Distributed resource management device and distributed resource management method

Abstract

To provide a distributed resource management device 1 that allows local governments and businesses promoting regional decarbonization to avoid opportunity losses due to output suppression in a situation where there is no information sharing between DERMS and DMS.SOLUTION: A distributed resource management device 1, which manages a plurality of distributed resources 42, includes a memory unit 17 that stores an output suppression history 21 that indicates the history of output suppression for the plurality of distributed resources 42, a learning unit 11 that learns the output suppression history 21 and creates an output suppression learning model, a prediction unit 12 that predicts the occurrence of output suppression for the plurality of distributed resources 42 based on the output suppression learning model and creates an output suppression prediction result, and an operation plan creation unit 13 that creates an operation plan for the plurality of distributed resources 42 in response to the output suppression based on the output suppression prediction result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a distributed resource management device and a distributed resource management method for managing a plurality of distributed energy resources (hereinafter simply referred to as distributed resources). [Background technology]

[0002] Toward decarbonization, a large amount of renewable energy (hereafter referred to as "RE") facilities is expected to be introduced in the future. In distribution systems, an increase in reverse power flow of renewable energy generated in each region to the upper system and an increase in forward power flow due to sudden loads such as rapid charging of electric vehicles (EVs) at consumer sites are expected, and it is necessary to appropriately manage these and maintain system stability.

[0003] Power generation, storage, and load equipment at consumer sites, as well as power generation and storage equipment directly connected to the grid, are collectively called Distributed Energy Resources (DER). A system that operates and manages DERs scattered throughout the distribution grid is called a Distributed Energy Resource Management System (DERMS). By properly operating a DERMS and managing congestion in the distribution grid, it is expected that the introduction of renewable energy can be promoted while reducing investment in distribution equipment.

[0004] Patent Document 1 discloses a configuration example and an operation example of a DERMS. Specifically, Patent Document 1 discloses a distributed energy resource management device that can provide a VPP aggregator with the range of active power control amount of each DER while satisfying constraints on the voltage and current of the grid in order to adjust supply and demand in the grid. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2023-088158 Summary of the Invention [Problem to be solved by the invention]

[0006] Generally, DERMS is operated in information sharing with DMS. Specifically, DERMS receives information related to the stability of the distribution system's voltage, current, etc. from DMS, and uses this information to create and control DER operation plans, thereby contributing to both system stabilization and DER utilization. When a general electricity transmission and distribution utility operates a DERMS, information sharing between the DMS and DERMS can be carried out without any problems.

[0007] On the other hand, in recent years, local governments have been working with private businesses to promote regional decarbonization, but the legal framework and platform for these businesses to share information with DMSs have not yet been established. In such cases, it is necessary to create DER operation plans without information sharing between DERMSs and DMSs.

[0008] In recent years, with the spread of renewable energy, the frequency of output suppression (also called output control) of renewable energy power generation facilities has tended to increase due to supply-demand imbalances and grid congestion. Figure 2 shows a typical example of the flow leading up to the decision to suppress output. Local governments and businesses promoting regional decarbonization submit renewable energy power generation plans to general electricity transmission and distribution utilities the day before power generation (step S11). Although not shown, retail electricity suppliers and other entities separately submit demand plans to general electricity transmission and distribution utilities the day before demand. The general electricity transmission and distribution utilities then issue instructions to revise the submitted plan based on the submitted supply-demand plan and grid congestion forecasts (steps S12 to S14). Finally, the plan is revised to balance supply and demand one hour before actual supply and demand (gate close).

[0009] However, if the final plan predicts that grid congestion will occur, output curtailment is instructed to the renewable energy power generation facility (step S15). For example, online control is performed on the power conditioner of the renewable energy power generation facility. This reduces power generation at the renewable energy power generation facility. Since no compensation is provided for the reduced power generation, output curtailment represents an opportunity loss for the power generation company. Because the output curtailment schedule is sent just before the output curtailment, local governments and businesses promoting regional decarbonization are unable to take measures to avoid output curtailment. Therefore, in a situation where there is no information sharing between the DERMS and DMS, it is difficult for local governments and businesses promoting regional decarbonization to avoid opportunity losses due to output curtailment.

[0010] Therefore, an object of the present invention is to reduce the opportunity loss for local governments and businesses as described above and to enable more efficient operation of distributed resources. [Means for solving the problem]

[0011] In order to solve the above problems, the present invention predicts the occurrence of output curtailment in distributed resources such as renewable energy power generation facilities, and creates an operation plan for the distributed resources in preparation for the predicted output curtailment, thereby reducing opportunity losses for local governments and businesses caused by output curtailment.

[0012] Specifically, the distributed resource management device manages a plurality of distributed resources and includes a memory unit that stores an output suppression history indicating the history of output suppression for the plurality of distributed resources, a learning unit that learns the output suppression history and creates an output suppression learning model, a prediction unit that predicts the occurrence of output suppression for the plurality of distributed resources based on the output suppression learning model and creates an output suppression prediction result, and an operation plan creation unit that creates an operation plan for the plurality of distributed resources in response to output suppression based on the output suppression prediction result.

