TY - GEN
T1 - Dynamic Economic Dispatch of Thermal-Wind-Storage Systems Based on Reinforcement Learning
AU - Li, Yuheng
AU - Hu, Chengfang
AU - Fu, Junjie
AU - Wang, Shuai
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper studies a dynamic economic dispatch (DED) problem which includes thermal and wind-storage hybrid units, aiming at minimizing the total generation cost and penalty costs involving generation regulation, load shedding, and wind curtailment. Each unit is assigned with a fixed, discrete, constrained virtual action set, and its cost function is unknown. Based on the developed model, a reinforcement learning algorithm is applied to solve the DED problem under the wind uncertainty. Simulation results illustrate the effectiveness of the algorithm.
AB - This paper studies a dynamic economic dispatch (DED) problem which includes thermal and wind-storage hybrid units, aiming at minimizing the total generation cost and penalty costs involving generation regulation, load shedding, and wind curtailment. Each unit is assigned with a fixed, discrete, constrained virtual action set, and its cost function is unknown. Based on the developed model, a reinforcement learning algorithm is applied to solve the DED problem under the wind uncertainty. Simulation results illustrate the effectiveness of the algorithm.
KW - Dynamic economic dispatch (DED)
KW - reinforcement learning
KW - storage
KW - wind power
UR - https://www.scopus.com/pages/publications/85144615091
U2 - 10.1109/DOCS55193.2022.9967708
DO - 10.1109/DOCS55193.2022.9967708
M3 - 会议稿件
AN - SCOPUS:85144615091
T3 - 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022
BT - 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022
Y2 - 28 October 2022 through 30 October 2022
ER -