KR20210146684A - A method for evaluating the optimal ess feasibility based on the characterictics of business demand - Google Patents

Abstract

A method for evaluating optimal energy storage system (ESS) feasibility in consideration of the characteristics of business site demand according to an embodiment of the present invention comprises the steps of: receiving feasibility evaluation request information from a person to be prospectively installed with an ESS; collecting past electricity demand data for a business site of the person to be prospectively installed with an ESS based on the business feasibility evaluation request information; providing ESS configuration applicable to the business site based on the past electricity demand data; predicting a future demand amount by clustering the past electricity demand data and reflecting future day characteristic information; and generating an ESS charging/discharging operation plan for maximizing the amount of electricity cost reduction based on the future demand amount and performing a feasibility analysis.

Description

{A METHOD FOR EVALUATING THE OPTIMAL ESS FEASIBILITY BASED ON THE CHARACTERICTICS OF BUSINESS DEMAND}

The present invention relates to an optimal ESS feasibility evaluation method in consideration of business site demand characteristics, and more specifically, maximizes internal rate of return (IRR) by providing an optimized report by comprehensively considering various feasibility evaluation factors when installing ESS. It is related to the optimal ESS feasibility evaluation method considering the characteristics of the demand of the workplace.

Energy Storage System (ESS) refers to a device that stores electrical energy and supplies it to be used when needed. The ESS includes a battery that stores electricity and related components such as a PCS (Power Conditioning System), EMS (Energy Management System), and BMS (Battery Management System) for efficiently managing the battery.

As the demand for electricity due to industrial development increases year by year, the amount of electricity used by consumers also tends to increase. However, increasing the power generation equipment has a significant limitation in reality. Accordingly, as part of the national energy policy, policies to encourage and support the installation of ESS to various consumers such as individual houses, apartment houses, office spaces, and factory facilities are continuously being promoted. However, it is burdensome to install ESS on its own from the point of view of the consumer, that is, the customer, because it takes a lot of cost to build an ESS, and the necessary components such as batteries, PCS, EMS, and BMS are diverse and complex. In addition, since electricity consumption and usage patterns are different depending on the characteristics of the installation site, such as houses, factory facilities, and business sites, efficient management and power to maximize the utilization effect for each customer and the energy saving effect of the entire system after the ESS is built There is a demand for supply control.

Therefore, before ESS installation, it is desirable to conduct an ESS feasibility evaluation so that an ESS suitable for consumers can be installed. In the case of general ESS feasibility evaluation, select a specific day and check how much electricity bills are saved when ESS is installed. Specifically, in the prior art, when an ESS feasibility evaluation requester requests an analysis, the evaluator makes a prediction for a specific day and then multiplies the calculated forecast value by the business period to deliver the expected profit and the estimated electricity cost savings to the ESS feasibility evaluation requester characterized in that

However, the amount of electricity used by the ESS installation site varies by season, and there are cases where the company does not participate in the project with its own budget. In addition, the number of holidays is different every year, and the battery used in the ESS deteriorates over time, and thus the amount of charge decreases. In addition, there are many cases where the evaluation requester does not know the ESS configuration suitable for the site where the ESS will be installed.

As such, the conventional ESS feasibility evaluation method has a problem in that the error rate of actual profit or actual savings is large because the ESS feasibility evaluation is performed without considering various variables.

The present invention has been devised to respond to the above-described technical problem, and an object of the present invention is to substantially supplement various problems caused by limitations and disadvantages in the prior art, and to evaluate various business feasibility factors when installing ESS. It is intended to provide an optimal ESS feasibility evaluation method considering the characteristics of the business site demand, which can maximize the internal rate of return (IRR) by providing an optimized report in comprehensive consideration.

In order to solve the above problems, the optimal ESS feasibility evaluation method in consideration of the demand characteristics of the workplace according to an embodiment of the present invention comprises the steps of: receiving business feasibility evaluation request information from a prospective ESS installation person; collecting historical electricity demand data for the business site of the person scheduled to install the ESS on the basis of the business feasibility evaluation request information; providing an ESS configuration applicable to the workplace based on the past power demand data; predicting future demand by clustering the past power demand data and reflecting future day characteristic information; and generating an ESS charging/discharging operation plan for maximizing the amount of electricity cost reduction based on the future demand amount and performing a feasibility analysis.

