KR102648702B1 - A method for evaluating the optimal ess feasibility based on the characterictics of business demand - Google Patents

Abstract

The optimal ESS business feasibility evaluation method considering workplace demand characteristics according to an embodiment of the present invention includes the steps of receiving business evaluation request information from a person scheduled to install an ESS; Collecting past power demand data for the workplace of the person scheduled to install the ESS based on the business feasibility evaluation request information; Providing an ESS configuration applicable to the business based on the past power demand data; Clustering the past power demand data and predicting future demand by reflecting future day characteristic information; And it may include performing a business feasibility analysis by creating an ESS charging/discharging operation plan to maximize electricity bill savings based on the future demand.

Description

Optimal ESS business feasibility evaluation method considering business demand characteristics {A METHOD FOR EVALUATING THE OPTIMAL ESS FEASIBILITY BASED ON THE CHARACTERICTICS OF BUSINESS DEMAND}

The present invention relates to an optimal ESS business feasibility evaluation method considering business demand characteristics. More specifically, it maximizes the internal rate of return (IRR) by providing an optimized report by comprehensively considering various business evaluation factors when installing ESS. This is about the optimal ESS business feasibility evaluation method considering the characteristics of workplace demand.

Energy Storage System (ESS) refers to a device that stores electrical energy and supplies it for use when needed. 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) to efficiently manage the battery.

As the demand for electricity due to industrial development increases every year, the amount of electricity used by consumers is also increasing. However, increasing power generation facilities has significant limitations in reality. Accordingly, as part of energy policy at the national level, policies to encourage and support the installation of ESS for various consumers such as individual homes, apartment complexes, office spaces, and factory facilities are continuously being promoted. However, not only does it cost a lot to build an ESS, but the required components such as batteries, PCS, EMS, and BMS are diverse and complex, so it is burdensome for the customer, that is, to install the ESS on their own. In addition, since power consumption and usage patterns vary depending on the characteristics of the installation location such as a house, factory facility, or business site, efficient management and power management is required to maximize the utilization effect for each customer and the energy saving effect of the entire system after ESS construction. There is a need for supply control.

Therefore, before installing ESS, it is advisable to conduct an ESS feasibility assessment to ensure that an ESS suitable for customers can be installed. In the case of a general ESS business feasibility evaluation, one specific day is selected to check how much electricity bills will be reduced when ESS is installed. Specifically, conventionally, when an ESS business feasibility evaluation requester requests analysis, the evaluator performs a forecast for a specific day, then multiplies the calculated forecast value by the project period and delivers the expected profit and expected electricity bill savings to the ESS feasibility evaluation requester. It is characterized by:

However, workplaces scheduled to install ESS vary in the amount of electricity they use depending on the season, and some do not participate in the project with their own budget. In addition, the number of holidays is different every year, and the batteries used in ESS have the characteristic of deteriorating over time and reducing the amount of charge. In addition, there are many cases where the evaluation requestor does not know the ESS configuration suitable for the business site where ESS is scheduled to be installed.

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

The present invention was created to respond to the technical problems described above, and the purpose of the present invention is to substantially complement various problems arising from the limitations and shortcomings of the prior art, and to evaluate various business feasibility factors when installing ESS. The purpose is to provide an optimal ESS business feasibility evaluation method considering workplace demand characteristics that can maximize the internal rate of return (IRR) by providing an optimized report with comprehensive consideration.

In order to solve the above-mentioned problems, the optimal ESS business evaluation method considering workplace demand characteristics according to an embodiment of the present invention includes the steps of receiving business evaluation request information from a person scheduled to install the ESS; Collecting past power demand data for the workplace of the person scheduled to install the ESS based on the business feasibility evaluation request information; Providing an ESS configuration applicable to the business based on the past power demand data; Clustering the past power demand data and predicting future demand by reflecting future day characteristic information; And it may include performing a business feasibility analysis by creating an ESS charging/discharging operation plan to maximize electricity bill savings based on the future demand.

