JP2023005124A - Power management device, power management system, and power management method - Google Patents

Abstract

To provide a power management device, a power management system, and a power management method that can appropriately collect operation plans and appropriately calculate a reference value related to power demand.SOLUTION: In a power management system in which a plurality of facilities connected to an electric power system, a power management server, and an electric power company are connected via a network, a power management server 200 being a power management device comprises: a management unit 210 that manages a facility that includes a distributed power supply connected with the electric power system; and a control unit 230 that, based on information on the actual result of power demand in the facility in a first period, specifies a predicted error in power demand in the facility that is referred to in a second period later than the first period, and when the predicted error is larger than a threshold, requests an operation plan from the facility for specifying a plan for power demand in the facility in the second period.SELECTED DRAWING: Figure 3

Description

The present invention relates to a power management device, a power management system, and a power management method.

Conventionally, as a mechanism for adjusting the balance of power supply and demand in a power system, there is also known a mechanism in which a power management apparatus requests facilities to reduce power consumption (DR; Demand Response) (for example, Patent Literature 1). DR is a mechanism for reducing the power equivalent to the requested power from the standard power (hereinafter referred to as the standard value), and it is important to accurately estimate (calculate) the standard value. The reference value may be referred to as baseline power.

For example, the reference value may be calculated based on actual information on power demand of the facility, weather information, calendar information (season, month, day of the week, etc.).

JP 2013-169104 A

By the way, in the calculation of the reference value, the calculation accuracy of the reference value is improved by obtaining the operation plan for specifying the power demand plan of the facility from the facility side terminal and calculating the reference value based on the operation plan. expected to improve.

However, providing the operation plan is complicated for the facility side, and it is assumed that it is difficult to constantly collect detailed information on the operation plan.

SUMMARY OF THE INVENTION Accordingly, the present invention has been made to solve the above-described problems. It is an object of the present invention to provide a system and power management method.

A power management apparatus according to the disclosure includes a management unit that manages a facility that includes a distributed power supply connected to a power system, and based on the performance information of the power demand of the facility in the first period, after the first period An operation plan for specifying a forecast error of the power demand of the facility referred to in a second period, and specifying a plan of the power demand of the facility in the second period when the forecast error is greater than a threshold. and a control unit that requests the facility side.

The power management system according to the disclosure includes a management unit that manages a facility including a distributed power supply connected to a power system, and based on the performance information of the power demand of the facility in the first period, After the first period An operation plan for specifying a forecast error of the power demand of the facility referred to in a second period, and specifying a plan of the power demand of the facility in the second period when the forecast error is greater than a threshold. and a control unit that requests the facility side.

A power management method according to the disclosure includes the steps of managing a facility including a distributed power source connected to a power system; The operation plan for specifying the power demand forecast error of the facility referred to in two periods, and specifying the power demand plan of the facility in the second period when the forecast error is greater than a threshold. and a step of requesting the facility side.

Advantageous Effects of Invention According to the present invention, it is possible to provide a power management device, a power management system, and a power management method that enable appropriate collection of operation plans and appropriate calculation of a reference value for power demand.

FIG. 1 is a diagram showing a power management system 100 according to an embodiment. FIG. 2 is a diagram showing a facility 300 according to an embodiment. FIG. 3 is a diagram showing the power management server 200 according to the embodiment. FIG. 4 is a diagram of a local controller 360 according to an embodiment. FIG. 5 is a diagram illustrating an example of a display image according to the embodiment; FIG. 6 is a diagram illustrating a power management method according to an embodiment. FIG. 7 is a diagram illustrating a power management method according to an embodiment.

Embodiments will be described below with reference to the drawings. In addition, in the following description of the drawings, the same or similar reference numerals are given to the same or similar parts. However, the drawings are schematic.

[Embodiment]
(power management system)
The power management system will be described below.

As shown in FIG. 1 , the power management system 100 has a power management server 200 , facilities 300 and a power company 400 . In FIG. 1, as the facility 300, facilities 300A to 300C are illustrated.

Each facility 300 is connected to the power grid 110 . Hereinafter, the power flow from the power system 110 to the facility 300 is referred to as power flow, and the power flow from the facility 300 to the power system 110 is referred to as reverse power flow.

