Virtual power plant formation control system and formation control method

Abstract

To further ensure the balance between the supply and demand of electric power by efficiently utilizing various energy resources to form a virtual power plant, regardless of an urban area and the peripheral area.SOLUTION: A virtual power plant formation control system in which a plurality of energy resources dispersed throughout the country are integrated and controlled via a network to function as if they were a single power plant, comprises: an attribute storage unit that stores attribute information which includes information indicating power use situations of a plurality of consumers; a learning model storage unit that stores a learning model which learns a correlation between attribute information, which is an explanatory variable, and a classification result of the attribute information, which is an objective variable; a learning unit that predicts the classification result to the attribute information using the learning model; and an integration control unit that selectively integrates the energy resources on the basis of a prediction result by the learning unit from among the plurality of energy resources, and controls the integrated energy resource depending on a demand response.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a formation control system and a formation control method for a virtual power plant. [Background technology]

[0002] In recent years, in order to realize a more stable and less wasteful power system, development has been progressing on virtual power plants (VPPs) that integrate and control various energy resources owned by consumers, such as distributed power sources and power storage facilities, and function as if they were a single power plant (see Patent Document 1). By forming a virtual power plant, it becomes possible to adjust the balance of supply and demand for electricity through demand response such as up DR (Demand Response) and down DR, instead of adjusting the output of large-scale power plants such as thermal power plants as has been done in the past. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-108301 A Summary of the Invention [Problem to be solved by the invention]

[0004] Comparing the formation of a virtual power plant in an urban area with the formation of a virtual power plant in a surrounding area surrounding the urban area, urban areas are often able to form a virtual power plant efficiently because they are rich in various types of energy resources such as power generation facilities and storage facilities, whereas surrounding areas are often biased toward certain types of energy resources (such as solar power generation facilities), making it difficult to form a virtual power plant. As a result, the current situation is that energy resources available in the surrounding area are not being effectively utilized as targets for virtual power plants.

[0005] Therefore, the present invention aims to provide a virtual power plant formation control system and a formation control method thereof that forms a virtual power plant by efficiently utilizing various energy resources that are ubiquitous, regardless of whether it is an urban area or a surrounding area, and further ensures the balance between power supply and demand. [Means for solving the problem]

[0006] The present invention, which is a main object of the present invention to solve the above-mentioned problems, is a system for providing a power supply system to a power grid owned by a plurality of consumers. Within the administrative area, including the urban area and surrounding areas A virtual power plant formation control system that integrates and controls multiple energy resources distributed in various locations via a network and functions as if they were a single power plant, the system comprising: an attribute storage unit that stores attribute information including information indicating the power usage status of the multiple consumers; a learning model storage unit that stores a learning model that learns the correlation between the attribute information, which is an explanatory variable, and a classification result of the attribute information, which is a target variable; and a learning unit that predicts the classification result for the attribute information using the learning model. When a down or up DR occurs, Among the plurality of energy resources, based on the prediction result by the learning unit A plurality of classification results corresponding to the lowering DR or the raising DR that can respond to the Selectively integrate energy resources 、 and an integrated control unit that controls the integrated energy resource. Other features of the present invention will become apparent from the accompanying drawings and the description of this specification. Effect of the Invention

[0007] According to the present invention, it is possible to form a virtual power plant by efficiently utilizing various energy resources that are ubiquitous, regardless of whether they are in an urban area or a surrounding area, and to more reliably balance the supply and demand of electricity. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is an overall view for explaining the formation of a virtual power plant according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram showing the functions of an aggregator server. [Figure 3A] 11 is a diagram showing an example of table data of attribute information A stored in an attribute storage unit. FIG. [Figure 3B] 13 is a diagram showing an example of table data of attribute information B stored in an attribute storage unit. FIG. [Figure 3C] 13 is a diagram showing an example of table data of attribute information C stored in an attribute storage unit. FIG. [Figure 4] FIG. 1 is a diagram illustrating an example of a power system. [Diagram 5] 10 is a flowchart showing an example of an operation of an integrated control unit when forming a virtual power plant. [Figure 6] 11 is a flowchart showing an example of an automated demand response flow. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] At least the following will become apparent from the description of this specification and the accompanying drawings.