In addition, the method further comprises the step of performing a future demand prediction based on whether the ESS sub-information is received from the ESS installation prospective person, but when the ESS sub information is received, based on the ESS configuration determined in advance from the ESS installation prospective person It is possible to predict future demand.

In addition, the ESS configuration may include a battery capacity and a PCS capacity.

In addition, the method further includes calculating a future charge after the step of generating the ESS charging/discharging operation plan, wherein the future charge may include at least one of a basic charge, a charge for an amount of electricity different to the load, VAT, and policy discount information. .

In addition, predicting the ESS configuration through the step of performing the feasibility analysis, and further comprising the step of calculating the installation cost according to the predicted ESS configuration, the installation cost is battery cost, PCS cost, construction cost, EMS cost And it may include at least one of ESS installation and management cost.

The method may further include calculating the ESS construction cost by summing the battery cost, PCS cost, construction cost, and EMS cost.

In addition, the step of performing the feasibility analysis further includes the step of performing a feasibility analysis based on the additionally collected financial information when the ESS investor does not invest based on the equity capital, the financial information is the borrowing principal; It may include at least one of investment money, deferral period, principal repayment period, principal and interest repayment period, monthly interest rate, monthly SG&A expenses, monthly depreciation and monthly corporate tax.

In addition, it may further include the step of producing an ESS evaluation report based on the predicted ESS configuration after the step of performing the business feasibility analysis.

In addition, the step of producing the ESS evaluation report, listing the predicted ESS configuration in the order of the internal rate of return and the return on investment capital; Displaying as a graph the usage of the day when the maximum demand occurred among the past demand; and displaying a change in the power usage pattern in a graph when the ESS is introduced; may further include.

In addition, the ESS charging/discharging operation plan can be generated based on the basic charge applied power and usage fee for optimal charging and discharging of the ESS.

Details of other embodiments are included in the detailed description and drawings.

The present invention provides a feasibility evaluation report close to actual ESS operation through a more accurate return review because, when a feasibility evaluation is requested prior to ESS installation, the feasibility evaluation is performed with various data rather than a simple calculation. satisfaction can be improved.

The present invention has the effect that the ESS installation planner selects the optimal ESS configuration suitable for his/her business characteristics, so that the efficient operation of funds is possible and the maximum profit can be obtained.

The effect according to the present invention is not limited by the contents exemplified above, and more various effects are included in the present specification.

1 is a flowchart for explaining an ESS business feasibility evaluation method according to an embodiment of the present invention.
2 is a flowchart illustrating a method for producing a business feasibility evaluation report according to an embodiment of the present invention.
3 is a table showing the internal rate of return for each ESS configuration according to an embodiment of the present invention.
4 is a table showing the return on investment capital for each ESS configuration according to an embodiment of the present invention.
5 is a graph showing the internal rate of return and the rate of return on investment according to the ESS configuration according to an embodiment of the present invention.
6 is a graph showing the demand on a specific day in the past when the maximum demand occurred according to the load time period according to an embodiment of the present invention.
7 is a graph showing the cash flow according to the configuration of the ESS when preparing the result report according to the embodiment of the present invention.
8 is a graph showing the break-even point for intuitive profit/loss determination of cash flow according to the ESS configuration.
9 is a graph showing a change in future power demand according to a load time period according to an ESS configuration according to an embodiment of the present invention.

The following is merely illustrative of the principles of the invention. Therefore, those skilled in the art can devise various devices that, although not explicitly described or shown herein, embody the principles of the invention and are included in the spirit and scope of the invention. Moreover, it should be understood that all conditional terms and examples listed herein are, in principle, expressly intended only for the purpose of understanding the inventive concept, and not limited to the specifically enumerated embodiments and states as such. do.

