JP7850595B2 - Resource management device, resource management method, and resource management program - Google Patents

Description

This invention relates to a resource management device, a resource management method, and a resource management program.

Renewable energy sources such as solar power are known to have output that fluctuates depending on weather conditions and time of day. In order to absorb these output fluctuations and make the most effective use of electricity generated from renewable energy sources, it is important to efficiently manage and control energy-related equipment that is widely and extensively distributed throughout the power grid.
Patent Document 1 discloses a method for controlling distributed energy equipment such as generators, energy storage devices, and hydrogen storage devices based on predicted energy demand and supply. According to Patent Document 1, by integrally controlling multiple energy devices of different types, it is possible to achieve optimal energy operation in a system powered by solar cells.

Patent Document 2 discloses a method for transmitting notification information to mobile vehicles equipped with batteries to adjust the supply and demand balance of a power system. This method includes a first aggregation device that manages the balance of power consumption based on power generation and demand forecasts, and a second aggregation device that provides electricity received from the first aggregation device based on a request from the first aggregation device. According to Patent Document 2, surplus power from multiple electric vehicles (EVs) equipped with batteries can be utilized to adjust the supply and demand balance of the power system.

Japanese Patent Publication No. 2004-312798 Japanese Patent Publication No. 2021-18608

Resources are not always limited to just one purpose; some can be used for multiple purposes.
In one type of use, for example, when transporting batteries or hydrogen storage devices as cargo, this transport resource plays the role of a power distribution resource through energy transport. In a multiple type of use, for example, when controlling electric vehicles such as passenger buses as energy resources, these buses fulfill both the roles of passenger transport and power transport.

When providing resources useful for multiple purposes to various systems, it is necessary to define the characteristics of each resource individually in advance to ensure ease of use for each recipient system. However, no means of managing and controlling the same resource across multiple systems with different purposes is disclosed in any of the literature.
Furthermore, by having multiple systems manage and control the same resource simultaneously, it is expected that the resource utilization rate will improve as multiple systems can use the same resource. However, conventional technologies assumed that the same resource would be managed and controlled so that only one system would occupy it during the same time period.

Therefore, the main objective of this invention is to improve the utilization rate of resources provided to multiple types of systems.

To solve the aforementioned problems, the resource management device of the present invention has the following features.
The present invention relates to a virtual resource capability conversion unit that, based on virtualization logic that virtualizes physical resources, creates virtual capability data indicating the capabilities that physical resources provide as virtual resources from physical resource measurement data obtained by measuring physical resources, which are equipment related to energy, and constructs a virtual resource that provides that virtual capability data.
The system includes a capability allocation unit that assigns the virtual resources constructed by the virtual resource capability conversion unit to a resource operation management system, thereby causing the resource operation management system to manage the actual resources.
The virtualization logic is prepared individually according to resource request data indicating the capability configuration of the physical resource requested by the resource operation management system.
The virtualization logic is characterized by converting the physical resource measurement data, which shows the movement history associated with the operation of the physical resource, into virtual capacity data, which shows the cargo transport capacity of the physical resource .
Other methods will be described later.

According to the present invention, the utilization rate of resources provided to multiple types of systems can be improved.

This is a configuration diagram showing the resource operation management system according to this embodiment. This is a diagram illustrating the configuration of the resource virtualization system according to this embodiment. This is a data flow diagram of the resource virtualization process of the resource virtualization system according to this embodiment. This is a flowchart showing the processing procedure for resource virtualization in the resource virtualization system according to this embodiment. This is a data flow diagram of the virtual resource capacity conversion unit according to this embodiment. This table shows an example of the resource request data to be acquired in this embodiment. This table shows an example of the virtualization definition data obtained in this embodiment. This table shows an example of resource constraint data related to this embodiment. This is an explanatory diagram of the processing of the virtualization logic "P0001" placed in the acquired capability conversion unit according to this embodiment. This is an explanatory diagram showing the virtualization logic "P0001," "P0002," and "P0003" related to this embodiment arranged within the capability conversion unit. This is an explanatory diagram of the processing of the virtualization logic "P0004" located within the acquired capability conversion unit according to this embodiment. This is an explanatory diagram of the processing of the virtualization logic "P0005", "P0002", and "P0006" located within the acquired capability conversion unit according to this embodiment. This graph shows virtual capability data to specifically explain "P0001" in Figure 9 of this embodiment. These are graphs to specifically explain "P0001," "P0002," and "P0003" in Figure 10 relating to this embodiment. This is a table showing an example of resource-related data related to this embodiment. This is a data flow diagram of the capability prediction unit according to this embodiment. This is a data flow diagram of the capacity allocation unit according to this embodiment. This table shows an example of the requested resource correspondence data related to this embodiment. This is an explanatory diagram illustrating how to allocate physical resources to the system according to this embodiment. This is an explanatory diagram showing an example of a data structure for virtualizing physical resources according to this embodiment. This is a hardware configuration diagram of the resource virtualization system according to this embodiment. This table shows an example of virtual capability data for each virtual resource in this embodiment.

The embodiments for carrying out the present invention will be described in detail with reference to the drawings.

First, an overview of this embodiment will be described with reference to Figures 19 and 20.
Figure 19 is an explanatory diagram illustrating the allocation of physical resources to the system. The horizontal axis in this diagram represents the time axis.
Schedule 1900 shows the mode of resource provision in the conventional method.
Schedule 1910 shows the mode of resource provision in this embodiment.

In Schedule 1900, the resource request period 1901 from Resource Operation Control System A and the resource request period 1902 from Resource Operation Control System B overlap for a portion of the time.
Therefore, the physical resources are exclusively provided to the predetermined resource operation control system A only during the resource provision period 1903. In this way, the conventional method has an exclusive recipient for the physical resources, resulting in a low utilization rate of the physical resources.
Furthermore, if it were to change the recipient of the physical resources from resource operation control system A to resource operation control system B, the specification data indicating the characteristics of the physical resources would have to be modified to match the resource requirements of resource operation control system B. However, since the resource requirements of resource operation control system A and resource operation control system B are not necessarily the same, changing the recipient was not easy.

On the other hand, Schedule 1910 converts one physical resource into one or more virtual resources, and each virtual resource is associated with virtual capability data that indicates its capabilities. For example, the following two virtual resources are generated from one physical resource:
Virtual resource A is associated with virtual capability data of an actual resource that meets the capabilities required by resource operation control system A. Specifically, since resource operation control system A is a grid monitoring and control system, it requires a power supply capacity of 1915 and a discharge capacity of 1917. Therefore, if the actual resource is an electric vehicle, virtual resource A is constructed in which the power supply capacity of its battery (1915) and the discharge capacity of 1917 are described as virtual capability data.
Virtual resource B is associated with virtual capability data of an actual resource that meets the capabilities required by resource operation control system B. Specifically, since resource operation control system B is a transportation system, a transportation capacity of 1916 is required. Therefore, if the actual resource is an electric vehicle, virtual resource B is constructed in which its transportation capacity of 1916 is described as virtual capability data.
Thus, even if the electric vehicles are physically the same, the virtual resources used by the recipient system to manage the electric vehicle are defined individually to satisfy the requirements of that system.

Furthermore, the differences between Schedule 1900 and Schedule 1910 will be explained from the perspective of managing actual resources.
In Schedule 1900, the resource request period 1901 and the resource provision period 1903 corresponded one-to-one, and one physical resource was exclusively provided to one system. In other words, managing a physical resource and using a physical resource were synonymous.
In schedule 1910, the resource request period 1901 from resource operation control system A is replaced by the resource management request period 1911. Similarly, the resource request period 1902 from resource operation control system B is replaced by the resource management request period 1912. As a result, resource operation control system A can continuously manage the physical resource to be managed via virtual resource A during the resource management request period 1911. On the other hand, resource operation control system B can continuously manage the physical resource to be managed via virtual resource B during the resource management request period 1912. In other words, one physical resource can be managed in parallel by two systems.

Management, in this context, involves notifying each system of the measured values of the physical resources related to the virtual capability data that each system requests from the physical resources. For example, resource operation control system A obtains power supply capacity 1915 and discharge capacity 1917 as virtual capability data, so virtual resource A is managed by resource operation control system A by constantly notifying it of the current battery level of the electric vehicle.
Similarly, since the resource operation control system B obtains the transport capacity 1916 as virtual capacity data, virtual resource A is managed by the resource operation control system B by constantly notifying the resource operation control system B of the electric vehicle's current location and the current number of occupants.

On the other hand, regarding the use of physical resources, unless the location of use in resource operation control system A and the location of use in resource operation control system B are the same, the same physical resource will be used exclusively by each system.
For example, an electric vehicle's battery is charged one morning by a power supply capacity of 1915 from resource operation control system A at location X.
After charging, the electric vehicle is requested by the resource operation control system B at point P to transport the occupants to point Q during the day, and begins transport with a transport capacity of 1916. After dropping off the occupants at point Q, the electric vehicle is requested by the resource operation control system A at point Y at night to discharge the battery power with a discharge capacity of 1917.

Here, if the tasks of supplying power from point X to point Y and transporting personnel from point P to point Q are close, for example, when point X and point P are close together and point Y and point Q are close together, both tasks can be executed in parallel in one day. In this way, electric vehicles can improve their utilization rate by managing separate systems during overlapping time periods and using different capacities with time lags. In other words, by converting physical resources into one or more virtual capacity data, simultaneous operation and control can be performed according to the resource requests from each resource operation and control system.

Figure 20 is an explanatory diagram showing an example of a data structure for virtualizing physical resources.
Each entity resource is assigned an entity resource ID, ranging from "AR001" to "AR006". For example, the entity resource for a private car is assigned the entity resource ID "AR001".
One or more virtual resources are constructed from a single physical resource. Each virtual resource is assigned a virtual resource ID, "VR001 to VR006". For example, from a physical resource with the ID "AR001", one virtual resource with the virtual resource ID "VR001" is constructed. Each virtual resource is associated with virtual capability data that it provides. The virtual capability data of a virtual resource is defined according to the requirements of the system to which the virtual resource is provided.

The virtual resource ID "VR003" is constructed from two physical resource IDs, "AR004" and "AR005". In this case, the capabilities provided by the two physical resources are combined to make them available as a single virtual resource.
Furthermore, two virtual resources, virtual resource IDs "VR002" and "VR005," are constructed from the physical resource with physical resource ID "AR002." In this case, a single physical resource, a truck equipped with a battery, can be defined as both virtual resource ID "VR002," which provides power transmission capabilities, and virtual resource ID "VR005," which provides adjustable power capabilities.

