KR101955185B1 - Maximum demand reduction system using energy storage system - Google Patents

Abstract

The present invention relates to a system for reducing a maximum demand power using an energy storage device of a power system including a microgrid. The system comprises: a peak reduction and smoothing unit including a serial and parallel prediction module, a peak reduction performance module, and a smoothing module; an ESS control means including a forced discharge performing unit; and a PCS including a system power control module, a DC voltage control module, and an AC voltage control module.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to an energy saving device,

The present invention relates to a maximum demand power reduction system using an energy storage system of a power system including a micro grid, and more particularly, to an energy storage system for storing power generated from a renewable generation source such as a grid power source, And a peak demand power reduction system using an energy storage device capable of reducing the peak demand power by performing peak reduction and load curve smoothing using the apparatus.

In order to solve the problem of power supply and demand, the electric power differential plan is designed to induce consumers to actively respond to price signals by applying different price rates according to the power use time zone, thereby to reduce the maximum power demand and to equalize the daily load. It is being applied in various forms in stead of electric fixed rate system.

In order to reduce the electric power usage fee based on the power differential charge plan, it is desirable to minimize the power use in the heavy-load time period in which the charge unit price is high and to maximize the electric power use in the light load period in which the charge unit charge is low.

However, there are cases in the industry where electricity use is directly related to production activities, and commercial facilities are not able to change electricity demand pattern arbitrarily due to reduction of heating and cooling load related to sales activities.

It is needless to say that the method of directly managing the power demand by directly controlling the conventional load can not be a suitable solution for the above problem.

In recent years, development of large capacity energy storage devices has been actively developed, and there has been a growing interest in the application of the energy storage devices to the management of the demand for power consumption of the energy storage devices. However, the development of concrete integrated operation methods has been limited.

Therefore, in an energy storage device that stores power generated from a renewable power source such as a grid power source or a solar power, a wind power, and a fuel cell power generation system, peak reduction and load curve smoothing are provided to actively cope with the maximum demand power I have proposed the technology.

KR 10-2009-0131354E (2009.12.29.)

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems of the prior art, and it is an object of the present invention to provide an energy storage device capable of efficiently managing peak demand power by using an energy storage device having functions of peak reduction and load curve smoothing .

In order to solve the above problems, a maximum demand power reduction system using an energy storage device according to the present invention includes a predicted load curve for predicting a load value with reference to past data and data generated through future prediction using a spline interpolation method, A predictive load value of the energy storage device, and a peak value for performing the operation range constraint condition of the SOC (State of Charge) by determining the effective power flow of the energy storage device for each time with reference to the capacity value and the predicted load value of the energy storage device. A peak reduction and smoothing unit including a smoothing module for smoothing the peak load curve reduced by the peak reduction execution module to perform accurate load prediction; An ESS control means for setting a minimum unit time of an energy storage device operation, extracting a current load size, and performing a charge / discharge of the energy storage device according to a demand response sequence; A system power control module for acquiring the charge / discharge performance information processed by the forced discharge performance unit and for charging / discharging the energy storage device, a DC voltage control module for charging 100% of SOC (State of Charge) And an AC voltage control module for receiving the reference value of the output current calculated by the control module and performing control to generate an output voltage reference value.

Here, the serial-parallel prediction module can predict a load measurement point in a range of 10 to 30 minutes in advance.

According to the present invention, in energy storage apparatus for storing power, peak reduction and load curve smoothing are provided to efficiently manage the energy of the energy storage device.

Also, in order to prevent the problem that the energy is exhausted when the prediction is wrong, it is possible to set the minimum unit time of the operation of the energy storage device so as to perform charging / discharging of the energy storage device according to the current load size .

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a general configuration diagram exemplarily showing an example of a maximum demand power reduction system using an energy storage device according to an embodiment; FIG.
FIG. 2 is a block diagram of an ESS control means exemplarily showing an example of a maximum demand power reduction system using an energy storage device according to an embodiment; FIG.
3 is a diagram illustrating a neural network structure including nodes, layers, and weights, which illustratively shows an example of a maximum demand power reduction system using an energy storage device according to an exemplary embodiment.
4 is a flowchart of a serial-parallel load predicting method.
5 is a graph showing an SOC locus.
6 is a PCS block diagram exemplarily showing an example of a maximum demand power reduction system using an energy storage device according to an embodiment.
FIG. 7 is a diagram exemplifying an outline of a maximum demand power reduction system using an energy storage device according to an embodiment; FIG.
8 is a diagram illustrating a prediction method using actual load data of a maximum demand power reduction system using an energy storage device according to an embodiment.
FIG. 9 is a graph showing prediction results with actual data extracted at intervals of 20 minutes; FIG.
10 is a circuit load graph corrected by the BESS operation.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

In the following description of the embodiments of the present invention, a detailed description of related arts will be omitted when it is determined that the gist of the embodiments disclosed herein may be obscured.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. , ≪ / RTI > equivalents, and alternatives.

