KR20210056667A - Apparatus and method for configuring a portfolio of distributed energy resources - Google Patents

Abstract

Disclosed are a device and method for configuring a set portfolio of various types of distributed energy resources to maximize a power transaction profit in a power market and to stably supply power to the power market. The device for configuring a set portfolio of distributed energy resources consisting of a plurality of non-renewable energy resources and a plurality of renewable energy resources according to an embodiment comprises: a set configuration unit configured to receive a set configuration condition of the distributed energy resources and configure sets of the distributed energy resources based on the set configuration condition; a data collection unit for collecting past weather forecast information, characteristic information of each distributed energy resource, and past operation information; and a simulation unit for calculating an electricity transaction bid amount for each set in a past time period by using the information collected by the data collection unit and determining a set in which an error rate is minimized by comparing the same with an actual generation amount for the same time period in the past.

Description

Apparatus and method for configuring a portfolio of distributed energy resources}

The present invention relates to an apparatus and method for constructing an aggregate portfolio of distributed energy resources in order to provide a reliable and safe aggregated resource to the power market and maximize the transaction profit of the aggregated resource.

Various distributed energy resources, such as combined cycle power plants, wind power plants, and solar power plants, are distributed throughout the building unit, community unit, regional unit, city unit, province unit, and nationwide unit. As such, it is necessary to efficiently manage distributed energy resources scattered and distributed over a wide range. In the power brokerage business and demand management business that collects distributed energy resources and sells them to the power market as a single aggregate resource, the penalties and profits of each aggregate resource vary according to the method of constructing a collective portfolio of resources.

In the demand management business, the penalty or incentive is determined according to the reduction fulfillment rate compared to the contracted capacity, and in the case of businesses participating in the electricity market such as the electricity brokerage business, the generation error rate compared to the bid amount. Therefore, in order to maximize the profits of electricity transactions, it is necessary to optimally construct a portfolio of distributed energy resources so that the reduction implementation rate can be maintained above the standards required by the market or the generation error rate below the specified standards. In addition, since the complexity increases in order to organize many distributed energy resources into an optimal set, an automated system for convenience of operation is required.

An object of the present invention is to provide an apparatus and method for constructing an aggregate portfolio of various types of distributed energy resources so as to maximize the power transaction profit in the power market and stably supply power to the power market.

An apparatus for configuring a collection portfolio of distributed energy resources comprising a plurality of non-renewable energy resources and a plurality of renewable energy resources according to an embodiment receives the set configuration condition of the distributed energy resources and distributes the distributed energy resource based on the set configuration condition. A set construction unit for configuring sets of energy resources; A data collection unit that collects past weather forecast information, characteristic information of each distributed energy resource, and past operation information; And a simulation unit that calculates a power transaction bidding amount for each of the past time slots for each set using the information collected by the data collection unit, and determines a set whose error rate is minimized by comparing it with the actual power generation amount for the same time slot in the past.

The simulation unit predicts the amount of generation by time of the past second period by using specific information for each renewable energy resource, operation information of the past first period, and weather prediction information of the past second period, and furthermore, each non-renewable energy resource A prediction unit that predicts the amount of power generation for each time period in the second period in the past by using the operation information of the first period in the past; A bidding amount calculating unit for calculating an amount of electric power transaction bidding for each of the second periods of the past for each of the sets, based on the estimated generation amount of each of the renewable energy resources and each of the non-renewable energy resources for each time period in the past second period; An actual power generation amount calculating unit for calculating an actual power generation amount for each of the past second periods for each of the sets based on past operation information of each distributed energy resource; And a determination unit configured to determine a set for which an error rate is minimized as an optimal set by comparing the electric power transaction bidding amount for each time slot of the past second period and the actual generation amount for each time slot of the past second period for each set.

The set configuration unit may receive one or more of a total number of resources, a minimum number of resources, a maximum power generation capacity, and a minimum power generation capacity as the set configuration condition.

The characteristic information may include power generation capacity, power generation efficiency, driving characteristics, location, and terrain information, and the past resource operation information may include power generation data and equipment state information for each past time period.

