steady price prediction model based on artificial neural network is presented. In cooperation with forecasted future prices, multi-agent reinforcement learning is adopted to make optimal decisions for different home appliances in a decentralized manner. To verify the performance of the proposed energy management scheme, simulations are conducted with non-shiftable, shiftable, and controllable loads. Experimental results demonstrate that the proposed DR algorithm can handle energy management for multiple appliances, minimize user energy bills, and dissatisfaction costs, and help the user to significantly reduce its electricity cost compared with a benchmark without DR.
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