KR20210019259A - A system for optimizing ess charging based on plan - Google Patents

Abstract

The present invention relates to an ESS charging/discharging optimization system based on a rate plan, capable of maximizing the economic benefits of a user and comprises: an energy information detection unit for detecting a plurality of energy information from a consumer for a certain period of time; a power demand prediction unit for calculating power demand and unit energy cost based on the detected plurality of energy information; and an optimal charging/discharging amount calculation unit for calculating an optimal charging/discharging amount that minimizes the unit energy cost for peak load reduction based on the power demand and charging power corresponding to the charging/discharging amount calculated for each time period, wherein the minimum value of the unit energy cost may be calculated to satisfy the condition that the objective function of the following equation becomes the minimum. In equation 1, variable ß is a value indicating the degree to which the battery wear-out cost is reflected in the total cost, and l_i - e_i indicates the amount of power brought from a system.

Description

Rate plan-based ESS charging/discharging optimization system {A SYSTEM FOR OPTIMIZING ESS CHARGING BASED ON PLAN}

The present invention relates to a rate plan-based ESS charging/discharging optimization system, and more particularly, to a rate plan-based ESS charging/discharging optimization system that can maximize user's economic benefit by calculating and charging the optimized charging capacity during heavy load time. will be.

In general, high-voltage power reception buildings, such as buildings and factories, are subject to a variable rate system in which the unit price of electric power usage is differentially charged for each time period. In the variable rate plan, the difference between the rate during the light load period and the unit price during the maximum load period is up to three times. With the introduction of a real-time electricity rate system, the difference in rate unit price is expected to increase further. Accordingly, in order to use electric energy economically, it is necessary to reduce the peak power so as to lower the contracted power, and to distribute the load from the power usage in the section where the unit price of power use is high to the section where the unit price is low.

Conventionally, charging and discharging is performed once a day to charge the battery at a time when the power usage fee is relatively low, and then discharge the battery at the time when the electricity usage fee is the highest, thereby reducing the overall charge. In other words, in order to reduce charges and maximize incentives, the ESS battery was charged to the maximum during the light load period according to the capacity and discharged to the maximum during the maximum load period.

However, due to problems such as the efficiency of the battery or the limit of the maximum charging/discharging range, the actual rechargeable battery capacity is small compared to the nominal battery capacity, which is the criterion for incentives. There could be cases where incentives were less effective. For example, when the battery charging/discharging efficiency is 90%, it is possible to discharge an amount of power corresponding to 81% of the battery capacity through one charging/discharging. In this case, there is a problem that the possible discharge amount is reduced by 19%, and the incentive that can be received may be reduced.

Therefore, in the present invention, when the battery is continuously charged/discharged from the light load time to the maximum load time due to the decrease in battery efficiency, the battery cannot be charged or discharged as much as the nominal battery capacity, and thus the battery is additionally charged and supplemented during the heavy load time. Regardless of the efficiency reduction, we are trying to provide a rate-based ESS charging/discharging optimization system that can maximize the economic benefits of users.

In addition, the problem to be solved by the present invention is to provide an ESS charging/discharging optimization system based on a rate plan that can maximize the economic benefit of users by calculating the supplemental charging capacity for an optimized heavy load period.

In addition, the problem to be solved by the present invention is to prepare for an unexpected moment, for example, an unexpected power peak, by controlling the charging to be performed in an area close to the end time of the heavy load time zone as possible, and at the same time reducing the power charge. It is to provide a system for ESS charging/discharging optimization based on a rate plan that can be minimized.

Another problem to be solved by the present invention is to provide an ESS charging/discharging optimization system based on a rate plan.

In order to solve the above-described problems, the charge plan-based ESS charging/discharging optimization system according to an embodiment of the present invention includes an energy information detector configured to detect a plurality of energy information from a customer for a predetermined period; A power demand prediction unit that calculates power demand and unit energy cost based on the detected plurality of energy information; And an optimum charge/discharge amount calculation unit for calculating an optimum charge/discharge amount for minimizing the unit energy cost to reduce the peak load based on the power demand, and for charging the electric power corresponding to the calculated charge/discharge amount for each time period. The minimum value may be calculated so as to satisfy the condition that the objective function of Equation 1 below becomes the minimum.

<Equation 1>

는 배터리 wear-out 비용을 전체 비용에 반영하는 정도를 나타내는 값이고,

는 계통으로부터 가져오는 전력량을 나타냄).(Here, variable

Is a value representing the degree to which the battery wear-out cost is reflected in the total cost,

Represents the amount of power taken from the grid).