[0013] The present invention also includes a distributed resource management method executed by the distributed resource management device, a distributed resource management program for causing the distributed resource management device to function as a computer, and a storage medium storing this program. [Effects of the Invention]

[0014] According to the present invention, by predicting output curtailment, more efficient operation of distributed resources can be achieved. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a diagram illustrating a schematic configuration of a distributed resource management device 1 according to a first embodiment. [Figure 2] FIG. 1 is a diagram showing a typical example of a conventional flow leading up to a decision on output suppression. [Figure 3] 1 is a diagram illustrating an operation flow of the distributed resource management device 1 according to the first embodiment. [Figure 4] 10 is an example of a graph showing an output suppression probability in an output suppression learning model created by an output suppression prediction unit 121 according to the first embodiment. [Figure 5] 10 is an example of a graph showing predicted power price values ​​created by a power price prediction unit 124 according to the first embodiment. [Figure 6] FIG. 2 is a diagram showing an example of a display screen 140 displayed by an output unit 14 according to the first embodiment. [Figure 7] FIG. 10 is a diagram illustrating a schematic configuration of a distributed resource management apparatus 1 according to a second embodiment. [Figure 8] FIG. 10 is a diagram illustrating a schematic configuration of a distributed resource management apparatus 1 according to a third embodiment. [Figure 9] FIG. 10 is a diagram illustrating a schematic configuration of a distributed resource management apparatus 1 according to a fourth embodiment. [Figure 10] FIG. 10 is a diagram illustrating a schematic configuration of a distributed resource management device 1 according to a fifth embodiment. [Figure 11] FIG. 13 is a diagram illustrating a schematic configuration of a distributed resource management apparatus 1 according to a sixth embodiment. [Figure 12] 1 is a schematic diagram illustrating an embodiment of the present invention. [Figure 13]1 is a configuration diagram showing an example of implementation of a distributed resource management device 1 according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] An embodiment of the present invention will be described below with reference to the drawings. Note that the same components in the drawings are given the same reference numerals, and detailed descriptions of overlapping parts will be omitted.

[0017] FIG. 12 is a schematic diagram illustrating this embodiment. In this embodiment, output curtailment of a plurality of distributed resources 42 (hereinafter, DERs 42), such as renewable energy power generation facilities, from a general power transmission and distribution utility that supplies power via a so-called power transmission and distribution network 80 is predicted, and an operation plan for the DERs 42 is created based on this prediction. For this reason, in FIG. 12, the power transmission and distribution network 80 is connected to power generation facilities 62, such as a hydroelectric power plant 62-1 and a thermal power plant 62-2, which are power supply sources, and supplies power to consumers 70, such as general households and factories. Note that hereinafter, the power transmission and distribution network 80 is also referred to as a system. Furthermore, the prediction of output curtailment is a concept that includes an output curtailment instruction, and can also be expressed as a prediction of output curtailment.

[0018] Furthermore, DERs 42, particularly wind power generation equipment 42-1 and solar power generation equipment 42-2, which are examples of renewable energy power generation equipment, are connected to the power transmission and distribution network 80, and power is supplied from these to the power transmission and distribution network 80 (so-called reverse power flow). Also, as a DER 42, a power storage device 42-3 having a storage battery is connected to the power transmission and distribution network 80 or other DERs 42 such as the solar power generation equipment 42-2. Power from the DER 42 or the power transmission and distribution network 80 is stored in this storage battery.

[0019] Furthermore, power generation facilities 62 such as a hydroelectric power plant 62-1 and a thermal power plant 62-2 generate power in accordance with control commands from power generation control devices 61-1 and 61-2, respectively. These power generation control devices 61-1 and 61-2 are managed by a central power distribution command system 60 connected via a network 91.

[0020] Furthermore, the DERs are managed by a distributed resource management device 1. For example, the distributed resource management device 1 sends a control command to an EMS (Energy Management System) 41 that controls the DERs. The EMS 41 then controls the operation of the DERs.

[0021] Here, the central power distribution command system 60 determines the need for output suppression for the DER 42. For example, the central power distribution command system 60 notifies the distributed resource management device 1 of an output suppression command. Then, the distributed resource management device 1 performs processing in accordance with the output suppression. In this embodiment, the occurrence of this output suppression is predicted, and an operation plan for the DER 42 is created.

[0022] For this purpose, the distributed resource management device 1 has a learning unit 11, a prediction unit 12, an operation plan creation unit 13, an output unit 14, an input unit 15, a DER control command unit 16, and a memory unit 17. First, the learning unit 11 learns history data 2 such as an output suppression history 21, and creates a learning model 29. For example, this learning model includes an output suppression learning model for the output suppression history 21. For this reason, an output suppression learning unit 111 included in the learning unit 11 learns the output suppression history 21 and creates the output suppression learning model. Note that the learning of the learning unit 11 is not limited to the output suppression history 21. An example of this will be described in an example below.

[0023] Furthermore, the prediction unit 12 predicts output suppression for a plurality of DERs 42 based on the created learning model 29, and creates an output suppression prediction result. For example, the output suppression prediction unit 121 of the prediction unit 12 predicts output suppression for a plurality of DERs 42 based on the output suppression learning model, and creates an output suppression prediction result. Note that the prediction by the prediction unit 12 is not limited to predicting output suppression. An example of this will be described later in the examples.

[0024] Furthermore, the operation plan creation unit 13 creates operation plans 34 for the multiple DERs 42 in response to output curtailment based on the output curtailment prediction results. Furthermore, the output unit 14 outputs the processing results of the distributed resource management device 1. For example, the output unit 14 outputs the created operation plan 34 and a control command, which will be described later. Here, the operation plan 34 may be displayed by the output unit 14 or may be displayed by a terminal device 50, which will be described later. Furthermore, the output unit 14 transmits the control command to the EMS 41. As a result, the EMS 41 can perform operation control in response to the predicted output curtailment. For this reason, the output unit 14 can be realized as a communication device or a display device.

[0025] The input unit 15 also receives information from other devices and user operations via the network 92. The information from other devices includes the past renewable energy power generation history 23 from the EMS 41 and weather forecast data 31 from the weather forecast data providing system 30. The user operations include instructions to create an operation plan 34 and control commands. Therefore, the output unit 14 can be realized as a communication device, a connection device having an interface function, or an input device.

[0026] Furthermore, the DER control command unit 16 generates control commands for controlling the multiple DERs 42 based on the generated operation plan 34. Furthermore, the memory unit 17 stores the history data 2, the learning model 29, the battery state quantity 32, the operation plan 34, and the battery deterioration model 35.

