JP7112937B2 - Information processing device, its method and computer program - Google Patents

Description

The present invention relates to an information processing device, its method, and a computer program.

Demand response (reduction of power consumption) has been advocated to stabilize power consumption by encouraging curtailment of power consumption and curbing power consumption during peak usage.

In the negawatt aggregation business, aggregators (aggregators) meet demand for negawatt power through methods such as setting prices and paying consideration to consumers who refrain from using them during peak hours. Collect from home. Negawatt refers to a reduction in power demand (negative power) in contrast to normal power demand (positive power). Power consumption can be reduced by collecting negawatts from consumers.

In the current system design, an aggregator concludes a contract regarding the amount of power reduction in advance with a system operator, a retail electricity supplier, a parent aggregator, etc. (hereinafter collectively referred to as the system operator). The aggregator gets a performance fee depending on whether it can achieve the contracted reduction amount. The aggregator can select a target consumer for a demand response request (DR request), which is a power reduction request, based on a request from the system operator, and can make a demand response request to the selected consumer. . The aggregator only needs to achieve the contracted reduction amount as the total amount of power reduction amount for the selected consumers. However, if the customer's reduction amount fluctuates from the expected amount, the aggregator's risk of failure may increase. In particular, in the case of fast DR, which takes a short time from DR request to DR implementation, there is a high possibility that consumers will not be able to respond in time during the first time slot of the DR implementation period.

JP 2017-228148 A

Embodiments of the present invention provide an information processing apparatus, method, and computer program that enable selection of consumers so as to achieve a desired amount of power reduction.

The information processing apparatus of the present embodiment selects a plurality of target consumers to be subjected to a first power reduction request from among the plurality of consumers based on power reduction histories of the plurality of consumers in response to the power reduction request, a processing unit that predicts the power reduction amount of the plurality of target consumers in response to the first power reduction request; a reduction target amount that is the power amount requested to be reduced by the first power reduction request; a plan creation unit that creates a discharge plan for a storage battery owned by at least one of the plurality of target consumers based on the difference from the total of the predicted power reduction amounts; and a communication unit configured to transmit the discharge plan to a consumer system of a target consumer of No. 1, and to transmit the discharge plan to a storage battery system including the storage battery.

1 is a block diagram of a schematic configuration of a demand response system according to a first embodiment; FIG. A functional block diagram of a demand response planning device. The figure which shows the example of reduction target information. The figure which shows the example of the reduction history of a consumer. The figure which shows the example of storage battery information. A diagram showing an example of a reduction scenario. FIG. 4 is a diagram for explaining an example of creating a discharge plan and success and failure of execution of a scenario; Explanatory drawing of the specific example of 1st Embodiment. The figure which shows the example of the output information which specifies the consumer selected by the DR planning device. The hardware block diagram of the demand-response planning apparatus which concerns on 1st Embodiment. 4 is a flowchart of operations according to the first embodiment; FIG. 11 is a diagram showing a specific example of scenario source data according to the second embodiment; The figure which shows the specific example of the scenario by the consumer selected in 3rd Embodiment. The figure which shows the specific example of the scenario by the consumer selected in 4th Embodiment.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

FIG. 1 is a block diagram of a schematic configuration of a demand response system (DR system) according to an embodiment of the invention. The DR system according to the present embodiment includes a demand response planning device (DR planning device) 101, which is an information processing device according to the present embodiment, a system operator system 201, and a consumer system operated by each of a plurality of consumers. 301. All or some of the multiple consumers have storage battery systems 311 . The consumer system 301 includes a communication device that communicates information with the DR planning device 101 , and the consumer system 301 of a consumer having a storage battery includes a storage battery system 311 . The storage battery system 311 includes a storage battery (secondary battery) 1 , a control device 2 that controls charging and discharging of the storage battery, and a communication device 3 that communicates with the DR planning device 101 . The DR planning device 101 and the grid operator system 201 and the DR planning device 101 and the consumer system 301 are connected via a communication medium. The communication medium may be a wide area network such as the Internet line, or a dedicated line. Also, the communication medium does not necessarily have to be a communication network, and may be another means of communication such as a telephone line. The communication device and storage battery system 311 included in the consumer system 301 may communicate with the DR planning device 101 via separate communication media, or may communicate with the DR planning device 101 via the same communication medium.

The grid operator system 201 is a system owned by a grid operator (for example, an electric power company), and issues a DR request, which is a power reduction request, to the aggregator. A DR planning device 101 is a system owned by an aggregator and creates a DR plan according to a DR request. The DR planning device 101 requests the consumer to reduce power according to the created DR plan. The DR planning device 101 can control at least one of charging and discharging of the storage battery 1 at the consumer through communication with the storage battery system 311 as part of the execution of the DR plan. In this embodiment, both discharge can be controlled, but both charging and discharging or only charging may be controlled.

FIG. 2 is a functional block diagram of the DR planning device 101, which is an information processing device according to this embodiment. The DR planning device 101 creates a DR plan for requesting a reduction in demand power to a consumer selected from among a plurality of consumers based on a DR request from a system operator, and confirms the success of the DR plan. One of the features is to create a discharge plan for the storage battery 1 that maximizes the probability.

The DR planning device 101 includes a DR plan optimization unit 20, a communication unit 30, an input/output I/F 27, a scenario creation unit 21, an operation input unit 28, a display unit 29, a customer reduction history DB_40, It has a reduction scenario DB_41, a reduction target amount DB_42, a plan creation parameter DB_43, a storage battery information DB_44, a charge/discharge plan DB_45, and a DR requesting consumer combination DB_46. The communication unit 30 includes an information communication unit 25 and a storage battery control unit 26 . The scenario creation unit 21 includes a consumer combination creation unit 22 . The DR plan optimization unit 20 includes a charge/discharge plan creation unit 23 and a processing unit 24 .

The information communication unit 25 communicates with the grid operator system 201 and the consumer system 301 via the communication medium described above. The consumer system 301 is a system for the consumer to receive information from the DR planning device 101 (aggregator) and send information to the aggregator. The customer system 301 may be a mobile terminal such as a smart phone, a dedicated device for communicating with the DR planning device 101, a general personal computer, or a telephone. An example of information that the DR planning device 101 notifies the consumer is a DR request that is sent to the consumer selected by the present embodiment. In addition, there is an instruction to transmit power consumption history in the customer's home, an instruction to transmit customer information, and the like. Information transmitted by the consumer to the DR planning device 101 includes information transmitted in accordance with the instructions of the DR planning device 101 . Information transmitted and received between the DR planning device 101 and the consumer system 301 is not limited to these.

The operation input unit 28 is for the operator of the aggregator to perform various operations or input data. The operation input unit 28 is, for example, a mobile terminal such as a keyboard, mouse, touch panel, or smart phone.

