Multiobjective daily Volt/VAr control in distribution systems with distributed generation using binary ant colony optimization

Turkish Journal of Electrical Engineering & Computer Sciences http://journals.tubitak.gov.tr/elektrik/ Research Article Turk J Elec Eng & Comp Sci (2013) 21: 613 – 629 c TÜBİTAK  doi:10.3906/elk-1110-16 Multiobjective daily Volt/VAr control in distribution systems with distributed generation using binary ant colony optimization Reza AZIMI, Saeid ESMAEILI∗ Department of Electrical Engineering, Bahonar University of Kerman, Kerman, Iran Received: 09.10.2011 • Accepted: 25.01.2012 • Published Online: 03.05.2013 • Printed: 27.05.2013 Abstract: This paper presents a multiobjective daily voltage and reactive power control (Volt/VAr) in radial distribution systems, including distributed generation units. The main purpose is to determine optimum dispatch schedules for onload tap changer (OLTC) settings at substations, substation-switched capacitors, and feeder-switched capacitors based on the day-ahead load forecast. The objectives are selected to minimize the voltage deviation on the secondary bus of the main transformer, total electrical energy losses, the number of OLTCs, and capacitor operation and voltage fluctuations in distribution systems for the next day. Since this model is the weighted sum of individual objective functions, an analytic hierarchy process is adopted to determine the weights. In order to simplify the control actions for OLTC at substations, a time interval-based control strategy is used for decomposition of a daily load forecast into several sequential load levels. A binary ant colony optimization (BACO) method is used to solve the daily voltage and reactive control, which is a nonlinear mixed-integer problem. To illustrate the effectiveness of the proposed method, the Volt/VAr control is performed in IEEE 33-bus and 69-bus distribution systems and its performance is compared with the genetic, hybrid binary genetic, and particle swarm optimization algorithms. The simulation results verify that the BACO algorithm gives better performances than other algorithms. Key words: Distributed generators, binary ant colony optimization, multiobjective, reactive power and voltage control 1. Introduction Volt/VAr control in distribution systems involves proper coordination among the on-load tap changer (OLTC) and all of the switched shunt capacitors in the distribution system to obtain an optimum voltage profile and optimum reactive power flows in the system according to the objective function and operating constraints [1]. The voltage and reactive power equipment in distribution systems are mostly operated based on an assumption that the voltage decreases along the feeder. On the other hand, the connection of distributed generation (DG) will fundamentally alter the feeder voltage profiles, which will obviously affect the voltage control in distribution systems. Recently, concerns about the global environment and energy security have raised expectations for distributed generators, such as wind-power generation, photovoltaic generation, fuel cells, and micro-gas turbines. Today’s improvements in the performance and efficiency of DG are encouraging an increase in amount of DG installed into electric power systems. The installation of DG into power systems has some merits, such as the reduction of transmission and distribution losses. On the other hand, it also brings some technical problems such as the occurrence of over-voltages or under-voltages on distribution feeders, injection of current harmonics, or islanding operation of DG. Therefore, it is essential to consider the impact of DGs ∗ Correspondence: s 613 AZIMI and ESMAEILI/Turk J Elec Eng & Comp Sci on power systems, especially on distribution networks, because the configuration of a distribution network is generally radial and the X/R ratio of distribution lines is small. Nowadays, research on the Volt/VAr control for distribution systems can be divided into 2 categories: offline setting control and real-time control. Research in offline setting control [2–4] aims to find dispatch schedules for switching capacitors and OLTC settings at substations for the day ahead according to optimization calculations based on load forecasts for the day ahead, while research for real-time control aims to control the aforementioned devices based on real-time measurements and experiences. The second category of control requires a higher level of distribution system automation and more hardware and software support [5]. Until recently, the majority of distribution systems did not reach such standards. Furthermore, it is very difficult for real-time control to consider the overall load change as well as the constraints of the maximum allowable switching operations for a number of Volt/VAr control devices. Recently, multiobjective optimization approaches for Volt/VAr control have become more attractive [6– 14]. However, the focus has been concentrated on power losses and voltage deviation. Relatively little effort has been directly involved with the number of OLTCs and capacitor operations. So far, various mathematical optimization algorithms, such as linear programming and gradient-based algorithms, in Volt/VAr control problems have been applied [15–17]. However, the optimal dispatch of all Volt/VAr control devices is a multiphase decision-making problem. For each hour, it is a discrete and nonlinear problem. Therefore, using traditional mathematical methods can be very complex and entails a heavy computational burden. In recent years, a wide variety of evolutionary algorithms [6–12], such as the genetic algorithm, particle swarm optimization, and honey bee mating optimization, have been used for the Volt/VAr control problem. The ant colony optimization (ACO) algorithm is one kind of heuristic biological modeling method to solve combinatorial optimization problems. The ACO method has been researched in various aspects and successfully applied to various optimization problems. The conventional ACO shows reasonable performance for small problems with moderate dimensions and searching space. However, it is not suitable for large-scale problems such as Volt/VAr control problems, because the size of the pheromone matrix grows exponentially along with the problem size [18]. In this study, a new algorithm named binary ant colony optimization (BACO) is proposed and implemented for Volt/VAr control problems to resolve the conventional ACO limitations. An ACO to determine the active and reactive power values of DGs, the tap positions of transformers, and reactive power values of capacitors was proposed in [7]. A time interval–based control strategy to reduce switching operations for OLTCs at substations was adopted in [3]. In [8], Niknam et al. proposed a costbased compensation methodology for daily Volt/VAr control in distribution networks, including DGs. A new optimization algorithm based on a chaotic improved honey bee mating optimization is proposed to determine the active power values of DGs, reactive power values of capacitors (...truncated)