KR20230106135A - Tso-dso coordination framework under high penetration levels of renewable energy sources - Google Patents

Abstract

Disclosed is a method of transmission system operation and distribution system operation in an environment of increasing renewable energy supply. The method, which is performed by a computing device including at least a processor, comprises: a first optimization process of generating a demand response plan for the day based on information on a predicted renewable energy generation amount, a predicted load, a predicted electric vehicle output, a past electricity market price, and a past transmission system power demand; and a second optimization process of generating the scheduling information of a battery energy storage system and an electric vehicle based on the demand response plan for the day, the electricity market price for the day, and the power demand information of the transmission system for the day. Therefore, the sufficient use of distributed flexible resources and a high consumer participation level can be achieved.

Description

How to cooperate with transmission system operation and distribution system operation in the environment of increasing supply of renewable energy {TSO-DSO COORDINATION FRAMEWORK UNDER HIGH PENETRATION LEVELS OF RENEWABLE ENERGY SOURCES}

The present invention relates to a method for cooperating with transmission system operation and distribution system operation in an environment where the supply of renewable energy increases.

Increasing system flexibility resources, demand response, and application of the latest technologies such as electric vehicles (EVs) and battery energy storage systems (BESS) are the key drivers of transmission system operation (TSO) and distribution system operation (DSO). Cooperation is required, and for this, precise definitions of each role and responsibility must be established.

In order to increase consumer participation as a prosumer and demand response agent along with maximization of system flexibility resource utilization, a precise level of cooperation in transmission and distribution system operation must be developed. In addition, in order to procure reserve power in the transmission system, the time segmentation method for the reserve power procurement process should be applied. Therefore, in each time frame in the distribution system, it is necessary to be able to specify distributed flexible resources capable of supplying reserve power within the system according to the type of auxiliary service.

Transmission systems are faced with a reduction in the number of existing thermal power plants that provide frequency response and inertia to the system. In order to solve these problems, transmission system operators and distribution system operators must maximize the utilization of distributed power and demand response through new operational cooperation.

The new transmission system operation (TSO) and distribution system operation (DSO) coordination method proposed in the present invention includes a one-day economic dispatch method, a same-day economic dispatch method, and real-time frequency control for distribution system operation. In addition, the present invention can promote market participation of consumers maximizing the benefits of system flexibility resources while satisfying transmission system operation requirements.

The economic dispatch proposed in the present invention includes demand response (DR), battery energy storage system (BESS) scheduling, and electric vehicle (EV) charging and discharging scheduling, which constitute a Mixed-Integer Quadratic Programming Problem. .

The present invention proposes a real-time frequency control method including droop control and inertia control of an electric vehicle based on economic power dispatch results.

The present invention proposes the application of distributed flexible resources in terms of distribution system operation to provide various types of auxiliary services in various time frames.

Republic of Korea Patent Publication No. 2021-0052925 (published on May 11, 2021) Republic of Korea Patent Registration No. 2276778 (2021.07.14. Notice) Republic of Korea Patent Publication No. 2019-0073340 (2019.06.26. Publication) Republic of Korea Patent Registration No. 1850426 (2018.04.19. Notice)

A technical problem to be achieved by the present invention is to provide a method for cooperating with next-generation transmission system operation and distribution system operation in an environment of increasing renewable energy supply.

A transmission system operation and distribution system operation method according to an embodiment of the present invention includes transmission system operation (TSO) and distribution system operation performed by a computing device including at least a processor. As a DSO) method, a first optimization process of generating a demand response plan for the day based on information on the predicted amount of renewable energy generation, the predicted load, the predicted amount of electric vehicle output, the past electricity market price, and the past power demand of the transmission system, and A second optimization process of generating battery energy storage system and electric vehicle scheduling information based on the demand response plan of the day, the electricity market price of the day, and the power demand amount information of the transmission system of the day is included.

Clearly define the roles and responsibilities of transmission system operators and distribution system operators. By proposing a new cooperative method between the two operators, it can contribute to solving the problem that the transmission system and the distribution system use distributed power at the same time.

In the present invention, a method for providing diversified auxiliary services in a subdivided time frame by maximally utilizing distributed flexible resources available from the perspective of a distribution system operator is proposed. This makes it possible to achieve the utilization of sufficient decentralized flexible resources and a high level of consumer participation.

In order to provide real-time auxiliary services in the distribution system for frequency fluctuations, droop control and inertial control of electric vehicles were applied based on economic dispatch. Through the present invention, the distribution system operator can utilize the EV as a flexible resource to provide a fast frequency response.

