Return of Interest Planning for Photovoltaics Connected with Energy Storage System by Considering Maximum Power Demand

Abstract

In this study, a general building of medium size with an Energy Storage Systems (ESS)-connected Photovoltaic (PV) system (energy storage system that is connected to a photovoltaic system) was chosen to develop a tool for a better economic evaluation of its installation and use. The newly obtained results, from the revised economic evaluation algorithm that was proposed in this study, showed the effective return of investment period (ROI) would be 8.62 to 12.77 years. The ratio of maximum power demand to contract demand and the falling cost of PVs and ESS was the factors that could affect the ROI. While using the cost scenario of PVs and ESS from 2019 to 2024, as estimated by the experts, the ROI was significantly improved. The ROI was estimated to be between 4.26 to 8.56 years by the year 2024 when the cost scenario was considered. However, this result is obtained by controlling the ratio of maximum power demand to contract demand. Continued favorable government policies concerning renewable energy would be crucial in expanding the supply and investment in renewable energy resources, until the required ROI is attained.

1. Introduction

Rapid advancement in state-of-the-art technology naturally leads to the need for development of more stable, reliable, and efficient methods to harvest electrical energy. Real-time alignment and synchronization between supply and demand is necessary since electrical energy is hard to store. To overcome this disadvantage, the system needs to have a certain amount of reserved power. Recently, Photovoltaic (PV) and Energy Storage Systems (ESS) have been mentioned as the possible solution to such problems. The importance of renewable energy is rising with new regulations from the United Nations Framework Convention on Climate Change (UNFCCC) and the increasing price of fossil fuels.

The Korean government is making the installation of ESS mandatory in public institutions. Not only do ESS systems work as an emergency electrical system and boost efficiency by saving on the electricity produced, they are also at the core of electric cars and power demand management.

Especially, governments around the world have a greater interest in the stability of the power grid and in the efficient usage of renewable energy after severe power blackout experienced around the world in beginning of the 21st century. Studies have been done to inspect how installation conditions of PV systems [1] and environmental elements could influence the output of PV systems [2], however there are still various characteristics that have a high probability of causing instability in power systems, such as random shadow casted on the PV module causing power loss [3], and this is a great setback in increasing the amount of energy generation. ESS-connected PV systems are nowadays actively used to compensate for such problems. Currently, ESS-connected PV with the highest renewable energy certificate (REC) of 5.0 [4] are being installed. When installing ESS, it is important to choose the right capacity that is suitable for the PV system while considering its capacity, generation time, maximum load, etc. If those conditions were not reviewed, then over or under estimation of PV and ESS system would occur, which resulted in over price or shortage of electricity. [5] Under the governmental policies, it would be difficult to achieve realistic conditions and result in over-estimated size of PV-ESS system. Study has been done to solve this problem by deriving optimized capacity of PV and ESS, while considering the power consumption of each household [6]

In the recent past, the investment in PV systems and wind power has been increasing every year. This is the result of rapid growth in the PV industry in China, which has the world’s biggest market economy. PV systems are expected to have a far bigger annual production as compared to other energy sources for the next five years while considering all of these aspects [7].

This study investigated investment costs and completed case analyses when PV and ESS are deployed in buildings that cannot afford optimized capacity calculations and economic evaluations. Next, we developed an economic evaluation algorithm, which can minimize the time before where a return on investment (ROI) is evident that can be applied to various cases. Based on this algorithm, the policies of the current government in supporting renewable energy initiatives were reviewed. This paper is a case study for national strategy of South Korea with specific LCOE, incentive policies, PV installation cost, etc.

2. Recent Trend of Government Policies on Solar Energy Generation and Saving System

2.1. Recent Trend in Government Policies for Power Producers

Many countries are in the process of announcing policies to encourage private enterprises to actively take part in renewable energy generation. In general, there are two policies for renewable energy generation: Feed in Tariff (FIT) plans, which offer a long-term contract to pay a higher price than the cost of generation; renewable energy Portfolio Standard (RPS), which requires large power producers to supply a certain portion of their electricity with renewable energy that can be bought with certified renewable energy generators. On January of 2012, the Korean government decided to abolish FIT, because it induced a rise in electricity bills and, instead, focus on RPS [8].

