Grid cooperation / distributed energy systems

Abstract

To realize a system which appropriately controls an apparatus which is associated with demand and supply of energy.SOLUTION: Each of a plurality of local devices 16 installed in a plurality of areas 12 includes a control server 20. A cloud device 22 includes a database 24 and a management server 26. The control server 20 transmits a plurality of pieces of energy data transmitted from a plurality of apparatuses 14 to the cloud device 22. The database 24 stores the energy data transmitted from a plurality of local devices 16. The management server 26 generates a plurality of prediction models corresponding to the plurality of areas, on the basis of the energy data stored in the database 24. The control server 20 derives a predicted value related to demand or supply of energy, on the basis of the prediction model provided from the cloud device 22, and controls the plurality of apparatuses 14 on the basis of the predicted value.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to data processing technology, and in particular to a grid cooperation / distributed energy system and information platform. [Background technology]

[0002] In order to achieve the reduction target of CO2 emissions, zero emissions in the power source field is almost essential, and the spread of distributed grids in which renewable energy accounts for the majority of the power source is important. However, renewable energy is a time-varying power source, and if its proportion increases, it will become difficult to control the power supply and demand balance that power companies have performed up until now. Therefore, a system is needed that can make detailed predictions of the demand and supply of power that fluctuates in real time within a distributed grid and control the devices related to the supply and demand of power (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-160932 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present inventors have recognized that a system configuration for appropriately controlling devices related to the supply and demand of energy, such as electricity, has not been adequately proposed up to now.

[0005] The present disclosure has been made based on the inventor's recognition of the above problems, and one objective is to realize a system that appropriately controls devices related to energy supply and demand. [Means for solving the problem]

[0006] In order to solve the above problem, an energy system according to an embodiment of the present disclosure includes a plurality of local devices installed in a plurality of areas, and a cloud device connected to the plurality of local devices via the Internet. Each of the plurality of local devices includes a control server that transmits a plurality of energy data related to energy demand or supply transmitted from a plurality of devices in the area in which the local device is installed to the cloud device. The cloud device includes a database that stores the plurality of energy data transmitted from each of the plurality of local devices, and a management server that uses the energy data stored in the database to generate, or requests an external device to generate, a plurality of prediction models related to energy demand or supply corresponding to the plurality of areas, and provides the prediction models corresponding to each area to the local devices installed in each area. The control server of each local device uses the prediction model provided by the cloud device to derive a prediction value related to energy demand or supply in the area in which the local device is installed, and controls a plurality of devices in the area in which the local device is installed based on the prediction value.

[0007] Another aspect of the present disclosure is an information platform. The information platform includes a plurality of local devices installed in a plurality of areas, and a cloud device connected to the plurality of local devices via the Internet. Each of the plurality of local devices includes a control server that transmits a plurality of energy data related to energy demand or supply transmitted from a plurality of devices in the area in which the local device is installed to the cloud device. The cloud device includes a database that stores the plurality of energy data transmitted from each of the plurality of local devices, and a management server that provides information based on the energy data stored in the database to an external device that executes information processing using the information. The energy data stored in the database of the cloud device is in a format in which a management tag is added to a value related to energy demand or supply, and the management server acquires the result of information processing by the external device in the format in which the management tag is added, and stores the result in the database.

[0008] Any combination of the above components and any conversion of the expressions of the present disclosure between an apparatus, a method, a program, a recording medium storing a program, etc. are also valid as aspects of the present disclosure. Effect of the Invention

[0009] According to the present disclosure, it is possible to realize a system that appropriately controls devices related to energy supply and demand. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing a configuration of an energy system according to an embodiment. [Diagram 2] FIG. 2 is a block diagram showing functional blocks of the control server. [Figure 3A] FIG. 13 is a diagram showing an example of structuring tagged energy data. [Figure 3B] FIG. 3B is a diagram showing an example of JSON data based on the definition in FIG. 3A. [Figure 3C] FIG. 13 is a diagram showing an example of structuring tagged energy data. [Figure 3D] FIG. 3D is a diagram showing an example of JSON data based on the definition of FIG. 3C. [Figure 3E] FIG. 3D is a diagram showing an example of JSON data based on the definition of FIG. 3C. [Figure 3F] FIG. 3D is a diagram showing an example of JSON data based on the definition of FIG. 3C. [Figure 4] 2 is a block diagram showing functional blocks of a management server. FIG. [Diagram 5] FIG. 2 is a sequence diagram showing the operation of the energy system of the embodiment. [Figure 6] FIG. 2 is a sequence diagram showing the operation of the energy system of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] FIG. 1 shows the configuration of an energy system 10 according to an embodiment. The energy system 10 is an information processing system (in other words, a computer system) that controls a plurality of devices installed in distributed areas and related to the supply and demand of energy. "Energy" in the embodiment can be said to be an energy resource, and is a source of power necessary for industry, transportation, consumer life, etc., and includes electricity, gas, heat, light, etc.

[0012] The energy system 10 includes a plurality of local devices 16 installed in a plurality of areas 12, and a cloud device 22 connected to the local devices 16 via the Internet 29. The cloud device 22 may be a virtual information processing device provided by a cloud computing service.