A detailed description of each drawing is provided in order to more fully understand the drawings cited in the detailed description of the present invention.
1 is a flowchart illustrating a first optimization process of the method proposed in the present invention.
2 is a flowchart illustrating a second optimization process of the method proposed in the present invention.
3 is a flowchart illustrating a process of generating control information of the method proposed in the present invention.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed in this specification are only illustrated for the purpose of explaining the embodiments according to the concept of the present invention, and the embodiments according to the concept of the present invention may be embodied in many forms and are not limited to the embodiments described herein.

Embodiments according to the concept of the present invention can apply various changes and can have various forms, so the embodiments are illustrated in the drawings and described in detail in this specification. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes all modifications, equivalents, or substitutes included in the spirit and scope of the present invention.

Terms such as first or second may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another, e.g. without departing from the scope of rights according to the concept of the present invention, a first component may be termed a second component and similarly a second component may be termed a second component. A component may also be referred to as a first component.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when a component is referred to as “directly connected” or “directly connected” to another component, it should be understood that no other component exists in the middle. Other expressions describing the relationship between components, such as "between" and "directly between" or "adjacent to" and "directly adjacent to", etc., should be interpreted similarly.

Terms used in this specification are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "having" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in this specification, but one or more other features It should be understood that it does not preclude the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in this specification, it should not be interpreted in an ideal or excessively formal meaning. don't

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these examples. Like reference numerals in each figure indicate like elements.

In the present invention, a method for cooperating with transmission system operation and distribution system operation is described. The transmission system operation and distribution system operation cooperation method may be named a transmission system operation and distribution system operation method, an optimization method, a control method, a control information generation method, and the like. Also, the method may be performed by a computing device including at least a processor and/or memory. That is, at least some of the steps constituting the method may be understood as operations of a processor of a computing device.

Also, a computing device may mean a single computing device that is not physically separated or a plurality of computing devices that are physically separated. For example, each of the first optimization process, the second optimization process, and the control information generating process constituting the method may be performed by a separate computing device. As another example, at least two of the first optimization process, the second optimization process, and the control information generating process constituting the method may be performed by one computing device. For example, a first optimization process may be performed by a first computing device, and a second optimization process and control information generating process may be performed by a second computing device. The computing device may be implemented as a personal computer (PC), a server, a tablet PC, a laptop computer, or the like.

1 is a flowchart illustrating a first optimization process included in the proposed method.

The first optimization process may be referred to as a one-day-ahead optimization process.

First, at least one of prediction information, that is, renewable energy prediction information 100, load prediction information 100, and system flexibility resource 101 prediction information is received (or collected). The renewable energy prediction information may refer to a predicted value of power generation by at least one of wind power generation (or wind power generators), photovoltaic power generation (or solar power generators), and other renewable energy. The load prediction information may refer to a predicted value of a load in the future (ie, a prediction time point or a prediction interval). Prediction information for a system flexibility resource, for example, an electric vehicle (or electric vehicle) may mean a predicted value for an output at a prediction time point (or prediction interval). As another example, prediction of the day before the electric vehicle may mean departure and arrival times of the electric vehicle, and moving distance of the electric vehicle. Here, the prediction amount or prediction information is information about the prediction amount at a predetermined point in time (or during a period), and may mean a result performed through an arbitrary prediction technique (eg, a known prediction technique). As a specific example, the prediction amount or prediction information is a result of prediction performed at an arbitrary time point one day before the prediction time point (prediction interval), and may mean prediction information for the next day. Accordingly, the first optimization process may be referred to as a one-day-before optimization process.

The received prediction information may be used in a process of generating probability distribution information for scenario generation 200 . In other words, it is possible to generate scenarios of one-day forecasts of wind power, solar power, and loads and one-day forecasts of electric vehicles using a probability density function (or probability distribution function).

Additionally, information about the scenario 200, market price 201, and grid power demand 202 is received (or collected). The scenario 200 may be generated based on prediction information, or the generated scenario may be received.

The goal of pre-day optimization is to meet the supply requirements of the transmission grid operation (TSO) with minimal operating costs (300). The output of the day-ahead optimization is the demand response scheduling (1000) for the day. BESS and EV scheduling plans may be excluded from this process due to uncertainty in the forecast one day in advance.

