Self-learning Prosumer in Competitive Local Energy Market
| Autor: | Tao Chen, Wencong Su, Junbo Zhao, Can Huang, Fengkai Hu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Battery (electricity)
Computer science 020209 energy 02 engineering and technology Environmental economics Business model Energy trading Available energy 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Energy market Prosumer Energy (signal processing) |
| Zdroj: | 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). |
| DOI: | 10.1109/isgt-asia.2019.8881496 |
| Popis: | In this paper, we study a prosumer’s energy trading behavior in a proposed local energy market (LEM). The prosumer will try to make the best use of its available energy resources, such as its PV panel and battery, with the self-learning capability to make a smart operation strategy. By adapting a deep reinforcement learning (DRL) technique and a rainflow battery aging mechanism, it can be taken as a contribution that the proposed intelligent and economical decision-making framework will facilitate more customers while encouraging active participation in the localized energy ecosystem. Meanwhile, more energy business models can be strategically redesigned on top of it. |
| Databáze: | OpenAIRE |
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