A single system issues one or more request IDs "R0001 to R0005". For example, a system called "Monitoring A" issues request ID "R0001" requesting virtual capability data for power supply, and also requests request ID "R0004" requesting virtual capability data for regulated power supply.
Resource allocation is the process of associating one or more virtual resource IDs with a single request ID. For example, in Figure 20, an arrow is connected to the virtual resource ID "VR001" for the request ID "R0001" requested by the "Monitoring A" system. This allows the "Monitoring A" system to manage the virtual capability data, specifically power supply, provided by the virtual resource ID "VR001". In reality, the private car with the physical resource ID "AR001," which corresponds to the virtual resource ID "VR001," provides the capability of power supply to the "Monitoring A" system.

Furthermore, the passenger bus with physical resource ID "AR003" provides the capability of power transmission to the "Monitoring B" system via virtual resource ID "VR002," while also providing the capability of regulating power to the "Monitoring A" system via virtual resource ID "VR005."
In this way, by virtualizing one physical resource into multiple virtual resources, it becomes possible to manage it simultaneously from multiple systems, as shown in schedule 1910 in Figure 19.

Figure 1 is a configuration diagram showing the resource operation management system 1. As explained in Figure 19, the resource operation management system 1 enables operation in multiple resource operation control systems, each with different operational objectives, by creating virtual resources from actual resources 5 that are in line with the operation of each resource operation control system. This enables more efficient operation and control of energy-related resources.
Therefore, the resource operation management system 1 consists of a resource virtualization device 3, a data management device 4, physical resources 5, a measurement device 6, a monitoring and control device 7, an information input/output terminal 8, an information distribution device 9, a resource operation control device 10, and controlled equipment 12. The communication path 11 is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network), and is a communication path 11 that connects the various devices and terminals constituting the resource operation management system 1 so that they can communicate with each other.

The data management device 4 stores measurement data of the physical resource 5, constraint data such as conditions and constraints related to the operation and control of the physical resource 5, and data of factors that affect the operation of the physical resource 5. The physical resource 5 is, for example, the following equipment.
- Generators that use fossil fuels - Generators that use renewable energy such as solar, geothermal, wind, and hydroelectric power - Stationary storage batteries - Mobile devices equipped with storage batteries, such as electric vehicles and electric buses - Substations and power transmission/distribution equipment such as transformers and phase-shifting equipment in substations - Individual equipment that produces, consumes, or stores energy, or a collection of equipment consisting of two or more such individual equipment. Equipment refers to utility equipment on the energy-consuming side, such as lighting, air conditioning, and power equipment.

The measurement data for the physical resource 5 includes at least data recording the operation of the physical resource 5 measured over time. The operation of the physical resource 5 includes, for example, the amount of power generated, consumed, stored, transmitted and distributed, and movement history of the physical resource 5.
The constraint data for physical resource 5 includes at least data indicating conditions and constraints related to the operation and control of physical resource 5. Conditions and constraints related to the operation and control of physical resource 5 include, for example, data indicating the operating range of quantities, times, and locations related to the operation of physical resource 5, such as the location of energy production, consumption, and storage, the possible time, and upper and lower limits of quantities, as well as data indicating costs related to operation.

The factor data 453A (Figure 3) acquired from the data management device 4 is, for example, the following data.
- Weather data such as temperature, humidity, solar radiation, wind speed, and atmospheric pressure - Fuel data such as trading volume and price of crude oil and natural gas - Transmission and distribution line data such as transmission and distribution line capacity - Generator operation status data such as generator operation or maintenance schedule - Calendar day data such as year, month, day, day of the week, and flag values indicating the type of day set arbitrarily - Data indicating the occurrence or absence of sudden events such as typhoons and events - Data showing economic conditions such as the number of energy consumers, industry trends, and business conditions index - Data showing the movement of people and vehicles such as express train occupancy rates, number of passengers, number of reserved seats, or road traffic conditions

The data management device 4 stores measurement data and constraint data for the physical resource 5 from a pre-set past date and time to the most recent observation date and time, via one of the measurement device 6, monitoring and control device 7, or information input/output terminal 8. The data management device 4 also searches for and transmits measurement data and constraint data for the physical resource 5 in response to data acquisition requests from other devices.

Figure 2 is a diagram showing the configuration of the resource virtualization system (resource management device) 2. The resource virtualization system 2 consists of a resource virtualization device 3 and a data management device 4.
The resource virtualization device 3 uses the data stored in the data management device 4 to convert the data of the physical resource 5 into "virtual resource data" that represents a virtual resource. Therefore, the resource virtualization device 3 has a virtual resource capacity conversion unit 351, a capacity prediction unit 352, a capacity allocation unit 353, and a control planning unit 354.

The virtual resource capability conversion unit 351 acquires measurement data and constraint data of the physical resource 5 from the data management device 4. The virtual resource capability conversion unit 351 converts the measurement data and constraint data of the physical resource 5 into "virtual capability data" that indicates the capability of the virtual resource in order to match the resource request content received from the resource operation control device 10, and configures the virtual resource based on the virtual capability data.
In other words, the virtual resource capability conversion unit 351 creates virtual capability data based on virtualization logic that virtualizes the physical resource 5, which is an energy-related facility, from the physical resource measurement data 451A obtained by measuring the physical resource 5, and constructs a virtual resource that provides that virtual capability data.
The virtualization logic is prepared individually according to the resource request data that indicates the capability status of the physical resource 5 requested by each resource operation management system 1 to which it is allocated. The virtualization logic is the logic that performs the conversion process from pre-configured physical resource measurement data 451A to virtual capability data in order to construct a virtual resource. In this specification, an ID starting with P, such as P0001, is assigned as the identifier for the virtualization logic.

The capacity prediction unit 352 identifies a model for predicting future values of virtual capacity data based on past virtual capacity data output by the virtual resource capacity conversion unit 351 and factor data 453A acquired from the data management device 4. Using the identified model, the capacity prediction unit 352 calculates a predicted value, which is the future value of the virtual capacity data at a predetermined future date and time.

The capacity allocation unit 353 matches the resource request details obtained from the resource operation control device 10 with the virtual capacity data calculated by the capacity prediction unit 352, and allocates virtual resources to the resource operation control device 10. In other words, the capacity allocation unit 353 assigns each virtual resource constructed by the virtual resource capacity conversion unit 351 to the resource operation management system 1, thereby allowing each resource operation management system 1 to manage the actual resources 5. Alternatively, the capacity allocation unit 353 may assign each virtual resource constructed by the virtual resource capacity conversion unit 351 to the resource operation management system 1 based on the predicted values calculated by the capacity prediction unit 352.

The control planning unit 354 generates a plan to control some or all of the physical resources 5 incorporated into the virtual resource. In other words, the control planning unit 354 generates a control plan for the physical resources 5 that constitute the virtual resource allocated by the capacity allocation unit 353, and instructs each allocated resource operation management system 1 to control the physical resources 5 based on that control plan. The virtual resource capacity conversion unit 351 modifies the virtual capacity data based on the control plan generated by the control planning unit 354.
The control planning unit 354 may calculate the time change in virtual capacity data related to energy as the physical resource 5 operates, and reflect that time change in the virtual capacity data created by the virtual resource capacity conversion unit 351.

The resource operation control device 10 is a device that, for example, operates and controls the generation, consumption, transportation, and trading of energy. Based on the virtual capacity data output by the resource virtualization device 3, the resource operation control device 10 combines the controlled equipment 12 and virtual resources that it manages to create and execute a physical equipment operation plan to achieve a predetermined goal. In the energy field, the physical equipment operation plan is, for example, an operation plan for energy production equipment, distribution equipment, consumption equipment, and storage equipment. Specifically, the physical equipment operation plan is the following plan:
- Planning the number of generators and batteries to be started and the output distribution of those generators and batteries. - Planning the charging or discharging control of electric vehicle chargers and charging stations. - Planning the distribution of gas and water flow rates and pressures through gas and water pipelines. - Planning the charging and discharging control of batteries to prioritize demand for electricity derived from renewable energy. - In demand response, which is the adjustment control of electricity demand, planning and implementing the distribution of demand adjustment amounts for electricity consumers or demand facilities of electricity consumers participating in demand response. - In the transportation sector, for example, planning the dispatch of transport vehicles such as buses and trucks for passenger and freight transport.

Furthermore, the equipment operation plan is not limited to direct execution by the entity using the resource operation control device 10; it may also be implemented indirectly. Indirect equipment operation, in the power sector, refers, for example, to the physical operation of equipment by another party based on a direct bilateral transaction contract or a transaction contract via an exchange. In this case, the execution plan of the transaction contract corresponds to the equipment operation plan.

The information input/output terminal 8 inputs data to the resource virtualization device 3 and the data management device 4, and displays the data stored or output by these devices. The data observation device 6 periodically measures or collects measurement data related to the operation of the physical resource 5 at predetermined time intervals and transmits it to the data management device 4. The information distribution device 9 transmits the measured and predicted values of factor data 453A that affect the operation of the physical resource 5 to the data management device 4. The monitoring and control device 7 monitors and controls the physical resource 5.

The data management device 4 consists of a CPU 31 (Central Processing Unit) 41 that comprehensively controls the operation of the data management device 4, an input device 42, an output device 43, a communication device 44, and a storage device 45. The data management device 4 is an information processing device such as a personal computer, a server computer, or a handheld computer.
The input device 42 consists of a keyboard or mouse, and the output device 43 consists of a display or printer. The communication device 44 is equipped with a NIC (Network Interface Card) for connecting to a wireless LAN or wired LAN. The storage device 45 is a storage medium such as RAM (Random Access Memory) or ROM (Read Only Memory). The output results of each processing unit and intermediate results may be output as appropriate via the output device 43.

The storage device 45 stores databases such as a real resource measurement data storage means 451, a real resource constraint data storage means 452, and a factor data storage means 453.
The physical resource measurement data storage means 451 holds the physical resource measurement data 451A.
The physical resource measurement data 451A is data that stores past observed values regarding the operation of physical resource 5. Observed values regarding the operation of physical resource 5 include, for example, the amount of power generated by a generator, the amount of charge and discharge of a storage battery, the amount of charge and remaining charge of an electric vehicle, the amount of discharge to the power grid, the movement history of a mobile object indicated by latitude and longitude or location identifiers such as GPS, and the movement history and transport content history of passenger transport and freight transport.