In addition, it further includes the step of predicting future demand based on whether or not ESS sub-information is received from the ESS installer, wherein when the ESS sub-information is received, the ESS is predicted based on the ESS configuration predetermined by the ESS installer. Future demand can be predicted.

Additionally, the ESS configuration may include battery capacity and PCS capacity.

In addition, a step of calculating a future rate is further included after the step of generating an ESS charging/discharging operation plan, and the future rate may include at least one of a base rate, a rate for different power amounts depending on the load, value-added tax, and policy discount information. .

In addition, it further includes predicting the ESS configuration through the step of performing the business feasibility analysis, and calculating an installation cost according to the predicted ESS configuration, wherein the installation cost includes battery cost, PCS cost, construction cost, and EMS cost. It may include at least one of ESS installation and management costs.

In addition, a step of calculating the ESS construction cost by adding the battery cost, PCS cost, construction cost, and EMS cost may be further included.

In addition, the step of performing the business feasibility analysis further includes the step of performing a business feasibility analysis based on additionally collected financial information when the ESS investor does not invest based on equity capital, and the financial information includes the borrowing principal amount, It may include at least one of the following: investment amount, grace period, principal repayment period, principal repayment period, monthly interest rate, monthly sales and administrative expenses, monthly depreciation, and monthly corporate tax.

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

In addition, the step of producing the ESS evaluation report includes listing the predicted ESS configuration in descending order of internal rate of return and return on invested capital; A step of graph displaying the usage on the day when the maximum demand occurred among the past demand; And it may further include displaying the change in power usage pattern upon introduction of the ESS in a graph.

In addition, the ESS charging and discharging operation plan can be created based on the basic power and usage charges for optimal charging and discharging of the ESS.

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

In the case where a business feasibility evaluation is requested prior to ESS installation, the present invention performs a business feasibility evaluation using various data rather than simple calculations, so it provides a business feasibility evaluation report that is close to actual ESS operation through a more accurate rate of return review, thereby helping the ESS feasibility evaluation requester. Satisfaction can be improved.

The present invention has the effect of enabling ESS installers to efficiently manage funds and obtain maximum profits by selecting the optimal ESS configuration suitable for their business characteristics.

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

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

The following merely illustrates the principles of the invention. Therefore, those skilled in the art will be able to invent various devices that embody the principles of the invention and are included in the concept and scope of the invention, although not clearly described or shown herein. In addition, all conditional terms and embodiments listed herein are, in principle, expressly intended solely for the purpose of enabling the inventive concept to be understood, and should be understood not as limiting to the embodiments and conditions specifically listed as such. do.

In addition, in the following description, ordinal expressions such as first, second, etc. are intended to describe objects that are equal and independent from each other, and the order includes main/sub or master/slave. It must be understood as meaningless.

The above-mentioned purpose, features and advantages will become clearer through the following detailed description in relation to the attached drawings, and accordingly, those skilled in the art in the technical field to which the invention pertains will be able to easily implement the technical idea of the invention. .

Each feature of the various embodiments of the present invention can be partially or fully combined or combined with each other, and as can be fully understood by those skilled in the art, various technical interconnections and operations are possible, and each embodiment may be implemented independently of each other. It may be possible to conduct them together due to a related relationship.

For clarity of interpretation of this specification, terms used in this specification will be defined below.

In this specification, “ESS configuration” refers to battery capacity and PCS capacity, which may be predetermined by the user prior to predictive evaluation. At this time, when the ESS configuration is predetermined by the user, it is desirable to understand that the user transmits 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 expected electricity bill savings of the business.

In this specification, “future day characteristic information” refers to information on days that are not normally weekdays or business days, such as public holidays, year-end and New Year holidays, etc.

In this specification, “future charges” include base charges, load-dependent power charges, and value-added taxes. At this time, it is desirable to understand that the basic fee is the basic fee in accordance with the general KEPCO terms and conditions.