는 k번째 시나리오의 t 시점에 EV가 충전소에 도착하는 것을 의미하며,

는 k번째 시나리오의 t 시점에 EV가 충전소를 떠나는 것을 의미한다. 따라서,

는 유효전력,

는 풍력발전량,

는 태양광 발전량을 의미한다. 그 외, EV의 행동에 따른 시나리오는 다음과 같이 나타낼 수 있다.Let i(i∈I) denote the number of buses, t(t∈T) denote the time, k(k∈K) denote the sequence of scenarios, and v(v∈V) denote the number of EVs.

means that the EV arrives at the charging station at time t of the kth scenario,

means that the EV leaves the charging station at time t of the kth scenario. thus,

is the active power,

is the amount of wind power,

is the amount of solar power. In addition, the scenario according to the EV's behavior can be expressed as follows.

[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

는 충전소의 동적 최대충전용량,

는 EV v의 정격에너지,

는 k번째 시나리오의 t 시점에서 EV v의 계통연계상태를 의미하는 이진 변수(1은 연계상태, 0은 분리상태를 의미),

는 EV의 동적 최대 충·방전량,

는 EV v의 정격전력,

는 k번째 시나리오의 t 시점에 EV가 충전소에 도착하여 에너지가 증가하는 것,

는 k번째 시나리오의 t 시점에 EV가 충전소를 떠나 에너지가 감소하는 것,

는 EV

가 일상 주행 후 소비한 전력을 의미한다. 수학식 1은 동적 최대 EV 에너지 용량은 k번째 시나리오의 t 시점에서 충전소에서 사용 가능한 모든 EV의 정격에너지 합을 나타내며, 이는 수학식 2의 동적 최대 EV 전력에도 동일하게 적용된다. 수학식 3은 k번째 시나리오의 t 시점에서 충전소의 에너지 증가가 주행 후 도착한 EV의 남은 에너지의 합임을 의미하며, 수학식 4는 t 시점에서 충전소의 에너지 감소는 충전이 완료되어 떠나는 EV의 합임을 의미한다.In the above equation,

is the dynamic maximum charging capacity of the charging station,

is the rated energy of EV v,

is a binary variable meaning the grid-connected state of EV v at time t of the k-th scenario (1 means connected state, 0 means disconnected state),

is the dynamic maximum charge and discharge amount of EV,

is the rated power of EV v,

is that the EV arrives at the charging station at time t of the kth scenario and the energy increases,

At time t of the k-th scenario, EV leaves the charging station and energy decreases,

is EV

means the power consumed after daily driving. In Equation 1, the dynamic maximum EV energy capacity represents the sum of the rated energies of all EVs available at the charging station at time t of the k-th scenario, which is equally applied to the dynamic maximum EV power in Equation 2. Equation 3 means that the energy increase of the charging station at time t of the kth scenario is the sum of the remaining energy of the EVs arriving after driving, and Equation 4 indicates that the energy decrease of the charging station at time t is the sum of EVs leaving after charging is completed. it means.

In addition to the scenario generation process 200 , the wholesale market price of electricity from the previous day and the predicted value of electric power demand on the transmission system side from the previous day are received (or collected). That is, 200, 201, and 202 in FIG. 1 constitute basic data input in terms of distribution system operation in the optimization process one day before. At this time, the responsibility of the distribution system operator is to meet the power supply requirement of the transmission system at a minimum price (300). If the distribution system operator fails to meet the power supply requirements of the transmission system operator, a penalty may be imposed. The electricity wholesale market price refers to the price at which the distribution system operator sells electricity to the transmission system operator. Distribution system operating cost F ₁ can be expressed as follows.

[Equation 5]

[Equation 6]

[Equation 7]

[Equation 8]

[Equation 9]

[Equation 10]

[Equation 11]