Power management server 200 , facility 300 and power company 400 are connected to network 120 . Network 120 may provide the circuits between these entities. For example, network 120 is the Internet. Network 120 may include a dedicated line such as a VPN (Virtual Private Network).

The power management server 200 is a server managed by businesses such as a power generation business, a power transmission and distribution business, a retail business, and a resource aggregator. The resource aggregator may be a power company that adjusts the power supply and demand balance of the power system 110 in a VPP (Virtual Power Plant). Adjustment of the power supply and demand balance may include trading (hereinafter, negawatt trading) in which the reduced power of the tidal power supplied from the power system 110 to the facility 300 (also referred to as the power demand of the facility 300) is exchanged for value. Balancing power supply and demand may include trading increased power for reverse flow power supplied from facility 300 to power system 110 for value. In the following, the request for adjusting the power supply and demand balance may be referred to as a power saving request. The resource aggregator may be an electric power company that provides reverse flow power to power generation companies, power transmission/distribution companies, retailers, and the like in the VPP.

The power management server 200 sends a control message that instructs the local control device 360 provided in the facility 300 to control the distributed power supply (for example, the solar cell device 310, the power storage device 320, or the fuel cell device 330) provided in the facility 300. to send. For example, the power management server 200 may transmit a power flow control message (for example, DR; Demand Response) requesting control of power flow, or may transmit a reverse power flow control message requesting control of reverse power flow. good. In addition, the power management server 200 may transmit power control messages that control the operating state of the distributed power sources. The control degree of tidal power or reverse tidal power may be represented by an absolute value (for example, XX kW) or by a relative value (for example, XX%). Alternatively, the degree of control of tidal power or reverse tidal power may be represented by two or more levels. The degree of control of tidal power or reverse tidal power may be represented by a power rate (RTP; Real Time Pricing) determined by the current power supply and demand balance, or may be represented by a power rate (TOU; Time Of Pricing) determined by the past power supply and demand balance. Use).

The facility 300 includes a solar cell device 310, a power storage device 320, a fuel cell device 330, a load device 340, a local control device 360, a power meter 380, and a power meter 390, as shown in FIG. have.

The solar cell device 310 is a distributed power source that generates power according to light such as sunlight. The solar cell device 310 may be an example of a distributed power supply that allows reverse power flow. The solar cell device 310 may be an example of a distributed power source to which a Feed-in Tariff (FIT) may be applied. The solar cell device 310 may be a distributed power source for which the feed-in tariff period has expired. For example, the solar cell device 310 is composed of a PCS (Power Conditioning System) and a solar panel.

Here, the power output from the solar cell device 310 may fluctuate depending on the amount of received light such as sunlight. Therefore, when considering the power generation efficiency of the solar cell device 310, the power output from the solar cell device 310 is variable power that can fluctuate depending on the amount of light received by the solar panel.

The power storage device 320 is a distributed power source that charges and discharges power. Power storage device 320 may be an example of a distributed power source in which reverse power flow is not allowed. The power storage device 320 may be an example of a distributed power source to which the fixed purchase price cannot be applied. For example, the power storage device 320 is composed of a PCS and power storage cells.

The fuel cell device 330 is a distributed power source that uses fuel to generate power. Fuel cell device 330 may be an example of a distributed power source in which reverse power flow is not allowed. Fuel cell device 330 may be an example of a distributed power source to which feed-in tariffs do not apply. For example, the fuel cell device 330 is composed of PCS and fuel cells.

For example, the fuel cell device 330 may be a solid oxide fuel cell (SOFC), a polymer electrolyte fuel cell (PEFC), or a phosphoric acid fuel cell. It may be a type fuel cell (PAFC; Phosphoric Acid Fuel Cell) or a molten carbonate type fuel cell (MCFC; Molten Carbonate Fuel Cell).

In embodiments, the solar cell device 310, the power storage device 320 and the fuel cell device 330 may be a regulated power source used for VPP. A regulated power supply is a power supply that contributes to the VPP among distributed power supplies provided in the facility 300 .

The load device 340 is a device that consumes power. For example, the load device 340 is an air conditioner, a lighting device, an AV (Audio Visual) device, or the like.

