Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
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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.