KR20160017682A - Method for managing peak shaving in large scale battery energy storage system - Google Patents

Abstract

The present invention relates to a method to manage peak shaving in a large battery energy storage system. According to the present invention, the method to manage peak shaving includes: a step of receiving system marginal prices (SMP) information and load data a day ago, secured for the management of a power system; and a step of rearranging first SMP information and load data of the previous long period at the end of the final timing of a time unit of the next long period when entering the next long period in order to maintain the entire charging/discharging scheduling section based on the SMP information; and a step of performing optimized charging/discharging scheduling at a corresponding real-time driving time by using an SMP shaving algorithm based on the rearranged input data. According to the present invention, in order to employ and manage the large battery energy storage system to store overproduced energy (electricity), since SMP shaving, which is a kind of long period control, is applied in real time, a difference between a high SMP moment and a low SMP moment is reduced to maximize economic gains.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a peak shaving operation method for a large-capacity battery energy storage system,

[0001] The present invention relates to a peak shaving operation method of a large capacity battery energy storage system, and in particular, by applying SMP (system marginal prices) shaving in real time, 0001] The present invention relates to a peak shaving operation method for a large-capacity battery energy storage system.

Rapid industrialization today has caused a worldwide surge in energy demand. Recently, the distribution and expansion policies of distributed power sources are being actively promoted in these environments.

The electric power storage device is a device that can store the generated electric power more than necessary and use it when necessary, thereby improving the efficiency of energy use and the utilization of renewable energy, and stabilizing the power system.

As the new renewable energy is increasing, power storage devices are being applied before power distribution and transmission. In the future, large capacity (MW class) power storage devices will be spread by substation. Accordingly, if a scheduling apparatus and method capable of integrally managing a large number of large-capacity power storage devices that are widely spread and supplied are provided, it will be very helpful for load control of power system and operation of power generation facilities.

Currently, researches on large capacity power storage devices are actively being carried out. With the global trend of smart grid, it is expected that power storage devices will be expanded and operated in the whole power system with the increase of renewable energy generation power.

Meanwhile, the application of the power storage device to the power system of the above-described kind can be divided into a long period and a short period according to the power generation cycle. The long-term operation mainly uses the power storage device as an energy source mainly for the purpose of generation and movement of energy use, and generally, it has a long charging period and a discharging period of one cycle per day.

Among the long-term and short-term roles of the battery energy storage system as a power storage device, in order to smoothly perform peak shaving to perform an energy time shift function through long-term charging and discharging, long- Time control strategy according to the state of the power conditioning system (PCS) that constitutes the battery energy storage system (BESS) and the change of the state of charge (SoC) acquired at the time of power saving.

Korean Registered Patent No. 10-1281309 (published on Mar. 3, 2013) Korean Patent Laid-Open No. 10-2014-0067746 (published on June 4, 2015)

SUMMARY OF THE INVENTION The present invention has been made in view of the above-mentioned problems, and it is an object of the present invention to provide a large-capacity battery energy storage system for storing over-produced energy (electric power), and to provide a system with an SMP (system marginal prices) The present invention provides a peak shaving operation method for a large-capacity battery energy storage system that can maximize economic gain by applying the peak shaving operation in real time.

According to an aspect of the present invention, there is provided a peak shaving operation method for a large-capacity battery energy storage system,

A peak shaving operation method by executing a peak shaving algorithm mounted on a power management system (PMS) of a large-capacity battery energy storage system,

a) receiving by the input unit of the PMS 1-day previous load data and SMP (system marginal prices) information secured in the power system operation;

b) The control unit of the PMS, based on the SMP information, maintains the entire charge / discharge scheduling interval, and at the time of entering the next long period, the first SMP information and load data of the previous long- Rearward of the final point of time; And

c) after the relocation of the SMP information and the load data, obtaining the solution of the constraint condition related to the object function and the object function of the SMP shaving using the relocated input data by the controller;

d) using the SMP shaving algorithm by the control unit on the basis of the relocated input data, the obtained objective function of the SMP shaving and the solution of the constraint condition related to the objective function, and performing charge / discharge scheduling And a step of performing the step of performing the step of performing the step

Here, in the step c), the objective function of the SMP shaving may be expressed by the following equation as an objective function to minimize the power purchase price.

In addition, in the step c), the constraint condition may include a discharge differential equation showing a change characteristic of a state of charge (SoC) at the time of charging and discharging the battery.