The physical resource constraint data storage means 452 holds physical resource constraint data 452A (Figure 8). The physical resource constraint data 452A is data that stores conditions and constraints related to the operation of the physical resource 5. The conditions and constraints related to the operation of the physical resource 5 include, for example, the following data.
- Operating time range, such as start and end dates and times for power generation, charging, energy storage and discharging, movement, etc. - Operating amount range, such as power generation amount, charging amount, energy storage and discharging amount, movement amount, etc. - Operating location range, such as location of power generation, charging, energy storage and discharging, and movement range. - Response time, which is the time required until the control response is completed when accepting control. - Whether or not it is possible to accept changes to the operating content of physical resource 5. - Whether or not the above conditions can be changed, the cost related to operation, or the cost incurred as a result of accepting control or changing conditions.

Factor data storage means 453 holds factor data 453A. Factor data 453A is data that stores observed and predicted values of various factors that affect the operation of the physical resource 5. Factors are, for example, the following data.
- Weather data such as temperature, humidity, solar radiation, wind speed, and atmospheric pressure - Energy consumption data such as electricity, gas, and water - Energy generation data such as solar power and wind power - Data on the status and schedule of generator shutdowns - Power system data such as available capacity, current, and voltage for each transmission line, distribution line, or substation - Market data such as trading volume and price of energy traded on exchanges - Calendar day data such as year, month, day, day of the week, and flag values indicating the type of day set arbitrarily - Data indicating the occurrence or absence of sudden events such as typhoons and events - Data indicating economic conditions such as the number of energy consumers, industry trends, and business conditions index - Data indicating the movement of people and mobile entities such as the occupancy rate, number of passengers, number of reserved seats on express trains, or road traffic conditions Also includes past observation data of the above-mentioned forecast targets, or the forecast result data itself.

The resource virtualization device 3 consists of a CPU 31 that comprehensively controls the operation of the resource virtualization device 3, an input device 32, an output device 33, a communication device 34, and a storage device 35. The resource virtualization device 3 is an information processing device such as a personal computer, a server computer, or a handheld computer.
The storage device 35 stores databases such as a virtualization definition data storage means 357, a virtualization logic pool data storage means 358, and a resource-related data storage means 359.

The virtualization definition data storage means 357 holds virtualization definition data 357A (Figure 7). The virtualization definition data 357A is data that shows the correspondence between the resource request content received from the resource operation control device 10 and the virtualization logic for constructing a virtual resource that satisfies the request.
The virtualization logic pool data storage means 358 holds the virtualization logic pool 358A. The virtualization logic pool 358A stores one or more pre-configured virtualization logics. Examples of virtualization logic are given below.
- Virtualization logic that converts physical quantities of a different type from the physical quantities in the physical resource measurement data 451A into virtual capability data. - Virtualization logic that generates new virtual capability data from a combination of two or more existing virtual capability data. - Virtualization logic that converts physical resource measurement data 451A, which shows the remaining amount of energy associated with the operation of physical resource 5, into virtual capability data that shows the positive or negative energy supply capacity of physical resource 5. - Virtualization logic that converts physical resource measurement data 451A, which shows the movement history associated with the operation of physical resource 5, into virtual capability data that shows the cargo transport capacity of physical resource 5. - Virtualization logic that uses physical resource measurement data 451A showing the positive or negative energy supply capacity of physical resource 5 and physical resource measurement data 451A showing the cargo transport capacity of physical resource 5 to convert into virtual capability data showing the energy transport capacity of physical resource 5.

The resource-related data storage means 359 holds resource-related data 359A (Figure 15). Resource-related data 359A is data indicating the correspondence between a virtual resource and the actual resource 5 that constitutes the virtual resource. For example, resource-related data 359A is shown as an arrow in Figure 20: private car "AR001" → virtual resource "VR001".

The storage device 35 also stores various computer programs, such as a virtual resource capacity conversion unit 351, a capacity prediction unit 352, a capacity allocation unit 353, and a control planning unit 354.
The virtual resource capacity conversion unit 351 executes the following processes in order.
- Input the actual resource measurement data 451A and the actual resource constraint data 452A.
- Using the virtualization logic in the virtualization logic pool 358A, the measurement data of each physical resource 5 included in the physical resource measurement data 451A is converted into virtual capability data. Information indicating which virtualization logic to use is stored in the virtualization definition data 357A.
- Resources that satisfy the resource requests received from the resource operation control device 10 are constructed as virtual resources by combining them with virtual capability data.
- Output the constructed virtual capability data.
- The selection is made based on the correspondence data between the resource request content received from the resource operation control device 10 and the virtualization logic.

The capacity prediction unit 352 receives the virtual capacity data output by the virtual resource capacity conversion unit 351 and the factor data 453A, identifies a prediction model that calculates a predicted value indicating the capacity at a predetermined future date and time, calculates the predicted value, and outputs the predicted value to the capacity allocation unit 353.
The capacity allocation unit 353 compares the predicted value of the virtual capacity data output by the capacity prediction unit 352 with the resource request content obtained from the resource operation control device 10, and determines the virtual resources to be provided to the resource operation control device 10.

The control planning unit 354 generates and outputs a control plan for the physical resource 5 that constitutes the virtual resource in any of the following cases:
- If the capacity allocation unit 353 determines that the predicted value of the virtual capacity data output by the capacity prediction unit 352 does not match the resource request content obtained from the resource operation control device 10 - If the resource request content from the resource operation control device 10 is changed The actual resources 5 that constitute the virtual resources are obtained from the resource relationship data 359A.

The processing and data flow of the resource virtualization system 2 will be described below with reference to Figures 3 and 4.
Figure 3 is a data flow diagram of the resource virtualization process in resource virtualization system 2.
The data management device 4 receives physical resource measurement data 451A from the measurement device 6 or the monitoring and control device 7 and stores it in the physical resource measurement data storage means 451. The data management device 4 also receives physical resource constraint data 452A from the information input/output terminal 8 and stores it in the physical resource constraint data storage means 452. The data management device 4 also receives factor data 453A from the information distribution device 9 and stores it in the factor data storage means 453.

The virtual resource capacity conversion unit 351 executes the following processes in order.
- Measurement data for one or more physical resources 5 recorded in the physical resource measurement data 451A is acquired from the physical resource measurement data 451A within a predetermined date and time range.
- Data regarding constraints and conditions related to the operation of the acquired physical resource 5 is obtained from the physical resource constraint data 452A.
- Using the resource request data input from the resource operation control device 10 via the capacity allocation unit 353 as a key, an identifier indicating the virtualization processing logic for converting the measurement data of the physical resource 5 into virtual capacity data is obtained from the virtualization definition data 357A.
- The virtualization processing logic is retrieved from the virtualization logic pool 358A using an identifier indicating the acquired virtualization processing logic as the key.
- The acquired virtualization processing logic is input with the acquired physical resource measurement data 451A and physical resource constraint data 452A for each physical resource 5, and converted into virtual capability data.
- Virtual resources are constructed by combining virtual capability data.
- Output the identifier indicating the constructed virtual resource and data indicating its capabilities as virtual capability data.
The virtual resource capability conversion unit 351 stores resource relationship data 359A, which indicates the correspondence between the virtual resource and the physical resource 5 that constitutes the virtual resource, in the resource relationship data storage means 359.

The capability prediction unit 352 executes the following processes in order.
- The virtual resource data output by the virtual resource capacity conversion unit 351 and factor data 453A are acquired from the data management device 4.
Identify predictive models for calculating predicted values of virtual capability data.
- Predicted values for virtual capability data are calculated by inputting predetermined future date and time factor data 453A into the identified prediction model.
- The predicted values of the data indicating capacity and the identifiers indicating virtual resources are combined and output as virtual resource capacity prediction result data.

The capacity allocation unit 353 executes the following processes in order.
- The system acquires virtual resource capacity prediction result data obtained from the capacity prediction unit 352 and resource request details obtained from the resource operation control device 10.
- Determine whether the predicted values of the virtual resource capacity data shown in the virtual resource capacity prediction result data satisfy the resource requirements.
If it is determined that the requirements are met, the virtual resource is registered as one to be used for the control and operation of the resource operation control device 10.
If it is determined that the requirements are not met, control plan generation instruction data is output to the control planning unit 354 to initiate the process of generating a control plan that modifies the operation of the physical resource 5 that constitutes the virtual resource.

The control planning unit 354 executes the following processes in order.
- Operation begins upon receipt of control plan generation instruction data from the capacity allocation unit 353.
- Resource-related data 359A is acquired, and a control plan is generated for one or more physical resources 5 that constitute the virtual resource for which control plan generation is targeted, and output as control plan data.
The output control plan data is input to the capacity prediction unit 352, and the capacity prediction unit 352 recalculates and outputs the predicted value of the virtual capacity data after the control plan has been executed.

Figure 4 is a flowchart showing the processing procedure for resource virtualization in the resource virtualization system 2. This flowchart shows a process that begins when one of the following events occurs, and the resource virtualization device 3 executes the processes from step S401 to step S405.
- The resource virtualization device 3 has received an input operation from the device user. - The resource operation control device 10 has received a resource request. - The pre-set execution time has arrived. In reality, processing is executed based on various computer programs stored in the CPU 31 and storage device 35 of the resource virtualization device 3, and various computer programs stored in the CPU 41 and storage device 45 of the data management device 4. For the sake of explanation, the processing entities will be described as the resource virtualization device 3 and the various computer programs that the resource virtualization device 3 possesses.

The virtual resource capacity conversion unit 351 acquires physical resource measurement data 451A and physical resource constraint data 452A from the data management device 4, and resource request content data (Figure 6) from the capacity allocation unit 353. Then, using the resource request content data and virtualization definition data 357A, it acquires virtualization processing logic from the virtualization logic pool 358A, and uses the acquired logic to convert the physical resource measurement data 451A into virtual capacity data. Finally, it combines the virtual capacity data to create a virtual resource (S401).

The capability prediction unit 352 identifies a prediction model for calculating predicted values of virtual capability data from the virtual resource data output by the virtual resource capability conversion unit 351 and the factor data 453A acquired from the data management device 4. Then, the capability prediction unit 352 calculates predicted values of virtual capability data by inputting the factor data 453A for predetermined future dates and times into the identified prediction model (S402).
The capability prediction unit 352 may also modify the predicted value of the virtual capability data calculated in S402 using the control plan for the physical resource 5 described later in S404.

The capacity allocation unit 353 obtains virtual resource capacity prediction result data from the capacity prediction unit 352 and obtains resource request details from the resource operation control device 10 (S403). The capacity allocation unit 353 then determines whether the predicted value of the virtual capacity data shown in the virtual resource capacity prediction result data satisfies the resource request details (S403A).
If the answer to S403A is Yes, the capacity allocation unit 353 registers the virtual resource that it determined in the determination in S403 to satisfy the resource request received from the resource operation control device 10 as a resource to be used for the control operation of the resource operation control device 10, and transmits it to the resource operation control device 10 (S405).