The determination unit may determine a set in which the average of the error rates for each time period is the minimum, as the optimal set.

The determination unit may add at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to the error rate to the optimal set.

The determination unit may add at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to a maximum error rate among the error rates for each time slot to the optimal set.

A method of constructing a collective portfolio of distributed energy resources comprising a plurality of non-renewable energy resources and a plurality of renewable energy resources according to an embodiment includes receiving the collective configuration condition of the distributed energy resources and dispersing the distributed energy resource based on the collective configuration condition. A set construction step of configuring sets of energy resources; A collection step of collecting past weather forecast information, characteristic information of each distributed energy resource, and past operation information; And a simulation step of calculating a power transaction bidding amount for each of the sets by using the collected information and comparing the actual power generation amount by the same time period in the past to determine a set in which the error rate is minimized.

In the simulation step, by using specific information for each renewable energy resource, operation information of the past first period, and weather prediction information of the past second period, the amount of power generation for each time period of the past second period is predicted, and each non-renewable energy Predicting the amount of power generation for each time period in the past second period by using the operation information of the past first period for each resource; Calculating an amount of electric power transaction bidding for each of the second periods of the past for each of the sets, based on the predicted generation amount of each of the renewable energy resources and each of the non-renewable energy resources for each time period in the past second period; Calculating an actual power generation amount for each time period in the past second period for each of the sets based on past operation information of each distributed energy resource; And comparing the power transaction bidding amount for each time slot of the past second period with the actual power generation amount for each time slot of the past second period for each set, and determining a set for which an error rate is minimized as an optimal set.

In the set configuration step, as the set configuration condition, one or more of a total number of resources, a minimum number of resources, a maximum power generation capacity, and a minimum power generation capacity may be received.

The characteristic information may include power generation capacity, power generation efficiency, driving characteristics, location, and terrain information, and the past resource operation information may include power generation data and equipment state information for each past time period.

In the determining step, a set in which the average of the error rates for each time period is minimum may be determined as an optimal set.

The determining may further include adding at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to the error rate to the optimal set.

In the adding step, at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to a maximum error rate among the error rates for each time period may be added to the optimal set.

According to an embodiment, in a business in which distributed energy resources are collected and traded with the power market as aggregate resources, the power transaction profit can be maximized by minimizing penalties and maximizing incentives.

According to an embodiment, by automatically configuring a collective portfolio of distributed energy resources scattered over a wide range, it is possible to maximize the convenience of resource managers and transaction operators, and to quickly configure energy resources.

1 is a diagram showing the configuration of an apparatus for configuring an aggregate portfolio constituting an aggregate portfolio of distributed energy resources according to an embodiment of the present invention.
2 is a diagram showing a detailed configuration of the simulation unit of FIG.
3 is a flowchart illustrating a method of configuring a collection portfolio of distributed energy resources according to an embodiment of the present invention.