Also, terms such as " comprising, " " comprising, " or " performed ", as disclosed in the following embodiments, mean that a component can be implanted unless otherwise specifically stated. It should be understood that the present invention is not limited to the components but includes other components.

In addition, the maximum demand power reduction system using the energy storage device described in the following embodiments is an energy storage device that stores power generated in a photovoltaic power generation system that is a new and renewable power source, and provides peak reduction and load curve smoothing, It is possible to efficiently manage the energy of the storage device.

Hereinafter, a maximum demand power reduction system using an energy storage device will be described in more detail.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is an overall configuration diagram illustrating an example of a maximum demand power reduction system using an energy storage device according to an embodiment; FIG.

 Referring to FIG. 1, a maximum demand power reduction system for efficiently managing a maximum demanded power of a renewable generation source converts AC power produced by a renewable energy generation source 100 into DC power, And converts the stored direct current power into alternating current power to be connected to the power system.

The renewable energy source 100 refers to a renewable energy source such as a wind power generator, a solar power generator, and the like. In the embodiment of the present invention, a solar power generator will be described as an example.

The energy storage device (ESS) 400 performs a function of storing power generated by a renewable energy generation source. In the present invention, an energy storage device configured in a distributed manner is used.

At this time, it is possible to reduce the peak of the energy storage device and smooth the load curves through the ESS control means 200 to efficiently manage the energy of the energy storage device, and to store energy under optimal conditions through the PCS 300 The device is charged and discharged. It provides a function of 100% charge through SOC (State of Charge).

FIG. 2 is a block diagram of an ESS control means exemplarily showing an example of a maximum demand power reduction system using an energy storage device according to an embodiment.

The ESS control means 200 includes a peak reduction and smoothing unit 210 and a forced discharge performing unit 220.

According to the above configuration, it is possible to monitor the power supply related state of the power supply network for supplying electric power to the customer and the electric energy storage device associated with the electric power supply network, and based on the predicted load information by time of the customer and the electric power charge information by time And the operation of the energy storage device and the power supply network is controlled according to the charging and discharging operation plan of the installed consumer energy storage device, thereby minimizing the charge for the electric power consumption.

At this time, the charging and discharging operation plan of the energy storage device can reduce the charging cost of the energy storage device and maximize the discharge effect.

Hereinafter, concrete configuration means for providing the above function will be described.

That is, the peak reduction and smoothing unit 210 includes a serial-parallel prediction module 211 for generating a prediction load curve for predicting a load value with reference to past data and data generated through future prediction using a spline interpolation method, A peak reduction execution module 212 for determining the active power flow of the energy storage device for each time by referring to the capacity value and the predicted load value of the energy storage device 400 and performing the SOC operating range constraint condition, And a smoothing module 213 for smoothing the peak load curve reduced by the execution module to perform accurate load prediction.

Since the load smoothing requires a fairly accurate load prediction, for example, a serial parallel prediction algorithm is applied to predict a load value in the range of 10 to 30 minutes in the future, and a battery energy storage system (BESS) State of Charge Changes to the load curve are removed by BESS while maintaining the change.

Here, peak reduction is preferably performed with smoothing to use the best BESS capacity.

Accordingly, in the present invention, the load prediction method is applied to apply the optimization algorithm, and since the capacity of the battery is limited to the set capacity, the accurate prediction algorithm can improve the work of the BESS more efficiently.

In addition, it may be effective to perform the prediction ahead of the current for the purpose of peak reduction, while predicting the load for 10-30 minutes for smoothing purposes. Preferably, smoothing can be performed with a 20 minute advance prediction.

In accordance with this demand, the serial-parallel prediction module 211 generates a predicted load curve for predicting a load value by referring to past data and data generated through future prediction using a spline interpolation method.