RPS is a system that obligates electricity suppliers to have facilities over 500 MW to supply a specified ratio of electricity generated from a renewable energy source. The ratio must be at least 2% of the whole, and it must be increased in steps of 0.5–1.0 percent annually, which results in 10% of the ratio by the year 2022 [9]. If suppliers cannot satisfy the regulated percentage of power from renewable energy sources, then they would need to purchase REC to cover the shortfall, or else pay a fine.

REC is a proof that the company has supplied energy using renewable energy source and used renewable energy facilities to generate power. If power companies cannot supply RPS with their own power generation, they are forced to acquire RECs from small to medium scale generating companies that are chosen through an open tender. A REC is issued with 1 REC per 1 MWh and it is further multiplied with an incentive. If the incentive is 1.5, then a PV power of 100 MWh is issued as 150 RECs. According to the Ministry of Trade, Industry, and Energy, ESS-connected PVs currently have the highest incentive of 5.0 and, from 2020 onwards, it will be readjusted to 4.0 [10].

REC trades are executed by auction, which forms a spot market. Buyers must bid for a sell order within a certain amount of time and the highest bidder becomes the buyer. Just like the stock market, the price fluctuates according to the demand and supply from buyers and sellers, respectively. If the fluctuation is too great, then the government invests its own RECs, so that the power companies and small-medium scale companies will not have to suffer due to the price fluctuation. Currently, the government possesses 600 thousand RECs for PV systems and 1.9 million RECs for non-PV systems. When government investments generate profit, it will be returned to the consumer in an electrical power fund [11].

2.2. Recent Trend in Government Policies for General Buildings

With the intent of boosting the distribution of renewable energy, the Ministry of Trade, Industry, and Energy, and the Korean Electric Power Corporation (KEPCO) are implementing various discount policies, such as the ESS electricity rate discount system and the electricity fee discount system for renewable energy, as shown in

Table 1. Energy Storage Systems (ESS) Related Electricity Rate Discount System.

Category	Content
Target	Customers who have installed an ESS for their own use to reduce the maximum power demand Excludes power generation, transmission, and distribution facilities.
Contract Power Demand	Determined based on the customer’s choice between transformer capacity and power converter capacity
Power meters	Installed a watt-hour meter that can measure by each time range
Rate applied	Electricity charges are calculated by adding the charges calculated for each time range Rate reduction according to” discount rules” is applied due to Charge/discharge of ESS
Discount rule	Base rate discount: The discount rate is decided by multiplying the drop rate of “Average maximum power demand” and “Base rate” Calculate the average maximum demand power reduction (kW): (A − B)/(C × 3 h) A: the total discharge amount of maximum load time B: is the total charge amount of maximum load time C: the number of weekdays in the month Electricity rate discount: 50% discount on electricity price is applied if ESS is charged during a light-load time range
Application period	77

.

Table 2. Electricity Fee Discount System for Renewable Energy.

Category	Content
Target	Applied to industrial and general customers who consume electricity generated by renewable energy for their own use Exceptions to the above rule occur when the consumption of renewable energy cannot be quantified Excludes the cases of mandatory installation of renewable energy facilities
Installation of power meters	Installation of watt-hour meters and accessories to measure total power generation and power transmission
Discount rule	50% discount of the price, obtained by multiplying the average price of the previous year's maximum and intermediate load time, is applied Total discount rate is limited to 50% of electricity bill of that month
Application period	Applied from 1 May 2017 to 1 May 2020

shows the government’s policies [12].

3. Analysis of Existing Cases

3.1. Case Study of Target Building

An existing building in process of installing ESS was chosen for the analysis to carry out a case study on an ESS-connected PV system. Subsequently, economic evaluation was completed under the assumption that the building had completed its planned deployment of the ESS-connected PV. The target building is Building G in Seoul and its electrical power aspects are displayed in

Table 3. Information of the Target Building.