The display unit 29 is a display device for displaying various information. Examples of the display device are a liquid crystal display device, an organic EL display device, or a CRT display device.

The input/output I/F 27 is connected to the operation input section 28 and the display section 29 and performs input/output interface operations. The connection between the operation input unit 28 and the display unit 29 may be wired or wireless.

The target reduction amount DB_42 stores information (reduction target information) on the target reduction amount of electric power based on the contract between the aggregator and the system operator.

FIG. 3 shows an example of reduction target information stored in the reduction target amount DB_42. The reduction target information is determined, for example, on a one-year contract basis based on the contract between the system operator and the aggregator. As a specific example, there is a statement such as "for an arbitrary time period between 8:00 and 17:00, a maximum duration of 8 hours is set, and power consumption is reduced by an amount equal to or greater than the reduction target amount". The grid operator will issue a DR request based on this contract. The issued DR request includes the designated period of the DR request (DR implementation period) and reduction target information for each time slot (slot) of the period. The target reduction amount DB_42 internally stores the reduction target information included in the DR request in association with the designated period (DR implementation period) of the DR request. However, if the value of the target reduction amount is determined in advance, the DR request may include only the information on the DR implementation period and may not include the value of the target reduction amount. Also, if the DR implementation period is predetermined, the DR request does not need to include information on the DR implementation period.

The reduction target information in FIG. 3 requests that consumers as a whole reduce 20 kWh in each 30-minute band (for 4 slots) from 13:00 to 15:00 as a designated period (notation of designated period are omitted). A reduction of 20 kWh in each slot is the target for the aggregator. The system operator system 201 transmits such a DR request to the DR planning device 101, for example, at a predetermined point in time or at an arbitrarily determined point in time (for example, a certain period of time before the DR period). For example, at the time of 12:45, a request is made to reduce the amount of electric power specified in the contract (for example, 20 kWh) in all 30-minute bands (for 4 slots) from 13:00 to 15:00.

The contract between the aggregator and the grid operator stipulates success conditions based on this reduction target amount. If the success condition is met, the grid operator may give the aggregator some incentive, and if not, the grid operator may impose some kind of penalty on the aggregator. Information on success conditions may be stored in the target reduction amount DB_42. Alternatively, information on the success condition may be written in the program code that implements the operation of this embodiment.

As a first example of a success condition, the total power reduction amount of consumers selected as targets of DR implementation may be greater than or equal to the reduction target amount in each of all slots.

As a second example of a success condition, the total power reduction amount of consumers selected as targets for DR implementation may fall within a predetermined target range in each of all slots. An example of the predetermined target range is 90% or more and 110% or less of the reduction target amount.

However, examples of success conditions are not limited to these.

In addition, the success conditions of the first and second examples above require that all slots satisfy the conditions (the conditions of the first example or the conditions of the second example). Partial success may be determined if In case of partial success, the penalty may be reduced.

The actual power reduction amount of the slot is calculated, for example, by the difference between the baseline power determined for each slot and the sum of the received power (power consumption) of the consumer requested by the DR. In addition to the standard baseline High 4 of 5 (with adjustment on the day) method, there are various methods for calculating baseline power, such as the High 4 of 5 (no adjustment on the day) method, the same day adoption method, and the pre-measurement method. Although techniques are known, any technique may be used to calculate the baseline power. For example, the information communication unit 25 may receive information on the power consumption of the consumer for which the DR request is made from a smart meter owned by the consumer, or the information communication unit 25 may receive the information from a server that manages the power consumption of the consumer. may receive. Alternatively, the power consumption information of the consumer for which a DR request has been made may be received by the system operator system 201 instead of the DR planning device 101 . The DR planning device 101 may determine the success or failure of the DR request, or the grid operator system 201 may determine the success or failure of the DR request. When the storage battery 1 of the consumer discharges, the amount of power reduction to be measured also includes the amount of discharge from the storage battery 1 .

The consumer reduction history DB_40 stores information on the power reduction history of each consumer in response to past DR requests (power reduction requests).

FIG. 4 shows an example of power reduction history of each consumer stored in the consumer reduction history DB_40. The information in FIG. 4 includes power reduction results of each consumer A, B, and C at past DR requests. Although the number of consumers is 3 here, it may actually be 4 or more, or may be 2. The date on which the DR request was made differs for each consumer. For each customer, the power reduction performance of 4 slots is illustrated for each day when a DR request is made.

For example, for customer A (a customer whose customer ID is A), 8/1 13:00-15:00, 8/3 13:00-15:00, and 8/5 13:00-15: There is a DR request in 00, and the power reduction results for slots 1 to 4 are shown. For example, slot 1 (13:00-13:30) on 8/1 is 8 kWh, slot 2 (13:30-14:00) is 10 kWh, slot 3 (14:00-14:30) is 11 kWh, slot 4 (14:30-15:00) shows that there was a power reduction of 10 kWh. An individual history ID for distinguishing the history of each day is given to the reduction performance of each day of the consumer. The history data for each day includes the consumer ID, DR implementation period, individual history ID, and the reduction performance of each slot.

From the information in FIG. 4, it can be understood that there are various trends in how each consumer responds to DR requests.

For example, consumer A is an example of a consumer that has variations in the start timing (rising timing) of DR implementation. 8/1 reduces power from the first slot, 8/3 only reduces power from the second slot, and 8/5 only from the third slot.

Consumer B is an example of a consumer whose power reduction amount varies between DR implementation days. 8/1 reduced power by an average of 13.5 kWh, but 8/5 reduced power by an average of 9.25 kWh and 8/8 by an average of 5.5 kWh.

Consumer C is an example of a consumer whose execution/non-execution of power reduction varies. 8/5 and 8/8 reduced power consumption by about 10 kWh on average, but 8/12 did not reduce power consumption and rather increased demand (power consumption) by 10 kWh or more. In the case of demand increase, the value of power reduction is represented by a negative value.

Here, the DR duration time (DR implementation period) on the DR request date is 4 slots, but if it is 5 slots or more, the results of 5 or more slots may be stored. Alternatively, as another method, the number of achievements to be stored may be fixed to 4 slots. In this case, if there are days with a DR implementation period longer than 4 slots, only the results for the first 4 slots may be retrieved. Conversely, if there is a day when the DR implementation period is less than four slots, the value of the insufficient slots may be calculated by complementing processing using a machine learning method or the like. Also, when the time zone for DR implementation on a certain day is different from the time zone on other days, correction may be made using a typical demand curve. Furthermore, if the baseline on the date of DR request or the demand curve immediately before the start of DR differs from the tendency of the baseline or demand curve on a normal day, the amount of reduction may be corrected based on the difference.

The storage battery information DB_44 stores storage battery information of the storage battery 1 of each consumer.