[0013] Each of the multiple areas 12 is also called a grid, and is an area that serves as a unit for managing energy supply and demand. For example, each area 12 is an area where the grid's contracted power ranges from 10,000 kW (kilowatts) to tens of thousands of kW. In each area 12, multiple devices 14 related to energy supply and demand are installed. The areas 12 may be provided in various organizational units such as regions, buildings, companies, and homes. The multiple devices 14 periodically transmit multiple energy data related to energy demand or supply to a local device 16 (a messaging server 18 described below).

[0014] 1, a power meter 14a, a solar cell device 14b, a storage battery device 14c, an air conditioning system 14d, a Building Energy Management System (BEMS) 14e, a weather measurement meter 14f, and a human activity measurement meter 14g are installed as multiple devices 14. The devices 14 installed in each area 12 may be different.

[0015] Power meter 14a measures the amount of power received from the power company and transmits energy data indicating the amount of power received every second to local device 16. Solar cell device 14b measures the amount of power generated by the solar cell and transmits energy data indicating the amount of power generated every second to local device 16. Although not shown, a fuel cell device that measures the amount of power generated by a fuel cell and transmits energy data indicating the amount of power generated every second to local device 16 may be installed in area 12.

[0016] The storage battery device 14c measures the charge and discharge amounts of the storage battery and transmits energy data indicating the charge or discharge amounts every second to the local device 16. The air conditioning system 14d includes a plurality of air conditioners and an air conditioning controller that controls the air conditioners. The air conditioning system 14d (air conditioning controller) transmits energy data indicating the operating state (starting, stopping, etc.), set temperature, amount of heat, etc. of each air conditioner to the local device 16 every minute.

[0017] The BEMS 14e transmits energy data (operating state, etc.) related to power supply equipment (e.g., gas engine generators) and power demand equipment installed in the managed building, and energy data indicating the measurement results of each of a plurality of sensors, every minute to the local device 16. The weather measurement meter 14f periodically measures meteorological conditions (e.g., weather, temperature, humidity, etc.), and transmits energy data indicating the measurement results (hereinafter also referred to as "weather-related data") to the local device 16 at predetermined time intervals.

[0018] The human activity measuring meter 14g periodically measures values ​​related to human activity (for example, the number of people present at a specific point, the number of people moving between specific points, etc.) and transmits energy data indicating the measurement results (hereinafter also referred to as "human activity related data") at predetermined time intervals to the local device 16. Although not shown, a management device for charging facilities for electric vehicles and fuel cell vehicles may be installed as the device 14 in the area 12.

[0019] Each of the multiple local devices 16 installed in the multiple areas 12 includes a messaging server 18 and a control server 20. There are no restrictions on the physical configuration of the local devices 16 installed in each area 12. For example, the local device 16 may be a single information processing device including the functions of both the messaging server 18 and the control server 20, or may include multiple information processing devices corresponding to each of the messaging server 18 and the control server 20. The messaging server 18 and the control server 20 in the embodiment are processes on a computer, in other words, instances running on a computer.

[0020] The messaging server 18 transfers energy data transmitted from the device 14 to the control server 20, and also transfers command data transmitted from the control server 20 to the device 14. The messaging server 18 temporarily holds the energy data transmitted from the device 14 in memory (typically a volatile memory such as a DRAM) without going through storage (in other words, an auxiliary storage device), and transfers the energy data transmitted from the device 14 to the control server 20 at high speed. In addition, the messaging server 18 temporarily holds command data transmitted from the control server 20 in memory without going through storage, and transfers the command data transmitted from the control server 20 to the device 14 at high speed.

[0021] The messaging server 18 acquires a plurality of energy data from a plurality of devices 14 in the area 12 (hereinafter also referred to as "local area") in which its local device 16 is installed. The messaging server 18 can be said to be an MQTT (Message Queuing Telemetry Transport) server, and an IEEE1888 gateway. The messaging server 18 acquires energy data transmitted from the plurality of devices 14 using various protocols (e.g., FTP (File Transfer Protocol), a unique TCP / IP protocol, etc.).

[0022] Furthermore, the messaging server 18 converts the energy data of various protocols transmitted from each device 14 into energy data of a common protocol independent of the protocol supported by each device 14. The messaging server 18 of the embodiment supports both MQTT and IEEE1888 as the common protocol. The messaging server 18 transmits and receives data that requires high-speed processing by MQTT communication, while transmitting and receiving data that does not require high-speed processing by IEEE1888 communication. For example, the messaging server 18 performs MQTT communication with the control server 20 to transfer energy data and command data at high speed.

[0023] The control server 20 controls the operation of the multiple devices 14 in the local area. The control server 20 transmits the multiple energy data acquired by the messaging server 18, i.e., the multiple energy data transmitted from the multiple devices 14 and forwarded by the messaging server 18, to the cloud device 22.

