JP7327111B2 - Management device, power adjustable amount estimation method, computer program, and system - Google Patents

Description

The present disclosure relates to a management device, a power adjustable amount estimation method, a computer program, and a system.

Patent Literature 1 discloses an energy management system that creates a charging plan for a power storage device. Patent Literature 2 discloses a demand response request device that obtains a predicted value of power consumption based on the actual charging performance of an electric vehicle. Demand response is the adjustment of power consumption by consumers in order to balance power supply and demand.

JP 2013-188031 A JP 2017-70211 A

When implementing demand response, the target of power regulation in demand response should be determined appropriately. In order to appropriately determine the target of power adjustment, it is desirable to grasp the amount of power that can be adjusted prior to implementing demand response. Here, the power adjustable amount is the power amount that is expected to be adjustable by implementing demand response in the future. Since the power demand changes over time, the power adjustable amount changes depending on the power consumption of individual consumers and also changes according to the implementation scenario such as when the demand response is implemented.

Therefore, in order to appropriately determine the power adjustment target, it is desired to estimate an appropriate power adjustment possible amount according to the implementation scenario of the demand response.

One aspect of the present disclosure is a management device that manages a plurality of consumer devices for demand response. The disclosed management device includes a storage unit storing actual power demand data for each of the plurality of consumer devices, and a power adjustable amount according to at least one demand response execution scenario based on the actual power demand data. and a calculation unit that executes a calculation process.

Another aspect of the present disclosure is a power adjustability estimation method. In the disclosed power adjustable amount estimation method, actual power demand data of each of a plurality of consumer devices is stored in a storage unit, and a processor reading the actual power demand data from the storage unit stores the actual power demand data. calculating a power adjustability according to at least one demand response implementation scenario based on.

Another aspect of the disclosure is a computer program product. A disclosed computer program causes a processor to execute a process of calculating a power adjustable amount according to at least one demand response implementation scenario based on actual power demand data of each of a plurality of consumer devices.

Another aspect of the disclosure is a system. The disclosed system includes a demand response control device and a management device that manages a plurality of consumer devices for demand response, and the management device stores actual power demand data of each of the plurality of consumer devices. and a calculation unit that calculates an adjustable power amount according to at least one demand response implementation scenario based on the actual power demand data, wherein the adjustable power amount is calculated as the It is configured to transmit to a demand response control unit.

According to the present disclosure, power adjustability is obtained according to a demand response implementation scenario.

FIG. 1 is a system configuration diagram for demand response. FIG. 2 is a hardware configuration diagram of the EV server. FIG. 3 is a software configuration diagram of the EV server. FIG. 4 is a demand response sequence. FIG. 5 is an explanatory diagram of calculation of the charge amount integrated value. FIG. 6 is a configuration diagram of a chargeable amount table. FIG. 7 is an explanatory diagram of calculation of the power adjustable amount. FIG. 8 is a configuration diagram of an adjustable amount table. FIG. 9 is a configuration diagram of a chargeable amount table. FIG. 10 is a configuration diagram of an adjustable amount table. FIG. 11 is an explanatory diagram of charging control.

[Description of Embodiments of the Present Disclosure]

(1) A management device according to an embodiment manages a plurality of consumer devices for demand response. The management device calculates a power adjustable amount according to at least one demand response execution scenario, based on a storage unit storing actual power demand data of each of the plurality of consumer devices, and the actual power demand data. and a calculation unit that executes the process. In this case, the power adjustable amount is calculated according to the demand response implementation scenario based on the actual power demand data.

(2) At least one demand response implementation scenario is preferably a plurality of demand response implementation scenarios. In this case, an appropriate demand response implementation plan can be formulated from a plurality of demand response implementation scenarios.

(3) It is preferable that at least one of the start time and the duration of the demand response is different for each of the plurality of demand response execution scenarios. In this case, an appropriate demand response implementation plan can be formulated from a plurality of implementation scenarios that differ in at least one of start time and duration.

(4) The demand response may include an increased demand response that increases power demand during the time period during which the demand response is implemented. In the process of calculating the power adjustable amount, based on the actual power demand data, a predictable chargeable amount in the implementation time period is calculated, and based on the predictable chargeable amount in the increased demand response Calculating the power adjustable amount may be included. In this case, the power adjustable amount is calculated from the chargeable amount predicted value in the first implementation time period.

(5) The increased demand response may include shifting at least the power information in the first time slot to a second time slot that is the implementation time slot of the demand response. The process of calculating the power adjustable amount may further include calculating a power demand forecast value for a time period including at least the first time period. Preferably, the power adjustable amount in the increased demand response is calculated based on the power demand predicted value and the chargeability predicted value. In this case, the power adjustable amount is calculated from the power demand predicted value in the first time period and the chargeable amount predicted value in the second time period.