[0027] The history data 2 is various types of history data such as an output suppression history 21. The learning model 29 is a learning model that indicates the learning results of the history data 2, such as an output suppression learning model. The battery state quantity 32 indicates the state (remaining battery capacity, etc.) of the battery of the power storage device 42-3. The operation plan indicates an operation plan created by the operation plan creation unit 13. For example, the operation plan 34 indicates an operation plan for output suppression based on the output suppression prediction result. The battery deterioration model 35 indicates a deterioration model of the battery of the power storage device 42-3, which is an example of the DER 42. The contents of these will be explained later. The memory unit 17 may be configured as a database system or a file server separate from the distributed resource management device 1.

[0028] The distributed resource management device 1 is also connected to a terminal device 50 used by a user via the output unit 14 and the input unit 15. The terminal device 50 receives an instruction to create an operation plan 34 and displays the created operation plan 34 in accordance with a user's operation. In this embodiment, the terminal device 50 also has an equipment specification planning unit 33. This equipment specification planning unit 33 has a configuration as described in Example 6 below, and may be omitted or provided in the distributed resource management device 1. The terminal device 50 can be realized by a computer such as a PC or a tablet. Furthermore, the terminal device 50 may be connected to the distributed resource management device 1 via a network 92. The distributed resource management device 1 is operated by a local government or a business operator. Therefore, the terminal device 50 is operated by a user of the local government or the business operator. By using the distributed resource management device 1, the local government or the business operator manages the operation of the DER 42.

[0029] The weather forecast data providing system 30 is a computer system that provides weather information, and provides weather forecast data 31 such as weather forecasts via a network 92. The weather forecast data 31 may include observation values ​​other than forecasts.

[0030] Network 91 and network 92 are networks that connect the above-mentioned devices. Network 91 is a wide-area network that connects the central power distribution command system 60 and the power generation control devices 61-1 and 61-2. For this reason, it is desirable that network 91 be realized by a dedicated line operated and used by the power transmission and distribution company.

[0031] The network 92 is also connected to the central power distribution command system 60 and the EMS 41-1 and 41-2. Therefore, network 91 can be realized by the Internet, but may also be realized by a dedicated line operated and used by an electricity transmission and distribution company. Furthermore, network 91 and network 92 may be realized by a single network.

[0032] 12 is now finished, and next, an implementation example of the distributed resource management device 1 that executes the main processing in this embodiment 1 will be described. In the following, an example in which the distributed resource management device 1 is realized by a server that executes processing according to a program will be described. Fig. 13 is a configuration diagram showing an implementation example of the distributed resource management device 1 in this embodiment.

[0033] In FIG. 13, the distributed resource management system 1 includes a processing unit 101, a communication unit 102, a main storage unit 103, and a secondary storage unit 104, which are connected to each other via a communication path.

[0034] Here, the processing device 101 can be realized by a processor such as a CPU, and executes calculations in accordance with a distributed resource management program 105, which will be described later. The communication device 102 has an interface function for connecting to the network 92, and corresponds to the output unit 14 and input unit 15 in FIG. 12.

[0035] Furthermore, the main storage device 103 can be realized by a so-called memory, and for the purposes of calculations in the processing device 101, a distributed resource management program 105 stored in a storage medium such as a secondary storage device 104 and information used in the processing of this distributed resource management program 105 are deployed therein. Furthermore, the secondary storage device 104 can be realized by a storage such as a hard disk drive, and stores programs and various information (history data 2, etc.). In this way, the secondary storage device 104 corresponds to the storage unit 17 in FIG. 12.

[0036] The distributed resource management program 105 stored in the secondary storage device 104 is composed of a learning module 106, a prediction module 107, an operation plan creation module 108, and a DER control command module 109. The modules of the distributed resource management program 105 have the following correspondence with the respective parts in FIG. 7 . Learning Section 11: Learning Module 106 Prediction unit 12: prediction module 107 Operation plan creation unit 13: Operation plan creation module 108 DER control command unit 16: DER control command module 109 That is, the processing device 101 executes the processes of the learning unit 11 , the prediction unit 12 , the operation plan creation unit 13 and the DER control command unit 16 in accordance with the distributed resource management program 105 .

[0037] The distributed resource management program 105 is installed in the distributed resource management device 1 by being distributed via the network 92 or stored in a storage medium. Each module may be configured as an independent program. Furthermore, the distributed resource management program 105 may be provided with an equipment specification planning module corresponding to the equipment specification planning unit 33.

[0038] The secondary storage device 104 also stores the history data 2, learning model 29, storage battery state quantity 32, operation plan 34, and storage battery degradation model 35 in this embodiment. This concludes the description of an implementation example of this embodiment, but the distributed resource management device 1 may also be implemented by a computer such as a PC or tablet that has an input device and a display device. In this case, the input unit 15 corresponds to the input device, and the output unit 14 corresponds to the display device. This concludes the description of this embodiment, but this embodiment can realize a distributed resource management device 1 that can avoid output suppression or reduce its effects. As a result, it is possible to reduce opportunity losses for local governments and businesses that result from output suppression. [Example]

[0039] First, a first embodiment will be described with reference to Fig. 1 to Fig. 7. Fig. 1 is a diagram showing a schematic configuration of a distributed resource management apparatus 1 of this embodiment. As shown in Fig. 1, the distributed resource management apparatus 1 of this embodiment includes a learning unit 11, a prediction unit 12, an operation plan creation unit 13, and an output unit 14.

[0040] The learning unit 11 learns the input history data 2 and creates a learning model. To this end, the learning unit 11 is composed of an output suppression learning unit 111, a renewable energy power generation learning unit 112, a local demand learning unit 113, and an electricity price learning unit 114, each of which creates various learning models. The created learning models are stored as learning models 29, as shown in Figs. 12 and 13.

