KR20230052562A - Method for controlling conservation voltage reduction for energy saving in low voltage distributed network and apparatus thereof - Google Patents

Abstract

The present invention relates to a CVR control method in an energy management system which comprises the following steps of: obtaining a plurality of measurement data from a plurality of customer loads connected to a power distribution network; estimating a ZIP coefficient for each load based on the obtained plurality of measurement data; calculating a system load of the distribution network based on the estimated ZIP coefficient for each load; and calculating a reactive power command value for each distribution power source by using an optimization algorithm in consideration of the calculated system load of the distribution network.

Description

CVR control method and device for energy reduction in low voltage distribution network

The present invention relates to a CVR control method and apparatus for energy reduction, and more particularly, to a CVR control method capable of adjusting system voltage of a low-voltage distribution network through reactive power compensation of distributed power sources connected to the low-voltage distribution network, and It's about the device.

Various technologies have been developed to stably and efficiently supply power to a distribution network. Among them, Conservation Voltage Reduction (CVR) is a method for reducing energy consumption by reducing power consumption by lowering voltage, and technology has been developed and empirical research has been conducted since the 1980s. In general, since the load changes with voltage, lowering the system voltage also reduces the size of the system load. The CVR technique based on this principle reduces the size of the grid load by forcibly lowering the grid voltage of the distribution network, thereby reducing energy consumption in the distribution network.

As shown in FIG. 1, the most basic CVR implementation method is to reduce the overall grid voltage of the distribution network by lowering the tap of the main transformer using an On Load Tap Changer (OLTC) installed at the grid connection point. way. However, in the case of using the existing method, when the terminal voltage of the distribution network reaches the limit value, the tap of the main transformer cannot be lowered any more, resulting in a problem that the grid voltage cannot be reduced. In addition, in the case of the existing method, the tap change of the main transformer frequently occurs, and accordingly, a problem occurs in the lifespan and durability of the OLTC.

In order to solve this problem, a CVR control technique through reactive power compensation of a photovoltaic generator connected to a power distribution network has been proposed. In the case of the method, there is an advantage in that the system load can be reduced by lowering the grid voltage of the distribution network through reactive power compensation of the photovoltaic generator. However, a problem of increasing the internal loss of the inverter may occur due to reactive power compensation of the photovoltaic generator. In addition, when the system voltage of the distribution network is lowered, the loss of the system line increases, which may rather increase energy consumption in the distribution network. Therefore, there is a need for a new CVR control technique for energy reduction in low voltage distribution networks.

Korean Registered Patent Publication No. 10-2055620 Korean Registered Patent Publication No. 10-1132107

The present invention aims to solve the foregoing and other problems. Another object is to provide a CVR control method and apparatus capable of calculating reactive power command values of distributed power sources connected to the low voltage distribution network by performing an optimization algorithm having an objective function and constraints for energy reduction in the low voltage distribution network. is in providing

Another object is to estimate the ZIP coefficient for each load based on the AMI data received from the consumer load connected to the low voltage distribution network, and to calculate the system load of the low voltage distribution network using the estimated ZIP coefficient for each load. It is to provide a CVR control method and device therefor.

According to one aspect of the present invention to achieve the above or other object, obtaining a plurality of measurement data from a plurality of customer loads connected to the power distribution network; estimating a ZIP coefficient for each load based on the obtained plurality of measurement data; calculating a system load of the distribution network based on the estimated ZIP coefficient for each load; and calculating a reactive power command value for each distributed power source using an optimization algorithm considering the calculated system load of the distribution network.

According to another aspect of the present invention, a data collection unit for collecting a plurality of measurement data from a plurality of customer loads connected to the distribution network; a ZIP coefficient estimator for estimating a ZIP coefficient for each load based on the collected plurality of measurement data; an optimization calculation unit that calculates the grid load of the distribution network based on the ZIP coefficient for each load estimated and performs an optimization algorithm considering the calculated grid load of the distribution network; and a reactive power compensation unit that calculates a reactive power command value for each distributed power source by performing the optimization algorithm and transmits the calculated reactive power command value to a plurality of distributed power sources connected to the distribution network. .

