DEP is assessed with the help of field studies, available data, and decentralized energy modeling. Conflicting objectives are considered in the implementation of DEP. Present work for the analysis of DEP at panchayat level, which is a cluster of village, has considered different scenarios. Energy planning at village level is the lowest level of the application of decentralized planning principle and district is the upper most level.
Decentralized planning involves scaling down energy planning to sub-national or regional scales. The kinds of objective functions and constraints that have to be included in the DEP have been enumerated in the present work. Present work presents the methodology for the DEP. DEP planning involves multiple objectives and different kinds of constraints. The geographical coverage and scale reflects the level at which the analysis takes place, which is an important factor in determining the structure of models. The main aspect of the energy planning at decentralized level would be to prepare an area-based DEP to meet energy needs and development of alternate energy sources at least-cost to the economy and environment. DEP is one of the options to meet the rural and small-scale energy needs in a reliable, affordable, and environmentally sustainable way. Finally, two numerical examplesĪre given to clarify the obtained results.ĭecentralized energy planning (DEP) is in the interest of efficient utilization of resources. A flowchart is also provided to illustrate the problem-solving steps. In addition, the motivation behind this study is toĮnable the decision makers to make the right decision considering the proposed solutions, while dealing with the uncertainĪnd imprecise data. Programming problems require that the decision variables be integers. Integer programming is used, since a lot of the linear Rough value optimal solutions and decision rough integer variables. Moreover, a-level at a = 0.5 is adopted to find some other Interval and represent completely satisfactory solutions. Represent rather satisfactory solutions the other is an FILP problem, where all of its coefficients are the lower approximation One of these problems is an FILP problem, where all of its coefficients are the upper approximation interval and In order to solve this problem, a proper methodology isĪdopted to solve the TFRILP problems by the slice-sum method with the branch-and-bound technique, through which twoįuzzy integers linear programming (FILP) problems with triangular fuzzy interval coefficients and variables were constructed. Market globalization and some other uncontrollable factors. Situations, the parameters of a linear programming problem model may not be defined precisely, because of the current Parameters and decision variables in the constraints and the objective function are triangular fuzzy rough numbers. Introduced in this paper in order to find rough value optimal solutions and decision rough integer variables, where all Finally, two numerical examples are given to clarify the obtained results.Ī suggested algorithm to solve triangular fuzzy rough integer linear programming (TFRILP) problems with a-level is
In addition, the motivation behind this study is to enable the decision makers to make the right decision considering the proposed solutions, while dealing with the uncertain and imprecise data. Integer programming is used, since a lot of the linear programming problems require that the decision variables be integers. Moreover, α-level at α = 0.5 is adopted to find some other rough value optimal solutions and decision rough integer variables. One of these problems is an FILP problem, where all of its coefficients are the upper approximation interval and represent rather satisfactory solutions the other is an FILP problem, where all of its coefficients are the lower approximation interval and represent completely satisfactory solutions. In order to solve this problem, a proper methodology is adopted to solve the TFRILP problems by the slice-sum method with the branch-and-bound technique, through which two fuzzy integers linear programming (FILP) problems with triangular fuzzy interval coefficients and variables were constructed. In real-life situations, the parameters of a linear programming problem model may not be defined precisely, because of the current market globalization and some other uncontrollable factors.
A suggested algorithm to solve triangular fuzzy rough integer linear programming (TFRILP) problems with α-level is introduced in this paper in order to find rough value optimal solutions and decision rough integer variables, where all parameters and decision variables in the constraints and the objective function are triangular fuzzy rough numbers.
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