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

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

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

Next, based on the received business feasibility evaluation request information, past power demand data for the business site where ESS is scheduled to be installed is collected (S102). Specifically, when a user transmits business feasibility evaluation request information for business feasibility evaluation, the analysis server uses data from KEPCO's iSmart service to transmit past power demand data of the business where the user wants to install ESS. do. Here, KEPCO's iSmart service refers to a power portal service that supports efficient power use and maximizes demand management effects by integrating the existing power consumption consulting (PCCS) and permanent demand management system. In other words, the analysis server of the present invention can collect basic power data of the workplace through the iSmart service.

Next, the analysis server provides an ESS configuration applicable to the business site based on the collected past power demand data of the business site (S103). At this time, the analysis server can check whether 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) specifically desired by the user, and future demand prediction is performed based on the ESS configuration predetermined by the user. (S104-1).

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

Subsequently, when all ESS configurations are created, 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 characteristic information is reflected to predict the future Predict demand (S106). Here, the future day characteristic information includes information about days that are not typically weekdays or business days, such as public holidays, year-end and New Year holidays, etc. In other words, the analysis server can predict future demand with high accuracy by performing clustering based on past power demand data. More detailed information regarding the clustering of the present invention is described in detail in Patent Application No. 10-2019-0159291, which is incorporated herein by reference.

Next, the future fee is predicted based on the future demand predicted in step S106 (S107). Here, it is desirable to understand that the predicted future demand is the demand predicted before the ESS is installed. Accordingly, the future demand predicted in this specification may be referred to as the demand without ESS installed.

Next, an ESS charging/discharging operation plan is established to maximize electricity bill savings based on the future demand predicted in step S106 (S108). The ESS charging/discharging operation plan includes the battery charge amount, discharge amount, and charging status. At this time, the ESS charging/discharging operation plan may be updated every 15 minutes, but the time interval may be adjusted depending on the embodiment. However, if the prediction cycle is too short, the PCS cannot charge and discharge normally and the battery cannot reliably measure changes in the state of charge (SoC). Therefore, the analysis server is characterized by establishing a charging and discharging strategy by calculating the minimum value of the objective function using linear programming under uncertainty to optimize the efficient operation of the ESS. In other words, the optimal charging/discharging strategy using linear programming is a method used to control the peak according to the prediction cycle. Using linear programming, it determines when and how much power (kWh) to charge and discharge, and based on this, the optimal charging/discharging strategy is used to control the peak according to the prediction cycle. Driving commands can be performed.

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

(Equation 1)

O is the objective function, Λ means the length between prediction times (Λ=N/24), ω means the daily base rate unit price, and u is the decision variable, the maximum power demand (kWh) between daily prediction times. means. Therefore, can be seen as meaning the predicted base fee. here, and As a decision variable means the amount of power discharged into the system at predicted time i, means the amount of power used for charging at predicted time i.

also, means the usage fee unit price at forecast time i, means the predicted or actual power consumption at prediction time i. Therefore, can be seen as referring to the actual electricity usage rate.

Therefore, the objective function is a variable that represents the fee for the prediction time, and the objective function is output based on the usage fee, actual power usage, discharge amount, and charge amount.

Accordingly, the fee can be minimized only when all four conditions below (Equations 2 to 4) are satisfied while finding the decision variable that minimizes the objective function.

Each of the four conditions is obtained using the formula below.

(Equation 2)

here, means contracted capacity (kW), is the amount of electricity (kWh) to which the basic rate was applied until the current month, that is, the power to which the basic rate was applied; means the maximum charge/discharge power amount (kWh) between the predicted times, is a decision variable that means the maximum power demand (kWh) between daily forecast times.

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

(Equation 3)

here, means the predicted or actual power consumption at prediction time i, means the discharge amount (kWh) at predicted time i, means the charging amount (kWh) at predicted time i, means the maximum power demand (kWh) between daily forecast times.

