Thanks to the unique features, deployment of battery energy storage systems in distribution systems is ever-increased. Therefore, new models are needed to capture the real-life characteristics. Beside active power, the battery energy storage system can exchange reactive power with the grid due to the inverterbased connection. Although some previous works have considered this issue, a detailed linear model suitable for the realistic large scale distribution systems is not addressed adequately. In this context, this paper proposes a mixed integer linear programming model for optimal battery energy storage system operation in distribution networks. The proposed model considers various parts of the battery energy storage system including battery pack, inverter, and transformer in addition to linear modeling of the reactive power and apparent power flow limit. Moreover, a linear power flow model is used to calculate voltage magnitudes and power losses with high accuracy. The proposed model is applied to the IEEE 33- bus test case and the results prove the accuracy and efficiency of the proposed model. The results demonstrate that considering reactive capability of the batteries offers new benefits including voltage profile improvement, decreasing reactive power flow in the network, reducing network losses, and releasing network and substation capacity.

Introduction

Nowadays, Energy Storage Systems (ESSs) are not new devices in the power systems. The emergence of these devices in the power systems was the deployment of the pumped hydro units for load leveling in Europe. Subsequently, development of the renewable power resources and need for smoothing generated power magnified role of the ESSs. In this stage, various ESS technologies including pumped hydro units, compressed air energy storage (CAES), thermal storage, hydrogen storage (along with fuel cell), flywheels, supercapacitor, superconducting magnetic energy storage (SMES) and various battery technologies have been utilized to renewable energy integration and time shift (Whittingham, 2012). With introducing smart grid concepts, the ESSs attract more attentions owing to the numerous and unique applications besides renewable energy assistant including price arbitrage (Saboori and Hemmati, 2016), peak shaving (Pimm et al., 2018), loss reduction (Saboori and Abdi, 2013), supply capacity, spinning reserve, load following, area regulation, transmission and distribution upgrade deferral (Kleinberg et al., 2014), congestion management (Hemmati et al., 2017), reactive support and power quality (Mahela and Abdul, 2016), reliability (Awad et al., 2014), and black start and restoration (Liu et al., 2016). Among various ESS technologies, Battery Energy Storage Systems (BESS) are becoming prominent technology for almost all applications owing to the unique feature including (Lawder et al., 2014).