KR20190026356A - Battery scheduling calibration method to enable optimal peak shaving operation - Google Patents

Abstract

According to an embodiment of the present invention, a charging and discharging scheduling method of an energy storage device which is connected to an electrical grid and supplies power to a load comprises the following steps: a charging and discharging scheduling system induces a depreciation density function in accordance with a characteristic of the energy storage device and sets an optimized limiting condition; the charging and discharging scheduling system generates a charging and discharging schedule of the energy storage device from the depreciation density function based on cost information of the electrical grid under the optimized limiting condition; and the charging and discharging scheduling system compares predicted demand data and real demand data and estimates corrective power to correct the charging and discharging schedule.

Description

[0001] The present invention relates to a battery scheduling calibration method and a battery scheduling method,

The present invention relates to a charging / discharging scheduling method for an energy storage device, and more particularly, to a method for estimating a margin power based on a prediction error and allocating the estimation power to minimize unnecessary waste of charge / And more particularly to a method and apparatus for scheduling energy storage devices.

The battery operation for reducing the maximum load power which is commonly used at present is to discharge the battery with a constant power at a time when the maximum load is expected to occur. Regardless of the error of the prediction, it is possible to reduce the load stably by a method in which the time of occurrence of the maximum load is not wrong, or when the prediction is inaccurate, the time zone is wide. However, this operation is not considered optimum operation because it does not take into account the loss due to unnecessary charging / discharging power, the battery depreciation, and the electric power usage charge.

An apparatus and method for charge / discharge scheduling by controlling the discharge depth of an energy storage device (Registration No. 10-1581684)

SUMMARY OF THE INVENTION It is an object of the present invention to provide a technical means for scheduling an energy storage device that minimizes unnecessary waste of charge / discharge power caused by a prediction error.

According to an aspect of the present invention, there is provided a charging / discharging scheduling method of an energy storage device connected to a grid in accordance with an embodiment of the present invention to supply power to a load, The scheduling system deriving a depreciation density function according to the characteristics of the energy storage device and setting a constraint condition of optimization; The charge and discharge scheduling system generating a charge and discharge schedule of the energy storage device from the depreciation density function based on cost information of the power network under the constraint of the optimization; And correcting the charge / discharge schedule by estimating a correction power by comparing the predicted demand data with the measured actual demand data by the charging / discharging scheduling system.

According to an embodiment of the present invention, the step of generating the charging / discharging schedule comprises: setting an operating cost of the power grid from the depreciation density function based on cost information of the power grid under the constraint condition of the optimization; And generating the charge / discharge schedule over time using a dynamic programming algorithm so that the operating cost of the power grid is minimized.

According to an embodiment of the present invention, the step of setting the operating cost of the power network may be a method of applying a weight based on demand power to the cost information of the power network.

According to an embodiment of the present invention, the step of estimating the correction power and correcting the charge / discharge schedule includes calculating a relation between a prediction error and a correction power using data of a past period of time, Estimating a correction power according to a prediction error using the relationship; And correcting the charge / discharge schedule by allocating the estimated correction power to the next section.

According to the embodiment of the present invention, the relationship between the prediction error and the correction power may be calculated by using a linear regression analysis on the data of the past certain period.

According to an embodiment of the present invention, the step of estimating the correction power and correcting the charge / discharge schedule includes dividing a period for calculating the maximum load power into a predetermined number of unit sections, estimating correction power for each unit section And the charge / discharge schedule is corrected by reflecting the estimated correction power in the next unit section.

According to the embodiment of the present invention, the section for calculating the maximum load power is 15 minutes, and the unit section is 1 minute.

The present invention also provides a computer-readable recording medium storing a program for causing a computer to execute the charging / discharging scheduling method.

According to an aspect of the present invention, there is provided a charging / discharging scheduling apparatus for an energy storage device connected to a grid in accordance with an embodiment of the present invention and supplying power to a load, An input unit for receiving characteristics of the apparatus and cost information of the power network; A memory for storing a charge and discharge scheduling program for generating a charge and discharge schedule of the energy storage device; And at least one processor for driving the charge and discharge scheduling program, wherein the charge and discharge scheduling program stored in the memory is operable to derive a depreciation density function according to the characteristics of the energy storage device, Generating a charge / discharge schedule of the energy storage device from the depreciation density function based on the cost information of the power network under the constraint condition of the optimization, estimating a correction power by comparing the predicted demand data with the measured actual demand data, And the discharge schedule is corrected.