The above-described objects, features, and advantages will become more apparent through the following detailed description in connection with the accompanying drawings, whereby those of ordinary skill in the technical field to which the present invention pertains can easily implement the technical idea of the present invention. There will be. In addition, in describing the present invention, when it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted. Hereinafter, a preferred embodiment according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram showing the configuration of an apparatus for configuring an aggregate portfolio constituting an aggregate portfolio of distributed energy resources according to an embodiment of the present invention. The collective portfolio configuration device 100 includes a memory, a memory controller, one or more processors (CPU), a peripheral interface, an input/output (I/O) subsystem, a display device, an input device, and a communication circuit. These components communicate via one or more communication buses or signal lines. The various components may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits. The memory may include high-speed random access memory, and may also include one or more magnetic disk storage devices, nonvolatile memory such as flash memory devices, or other nonvolatile semiconductor memory devices. In some embodiments, the memory is a storage device that is located remotely from one or more processors, such as a communication circuit, the Internet, an intranet, a local area network (LAN), a wide LAN (WLAN), a storage area network (SAN), and the like, Or a network attached storage device that is accessed through a communication network (not shown), such as a suitable combination thereof. Access to the memory by other components of the mobile terminal, such as the processor and peripheral interface, may be controlled by the memory controller. The peripheral interface connects the input/output peripheral device of the aggregate portfolio configuration device 100 with the processor and the memory. One or more processors execute various software programs and/or instruction sets stored in a memory to perform various functions for the aggregate portfolio configuration device 100 and process data. The I/O subsystem provides an interface between an input/output peripheral device of the aggregate portfolio component device 100 such as a display device and an input device and a peripheral interface. The processor is a processor configured to perform an operation related to the collective portfolio configuration device 100 and perform instructions. For example, by using instructions retrieved from a memory, input and output data between components of the aggregate portfolio configuration device 100 You can control reception and operation. In some embodiments, the software component has an operating system, a graphics module (instruction set), and an application (instruction set) mounted (installed) in memory. The operating system may be, for example, a built-in operating system such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS or VxWorks, Android, etc., and general system tasks (e.g., memory management, storage Device control, power management, etc.) to control and manage various software components and/or devices, and facilitate communication between the various hardware and software components. As shown in FIG. 1, the collective portfolio configuration apparatus 100 includes a collection configuration unit 110, a data collection unit 120, and a simulation unit 130, which are implemented as a program and stored in a memory, and a processor It can be executed by, or can be implemented and executed by the hardware and software described above.

The set configuration unit 110 receives a set configuration condition of distributed energy resources. The set configuration unit 110 may receive a set configuration condition from a user through an input device. Here, the distributed energy resources consist of non-renewable energy resources, renewable energy resources, and other resources. Non-renewable energy resources include thermal energy, nuclear energy, combined cycle power generation, etc., and renewable energy resources include solar energy, hydro energy, wind energy, tidal energy, etc., and other resources include energy storage systems (ESS: Energy Storage System), fuel cells, electric vehicles, and demand response resources. The set configuration condition may include one or more of the total number of resources, the minimum number of resources, the maximum power generation capacity, the minimum power generation capacity, and the like. The set construction unit 110 constructs a set of the number of various cases by using non-renewable energy resources and renewable energy resources among distributed energy resources based on the received set construction condition. For example, if the minimum power generation capacity and the total number of resources are received as the set configuration condition, distributed energy resources are combined to form a set of the number of cases meeting the corresponding condition. Other resources are added to the optimal set as buffer resources, as described later.

The data collection unit 120 collects past weather prediction information, characteristic information of each distributed energy resource, and past operation information. The data collection unit 120 may receive input from a user through an input device, or may be received from another system through online. For example, past weather forecast information may be collected by crawling from the Meteorological Agency server. The characteristic information of the distributed energy resource includes resource capacity information (e.g., power generation capacity, storage capacity, reduction contract capacity, etc.), operation characteristic information (e.g., power generation restriction time, maximum/minimum Soc of battery, etc.), power generation efficiency ( For example, it includes solar panel power generation efficiency, battery storage efficiency, etc.), location information (address, latitude, longitude, etc.), terrain information (height, etc.). The past operation information includes state event information such as power generation data by past time, reduction performance data by past time, power usage data by time, and failure by power generation facility.

The simulation unit 130 uses the information collected by the data collection unit 120 to calculate the electric power transaction bidding amount for each of the past time slots for each set of the number of cases configured by the set configuration unit 110 and the same in the past. The set in which the error rate is minimized is determined by comparing it with the actual generation amount by time. FIG. 2 is a diagram showing a specific configuration of the simulation unit 130 of FIG. 1. Referring to FIG. 2, the simulation unit 130 includes a prediction unit 131, a bid amount calculation unit 132, and an actual power generation amount calculation unit. It includes 133 and a decision unit 134.

The prediction unit 131 predicts the generation amount of each renewable energy resource according to the past time period, and also predicts the generation amount of each non-renewable energy resource according to the same time period in the past. For example, the amount of power generation by time of the past October 1 is predicted for each renewable energy resource and each non-renewable energy resource.

