A multi‐objective multi‐agent deep reinforcement learning approach to residential appliance scheduling. Issue 4 (12th May 2022)

• Record Type: Journal Article
• Authors: Lu, Junlin; Mannion, Patrick; Mason, Karl
• Other Names: Hua Weiqi guestEditor.; Luo Fengji guestEditor.; Du Liang guestEditor.; Chen Sijie guestEditor.; Kim Taesic guestEditor.; Morstyn Thomas guestEditor.; Robu Valentin guestEditor.; Zhou Yue guestEditor.
• Is Part Of: IET smart grid. Volume 5:Issue 4(2022)
• Page Start: 260
• Page End: 280
• Publication Date: 2022-05-12
• Subjects: deep reinforcement learning -- multi‐objective optimization -- residential energy management; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/stg2.12068
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract: Residential buildings are large consumers of energy. They contribute significantly to the demand placed on the grid, particularly during hours of peak demand. Demand‐side management is crucial to reducing this demand placed on the grid and increasing renewable utilisation. This research study presents a multi‐objective tunable deep reinforcement learning algorithm for demand‐side management of household appliances. The proposed tunable Deep Q‐Network (DQN) algorithm learns a single policy that accounts for different preferences for multiple objectives present when scheduling appliances. These include electricity cost, peak demand, and punctuality. The tunable Deep Q‐Network algorithm is compared to two rule‐based approaches for appliance scheduling. When comparing the 1‐month simulation results for the tunable DQN with an electricity cost rule‐based benchmark method, the tunable DQN agent provides a statistically significant improvement of 30%, 18.2%, and 37.3% for the cost, peak power, and punctuality objectives. Moreover, the tunable Deep Q‐Network can produce a range of appliance scheduling policies for different objective preferences without requiring any computationally intensive retraining. This is the key advantage of the proposed tunable Deep Q‐Network algorithm for appliance scheduling.