Abstract
The results of research into the use of fuzzy set based models and methods of multicriteria decision making for solving power engineering problems are presented. Two general classes of models related to multiobjective (X，M> models) and multiattribute (X，R> models) problems are considered. The analysisX，M> of models is based on the use of the Bellman-Zadeh approach to decision making in a fuzzy environment. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. Several techniques based on fuzzy preference modeling are considered for the analysis of X，R> models. A review of the authors’ results associated with the application of these models and methods for solving diverse types of problems of power system and subsystems planning and operation is presented. The recent results on the use ofX，M> andX，R> models and methods of their analysis for the allocation of reactive power sources in distribution systems and for the prioritization in maintenance planning in distribution systems, respectively, are considered.
Highlights
The analysis of X, M models is based on the use of the Bellman-Zadeh approach to decision making in a fuzzy environment
Diverse types of uncertainty [1] are commonly encountered in a wide range of optimization and decision making problems related to planning and operation of power systems and subsystems
The uncertainty of goals is an essential kind of uncertainty related to a multicriteria nature of many power engineering problems
Summary
Diverse types of uncertainty [1] are commonly encountered in a wide range of optimization and decision making problems related to planning and operation of power systems and subsystems. It is necessary to solve some questions related to normalizing objective functions, selecting principles of optimality, and considering priorities of objective Their solution and, development of multiobjective methods is carried out in the following directions [9,10,11]: scalarization techniques, imposing constraints on objectives, utility function method, goal programming, and using the principle of guaranteed result. When applying the Bellman-Zadeh approach to decision making in a fuzzy environment [14], this concept is defined with reasonable validity: the maximum degree of implementing goals serves as a criterion of optimality This conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions.
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