The local control device 360 is a device (EMS; Energy Management System) that manages the power of the facility 300 . The local control device 360 may control the operating state of the solar cell device 310, may control the operating state of the power storage device 320 provided in the facility 300, and may control the operating state of the fuel cell device 330 provided in the facility 300. may be controlled. Details of the local controller 360 will be described later (see FIG. 4).

In an embodiment, communication between power management server 200 and local controller 360 occurs according to a first protocol. On the other hand, communication between the local controller 360 and distributed power sources is performed according to a second protocol different from the first protocol. For example, as the first protocol, a protocol conforming to Open ADR (Automated Demand Response) or a unique dedicated protocol can be used. For example, the second protocol can use a protocol conforming to ECHONET Lite (registered trademark), SEP (Smart Energy Profile) 2.0, KNX, or a unique dedicated protocol. The first protocol and the second protocol may be different. For example, even if both are proprietary protocols, they may be protocols created according to different rules. However, the first protocol and the second protocol may be protocols created according to the same rules.

The wattmeter 380 is an example of a trunk wattmeter that measures tidal power from the power system 110 to the facility 300 and reverse power from the facility 300 to the power system 110 . For example, power meter 380 is a smart meter belonging to power company 400 .

Here, the wattmeter 380 transmits to the local control device 360 a message including an information element indicating the integrated value of the power flow or reverse power flow per unit time (for example, 30 minutes). Power meter 380 may transmit messages autonomously or may transmit messages at the request of local controller 360 . The power meter 380 may transmit to the power management server 200 a message including an information element indicating the power flow power or the reverse power flow power in each unit time.

The wattmeter 390 is an example of an individual wattmeter that measures the power of each distributed power supply. The power meter 390 may be provided at the output of the PCS of the distributed power supply and may be considered part of the distributed power supply. In FIG. 2 , a wattmeter 391 , a wattmeter 392 , and a wattmeter 393 are provided as the wattmeter 390 . A power meter 391 measures the output power of the solar cell device 310 . Power meter 392 measures the discharged power of power storage device 320 . Power meter 392 may measure the charging power of power storage device 320 . A power meter 393 measures the individual output power of the fuel cell device 330 .

Here, the power meter 390 may transmit a message including an information element indicating the power of the distributed power supply to the local controller 360 at intervals shorter than the unit time (for example, 1 minute). The individual output power of distributed power sources may be represented by an instantaneous value or an integrated value. Power meter 390 may transmit messages autonomously or may transmit messages at the request of local controller 360 .

Returning to FIG. 1, power company 400 is an entity that provides infrastructure such as power system 110, and is, for example, a power generator or a power transmission and distribution company. The power company 400 may entrust various operations to an entity that manages the power management server 200 . The power company 400 may send a request message to the power management server 200 requesting adjustment of the power supply and demand balance of the power system 110 .

(power management server)
The power management server will be described below. As shown in FIG. 3 , the power management server 200 has a management section 210 , a communication section 220 and a control section 230 . The power management server 200 may be an example of a VTN (Virtual Top Node). In embodiments, the power management server 200 is an example of a power management device.

The management unit 210 is configured by a storage medium such as a non-volatile memory and/or HDD, and manages information regarding the facility 300 . For example, the information about the facility 300 includes the types of distributed power sources provided in the facility 300, the specifications of the distributed power sources provided in the facility 300, and the like. The specifications may include the rated power generation of the solar cell device 310 , the rated charging power of the power storage device 320 , the rated charging power of the power storage device 320 , and the rated output power of the fuel cell device 330 . The specifications may include the rated capacity of power storage device 320, the maximum charge/discharge power, and the like.

In the embodiment, the management unit 210 constitutes a management unit that manages the facility 300 including distributed power sources connected to the power grid 110 .

The communication unit 220 is configured by a communication module and communicates with the local control device 360 via the network 120 . The communication module can be a wireless communication module conforming to standards such as IEEE802.11a/b/g/n, ZigBee, Wi-SUN, LTE, 5G, 6G, etc., and a wired communication module conforming to standards such as IEEE802.3 It may be a communication module.

As described above, the communication unit 220 communicates according to the first protocol. For example, communication unit 220 transmits a first message to local controller 360 according to a first protocol. Communication unit 220 receives a first message response from local controller 360 according to a first protocol.