According to another aspect of the present invention, the process of obtaining a plurality of measurement data from a plurality of customer loads connected to the distribution network; estimating a ZIP coefficient for each load based on the obtained plurality of measurement data; calculating a grid load of the distribution network based on the estimated ZIP coefficient for each load; and a computer program stored in a computer-readable recording medium so that a process of calculating a reactive power command value for each distributed power source using an optimization algorithm considering the calculated grid load of the distribution network can be performed on a computer.

The effects of the CVR control method and apparatus for energy reduction according to embodiments of the present invention will be described as follows.

According to at least one of the embodiments of the present invention, reactive power compensation through a plurality of distributed power sources connected to the distribution network is performed using an optimization algorithm considering at least one of system load, system loss, and inverter internal loss of the low voltage distribution network. By doing this, it is possible to appropriately reduce the system voltage of the low-voltage distribution network, and through this, there is an advantage in that energy consumption in the distribution network can be effectively reduced.

However, the effects that can be achieved by the CVR control method and apparatus for energy reduction according to embodiments of the present invention are not limited to those mentioned above, and other effects not mentioned are the present invention from the description below. It will be clearly understood by those skilled in the art.

1 is a diagram showing a CVR technique for regulating power grid voltage using an OLTC;
2 is a diagram showing the configuration of a power distribution system according to an embodiment of the present invention;
3 is a flowchart illustrating a CVR control method according to an embodiment of the present invention;
4 is a diagram referenced to explain the objective function and constraints of the optimization algorithm used in the CVR control method of FIG. 3;
5 is a flowchart illustrating a ZIP coefficient estimation method according to an embodiment of the present invention;
FIG. 6 is a diagram referenced to explain the ZIP coefficient estimation method of FIG. 5;
FIG. 7 is a diagram referenced to explain a data selection method used in the ZIP coefficient estimation method of FIG. 5;
8 is a configuration block diagram of a CVR control device according to an embodiment of the present invention.

Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings, but the same or similar elements are given the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "unit" for components used in the following description are given or used together in consideration of ease of writing the specification, and do not have meanings or roles that are distinct from each other by themselves. That is, the term 'unit' used in the present invention means a hardware component such as software, FPGA or ASIC, and 'unit' performs certain roles. However, 'part' is not limited to software or hardware. A 'unit' may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, 'unit' refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. The functionality provided within the components and 'parts' may be combined into a smaller number of elements and 'parts' or further separated into additional elements and 'parts'.

In addition, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the embodiment disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, the technical idea disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and technical scope of the present invention , it should be understood to include equivalents or substitutes.

The present invention proposes a CVR control method and device capable of calculating reactive power command values of distributed power sources connected to the low voltage distribution network by performing an optimization algorithm having an objective function and constraints for energy reduction in the low voltage distribution network. do. In addition, the present invention estimates the ZIP coefficient for each load based on the AMI data received from the consumer load connected to the low voltage distribution network, and calculates the system load of the low voltage distribution network using the estimated ZIP coefficient for each load. A CVR control method and device are proposed.

Hereinafter, in the present specification, adjusting the system voltage of the low voltage distribution network through reactive power compensation of distributed power sources is exemplified, but is not necessarily limited thereto, and can be applied not only to the low voltage distribution network but also to the high voltage distribution network and other power networks. It will be apparent to those skilled in the art. In addition, although the subject performing the reactive power compensation is exemplified and described as a solar power generator, it is not necessarily limited thereto, and it will be apparent to those skilled in the art that all distributed power sources capable of compensating for reactive power may be included.

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

2 is a diagram showing the configuration of a power distribution system according to an embodiment of the present invention.

Referring to FIG. 2 , the power distribution system 100 according to an embodiment of the present invention includes a main transformer 110, a power distribution network 120, and an energy management system managing the power distribution network 120 (Energy Management System, EMS, 120) may be included.