Category	Information
Electric power rate	General service (A) (High voltage)
Contract demand [kW]	13,000
Basic Charge [USD]	7.39
Real demand [kW]	4124
Monthly base rate [USD]	30,471.2

,

Table 4. Seasonal Electricity Rate of the Target Building ($/kWh).

Season	Off Peak Load Time	Mid Load Time	Peak Load Time
Spring	0.05	0.07	0.1
Summer	0.05	0.1	0.17
Autumn	0.05	0.07	0.1
Winter	0.06	0.1	0.15

and

Table 5. Configuration of Electricity Bill.

Classification	Summer, Spring, Autumn	Winter
Off peak load time	23:00–09:00	23:00–09:00
Mid. load time	09:00–10:00 12:00–13:00 17:00–23:00	09:00–10:00 12:00–17:00 20:00–22:00
Peak load time	10:00–12:00 13:00–17:00	10:00–12:00 17:00–20:00 22:00–23:00

. In general, real demand is smaller than contract demand, which is set by the main power distributor, with several reasons, such as high vacancy rate of the building.

In this paper, currency ratio for the South Korean Won to United Stated Dollar was 1000 KRW to 0.88 USD, and it is applied to all of the tables.

3.2. Case Study of Target System

To analyze the electrical energy of the target building, I-Smart of KEPCO, which is the database of the maximum power demand of building, was used to derive the building’s maximum power demand, and the month of the year in which that maximum demand occurred, as shown in

Table 6. Maximum Demand Power of the Building (2015).

Month.	Maximum Power Demand [kW]
Jan.	3271
Feb.	3279
Mar.	3248
Apr.	3356
May	3509
Jun.	3717
Jul.	4124
Aug.	4124
Sep.	3671
Oct.	3448
Nov.	3233
Dec.	3194

. According to our analysis, the highest demand was during July and August. Since nation-wide issues concerning electricity demand are mostly debated and acted upon in August, an economic evaluation of PV and ESS was completed for each time period in August.

Analysis showed that, on the 6th of August, the target building demanded and consumed its maximum power and the maximum value was 4121.16 [kW]. According to KEPCO, which is the main power distributor of the nation, the electricity fee could decrease as the real power demand drop. However, there is a limit on just how much the electricity fee could drop, and this is the concept of “base rate”. It decreases until the real demand becomes 30% of the contract demand. Real demand was set to 30%, which is 3900 [kW], for economic feasibility, since the contract demand of the target building is 13,000 [kW]. PV with 200 [kW] was selected to cover the difference between 3900 [kW] and maximum power demand of 4121 [kW] measured on August, as shown in Table 6. The PV power was set at 200 kW and generating time was set to be 3 h, which is part of the national strategy. The power generated by PV is calculated by multiplying the capacity of PV, solar charging time, and the average insolation.

Table 7. System Overview of the Target Building.

PV Capacity	200 [kW]	Peak Savings Limitation (30% of Contract Demand)	3900 [kW]
Solar charging time	3 h	PCS capacity	250 [kW]
Battery capacity	586 [kWh]	Battery capacity (Depth of Discharge 90%)	527.4 [kWh]
Operation time at peak load	3 h	Operation time at mid. load	1 h
Discharge capacity at peak load	377.4 [kWh]	Discharge capacity at mid. Load	150 [kWh]
Battery charging efficiency	98.48%
Battery discharging efficiency	95.76%	Charge/Discharge efficiency	97.20%

shows the system that is designed with those conditions.

The PCS (power conditioning system) was chosen with a capacity of 250 kW, because the market standard is 250 kW; there are no products with 200 kW capacities. In the case of Korea, the average time of PV generation is stated as 3.7 h; hence, this study set the time as 3 h

The battery degradation rate was set at 1.9%. In general, the time when PV system is capable of generation and, when maximum power demand takes place, are not coincided with each other. With this national strategy, a simulation was done, so that the PV system would charge the ESS during the time period 10:00 h to 14:00 h and then discharge during the period 14:00 h to 18:00 h, which is the time when the maximum demand exceeds the contract demand and it would result in paying for extra charge that main power distributor had set in beforehand, as shown in Figure 1.

