Abstract
It is important for power system stability and economic operation to effectively suppress the power fluctuation from the large-scale grid-connected photovoltaic (PV) stations on the point of common coupling (PCC). Energy storage system (ESS) may effectively provide the power support to smooth the PV output power to the main grid. However, there are some drawbacks for PV power forecast, such as low accuracy, the frequently charging and discharging exchange of ESS, which may result in low stability and economic benefits. Based on this motivation, the modified cycling decay learning rate- deep deterministic policy gradient (CDLR-DDPG) approach is proposed in this paper, to implement the online intelligent economic dispatch for the PV-PHS complementary power generation system considering both the power fluctuation on PCC and the promise of full absorption of PV. The Markov decision process isintroduce to convert this dispatch model and the CDLR-DDPG algorithm is adopted to solve it. Finally, a case study is carried out to evaluate the performance of intelligent economic dispatch model based on the real PV plant obtained in Xiaojin County, Sichuan province, China. The simulation results reveal that the intelligent dispatch strategy can effectively mitigate the power fluctuation and enhance the economic efficiency, that is, the power fluctuation on PCC is reduced by 12.7% and the economic revenue of complementary system is increased by 4.95%, simultaneously.
| Translated title of the contribution | Intelligent Economic Dispatch for PV-PHS Integrated System: a Deep Reinforcement Learning-Based Approach |
|---|---|
| Original language | Chinese (Traditional) |
| Journal | Diangong Jishu Xuebao/Transactions of China Electrotechnical Society |
| Volume | 35 |
| Issue number | 13 |
| Pages (from-to) | 2757-2768 |
| Number of pages | 12 |
| ISSN | 1000-6753 |
| DOIs | |
| Publication status | Published - 10 Jul 2020 |