According to another feature of the present invention, the objective function may be calculated to satisfy all of the plurality of constraint conditions of Equation 2 below.

(Equation 2)

는 양수일 경우 방전을 나타내고, 음수일 경우 충전을 나타냄)(here,

Is a positive number indicates discharge, a negative number indicates charging)

According to another feature of the present invention, the optimum charge/discharge amount calculation unit may charge power corresponding to the optimum charge/discharge amount in at least some time periods including the end time of the heavy load time period.

According to another feature of the present invention, the unit energy cost may be calculated to satisfy Equation 3 below.

(Equation 3)

는 고정된 DOD값 D를 기준으로 1회의 충방전을 할 때 발생하는 에너지를 나타내고,

는 사이클 수를

만큼 반복할 경우 배터리 사용 전반에 걸쳐 발생하는 에너지양을 나타내고, 상수

는 배터리 효율을 나타냄(0≤

≤1)).(here,

Represents the energy generated when charging and discharging once based on the fixed DOD value D,

Is the number of cycles

Represents the amount of energy generated throughout battery use when repeated

Represents the battery efficiency (0≤

≤1)).

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

The present invention can maximize the economic benefit of the user by calculating the supplemental charging capacity in the optimized heavy load time period.

The present invention controls and controls to perform charging in an area close to the end time of the heavy load time zone, thereby preparing for unexpected moments such as occurrence of an unexpected power peak, and at the same time minimizing power charges.

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

1 is a schematic diagram of an ESS charging/discharging optimization system based on a rate plan according to an embodiment of the present invention.
2 is an overall configuration diagram of an ESS charging/discharging optimization system based on a rate plan according to an embodiment of the present invention.
3 is a flowchart illustrating a method of optimizing charge/discharge based on a rate plan according to an embodiment of the present invention.
4 is an exemplary diagram for explaining an incentive rate of an energy storage device relative to an electric energy rate according to an embodiment of the present invention.
5 is an exemplary diagram for explaining a battery deterioration cost according to an embodiment of the present invention.
6 is an exemplary diagram for explaining a daily SOC (State Of Charge) change according to an embodiment of the present invention divided into comparative examples and examples.
7 is an exemplary diagram for explaining changes in accumulated revenue before and after application of an optimization scenario according to an embodiment of the present invention.

1 is a schematic diagram of an ESS charging/discharging optimization system based on a rate plan according to an embodiment of the present invention. 2 is a block diagram of an ESS charging/discharging optimization system based on a rate plan according to an embodiment of the present invention. 3 is an exemplary diagram for explaining an incentive rate of an energy storage device compared to an electric energy rate according to an embodiment of the present invention. FIG. 4 is an exemplary diagram for explaining a comparison between a charge reduction effect and battery operation according to a movement of demand according to an embodiment of the present invention.

Referring to FIG. 1, a rate plan-based ESS charging/discharging optimization system 100 is a system for optimizing power rates by applying an optimization scenario, and an energy storage device linked to the power consumption and customer price 10 ( 20) can monitor the power supply related status. Here, the power charge may be defined as the power usage charge minus the ESS incentive. Accordingly, optimizing (or minimizing) power bills is preferably understood as minimizing power usage charges while maximizing ESS incentives. In addition, the ESS charging/discharging optimization system 100 based on a rate plan may receive power supplied through a power grid from a customer, or may receive power through a network network.

In addition, the charge plan-based ESS charging/discharging optimization system 100 stores energy according to the charging/discharging operation plan of the energy storage device 20 established based on predicted load information for each time period of the customer 10 or power rate information for each time period. By controlling the device 20, it is possible to minimize the electric power usage charge of the customer 10. Specifically, referring to FIG. 2, the ESS charging/discharging optimization system 100 based on a rate plan includes an energy information collection unit 110, a demand power prediction unit 120, an economic evaluation unit 140, and an optimal charge/discharge amount calculation unit ( 150) and a control unit 130 may be included.

The energy information detection unit detects energy information required to calculate an optimized amount of supplemental charging power for a heavy load for a certain period of time. For example, the energy information may include the number of cycles representing the sum of the total discharge and partial discharge cycles over the entire battery usage time, a discharge depth (DOD, Depth Of Discharge), and a remaining capacity (SOC, State Of Charge).

