Approximate and reinforcement learning techniques to solve non-convex economic dispatch problems

M. Abouheaf, S. Haesaert, W.-J. Lee, F.L. Lewis

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)
2 Downloads (Pure)

Abstract

Economic Dispatch is one of the power systems management tools. It is used to allocate an amount of power generation to the generating units to meet the active load demands. The Economic Dispatch problem is a large-scale nonlinear constrained optimization problem. In this paper, two novel techniques are developed to solve the non-convex Economic Dispatch problem. Firstly, a novel approximation of the non-convex generation cost function is developed to solve non-convex Economic Dispatch problem with the transmission losses. This approximation enables the use of gradient and Newton techniques to solve the non-convex Economic Dispatch problem. Secondly, Q-Learning with eligibility traces technique is adopted to solve the non-convex Economic Dispatch problem with valve point loading effects, multiple fuel options, and power transmission losses. The eligibility traces are used to speed up the Q-Learning process. This technique showed superior results compared to other heuristic techniques.
Original languageEnglish
Title of host publicationProceedings of the 11th International Multi-Conference on Systems, Signals and devices (SSD 2014), 11-14 February 2014, Barcelona, Spain
PublisherInstitute of Electrical and Electronics Engineers
Pages1-8
DOIs
Publication statusPublished - 2014
Eventconference; Multi-Conference on Systems, Signals & Devices (SSD); 2014-02-11; 2014-02-14 -
Duration: 11 Feb 201414 Feb 2014

Conference

Conferenceconference; Multi-Conference on Systems, Signals & Devices (SSD); 2014-02-11; 2014-02-14
Period11/02/1414/02/14
OtherMulti-Conference on Systems, Signals & Devices (SSD)

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