는 배전계통 운영자와 송전계통 운영자 간 전력교환수익 또는 총비용,

는 송전계통 운영자와 배전계통 운영자 간 판매하거나 구입하는 전력량,

은 송전계통 운영자와 배전계통 운영자 간 판매하거나 구입하는 가격,

은 고객에게 전력을 팔아서 얻은 배전계통 운영자의 수익,

는 수요반응레벨 j에서의 가격비율,

은 수요반응 없는 고객에 대한 원가,

는 가격레벨 j에 대한 이진 결정,

는 가격레벨 j의 수요반응 비율,

는 EV 소유자에게 배전계통 운영자가 주는 보조금,

는 EV 소유자와 계약된 충·방전 가격,

는 EV의 충·방전 전력량,

는 신재생에너지원 구매비용,

는 신재생에너지원 사업자로부터 구매한 전력량,

는 신재생에너지원 구매비용,

는 총 배터리 에너지 저장 시스템의 운영 비용,

는 배터리 에너지 저장 시스템의 비용 상관계수,

는 배터리 에너지 저장 시스템의 충·방전 전력량,

은 송전계통 운영자의 요구량을 충족하지 못할 시 배전계통 운영자에게 부과되는 벌금(패널티),

는 벌금 상관계수,

는 음이 아닌 송전계통 운영자 불균형 전력을 의미한다.In the above equation, j is the demand response price level, J is a set of demand response price levels, τ is the length of time, π _k is the probability of the kth scenario,

is the revenue or total cost of electricity exchange between the distribution grid operator and the transmission grid operator,

is the amount of electricity sold or purchased between transmission grid operators and distribution grid operators,

the price of sale or purchase between the transmission grid operator and the distribution grid operator;

is the distribution system operator's revenue from selling electricity to customers;

is the price ratio at the demand response level j,

is the cost for customers without demand response,

is the binary decision for price level j,

is the demand response rate of price level j,

is a subsidy given by distribution grid operators to EV owners;

is the charging and discharging price contracted with the EV owner,

is the amount of charging and discharging power of the EV,

is the purchase cost of renewable energy sources,

is the amount of electricity purchased from a renewable energy source operator,

is the purchase cost of renewable energy sources,

is the operating cost of the total battery energy storage system,

is the cost correlation coefficient of the battery energy storage system,

is the amount of charge and discharge power of the battery energy storage system,

is a fine (penalty) imposed on distribution system operators when they do not meet the demand of transmission system operators;

is the penalty correlation coefficient,

denotes a non-negative grid operator unbalanced power.

In the above process, in order to reflect the uncertainties of solar power, wind power, and load and to satisfy the demand of the transmission system, the distribution system operator performs a demand response through optimization 301 and supplies power to the battery energy storage system and the electric vehicle. Therefore, a whole-day DR plan can be derived through the one-day optimization process. Due to the uncertainty of the forecast the day before, scheduling for battery energy storage systems and electric vehicles is not performed in this process. The battery energy storage system and EV scheduling are performed in the second optimization process (same-day optimization process) considering uncertainty.

2 is a flowchart illustrating a second optimization process of the method proposed in the present invention.

The second optimization process may be referred to as the same-day optimization process. Considering the convenience of consumers, DR scheduling can be fixed after optimization one day in advance. This means that the distribution system operator does not require load changes through demand response from consumers on the same day. However, the battery energy storage system and EV scheduling are optimized during the same day optimization process taking into account short-term uncertainties. Whole-day demand response plan (1000), solar power, wind power, load, electric vehicle uncertainty (1001), electricity price for the day (1002), and power demand for operation of the transmission system for the day (1003) Configures the basic input data of the operation. At this time, the responsibility of the distribution system operator is to supply the power supply demand of the transmission system operator at the minimum price through the battery energy storage device (Battery Energy Storage System) and EV scheduling, and the optimized EV output is set as the reference point for real-time frequency control. do. If the distribution system operator fails to meet the power supply requirements of the transmission system, the cost in 302 may be applied as a penalty to the distribution system operator.

은 다음과 같이 나타낼 수 있다.The second optimization process is a rolling optimization process. In this process, deterministic prediction of solar and wind generator outputs and loads is used, and accordingly, solar and wind generators and loads are regarded as deterministic parameters. The characteristics of electric vehicles are also deterministically fixed. Apart from this, in each rolling optimization process considering the uncertainty, only the first lag decision is implemented and the others are not used. Accordingly, it can be more usefully used when the accuracy of solar power, wind power, and load prediction increases in the near future. During the rolling optimization process, the battery energy storage system and EV scheduling plan are optimized again. Distribution system operating cost F ₂ and

can be expressed as:

[Equation 12]

[Equation 13]

는 일일 수요반응 스케줄링 계획(1000)을 의미하며, τ는 0.25h를 나타낸다. 수학식 12의 변수들은 수학식 1 내지 수학식 4와 유사한 의미와 계산 과정을 거치지만, 수학식 5 내지 수학식 11에서는 더 이상 시나리오를 고려하지 않는다.In the above equation,

denotes the daily DR scheduling plan (1000), and τ denotes 0.25h. The variables of Equation 12 undergo similar meanings and calculation processes to those of Equations 1 to 4, but scenarios are no longer considered in Equations 5 to 11.