In addition, in the following description, ordinal expressions such as first, second, etc. are for describing objects that are equal and independent of each other, and in the order of main/sub or master/slave It should be understood as meaningless.

The above-described objects, features and advantages will become more apparent through the following detailed description in relation to the accompanying drawings, and accordingly, those of ordinary skill in the art to which the invention pertains will be able to easily practice the technical idea of the invention. .

Each feature of the various embodiments of the present invention may be partially or wholly combined or combined with each other, and as those skilled in the art will fully understand, technically various interlocking and driving are possible, and each embodiment may be implemented independently of each other, It may be possible to implement together in a related relationship.

For clarity of interpretation of the present specification, terms used herein will be defined below.

As used herein, “ESS configuration” refers to battery capacity and PCS capacity, and may be predetermined by a user prior to predictive evaluation. At this time, when the ESS configuration is determined in advance by the user, it is preferable that the user is understood to transmit ESS sub-information to the analysis server.

Accordingly, in this specification, "appropriate ESS configuration" means the capacity of the battery and PCS to maximize the expected profit and the expected electricity cost reduction of the business site.

In the present specification, “future day characteristic information” means information on days other than weekdays and business days, such as public holidays and year-end and New Year holidays.

As used herein, the term “future charge” includes basic charge, other electricity charges for load, and VAT. In this case, it is preferable to understand that the basic rate is a basic rate according to the general KEPCO Terms and Conditions.

Hereinafter, an ESS feasibility evaluation method and report production process according to an embodiment of the present invention will be described with reference to FIGS. 1 to 5 .

1 is a flowchart for explaining an ESS business feasibility evaluation method according to an embodiment of the present invention. 2 is a flowchart illustrating a method for producing a business feasibility evaluation report according to an embodiment of the present invention. 3 is a table showing the internal rate of return for each ESS configuration according to an embodiment of the present invention. 4 is a table showing the return on investment capital for each ESS configuration according to an embodiment of the present invention. 5 is a graph showing the internal rate of return and the rate of return on investment according to the ESS configuration according to an embodiment of the present invention. Steps S101 to S110 in FIG. 1 show the ESS feasibility evaluation process, and steps S11 to S114 in FIG. 2 show the ESS feasibility evaluation report production process. The ESS feasibility evaluation system according to an embodiment of the present invention is characterized in that it includes an analysis server and a database, and it is preferable to understand that the ESS feasibility evaluation method is collectively performed within the analysis server.

First, the analysis server receives the business feasibility evaluation request information from the user (S101). In this specification, a 'user' is a person who intends to install an ESS, and may also be referred to as an ESS installation prospect.

Next, based on the received business feasibility evaluation request information, historical power demand data for the business site where the ESS is scheduled to be installed is collected (S102). Specifically, when the user transmits the feasibility evaluation request information for feasibility evaluation, the analysis server uses the data of the KEPCO iSmart service to transmit the past power demand data of the business site where the user intends to install the ESS. do. Here, the KEPCO iSmart service refers to a power portal service that can support efficient power use and maximize the effect of demand management by integrating the existing power consumption consulting (PCCS) and regular demand management system. That is, the analysis server of the present invention can collect basic power data of the workplace through the i-smart service.

Next, the analysis server provides an ESS configuration applicable to the workplace based on the collected past power demand data of the workplace (S103). At this time, the analysis server may check whether the ESS sub-information is received from the user (S104). In other words, when ESS sub-information is received from the user, it is determined that there is an ESS configuration (battery capacity and PCS capacity) that the user specifically wants, and future demand forecasting is performed based on the ESS configuration predetermined by the user. to (S104-1).

However, if the ESS sub-information is not received from the user, it is determined that there is no ESS configuration that the user specifically wants, and the ESS configuration suitable for the user is created while changing the battery capacity and the PCS capacity (S104-2). Specifically, the analysis server generates a plurality of input values for the PCS capacity, and generates a plurality of ESS configurations by generating a value multiplied by 1 to 3 times the input PCS capacity as an input value of the battery capacity. do. Here, the battery capacity is characterized in that the generated PCS capacity is generated while increasing at intervals of 0.25 times between 1 to 3 times.

