KR102620315B1 - Distribution grid optimization system and method - Google Patents

Abstract

The present invention collects measurement data from a distributed power source, predicts the distributed power generated from the distributed power source based on the measurement data, and determines the control pattern of the active network manager based on the predicted distributed power source. Provides an optimization system.

Description

Distribution grid optimization system and method {DISTRIBUTION GRID OPTIMIZATION SYSTEM AND METHOD}

The present invention relates to a distribution system optimization system and method, and more specifically, to a distribution system optimization system and method that can increase the capacity of distributed power sources.

In general, the capacity of a distribution system can be increased through expansion or reconfiguration of the system, and at this time, various external devices are used to maintain technical constraints within an acceptable range.

In this regard, expansion of the distribution system can increase capacity, but has the disadvantage of requiring a lot of cost because new infrastructure is built.

Meanwhile, Active Network Management (ANM) is one of the methods of maintaining technical constraints within an acceptable range by using voltage regulation and reactive power compensation to increase the capacity of distributed power sources connected to the distribution system.

For example, OLTC (On-Load Tap Changer), voltage regulator, SCB (Switched Capacitor Bank), grid reconfiguration, etc. are included in ANM. At this time, OLTC adjusts the voltage by changing the tap position of the transformer. Additionally, the SCB supplies reactive power to the grid to regulate the voltage. However, these devices have the disadvantage of being limited in the number of switching operations and having a slow response time.

Meanwhile, system reconfiguration optimally reconfigures the topology and increases the capacity of distributed power by operating tie switches and sectional switches without expanding the system. In this regard, according to the IEEE 1547-2018 standard, smart inverter-based distributed power sources are being used to comply with constraints while increasing capacity using the functions of smart inverters.

However, in order to increase the distributed power capacity of the distribution system and ensure efficient operation, cooperation between existing ANMs and smart inverters is required.

Domestic published patent 10-2020-0048744 (2020.05.08.)

The technical problem to be solved by the present invention is to provide a distribution system optimization system and method that increases the capacity of distributed power sources by optimizing ANM (Active Network Management) linked to the distribution system.

One aspect of the present invention is a system power supply that supplies power; an active network manager that controls power supplied from the system power supply; A distributed power source that generates distributed power using the natural environment and supplies the distributed power to a bus connected to the system power supply; and collecting measurement data generated by measuring the amount of the natural environment used in the distributed power source, predicting distributed power generated by the distributed power source based on the measurement data, and predicting distributed power generated by the distributed power source based on the predicted distributed power source. It may include a smart inverter that determines the control pattern of the active network manager.

In addition, the active network manager includes a voltage regulator that adjusts the voltage transmitted from the system power source to the busbar; and a reactive power compensator that compensates for reactive power in the busbar.

Additionally, the smart inverter can generate an environmental model using the measurement data and predict the distributed power based on the environmental model.

In addition, the smart inverter determines one voltage tap position among a plurality of voltage tap positions provided in the active network manager, and determines the suitability of the determined voltage tap position from the predicted distributed power source based on a preset optimization algorithm. can be judged.

Additionally, the smart inverter may determine a reactive power compensation pattern of the active network manager based on the determined voltage tap position.

In addition, the optimization algorithm determines the suitability of the determined voltage tap position based on the distributed power source predicted at a preset time interval within a preset time range and the power usage pattern of the load connected to the busbar, and determines the suitability. If the determined number of repetitions is lower than the preset maximum number of repetitions, the suitability of a voltage tap position different from the determined voltage tap position among the plurality of voltage tap positions is determined, and if the number of repetitions reaches the maximum number of repetitions It may be arranged to determine the control pattern of the active network manager based on the suitability of each of the plurality of voltage tap positions.

Another aspect of the present invention is a distribution system optimization method in a distribution system optimization system including grid power, active network manager, distributed power, and smart inverter, measuring the amount of natural environment used in the distributed power. collecting measurement data generated by; predicting distributed power generated from the distributed power source based on the measurement data; determining a control pattern of the active network manager based on predicted distributed power; and controlling the active network manager based on the control pattern.

In addition, the active network manager includes a voltage regulator that regulates the voltage transmitted from the system power source to the busbar; and a reactive power compensator that compensates for reactive power in the busbar.

In addition, the step of predicting distributed power based on the measurement data may include creating an environmental model using the measurement data and predicting the distributed power based on the environmental model.

In addition, the step of determining the control pattern of the active network manager based on the distributed power source includes determining one voltage tap position among a plurality of voltage tap positions provided in the active network manager and based on a preset optimization algorithm. The suitability of the determined voltage tap position can be determined from the predicted distributed power source.

Additionally, the step of determining the control pattern of the active network manager based on the distributed power source may determine a reactive power compensation pattern of the active network manager based on the determined voltage tap position.