(6) The demand response may include a decreasing demand response that reduces the power demand during the execution time period of the demand response. In the process of calculating the power adjustable amount, based on the actual power demand data, a power demand forecast value during the time period during which the decreased demand response is implemented is calculated, and the power demand during the time period during which the decreased demand response is performed is calculated. Calculating the power adjustability in the reduced demand response based on the predicted value can be included.

(7) The management device can further include a control unit. The control unit calculates a difference between the target value and the actual value based on a target value for power adjustment set based on the adjustable power amount and an actual value for power adjustment during the duration of the demand response. power demand of the plurality of consumer devices during the duration of the demand response so that In this case, the actual value can be brought closer to the target value.

(8) Adjusting the power demand of each of the plurality of consumer devices increases or decreases the number of target consumer devices that consume power among the plurality of consumer devices during the duration of the demand response. can include allowing In this case, the power demand can be adjusted by increasing or decreasing the number of target consumer devices.

(9) The consumer device preferably includes a storage battery. In this case, the charging of the storage battery can be considered as power demand.

(10) The consumer device preferably includes a stationary storage battery. However, the storage battery may be a storage battery other than the stationary type. Note that the consumer device may be any device capable of controlling power demand, and may be a device other than a storage battery.

(11) In the power adjustable amount estimation method according to the embodiment, the actual power demand data of each of a plurality of consumer devices is stored in a storage unit, and the processor reading the actual power demand data from the storage unit performs the Calculating power adjustability according to at least one demand response implementation scenario based on the power demand performance data.

(12) A computer program according to an embodiment causes a processor to execute a process of calculating a power adjustable amount according to at least one demand response implementation scenario based on actual power demand data of each of a plurality of consumer devices. . A computer program is stored in a computer-readable, non-transitory storage medium.

(13) A system according to an embodiment includes a demand response generalizing device and a management device that manages a plurality of consumer devices for demand response. The management device calculates a power adjustable amount according to at least one demand response execution scenario, based on a storage unit storing actual power demand data of each of the plurality of consumer devices, and based on the actual power demand data. and a calculation unit that executes a process for calculating the The management device is configured to transmit the power adjustability amount to the demand response manager.

[Details of the embodiment of the present disclosure]

FIG. 1 shows a system 10 for demand response. The system 10 includes a virtual power plant (VPP) server 30 and a consumer device server 100 .

The VPP server 30 manages a plurality of power generation related devices so that they function as one power plant. The plurality of power generation-related equipment includes, for example, equipment of the grid operator 21, equipment of the renewable energy power producer 22, and equipment of the electricity retailer 23. The VPP server 30 also operates as a device (demand response control device) that controls demand responses (DR).

The VPP server 30 of the embodiment determines a target value for power adjustment based on the power adjustable amount transmitted from the consumer device server 100 . The VPP server 30 also determines the start time and duration of the demand response. The target value for power adjustment in the demand response and the start time and duration of the demand response are sent to the consumer device server 100 as an adjustment request.

The consumer device server 100 operates as a management device that manages charging and the like in a plurality of consumer devices. The consumer device is, for example, a stationary storage battery. Stationary storage batteries are installed in homes, for example, and supply electric power to electric devices in the homes. The consumer device may be an electric vehicle (EV) 70 . In the following, charging of a storage battery (not shown) included in the EV 70 will be described as an example of power consumption in the consumer device. The consumer device server 100 of the embodiment executes control for supporting EV charging control. The customer device server 100 is hereinafter referred to as an EV server 100 .

The EV server 100 of the embodiment estimates the power adjustable amount and transmits the power adjustable amount to the VPP server 30 . EV server 100 also controls charging of EV 70 based on the adjustment request received from VPP server 30 . In the embodiment, EV server 100 controls the charging operation at EV switch 60 connected to EV 70 for charging. For controlling the charging operation, EV server 100 transmits a charging control command to EV switch 60 . EV server 100 performs feedback control so that the actual value of power adjustment approaches the target value of power adjustment.

EV server 100 acquires power information indicating the charging power for EV 70 from EV switch 60 in order to grasp the actual value of power adjustment used for feedback control. When the EV 70 is connected to the EV switch 60, the EV server 100 can acquire power information, for example, in a one-minute cycle. Note that EV server 100 may acquire power information from EV 70 .

EV server 100 is connected to telematics server 50 . The telematics server 50 collects vehicle information (probe data) from vehicles such as the EV 70 . The vehicle information includes the state of charge (SoC) of the storage battery installed in the EV 70 . The EV server 100 acquires vehicle information of the EV 70 under the control of the EV server 100 from the telematics server 50 . The EV server 100 can acquire vehicle information, for example, in an hourly cycle.