The power demand prediction unit 120 calculates the current power demand, and calculates a cumulative peak value of power demand and a unit energy cost for the last several months. For example, the past few months may correspond to the winter and summer of the 12 months preceding the current month. The power demand predictor 120 predicts a peak value of the power demand for the current month based on data on past power usage detected from the energy information detector. Specifically, the power demand prediction unit 120 may predict the peak value of the power demand for the current month based on the power demand data for the last two weeks.

The economic evaluation unit 140 evaluates economic feasibility by converting into energy generated when the battery charging and discharging is repeated as many times as the number of cycles. Detailed contents related to this will be described later.

The analysis unit compares and analyzes the predicted peak value of the power demand for the current month predicted by the power demand predictor 120 and the cumulative peak value of the power demand calculated from the power demand detection unit for the past few days (or months) by time or season. Specifically, the comparison and analysis unit may compare and analyze the predicted peak value for the demanded power for the current month calculated based on the past power usage data for the past one week and the cumulative peak values in winter and non-winter. have.

The optimum charge/discharge amount calculation unit 150 calculates the optimum charge/discharge amount of the energy storage device 20 for peak load reduction based on the analysis of the analysis unit. That is, the charging/discharging state of the battery can be adjusted by calculating the charging/discharging amount of the energy storage device from the objective function obtained by subtracting the ESS incentive and the battery wear-out cost from the usage fee calculated based on the amount of power brought from the system. A detailed operation of the optimal charge/discharge amount calculation unit 150 will be described later.

The energy storage device 20 generally refers to a means capable of storing power and performing power charging and discharging when necessary, and may be, for example, an ESS (Energy Storage System), and the energy storage device 20 manages customer power demand It may be installed and operated at individual customers for this purpose. In this case, the charging/discharging operation plan of the energy storage device 20 may be designed to reduce the charging cost of the energy storage device 20 and maximize the discharge effect. Details on the cost reduction according to the amount of charge and discharge will be described later.

In addition, the power rate may vary according to time or season as shown in the following table.

<Table 1>

The numbers shown in Table 1 exemplarily represent the electricity rates for each time and season, and it can be seen that the unit price of the electricity rates in the maximum load time period is approximately three times higher than that in the light load time period. In other words, since different power rates are applied for each season and for each time period, it is necessary to reflect the demand movement, that is, to move the amount of electricity in the maximum load time zone to the light load time zone.

Specifically, the economic benefit that can be obtained when power is moved from the maximum load period to the light load period increases in proportion to the amount of power transferred. However, the result does not include incidental costs to operate the battery, and if the operating cost is high, the economic benefit from charging and discharging will inevitably decrease. Accordingly, a rough representation of the cost change according to the ESS operation is as shown in FIG. 4. Specifically, the solid line represents the economic benefit that can be obtained when the demand is shifted from the maximum load time zone to the light load time zone, the dotted line represents a scenario for battery operation cost, and the dotted line cost does not exceed the cost of the solid line. It is advisable to interpret that only there is an economic benefit.

In general, the charge power power usage rate discount of the energy storage device 20 is divided into a basic rate discount and a power consumption rate discount, and incentives are paid based on the ESS charge amount during the light load period and the ESS discharge amount during the maximum load period. . Specifically, the basic rate discount is to apply a 50% discount on the amount of electricity used to charge the ESS during the light load period, and the electricity rate discount is a reduction in the amount multiplied by the base rate unit price to the average maximum power demand reduction. Is to do. In this case, the average maximum power demand reduction may be calculated as in Equation 1 below.

<Equation 1>

Here, A represents the total amount of discharge during the maximum load time on the weekday of the month, B represents the total amount of charge during the maximum load time on the weekday of the month, and C represents the number of weekdays in the month.

Accordingly, the power rate and the ESS incentive rate may be as shown in FIG. 3. In other words, since the incentive is very high compared to the electricity rate, it is necessary to optimize the amount of ESS charging and discharging during the peak load period. Here, the power charge excluding the incentive and the basic charge is calculated as shown in Equation 2 below.

<Equation 2>

)이라고 가정할 경우, 수학식 2는 하기 수학식 3과 같이 정리될 수 있다.Here, the subscripts off, mid, and on denote light load, intermediate load, and maximum load, respectively, and e _off denotes the sum of the ESS charge/discharge amounts in the light load time period. At this time, the sum of the daily charge and discharge amount is 0 (

), Equation 2 may be summarized as Equation 3 below.