In addition, the optimization algorithm determines the suitability of the determined voltage tap position based on the distributed power source predicted at a preset time interval within a preset time range and the power usage pattern of the load connected to the busbar, and determines the suitability. If the determined number of repetitions is lower than the preset maximum number of repetitions, the suitability of a voltage tap position different from the determined voltage tap position among the plurality of voltage tap positions is determined, and if the number of repetitions reaches the maximum number of repetitions It may be arranged to determine the control pattern of the active network manager based on the suitability of each of the plurality of voltage tap positions.

According to one aspect of the present invention described above, by providing a distribution system optimization system and method, it is possible to optimize ANM (Active Network Management) linked to the distribution system by predicting changes in power that can be generated from distributed power sources.

Figure 1 is a block diagram of a distribution system optimization system according to an embodiment of the present invention.
Figure 2 is an example of a schematic diagram to which a distribution system optimization system according to an embodiment of the present invention can be applied.
Figures 3 to 5 are graphs showing an example of a control pattern determined in the smart inverter of Figure 1.
Figure 6 is a flowchart of a distribution system optimization method according to an embodiment of the present invention.
Figure 7 is a detailed flowchart of the step of determining the control pattern of the active network manager based on the distributed power of Figure 6.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the disclosure of the present invention is complete and to be understood by those skilled in the art. It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims.

In describing embodiments of the present invention, if a detailed description of a known function or configuration is judged to unnecessarily obscure the gist of the present invention, the detailed description will be omitted. The terms described below are terms defined in consideration of functions in the embodiments of the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, the definition should be made based on the contents throughout this specification.

Figure 1 is a block diagram of a distribution system optimization system according to an embodiment of the present invention.

The distribution grid optimization system may include a grid power source (10), an active network manager (20), a distributed power source (30), and a smart inverter (40).

The system power source 10 may be provided to supply power to the load 50 through a bus, and for this purpose, the system power source 10 may mean a distribution system or a power source of the distribution system.

The active network manager 20 can control power supplied from the grid power source 10. For example, the active network manager 20 may adjust the voltage transmitted from the system power source 10 to the busbar, or the active network manager 20 may compensate for reactive power in the busbar.

To this end, the active network manager 20 may include a voltage regulator 21 and a reactive power compensator 23.

At this time, the voltage regulator 21 can regulate the voltage transmitted from the system power source 10 to the busbar, and for this purpose, the voltage regulator 21 is provided with a plurality of voltage taps to adjust the voltage according to the positions of the voltage taps. It can be.

In one embodiment, the voltage regulator 21 may use an On-Load Tap Changer (OLTC).

Meanwhile, the reactive power compensator 23 can compensate for reactive power in the busbar. To this end, the reactive power compensator 23 may be arranged to absorb or supply reactive power depending on the voltage or active power of the busbar. .

For example, the reactive power compensator 23 may stop compensating control of reactive power when the voltage level of the busbar is within an acceptable range. In addition, the reactive power compensator 23 can absorb or supply reactive power to the busbar when the voltage magnitude of the busbar is outside the allowable range. In this case, the reactive power compensator 23 is activated when the voltage magnitude of the busbar is outside the allowable range. The amount of reactive power absorbed or supplied can be constantly increased until the voltage level of the busbar reaches the critical value.

Accordingly, the reactive power compensator 23 can supply or absorb reactive power set to a certain level when the voltage level of the bus bar outside the allowable range exceeds the threshold.

The distributed power source 30 can generate distributed power using the natural environment. Here, the natural environment used by the distributed power source may mean light, heat, wind, etc., and accordingly, the distributed power source 30 is a solar power generator (PV, Photovoltaic) or a wind turbine generator (WTGS). System), etc.

Additionally, distributed power may refer to power generated through distributed power sources such as solar power generators or wind power generators and supplied to the busbar.

The smart inverter 40 can determine the control pattern of the active network manager 20. Here, the control pattern of the active network manager 20 may include the voltage tap position of the voltage regulator 21 and the reactive power compensation pattern of the reactive power compensator 23.

In other words, the active network manager 20 can control the voltage regulator 21 based on the voltage tap position provided in the control pattern, and the active network manager 20 can control the voltage regulator 21 based on the reactive power compensation pattern provided in the control pattern. Thus, the reactive power compensation pattern of the reactive power compensator 23 can be controlled.

At this time, the reactive power compensation pattern may be set to indicate the size of the reactive power that the reactive power compensator 23 supplies to the busbar or absorbs from the busbar. To this end, the reactive power compensation pattern is set to the reactive power compensator 23 ) can be set to indicate a pattern for selecting one or more capacitors among a plurality of capacitors provided in ).

At this time, the control pattern may be set as a temporal sequence, and in this case, the active network manager 20 may control the voltage tap position and the reactive power compensation pattern based on the control pattern corresponding to the current time.

In this regard, the smart inverter 40 can collect measurement data generated by measuring the amount of the natural environment used by the distributed power source 30, and the smart inverter 40 can generate distributed power based on the collected measurement data. The distributed power generated from the type power source 30 can be predicted.