(Equation 4)

here, means discharge efficiency, means charging efficiency.

(Equation 5)

here, means the maximum charge/discharge power amount (kWh) between the predicted times.

Previously, Equation 3 refers to conditions for peak reduction and backflow prevention. In other words, this means preventing backflow of power through overdischarge while performing peak reduction by preventing net demand from exceeding a certain value.

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

Next, when the prediction is completed through the previous steps, the installation cost according to the predicted ESS configuration is calculated (S110). For example, the installation cost includes battery cost, PCS cost, construction cost, EMS cost, and ESS installation management cost, and the calculation formula for each cost is as described below.

*65Specifically, the battery cost (a) is calculated by multiplying the battery unit price per kWh by the battery capacity (battery cost = battery unit price per kWh * battery capacity).

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

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

Additionally, the EMS cost (d) is calculated by multiplying the installation price per kWh by the battery capacity (EMS cost = installation price per kWh * battery capacity).

After completing all of the above calculations, the ESS construction cost can be calculated. In other words, 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 input values (ESS installation and management cost = ESS construction cost * construction cost / (1 - Construction costs)).

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

Afterwards, cash flows such as profits, electricity bill savings, and operating costs according to the ESS configuration are calculated. The profit or electricity bill savings (h) is calculated by subtracting the expected cost calculated through simulation from the actual expected cost (savings = actual expected cost - expected cost saved through simulation). Here, the profit from ESS operation (i) is calculated by additionally reducing the VAT on the electricity bill reduction from the electricity bill savings (h). Calculation of profit from ESS operation = savings cost - VAT on the savings cost).

Batteries have the characteristic of hardening (aging) as they are used and decreasing their charge capacity (i.e., worsening profitability). In order to reflect this in the cost, the battery hardening rate is designated and calculated as the cost. Therefore, the profit (j) applied to the battery curing rate is calculated by multiplying the monthly profit by (1 - annual battery curing percentage / 100 / 12), and the value obtained at this time is preferably understood as a monthly value.

Meanwhile, when installing an ESS, there may be cases where the ESS installer and the ESS investor (hereinafter referred to as 'ESS installation business operator') are different. In this case, the analysis server may perform a business feasibility analysis from the investor's perspective according to another embodiment.

Specifically, it is calculated that the investor will recover a certain portion of the installer's electricity bill savings, and the business feasibility analysis is performed by setting the recovery period and recovery rate as input values. In general, ESS investors are characterized by dividing profits into two stages through profit distribution (k). First, investors receive profit distribution at a rate that specifies a high recovery rate in the first stage. Afterwards, when the specified period has elapsed, the investor receives profits distributed at the rate specified as the general recovery rate in the second stage. Here, both the period and ratio can be specified as input values entered at the time of business feasibility analysis.

Meanwhile, according to another embodiment, cases where ESS investors do not invest entirely with their own capital should also be considered. In this case, the analysis server collects additional financial information and performs business feasibility analysis.

Specifically, the analysis server can collect financial information such as borrowing principal, investment amount, grace period, principal repayment period, principal repayment period, monthly interest rate, monthly sales and administrative expenses, monthly depreciation, and monthly corporate tax. Here, the method of calculating each financial information is as described later.

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

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

In addition, among financial information, the monthly interest rate (n) is necessary information because business feasibility analysis is performed on a monthly basis. At this time, 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 (months) = principal repayment period (years) * 12) and the principal repayment period (principal repayment period (months) = grace period + principal repayment period) are divided.

Accordingly, the analysis server also collects the grace period (grace period (months) = grace period (years) * 12), which is the difference between the principal and interest repayment period, and can be specified as an input value at the time of business feasibility analysis.