FIG. 5 shows an example of storage battery information of each consumer stored in the storage battery information DB_44. The information in FIG. 5 includes the presence/absence of a storage battery at each of the consumers A, B, and C, and if there is a storage battery, the power output amount per unit time (per slot) and the value of the battery capacity. Examples other than the power output amount and capacity include the charge amount (charging speed) of power per unit time (per slot), the response delay time for communication (after the DR planning device 101 sends an instruction to the storage battery, the instruction is information such as the time until the data is reflected), the degree of deterioration of the storage battery, and the priority with which the storage battery is used for discharging or charging during DR. Examples using the charging speed, response delay time, degree of deterioration, priority, etc. will be described later in the embodiments. When a consumer owns two or more storage batteries with different characteristics, information may be registered for each storage battery.

The customer combination creating unit 22 in the scenario creating unit 21 creates a customer combination s(k) for which a DR request (first power reduction request) is made. As a method of creating the consumer combination s(k), there is a method of creating at random or a method of creating all combinations. In addition, a combination close to the k-1th consumer combination s(k-1) created immediately before (replace some of the consumers with other consumers, delete them, add new consumers, etc.) ) is also available. Alternatively, a known meta-heuristic method such as simulated annealing, tabu search, genetic algorithm, etc. may be used to create a consumer combination. For consumers A, B, and C, examples of consumer combinations are (A, B, C), (A, B), (A, C), and (B, C). If a single customer is permitted as a customer combination, customer A, customer B, and customer C should be added in addition to these combinations.

The scenario creation unit 21 uses the consumer reduction history DB_40 to create a power reduction scenario (hereinafter simply referred to as scenario) during the DR implementation period for each consumer combination created by the consumer combination creation unit 22. . A scenario represents a predicted value of total savings per slot for a consumer combination. Specifically, the scenario extracts the history data of each customer included in the customer combination for an arbitrary day (DR request date) from the customer reduction history DB_40, and calculates the power reduction amount of the extracted history data for each slot. is the sum of The scenario creating unit 21 stores the created scenario in the reduction scenario DB_41. The reduction scenario DB_41 stores scenarios created by the scenario creating unit 21 .

FIG. 6A shows an example of scenarios stored in the reduction scenario DB_41. Multiple scenarios are generated for each consumer combination. Specifically, 27 scenarios ABC1 to ABC27 for the consumer combination (A, B, C), 9 scenarios AB1 to AB9 for the consumer combination (A, B), the consumer combination (A, Nine scenarios AC1 to AC9 are generated for C), and nine scenarios BC1 to BC9 are generated for the customer combination (B, C). Customers marked with "*" are not included in the customer combination for which the scenario is generated. For example, scenario AB1 is generated from historical data of consumers A and B, and historical data of consumer C is not used. In the example of FIG. 4, each customer has three pieces of historical data, so if the customer combination is (A, B, C), a total of 3×3×3=27 scenarios are generated. . For a combination of two consumers, 3×3=9 scenarios are generated for each. However, only some of the scenarios may be selected at random. For example, in the case of the customer combination (A, B, C), some of the 27 scenarios may be randomly selected. In addition, it may be devised to include history data of a day common to a plurality of consumers in the same scenario.

Each scenario includes an individual history ID of each consumer used to generate the scenario, and the total value of power reduction amount for each slot of the history data of the individual history ID. For example, in scenario ABC4, the history data of customer A on the day with individual history ID=1, the history data of customer B on the day with individual history ID=2, and the history data of customer C with individual history ID=1 are stored. , summed for each slot.

Specifically, if the power reduction amounts for slots 1, 2, 3, and 4 are expressed as (x1, x2, x3, x4), the power reduction amounts for customer A's individual history ID=1 are (8, 10, 11, 10), the power reduction amount with individual history ID=2 for customer B is (8, 12, 9, 8), the power reduction amount with individual history ID=1 for customer C is (10, 11, 10, 11). Therefore, the predicted value of the power reduction amount for each slot in scenario ABC4 is calculated by summing the power reduction amounts of these consumers.
That is, it is calculated like (8, 10, 11, 10) + (8, 12, 9, 8) + (10, 11, 10, 11) = (26, 33, 30, 29).

The DR plan optimizing unit 20 selects a consumer as a DR implementation target based on the reduction scenario DB_41, and based on the predicted value of the power reduction amount of the selected consumer (target demand), the selected consumer possesses Create at least one discharge plan for the storage battery, and create a DR plan including the selected consumer information and the created discharge plan. Details of the DR plan optimization unit 20 will be described below.

The charge/discharge plan creation unit 23 of the DR plan optimization unit 20 creates a discharge plan for each scenario for each customer combination created by each customer combination creation unit 22 . The created discharge plan is stored in charge/discharge plan DB_45. The processing unit 24 of the DR plan optimizing unit 20 determines success or failure of scenario execution based on the created discharge plan and scenario.

The discharge plan is to plan the discharge amount of the storage battery (the storage battery owned by the consumer included in the scenario) for each slot. The amount of discharge from the storage battery is treated as the power reduction amount in DR implementation. This is because the power consumption of the system can be reduced by the amount of power discharged. Creating a discharge plan includes setting the amount of discharge for each slot to zero. For example, if the scenario can be executed successfully without discharge, the discharge amount for each slot may be set to zero.

Successful execution of the scenario means that all consumers included in the scenario were requested to implement DR, all consumers reduced power according to the scenario, and the storage batteries of the consumers discharged according to the discharge plan. It means that the success condition is satisfied when the discharge is not performed (including the case when the discharge amount is zero). Failure to execute the scenario means that a discharge plan that satisfies the constraints of the storage battery information (storage battery constraints) cannot be created, all the consumers included in the scenario are requested to implement DR, and all consumers receive electricity according to the scenario. It means that the success condition cannot be satisfied even if the number is reduced. The charging/discharging plan creation unit 23 determines the success or failure of execution of each scenario for each consumer combination, and calculates the success rate rate for each consumer combination based on the ratio of the number of successes and failures.

An example of creating a discharge plan and an example of determining success/failure of execution of a scenario will be described with reference to FIG. FIG. 7(A) shows an example in which the scenario can be executed successfully by the created discharge plan, and FIG. An example of failure is shown.

In FIG. 7A, the power reduction amounts for slots 1 to 4 in a scenario with a consumer combination (A, B, C) are (12, 16, 22, 25). In this case, if the discharge amount of the storage battery of consumer A is determined so that the power reduction amount is 20 kWh or more in order from the first slot 1, the discharge amount of slots 1 to 4 in the discharge plan is (8 , 4, 0, 0). In this case, the total reduction amount for slots 1-4 is (20, 20, 22, 25).

The discharge amount of the storage battery in each slot is equal to or less than the output 10 kWh/slot defined in the storage battery information DB_44 (Fig. 4). The total discharge amount of all slots is 12 (=8+4+0+0) kWh, which is less than or equal to 20 kWh, which is the capacity of the storage battery of consumer A determined in the storage battery information DB_44. Therefore, this discharge plan satisfies the constraints of the battery information.