According to an embodiment of the present invention, the charging and discharging scheduling program stored in the memory may be configured to set the operating cost of the power grid from the depreciation density function based on the cost information of the power grid under the constraint conditions of the optimization, And the charge / discharge schedule is generated over time using a dynamic programming algorithm so that the cost is minimized.

According to an embodiment of the present invention, the charge / discharge scheduling program stored in the memory may be configured to apply a weight based on the demanded power to the cost information of the power network to set the operating cost of the power network.

According to the embodiment of the present invention, the charge / discharge scheduling program stored in the memory calculates a relationship between a prediction error and a correction power using data of a past period of time, and calculates a prediction error , And corrects the charge / discharge schedule by assigning the estimated correction power to the next section.

According to an embodiment of the present invention, the relationship between the prediction error and the correction power may be calculated using a linear regression analysis on the data of the past certain period.

According to the embodiment of the present invention, the charging / discharging scheduling program stored in the memory divides a section for calculating the maximum load power into a predetermined number of unit sections, estimates correction power for each unit section, And the charge / discharge schedule is corrected by reflecting the estimated correction power.

According to an embodiment of the present invention, the correction power is estimated based on the prediction error, and the calculated correction power is allocated to the scheduled charge / discharge power in real time, thereby making it possible to reduce the maximum load power by correcting to an output close to optimal operation. In addition, dynamic programming algorithms can reduce the maximum load power without setting a separate threshold by applying price weights.

1 is a diagram illustrating a relationship between a power grid, an energy storage device, and a load to which a charging / discharging scheduling apparatus for an energy storage device according to an embodiment of the present invention is applied.
Fig. 2 is a graph showing the comparison between predicted power demand and actual power demand and the results thereof.
3 is a block diagram of a charge and discharge scheduling apparatus for an energy storage apparatus according to an embodiment of the present invention.
4 is a graph showing the relationship between the prediction error and the correction power for 355 days.
FIGS. 5 and 6 are diagrams illustrating a process of correcting charge / discharge schedules by allocating correction power.
7 is a block diagram of a charging / discharging scheduling process of an energy storage device according to another embodiment of the present invention.
8 to 13 are diagrams for explaining the effect of charging / discharging scheduling of the energy storage device according to the embodiment of the present invention.
14 is a flowchart of a charging / discharging scheduling method of an energy storage device according to an embodiment of the present invention.
15 and 16 are flowcharts of a charge / discharge scheduling method of an energy storage device according to another embodiment of the present invention.

Prior to the description of the concrete contents of the present invention, for the sake of understanding, the outline of the solution of the problem to be solved by the present invention or the core of the technical idea is first given.

1 is a general state diagram in which a grid, an energy storage device and a load are connected. In the case of using an energy storage device auxiliary to the power network in order to supply necessary power to the conventional load side in the situation as shown in FIG. 1, optimization is performed by using prediction data for minimizing unnecessary waste and operating for optimal load reduction To perform battery scheduling. We utilize the depreciation density function to account for the battery depreciation, and we perform optimization using dynamic programming. However, it is very difficult to set a proper limit because dynamic programming is limited in terms of its characteristics.

In addition, a loss occurs due to a definite battery charge / discharge scheduling that does not consider the prediction error. As shown in FIG. 2 (a), the forecasted result does not match the actual demand, but if there is no significant difference in the demand pattern, the usage charge and depreciation do not cause a loss as compared with the optimal operation. However, since the operation for reducing the maximum load power is extremely sensitive to the error, as shown in FIG. 2 (b), a larger load power is generated than the optimal operation. In fact, although the power consumption rate does not show a significant difference in Table 1, it can be seen that the maximum load power has greatly increased from the optimum value.