As shown in FIG. 2, the EV server 100 is configured by a computer having a processor 101 and a storage unit 102 connected to the processor 101 . A computer program 105 that causes a computer to function as the EV server 100 is stored in the storage unit 102 . The processor 101 reads and executes a computer program 105 stored in the storage unit 102 . The computer program 105 has code that causes the processor 101 to perform various processes that will be described later. Note that the storage unit 102 of the embodiment includes a temporary storage device and a secondary storage device. Storage unit 102 may include a tertiary storage device.

A SoC model 112 is stored in the storage unit 102 . The SoC model 112 indicates the state of charge (SoC) of the storage battery installed in each of the plurality of EVs 70 . Storage unit 102 stores full charge determination data 122 . The full charge determination data 122 stores determination data indicating whether or not the storage battery mounted in each of the plurality of EVs 70 is fully charged. A power statistical model 132 is stored in the storage unit 102 . As shown in FIG. 3 , the power statistical model 132 of the embodiment has, as an example, a charge performance database 133, a chargeable amount table 134, and an adjustable amount table 135.

The EV server 100 further includes an interface 103 for communication with external devices. The external device is the VPP server 30, the user terminal 40, the telematics server 50, or the EV switch 60, for example.

FIG. 3 shows the processing performed in the EV server 100. As shown in FIG. The processing shown in FIG. 3 is executed by processor 101 according to computer program 105 .

The EV server 100 executes update processing of the SoC model 112 . In other words, the processor 101 of the EV server 100 operates as the SoC model updater 111 . The SoC model update unit 111 updates the SoC of the storage battery of each EV 70 in the SoC model 112 based on the SoC included in the vehicle information acquired from each EV 70 . The SoC model update unit 111 also updates the SoC of the storage battery of each EV 70 in the SoC model 112 based on the power information acquired from the EV switch 60 as well.

The EV server 100 executes update processing of the power statistical model 132 . In other words, processor 101 of EV server 100 operates as power statistical model updater 131 . EV server 100 executes a process of calculating the power adjustable amount. That is, the processor 101 of the EV server 100 operates as the power adjustment amount calculator 140 . The EV server 100 executes charging control processing for the EV 70, which is a consumer device. That is, the processor 101 of the EV server 100 operates as a consumer device control unit (charging control unit) 150 . The EV server 100 executes a consumer (user) participation process. In other words, processor 101 of EV server 100 operates as participation processing unit 160 .

FIG. 4 shows the control time sequence for demand response. In step S11, the power adjustable amount calculation unit 140 of the EV server 100 estimates the power adjustable amount for the next day (demand response implementation day) on the day before the demand response implementation date.

In step S<b>12 , EV server 100 transmits the power adjustable amount to VPP server 30 . In step S13, the VPP server 30 determines a target value for power adjustment based on the power adjustable amount. The target value is determined in consideration of the power supply capability during the demand response implementation time period within the range of the power adjustable amount.

In step S<b>14 , the VPP server 30 transmits to the EV server 100 an adjustment request including the target value and the start time and duration of the demand response. In step S<b>15 , the participation processing unit 160 of the EV server 100 transmits a participation request to the terminal 40 of the user who has the EV 70 . The request for participation is sent by e-mail, for example. The user terminal 40 transmits to the EV server 100 whether or not to participate.

In step S<b>17 , the charging control unit 150 of the EV server 100 controls charging of the EV 70 of the user who can participate in the demand response. EV charging control is performed by feedback control that reduces the difference between the target value for power adjustment and the actual value for power adjustment. Feedback control will be described later.

In step S<b>20 after executing the demand response, the EV server 100 transmits the power adjustment result value to the VPP server 30 .

In the embodiment, the power adjustable amount is calculated as the power amount that can be adjusted in the future demand response based on the past charging performance data, which is the power demand performance data. Also, the power adjustable amount is calculated according to a future demand response implementation scenario (implementation time period). In an embodiment, an implementation scenario is a demand response implementation window defined by a demand response start time and a demand response duration. Note that the implementation scenario may be defined by a start time and an end time. In the embodiment, the power adjustable amount is calculated for each of a plurality of implementation scenarios.

5 to 8 show the procedure for calculating the power adjustable amount for increased demand response (hereinafter referred to as "increased DR"). Here, the increased demand response shifts the power demand in the first time slot (cut time slot), which is a time slot other than the demand response implementation time slot, to the second time slot, which is the demand response implementation time slot.

In the process of calculating the power adjustable amount in the increased DR, first, for each EV 70, the total value P _C of the accumulated charged amount in the first time slot and the accumulated charged amount in the second time slot is calculated (Fig. 5). In addition, hereinafter, "the total value of the accumulated charge amount value in the first time slot and the accumulated charge amount value in the second time slot" will be referred to as "accumulated charge amount value P _C ". Subsequently, the chargeable amount _PA in the second time period is calculated for each EV 70 (see FIG. 6). Then, the power adjustable amount _{P_ctrl} in the second time period is calculated from the chargeable amount _{P_AT} of all EVs 70 (see FIG. 7). The adjustable amount P _ctrl is calculated for each of multiple implementation scenarios defined by the start time and duration (see FIG. 8). Also, the adjustable amount P _ctrl is calculated for each day of the week (see FIG. 8). The details of the calculation process of the power adjustable amount in the increased DR will be described below.