In the embodiment, the communication unit 220 may constitute a receiving unit that receives performance information of power demand of the facility 300 from the facility 300 . The communication unit 220 may receive performance information on the power generated by the solar cell device 310 from the facility 300 . The communication unit 220 may receive performance information on the charge/discharge power of the power storage device 320 from the facility 300 . The communication unit 220 may receive output power performance information of the fuel cell device 330 from the facility 300 . The communication unit 220 may receive power consumption performance information of the load device 340 from the facility 300 .

The power demand performance information of the facility 300 may be the power flow performance information measured by the power meter 380 . Performance information may be received from power meter 380 and may be received from local controller 360 .

Control unit 230 may include at least one processor. The at least one processor may be composed of a single integrated circuit (IC) or may be composed of a plurality of communicatively connected circuits (such as integrated circuits and/or discrete circuits).

The control unit 230 controls each component provided in the power management server 200 . For example, the control unit 230 instructs the local control device 360 provided in the facility 300 to control distributed power sources provided in the facility 300 by transmitting a control message. The control message may be a power flow control message, a reverse power flow control message, or a power control message, as described above.

In the embodiment, the control unit 230 identifies the prediction error of the power demand of the facility 300 that is referenced in the second period after the first period based on the performance information of the power demand of the facility 300 in the first period. , configures a control unit that requests the facility 300 side to make an operation plan for specifying the power demand plan of the facility 300 in the second period when the prediction error is larger than the threshold.

The second period is a period for adjusting the power supply and demand balance of the power system 110 . For example, the second period may be a DR period in which suppression of tidal power (demand power) is requested, or an output suppression period in which suppression of reverse tidal power (output power) is requested.

The power demand of facility 300 that is referred to in the second period is used to calculate a reference power (hereinafter referred to as a reference value) that is referred to when adjusting the power supply and demand balance of power system 110 . In the following, the power demand of facility 300 referred to in the second period may be referred to as a reference value. The power management server 200 needs to reduce the power corresponding to the requested power requested in the request message from the reference value. The reference value may be referred to as baseline power. The baseline power may be an average value of demand power for a certain period before the DR period. The certain period of time may be determined according to the substance of the negawatt transaction, or may be determined by the electric power company 400 .

For example, the control unit 230 uses a prediction model generated by learning the power demand of the facility 300 in the first period to calculate the prediction error of the power demand (reference value) of the facility 300 referenced in the second period. may be specified.

The prediction model may be generated by learning the performance information of power demand and learning parameters that affect the power demand. By inputting the learning parameters and the corresponding input parameters into the prediction model, the prediction model may output the predicted value and the prediction error of the power demand (reference value). The learning parameters may include at least a parameter specifying time. Learning parameters may include parameters specifying the day of the week, month, season, weather, temperature, humidity, and the like.

The prediction model may be generated by learning the performance information of the generated power of the solar cell device 310 and learning parameters that affect the generated power. The learning parameters may include parameters specifying the weather, temperature, humidity, amount of solar radiation, etc. that affect the power generated by the solar cell device 310 .

The prediction model may be generated by learning the performance information of the power consumption of the facility 300 (for example, the load device 340) and learning parameters that affect the power consumption. The learning parameters may include at least a parameter specifying time. Learning parameters may include parameters specifying the day of the week, month, season, weather, temperature, humidity, and the like.

The predictive model may learn parameters related to power storage device 320 as one of the learning parameters. The parameters related to the power storage device 320 may include parameters indicating specifications of the power storage device 320 (eg, rated capacity, maximum charge/discharge power, etc.). The parameters related to the power storage device 320 may include parameters indicating the remaining amount of power storage of the power storage device 320 .

The operation plan requested to the facility 300 side may include the power demand plan of the facility 300 itself. The operation plan may include an operation plan (charging/discharging plan) of power storage device 320 . The operation plan may include an operation plan for the fuel cell device 330 . The operation plan may include an operation plan (power consumption) of the load device 340 . In such a case, the operation plan may include an operation plan (power consumption) plan for the load device 340 whose power consumption is assumed to be greater than the threshold.

Although not particularly limited, the operation plan may be input by a user of facility 300 . The operation plan may be input in file format.

