## 1 Introduction  
Energy storage systems (ESSs) represent a promising technology for incorporation with existing power systems. Lately, interest in using ESS has been rekindled, especially considering the perfect services that ESSs can offer. Applications of ESSs in power systems are usually motivated by technical and/or economic benefits ([1, 2]). For economic purposes, ESSs can be used by the utility and end-user customers to gain benefits. On the utility side, the literature shows several algorithms for optimizing the deployment of ESS for the deferral of feeder capacity upgrades [1, 2, 3, 4, 5, 6]. One of the early studies to investigate the potential of storage systems in the deferral of distribution grid upgrades was published in 1998 [3]. In [4], the optimal size of distribution feeders with a battery and a photovoltaic system was determined. Sandia National Laboratories analyzed the impact of modular storage systems on deferring asset upgrade projects for electric utilities at the transmission and distribution levels [5]. [PERSON] et al. proposed an algorithm for utility distribution planning benefiting from the deferral potentials of storage systems [6]. On the other hand, energy storage is used by end-users to minimize utility charges, e.g., demand charges (DCs), in some jurisdictions. Energy price arbitrage (EPA) is an interesting way to gain profit by owning energy storage. EPA refers to energy trading, when allowable, within an electricity market, by purchasing energy from the grid (charging ESS) at a low price and selling it back to the grid (discharging ESS) at times of significantly higher grid prices. Therefore, battery systems may take advantage of the spot price volatility from off-peak time to peak time to gain profit. The literature shows several efforts that have been devoted to optimizing the deployment of BESSs for the purpose of maximizing EPA revenue [7; 8; 9; 10; 11; 12]. Recently, an optimization algorithm for BESSs to maximize EPA benefits and provide power factor corrections was presented in [7]. [PERSON] et al. developed another algorithm for EPA based on BESS's liquid air technology [8]. In [9], the economic viability of BESSs was assessed for EPA in the Chilean power system. A scheduling algorithm for BESSs (employed for both the frequency response service and EPA) was proposed in [10]. A multi-objective technique to optimize several technologies for BESSs was presented in [11]. [PERSON] et al. evaluated the economic potential of a BESS for EPA in New York [12]. In the context of decreasing the energy bills for end-user customers, several studies have been conducted to minimize utility charges, such as DC [13; 14; 15; 16]. In this paper, a technique that employs battery systems to minimize the global adjustment charges (GACs) of class-A customers in Ontario is presented. The GAC of a class-A customer is based on the contribution of the customer's facility to the provincial load during the top five peak hours over a twelve-month period. In the literature, some attempts to control the GAC are presented based on the prediction of Ontario's top five peak hours, such that clients can reduce their loads, and therefore their contributions to the total provincial demand during the five annual peaks [17; 18; 19]. The GAC is defined by the Independent Electricity System Operator (IESO) of Ontario, as \"global adjustment covers the cost of building new electricity infrastructure in the province, as well as delivering Ontario's conservation programs--ensuring that enough electricity supply will be available over the long term\" [20].