As shown in FIG. 5 , the charging performance database 133 stores charging performance time-series data for each day of the week for a plurality of EVs 70 . In FIG. 5, EV_ID1 indicates the EV 70 whose identifier is ID1, EVID2 indicates the EV 70 whose identifier is ID2, and EV_IDn indicates the EV 70 whose identifier is IDn. The charging performance time-series data for each day of the week is generated from past power information (charging performance data of the EV 70) for the corresponding day of the week. The charging performance time-series data indicates temporal changes in the charging power amount to the EV 70 .

Power adjustable amount calculation unit 140 of EV server 100 refers to the charging time-series data for the day of the week on which the demand response is performed, and calculates the integrated charging amount value P _C for each EV 70 in the first time period and the second time period. calculate. Here, it is assumed that the day of the week on which the demand response is implemented is Monday, and the power adjustable amount is calculated on Sunday, which is the previous day.

Also, here, the first time period (the time period in which power demand is cut due to the increased DR) is assumed to be from 19:00 on Sunday, which is the previous day, to 11:00 on Monday, which is the current day. To simplify the explanation, the start time of the cut time, which is the first time period, is fixed at 19:00 on Sunday, which is the previous day. It may vary accordingly. At the cut start time, the EV server 100 turns off the EV switch 60, suspends charging until the increased DR start time, and starts charging at the increased DR start time.

Also, here, the end time of the first time period coincides with the start time of the increased DR, but does not have to coincide with the start time of the increased DR. The end time of the first time period, that is, the start time of the increased DR differs depending on the scenario for implementing the increased DR. ) at 11:00 on Monday. The second time slot also depends on the implementation scenario, but here the duration of the second time slot is 3 hours. That is, the end time of the second time slot is 14:00.

Calculation unit 140 calculates the charge amount integrated value (predicted value) _PC for each EV 70 from 19:00 on Sunday, the previous day, to 14:00 on Monday, which is the current day. For example, as shown in FIG. 8, the charge amount integrated value (predicted value) _PC of the EV 70 is calculated based on the past Sunday charging performance data and the past Monday charging performance data. In this case, the charge amount integrated value (predicted value) P _C is obtained when charging is performed from 19:00 on Sunday to 14:00 on Monday (the first time slot and the second time slot). , is calculated as an integrated value of the electric energy expected to be charged. That is, the charge amount integrated value P _C is a power demand forecast value in the first time period and the second time period.

Calculation unit 140 then calculates, for each EV 70, the chargeable amount P _A in the second time period in which the power demand should be increased as the increase DR.

Calculation unit 140 calculates the chargeable amount _PA as the amount of electric power per unit time. The unit time here is, for example, 30 minutes. In the embodiment, the unit time is equal to the time interval at which control is performed in the charging control process, which will be described later.

As shown in FIG. 6, the chargeable amount P _A per unit time is calculated by dividing the charge amount integrated value P _C by the duration of the increase DR (the length of the second time period). Here, the duration of the raised DR is indicated by the number of unit time (30 minutes). That is, the duration of the raised DR is 3h/0.5=6. Therefore, the value obtained by dividing the charge amount integrated value P _C by 6 is the chargeable amount P _A per unit time. The chargeable amount _PA is a predicted value of the chargeable amount in the second time period (chargeable amount predicted value).

The chargeable amount P _A per unit time is calculated for each of a plurality of implementation scenarios consisting of combinations of a plurality of raised DR start times and a plurality of durations, as shown as a chargeable amount table _Tm1 in FIG. The chargeable amount table T _m1 in FIG. 6 indicates the chargeable amount for each of a plurality of implementation scenarios for EV_ED1 on Monday.

In the chargeable amount table _Tm1 , the start time is set every 30 minutes during a day (here, Monday). Also, in the chargeable amount table _Tm1 , the duration is set every 30 minutes from 30 minutes to 240 minutes.

If the time period during which the increased DR is implemented is different, the charge amount integrated value _PC will fluctuate, and as a result, the chargeable amount _PA will also fluctuate. Also, if the duration of the increased DR is different, the chargeable amount _PA per unit time will vary even if the charge amount integrated value _PC is the same. In this way, the chargeable amount _PA differs for each implementation scenario of the increased DR. In this embodiment, the chargeable amount _PA is calculated according to each of a plurality of implementation scenarios.