In order to reduce the maximum load power by compensating the charging / discharging schedule in consideration of the prediction error, an energy storage (not shown) connected to the grid according to an embodiment of the present invention for supplying power to the load wherein the charge / discharge scheduling system comprises: a charge / discharge scheduling system for deriving a depreciation density function according to characteristics of an energy storage device and setting a constraint condition for optimization; Generating a charge / discharge schedule of the energy storage device from the depreciation cost function based on the cost information of the energy storage device, and estimating a correction power by comparing the predicted demand data with the measured actual demand data, And correcting the charge / discharge schedule.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. It will be apparent to those skilled in the art, however, that these examples are provided to further illustrate the present invention, and the scope of the present invention is not limited thereto.

BRIEF DESCRIPTION OF THE DRAWINGS The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings, in which: It is to be noted that components are denoted by the same reference numerals even though they are shown in different drawings, and components of different drawings can be cited when necessary in describing the drawings. In the following detailed description of the principles of operation of the preferred embodiments of the present invention, it is to be understood that the present invention is not limited to the details of the known functions and configurations, and other matters may be unnecessarily obscured, A detailed description thereof will be omitted.

3 is a block diagram of a charge and discharge scheduling apparatus for an energy storage apparatus according to an embodiment of the present invention.

The charging / discharging scheduling apparatus 100 of the energy storage apparatus according to an embodiment of the present invention includes an input unit 10, a processor 20, and a memory 30. And a control unit for controlling the energy storage device according to the charge / discharge schedule.

In order to utilize the optimization technique for optimal operation of the energy storage device, predictive demand data is required, and because demand data is predicted and used, loss due to prediction error occurs. Therefore, by additionally adding the correction power, the loss due to the prediction error can be minimized and the optimal charge / discharge scheduling can be approximated. Maximum demand power can be saved through optimal charge / discharge scheduling.

The input unit 10 receives the characteristics of the energy storage device and the cost information of the power network. The characteristics of the energy storage device necessary for generating and calibrating the charging / discharging schedule of the energy storage device, and the cost information of the power network. In addition, other data necessary for charging / discharging scheduling, such as past demand data, can be input.

The memory 30 stores a charge / discharge scheduling program for generating a charge / discharge schedule of the energy storage device. The memory 30 stores the generated charge / discharge schedule and can store past demand data.

The processor 20 drives the charge / discharge scheduling program.

Wherein the charging and discharging scheduling program stored in the memory is operable to derive a depreciation density function according to the characteristics of the energy storage device and set a constraint condition of optimization, A charge / discharge schedule of the energy storage device is generated from the function, and the charge / discharge schedule is corrected by comparing the predicted demand data with the measured actual demand data to estimate a correction power.

In order to generate charge and discharge schedules based on the characteristics of energy storage devices and the cost information of the grid, we first derive the depreciation density function according to the characteristics of the energy storage device and set the optimization conditions. And generates a charge / discharge schedule of the energy storage device from the depreciation density function based on cost information of the power network under the constraint of the optimization.

Generating a charge / discharge schedule by setting an operating cost of the power network from the depreciation density function based on the cost information of the power network under the constraint of the optimization; and performing dynamic programming to minimize the operating cost of the power network, Algorithm to generate the charge / discharge schedule over time.

In setting the operation cost of the power network, the operation cost of the power network can be set by applying a weight according to the demand power to the cost information of the power network in order to suppress the maximum load power.

The generation of charge / discharge schedules can be performed through the following specific process.

The state of charge (SoC) of the energy storage device over time is defined as the state s _t in order to perform the optimal charge / discharge scheduling utilizing the dynamic programming algorithm. Then, the state transition function according to time is defined as follows according to charge / discharge power p _t and unit time Δt.

Costs are generated by this state change, which defines a transition cost function J (s _t , p _t ). It consists of power use rate c _t and the battery depreciation density function w (s) with time. In order to suppress the maximum load power, a self-pricing factor was added to the rate c _t in order to generate disadvantages according to the demand power. α and β are the anthropogenic variables for adjusting each weight.

According to the dynamic programming algorithm, the same problem is repeatedly solved through backward induction. The optimal state value function V ^* (s _t ) for this is defined as follows.

Inner releasing the equation to-Bellman equation for optimal energy storage device SoC scheduled out, it is possible to obtain an optimal energy storage system charging and discharging electric power p ^* _t in time through it.