Two strategies, for operating the system implemented with a PV system and ESS, were considered: reducing the base rate by controlling the maximum power demand and reducing the base rate by controlling schedules, so that the ESS would charge during a light load and discharge during maximum load, as shown in Figure 2.

3.3. Result of System Analysis from Case Study

Table 8. Information of the Target Building.

Category	Content
Demand charge savings	Peak cut amount × Unit price of demand charge
Demand charge discount	The demand charge is discounted by three times the decline in the peak demand.
Energy charge savings	[Discharge quantity × (energy charge unit price of peak/mid load condition)] − [Charge quantity × (energy charge unit price of light-load condition)]
Energy charge discount	50% discount to the energy charge when off-peak hour (for ESS charging) is applied.
Additional discount	Additional discount is applied differently for each cases: If the capacity of ESS is less than 5% of the contract demand, there are no discount: If the capacity of ESS is between 5% and 10% of the contract demand, additional discount that is 20% of renewable energy discount takes place: If the capacity of ESS is greater than 10% of the contract demand, additional discount, 50% of renewable energy discount, takes place

shows the summary of the simulation parameters used in the economic evaluation.

Table 9. Test Result from the Case Analysis.

Category	Item	Price [$]
Investment cost (including VAT)		545,068.87
Savings(10years)	Energy charge savings	210,734.18
	Reduced by ESS capacity ratio	107,977.69
	Reduction of power-based funds	11,792.34
	Sub total	330,504.21
	Total savings (including VAT)	406,195,958
Payback period (years)		17.55

shows the result of economic analysis based on the data discussed so far. The charge and discharge efficiency of ESS was set to 6%. Basic rate discounts, discounts for reducing the peak, and reduced power use saving were separately applied for before 2020 and after 2021. As can be seen in Table 9, the energy savings for 10 years is about $210,000. Reducing the ESS battery capacity ratio could save approximately $100,000, and reducing power-based funds could save about $11,000. In total, savings of about $330,000 resulted in 10 years. The installation fee of the system was estimated to be around $540,000 with a 586 [kWh] battery and a PCS of 250 kW. Under these assumptions, the ROI period will be about 17.55 years.

Table 10. Investment Cost Analysis in the Test Case.

Item	Capacity	Unit Price	Price [$]
ESS [kWh]	586	340.12	222,680,000
PCS [kW]	250	40,277.83	40,277.83
PMS	-	40,277.83	40,277.83
Construction Cost	-	44,753.14	44,753.14
PV [kW]	200	1102.36	220,471.88
Sum	-	-	545,093.27

details investment costs for each condition.

4. Development of the Profit Rate of an ESS-Connected PV Analysis Method

4.1. Development of Economical Evaluation Method

The capacity decision method based on a basic economical evaluation was examined to develop an economical evaluation method that is optimized for a system comprising an ESS-connected PV system deployed in a building, as shown in Figure 3. The maximum power demand of the building is analyzed, followed by an analysis of the peak power curve during either summer or winter [13]. The peak power cut, which should be over 30% of the contract demand, is decided and the capacity of the PV system is chosen based on this value. Next, the PV system’s generating time is calculated based on the building’s location, cardinal points, and other factors. Finally, the capacity of the ESS is estimated from that data [14].

The estimated capacity of the ESS decides the capacity of the PCS. From this, an estimate of the total electricity cost savings due to an ESS charge/discharge, the ESS base fare discount, and the discount rate for renewable energy is derived for analyzing the ROI period [15,16].

When an economical evaluation is carried out with the conventional method, the system could be excessively designed, or optimum economic feasibility could not be achieved. This study developed an algorithm to design an optimized system without such flaws, as shown in Figure 4.

Normally, if a system is installed with a large capacity of PV system and ESS, renewable energy generation is stimulated, which makes it more convenient; however, the installation costs escalate greatly. On the other hand, if the capacities of the PV system and ESS are small, a low installation cost becomes an advantage, but their role as renewable energy providers would be diminished.