Calculation unit 140 obtains an average value _PAA by averaging the calculated chargeable amount P _A and the previously calculated chargeable amount P _A (value already stored in chargeable amount table T _m1 ). The average value _PAA is stored in the chargeable amount table _Tm1 as the chargeable amount.

As shown in the chargeable amount table 134 of FIG. 6 , the chargeable amount table _Tm1 for multiple implementation scenarios is generated for each day of the week and for each of the multiple EVs 70 .

Subsequently, calculation unit 140 totals the chargeable amounts of all EVs 70 in a specific implementation scenario on a specific day of the week by referring to chargeable amount table 134 in FIG. 6 . Here, on Monday, which is the increased DR implementation day, the chargeable amount P _AT of all EVs 70 per unit time in the increased DR implementation scenario in which the start time is 11:00 and the duration is 3 hours is calculated by calculating unit 140. (see FIG. 7). The chargeable amount _PAT is calculated for each of a plurality of implementation scenarios and for each day of the week.

Subsequently, calculation unit 140 subtracts the baseline during the increased DR implementation time zone from chargeable amount P _AT to calculate power adjustable amount P _ctrl . Here, the baseline is calculated from past actual charging amounts of all the EVs 70 during the increased DR implementation time period. The baseline indicates the amount of power that the EV 70 normally consumes for charging. Thus, in the embodiment, the power adjustable amount P _ctrl is calculated as the power adjustment margin by subtracting the baseline from the chargeable amount P _AT .

Note that in the embodiment, the baseline is obtained as a value every 30 minutes, so the power adjustable amount P _ctrl is also obtained as a different value every 30 minutes. However, here, the power adjustable amount P _ctrl obtained by subtracting the maximum value Bm of the baseline during the increased DR implementation time period, that is, the minimum value of the power adjustable amount P _ctrl is set to It is the power adjustable amount.

As shown in FIG. 8, the power adjustable amount P _ctrl and the chargeable amount P _AT are calculated for each of a plurality of implementation scenarios and stored in the adjustable amount table T _{m_ctrl} . Also, the adjustable amount table _{Tm_ctrl} is calculated for each day of the week and stored in the adjustable amount table 135 for each day of the week.

EV server 100 transmits the adjustable amount table _{Tm_ctrl} for Monday to VPP server 30 as data indicating adjustable amounts for each of a plurality of implementation scenarios on Sunday, which is the day before raising DR on Monday.

9 and 10 show the procedure for calculating the power adjustable amount for the downward demand response (hereinafter referred to as "downward DR"). The reduced demand response reduces the power demand during the demand response implementation time period.

In the process of calculating the power adjustable amount in the increased DR, first, for each EV 70, the chargeable amount _PC in the decreased DR implementation time period is calculated (see FIG. 9). Next, from the chargeable amount _PCT of all EVs 70, the power adjustable amount Pctrl in the lowered DR implementation time period is calculated (see FIG. 10). The details of the calculation process of the power adjustable amount in the lowered DR will be described below.

First, power adjustable amount calculation unit 140 refers to the charging time-series data for the day of the week on which the lower DR is implemented, and for each EV 70, the chargeable amount per unit time (30 minutes) in the lower DR implementation time zone is Calculate _PC . Here, it is assumed that the day of the week on which the lower DR is implemented is Monday.

Also, here, it is assumed that the down DR implementation time period is from 11:00 to 13:00 on Mondays. That is, the start time is 11:00 and the duration is 2 hours.

Calculation unit 140 calculates chargeable amount P _C for each EV 70 from 11:00 to 13:00 on Monday for each unit time (every 30). The chargeable amount P _C is predicted to be charged if charging is performed from 11:00 to 13:00 on Monday, which is the date of the lower DR, based on the charging performance data for past Mondays. calculated as the amount of power consumed. That is, the chargeable amount P _C here is a predicted value of power demand that can be reduced by the lowered DR during the lowered DR implementation time period.

The chargeable amount P _C for each unit time is calculated for each of a plurality of implementation scenarios consisting of a combination of a plurality of lowered DR start times and a plurality of durations, as shown as a chargeable amount table _Tm1 in FIG. 9 . The chargeable amount table _Tm1 in FIG. 9 indicates the chargeable amount for each of a plurality of implementation scenarios for EV_ED1 on Monday.

In the chargeable amount table _Tm1 , the start time is set every 30 minutes during a day (here, Monday). Also, in the chargeable amount table _Tm1 , the duration is set every 30 minutes from 30 minutes to 240 minutes.

Since the power demand fluctuates with time, the chargeable amount P _C fluctuates if the time zone for implementing the lowered DR is different. In this way, the chargeable amount P _C differs for each scenario in which the lowered DR is implemented. In this embodiment, the chargeable amount _PC is calculated according to each of a plurality of implementation scenarios.