Therefore, in the case of FIG. 7A, the amount of power reduction for all slots is 20 kWh or more, and the discharge plan satisfies the constraints of the storage battery information. , execution of the DR request is successful. Therefore, execution of this scenario is judged to be successful. Considering the margin, even if the reduction amount for each slot is determined by adding a certain amount of power to the reduction amount specified in the contract (20 kWh), and determining the discharge amount so that it is equal to or greater than the added power amount as much as possible. good.

In addition, in the storage battery of the consumer, a certain amount of power or a predetermined percentage of the battery capacity remains at the time when a DR request may be issued from the aggregator to the consumer or from the system operator to the aggregator. Suppose there is Such a thing may be realized by a contract established between the aggregator and the consumer, or may be realized by another method. The storage battery may be charged by any method, such as charging at night or charging with power generated by a photovoltaic panel.

Here, only consumer A among consumers A to C has a storage battery, but if other consumers have storage batteries, the amount of discharge of the storage battery of one or more consumers is determined. , the sum of the determined amount of discharge and the amount of power reduction should be 20 kWh or more for each slot.

On the other hand, in FIG. 7B, the power reduction amounts for slots 1 to 4 are (11, 13, 15, 25) in a certain scenario. In this case, when the discharge amount of the storage battery of consumer A is determined so that the power reduction amount is 20 kWh or more in order from the first slot 1, the discharge amount of slots 1 to 4 in the discharge plan is (9 , 7, 5, 0). The total reduction will be (20,20,20,25).

However, in this case, the total amount of discharge in all slots of the storage battery of consumer A is 21 (=9+7+5+0) (kWh). This value exceeds 20 kWh, which is the capacity defined in the storage battery information DB_44. Therefore, execution of this scenario cannot be realized and is judged to be a failure.

Depending on the scenario, even if the amount of discharge in each slot is zero (even if the discharge plan is not performed), the execution of the scenario is determined to be successful. For example, if DR is requested to all consumers included in scenario ABC4 in FIG. Since the total reduction amount is 20 kWh or more, the DR implementation is successful. Scenario 1, Scenario 2, etc. are also successful.

By the method described above, the charge/discharge plan creation unit 23 generates a storage battery discharge plan for each scenario for each consumer combination, and the processing unit 24 determines success or failure of execution of each scenario based on the discharge plan. to judge. For example, in the case of the consumer combination (A, B, C), there are 27 scenarios, 18 of which are successfully executed, and the success rate rate=18/27=66.7%. Success rates can be similarly calculated for other consumer combinations (A, C), (A, B), and (B, C).

FIG. 8 shows the power reduction amount Q for each slot, the discharge amount P for each slot in the discharge plan, and the power reduction amount S (power A result table summarizing the sum of the power reduction amount Q and the discharge amount P), the minimum value of the power reduction amount S for each slot, and the success or failure flag of scenario execution. However, in this example, only the customer combination (A, B) portion is shown, and the table portions of other customer combinations are omitted. The success rate rate can be calculated by calculating the ratio of the number of successes (o) and the number of failures (x) for each consumer combination.

Since the success rate rate of the consumer combination (A, B) is the highest here, the processing unit 24 selects the consumers A and B as the consumers to which the DR request is made. The planning parameter DB_43 stores calculation parameters used when the processing unit 24 performs the processing of the present embodiment (details will be described later).

The processing unit 24 also determines a discharge plan (applicable discharge plan) to be applied to the selected consumer. The applied discharge plan may be, for example, one arbitrarily selected from the discharge plans of each scenario of the selected consumer combination, or the average of the discharge plans of each scenario (however, it is necessary to satisfy the constraints of the storage battery information). , may be determined in other ways.

The processing unit 24 generates output information (DR plan) including information specifying the selected consumer and the applicable discharge plan. The generated DR plan is stored in the DR-requested consumer combination DB_46. At this time, the DR plan may be associated with the DR request information received from the grid operator system 201 .

The processing unit 24 may display the generated DR plan on the display unit 29 .

FIG. 9 shows a display example of the display unit 29. As shown in FIG. An example of information specifying a selected consumer in the DR plan is shown. The customers to whom the DR implementation is requested are marked with ○, and the other customers are marked with ×. Information on the applicable discharge plan may also be displayed on the display unit 29 .

Based on the DR plan, the information communication unit 25 transmits a DR implementation request to the consumer's system 301 of the selected consumer at the DR start time or a certain time before it. The request for DR implementation may include information on the duration of the DR request. In addition, the request for implementing DR may include information on the power reduction amount expected of the consumer. The power reduction amount expected of the consumer may be a predetermined value, a value determined in a contract with the consumer, or any other value.

The storage battery control unit 26 transmits the applied discharge plan (that is, the discharge instruction for each slot) included in the DR plan to the storage battery system 311 of the customer (in this example, the customer A). The control device 2 of the storage battery system 311 charges and discharges the storage battery 1 according to the applicable discharge plan. This controls the discharge of the storage battery 1 . When the device 101 calculates the power reduction amount of each selected consumer in real time and determines that there is a high possibility that the success condition will not be satisfied When a predetermined percentage of the power consumption cannot be reduced), a discharge instruction (discharge plan correction instruction) may be output to the storage battery system 311 to discharge even if it is not specified in the discharge plan ( However, the restrictions on the storage battery information shall be satisfied). Information on the real-time power reduction amount of the selected consumer is displayed on the display unit 29, and when the operator determines that it is necessary, the operator inputs an instruction to discharge the storage battery system 311 using the operation input unit 28, A discharge instruction may be output to the storage battery system 311 based on the instruction.

The DR planning device 101 may perform the process of determining the combination of consumers when receiving the DR request from the grid operator system 201, or may perform the process in advance before receiving the DR request.

FIG. 10 shows the hardware configuration of a DR planning device (information processing device) 101 according to this embodiment. The information processing device 101 according to this embodiment is configured by a computer device 150 . The computer device 150 includes a CPU 151 , an input interface 152 , a display device 153 , a communication device 154 , a main storage device 155 and an external storage device 156 , which are interconnected by a bus 157 .

A CPU (Central Processing Unit) 151 executes a computer program that implements the above-described functional configurations of the information processing apparatus 101 on the main storage device 155 . Each functional configuration is realized by the CPU 151 executing a computer program.

The input interface 152 is a circuit for inputting to the information processing apparatus 101 an operation signal from an input device such as a keyboard, mouse, and touch panel.

The display device 153 displays data or information output from the information processing device 101 . The display device 153 is, for example, an LCD (liquid crystal display), a CRT (cathode-ray tube), and a PDP (plasma display), but is not limited thereto. Data or information output from the computer device 150 can be displayed by this display device 153 .