Here, the measurement data may mean information expressed in numbers by measuring the natural environment used in the distributed power source 30. For example, the measurement data may include information such as solar radiation, ambient temperature of the distributed power source, and wind speed. may include.

At this time, the smart inverter 40 may collect measurement data according to a temporal series. For example, the smart inverter 40 may collect measurement data measured at 1-hour intervals.

In this case, the smart inverter 40 can predict distributed power according to the same temporal series as the measurement data.

Through this, the smart inverter 40 can determine the control pattern of the active network manager 20 based on the predicted distributed power.

More specifically, the smart inverter 40 can create an environmental model using measurement data, and the smart inverter 40 can predict distributed power based on the generated environmental model.

Here, the environmental model may mean created to numerically represent changes in the environment over time based on measurement data. Additionally, the environmental model may be understood as sampling of the measurement data.

In one embodiment, the smart inverter 40 may generate an environmental model representing changes in solar radiation through Equation 1 below.

는 샘플링된 일사량을 의미할 수 있으며,

및

는 베타 분포의 형상 매개변수를 의미할 수 있다. 또한,

는 감마 함수를 의미할 수 있다.The smart inverter 40 can create an environmental model of solar radiation using Equation 1. Here, f may mean an environmental model,

may refer to the sampled solar radiation,

and

may mean the shape parameter of the beta distribution. also,

may mean a gamma function.

및

는 측정 데이터로부터 일사량의 변화가 가장 크게 나타나도록 설정될 수 있으며, 이를 위해,

및

는 최우추정법(MLE, Maximum Likelihood Estimation)을 이용하여 설정될 수 있다. 여기에서, 최우추정법은 데이터에 의한 확률을 최대화하는 값을 이용하여 모집단의 모수를 추정하는 기법으로 이해할 수 있다.At this time,

and

can be set to show the greatest change in solar radiation from the measured data, and for this purpose,

and

Can be set using Maximum Likelihood Estimation (MLE). Here, the maximum likelihood estimation method can be understood as a technique for estimating population parameters using values that maximize the probability of data.

Accordingly, the smart inverter 40 can generate an environmental model in the beta distribution through simulation such as Monte Carlo Simulation (MCS), which is a known technology.

Accordingly, the smart inverter 40 can predict the distributed power of the distributed power source 30 through Equation 2 below.

은 분산형 전원(30)의 셀 온도를 의미할 수 있고,

는 분산형 전원(30)의 주변 온도를 의미할 수 있다. 또한,

는 샘플링된 일사량을 의미할 수 있으며, NOCT(Nominal Operating Cell Temperature)는 공칭 동작 셀 온도를 의미할 수 있다. 또한,

은 셀 전류를 의미할 수 있고,

는 단락 전류를 의미할 수 있다. 또한,

는 주변 온도의 변화에 따른 전류의 변화를 나타내는 전류 온도 계수를 의미할 수 있으며,

는 주변 온도의 변화에 따른 전압의 변화를 나타내는 전압 온도 계수를 의미할 수 있다. 또한,

은 셀 전압을 의미할 수 있고,

는 개방 회로 전압을 의미할 수 있다. 또한,

는 분산형 전원(30)의 출력 전력을 의미할 수 있고,

은 분산형 전원(30)의 모듈 개수를 의미할 수 있다. 또한,

는 최대 전력 지점에서의 전압을 의미할 수 있고,

는 최대 전력 지점에서의 전류를 의미할 수 있다.From here,

may mean the cell temperature of the distributed power source 30,

may mean the ambient temperature of the distributed power source 30. also,

may mean the sampled solar radiation, and NOCT (Nominal Operating Cell Temperature) may mean the nominal operating cell temperature. also,

may mean the cell current,

may mean short-circuit current. also,

may refer to the current temperature coefficient indicating the change in current according to the change in ambient temperature,

may mean a voltage-temperature coefficient indicating a change in voltage according to a change in ambient temperature. also,

may mean the cell voltage,

may mean open circuit voltage. also,

may mean the output power of the distributed power source 30,

may mean the number of modules of the distributed power source 30. also,

may mean the voltage at the maximum power point,

may mean the current at the maximum power point.

Accordingly, the smart inverter 40 can calculate the output power of the distributed power source 30 using solar radiation. At this time, the output power can be understood to mean the distributed power generated by the distributed power source 30. .

In another embodiment, the smart inverter 40 may generate an environmental model representing changes in wind speed through Equation 3 below.

는 평균 풍속을 의미할 수 있다. 또한, b는 Weibull 분포의 형상 매개변수를 의미할 수 있고, c는 Weibull 분포의 척도 매개변수를 의미할 수 있다.The smart inverter 40 can create an environmental model of wind speed using Equation 3. Here, f may mean an environmental model,

may mean the average wind speed. Additionally, b may mean the shape parameter of the Weibull distribution, and c may mean the scale parameter of the Weibull distribution.

At this time, b and c can be set so that the change in wind speed appears the largest from the measurement data. To this end, b and c can be set using the maximum likelihood estimation method.