3 shows a multi-value neural network structure including an input layer, a hidden layer and an output layer, where the input layer includes two parts: input data v and previous output data y.

If the input data (v), the previous output data (y) and the weights (i.e. W ¹ _{n 0} , W ² _l0 , W ¹ _nm , W ² _ln etc.) and the activation function (f _c ) Is a normal artificial neural network (ANN), and if the parameter mentioned above has a complex value, the neural network can be regarded as a CVNN (Complex-Valued Neural Network).

)을 나타낸다.Equation (1) represents the l-th output value (predicted value,

).

수식 (1)

Equation (1)

는 출력값(예측값), f_c는 활성함수, W_l0 ²과 W² _ln은 가중치, H_n은 n번째 은닉 뉴런이다.here,

The output value (prediction value), f _c is the active function, _l0 W ² and W ² are weight _ln, H _n is the n-th hidden neuron.

The output of the n-th concealed neuron (H _n ) is expressed as Equation (2).

수식 (2)

Equation (2)

Where H _n is the nth hidden neuron, f _c is the activation function, W _n0 ² and W _lnm ² are the weights, and X _m is the input vector.

At this time, X _m = [v (k - 1), v (k - 2), ..., , v (k - p), y (k - 1), y (k - 2), ... , y (k - i) .m, n, i, l and p are positive integers.

In the above equation (2), if a division sigmoid function is regarded as an activation function, it can be represented by equation (3).

수식 (3)

Equation (3)

Where f _c (z) is the activation function.

을 나타내고, 비분할 대신 분할 시그모이드 함수를 사용하여 함수의 특이성 문제를 피할 수 있다.If z = x + jy in equation (3), j =

And it is possible to avoid the problem of the specificity of the function by using the division sigmoid function instead of the division.

Thus, complex backpropagation algorithm applies weight and ⁽² _l0 ΔW, ΔW ² _ln, ΔW ¹ _n0 And? W ¹ _nm ), the error can be calculated by the following equation (4).

수식 (4)

Equation (4)

는 예측 데이터이다.Here, e _l (k) is an error for the l-th actual output, y _l (k) is input data,

Is prediction data.

At this time, the serial-parallel prediction module 211 generates a predicted load curve for predicting a load value by referring to past data and data generated through future prediction using the spline interpolation method.

That is, daily historical data is used in addition to the load history data, and past data extends from 20 minutes to the present moment.

In addition, a load measurement point calculated by parallel prediction (CVNN) is also needed.

As a result, we must have a set of data that includes the past data and the points obtained through future predictions, and spline interpolation is suitable for analysis of these data sets.

Then, the new points generated by the spline interpolation method are averaged, and the generated points consist of one component as the final predicted value.

This point is compared with the actual value at the time when the future time point is reached.

Then, the weight of the error is added to the average value to make a final predicted value.

At the first prediction point, this error does not exist and the above step is executed for prediction after the first point.

This process is referred to as PI control as shown in FIG. 4 because it imitates the PI control mechanism, and the predicted time of 20 minutes may be changed in a range of 10 to 30 minutes according to user setting.

This information is used for optimal BESS control to achieve the purpose.

The SOC trajectory is found by solving the peak reduction problem, and the BESS power is determined to follow the acquired trajectory.

Then, as far as possible, the daytime fluctuations are limited while adhering to the planned SOC trajectory.

The peak reduction performing module 212 determines the active power flow of the energy storage device by referring to the capacity value and the predicted load value of the energy storage device 400 and performs the SOC operating range constraint condition.

The BESS should be charged early in the morning to reduce the peak, and the BESS power flow should be flattened to account for the constraints.

Thus, the cost function is defined as minimizing the total load deviation from the straight line.

The total load consists of the predicted load and the BESS power. In other words, the BESS tries to keep the load curve in a straight line that knows the capacity, and the mathematical objective function (fm) is like Equation (5).

수식 (5)

Equation (5)

Here, f (m) is the mathematical objective function, N is the number of time steps in the predicted horizon, L (t) is the predicted load at time t, and P (t) means BESS power at time t.

The objective function J (t) at the time step t applied to Equation (5) is defined as Equation (6) with the total load multiplication time variation coefficient flowing through the distribution transformer.