The communication device 154 is a circuit for the information processing device 101 to communicate wirelessly or by wire with an external device. Information can be input from an external device via the communication device 154 . Information input from an external device can be stored in the DB. A portion of the information communication unit 25 and the storage battery control unit 26 that has a communication function can be constructed on the communication device 154 .

The main storage device 155 stores a program that implements the processing of this embodiment, data necessary for executing the program, data generated by executing the program, and the like. The program is expanded on the main storage device 155 and executed. The main storage device 155 is, for example, RAM, DRAM, or SRAM, but is not limited thereto. Various DBs and storage units in each embodiment may be constructed on the main storage device 155 .

The external storage device 156 stores the above programs, data necessary for executing the programs, data generated by executing the programs, and the like. These programs and data are read into the main memory device 155 during the processing of this embodiment. The external storage device 156 is, for example, a hard disk, an optical disk, a flash memory, and a magnetic tape, but is not limited to these. Various DBs and storage units in each embodiment may be constructed on the external storage device 156 .

The program described above may be pre-installed in the computer device 150, or may be stored in a storage medium such as a CD-ROM. Also, the program may be uploaded on the Internet.

The computer device 150 may include one or more of each of the processor 151, the input interface 152, the display device 153, the communication device 154, and the main storage device 155, and may be connected to peripheral devices such as printers and scanners. may be

Further, the information processing apparatus 101 may be configured by a single computer device 150, or may be configured as a system composed of a plurality of computer devices 150 interconnected.

FIG. 11 is a flow chart showing the operation of the DR planning device 101 of this embodiment. This operation may be performed when a DR request is received from the system operator system 201, or may be performed at a predetermined time (for example, a predetermined time before the time at which the DR request may be received), or otherwise. (for example, the timing instructed by the operator).

First, the DR planning device 101, through at least one of the input/output I/F 27 and the information communication unit 25, stores information to be stored in at least one of the consumer reduction history DB_40, the target reduction amount DB_42, the plan creation parameter DB_43, and the storage battery information DB_44. The acquired information is stored in these DBs (step S1). If information is stored in these DBs in advance, it is not necessary to store the information in this step.

Next, in step S2, the scenario creation unit 21 generates a consumer combination, and based on the history data of each consumer in the consumer reduction history DB_40, for each consumer combination, one or more scenarios of electric energy reduction are created. to create The created scenario is stored in the reduction scenario DB_41. The consumer combination may be generated for the consumers included in the consumer reduction history registered in the consumer reduction history DB_40, or a DB in which the information of the consumers is registered is separately prepared and the DB is used. may be used.

Next, in step S3, the processing unit 24 initializes rate_max, which is a parameter for storing the success rate, to 0%. The initialized parameters are stored in the planning parameter DB_43.

Next, for k=1 to Kmax, a customer combination s(k) is generated sequentially from k=1, and steps S4 to S7 are performed each time a customer combination s(k) is generated. Kmax corresponds to, for example, the maximum number of combinations of consumers. Details of steps S4 to S7 will be described below.

In step S4, 1 is added to the previous k to generate the k-th customer combination s(k). For the first time, the process is performed with k=1.

Next, in step S5, for each scenario for the combination of consumers s(k), a discharge plan ( b) is created. Success or failure of execution of each scenario is determined based on the created discharge plan and storage battery information. Based on the ratio of the number of times of success and failure, the success rate of DR implementation is calculated.

In step S6, it is determined whether the success rate rate calculated in step S5 is greater than rate_max. If rate is greater than rate_max (YES), in step S7, rate_max is updated by the success rate rate. Moreover, s* is updated by the consumer combination s(k) at this time. Also, the applicable discharge plan br is determined based on the discharge plan b corresponding to the successful scenario among the scenarios of the consumer combination s(k), and b* is updated by the determined applicable discharge plan br. The applied discharge plan br may be, for example, one discharge plan selected from the discharge plans b corresponding to the successful scenarios, or may be the average value of the discharge plans b corresponding to the successful scenarios (however, the storage battery information is limited must be satisfied), or may be determined in other ways. Initial values of s* and b* may be arbitrary. On the other hand, if rate is less than or equal to rate_max (NO), rate_max, s*, and b* are not updated.

When the processing of steps S4 to S7 is completed for the Kmax-th consumer combination s (Kmax), as a result, the value indicating the consumer combination with the highest success rate among the Kmax consumer combinations becomes s*. Also, the discharge plan of the storage battery in that case is stored in b*.

As described above, according to the present embodiment, a DR that increases the success rate of executing a DR request from a system operator by absorbing fluctuations in the power reduction amount of the consumer during DR implementation using the storage battery of the consumer. It is possible to create a plan (select a consumer to whom a DR request is made and determine an applicable discharge plan).

In the present embodiment, one or more scenarios are created by combining historical data for each consumer combination, and a consumer combination with a high success rate of scenario execution is selected. A DR request target consumer may be selected using the reduction prediction model. For example, the predicted reduction amounts for slots 1 to 4 are sampled from a probability distribution (corresponding to a reduction amount prediction model) such as a normal distribution, and one or more pieces of sampling data corresponding to history data are generated for each customer. The same processing as described above is performed by replacing the history data of the above-described embodiment with the sampling data. The parameters that define the functional form of the probability distribution may be obtained in advance by parameter estimation using the customer deletion history DB_40. Also, a probability distribution may be generated for each consumer combination. In this case, the predicted reduction amounts of slots 1 to 4 may be sampled for each consumer combination. In addition, if there is a common trend in the amount of reduction among multiple consumers (for example, the reason may be that they are in the same industry), a Bayesian method that uses a common prior distribution and performs sampling method may be used. Furthermore, at the initial stage when there is no information on the reduction history of consumers, it is possible to use a probability distribution with a large variance and use a method that increases the accuracy as information is collected (for example, reinforcement learning, banded algorithm, etc.). good.

(Second embodiment)
In the first embodiment, the success rate of executing a plurality of scenarios generated for each consumer combination is calculated, and the consumer combination with the highest success rate and the applicable discharge plan are determined. In the second embodiment, the problem is modeled using mathematical programming, and the problem is solved using a mathematical programming solver or the like to obtain the same result as in the first embodiment. Examples of mathematical programming solvers are Gurobi and CPLEX.

In the present embodiment, the scenario creating unit 21 creates original scenario data by combining history data of all consumers. The original scenario data is stored in the reduction scenario DB_41.

FIG. 12 shows an example of scenario source data stored in the reduction scenario DB_41.

The processing unit 24 extracts data related to the consumer combination from each scenario source data for each of the plurality of consumer combinations. The extracted data corresponds to the scenario of the customer combination in the first embodiment. As in the first embodiment, a discharge plan is generated for the extracted data (hereinafter referred to as a scenario), it is determined whether the scenario can be successfully executed, and the number of scenarios successfully executed is counted. A consumer combination with the largest count value is selected from a plurality of consumer combinations. We formulate the process described here as a problem in mixed-integer linear programming as follows. s.t. means constraints.