Accordingly, the smart inverter 40 can generate an environmental model in the Weibull distribution through simulation such as Monte Carlo simulation, which is a known technology.

Accordingly, the smart inverter 40 can predict the distributed power of the distributed power source 30 through Equation 4 below.

는 분산형 전원(30)의 출력 전력을 의미할 수 있고,

은 분산형 전원(30)에 마련된 풍력 터빈의 정격 출력을 의미할 수 있다. 또한,

는 평균 풍속을 의미할 수 있고,

는 풍력 터빈이 발전을 시작하는 시점에서의 속도인 컷인(Cut-In) 속도를 의미할 수 있다. 또한,

은 풍력 터빈의 속도를 의미할 수 있고,

는 손상에 대한 안전 범위 내에서 풍력 터빈의 최대 속도인 컷아웃(Cut-Out 또는 Cut-Off) 속도를 의미할 수 있다.From here,

may mean the output power of the distributed power source 30,

may mean the rated output of the wind turbine provided in the distributed power source 30. also,

may mean the average wind speed,

May refer to the cut-in speed, which is the speed at which the wind turbine starts generating power. also,

may refer to the speed of the wind turbine,

May refer to the cut-out (Cut-Out or Cut-Off) speed, which is the maximum speed of the wind turbine within the safe range for damage.

Accordingly, the smart inverter 40 can calculate the output power of the distributed power source 30 using wind speed, and at this time, the output power can be understood to mean the distributed power generated by the distributed power source 30. .

In this way, the smart inverter 40 can predict distributed power based on measurement data, and at this time, the predicted distributed power may be predicted differently depending on the time series.

For example, when the distributed power source 30 generates power using solar radiation, the size of the distributed power during the day may be predicted to be larger than the size of the distributed power during the night.

In this regard, the smart inverter 40 can determine one voltage tap position among a plurality of voltage tap positions provided in the active network manager 20, and the smart inverter 40 can, based on a preset optimization algorithm, The suitability of the voltage tap position determined above can be determined from the distributed power predicted above.

Here, the optimization algorithm can be prepared to determine the optimal control pattern according to the change in distributed power and the power usage pattern of the load 50 connected to the busbar. At this time, the optimal control pattern is the voltage of the voltage regulator 21. It may mean a control pattern set to minimize switching of the tap position and switching of the reactive power supply and absorption of the reactive power compensator 23.

To this end, the optimization algorithm can determine the suitability of the voltage tap location based on the power usage pattern of the distributed power and the load 50 predicted at a preset time interval within a preset time range, and the optimization algorithm determines the suitability. If the determined number of repetitions is lower than the preset maximum number of repetitions, the suitability of a voltage tap position different from the voltage tap position determined above among a plurality of voltage tap positions can be determined, and the optimization algorithm can determine the suitability of the voltage tap position when the number of repetitions reaches the maximum number of repetitions. In this case, the control pattern of the active network manager 20 may be determined based on the suitability of each of the multiple voltage tap positions.

In this regard, in one embodiment, the smart inverter 40 may determine a control pattern using a slime mold algorithm (SMA).

Here, the slime mold algorithm can be understood as a type of optimization technique through metaheuristic or advanced reasoning, and the slime mold algorithm is used by slime molds, slime molds, and slime molds to search for food, approach food, and , can be performed through mathematical equations that mimic the behavior of catching prey.

Equation 5 below can be understood as a formula representing the behavior of searching, approaching, and catching food used in the slime mold algorithm.

는 현재 시점까지의 슬라임 몰드 중 농도가 가장 높은 슬라임 몰드의 위치를 의미할 수 있다. 또한,

와

는 무작위로 선택된 슬라임 몰드의 위치를 의미할 수 있고, l은 반복 횟수를 의미할 수 있다. 또한,

는 아래에서 설명할 매개변수 a와 -a 사이의 매개변수를 의미할 수 있으며,

는 1에서부터 0까지 선형적으로 감소하는 매개변수를 의미할 수 있다. 또한, p는 적합도에 따라 결정되는 값으로써, 아래에서 상세히 설명하도록 한다. 또한, W는 슬라임 몰드의 가중치를 의미할 수 있다.Here, . Additionally, y may mean a constant parameter,

may mean the location of the slime mold with the highest concentration among the slime molds up to the present time. also,

and

may mean the position of a randomly selected slime mold, and l may mean the number of repetitions. also,

may mean a parameter between parameter a and -a, which will be explained below,

may mean a parameter that decreases linearly from 1 to 0. In addition, p is a value determined according to suitability and will be explained in detail below. Additionally, W may mean the weight of the slime mold.

는 현재 시점까지 반복되어 산출된 적합도 중 가장 높은 적합도를 의미할 수 있다.Equation 6 can be understood as a formula for calculating p in Equation 5. Here, z may mean any value from 1 to n, and S may mean suitability at a specific slime mold location.

may mean the highest degree of suitability among the degrees of suitability repeatedly calculated up to the current point.

