TY - JOUR
T1 - Distributed Reinforcement Learning Algorithm for Dynamic Economic Dispatch with Unknown Generation Cost Functions
AU - Dai, Pengcheng
AU - Yu, Wenwu
AU - Wen, Guanghui
AU - Baldi, Simone
N1 - Accepted Author Manuscript
PY - 2020
Y1 - 2020
N2 - In this article, the dynamic economic dispatch (DED) problem for smart grid is solved under the assumption that no knowledge of the mathematical formulation of the actual generation cost functions is available. The objective of the DED problem is to find the optimal power output of each unit at each time so as to minimize the total generation cost. To address the lack of a priori knowledge, a new distributed reinforcement learning optimization algorithm is proposed. The algorithm combines the state-action-value function approximation with a distributed optimization based on multiplier splitting. Theoretical analysis of the proposed algorithm is provided to prove the feasibility of the algorithm, and several case studies are presented to demonstrate its effectiveness.
AB - In this article, the dynamic economic dispatch (DED) problem for smart grid is solved under the assumption that no knowledge of the mathematical formulation of the actual generation cost functions is available. The objective of the DED problem is to find the optimal power output of each unit at each time so as to minimize the total generation cost. To address the lack of a priori knowledge, a new distributed reinforcement learning optimization algorithm is proposed. The algorithm combines the state-action-value function approximation with a distributed optimization based on multiplier splitting. Theoretical analysis of the proposed algorithm is provided to prove the feasibility of the algorithm, and several case studies are presented to demonstrate its effectiveness.
KW - Distributed reinforcement learning
KW - dynamic economic dispatch (DED)
KW - multiplier splitting
KW - state-action-value function approximation
UR - http://www.scopus.com/inward/record.url?scp=85078465682&partnerID=8YFLogxK
U2 - 10.1109/TII.2019.2933443
DO - 10.1109/TII.2019.2933443
M3 - Article
AN - SCOPUS:85078465682
VL - 16
SP - 2258
EP - 2267
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
IS - 4
ER -