Calculation unit 140 obtains an average value _PCA by averaging the calculated chargeable amount P _C with the previously calculated chargeable amount P _C (the value already stored in the chargeable amount table T _m1 ). The average value _PCA is stored in the chargeable amount table _Tm1 as the chargeable amount.

As shown in the chargeable amount table 134 of FIG. 9 , the chargeable amount table _Tm1 for multiple implementation scenarios is generated for each day of the week and for each of the multiple EVs 70 .

Next, calculation unit 140 totals the chargeable amounts of all EVs 70 in a specific implementation scenario on a specific day of the week by referring to chargeable amount table 134 of FIG. 9 . Here, on Monday, which is the lower DR implementation day, the chargeable amount _PCT of all EVs 70 per unit time in the scenario where the start time is 11:00 and the duration is 2 hours is calculated by the calculation unit. 140 (see FIG. 10). The chargeable amount _PCT is calculated for each of a plurality of implementation scenarios and for each day of the week.

Subsequently, the calculation unit 140 sets the minimum value of the chargeable amount _{P_CT} for each unit time as the power adjustable amount _{P_ctrl} during the increased DR implementation time period.

As shown in FIG. 10, the power adjustable amount P _ctrl is calculated for each of a plurality of implementation scenarios and stored in the adjustable amount table T _{m_ctrl} . Also, the adjustable amount table _{Tm_ctrl} is calculated for each day of the week and stored in the adjustable amount table 135 for each day of the week.

EV server 100 transmits the adjustable amount table _{Tm_ctrl} for Monday to VPP server 30 as data indicating adjustable amounts for each of a plurality of implementation scenarios on Sunday, which is the day before the lowered DR is implemented on Monday.

The VPP server 30 determines whether to implement an increase DR or a decrease DR, taking into account fluctuations in the amount of power supply, based on the adjustable amount for each of the multiple implementation scenarios for each of the increase DR and the decrease DR. do. The VPP server 30 also determines the demand response start time, duration, and power adjustment target value for the increase DR or decrease DR. The power target value is determined in consideration of fluctuations in the amount of power supply within the range of the power adjustable amount. The power target value may be determined in consideration of the user participation probability (predicted value) in the demand response. Note that the power target value is determined as a value every 30 minutes.

FIG. 11 shows charging control of the EV switch 60 by the EV server 100 during the demand response period. The charging control unit 150 selects the EV 70 (target EV; target consumer device) to which charging control is to be performed at the start of execution of the demand response. This selection is called initial selection. After the initial selection, charging control unit 150 reselects the target EV for each selection control period, which is every 30 minutes. Re-selection is repeated during the demand response period. By reselecting the target EV, it is possible to reduce the bias of the EVs 70 to be charged. The target EV is selected in the following order of priority until the chargeable amount for 30 minutes reaches the power target value.
1st priority: The number of selections should be small.
Second priority: being the target EV in the last 30 minutes.
Third priority: that the charging phase is a stable period.
Fourth priority: Large chargeable capacity.

The “first priority: less number of times of selection” is a criterion for reducing bias in charging EVs 70 by selecting EVs 70 with less number of times of selection as target EVs. “Second priority: being a target EV in the last 30 minutes” is a criterion for minimizing the number of times of switching between charging and non-charging to prevent deterioration of the storage battery. "Third priority: the charging phase is in the stable period" is a criterion for preferentially charging the storage battery in the stable period. It should be noted that the charging phase being in the stable period refers to a state in which the charging state does not reach near full charge and the charging current is stable. The charge phase is determined based on full charge determination data 122 . “Fourth priority: large chargeable capacity” is a criterion for preferentially charging a storage battery with a large remaining charge capacity. The chargeable amount is determined based on the SoC model 112 .

The charge control unit 150 transmits a charge control command (ON command) for charging to the EV switch 60 connected to the target EV (step S18 in FIG. 4). The EV switch 60 that has received the ON command charges the target EV. The EV switch 60 transmits the charging power to the target EV to the EV server 100 as a charging performance value (step S19 in FIG. 4). Transmission of the actual charging value to the EV server 100 is performed, for example, every minute.

During the demand response time period, the charging control unit 150 increases or decreases the number of target EVs for each feedback control period, which is every five minutes. The number of target EVs is increased or decreased based on the actual charging value received from EV switch 60 .

For example, as shown in FIG. 11, the estimated power consumption based on the actual charging value at 11:05 is excessive compared to the target value. Therefore, the charging control unit 150 reduces the number of target EVs (the number of charging vehicles) at 11:05. Also, the estimated power consumption based on the actual charging value at 11:15 is insufficient compared to the target value. Therefore, charging control unit 150 increases the number of target EVs at 11:15. By performing such feedback control, the difference between the power adjustment target value and the actual value can be reduced, and the actual value can be brought closer to the target value.