<Equation 3>

는 배터리를 사용하지 않았을 경우 지불해야하는 기존의 전력 사용 요금을 나타낸다. 경부하 시간대와 최대 부하 시간대의 배터리 충 · 방전량에 따라 요금 절감액이 달리지는데, 가장 경제적인 충 · 방전 전략은 경부하 시간대에 최대 충전, 최대부하 시간대에 최대 방전하는 것이다. 특히, 본 발명에서는 부하량이 요금적용전력값을 초과할 것으로 예상되는 경우, 요금적용전력을 초과하는 피크 값을 줄이는 것을 주목적으로 한다.here,

Denotes the existing power usage fee that is to be paid if the battery is not used. Charge savings vary depending on the amount of charge and discharge of the battery in the light load period and the maximum load period. The most economical charging and discharging strategy is to charge the maximum during the light load period and discharge the maximum during the maximum load period. In particular, in the present invention, the main purpose is to reduce the peak value exceeding the charge applied power when the load amount is expected to exceed the charge applied power value.

Hereinafter, a method of minimizing power usage charges using the ESS charging/discharging optimization system 100 based on a rate plan to which the optimization scenario is applied will be described in detail with reference to FIGS. 5 to 7.

5 is an exemplary diagram for explaining a battery deterioration cost according to an embodiment of the present invention. 6 is an exemplary diagram for explaining a daily SOC (State Of Charge) change according to an embodiment of the present invention divided into comparative examples and examples. 7 is an exemplary diagram for explaining changes in accumulated revenue before and after application of an optimization scenario according to an embodiment of the present invention.

Before describing the optimization scenario according to an embodiment of the present invention, related terms will be described first.

The number of cycles is a number representing the sum of full and partial discharge cycles over the life of the battery. The battery is guaranteed to operate for that number of cycles, and this value is a result that the battery manufacturer can obtain through repeated experiments under limited circumstances. At this time, in order for the battery to exhibit the best performance, the number of cycles should not exceed the expected number of cycles, and it is desirable to replace the battery if it exceeds this. For example, as shown in Table 2 below, charging and discharging may be repeated about 26,000 times based on DOD 10, while charging and discharging may be performed about 5,000 times based on DOD 80.

<Table 2>

As shown in Table 2, if the number of cycles is small, the smaller the DOD, the more efficient it may be, but since the amount of energy transferred is different, such a simple comparison is meaningless. Therefore, it is desirable to evaluate the economy by converting the energy generated when charging and discharging is repeated as many times as the number of cycles.

The discharge depth (D0D, Depth Of Discharge) is a percentage of the current discharge amount compared to the battery capacity. In a fully charged state, the discharge depth (DOD) value is 0, and the value may increase as the discharge increases.

The residual capacity (SOC, State Of Charge) is a percentage of the current charge compared to the battery capacity. In a fully charged state, the residual capacity (SOC) value is 100, and the value may decrease as the discharge increases.

The end of life of a battery means that the number of battery cycles has reached the expected limit, and the ability to maintain a charged state has also declined.

The unit energy cost is indexed by calculating the cost of charging and discharging 1 kWh of electric power, and can be expressed as W(D). Therefore, the expected number of battery cycles represents the number of times that charging and discharging can be repeatedly performed from a fully charged state to a specific DOD state. That is, if the expected number of cycles for DOD 50 is 7,200, it is preferable to understand that the number of times that half of the battery charge can be used and then fully charged again and repeated this is 7,200 times.

In addition, the unit energy cost can be generally calculated by dividing the battery price by the total generated energy as shown in Equation 4 below.

<Equation 4>

는 고정된 DOD값 D를 기준으로 1회의 충방전을 할 때 발생하는 에너지를 나타내고,

는 사이클 수를

만큼 반복할 경우 배터리 사용 전반에 걸쳐 발생하는 에너지양을 나타내고, 상수

는 배터리 효율을 나타낸다. 이때,

는 효율이 떨어질수록 단위 에너지 비용이 증가함을 나타내는 0 내지 1 사이의 숫자이다.here,

Represents the energy generated when charging and discharging once based on the fixed DOD value D,

Is the number of cycles

Represents the amount of energy generated throughout battery use when repeated

Represents the battery efficiency. At this time,

Is a number between 0 and 1 indicating that the unit energy cost increases as the efficiency decreases.