The main transformer 110 is disposed between the power transmission network 50 and the power distribution network 120 to step down the voltage applied from the power transmission network 50 at a predetermined rate and transmit it to the power distribution network 120. can

The power distribution network 120 is connected to the power transmission network 50, which is an upper system, through the main transformer 110, and may play a role in distributing power supplied from the power transmission network 50 to consumers. The distribution network 120 may be composed of a high-voltage distribution network for distributing a high voltage corresponding to 13.2 kV and a low-voltage distribution network for distributing a low voltage corresponding to 220V. An auxiliary transformer 122 may be installed between the high voltage distribution network and the low voltage distribution network to convert the distribution system voltage.

The high voltage distribution network may include a primary feeder 121 and a plurality of secondary transformers 122 for supplying high voltage. The high voltage distribution network may be connected to a plurality of low voltage distribution networks through a plurality of secondary transformers 122 .

The low-voltage distribution network may include a secondary feeder 123 for supplying low voltage, a measurement device (or a linkage device, 124), a plurality of distributed power sources 125, and a plurality of consumer loads 126. . Hereinafter, the plurality of distributed power sources 125 described in this embodiment will be described as an example of a photovoltaic generator (PV) capable of compensating for reactive power.

The energy management system 130 may perform a function of managing energy of the power distribution network 120 . The energy management system 120 is connected to a plurality of distributed resources constituting the power distribution network 110 through a communication network, and can transmit/receive data with the plurality of distributed resources through the communication network.

The energy management system 130 may include a CVR control device 135 for energy reduction in a low voltage distribution network. Meanwhile, in this embodiment, although the CVR control device 135 is exemplified to be installed in the energy management system 130, it is not necessarily limited thereto, and it can be installed independently of the energy management system 130. Those skilled in the art will be self-evident.

The CVR control device 135 may perform a function of adjusting the system voltage of the corresponding distribution network through reactive power compensation of the distributed power sources 125 linked to the low voltage distribution network. That is, the CVR control device 135 estimates the ZIP coefficient for each load based on the advanced metering infrastructure (AMI) data received from the plurality of customer loads 126 connected to the low voltage distribution network, and calculates the ZIP coefficient for each load. The system load of the low voltage distribution network can be calculated using In addition, the CVR control device 135 performs an optimization algorithm having an objective function considering the system load, system loss, and inverter internal loss of the low voltage distribution network and predetermined constraints to invalidate the plurality of distributed power sources linked to the low voltage distribution network. Power command value can be calculated.

The CVR control device 135 transmits/receives data in conjunction with a plurality of distributed resources linked to the low voltage distribution network, such as the measurement device 124, a plurality of distributed power sources 125, and a plurality of customer loads 126. can For example, the CVR control device 135 may acquire measurement data at a point of common coupling (PCC) from the measurement device 124 . In addition, the CVR control device 135 may obtain AMI data from a plurality of smart meters installed in a plurality of consumers 126 . In addition, the CVR control device 135 may provide a reactive power command value for adjusting the distribution grid voltage to the plurality of distributed power sources 125 .

Hereinafter, the CVR control device and its control method will be described in more detail.

3 is a flowchart illustrating a CVR control method according to an embodiment of the present invention, and FIG. 4 is a diagram referenced to describe an objective function and constraints of an optimization algorithm used in the CVR control method of FIG. 3 .

3 and 4, the CVR control device 135 according to the present invention obtains AMI data from a plurality of customer loads connected to the low voltage distribution network, and based on the obtained AMI data, the ZIP coefficient for each load It can be estimated (S310). Here, the AMI data may include voltage and power information of a consumer load. A more detailed description of the method for estimating the ZIP coefficient for each load will be described later with reference to FIGS. 5 to 7 .