At this time, the discharge differential equation can be expressed by the following equation.

In performing the optimized charging / discharging scheduling in step d), preferably, state information of a power conditioning system (PCS) module applicable to real-time operation and charging state information of a battery belonging to each PCS module Charge / discharge scheduling is carried out.

In addition, in performing the optimized charge / discharge scheduling in step d), charge / discharge scheduling optimized for 1 to 48 periods is performed in the first long-term time unit, Performs optimized charge / discharge scheduling for the 1 to 48-interval data generated by moving the first data to the last interval.

According to the present invention, when introducing and operating a large-capacity battery energy storage system for storing over-produced energy (electric power), SMP (system marginal prices) shaving, which is a kind of long- Thereby maximizing the economic gain by reducing the difference between the high and low SMP points.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart illustrating an implementation of a peak shaving operating method of a large capacity battery energy storage system in accordance with an embodiment of the present invention; FIG.
2 is a circuit diagram of an equivalent model of the BESS employed to implement the method of the present invention;
3 is a diagram illustrating a process of dividing data of one day into data of a plurality of intervals according to the method of the present invention and optimizing the scheduling at each time.
Figure 4 shows SMP data of any particular day for SMP shaving by the method according to the present invention.
5 is a diagram showing a result of applying a real-time SMP shaving algorithm employed in the present invention to an RT-mode simulator by using load data and SMP data of a specific day;
FIG. 6 is a graph showing characteristics of a reduced load curve and a reduction load curve to which a charge / discharge strategy based on SMP shaving employed in the present invention is applied; FIG.

The terms and words used in the present specification and claims should not be construed as limited to ordinary or dictionary terms and the inventor can properly define the concept of the term to describe its invention in the best way Should be construed in accordance with the principles and meanings and concepts consistent with the technical idea of the present invention.

Throughout the specification, when an element is referred to as "comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise. Also, the terms " part, "" module, "and" device " Lt; / RTI >

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

FIG. 1 is a flowchart illustrating an operation procedure of a peak shaving operation method of a large-capacity battery energy storage system according to an embodiment of the present invention.

1, a peak shaving operation method of a large capacity battery energy storage system according to the present invention includes a peak shaving operation performed by a peak shaving algorithm installed in a power management system (PMS) (not shown) of a large capacity battery energy storage system, As an operation method, the load data and the SMP (system marginal prices) information of the one day before secured in the operation of the power system are input from an external information provider (for example, KEPCO) by the input unit of the PMS (Step S101).

Then, the control unit of the PMS controls the first SMP information and the load data of the previous long period to enter the next long period to maintain the full charging / discharging scheduling interval of the battery based on the SMP information, Rearranged at the tail end of the unit (step S102). This will be described later.

When the relocation of the SMP information and the load data is completed as described above, the control unit obtains the solution of the constraint condition related to the objective function and the objective function of the SMP shaving using the relocated input data (step S103).

At this time, the objective function of the SMP shaving can be expressed by the following equation as an objective function to minimize the power purchase price.

는 k시점에서의 쉐이빙이 이루어진 부하 수준, M은 시작점, N은 끝점을 각각 나타낸다.Here, C (k) represents SMP information,

Denotes the load level at which the shaving is performed at point k, M denotes the starting point, and N denotes the end point.

In addition, the constraint condition may include a discharge differential equation showing a change characteristic of a state of charge (SoC) at the time of charging and discharging the battery in step S103.

At this time, the discharge differential equation can be expressed by the following equation.

The above-described discharge differential equation will be described later.

When the solution of the constraint condition related to the objective function of the SMP shaving and the objective function of the SMP shaving is obtained, the solution of the constrained condition related to the objective function of the obtained SMP shaving and the relocated input data in the step S 102 , And performs the charge / discharge scheduling optimized at the corresponding real-time operation time using the SMP shaving algorithm by the controller (step S104).

In performing the optimized charge / discharge scheduling, it is preferable that charge / discharge is performed in consideration of state information of a power conditioning system (PCS) module applicable to real-time operation and charge state information of a battery belonging to each PCS module, And performs scheduling.

At this time, in performing the optimized charge / discharge scheduling, optimized charge / discharge scheduling is performed for 1 to 48 periods in the first long-term time unit, and first charge / discharge scheduling is performed in the second long- To the last interval, and performs optimized charge / discharge scheduling on the generated 1 to 48-interval data.