Next, when all ESS configurations are generated, clustering is performed based on the past power demand data (hereinafter also referred to as 'past power demand') received in step S102, and future day characteristics information is reflected in the future Predict the demand (S106). Here, the future day characteristic information includes information on days other than weekdays and business days, such as public holidays and year-end and New Year holidays. That is, the analysis server can accurately predict future demand by performing clustering based on past power demand data. More details regarding clustering of the present invention are described in detail in Patent Application No. 10-2019-0159291, which is incorporated herein by reference.

Next, a future charge is predicted based on the future demand amount predicted in step S106 (S107). Here, it is preferable that the predicted future demand is understood to be the predicted demand before the ESS is installed. Accordingly, in the present specification, the predicted future demand may be referred to as a demand in a state where the ESS is not installed.

Next, based on the future demand predicted in step S106, an ESS charge/discharge operation plan for maximizing the amount of electricity cost savings is established (S108). The ESS charge/discharge operation plan includes the amount of charge and discharge of the battery and the state of charge. In this case, the ESS charging/discharging operation plan may be updated every 15 minutes, but the time interval may be adjusted according to the embodiment. However, if the cycle is too short, the PCS cannot charge and discharge normally and the battery cannot reliably measure the change in the state of charge (SoC). Therefore, the analysis server is characterized in that it establishes a charge/discharge strategy by calculating the minimum value of the objective function using a linear programming method (Linear Programming Under Uncertainty) to optimize for efficient operation of the ESS. In other words, the optimal charging/discharging strategy using the linear programming method is a method used to adjust the peak according to the prediction period. Through the linear programming method, when and how much power (kWh) to be charged and discharged, the optimal charge/discharge strategy is determined. Able to execute driving commands.

Specifically, the linear programming method finds the decision variable whose objective function is the minimum and updates it in the memory. At this time, the objective function is calculated as shown in Equation 1 below.

(Equation 1)

는 예측 기본 요금을 의미하는 것으로 볼 수 있다. 여기서,

및

도 결정 변수로서

는 예측 시간 i의 계통으로 방전된 전력량을 의미하고,

는 예측 시간 i의 충전에 사용된 전력량을 의미한다.O is the objective function, Λ is the length between prediction times (Λ=N/24), ω is the daily basic rate unit price, and u is the determining variable, the maximum power demand (kWh) between the daily prediction times. means Therefore,

can be seen as meaning the predicted base rate. here,

and

as a determinant variable

means the amount of power discharged to the system at the predicted time i,

denotes the amount of power used for charging at the predicted time i.

는 예측 시간 i의 사용량 요금 단가를 의미하며,

는 예측 시간 i의 예측 또는 실측 전력사용량을 의미한다. 이에,

는 실제 사용된 전력사용량 요금을 의미하는 것으로 볼 수 있다.In addition,

is the unit price of usage fee for the forecast time i,

denotes the predicted or measured power consumption of the prediction time i. Therefore,

can be seen as meaning the actual electricity consumption rate.

Accordingly, the objective function is a variable representing the charge for the predicted time, and the objective function is output based on the usage charge, the measured power consumption, the discharge amount, and the charge amount.

Accordingly, the fee can be minimized only when all of the following four conditions (Equations 2 to 4) are satisfied while finding the determinant variable for which the objective function is minimized.

Each of the four conditions is obtained by the following formula.

(Equation 2)

는 계약용량(kW)을 의미하며,

는 당월 이전까지 기본요금적용한 전력량(kWh) 즉, 기본요금적용전력이며,

는 예측 시간 사이의 최대충방전 전력량(kWh)을 의미하며,

는 일간 예측 시간 사이의 최대전력수요량(kWh)을 의미하는 결정변수이다.here,

is the contract capacity (kW),

is the amount of electricity applied to the basic rate (kWh) before the current month, that is, the electricity to which the basic rate is applied,

is the maximum amount of charge/discharge power (kWh) between the predicted times,

is a determinant variable meaning the maximum amount of electricity demand (kWh) between the daily forecast times.