)를 계산할 수 있다. 아래 수학식 1 및 2에 정의된 바와 같이, 분산전원을 통해 무효전력을 보상하게 되면, 배전 계통 전압이 감소하게 되고, 그에 따라 배전 계통 부하가 감소하게 된다.The CVR controller 135 may calculate the system load (ie, system load) of the low voltage distribution network using the estimated ZIP coefficient for each load (S320). At this time, the CVR control device 135 uses Equations 1 and 2 below to load the system load of the low voltage distribution network (

) can be calculated. As defined in Equations 1 and 2 below, when reactive power is compensated through distributed power sources, the distribution system voltage is reduced, and the distribution system load is reduced accordingly.

는 계통 부하이고,

는 i번째 부하 전력,

는 정격 유효전력, V_i는 i번째 부하전압,

는 정격전압,

,

,

는 i번째 부하의 ZIP 계수임. here,

is the system load,

is the ith load power,

is the rated active power, V _i is the ith load voltage,

is the rated voltage,

,

,

is the ZIP coefficient of the ith load.

The CVR control device 135 may calculate system loss of the corresponding distribution network based on measurement data obtained from the low voltage distribution network (S330). Here, the system loss may consist of system line loss and transformer loss. The system line loss and transformer loss may be defined as Equations 3 and 4 below. And, the system loss can be expressed as the sum of the system line loss and the transformer loss as shown in Equation 5 below. As defined in Equations 3 to 5 below, when reactive power is compensated through distributed power, the distribution system voltage decreases, and the distribution system current increases accordingly, resulting in increased system line loss and transformer loss.

는 계통 손실,

는 계통 선로 손실,

는 변압기 손실,

는 계통 부하,

는 계통 전압,

는 계통 전류,

는 선로 저항,

는 변압기 저항임.here,

is the system loss,

is the grid line loss,

is the transformer loss,

is the grid load,

is the grid voltage,

is the grid current,

is the line resistance,

is the transformer resistance.

The CVR control device 135 may calculate inverter internal losses of a plurality of distributed power sources connected to the corresponding distribution network based on measurement data obtained from the low voltage distribution network (S340). Here, the internal loss of the inverter may be composed of a switching loss and a conduction loss. The switching loss and conduction loss are different depending on the type of each inverter device, and can be expressed as Equations 6 and 7 below by performing curve fitting based on the specifications of the data sheet. In addition, the internal loss of the inverter can be expressed as the sum of switching loss and conduction loss as shown in Equation 8 below. As defined in Equations 6 to 8 below, when reactive power is compensated for through distributed power, the distribution system voltage decreases, and the distribution system current increases accordingly, resulting in increased switching loss and conduction loss.

는 인버터 내부 손실,

은 전도 손실,

은 스위칭 손실,

,

,

는 파라미터이고,

는 인버터 내부에 흐르는 전류임.here,

is the inverter internal loss,

silver conduction loss,

is the switching loss,

,

,

is a parameter,

is the current flowing inside the inverter.

The CVR control device 135 may perform an optimization algorithm having an objective function and predetermined constraints for energy reduction in the low voltage distribution network (S350).

For example, as shown in FIG. 4 , the objective function of the optimization algorithm aims at minimizing the sum of the grid load, grid loss, and inverter internal loss of the low voltage distribution network. That is, the objective function (F) of the optimization algorithm may be defined as in Equation 9 below.

는 계통 부하이고,

는 계통 손실이고,

는 인버터 내부 손실임.here,

is the system load,

is the system loss,

is the inverter internal loss.

On the other hand, this embodiment exemplifies that the objective function of the optimization algorithm is to minimize the sum of grid load, grid loss, and inverter internal loss of the distribution network, but is not necessarily limited thereto, and the grid load and grid of the distribution network The objective function of the optimization algorithm can be that the sum of the losses is minimized. That is, since the internal loss of the inverter has a relatively small value, the component may be omitted.

Constraints of the optimization algorithm may include a first constraint on the system voltage range of the low-voltage power distribution network and a second constraint on the reactive power compensation amount of distributed power sources linked to the low-voltage power distribution network.