Under this premise, the threshold value may be determined according to the power that can be adjusted by the distributed power supply (hereinafter, power storage device 320). The power that can be adjusted by power storage device 320 may be specified based on at least one of the rated capacity of power storage device 320 and the remaining power amount of power storage device 320 . The thresholds may include a first threshold and a second threshold that is greater than the first threshold.

Here, the control unit 230 may present information elements regarding prediction errors to the facility 300 side. The information element regarding the prediction error may be an information element indicating the prediction error itself, and may include the success probability of successfully adjusting the power supply and demand balance by controlling the power storage device 320 in the second period. The prediction error is the prediction error of the reference value referred to in the second period, and has a very high correlation with the probability of success, so it is possible to specify the probability of success based on the prediction error. The information element about the prediction error may be an information element representing the level of prediction error (e.g., large, medium, small, etc.), or an information element representing the level of probability of success (e.g., low, medium, high, etc.). There may be.

Information elements related to forecast errors are information elements for cases where forecast errors are specified without using the operation plan (hereinafter referred to as information elements (no operation plan)) and information elements for cases where forecast errors are specified using the operation plan (hereinafter referred to as , information element (with operation plan)). Information elements related to prediction errors may include both an information element (without operation plan) and an information element (with operation plan). According to such a configuration, the user of the facility 300 can understand that the provision of the operation plan increases the probability of success, and the motivation to provide the operation plan can be improved.

The control unit 230 requests the first operation plan having the data granularity of the first level when the prediction error is greater than the first threshold, and requests the first operation plan having the data granularity of the first level when the prediction error is greater than the second threshold. A second run plan with a fine second level of data granularity may be requested.

Here, the data granularity may be defined by the time unit of the operation plan. Although not particularly limited, the first level data granularity may be daily data granularity, and the second level data granularity may be hourly data granularity. Data granularity may be defined by the number of items in the operational plan. Although not particularly limited, the first level of data granularity is the data granularity including the power demand of the facility 300, and the second level of data granularity is the power demand of the facility 300 and the operation of the power storage device 320. The data granularity may include planning items.

Information elements related to forecast errors are information elements for cases in which forecast errors are specified using the first operation plan (hereinafter referred to as information elements (first operation plan)) and information elements for cases in which forecast errors are specified using the second operation plan. At least one of information elements (hereinafter referred to as information elements (second operation plan)) may be included. Information elements related to prediction errors may include both information elements (first operation plan) and information elements (second operation plan). The user of the facility 300 can understand that the provision of the second operation plan with finer data granularity increases the probability of success, and the motivation to provide the second operation plan with finer data granularity can be improved.

The control unit 230 generates display data (image) for requesting an operation plan from the facility 300 side, and performs communication so as to transmit the display data (image) to a terminal belonging to a user of the facility 300 (hereinafter referred to as a user terminal). 220 may be controlled. The communication unit 220 may directly transmit the display data (image) to the user terminal, or directly transmit the display data (image) to the user terminal via the local controller 360 or the like.

Similarly, the control unit 230 may control the communication unit 220 to generate display data (image) regarding information elements related to prediction errors and transmit the display data (image) to the user terminal. The communication unit 220 may directly transmit the display data (image) to the user terminal, or directly transmit the display data (image) to the user terminal via the local controller 360 or the like.

The display data (image) related to the information element related to the prediction error may be transmitted together with the display data (image) for requesting the facility 300 side for the operation plan.

(local controller)
In the following, the local controller will be described. As shown in FIG. 4 , the local control device 360 has a first communication section 361 , a second communication section 362 and a control section 363 . The local controller 360 may be an example of a VEN (Virtual End Node).

The first communication unit 361 is configured by a communication module and communicates with the power management server 200 via the network 120 . The communication module can be a wireless communication module conforming to standards such as IEEE802.11a/b/g/n, ZigBee, Wi-SUN, LTE, 5G, 6G, etc., and a wired communication module conforming to standards such as IEEE802.3 It may be a communication module.

As described above, the first communication unit 361 communicates according to the first protocol. For example, the first communication unit 361 receives the first message from the power management server 200 according to the first protocol. The first communication unit 361 transmits the first message response to the power management server 200 according to the first protocol.

The second communication unit 362 is configured by a communication module and communicates with distributed power sources. The communication module can be a wireless communication module complying with standards such as IEEE802.11a/b/g/n, ZigBee, Wi-SUN, LTE, 5G, 6G, etc., such as IEEE802.3 or proprietary proprietary protocols. It may be a standard-compliant wired communication module.

