KR102014201B1 - Energy storage system for performing peak reduction and smoothing of power grid - Google Patents

Abstract

The present invention relates to an energy storage system which can efficiently manage energy by reducing a peak and smoothing a load curve. The energy storage system for performing peak reduction and smoothing according to the present invention comprises: a peak reduction and smoothing unit including a serial and parallel estimation module for estimating a load value using a spline interpolation method by referring to past data and data generated through future estimation, a peak reduction module for determining effective power flow of an energy storage device for each time to perform an operation range restriction of an state of charge (SOC) by referring to a capacity value and an estimated load value of the energy storage device, and a smoothing module for smoothing a peak load curve reduced by the peak reduction module to estimate a correct load; a forced discharge performing unit for setting a minimum unit time of an operation of the energy storage device, extracting a size of a current load and charging or discharging the energy storage device according to a demand response sequence; and a PCS having a system power control module for obtaining charge or discharge performing information processed by the forced discharge unit performing unit to charge or discharge the energy storage device, a direct current voltage control module for charging the SOC by 100%, and an alternating current voltage control module for receiving a reference value of an output current calculated by the system power control module to perform a control operation and to generate a reference value of an output voltage.

Description

Energy storage system for peak reduction and smoothing {ENERGY STORAGE SYSTEM FOR PERFORMING PEAK REDUCTION AND SMOOTHING OF POWER GRID}

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an energy storage system for performing peak reduction and smoothing of a power system including microgrids. The present invention relates to an energy storage system for storing power generated from renewable energy sources such as grid power, solar power, wind power, and fuel cell power generation systems. A storage device relates to an energy storage system capable of efficiently managing energy in an energy storage device by providing peak reduction and load curve smoothing.

In order to solve the power supply and demand problem, the electricity differential rate plan is designed to induce consumers to respond to the price signal by applying different price rates according to the time of use of the electricity, thereby reducing the maximum power demand and leveling the daily load. It is being applied in various forms in place of the fixed power plan.

In order to reduce the power usage fee of the customer on the basis of the power differential rate plan, it is desirable to minimize the power usage in the heavy load time period with a high price rate and to maximize the light usage during the light load time period with a low price rate.

However, there are cases where it is not practical to change power demand patterns arbitrarily in industry, such as electricity use is directly related to production activities, and commercial facilities are also involved in reducing heating and cooling loads.

Of course, the conventional method of managing power demand by directly controlling the load may not be an appropriate solution to the above problem.

On the other hand, recently, as the development of large-capacity energy storage devices has been actively progressed, there has been a growing interest in the application for consumer power demand management of the energy storage devices, but the development of the specific integrated operation method is insignificant.

Therefore, in an energy storage device that stores electric power generated from a renewable power source such as a grid power source or a solar power, wind power, fuel cell power generation system, etc., peak reduction and load curve smoothing are provided to provide energy of the power system and the energy storage device. It is to propose a technology that can be managed efficiently.

KR 10-2009-0131354 A (2009.12.29.)

The present invention has been made to solve the problems of the prior art, the problem to be solved in the present invention in the energy storage device for storing power, providing peak reduction and load curve smoothing to provide energy of the energy storage device The purpose of this is to manage them efficiently.

In order to solve the above problems, in the energy storage system for performing peak reduction and smoothing of the power system according to the present invention, spline interpolation is performed to estimate load values by referring to historical data and data generated through future prediction. In order to determine the effective power flow of the energy storage device for each time by referring to the capacity value of the energy storage device and the predicted load value, an operation range constraint of the SOC (State of Charge) is generated. A peak reduction and smoothing unit including a peak reduction performing module for performing the same, and a smoothing module for performing accurate load prediction by smoothing the peak load curve reduced by the peak reduction performing module; ESS control means including a forced discharge execution unit for setting the minimum unit time of the operation of the energy storage device, extracting the current load size, and performing charging or discharging of the energy storage device according to the demand response sequence; And a system power control module for charging and discharging the energy storage device by acquiring charge and discharge performance information processed by the forced discharge performing unit, a DC voltage control module for charging 100% of SOC, and the system power. It characterized in that it comprises a PCS including an AC voltage control module for generating an output voltage reference value by performing the control by receiving the reference value of the output current calculated in the control module.

Here, the serial-parallel prediction module is characterized in that the load measurement point can be predicted in advance in the range of 10 to 30 minutes.

According to the present invention, in the energy storage device for storing electric power, it is possible to provide peak reduction and smoothing the load curve to effectively manage energy of the energy storage device.