The prediction unit 131 predicts the amount of generation of each renewable energy resource for each past time using a generation prediction model learned in advance by machine learning. For example, in the case of predicting the amount of power generation for each time period in the past second period, the input value input to the power generation prediction model includes characteristic information of renewable energy resources, operation information of a first period that is more past than the second period, and It is weather prediction information of the second period predicted in the first period. The prediction unit 131 uses these input values to predict the generation amount of each renewable energy resource for each time period in the past second period through a power generation prediction model learned in advance by machine learning.

In the case of the non-renewable energy resource, since the prediction unit 131 can output a fixed amount of power generation according to the input fuel without being affected by weather conditions, etc., the generation amount is not predicted by machine learning like a renewable energy resource, As the amount of power generated in a specific period, the amount of power generated in a period prior to that specific period is simply predicted as the amount of power generated in that specific period. For example, in predicting the power generation amount for each time period in the second period, the prediction unit 131 uses the operation information of the first period that is more in the past than the second period to determine the power generation amount for the same time period in the first period. It is assumed to be the amount of power generated by time of the period. If the power generation amount of the past October 1, 2018 is predicted for thermal power generation, the power generation amount on September 30, the previous day, or the same generation amount in the same month of the previous year of the year, or the amount of power generation on the same day a week ago, the corresponding It can be assumed as the amount of power generation on October 1.

The bidding amount calculating unit 132 is based on the predicted generation amount of each renewable energy resource and each non-renewable energy resource for each past time period, and bidding for power transaction by the same past time period for each set configured by the set configuration unit 110 Calculate the quantity. For example, when a first renewable energy resource and a second non-renewable energy resource belong to the first set, the power transaction bid amount of the first set is calculated based on their predicted generation amount. In this way, each set calculates the amount of bidding for electric power transactions for each past time period. The bidding amount calculating unit 132 may calculate a value obtained by summing the predicted power generation amount of each resource as it is, as a bid amount, and at this time, may calculate the bidding amount in consideration of the inactivity of the weather. For example, the amount of sunlight can be corrected by the average or median of the errors of past days that were predicted for similar weather (eg, similar time zone/insolation/temperature/humidity/wind speed, etc.).

The actual generation amount calculation unit 133 calculates the actual generation amount for each time slot in the same period as the period in which the power transaction bid amount for each set was calculated based on the past operation information of each distributed energy resource. That is, by using the actual power generation amount of each renewable energy resource and the actual power generation amount of each non-renewable energy resource during the period, the actual power generation amount for each set of past time periods is calculated. For example, if a first renewable energy resource and a second non-renewable energy resource belong to the first set, and the power transaction bidding amount for the second period in the past is calculated for the first set, The actual power generation amount of the first set is calculated by summing the actual power generation amount of the first renewable energy resource and the actual power generation amount of the second non-renewable energy resource.

The determination unit 134 calculates the amount of electric power transaction bidding for each set, calculated by the bidding amount calculating unit 132, and the actual power generation amount for each set, calculated by the actual generation amount calculating unit 133. By comparison, the set in which the error rate (nMAE, MAPE, etc.) is minimized is determined as the optimal set. Here, the error rate may be an average of the error rates for each time period. In the case of renewable energy resources, output variability can be large depending on the weather. Therefore, even if it is an optimal set, stable power generation may not be achieved due to fluctuations in the output of renewable energy resources belonging to the set depending on weather conditions, so fuel cells, energy storage systems (ESS), electric vehicles, and demand response resources that can increase or decrease the output quickly It is preferable to add, etc. to the optimal set as a buffer resource.

The determination unit 134 may add another resource having an output and a capacity corresponding to the error rate, for example, at least one of an electric vehicle, an energy storage system, and a demand response resource to the determined optimal set. More preferably, the determination unit 134 may add at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to a maximum error rate among error rates for each time slot to the optimal set. . For example, it is possible to add other resources so that the output capacity of other resources is equal to the difference between the bidding amount and the actual generation amount at the time when the actual generation amount was insufficient in the past. Conversely, when the actual amount of power generation in the past was in the maximum surplus state, an electric vehicle or an energy storage system may be added so that energy can be charged as much as the difference value at that time. At this time, the determination unit 134 includes characteristic information such as reduction contract capacity, storage capacity, maximum/minimum SoC of the battery, battery storage efficiency, etc. of other resources collected by the data collection unit 120, and the past time of other resources. Other resources corresponding to the error rate are added to the optimal set by referring to past operation information such as reduction performance data and power usage by time.