Hereinafter, the peak shaving operation method of the large-capacity battery energy storage system according to the present invention will be described in further detail.

<Peak shaving algorithm>

The peak shaving algorithm adopted in the present invention establishes the discharge scheduling at the peak load of the next day by inputting the pre-load curve and the system marginal prices (SMP) information that can be ensured in the power system operation. However, in real-time operation, the charging / discharging strategy for peak shaving needs to be re-executed considering the state information of the applicable PCS module and the state of charge (SoC) information of the battery belonging to each PCS module. In this case, as the time progresses, the time range for performing the peak shaving algorithm gradually decreases, and it is possible to establish a strategy (in case of SMP-based strategy establishment) that maximizes the profit in a relatively short time as the next day . In the present invention, a real-time peak shaving module based on economic model prediction control suitable for real-time operation is introduced.

<Long-term operation of SMP shaving and BESS model>

The applicability of the energy storage device can be divided into a long - term role and a short - term role according to the discharge cycle. Peak shaving is a key feature of battery energy storage in long-term operation. In order to smoothly perform peak shaving to perform the T & D function of the energy time shift, there are several long-term operation functions for maintaining the stability of the system. However, in order to smoothly perform peak shaving, A real-time control strategy based on the state of the unit PCS constituting the BESS (battery energy storage system) is indispensably required.

A specific BESS model is needed to determine charging and discharging scheduling of BESS for long - term operation, and the most important characteristic to be considered for this is the characteristic of battery SoC. In the present invention, an equivalent model of BESS including a voltage type bi-directional converter is introduced, and the parameters included therein are expressed in a form applicable to charge and discharge scheduling.

Fig. 2 is a diagram showing a circuit configuration of an equivalent model of the BESS employed to implement the method of the present invention. Fig.

2, and R _c is the resistance component representing a loss appears at the time of high load operation in the bi-directional converter in the circuit of an equivalent model of the BESS, R _s1 and R _s2 is to express the DC-side loss of the battery at the time of high load operation Respectively. C _s is a series capacitance component, and R _p is a parallel resistance component for expressing the natural discharge loss of the battery. V _dco represents the DC voltage when the battery is opened.

In the present invention, a discharge differential equation is introduced by focusing on the SoC variation during charging and discharging so as to be applicable to the SMP shaving algorithm through consideration of the equivalent BESS model as described above.

는 k시점에서의 배터리의 충전에너지,

는 배터리의 자연 방전 손실에 의한 자연 방전 손실 계수,

는 정상상태 부하 운전 시 BESS(battery energy storage system) 모델의 양방향 컨버터의 저항 성분에 의한 손실 계수,

는 배터리의 직렬저항에 의한 손실 계수,

는 k시점에서의 배터리의 방전전력,

는 단위 시간을 각각 나타낸다. 본 발명에서는 이러한 단위 시간을 30분으로 가정하기로 한다.here,

Is the charge energy of the battery at time k,

The natural discharge loss coefficient due to the natural discharge loss of the battery,

Is the loss factor due to the resistance component of the bidirectional converter of the battery energy storage system (BESS) model during steady state load operation,

Is the loss coefficient due to the series resistance of the battery,

Is the discharge power of the battery at time k,

Represents the unit time. In the present invention, it is assumed that the unit time is 30 minutes.

<SMP Shaving Algorithm>

The SMP Shaving algorithm introduced in the present invention applies SMP information at each time point to minimize the purchase price of the power. This is an algorithm for energy time shift of energy use time at the point of time using SMP information. The objective function of SMP Shaving used in this algorithm can be expressed as Equation (2) below, and the constraints related to the objective function can be expressed by Equations (3) to (8).

는 k시점에서의 쉐이빙이 이루어진 부하 수준, M은 시작점, N은 끝점을 각각 나타낸다.Here, C (k) represents SMP information,

Denotes the load level at which the shaving is performed at point k, M denotes the starting point, and N denotes the end point.