The first constraint may be defined as in Equation 10 below. As defined in Equation 10 below, the objective function of the optimization algorithm must satisfy the constraint condition that the distribution grid voltage does not deviate from the grid voltage maintenance range set by the grid operator.

는 배전 계통 전압이고,

는 계통 운영자가 설정한 계통 전압의 최소값이고,

는 계통 운영자가 설정한 계통 전압의 최대값임.here,

is the distribution grid voltage,

is the minimum value of the grid voltage set by the grid operator,

is the maximum value of the grid voltage set by the grid operator.

The second constraint may be defined as in Equation 11 below. As defined in Equation 11 below, the objective function of the optimization algorithm must satisfy the constraint that the reactive power compensation amount of the distributed power source does not exceed the maximum allowable reactive power compensation amount of the distributed power source.

는 분산전원(즉, PV)의 무효전력 보상량이고,

는 분산전원의 최대 허용 무효전력 보상량이고,

는 분산전원의 정격 유효전력이고,

는 분산전원의 정격 피상전력임.here,

Is the reactive power compensation amount of the distributed power source (i.e., PV),

is the maximum allowable reactive power compensation amount of the distributed power supply,

is the rated active power of the distributed power source,

is the rated apparent power of the distributed power supply.

The CVR control device 135 may calculate a reactive power command value for each distributed power source by performing an optimization algorithm having the above-described objective function and constraint conditions (S360).

The CVR controller 135 may perform reactive power compensation by transmitting the calculated reactive power command value for each distributed power source to a plurality of distributed power sources connected to the low voltage distribution network (S370). By adjusting (reducing) the system voltage of the distribution network to an optimal voltage value through reactive power compensation of these distributed power sources, power consumption in the distribution network can be effectively reduced.

The CVR control device 135 may perform reactive power compensation for the CVR by repeatedly performing the above-described operations of steps 310 to 370 at a predetermined time period (eg, 15 seconds).

Meanwhile, in the present embodiment, steps 320 to 340 are sequentially performed, but are not necessarily limited thereto, and steps 320 to 340 may be performed almost simultaneously or the order of operations may be changed to those skilled in the art. It will be self-explanatory.

As described above, the CVR control method according to an embodiment of the present invention uses an optimization algorithm that considers at least one of the system load, system loss, and inverter internal loss of the low-voltage power distribution network, and uses a plurality of distribution linked to the corresponding distribution network. By performing reactive power compensation through the power sources, it is possible to appropriately reduce the grid voltage of the low-voltage distribution network, and through this, it is possible to effectively reduce energy consumption in the corresponding distribution network.

5 is a flowchart illustrating a ZIP coefficient estimation method according to an embodiment of the present invention, FIG. 6 is a diagram referenced to explain the ZIP coefficient estimation method of FIG. 5, and FIG. 7 is a ZIP coefficient estimation method of FIG. 5 It is a drawing referenced to explain the data selection method used in

5 to 7, the CVR control device 135 according to the present invention may select a predetermined number (eg, m) of AMI data from among AMI data stored in the database (S510). Here, the predetermined number (m) corresponds to the number of AMI data collected from a plurality of customer loads for a certain period of time (eg, 1 hour). The ZIP coefficient for each load is estimated based on the AMI data collected for a certain period of time for each consumer load.

The database may classify and store AMI data collected from a plurality of consumer loads linked to the low voltage distribution network by load. In addition, the database may classify and store AMI data collected from a plurality of customer loads connected to the low voltage distribution network by category. For example, the database includes a first database for storing real-time AMI data for each time zone of the current day, a second database for storing AMI data for each time zone for the previous day, and a third database for storing AMI data for each time zone according to characteristics of each season and/or day of the week. Can contain databases. The AMI data may include voltage information and power information of a consumer load.

The predetermined number of AMI data may be selected from AMI data stored in the first database and AMI data stored in the second database according to the first data selection method. Alternatively, the predetermined number of AMI data may be selected from AMI data stored in the first database and AMI data stored in the third database according to the second data selection method. The first and second data selection methods will be described later.