3 is a flowchart illustrating a method of configuring a collection portfolio of distributed energy resources according to an embodiment of the present invention.

Referring to FIG. 3, in step S301, the aggregate portfolio configuration apparatus 100 receives a set configuration condition of distributed energy resources. The set configuration condition may be received from the user through the input device. Here, the distributed energy resources consist of non-renewable energy resources, renewable energy resources, and other resources. Non-renewable energy resources include thermal energy, nuclear energy, combined cycle power generation, etc., and renewable energy resources include solar energy, hydro energy, and wind energy, and other resources include energy storage systems, fuel cells, electric vehicles, and demand. Includes reaction resources, etc. The set configuration condition may include one or more of the total number of resources, the minimum number of resources, the maximum power generation capacity, the minimum power generation capacity, and the like.

In step S302, the aggregate portfolio configuration apparatus 100 constructs a set of various cases by using non-renewable energy resources and renewable energy resources among distributed energy resources based on the received aggregate configuration condition. For example, if the minimum power generation capacity and the total number of resources are received as the set configuration condition, distributed energy resources are combined to form a set of the number of cases meeting the corresponding condition.

In step S303, the collective portfolio configuration apparatus 100 collects past weather prediction information, characteristic information of each distributed energy resource, and past operation information. The collective portfolio configuration device 100 may receive input from a user through an input device, or may receive from another system through online. For example, past weather forecast information may be collected by crawling from the Meteorological Agency server.

The characteristic information of the distributed energy resource includes resource capacity information (e.g., power generation capacity, storage capacity, reduction contract capacity, etc.), operation characteristic information (e.g., power generation restriction time, maximum/minimum Soc of battery, etc.), power generation efficiency ( For example, it includes solar panel power generation efficiency, battery storage efficiency, etc.), location information (address, latitude, longitude, etc.), terrain information (height, etc.). The past operation information includes state event information such as power generation data by past time, reduction performance data by past time, power usage data by time, and failure by power generation facility.

In step S304, the collective portfolio configuration apparatus 100 predicts the amount of power generation for each resource in the past time period. For example, the amount of power generation by time of the past October 1 is predicted for each renewable energy resource and each non-renewable energy resource. More specifically, the collective portfolio configuration device 100 predicts the amount of generation of each renewable energy resource for each past time using a power generation prediction model learned in advance by machine learning, and in the case of a non-renewable energy resource, it is affected by weather conditions, etc. Since it is possible to output a fixed amount of power according to the input fuel without receiving any power, the amount of power generated in a period prior to that specific period is not predicted by machine learning like a renewable energy resource. Simple prediction by power generation.

When predicting the past power generation amount of the renewable energy resource, for example, when predicting the power generation amount for each time period in the past second period, the input value input to the power generation prediction model includes characteristic information of the renewable energy resource and is more past than the second period. This is the operation information of the first period and weather prediction information of the second period predicted in the first period. The collective portfolio configuration apparatus 100 predicts the amount of generation of each renewable energy resource for each time period in the past second period through a power generation prediction model using these input values.

When predicting the past power generation amount of non-renewable energy resources, for example, in predicting the power generation amount for each time period in the second period, the collective portfolio configuration device 100 uses the operation information of the first period that is more past than the second period. Thus, the generation amount for the same time period in the first period is predicted as the generation amount for each time period in the second period. If the power generation amount of the past October 1, 2018 is predicted for thermal power generation, the power generation amount of the previous day, September 30, or the same amount of power generation in the same month of the previous year of the relevant year, is predicted as the power generation amount of the relevant October 1.

In step S305, the collective portfolio configuration device 100, based on the predicted generation amount for each of the renewable energy resources and each non-renewable energy resource in the past time zone in the step S304, the same for each set configured in the step S302 past time zone. Calculate the amount of bidding for electricity transactions. For example, when a first renewable energy resource and a second non-renewable energy resource belong to the first set, the power transaction bidding amount of the first set is calculated based on their predicted generation amount. In this way, for each set, the amount of electric power transaction bidding for each past time period is calculated.