If the answer in S403A is No, then control plan generation instruction data is output to the control planning unit 354 to initiate the process of generating a control plan that modifies the operation of the physical resource 5 that constitutes the virtual resource, thereby determining whether or not to issue an instruction to change the resource configuration (S403B).
If the answer to S403B is Yes, the program returns to S401 and performs a change in the resource configuration, which changes the calculation result of S402, which is executed again.
If the answer in S403B is No, the control planning unit 354 acquires resource-related data 359A and generates a control plan for one or more physical resources 5 that constitute the virtual resource for which the control plan is to be generated (S404). The generated control plan data is input to the capability prediction unit 352, which changes the calculation result of S402, which is executed again.
Thus, regardless of whether the branch result of S403B is Yes or No, the calculation result of S402, which is executed again, will change, so the judgment result of S403A, which is executed again, may change to Yes.

Furthermore, the capacity allocation unit 353 outputs virtual resource regeneration instruction data to the virtual resource capacity conversion unit 351 to re-execute the virtual resource formation process in any of the following cases. Then, the process is restarted from S401.
- If the number of times control plan generation instruction data is output to the control planning unit 354 exceeds a predetermined number. - If no change is confirmed in the predicted value of the virtual capacity data obtained from the capacity prediction unit 352.

Detailed embodiments of each component will be described below with reference to Figures 5 to 7.
Figure 5 is a data flow diagram of the virtual resource capacity conversion unit 351.
The virtual resource capability conversion unit 351 converts the actual resource measurement data 451A into virtual capability data. It then constructs a virtual resource by combining the virtual capability data and outputs an identifier indicating the constructed virtual resource and data indicating its capability as virtual capability data.

The capacity conversion definition extraction unit 351A obtains resource request data from the capacity allocation unit 353.
Figure 6 is a table showing an example of the resource request data to be acquired. The table in Figure 6 stores the following data in each column.
- The first column contains the "Request ID," which identifies the request details.
- The second column contains the "Requesting System Name," which indicates the name of the resource operation control system 10 that submitted the request.
- The third column shows the name of the requested resource, "Requested Resource Name".
- The fourth column shows the "request period," which indicates the duration of the resource request.
- The fifth column shows the requested amount of resources.
- The sixth column shows the requirements for resource quality, labeled "Requirements".
- The 7th column shows the "request location," indicating the details of the request to the resource supply location.

The first row of the table in Figure 6 contains the following data as the request content for request ID "R0001".
- The requested system name is a request submitted by "System Monitoring and Control System A".
The requested resource is a "power supply resource."
The application period is one year, from "2020/01/01 00:00 to 2021/12/31 23:59".
The requested quantity is "more than 100 MW per hour in terms of supply."
The required location is "to supply to point A".
• The required quality is "-", indicating that there are no quality requirements.

The second row of the table in Figure 6 contains the following data as the request content for request ID "R0002".
- The requested system name is a request submitted by "System Monitoring and Control System B".
The requested resource is "power transmission resource".
The request period is one day, from 00:00 on 2020/01/01 to 23:59 on 2021/01/01.
The required quantity is "more than 10 MW per hour in terms of transport volume."
The requirements for resource quality are that "the acceptable range for fluctuations in transport volume is within ±3%, and the acceptable range for the start and end times of transport is within ±30 minutes."
The required location is "transportation from location A to location B".

Figure 22 is a table showing an example of virtual capability data for each virtual resource. The table in Figure 22 stores the following data in at least each column:
The first column stores data indicating the identifier of the virtual resource formed in the virtual resource capability conversion unit 351.
The second column contains data indicating the date and time.
The third column contains data showing the virtual capacity value of the virtual resource shown in the first column for each date and time shown in the second column.
The fourth column contains data indicating the unit of the virtual capability value shown in the third column.
The fifth column contains data showing the operational status of the virtual resource shown in the first column, at the date and time shown in the second column.
The sixth column contains data indicating the location where the virtual resource shown in the first column was running at the date and time shown in the second column.

In Figure 22, for the sake of simplicity, all virtual resource identifiers shown in the first column are set to "VR001". Similarly, for the sake of simplicity, the dates and times shown in the second column are set to one-hour intervals, ranging from 0:00 on January 1, 2021 to 23:00 on December 31, 2021.
Here, the virtual resource ID "VR001" is the virtual resource associated with the request ID "R0001," as shown in Figure 20. The request ID "R0001" is the request received from "System Monitoring and Control System A," as shown in the first line of the resource request data in Figure 6.

According to the request details in the first row of Figure 6, the request period is "from January 1, 2021, 00:00 to December 31, 2021, 23:59", the request quantity is "a supply of 100 MW/h or more", and the request location is "Location A".
The virtual capacity data shown in Figure 22 indicates that virtual resource ID "VR001" has a power supply capacity of 100 MW/h or more at location A from "0:00 on January 1, 2021" to "23:00 on December 31, 2021". Therefore, the requirement satisfaction evaluation unit 353B determines that virtual resource "VR001" satisfies the requirement of requirement ID "R0001".

Figure 7 is a table showing an example of the acquired virtualization definition data 357A. The capability conversion definition extraction unit 351A acquires the virtualization definition data 357A. The table in Figure 7 stores the following data in each column.
The first column stores identifiers for resource requests received from the resource operation control device 10 via the capacity allocation unit 353.
The second column stores identifiers for virtualization logic that are configured and registered to correspond to resource requests.
The third column contains data indicating the operational details of the physical resource 5 applied to the processing of the virtualization logic.

For example, the first line indicates that, in response to the resource request "R0001," the virtualization logic "P0001" is used to configure and register the conversion of the actual resource measurement data 451A of the actual resource 5, whose operation is "discharge or charge/discharge," into virtual capacity data.

The second line also indicates the following:
- The resource request for "R0002" utilizes multiple virtualization logics: "P0001", "P0002", and "P0003".
First, the data is processed in "P0001" and "P0002" respectively, and then the processing result data from each is entered into "P0003" and processed there.
- The system is configured to convert the actual resource measurement data 451A of the actual resource 5, whose operation is "discharge or storage/discharge and movement," into virtual capacity data.

The fourth line also indicates the following:
For request ID "R0004", three virtualization logics are used: "P0005", "P0002", and "P0006".
The virtualization logic for "P0002" uses the same logic as the virtualization logic used for request ID "R0002".
The capacity conversion definition extraction unit 351A uses the identifier of the resource request content obtained from the capacity allocation unit 353 as a key to obtain the identifier of the virtualization logic and processing order information from the virtualization definition data 357A shown in Figure 6, and outputs it to the capacity conversion unit 351B.

Figure 8 is a table showing an example of the actual resource constraint data 452A. The capacity conversion unit 351B first acquires the actual resource measurement data 451A and the actual resource constraint data 452A from the data management device 4. The table in Figure 8 stores the following data in each column.
The first column is the identifier for entity resource 5.
The second column contains data indicating the name of the physical resource 5.
The data from the third column onward shows the details of the constraints for each entity resource 5.
The third column contains data indicating the operational capabilities of each entity resource 5.
The fourth column contains data indicating whether or not control of each entity resource 5 is accepted.
The fifth column contains data indicating the period and time slots during which each entity resource 5 is operational.
The sixth column shows the cost incurred when each of the five physical resources operates as a virtual resource.
The seventh column shows whether or not the constraint data for each entity resource 5 can be changed.

For example, the first instance resource ID "AR001" and the instance resource name "Private Car" for instance resource 5 represent the following:
- Capable of both "storage and discharge" and "mobility" operation.
- Acceptance of the control is "not possible".
The operating hours are from 08:00 to 16:00.
There are "no" costs incurred when operating as a virtual resource.
Entity resource 5 presents the constraint that "changes to the aforementioned constraints are not permitted".

Note that the physical resource 5 is not necessarily limited to a single physical device. For example, it may be a collection of physical device enclosures, such as the "factory customer" shown with physical resource ID "AR005," or a collection of multiple physical resources 5, such as the "virtual resource A" shown with physical resource ID "AR007."

The capability conversion unit 351B then retrieves virtualization logic from the virtualization logic pool 358A using the virtualization logic identifiers "P0001 to P0006, etc." obtained from the capability conversion definition extraction unit 351A. The capability conversion unit 351B then processes the virtualization logic according to the processing order obtained from the capability conversion definition extraction unit 351A.

The specific processing flow of the capability conversion unit 351B will be explained below using Figures 7 to 15. The virtualization logic within the capability conversion unit 351B is complex because the capabilities required of the physical resource 5 vary widely depending on the requesting system.
As mentioned above, Figure 7 shows the virtualization definition data 357A obtained from the capability conversion definition extraction unit 351A, which is data indicating the ID of the virtualization logic to be applied and the processing order of the virtualization logic for each identifier of the request content data.
For example, the first row of Figure 7 indicates that the virtualization logic for "P0001" is to be applied to the request ID "R0001". Therefore, the capability conversion definition extraction unit 351A obtains the virtualization logic for "P0001" from the virtualization logic pool 358A and places it in the capability conversion unit 351B.

Figure 9 is an explanatory diagram of the processing of the virtualization logic of "P0001" located within the acquired capability conversion unit 351B.
First, the virtualization logic "P0001 (351B1)" located in the capacity conversion unit 351B acquires actual resource measurement data 451A from the data management device 4 in the charge level estimation unit 351B11 and estimates time-series data of the charge level. The actual resource measurement data 451A to be acquired is indicated as "discharge or charge/discharge" in the "Operation details" section of Figure 7.
Therefore, the remaining charge estimation unit 351B11 acquires at least "AR001", "AR002", "AR003", "AR004", "AR005", "AR006", and "AR007", which are indicated as "energy storage or energy storage/discharging" in the "operable content" of the actual resource constraint data in Figure 8.

Then, the remaining charge estimation unit 351B11 performs the remaining charge estimation process by executing either (estimation method 1) or (estimation method 2) below on the acquired physical resource measurement data 451A of each physical resource 5.
(Estimation Method 1) If the acquired actual resource measurement data 451A is the time-series data of the remaining charge itself, it is output as is.
(Estimation Method 2) If the acquired physical resource measurement data 451A is time-series data of the charging history of a charger or time-series data of the charging history of each device such as an electric vehicle connected to the charger, the amount of charge from the start to the end of charging is calculated. Then, the amount of charge calculated from the past maximum charge is divided by the value obtained to determine the remaining charge at the start of charging. Then, the remaining charge during charging is estimated using the amount of charge. From the end of charging until the end of charger connection, the amount of charge is estimated to be the sum of the remaining charge at the start of charging and the amount of charge charged during charging.

