Published
Implementation of an evolutionary algorithm in planning investment in a power distribution system
Implementación de un Algoritmo Evolutivo en la Planeación de Inversiones de un Sistema de Distribución de Energía.
DOI:
https://doi.org/10.15446/ing.investig.v31n2SUP.25222Keywords:
Evolutionary Algorithm, Distribution system planning, SPEA, Multi-objective optimization, optimal investment plan (en)Algoritmos Evolutivos, Planeación de sistemas de Distribución, Optimización Multi objetivo, Plan de inversiones óptimo (es)
The definition of an investment plan to implement in a distribution power system, is a task that constantly faced by utilities. This work presents a methodology for determining the investment plan for a distribution power system under a short-term, using as a criterion for evaluating investment projects, associated costs and customers benefit from its implementation. Given the number of projects carried out annually on the system, the definition of an investment plan requires the use of computational tools to evaluate, a set of possibilities, the one that best suits the needs of the present system and better results. That is why in the job, implementing a multi objective evolutionary algorithm SPEA (Strength Pareto Evolutionary Algortithm), which, based on the principles of Pareto optimality, it deliver to the planning expert, the best solutions found in the optimization process. The performance of the algorithm is tested using a set of projects to determine the best among the possible plans. We analyze also the effect of operators on the performance of evolutionary algorithm and results..
La definición de un plan de inversiones a realizar en un sistema de distribución, es una tarea a la que constantemente se deben enfrentar las empresas de distribución. Este trabajo, presenta una metodología para la determinación del plan de inversiones en un sistema de distribución, bajo un horizonte de corto plazo, empleando como criterio de evaluación de los proyectos de inversión: los costos asociados y los clientes beneficiados con su ejecución. Dada la cantidad de proyectos que se ejecutan anualmente sobre el sistema, la definición de un plan de inversiones requiere del uso de herramientas computacionales que permitan evaluar, de un conjunto de posibilidades, la que más se ajuste a las necesidades del sistema y mejores resultados presente. Es por esto que dentro del trabajo, se implementa un algoritmo evolutivo multi objetivo SPEA (Strength Pareto EvolutionaryAlgortithm), el cual, basado en los principios de optimalidad de Pareto, entregan al experto en planeación, las mejores soluciones halladas dentro del proceso de optimización, según los objetivos planteados. El desempeño del algoritmo es probado empleando un conjunto de proyectos, hasta determinar los mejores planes de entre los posibles. Se analiza además, el efecto que tienen los operadores evolutivos sobre el desempeño del algoritmo y los resultados del mismo.
References
Bernal-Agustín, J. L. “Application of genetic algorithms to the optimal design of power distribution systems,” (in Spanish) Ph.D. dissertation, Dept. Elect. Eng., Univ. Zaragoza, Zaragoza, Spain, 1998.
Boardman, J. T and Meckiff, C. C. “A branch and bound formulation of an electricity distribution planning problem,” IEEE Trans. Power App. Syst., vol. 104, pp. 2112-2118, Aug. 1985.
Carreno, E.M.; Moreira, N.; Romero, R.;, “Distribution network reconfiguration using an efficient evolutionary algorithm,” Power Engineering Society General Meeting, 2007. IEEE, pp.1-6, 24-28 June 2007.
Coello, C. A; Veldhuizen, D. A. V and Lamont, G. B; Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Second Edition, MA: Kluwer, 2007.
Coello, C.A., “Evolutionary Multi-Objective Optimization: A Historical View of the Field,” IEEE Computational Intelligence Magazine, February 2006, pp. 28-36.
CREG Comisión de Regulación de Energía y Gas (CREG); Resolución 097-2008, Por la cual se aprueban los principios generales y la metodología para el establecimiento de los cargos por uso de los Sistemas de Transmisión Regional y Distribución Local. [Online]. Available: http://www.creg.gov.co.
Ferreira, L. A. F. M; Carvalho, P. M. S; Jorge, L.A; Grave, S. N. C and Barruncho, L. M. F; “Optimal Distribution Planning by Evolutionary Computation ó How to Make it Work,” Transmission and Distribution Conference and Exposition (TDCE' 2001), Atlanta, USA, 2001.
Fletcher, R.H. and Strunz, K.; “Optimal Distribution System Horizon Planning-Part I: Formulation,” Power Systems, IEEE Transactions, vol.22, no.2, pp.791-799, May 2007.
Gönen.T and Foote, B. L. “Distribution system planning using mixed integer programming,” Proc. Inst. Elect. Eng. C, vol. 128, no. 2, pp.70-79, Mar. 1981.
Khator, S. K. and Leung, L. C. “Power distribution planning: A review of models and issues,” IEEE Trans. Power Syst., vol. 12, no. 3, pp. 1151- 1159, Aug. 1997.
Khodr, H.M., Vale, Z., Ramos, C., Faria, P., “Optimization techniques for power distribution planning with uncertainties: A comparative study,” Power & Energy Society General Meeting, 2009. PES '09. IEEE, pp.1-8, 26-30 July 2009.
Ramirez-Rosado I.J and Dominguez-Navarro, J.A. “New multi objective tabu search algorithm for fuzzy optimal planning of power distribution systems,” IEEE Trans. Power Syst., Vol. 21, Issue 1, Pp.224-233, Feb. 2006.
Rivas-Davalos, F., Moreno-Goytia, E., Gutierrez-Alacaraz, G., Tovar-Hernandez, J., “Evolutionary Multi-Objective Optimization in Power Systems: State-of-the-Art,” Power Tech, 2007 IEEE Lausanne, pp.2093-2098, 1-5 July 2007.
RuiXu; Wunsch, D., II;, “Survey of clustering algorithms,” Neural Networks, IEEE Transactions on, vol.16, no.3, pp.645-678, May 2005.
Zitzler, E. and Thiele, L., “Multi objective evolutionary algorithms: a comparative case study and the strength Pareto approach,” Evolutionary Computation, IEEE Transactions, vol.3, no.4, pp.257-271, Nov 1999.
Dimensions
PlumX
Article abstract page views
Downloads
How to Cite
License
Copyright (c) 2011 Carlos Andrés García Montoya, Steiner Mendoza Toro
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors or holders of the copyright for each article hereby confer exclusive, limited and free authorization on the Universidad Nacional de Colombia's journal Ingeniería e Investigación concerning the aforementioned article which, once it has been evaluated and approved, will be submitted for publication, in line with the following items:
1. The version which has been corrected according to the evaluators' suggestions will be remitted and it will be made clear whether the aforementioned article is an unedited document regarding which the rights to be authorized are held and total responsibility will be assumed by the authors for the content of the work being submitted to Ingeniería e Investigación, the Universidad Nacional de Colombia and third-parties;
2. The authorization conferred on the journal will come into force from the date on which it is included in the respective volume and issue of Ingeniería e Investigación in the Open Journal Systems and on the journal's main page (https://revistas.unal.edu.co/index.php/ingeinv), as well as in different databases and indices in which the publication is indexed;
3. The authors authorize the Universidad Nacional de Colombia's journal Ingeniería e Investigación to publish the document in whatever required format (printed, digital, electronic or whatsoever known or yet to be discovered form) and authorize Ingeniería e Investigación to include the work in any indices and/or search engines deemed necessary for promoting its diffusion;
4. The authors accept that such authorization is given free of charge and they, therefore, waive any right to receive remuneration from the publication, distribution, public communication and any use whatsoever referred to in the terms of this authorization.