은 최대 반복 횟수를 의미할 수 있다.Additionally, Equation 7 can be understood as a formula showing the process of calculating u_b and a above. Here, l may mean the number of repetitions,

may mean the maximum number of repetitions.

는 현재 시점까지 반복하여 산출된 적합도 중 가장 높은 적합도를 의미할 수 있고,

는 현재 시점까지 반복하여 산출된 적합도 중 가장 낮은 적합도를 의미할 수 있다.Additionally, Equation 8 can be understood as a formula for calculating the weight of the slime mold described above. From here,

may mean the highest fitness among those repeatedly calculated up to the current point,

may mean the lowest suitability among the suitability repeatedly calculated up to the current point.

,

, w를 수정하도록 마련될 수 있다.Through this, the slime mold algorithm can extract the position of a slime mold that exists in a random position from the first repetition number, and when the repetition number changes, the slime mold algorithm can perform the above a, p,

,

, can be arranged to modify w.

Accordingly, when the number of repetitions reaches the maximum number of repetitions, the slime mold algorithm can list a number of suitability values calculated during the repetition process according to the rank of suitability.

In this regard, the smart inverter 40 can correspond to the maximum and minimum values of the distributed power to the upper and lower limits of the slime mold search range, and the voltage tap position of the voltage regulator can correspond to the position of the slime mold.

Accordingly, the smart inverter 40 can extract a random voltage tap position between the maximum and minimum values of the distributed power, and the smart inverter 40 can select the voltage tap according to the power usage pattern of the distributed power and the load 50. The suitability of the location can be judged.

Equations 9 to 12 below can be understood as equations used in the process of determining the suitability of the voltage tap position.

는 모선의 유효 전력을 의미할 수 있고,

는 모선의 무효 전력을 의미할 수 있다. 또한,

는 시간 t일 때, 일사량을 이용한 분산형 전원(30)에서 i번 모선에 입력되는 유효 전력을 의미할 수 있고,

는 시간 t일 때, 풍속을 이용한 분산형 전원(30)에서 i번 모선에 입력되는 유효 전력을 의미할 수 있다. 또한,

는 시간 t일 때, 일사량을 이용한 분산형 전원(30)에서 i번 모선에 입력되는 무효 전력을 의미할 수 있고,

는 시간 t일 때, 풍속을 이용한 분산형 전원(30)에서 i번 모선에 입력되는 무효 전력을 의미할 수 있다. 또한,

는 시간 t일 때, i번 모선에서 소비되는 유효 전력을 의미할 수 있고,

는 시간 t일 때, i번 모선에서 소비되는 무효 전력을 의미할 수 있다. 또한,

는 i번 모선의 전압을 의미할 수 있고,

는 모선의 개수를 의미할 수 있으며, Y는 Y-bus 행렬의 요소를 의미할 수 있고,

는 i번 모선과 j번 모선 사이의 선로 임피던스를 의미할 수 있으며,

는 i번 모선 또는 j번 모선의 위상각을 의미할 수 있다.From here,

may mean the active power of the busbar,

may mean the reactive power of the busbar. also,

may mean the active power input to the i bus from the distributed power source 30 using solar radiation at time t,

may mean the active power input to the i bus from the distributed power source 30 using wind speed at time t. also,

may mean the reactive power input to the i bus from the distributed power source 30 using solar radiation at time t,

may mean the reactive power input to the i bus from the distributed power source 30 using wind speed at time t. also,

may mean the active power consumed by bus i at time t,

may mean the reactive power consumed in bus i at time t. also,

may mean the voltage of the i bus,

may mean the number of bus bars, Y may mean the elements of the Y-bus matrix,

may mean the line impedance between the i bus and the j bus,

may mean the phase angle of the i bus bar or the j bus bar.

과

는 모선에 설정된 각 노드에서의 전압 최소값과 최대값을 의미할 수 있고,

는 i번 모선과 j번 모선 사이에 존재하는 선로의 전류 용량 한계를 의미할 수 있다.From here,

class

may mean the minimum and maximum voltage values at each node set in the busbar,

may mean the current capacity limit of the line that exists between the i bus and the j bus.

및

는 일사량을 이용한 분산형 전원(30)의 최소 유효 전력과 최대 유효 전력을 의미할 수 있고,

및

는 풍속을 이용한 분산형 전원(30)의 최소 유효 전력과 최대 유효 전력을 의미할 수 있다. 또한,

및

는 일사량을 이용한 분산형 전원(30)의 최소 피상 전력과 최대 피상 전력을 의미할 수 있고,

및

는 풍속을 이용한 분산형 전원(30)의 최소 피상 전력과 최대 피상 전력을 의미할 수 있다.From here,

and

may mean the minimum active power and maximum active power of the distributed power source 30 using solar radiation,

and

May mean the minimum active power and maximum active power of the distributed power source 30 using wind speed. also,

and

may mean the minimum apparent power and maximum apparent power of the distributed power source 30 using solar radiation,

and

May mean the minimum apparent power and maximum apparent power of the distributed power source 30 using wind speed.

