This paper proposes a comprehensive framework for generation and transmission planning of renewable dominated power systems, which is formulated as a stochastic multi-objective problem. In this regard, a Normalized No...
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This paper proposes a comprehensive framework for generation and transmission planning of renewable dominated power systems, which is formulated as a stochastic multi-objective problem. In this regard, a Normalized Normal Constraint (NNC) solution approach is proposed to solve the introduced stochastic multiobjective generation and transmission planning (GTP) problem. The NNC is utilized in this paper as a relation between different objective functions with different dimensions to find the optimal weighting factors of these objectives. The NNC is applied for solving the GTP problem with objective functions including the investment and operation costs along with the transmission losses, while considering the cost of unserved energy, as well as the uncertainty of load and Renewable Energy Resources (RERs). A fuzzy-based decision making framework is utilized to select the best solution among the optimal non-dominated solution points. A scenario-based approach is used to model the uncertainties. The Garver 6-bus and IEEE 118-bus test systems are utilized to perform the numerical analysis. The simulation results validate the performance and importance of the proposed model, as well as the effectiveness of the NNC to find the evenly distributed Pareto solutions of the multiobjective problems.
This paper presents new classes generalizations of second order (K, eta)-pseudobonvexity functions, strongly second order (K, eta)-pseudobonvexity functions, and establishes a pair Mond-Weir type duality multiobjectiv...
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This paper presents new classes generalizations of second order (K, eta)-pseudobonvexity functions, strongly second order (K, eta)-pseudobonvexity functions, and establishes a pair Mond-Weir type duality multiobjective nonlinear programs over these new classes generalizations. The weak duality, strong duality, converse duality, and self-duality theorems are presents under these generalizations functions. Finally, we introduced two nontrivial numerical examples to illustrating results of the weak duality theorem and strong duality theorem. (C) 2022 Elsevier Masson SAS. All rights reserved.
We present a method to capture decision maker's preferences in multiobjective problems and we discuss its use as a base for a decision maker -- multiobjective optimization model interface. We illustrate the idea o...
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ISBN:
(纸本)9782917490204
We present a method to capture decision maker's preferences in multiobjective problems and we discuss its use as a base for a decision maker -- multiobjective optimization model interface. We illustrate the idea on a small but illustrative numerical example of the airport gate assignment problem.
A Lagrange multiplier theorem is established for a nonsmooth constrained multiobjective optimization problems where the objective function and the constraints are compositions of V-invex functions, and locally Lipschi...
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A Lagrange multiplier theorem is established for a nonsmooth constrained multiobjective optimization problems where the objective function and the constraints are compositions of V-invex functions, and locally Lipschitz and Gåteaux differentiable functions. Furthermore, a vector valued Lagrangian is introduced and vector valued saddle point results are presented. A scalarization result and a characterization of the set of all conditionally properly efficient solutions for V-invex composite problems are also discussed under appropriate conditions.
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