KR20160062684A - Probabilistic Model-based Distributed Resource Management System and Method thereof - Google Patents

Abstract

The present invention relates to a system for managing virtual distributed resources based on a probabilistic model and a method thereof. The system for managing virtual distributed resources based on a probabilistic model according to an embodiment of the present invention comprises: a resource collecting unit for collecting distributed resource state information from multiple distributed resources; and a virtual distributed resource management server for monitoring the multiple distributed resources by using the distributed resource state information collected by the resource collecting unit, and for calculating availability and a moving probability of each of the multiple distributed resources, thereby moving the distributed resources. The system for managing virtual distributed resources based on a probabilistic model can hierarchically manage distributed resources by monitoring, in real time, assets of power generation resources based on distributed energy resources, and provide stable energy by clustering virtual power generation energy by securing power generation resources near an energy-needed location.

Description

 TECHNICAL FIELD [0001] The present invention relates to a virtual distributed resource management system based on probabilistic models,

The present invention relates to a stochastic model-based virtual distributed resource management system and method, and more particularly, to a technique capable of efficiently and integrally managing distributed energy resources.

The existing distributed energy resources are installed locally in the building or in some areas when the power system can not receive the energy, and supply and receive the energy through the power generation resources.

However, in the conventional distributed energy resource management technology, there is a problem that the energy of the distributed power generation resources can be supplied only to the inside or adjacent area of the building which can supply the energy of the distributed resources.

In particular, heterogeneous distributed energy resources and distributed energy resources installed in a large area are not integratedly managed in terms of energy generation assets, so that the utilization of distributed energy resources that can be utilized in an emergency is deteriorating. As a result, the efficient management of distributed energy resources and the limit of energy supply are encountered.

Therefore, it is necessary not only to manage as a systematic and hierarchical energy generation asset of distributed energy resources, but also to monitor the status of distributed resources for emergency energy supply and demand in a real-time manner and supply them as stable distributed demand and development resources. It is necessary to manage the resources of the probabilistic model base.

Patent Publication No. KR 2014-0076037

The embodiment of the present invention monitors the distributed energy resources periodically to manage the state of the power generation resources based on the dispersed energy resources when the consumer fails to receive energy supply / demand from the power system at the time of emergency or power peak, The present invention provides a stochastic model-based virtual distributed resource management system and method for stably providing energy.

The technical problems of the present invention are not limited to the above-mentioned technical problems, and other technical problems which are not mentioned can be understood by those skilled in the art from the following description.

A probabilistic model-based virtual distributed resource management system according to an embodiment of the present invention includes: a resource collection unit for collecting distributed resource state information from a plurality of distributed resources; And a virtual distributed resource management server for monitoring the plurality of distributed resources using the distributed resource status information collected by the resource collection unit and calculating availability and response probabilities of the plurality of distributed resources, .

The virtual distributed resource management server further includes a virtual distributed resource management server for analyzing the operating costs of the distributed distributed resources among the distributed resources and providing the result to the virtual distributed resource management server when receiving the activeable resource report from the virtual distributed resource management server can do.

Wherein the virtual distributed resource management server comprises: a monitoring unit operable to monitor a state of the plurality of distributed resources in cooperation with the resource collection unit; A availability analyzer for analyzing availability of each distributed resource using the history of the plurality of distributed resources and the distributed resource status information received from the resource collector; A relative probability calculation unit for calculating the probability of success of the plurality of distributed resources and determining whether the probability of success of each distributed resource is equal to or greater than a reference value; And a profile manager for storing and managing a profile of distributed resources adjacent to a target point among distributed resources having a correlation success probability of a reference value or more among the plurality of distributed resources.

The availability analysis unit may analyze availability using at least one of a past active time of the plurality of distributed resources, an active condition, a current state of distributed resources, and a communication state.

Wherein the distributed resource management server comprises: a scheduling unit for clustering distributed resources adjacent to the target point and scheduling the distributed resources; And a response requesting unit for requesting to respond to the distributed resources according to the scheduling.

Wherein the activity probability calculation unit calculates an activity success probability by dividing the number of active responses of the distributed resources by the number of times of the active request and determines whether the active success probability is higher than the average activity success probability of all the distributed resources, Distributed resources higher than probability can be listed.

The scheduling unit may determine whether scheduling is possible based only on the distributed resources of the region adjacent to the target point among the distributed resources having the correlation success probability of the reference value or more and if not, scheduling may include the distributed resources outside the region adjacent to the target point.

