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The measure of Reactive power optimization of main power network mainly considers on-load tap changer, the optimal capacity of the capacitor, the voltage of generator under the steady load. In This paper, a multi-objective optimization methodology to the Optimal Reactive Power Flow (ORPF) problem is proposed in which the e-constrained approach is implemented for the Multi-objective Mathematical Programming (MMP) formulation. The objective functions of the proposed model include optimize the total fuel cost, the active power losses, and the system load ability. Since the control variables include discrete variables (var sources and transformer tap ratios), the ORPF is inherently a mixed-integer nonlinear programming (MINLP) problem. The optimum tap settings of transformers are directly determined in terms of the admittance matrix of the network since the admittance matrix is constructed in the optimization framework as additional equality constraints. To show the effectiveness of proposed method the results are compared with single-objective, two-objectives and three-objectives. Finally, the method has been implemented in GAMS and solved using DICOPT solver to obtain optimal solutions. In this research, for selection of the best compromising answer among the Pareto optimal solutions has been designed a fuzzy decision-maker tool. The algorithm is tested on standard IEEE 14- bus test system. Simulation results show that the proposed algorithm is able to control reactive power flow effectively and optimize the selected objective functions.

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Abstract:
Short-Term Scheduling of a standalone microgrid in the presence of renewable energy sources which is used to determine optimal daily operation is presented. The Necessity of this work is to use green and low-cost distributed energy resources that leads to lower costs and less pollution to the environment. Here, the commitment decisions are determined by minimizing the Lagrangian Function of the optimal power and then a duality-based analysis isapplied to derive closedform solutions. Finally, an efficient subgradientbased method is introduced to numerically compute the optimal solutions. The under study microgrid consists of five DG units, including solar photovoltaic, wind turbines, micro turbines and fuel cells. In islanded mode, the unit commitment problem is solved with respect of load demand and electrical power generated by WT and PV in different hours, so as to meet the load demand and related system operating constraints, several senarios are studied. The intermittency nature of the renewable energy sources, as well as microgrid’s capacity to operate either in parallel with, or autonomously of, the traditional power grid, pose new challenges to this classic optimization task. Using of an Energy Storage System with guidelines in deciding of its size is proposed to compensate the intermittent of the renewable energy sources.

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Charging electric vehicles in the distribution network is one of the most basic solutions for technical and economic management of energy distribution. In many traditional charging methods, the condition of fully charging cars when leaving the parking lot has always been a problem. But in this article, each car is intelligently charged only based on the amount of energy required to travel its daily journeys. In order to implement this smart method, owners of electric vehicles provide information about the number of trips and the length of their route to the parking charge management, and based on the specifications of the vehicles and the initial energy level of their battery when entering the parking lot, the amount The charge required for them is determined. Then, the charging manager plans the charging based on the tariff of energy consumption time, limitation of distribution network transformers, type of charge level selected (normal or fast) so that charging costs are minimized by observing technical and economic constraints. . The result of intelligent car charging in normal and fast charging conditions and in different limitations of the distribution network and in the presence or absence of load response program is compared with each other. YALMIP and MOSEK software have been used to solve the mixed integer linear programming model.