As described above, the second communication unit 362 communicates according to the second protocol. For example, the second communication unit 362 transmits the second message to the distributed power sources according to the second protocol. The second communication unit 362 receives the second message response from the distributed power sources according to the second protocol.

Control unit 363 may include at least one processor. The at least one processor may be composed of a single integrated circuit (IC) or may be composed of a plurality of communicatively connected circuits (such as integrated circuits and/or discrete circuits).

The control unit 363 controls each component provided in the local control device 360 . Specifically, in order to control the power of the facility 300 , the control unit 363 instructs the equipment to set the operating state of the distributed power sources by transmitting the second message and receiving the second message response. The control unit 363 may instruct the distributed power sources to report the information of the distributed power sources by sending the second message and receiving the second message response in order to manage the power of the facility 300 .

(display image)
The display image will be described below. Here, a case is exemplified in which the display image may include a display image related to information elements related to prediction errors and a display image for requesting the facility 300 to provide an operation plan. Further, a case is illustrated where the threshold includes a first threshold and a second threshold (>first threshold). The information element regarding the prediction error is represented by the level of success probability (in FIG. 5, success expectation).

First, a case where the prediction error is smaller than the first threshold will be described with reference to the upper part of FIG. In such a case, the operation plan need not be requested from the facility 300 side.

As shown in the upper part of FIG. 5, the display image may include a display image related to the information element related to the prediction error (demand response success expectation: high in the upper part of FIG. 5). The display image may not include the display image for requesting the operation plan from the facility 300 side.

Second, a case where the prediction error is greater than the first threshold will be described with reference to the middle part of FIG. In such cases, a first run plan with a first level of data granularity may be requested.

As shown in the middle part of FIG. 5, the display image may include a display image related to the information element related to the prediction error (in the middle part of FIG. 5, demand response success expectation: medium). The display image may include a display image for requesting the first operation plan from the facility 300 side (in the middle part of FIG. 5, please input the operation information for each day).

Third, a case where the prediction error is greater than the second threshold will be described with reference to the lower part of FIG. In such cases, a second run plan with a second level of data granularity that is finer than the first level may be requested.

As shown in the lower part of FIG. 5, the display image may include a display image related to the information element related to the prediction error (demand response success expectation: low in the lower part of FIG. 5). The display image may include a display image for requesting the second operation plan from the facility 300 side (in the lower part of FIG. 5, please input the operation information in units of one hour).

Although not particularly limited, the display image may include an image that visualizes the state of charge (SOC; State Of Charge [%]) and the operation plan (Discharge [kW]) of the power storage device 320 . Note that FIG. 5 illustrates a case where the Discharge is constant at 0 kW and the SOC is also constant. Also, when Discharge is a positive value, Discharge represents the discharged power of power storage device 320 , and when Discharge is a negative value, Discharge represents the charged power of power storage device 320 .

(Power management method)
The power management method is described below. A case in which the second period is the DR period will be exemplified below.

First, generation of a prediction model will be described with reference to FIG. Generation of the prediction model is performed in the first period prior to the DR period.

As shown in FIG. 6, in step S10, the power management server 200 collects various information. Although not particularly limited, the power management server 200 may collect various types of information from an external server that manages the weather.

The information collected may include learned parameters that affect the power demand of facility 300 . The collected information may include learning parameters that affect the power generated by the solar cell device 310 . The information collected may include learning parameters that affect the power consumption of load device 340 .

Additionally, the information collected may include information about facility 300 . The information about the facility 300 includes the types of distributed power sources provided in the facility 300, the specifications of the distributed power sources provided in the facility 300, and the like.

In step S<b>11 , the power management server 200 receives performance information from the facility 300 . The performance information may include performance information of the power demand of the facility 300 . The track record information may include track record information of the power generated by the solar cell device 310 . The performance information may include performance information of the power consumption of the load device 340 .

In step S12, the power management server 200 generates a prediction model based on the information collected in step S10 and the information received in step S11.

Secondly, processing related to the prediction error of the reference value referred to in the DR period will be described with reference to FIG.