The minimum remaining charge estimation unit 351B12 acquires time-series data of estimated remaining charge values from the past to the latest date and time output by the remaining charge estimation unit 351B11 and the physical resource constraint data 452A, and estimates the minimum allowable remaining charge value of the physical resource 5. Specifically, it estimates the minimum value in the time-series data of estimated remaining charge values from the past to the latest date and time output by the remaining charge estimation unit 351B11 as the minimum allowable remaining charge value of the physical resource 5.
Furthermore, if the actual resource constraint data 452A contains data indicating the constraint condition for the minimum allowable remaining charge of the resource, the value of that constraint condition is estimated as the minimum allowable remaining charge.

The power supply capacity calculation unit 351B13 obtains time-series data of estimated remaining charge values from the past to the latest date and time, output by the remaining charge estimation unit 351B11, and the minimum allowable remaining charge value output by the minimum remaining charge estimation unit 351B12, and calculates the power supply capacity of the actual resource 5.

Figure 13 is a graph showing virtual capability data to specifically explain "P0001" in Figure 9.
The white (uncolored) bar graph shown on the left side of Figure 13 represents the time-series data of the estimated remaining charge of the physical resource 5 from the past to the most recent date and time, output by the remaining charge estimation unit 351B11. The dotted line (1303) in the figure indicates the minimum allowable remaining charge estimated and output by the minimum remaining charge estimation unit 351B12. Here, the time periods 1301 and 1302, when the device is connected to the charger after charging is complete, are time periods when power can be supplied (reverse power supply).

The gray (filled) bar graph shown on the right side of Figure 13 represents virtual capacity data for power supply capacity.
The power supply capacity calculation unit 351B13 converts the data into power supply capacity data shown in 1304 and 1305 of the figure on the right, for the time periods 1301 and 1302 when the device is connected to the charger after charging is complete. When the power supply capacity calculation unit 351B13 calculates the power supply capacity data, for physical resources 5 that have operational constraints in the acquired physical resource constraint data 452A, the data is converted to power supply capacity data after satisfying the constraints. For example, in the physical resource constraint data 452A shown in Figure 8, the operating time period for physical resource "AR001" is indicated as "08:00 to 16:00". Therefore, for time periods other than this, the power supply capacity data converted from the physical resource measurement data 451A of the physical resource 5 is always converted to zero.

Furthermore, other examples of virtualization logic placement in the capability conversion unit 351B will be explained. For example, the request ID "R0002" shown in the second row of Figure 7 is configured to apply three virtualization logics: "P0001," "P0002," and "P0003." Moreover, the processing procedure is configured to first operate "P0001" and "P0002" respectively, and then operate "P0003" using the resulting data as input to convert it into virtual capability data. Therefore, the capability conversion definition extraction unit 351A acquires the virtualization logics "P0001," "P0002," and "P0003" from the virtualization logic pool 358A and places them in the capability conversion unit 351B according to the configured processing order.

Figure 10 is an explanatory diagram showing the virtualization logics "P0001", "P0002", and "P0003" arranged within the capability conversion unit 351B.
The virtualization logic "P0001 (351B1)" located in the capacity conversion unit 351B includes a charge level estimation unit 351B11.
The charge level estimation unit 351B11 acquires the physical resource measurement data 451A of the physical resource 5 that matches the operation content data shown in the third column of the request ID "R0002" in Figure 7, and which is "discharging or charging/discharging, and moving," from the data management device 4. Then, the charge level estimation unit 351B11 uses the acquired physical resource measurement data 451A to estimate the time-series data of the power supply capacity of each physical resource 5. Here, "P0001 (351B1)" is the same as the "P0001 (351B1)" mentioned above, and the data indicating the power supply capacity is calculated according to the processing procedure described above.

The station location estimation unit 351B21 of the virtualization logic "P0002 (351B2)" acquires the physical resource measurement data 451A of the physical resource 5 that matches the operation content data shown in the third column of request ID "R0002" in Figure 7, namely "discharge or storage/discharge, and movement," from the data management device 4. Then, the station location estimation unit 351B21 uses the acquired physical resource measurement data 451A to estimate the time-series data of the departure location during movement for each physical resource 5.

The method for estimating time-series data is as follows: For example, if the actual entity of the physical resource measurement data 451A is time-series data of latitude and longitude information for each time period, such as GPS data, then the time periods and locations where the entity is stationary at the same latitude and longitude within a predetermined time range are extracted, and these locations are estimated as stationary locations. Next, the transport capacity calculation unit 351B22 receives the time-series data of the stationary locations of the physical resource 5 from the past to the present, calculated by the stationary location estimation unit 351B21, and converts it into data indicating the mobility capacity of the physical resource 5.
The stop location estimation unit 351B21 estimates, for example, that a certain estimated stop location has the capacity to "collect" some kind of cargo during the time it is stopped at that stop. The stop location estimation unit 351B21 also estimates that the cargo has the capacity to "arrive" at the next stop, and estimates that the cargo has the capacity to "transport" between the two stops, and converts this into data indicating the aforementioned series of transport capacities.

Finally, the power transmission capacity calculation unit 351B31 of "P0003 (351B3)" receives the data indicating the power supply capacity, the data indicating the mobility capacity, and the physical resource constraint data 452A calculated by P0001 and P0002, respectively. The power transmission capacity calculation unit 351B31 converts this into data indicating the power transmission capacity of the physical resource 5.

Figure 14 is a graph that provides a detailed explanation of "P0001,""P0002," and "P0003" in Figure 10.
Graph 1401 shows time-series data of the remaining charge of a certain physical resource 5 estimated by the remaining charge estimation unit 351B11 of P0001. The power supply capacity calculation unit 351B13 then takes the estimated time-series data of the remaining charge shown in Graph 1401 as input and calculates the data shown in Graph 1402 as data indicating the power supply capacity of the physical resource 5.
For example, the physical resource 5 is being charged with electricity from the charger during time period 1401A in graph 1401. Therefore, the power supply capacity calculation unit 351B13 calculates the negative supply capacity shown in time period 1402A of graph 1402, assuming that the physical resource 5 has a "negative supply capacity" during that time period.
Then, in time period 1401B of graph 1401, the power supply capacity calculation unit 351B13 estimates that the physical resource 5 is connected to the charger but is not being charged, and estimates that it has the capacity to supply (reverse power) the remaining charge through the charger during this time period. As a result, it calculates a positive supply capacity as shown in time period 1402B of graph 1402.

Furthermore, power supply capacity decreases over time after power supply begins. Therefore, as shown in time zone 1402B, the positive supply capacity is calculated to decay over time. The degree of decay may be calculated based on the previously acquired specification information of the hourly power supply amount of the physical resource 5 in question. Alternatively, it may be calculated based on the specification information of a physical resource 5 of the same type, estimated from past power supply capacity data of the physical resource 5, or calculated according to a predetermined coefficient.

Next, graph 1403 shows the time-series data of the stopping location and stopping time of the physical resource 5 estimated by the stopping location estimation unit 351B21 in P0002 (351B2). This means that the physical resource 5 is estimated to have stopped at "location A" in time period 1403A, then moved after some time, and stopped at "location B" in time period 1403B. Then, P0002 (351B2) estimates data indicating the transport capacity of the physical resource 5 from the estimated stopping location result data shown in graph 1403. Specifically, as shown in graph 1404, it is estimated that the physical resource 5 has the capacity to "collect" while stopped at location A (time period 1404A). This means that it is presumed to have the capacity to "transport" the next collected cargo (time zone 1404B), and then presumed to have the capacity to "receive" the cargo at point B (time zone 1404C).

Finally, the power transmission capacity calculation unit 351B31 of P0003 receives the data showing the power supply capacity of the physical resource 5 calculated by P0001 (graph 1402), the data showing the transmission capacity of the physical resource 5 calculated by P0002 (graph 1404), and the physical resource constraint data 452A, and converts them into data showing the power transmission capacity shown in graph 1405. Specifically, 1402A in graph 1402 is estimated to have negative supply capacity (charging) at point A, and at the same time, as shown in time period 1404A in graph 1404, it is estimated to have collection capacity at point A.

Therefore, the power transmission capacity calculation unit 351B31 estimates that, as shown in time period 1405A of graph 1405, the actual resource 5 has the capacity to receive electricity as cargo at point A during that time period. Similarly, in time period 1402B of graph 1402, it is estimated that point B has a positive supply capacity (reverse current), and simultaneously, as shown in time period 1404C of graph 1404, it is estimated that point B has the capacity to receive cargo.

Therefore, the power transmission capacity calculation unit 351B31 estimates that the actual resource 5 has the capacity to unload electricity at point B during the time period 1405B shown in graph 1405. Based on the estimation result that the resource has the capacity to transport goods during the time period 1404B shown in graph 1404, when it moves from point A to point B, the power transmission capacity calculation unit 351B31 estimates that the resource has the capacity to load electricity at point A, transport it to point B, and unload it at point B.

Furthermore, other arrangement examples of the virtualization logic in the capacity conversion unit 351B will be described. The request ID "R0003" shown in the third row of Figure 7 is defined to apply the actual resource measurement data 451A of the actual resource 5, whose operation is "discharge or storage/discharge, and is renewable energy," to the virtualization logic "P0004" to convert it into virtual capacity data.

Figure 11 is an explanatory diagram of the processing of the virtualization logic of "P0004" located within the acquired capability conversion unit 351B.
The renewable energy supply capacity calculation unit 351B41, located at P0004 (351B4), acquires physical resource measurement data 451A and physical resource constraint data 452A from the data management device 4. The renewable energy supply capacity calculation unit 351B41 then takes the acquired physical resource measurement data 451A and physical resource constraint data 452A as input and converts them into data indicating the renewable energy supply capacity of each physical resource 5.
For example, if the data stored in the physical resource measurement data 451A is the remaining charge and discharge amount of the physical resource 5 as a battery, the value of the amount of electricity derived from renewable energy is separated from the data of the remaining charge and discharge amount, and the separated value is calculated as data indicating the renewable energy supply capacity. The method for separating the value of electricity derived from renewable energy may be a known method such as traceability of renewable energy generation. If the data stored in the physical resource measurement data 451A is originally actual data on the amount of renewable energy generation, that actual data is output as is.

Furthermore, other examples of virtualization logic configurations in the capacity conversion unit 351B will be explained. The request ID "R0004" shown in the fourth row of Figure 7 is defined to apply the actual resource measurement data 451A of the actual resource 5, whose operation is "cargo transport," to the virtualization logics "P0005," "P0002," and "P0006" to convert it into virtual capacity data.