[0024] 2 is a block diagram showing the functional blocks of the control server 20. Each block shown in the block diagram of the present disclosure can be realized in terms of hardware by elements and mechanical devices such as a computer's CPU and memory, and in terms of software by a computer program, etc., but here, the functional blocks realized by the cooperation of these are depicted. Those skilled in the art will understand that these functional blocks can be realized in various ways by combining hardware and software.

[0025] The control server 20 includes a control unit 30, a storage unit 32, and a communication unit 34. The control unit 30 executes various data processing operations. The storage unit 32 stores data referenced or updated by the control unit 30. The communication unit 34 communicates with an external device according to a predetermined communication protocol. In the embodiment, the control unit 30 transmits and receives data to and from the messaging server 18 by MQTT communication via the communication unit 34. The control unit 30 also transmits and receives data to and from the cloud device 22 (a management server 26 described below) by HTTP communication via the communication unit 34.

[0026] The storage unit 32 includes an energy data storage unit 36 ​​and a model storage unit 38. The energy data storage unit 36 ​​stores tagged energy data, which will be described later. The model storage unit 38 stores prediction model data provided from the cloud device 22. The control unit 30 uses the prediction model provided from the cloud device 22 to derive a prediction value regarding energy demand or supply in the local area, and controls a plurality of devices 14 installed in the local area based on the prediction value.

[0027] The control unit 30 includes an energy data acquisition unit 40, an energy data conversion unit 42, an energy data transmission unit 44, a model acquisition unit 46, a supply and demand prediction unit 48, a command generation unit 50, and a command transmission unit 52. The processing of each functional block will be described later.

[0028] A computer program including modules corresponding to the multiple functional blocks of the control unit 30 may be stored in a computer-readable and non-transitory recording medium, and may be installed in the storage of the local device 16 via the recording medium. Alternatively, the computer program may be downloaded via a network and installed in the storage of the local device 16. The CPU of the local device 16 may load the computer program into the main memory of the local device 16 and execute it to fulfill the functions of the multiple functional blocks.

[0029] Returning to Fig. 1, the cloud device 22 includes a database 24 and a management server 26. There are no restrictions on the physical configuration of the cloud device 22. For example, the cloud device 22 may be a single information processing device including the functions of the database 24 and the management server 26, or may include multiple information processing devices corresponding to the database 24 and the management server 26, respectively. The database 24 and the management server 26 in the embodiment are assumed to be processes on a computer, in other words, instances running on a computer.

[0030] The database 24 is a NoSQL database that stores a plurality of energy data (tagged energy data in the embodiment) transmitted from each of the plurality of local devices 16 (control server 20). Specifically, the database 24 is a document-oriented database, MongoDB (registered trademark).

[0031] The tagged energy data is data in JSON (JavaScript Object Notation) ("JavaScript" is a registered trademark) format, specifically, energy data in a format in which management tags are added to values ​​related to energy demand or supply. The tag may indicate various attributes related to the value indicated by the original energy data. For example, the tag may indicate (1) attribute information related to the device 14 that transmitted the original energy data, (2) attribute information related to the area in which the device 14 is installed, or (3) the item name.

[0032] The tagged energy data will be described with reference to Figs. 3A to 3F. Fig. 3A shows an example of structuring the tagged energy data. For example, "time" and "value" are items in which the current date and time (measurement date and time) and the measurement value (power consumption, etc.) detected by the sensor are set. After "type1", various attribute information related to the sensor is set. In the tagged energy data of the embodiment, "time" and "value" are required items. In the tagged energy data of the embodiment, "time" and "value" are set at the beginning, and various attribute information is added appropriately thereafter. Note that each attribute information may be set in a hierarchical structure. As the attribute information, for example, "type1" is an item in which the name of the sensor (point name) is set. "type2" is an item in which attribute information related to weather and power reception is set. "Location" is an item in which the location information of the sensor is set. "Building" is an item in which information of the building in which the sensor is installed is set.

[0033] Fig. 3B shows an example of JSON data based on the definition in Fig. 3A. Energy data 80 (JSON data) in the figure indicates the outside air temperature detected by a specific temperature sensor at 9:00 a.m. on November 28, 2019.

[0034] In this way, the tagged energy data of the embodiment is data related to energy demand or supply that is structured based on scientific knowledge so that it is easy for humans to understand. By using the JSON format, multiple tags such as "time", "value", "type", "location", "building", etc. can be freely added to one piece of data (corresponding to one sensor, etc.), and tags reflecting a hierarchical structure can also be freely added. In addition, the designed JSON format data can be efficiently managed by a NoSQL database (MongoDB in the embodiment). Note that when a new data item is added to the energy data, it can be handled by adding the new data item to a setting file (for example, a table in which "type", "location", "building", etc. have one-to-one correspondence).

[0035] FIG. 3C shows an example of structuring tagged energy data. This figure shows a structure in which the structure shown in FIG. 3A is divided into multiple collections. Collection 82a is a first collection including "time," "value," and "ID." Collection 82b is a second collection including "type," "location," and "Building_ID." Collection 82c is a third collection including "number of floors," "number of rooms," and "site area." Collections 82a and 82b are related by "ID," and collections 82b and 82c are related by "Building_ID."