및

는 무효 전력 보상기(23)에서의 Volt/VAr 제어 곡선의 기울기의 최소값과 최대값을 의미할 수 있고,

및

는 무효 전력 보상기(23)에서의 Volt/VAr 제어 곡선의 dead band의 최소값과 최대값을 의미할 수 있다. 또한,

및

는 무효 전력 보상기(23)에서 스위칭 가능한 커패시터의 최소값과 최대값을 의미할 수 있고,

과

는 전압 조정기(21)에서 조절 가능한 전압 탭의 최소값과 최대값을 의미할 수 있다.From here,

and

may mean the minimum and maximum values of the slope of the Volt/VAr control curve in the reactive power compensator 23,

and

may mean the minimum and maximum values of the dead band of the Volt/VAr control curve in the reactive power compensator 23. also,

and

may mean the minimum and maximum values of the switchable capacitor in the reactive power compensator 23,

class

may mean the minimum and maximum values of the voltage tap that can be adjusted in the voltage regulator 21.

Equations 9 to 12 can be understood as limiting conditions for the active network manager 20. Accordingly, the smart inverter 40 can compare the control state and constraint conditions of the active network manager 20 determined based on the optimization algorithm described above, and according to this, the smart inverter 40 can compare the active network manager 20 The suitability for the control state can be determined.

In this regard, the smart inverter 40 may determine the reactive power compensation pattern of the active network manager 20 based on the determined voltage tap position.

Equation 13 below can be understood as an equation prepared to determine the reactive power compensation pattern.

내지

는 제 1 임계 전압 내지 제4 임계 전압을 의미할 수 있다. 또한, m은 무효 전력 보상기(23)에서의 Volt/VAr 제어 곡선의 기울기를 의미할 수 있으며, d는 무효 전력 보상기(23)에서의 Volt/VAr 제어 곡선의 dead band의 범위를 의미할 수 있다. 또한,

은 전압 조정기(21)에서 선택된 전압 탭의 설정 전압을 의미할 수 있다. 또한,

는 스마트 인버터(40)의 최대 무효 전력을 의미할 수 있다.From here,

inside

may mean a first to fourth threshold voltage. In addition, m may mean the slope of the Volt/VAr control curve in the reactive power compensator 23, and d may mean the range of the dead band of the Volt/VAr control curve in the reactive power compensator 23. . also,

may mean the set voltage of the voltage tap selected in the voltage regulator 21. also,

May mean the maximum reactive power of the smart inverter 40.

Equation 14 below can be understood as a formula used in the process of calculating the maximum reactive power of the smart inverter 40.

는 스마트 인버터(40)의 최대 무효 전력을 의미할 수 있고, S는 분산형 전원(30)의 피상 전력을 의미할 수 있으며, P는 분산형 전원(30)의 유효 전력을 의미할 수 있다.From here,

may mean the maximum reactive power of the smart inverter 40, S may mean the apparent power of the distributed power source 30, and P may mean the active power of the distributed power source 30.

Meanwhile, the smart inverter 40 can determine the suitability at different voltage tap positions by repeating the above suitability determination process for a preset maximum number of repetitions. Accordingly, the smart inverter 40 can repeat the maximum number of repetitions. When the number of times is reached, the control pattern of the active network manager 20 can be determined by selecting the highest suitability among the plurality of determined suitability.

Through this, the distribution grid optimization system can maximize the capacity of the distributed power source 30 that can be connected to the grid power source 10, and the distribution grid optimization system can maximize the loss of the active network manager 20 or the distributed power source 30. can be minimized, and the distribution system optimization system can minimize the voltage fluctuations of the busbar.

As such, in one embodiment, the smart inverter 40 can set the optimal control pattern of the smart inverter 40 and the active network manager 20 using the slime mold algorithm, but the slime mold algorithm of the present invention is simply a smart mold algorithm. As a means for setting the optimal control pattern of the inverter 40 and the active network manager 20, this can be replaced with another known optimization algorithm.

Figure 2 is an example of a schematic diagram to which a distribution system optimization system according to an embodiment of the present invention can be applied.

Referring to Figure 2, it can be seen that the system power source 10 and the voltage regulator 21 are connected and the power input from the system power source 10 is supplied to the busbar through the voltage regulator 21, and distribution using solar radiation is performed. It can be confirmed that the distributed power generated from the type power source 30a and the distributed power source 30b using wind speed is supplied to the busbar through the smart inverter 40.

In addition, it can be confirmed that the reactive power compensator 23 compensates for the reactive power, and it can be confirmed that a number of loads 50 are connected to the busbar and receive power.

Figures 3 to 5 are graphs showing an example of a control pattern determined in the smart inverter of Figure 1.

Referring to Figures 3 and 4, the control pattern of the voltage regulator 21 set at 1-hour intervals within a 24-hour range can be confirmed.

