Stochastic optimization of home energy management system using clustered quantile scenario reduction

Kim, Min‐Soo; Park, T.-J.; Jeong, Jaeik; Kim, Hongseok

doi:10.1016/j.apenergy.2023.121555

Cited by 9 publications

(1 citation statement)

References 27 publications

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“…Tostado et al in (Tostado-Véliz et al, 2023a) develops a novel SP-based home energy management model considering negawatt trading. Kim et al in (Kim et al, 2023) proposes an SP-based algorithm to reduce computation time while preserving the stochastic properties of generated scenarios based on the Wasserstein-1 distance. Nevertheless, the SP-based method requires both vast computational ability and accurate distribution of random variables that may not be realized in practice (Xiong et al, 2023a).…”

Section: Related Workmentioning

confidence: 99%

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%