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].

$${\displaystyle \sum }_{\mathit{i}=\mathbf{0}}^{\mathit{n}}\left[\frac{\left(\mathit{a}\left(\mathit{i}\right)+\mathit{b}\left(\mathit{i}\right)+\mathit{c}\left(\mathit{i}\right)+\mathit{d}\left(\mathit{i}\right)\right)}{{\left(\mathbf{1}+\mathit{r}\right)}^{\mathit{i}}}\right]-\left[{\mathit{C}}_{\mathbf{0}}+{\displaystyle \mathbf{\sum }}_{\mathit{i}=0}^{\mathit{n}}\frac{\mathit{O}\mathit{M}\left(\mathit{i}\right)}{{\left(\mathbf{1}+\mathit{r}\right)}^{\mathit{i}}}\right]$$

(1)

• a(i): the profit from the renewable energy electricity fee discount
• b(i): the profit from the reduced usage fee
• c(i)the profit from the ESS battery capacity
• d(i): the profit from the reduction of power fund
• C₀: the initial investment
• OM(i): the annual maintenance fee
• r: the discount rate
• 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 m², 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. The PV, ESS, PCS capacities of each case were decided by the following rules: the capacity of the PV system needs to be equal or greater than capacity of PCS, because PCS does not operate with PV and ESS simultaneously; the capacity of the ESS is set so that the depth of discharge would be below 90%. The capacity of the PV can be freely decided, but the capacity of PCS must follow the standard. Therefore, in cases 2 and 4, the PV capacity does not match PCS capacity. Case 7 has an increased PV capacity of 800 kW since there are no PCS systems with a capacity between 500 kW and 800 kW.

For each combination, a simulation was carried out based on the test model to review the ROI period. Case 1 is the most general case of a PV-ESS combination. The maximum power demand-per-month for 2015 was checked. July and August were chosen for the highest values of power demanded. Next, the maximum power demand for each day and hour of August was reviewed. The 6th of August showed the highest demand; therefore, the analysis was completed with the goal of reducing this particular peak in demand. The PV system was set to charge during the time period from 11:00 h to 14:00 h and discharge during 14:00 h to 18:00 h to save cost. For this case, the total investment would be $676,667.5 and profit would be $590,741.47 in ten years, resulting in 10.45 years of ROI period. An economic evaluation was completed with a similar case, but with different capacities of the PV system and ESS.

Table 11. System Economic Evaluation (Total).

Case	PV [kW]	ESS [kWh]	PCS [kW]	ROI [Year]	Initial Investment [$]
Case 1	200	705	250	10.45	676,599.55
Case 2	250	846	250	8.62	796,706.94
Case 3	300	987	300	9.72	988,402.68
Case 4	350	1128	350	9.32	1,072,715.88
Case 5	400	1128	500	9.46	1,157,727.07
Case 6	500	1142	500	10.56	1,371,794.18
Case 7	800	1692	800	12.77	1,996,098.43

presents different properties of the major facilities for each case. After running the simulations for each case, the cases with PV 250 kW, ESS 846 kWh, and PCS 250 kW showed the shortest ROI period of 8.62 years. On the other hand, the case with PV 700 kW, ESS 1,692 kWh, and PCS 800 kW showed an ROI of 12.77, which is the worst result. It is clear that the economic feasibility depends upon the combination of the features of the PV system, ESS, and PCS. In most cases, the engineer chooses a major parameter while using commercially suggested value, and then adjusts the parameter repeatedly until a seemingly optimized result is achieved from an economic evaluation. This would lead to either over and/or underestimated sizes of the capacities. Ideally, the system must be designed by a skilled engineer to display an optimized economic feasibility, which is a challenging task. External factors would affect the system and unlike in the simulation, economic feasibility might not be attained, even if the system was designed perfectly.

The conditions for simulation are as follows:

• Energy generated from the PV system is consumed by the building itself and there is no other grid that is connected.
• REC is not considered because the economic evaluation is applied to a general building.
• Annual maintenance cost is set to 6% and it is included in total construction cost.
• Operating hours of the PV system is set from 11:00 h to 14:00 h, and the energy that is generated from sunrise to 11:00 h is not considered in the economic evaluation.

Table 11 shows the detailed information of each case. The initial investment cost of each case consists of the cost of PV, ESS, PCS, PMS, and construction. The ROI of each case differs, because their initial investment cost differs.

