• An Evolutionary Generation Scheduling in an Open Electricity Market

      Dahal, Keshav P.; Siewierski, T.A.; Galloway, S.J.; Burt, G.M.; McDonald, J.R. (2004)
      The classical generation scheduling problem defines on/off decisions (commitment) and dispatch level of all available generators in a power system for each scheduling period. In recent years researchers have focused on developing new approaches to solve nonclassical generation scheduling problems in the newly deregulated and decentralized electricity market place. In this paper a GA-based approach has been developed for a system operator to schedule generation in a market akin to that operating in England and Wales. A generation scheduling problem has been formulated and solved using available trading information at the time of dispatch. The solution is updated after information is obtained in a rolling fashion. The approach is tested for two IEEE network-based problems, and achieves comparable results with a branch and bound technique in reasonable CPU time.
    • Evolutionary hybrid approaches for generation scheduling in power systems

      Dahal, Keshav P.; Aldridge, C.J.; Galloway, S.J. (2007)
    • GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

      Dahal, Keshav P.; Burt, G.M.; McDonald, J.R.; Galloway, S.J. (2000)
      Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems
    • Generation scheduling using genetic algorithm based hybrid techniques

      Dahal, Keshav P.; Galloway, S.J.; Burt, G.M.; McDonald, J.R. (IEEE, 2001)
      The solution of generation scheduling (GS) problems involves the determination of the unit commitment (UC) and economic dispatch (ED) for each generator in a power system at each time interval in the scheduling period. The solution procedure requires the simultaneous consideration of these two decisions. In recent years researchers have focused much attention on new solution techniques to GS. This paper proposes the application of a variety of genetic algorithm (GA) based approaches and investigates how these techniques may be improved in order to more quickly obtain the optimum or near optimum solution for the GS problem. The results obtained show that the GA-based hybrid approach offers an effective alternative for solving realistic GS problems within a realistic timeframe.
    • A knowledge-based genetic algorithm for unit commitment

      Aldridge, C.J.; McKee, S.; McDonald, J.R.; Galloway, S.J.; Dahal, Keshav P.; Bradley, M.E.; Macqueen, J.F. (2001)
      A genetic algorithm (GA) augmented with knowledge-based methods has been developed for solving the unit commitment economic dispatch problem. The GA evolves a population of binary strings which represent commitment schedules. The initial population of schedules is chosen using a method based on elicited scheduling knowledge. A fast rule-based dispatch method is then used to evaluate candidate solutions. The knowledge-based genetic algorithm is applied to a test system of ten thermal units over 24-hour time intervals, including minimum on/off times and ramp rates, and achieves lower cost solutions than Lagrangian relaxation in comparable computational time.