[0041] Here, the historical data 2 is information indicating the history of the operation and status of distributed resources. The historical data 2 includes output suppression history 21, past weather data history 22, past renewable energy power generation history 23, past local demand history 24, past electricity price history 25, and calendar information 26. The historical data 2 is learned by each component of the learning unit 11. That is, the output suppression learning unit 111, the renewable energy power generation learning unit 112, the local demand learning unit 113, and the electricity price learning unit 114 each learn data from the historical data 2 related to their respective learning targets. For example, to create a power suppression learning model, it is effective to learn, for example, the history of past output suppression occurrences, the weather data, electricity supply and demand, and calendar information (such as seasons, weekday / holiday classifications) at that time. The history of output suppression occurrences can be based on output suppression instructions from a power transmission and distribution company or operation history acquired from distributed resources such as renewable energy power generation facilities.

[0042] Therefore, the output suppression learning unit 111 creates an output suppression learning model using at least the output suppression history 21, and further using past weather data history 22, past renewable energy power generation history 23, past local demand history 24, and calendar information 26. This output suppression learning model is a model that describes the relationship between the probability of output suppression occurring and the weather data, power supply and demand, and calendar information.

[0043] Furthermore, the renewable energy power generation learning unit 112 learns the past renewable energy power generation history 23 and creates a renewable energy power generation learning model that indicates a learning model for power generation at the renewable energy power generation facility. As a result, the renewable energy power generation prediction unit 122, which will be described later, predicts the power generation status at the renewable energy power generation facility. The renewable energy power generation learning unit 112 is an example of a distributed resource learning unit that learns the operation history of the DER 42 and creates an operation learning model. As a result, the regional demand prediction unit 123, which will be described later, creates demand prediction results based on the created renewable energy power generation model. At this time, it is desirable to further use the regional demand model, which will be described later. The region refers to an area where the target local government or business is promoting decarbonization.

[0044] Furthermore, the local demand learning unit 113 creates a local demand model by learning the past local demand history 24 that indicates past local demand. As a result, the local demand forecasting unit 123 can forecast local demand based on the local demand model.

[0045] Furthermore, the power price learning unit 114 learns from the past power price history 25, which indicates the history of past power prices, to create a power price model. Here, it is desirable to use the power selling price from the renewable energy business operator as the power price. As a result, the power price prediction unit 124 predicts the power price in the region based on this power price model. Note that the learning algorithm of the learning unit 114 can be a known method such as machine learning.

[0046] Furthermore, the prediction unit 12 predicts the operation and status of the plurality of distributed resources (DER 42) based on the learning model created by the learning unit 11, and creates a prediction result. In particular, the prediction unit 12 predicts output suppression for the plurality of distributed resources (DER 42) based on the learning model created by the learning unit 11, and creates an output suppression prediction result. In this case, more preferably, the prediction unit 12 receives the learning model and weather forecast data 31 as input and creates an output suppression prediction result.

[0047] For this prediction, the prediction unit 12 is composed of an output suppression prediction unit 121, a renewable energy power generation prediction unit 122, a regional demand prediction unit 123, and an electricity price prediction unit 124. Furthermore, within the prediction unit 12, the prediction results of the renewable energy power generation prediction unit 122 and the regional demand prediction unit 123 are also used as input data for the output suppression prediction unit 121 and the electricity price prediction unit 124. For example, the output suppression prediction unit 121 predicts output suppression based on the output suppression learning model of the output suppression learning unit 111, the renewable energy power generation prediction of the renewable energy power generation prediction unit 122, the regional demand forecast of the regional demand prediction unit 123, and the weather forecast data 31. Note that a known method can be used as the prediction algorithm of the prediction unit 12.

[0048] Furthermore, the operation plan creation unit 13 creates operation plans for the multiple DERs 42) based on the prediction results by the prediction unit 12. More preferably, the operation plan creation unit 13 creates the operation plan based also on the storage battery state quantity 32. More specifically, the operation plan creation unit 13 creates the operation plan from at least one of a storage battery charge / discharge plan, an EV charging plan, a water heater operation plan, and predictions of power demand and renewable energy power generation for these devices. This operation plan may include a discharge plan for the power storage device 42-3 and a plan to sell power resulting from this discharge, etc.

[0049] Here, the battery state quantity 32 is a result of measuring in real time the state of the battery of the power storage device 42-3 managed by the distributed resource management device 1, and may be, for example, a physical quantity such as a remaining battery capacity (State of Charge: SoC). A specific example of operation plan creation by the operation plan creation unit 13 will be described later with reference to Figs. 4 and 5.

[0050] Furthermore, the output unit 14 outputs the operation plan output by the operation plan creation unit 13. For example, the output unit 14 displays the operation plan to a user by a method such as a screen display, or transmits the operation plan to another device such as a terminal device 50. A specific example of this output will be described later with reference to FIG. 6.

[0051] Next, FIG. 3 is a diagram illustrating an operation flow of the distributed resource management device 1 according to the first embodiment. Compared with the conventional flow of FIG. 2, steps S101 and S102 are performed in advance. That is, in FIG. 3, a local government or business operator promoting regional decarbonization causes the prediction unit 12 of the distributed resource management device 1 to predict output suppression (step S101). If the prediction results indicate a period of high risk of output suppression, the operation plan creation unit 13 of the distributed resource management device 1 creates an operation plan in preparation for output suppression (step S102). As part of the operation plan creation, a charging plan is created to charge the storage battery with power generated during that period. Furthermore, as part of the operation plan creation, a discharging plan is created to discharge the storage battery in advance by selling power or consuming power in demand equipment, etc., in order to ensure the remaining capacity of the storage battery required to execute the charging plan. As a result, an operation plan is created that does not perform (or reduces) reverse power flow by charging the storage battery with power generated by renewable energy based on the charging plan and the discharging plan. Then, this operation plan is submitted to the general electricity transmission and distribution company (step S11). For this purpose, for example, the distributed resource management device 1 transmits the operation plan to the central electricity distribution command system 60. Step S11 in Fig. 3 is similar to step S11 in Fig. 2 in the sense of submitting an operation plan, but the contents of the operation plan function are different.

