Abstract This paper proposes a multi-objective chance-constrained optimization approach for allocating wind generation and photovoltaic. For reflecting the uncertainty and relevance among wind speed, solar radiation and load, and accelerating the computation, a kind of state selection strategy based on statistical methods is proposed. To evaluate profits from distributed generation more comprehensively, three indices are introduced into the objective function, namely cost index, power loss index and voltage deviation index. To balance the relationship between risk and return, the chance-constrained optimization is adopted. In the process of solution, firstly the novel method integrating the differential evolution for multi-objective optimization and dynamic non-dominated sorting is proposed to get a set of the Pareto-optimal solutions, after that fuzzy multi-attribute decision making method based on information entropy is adopted to select the best compromise solution from the Pareto-optimal solutions. The case studies show that the proposed optimal model is rational, and the algorithm is effective.


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    Title :

    An Improved Multi-objective Differential Evolution Algorithm for Allocating Wind Generation and Photovoltaic


    Contributors:
    Ge, Xiao-lin (author) / Xia, Shu (author)


    Publication date :

    2014-01-01


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English