2012
DOI: 10.1002/etep.1672
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Multi-objective transmission expansion planning based on reliability and market considering phase shifter transformers by fuzzy-genetic algorithm

Abstract: SUMMARY In this paper, a new framework is presented for multi‐objective transmission expansion planning which expansion options are combination of transmission lines and phase shifter transformers. This framework is based on a multiple criteria decision making whose fundamental elements are reliability and market. Investment cost, congestion cost, users' benefit and expected customer interruption cost are considered in the optimization as four objectives. The proposed model is a non‐convex optimization problem… Show more

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Cited by 32 publications
(22 citation statements)
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“…Because, it is hard to express such a two stage optimization problem with analytical equations. In this paper, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the upper stage of TEP problem which has integer structure [18,20,[28][29][30]. In this methodology, trade-off between the objective functions can be evaluated after the optimization is executed and also the planners can reflect their preferences to the planning process.…”
Section: Optimization Frameworkmentioning
confidence: 99%
See 1 more Smart Citation
“…Because, it is hard to express such a two stage optimization problem with analytical equations. In this paper, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the upper stage of TEP problem which has integer structure [18,20,[28][29][30]. In this methodology, trade-off between the objective functions can be evaluated after the optimization is executed and also the planners can reflect their preferences to the planning process.…”
Section: Optimization Frameworkmentioning
confidence: 99%
“…Thus, the upper stage of optimization has not a suitable structure to solve by mathematical optimization models. Genetic algorithm that is a derivative-free nonlinear solver offers the good opportunity to solve that kind of problems [11,27,28]. Because, it is hard to express such a two stage optimization problem with analytical equations.…”
Section: Optimization Frameworkmentioning
confidence: 99%
“…[40] and modified for the aforementioned test system according to Table 5. The base case of DC power flow was checked with [33], which indicates the obtained results are accurate. On the other hand, in this study, the proposed problem is performed under three different cases for comparison purposes.…”
Section: Simulation and Case Studiesmentioning
confidence: 99%
“…In this structure, each participant presents an hourly price bid in the form of marginal cost or marginal benefit functions [$/MWh]. Also, the ISO performs market-clearing process by considering the bids presented by participants, environmental issues and transmission constraints [33,34]. Hence, in this paper, operation of the electricity market is carried out by optimization of two groups of conflicting objectives, namely, community welfare and atmospheric emission subject to the security and physical constraints.…”
Section: Electricity Market Clearing Modelmentioning
confidence: 99%
“…By taking into account of the individual minimum and maximum values of each objective function, the membership function,μfktrueX¯, for each objective function can be determined in a subjective manner. Then, to solve the proposed model as a multi‐objective optimization problem with K objective functions, the final solution is selected based on Equation . italicmaxkitalicmintrueX¯μfktrueX¯;k=1,2,K…”
Section: Solution Algorithm For the Proposed Plas Problemmentioning
confidence: 99%