As shown in FIG. 7, in step S20, the power management server 200 collects various information. Although not particularly limited, the power management server 200 may collect various types of information from an external server that manages the weather.

The information collected is the input parameters that are input to the prediction model, and the input parameters are the parameters that correspond to the learning parameters. Accordingly, the information collected may include learned parameters that affect the power demand of facility 300 . The collected information may include learning parameters that affect the power generated by the solar cell device 310 . The information collected may include learning parameters that affect the power consumption of load device 340 .

In step S21, the power management server 200 acquires the predicted value and prediction error of the power demand (reference value) from the prediction model by inputting the input parameters into the prediction model.

In step S22, when the prediction error is greater than the threshold, the power management server 200 sends a message requesting input of an operation plan for identifying the power demand plan of the facility 300 in the DR period (in FIG. 7, an input request ) to the facility 300 .

In such a case, the power management server 200 may present to the facility 300 side information elements related to the prediction error together with the operation plan request. For example, the power management server 200 requests the facility 300 to input display data of a display image (see FIG. 5) that can include a display image for requesting the facility 300 side for an operation plan and a display image related to information elements related to prediction errors. may be sent to

At step S<b>23 , each facility 300 displays a display image (see FIG. 5 ) based on the display data received from the power management server 200 . For simplification of explanation, a case where each facility 300 displays the display image is exemplified, but the display image may be displayed by the user terminal as described above.

In step S<b>24 , the power management server 200 receives a response message (input response in FIG. 7 ) to the input request from each facility 300 . The input response may include an operating plan for specifying the power demand plan for the facility 300 during the DR period.

In step S30, the power management server 200 receives the request message. The request message may be received from power company 400 .

In step S<b>31 , the power management server 200 determines control details for the power storage device 320 based on the predicted value and prediction error of the power demand (reference value) from the prediction model, and issues control instructions corresponding to the control details to each facility 300 . Send to The power management server 200 may determine the content of control for the power storage device 320 based on the rated capacity and the remaining amount of power storage of the power storage device 320 .

(Action and effect)
In the embodiment, the power management server 200 identifies the prediction error of the power demand of the facility 300 referenced in the second period after the first period, based on the performance information of the power demand of the facility 300 in the first period. Then, when the prediction error is equal to or greater than the threshold, the facility 300 is requested to provide an operation plan for specifying the power demand plan for the facility 300 in the second period. According to such a configuration, an operation plan is requested when the prediction error is equal to or greater than the threshold. While reducing the number of man-hours, it is possible to appropriately collect operation plans. Appropriate collection of operation plans enables proper calculation of reference values for power demand.

In an embodiment, the power management server 200 may present information elements related to prediction errors to the facility 300 side when requesting an operation plan. According to such a configuration, it is possible to improve the motivation to input the operation plan.

In an embodiment, the power management server 200 requests a first operation plan with a first level of data granularity if the prediction error is greater than a first threshold, and if the prediction error is greater than a second threshold: A second run plan may be requested that has a second level of data granularity that is finer than the first level. According to such a configuration, it is possible to appropriately collect operation plans while suppressing an increase in the man-hours of the user of the facility 300 .

Furthermore, since it becomes easier for the power management server 200 to collect operation plans, it is possible to identify data that contributes to the improvement of the reference value prediction accuracy from the operation plan. and time can be reduced. The data contributing to the improvement of the prediction accuracy of the reference value include the type of power demand of the facility 300, the type of distributed power sources provided in the facility 300, the type of load equipment 340 provided in the facility 300, and the like. Note that data that contributes to improving the prediction accuracy of the reference value can be used for prediction of the reference value of the new facility 300 .

[Other embodiments]
Although the present invention has been described by the above-described embodiments, the statements and drawings forming part of this disclosure should not be construed as limiting the present invention. Various alternative embodiments, implementations and operational techniques will become apparent to those skilled in the art from this disclosure.

Although the power management server 200 generates the prediction model in the above disclosure, the above disclosure is not limited to this. A prediction model may be generated by a third party other than the power management server 200 .

In the above disclosure, power management server 200 controls power storage device 320, but the above disclosure is not limited to this. The power management server 200 has a function of requesting an operation plan for specifying the power demand plan of the facility 300 in the second period based on the prediction error of the power demand (reference value) referred to in the second period. It's fine if you do. Therefore, the power management server 200 does not have to have the function of actually controlling the power storage device 320 . In such a case, the power management server 200 may be read as a data providing device or a data requesting device.