는 k시점에서의 규모 축소된 부하 수준을 의미하고, 수학식 4는 수학식 1과 같다. 수학식 5는 배터리의 방전 전력은 최대, 최소의 방전 전력 범위 내에 있음을 의미하는 제약조건이고, 수학식 6은 배터리에서 에너지의 과다방전과 과다충전을 억제하기 위한 제약조건이다. 수학식 7의

는 스케줄링의 시작점에서의 초기 SoC를 나타내고, 수학식 8은 스케줄링의 시작점과 끝점을 의미한다.In Equation (3)

Denotes the reduced load level at time k, and Equation (4) is Equation (1). Equation (5) is a constraint condition that the discharge power of the battery is within the maximum and minimum discharge power ranges, and Equation (6) is a constraint condition for suppressing overdischarging and overcharging of energy in the battery. Equation (7)

Represents the initial SoC at the starting point of the scheduling, and Equation (8) represents the starting point and the end point of the scheduling.

<Real-time module implementation problem of SMP Shaving>

In the domestic power market, SMP data is provided based on the predicted load data the day before. The battery charging and discharging strategy can be established by using the given SMP data and it can be reflected in real time operation. However, it is possible to quickly cope with the change of the state of the PCS module due to the internal failure and the abnormal change of the SoC of the battery included in each PCS it's difficult. Therefore, the SoC information of each PCS module should be measured in real time, and this should be reflected in the scheduling decision.

Since the SMP information is given once a day, the overall scheduling interval is generally shorter. In the latter part of the scheduling period of 1 day, undesirable shaving may be caused by this problem. Therefore, when applying the concept of economic model predictive control, the entire scheduling interval should be set not to change.

The SMP shaving algorithm for real-time operation introduced in the present invention determines charge and discharge scheduling using previously provided SMP information. In order to maintain SMP, which is the standard of wholesale price, there is no fluctuation due to the load change. In order to maintain the entire scheduling interval, the SMP and the load data before the entry into the next long term And rearranged at the rear to perform charge / discharge scheduling. At this time, in finding the solutions of the above Equations 2 to 8 using the input data rearranged in this manner, for example, M is set to 1 and N is set to 48 to establish a control strategy to maximize the overall profit for a sufficiently long period. The amount of control at that point applies the charge or discharge amount determined in the first time unit.

The real-time SMP shaving algorithm according to the present invention divides data of 24 hours a day into 48 segments of data every 30 minutes to optimize the scheduling at each time to establish a more effective real-time operation strategy.

As shown in FIG. 3, when real-time SMP shaving algorithm strategy is applied, optimized charging / discharging scheduling is performed for 1 to 48 intervals in the first long-term time unit, The first data is shifted to the last segment, and the scheduling is optimized for the generated data of 1 to 48 sectors.

<Simulation and Results>

Figure 4 is a view showing SMP data for any particular day (e.g., March 21) for SMP shaving.

Since each time-specific SMP in the electric power market is determined for 24 intervals one day before, 24 SMP data per day is divided into 48 intervals of 30 minutes interval for application to the real-time SMP shaving algorithm introduced in the present invention .

The RT (real time) mode simulator, which is applied to test the real time SMP shaving algorithm, is a program that can simulate real time operation module of PMS. In real time operation mode, 30 minutes corresponding to the long time unit, The control amount in the current interval determined in the long term mode is distributed to each PCS module in consideration of the change in the operating state of BESS that can occur in each section. Therefore, in PCS, 1 day is expressed as 48 long period intervals and 720 RT mode intervals.

5 is a diagram showing a result of applying a real-time SMP shaving algorithm to an RT mode simulator using load data and SMP data for a specific day (e.g., March 21).

는 각각 0.05, 0.025, 0의 값을 적용하였다. 도 5의 그래프는 "Pbat", "SoC" 및 "Benefit"의 곡선으로 이루어져 있다. "Pbat"은 BESS의 유효전력 출력을 의미하고, 이 값이 음(-)일 때는 배터리가 충전되는 상태이며, 이에 따라 "SoC"가 증가된다. 반대로 "Pbat"이 양(+)일 때는 배터리가 방전되고 있는 상태를 나타내고, 이 상태에서는 "SoC"가 감소된다. "Benefit"은 SMP 쉐이빙 알고리즘을 사용하였을 경우에 축적된 이득을 의미하며, 이때 "Benefit"은 "Pbat"과 해당 시점에서의 SMP, 그리고 RT 모드 시간단위의 간격(1/30 hour)에 의하여 변화된다. 본 시뮬레이션에서 최종 시점의 축적된 이득은 459.937[kWon]이다.Referring to FIG. 5, in this simulation, 2.4 [MWh] is applied to the initial SoC of BESS, and the corresponding