In addition, when installing ESS, an additional monthly SG&A fee (p) is incurred, which is calculated by collecting the annual SG&A ratio (percentage), multiplying it by the monthly profit (j), and then dividing by 1200 (monthly SG&A fee = monthly profit * SG&A ratio (percentage) / (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 to date (monthly depreciation = ESS total installation cost / (operating period (per year) ) * 12) * Wednesday of last month). Here, the operating period can be specified as an input value at the time of business feasibility analysis.

In addition, if profit is generated when installing ESS, monthly corporate tax (r) is incurred, and monthly corporate tax (r) can be calculated using Equation 6 below.

(Equation 6)

Monthly corporate tax = (Monthly profit - Monthly profit 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 can be calculated using Equation 7 below.

(Equation 7)

Final cost = monthly profit distribution (k) + monthly loan repayment, interest (o) + monthly sales and administrative expenses (p) + monthly corporate tax (r)

Next, after installing the ESS, calculate the break-even point (BEP), which is one of the business feasibility indicators. Break-even point (BEP) returns the date when break-even occurs, and returns the value calculated using only owner-owned capital (investor capital) and the value calculated using project capital (sum of investor capital and financial capital). I do it.

Once the calculation of the break-even point (BEP) is completed, the return on investment (ROI) and 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 calculating self-owned capital and calculating total project capital (including loans).

Referring to Figure 2, after the ESS business feasibility evaluation process (steps S101 to S110), a process is performed to visualize the evaluation requester and display it as a report for easy viewing (steps S111 to S115).

Specifically, the analysis server lists the ESS configurations predicted within the ESS business feasibility evaluation process in order of good (or high) internal rate of return (IRR) and return on invested capital (ROI), as shown in Figures 3 and 4, respectively. Do it (S111). Here, the ESS configuration for the internal rate of return (IRR) and return on invested capital (ROI) listed can be adjusted and output as needed. That is, since only a portion of the internal rate of return (IRR) and return on investment (ROI) are shown as in Figures 3 and 4, the internal rate of return (IRR) for the battery (Bat) and PCS is shown on the graph as shown in Figure 5. ) and return on investment (ROI), and then counterintuitive data can be supplemented. In Figure 5, it can be seen that the internal rate of return (IRR) is indicated by a solid red line, and the return on invested capital (ROI) is indicated by a solid blue line.

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

Next, when the internal rate of return (IRR) and return on invested capital (ROI) are completed, the usage amount on the day when the highest demand occurred among past demands is displayed in a graph (S112). At this time, the graph shows the amount of electricity demand by load time for the date selected as the day when the maximum demand occurred by season (spring/summer/autumn/winter). For example, Figure 6 shows the power demand by load time on August 16, 2019, when the past power demand during the summer peaked. In Figure 6, it can be seen that different colors are used depending on the load time zone, and it is preferable to understand that light blue represents the light load time zone, apricot color represents the medium load time zone, and orange represents the maximum load time zone.

Subsequently, when the output of past demand volume in step S112 is completed, financial information according to the ESS configuration can be output. Financial information according to ESS configuration may include the cost (KRW) of ESS installation, as shown in <Table 1> below.

<Table 1>

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

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

Next, the expected profit and break-even point for each ESS introduction configuration are displayed on the graph (S113). Expected profits by ESS introduction configuration can be shown each year, as shown in Figure 7. Here, the values represented by each bar graph are as shown in Table 2 below.

<Table 2>

In addition, since Figure 7 shows only the profits coming in every year, it is impossible to intuitively judge profit and loss with only the numbers shown in Figure 7, so after displaying each profit on a graph as shown in Figure 8, the break-even point is calculated. can do.

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

Next, changes in power usage patterns upon introduction of ESS are displayed on a graph (S114). As shown in Figure 9, even when ESS is introduced, the power demand for the date selected as the day of maximum demand by season (spring/summer/autumn/winter) is displayed by load time. At this time, the load time zone is characterized by following the KEPCO standards at the time of conducting the business feasibility analysis. As described above in FIG. 9 , different colors may be displayed for each load time period.

