Distributed Reinforcement Learning Algorithm for Dynamic Economic Dispatch With Unknown Generation Cost Functions

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...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 16; no. 4; pp. 2258 - 2267
Main Authors: Dai, Pengcheng, Yu, Wenwu, Wen, Guanghui, Baldi, Simone
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
Cost function
Distributed reinforcement learning
dynamic economic dispatch (DED)
Economics
Heuristic algorithms
Machine learning
Mathematical analysis
multiplier splitting
Optimization
Power dispatch
Reinforcement learning
Smart grid
Smart grids
state-action-value function approximation
ISSN:1551-3203, 1941-0050
Online Access:Get full text
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Summary: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.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2933443