[0010] FIG. 1 is an overall diagram for explaining the formation of a virtual power plant according to this embodiment. The virtual power plant is operated by an aggregator. In this embodiment, the aggregator is a general term for a resource aggregator and an aggregation coordinator, and may refer to either the resource aggregator or the aggregation coordinator, or both. The resource aggregator controls the energy resources based on a contract with each consumer who owns the energy resources. The aggregation coordinator bundles the amount of power controlled by each resource aggregator, and trades power with a general power transmission and distribution business operator and a retail electricity business operator. In this embodiment, the general power transmission and distribution business operator and the retail electricity business operator are collectively referred to as an electricity business operator. Therefore, the electricity business operator may refer to either the general power transmission and distribution business operator and the retail electricity business operator, or both. In this embodiment, for example, it is assumed that a management area 10 in which a virtual power plant is to be formed is determined by an aggregator. In this management area 10, a variety of energy resources, such as energy generation, energy storage, and energy saving, owned by a plurality of consumers are distributed and installed. The scale of the management area 10 may be a relatively small one, such as a city, ward, town, or village, or a relatively large one, such as one spanning prefectures.

[0011] In this embodiment, the management area 10 is roughly divided into, for example, an urban area 10A and a surrounding area 10B surrounding the urban area 10A. In the urban area 10A, commercial facilities and industrial facilities for economic activities are densely concentrated, and many energy resources 20 are installed in a concentrated manner. On the other hand, in the surrounding area 10B, there tend to be more houses than facilities for economic activities, and the energy resources 20 are installed sparsely, although in smaller numbers than in the urban area 10A. Note that a plurality of energy resources distributed in the urban area 10A is referred to as 20A, and a plurality of energy resources distributed in the surrounding area 10B is referred to as 20B. These energy resources 20A and 20B are connected to a power grid (not shown), but are selectively integrated so as to be communicable via a network 30 such as IoT, according to an instruction from an aggregator server 50 described later.

[0012] The virtual power plant is formed by integrating, via the network 30, the energy resources 20A and 20B (for example, within the bold frame marked "VPP" in FIG. 1) that correspond to information corresponding to attribute information on a plurality of consumers and the energy resources 20A and 20B from among the energy resources 20A and 20B in the management area 10, regardless of the urban area 10A or the surrounding area 10B. The virtual power plant formed in this manner operates to intentionally increase or decrease the demand for power in response to demand responses such as an up-DR or down-DR that occur according to the supply amount of power at that time, so as to contribute to balancing the demand amount of power by consumers with the supply amount of power. In other words, the virtual power plant functions as a virtual power plant. Here, in this embodiment, the attribute information is used as an explanatory variable, the classification result of the attribute information is used as an objective variable, and a learning model that learns the correlation between these attribute information and the classification result is prepared, and the energy resources 20A and 20B are selectively integrated according to the classification result of the attribute information predicted using this learning model (information corresponding to the attribute information) to form the virtual power plant.

[0013] The energy resources 20A, 20B are connected to a relay system 40 (also called a power management system) that relays communications between a GW (gateway), HEMS (Home Energy Management System), BEMS (Building Energy Management System), FEMS (Factory Energy Management System), etc. so that a virtual power plant can be formed through a network 30. Furthermore, the energy resources 20A, 20B are communicatively connected to an aggregator server 50 via the relay system 40 and the network 30 to integrate the energy resources 20A, 20B and thereby form a virtual power plant.

[0014] Demand response is a demand response command for changing the power demand pattern by controlling the power demand by the multiple energy resources 20A and 20B forming the virtual power plant so that the power supply and the power demand by the consumer are balanced. There are two types of demand response: up DR and down DR. Up DR is a command for increasing the power demand (consumption) by the multiple energy resources 20A and 20B in the virtual power plant when the power demand is less than the power supply. For example, when there is a risk that the power supply by a photovoltaic power generation system connected to the power grid will be oversupplied due to the influence of fine weather, an up DR is generated. For example, when the energy resources 20A and 20B forming the virtual power plant include a storage battery (storage energy resource), the demand for the storage battery is increased to charge the storage battery with the amount of power that would be oversupplied. On the other hand, down DR is a command for decreasing the power demand by the multiple energy resources 20A and 20B in the virtual power plant when the power demand is greater than the power supply. For example, when there is a high demand for air conditioners and the like during a heatwave and there is a risk of a shortage in the supply of electricity, a down DR is generated to reduce the demand for any of the energy resources 20A, 20B in the virtual power plant. In this way, changing the demand pattern of the multiple energy resources 20A, 20B in the virtual power plant can contribute to balancing the supply and demand of electricity.