3 is a flowchart illustrating a process of generating control information of the method proposed in the present invention.

The process of generating control information is a process of generating real-time frequency control information. Even at each time interval of the second optimization process, outputs of solar and wind power generators may rapidly fluctuate under special conditions, such as rapid movement of clouds or strong winds. This may cause sudden frequency fluctuations in the entire power system, resulting in an acceptable frequency range. In the third process, the electric vehicle charge/discharge scheduling result (10000) derived from rolling optimization and the real-time measured value (10001) of solar power, wind power, and load are used as basic input data for distribution system operation. The distribution system operator can control the electric vehicle output through droop control and inertia control (real-time control) 10002 to mitigate frequency variability of the system. The real-time frequency control process derives the real-time output 10003 of the electric vehicle. That is, in the process of generating control information, the real-time output of the electric vehicle can be derived, and accordingly, the distribution system operator can mitigate frequency variability of the system.

[Equation 14]

는 EV의 실시간 출력(10003),

는 EV의 스케줄링 계획(10000),

는 계통 주파수 변동,

은 관성 제어 상수,

는 드룹 제어 상수를 의미한다.In Equation 14,

is the real-time output of EV (10003),

is the EV's scheduling plan (10000),

is the grid frequency variation,

is the inertial control constant,

denotes a droop control constant.

In the present invention, the distribution system operator is obliged to manage the battery energy storage system, the electric vehicle, and the load. The transmission system manager has only the duty to deliver the energy requirements to the distribution system operator, without the need to manage distributed power sources. The problem of simultaneous management of distributed power by the transmission system manager and the distribution system manager can be solved through the method of cooperating between the operation of the transmission system and the operation of the distribution system proposed in the present invention.

In the present invention, demand response, battery energy storage devices (battery energy storage systems), and electric vehicles operate in different subdivided time frames to provide different types of grid services. Demand response is planned in the first process (first optimization process), the battery energy storage system is mainly planned in the second process (second optimization process), and the charging and discharging schedule of the electric vehicle is mainly planned in the second and third processes (control information generation). process) is planned and controlled. Through this method, it is possible to maximize the full utilization of distributed flexibility resources and the participation of consumers in the power system. In addition, a predetermined optimization technique may be used in the present invention, and a Mixed-Integer Quadratic Programming Problem may be used as an example.

In the present invention, real-time droop control and real-time inertia control based on economic power dispatch results are applied to electric vehicle control in order to mitigate frequency fluctuations of the system. In this way, distribution grid operators can take advantage of the flexibility of EVs to provide fast frequency response throughout the grid.

The device described above may be implemented as a hardware component, a software component, and/or a set of hardware components and software components. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a Programmable Logic Unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Also, other processing configurations are possible, such as a parallel processor.

Software may include a computer program, code, instructions, or a combination of one or more of these, and may configure a processing device to operate as desired or process independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in the transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - Includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, ROM, RAM, flash memory, etc. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the present invention has been described with reference to the embodiments shown in the drawings, this is only exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the attached claims.

Claims (4)

In the transmission system operation (TSO) and distribution system operation (DSO) method performed by a computing device including at least a processor,
A first optimization process of generating a demand response plan for the day based on information about renewable energy generation forecast, load forecast, electric vehicle output forecast, past electricity market price, and past transmission system power demand; and
A second optimization process of generating battery energy storage system and electric vehicle scheduling information based on the demand response plan of the day, the electricity market price of the day, and the power demand information of the transmission system of the day.
Transmission system operation and distribution system operation method. According to claim 1,
Further comprising a control information generation process for generating output information of an electric vehicle to mitigate frequency variability of the entire system,
Transmission system operation and distribution system operation method. According to claim 2,
The predicted amount of renewable energy generation means the predicted amount of wind power generation and solar power generation the day before,
The predicted amount of the electric vehicle output means the predicted amount for the departure, arrival time, and moving distance of the electric vehicle,
The past power market price means the power market price of the day before,
The past transmission system power demand means the transmission system power demand of the previous day,
Transmission system operation and distribution system operation method. According to claim 3,
The control information generation process,
Generating output information of the electric vehicle using a mathematical formula;
The above formula is

ego,
remind

is the real-time output of the electric vehicle,

Is the scheduling plan of the electric vehicle,

is the grid frequency variation,

is the inertial control constant,

denotes the droop control constant,
Transmission system operation and distribution system operation method.

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