Previously, Equation 2 is an expression for obtaining the first condition for finding a decision variable that minimizes the objective function, and peak reduction is not performed if there is no basic charge reduction effect.

(Equation 3)

는 예측 시간 i의 예측 또는 실측 전력사용량을 의미하며,

는 예측 시간 i의 방전량(kWh)을 의미하며,

는 예측 시간 i의 충전량(kWh)을 의미하며,

는 일간 예측 시간 사이의 최대전력수요량(kWh)을 의미한다.here,

is the predicted or measured power consumption of the predicted time i,

is the discharge amount (kWh) of the predicted time i,

is the charge amount (kWh) of the predicted time i,

is the maximum power demand (kWh) between the daily forecast times.

(Equation 4)

는 방전효율을 의미하고,

는 충전효율을 의미한다.here,

is the discharge efficiency,

is the charging efficiency.

(Equation 5)

는 예측 시간 사이의 최대 충방전 전력량(kWh)을 의미한다.here,

denotes the maximum amount of charge/discharge power (kWh) between the predicted times.

Previously, Equation 3 means a condition for peak reduction and backflow prevention. In other words, it means to prevent the reverse flow of power through overdischarge while performing peak reduction by preventing the net demand from exceeding a specific value.

Next, when the prediction of the ESS charging/discharging operation plan is completed, future charges are calculated based on this (S109). Here, the future rate may include basic rate, electric power rate according to load, value added tax, and policy discount information.

Next, when all predictions are completed by the preceding steps, the installation cost according to the ESS configuration predicted first is calculated (S110). For example, the installation cost includes battery cost, PCS cost, construction cost, EMS cost, and ESS installation and management cost, and the calculation formula for each cost is as described below.

Specifically, the battery cost (a) is calculated by multiplying the battery unit price per kWh by the battery capacity (battery cost = battery unit cost per kWh * battery capacity).

In addition, the PCS cost (b) is calculated by multiplying the PCS unit price per kW by the PCS capacity (PCS cost = PCS unit price per kW * PCS capacity).

In addition, the construction cost (c) is calculated by multiplying the installation cost per kWh by the battery capacity (construction cost = installation cost per kWh * battery capacity).

In addition, the EMS cost (d) is calculated by multiplying the installed unit cost per kWh by the battery capacity (EMS cost = installed unit cost per kWh * battery capacity).

After completing all of the above calculations, the ESS construction cost can be calculated. That is, the ESS construction cost (e) can be expressed as (a) + (b) + (c) + (d) (ESS construction cost = battery cost (a) + PCS cost (b) + construction cost ( c) + EMS cost (d)).

In addition, the ESS installation and management cost (f) is additionally calculated, which is a certain percentage of the ESS construction cost (e) and is calculated through the input value (ESS installation and management cost = ESS construction cost * construction cost / (1 - construction cost)).

Finally, the total ESS installation cost (g) can be calculated as (e) + (f) (total ESS installation cost = ESS construction cost (e) + ESS installation and management cost (f)).

Thereafter, cash flows such as revenue, electricity cost savings, and operating costs according to the ESS configuration are calculated. The revenue or electricity cost savings (h) is calculated by subtracting the estimated cost calculated by simulation from the actual estimated cost (reduction cost = actual estimated cost - estimated cost saved by simulation). Here, the revenue from ESS operation (i) is calculated by additionally deducting the VAT on the electricity bill savings from the electricity bill savings (h).

Batteries have the characteristic of being hardened (aged) as they are used, and thus having a reduced charge (ie, poor profitability). To reflect this also in the cost, the battery curing rate is designated as a cost and calculated. Accordingly, the battery curing rate applied revenue (j) is calculated by multiplying the monthly revenue by (1 - the annual battery curing percentage / 100 / 12), and the resulting value is preferably understood as a monthly value.

