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

Journal article (2020)

Authors

Pengcheng Dai Southeast University

Wenwu Yu Nantong University, Southeast University

Guanghui Wen Southeast University

S. Baldi Team Bart De Schutter - Mechanical, Maritime and Materials Engineering , Southeast University

Research Group

Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI

https://doi.org/10.1109/TII.2019.2933443

Distributed reinforcement learning Dynamic economic dispatch (DED) Multiplier splitting State-action-value function approximation

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To reference this document use:

http://resolver.tudelft.nl/uuid:49d9f5f6-8fbc-419f-a599-3368d6cecf0d

Published Date

2020

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Bart De Schutter

Abstract

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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