[0052] Then, for example, in the general electricity transmission and distribution utility, the central electricity distribution command system 60 transmits to the distributed resource management device 1 an instruction to modify the submitted plan based on the supply and demand plan and the grid congestion forecast. However, since the operation plan submitted by the local government / business does not involve (or reduces) reverse power flow, the possibility that the local government / business will receive an instruction to modify the plan is lower than in the conventional flow of FIG. 2. If an instruction to modify the plan is received based on the grid congestion forecast (step S12), the local government / business creates an operation plan that modifies the amount of charge to the storage battery as necessary and submits it to the general electricity transmission and distribution utility (step S13).

[0053] In these cases, a plan modification instruction is sent from the central power distribution command system 60 to the distributed resource management device 1, and the distributed resource management device 1 modifies the operation plan and transmits it to the central power distribution command system 60.

[0054] After the gate closes, the general electricity transmission and distribution company may issue an order to curtail output based on grid congestion forecasts. However, since the local government or business in question has an operation plan that does not involve (or reduces) reverse power flow by charging renewable energy generation into storage batteries, it is possible (or the possibility of avoiding it is increased) to avoid output curtailment. The above flow makes it possible to reduce opportunity losses for renewable energy generation due to output curtailment.

[0055] A specific method for creating an operation plan in the above operational flow will be described with reference to FIGS. 4 and 5. FIG. 4 is an example of a graph showing the output suppression probability in the output suppression learning model created by the output suppression prediction unit 121 in the first embodiment. FIG. 4 shows the output suppression probability, which indicates the probability that output suppression will occur for each hour in a predetermined time slot. A period (t2 to t3) in which the output suppression probability is higher than a predetermined threshold value P0 is labeled as a period of high output suppression risk (output suppression risk period). In addition, a pre-preparation period (t1 to t2 in FIG. 4) is set before the output suppression risk period. The length of the pre-preparation period may be a predetermined value, or may be adjusted according to the transition of the value of the predicted output suppression probability.

[0056] The output suppression risk period and advance preparation period are extracted by the output suppression prediction unit 121. To this end, the output suppression prediction unit 121 calculates the output suppression probability for a predetermined time period, divides this time period into a plurality of output suppression-related periods according to the output suppression probability, and extracts the output suppression risk period and advance preparation period from these. The processing cycle of the operation flow in FIG. 3, such as one day, can be used as the predetermined time period. As a result, the operation plan creation unit 13 creates an operation plan according to the divided output suppression-related period. As will be explained in detail later, the operation plan creation unit 13 extracts the output suppression risk period and advance preparation period from the output suppression-related period, and creates an operation plan according to these.

[0057] 5 is an example of a graph showing predicted power prices (for example, power prices in the spot market or the hourly market) created by the power price prediction unit 124 according to the first embodiment. In this example, a basic guideline for creating an operation plan based on power prices is to ensure profits by purchasing power during a power purchase promotion period when the power price is low and selling power during a power sales promotion period when the power price is high. Here, a low power price means a price that is lower than a predetermined power purchase promotion threshold value X L The following applies to the electricity purchase promotion period. a ~t b , t e It means the following.

[0058] In addition, high electricity prices are those that exceed the predetermined threshold value X H The above means that the electricity sales promotion period is the period shown in Figure 5. c ~t d As a result, in the example of FIG. 5, during the above-mentioned advance preparation period (t1 to t2 in FIG. 5), the electricity sales promotion period (t a ~t b ), and when output curtailment is implemented, the storage battery can be charged with electricity generated by renewable energy. In other words, this operation plan sells electricity (discharges the storage battery) during the overlapping period when the advance preparation period and the electricity sales promotion period overlap, and when output curtailment is implemented, the storage battery can be charged with electricity generated by renewable energy.

[0059] In addition, the power charged during output curtailment is consumed by multiple DERs 42 after the curtailment is lifted, or is sold during the power sales promotion period (t in Figure 5). e The operation plan creating unit 13 may determine whether to sell the electricity in the future (hereafter) by referring to the prediction result of the prediction unit 12 of this embodiment. This determination may be made by the operation plan creating unit 13 or by the user based on the prediction result displayed via the output unit 14.

[0060] If there is no power sales promotion period during the advance preparation period or if high demand within the region is predicted, the operation plan creation unit 13 may create an operation plan for consuming power within the region instead of selling power as described above. While the above description assumes that the means for avoiding output suppression is charging and discharging a storage battery, other methods for avoiding output suppression are possible, such as controlling the operating hours of demand equipment (e.g., a hot water heater) to consume renewable energy power within the region and prevent reverse power flow. Here, demand equipment includes demand equipment at the consumer 70 and demand equipment installed in a house equipped with a DER 42, such as a solar power generation system 42-2. The operation plan creation unit 13 creates an operation plan from these various options in accordance with predetermined conditions, such as optimization from an economic perspective. The operation plan may also take into account the performance and status of the storage battery of the power storage facility 42-3. For example, the operation plan creation unit 13 creates an operation plan for charging the storage battery so as to avoid full charging to avoid deterioration.

[0061] A specific example of the output of the output unit 14 will be described below with reference to Fig. 6. Fig. 6 is a diagram showing an example of a display screen 140 displayed by the output unit 14 according to the first embodiment. Here, the description will be given assuming that the output unit 14 displays the display screen 140 as a display device, but the display screen 140 may also be displayed on a terminal device 50.