At this time, Figure 3 can be understood as the voltage tap position changing 23 times, and Figure 4 can be understood as the voltage tap position changing 12 times.

Accordingly, it can be understood that the suitability of the control pattern is higher in the graph of FIG. 4, where there is less variation in the voltage tap position, than in the graph of FIG. 3.

Meanwhile, Figure 5 can be understood as the Volt/VAr control curve of the reactive power compensator 23, where m may mean the slope of the Volt/VAr control curve, and d may represent the dead band of the Volt/VAr control curve. It can mean.

보다 낮은 경우에

만큼의 무효 전력을 공급할 수 있고, 무효 전력 보상기(23)는 모선의 전압이

내지

의 범위에 존재하는 경우에 -m의 기울기에 따라 무효 전력의 크기를 변화시키며 공급할 수 있다.Accordingly, the reactive power compensator 23 adjusts the voltage of the bus bar to

In case of lower

It can supply as much reactive power as possible, and the reactive power compensator 23 reduces the voltage of the busbar to

inside

If it exists in the range, the size of reactive power can be changed and supplied according to the slope of -m.

보다 높은 경우에

만큼의 무효 전력을 흡수할 수 있고, 무효 전력 보상기(23)는 모선의 전압이

내지

의 범위에 존재하는 경우에 -m의 기울기에 따라 무효 전력의 크기를 변화시키며 흡수할 수 있다.In addition, the reactive power compensator 23 reduces the voltage of the busbar.

In case of higher

It can absorb as much reactive power as possible, and the reactive power compensator 23 reduces the voltage of the busbar to

inside

If it exists in the range of , the size of reactive power can be changed and absorbed according to the slope of -m.

내지

의 범위에 존재하는 경우에 무효 전력의 보상 동작을 중단할 수 있다.Meanwhile, the reactive power compensator 23 reduces the voltage of the busbar to

inside

If it exists in the range of , the compensation operation of reactive power can be stopped.

Figure 6 is a flowchart of a distribution system optimization method according to an embodiment of the present invention.

Since the distribution system optimization method according to an embodiment of the present invention is carried out on substantially the same configuration as the distribution system optimization system shown in FIG. 1, the same reference numerals are assigned to the same components as the distribution system optimization system shown in FIG. , repeated explanations will be omitted.

The distribution system optimization method includes the steps of collecting measurement data from distributed power (100), predicting distributed power based on the measured data (200), and determining the control pattern of the active network manager based on the distributed power ( 300) and controlling the active network manager based on the control pattern (400).

The step 100 of collecting measurement data from the distributed power source may be a step in which the smart inverter 40 collects measurement data from the distributed power source 30 .

Step 200 of predicting distributed power based on measurement data may be a step in which the smart inverter 40 predicts distributed power generated from the distributed power source 30 based on the measured data.

The step 300 of determining the control pattern of the active network manager based on the distributed power may be a step in which the smart inverter 40 determines the control pattern of the active network manager 20 based on the predicted distributed power.

The step 400 of controlling the active network manager based on the control pattern may be a step in which the smart inverter 40 or the active network manager 20 controls the active network manager 20 based on the control pattern.

Figure 7 is a detailed flowchart of the step of determining the control pattern of the active network manager based on the distributed power of Figure 6.

The step of determining the control pattern of the active network manager (300) may include the step of determining the voltage tap position (310), and the step of determining the voltage tap position (310) may include the step of determining the voltage tap position (310) provided in the active network manager (20). This may be a step of determining one voltage tap position among a plurality of voltage tap positions.

The step 300 of determining the control pattern of the active network manager may further include a step 320 of determining the suitability of the voltage tap position, and the step 320 of determining the suitability of the voltage tap position may be performed within a preset time range. This may be a step of determining the suitability of the voltage tap location based on the power usage pattern of the distributed power source and the load 50 predicted at a preset time interval within the process.

The step 300 of determining the control pattern of the active network manager may further include the step 330 of determining whether the voltage tap position satisfies the constraint condition, and the step 330 of determining whether the voltage tap position satisfies the constraint condition. ) may be a step of determining whether the voltage tap position satisfies preset constraints in the process of determining the suitability of the voltage tap position.

The step 300 of determining the control pattern of the active network manager may further include a step 340 of resetting the suitability based on the constraint condition, and the step 340 of resetting the suitability based on the constraint condition may include a voltage tap In the step 330 of determining whether the position satisfies the constraint condition, if the voltage tap position does not satisfy the constraint condition, the suitability may be reset.

At this time, the smart inverter 40 may reduce the suitability according to the number of constraints that the voltage tap position does not satisfy. In addition, the smart inverter 40 may reduce the suitability if the voltage tap position does not satisfy the constraint conditions. In some cases, a fitness penalty can be imposed in a variety of ways.

The step 300 of determining the control pattern of the active network manager may further include a step 350 of determining whether the number of repetitions satisfies the maximum number of repetitions.

