The integration of distributed generations (solar power, wind power), energy storage devices, and electric vehicles, causes unpredictable disturbances in power grids. It has become a top priority to coordinate the distributed generations, loads, and energy storages in order to better facilitate the utilization of new energy. Therefore, a novel algorithm based on deep reinforcement learning, namely the deep PDWoLF-PHC (policy dynamics based win or learn fast-policy hill climbing) network (DPDPN), is proposed to allocate power order among the various generators. The proposed algorithm combines the decision mechanism of reinforcement learning with the prediction mechanism of a deep neural network to obtain the optimal coordinated control for the source-grid-load. Consequently it solves the problem brought by stochastic disturbances and improves the utilization rate of new energy. Simulations are conducted with the case of the improved IEEE two-area and a case in the Guangdong power grid. Results show that the adaptability and control performance of the power system are improved using the proposed algorithm as compared with using other existing strategies.
Bibliographical noteFunding Information:
Manuscript received August 9, 2019; revised March 3, 2020; accepted March 12, 2020. Date of online publication April 6, 2020; date of current version May 20, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant No. 51707102. L. Xi, L. P. Zhou, and Y. C. Xu are with the College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China. L. Liu is with the State Grid Xianning Electric Power Supply Company, Xianning 437100, China. D. L. Duan is with the Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, USA. L. Q. Yang is with the Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA. S. X. Wang (corresponding author, e-mail: firstname.lastname@example.org) is with the Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China. DOI: 10.17775/CSEEJPES.2019.01840
- Automatic generation control
- deep reinforcement learning
- power order allocation