In step S306, the collective portfolio configuration apparatus 100 calculates the actual generation amount for each time period in the same past period as the period in which the power transaction bidding amount was calculated for each of the sets based on the past operation information of each distributed energy resource. That is, by using the actual power generation amount of each renewable energy resource and the actual power generation amount of each non-renewable energy resource during the period, the actual power generation amount for each set of past time periods is calculated. For example, if a first renewable energy resource and a second non-renewable energy resource belong to the first set, and the power transaction bidding amount for the second period in the past is calculated for the first set, The actual power generation amount of the first set is calculated by summing the actual power generation amount of the first renewable energy resource and the actual power generation amount of the second non-renewable energy resource.

In step S307, the aggregate portfolio configuration device 100 compares the power transaction bidding amount for each set in the past time period calculated in step S305 with the actual generation amount in the past time period for each set calculated in the step S306, so that the error rate is The minimized set is determined as the optimal set. Here, the error rate may be an average of the error rates for each time period. In the case of renewable energy resources, output variability can be large depending on the weather. Therefore, even if it is an optimal set, stable power generation may not be achieved due to fluctuations in the output of renewable energy resources belonging to the set depending on weather conditions, so fuel cells, energy storage systems (ESS), electric vehicles, and demand response resources capable of rapid output increase or decrease. It is preferable to add, etc. to the optimal set as a buffer resource. The collective portfolio configuration apparatus 100 may add at least one resource of an electric vehicle, an energy storage system, and a demand response resource, for example, to the determined optimal set, other resources having output and capacity corresponding to the error rate. . More preferably, the aggregate portfolio configuration device 100 may add at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and capacity corresponding to the maximum error rate among the error rates for each time slot to the optimal set. have. At this time, the collective portfolio configuration device 100 includes characteristic information such as reduction contract capacity, storage capacity, maximum/minimum SoC of battery, battery storage efficiency, etc. of other resources collected in step S303, and reduction performance of other resources by past time. Other resources corresponding to the error rate are added to the optimal set by referring to past operation information such as data and power usage by time.

While this specification includes many features, such features should not be construed as limiting the scope or claims of the invention. In addition, features described in separate embodiments herein may be combined and implemented in a single embodiment. Conversely, various features described in a single embodiment in the present specification may be individually implemented in various embodiments, or may be properly combined and implemented.

Although the operations have been described in a specific order in the drawings, it should not be understood that such operations are performed in a specific order as shown, or as a series of consecutive sequences, or that all described operations are performed to obtain a desired result. . Multitasking and parallel processing can be advantageous in certain environments. In addition, it should be understood that classification of various system components in the above-described embodiments does not require such classification in all embodiments. The above-described program components and systems may generally be implemented as a package in a single software product or multiple software products.

The method of the present invention as described above may be implemented as a program and stored in a recording medium (CD-ROM, RAM, ROM, floppy disk, hard disk, magneto-optical disk, etc.) in a form that can be read by a computer. This process can be easily performed by a person of ordinary skill in the art to which the present invention pertains, and thus will not be described in detail.

The present invention described above, for those of ordinary skill in the art to which the present invention pertains, various substitutions, modifications and changes are possible within the scope of the technical spirit of the present invention. It is not limited by the drawings.

100: collective portfolio configuration device
110: set component
120: data collection unit
130: simulation unit
131: prediction unit
132: Bid amount calculation unit
133: Actual power generation calculation unit
134: decision part

Claims (15)

