| Autor: |
Tingzhe Pan, Zean Zhu, Hongxuan Luo, Chao Li, Xin Jin, Zijie Meng, Xinlei Cai |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Thermal Engineering, Vol 4 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2813-0456 |
| DOI: |
10.3389/fther.2024.1391602 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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