4.2. Analysis of the Result of Economic Evaluation

The result of conventional economic evaluation performed for the target building showed that it would take over 15 years of ROI time, which implies that there is no economic feasibility. An improved result was obtained with the newly developed economic evaluation, but it still had at least 8.62 and at most 12.77 years of ROI period. This outcome would make the landlords reluctant to invest in the renewable energy industry.

Although it is true that the initial cost of installing facilities has become cheaper, it still is difficult to secure economic feasibility without government aid. As mentioned earlier, there are aid policies provided by the government, such as the ESS electricity rate discount system and the electricity fee discount system for renewable energy.

The interesting fact about government aid policy is that as a part of the strategy to control national power demand, if the maximum power demand is set to less than 30% of contract demand, then that 30% is reclassified as the price of applied power. The discount for the basic rate does not get applied when the ESS is installed and the power demand goes below the 30% level. To overcome this problem, in most cases, the ESS is set to have a power demand that is higher than 30% of the contract demand. These constraints restrain the influence of battery capacity, which is the major factor to be considered while installing an economically feasible PV and ESS system [23,24,25].

The ratio of maximum power demand to contract demand was set as a control factor of the economic evaluation algorithm. To find the ratio that would result in the ROI below five years, a simulation was done with the existing ratio of 30% set as 23%, and 16% for two different cases: a combination of PV = 500 [kW], ESS = 1667 [kWh], PCS = 500 kW, which is 16% of the contract demand; a combination of PV = 800 [kW], ESS = 2667 [kWh], PCS = 500 [kW], which is 23% of the contract demand. The capacity of PV does not need to match the capacity of the PCS anymore since the ratio had changed. A reduction in the time required for a ROI, which is currently 8.62 years, would be observed if the ratio exceeds its limits and falls below 30%. If the required investment increases due to an enlarged battery capacity and higher installation cost, then the maximum power demand will become less important. However, cost cutting would occur more rapidly, so, when compared to the conventional case, the time that is required for an ROI will increase by 1.33 years at the most [23,26]

4.3. Analysis of Economic Feasibility as Price of PV/ESS Changes

As a result of the economic evaluation done above, the ROI period derived with the conventional method showed 15 years and with the newly developed algorithm, it was slightly longer than eight years. If policies about contract demand were assumed to be revised, the ROI period will be at least seven years, which would still not be enough to attain economic feasibility. However, as technology evolves, the price of a PV system and ESS battery shows a decrease of 3 to 7% annually, as seen in Figure 5. A simulation was carried out for a time range from 2019 to 2024 based on the price reduction rate in 2018 to analyze the effect of the price decrease in facilities, as shown in Figure 5a,b.

Table 12. Price Trend Expected of Rechargeable Batteries and PV Cells.

Category	Battery		PV
Year(20xx)	Standard	Drop Ratio	Standard	Drop Ratio
18	302	1	0.93	1
19	282	0.93	0.9	0.97
20	262	0.87	0.86	0.92
21	245	0.81	0.79	0.85
22	228	0.75	0.76	0.82
23	212	0.7	0.73	0.78
24	197	0.65	0.7	0.75

shows exactly how much the price of ESS and PV systems drop each year from 2018 to 2024.

The ratio of maximum power demand to contract demand was revised as the price fell.

Table 13. Return on investment (ROI) Analysis [year].

Case NO. PV [kW]/ESS [kWh]/PCS [kW]	2018	2019	2020	2021	2022	2023	2024
[Case 1] 200/705/250	10.45	9.69	8.85	7.85	7.17	6.48	5.87
[Case 2] 250/846/250	8.62	8.10	7.52	6.83	6.35	5.88	5.45
[Case 3] 300/987/300	9.72	9.18	8.57	7.86	7.37	6.88	6.44
[Case 4] 350/1128/350	9.32	8.77	8.15	7.40	6.9	6.39	5.94
[Case 5] 400/1128/500	9.46	8.92	8.30	7.55	7.05	6.55	6.10
[Case 6] 500/1669/500	10.56	9.98	9.30	8.48	7.96	7.41	6.93
[Case 7] 800/1,692/800	12.77	12.11	11.32	10.35	9.75	9.11	8.56
[Case8] 500/1667/500	8.04	7.52	6.94	6.25	5.79	5.31	4.90
[Case9] 800/2667/500	7.4	6.88	6.31	5.62	5.15	4.68	4.26