[0062] In FIG. 6, the display screen 140 has three items: an operation plan display, an output curtailment prediction display, and an electricity price prediction display. The operation plan display displays the numerical values ​​of the operation parameters of distributed resources such as storage batteries, EVs, and hot water heaters. The operation plan display also displays graphs of the demand forecast, renewable energy power generation forecast, storage battery discharge plan, and power sales plan. The output curtailment prediction display also displays a graph of the output curtailment probability shown in FIG. 4, and displays an alert if there is a period of high output curtailment risk. The electricity price prediction display also displays a graph of the electricity price prediction value shown in FIG. 5. In this way, the output unit 14 presents the operation plan and various prediction values ​​to the user. This provides the user with a basis for determining how to avoid output curtailment. [Example]

[0063] Next, a second embodiment will be described with reference to Fig. 7. Fig. 7 is a diagram showing a schematic configuration of a distributed resource management device 1 according to the second embodiment. As shown in Fig. 7, the distributed resource management device 1 of this embodiment differs from the first embodiment (Fig. 1) in that a DER operation history 27 is added to the history data 2 input to the learning unit 11. The other configurations are the same as those of the first embodiment (Fig. 1). Therefore, the following description will focus on this difference.

[0064] The DER operation history 27 is, for example, the charge / discharge history of the storage battery, the operation history of the water heater, etc. These are data including information on the time change in electricity demand, the supply / demand gap, and the storage battery state (charge / discharge efficiency, etc.). In addition, the DER operation history 27 is mainly used as learning data for the local demand learning unit 113. As a result, it is possible to improve the accuracy of the local demand forecast compared to Example 1 and each example described later. As a result of the improved accuracy of the local demand forecast, the accuracy of the operation plan created by the operation plan creation unit 13 improves, and it becomes possible to reduce opportunity losses caused by output suppression for local governments and businesses promoting local decarbonization. [Example]

[0065] Next, a third embodiment will be described with reference to Fig. 8. Fig. 8 is a diagram showing a schematic configuration of a distributed resource management device 1 according to the third embodiment. As shown in Fig. 8, the distributed resource management device 1 of this embodiment differs from the first embodiment (Fig. 1) in that a remote base output suppression history 28 is added to the history data 2 input to the learning unit 11. The other configurations are the same as those of the first embodiment (Fig. 1). Therefore, the following description will focus on this difference.

[0066] The other-base output suppression history 28 is a history of past output suppression at other bases different from the local government / operator (home base). Here, the other bases refer to bases in the same distribution system as the home base. Since the DER operating operator at the other base may generally be different from the operator at the home base, Example 3 is implemented under the assumption that information sharing between the above operators is performed.

[0067] In addition, the other base output suppression history 28 is mainly used as learning data for the output suppression learning unit 111. As a result, it is possible to improve the accuracy of output suppression prediction compared to Example 1 and each of the examples described below. The reason for this is as follows: Even if output suppression was not performed at the own base in the past output suppression history, if there is a case where output suppression was performed at another base under the same system conditions and weather conditions, it is useful to label and learn such a condition as one with a high risk of output suppression. As a result of the improved accuracy of output suppression prediction, the accuracy of the operation plan created by the operation plan creation unit 13 improves, and it becomes possible to reduce opportunity losses due to output suppression for local governments and businesses promoting regional decarbonization. [Example]

[0068] Next, a fourth embodiment will be described with reference to Fig. 9. Fig. 9 is a diagram showing a schematic configuration of a distributed resource management device 1 according to the fourth embodiment. As shown in Fig. 9, the distributed resource management device 1 of this embodiment differs from the first embodiment (Fig. 1) in that a storage battery deterioration model 35 is added. The other configurations are the same as those of the first embodiment (Fig. 1). Therefore, the following description will focus on this difference.

[0069] The battery degradation model 35 is a model that describes the influence of the operating parameters of the battery (for example, the charge / discharge rate and the upper and lower limits of the SoC in the charge / discharge cycle) on future degradation of the battery. The state of health (SoH) of the battery is often used as an evaluation index.

[0070] The battery degradation model 35 is also used as input data for the operation plan creation unit 13. Specifically, when the operation plan creation unit 13 creates an operation plan for the battery, the model is used to set operation parameters for suppressing the progression of future battery degradation. For this reason, the battery degradation model 35 is a model that indicates the aging and degradation progression of the battery. When creating an operation plan for the battery, it is desirable to evaluate the current SoH of the battery based on information on the battery state quantity 32 and set the operation parameters of the battery based on the evaluation results. As a result, it is possible to suppress deterioration of the battery and operate it for a longer lifespan, compared to Example 1 and the other examples described below. Operating a battery for a longer lifespan enables local governments and businesses promoting regional decarbonization to increase capital investment efficiency and improve business profitability. [Example]

[0071] Next, a fifth embodiment will be described with reference to Fig. 10. Fig. 10 is a diagram showing a schematic configuration of a distributed resource management device 1 according to the fifth embodiment. As shown in Fig. 10, the distributed resource management device 1 of this embodiment differs from the first embodiment (Fig. 1) in that a DER control command unit 16 is added. The other configurations are the same as those of the first embodiment (Fig. 1). Therefore, the following description will focus on this difference.

[0072] The DER control command unit 16 generates a control command based on the operation plan generated by the operation plan generation unit 13. The control command is transmitted to an EMS (Energy Management System) 41 that manages multiple distributed resources, i.e., DERs 42, and the EMS 41 controls each of the DERs 42.