Figure 12 is an explanatory diagram of the processing of the virtualization logic "P0005", "P0002", and "P0006" located within the acquired capability conversion unit 351B.
First, P0005 (351B5), located in the capacity conversion unit 351B, acquires physical resource measurement data 451A from the data management device 4. Based on the "cargo transport" operation shown in the third column of Figure 7, the P0005 acquires at least the physical resource measurement data 451A for the physical resource ID "AR003" whose "operable content" in the participant name of the physical resource constraint data 452A shown in Figure 8 is "cargo transport".

The P0005's available capacity estimation unit 351B51 receives the physical resource measurement data 451A and estimates the available capacity of each physical resource 5 at each given time. For example, if the measurement data content of the physical resource measurement data 451A is the number of passengers measured at each time or at a specific location, the unit calculates the number of passengers that can be accommodated by dividing the maximum number of passengers by the number of passengers, and outputs this as the estimated available capacity for each time or location.

The capacity-cargo conversion unit 351B52 of P0005 generates a function for calculating the possible cargo load from the available capacity of the physical resource 5. The method for generating the function could be, for example, by pre-setting data for the correspondence between available capacity and possible cargo load, such as "5 items of 80 cm size" if the available capacity is "10 people," and "2 items of 80 cm size" if the available capacity is "5 people." Alternatively, it could be a method of identifying a model that calculates the possible cargo load for a given available capacity using a regression model such as a multiple regression model based on past cargo loading history and the available capacity data at that time.

The cargo capacity calculation unit 351B53 then inputs the data indicating the available capacity calculated by the available capacity estimation unit 351B51 into a function that calculates the possible cargo load from the available capacity calculated by the capacity cargo conversion unit 351B52, thereby calculating data indicating the possible cargo load capacity of the physical resource 5. In this calculation, during periods when the constraints of the physical resource 5 shown in the physical resource constraint data 452A cannot be satisfied, the possible cargo load is calculated as zero.

Furthermore, in P0002 (351B2) located in the capacity conversion unit 351B, data indicating the transport capacity of the physical resource 5 is calculated using the method described in Figure 10.

Then, P0006 (351B6), located in the capacity conversion unit 351B, receives the data indicating the cargo load capacity calculated by P0005, the data indicating the transport capacity calculated by P0002, and the physical resource constraint data 452A into the cargo transport capacity calculation unit 351B61, and calculates the data indicating the cargo transport capacity of each physical resource 5. For example, if the cargo load capacity at time T is "five 80cm-sized cargo items," and the transport capacity at time T is "collection," and the transport capacity at time T+N is "arrival," then the physical resource 5 is calculated to have the cargo transport capacity to transport "five 80cm-sized cargo items" from time T to time T+N.

Based on the processing operations described above, the capacity conversion unit 351B converts the measurement data of each physical resource 5 stored in the physical resource measurement data 452A into virtual capacity data according to the resource request data obtained from the capacity allocation unit 353.
The resource formation unit 351C generates one virtual capability data by summing the virtual capability data of each physical resource 5 output by the capability conversion unit 351B at each given time, and outputs data including the identifier of the virtual resource, the identifiers of the physical resources 5 that constitute the virtual resource, and the virtual capability data to the capability prediction unit 352 as virtual resource data. The resource formation unit 351C may select all of the physical resources 5 output by the capability conversion unit 351B, or it may select some of the physical resources 5, as a method for selecting the physical resources 5 to be added to (associated with) the virtual resource.

For example, in Figure 20, the virtual resource VR001 is composed of only "AR001" selected from the physical resources 5 "AR001 to AR006". The resource formation unit 351C selected "AR001" as the physical resource 5 that corresponds to at least one of the following listed items in response to the request content of request R0001 corresponding to the virtual resource VR001.
Five tangible resources capable of providing the necessary functions to meet the requirements.
Five physical resources capable of providing the required capacity over the requested period.
Five physical resources that meet the required cost

Here, the resource formation unit 351C may select the actual resource 5 to be incorporated into the virtual resource using the predicted value calculated by the capacity prediction unit 352 instead of the measured value calculated by the capacity conversion unit 351B. For example, the resource formation unit 351C may select the actual resource 5 such that the expected value of the predicted virtual capacity data for the virtual resource at any future date and time calculated by the capacity prediction unit 352, the predicted value of quantiles such as the 25th percentile, and the interval estimates such as confidence intervals and prediction intervals satisfy predetermined thresholds and quality conditions specified in the resource request content data.
In this case, the resource formation unit 351C outputs control data to the capacity prediction unit 352, instructing it to return the prediction result data. The output of control data continues until the prediction result data of the virtual capacity data exceeds a predetermined threshold or until the difference in the change between the previous and current values of the prediction result data falls below a predetermined value.
This makes it possible to create virtual resources that guarantee the quantity and quality of their capabilities.

Alternatively, the resource formation unit 351C may select the physical resource 5 to be incorporated into the virtual resource such that the interval estimates, such as the confidence interval or prediction interval, of the predicted value of the virtual capacity data for any future date and time of the virtual resource calculated by the capacity prediction unit 352 are minimized. In this case, the resource formation unit 351C outputs control data instructing the capacity prediction unit 352 to return the prediction result data. The output of control data continues until the predicted result data of the virtual capacity data exceeds a predetermined threshold or until the difference in the change between the previous and current values of the prediction result data falls below a predetermined value.
Alternatively, the resource formation unit 351C may select the physical resources 5 to be incorporated into the virtual resources such that at least one of the expected value, quantile prediction value, or interval prediction value of the virtual resource's capacity at a predetermined date and time calculated by the capacity prediction unit 352 satisfies a predetermined standard value.

In this way, the virtual resource capability conversion unit 351 constructs a virtual resource by combining multiple pre-stored virtualization logics to satisfy the capability mode of the physical resource requested by the resource operation management system.
Alternatively, the virtual resource capacity conversion unit 351 constructs virtual resources based on the predicted values of the virtual capacity data calculated by the capacity prediction unit 352.
Here, the virtual resource capability conversion unit 351 may determine a combination of physical resources 5 that constitute the virtual resources such that the information indicating the attributes of the virtual resources (controllability and cost) generated based on information indicating the constraints of each physical resource 5 satisfies predetermined criteria.
This makes it possible to create virtual resources with minimal uncertainty regarding the quantity and quality of their capabilities.

The resource formation unit 351C outputs data showing the correspondence between the formed virtual resource and the actual resource 5 that constitutes the virtual resource as resource relationship data 359A, and stores it in the resource relationship data storage means 359.
Figure 15 is a table showing an example of resource-related data 359A. The table in Figure 15 stores the following data in each column.
The first column stores identifier data that identifies each virtual resource created by the resource formation unit 351C.
The second column stores the identifier data for the physical resource 5 that constitutes the virtual resource.
The third and fourth columns store the start and end dates and times to which each entity resource 5 is associated as a component of each virtual resource.

The table in Figure 15 stores the following data in each row.
The first line indicates that the virtual resource ID "VR001" is associated with the physical resource "AR001" as a component, and that the period during which the physical resource "AR001" is a component is from "2021/01/01 13:24" to "2021/01/01 14:43".
- The virtual resource "VR002" in the second line is associated with the physical resource 5 of "AR002" as a component.
As shown in the third line, the virtual resource "VR002" is associated with "AR003" as a component, but at a different time point than "AR002".
In this manner, the resource formation unit 351C forms a virtual resource such that multiple physical resources 5 become constituent elements of a single virtual resource, either in the same or different time periods.

Furthermore, the physical resource "AR003" that constitutes the virtual resource "VR002" is also physical resource 5 that constitutes the virtual resource "VR005" shown in row 7. And as shown in the periods in columns 3 and 4, "AR003" is also simultaneously configured in multiple virtual resources, "VR002" and "VR005".
Here, the virtual resource "VR002" is a virtual resource created for the purpose of providing the requested resource "power transport resource" shown in the second line of the resource request data in Figure 6, and "VR005" is a virtual resource created for the purpose of providing the "cargo transport resource" shown in the fifth line of the resource request data in Figure 6.

Here, the physical resource "AR003" is named "Customer Bus" as shown in the third row of the physical resource constraint data 452 in Figure 8, and its operations are "storage and discharge, movement, and cargo transport." In other words, "AR003" is a physical resource 5 that, in the movement state, can simultaneously provide capabilities as two virtual resources: "power transport capacity using rechargeable batteries" and "cargo transport capacity using passenger space."
Therefore, the resource formation unit 351C indicates that it simultaneously configures the physical resource 5 of "AR003," which has data indicating both "power transport capacity" and "cargo transport capacity" at the same time, into "VR002" and "VR005."
This concludes the explanation of the virtual resource capacity conversion unit 351.

Figure 16 is a data flow diagram of the capacity prediction unit 352. The capacity prediction unit 352 receives the virtual capacity data output by the virtual resource capacity conversion unit 351 and the factor data 453A, and outputs a predicted value of the virtual capacity data for a predetermined future date and time.

Specifically, the model identification unit 352A first receives the virtual capability data output by the virtual resource capability conversion unit 351 and the factor data 453A, and identifies a predictive model that takes the values of each factor included in the factor data 453A as input and outputs the values of the virtual capability data. A known method may be applied to identify the predictive model. A known method is, for example, one of the following methods.
- Methods that assume linearity, such as linear regression models like multiple regression models or generalized linear models like logistic regression. - Methods that assume autoregression, such as ARX (AutoRegressive with Exogenous) models. - Methods that utilize reduction estimators such as Ridge regression, Lasso regression, and ElasticNet. - Methods that utilize dimensionality degeneraters such as partial least squares and principal component regression. - Nonlinear models using polynomials, or regression models called nonparametric, such as support vector regression, regression trees, Gaussian process regression, and neural networks. - In addition to inductive or interpolative methods such as regression models, deductive or extrapolative methods such as agent simulation are also acceptable.

Furthermore, when the model identification unit 352A receives input of control plan data for the physical resource 5 created by the control planning unit 354, which has received a start signal from the capacity allocation unit 353, it also uses the control plan data for the physical resource 5 in the prediction model identification process. For example, consider a scenario where the initial operation state at time T of the physical resource "AR00X", which has a control acceptance status of "acceptable", is "not operating", and therefore the value of the virtual capacity data is initially zero.
In this scenario, if the control planning unit 354 generates control planning data that changes the operation of "AR00X" at time T to "operate", the model identification unit 352A changes the value of the virtual capability data of the actual resource "AR00X" at time T to a value greater than zero, and then executes the prediction model identification process. The method for changing the value of the virtual capability data is, for example, the following method.
- A method to change the virtual capability data value to the same value as the value of the virtual capability data at the time immediately preceding time T. - A method to change it to a predetermined value. - A method to change the time and factor data 453A value to the same value or the average value of a similar past date and time.

