Static Economic Dispatch Incorporating UPFC Using Artificial Bee Colony Algorithm
01 Jan 2016-pp 757-769
TL;DR: The impact of unified power flow controller (UPFC) in static economic dispatch (SED) using artificial bee colony (ABC) algorithm that imitates the foraging behavior of honey bees is used as an optimization tool.
Abstract: Static economic dispatch is a real-time problem in power system network. Here, the real power output of each generating unit is calculated with respect to forecasted load demand over a time horizon while satisfying the system constraints. This paper explains the impact of unified power flow controller (UPFC) in static economic dispatch (SED) using artificial bee colony (ABC) algorithm. UPFC is a converter (shunt and series)-based FACTS device, which can control all the parameters in a transmission line individually or simultaneously. ABC algorithm that imitates the foraging behavior of honey bees is used as an optimization tool. The impact of UPFC in reducing the generation cost, loss, and improving voltage profile, power flow are demonstrated. The studies are carried out in an IEEE 118 bus test system and a practical South Indian 86 bus utility.
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TL;DR: It is observed that the combination of DEED with the optimal positioning of FACTS in an interconnected network constitutes an efficient technico-ecological means to act in the direction of reduction on the triplet consisting of (gas emissions, losses, production cost).
Abstract: In an energy environment with multiple production sources, operators are generally confronted with the optimal choice of sources which minimizes polluting gas emissions, losses and marginal production costs while meeting the contractual requirements for maintaining voltage in the ranges required. The present work consisted of optimizing an energy mix in the presence of multi-STATCOM in an interconnected network. Indeed, the (DEE) is a concrete real time problem in electrical energy production systems. This paper shows the impact of STATCOM on static DEE (DEES) and on dynamic DEE (DEED) using the modern genetic algorithm of type U-NSGA-III, which is based on non-dominance sorting. The optimal positioning of two STATCOMs in the application network associated with dynamic dispatching has contributed to the reduction of the total production cost, toxic gas emissions, active losses and then to the improvement of the voltage profiles and the transit of power in the branches. It is observed that the combination of DEED with the optimal positioning of FACTS in an interconnected network constitutes an efficient technico-ecological means to act in the direction of reduction on the triplet consisting of (gas emissions, losses, production cost). The relevance of the results obtained compared to the real case of operating the CEB's interconnected network, justifies the performance of the algorithmic tools developed in the context of this work.
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Cites background from "Static Economic Dispatch Incorporat..."
...Based on Kron's formula [12] [13], 4,* can be expressed as follows:...
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...The installation of FACTS (in particular STATCOM in our case) improves the flow of active power by providing reactive power to transmission lines [13]....
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References
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Book•
24 Dec 1999
TL;DR: The Flexible AC Transmission System (FACTS)—a new technology based on power electronics—offers an opportunity to enhance controllability, stability, and power transfer capability of ac transmission systems.
4,217 citations
01 Jan 2008
TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.
Abstract: Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.
3,242 citations
TL;DR: In this article, a genetic-based algorithm was proposed to solve an economic dispatch problem for valve point discontinuities, which utilizes payoff information of candidate solutions to evaluate their optimality.
Abstract: A genetics-based algorithm is proposed to solve an economic dispatch problem for valve point discontinuities. The algorithm utilizes payoff information of candidate solutions to evaluate their optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are circumvented. The formulations of an economic dispatch computer program using genetic algorithms are presented and the program's performances using two different encoding techniques are compared. The results are verified for a sample problem using a dynamic programming technique. >
1,224 citations
18 Jun 2007
TL;DR: The ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems to show superior performance on these kind of problems.
Abstract: This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems .
1,218 citations
TL;DR: The performance of evolutionary programs on ELD problems is examined and modifications to the basic technique are proposed, where adaptation is based on scaled cost and adaptation based on an empirical learning rate are developed.
Abstract: Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.
1,207 citations