Minimization of voltage deviation and power losses in power networks using Pareto optimization methods

Authors:
Francisco G. Montoya;Raúl Baños;Consolación Gil;Antonio Espín;Alfredo Alcayde;Julio Gómez
Affiliations:
Department of Rural Engineering, University of Almería, Carretera de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain;Department of Computer Architecture and Electronics, University of Almería, Carretera de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain;Department of Computer Architecture and Electronics, University of Almería, Carretera de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain;Department of Civil Engineering, University of Granada, Campus de La Cartuja, 18071 Granada, Spain;Department of Rural Engineering, University of Almería, Carretera de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain;Department of Languages and Computation, University of Almería, Carretera de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

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Abstract

Voltage regulation is an important task in electrical engineering for controlling node voltages in a power network. A widely used solution for the problem of voltage regulation is based on adjusting the taps in under load tap changers (ULTCs) power transformers and, in some cases, turning on Flexible Alternating Current Transmission Systems (FACTS), synchronous machines or capacitor banks in the substations. Most papers found in the literature dealing with this problem aim to avoid voltage drops in radial networks, but few of them consider power losses or meshed networks. The aim of this paper is to present and evaluate the performance of several multi-objective algorithms, including hybrid approaches, in order to minimize both voltage deviation and power losses by operating ULTCs located in high voltage substations. In particular, a well-known multi-objective algorithm, PAES, is used for this purpose. PAES finds a set of solutions according to Pareto-optimization concepts. Furthermore, this algorithm is hybridized with simulated annealing and tabu search to improve the quality of the solutions. The implemented algorithms are evaluated using two test networks, and the numerical results are analyzed with two metrics often used in the multi-objective field. The results obtained demonstrate the good performance of these algorithms.