[0036] FIG. 3D shows an example of JSON data based on the definition in FIG. 3C. JSON data 84a in FIG. 3D is the result of dividing the JSON data in FIG. 3B, and shows data corresponding to collection 82a in FIG. 3C. FIG. 3E shows an example of JSON data based on the definition in FIG. 3C. JSON data 84b in FIG. 3E is the result of dividing the JSON data in FIG. 3B, and shows data corresponding to collection 82b in FIG. 3C. FIG. 3F shows an example of JSON data based on the definition in FIG. 3C. JSON data 84c in FIG. 3F is the result of dividing the JSON data in FIG. 3B, and shows data corresponding to collection 82c in FIG. 3C.

[0037] In the database 24 (MongoDB), energy data that was expressed in one collection (in other words, one tree structure) is divided into multiple collections, so that the amount of data stored in the database 24 can be reduced. Also, the search speed can be improved.

[0038] Returning to Fig. 1, the management server 26 manages the energy demand or supply of multiple devices 14 installed in multiple areas. The management server 26 executes a process of generating prediction models corresponding to the multiple areas 12, i.e., multiple prediction models related to the energy demand or supply in the multiple areas 12, using the tagged energy data stored in the database 24. The management server 26 provides the prediction models corresponding to each area 12 to the local devices 16 (control servers 20) installed in each area 12.

[0039] The prediction model is a mathematical model that derives a predicted value related to at least one of the energy demand and supply in a specific area 12, and may be generated by a method such as machine learning or multiple regression analysis. For example, the prediction model may include a function or prediction formula that receives, as input, an energy demand actual value (or its time series data) and a supply actual value (or its time series data) indicated by the tagged energy data of the specific area 12, and outputs an energy demand forecast value and a supply forecast value in the area 12. The prediction model may also be a regression formula that uses the energy demand actual value (or its time series data) and the supply actual value (or its time series data) indicated by the tagged energy data of the specific area 12 as explanatory variables, and uses the energy demand forecast value and the energy supply forecast value in the area 12 as objective variables.

[0040] Furthermore, the management server 26 executes a process of generating a control model that estimates optimal control content for the multiple devices 14, based on a predicted value for at least one of energy demand and supply derived by the prediction model, for each of the multiple areas 12. That is, the management server 26 executes a process of generating multiple control models corresponding to the multiple areas 12. The management server 26 provides the control model corresponding to each area 12 to the local device 16 (control server 20) installed in each area 12.

[0041] Like the prediction model, the control model may also be a mathematical model generated by a method such as machine learning. For example, the control model may include a control formula that receives a predicted value output by the prediction model as an input and outputs control details for turning on / off the solar cell device 14b, for the purpose of stable power supply and high-efficiency operation (e.g., peak cut control) in a building or grid. The control model may also include a control formula that outputs control details for starting / stopping the storage battery device 14c, or control details for charging / discharging. The control model may also include a control formula that outputs control details for starting / stopping the air conditioning system 14d, or control details for temperature adjustment. The control model may also include a control formula that outputs control details for starting / stopping the equipment (such as a gas engine generator) managed by the BEMS 14e.

[0042] Furthermore, the cloud device 22 (management server 26) communicates with an external information utilization device 90 via the Internet 29. The information utilization device 90 is a device or system that executes various information processing by utilizing information based on energy data (hereinafter also referred to as "energy-related information") provided by the cloud device 22 (management server 26). For example, the information utilization device 90 includes a device that executes a system design application that utilizes energy data. Furthermore, the information utilization device 90 includes various simulators and databases, such as a social data analysis device and a climate forecast simulator.

[0043] Furthermore, the cloud device 22 (management server 26) communicates with an external power market device 92 via the Internet 29. The power market device 92 is a device or system that manages information related to the power market (hereinafter also referred to as "power market information"). The power market information may include a power trading market price that changes with time or region and its future predicted value. In other words, the power market information may include the present value of power (e.g., the value of each of kWh, kW, and ΔkW) in each of the multiple areas 12 and a predicted value of the future value of power. The power market device 92 may periodically transmit the latest power market information to the cloud device 22 at a predetermined cycle. Furthermore, the power market device 92 may transmit the latest power market information to the cloud device 22 every time a request is received from the cloud device 22.

[0044] 4 is a block diagram showing functional blocks of the management server 26. The management server 26 includes a control unit 60 and a communication unit 62. The control unit 60 executes various data processing operations. The communication unit 62 communicates with external devices according to a predetermined communication protocol. The communication unit 62 communicates with the control server 20, the database 24, and the supercomputer 28 via the communication unit 62.

[0045] The control unit 60 includes an energy data acquisition unit 64, an energy data storage unit 66, a model generation unit 68, a model provision unit 70, an energy data update unit 72, an energy-related information provision unit 74, an external processing result acquisition unit 76, an electricity market information acquisition unit 78, and an electricity market information provision unit 79. The processing of each functional block will be described later. A computer program including modules corresponding to the multiple functional blocks of the control unit 60 may be stored in a computer-readable and non-transitory recording medium, or may be installed in the storage of the cloud device 22 via the recording medium. Alternatively, the computer program may be downloaded via a network and installed in the storage of the cloud device 22. The CPU of the cloud device 22 may perform the functions of the multiple functional blocks by reading the computer program into the main memory of the cloud device 22 and executing it.