The CVR control device 135 may calculate a data density function based on the predetermined number of AMI data for each consumer load (S520). In this case, the CVR control device 135 may calculate the data density function using a density estimation algorithm, which is one of machine learning algorithms. The density estimation algorithm can be classified into a parametric 'density estimation algorithm' and a non-parametric 'density estimation algorithm'.

Hereinafter, in this embodiment, calculation of a data density function using a kernel density estimation algorithm, which is one of non-parametric density estimation algorithms, will be described as an example. The kernel density estimation algorithm (KDE) is a histogram that generates a kernel for each data by estimating the density based on the data.

The CVR control device 135 may acquire real-time AMI data from a plurality of customer loads at a predetermined time period (eg, 15 seconds) (S530). Here, the AMI data received at regular intervals may be one data or two data for each consumer load, but is not necessarily limited thereto. Hereinafter, in this embodiment, for convenience of description, one AMI data is received for each customer load.

The CVR control device 135 may calculate data density of the acquired real-time AMI data using the calculated data density function (S540). For example, as shown in FIG. 7 , the data density of real-time AMI data may be defined as in Equation 12 below.

Here, p() is the data density function, and (V, P) is real-time AMI data.

The CVR control device 135 may check whether the data density of the real-time AMI data corresponding to the current time point exceeds a predetermined threshold value ε. This is to check whether the load configuration corresponding to the current time of the day and the load configuration corresponding to the same time of the previous day are the same.

As a result of the check, if the data density of the real-time AMI data corresponding to the current time exceeds a predetermined threshold, the CVR control device 135 selects a predetermined number (eg, m) of AMI data according to the first data selection method. can be selected (S550).

For example, as shown in FIG. 7, when the current time point is 7 o'clock and the data density of the real-time AMI data exceeds the threshold value, the CVR controller 135 determines the load configuration at the current time today and the load configuration at the same time point the previous day. These can be judged to be identical to each other. Accordingly, the CVR control device 135 may select one real-time AMI data received at 7:00 and m-1 AMI data received from 7:00:15 to 8:00 the previous day.

If the current time point is 7:00:15 after a certain amount of time has elapsed and the data density of the real-time AMI data exceeds the threshold, the CVR controller 135 calculates two real-time AMI data received today and 7:00 the previous day. You can select m-2 AMI data received from minute 30 to before 8:00. If the current point in time is 7:59:45 after a certain amount of time has elapsed and the data density of the real-time AMI data exceeds the threshold value, the CVR controller 135 may select m AMI data received during that time today. .

On the other hand, as a result of the check, if the data density of the real-time AMI data corresponding to the current time point does not exceed a predetermined threshold, the CVR control device 135 selects a predetermined number (eg, m) according to the second data selection method. AMI data of can be selected (S560).

For example, as shown in FIG. 7, if the current time is 7:00 and the data density of the real-time AMI data does not exceed the threshold, the CVR controller 135 calculates the load configuration at the current time today and the load at the same time the previous day. It can be determined that the configurations are not identical to each other. Accordingly, the CVR control device 135 receives one real-time AMI data received at 7:00 and m-1 received in the time zone corresponding to the characteristics of each season and / or day (ie, from 7:00:15 to 8:00) You can select AMI data.

If the current time point is 7:00:15 after a certain amount of time has elapsed and the data density of the real-time AMI data does not exceed the threshold, the CVR controller 135 determines the two real-time AMI data received today and seasonal and/or m-2 pieces of AMI data received in the time zone corresponding to the characteristics of each day of the week (ie, from 7:00:30 to before 8:00) may be selected. If the current point in time is 7:59:45 after a certain amount of time has elapsed and the data density of the real-time AMI data does not exceed the threshold value, the CVR controller 135 can select m AMI data received during that time today. there is.