A method for managing a virtual distributed resource based on a probabilistic model according to an embodiment of the present invention includes collecting distributed resource state information from distributed resources and monitoring distributed resources; Analyzing the availability of the distributed resources using the distributed resource state information and the history information of the distributed resources; Calculating a probability of success of the distributed resources; Listing the distributed resources adjacent to the target point based on the probability of success of the interaction; Performing drive scheduling using the list-up distributed resources; And clustering and reacting distributed resources according to the drive scheduling.

The step of performing the drive scheduling may further include a step of performing drive scheduling including a distributed resource outside the area adjacent to the target point when energy supply to the target spot is insufficient for the distributed resource adjacent to the target spot.

The step of calculating the probability of success of the distributed resources may calculate the probability of success by dividing the number of active responses to each distributed resource by the number of active requests.

The step of performing drive scheduling using the listed up distributed resources may list distributed resources whose computation success probabilities for each distributed resource are greater than the average computation probability of the distributed resources.

Analyzing availability of the distributed resources; The availability of the distributed resource can be determined using at least one of the current charging state, the communication state, the driving fuel state, the past active state, and the active state.

The step of performing drive scheduling using the listed up distributed resources may analyze the operation cost of the distributed resources and perform drive scheduling according to the result.

This technology monitors the assets of power generation resources in real time based on distributed energy resources and hierarchically manages distributed demand resources with high probability of responding in case of emergency or power peak, as active assets, and provides energy supply and demand pattern And it is possible to provide stable energy by clustering virtual power generation energy by securing generation resources adjacent to the supply area.

1 is a block diagram of a stochastic model-based virtual resource management system according to an embodiment of the present invention.
2 is a detailed configuration diagram of a resource management server according to an embodiment of the present invention.
FIG. 3 is a flowchart illustrating a distributed resource replica method of a probabilistic model-based virtual resource management system according to an embodiment of the present invention.
4 is a flowchart illustrating a distributed resource management method of a virtual distributed resource management server according to an embodiment of the present invention.
FIG. 5A is a graph showing a development pattern and a development timing of a first distributed resource according to an embodiment of the present invention. FIG.
FIG. 5B is a graph showing a development pattern and a development timing of a second distributed resource according to an embodiment of the present invention. FIG.
FIG. 5C is a graph showing a development pattern and development timing of a third distributed resource according to an embodiment of the present invention. FIG.
FIG. 5D is a graph showing a development pattern and a development timing of a fourth distributed resource according to an embodiment of the present invention. FIG.
FIG. 6 is a graph illustrating a method of approximating active scheduling of distributed resources according to an embodiment of the present invention.
7 is a configuration diagram of a computer system to which a distributed resource management system according to an embodiment of the present invention is applied.

Hereinafter, some embodiments of the present invention will be described in detail with reference to exemplary drawings. It should be noted that, in adding reference numerals to the constituent elements of the drawings, the same constituent elements are denoted by the same reference numerals whenever possible, even if they are shown in different drawings. In the following description of the embodiments of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the difference that the embodiments of the present invention are not conclusive.

In describing the components of the embodiment of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are intended to distinguish the constituent elements from other constituent elements, and the terms do not limit the nature, order or order of the constituent elements. Also, unless otherwise defined, 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 this 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 relevant art and are to be interpreted in an ideal or overly formal sense unless explicitly defined in the present application Do not.

The present invention monitors the state of a distributed energy resource in real time, identifies the profile of dispersed resources, the number of times of success and the number of times of successful operation and the adjacent area condition at the time of normal operation, and stably performs energy clustering stably at power peak or emergency. Provides a method and system for managing distributed energy resources as an asset to provide distributed energy to the customer as an operating cost.

Hereinafter, embodiments of the present invention will be described in detail with reference to FIGS. 1 to 7. FIG.

1 is a block diagram of a stochastic model-based virtual resource management system according to an embodiment of the present invention.

The probabilistic model-based virtual resource management system according to an embodiment of the present invention includes a plurality of resource collection units 200 connected to a virtual distributed resource management server 100, a virtual distributed resource management server 100, A plurality of distributed energy resources 300 and a virtual distributed resource operation server 400 each managed by a plurality of resource collection units 200.