In the apparatus for constructing a collective portfolio of distributed energy resources comprising a plurality of non-renewable energy resources and a plurality of renewable energy resources,
An aggregation configuration unit configured to receive the aggregation configuration condition of the distributed energy resources and configure the aggregation configuration of the distributed energy resources based on the aggregation configuration condition;
A data collection unit that collects past weather forecast information, characteristic information of each distributed energy resource, and past operation information; And
An apparatus comprising a simulation unit that calculates a bidding amount for electric power transactions for each of the past time periods for each of the sets using the information collected by the data collection unit, and determines a set in which an error rate is minimized by comparing it with the actual generation amount for the same time period in the past. The method of claim 1,
The simulation unit,
By using specific information for each renewable energy resource, operation information for the past first period, and weather forecast information for the past second period, the amount of power generation for each time period of the past second period is predicted, and the past first period for each non-renewable energy resource. A prediction unit that predicts the amount of power generation for each time period in the past second period by using the operation information of the period;
A bidding amount calculating unit for calculating an amount of electric power transaction bidding for each of the second periods of the past for each of the sets, based on the estimated generation amount of each of the renewable energy resources and each of the non-renewable energy resources for each time period in the past second period;
An actual power generation amount calculation unit for calculating an actual power generation amount for each of the past second periods for each of the sets based on past operation information of each distributed energy resource; And
And a determination unit configured to determine a set whose error rate is minimized as an optimal set by comparing the amount of electric power transaction bidding for each time slot in the past second period and the actual generation amount for each time slot in the past second period for each of the sets. The method of claim 2,
The set configuration unit,
As the set configuration condition, an apparatus characterized in that receiving at least one of a total number of resources, a minimum number of resources, a maximum power generation capacity, and a minimum power generation capacity. The method of claim 2,
The characteristic information,
Including power generation capacity, power generation efficiency, driving characteristics, location and topography information,
The past resource operation information,
Device, characterized in that it comprises the power generation data and equipment status information for each time period in the past. The method of claim 2,
The determination unit,
An apparatus, characterized in that the set in which the average of the error rates for each time period is minimum is determined as an optimal set. The method of claim 2,
The determination unit,
And adding at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to the error rate to the optimal set. The method of claim 6,
The determination unit,
And adding at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to a maximum error rate among error rates for each time period to the optimal set. In the method of constructing a collective portfolio of distributed energy resources consisting of a plurality of non-renewable energy resources and a plurality of renewable energy resources,
A set construction step of receiving the set construction condition of the distributed energy resources and configuring the sets of distributed energy resources based on the set construction condition;
A collection step of collecting past weather forecast information, characteristic information of each distributed energy resource, and past operation information; And
And a simulation step of calculating a power transaction bidding amount for each of the sets using the collected information and comparing it with the actual generation amount for the same time period in the past to determine a set in which an error rate is minimized. The method of claim 8,
The simulation step,
By using specific information for each renewable energy resource, operation information for the past first period, and weather forecast information for the past second period, the amount of power generation for each time period of the past second period is predicted, and the past first period for each non-renewable energy resource. Predicting the amount of power generation for each time period in the past second period by using the operation information of the period;
Calculating an amount of electric power transaction bidding for each of the second periods of the past for each of the sets, based on the predicted generation amount of each of the renewable energy resources and each of the non-renewable energy resources for each time period in the past second period;
Calculating an actual amount of power generation for each time period in the past second period for each of the sets based on past operation information of each distributed energy resource; And
And determining a set whose error rate is minimized as an optimal set by comparing the amount of electric power transaction bidding for each time slot in the past second period and the actual generation amount for each time slot in the past second period for each set. The method of claim 9,
The set configuration step,
As the set configuration condition, at least one of a total number of resources, a minimum number of resources, a maximum power generation capacity, and a minimum power generation capacity is received. The method of claim 9,
The characteristic information,
Including power generation capacity, power generation efficiency, driving characteristics, location and topography information,
The past resource operation information,
A method comprising the power generation amount data and equipment status information for each past time period. The method of claim 9,
The determining step,
A method, characterized in that the set in which the average of the error rates for each time period is minimum is determined as an optimal set. The method of claim 9,
The determining step,
And adding at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to the error rate to the optimal set. The method of claim 13,
The step of adding,
And adding to the optimal set at least one of an electric vehicle, an energy storage system, and a demand response resource having an output and a capacity corresponding to a maximum error rate among error rates for each time period. A computer program recorded on a recording medium as a computer program for executing the method according to any one of claims 8 to 14 through a computer system.

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