Contents lists available at ScienceDirect Journal of Energy Storage journal homepage: www.elsevier.com/locate/est Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms L.F. Grisales-Noreña a, ⁎ , Oscar Danilo Montoya b , Walter Gil-González c a Departamento de Electromecánica y Mecatrónica, Instituto Tecnológico Metropolitano Medellín, Colombia b Programa de Ingeniería Eléctrica e Ingeniería Electrónica, Universidad Tecnológica de Bolívar, Km 1 vía Turbaco, 131001, Cartagena, Colombia c Universidad Tecnológica de Pereira, AA: 97 – Post Code: 660003, Pereira, Colombia ARTICLEINFO Keywords: Chu & Beasley genetic algorithm Capacitor banks Energy storage systems Master-slave algorithm Optimal power fow Radial distribution networks ABSTRACT This paper presents a method to fnd the optimal location, selection, and operation of energy storage systems (ESS- batteries-) and capacitors banks (CB) in distribution systems (DS). A mixed-integer non-linear program- ming model is proposed to formulate the problem. In this model, the minimization of energy loss in the DS is selected as an objective function. As constraints are considered: the active and reactive energy balance, voltage regulation, the total number energy storage devices that can be installed into network, as well as the operative bounds associated with the ESS (time of charge-discharge and energy capabilities). Three operating scenarios for the DS are analyzed by adopting the method proposed in this work. The frst scenario is an evaluation of the base case (without batteries and CB), in which the initial conditions of the DS are determined. The second scenario considers the location of the ESS composed by redox fow batteries. Finally, the third scenario includes the installation of REDOX fow batteries with CB in parallel to correct operating problems generated by battery charging, and improve their impact on the grid. A master-slave strategy is adopted to solve the problem here discussed, implementing a Chu & Beasley genetic algorithm in both stages as an optimization technique. The proposed method is tested in a 69-node test feeder, where numerical results demonstrate its efectiveness. 1. Introduction Nowadays, the location of energy storage systems (ESS) in dis- tribution systems (DS) is widely debated worldwide [1–5]. This pro- blem poses two big challenges. First, where should ESS be located and what capacity should they have? This question is important because the level of efciency the network can reach depends on such location and selecting [6–9]. (Here, the word efciency is interpreted as the energy loss reduction in addition, to the voltage improvement between two possible operative scenarios, i.e., prior and after location and di- mensioning of ESS in the network). If an incorrect decision is made when the devices are integrated, the operating state of the DS may be compromised, thus increasing the level of energy loss, worsening vol- tage profles, and negatively impacting the technical operating condi- tions of the network as a whole [10]. Additionally, cost overruns as- sociated with ESS oversizing can occur [11,12]. The second challenge, not less important, it is the optimal dispatch of the ESS during the time window under analysis (typically, 24 h) so that they charge at hours when power consumption or electricity prices are low and discharge when consumption or prices are high according to the case. Such hours are generally peak hours, when demand is higher and, as a result, power losses increase, and voltage profles are worse [13]. For these reasons, is considered that the problem of optimal integration of ESS in DS is a complex optimization problem. Due to that this is a mixed-in- teger non-linear programming (MINLP) model, that analyses two main aspects is proposed: the frst problem is related with location and se- lecting of the ESS, and the second one that corresponds of fnding the scheme operation associated with these devices. The specialized literature in this feld includes diferent alternatives that have been proposed to solve the problem of locating, selection, and coordination of ESS in DS. Here, we present the most important con- tributions in the specialized literature in the opinion of the authors. A two-stage method to locate, select and operate an ESS in a elec- trical distribution system is implemented in [14]. The frst stage enables the location of the ESS by analyzing the charging capacity; the second stage determines the operation of the ESS and network losses using an optimal power fow approach. The charge-discharge cycles of the ESS are analyzed to determine the operation intervals of the ESS required to maintain adequate voltage profles in case of changes in the demand. In [15], the authors employ dynamic programming to fnd the optimal https://doi.org/10.1016/j.est.2019.100891 Received 11 March 2019; Received in revised form 24 July 2019; Accepted 3 August 2019 ⁎ Corresponding author. E-mail addresses: luisgrisales@itm.edu.co (L.F. Grisales-Noreña), o.d.montoyagiraldo@ieee.org (O.D. Montoya), wjgil@utp.edu.co (W. Gil-González). Journal of Energy Storage 25 (2019) 100891 Available online 20 August 2019 2352-152X/ © 2019 Elsevier Ltd. All rights reserved. T