The virtual distributed resource management server 100 supplies stable energy to the customer in an emergency or at a power peak. That is, the virtual distributed resource management server 100 hierarchically manages each distributed energy resource 300 as a power generation resource asset, and virtually clusters the energy resource, so that energy can be supplied to the customer as an optimal operation cost. At this time, the customer means houses, factories, buildings, etc. that require power. For this, the virtual distributed resource management server 100 collects status information of the distributed resources through the resource collection unit 200, monitors the status of the distributed resources, and manages the availability of the distributed resources using the status information and the history information of the distributed resources. After analysis, we calculate the probability of success for the available distributed resources, list the distributed resources whose probability of success is above the reference value, perform the scheduling using the distributed distributed resources, and respond to the distributed resources by clustering.

The resource collection unit 200 physically clusters the distributed energy resources 300 and provides status information on distributed energy resources to the virtual distributed resource management server 100 through the Internet.

The distributed energy resource 300 includes a distributed energy resource controller that controls energy resources and distributed energy resources provided to the customer. Examples of distributed energy resources include renewable energy (solar, wind, etc.), cogeneration energy, emergency generators, and energy storage devices, and these various distributed energy resources can be built in or near buildings.

The virtual distributed resource management server 400 analyzes the operation costs of the distributed distributed resources in cooperation with the virtual distributed resource management server 100 and transmits the history information about the distributed resources having the optimal cost to the virtual distributed resource management server 100. [ .

2 is a detailed configuration diagram of the resource management server 100 according to the embodiment of the present invention.

The resource management server 100 according to the embodiment of the present invention includes a monitoring unit 110, a availability analysis unit 120, a probability calculation unit 130, a profile management unit 140, a scheduling unit 150, (160).

The monitoring unit 110 monitors the status of a plurality of distributed resources in cooperation with the resource collection unit 200.

The availability analysis unit 120 analyzes the availability of each distributed resource using the history information of a plurality of distributed resources and the distributed resource status information received from the resource collection unit 200. The availability analysis unit 120 may analyze the availability using at least one of past past living time of a plurality of distributed resources, living conditions, current state of distributed resources, and communication state.

The likelihood probability calculation unit 130 calculates the probability of success of the plurality of distributed resources and determines whether the probability of success of the respective distributed resources is equal to or greater than a reference value. The activity probability calculation unit 130 calculates an activity success probability by dividing the number of active responses of distributed resources by the number of times of active requests and determines whether the activity success probability is higher than the average activity success probability of all distributed resources, Manage the profile by listing the distributed resources that are higher than the probability of success.

The asset profile management unit 140 stores and manages a profile of distributed resources adjacent to a target point among distributed resources having a correlation success probability of a plurality of distributed resources or more.

The scheduling unit 150 clusters the distributed resources adjacent to the target point and schedules the listed distributed resources. The scheduling unit 150 determines whether scheduling is possible based only on distributed resources of the region adjacent to the target point among the distributed resources having the probability of success probability equal to or greater than the reference value and scheduling including the distributed resources outside the region adjacent to the target point if impossible.

The activity request unit 160 requests the activity with the corresponding distributed resources according to the scheduling.

Hereinafter, a distributed resource management flow for virtual power generation energy clustering of distributed energy resources according to an embodiment of the present invention will be described with reference to FIG.

First, the virtual distributed resource management server 100 periodically requests the resource collection unit 200 to monitor the distributed resource status (S101).

The resource collection unit 200 requests distributed resource status information from the distributed resource resources 300 in steps S102 and S103. The distributed resource resource 300 receives the distributed resource status information from the resource collection unit 200 (S104, S105). At this time, the distributed resource status information may include a power generation status, a manual control mode, a charging status, a driving fuel status, a networking status, and the like.

Thereafter, the resource collection unit 200 reports information about whether or not the distributed resources can be activated to the virtual distributed resource management server 100 using the status information of the distributed resources of the distributed energy resource 300 (S106). At this time, the resource collecting unit 200 determines whether or not the distributed resources can be activated according to the charged state of the distributed resources, whether networking (communication) is possible, whether the mode is manual control mode, or the like. That is, when the charged state of the distributed resource is high, the resource collecting unit 200 determines that the distributed resource is available, if the networking is possible, the non-passive control mode, and if the charged state is low, It is judged that it is impossible to stir. Here, the manual control mode means a state in which the operator can manually control in the field instead of the remote control.

Then, the virtual distributed resource management server 100 recognizes the status of the distributed resource using the affinity information and the distributed resource status information reported from the resource collection unit 200, And manages the profile of distributed resources (S107).

