Study of Reconfiguration for the Distribution System With Distributed Generators

Wu, Yuan‐Kang; Lee, Ching-Yin; Liu, Le-Chang; Tsai, Shao-Hong

doi:10.1109/tpwrd.2010.2046339

Cited by 300 publications

(163 citation statements)

References 14 publications

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“…Table 2 shows the fitness found with the different algorithms after minimizing the annual energy losses and annual renewable energy costs, according to (1).…”

Section: Resultsmentioning

confidence: 99%

“…The optimization problem was defined as the minimization of energy losses for the different hours of a day and the total annual cost related to the renewable energies used in the distribution network, as shown in (1).…”

Section: Objective Functionmentioning

confidence: 99%

“…This helps the bats determine the objects' size, form, and position so they can move accordingly [27]. The algorithm was proposed in [28], and the steps are as follows: 1) Generate the initial population of bats 2) Generate the velocities of bats using (4) The frequency fi can be calculated using (16), and the velocity is updated using (17) [27]. (17) Where fmax is the maximum frequency, fmin is the minimum frequency, and β is a normally distributed random number to generate different frequencies.…”

Section: Bat-inspired Algorithmmentioning

confidence: 99%

“…Some of the solutions proposed in literature are network reconfiguration [1], [2]; feeder restructuring [3]; capacitor placement [3]; and distributed generation (DG) [4]- [10].…”

Section: Introductionmentioning

confidence: 99%

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Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

Candelo-Becerra

Riaño

Santander-Mercado

2015

TecnoL.

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Electric power losses have a significant impact on the total costs of distribution networks. The use of renewable energy sources is a major alternative to improve power losses and costs, although other important issues are also enhanced such as voltage magnitudes and network congestion. However, determining the best location and size of renewable energy generators can be sometimes a challenging task due to a large number of possible combinations in the search space. Furthermore, the multiobjective functions increase the complexity of the problem and metaheuristics are preferred to find solutions in a relatively short time. This paper evaluates the performance of the cuckoo search (CS), harmony search (HS), and bat-inspired (BA) algorithms for the location and size of renewable distributed generation (RDG) in radial distribution networks using a multiobjective function defined as minimizing the energy losses and the RDG costs. The metaheuristic algorithms were programmed in Matlab and tested using the 33-node radial distribution network. The three algorithms obtained similar results for the two objectives evaluated, finding points close to the best solutions in the Pareto front. Comparisons showed that the CS obtained the minimum results for most points evaluated, but the BA and the HS were close to the best solution. KeywordsCuckoo search, bat-inspired algorithm, renewable distributed generation, harmony search, photovoltaic energy systems, wind energy systems. ResumenLas pérdidas eléctricas tienen un impacto significativo en los costos totales de las redes de distribución. El uso de las energías renovables es una gran alternativa para mejorar las pérdidas y los costos, aunque también otros problemas en las magnitudes de las tensiones y la congestión de la red pueden ser mejorados. Sin embargo, determinar la mejor localización y dimensionamiento de generadores eléctricos renovables puede ser a veces una tarea difícil debido al gran número de combinaciones posibles existentes en el espacio de búsqueda. Además, el uso de funciones multiobjetivo incrementa la complejidad del problema y se prefiere usar las metaheurísticas para encontrar soluciones en un tiempo relativamente corto. En este trabajo se evalúa el desempeño de los algoritmos inspirados en búsqueda cuco, búsqueda armónica y murciélagos para la localización y dimensionamiento de la generación distribuida renovable en redes de distribución radiales, usando funciones como la minimización de las pérdidas de energía y los costos de la generación distribuida renovable. Las metaheurísticas fueron programadas en Matlab y evaluadas usando la instancia denominada red de distribución radial de 33 nodos. Los tres algoritmos evaluados obtuvieron resultados similares para los dos objetivos evaluados, encontrando Frentes de Pareto cercanos a las mejores soluciones. La comparación realizada mostró que la búsqueda cuco obtiene los mejores resultados, pero los algoritmos inspirados en murciélagos y búsqueda armónica obtuvieron resultados cercanos a la mejor soluci...

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“…Table 2 shows the fitness found with the different algorithms after minimizing the annual energy losses and annual renewable energy costs, according to (1).…”

Section: Resultsmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%

Section: Bat-inspired Algorithmmentioning

confidence: 99%

“…Some of the solutions proposed in literature are network reconfiguration [1], [2]; feeder restructuring [3]; capacitor placement [3]; and distributed generation (DG) [4]- [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

Candelo-Becerra

Riaño

Santander-Mercado

2015

TecnoL.

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“…Additionally, this approach alters the essence of the original technical problem. On the other hand, some authors have studied this problem using aggregation functions, converting the multi-objective problem into a single objective one that assumes a (weighted or not) sum of the selected objective functions [23][24][25][26][27][28][29][30]. The major difficulty in this kind of problem consists in the incompatibility of different criteria.…”

Section: Introductionmentioning

confidence: 99%

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

et al. 2013

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Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.

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Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Hesaroor

Das

2019

Int Trans Electr Energ Syst

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Summary Renewable energy sources are becoming popular day by day for distributed generation as technology is becoming cheaper and modular. Power loss reduction is a powerful incentive for a distribution system operator to promote distributed generation. This paper presents a method to minimize annual energy losses through the integration of nondispatchable renewable distributed generation (DG) units and network reconfiguration under varying load demand condition. The optimal location and DG power level are calculated, and then these data are used to determine DG plant size taking into account the nonavailability of renewable energy at certain times. A network reconfiguration strategy is proposed, which takes into account both the time‐varying load and the renewable energy source such that annual energy loss is minimum. The proposed methodology is applied to 33‐node and 118‐node test distribution system with different scenarios. Results show a substantial reduction in energy loss. A cost‐benefit analysis is also carried out.

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Study of Reconfiguration for the Distribution System With Distributed Generators

Cited by 300 publications

References 14 publications

Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

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