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A hybrid constraints handling strategy for multiconstrained multiobjective optimization problem of microgrid economical/environmental dispatch - MaRDI portal

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A hybrid constraints handling strategy for multiconstrained multiobjective optimization problem of microgrid economical/environmental dispatch (Q1693797)

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scientific article; zbMATH DE number 6833048
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English
A hybrid constraints handling strategy for multiconstrained multiobjective optimization problem of microgrid economical/environmental dispatch
scientific article; zbMATH DE number 6833048

    Statements

    A hybrid constraints handling strategy for multiconstrained multiobjective optimization problem of microgrid economical/environmental dispatch (English)
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    31 January 2018
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    Summary: MicroGrid (MG) Economical/Environmental Dispatch (MGEED) problem is a complex multiobjective optimization topic. Since the generators are diversified and the operation mode changes frequently, the MGEEED problem always has different types of constraints, such as the load balance constraints and the ramp rates constraints, which make it a nonlinear nonconvex optimization problem. In this paper, the mathematical model of a typical MG system applied in northwest China is established. Then, a Hybrid Constraints Handling Strategy (HCHS) based on Nondominated Sorting Genetic Algorithm II (NSGAII) is proposed to deal with the typical constraints, by which the constraints violations can be removed in several steps during the evolutionary process. A dimensionality reduction method is introduced to simplify the optimization model. And an individual repair approach is designed for the violations of ramp rates constraints. In order to balance the weights of various types of constraints, the process of constraints handling in standard NSGAII is revised. Thereafter, HCHS-NSGAII is applied to some typical MGEED problems, considering all kinds of typical constraints. The results show that HCHS-NSGAII can obtain feasible Pareto sets with satisfactory convergence and distribution, which is efficient in handling complex practical industrial MGEED problems with the change of constraints combinations.
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    complex multiobjective optimization
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    microgrid economical/environmental dispatch
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    nonlinear nonconvex optimization problem
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    genetic algorithm
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