However, since the actual usage pattern is mixed with various cases such as starting discharging before fully charging or changing the charging/discharging schedule in the middle, it may be difficult to obtain the unit energy cost only by the method shown in Equation 4 above. . Specifically, a general battery discharges the battery when the power usage fee is high, and charges the battery when the electricity usage fee is low, thereby reducing the overall charge. In order to obtain economic benefits through the use of such batteries, a new renewable energy power usage fee discount system, that is, an incentive system is being activated. Here, for the purpose of simply reducing rates and maximizing incentives, discharge as much as possible during the maximum load period and maximum charge during the light load period. However, when the battery is operated in this way, there is a problem that the battery consumption becomes faster because the number of cycles increases.

In addition, there is a problem that the battery cannot be 100% charged due to efficiency problems in general. For example, if charging is only 90% due to the problem of battery efficiency, the discharge is only 90%, so the incentive cannot be covered as much.

Accordingly, the ESS charging/discharging optimization system 100 based on a rate plan according to an embodiment of the present invention can maximize the user's economic benefit by charging the battery as much as the remaining charging capacity that has not yet been charged during the heavy load period. Hereinafter, a detailed description related to this will be given.

First, by defining a wear density function of a battery, and calculating an energy cost function for a complex battery state change through this, it is possible to estimate and calculate a unit energy cost against a random use pattern of the battery. When the above-described contents are expressed in a graph, as shown in FIG. 5, a section in which the unit energy cost is high is displayed in a dark color.

Accordingly, the optimum charge/discharge amount calculation unit 150 may optimize charge/discharge by minimizing the objective function of Equation (5) to minimize the energy cost per battery by appropriately adjusting the number of battery cycles.

<Equation 5>

는 배터리 wear-out 비용을 전체 비용에 반영하는 정도를 나타내는 값이며, 사용자에 의해 입력되는 변수이다. 이때, 변수

가 0일 경우, 배터리의 상태에 관계없이 요금 절감과 인센티브 최대화를 목적으로 최적화를 계산한다. 이에 반해, 변수

가 커질 경우 배터리 사용을 줄이면서 비용을 최소화하는 계산을 수행한다.Where, the variable

Is a value representing the degree to which the battery wear-out cost is reflected in the total cost, and is a variable input by the user. At this time, the variable

If is 0, the optimization is calculated for the purpose of reducing charges and maximizing incentives regardless of the state of the battery. On the other hand, the variable

When is large, calculations are performed to minimize cost while reducing battery usage.

을 기준으로 계산한 사용량 요금에서 ESS 인센티브와 배터리 wear-out 비용을 뺀 값으로 정의될 수 있다. 이때, 배터리 wear-out 비용은 상기 수학식 4를 ESS의 DoD에 따라 적분한 것으로 정의된다.In addition, the objective function is the amount of power brought from the system as shown in Equation 6 below.

It can be defined as a value obtained by subtracting the ESS incentive and battery wear-out cost from the usage charge calculated based on. In this case, the battery wear-out cost is defined as the integration of Equation 4 according to the DoD of the ESS.

<Equation 6>

At this time, the objective function is minimized to satisfy Equations 7 to 11 below. That is, it is preferable that Equations 7 to 11 are understood as constraints for minimizing the objective function.

<Equation 7>

Equation 7 is an equation representing the maximum charge/discharge limit of the battery, and may be calculated according to the PCS capacity or set to an arbitrary value.

<Equation 8>

에서 ess발전량

을 뺀 값이므로 이는 한전으로부터 가져오는 전력 사용량과 동일한 것을 의미한다. 여기서,

에 대한 조건은 피크 제한 조건과 동일하다.Equation 8 is an equation that means the constraint on the amount of power that the customer 10 brings from the power grid, and the power consumption for each time

Ess power generation

Since it is the value minus, this means the same as the amount of power consumed by KEPCO. here,

The condition for is the same as the peak limit condition.

<Equation 9>

Equation 9 is an equation representing a constraint that the value of SOC must fall within the range of battery operating capacity.

<Equation 10>

는 충방전 효율을 나타내는 변수로서, 충전일 경우와 방전일 경우 각각 다른 값으로 설정할 수 있다. 또한,

가 양수이면 방전을, 음수이면 충전을 의미한다. 예컨대, 배터리의 용량이 100kWh이고, SOC가 20%에서 10%로 감소할 ‹š 방전 효율

이 80%이면 배터리의출력 전력은

가 될 수 있다. 이에 반해, SOC가 10%에서 20%로 증가할 때 충전 효율

이 80%이면 배터리의 소모 전력은

가 될 수 있다.Equation 10 is an equation showing the relationship between the charge/discharge amount of the ESS and the SOC. At this time,

Is a variable representing the charging/discharging efficiency, and can be set to different values for charging and discharging. Also,

A positive number indicates discharge, and a negative number indicates charging. For example, the capacity of the battery is 100 kWh, and the SOC is reduced from 20% to 10%.