[0015] The energy generating resource, which is one type of the energy resources 20A and 20B, is a resource that generates electricity, and examples thereof include a photovoltaic power generation system, a cogeneration system, a system that utilizes renewable energy heat (geothermal heat, solar heat, snow and ice heat, etc.), and a fuel cell vehicle. The energy storage resource, which is another type of the energy resources 20A and 20B, is a resource that stores electric energy, and examples thereof include a home storage battery, a system storage battery, and a heat pump water heater. The energy saving resource, which is another type of the energy resources 20A and 20B, is a resource that saves electricity, and examples thereof include a business / industrial EMS (Energy Management System), IoT-based lighting fixtures, air conditioning equipment, and refrigeration equipment. The virtual power plant is formed by selectively integrating the energy generating resource, the energy storage resource, and the energy saving resource included in these multiple energy resources 20A and 20B based on the attributes of multiple consumers and the multiple energy resources 20A and 20B.

[0016] The aggregator server 50 is a server managed by an aggregator. As described above, the aggregator is a general term for a resource aggregator and an aggregation coordinator, so there are servers managed by resource aggregators (also referred to as resource aggregator servers) and servers managed by aggregation coordinators (also referred to as aggregation coordinator servers), but in this embodiment, the resource aggregator servers and aggregation coordinator servers are collectively referred to as the aggregator server 50. The aggregator server 50 is configured by a computer as hardware, and various functions as the aggregator server 50 are realized by software processing of the computer.

[0017] The electric utility server 60 is a server managed by an electric utility. As described above, the electric utility is a general term for the general electricity transmission and distribution utility and the retail electricity utility, and therefore includes a server managed by the general electricity transmission and distribution utility (also referred to as the general electricity transmission and distribution utility server) and a server managed by the retail electricity utility (also referred to as the retail electricity utility server). In this embodiment, the general electricity transmission and distribution utility server and the retail electricity utility server are collectively referred to as the electric utility server 60. The electric utility server 60 generates a demand response to the aggregator server 50, and receives the result of controlling the operation of the energy resources 20A and 20B according to the demand response from the aggregator server 50. The electric utility server 60 is configured by a computer as hardware, and realizes various functions as the electric utility server 60 by software processing of the computer.

[0018] The aggregator server 50 selectively integrates multiple energy resources 20A, 20B installed within the management area 10, regardless of whether it is an urban area 10A or a surrounding area 10B, via the network 30 based on prediction results classified by a learning model from multiple consumers who own the energy resources 20A, 20B and predetermined attributes related to the energy resources 20A, 20B, thereby forming a virtual power plant.

[0019] FIG. 2 is a block diagram showing the functions of the aggregator server 50. As shown in FIG. The aggregator server 50 includes, as means for forming a virtual power plant, an attribute storage unit 510, a learning model storage unit 520, a learning unit 530, and an integrated control unit 540. The aggregator server 50 is configured as hardware by a computer similar to the electric utility server 60, and realizes the functions of the attribute storage unit 510, the learning model storage unit 520, the learning unit 530, and the integrated control unit 540 through software processing of the computer.

[0020] The attribute storage unit 510 stores in advance attribute information required when the learning unit 530 obtains a predetermined classification result using the learning model stored in the learning model storage unit 520. For example, the attribute information includes attribute information A indicating the power usage status of multiple consumers who own the multiple energy resources 20A and 20B that are distributed and installed in the management area 10, attribute information B indicating the power system to which the multiple energy resources 20A and 20B are connected, and attribute information C indicating the geographical positions of the multiple energy resources 20A and 20B, for multiple energy resources 20A and 20B that are distributed and installed in the management area 10. The attribute storage unit 510 is configured using hardware such as a hard disk or a semiconductor nonvolatile memory, and is capable of functions such as referring to and updating the attribute information by software processing.