[0046] Returning to Fig. 1, the supercomputer 28 is a computer having hardware and software optimized to achieve high-speed computing power. The management server 26 of the cloud device 22 is connected to the supercomputer 28 via the Internet 29 or a LAN. The management server 26 passes the tagged energy data stored in the database 24 to the supercomputer 28 and requests the supercomputer 28 to generate multiple prediction models corresponding to multiple areas 12.

[0047] The operation of the energy system 10 having the above configuration will be described. 5 is a sequence diagram showing the operation of the energy system 10 of the embodiment. For ease of explanation, the diagram shows an example in which the cloud management server 26 executes processing related to one area 12. In reality, the cloud-side management server 26 executes processing related to multiple areas 12 in parallel.

[0048] A plurality of devices 14 installed in the area 12 periodically transmit energy data in various protocols to the messaging server 18 at predetermined time intervals (S10). The messaging server 18 transmits energy data in a common protocol (e.g., MQTT message format) based on the energy data received from the devices 14 to the control server 20 (S12).

[0049] The energy data acquiring unit 40 of the control server 20 acquires the energy data transmitted from the messaging server 18. The energy data converting unit 42 of the control server 20 converts the energy data acquired by the energy data acquiring unit 40 into tagged energy data in a format in which a management tag is added, as exemplified in Fig. 3 (S14). The energy data converting unit 42 stores the converted tagged energy data in the energy data storage unit 36 ​​(S16).

[0050] The energy data conversion unit 42 may store a correspondence relationship between the format of the energy data transmitted from each device 14 and the format of the tagged energy data. The energy data conversion unit 42 may also store a tag assignment rule for the value of each item of the energy data transmitted from each device 14. The energy data conversion unit 42 may generate tagged energy data by assigning a tag determined by the above correspondence relationship or the assignment rule to the value of each item of the energy data transmitted from each device 14. The tagged energy data may include, as transmission source information of the original energy data, identification information of the transmission source device 14 and identification information of the area in which the device is installed.

[0051] The energy data transmission unit 44 of the control server 20 transmits the tagged energy data generated by the energy data conversion unit 42 to the management server 26 (S18). The energy data acquisition unit 64 of the management server 26 acquires the tagged energy data transmitted from the control server 20. The energy data storage unit 66 of the management server 26 stores the tagged energy data acquired by the energy data acquisition unit 64 in the database 24 (S20).

[0052] The energy data update unit 72 of the management server 26 changes the structure of the energy data stored in the database 24 in response to an operation by the administrator. For example, the administrator may input an operation to the management server 26 instructing to divide the existing energy data into multiple collections. In this case, the administrator may provide a definition of the structure of each of the multiple collections and a definition of data that associates the collections with each other. In response to the administrator's instruction to divide the collections, the energy data update unit 72 may (1) divide the existing energy data stored in the database 24 into multiple collections, (2) automatically set data that associates the collections with each other, and (3) store the JSON data after division into the multiple collections in the database 24 as new energy data.

[0053] The energy-related information providing unit 74 of the management server 26 provides the information utilization device 90 with energy-related information based on the energy data stored in the database 24. The energy-related information may include the energy data itself stored in the database 24, or may include data obtained by aggregating the energy data using a predetermined algorithm and various statistical information (such as the average value of values ​​indicated by energy data at multiple points).

[0054] The energy-related information providing unit 74 may periodically transmit energy-related information to the information utilization device 90 at a predetermined cycle (for example, once an hour). Furthermore, each time the energy-related information providing unit 74 receives a request from the information utilization device 90, it may generate energy-related information in a format specified by the request and transmit it to the information utilization device 90. For example, the information utilization device 90 may specify a tag of an item to be acquired in the energy data. The energy-related information providing unit 74 may provide the information utilization device 90 with the value of an item identified by a tag specified by the information utilization device 90 (the item to which the tag is assigned) from among the values ​​of multiple items included in the energy data.

[0055] The energy data updating unit 72 may update the energy data stored in the database 24 so that the data has a data format required by the information utilization device 90. For example, the energy data updating unit 72 may add a new tag or change the structure based on the data format required by the information utilization device 90 in response to an operation by an administrator.

[0056] The information utilization device 90 transmits information processing results (e.g., prediction results and simulation results) based on the energy-related information to the cloud device 22 (management server 26). The information processing results transmitted from the information utilization device 90 to the cloud device 22 (management server 26) are data in a format (JSON format in this embodiment) to which management tags are added as shown in FIGS. 3A to 3F. The external processing result acquisition unit 76 of the management server 26 acquires the information processing results transmitted from the information utilization device 90 and stores them in the database 24. The database 24 accumulates information processing results (e.g., prediction results and simulation results) by the information utilization device 90 based on the energy-related information.