In addition, in order to prevent the problem that the energy is exhausted when the prediction is wrong, 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. Done.

1 is an overall configuration diagram showing an example of an energy storage system for performing peak reduction and smoothing according to an embodiment.
2 is an exemplary ESS control means block diagram illustrating an example of an energy storage system for performing peak reduction and smoothing according to an embodiment.
3 is a diagram illustrating a neural network structure including nodes, layers, and weights illustrating an example of an energy storage system for performing peak reduction and smoothing according to an embodiment.
4 is a flowchart of a method for predicting a series-parallel load.
5 is a graph showing the SOC trajectory.
6 is a PCS block diagram illustrating an example of an energy storage system for performing peak reduction and smoothing according to an embodiment.
7 is a diagram illustrating an example of an external appearance of an energy storage system for performing peak reduction and smoothing according to an embodiment.
8 is a diagram illustrating a prediction method using actual load data of an energy storage system for performing peak reduction and smoothing according to an embodiment.
9 is a graph showing prediction results with actual data extracted at 20 minute intervals.
10 is a circuit load graph corrected by the BESS operation.

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

In the following description of the embodiments disclosed herein, if it is determined that the detailed description of the related known technology may obscure the gist of the embodiments disclosed herein, the detailed description thereof will be omitted.

In addition, the accompanying drawings are intended to facilitate understanding of the embodiments disclosed herein, but are not limited to the technical spirit disclosed herein by the accompanying drawings, all changes included in the spirit and scope of the present invention. It should be understood to include equivalents and substitutes.

In addition, the terms "comprise", "comprise" or "consist" as described in the following embodiments mean that the corresponding component may be included, unless otherwise stated. It is to be understood that the components are not included, but are further provided with other components.

In addition, the energy storage system for performing the peak reduction and smoothing disclosed in the following embodiments in the energy storage device for storing the power generated in the photovoltaic power generation system as a renewable energy source, by providing a peak reduction and load curve smoothing It provides a feature that can efficiently manage the energy of the energy storage device.

Hereinafter, an energy storage system for performing peak reduction and smoothing will be described in more detail.

1 is an overall configuration diagram showing an example of an energy storage system for performing peak reduction and smoothing according to an embodiment.

 Referring to FIG. 1, an energy management system for performing peak reduction and smoothing of renewable energy sources converts AC power produced from renewable energy sources 100 into DC power and stores them in an energy storage device 400. And it converts the stored DC power into AC power and performs the function of linking to the power system.

The renewable energy generation source 100, for example, means a renewable energy generation source, such as a wind power generator, a photovoltaic generator, in the embodiment of the present invention will be described with an example of a solar generator.

The energy storage device 400 (ESS) performs a function for storing electric power generated by a renewable energy generation source, and in the present invention, a distributed energy storage device is used.

In this case, the ESS control means 200 provides peak reduction and load curve smoothing of the energy storage device to efficiently manage the energy of the energy storage device, and stores the energy under the optimal conditions through the PCS 300. It charges and discharges the device, which provides 100% charge through the state of charge (SOC).

FIG. 2 is a block diagram of an ESS control means exemplarily showing an example of an energy management system for performing peak reduction and smoothing of a renewable energy source according to an embodiment.

The ESS control means 200 is largely comprised of a peak reduction and smoothing unit 210 and a forced discharge performing unit 220.

When configured as described above, the power supply network for supplying power to the customer and the power supply-related state of the consumer energy storage device connected to the power supply network are monitored and based on the customer's time zone predicted load information and time zone power charge information. According to the charging and discharging operation plan of the customer energy storage device is established by controlling the operation of the energy storage device and the power supply network to minimize the customer power usage fee.

At this time, the charge and discharge operation plan of the energy storage device is to reduce the charging cost of the energy storage device and to maximize the discharge effect.

Next, specific configuration means for providing the function will be described.

That is, the peak reduction and smoothing unit 210 uses a spline interpolation method to generate a predictive load curve for predicting a load value by referring to historical data and data generated through future prediction. Peak reduction performance module 212 for performing the operating range constraint of the SOC by determining the effective 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, the peak reduction It characterized in that it comprises a smoothing module 213 for performing a smooth load prediction by performing smoothing on the peak load curve reduced by the performing module.