수식 (6)

Equation (6)

Where L (t) is the predicted load at time t and P (t) is the BESS power at time t, where J (t) is the objective function at time t, W (t) is power at time t, .

The power (W (t)) is determined by the square of the total power flow, in order to apply the load to the high load flowing through the distribution transformer.

수식 (7)

Equation (7)

Where W (t) is the power at time t, L (t) is the predicted load at time t, and P (t) is BESS power at time t.

Equation (7) is introduced into Equation (6) to obtain the first derivative and is calculated for the horizontal line result in Equation (5), and the SOC constraint is defined by Equation (8) and Equation (9) as follows.

수식 (8)

Equation (8)

수식 (9)

Equation (9)

Here, the SOC of BESS is updated by the following equation (10).

수식 (10)

Equation (10)

In the above, SOC _min and SOC _max mean the minimum and maximum allowable SOC, and E _tot And E (t) represent the capacity of the BESS capacity and the capacity of the stored energy at time t, respectively.

Δt represents the planned time resolution.

Calculating Equation (5), it is possible to calculate the BESS active power flow and the resulting SOC at each time step.

The smoothing module 213 performs smoothing on the peak load curve reduced by the peak reduction execution module to perform accurate load prediction.

Here, smoothing is an add-on function that corrects the peak reduction problem to take advantage of the stochastic variability in the load curve. That is, the level is determined at each time interval and the BESS is about to maintain this full line, and BESS is optimized to reduce the peak while maintaining the level of smoothing during each interval.

This level should be determined in such a way that the SOC deviation is minimized at the end of each time interval.

Thus, the level of smoothing is defined as the average of the current and predicted loads.

As a result, the BESS charging and discharging amount should be almost the same, so that the SOC deviation is kept at the minimum level.

The more the prediction is equal to the actual value, the less SOC deviation occurs at the end of each cycle.

The error of SOC _e with respect to SOC at time t is defined as Equation (11) as follows.

수식 (11)

Equation (11)

Where SOCe (t) is the SOC error at time t, P (t) is the BESS power at time t, E _tot The BESS capacity at time t, P _x (t), represents the BESS supplemental power provided by the SOC correction.

The smoothing error (SM _e ) at time t is expressed as Equation (12).

수식 (12)

Equation (12)

Here, SMe (t) is a smoothing error at time t, P (t) is BESS power at time t, and SL (t) is a smoothing level at time t.

The smoothing level SL (t) in the equation (12) is defined by the following equation (13).

수식 (13)

Equation (13)

Where SL (t) is the smoothed level at time t, L (t) is the predicted load at time t, P (t) is BESS power at time t and f (t + Δt) is the predicted load ahead.

The residual condition indicating the SOC deviation from the set planned point is added to compensate for the SOC deviation. In addition, f, which means predicting the preceding load, is responsible for the smoothing level calculation, and the smoothing level is updated at the end of each cycle.

The total cost is expressed as a weighted sum of the SOC and the smoothing error as in Equation (14).

수식 (14)

Equation (14)

Assuming that SMe (t) is a smoothing error at time t and that α and β are coefficients for each component of the cost function, the optimal BESS power is calculated by minimizing the cost function as in equation (15).

수식 (15)

Equation (15)

Where Px is the optimal BESS power.

After calculating P _X , the BESS power is updated by the reference P _X as shown in equation (16).

수식 (16)

Equation (16)

Where P (t) is the BESS power at time t and P _X (t) is the optimal BESS power at time t.

The peak reduction is performed by using the equations (5) to (9) for obtaining the load using the CVNN prediction method shown in the equations (1) to (4).

According to the pure peak reduction scheme, the load curve is kept as flat as possible based on the predicted curve and SOC constraints.

Calculate equations (5) to (10) and present the target SOC trajectory shown in Fig. 5 in which BESS absorbs or injects active power into the distribution circuit.

As shown in FIG. 5, SOC is increasing to shift the load curve through charging from midnight to 6 am.

The concave portion of the load curve changes around 6:00 am, releasing some of the stored energy until about 10 am to lower the load curve.

After locally minimizing the SOC trajectory occurring at approximately 10 am, BESS charges up to 2 pm to increase the load slightly below the flat line of approximately 800 kW.

By 2 pm or until 6 pm, the BESS will start releasing to maintain its stored energy and reduce its peak.