That is, the virtual distributed resource management server 100 monitors the location and the active state of the distributed resource using the affinity information and the distributed resource status information reported from the resource collection unit 200, and updates the updated information with the latest information. In addition, the virtual distributed resource management server 100 analyzes the availability of each distributed resource using history information about each distributed resource previously stored, affinity information reported from the resource collection unit 200, and distributed resource status information do. In addition, the virtual distributed resource management server 100 calculates, based on a probabilistic model, whether or not it is possible to deal with each distributed resource in an emergency or at a power peak. In addition, the virtual distributed resource management server 100 clusters and manages the profiles of the distributed resources in the regions adjacent to the target point among the distributed resources having a high probability of success of interaction. At this time, the target point is the area of the consumer who needs energy.

Thereafter, the virtual distributed resource management server 100 provides the virtual distributed resource management server 400 with information about distributed resources that can be interacted based on the relay probability (S108).

Accordingly, the virtual distributed resource management server 400 analyzes the optimum point of the operation cost of the distributed resource asset that can be coordinated (S109). At this time, the virtual distributed resource management server 400 can analyze the operation cost by using information such as the distance from the target point to the distributed resource, the fuel of the distributed resource, and the like.

Then, the virtual distributed resource management server 400 provides the virtual distributed resource management server 100 with the history information including the operation cost of distributed resources and the like (S110).

Then, the virtual distributed resource management server 100 schedules the distributed resources using the active history information received from the virtual distributed resource management server 400 and the profile information of the distributed resources adjacent to the target point, and clusters the distributed resources ( S111).

Thereafter, the virtual distributed resource management server 100 requests the distributed resources (301, ..., 300m) to drive the generated resources according to the distributed resource scheduling plan (S112, S113) 301, ..., 300m) (S114, S115). At this time, the virtual distributed resource management server 100 may provide the virtual distributed resource management server 400 with the history information of starting the distributed resource management.

Hereinafter, a distributed resource management method of a virtual distributed resource management server according to an embodiment of the present invention will be described in detail with reference to FIG.

The virtual distributed resource management server 100 periodically monitors the status of the distributed distributed resources (S201).

The virtual distributed resource management server 100 checks whether the distributed resource is available or not by using the status information of the distributed resource and the history information of the distributed resource (S202). In other words, the virtual distributed resource management server 100 uses the history information of the distributed resource to store information such as the past active time of the distributed resource, the active condition, the active state at the time of emergency, State, network (communication) availability, etc., to determine whether the distributed resource is available.

Thereafter, the virtual distributed resource management server 100 calculates the probability of success of the distributed resources (S203). That is, the probability of success of the distributed resources can be calculated by dividing the number of active responses by the number of active requests. The virtual distributed resource management server 100 determines whether the probability of success of the distributed resources is higher than the average probability of all the distributed resources. That is, if the probability of success is higher than the average probability of occurrence, it is determined that the probability of success is high.

Next, the virtual distributed resource management server 100 manages and clusters the profiles of the distributed resources adjacent to the target point to which the energy is to be supplied, among the distributed resources having a high success probability of operation (S204).

That is, the virtual distributed resource management server 100 clusters power generation resources adjacent to the target point when the probability of occurrence of an emergency or power peak is greater than or equal to a reference value, and if the probability of occurrence of an emergency or a power peak is smaller than a reference value, And continue to check whether the assets of the power generation resources are available.

As described above, the virtual distributed resource management server 100 clusters the distributed resources, and then plans the active schedules of the responsive distributed resources (S205). At this time, it is possible to adjust the agitation point in consideration of the variables such as generation amount of various distributed energy resources, generation duration, and power generation stability.

At this time, it is determined whether or not a desired amount of energy can be supplied only by adjacent distributed resources (S206). If it is impossible to supply a desired amount of energy by only adjacent distributed resources, among distributed resources outside the adjacent region, And performs scheduling (S207).

When the active schedule is determined, the virtual distributed resource management server 100 clusters the distributed resources according to the scheduling (S208), and stores the distributed resource active history information (S209).

Thereafter, a request is made to respond to the distributed resources (S210). In this case, due to various characteristics of distributed energy resources, it is difficult to independently supply stable energy to one kind of distributed resource when power generation is requested, so that clustering is performed.

Thereafter, the virtual distributed resource management server 100 provides the virtual distributed resource management server 400 with the history of the generated power generation resources for energy supply and demand which may be generated in an emergency, It is possible to manage its history so that it can be driven.