On the other hand, there may be cases where the ESS installer and the ESS investor (hereinafter also referred to as ‘ESS installer’) are different when installing the ESS. In this case, the analysis server may perform a business feasibility analysis from an investor's point of view according to another embodiment.

Specifically, it is characterized in that the investor recovers a certain portion of the electricity bill savings of the installer, and analyzes the business feasibility by setting the redemption period and the redemption ratio as input values. In general, ESS investors are characterized in that profits are distributed in two stages through profit distribution (k). First, investors receive a distribution of profits in the ratio designated with a high recovery rate in the first stage section. After that, when the specified period has elapsed, the investor receives the dividend at the ratio specified for the general recovery rate in the second stage section. Here, both the period and the ratio can be designated as input values entered at the time of business feasibility analysis.

On the other hand, according to another embodiment, it is also necessary to consider the case where the ESS investor does not fully invest with his/her own capital. In this case, it is characterized in that the analysis server additionally collects financial information to perform business feasibility analysis.

Specifically, the analysis server can collect borrowing principal, investment money, deferral period, principal repayment period, principal and interest repayment period, monthly interest rate, monthly SG&A expenses, monthly depreciation and monthly corporate tax as financial information. Here, a method of calculating each financial information will be described later.

First, the borrowing principal (l) is calculated by multiplying the total ESS installation cost by the loan percentage and dividing by 100 (borrowing principal = ESS total installation cost * loan ratio / 100).

In addition, the investment amount (m) is calculated by subtracting the borrowing principal (l) from the total ESS installation cost (g) (investment amount = total ESS installation cost - borrowing principal).

In addition, the monthly interest rate (n) among financial information is necessary information because business feasibility analysis is performed on a monthly basis. In this case, the monthly interest rate (n) can be calculated by dividing the annual interest rate percentage by 1200 to calculate the monthly interest rate (o) (monthly interest rate = annual interest rate / 1200)

In addition, there may be cases where the principal repayment period (principal repayment period (month) = principal repayment period (year) * 12) and principal and interest repayment period (principal and principal repayment period (month) = deferral period + principal repayment period) are divided.

Accordingly, the analysis server also collects the deferral period (delay period (month) = deferral period (year) * 12), which is the difference between the principal and principal repayment period and the principal repayment period, and can be designated as an input value at the time of business feasibility analysis.

In addition, additional monthly SG&A expenses (p) are incurred when installing ESS, which is calculated by collecting the annual SG&A ratio (percent), multiplying it by the monthly revenue (j), and dividing by 1200 (monthly SG&A expense = monthly revenue * SG&A ratio (percent) / (12 * 100)).

Because ESS is a type of product, depreciation exists. Monthly depreciation (q) can be calculated by dividing the total ESS installation cost (g) by the operating period (months) and then multiplying by the number of months operated so far (monthly depreciation = total ESS installation cost / (operation period (years)) ) * 12) * last month number). Here, the operating period may be designated as an input value at the time of business feasibility analysis.

In addition, when revenue is generated during installation of the ESS, monthly corporate tax (r) is generated, and the monthly corporate tax (r) can be calculated by Equation 6 below.

(Equation 6)

Monthly corporate tax = (monthly revenue - monthly revenue distribution - monthly SG&A - monthly interest - monthly depreciation) * corporate tax rate / 12

Accordingly, the final cost (hereinafter, also referred to as 'final predicted cost') finally calculated through the above-described financial information calculation process may be calculated by Equation 7 below.

(Equation 7)

Final cost = monthly revenue share (k) + monthly loan repayment, interest (o) + monthly SG&A (p) + monthly corporate tax (r)

Next, after installing the ESS, the Break Even Point (BEP), which is one of the indicators of business feasibility, is calculated. Break-even point (BEP) returns the date at which the break-even occurs, and returns a value calculated using only self-owned capital (investor capital) and a value calculated using project capital (sum of investor capital and financial capital) will do

When the calculation of the break-even point (BEP) is completed, a return on investment (ROI) and an internal rate of return (IRR) are calculated based on the calculated value (S110). At this time, the internal rate of return (IRR) is characterized by performing the calculation for the self-owned capital and the calculation for the total project capital (including loans).

