Abstract
In this paper, a cooperative metaheuristic based on ant colony optimization and genetic algorithm is developed for constrained predictive control of power systems. The classical Unit Commitment solution is an open loop control for power systems which cannot be applied to real system, since it is affected by important uncertainties, a typical source being the consumer load. Predictive control offers an efficient way to use optimization results in a closed loop framework, implying the online solution of successive constrained mixed optimization problems. The algorithm proposed here is able to explicitly deal with constraints, and to quickly find high quality suboptimal solutions for computationally involving predictive control schemes. Simulation results show the efficiency of the developed method, even for Unit Commitment problems with underestimated consumer demand.
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Sandou, G., Olaru, S. (2007). Ant Colony and Genetic Algorithm for Constrained Predictive Control of Power Systems. In: Bemporad, A., Bicchi, A., Buttazzo, G. (eds) Hybrid Systems: Computation and Control. HSCC 2007. Lecture Notes in Computer Science, vol 4416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71493-4_39
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DOI: https://doi.org/10.1007/978-3-540-71493-4_39
Publisher Name: Springer, Berlin, Heidelberg
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