[0021] Fig. 3A shows an example of table data of attribute information A stored in attribute storage unit 510. Fig. 3B shows an example of table data of attribute information B stored in attribute storage unit 510. Fig. 3C shows an example of table data of attribute information C stored in attribute storage unit 510.

[0022] The attribute information A is, for example, information that captures the power consumption (power consumption curve) for each predetermined time in one day of each of multiple consumers who own the energy resources 20A and 20B as the power usage status. The predetermined time may be, for example, 15 minutes or one hour. Furthermore, this one day may be divided into, for example, weekdays and weekends and holidays. In this case, the power consumption for each predetermined time on weekdays may be, for example, the average value of the power consumption from Monday to Friday for each predetermined time, while the power consumption for each predetermined time on weekends and holidays may be, for example, the average value of the power demand for Saturday, Sunday, and holiday for each predetermined time. When the power consumption for each predetermined time in one day is divided into weekdays and weekends and holidays, this information may be updated, for example, on a weekly basis and stored in the attribute storage unit 510. This makes it possible to effectively use information indicating the latest power usage status of multiple consumers for the integration of virtual power plants. For example, assume that multiple consumers are X1 to Xn, information indicating the power usage status of each of the consumers X1 to Xn on weekdays is W11 to W1n, and information indicating the power usage status of each of the consumers X1 to Xn on holidays and weekends is W21 to W2n.

[0023] FIG. 4 is a diagram showing an example of a power system, and for example, all areas shown in FIG. 4 correspond to the management area 10. FIG. 4 shows, for example, two substations H1 and H2, buses K1 and K2 connected to the substations H1 and H2, distribution lines F1 and F2 branching off from the bus K1, and distribution lines F3 and F4 branching off from the bus K2. For example, the distribution lines F1 and F2 are formed so as to straddle an urban area 10A, and a plurality of energy resources 20A (white circles) are installed in a dispersed and dense manner within this urban area 10A. Furthermore, the distribution lines F3 and F4 are formed so as to straddle a peripheral area 10B, and a plurality of energy resources 20B (white triangles) are installed in a dispersed manner within this peripheral area 10B.

[0024] The attribute information B is, for example, information indicating the power distribution lines F1-F4 to which the multiple energy resources 20A, 20B are connected. For example, if the multiple energy resources distributed in the urban area 10A across the power distribution lines F1, F2 are 20A1-20An, the multiple energy resources 20A1-20An are associated with information on the power distribution lines F1, F2 connected thereto and information indicating the consumers X1-Xn who own the corresponding energy resources 20A1-20An, and are stored in the attribute storage unit 510 as the attribute information B. Also, if the multiple energy resources distributed in the surrounding area 10B across the power distribution lines F2-F4 are 20B1-20Bn, the multiple energy resources 20B1-20Bn are associated with information on the power distribution lines F2-F4 connected thereto and information indicating the consumers X1-Xn who own the corresponding energy resources 20B1-20Bn, and are stored in the attribute storage unit 510 as the attribute information B. Here, the information indicating the consumers X1 to Xn may be the same information as the consumers X1 to Xn in the attribute information A, or may be information corresponding to the consumers X1 to Xn in the attribute information A. In other words, any information may be used as long as the consumers X1 to Xn in the attribute information A and B match each other.