0.025, and 0, respectively. The graph of FIG. 5 consists of curves of "Pbat &quot;," SoC "and" Benefit &quot;."Pbat" refers to the active power output of the BESS, and when this value is negative (-), the battery is in a state of charge and thus "SoC" is increased. Conversely, when "Pbat" is positive, it indicates that the battery is being discharged, and in this state, "SoC" is decreased. "Benefit" refers to the accumulated gain when using the SMP shaving algorithm, where "Benefit" is the "Pbat", the SMP at that time, and the time interval (in 1/30 hour) do. In this simulation, the accumulated gain at the end point is 459.937 [kWon].

6 is a graph showing the characteristics of a reduced-size load curve and a reduced load curve.

Referring to FIG. 6, 'ScaledLoad' corresponding to the reduced load curve means a load value reduced to a level of load that can be accommodated by the BESS located at the corresponding point, and 'CutLoad' It is the curve to which charge / discharge strategy (change of 'Pbat') by SMP shaving is applied. In the present invention, since the SMP shaving is performed, the load curve (load data) and the systematic marginal cost (SMP) data are inputted as a result for minimizing the power purchase cost according to the objective function of the SMP shaving, Accordingly, it is an object of the present invention to enable BESS charge / discharge scheduling to be performed most economically at the time of real-time operation. 4 and 5 to 6, it can be seen that the charging and discharging of the battery are properly performed at a time corresponding to each other.

As described above, in the peak shaving operation method of the large capacity battery energy storage system according to the present invention, when a large capacity battery energy storage system for storing the over-produced energy (power) is introduced and operated, (system marginal prices). By applying shaving in real time, it is possible to maximize the economic gain by reducing the difference between high and low SMP points.

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 many variations and modifications may be made without departing from the spirit and scope of the invention. Be clear to the technician. Accordingly, the true scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of the same should be construed as being included in the scope of the present invention.

Claims (6)

A peak shaving operation method by executing a peak shaving algorithm mounted on a power management system (PMS) of a large-capacity battery energy storage system,
a) receiving by the input unit of the PMS 1-day previous load data and SMP (system marginal prices) information secured in the power system operation;
b) The control unit of the PMS, based on the SMP information, maintains the entire charge / discharge scheduling interval, and at the time of entering the next long period, the first SMP information and load data of the previous long- Rearward of the final point of time; And
c) after the relocation of the SMP information and the load data, obtaining the solution of the constraint condition related to the object function and the object function of the SMP shaving using the relocated input data by the controller;
d) using the SMP shaving algorithm by the control unit on the basis of the relocated input data, the obtained objective function of the SMP shaving and the solution of the constraint condition related to the objective function, and performing charge / discharge scheduling And performing a peak shaving operation of the mass storage battery energy storage system.
The method according to claim 1,
Wherein the objective function of the SMP shaving in the step c) is expressed as an objective function that minimizes a power purchase price, and is represented by the following equation.

Here, C (k) represents SMP information,

Denotes the load level at which the shaving is performed at point k, M denotes the starting point, and N denotes the end point.
The method according to claim 1,
Wherein the constraint condition in the step c) includes a discharge differential equation showing a change characteristic of a state of charge (SoC) at the time of charging and discharging the battery.
The method of claim 3,
Wherein the discharge differential equation is expressed by the following equation: &lt; EMI ID = 1.0 &gt;

here,

Is the charge energy of the battery at time k,

The natural discharge loss coefficient due to the natural discharge loss of the battery,

Is the loss factor due to the resistance component of the bidirectional converter of the battery energy storage system (BESS) model during steady state load operation,

Is the loss coefficient due to the series resistance of the battery,

Is the discharge power of the battery at time k,

Represents the unit time.
The method according to claim 1,
In performing the optimized charge / discharge scheduling in the step d), charge / discharge is performed in consideration of state information of a power conditioning system (PCS) module applicable to real time operation and charge state information of a battery belonging to each PCS module, Wherein the peak shaving operation is performed by using the peak shaving operation.
The method according to claim 1,
In performing the optimized charge / discharge scheduling in step d), charge / discharge scheduling optimized for 1 to 48 periods is performed in the first long-term time unit, Th data is shifted to the last period, and the optimized charge / discharge scheduling is performed on the generated 1 to 48-segment data.

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