The serial-parallel prediction module 211 can predict a predicted load value of a set time (for example, 20 minutes) in a fairly accurate manner.

The predicted load value is used to perform peak reduction and load curve smoothing of the power distribution.

The reason for configuring the peak reduction and smoothing unit 210 as described above is for the purpose of peak reduction, and the BESS can provide the characteristic of following the SOC locus obtained by the optimization algorithm using the CVNN prediction approach.

On the other hand, the operation of actual charge and discharge only changes the load curve for the time.

Since introduction of the energy storage system (ESS) according to the present invention reduces the maximum electric power demand, it is possible to reduce the electricity base rate (electric power charge) and reduce the electric power consumption at a time when the energy cost is high, (Electric power charge) can be obtained at the same time.

In addition, if the maximum load is reduced due to the introduction of the energy storage device (ESS), it is also possible to additionally delay the extension of the electric power facility.

For maximum load reduction and energy leveling, the existing energy storage (ESS) algorithm uses a demand response algorithm based on the same predicted load pattern.

This can improve the control reliability by implementing the optimal charge / discharge schedule for the regular load, but there is a slight risk for the unfixed load.

If the unstructured load is difficult to predict, the larger the error, the larger the error range becomes. The failure of control due to erroneous control can cause a great economic loss to the customer.

Typically, the short-term demand forecast error is expected to be around 20%.

If an error occurs in the ideal daily demand target value, it hinders efficient operation of the energy storage system (ESS). If the predicted value is derived by more than 20% of the actual target value, the load does not reach the predicted value ESS has a problem that it does not work at all for one day.

Conversely, if the energy storage unit (ESS) predicts 20% less than the target value, the energy stored in the energy storage unit (ESS) is discharged before reaching the target value. If the target value is reached, And the increase of the load can not be suppressed.

If the prediction is successful, it will be possible to effectively distribute the energy of the energy storage device (ESS) for about 240 minutes to maintain the maximum load, effectively restricting the maximum load, but the wrong prediction due to the prediction error is the maximum output of the energy storage device Causing the energy to be discharged, resulting in a problem of consuming all of the energy after about one hour.

In order to solve the above problem, the forced discharge performing unit 220 is configured in the present invention.

The forced discharge performing unit 220 sets a minimum unit time for operation of the energy storage device, extracts a current load size, and performs charging / discharging of the energy storage device according to a demand response sequence.

That is, the minimum unit time for operating the energy storage device is set, and the current load size is extracted.

Thereafter, the charging or discharging is determined according to the demand reaction sequence.

At this time, the energy storage device is operated in 24 hours a day by checking the current time, and the daily power demand target value is defined as the set sequence at the end of the day.

7, the ESS control means 200 of the present invention can have the appearance of FIG. 7, and a front panel is present on the front surface. The front panel includes a display panel 250 The display panel generates a predicted load curve for predicting a load value with reference to past data processed by the serial-parallel prediction module 211 and data generated through future prediction. .

Then, the active power flow of each time energy storage device processed by the peak reduction execution module 212 is determined, and the operation range restriction condition of the SOC is performed. Data corresponding to the operation range restriction condition is provided to the display panel can do.

The smoothing module 213 performs smoothing on the peak load curve reduced by the peak reduction execution module to perform accurate load prediction, and the corresponding load prediction curve is displayed.

In addition, a power on / off button 260, a setting button 261 for setting a minimum unit time of operation of the energy storage device, up-down buttons 262 and 263 and a current charge / And a communication port 265 for performing communication with an external terminal.

6 is a PCS block diagram exemplarily showing an example applied to a maximum demand power reduction system using an energy storage device according to an embodiment.

The PCS 300 includes a system power control module 310 for acquiring charge / discharge performance information by the forced discharge performing unit and for charging / discharging the energy storage device, And an AC voltage control module 330 for receiving an output current reference value calculated by the grid power control module 310 and generating an output voltage reference value.

In the grid-connected operation, the system synchronization is performed to link the system and the PCS at the beginning of the operation. After the system synchronization is completed, the operation is performed according to the command.

In the case of the conventional operation method, a method of operating the switchgear and synchronizing the output of the PCS with the transformer of the PCS and synchronizing the output of the PCS with the voltage of the secondary side of the transformer is mainly used. In this case, An inrush current twice or more of the rated value may be generated and the problem may be caused to be applied to the system.

