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Immune Procedure for Optimal Scheduling of Complex Energy Systems

  • Enrico Carpaneto
  • Claudio Cavallero
  • Fabio Freschi
  • Maurizio Repetto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4163)

Abstract

The management of complex energy systems where different power sources are active in a time varying scenario of costs and prices needs efficient optimization approaches. Usually the scheduling problem is is formulated as a Mixed Integer Linear Programming (MILP) to guarantee the convergence to the global optimum. The goal of this work is to propose and compare a hybrid technique based on Artificial Immune System (AIS) and linear programming versus the traditional MILP approach. Different energy scheduling problem cases are analyzed and results of the two procedures are compared both in terms of accuracy of results and convergence speed. The work shows that, on some technical cases, AIS can efficiently tackle the energy scheduling problem in a time varying scenario and that its performances can overcome those of MILP. The obtained results are very promising and make the use of immune based procedures available for real-time management of energy systems.

Keywords

Optimal Schedule Linear Programming Problem Mixed Integer Linear Programming Problem Thermal Storage Schedule Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Enrico Carpaneto
    • 1
  • Claudio Cavallero
    • 1
  • Fabio Freschi
    • 1
  • Maurizio Repetto
    • 1
  1. 1.Dept. of Electrical EngineeringPolitecnico di TorinoTorinoItaly

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