In operation of the actual energy storage device according to the charge / discharge schedule of the energy storage device generated through the above process, the margin power is estimated and reflected to reduce the loss due to the prediction error.

In order to estimate the correction power, past demand data is used.

Calculating a correlation between a prediction error and a correction power using data of a past period of time, estimating a correction power according to a prediction error using the relationship between the prediction error and the correction power, So that the charge / discharge schedule can be corrected. The relationship between the prediction error and the correction power can be calculated using linear regression analysis on the data of the past certain period.

In order to estimate the correction power, the relationship between the prediction error and the correction power (Margin power) is derived by comparing the past measured demand data with the prediction based optimization result. The margin power μ _t is defined as the difference between the optimum charge / discharge power p _t ^* and the charge / discharge power p _t ^{e *} according to the prediction-based optimization, and can be expressed as follows.

FIG. 4 shows the relation between the prediction error and the correction power for 355 days. As can be seen from the graph, the margin power has a linear relationship with the prediction error. The slope and the slice must be obtained do. To do this, linear regression is used to infer relationships through data from a certain period of time.

The slope r ₁ and the slice r ₂ are obtained by linear regression analysis, and the correction power (margin power) is obtained according to the prediction error.

As described above, in order to correct the charging / discharging schedule by using the estimated correction power, a section for calculating the maximum load power is divided into a predetermined number of unit sections, the correction power is estimated for each unit section, The charging / discharging schedule can be corrected by reflecting the estimated correction power. Here, in the Korean power grid environment, the period for calculating the maximum load power is 15 minutes, and the unit period is preferably 1 minute. It is natural that the unit interval can be set differently depending on the environment or situation of the power network.

The prediction error is defined as the difference between the demand demand data and the actually measured demand power. Since the scheduled charge / discharge power is already outputted at the moment when the error is measured, there is no way to reflect the corrected power even if the correction power is estimated. In order to solve this problem, as shown in FIG. 5, a section for calculating the maximum load power is divided into a predetermined number of unit sections, a correction power is estimated for each unit section, and the estimated correction power is reflected in the next unit section By correcting the charging / discharging schedule, it is possible to reflect the correction power even for the past time point.

The maximum load power is defined as the average power of the 15-minute power consumed. In fact, the watt-hour meter can measure the power in a shorter time interval. Therefore, as shown in FIG. 6, a correction power is allocated using an error measured for a time shorter than 15 minutes.

For example, if the power is measured in units of one minute, the prediction error is compared with the average power and the predicted demand, the correction power is estimated, and the charge / discharge power of the energy storage device is corrected for the next one minute, And 14 correction powers are allocated for a minute to correct the charge / discharge schedule. The correction power corresponding to the last minute can not be allocated, and the correction power for 1 minute is negligibly small, so it does not have a large effect. This can reduce the maximum power load.

7 is a block diagram of a charge / discharge scheduling process of an energy storage device according to another embodiment of the present invention. First, charge / discharge scheduling of the energy storage device is performed through optimization of the prediction demand data base. At this time, the depreciation density function is derived according to the characteristics of the energy storage device, and the optimization condition is set. In addition, the optimum charging and discharging schedule is obtained by considering the electricity usage fee and the basic charge according to the Korean contract power. Next, the relationship between the marginal power and the error is compared by using the linear regression analysis by comparing the past measured actual demand data based optimal scheduling result with the predictive based scheduling result. In this way, the correction power is estimated based on each measured error, and it is allocated to the scheduled charge / discharge power in real time to be corrected to an output close to optimal operation.

8 to 13 are diagrams for explaining the effect of charging / discharging scheduling of the energy storage device according to the embodiment of the present invention. In the demand power data of Sogang University in 2015, This is the simulation result.

In FIG. 8, a black straight line (Regression) represents the relationship between the correction error and the prediction error obtained through linear regression analysis. The training data are the correction power and the prediction error data corresponding to the past 10 matches, and the red data represent the correction power and the prediction error of the same day. It can be confirmed that the relationship is almost similar to that obtained in the linear regression analysis.

9 is a result of applying the energy storage output obtained by each method. The optimal result obtained under the assumption that the red solid line (Opt_path) is known for all the demanded electric power is the result of applying the charge / discharge scheduling obtained by the predictive data-based optimum technique. And a blue solid line (Proposed_path) is applied to charge / discharge scheduling according to the embodiment of the present invention. It can be confirmed that the result after the correction is nearly optimal.

