a b s t r a c tPower loss is one of the most important issues when dealing with distribution networks. Due to the nature of power loss, it can be an inseparable part of these networks. This is while an optimal reconfiguration is a great optimization procedure to power loss reduction in distribution networks. Moreover, to perform optimal dynamic reconfiguration, determining optimal time intervals and detecting the most proper time points greatly affects the total benefit achieved from this process. This benefit includes the cost of reconfiguration and the benefit of power loss reduction.This paper proposes an evolutionary approach for optimal time interval determination. In this paper basic reconfiguration models are discussed to form an optimal time interval model gradually. The Genetic Algorithm (GA) is used to solve the suggested model while the proposed method is implemented on an IEEE-33 Bus network. In order to examine the effectiveness of the proposed method, a comparison is done with other similar procedures. Also, in order to validate the numerical results, further compression is done with a method using a Binary Particle Swarm Optimization (BPSO) algorithm rather than the GA.
a b s t r a c tPower loss reduction can be considered as one of the main purposes for distribution system operators, especially for recent non-governmental networks. Reconfiguration is an operation process widely used for this optimization by means of changing the status of switches in a distribution network. Some major points such as time-varying loads and the number of switchings, which are often neglected or not applied simultaneously in most previous studies, are the main motivation behind this study. In this paper, a new probabilistic approach is proposed to perform an optimal reconfiguration in order to reduce the total cost of operation, including the cost of switching and benefit of loss reduction. Considering time-varying loads, the proposed method can obtain an optimal balance between the number of switchings and the power loss. The effectiveness of the suggested method is demonstrated through several experiments and the results are compared with those of other reliable methods in several cases.
One of the most challenging issues of transmission grids in restructured power networks is determining the real transmission service cost. In this paper, a method for calculating the transmission service in different grids is presented. In the proposed method, the cost of power loss and congestion of transmission services are considered. Using this integrated model, the strengths and weaknesses of a power grid can be straightforwardly analyzed. All simulations have been done on IEEE nine-bus test system. Computing the transmission service cost, the next step is to optimize power loss and congestion using NSGA-II which is a two objective genetic algorithm method in which optimal power flow is implemented and power loss and congestion are considered as objective functions. Numerical results prove the effectiveness of the proposed optimizing method in which decreasing the cost of power loss and congestion have a direct effect on improving the quality of transmission services.
Load frequency control in power systems introduces as one of the most important items in order to supply reliable electric power with good quality. The goals of the Load Frequency Control (LFC) are to maintain zero steady state errors in a two area interconnected power system. To achieve this goal a fast controller with having no steady-state error will be necessary to be included in power systems. In this paper a new genetic algorithm based method is presented to obtain optimal gains of this controller included in two-area interconnected power system. Simulation results in comparison with correspondence methods confirm the efficiency of proposed method through fastdamping steady-state deviations in power and frequency with presence of step load disturbance.
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