Referring to FIG. 2 , after the ESS feasibility evaluation process (steps S101 to S110), the process of visualizing the evaluation requestor for easy viewing and displaying as a report is performed (steps S111 to S115).

Specifically, as shown in FIGS. 3 and 4, the analysis server lists the ESS configurations predicted within the ESS feasibility evaluation process in order of good (or high) internal rate of return (IRR) and return on investment (ROI). do (S111). Here, the ESS composition for the listed internal rate of return (IRR) and return on investment (ROI) can be output by adjusting the required amount. That is, since the internal rate of return (IRR) and the return on investment (ROI) are only partially shown as shown in FIGS. 3 and 4, the internal rate of return (IRR) for the battery (Bat) and the PCS on the graph as shown in FIG. ) and return on investment (ROI), you can supplement the non-intuitive data. In FIG. 5 , it can be seen that the internal rate of return (IRR) is indicated by a red solid line, and the return on investment (ROI) is indicated by a blue solid line.

6 is a graph showing the demand on a specific day in the past when the maximum demand occurred according to the load time period according to an embodiment of the present invention.

Next, when the output of the internal rate of return (IRR) and the return on investment (ROI) is completed, the usage of the day when the maximum demand occurred among the past demand is displayed as a graph (S112). In this case, the graph shows the amount of power demand for each season (spring/summer/autumn/winter) and the power demand for the selected day as the day when the maximum demand occurs for each load time period. For example, FIG. 6 shows the power demand for each load time period for August 16, 2019, when the past power demand during the summer was the highest. It can be seen that different colors are distinguished according to the load time zone in FIG. 6 , and it is preferable to understand that the sky blue represents the light load time zone, the apricot color represents the medium load time zone, and the orange color represents the maximum load time zone.

Then, when the output of the past demand by step S112 is completed, financial information according to the ESS configuration may be output. Financial information according to the ESS configuration may include costs (KRW) according to the ESS installation, as shown in Table 1 below.

<Table 1>

In other words, the financial information according to the ESS configuration includes information on the purchase price of the battery (Battery) and PCS (PCS), the on-site installation cost (inst), and the operation and management cost (ems, mng) as shown in Table 1 above. can In Table 1, the unit of amount for each financial information is based on thousand won (millions), but may be changed as needed.

7 is a graph showing the cash flow according to the configuration of the ESS when preparing the result report according to the embodiment of the present invention. 8 is a graph showing the break-even point for intuitive profit/loss determination of cash flow according to the ESS configuration.

Next, the expected profit and break-even point for each ESS introduction configuration are displayed on the graph (S113). The expected revenue for each ESS introduction configuration can be shown every year, as shown in FIG. 7 . Here, the numerical values indicated by each bar graph are as shown in Table 2 below.

<Table 2>

In addition, since it is impossible to intuitively determine profit or loss only with the numerical value shown in FIG. 7 because only the annual revenue is shown in FIG. 7, the break-even point is calculated after displaying each profit on the graph as shown in FIG. can do.

9 is a graph showing the change in the future power demand according to the load time period by the ESS configuration according to the embodiment of the present invention.

Then, when the ESS is introduced, the change in the power usage pattern is displayed on the graph (S114). As shown in FIG. 9 , even when the ESS is introduced, the amount of power demand for the day selected as the day when the maximum demand occurs by season (spring/summer/autumn/winter) is displayed for each load time zone. In this case, the load time zone is characterized in that it follows the KEPCO standard at the time when the business feasibility analysis is performed. As described above in FIG. 9 , different colors may be displayed for each load time period.

The blue solid line shows the power demand when no ESS is introduced (W/O ESS Usage), and the yellow dotted line shows the power demand with the ESS introduced (With ESS Usage).

The above-mentioned steps are basically repeated until the output of all the components included in the list shown in FIGS. 3 to 4 is completed, but this may be limited when the requestor of the ESS feasibility evaluation requests it.