The step 300 of determining the control pattern of the active network manager may further include a step 360 of adding the number of repetitions, and the step 360 of adding the number of repetitions determines whether the number of repetitions satisfies the maximum number of repetitions. In step 350, if the number of repetitions is less than the maximum number of repetitions, the number of repetitions may be added once.

The step 300 of determining the control pattern of the active network manager may further include a step 370 of re-determining the voltage tap position, and the step 370 of re-determining the voltage tap position may be performed based on a preset optimization algorithm. This may be a step of determining a voltage tap position that is different from the voltage tap position determined in .

The step 300 of determining the control pattern of the active network manager may further include the step 380 of determining the voltage tap position with the highest suitability, and the step 380 of determining the voltage tap position with the highest suitability is In step 350 of determining whether the number of repetitions satisfies the maximum number of repetitions, if the number of repetitions is greater than or equal to the maximum number of repetitions, the control pattern of the active network manager is determined based on the suitability determined for different voltage tap positions. This may be a step.

The above description is merely an illustrative explanation of the technical idea of the present invention, and those skilled in the art will be able to make various modifications and variations without departing from the essential quality of the present invention. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but are for illustrative purposes, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention shall be interpreted in accordance with the claims below, and all technical ideas within the scope equivalent thereto shall be construed as being included in the scope of rights of the present invention.

10: Grid power
20: Active network manager
21: voltage regulator
23: Reactive power compensator
30: Distributed power
30a: Distributed power using solar radiation
30b: Distributed power using wind speed
40: Smart inverter
50: load

Claims (12)

Grid power that supplies power to loads connected through a bus;
an active network manager that controls power supplied from the system power supply;
A distributed power source that generates distributed power using the natural environment and supplies the distributed power to the busbar; and
Collect measurement data generated by measuring the amount of the natural environment used by the distributed power source, predict distributed power generated from the distributed power source based on the measured data, and predict the distributed power generated from the distributed power source based on the measured distributed power source. Control of the active network manager by determining one voltage tap position among a plurality of voltage tap positions provided in the network manager and determining the suitability of the determined voltage tap position from the predicted distributed power source based on a preset optimization algorithm. A distributed power optimization system that includes smart inverters that determine patterns. The method of claim 1, wherein the active network manager:
a voltage regulator that adjusts the voltage transmitted from the system power source to the bus bar based on the control pattern; and
A distributed power optimization system comprising a reactive power compensator that compensates for reactive power in the busbar based on the control pattern. The method of claim 1, wherein the smart inverter,
A distributed power optimization system that generates an environmental model using the measurement data and predicts the distributed power based on the environmental model. delete The method of claim 1, wherein the smart inverter,
A distributed power optimization system that determines a reactive power compensation pattern of the active network manager based on the determined voltage tap position. The method of claim 1, wherein the optimization algorithm is:
The suitability of the determined voltage tap position is determined based on the distributed power predicted at a preset time interval within a preset time range and the power use pattern of the load, and the number of repetitions for determining the suitability is greater than the preset maximum number of repetitions. In the low case, the suitability of a voltage tap position different from the determined voltage tap position among the plurality of voltage tap positions is determined, and when the number of repetitions reaches the maximum number of repetitions, the suitability of each voltage tap position for the plurality of voltage tap positions is determined. A distributed power optimization system arranged to determine the control pattern based on suitability. In a distribution system optimization method in a distribution system optimization system including grid power, active network manager, distributed power, and smart inverter,
collecting measurement data generated by measuring the amount of natural environment used by the distributed power source;
predicting distributed power generated from the distributed power source based on the measurement data;
Based on the predicted distributed power, one of the plurality of voltage tap positions provided in the active network manager is determined, and the suitability of the determined voltage tap position is determined from the predicted distributed power based on a preset optimization algorithm. determining a control pattern of the active network manager; and
A distributed power optimization method comprising: controlling the active network manager based on the control pattern. In clause 7,
The step of controlling the active network manager is,
adjusting the voltage transmitted from the system power source to the bus bar based on the control pattern; and
Compensating for reactive power in the busbar based on the control pattern. The method of claim 7, wherein predicting distributed power based on the measurement data comprises:
A distributed power optimization method for generating an environmental model using the measurement data and predicting the distributed power based on the environmental model. delete The method of claim 7, wherein determining a control pattern of the active network manager based on the distributed power supply comprises:
A distributed power optimization method for determining a reactive power compensation pattern of the active network manager based on the determined voltage tap position. The method of claim 7, wherein the optimization algorithm is:
The suitability of the determined voltage tap position is determined based on the distributed power predicted at a preset time interval within a preset time range and the power use pattern of the load connected to the system power through a bus bar, and the number of repetitions for determining the suitability. If is lower than the preset maximum number of repetitions, determine the suitability of a voltage tap position different from the determined voltage tap position among the plurality of voltage tap positions, and if the number of repetitions reaches the maximum number of repetitions, determine the suitability of the plurality of voltage tap positions Distributed power optimization method provided to determine the control pattern based on the respective suitability for voltage tap positions.

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