When increasing the number of target EVs, target EVs are additionally selected from non-target EVs in the following order of priority until the expected power consumption reaches the target value.
1st priority: The number of selections should be small.
Second priority: that the charging phase is stable.
Third priority: Large chargeable capacity.

When reducing the number of target EVs, EVs 70 to be excluded from the target EVs are selected in the following order of priority until the expected power consumption reaches the target value. When removed from the target EV, charging is turned off.
First priority: the charging phase is other than the stable phase (such as a fully charged state).
Second priority: Large chargeable capacity.
Third priority: The number of selections should be small.

[Appendix]

It should be noted that the embodiments disclosed this time should be considered as examples in all respects and not restrictive. The scope of the present invention is indicated by the scope of the claims rather than the above-described meaning, and is intended to include meanings equivalent to the scope of the claims and all modifications within the scope.

10: System 21: Grid operator 22: Renewable energy generator 23: Retailer 30: VPP server 40: User terminal 50: Telematics server 60: EV switch 70: EV
100: EV server 101: processor 102: storage unit 103: interface 105: computer program 111: SoC model update unit 112: SoC model 122: full charge determination data 131: power statistical model update unit 132: power statistical model 133: charging performance Database 134 : Chargeable amount table 135 : Adjustable amount table 140 : Power adjustable amount calculation unit 150 : Charging control unit 160 : Participation processing unit

Claims (20)