5A to 5D show development patterns and development points of various distributed resources according to an embodiment of the present invention. According to the present invention, energy supply and demand can be performed at an optimal operating cost in an emergency using the power generation characteristics and the dispersion probability of distributed energy resources. As a result, if various distributed energy resources exist in the same area and power generation resources are available, n distributed energy resources can be used as clustering.

However, if various distributed energy resources do not exist in the same area, the optimal available resources are configured as virtual power generation energy to supply and receive energy. Here, the probability mass function of each distributed resource X is expressed by Equation 1 below.

의 범위 안에 있고, n은

인 정수이다. 응동 성공 확률이 p인 n번의 독립시행에서 성공 횟수는 항상 이항 랜덤변수가 된다. 일반적으로 이항 (n, p) 랜덤변수에 대해서 n은 시행의 횟수이고, p는 응동 성공 확률이다. X can be defined as a binary ( n , p ) random variable. Where p is

Lt; / RTI >

Lt; / RTI > In n independent trials where the probability of success is p , the number of successes is always a binary random variable. Generally, for a binary ( n , p ) random variable, n is the number of trials and p is the probability of success.

FIG. 5B is an example of the success probability model for the second distributed resource, FIG. 5C is an example of defining the success probability model for the second distributed resource at the end, and FIG. 5D is an example of defining the success probability model for the last distributed resource. This probability model is not limited to the probability model proposed in the present invention but can be defined in various ways.

FIG. 6 shows a method of approximating active scheduling of distributed generation resources. The cumulative distribution function of the random variable X can be defined as shown in Equation (2) below.

That is, for any random number x , the cumulative distribution function means the probability that the random variable X is not greater than x .

에 대하여

가 성립한다. 각각의 분산자원의 누적분포함수는 아래 수학식 3과 같이 표현할 수 있다.Therefore,

about

. The cumulative distribution function of each distributed resource can be expressed as Equation 3 below.

또는

로 할 수 있다. 따라서, X의 기대값은

으로 정의한다. 결국 이항 (n, p) 랜덤변수 X에 대해 기대값은

이 된다. 그러므로, 각각의 분산자원의 응동 기대값은 아래 수학식 4와 같다.At this time, the expected value of the random variable X , which is a distributed resource, can be derived. That is, it corresponds to dividing the measured value by the total number of additions, so the notation for the expected value of the random variable X is

or

. Thus, the expected value of X is

. Finally, for the binomial ( n , p ) random variable X , the expected value is

. Therefore, the expected expectation value of each distributed resource is given by Equation (4) below.

Considering the characteristics of power generation resources shown in the graph of FIG. 6, a distributed resource having a high probability of occurrence is prepared in the interval [a, b] and a distributed resource profile is extracted from a distributed resource profile To provide stable energy. In this case, when there are a plurality of distributed resource assets having a high probability of occurrence in the [a, b] interval, the distributed resources are clustered to be responsive. If there is a distributed resource with a high probability of responding to [a, b] and [e, g], but there is no distributed resource with a high probability of responding to [b, e] If there is a distributed resource with a high probability of responding to the [c, d] section including the interval, the distributed resource is continuously transmitted to provide energy to the customer. In other words, by selecting distributed resources with high probability of clustering, it is possible to cluster distributed resources adjacent to the target point and optimize the supply and demand plan of virtual generation energy using energy storage devices or other distributed energy resources.

Thus, the present invention monitors the distributed energy resources periodically and manages the state of the power generation resources based on the dispersed energy resources when the customer can not receive energy supply / demand from the power system at the time of emergency or power peak, It is possible to cluster and supply the virtual power generation energy with the optimal operation cost.

7 is a configuration diagram of a computer system to which a distributed resource management system according to an embodiment of the present invention is applied.

7, a computing system 1000 includes at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, (1600), and a network interface (1700).

The processor 1100 may be a central processing unit (CPU) or a memory device 1300 and / or a semiconductor device that performs processing for instructions stored in the storage 1600. Memory 1300 and storage 1600 may include various types of volatile or non-volatile storage media. For example, the memory 1300 may include a ROM (Read Only Memory) and a RAM (Random Access Memory).

Thus, the steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by processor 1100, or in a combination of the two. The software module may reside in a storage medium (i.e., memory 1300 and / or storage 1600) such as a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, You may.