The prediction value calculation unit 352B calculates predicted result data for virtual capability data values by inputting observed or predicted values of factor data 453A for the target date and time to the prediction model output by the model identification unit 352A. The form of the calculated prediction result data includes expected values, predicted values of quantiles such as the 25th percentile, and interval estimates such as confidence intervals and prediction intervals. The calculated prediction result data is output to the capability allocation unit 353.

If control data indicating an instruction to return the prediction result data is input from the virtual resource capacity conversion unit 351, the prediction result data is output to the virtual resource capacity conversion unit 351, not to the capacity allocation unit 353. Upon receiving the prediction result data, the virtual resource capacity conversion unit 351 modifies the configuration of the physical resource 5 associated with the virtual resource using the prediction result data, as described in the resource formation unit 351C. If there is no longer any input of control data indicating an instruction to return the prediction result data from the virtual resource capacity conversion unit 351, the prediction result data is output to the capacity allocation unit 353.
This concludes the operation of the capability prediction unit 352.

Figure 17 is a data flow diagram of the capacity allocation unit 353. The capacity allocation unit 353 receives data indicating the resource request from the resource operation control device 10 and outputs resource request data to the virtual resource capacity conversion unit 351. Subsequently, the capacity allocation unit 353 receives prediction result data for virtual capacity data from the capacity prediction unit 352 and outputs data indicating that the virtual resources have been set for use in the operation control of the resource operation control device 10.

Specifically, the resource request data generation unit 353A first acquires data indicating the resource request from the resource operation control device 10 and generates resource request data as shown in Figure 6. The generated resource request data is output to the virtual resource capacity conversion unit 351. Subsequently, as explained above, the virtual resource capacity conversion unit 351 outputs virtual capacity data, and then the capacity prediction unit 352 outputs the predicted result data for the virtual capacity data.

Next, the request satisfaction evaluation unit 353B receives the predicted result data of virtual capacity data output by the capacity prediction unit 352 and the resource request content data output by the resource request data generation unit 353A as input, and determines whether the predicted result data of virtual capacity data satisfies the resource request content. For example, the resource request content for request ID "R0001" shown in the first row of Figure 6 is a request period of "2021/01/01 00:00 to 2021/12/31 23:59", a request amount of "supply amount of 100MW or more per hour", and a request location of "supply to location A". The evaluation unit 353B then determines whether the predicted result data of virtual capacity data output by the capacity prediction unit 352 satisfies each request. If it determines that the requests are satisfied, the request satisfaction evaluation unit 353B generates request resource correspondence data (Figure 18) that shows the correspondence between the resource requests received from each resource operation control device 10 and the virtual resources. The requested resource correspondence data is, for example, the data that associates the virtual resource "VR001" in Figure 20 with the request ID "R0001".

Figure 18 is a table showing an example of requested resource correspondence data. The table in Figure 18 stores the following data in each column.
The first column stores data indicating the identifier of the virtual resource formed in the virtual resource capability conversion unit 351.
The second and third columns contain data indicating the start and end dates and times when each virtual resource becomes available.
The fourth column contains data indicating whether or not the virtual resource is eligible for control.
The fifth column stores data indicating the identifier of the request from the resource operation control device 10, which has been set by the request satisfaction evaluation unit 353B to provide each virtual resource.
The sixth column stores data indicating the characteristics of the virtual resource. In the example in Figure 18, it shows the response speed, which is the time from receiving the control signal to reaching a predetermined control value.

The first row of the table in Figure 18 indicates that the virtual resource ID "VR001" is available for the period from "2021/01/01 00:00 to 2021/12/31 23:59" and that control acceptance is "not possible". This virtual resource has been determined to satisfy the request for request ID "R0001" as a result of the determination process of the request satisfaction evaluation unit 353B, and therefore, as shown in the fifth column, it is set to be associated with the corresponding request "R0001".
The seventh row of the table in Figure 18 indicates that the virtual resource ID "VR007" is not associated with any response request, meaning it is an idle virtual resource.

As illustrated in other rows of Figure 18, the request satisfaction evaluation unit 353B determines whether the predicted result data for each virtual capability data satisfies each resource request. If it does, it associates and sets the ID of each resource request data. The set request resource correspondence data is transmitted to the resource operation control device 10. The resource operation control device 10 determines whether to operate the received virtual resource. If it decides to operate it, it sends signal data to the resource virtualization device 3 to execute operation control. The resource virtualization device 3 then, if the virtual resource is controllable and requires control, sends a control signal to the actual resource 5 constituting the virtual resource via the control planning unit 354.

Furthermore, if the request satisfaction evaluation unit 353B determines that the predicted result data of the virtual capacity data does not satisfy the resource requirements indicated in the resource request content data, and if the virtual resource is a controllable virtual resource, it outputs data indicating the details of the satisfaction violation and data indicating an instruction to create a control plan for the virtual resource to the control planning unit 354. Upon receiving the data indicating an instruction to create a control plan, the control planning unit 354 generates controllable physical resources 5 among the physical resources 5 constituting the virtual resource in order to resolve the satisfaction violation. The created control plan data is input to the capacity prediction unit 352 as described above, and the capacity prediction unit 352 recalculates the predicted result data of the virtual capacity data. Then, using the recalculated predicted result data, the request satisfaction evaluation unit 353B performs the determination again. The above operations are repeated until the request satisfaction evaluation unit 353B determines that the resource request content is satisfied or until a predetermined number of times is exceeded.

Furthermore, if the aforementioned operations exceed a predetermined number of times, or if the virtual resource that the request content is determined not to be satisfied is an uncontrollable virtual resource, the request satisfaction evaluation unit 353B outputs data indicating the content of the satisfaction violation and data indicating an instruction to create a control plan for the virtual resource to the control planning unit 354. Upon receiving the data indicating an instruction to change the configuration of the physical resource 5, the virtual resource capacity conversion unit 351 modifies the physical resource 5 that constitutes the virtual resource to resolve the request violation. The virtual capacity data with the modified configuration of the physical resource 5 is then input to the capacity prediction unit 352 to recalculate the predicted result data for the virtual capacity data, and the request satisfaction evaluation unit 353B makes another determination using the recalculated predicted result data. The above operations are repeated until the request satisfaction evaluation unit 353B determines that the resource request content is satisfied or until a predetermined number of times is exceeded.
This concludes the operation of the capacity allocation unit 353.

The data flow and processing operation of the control planning unit 354 will be explained.
The control planning unit 354 starts operation upon receiving data from the capacity allocation unit 353 indicating support for the generation of a control plan. The control planning unit 354 receives data from the capacity allocation unit 353 indicating the identifier of the virtual resource to be used for control plan generation, data indicating the content of the resource request content satisfaction violation, resource relationship data 359A, and physical resource constraint data 452A, and outputs control plan data for the physical resource 5.

Specifically, the control planning unit 354 first receives the identifier of the virtual resource to be used for control plan generation and resource relationship data 359A from the capacity allocation unit 353. Then, using the identifier of the virtual resource to be used for control plan generation as a key, it obtains the identifiers of the physical resources 5 that constitute the virtual resource from the resource relationship data 359A. Next, the control planning unit 354 receives the physical resource constraint data 452A and obtains data indicating the constraint conditions of each physical resource 5 from the physical resource constraint data 452A using the identifiers of the physical resources 5 obtained in the above operation as a key.
Then, from the constraints of the physical resources 5, the control plan unit 354 extracts the physical resources 5 for which control is possible. The control plan unit 354 then generates and outputs a control plan for each of the extracted physical resources 5 so as to resolve any violations of the resource request content output by the capacity allocation unit 353.

Let's explain with a specific example. We will explain with the example where the virtual resource to be used for control plan generation, input from the capacity allocation unit 353, is "VR002", and the data indicating the content of the sufficiency violation is "insufficient predicted value of the data indicating capacity at time 17:00".
First, the control planning unit 354 obtains "AR002" and "AR003," which are identifiers of the physical resources 5 that constitute the virtual resource "VR002," from the resource relationship data 359A shown in Figure 15. Next, the control planning unit 354 obtains data showing the constraint conditions of the physical resources "AR002" and "AR003" from the physical resource constraint data 452A shown in Figure 8, and extracts "AR002," which is the physical resource 5 for which the controllability of the constraint conditions is "possible."

Here, the actual resource "AR002" shown in the second row of resource relationship data 359A in Figure 15 has an operation termination date and time of "2021/01/01 16:21", indicating that it was not operational at the time "17:00" shown in the data indicating the content of the sufficiency violation.
On the other hand, the operational time for the entity resource "AR002" shown in the second row of the entity resource constraint data 452A in Figure 8 is "09:00 to 23:00", and operation at "17:00" as shown in the data indicating the content of the satisfaction violation satisfies the constraint for the entity resource "AR002".

Therefore, the control planning unit 354 generates control plan data to operate the physical resource "AR002" at "time 17:00" as indicated in the data indicating the content of the sufficiency violation, in order to resolve the "insufficient predicted value of the data indicating capacity" as indicated in the data indicating the content of the sufficiency violation.
The generated control plan data is input to the capacity prediction unit 352. The operations described above are then performed. Furthermore, when the resource operation control device 10 inputs data to the resource virtualization device 3 indicating the execution of operation control of a virtual resource, the control plan unit 354 transmits a control signal to the control device of the physical resource 5 based on the aforementioned control plan data.

With this, the operation of the control planning unit 354 is completed, and simultaneously, the computational processing of the resource virtualization system 2 in this embodiment is finished.

In the above embodiment, the virtual resource capability conversion unit 351 was described as determining the physical resources 5 that constitute the virtual resources based on the prediction result data of the virtual capability data output by the capability prediction unit 352. However, the determination may not be limited to this description, and may be made according to the state of the virtual capability data from the past to the latest date and time.
For example, when the time-series data of the virtual capability data of each physical resource 5 from the past to the latest date and time is aggregated, the time-series data representing the aggregated capability of the virtual resource may be configured to be stationary data. By configuring it in this way, the stationarity of the virtual capability data is ensured, and the prediction accuracy of the virtual capability data in the capability prediction unit 352 is improved or stabilized.