A management device that manages a plurality of consumer devices for demand response,
a storage unit that stores actual power demand data for each of the plurality of consumer devices;
a calculation unit that calculates a power adjustable amount according to at least one demand response implementation scenario based on the actual power demand data;
with
The demand response includes an increase demand response that increases the power demand during the execution time period of the demand response,
The process of calculating the power adjustable amount includes:
calculating a predictable chargeable amount in the implementation time period based on the actual power demand data;
calculating the power adjustable amount in the increased demand response based on the predictable chargeable amount
including
management device. The management device according to claim 1, wherein the at least one demand response implementation scenario is a plurality of the demand response implementation scenarios. 3. The management device according to claim 2, wherein each of the plurality of demand response implementation scenarios differs in at least one of a start time and a duration of the demand response. The increased demand response includes shifting the power demand in at least a first time slot to a second time slot, which is the implementation time slot of the demand response,
The process of calculating the power adjustable amount further includes calculating a power demand forecast value in a time slot including at least the first time slot,
The management device according to claim 1 , wherein the power adjustable amount in the increase demand response is calculated based on the power demand predicted value and the chargeable amount predicted value. The demand response includes a lowering demand response that reduces the power demand during the execution time period of the demand response,
The process of calculating the power adjustable amount includes:
Based on the actual power demand data, calculate the power demand forecast value in the implementation time zone of the lowered demand response,
5. The management according to any one of claims 1 to 4 , comprising calculating the power adjustable amount in the lowered demand response based on the predicted power demand value in the time period during which the lowered demand response is implemented. Device. Based on the target value of power adjustment set based on the power adjustable amount and the actual value of power adjustment during the duration of the demand response, the difference between the target value and the actual value is reduced. 6. The management device according to any one of claims 1 to 5, further comprising a control unit that adjusts power demand of the plurality of consumer devices during the duration of the demand response. Adjusting the power demand of each of the plurality of consumer devices means increasing or decreasing the number of target consumer devices that consume power among the plurality of consumer devices during the duration of the demand response. The management device according to claim 6 , comprising: The management device according to any one of claims 1 to 7 , wherein the consumer device includes a storage battery. The management device according to claim 8 , wherein the consumer device includes a stationary storage battery. A management device that manages a plurality of consumer devices for demand response,
a storage unit that stores actual power demand data for each of the plurality of consumer devices;
a calculation unit that calculates a power adjustable amount according to at least one demand response implementation scenario based on the actual power demand data;
with
The demand response includes a lowering demand response that reduces the power demand during the execution time period of the demand response,
The process of calculating the power adjustable amount includes:
Based on the actual power demand data, calculate the power demand forecast value in the implementation time zone of the lowered demand response,
calculating the power adjustable amount in the lowered demand response based on the predicted power demand value in the time period during which the lowered demand response is implemented;
including
management device. A management device that manages a plurality of consumer devices for demand response,
a storage unit that stores actual power demand data for each of the plurality of consumer devices;
a calculation unit that calculates a power adjustable amount according to at least one demand response implementation scenario based on the actual power demand data;
Based on the target value of power adjustment set based on the power adjustable amount and the actual value of power adjustment during the duration of the demand response, the difference between the target value and the actual value is reduced. and a control unit that adjusts the power demand of the plurality of consumer devices during the duration of the demand response.
management device. storing actual power demand data for each of a plurality of consumer devices in a storage unit;
a processor that reads the actual power demand data from the storage unit, based on the actual power demand data, calculates a power adjustable amount according to at least one demand response execution scenario ;
The demand response includes an increase demand response that increases the power demand during the execution time period of the demand response,
Calculating the power adjustable amount includes:
calculating a predictable chargeable amount in the implementation time period based on the actual power demand data;
calculating the power adjustable amount in the increased demand response based on the predictable chargeable amount
including
Power adjustable amount estimation method. storing actual power demand data for each of a plurality of consumer devices in a storage unit;
A processor that reads the actual power demand data from the storage unit calculates a power adjustable amount according to at least one demand response execution scenario based on the actual power demand data.
including
The demand response includes a lowering demand response that reduces the power demand during the execution time period of the demand response,
Calculating the power adjustable amount includes:
Based on the actual power demand data, calculate the power demand forecast value in the implementation time zone of the lowered demand response,
calculating the power adjustable amount in the lowered demand response based on the predicted power demand value in the time period during which the lowered demand response is implemented;
including
Power adjustable amount estimation method. storing actual power demand data for each of a plurality of consumer devices in a storage unit;
a processor that reads the actual power demand data from the storage unit, based on the actual power demand data, calculates a power adjustable amount according to at least one demand response execution scenario;
Based on the target value of power adjustment set based on the power adjustable amount and the actual value of power adjustment during the duration of the demand response, the difference between the target value and the actual value is reduced. and adjusting the power demands of the plurality of consumer devices during the duration of the demand response.
including
Power adjustable amount estimation method. to the processor,
A computer program for executing a process of calculating a power adjustable amount according to at least one demand response implementation scenario based on actual power demand data of each of a plurality of consumer devices,
The demand response includes an increase demand response that increases the power demand during the execution time period of the demand response,
The process of calculating the power adjustable amount includes:
calculating a predictable chargeable amount in the implementation time period based on the actual power demand data;
calculating the power adjustable amount in the increased demand response based on the predictable chargeable amount
including
computer program . to the processor,
Execute a process of calculating a power adjustable amount according to at least one demand response execution scenario based on actual power demand data of each of a plurality of consumer devices.
A computer program,
The demand response includes a lowering demand response that reduces the power demand during the execution time period of the demand response,
The process of calculating the power adjustable amount includes:
Based on the actual power demand data, calculate the power demand forecast value in the implementation time zone of the lowered demand response,
calculating the power adjustable amount in the lowered demand response based on the predicted power demand value in the time period during which the lowered demand response is implemented;
including
computer program. to the processor,
Execute a process of calculating a power adjustable amount according to at least one demand response execution scenario based on actual power demand data of each of a plurality of consumer devices.
A computer program,
A difference between the target value and the actual value is determined in the processor based on the target value for power adjustment set based on the power adjustable amount and the actual value for power adjustment during the duration of the demand response. further executing a process of adjusting the power demand of the plurality of consumer devices during the duration of the demand response so as to reduce the power demand
computer program. a demand response control device;
a management device that manages a plurality of consumer devices for demand response;
with
The management device
a storage unit that stores actual power demand data for each of the plurality of consumer devices;
a calculation unit that calculates a power adjustable amount according to at least one demand response implementation scenario based on the actual power demand data;
and is configured to transmit the power adjustable amount to the demand response control device,
The demand response includes an increase demand response that increases the power demand during the execution time period of the demand response,
The process of calculating the power adjustable amount includes:
calculating a predictable chargeable amount in the implementation time period based on the actual power demand data;
calculating the power adjustable amount in the increased demand response based on the predictable chargeable amount
including
system. a demand response control device;
a management device that manages a plurality of consumer devices for demand response;
with
The management device
a storage unit that stores actual power demand data for each of the plurality of consumer devices;
a calculation unit that calculates a power adjustable amount according to at least one demand response implementation scenario based on the actual power demand data;
and is configured to transmit the power adjustable amount to the demand response control device,
The demand response includes a lowering demand response that reduces the power demand during the execution time period of the demand response,
The process of calculating the power adjustable amount includes:
Based on the actual power demand data, calculate the power demand forecast value in the implementation time zone of the lowered demand response,
calculating the power adjustable amount in the lowered demand response based on the predicted power demand value in the time period during which the lowered demand response is implemented;
including
system. a demand response control device;
a management device that manages a plurality of consumer devices for demand response;
with
The management device
a storage unit that stores actual power demand data for each of the plurality of consumer devices;
a calculation unit that calculates a power adjustable amount according to at least one demand response implementation scenario based on the actual power demand data;
and is configured to transmit the power adjustable amount to the demand response control device,
Based on the target value of power adjustment set based on the power adjustable amount and the actual value of power adjustment during the duration of the demand response, the difference between the target value and the actual value is reduced. , further comprising a control unit that adjusts the power demand of the plurality of consumer devices during the duration of the demand response
system.

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