## 1 Introduction
The global adjustment amount to be paid by class-A customers is set monthly by the IESO to reflect two main items; firstly, the differences between the wholesale market price for electricity, known as Hourly Ontario Energy Price (HOEP) and, the sum of regulated rates for Ontario Power Generation's nuclear and hydroelectric generating stations and payments for building or refurbishing infrastructure, such as gas-fired and renewable facilities and other nuclear facilities, as well as the contracted rates paid to a number of generators across the province. Secondly, the cost of delivering conservation programs. As per the IESO definition, the coincidental peak hours are the top five hours of energy consumption in Ontario, where class-A customers pay (GAC) according to their relative contribution to these top five peaks [21]. Figure 1 shows that the average GAC keeps increasing every year. For instance, in 2019, the GAC was almost equal to five times the average energy cost, as shown in the following figure [22]. Figure 1: Historical Ontario average global adjustment price and average weighted energy price [22]. Class-A customers are defined by (the IESO) as \"Customers with peak demand greater than 1 MW but less than or equal to 5 MW\". Moreover, \"Customers who participate in the Industrial Conservation Initiative (ICI), pay global adjustment (GA) based on their percentage contribution to the top five peak Ontario demand hours over a twelve-month period. Customers participating in this initiative are referred to as Class A\" [23]. Different from publications available in the literature, this paper proposes a methodology to figure out the optimal system size and scheduling profile of BESS to minimize the utility charges (specifically GAC and DC) for class-A customers in Ontario. The research work is motivated by and specific to the class-A tariff structure and the jurisdiction regulations of the IESO in Ontario. Such tariff and regulations together add significant financial benefits to the classical peak shaving service energy storage can offer to large commercial and industrial facilities. Furthermore, the proposed formulation considers profit maximization through stacking more revenue streams, such as energy arbitrage and participation in IESO demand response programs. In addition, the proposed BESS provides backup power supply services to the class-A facilities during grid outages which increases the overall supply resiliency. The model is developed, coded, and tested based on actual load profile data of existing class-A facilities and the corresponding IESO energy market data. The results show the effectiveness of the developed optimization algorithm in minimizing the energy bills of the customer. Findings of this research work also maximize the overall profits of the system such that a viable investment opportunity can be pursued. In other words, value stacking and revenue maximization of the BESS increases the return on investment (ROI) of the project above the threshold that attracts investors into the business. The contributions of this work can be summarized as follows. Firstly, a novel solution is presented to minimize the annual energy bill for large electricity customers in Ontario. Secondly, a methodology is developed to obtain the optimal size of BESS units that minimizes total energy costs, including the global adjustment cost, for class-A customers. Various revenue streams are stacked to maximize the total gain. The proposed technique is flexible and can be applied to different class-A facilities with different load profiles and operation schemes. Thirdly, the proposed straightforward optimization model has a commercial opportunity to attract a great deal of attention from many class-A customers in Ontario, the IESO, and independent third-party energy market investors. Finally, the proposed methodology can be extended to other markets and jurisdictions wherever the total energy cost can be controlled through a behind-the-meter distributed energy resource like a BESS.