The CVR control device 135 may derive a two-dimensional function by performing curve fitting based on a predetermined number of AMI data selected according to the first data selection method or the second data selection method (S570). . For example, as shown in FIG. 6, the CVR control device 135 calculates a two-dimensional function having the voltage (V) axis as the x axis and the power (P) axis as the y axis based on the data distribution of m pieces of AMI data. there is.

The CVR control device 135 may estimate the ZIP coefficient for each load based on the derived two-dimensional function (S580). For example, as shown in FIG. 6 , the CVR control device 135 may estimate the ZIP coefficient for each load using a two-dimensional function as defined in Equation 13 below.

는 i번째 부하의 유효전력,

는 정격 유효전력,

는 i번째 부하의 무효전력,

는 정격 무효전력, V_i는 i번째 부하전압,

는 정격전압,

,

,

,

,

,

는 i번째 부하의 ZIP 계수임. here,

is the active power of the ith load,

is the rated active power,

is the reactive power of the ith load,

is the rated reactive power, V _i is the ith load voltage,

is the rated voltage,

,

,

,

,

,

is the ZIP coefficient of the ith load.

The CVR control device 135 may estimate the ZIP coefficient for each load by repeatedly performing the above-described operations of steps 510 to 580 at a predetermined time period (eg, 15 seconds). At this time, the CVR controller 135 may select a predetermined number of AMI data for each consumer load and estimate the ZIP coefficient by curve fitting the selected data.

As such, in this embodiment, a more accurate ZIP coefficient can be estimated by selecting a predetermined number of AMI data using a kernel density estimation algorithm, which is one of the machine learning techniques. Through this ZIP coefficient estimation, it is possible to more accurately calculate the system load used in the optimization algorithm for energy reduction in the low voltage distribution network.

8 is a configuration block diagram of a CVR control device according to an embodiment of the present invention.

Referring to FIG. 8 , the CVR control devices 135 and 200 according to an embodiment of the present invention include a data collection unit 210, a ZIP coefficient estimation unit 220, an optimization operation unit 230, and a reactive power compensator 240. ) may be included. The components shown in FIG. 8 are not essential to implement the CVR control device, so the CVR control device described herein may have more or less components than those listed above.

The data collection unit 210 may collect data in conjunction with a plurality of distributed resources linked to the low voltage distribution network, such as a measuring device, a plurality of distributed power sources, and a plurality of customer loads.

For example, the data collection unit 210 may collect measurement data from a measurement device at a grid connection point (PCC). In addition, the data collection unit 210 may collect AMI data from a plurality of consumer loads connected to the low voltage distribution network.

The data collection unit 210 may classify and store AMI data collected from a plurality of customer loads by load. In addition, the data collection unit 210 may classify and store AMI data collected from a plurality of customer loads by category. For example, the data collection unit 210 may classify and store real-time AMI data for each time zone of today in a first storage area, classify and store AMI data for each time zone of the previous day in a second storage area, and store the data for each season and/or day of the week. AMI data for each time period according to characteristics may be classified and stored in the third storage area.

The ZIP coefficient estimator 220 selects a predetermined number of AMI data from a plurality of AMI data collected from a plurality of customers connected to the low voltage distribution network, and estimates the ZIP coefficient for each load based on the selected AMI data can do. At this time, the ZIP coefficient estimator 220 may calculate a data density function using a kernel density estimation algorithm and select a predetermined number of AMI data using the calculated data density function.

The optimization calculator 230 may calculate the system load (ie, system load amount) of the low voltage distribution network using the ZIP coefficient for each load. In addition, the optimization calculator 230 may calculate system loss of the corresponding power distribution network based on measurement data obtained from the low voltage power distribution network. In addition, the optimization operation unit 230 may calculate inverter internal losses of a plurality of distributed power sources linked to the corresponding distribution network based on measurement data obtained from the low voltage distribution network.

