Transmission lines are widely used for transferring electrical energy from power plants to loads, interconnecting load centers and improving reliability of power systems. Due to recent society developments, the need for electrical energy has increased which in turn requ More
Transmission lines are widely used for transferring electrical energy from power plants to loads, interconnecting load centers and improving reliability of power systems. Due to recent society developments, the need for electrical energy has increased which in turn requires more investment in constructing additional electrical transmission lines. Power system restructuring and deregulation has increased uncertainties in transmission expansion planning and made investment in electrical transmission lines more complicated and less appealing for private parties. This paper proposes a new approach for transmission line expansion planning in deregulated networks. To do that, a multi objective programming problem which consists of various objective functions such as minimizing capital investment for constructing new transmission lines, minimizing congestion in transmission lines and maximizing the investment from private parties is suggested such that access to competitive, economic and reliable energy market is facilitated. To solve the proposed multi objective optimization problem, the Pareto differential evolution algorithm is used. Applying this algorithm to the proposed multi objective programming problem generates set of optimal plans that shows
the best compromise between objective functions. The final plan, among the generated plans, is selected using a max-min fuzzy decision making. The proposed method is applied on the IEEE 24 bus test system and effectiveness of the proposed method is verified.
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Plug in electric vehicles besides environment pollution reduction can help power system operation. One of the most important capabilities of them is providing activeand reactive power. This paper considers grid constraints, technical concerns and market price and propos More
Plug in electric vehicles besides environment pollution reduction can help power system operation. One of the most important capabilities of them is providing activeand reactive power. This paper considers grid constraints, technical concerns and market price and proposes a framework to allocate the PEV capacity such that operational cost paid by distribution system operator (DSO) to power provider of active and reactive power is minimized. For this purpose, an objective function is defined that includes the payment for each power provider. This objective function is minimized based on particle swarm optimization subject to grid and vehicles constraints. In this framework, the PEVs compete with generator to produce active and reactive power. In order to accelerate the optimization process and prevent the algorithm from being trapped in local optima, new heuristic approaches are included to the original PSO algorithm. To evaluate the effectiveness of the propose method, it is implemented on the low voltage with 134 customer and including the other power providers and the amount of each participants production and payment cost to each component is determined.
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Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Mo More
Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Moreover, due to the large number of features in microarrays, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Hence, appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, a modified multi-objective cuckoo search algorithm is used to feature selection and sample selection to find the best available solutions. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. The proposed algorithm is applied on six cancer datasets and its results are compared with other existing methods. The results show that the proposed method has higher accuracy and validity in comparison to other existing approaches and is able to select the small subset of informative genes in order to increase the classification accuracy.
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