shows how the ratio would change with the new factors being taken into consideration. As the price of the ESS and PV system, which comprise the major portion of the entire system, drops, the ROI time changes. In 2018, it is recalculated to fall in the range from at least 7.4 years to at most 12.77 years. In 2022, it is recalculated to fall in the range from least 6.31 to at most 11.32 years. Finally, ROI will range between 4.26 years to 8.56 years in 2024.

In the controlled cases 8 and 9, the ROI time falls below five years, which is the request of the landlord, on the 31st day of 2022 and the 75th day of 2023. However, cases 8 and 9 have a revised ratio of contract demand to maximum power demand, which is 16% and 23%, respectively. An analysis was done on those cases without a revised ratio, in the case where in 2024 the price of the PV system and the ESS drops by 25% and 35%, respectively. Case 2, which showed the lowest ROI time, still showed over 5.45 years of ROI under such conditions, as shown in

Table 14. Price Trend Expected of Rechargeable Batteries.

	Insolation [h]	ROI [year]
PVCapacity [kW]		3	4	5	6
200		10.45	10.09	11.16	13.24
250		8.62	8.81	9.19	9.59
300		9.72	9.8	10.39	10.76
350		9.32	9.92	10.44	10.77
400		9.46	10.26	10.24	10.56
500		10.56	10.62	10.9	11.19
800		12.77	14.28	12.8	13.04

and Figure 6. This result implies that continuous support from the government to activate the renewable energy industry is needed.

4.4. Economic Evaluation with Verifying PV System Generation Times

The analysis introduced above is based on a condition where the PV system is set to generate for 3 h a day. However, this time could differ depending on the region. An economic evaluation of the effects of a varying time of PV power generation was completed on a target building in Ansan-city. The second building was analyzed in this paper, since the two of them have distinctly different load pattern because of their locations and purpose. The generation duration was set to be between 3 to 6 h per day and Table 14 presents the result of the evaluation.

4.5. Economic Evaluation on a System Only with ESS

The reviewed cases 1 to 7 were assumed to have both a PV system and an ESS. Among those seven cases, three were randomly chosen and they were subjected to an economic evaluation with a system configuration where only the ESS is installed.

Table 15. System configuration when only ESS is installed.

		Cases with PV + ESS			Cases with ESS Only
Cases		Case2	Case4	Case7	Case2-1	Case4-1	Case 7-1
Facility Condition	PV [kW]	250	350	800	0	0	0
	ESS [kWh]	846	1128	1692	846	1128	1692
	PCS [kW]	250	350	800	250	350	800

shows the conditions of the three cases and the result of the evaluation done to those cases are shown in

Table 16. Economic evaluation when only ESS is installed.

	Case Number	ROI [year]
Specifics		2	4	7
Case with PV + ESS		8.62	9.32	12.77
Case with ESS only		7.12	6.96	7.28
Reduction rate [%]		17.4	25.3	43.0
Average reduction rate [%]		28.6

.

As a result of the simulation in each case, the ROI time of the ESS-only case was reduced by 28.6% on average, when compared to the ROI of the PV-ESS case. This shows that the cost of the PV system is a large fraction of the entire system cost. Rather than charging during the daytime and discharging during the nighttime, charging with nighttime power is more favorable in achieving economic feasibility. In case 2, the cost of installing a PV system is around $300,000 that is about 42.1% of the total construction cost. When a PV system is not installed in the building, the cost of installing facilities greatly decreases. However, the total cost of construction decreases even more, so the economic feasibility is ultimately improved. This result is because of the current electric fee system that makes it possible to use nighttime power at a very low price. However, at some point in the future, using an ESS-connected PV system that can generate and consume energy by itself would be more economically feasible since the government is trying to strengthen the policies regarding the electric fee so that the price of nighttime power would increase.