：シナリオ元データｓの場合に時刻（スロット）ｔにおける需要家ｉの蓄電池の放電量
l^s：シナリオ元データｓの場合にＤＲ要請の実行に成功するか否かの変数（失敗の場合は０、成功の場合は１） The variables are defined as follows.
x _i : Variable indicating whether to issue a DR request to consumer i (whether to select consumer i) 1 when issuing a DR request (when selecting customer i), 0 when not issuing a DR request is. For example, if consumers 1 to 3 exist, (x _1, x _2, x ₃ )=(1, 1, 0) means that consumers 1 and 2 are selected and consumer 3 is not selected. do. That is, it means selecting the consumer combination (1, 2).

: Discharge amount of storage battery of consumer i at time (slot) t in the case of scenario original data s
l ^s : Variable indicating whether or not the DR request is successfully executed for scenario source data s (0 for failure, 1 for success)

：シナリオ元データｓの場合にスロットｔにおいて需要家ｉが削減する電力量（ｋＷｈ）
A_i：需要家ｉの蓄電池の１スロット当たりの出力量（ｋＷｈ／ｓｌｏｔ）すなわち放電速度
B_i：需要家ｉの蓄電池の容量（ｋＷｈ）
C：削減目標量（ｋＷｈ）
M：十分大きな定数 Also, the constants are defined as follows.
I= {1, 2, …, N}: a set of consumers
T= {1, 2, …, T}: a set of slots
S= {1, 2, …, S}: Set of original scenario data

: Power consumption (kWh) reduced by consumer i at slot t for scenario original data s
A _i : output amount (kWh/slot) per slot of storage battery of consumer i, i.e. discharge rate
B _i : Storage battery capacity of consumer i (kWh)
C: Reduction target amount (kWh)
M: constant large enough

は、ＤＲ要請の成功回数を表す。従って、この計画を解くことで、当該成功回数を最大化する、需要家ｉの組合せと、当該組合せにおける需要家の蓄電池のスロット毎の出力量とを求める。なお、成功回数ではなく、成功率を最大化する場合は、(1)の式を、

に置き換えればよい。 of formula (1)

represents the number of successful DR requests. Therefore, by solving this plan, the combination of the customer i that maximizes the number of successes and the output amount for each slot of the storage battery of the customer in the combination are obtained. If you want to maximize the success rate instead of the number of successes, the formula in (1) is

should be replaced with

Formula (2) states that the success condition for a certain scenario (when a certain consumer combination is specified in certain scenario original data) is that the sum of the power reduction amount and the discharge amount is equal to or greater than the reduction target amount for each slot. represents (1-l ^s )M is a term for satisfying a constraint condition (the left side of equation (2) is equal to or greater than the target reduction amount C) when the execution of a certain scenario fails. By setting M to a sufficiently large constant, the left-hand side can always be made larger than the target power consumption when execution of the scenario fails. In the case of success, (1−l ^s )M becomes zero, so the constraint condition is that the sum of the power reduction amount and the discharge amount is equal to or greater than the reduction target amount C for each slot.

Equation (3) expresses that the discharge amount of each slot is equal to or less than Ai at consumer _i . In addition, when the DR request is not made to the customer i (when x _i =0), it also indicates that the storage battery of the customer i is not used.

Equation (4) expresses that the total discharge amount of the storage battery at consumer i is equal to or less than the capacity B _i of the storage battery.

は、図１２のシナリオ元データにより設定される。例えば、

は、シナリオ元データ４において需要家２（需要家Ｂ）のスロット１の電力削減量を表す。

の値は、図１２から１０ｋＷｈとなる。
本例においては、需要家１（需要家Ａ）のみが蓄電池を保有しており（図５参照）、Ａ_１＝１０（ｋＷｈ／ｓｌｏｔ）、Ｂ_１＝２０（ｋＷｈ）である。需要家２（需要家Ｂ）及び需要家３（需要家Ｃ）は蓄電池を保有していないため、Ａ_２＝Ｂ_２＝Ａ_３＝Ｂ_３＝０である。
目標電力量は各スロットでＣ＝２０、である（図３参照）。 As a specific example, when using the reduction target amount (reduction contract amount) in FIG. 3, the storage battery information in FIG. 5, and the scenario original data in FIG. When,
The consumer set is I = {1, 2, 3},
The slot set is T = {1, 2, 3, 4},
The scenario source data set is S = {1, 2, 3, …, 27}.

is set by the original scenario data of FIG. for example,

represents the amount of power reduction for slot 1 of customer 2 (customer B) in scenario source data 4 .

is 10 kWh from FIG.
In this example, only consumer 1 (consumer A) has a storage battery (see FIG. 5), A ₁ =10 (kWh/slot) and B ₁ =20 (kWh). Since consumer 2 (consumer B) and consumer 3 (consumer C) do not have storage batteries, A ₂ =B ₂ =A ₃ =B ₃ =0.
The target power budget is C=20 in each slot (see FIG. 3).

でもよい。または、上記シナリオの実行が成功したシナリオ元データにおいて需要家（Ａ，Ｂ）に対して決定された

の平均でもよい。その他の方法で決定してもよい。 Solving this example problem yields the optimal solution where (x1,x2,x3)=(1,1,0). This means that consumers A and B are selected as DR request targets. Also, the applicable discharge plan in this case may be calculated in the same manner as in the first embodiment. For example, for the customer (A, B), any one of the scenario original data that successfully executed the scenario for the customer (A, B) was determined for the customer (A, B)

It's okay. Alternatively, it was determined for the consumer (A, B) in the scenario original data in which the above scenario was successfully executed

may be the average of Other methods may be used.

In the above example, a plurality of original scenario data (27 pieces of scenario original data in the example of FIG. 12) were used. may be created. In this case, |S|=1. In this case, since the problem is simplified, speeding up of processing can be expected.

(Third embodiment)
When DR implementation is started for the consumer selected in the second embodiment described above, the power consumption increases unexpectedly, and the power reduction amount is unlikely to reach the contract reduction amount (reduction target amount). It is possible. In that case, the DR planning device 101 can communicate in real time with the storage battery system 311 of the selected consumer and control the storage battery 1 so as to increase the output amount (discharge amount). However, there are situations where the response of the storage battery 1 to the control command is slow, making real-time control difficult. In this case, it is difficult to change the discharge plan halfway through. In the present embodiment, assuming such a situation, a constraint condition is added to make the discharge plan common to all scenarios in the second embodiment when performing the DR plan.