[0057] The model generation unit 68 of the management server 26 periodically executes a process of generating a prediction model and a control model for each area 12 based on the tagged energy data of each area 12 stored in the database 24. The generation interval of the prediction model and the control model for each area 12, in other words, the update interval of these models, may be, for example, two weeks. In the embodiment, the model generation unit 68 requests an external device to create a prediction model and a control model for each area 12, and causes the external device to create these models, but as a modified example, the model generation unit 68 may generate these models by itself. Furthermore, the model generation unit 68 may execute a process of generating a prediction model and a control model for each area 12 based on the information processing result (e.g., prediction result or simulation result) based on the energy-related information by the information utilization device 90 stored in the database 24 in addition to the tagged energy data of each area 12 stored in the database 24.

[0058] In the embodiment, the model generation unit 68 transmits the tagged energy data of each area 12 stored in the database 24 to the supercomputer 28, and requests the supercomputer 28 to generate a prediction model and a control model for each area 12 (S22). The supercomputer 28 generates a prediction model and a control model for each area 12 (S24), and transmits the data to the management server 26 (S26). By having the external supercomputer 28 be responsible for generating the prediction model and the control model, the load of model generation can be shifted to the supercomputer 28, which has high-speed computing power, and the load of model generation on the cloud device 22 (management server 26) can be reduced.

[0059] The model providing unit 70 of the management server 26 transmits the prediction model data and control model data for each area 12 provided by the supercomputer 28 to the control server 20 of each area 12 (S28). The model acquiring unit 46 of the control server 20 acquires the prediction model data and control model data provided by the management server 26, and stores the data in the model storage unit 38. If an existing prediction model and control model are already stored in the model storage unit 38, the model acquiring unit 46 replaces (i.e. updates) those models with the prediction model and control model newly provided by the management server 26 (S30).

[0060] Fig. 6 is also a sequence diagram showing the operation of the energy system 10 of the embodiment. The process shown in the figure is executed in parallel in a plurality of areas 12. In the embodiment, the latest tagged energy data is stored and accumulated in the energy data storage unit 36 ​​of the control server 20 at intervals of several seconds to several minutes. In addition, the prediction model and the control model stored in the model storage unit 38 of the control server 20 are updated to the latest models every two weeks.

[0061] The supply and demand prediction unit 48 of the control server 20 derives a demand prediction value and a supply prediction value of energy in the area 12 by inputting values ​​indicated by the tagged energy data stored in the energy data storage unit 36 ​​into the prediction model stored in the model storage unit 38 (S40). The command generation unit 50 of the control server 20 generates command data for controlling the operation of the multiple devices 14 in the area 12 based on the control model stored in the model storage unit 38 (S42). For example, the command generation unit 50 may input at least one of the demand prediction value and the supply prediction value derived by the supply and demand prediction unit 48 into the control model, and generate command data based on the control content of the devices 14 output from the control model.

[0062] The command transmitting unit 52 of the control server 20 transmits a set of command data for the device 14 generated by the command generating unit 50 and destination information (information indicating the device 14 to which the command data is to be transmitted) to the messaging server 18 (S44). The messaging server 18 transfers the command data transmitted from the control server 20 to the device 14 indicated by the destination information (S46). The device 14 sets its own operation based on the command data transmitted from the messaging server 18 (S48). The device 14 may start or stop an operation, or change various setting values, based on the command data.

[0063] For example, the supply and demand prediction unit 48 may derive a prediction value every minute or every second, and the command generation unit 50 may generate command data instructing the solar cell device 14b (or the fuel cell device) to start or stop. In this case, the operation mode (started state or stopped state) of the solar cell device 14b (or the fuel cell device) is controlled every minute or every second. Also, the supply and demand prediction unit 48 may derive a prediction value every minute, and the command generation unit 50 may generate command data instructing the storage battery device 14c to charge or discharge. In this case, the operation mode (charged state or discharged state) of the storage battery device 14c is controlled every second.

[0064] Also, the supply and demand prediction unit 48 may derive a prediction value every minute, and the command generation unit 50 may generate command data instructing the air conditioning system 14d (each air conditioning device) to be on, off, or to set a temperature. In this case, the operation mode of the air conditioning system 14d (each air conditioning device) is controlled every minute. Also, the supply and demand prediction unit 48 may derive a prediction value every minute, and the command generation unit 50 may generate command data instructing the start or stop of the BEMS 14e (here, a gas engine generator). In this case, the operation mode of the BEMS 14e (gas engine generator) is controlled every minute. As described above, the messaging server 18 transfers the command data to the device 14 by on-memory processing without holding the command data in storage. This allows the operation of the device 14 to be switched in a short time.

[0065] Further, the power market information acquisition unit 78 of the management server 26 of the cloud device 22 acquires the power market information transmitted from the power market device 92 periodically or on demand. In the embodiment, the power market device 92 is provided outside the energy system 10, but as a modified example, the energy system 10 may be provided with a device that independently calculates the power market information (e.g., a predicted value of the market value of electricity), and the power market information acquisition unit 78 may acquire the power market information from the device. Furthermore, the power market information acquisition unit 78 may acquire the power market information from both the external power market device 92 and the above-mentioned device inside the energy system 10. The power market information provision unit 79 of the management server 26 transmits the power market information acquired by the power market information acquisition unit 78 to the local device 16 of each area 12.