In the above disclosure, the power storage device 320 is mainly illustrated as a distributed power source (adjusted power source) controlled by the power management server 200 . However, the above disclosure is not so limited. A distributed power source controlled by the power management server 200 may include a fuel cell device 330 .

In the above disclosure, the solar cell device 310 was exemplified as a distributed power supply that allows reverse power flow. However, the above disclosure is not so limited. Distributed power sources that allow reverse power flow may include distributed power sources that output power using renewable energy. Such distributed power sources may include one or more distributed power sources selected from among wind power plants, hydro power plants, geothermal power plants and biomass power plants. A reverse power flow of the discharged power of the power storage device 320 may be allowed, and a reverse power flow of the output power of the fuel cell device 330 may be allowed.

Although not specifically mentioned in the above disclosure, power storage device 320 may include a stationary power storage device or may include a power storage device mounted on an electric vehicle.

The above disclosure exemplifies the case where the local control device 360 is provided in the facility 300 . However, the above disclosure is not so limited. Local controller 360 may be provided by a cloud service implemented by a server or the like provided on network 120 .

Although not specifically mentioned in the disclosure above, the power may be an instantaneous value (kW) or an integrated value (kWh) per unit time.

DESCRIPTION OF SYMBOLS 100... Power management system, 110... Power system, 120... Network, 200... Power management server, 210... Management part, 220... Communication part, 230... Control part, 300... Facility, 310... Solar battery device, 320... Power storage device , 330... fuel cell device, 340... load device, 360... local control device, 361... first communication unit, 362... second communication unit, 363... control unit, 380... power meter, 390... power meter, power meter, 392... Power meter, 393... Power meter, 400... Power company

Claims (11)

a management unit that manages a facility that includes distributed power sources connected to a power grid;
Based on the performance information of the power demand of the facility in the first period, a prediction error of the power demand of the facility referred to in a second period after the first period is specified, and the prediction error is greater than a threshold. and a control unit that requests an operation plan for specifying a plan for power demand of the facility in the second period from the facility side when the power demand is large. 2. The power management apparatus according to claim 1, wherein said threshold is determined according to power that can be adjusted by said distributed power sources. 3. The power management apparatus according to claim 1, wherein said second period is a period for adjusting the power supply and demand balance of said power system. The control unit presents information elements related to the prediction error to the facility side,
The information element regarding the prediction error includes at least one of an information element for specifying the prediction error without using the operating plan and an information element for specifying the prediction error using the operating plan. The power management apparatus according to any one of claims 1 to 3. The threshold includes a first threshold and a second threshold that is greater than the first threshold. The control unit has a first level of data granularity when the prediction error is greater than the first threshold. Requesting a first operational plan, and requesting a second operational plan having a second level of data granularity that is finer than the first level if the prediction error is greater than the second threshold. 5. The power management device according to any one of 4. The control unit presents information elements related to the prediction error to the facility side,
The information element related to the prediction error is at least one of an information element for specifying the prediction error using the first operation plan and an information element for specifying the prediction error using the second operation plan. 6. The power management apparatus of claim 5, comprising: The power management apparatus according to any one of claims 1 to 6, wherein the operation plan includes a power demand plan itself of the facility. The power management apparatus according to any one of claims 1 to 7, wherein said operation plan includes an operation plan for load devices of said facility. The power management apparatus according to any one of claims 1 to 8, wherein the operation plan includes an operation plan for the distributed power sources. a management unit that manages a facility that includes distributed power sources connected to a power grid;
Based on the performance information of the power demand of the facility in the first period, a prediction error of the power demand of the facility referred to in a second period after the first period is specified, and the prediction error is greater than a threshold. and a control unit that requests an operation plan for specifying a power demand plan of the facility in the second period from the facility side when the power demand is large. managing a facility that includes distributed power sources connected to a power grid;
Based on the performance information of the power demand of the facility in the first period, a prediction error of the power demand of the facility referred to in a second period after the first period is specified, and the prediction error is greater than a threshold. and requesting, from the facility side, an operation plan for specifying the power demand plan of the facility in the second period if the power demand is large.

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