Hybrid robust-stochastic optimal scheduling for multi-objective home energy management with the consideration of uncertainties

Xiong, Binyu; Wei, Feng; Wang, Yifei; Xia, Kairui; Su, Fuwen; Fang, Yingjia; Gao, Zuchang; Wei, Zhongbao

doi:10.1016/j.energy.2023.130047

Cited by 6 publications

(2 citation statements)

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“…Then, how to obtain the optimal dispatch strategy of the HEMS is a crucial problem (Xiong et al, 2024). Normally, the optimization-based methods such as stochastic programming method (SP) (Hussain et al, 2023) and robust optimization method (RO) (Wang et al, 2024) are utilized to solve the optimization problem of HEMS.…”

Section: Related Workmentioning

confidence: 99%

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Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Pan,

Zhu,

Luo

et al. 2024

Front. Therm. Eng.

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With the increase in the integration of renewable sources, the home energy management system (HEMS) has become a promising approach to improve grid energy efficiency and relieve network stress. In this context, this paper proposes an optimization dispatching strategy for HEMS to reduce total cost with full consideration of uncertainties, while ensuring the users’ comfort. Firstly, a HEMS dispatching model is constructed to reasonably schedule the start/stop time of the dispatchable appliances and energy storage system to minimize the total cost for home users. Besides, this dispatching strategy also controls the switching time of temperature-controlled load such as air conditioning to reduce the energy consumption while maintaining the indoor temperature in a comfortable level. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called deep deterministic policy gradient. The example results verify the effectiveness and superiority of the proposed method. The energy cost can be effectively reduced by 21.9% at least compared with other benchmarks and the indoor temperature can be well maintained.

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Section: Related Workmentioning

confidence: 99%

“…Moreover, compared with the optimization-based methods in Refs. (Hussain et al, 2023;Wang et al, 2024;Xiong et al, 2024), the applied DDPG method can achieve fast decision making since the learned policy can be generalized to other situations without resolving the optimization model after the agent is trained.…”

Section: Related Workmentioning

confidence: 99%