Since the load smoothing requires a fairly accurate load prediction, for example, a series-parallel prediction algorithm is applied to predict load values in the next 10 to 30 minutes, and BESS (Battery Energy Storage System) SOC (minimum level) is used. While maintaining the state of charge change, the variation on the load curve is eliminated by the BESS.

Here, peak reduction is preferably performed with smoothing in order 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, while it is necessary to estimate the load for the future ahead of the present for the purpose of peak reduction, it may be effective to perform through the prediction in the range of 10 to 30 minutes for the smoothing purpose. Preferably, the smoothing may be performed through 20 minutes earlier prediction.

According to such a request, the serial-parallel prediction module 211 generates a prediction load curve for predicting a load value by referring to data generated through past data and future prediction using spline interpolation.

3 shows a multi-value neural network structure comprising an input layer, a hidden layer and an output layer, wherein the input layer comprises 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 _n0 , W ² _l0 , W ¹ _nm , W ² _ln, etc.) and the activation function f _c represent actual values, the neural network Is a normal artificial neural network (ANN), and if it has a complex value in the aforementioned parameter, the neural network can be regarded as a complex-valued neural network (CVNN).

)을 나타낸다.Equation (1) is the lth output value of the neural network (predicted value,

).

수식 (1)

Formula (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 hidden neuron H _n is represented by Equation (2).

수식 (2)

Formula (2)

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

Where x _m = [v (k-1), v (k-2),..., Including complex value input m (m = i + p). , v (k-p), y (k-1), y (k-2),... , y (k-i). The total values of m, n, i, l and p are positive integers.

In Equation (2), the split sigmoid function may be expressed as Equation (3) if it is regarded as an active function.

수식 (3)

Formula (3)

Where f _c (z) is the activity function.

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

By using the split sigmoid function instead of undivided, we can avoid the problem of the specificity of the function.

Therefore, the application of complex backpropagation algorithm and weights (ΔW ² _l0 , ΔW ² _ln , ΔW ¹ _n0) And ΔW ¹ _nm ), the error can be calculated by the following equation (4).

수식 (4)

Formula (4)

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

Is prediction data.

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

In other words, daily historical data is used in addition to the load history data. The historical data continues from 20 minutes ago to the present moment.

In addition, load measurement points calculated by parallel prediction (CVNN) are also required.

As a result, you must have a set of data that includes points from historical data and future predictions, and spline interpolation is suitable for analysis of these data sets.

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

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

Then, the weight of the error is added to the mean value to make the final prediction value.

This error does not exist at the first prediction point, and the above steps are performed for prediction after the first point.

Since this process mimics the proportional integration (PI) control mechanism, it is referred to as PI control as shown in FIG. 4, and the estimated time of 20 minutes can be changed and applied in the range of 10 to 30 minutes according to a user setting.

The 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 trajectory obtained.

Next, midday variation is limited while adhering to the planned SOC trajectory as much as possible.

The peak reduction performance module 212 performs the operating range constraint of the SOC by determining the effective 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.

Charge the BESS early in the morning for peak reduction, and the BESS power flow should flatten the load curve to account for constraints.

The cost function is therefore defined as minimizing the total load deviation from the straight line.

The total load consists of the predicted load and the BESS power, that is, the BESS attempts to maintain the load curve in a straight line of known capacity, and the mathematical objective function fm is given by equation (5).

수식 (5)

Formula (5)

Where f (m) is the mathematical objective function, N is the number of time steps in the prediction horizon, L (t) is the predictive load in time step t, and P (t) is the BESS power in time step t.

The objective function J (t) in time step t applied to Equation (5) is defined as Equation (6) as the total load multiplied by the time-varying coefficient of time flowing through the distribution transformer.

수식 (6)

Formula (6)

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

In order to impose a high load on the distribution transformer, the power W (t) is set to the square of the total power flow.

수식 (7)

Formula (7)

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

Equation (7) is introduced into Equation (6) to obtain the first derivative and calculate for horizontal results in Equation (5), where the SOC constraints are defined by Equations (8) and (9) as follows.

수식 (8)

Formula (8)

수식 (9)

Formula (9)

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

수식 (10)

Formula (10)

In the above, SOC _min and SOC _max means the minimum and maximum allowable SOC, E _tot And E (t) represents the capacity of the BESS capacity and the storage energy, respectively, at time step t.

Δt represents the planned time resolution.

By calculating Equation (5), it is possible to calculate the BESS effective power flow and SOC resulting from each time step.

The smoothing module 213 performs smooth load prediction by performing smoothing on the peak load curve reduced by the peak reduction performance module.