[0066] The control server 20 of the local device 16 in each area 12 may control the multiple devices 14 in the area 12 in which the local device is installed (also referred to here as the "own area") based on the electricity market information transmitted from the management server 26 of the cloud device 22, that is, may control the balance between the demand and supply of electricity in the own area. For example, the control server 20 may control the operation of electricity demanding equipment such as the air conditioning system 14d, or may control charging or discharging of the storage battery device 14c, based on the current present value and predicted future value of electricity in the own area indicated by the electricity market information.

[0067] In this case, the model generating unit 68 of the management server 26 may generate a new control model corresponding to each area 12. The new control model may estimate optimal control contents for the plurality of devices 14 based on electricity market information (e.g., predicted values ​​of the present value and future value of electricity) together with or instead of a predicted value related to at least one of the energy demand and supply derived by the prediction model. Alternatively, the model generating unit 68 may update an existing control model for each area 12 to a new control model that estimates control contents for the plurality of devices 14 by taking into account the electricity market information of the area 12. The model providing unit 70 of the management server 26 may transmit the new control model corresponding to each area 12 to the local device 16 of each area 12 and store it in the model storage unit 38.

[0068] For example, in the energy system 10, the cloud device 22 and the supercomputer 28 can create highly accurate prediction models and control models by utilizing information obtained from the electricity market device 92 and the information utilization device 90 in addition to the multiple local devices 16. Then, in the energy system 10, the highly accurate prediction models and control models are used to control the local devices and equipment 14 in each area 12. This enables optimal control based on the value of electricity that varies depending on the time and area (such as the value of Δkw, the value of kw, the value of kwh, and the value of CO2 emission reductions, etc.), which was previously difficult.

[0069] According to the energy system 10 of the embodiment, the database 24 of the cloud device 22 centrally manages energy data relating to the multiple areas 12, and the management server 26 of the cloud device 22 collectively generates prediction models and control models for each area 12 for controlling the devices 14 in each area 12. Because a variety of energy data relating to the multiple areas 12 is accumulated in the database 24 of the cloud device 22, it is possible to generate highly accurate prediction models and control models. Furthermore, although a large amount of calculation is required to generate the prediction models and control models, by performing the generation process of the prediction models and control models on the cloud side, it is possible to realize a system in which various companies, households, etc. can easily participate.

[0070] Moreover, the database 24 of the cloud device 22 is a NoSQL database, and stores tagged energy data in JSON format. This makes it possible to efficiently read out a large amount of data generated when generating a prediction model and a control model. Furthermore, by using tags (data on the sending device, area 12, item name, etc.) included in the tagged energy data, it is possible to efficiently generate a prediction model and a control model.

[0071] Moreover, according to the energy system 10 of the embodiment, an information platform can be realized that provides energy-related information based on energy data related to a plurality of areas 12 centrally managed in the database 24 to an information utilization device 90 that utilizes the information. That is, according to the energy system 10 of the embodiment, an information platform that supports utilization of energy data in an external device can be realized by linking a plurality of local devices 16 in a plurality of areas 12 with the cloud device 22. Furthermore, according to the energy system 10 of the embodiment, a two-way information platform can be realized by acquiring and storing the information processing results based on the energy-related information by the information utilization device 90, and for example, the information processing results of the information utilization device 90 can be utilized for energy management in the energy system 10. For example, a highly accurate prediction model and control model can be created by utilizing the data collected from the plurality of local devices 16 and the information processing results (for example, prediction results and simulation results) based on the energy-related information by the information utilization device 90.

[0072] Furthermore, according to the energy system 10 (in other words, an information platform) of the embodiment, a society can be realized in which various electricity-related processes (such as electricity trading) can be optimally carried out in response to electricity market information. In this way, energy has new value as data, and an "ambient energy society" can be established, a human-centered energy society in which people are freed from the limitations of energy consumption. The "ambient energy society" is defined as a human-centered energy society in which energy, primarily renewable energy, is used wisely through IoT, ICT, and AI analysis of energy big data, and a variety of services are created using energy big data, freeing people from the constraints of energy use, such as energy costs and CO2 emissions.

[0073] The present disclosure has been described above based on examples. These examples are merely illustrative, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each processing step, and that such modifications are also within the scope of the present disclosure.

[0074] A first modified example will be described. As described above, at least one of a human activity measurement meter 14g that transmits human activity related data to a local device 16 (messaging server 18) and a weather measurement meter 14f that transmits weather related data to a local device 16 (messaging server 18) may be installed in a certain area 12 (herein referred to as a "specific area"). The control server 20 of the local device 16 in the specific area may receive human activity related data and weather related data via the messaging server 18, and may further transmit at least one of the human activity related data and weather related data to the cloud device 22 in addition to the energy data. The human activity related data and weather related data transmitted to the cloud device 22 may be tagged for management purposes, similar to the energy data.