If this is 80%, the output power of the battery is

Can be. In contrast, charging efficiency when SOC increases from 10% to 20%

If this is 80%, the power consumption of the battery is

Can be.

<Equation 11>

Equation 11 is an equation representing the boundary condition of an ESS system operated on a daily basis. That is, it means that the sum of the daily charge and discharge amounts is set to be zero.

Accordingly, referring to FIG. 6, (i) is a comparative example graph, (ii) is an example graph, and non-winter corresponds to spring, summer, and autumn, and winter corresponds to winter. Referring to the non-winter graph and the winter graph of graph (i), it can be seen that it is a form of charging to the maximum in the light load time (off) and discharging to the maximum in the maximum load time (on). On the other hand, referring to the non-winter graph and the winter graph of graph (ii), it can be seen that the charging amount is reduced in the light load period (off) and additional charging is performed in the mid-load period (mid) (Fig. 6 graph (ii) O).

Accordingly, the ESS charging/discharging optimization system 100 based on a rate plan according to an embodiment of the present invention can reduce charges and maximize incentives regardless of the state of the battery by additionally charging during the heavy load period. That is, the ESS charging/discharging optimization system 100 based on a rate plan according to an embodiment of the present invention has an effect of minimizing cost (or maximizing profit) while using less battery.

In particular, as shown in Figure 6, the charge plan-based ESS charging and discharging optimization system 100 according to an embodiment of the present invention is the day time of the heavy load time in the spring, summer, and autumn when energy is used a lot during the day time. In winter, when energy is used a lot during the night time, charging is additionally performed during the night time of the heavy load time.

In addition, it is possible to prepare for a sudden situation by charging in the region closest to the end time of the medium and long load time. For example, if the amount of usage suddenly increases after charging in the first or mid-load time zone, the amount charged in the mid-load time zone may be insufficient or excessively charged. It is desirable to charge.

Therefore, as shown in FIG. 7, the ESS charging/discharging optimization system 100 based on a rate plan according to an embodiment of the present invention can maximize the user's profit by gradually increasing the accumulated profit compared to the prior art by applying the optimization scenario. There is an effect.

Although the 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 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 to explain the technical idea, and the scope of the technical idea 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 limiting. The scope of protection of the present invention should be interpreted by the following claims, and all technical ideas within the scope equivalent thereto should be interpreted as being included in the scope of the present invention.

10: customer 20: energy storage device
100: rate plan-based ESS charging/discharging optimization system 110: energy information collection unit
120: power demand prediction unit 130: control unit
140: economic evaluation unit 150: optimal charge/discharge amount calculation unit

Claims (4)

An energy information detection unit for detecting a plurality of energy information of a customer for a predetermined period;
A power demand predictor for calculating a demand power and a unit energy cost based on the detected plurality of energy information; And
Comprising an optimum charge/discharge amount calculation unit for calculating an optimum charge/discharge amount at which the unit energy cost is minimized in order to reduce the peak load based on the power demand, and for charging power corresponding to the calculated charge/discharge amount for each time period,
The minimum value of the unit energy cost is calculated to satisfy the condition that the objective function of Equation 1 below is minimum,
(Equation 1)

(Here, variable

Is a value representing the degree to which the battery wear-out cost is reflected in the total cost,

Represents the amount of power drawn from the grid)
Rate plan-based ESS charging/discharging optimization system. The method of claim 1, wherein the objective function is calculated to satisfy all of a plurality of constraint conditions of Equation 2 below,
(Equation 2)

(here,

Is a positive number indicates discharge, a negative number indicates charging)
Rate plan-based ESS charging/discharging optimization system. The method of claim 1, wherein the optimum charge/discharge amount calculation unit charges power corresponding to the optimum charge/discharge amount in at least some time periods including an end time of the heavy load time period among the heavy load time periods,
Rate plan-based ESS charging/discharging optimization system. The method of claim 1, wherein the unit energy cost is calculated to satisfy Equation 3 below,
(Equation 3)

(here,

Represents the energy generated when charging and discharging once based on the fixed DOD value D,

Is the number of cycles

Represents the amount of energy generated throughout battery use when repeated

Represents the battery efficiency (0≤

≤1)).
Rate plan-based ESS charging/discharging optimization system.

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