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

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

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

As a result, SOC deviations are kept to a minimum level since the BESS charge and discharge amounts should be about the same.

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)

Formula (11)

Where SOCe is SOC error, P (t) is BESS power at time step t, and E _tot is In time step t, the BESS capacity, P _x (t), means the BESS supplemental power provided with SOC correction.

The smoothing error SM _e at time t is represented by Equation (12).

수식 (12)

Formula (12)

Where SMe is the smoothing error at time t, P (t) is the BESS power at time step t, and SL (t) is the smoothing level at time t.

In Equation (12), the smoothing level SL (t) is defined by the following Equation (13).

수식 (13)

Formula (13)

Where SL (t) is the smoothing level, L (t) is the predictive load at time step t, P (t) is the BESS power at time step t, and f (t + Δt) is the preceding predictive load.

Residual conditions representing SOC deviations from the set planned points are added to compensate for SOC deviations. In addition, f, which implies the previous load prediction, is responsible for calculating the smoothing level, and the smoothing level is updated at the end of each period.

The total cost is expressed as the weighted sum of the SOC and smoothing error, as shown in equation (14).

수식 (14)

Formula (14)

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

수식 (15)

Formula (15)

Where Px is the optimal BESS power.

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

수식 (16)

Formula (16)

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

Peak reduction is performed using Eqs. (5) to (9), where load is obtained using the CVNN prediction method shown in Equations (1) to (4).

According to the pure peak reduction method, the load curve is kept as flat as possible based on the prediction curve and the SOC constraint.

Equations (5) to (10) are calculated to present the target SOC trajectory shown in FIG. 5 in which the 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 am, releasing some of the stored energy until about 10 am to lower the load curve.

After local minimization in the SOC trajectory that occurs at about 10 am, the BESS charges until 2 pm to increase the load slightly below the flat line of about 800 kW.

After 2 pm and 6 pm, the BESS will begin emitting to maintain stored energy and reduce the peak.

The serial / parallel prediction module 211 may predict a predicted load value of a predetermined time (for example, 20 minutes) fairly accurately.

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

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 a characteristic that follows the SOC trajectory obtained by the optimization algorithm using the CVNN prediction approach.

On the other hand, in the actual charging and discharging operation, the load curve changes only for the corresponding time.

The introduction of ESS reduces the maximum power demand, which can reduce the basic electricity bill (power bill) and reduce the electricity consumption during high energy costs, thereby simultaneously reducing the electrical energy bill (power bill).

In addition, if the maximum load is reduced due to the introduction of the ESS, additional capacity expansion may also be delayed.

The existing ESS algorithm for maximum load reduction and energy leveling uses a demand response algorithm based on the expected load pattern on the day.

This can improve control reliability by implementing an optimal charge / discharge schedule for a standardized load, but there are some risks for unstructured loads.

If the unstructured load is difficult to predict, the larger the error, the larger the error range, and a control failure due to incorrect control may cause a large economic loss to the customer.

Typically, errors in short-term demand forecasts are expected to be ± 20%.

An error in the ideal daily demand target would interfere with the efficient operation of the ESS. If the predicted value is derived more than 20% above the actual target value, the ESS will not reach the forecast at all. There is a problem that does not work.

On the contrary, if it is predicted to be 20% less than the target value, the ESS discharges the energy stored in the ESS before reaching the target value, and when the actual target value is reached, the remaining energy is insufficient to suppress the increase in load. You will not be able to.

If the prediction is successful, the maximum load can be effectively suppressed by allocating the energy of the ESS for about 240 minutes while the maximum load is maintained, but due to the prediction error, the wrong prediction causes the energy to discharge to the maximum output of the ESS. After time, a problem of consuming all the energy occurs.

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

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

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

Then, it is determined whether to charge or discharge according to the demand response sequence.

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

On the other hand, as shown in Figure 7, the ESS control means 200 of the present invention, may have the appearance of Figure 7, the front panel is present, the front panel is a display panel 250 is configured The display panel may generate a predicted load curve for predicting a load value by referring to past data processed by the serial-parallel prediction module 211 and data generated through future prediction. Will be provided.

In addition, the effective power flow of the energy storage device for each time processed by the peak reduction module 212 is determined, and the operating range constraint of the SOC is performed, and data corresponding to the operating range constraint is provided to the display panel. can do.

The smoothing module 213 performs smooth load prediction by performing smoothing on the peak load curve reduced by the peak reduction execution module, thereby displaying the corresponding load prediction curve.