[0025] The attribute information C is information indicating the geographical positions where the multiple energy resources 20A, 20B are installed in the urban area 10A and the surrounding area 10B, respectively. The multiple energy resources 20A1-20An distributed in the urban area 10A in Fig. 4 are associated with information indicating the geographical positions of the multiple energy resources 20A1-20An and information indicating the consumers X1-Xn who own the corresponding multiple energy resources 20A1-20An, and are stored in the attribute storage unit 510 as the attribute information C. Similarly, the multiple energy resources 20B1-20Bn distributed in the surrounding area 10B in Fig. 4 are associated with information indicating the geographical positions of the multiple energy resources 20B1-20Bn and information indicating the consumers X1-Xn who own the corresponding multiple energy resources 20B1-20Bn, and are stored in the attribute storage unit 510 as the attribute information C. Here, the information indicating the consumers X1-Xn may be the same as the consumers X1-Xn in the attribute information A, or may be information corresponding to the consumers X1-Xn in the attribute information A. In other words, any information may be used as long as the consumers X1-Xn in the attribute information A and C match. In addition, the information indicating the geographical positions of the multiple energy resources 20A and 20B may be, for example, address information or longitude and latitude information indicating the installation positions of the corresponding energy resources 20A and 20B, but in this embodiment, it is address information. For example, the address information of the energy resources 20A1-20An is AD11-AD1n, and the address information of the energy resources 20B1-20Bn is AD21-AD2n.

[0026] The learning model storage unit 520 prestores a learning model that is machine-learned by extracting feature amounts to obtain correlations between the attribute information A to C and various classification results, with the attribute information A to C as explanatory variables and various classification results as objective variables. The learning model may be a model that obtains various classification results from each of the attribute information A to C, or a model that obtains various classification results from a combination of two or more of the attribute information A to C.

[0027] The learning unit 530 predicts the classification to which the input attribute information A to C should belong, using the learning model stored in the learning model storage unit 520. For example, in the case of a classification for reducing power consumption from 12:00 to 15:00 on weekdays in the management area 10, the learning unit 530 predicts the energy resources 20A and 20B that belong to the above classification from, for example, the attribute information A, using the learning model. In addition, in order to improve the accuracy of the classification prediction result, the learning unit 530 may predict the energy resources 20A and 20B that belong to the above classification from, for example, the attribute information A and B, using the learning model. Furthermore, when taking into account the geographical conditions of the energy resources 20A and 20B, the learning unit 530 may predict the energy resources 20A and 20B that belong to the above classification, using the learning model, giving priority to, for example, the attribute information A and B over the attribute information C.

[0028] The integrated control unit 540 selectively integrates, via the network 30, a plurality of energy resources 20A, 20B that are determined to be optimal when responding to demand response from among a plurality of energy resources 20A distributed within the urban area 10A and a plurality of energy resources 20B distributed in the surrounding area 10B, according to the prediction results of the learning unit 530, to form a virtual power plant.

[0029] The integration control unit 540 selectively integrates the multiple energy resources 20A, 20B according to the prediction result of the learning unit 530, which prioritizes the attribute information A and B over the attribute information C as described above.

[0030] Here, the attribute information A includes information indicating the power consumption of each of the consumers X1 to Xn for each predetermined time in one day. Therefore, the integrated control unit 540 selects the consumers X1 to Xn that consume similar power according to the prediction result of the learning unit 530 using the attribute information A, and integrates the energy resources 20A and 20B owned by the selected consumers X1 to Xn via the network 30. As an example, when it is necessary to reduce the power demand from 12:00 to 15:00 on weekdays by a downward DR, the integrated control unit 540 selects only the information W11 to W1n included in the classification indicating the power usage situation in which the power consumption from 12:00 to 15:00 on weekdays becomes larger than a certain amount according to the prediction result of the learning unit 530 using the attribute information A, and integrates the energy resources 20A and 20B owned by the consumers X1 to Xn that correspond to the information W11 to W1n indicating the selected power usage situation via the network 30. The integrated control unit 540 can then efficiently reduce the amount of power consumed by the energy resources 20A and 20B in the integrated virtual power plant between 12:00 and 15:00 on weekdays in accordance with the down DR. In this way, the attribute information A is useful information for predicting the demand of the energy resources 20A and 20B constituting the virtual power plant in response to demand response, with respect to how the demand is controlled. In addition, the consumers X1 to Xn selected from the prediction result of the learning unit 530 using the attribute information A have similar power usage conditions, and therefore have high priority as information used for the virtual power plant to operate efficiently in one demand response.

