## 4 Development of the Profit Rate of an ESS-Connected PV Analysis Method
### Development of Economical Evaluation Method
For these reasons, this study has come up with an algorithm that can inspect and evaluate economic feasibility. Initially, an artificial intelligence (AI) program for evaluating the optimized economic feasibility for materials and construction cost would be one of possible algorism to fulfill the aim of the solution. It is inevitable to analyze and evaluate the characteristics, such as the specification and the commercial terms and conditions of the available suppliers of the dedicated PV, ESS, and PCS, in order to decide the actual construction and equipment cost with others incurred. Moreover, the applicable AI methodology developed until now have been tried to realize these complicated algorisms to counter measures to the change, as per the basis of real time. However, the limits due to the variety of cost and the specific requirement conditions as per customer demand come to reach the difficulties to achieve the aim of proper algorisms [17; 18; 19; 20; 21]. The algorithm is developed by upgrading the conventional one, which is derived through simple data fitting iteration to obtain a power generation of the PV system, by obtaining more realistic input data of power generation and the consumption of the building to derive more accurate ROI [22]. When the algorithm executes, it obtains the maximum power demand of the building and analyzes the peak cut capacity by inspecting the peak-cut curve with the data that were obtained from KEPCO's I-smart. The PV system capacity is decided by factoring in the analyzed peak cut capacity. The capacity of the ESS is decided based on the maximum power demand/usage per day and the discharging rate of the PV and ESS system. At the economic evaluation step, the net benefit present value, internal rate of return, and other such factors is considered. Next, the net present value (NPV) is calculated with the Equation (1), after considering the installation cost, the operating profit, and other relevant details [23; 24; 25]. \[\sum_{i=0}^{n}\left[\frac{(\mathbf{a}(\mathbf{i})+\mathbf{b}(\mathbf{i})+\mathbf{c}(\mathbf{i})+\mathbf{d} (\mathbf{i}))}{(\mathbf{1}+\mathbf{r})^{i}}\right]-\left[\mathbf{C_{0}}+\sum_{i=0}^{n}\frac{ \mathbf{OM}(\mathbf{i})}{(\mathbf{1}+\mathbf{r})^{i}}\right] \tag{1}\]  
* \(\mathbf{a}(\mathbf{i})\): the profit from the renewable energy electricity fee discount
* \(\mathbf{b}(\mathbf{i})\): the profit from the reduced usage fee
* \(\mathbf{c}(\mathbf{i})\)the profit from the ESS battery capacity
* \(\mathbf{d}(\mathbf{i})\): the profit from the reduction of power fund
* \(\mathbf{C_{0}}\): the initial investment
* \(\mathbf{OM}(\mathbf{i})\): the annual maintenance fee
* \(\mathbf{r}\): the discount rate
* \(\mathbf{n}\): the service period  
An economic evaluation is performed while using the calculated NPV and if the result does not meet the operator's terms then the system recalculates the parameters to obtain the economic feasibility. The algorithm could satisfy under any condition when data acquisition of building maximum demand power is accomplished. Subsequently, based on such figure, peak curve analysis would be done deriving peak cut capacity. Under the assumption of the same price range of PV and ESS, the optimized capacity of PV and ESS could then be obtained by applying cost of local electricity. Various combinations of PV systems, ESS, and PCS with different capacities for new buildings were reviewed to attain the minimized ROI period. The building is located at Ansan-city, Korea. It has a total floor area of 11,473 m\({}^{2}\), two stories, and it is mainly used as a leather processing factory. The capacity of the PV system was set to lie in a range from 200 kW to 800 kW while considering the building's total power usage and area suitable for installing a PV system.