Evolutionary hybrid approaches for a power system scheduling problem

K. Dahal, C. Aldridge, S.J. Galloway

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Generation scheduling (GS) in power systems is a tough optimisation problem which continues to present a challenge for efficient solution techniques. The solution is to define on/off decisions and generation levels for each electricity generator of a power system for each scheduling interval. The solution procedure requires simultaneous consideration of binary decision and continuous variables. In recent years researchers have focused much attention on developing new hybrid approaches using evolutionary and traditional exact methods for this type of mixed-integer problems. This paper investigates how the optimum or near optimum solution for the GS problem may be quickly identified. A design is proposed which uses a variety of metaheuristic, heuristics and mathematical programming techniques within a hybrid framework. The results obtained for two case studies are promising and show that the hybrid approach offers an effective alternative for solving the GS problems within a realistic timeframe.
LanguageEnglish
Pages2050-2068
Number of pages18
JournalEuropean Journal of Operational Research
Volume177
Issue number3
Early online date25 Jan 2006
DOIs
Publication statusPublished - 16 Mar 2007

Fingerprint

Hybrid Approach
Power System
Scheduling Problem
Scheduling
Heuristic programming
Exact Method
Mathematical programming
Continuous Variables
Electricity
Efficient Solution
Mathematical Programming
Metaheuristics
Continue
Heuristics
Generator
Binary
Optimization Problem
Interval
Integer
Power system

Keywords

  • evolutionary computations
  • genetic algorithms
  • knowledge-based systems
  • power systems
  • scheduling

Cite this

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Evolutionary hybrid approaches for a power system scheduling problem. / Dahal, K.; Aldridge, C.; Galloway, S.J.

In: European Journal of Operational Research, Vol. 177, No. 3, 16.03.2007, p. 2050-2068.

Research output: Contribution to journalArticle

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