Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Thillainathan Logenthiran, Wai Lok Woo, Van Tung Phan

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.
Original languageEnglish
Pages (from-to)356-364
JournalInternational Journal of Electrical Power & Energy Systems
Volume64
Early online date13 Aug 2014
DOIs
Publication statusPublished - Jan 2015

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