After that, when the ESS feasibility analysis evaluation result report is saved (S115), the ESS feasibility evaluation is completed. At this time, the ESS business feasibility analysis evaluation result report is saved as a basic computer file. However, upon the request of the ESS feasibility evaluation requester, it may be provided in various ways and modified.

As such, the present invention evaluates ESS feasibility by providing a feasibility evaluation report close to actual ESS operation through a more accurate return review because, when a feasibility evaluation is requested before installation of the ESS, the feasibility evaluation is performed with various data rather than a simple calculation. It can improve the satisfaction of the requester.

Accordingly, the ESS installation prospect of the present invention can efficiently manage funds and obtain maximum profits by selecting the optimal ESS configuration suitable for their business characteristics.

Although embodiments of the present invention have been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and various modifications may be made within the scope without departing from the technical spirit of the present invention. . Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical spirit of the present invention, but to explain, and the scope of the technical spirit of the present invention is not limited by these embodiments. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The protection scope of the present invention should be construed by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

Claims (10)

Receiving business feasibility evaluation request information from a prospective ESS installation person;
collecting historical electricity demand data for the business site of the ESS installation prospect based on the business feasibility evaluation request information;
providing an ESS configuration applicable to the workplace based on the past power demand data;
predicting future demand by clustering the past power demand data and reflecting future day characteristic information; and
An optimal ESS feasibility evaluation method in consideration of business site demand characteristics, comprising the step of generating an ESS charging/discharging operation plan for maximizing the amount of electricity cost reduction based on the future demand and performing a feasibility analysis. According to claim 1,
Further comprising the step of performing a future demand forecast based on whether the ESS sub-information received from the ESS installation prospective person,
When the ESS sub-information is received, the optimal ESS feasibility evaluation method in consideration of business site demand characteristics, in which the future demand is predicted based on the ESS configuration predetermined by the ESS installation person. According to claim 1,
The ESS configuration includes battery capacity and PCS capacity, an optimal ESS feasibility evaluation method in consideration of business site demand characteristics. According to claim 1,
Further comprising the step of calculating future charges after the step of generating the ESS charging and discharging operation plan,
The optimal ESS feasibility evaluation method in consideration of business site demand characteristics, wherein the future rate includes at least one of a basic rate, an electricity rate different to the load, value-added tax, and policy discount information. According to claim 1,
Predicting the ESS configuration through the step of performing the business feasibility analysis, further comprising the step of calculating the installation cost according to the predicted ESS configuration,
The installation cost includes at least one of a battery cost, a PCS cost, a construction cost, an EMS cost, and an ESS installation and management cost, the optimal ESS feasibility evaluation method in consideration of the business site demand characteristics. 6. The method of claim 5,
The optimal ESS feasibility evaluation method in consideration of business site demand characteristics, further comprising the step of calculating the ESS construction cost by adding the battery cost, PCS cost, construction cost, and EMS cost. According to claim 1,
The step of performing the feasibility analysis further includes the step of performing a feasibility analysis based on additionally collected financial information when the ESS investor does not invest based on their own capital,
The financial information includes at least one of borrowing principal, investment amount, deferral period, principal repayment period, principal and interest repayment period, monthly interest rate, monthly SG&A expense, monthly depreciation and monthly corporate tax. 6. The method of claim 5,
Optimum ESS feasibility evaluation method in consideration of business site demand characteristics, further comprising the step of producing an ESS evaluation report based on the predicted ESS configuration after performing the feasibility analysis. 9. The method of claim 8,
The step of producing the ESS evaluation report is,
Listing the predicted ESS composition in the order of the internal rate of return and the return on investment capital;
Displaying as a graph the usage of the day when the maximum demand occurred among the past demand; and
An optimal ESS feasibility evaluation method in consideration of business site demand characteristics, further comprising; displaying the change in power usage pattern in a graph when ESS is introduced. According to claim 1,
The ESS charging/discharging operation plan is an optimal ESS feasibility evaluation method in consideration of the characteristics of the business site, which is generated based on the basic rate applied power and the usage rate for the optimal charging and discharging of the ESS.

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