を、全シナリオ元データ共通の変数

に変更した上で、下記のように定式化すればよい。式(7)、(11)、(12)は、式(1)、(5)、(6)と同じである。式(8)、(9)、(10)は、式(2)、(3)、(4)において上記変数の置換を行った式に相当する。 In this case, the variables of the discharge plan in the second embodiment

, a variable common to all scenario original data

After changing to , it can be formulated as follows. Equations (7), (11) and (12) are the same as Equations (1), (5) and (6). Equations (8), (9) and (10) correspond to equations (2), (3) and (4) with the above variables replaced.

In this case, the optimum solution of (x1, x2, x3)=(1, 1, 1) is obtained by the processing unit 24 solving the problem. The applicable discharge plan may be determined in the same manner as in the second embodiment.

FIG. 13 shows each scenario when consumers 1 to 3 (consumers A to C) are selected in each scenario original data based on the above optimum solution, that is, when DR requests are made to all of consumers A to C. 8 shows a table storing information of items similar to those in FIG. Scenario information when other consumer combinations (A, B), (A, C), and (B, C) are selected is omitted. This table is created by the processing unit 24 . A discharge plan is common to all scenarios. That is, in all scenarios 1 to 27, the discharge amount is 6 kWh in slot 1 (t1), 3 kWh in slot 2 (t2), and 0 kWh in slot 3 (t3) and slot 4 (t4). In this example, 18 scenarios out of scenarios 1 to 27 have been successfully executed. Therefore, the success rate rate=18/27=66.7%.

(Fourth embodiment)
This embodiment deals with a case where the storage battery can be charged during the DR implementation period. In the middle of the DR implementation period, if there is a margin in the target range of the reduction target amount or deletion amount of the slot, the storage battery is charged and used for discharging in the subsequent slot, so that the storage battery can be effectively used. Available.

を、正の値のみでなく、負の値もとれるように定義する。正の場合は、放電を表し、負の場合は、充電を表す。本実施形態では、混合整数線形計画の問題は、下記のように定式化される。 In this case, the variables of the discharge plan in the second embodiment

is defined to be positive as well as negative. If positive, it represents discharging; if negative, it represents charging. In this embodiment, the mixed-integer linear programming problem is formulated as follows.

Constants newly introduced for the second embodiment are shown below.
A' _i : Charging amount per slot (kWh/slot) of the storage battery of consumer i, that is, charging speed
B _i0 : Initial chargeable capacity of storage battery of consumer i (initial charge capacity is B _i -B _i0 ) (kWh)

Equations (14) and (15) require that the power reduction amount for each slot be within the range of 90% to 110% (18 kWh to 22 kWh) of the target reduction amount of 20 kWh.

Equation (16) expresses that the amount of charge in each slot of customer _i is A'i or less in addition to the fact that the amount of discharge in each slot is A _i or less in customer i.

As a specific example, if the charging speed is set to (A' _i =10 (kWh/slots)) and the above problem is solved, the optimum solution of (x1, x2, x3)=(1, 1, 1) is obtained. The applicable charge/discharge plan (charge/discharge amount of each slot) may be determined in the same manner as the applicable discharge plan of the second embodiment. When solving the above problem, the values of the discharge amount and the charge amount that make the execution of the scenario successful in a certain combination of consumers each have a range, and any value within the range may be adopted. As an example, in the case of charging, the amount of charge is determined as large as possible so that the reduction amount is at or near the lower limit of the target range (but larger than the lower limit), and in the case of discharging, the reduction amount is the lower limit of the target range. Alternatively, a discharge amount as small as possible may be determined so as to be close to it (but larger than the lower limit).

FIG. 14 shows each scenario when consumers 1 to 3 (consumers A to C) are selected in each scenario original data based on the above optimum solution, that is, when DR requests are made to all of consumers A to C. 8 shows a table storing information of items similar to those in FIG. However, the "discharge amount" in FIG. 8 is changed to "charge/discharge amount", and the "removal amount including discharge plan" is changed to "removal amount including charge/discharge plan". The "removal amount including charging/discharging plan" is obtained by adding the discharging amount or subtracting the charging amount from the total removed amount of the customers A and B. In this example, since only the customer A owns the storage battery, the storage battery for the customer A is the only one to be charged and discharged. If storage batteries of a plurality of consumers are to be targeted, the total amount of discharge or the total amount of charge of the plurality of consumers is entered in the item of "charge/discharge amount".

For example, in scenario ABC4, 2 kWh is discharged in slot 1 (t1), 4 kWh is charged in slot 2 (t2), 2 kWh is charged in slot 3 (t3), and both discharging and charging are performed in slot 4 (t4). do not have. Even if 4 kWh is charged in slot 2 (t2), the amount of removal including the charge/discharge plan is 18 (=22-4) kWh, so it falls within the target range of the amount of removal (18 kWh or more and 22 kWh or less). Similarly, even if 2 kWh is charged in slot 3 (t3), the amount of removal including the charge/discharge plan is 18 (=20-2) kWh, so it falls within the target range of the amount of removal (18 kWh or more and 22 kWh or less). By charging the storage battery when there is a margin with respect to the target range of the reduction amount in this way, the charged power can be utilized for subsequent discharge. Therefore, by determining the amount of charge to be at or near the lower limit of the target range of the amount of deletion, the storage battery can be used more efficiently while successfully executing the DR request.

In the example of FIG. 14, 21 out of 27 scenarios are successful. Therefore, the success rate rate=21/27=77.8%.

(Fifth embodiment)
In the first to fourth embodiments, the storage battery control unit 26 of the DR planning device 101 charges the storage battery of the consumer during the period from when the DR request is received from the system operator system 201 to when the DR is started. can be controlled as follows. The storage battery to be charged may be a storage battery owned by a consumer whose remaining power amount has not reached a predetermined value defined in a contract (for example, power amount required at the start of DR). In this case, the consumer needs to charge the battery to a predetermined value by the start time of DR implementation.

In addition, as another example of the storage battery to be charged, the storage battery of all consumers, the storage battery of a certain number of consumers randomly selected from all consumers, for example, may be used. You may choose different storage batteries.

The charging may be full charging, charging up to a predetermined value, or defining the charging amount by other methods. For example, if a DR request is received at 12:30 and the DR request specifies 13:00 as the DR start time, the storage battery selected by the above method is fully charged between 12:30 and 13:00. . Thereby, the amount of discharge used for DR can be increased.

(Sixth embodiment)
In the first to fourth embodiments, it is assumed that all the electric power of the storage battery of the consumer can be used for discharging, but a maximum of 80% of the rated capacity can be used for discharging, and 20% is left. may be added. As a result, it is possible to use the storage battery in consideration of the customer's demand for the storage battery (such as wanting to use it for other purposes), for example, leaving some power for emergency use. Note that 80% and 20% are examples, and other numerical values may be used.