In the PCS of the present invention, the output terminal of the PCS is initially connected to the transformer through the ACB, the output of the PCS is gradually increased so that the phase voltage and the magnitude of the system voltage are the same, the voltage of the system is matched with the output voltage of the PCS And operates the switch gear to operate in association with the system.

In this case, the transient current generated during the grid connection can be limited to within 10% of the rated current.

In the present invention, the grid connection operation is performed in two control modes.

Constant power control (CP) mode that charges / discharges energy according to the upper power command value and DC voltage control that charges the energy storage device by SOC while maintaining battery voltage, CV) mode.

In this case, the grid power control mode is performed by the grid power control module 310, and the DC voltage control mode is performed by the DC voltage control module 320.

In case of grid power control, the charge and discharge power is controlled by grasping the situation of the system and the battery power of the energy storage device. In case of DC voltage control, check the voltage and SOC of the energy storage device and check the DC voltage .

The determination of the grid power control and the DC voltage control is made by the central control unit for obtaining the charging / discharging performance information by the forced discharge performing unit and analyzing the corresponding information. In order to obtain the desired output, the AC voltage control module .

The AC voltage control module 330 receives the output current reference value calculated by the grid power control module and generates an output voltage reference value.

The output voltage reference value is reflected in the actual output from the PCS.

The actual output produces a voltage with the rated magnitude and frequency, and the output power is determined by the load. The magnitude and frequency of the output voltage are used as reference values of the system voltage magnitude and frequency input to the PCS as parameters.

In summary, the output current reference value is input to the AC voltage control module, and the corresponding voltage is output based on the output voltage reference value required for the system.

The grid power control module 310 performs a function of generating a current reference value based on the magnitude of the grid voltage. Here, since the power is proportional to the voltage and the current, when the magnitude of the system voltage increases in a state where the power is kept constant, the output current of the energy storage device 400 (ESS) decreases, The output current of the energy storage device 400 (ESS) is increased.

The AC voltage control module 330 receives the current reference value calculated by the grid power control module and performs PI control to generate an output voltage reference value.

At this time, the output voltage reference value generated by the AC voltage control module 330 is generated by reflecting the grid voltage magnitude as the forward bias compensation to improve the characteristics of the PI control output for the error between the output current and the feedback current.

The DC voltage control module 320 compares the reference value of the DC voltage with the current DC voltage magnitude and calculates an output current reference value to maintain a constant power. The AC voltage control module 330 calculates the output voltage reference value, .

8 is a graph showing the predicted load curves obtained from equations (1) to (4).

The predicted results are compared with the CVNN predicted load without Kalman filter (Fig. 8 (a)) and the Kalman filter applied CVNN predicted load (Fig. 8 (b) The smoothed prediction data appeared in two different forms.

The Kalman filter is a recursive filter that tracks the dynamic state that contains noise and is based on measurements made over time. That is, the Kalman filter is an optimal state estimation process applied to a dynamic system including an irregular outer space. The Kalman filter is an optimal state estimation process, Is a linear, unbiased, minimum error variance recursive algorithm for optimally estimating the unknown state variables of the dynamic system.

8, the CVNN predicted load (FIG. 8 (a)) to which the Kalman filter is not applied has a large change amplitude, whereas the CVNN predicted load (FIG. 8 )), The variation width was relatively small.

The serial-parallel method is used for predicting the load data in the next 10 to 30 minutes. For example, the predicted result with the actual data extracted every 20 minutes is shown in FIG.

Referring to FIG. 9, it can be seen that the predicted load is derived according to the actual load, as a result of 20 minutes predicted with actual load data according to the present invention.

The compensated load flow through the transformer is shown in Fig.

As can be seen in FIG. 10, the correction load is nearly flat until 8 am, but the difference between the actual load and the predicted load appears due to the prediction error.

In addition, the predicted and actual loads vary considerably at midday, so the actual load is derived higher than the compensated load. In this case, the energy storage device emits the stored energy between 16:00 and 21:00, keeping the compensating load flat.

As described above, the prediction method is presented and the results are compared with the actual load values.

As a result of the comparison, the present invention uses the past prediction data and predicts the load measurement point 20 minutes in advance with considerable accuracy.

The predicted load value is used to perform peak reduction and load curve smoothing of the power distribution.