In addition, at the bottom of the display panel, a power on / off button 260, a setting button 261 for setting a minimum unit time of an energy storage device, an up-down button 262 and 263, and current charging / discharging performance can be checked. It includes a lamp 264, and includes a communication port 265 for performing communication with the external terminal.

6 is a PCS block diagram illustrating an example of an energy storage system for performing peak reduction and smoothing according to an embodiment.

The PCS 300 obtains the charging / discharging performance information by the forced discharge performing unit and charges and discharges the energy storage device by the system power control module 310 and the energy storage device 400 (ESS) by SOC 100%. A DC voltage control module 320 for charging and an AC voltage control module 330 for generating an output voltage reference value by receiving the output current reference value calculated by the grid power control module 310 are included.

In system linkage operation, system synchronization is performed to link the system with the PCS at the beginning of operation. After the system synchronization is completed, it operates according to the command.

In the conventional driving method, a switch gear is operated to link the system and the output transformer of the PCS and to synchronize the output of the PCS with the secondary voltage of the transformer. However, in this case, when connected without an initial charging circuit, in a transient state Inrush current more than twice the rating can occur and cause problems to be applied to the system.

The PCS of the present invention initially connects the output terminal of the PCS to the transformer through the ACB and gradually increases the output of the PCS so that the phase and magnitude of the grid voltage are equal to pressurize the transformer so that the voltage of the grid and the output voltage of the PCS match. After confirming that, the switchgear is operated to connect with the system.

In this case, it is possible to limit the transient current generated during grid linkage to within 10% of the rated current.

In the present invention, the grid-connected operation consists of two control modes.

Constant Power Control (CP) mode, which charges and discharges energy according to the upper power command value, and Constant Voltage Control, which charges the energy storage device by 100% while maintaining the voltage of the battery. CV) mode.

At this time, the system power control mode of the respective modes is performed by the system power control module 310, the DC voltage control mode is performed by the DC voltage control module 320.

In the case of grid power control, the charging and discharging power is controlled by checking the system situation and the battery power of the energy storage device.In the case of the DC voltage control, the DC voltage is supplied to enable full charging while checking the voltage and SOC of the energy storage device. To control.

The determination of the grid power control and the DC voltage control is made by the central control unit which acquires charging / discharging performance information by the forced discharge execution unit and analyzes the information, and sets the command value to the AC voltage control module 330 to obtain a desired output. Enter it.

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

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

The actual output generates a voltage of 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 grid voltage and frequency input as parameters to the PCS.

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 generates a current reference value based on the magnitude of the grid voltage. Herein, since the power is proportional to the voltage and the current, when the magnitude of the grid voltage increases while the power is kept constant, the output current of the energy storage device 400 (ESS) decreases. In the case of decreasing, the output current of the energy storage device 400 (ESS) increases.

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 system voltage magnitude as a forward compensation to improve the characteristics of the PI control output for the error of the output current and the feedback current.

On the other hand, the DC voltage control module 320 calculates the output current reference value to maintain a constant power by comparing the reference value of the DC voltage and the current DC voltage magnitude, and the output voltage reference value AC voltage control module 330 To be provided.

8 shows a graph of the predictive load curves obtained from Equations (1) to (4).

The prediction results were compared with the CVNN prediction load without the Kalman Filter (FIG. 8 (a)) and the CVNN prediction load with the Kalman Filter (FIG. 8 (b)). Smoothed prediction data appeared in different forms in two cases.

The Kalman filter is a recursive filter that tracks the mechanical state of 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 outlier, and data including noise measured at discrete real time intervals is included. It is a recursive algorithm of linear, unbiased, and minimum error variance for optimal estimation of unknown state variables of dynamic systems.

Referring to FIG. 8, the CVNN prediction load without the Kalman Filter (FIG. 8A) has a large amplitude of change, whereas the CVNN prediction load with the Kalman Filter is applied (FIG. 8B). In)), the width of change is relatively small.

The serial-to-parallel method is then used to predict load data in the range of 10-30 minutes, for example the prediction results with actual data extracted at 20-minute intervals are shown in FIG.

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

The compensation load flow through the transformer is shown in FIG.

As can be seen in FIG. 10, the corrected load is almost flat until 8 am, but due to a prediction error, the actual load and the predicted load appear to have a predetermined difference.