An exemplary storage medium is coupled to the processor 1100, which can read information from, and write information to, the storage medium. Alternatively, the storage medium may be integral to the processor 1100. [ The processor and the storage medium may reside within an application specific integrated circuit (ASIC). The ASIC may reside within the user terminal. Alternatively, the processor and the storage medium may reside as discrete components in a user terminal.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention.

Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, 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 should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

100: virtual distributed resource management server
200: resource collection unit
300: Distributed energy resources
400: virtual distributed resource application server
110:
120: Availability Analysis Unit
130:
140: Profile manager
150: Scheduling unit
160:

Claims (13)

A resource collection unit for collecting distributed resource state information from a plurality of distributed resources; And
A virtual distributed resource management server for monitoring the plurality of distributed resources by using the distributed resource status information collected by the resource collection unit and calculating availability and response probabilities of the plurality of distributed resources,
Based virtual distributed resource management system. The method according to claim 1,
A virtual distributed resource management server for analyzing the operating costs of the distributed distributed resources among the distributed resources and providing the result to the virtual distributed resource management server,
And a virtual distributed resource management system based on the probabilistic model. The method according to claim 1,
Wherein the virtual distributed resource management server comprises:
A monitoring unit operable to monitor the status of the plurality of distributed resources in cooperation with the resource collection unit;
A availability analyzer for analyzing availability of each distributed resource using the history of the plurality of distributed resources and the distributed resource status information received from the resource collector;
A relative probability calculation unit for calculating the probability of success of the plurality of distributed resources and determining whether the probability of success of each distributed resource is equal to or greater than a reference value; And
A profile management unit for storing and managing a profile of distributed resources adjacent to a target point among distributed resources among the plurality of distributed resources having a correlation success probability of a reference value or more;
And a virtual distributed resource management system based on the probabilistic model. The method of claim 3,
The availability analysis unit,
Wherein the availability is analyzed using at least one of a past active time of the plurality of distributed resources, an active condition, a current state of distributed resources, and a communication state. The method of claim 3,
Wherein the distributed resource management server comprises:
A scheduling unit for clustering distributed resources adjacent to the target point and scheduling the distributed resources; And
And a response request unit for requesting response with corresponding distributed resources according to the scheduling,
And a virtual distributed resource management system based on the probabilistic model. The method of claim 3,
The activity probability calculation unit
The method of claim 1, further comprising the steps of: calculating an activity success probability by dividing the number of active responses of the distributed resources by the number of active requests; determining whether the active success probability is higher than an average success probability of all distributed resources; Wherein the virtual distributed resource management system is a virtual distributed resource management system based on a probabilistic model. The method of claim 5,
The scheduling unit may include:
Wherein the scheduling is performed by including a distributed resource outside the region adjacent to the target point if it is determined that scheduling is possible based only on the distributed resources of the region adjacent to the target point among the distributed resources having the correlation success probability of the reference value or more, Virtual Distributed Resource Management System. Monitoring distributed resources by collecting distributed resource state information from distributed resources;
Analyzing the availability of the distributed resources using the distributed resource state information and the history information of the distributed resources;
Calculating a probability of success of the distributed resources;
Listing the distributed resources adjacent to the target point based on the probability of success of the interaction;
Performing drive scheduling using the list-up distributed resources; And
Clustering and reacting distributed resources according to the drive scheduling;
Based virtual distributed resource management method. The method of claim 8,
Wherein the step of performing drive scheduling comprises:
Performing driving scheduling including a distributed resource outside an area adjacent to the target point when energy supply to the target spot is insufficient for the distributed resource adjacent to the target spot;
The method comprising the steps of: (a) The method of claim 9,
The step of calculating the probability of success of the distributed resources,
Wherein the probability of success is calculated by dividing the number of active responses to each distributed resource by the number of times of requests for interaction. The method of claim 9,
Wherein the step of performing drive scheduling using the listed up distributed resources comprises:
And distributing the distributed resources whose probability of success of computation for each distributed resource is greater than the average probability of the distributed resources. The method of claim 8,
Analyzing availability of the distributed resources;
Wherein the availability is determined using at least one of a current charging state, a communication state, a driving fuel state, a past reaction time, and a reaction time of the distributed resource, based on a probabilistic model-based virtual distributed resource management Way. The method of claim 8,
Wherein the step of performing drive scheduling using the listed up distributed resources comprises:
And analyzing operation costs of the distributed resources and performing drive scheduling according to a result of the analysis.

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