Further, the demand for the storage battery from the consumer is not limited to the amount of power to be stored in the storage battery. For example, there may be consumers who desire to suppress deterioration of storage batteries. Therefore, a priority may be set for each consumer's storage battery, and a DR plan may be created so that the storage battery with the highest priority is preferentially used for discharging or charging. In addition, storage batteries with higher priority may be preferentially used for real-time discharging or charging. A DR plan may also be created so that a limit on the number of times of use is set for the storage battery, and a storage battery whose number of times of use has reached the limit is not used for discharging or charging during DR. Information on the priority or the number of times of use may be stored in the storage battery information DB_44 (see FIG. 5).

(Seventh embodiment)
If there is a delay in the response of the customer's storage battery to the command from the DR planning device 101, the response delay may be taken into consideration in selecting the customer or the storage battery. For example, assume a case where a DR request and a discharge plan are transmitted to the consumer 10 minutes before the DR start time. For example, if the battery response delay is 15 minutes (e.g., it takes 15 minutes from the time you send an instruction to the battery to the time it processes the instruction), the discharge plan (including the discharge plan for the first slot) will be executed 10 minutes in advance. Even if it is sent to the storage battery, it will not be in time for the DR start time. In such a case, it is possible not to select a storage battery of a customer with a response delay of 10 minutes or more as a storage battery that needs to be discharged in the first slot. The amount of delay in the response of the storage battery may be measured by, for example, transmitting an instruction message to the storage battery and measuring the time until receiving the response message. The response delay amount may depend on the network delay and the load condition of the storage battery 1 .

(Eighth embodiment)
Although the first to seventh embodiments dealt with cases where a power reduction request is made as a DR request, the present invention can also be applied to a case where a power consumption request is made to encourage power consumption. In this case, a target amount of power consumption is determined between the aggregator and the grid operator. Based on the power consumption history of each consumer, the consumers to whom the DR request is made may be selected such that the total power consumption of the selected consumers is greater than or equal to the target amount during the DR requested period. Also, for the time period when the target amount is not reached, the charging plan for the storage battery may be created so that the sum of the charge amount and the sum of the power consumption of the selected consumer is equal to or greater than the target amount. In addition, by appropriately rereading the description of each of the above-described embodiments, it can be applied to the case of a power consumption request.

It should be noted that the present invention is not limited to the above-described embodiments as they are, and can be embodied by modifying constituent elements without departing from the gist of the present invention at the implementation stage. Also, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the above embodiments. Further, for example, a configuration in which some components are deleted from all the components shown in each embodiment is also conceivable. Furthermore, components described in different embodiments may be combined as appropriate.

20: DR plan optimization unit 21: Scenario creation unit 23: Charge/discharge plan creation unit (plan creation unit)
22: Consumer combination creation unit 24: Processing unit 25: Information communication unit 27: Input/output I/F
28: Operation input unit 29: Display unit 41: Reduction scenario DB
40: Customer reduction history DB
42: Reduction Target Amount DB
43: Plan creation parameter DB
44: Storage battery information DB
45: Charge and discharge plan DB
46: DR request consumer combination DB
101: DR planning device (demand response planning device)
201: Grid operator system 301: Customer system 311: Customer storage battery 150: Computer device 151: Processor (CPU)
152: Input interface 153: Display device 154: Communication device 155: Main storage device 156: External storage device 157: Bus

Claims (9)

selecting a plurality of target consumers to be subjected to a first power reduction request from among the plurality of consumers based on power reduction histories of the plurality of consumers in response to the power reduction request; a processing unit that predicts the reduced power amount of the target consumer of
of the plurality of target consumers based on the difference between the reduction target amount, which is the amount of power requested to be reduced by the first power reduction request, and the total of the power reduction amounts predicted for the plurality of target consumers; a plan creation unit that creates a discharge plan for at least one storage battery;
a communication unit that transmits the first power reduction request to the consumer systems of the plurality of target consumers and transmits the discharge plan to a storage battery system that includes the storage battery;
Information processing device with For a time period in which the total predicted power reduction amount is smaller than the target reduction amount, the plan creation unit determines that the sum of the predicted total power reduction amount and the discharge amount of the storage battery in the time period is The information processing apparatus according to claim 1, wherein the discharge plan for the storage battery is created so as to be equal to or greater than the target reduction amount or within a range of ±10% of the target reduction amount . The plan creation unit creates a charging plan for the storage battery,
The plan creation unit subtracts the charge amount of the storage battery in the time period from the total predicted power reduction amount for a time period in which the predicted total power reduction amount is greater than the target reduction amount. creating the charging plan for the storage battery so that the value is equal to or greater than the reduction target amount;
The information processing apparatus according to claim 1 or 2, wherein the communication unit transmits the charging plan to the consumer system of the at least one target consumer. The communication unit communicates with the consumer system or the system operator system during the period requested to reduce power by the power reduction request, calculates the power reduced by the target consumer, and calculates the power reduction A storage battery control unit that determines a discharge amount to be added to the discharge plan based on the difference between the calculated power and the reduction target amount, and controls the storage battery system to discharge the determined discharge amount. 4. The information processing device according to any one of 3. Two or more of the target consumers own storage batteries,
A priority is set for the storage battery,
The processing unit selects a storage battery to be discharged from the storage batteries owned by the two or more target owners based on the priority, and creates the discharge plan for the selected storage battery. The information processing device according to item 1. Two or more of the target consumers own storage batteries,
2. The processing unit selects a storage battery to be discharged from among the storage batteries owned by the two or more target consumers based on the response delay time of the storage battery, and creates the discharge plan for the selected storage battery. 5. The information processing device according to any one of 1 to 4. The information processing according to any one of claims 1 to 6, wherein the processing unit predicts the power reduction amount of the plurality of target consumers based on the distribution of the power reduction amount of each of the plurality of target consumers. Device. selecting a plurality of target consumers to be subjected to a first power reduction request from among the plurality of consumers based on power reduction histories of the plurality of consumers in response to the power reduction request; A step of predicting the reduced power amount of the target consumer of
of the plurality of target consumers based on the difference between the reduction target amount, which is the amount of power requested to be reduced by the first power reduction request, and the total of the power reduction amounts predicted for the plurality of target consumers; creating a discharge plan for at least one of the storage batteries;
transmitting the first power reduction request to consumer systems of the plurality of target consumers and transmitting the discharge plan to a storage battery system including the storage battery;
An information processing method comprising selecting a plurality of target consumers to be subjected to a first power reduction request from among the plurality of consumers based on power reduction histories of the plurality of consumers in response to the power reduction request; A step of predicting the reduced power amount of the target consumer of
of the plurality of target consumers based on the difference between the reduction target amount, which is the amount of power requested to be reduced by the first power reduction request, and the total of the power reduction amounts predicted for the plurality of target consumers; creating a discharge plan for at least one of the storage batteries;
transmitting the first power reduction request to consumer systems of the plurality of target consumers and transmitting the discharge plan to a storage battery system including the storage battery;
A computer program that causes a computer to execute

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