4.6. Proposal for the Policies

The result of economic evaluation done above shows that, with the conventional evaluation method, the time that is required for an ROI was over 15 years and with the new evaluation method, the ROI period required was eight years. When the annual price reduction of PV systems and ESS is considered, the result showed some improvement; still, without continuous support of renewable energy policies from government, it appears to be difficult to secure economic feasibility. The current government’s renewable energy support policies could be categorized into three cases: contract demand related discount; discount on renewable energy electricity; and, ESS exclusive plan.

The simulation, wherein each case of the governmental support policies was applied at the target building in Ansan-city with combination of PV = 250 kW, ESS = 846 kWh, and PCS = 250 kW, was executed. The percentages assigned to each case were a 15.7% discount on renewable energy electricity, 39.7% on the ESS exclusive plan, and 3.6% as the contract demand related discount. The ESS exclusive plan had gone through some revisions, so that it could be initiated in April of 2016. The plan is now confirmed to be operational until March of 2026 and, since the price of the ESS is estimated to drop until 2026, it appears to be a reasonable period. On the other hand, a discount in the renewable energy electricity policy was initiated from May of 2017, but it is scheduled to end in late December of 2020.

According to table below, the renewable energy electricity discount policy is making a great contribution in supplying renewable energy by consuming up to 15% of the total discount. The price of PV and ESS will each drop by 22% and 30% at most, which would lead to an improvement of ROI if the government decides to keep this policy up to year 2023.

As for the ESS exclusive plan, the problem is with the limit that is set the ratio of maximum power demand to contract demand. The policy must be reconsidered to change the limit to 30%. This kind of regulation is implemented, because of KEPCO’s power management policy for buildings. Most of buildings in Korea go through construction with excessive planning. In this process, the maximum power demand is set to be below 40% of the initially planned power demand. This causes difficulties in effectively utilizing ESS. Every time, the ratio drops by 10% from the existing 30%, the cost of an ESS exclusive plan rises by 15%, and this must be investigated and revised. It would be too difficult if policies for general buildings are covered. It seems appropriate to decrease the limit of the ratio, especially for buildings where the ESS tariff system has been deployed, from 30% to 20% within five years.

Table 17. Improvement of government policy on renewable energy.

Case	Improvement Plan
Contract demand Related	Exclusive ESS plan 5 years grace period Lowering the 30% limit to 20%
Discount on renewable energy electricity	The validity period until 2023
ESS exclusive plan	Maintaining the current system

lists improvement plans for government policies on renewable energy based on this paper.

5. Conclusions

In this study, for purpose of developing a tool for enhanced economic evaluation, a general building of a medium size with an ESS-connected PV system was analyzed. As a result of preexisting economic evaluation, the ROI period was calculated to be 17.55 years, which would be very poor result. An algorithm was developed to perform an optimized economic evaluation and the improved resultant ROI period with the new algorithm was between 8.62 and 12.77 years; however, it would still be difficult to please the landlord, since the original requirement was five years for ROI. This algorithm would be particularly essential in monitoring and maintaining both ground and floating and marine based photovoltaics for the future where installation environments are quite sensitive to natural disasters.

According to the government’s policy, there is no discount on the basic rate fee when power demand of the building is lower than 30% of the contract demand. Case studies from this study set the ratio of maximum power demand per contract demand to 31.7% to fulfill the ROI condition, but still failed to secure economic feasibility. Installing an ESS and PV system could not show any positive effect on the ROI period since commercial buildings in the nation have less than 40% of the ratio. An economic evaluation was conducted again with the same case as above in addition to cost drop scenario of PV and ESS. For cases 8 and 9, as shown in Table 13, the result was 4.9 and 4.26 in 2024. However, this was a case where the ratio of maximum power demand to contract demand was adjusted to 23% and 16 percent, respectively, whereas cases 1 to 7 followed the governmental regulation that is 30%. In actual cases where such adjustment does not take place, the ROI period is still over five years. It would be clear that continual government support and modification of the related policies are essential for invigorating and expanding the renewable energy business.