For peak reduction purposes, the energy storage device follows the SOC locus obtained by the optimization algorithm using the predictive approach of the present invention.

This method can cause a serious error to an inaccurate predicted load which causes improper charging / discharging of the energy storage device at a time when the circuit load becomes worse.

Thus, the smoothing function is provided to reduce the variation during the peak reduction operation.

This method also relies on the serial-parallel prediction method, while adding smoothing to the reduced peak load curve.

The smoothing approach not only shifts the load but also reduces the stochastic load variation due to the PV generation.

For optimal operation, there are some user-interactive parameters that can be matched with other variables in the power system.

A serial-parallel method that benefits from real-time data of circuit loads and weather forecasts may help to obtain better load estimates for the next time step.

The energy consumption of the energy storage device can be efficiently managed by providing the peak reduction and the load curve smoothing through the above-described configuration and operation, thereby effectively reducing the maximum demand power and reducing the energy consumption In order to prevent the exhaustion of the energy storage device, the minimum unit time of the operation of the energy storage device is set so that the energy storage device can be charged or discharged according to the current load size.

The maximum demand power reduction system using the energy storage device of the renewable power generation source described above can be implemented in the form of program instructions that can be executed through various computer components and can be recorded in a computer readable medium.

The computer readable medium may be any medium accessible by the processor. Such media can include both volatile and nonvolatile media, removable and non-removable media, communication media, storage media, and computer storage media.

A communication medium may include computer readable instructions, data structures, program modules, other data of a modulated data signal such as a carrier wave or other transmission mechanism, and may include any other form of information delivery medium known in the art.

The storage medium may be any type of storage medium such as RAM, flash memory, ROM, EPROM, electrically erasable read only memory (" EEPROM "), registers, hard disk, removable disk, compact disk read only memory Or any other type of storage medium.

Computer storage media includes removable and non-removable, nonvolatile, and nonvolatile storage media implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data, Volatile media.

Such computer storage media may be embodied as program instructions, such as RAM, ROM, EPROM, EEPROM, flash memory, other solid state memory technology, CDROMs, digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage, Lt; RTI ID = 0.0 > and / or < / RTI >

Examples of program instructions may include machine language code such as those produced by a compiler, as well as high-level language code that may be executed by a computer using an interpreter or the like.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

100: New and renewable power generation source
200: ESS control means
300: PCS
400: Energy storage device

Claims (7)

A system for reducing the maximum demand power using an energy storage device of a system power supply or a renewable power generation source,
A peak reduction and smoothing unit for performing a peak reduction of the energy storage device, a minimum unit time of the energy storage device operation, extracting a current load size, performing charge / discharge of the energy storage device according to a demand response sequence, And a PCS for controlling charging and discharging of the energy storage device,
Wherein the peak reduction and smoothing unit comprises:
A serial-parallel prediction module for generating a predicted load curve for predicting a load value by referring to past data and data generated through future prediction using a spline interpolation method;
A peak reduction execution module for calculating a battery energy storage system (BESS) active power flow and an SOC (State of Charge) by determining an active power flow of the energy storage device for each time with reference to a capacity value and a predicted load value of the energy storage device, ; And
A smoothing module for smoothing the peak load curve reduced by the peak reduction performing module to perform load prediction;
/ RTI >
The PCS,
A system power control module for controlling charging and discharging power by sensing a state of a system and a battery power of the energy storage device according to a system power control mode;
A direct current voltage control module for controlling the battery of the energy storage device to be fully charged based on the voltage of the energy storage device and the SOC according to the direct current voltage control mode; And
An AC voltage control module for generating an output voltage reference value through PI control using the output current of the energy storage device as a current reference value in the grid power control module;
And,
Wherein the output voltage reference value generated by the AC voltage control module includes:
And is reflected on the actual output outputted from the PCS.
The method according to claim 1,
The serial-parallel prediction module includes:
And the load measuring point can be predicted in the range of 10 to 30 minutes in advance.
delete The method according to claim 1,
The AC voltage control module includes:
Wherein the output voltage reference value is generated by reflecting the grid voltage magnitude as a forward compensation to improve the characteristics of the controller in the controller output with respect to the error between the output current and the feedback current.
delete The method according to claim 1,
Wherein the predicted load value of the deserializer
Wherein the peak power reduction system is used to perform peak reduction and load curve smoothing of the power distribution system.
delete

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