[0073] By being equipped with the DER control command unit 16 in this manner, the distributed resource management device 1 of this embodiment has the functions of both operation plan creation and control command, which has the advantage for users of being able to achieve integrated operations and labor savings. [Example]

[0074] Next, a sixth embodiment will be described with reference to Fig. 11. Fig. 11 is a diagram showing a schematic configuration of a distributed resource management device 1 according to the sixth embodiment. As shown in Fig. 11, the distributed resource management device 1 of this embodiment differs from the first embodiment (Fig. 1) in that equipment specification information created by an equipment specification planning unit 33 is input to an operation plan creation unit 13. The other configurations are the same as those of the first embodiment (Fig. 1). Therefore, the following description will focus on this difference.

[0075] The equipment specification planning unit 33 plans specifications such as the required capacity of each device in the preliminary stage of introducing and installing distributed resources such as storage batteries, EVs, and water heaters. The equipment specification information created by the equipment specification planning unit 33 is, for example, information such as the capacity and charge / discharge rate of storage batteries, and the tank capacity and power consumption of water heaters. Generally, the distributed resource management device 1 is classified as an operational solution that is used in daily operations. In contrast, the equipment specification planning unit 33 is classified as a planning solution, and the two usually exist as separate systems. In this embodiment, the equipment specification planning unit 33 also exists outside the distributed resource management device 1, but information is shared between the two.

[0076] In the sixth embodiment, by receiving the equipment specification information created by the equipment specification planning unit 33, it is ensured that when the operation plan creation unit 13 creates an operation plan, an executable plan is created taking the equipment specifications into consideration. From another perspective, if the equipment specification planning unit 33 and the distributed resource management device 1 do not share information, the user needs to perform an operation to impose the equipment specification information as a constraint on the operation plan created by the operation plan creation unit 13. In contrast, the sixth embodiment has the advantage of realizing integration of operations and labor saving. [Example]

[0077] Next, as Example 7, the relationship between the opportunity loss reduction effect of renewable energy power generation facilities, which are an example of DER42 of local governments and businesses in each example, and the storage battery capacity and output suppression frequency will be described.

[0078] First, if the renewable energy power generation facility is unable to generate power due to output suppression, the annual loss L1 is expressed by the following (Equation 1).

[0079] L1 = [R × (1-α) × P S +R×α×P P ]×S···(Number 1) In (Equation 1), the amount of electricity generated by renewable energy power generation facilities is R (kWh / day), the rate at which renewable energy generated electricity is consumed by the demand equipment of the local government or business is α (0≦α≦1), and the selling price of surplus renewable energy generated electricity is P S (yen / kWh), and the purchase price of the insufficient renewable energy generated electricity is P P (yen / kWh), and the frequency of output curtailment is S (days / year).

[0080] In other words, since renewable energy generation is not performed, the electricity demand that is normally met by in-house generation must be purchased from outside, and there is no profit to be gained by selling surplus renewable energy generated electricity. For example, if R=500kWh / day, α=0.6, and P S = 10 yen / kWh, P P = 20 yen / kWh, S = 30 days / year, L1 = 240,000 yen / year.

[0081] Based on this, in the seventh embodiment, the effect of reducing opportunity loss when output suppression is avoided by charging and discharging the storage battery can be estimated as follows.

[0082] First, assuming that the capacity of the storage battery is B (kWh) and that there is no reverse power flow of renewable energy generated electricity, we can roughly estimate that the amount of storage battery capacity B can be used as in-house power generation for the amount of power generated by the renewable energy power generation facility R. In this case, the annual loss L2 can be calculated using (Equation 2).

[0083] L2=[R×(1-Min(1,B / R))×(1−α)×P S +R×(1-Min(1,B / R))×α×P P ]×S (Number 2) Here, Min(x, y) is a function representing the minimum value of x and y. In other words, if the storage battery capacity B is smaller than the renewable energy power generation facility's power generation capacity R, the annual loss decreases linearly as the storage battery capacity B increases. If the storage battery capacity B is greater than the renewable energy power generation facility's power generation capacity R, all of the renewable energy power can be charged to the storage battery, so increasing the storage battery capacity B beyond that does not affect the recovery of opportunity losses. For example, if B = 200 kWh, L2 = 144,000 yen / year, a 40% reduction compared to L1. Note that in reality, the self-consumption rate α also changes depending on the storage battery capacity B, so the above is an approximate evaluation. Furthermore, to optimize business viability, an evaluation that takes into account the battery purchase price (yen / kWh), useful life (years), and equipment depreciation costs is also required. By designing the storage battery capacity based on the above concepts and then applying Example 7, opportunity losses can be effectively reduced.

[0084] Therefore, in the seventh embodiment, the operation plan creation unit 13 calculates annual loss amounts L1 and L2 using (Equation 1) and (Equation 2). Next, the operation plan creation unit 13 outputs the opportunity loss reduction effect corresponding to the difference between L1 and L2 via the output unit 14. Note that the "annual" loss is an example of loss over a predetermined period and is not limited to a year. In this way, the operation plan creation unit 13 calculates the difference in the loss amount over a predetermined period depending on whether the storage battery is charged or not depending on the capacity B of the storage battery. Then, the operation plan creation unit 13 calculates the opportunity loss reduction effect corresponding to the difference. Here, the opportunity loss reduction effect corresponding to the difference may be the difference itself, or the above-mentioned ratio (40% in the above example) may be used. As a result, in the seventh embodiment, the user can grasp the opportunity loss reduction effect. Note that the configuration of the seventh embodiment can use the configurations of the first to sixth embodiments. In this case, a configuration combining at least two of the first to sixth embodiments may be used.