FIG. 10 is a graph showing the relationship between the demand power measured in the first minute and the existing predicted demand data, and actual measured data in units of one minute can not be obtained. Therefore, the virtual one minute actual data is generated by mixing Gaussian noise, As shown in FIG.

FIG. 11 shows a result of estimating the correction power through the relationship obtained from the above-described linear regression analysis using the error measured for one minute, and allocating the correction power according to the charge-discharge scheduling according to the embodiment of the present invention. The result indicated by the blue dotted line is a result of the charge / discharge scheduling according to the embodiment of the present invention, and the blue solid line is a result of estimating the correction power in units of 15 minutes in which the allocation is impossible. There is little difference between the two results.

12 shows the maximum load power of each month obtained through each method for 355 days. The black line (wo ESS) is the result of battery-free operation, and the day-ahead is the result of applying predictive data-based optimization scheduling. The red solid line (Opt) is the optimal result on the assumption that all the data are known, and the blue solid line is the result of applying the charge / discharge scheduling according to the embodiment of the present invention.

Figure 13 shows the power usage fee charged each month through each method. Unlike the case of not using the battery unlike the maximum load power, there is almost no difference.

Table 2 shows the results according to each method. The maximum profitable battery charge is about 140 million won under the assumption that all data are known. However, as a result of applying the prediction-based optimization result, about 30 million won is lost due to the prediction error, and only about 110 million won of profit can be obtained. However, when the charge / discharge scheduling according to the embodiment of the present invention is applied, it can be seen that only loss of about 8 million won is generated and the existing loss can be saved by about 60%.

FIG. 14 is a flowchart of a charge / discharge scheduling method of an energy storage device according to an embodiment of the present invention, and FIGS. 15 and 16 are flowcharts of a charge / discharge scheduling method of an energy storage device according to another embodiment of the present invention. 14 to 16 correspond to the charging and discharging scheduling apparatus and the process of the energy storage apparatuses of FIGS. 1 to 13, and a duplicate description will be omitted.

In a charge / discharge scheduling method of an energy storage device connected to a grid (Grid) to supply power to a load, the charge / discharge scheduling system calculates a charge / Derive density function and set constraint of optimization.

In step S120, the charge / discharge scheduling system generates a charge / discharge schedule of the energy storage device from the depreciation density function based on the cost information of the power network under the constraint condition of the optimization.

More specifically, step S120 may be implemented in step S210 and step S220.

In operation S210, an operating cost of the power grid is set based on the cost information of the power grid under the constraint condition of the optimization. In operation S220, dynamic programming is performed so that the operating cost of the power grid is minimized. Algorithm to generate the charge / discharge schedule over time. In setting the operating cost of the power network in step S210, a weight corresponding to the demand power may be applied to the cost information of the power network. The maximum load power can be suppressed by applying the weight according to the demanded power.

After the charge / discharge schedule is generated in step 120, the charge / discharge schedule is corrected by estimating the correction power in actual application. To this end, in step S130, the charge / discharge scheduling system calculates predicted demand data and measured actual demand data And corrects the charge / discharge schedule by estimating the correction power.

More specifically, step S130 may be implemented in step S310 and step S320.

In step S310, a relation between a predictive error and a correction power is calculated using past past period data, a correction power according to a prediction error is estimated using the relationship between the prediction error and the correction power, and in step S320, And the corrected charge is corrected to the next period to correct the charge / discharge schedule. Here, the relationship between the prediction error and the correction power can be calculated using linear regression analysis on the data of the past certain period. Further, in order to reflect the correction power in real time, a section for calculating the maximum load power is divided into a predetermined number of unit sections, a correction power is estimated for each unit section, and the estimated correction power is reflected in the next unit section The charging and discharging schedule can be corrected. In this case, the period for calculating the maximum load power to meet the Korean power situation is 15 minutes, and the unit period can be set to 1 minute.