Furthermore, the determination may be based not only on virtual capability data, but also on data indicating the attributes of virtual resources. For example, the controllability conditions described in the physical resource constraint data 452A may be referenced, and the system may be composed of only physical resources 5 that are "controllable" or only physical resources 5 that are "uncontrollable".
This configuration allows for the management of virtual resources by separating the certainty and uncertainty of the quantity and quality of the virtual resources' capabilities. Alternatively, the configuration may be made by referring to the "cost" constraints of each resource shown in the physical resource constraint data 452A, so as to minimize the sum of the costs of each physical resource 5 that constitute the virtual resource. This configuration makes it possible to minimize the costs associated with the operation of virtual resources.

In the above embodiment, the capacity allocation unit 353 was described as always corresponding to one of the resource requests. However, the description is not limited to this, and a virtual resource may exist even if there is no corresponding resource request.
For example, a virtual resource that satisfies the currently existing resource request or the past resource request can be formed using the virtual resource capacity conversion unit 351, the capacity prediction unit 352, and the control planning unit 354, although the capacity allocation unit 353 may set the resource request to be "none". This makes it possible to immediately provide a virtual resource when a similar resource request is newly input from the resource operation control device 10, thereby improving the control response of the resource operation control device 10.

In the above embodiment, the control planning unit 354 was described as generating a control plan for a physical resource. However, it may also generate a control plan for a virtual resource.
For example, the control planning unit 354 starts operation upon receiving control plan generation instruction data from the capacity allocation unit 353.
The control planning unit 354 extracts virtual resources from the requested resource correspondence data shown in Figure 18 that are not allocated to the request and are not scheduled to be operational, or virtual resources that have already been allocated and can simultaneously satisfy the request currently being satisfied. The control planning unit 354 then incorporates the extracted virtual resources into the virtual resources corresponding to the request currently being satisfied.
This allows for improved utilization of already configured virtual resources while also increasing the likelihood of fulfilling requests.

In the above embodiment, the capability prediction unit 352 was described as performing prediction processing using the virtual capability data output by the virtual resource capability conversion unit 351 as is. However, the capability prediction unit 352 may separate the virtual capability data into virtual capability data corresponding to controllable physical resources and virtual capability data corresponding to uncontrollable physical resources, and perform prediction processing using the virtual capability data corresponding to uncontrollable physical resources.
In this case, the predicted virtual capacity data corresponding to the controllable physical resources is calculated as a planned control amount based on the control plan for the physical resources generated in the control planning unit 354, and is added to the predicted result data of the virtual capacity data corresponding to the uncontrollable physical resources to calculate the final predicted result data.
This allows us to prevent a decrease in the modeling accuracy of prediction models for virtual capability data by pre-separating virtual capability data that corresponds to controllable physical resources.

Figure 21 is a hardware configuration diagram of resource virtualization system 2.
Each device in the resource virtualization system 2 is configured as a computer 900 having a CPU 901, RAM 902, ROM 903, HDD 904, communication I/F 905, input/output I/F 906, and media I/F 907.
The communication interface 905 is connected to an external communication device 915. The input/output interface 906 is connected to an input/output device 916. The media interface 907 reads and writes data to the recording medium 917. Furthermore, the CPU 901 controls each processing unit by executing a program (also called an application or app) loaded into the RAM 902. This program can also be distributed via a communication line or by recording it on a recording medium 917 such as a CD-ROM and distributing it thereafter.

The embodiments of the present invention have been described above, but the present invention is not limited to the embodiments described above, and various modifications can be made without departing from the scope of the claims. For example, the embodiments described above illustrate the present invention in detail, and it is not necessary to have all the configurations described. Furthermore, it is possible to add configurations from other embodiments to the present invention. In addition, some parts of the configuration can be added, deleted, or replaced.

Furthermore, it is possible to add, delete, or replace some of the configurations in each embodiment. In addition, some or all of the above configurations, functions, processing units, processing means, etc., may be implemented in hardware, for example, by designing them as integrated circuits.
Furthermore, each of the aforementioned configurations and functions may be implemented in software by the processor interpreting and executing programs that realize each of these functions.

The information such as programs, tables, and files that implement each function can be stored in memory, storage devices such as hard disks and SSDs (Solid State Drives), or recording media such as IC (Integrated Circuit) cards, SD cards, and DVDs (Digital Versatile Discs). Cloud storage can also be utilized.
Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and do not necessarily represent all control lines and information lines in the actual product. In practice, it can be assumed that almost all components are interconnected. In addition, the communication means connecting each device is not limited to wireless LAN, but may be changed to wired LAN or other communication methods.

1. Resource operation management system 2. Resource virtualization system (resource management device)
3. Resource virtualization device 4. Data management device 5. Physical resources 6. Measurement device 7. Monitoring and control device 8. Information input/output terminal 9. Information distribution device 10. Resource operation control device 11. Communication path 12. Controlled equipment 31. CPU
32 Input device 33 Output device 34 Communication device 35 Storage device 41 CPU
42 Input device 43 Output device 44 Communication device 45 Storage device 351 Virtual resource capacity conversion unit 352 Capacity prediction unit 353 Capacity allocation unit 354 Control planning unit 451A Physical resource measurement data

Claims (14)

A virtual resource capability conversion unit creates virtual capability data indicating the capabilities that the physical resources provide as virtual resources, based on virtualization logic that virtualizes the physical resources, from physical resource measurement data obtained by measuring physical resources, which are equipment related to energy, and constructs virtual resources that provide that virtual capability data.
The system includes a capability allocation unit that assigns the virtual resources constructed by the virtual resource capability conversion unit to a resource operation management system, thereby causing the resource operation management system to manage the actual resources.
The virtualization logic is prepared individually according to the resource request data indicating the capability state of the physical resource requested by the resource operation management system.
The resource management device is characterized in that the virtualization logic converts the physical resource measurement data, which shows the movement history associated with the operation of the physical resource, into virtual capacity data, which shows the cargo transport capacity of the physical resource . A virtual resource capability conversion unit creates virtual capability data indicating the capabilities that the physical resources provide as virtual resources, based on virtualization logic that virtualizes the physical resources, from physical resource measurement data obtained by measuring physical resources, which are equipment related to energy, and constructs virtual resources that provide that virtual capability data.
The system includes a capability allocation unit that assigns the virtual resources constructed by the virtual resource capability conversion unit to a resource operation management system, thereby causing the resource operation management system to manage the actual resources.
The virtualization logic is prepared individually according to the resource request data indicating the capability state of the physical resource requested by the resource operation management system.
Resource management device characterized in that the virtualization logic converts the physical resource measurement data, which indicates the positive or negative energy supply capacity of the physical resource, and the physical resource measurement data, which indicates the cargo transport capacity of the physical resource, into virtual capacity data indicating the energy transport capacity of the physical resource . The resource management device further includes a capacity prediction unit that calculates predicted values, which are future values of the virtual capacity data.
The resource management device according to claim 1 or 2, characterized in that the capacity allocation unit allocates the virtual resources constructed by the virtual resource capacity conversion unit to the resource operation management system based on the predicted values calculated by the capacity prediction unit. The resource management device further includes a control planning unit,
The control planning unit generates a control plan for the physical resources that constitute the virtual resources allocated by the capacity allocation unit, and causes the resource operation management systems to which the resources are allocated to control the physical resources based on that control plan.
The resource management device according to claim 1 or claim 2 , characterized in that the virtual resource capacity conversion unit modifies the virtual capacity data based on the control plan generated by the control planning unit. The resource management device according to claim 1 or 2, characterized in that the virtual resource capacity conversion unit constructs the virtual resource by combining a plurality of virtualization logics stored in advance to satisfy the capacity mode of the physical resource required by the resource operation management system. The virtual resource capacity conversion unit is characterized by constructing the virtual resource based on the predicted value of the virtual capacity data calculated by the capacity prediction unit.
The resource management device according to claim 3 . The virtual resource capability conversion unit is characterized in that, in the process of combining a plurality of virtualization logics stored in advance, it determines the combination of physical resources that constitute the virtual resource such that the information indicating the attributes of the virtual resource, generated based on information indicating the constraints of each physical resource, satisfies predetermined criteria.
The resource management device according to claim 5 . The control planning unit is characterized by calculating the time change of the virtual capacity data related to energy in conjunction with the operation of the physical resource, and reflecting that time change in the virtual capacity data created by the virtual resource capacity conversion unit.
The resource management device according to claim 4 . The resource management device according to claim 1 or claim 2 , characterized in that the virtualization logic converts the physical quantity of the actual resource measurement data into virtual capacity data that represents a different type of physical quantity from the physical quantity of the actual resource measurement data. The resource management device according to claim 1 or claim 2 , characterized in that the virtualization logic generates new virtual capability data from a combination of two or more types of virtual capability data. The resource management device according to claim 1 or claim 2, characterized in that the virtualization logic converts the physical resource measurement data, which shows the remaining amount of energy associated with the operation of the physical resource, into virtual capacity data, which shows the positive or negative energy supply capacity of the physical resource . The resource management device has a virtual resource capacity conversion unit and a capacity allocation unit.
The virtual resource capability conversion unit creates virtual capability data indicating the capability that the physical resource provides as a virtual resource, based on virtualization logic that virtualizes the physical resource, from physical resource measurement data obtained by measuring the physical resource, which is equipment related to energy, and constructs a virtual resource that provides that virtual capability data.
The capacity allocation unit assigns the virtual resources constructed by the virtual resource capacity conversion unit to the resource operation management system, thereby allowing the resource operation management system to manage the actual resources.
The virtualization logic is prepared individually according to the resource request data indicating the capability state of the physical resource requested by the resource operation management system.
The resource management method is characterized in that the virtualization logic converts the physical resource measurement data, which shows the movement history associated with the operation of the physical resource, into virtual capacity data, which shows the cargo transport capacity of the physical resource . The resource management device has a virtual resource capacity conversion unit and a capacity allocation unit.
The virtual resource capability conversion unit creates virtual capability data indicating the capability that the physical resource provides as a virtual resource, based on virtualization logic that virtualizes the physical resource, from physical resource measurement data obtained by measuring the physical resource, which is equipment related to energy, and constructs a virtual resource that provides that virtual capability data.
The capacity allocation unit assigns the virtual resources constructed by the virtual resource capacity conversion unit to the resource operation management system, thereby allowing the resource operation management system to manage the actual resources.
The virtualization logic is prepared individually according to the resource request data indicating the capability state of the physical resource requested by the resource operation management system.
Resource management method characterized in that the virtualization logic converts the physical resource measurement data, which indicates the positive or negative energy supply capacity of the physical resource, and the physical resource measurement data, which indicates the cargo transport capacity of the physical resource, into virtual capacity data indicating the energy transport capacity of the physical resource . A resource management program for causing a computer to function as a resource management device according to claim 1 or claim 2 .