The solid blue line represents the power demand when ESS is not introduced (W/O ESS Usage), and the yellow dotted line represents the power demand when ESS is introduced (With ESS Usage).

The above-described steps are basically repeated until the output of all configurations included in the list shown in FIGS. 3 and 4 is completed, but this can be limited when requested by the ESS business feasibility evaluation requester.

Afterwards, if the ESS business feasibility analysis evaluation result report is saved (S115), the ESS business feasibility evaluation is completed. At this time, the ESS business feasibility analysis evaluation result report is saved as a basic computer file. However, at the request of the ESS business feasibility evaluation requestor, it may be provided in various ways and means.

In this way, when a business feasibility evaluation is requested before ESS installation, the present invention performs a business feasibility evaluation using various data rather than simple calculations, so it provides a business feasibility evaluation report that is close to actual ESS operation through a more accurate rate of return review, thereby evaluating ESS feasibility. Requester satisfaction can be improved.

Accordingly, those planning to install the ESS of the present invention can manage their funds efficiently 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 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 idea of the present invention, but are for illustrative purposes, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive. The scope of protection of the present invention should be interpreted in accordance with the claims below, and all technical ideas within the equivalent scope should be construed as being included in the scope of rights of the present invention.

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Claims (17)

Receiving business evaluation request information from a person planning to install ESS;
Collecting past power demand data for the workplace of the person scheduled to install the ESS based on the business feasibility evaluation request information;
Providing an ESS configuration applicable to the business based on the past power demand data;
Clustering the past power demand data to predict future demand; and
In order to maximize electricity bill savings, a decision variable is calculated and established to minimize the objective function output based on the usage rate, actual power usage, discharge amount, and charge amount, and a step of generating an ESS charging/discharging operation plan based on the future demand. Includes,
The objective function is calculated according to Equation 1 below, and the minimum value of the objective function calculated according to Equation 1 is the decision variable of the objective function,
The calculation of the decision variable is an ESS business feasibility evaluation method that takes into account business demand characteristics that satisfies the conditions of Equation 2 below.
(Equation 1)

(Here, Λ is the length between prediction times (Λ=N/24), ω is the daily basic rate unit price, and u represents the maximum power demand (kWh) between daily prediction times, represents the actual electricity usage rate)
(Equation 2)

(here, is the contract capacity (kW), is the amount of electricity (kWh) to which the base rate was applied until the current month, is the maximum charge/discharge power amount (kWh) between the predicted times, represents the maximum power demand (kWh) between daily forecast times)
delete delete delete delete delete delete delete delete delete delete According to paragraph 1,
Battery hardening rate, battery cost, PCS cost, and EMS cost are further specified as ESS installation costs.
The battery curing rate is the monthly revenue multiplied by (1 - annual battery curing percentage / 100 / 12),
The battery cost is the battery unit price per kWh multiplied by the battery capacity,
The PCS cost and the EMS cost are the installation price per kWh multiplied by the battery capacity. An ESS business feasibility evaluation method considering the demand characteristics of the workplace.
delete delete According to paragraph 1,
The objective function is an ESS business feasibility evaluation method considering business demand characteristics that satisfies the conditions calculated according to Equation 3 below.
(Equation 3)

(here, is the predicted or actual power consumption at prediction time i, is the discharge amount (kWh) at predicted time i, represents the charging amount (kWh) at forecast time i, u represents the maximum power demand (kWh) between daily forecast times)
According to paragraph 1,
The objective function is an ESS business feasibility evaluation method considering workplace demand characteristics that satisfies the conditions calculated according to Equation 4 below.
(Equation 4)

(here, is the discharge efficiency, represents charging efficiency)
According to paragraph 1,
The objective function is an ESS business evaluation method considering business demand characteristics that satisfies the conditions calculated according to Equation 5 below.
(Equation 5)

(here, represents the maximum charge/discharge power amount (kWh) between prediction times)