Combined heat and power economic dispatch by mesh adaptive direct search algorithm

Research highlights? In this study, an optimization method, namely mesh adaptive direct search (MADS) is introduced to solve combined heat and power (CHP) economic dispatch problem. ? MADS is a recently developed algorithm that is supported by a thorough convergence analysis. The MADS method is illustrated using three test cases taken from the literature. ? Latin hypercube sampling (LHS), particle swarm optimization (PSO) and design and analysis of computer experiments (DACE) algorithms are employed as effective search strategies in MADS to solve the CHPED problems. The results clearly demonstrate that the MADS-based methods are practical and valid for CHPED applications. ? The MADS-DACE algorithm performs superior than or as well as the other recent methods in terms of solution quality, handling constraints and computation time. The optimal utilization of multiple combined heat and power (CHP) systems is a complex problem. Therefore, efficient methods are required to solve it. In this paper, a recent optimization technique, namely mesh adaptive direct search (MADS) is implemented to solve the combined heat and power economic dispatch (CHPED) problem with bounded feasible operating region. Three test cases taken from the literature are used to evaluate the exploring ability of MADS. Latin hypercube sampling (LHS), particle swarm optimization (PSO) and design and analysis of computer experiments (DACE) surrogate algorithms are used as powerful SEARCH strategies in the MADS algorithm to improve its effectiveness. The numerical results demonstrate that the utilized MADS-LHS, MADS-PSO, MADS-DACE algorithms have acceptable performance when applied to the CHPED problems. The results obtained using the MADS-DACE algorithm are considerably better than or as well as the best known solutions reported previously in the literature. In addition to the superior performance, MADS-DACE provides significant savings of computational effort.

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