[0085] The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with another configuration. For example, at least two of Examples 1 to 7 may be combined. [Explanation of symbols]

[0086] 1...Distributed resource management device, 11...Learning unit, 12...Prediction unit, 13...Operation plan creation unit, 14...Output unit, 16...DER control command unit, 111...Output suppression learning unit, 112...Renewable energy power generation learning unit, 113...Regional demand learning unit, 114...Power price learning unit, 121...Output suppression prediction unit, 122...Renewable energy power generation prediction unit, 123...Regional demand prediction unit, 124...Power price prediction unit, 2...Historical data, 21...Output suppression history, 22...Past weather data history, 23...Past renewable energy power generation history, 24...Past regional demand history, 25...Past electricity price history, 26...Calendar information, 27...DER operation history, 28...Output suppression history at other locations, 31...Weather forecast data, 32...Storage battery state quantity, 33...Equipment specification planning unit, 34...Operation plan, 35...Storage battery deterioration model, 41...EMS, 42...DER.

Claims

1. A distributed resource management device for managing a plurality of distributed resources, a storage unit configured to store an output suppression history indicating a history of output suppression for the plurality of distributed resources; a learning unit that learns the output suppression history and creates an output suppression learning model; a prediction unit that predicts the occurrence of output suppression for the plurality of distributed resources based on the output suppression learning model and creates an output suppression prediction result; A distributed resource management device including an operation plan creation unit that creates an operation plan for the plurality of distributed resources in response to output suppression based on the output suppression prediction result.

2. 2. The distributed resource management apparatus according to claim 1, the plurality of distributed resources includes renewable energy power generation facilities; The prediction unit an output suppression prediction unit that creates the output suppression prediction result; a power generation prediction unit that predicts the amount of power generated by the renewable energy power generation facility and creates a power generation prediction result; a demand forecasting unit that forecasts power demands of the plurality of distributed resources and creates a demand forecast result; The operation plan creation unit is a distributed resource management device that creates the operation plan based on the power generation prediction result and the demand prediction result.

3. 2. The distributed resource management apparatus according to claim 1, the prediction unit has an output suppression prediction unit that calculates an output suppression probability indicating a probability that the output suppression will occur in a predetermined time period, and classifies the time period into output suppression-related periods according to the output suppression probability; The operation plan creation unit is a distributed resource management device that creates the operation plan according to the divided output suppression-related period.

4. 4. The distributed resource management device according to claim 3, the plurality of distributed resources include a power storage facility and a renewable energy power generation facility; The output suppression prediction unit extracts an output suppression risk period in which the output suppression probability is equal to or greater than a threshold value from the output suppression related period, The operation plan creation unit creates, as the operation plan, at least one of an operation plan for charging the power storage equipment with the power generated by the renewable energy power generation equipment during the output suppression risk period, and an operation plan for reducing reverse power flow to the distribution system to which the renewable energy power generation equipment is connected during the output suppression risk period.

5. 4. The distributed resource management device according to claim 3, the plurality of distributed resources includes a power storage facility; The output suppression prediction unit extracts, from the output suppression related period, a preparation period that precedes an output suppression risk period in which the output suppression probability is equal to or greater than a threshold, The operation plan creation unit creates, as the operation plan, an operation plan for discharging from the power storage equipment during the advance preparation period.

6. 5. The distributed resource management system according to claim 4, The distributed resource management apparatus further includes a power price prediction unit configured to predict power prices and generate power price prediction results.

7. 7. The distributed resource management system according to claim 6, The power price prediction unit is a distributed resource management device that extracts, from the power price prediction results, power sales promotion periods in which the power price exceeds a predetermined power sales promotion threshold and power purchase promotion periods in which the power price falls below a predetermined power purchase promotion threshold.

8. 8. The distributed resource management device according to claim 7, The output suppression prediction unit extracts a preparation period prior to the output suppression risk period from the output suppression related period, The operation plan creation unit is a distributed resource management device that, when there is an overlapping period in which the advance preparation period and the electricity sales promotion period overlap, creates an operation plan as the operation plan to sell the electricity discharged from the energy storage equipment during the overlapping period.

9. 9. The distributed resource management apparatus according to claim 8, The learning unit further includes a renewable energy power generation learning unit that learns a past renewable energy power generation history indicating a power generation history of the renewable energy power generation facility and creates a renewable energy power generation model, The distributed resource management device includes a demand prediction unit that creates a demand prediction result based on the renewable energy power generation model.

10. 2. The distributed resource management apparatus according to claim 1, The learning unit is a distributed resource management device having an output suppression learning unit that learns the output suppression history of a second plurality of distributed resources installed in a distribution system to which renewable energy power generation equipment included in the distributed resources is connected.

11. 6. The distributed resource management system according to claim 5, the storage unit further stores a battery deterioration model indicating a relationship between an operating condition of the power storage equipment and deterioration of the power storage equipment; The distributed resource management device is characterized in that the operation plan creation unit selects operating conditions for the storage equipment so as to suppress future deterioration of the storage equipment based on a deterioration prediction output according to the storage battery deterioration model.

12. 2. The distributed resource management apparatus according to claim 1, The distributed resource management device further comprises a control command unit that generates control commands for controlling the plurality of distributed resources based on the operation plan.

13. 2. The distributed resource management apparatus according to claim 1, The operation plan creation unit is a distributed resource management device that creates the operation plan based on equipment specification information of the plurality of distributed resources.

14. 2. The distributed resource management system of claim 1, the distributed resources include a power storage device having a storage battery; The operation plan creation unit calculates a difference in the amount of loss for a predetermined period depending on whether or not the storage battery is charged, and calculates an opportunity loss reduction effect depending on the difference.

15. A distributed resource management method executed by a distributed resource management device that manages a plurality of distributed resources, comprising: the storage unit stores an output suppression history indicating a history of output suppression for the plurality of distributed resources; a learning unit that learns the output suppression history and creates an output suppression learning model; a prediction unit predicting occurrence of output suppression for the plurality of distributed resources based on the output suppression learning model, and creating an output suppression prediction result; A distributed resource management method in which an operation plan creation unit creates an operation plan for the plurality of distributed resources in response to output curtailment based on the output curtailment prediction result.

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