[0075] The database 24 of the cloud device 22 may store at least one of tagged human activity related data and weather related data transmitted from the local device 16 in addition to the tagged energy data stored in the database 24. The management server 26 of the cloud device 22 may generate a prediction model corresponding to a specific area by itself or by requesting a supercomputer 28 to generate the prediction model using at least one of the human activity related data and weather related data stored in the database 24 in addition to the tagged energy data stored in the database 24. The prediction model may include a function or a prediction formula that further receives at least one of the values ​​of the human activity related data and the weather related data as input and outputs a demand prediction value and a supply prediction value of energy in the area 12. According to this modification, a highly accurate prediction model for the area 12 can be generated by taking into account the activity state of people and the weather state in the area 12.

[0076] Furthermore, human activity is a strong governing factor in electricity demand, and the highly accurate prediction model obtained by the energy system 10 (information platform) can also be applied as a model for predicting human activity within the area 12. The prediction results can be used for various services (such as efficient time sales) to create new value and contribute to the establishment of the above-mentioned human-centered energy society, the "ambient energy society."

[0077] A second modified example will be described. In the above embodiment, the energy data transmitted from the local device 16 to the cloud device 22 and stored in the database 24 of the cloud device 22 is JSON format data, but the energy data is not limited to JSON format. The energy data may be other types of structured data to which management tags are added. For example, the energy data may be HTML format data or XML format data.

[0078] Any combination of the above-mentioned examples and modifications is also useful as an embodiment of the present invention. A new embodiment resulting from the combination has the effects of each of the combined examples and modifications. It will also be understood by those skilled in the art that the functions to be performed by each component recited in the claims can be realized by each component shown in the examples and modifications alone or in cooperation with each other. [Explanation of symbols]

[0079] 10 energy system, 14 equipment, 16 local device, 18 messaging server, 20 control server, 22 cloud device, 24 database, 26 management server, 28 supercomputer, 29 internet.

Claims

1. A plurality of local devices installed in a plurality of areas; a cloud device connected to the plurality of local devices via the Internet; Equipped with Each of the plurality of local devices A control server that transmits a plurality of energy data related to energy demand or supply transmitted from a plurality of devices in an area in which the control server is installed to the cloud device; The cloud device includes: a database that stores a plurality of energy data transmitted from each of the plurality of local devices; a management server that provides information based on the energy data stored in the database to an information utilization device that executes information processing using the information; The energy data stored in the database of the cloud device is in a format in which a management tag is added to a value related to energy demand or supply, the plurality of energy data include energy data indicating a measurement value by a sensor, The energy data indicating the measurement value by the sensor includes, as the management tag, at least a tag indicating an item in which the measurement value by the sensor is set and a tag indicating an item in which two or more attribute information related to the data is set; the information based on the energy data provided by the management server to the information utilization device includes, as attribute information of the energy data, at least information indicating a measurement time by the sensor and location information of the sensor; the management server acquires a result of information processing by the information utilization device, the result of information processing being in a format to which the management tag is added, and a prediction result or a simulation result based on the energy data, and stores the result in the database; the management server passes the energy data stored in the database and the results of information processing by the information utilization device to a supercomputer, causes the supercomputer to generate a plurality of prediction models related to energy demand or supply corresponding to the plurality of areas, and provides the prediction models corresponding to each area to local devices installed in each area; the control server of each local device derives a predicted value related to energy demand or supply in the area in which the local device is installed, using the prediction model provided by the cloud device, and controls a plurality of appliances in the area in which the local device is installed based on the predicted value; The plurality of devices include a power consumption device, a renewable energy power supply device, and a power storage device; new energy data transmitted from each of the plurality of local devices is accumulated in the database, and the management server periodically updates the prediction model. Energy system.

2. The database of the cloud device is a NoSQL database. The energy system of claim 1 .

3. The management server of the cloud device is capable of changing the structure of the energy data stored in the database in response to an operation by an administrator.

3. The energy system according to claim 1 or 2.

4. Each of the local devices further includes a messaging server that acquires a plurality of energy data related to energy demand or supply from a plurality of devices in an area in which the local device is installed, and transmits the plurality of energy data to the control server; The control server of each local device transmits command data for controlling a plurality of devices in the area in which the local device is installed to the messaging server; The messaging server of each local device stores the command data transmitted from the control server in a memory without using a storage device, and transfers the command data to a plurality of devices in the area in which the local device is installed.

4. An energy system according to claim 1.

5. The device installed in the certain area further transmits at least one of the human activity related data and the weather related data to a local device in the certain area; The control server of the local device in the certain area further transmits at least one of the human activity related data and the weather related data to the cloud device; The management server of the cloud device causes the supercomputer to generate a prediction model corresponding to the certain area using at least one of the human activity-related data and the weather-related data transmitted from the control server in addition to the energy data.

5. An energy system according to any one of claims 1 to 4.

6. A management server of the cloud device acquires information related to the electricity market from an external device and transmits the information to a local device in each area; A control server of a local device in each area controls a plurality of devices in the area in which the local device is installed based on the information related to the electricity market transmitted from the management server.

6. An energy system according to any one of claims 1 to 5.

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