In addition, since the forecast and actual loads vary considerably around noon, the actual load is derived higher than the corrected load. In this case, the energy storage device released the stored energy between 16 o'clock and 21 o'clock to keep the correction load flat.

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

As a result of the comparison, the present invention uses historical prediction data and was able to predict load measurements 20 minutes ahead of time fairly accurately.

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

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

This method can cause serious errors with inaccurate predicted loads that cause inadequate charging / discharging of the energy storage device at some point that worsens the circuit load.

This provides a smoothing function to reduce fluctuations during peak reduction operations.

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

The smoothing approach not only shifts the load, but also reduces the probabilistic load fluctuations usually caused by PV generation.

There are some user interactive parameters that can be matched with other variables in the power system for optimal operation.

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

Through the above configuration and operation, it is possible to efficiently manage the energy of the energy storage device by providing peak reduction and load curve smoothing, and to prevent energy exhaustion when the prediction is wrong. By setting the minimum unit time of the storage device operation to provide the effect of performing the charge / discharge of the energy storage device according to the current load size.

The energy management system for performing peak reduction and smoothing of the renewable energy source described above may be implemented in the form of program instructions that can be executed through various computer components and recorded in a computer readable medium.

Computer-readable media can be any media that can be accessed by a processor. Such media can include both volatile and nonvolatile media, removable and non-removable media, communication media, storage media and computer storage media.

The communication medium may include other data of a modulated data signal, such as computer readable instructions, data structures, program modules, carriers or other transmission mechanisms, and may include any other form of information transmission medium known in the art.

Storage media may be RAM, flash memory, ROM, EPROM, electrically erasable read only memory ("EEPROM"), registers, hard disks, removable disks, compact disk read only memory ("CD-ROM"), or any known And other forms of storage media.

Computer storage media are removable and non-removable, volatile and non-removable, 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 include program instructions such as RAM, ROM, EPROM, EEPROM, flash memory, other solid state memory technologies, CDROMs, digital versatile disks (DVDs), or other optical storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices, and the like. It may include a hardware device specifically configured to store and perform.

Examples of program instructions may include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

Although the preferred embodiment of the present invention has been described above, the scope of the present invention is not limited thereto, and it should be understood that the scope of the present invention extends to the range that is substantially equivalent to the embodiment of the present invention. Various modifications can be made by those skilled in the art without departing from the spirit of the invention.

100: renewable energy generation
200: ESS control means
300: PCS
400: energy storage device

Claims (7)

A grid power supply; and a distributed energy storage device; And an energy storage system for performing peak reduction and smoothing of a renewable power source including a wind power generator or a solar power generator.
Set the peak reduction and smoothing unit performing peak reduction of the energy storage device and the minimum unit time of the operation of the energy storage device, extract the current load size, and perform charge / discharge of the energy storage device according to the demand response sequence. ESS control means including a forced discharge performing unit for controlling and the charge and discharge of the energy storage device PCS,
The peak reduction and smoothing unit,
A parallel-parallel prediction module for generating a predictive load curve for predicting a load value by referring to past data and data generated through future prediction using spline interpolation;
Peak reduction performance module that calculates the BESS (Battery Energy Storage System) effective power flow and SOC (State of Charge) by determining the effective 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. ; And
A smoothing module performing smoothing on the peak load curve reduced by the peak reduction performing module to perform load prediction;
Including;
The PCS,
A system power control module configured to perform charging / discharging power control by identifying a system situation and battery power of the energy storage device according to a system power control mode;
A DC voltage control module controlling to fully charge a battery of the energy storage device based on the voltage of the energy storage device and the SOC according to the DC voltage control mode; And
An AC voltage control module configured to generate 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;
It is configured to include,
The serial and parallel prediction module,
Energy storage system for performing peak reduction and smoothing, characterized in that the load measurement point can be predicted in the range of 10 to 30 minutes in advance.
delete The method according to claim 1,
The output voltage reference value generated by the AC voltage control module is
Energy storage system for performing peak reduction and smoothing, characterized in that reflected to the actual output output from the PCS.
The method according to claim 1,
The AC voltage control module,
Energy storage system for peak reduction and smoothing, characterized in that the output voltage reference value is generated by reflecting the system voltage magnitude as a forward compensation to improve the characteristics of the controller in the controller output against the error of the output current and the feedback current. .
delete The method according to claim 1,
The predicted load value of the serial-to-parallel prediction module is
An energy storage system for performing peak reduction and smoothing, characterized in that it is used to perform peak reduction and load curve smoothing of a distribution network.
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