The method according to an embodiment of the present invention may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

As described above, the present invention has been described with reference to particular embodiments, such as specific elements, and specific embodiments and drawings. However, it should be understood that the present invention is not limited to the above- And various modifications and changes may be made thereto by those skilled in the art to which the present invention pertains.

Accordingly, the spirit of the present invention should not be construed as being limited to the embodiments described, and all of the equivalents or equivalents of the claims, as well as the following claims, belong to the scope of the present invention .

100: charge / discharge scheduling device of energy storage device
10: Input unit
20: Processor
30: Memory

Claims (14)

A charge / discharge scheduling method of an energy storage device connected to a grid (Grid) to supply power to a load (Load)
Wherein the charge and discharge scheduling system derives a depreciation density function according to the characteristics of the energy storage device and sets a constraint condition of optimization;
The charge and discharge scheduling system generating a charge and discharge schedule of the energy storage device from the depreciation density function based on cost information of the power network under the constraint of the optimization; And
Wherein the charging / discharging scheduling system comprises comparing the predicted demand data with the measured actual demand data to estimate a correction power to correct the charge / discharge schedule. The method according to claim 1,
Wherein the step of generating the charge /
Setting an operating cost of the power grid from the depreciation density function based on cost information of the power grid under the constraints of the optimization; And
Wherein said charging and discharging schedule is generated over time using a dynamic programming algorithm such that the operating cost of said power grid is minimized. 3. The method of claim 2,
Wherein setting the operating cost of the power network comprises:
Wherein a weight according to demand power is applied to cost information of the power network. The method according to claim 1,
Wherein the step of estimating the correction power to correct the charge /
Calculating a relationship between a prediction error and a correction power using data of a past period of time and estimating a correction power according to a prediction error using the relationship between the prediction error and the correction power; And
And correcting the charge / discharge schedule by allocating the estimated correction power to the next section. 5. The method of claim 4,
The relationship between the prediction error and the correction power is expressed as:
Wherein the calculation is performed using linear regression analysis on the data of the past period of time. The method according to claim 1,
Wherein the step of estimating the correction power to correct the charge /
And the charging / discharging schedule is corrected by reflecting the estimated correction power in the next unit section. [0031] According to another aspect of the present invention, How to. The method according to claim 6,
Wherein the section for calculating the maximum load power is 15 minutes and the unit section is 1 minute. A computer-readable recording medium storing a program for causing a computer to execute the method according to any one of claims 1 to 7. An apparatus for charging / discharging an energy storage device connected to a grid (Grid) to supply power to a load (Load)
An input unit for receiving characteristics of the energy storage device and cost information of the power network;
A memory for storing a charge and discharge scheduling program for generating a charge and discharge schedule of the energy storage device; And
And at least one processor for driving the charge / discharge scheduling program,
Wherein the charge / discharge scheduling program stored in the memory comprises:
Wherein the energy storage device is configured to derive a depreciation density function according to the characteristics of the energy storage device and set a constraint condition of optimization and to determine from the depreciation density function based on cost information of the power network under the constraint condition of optimization, And compares the predicted demand data with the measured actual demand data to estimate the correction power to correct the charge / discharge schedule. 10. The method of claim 9,
Wherein the charge / discharge scheduling program stored in the memory comprises:
Setting an operating cost of the power network from the depreciation density function based on cost information of the power network under the constraint condition of the optimization and setting the operating cost of the power network using the dynamic programming algorithm And generates a charge / discharge schedule. 11. The method of claim 10,
Wherein the charge / discharge scheduling program stored in the memory comprises:
Wherein the operating cost of the power network is set by applying a weight based on demand power to the cost information of the power network. 10. The method of claim 9,
Wherein the charge / discharge scheduling program stored in the memory comprises:
Calculating a correlation between a prediction error and a correction power using data of a past period of time, estimating a correction power according to a prediction error using the relationship between the prediction error and the correction power, Thereby correcting the charge / discharge schedule. 13. The method of claim 12,
The relationship between the prediction error and the correction power is expressed as:
Wherein the calculation is performed using linear regression analysis on the data of the past period of time. 10. The method of claim 9,
Wherein the charge / discharge scheduling program stored in the memory comprises:
And the charging / discharging schedule is corrected by reflecting the estimated correction power in the next unit section. [0031] According to another aspect of the present invention, .

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