The optimization calculator 230 may perform an optimization algorithm having an objective function for energy reduction in a low voltage distribution network and predetermined constraints based on the calculated information. The objective function of the optimization algorithm aims at minimizing the sum of grid load, grid loss, and inverter internal loss in the low voltage distribution network. Constraints of the optimization algorithm may include a first constraint on the system voltage range of the low-voltage power distribution network and a second constraint on the reactive power compensation amount of distributed power sources connected to the low-voltage power distribution network.

The reactive power compensator 240 may calculate a reactive power command value for each distributed power source by performing an optimization algorithm having the above-described objective function and constraints. The reactive power compensator 240 may perform reactive power compensation by transmitting the calculated reactive power command value for each distributed power source to a plurality of distributed power sources linked to a low voltage distribution network.

As described above, the CVR control device according to an embodiment of the present invention uses an optimization algorithm that considers at least one of the system load, system loss, and inverter internal loss of the low-voltage power distribution network, and uses a plurality of distribution linked to the corresponding distribution network. By performing reactive power compensation through the power sources, it is possible to appropriately reduce the grid voltage of the low-voltage distribution network, and through this, it is possible to effectively reduce energy consumption in the corresponding distribution network.

The above-described present invention can be implemented as computer readable code on a medium on which a program is recorded. The computer-readable medium includes all types of recording devices in which data that can be read by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. there is Accordingly, the above detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

100: distribution system 110: main transformer
120: distribution network 130: energy management system
135/200: CVR control device 210: data collection unit
220: ZIP coefficient estimation unit 230: optimization calculation unit
240: reactive power compensation unit

Claims (11)

In the CVR (Conservation Voltage Reduction) control method in the energy management system,
obtaining a plurality of measurement data from a plurality of customer loads linked to a power distribution network;
estimating a ZIP coefficient for each load based on the obtained plurality of measurement data;
calculating a system load of the distribution network based on the estimated ZIP coefficient for each load; and
CVR control method comprising the step of calculating a reactive power command value for each distributed power source using an optimization algorithm in consideration of the calculated grid load of the distribution network. According to claim 1,
The CVR control method, characterized in that the measurement data is AMI (Advanced Metering Infrastructure) data including voltage information and power information. According to claim 1,
The CVR control method further comprising calculating a system loss of the corresponding distribution network based on the measurement data obtained from the distribution network. According to claim 3,
The CVR control method further comprising calculating an inverter internal loss of a plurality of distributed power sources connected to the distribution network based on the measurement data obtained from the distribution network. According to claim 4,
The CVR control method according to claim 1 , wherein the objective function of the optimization algorithm aims at minimizing a sum of grid load, grid loss, and inverter internal loss of the distribution network. According to claim 4,
CVR control characterized in that the constraints of the optimization algorithm include a first constraint on the system voltage range of the distribution network and a second constraint on the amount of reactive power compensation of distributed power sources connected to the distribution network. method. According to claim 1,
The CVR control method characterized in that for transmitting the calculated reactive power command value for each distributed power source to a plurality of distributed power sources connected to the distribution network. The method of claim 1, wherein the estimating step,
CVR control method, characterized in that for selecting a predetermined number of measurement data from the plurality of obtained measurement data, and estimating the ZIP coefficient for each load based on the selected measurement data. The method of claim 8, wherein the estimating step,
A CVR control method comprising: calculating a data density function using a predetermined density estimation algorithm, and selecting a predetermined number of measurement data using the calculated data density function. A computer program stored in a computer-readable recording medium so that the method according to any one of claims 1 to 9 can be performed on a computer. a data collection unit that collects a plurality of measurement data from a plurality of consumer loads linked to a distribution network;
a ZIP coefficient estimator for estimating a ZIP coefficient for each load based on the collected plurality of measurement data;
an optimization calculation unit that calculates the grid load of the distribution network based on the ZIP coefficient for each load estimated and performs an optimization algorithm considering the calculated grid load of the distribution network; and
Conservation Voltage Reduction (CVR) control including a reactive power compensation unit that calculates a reactive power command value for each distributed power source by performing the optimization algorithm and transmits the calculated reactive power command value to a plurality of distributed power sources connected to the distribution network. Device.

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