[0031] The attribute information B is information indicating which of the power distribution lines F1 to F4 the multiple energy resources 20A and 20B are connected to. The power distribution lines F1 and F2 are power lines branching off from a common bus K1, so they are strongly correlated, and the distance between the power distribution lines F1 and F2 is relatively short compared to other power systems (for example, the power distribution lines F3 and F4). Similarly, the power distribution lines F3 and F4 are power lines branching off from a common bus K2, so they are strongly correlated, and the distance between the power distribution lines F3 and F4 is relatively short compared to other power systems (for example, the power distribution lines F1 and F2). For this reason, the multiple energy resources 20A and 20B connected to the power distribution lines F1 and F2 (or the power distribution lines F3 and F4) are highly likely to be affected by the same natural environment (weather, disasters, etc.). The same can be said about the energy resources 20A and 20B commonly connected to any one of the power distribution lines F1 to F4. Therefore, since the energy resources 20A, 20B connected to at least one of the distribution lines F1, F2 and at least one of the distribution lines F3, F4 are likely to have similar power usage conditions, they become high priority information to be used by the virtual power plant to operate efficiently in a single demand response.

[0032] The attribute information C is information indicating the geographical positions of the energy resources 20A and 20B, and depending on the conditions of the demand response, the energy resources 20A and 20B distributed in geographically close positions (e.g., the energy resources 20A and 20B connected to the distribution lines F1 and F2) or the energy resources 20A and 20B distributed in geographically distant positions (e.g., the energy resources 20A and 20B connected to the distribution lines F1 and F4) can be used as information for forming a virtual power plant. In order to balance the demand for power consumed by consumers against the supply of power, the attribute information A and B are used preferentially over the attribute information C in this embodiment.

[0033] In this embodiment, the attribute information A to C is used as information to be input to the learning unit 530 as an explanatory variable, but the attribute information is not limited to this. In addition to the attribute information A to C, for example, weather forecast information, event information, hazard information, etc. of the region in which the energy resources 20A, 20B are separated may be appropriately used as information for integrating the multiple energy resources 20A, 20B.

[0034] 5 is a flowchart showing an example of the operation of the integrated control unit 540 when forming a virtual power plant. For ease of explanation, it is assumed that the attribute information A and B stored in the attribute storage unit 510 are input to the learning unit 530 when forming the virtual power plant. In addition, it is assumed that the demand response is, for example, a downward DR, which is a command to reduce the amount of power consumption by a certain amount from 12:00 to 15:00 on weekdays as described above.

[0035] First, the integrated control unit 540 judges whether or not a demand response has been received from the electric utility server 60 (step S10). If the integrated control unit 540 has not received a demand response (step S10: NO), the integrated control unit 540 repeats the judgment operation of step S10 until a demand response is received. On the other hand, if the integrated control unit 540 has received a demand response (step S10: YES), the integrated control unit 540 selects a plurality of energy resources 20A, 20B corresponding to a classification result that is capable of responding to the above-mentioned lowering DR from among the classification results obtained by the learning unit 530 using the learning model stored in the learning model storage unit 520 (step S20).

[0036] Next, the integration control unit 540 integrates the selected energy resources 20A, 20B via the network 30 to form a virtual power plant. For example, as shown in Fig. 4, among the energy resources 20A, 20B connected to the power distribution line F2, the energy resources 20A, 20B in the area surrounded by a thick line are integrated by the integration control unit 540 to form a virtual power plant. This virtual power plant is formed by selectively integrating the energy resource 20A included in the urban area 10A and the energy resource 20B included in the peripheral area 10B (step S50).

[0037] FIG. 6 is a flowchart showing an example of an automated demand response process.

[0038] First, a demand response indicating the above-mentioned lowered DR is generated from the electric utility server 60 (step S100).

[0039] Next, when the aggregator server 50 receives the lowered DR on a weekday (step S110), the energy resources 20A and 20B capable of responding to the lowered DR are integrated via the network 30 based on the attribute information A and B in the procedure shown in FIG. 5 to form a virtual power plant (step S120). Here, in FIG. 5, the procedure using the classification result of the learning unit 530 based on the attribute information A and B when forming the virtual power plant is shown, but depending on the conditions of the lowered DR, the classification result of the learning unit 530 based on the attribute information A to C may be used to more reliably respond to the lowered DR. For example, the attribute information C is useful information when it is desired to further narrow down the geographical area of ​​the virtual power plant. In this way, the aggregator server 50 forms a virtual power plant using at least one of the attribute information A, B, and C. For example, depending on the conditions of the DR, the aggregator server 50 may form a virtual power plant using only the attribute information A without using the attribute information B, or may form a virtual power plant using the attribute information A and C.

[0040] When the formation of the virtual power plant is completed, the integrated control unit 540 instructs the multiple energy resources 20A, 20B forming the virtual power plant to reduce their power consumption by a certain amount between 12:00 and 15:00 on weekdays in accordance with the lowering DR. This instruction is given to the relay system 40 connected to the energy resources 20A, 20B, which controls the reduction in the power consumption by the energy resources 20A, 20B to reach the certain amount (step S130). For example, if the energy resources 20A, 20B are lighting equipment or air conditioning equipment, control to suppress the output of these equipment is automatically performed.

[0041] When the period for reducing power consumption within the virtual power plant (12:00-15:00 on weekdays) has elapsed, the integrated control unit 540 acquires and compiles actual data indicating the amount of reduction in power consumption from the relay systems 40 connected to the energy resources 20A, 20B that have reduced their power consumption within the virtual power plant, and transmits this compiled data to the electric utility server 60 (step S140).

[0042] When the electric utility server 60 acquires the aggregated data from the integrated control unit 540, it performs a process to pay the aggregator server 50 a remuneration according to the amount of reduction in power consumption indicated by the aggregated data (step S150). After confirming through the aggregator server 50 that the process of receiving the remuneration according to the amount of reduction in power consumption has been completed, the aggregator pays the remuneration to the consumers who own the energy resources 20A and 20B in the virtual power plant, thereby completing a series of negawatt transactions. Of course, whether or not a remuneration is paid to a consumer in response to a demand response depends on the contents of the consumer's contract, and this embodiment is merely an example.

[0043] As described above, the virtual power plant formation control system integrates and controls multiple energy resources 20A, 20B owned by multiple consumers X1 to Xn and distributed in various locations via the network 30, and functions as if they were a single power plant. The system includes an attribute memory unit 510 in which attribute information including information A indicating the power usage status of the multiple consumers X1 to Xn is stored, a learning model memory unit 520 in which a learning model that learns the correlation between the attribute information, which is an explanatory variable, and the classification result of the attribute information, which is an objective variable, is stored, a learning unit 530 that predicts the classification result for the attribute information using the learning model, and an integration control unit 540 that selectively integrates the multiple energy resources 20A, 20B from the multiple energy resources 20A, 20B based on the prediction result by the learning unit 530, and controls the integrated multiple energy resources 20A, 20B in accordance with the demand response.

[0044] The attribute information further includes information indicating the power system to which the multiple energy resources 20A and 20B are connected.

[0045] Moreover, the attribute information further includes information indicating the geographical positions of the multiple energy resources 20A and 20B.

[0046] In addition, the integrated control unit 540 selectively integrates the multiple energy resources 20A, 20B by prioritizing prediction results related to information indicating the power usage status of the multiple consumers X1 to Xn and information indicating the power grid to which the multiple energy resources 20A, 20B are connected over prediction results related to information indicating the geographical positions of the multiple energy resources 20A, 20B among the prediction results by the learning unit 530.

[0047] In addition, the multiple energy resources 20A, 20B include energy generating resources, energy storing resources, and energy saving resources, and the integrated control unit 540 selectively integrates any one of the energy generating resources, energy storing resources, and energy saving resources from among the multiple energy resources 20A, 20B based on the prediction result of the learning unit 530.

[0048] Furthermore, according to this embodiment, the learning unit 530 can use a machine learning function such as artificial intelligence to accurately classify the attribute information A to C into multiple groups that can respond to demand response, and regardless of the urban area 10A or the surrounding area 10B, it is possible to form a virtual power plant by efficiently utilizing various energy resources 20A, 20B that are ubiquitous, thereby making it possible to more reliably balance the supply and demand of electricity.

[0049] The above embodiment is for facilitating understanding of the present invention, and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and the present invention also includes equivalents thereof. In the present embodiment, the virtual power plant operates to reduce the amount of power consumption according to the lowering DR, but is not limited to this. The virtual power plant can also operate to increase the amount of power consumption according to the increasing DR. In other words, the integrated control unit 540 in the aggregator server 50 can form and control the virtual power plant so as to efficiently respond to both the increasing DR and the decreasing DR. [Explanation of symbols]

[0050] 10. Administration Area 10A Urban Area 10B Surrounding area 20A, 20B Energy Resources 30 Network 40 Relay System 50 Aggregator Server 60 Electricity supplier server 510 Attribute storage section 520 Learning model memory unit 530 Learning Department 540 Integrated Control Unit

Claims

1. A virtual power plant formation control system that integrates and controls a plurality of energy resources owned by a plurality of consumers and distributed throughout a management area including an urban area and surrounding areas via a network, and functions as if they were a single power plant, an attribute storage unit that stores attribute information including information indicating power usage status of the plurality of consumers; a learning model storage unit that stores a learning model that learns a correlation between the attribute information, which is an explanatory variable, and a classification result of the attribute information, which is a target variable; a learning unit that predicts the classification result for the attribute information by using the learning model; an integrated control unit that selectively integrates, from among the plurality of energy resources, a plurality of energy resources that correspond to a classification result that is capable of responding to the lowering DR or the raising DR based on a prediction result by the learning unit when a lowering DR or an raising DR occurs, and controls the integrated energy resources; A formation control system for a virtual power plant equipped with

2. The attribute information further includes information indicating a power grid to which the plurality of energy resources are connected. The virtual power plant formation control system according to claim 1 .

3. The attribute information further includes information indicating geographical locations of the plurality of energy resources. The virtual power plant formation control system according to claim 2 .

4. The integration control unit selectively integrates the energy resources by prioritizing prediction results regarding information indicating power usage conditions of the plurality of consumers and information indicating a power grid to which the plurality of energy resources are connected over prediction results regarding information indicating geographical positions of the plurality of energy resources among prediction results by the learning unit. The virtual power plant formation control system according to claim 3 .

5. The plurality of energy resources include an energy generating resource, an energy storing resource, and an energy saving resource, The integrated control unit selectively integrates any one of the energy generating resource, the energy storing resource, and the energy saving resource from among the plurality of energy resources based on a prediction result of the learning unit. A formation control system for a virtual power plant according to any one of claims 1 to 4.

6. A method for forming and controlling a virtual power plant in which a plurality of energy resources owned by a plurality of consumers and distributed throughout a management area including an urban area and surrounding areas are integrated and controlled via a network to function as if they were a single power plant, comprising: predicting the classification result for the attribute information using a learning model that learns a correlation between attribute information including information indicating the power usage status of the plurality of consumers, which is an explanatory variable, and a classification result of the attribute information, which is a target variable; When a lowering DR or an increasing DR occurs, a plurality of energy resources corresponding to a classification result that is capable of responding to the lowering DR or the increasing DR based on a prediction result by the learning model are selectively integrated from among the plurality of energy resources, and the integrated energy resource is controlled. A method for controlling the formation of a virtual power plant.

7. The attribute information further includes information indicating a power grid to which the plurality of energy resources are connected. The method for controlling formation of a virtual power plant according to claim 6.

8. The attribute information further includes information indicating geographical locations of the plurality of energy resources. The method for controlling formation of a virtual power plant according to claim 7.

9. Among the prediction results based on the learning model, a prediction result regarding information indicating the power usage status of the plurality of consumers and information indicating a power grid to which the plurality of energy resources are connected is prioritized over a prediction result regarding information indicating the geographical positions of the plurality of energy resources, and the energy resources are selectively integrated. The method for controlling formation of a virtual power plant according to claim 8.

10. The plurality of energy resources include an energy generating resource, an energy storing resource, and an energy saving resource, Selectively integrating any one of the energy generating resource, the energy storing resource, and the energy saving resource from among the plurality of energy resources based on a prediction result of the learning model. A method for controlling the formation of a virtual power plant according to any one of claims 6 to 9.

Images