JP2024006334A - Output estimation device, output estimation program, and output estimation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an output estimation device, an output estimation program, and an output estimation method for accurately adjusting demands with a simple configuration.

SOLUTION: A smart meter data acquisition unit 101 collects the effective and reactive power consumption of a plurality of consumers. A load characteristic calculation unit 102 calculates load characteristics using the effective and reactive power consumption of the non-possession consumers of the plurality of consumers that do not have a charging/discharging device. An estimation unit 105 estimates the charging/discharging amount of the charging/discharging device of the target consumer to be estimated who owns a charging/discharging device on the basis of the effective and reactive power consumption of the target consumer to be estimated and the load characteristics.

SELECTED DRAWING: Figure 2

COPYRIGHT: (C)2024,JPO&INPIT

Description

The present invention relates to an output estimation device, an output estimation program, and an output estimation method.

In order to realize a low-carbon society and use energy efficiently, it is expected that the number of consumers using energy devices such as photovoltaics (PV) and storage batteries will increase in the future. If charging and discharging by each customer's storage battery is concentrated in a specific time period, the power demand during that time period will increase, and there is a risk that the load factor of the power system will decrease. When the load factor of the power system decreases, the operational efficiency of the power system decreases.

In this way, it is conceivable that demand fluctuations in the power system become complicated due to charging and discharging of storage batteries. As demand fluctuations in the power system become more complex, it is expected that the rate of change in demand will increase more than ever. When the rate of change in demand increases, the difference between the minimum demand and the maximum demand increases, and the difference must be rapidly filled in a short period of time, increasing the amount of adjustment required for the power system. Furthermore, if the power system is not able to keep up with the adjustment, there is a possibility that a situation may arise in which power cannot be supplied.

In order to avoid such a situation, it is desired to consider measures such as reducing the demand change rate using consumer equipment such as heat pump water heaters and storage batteries. When considering countermeasures using consumer equipment, it is expected that a business will be established in which intermediaries called aggregators and retail electricity utilities that utilize consumer-side resources can earn compensation by contributing to the demand adjustment of grid operators. be done.

One possible method for adjusting demand using consumer equipment is to utilize demand response (DR). Demand response is a technology that adjusts demand by providing economic benefits. For example, one possible use of demand response is to adjust demand by controlling a consumer's storage battery by giving incentives or penalties to the charging and discharging of the consumer's storage battery.

In order to appropriately adjust demand, it is necessary to understand the charging and discharging of consumer equipment such as storage batteries. Therefore, retail electricity companies and aggregators install sensors between smart meters and storage batteries to obtain information on charging and discharging the storage batteries, and issue demand response commands using the information on charging and discharging the storage batteries obtained from the sensors. Demand adjustment is being carried out.

As a demand adjustment technique using demand response, for example, the total charging power curve for each time period for charging each storage battery is determined under conditions where the daily charging and discharging amount can be obtained in advance, and the There is a prior art technique that determines electricity rates according to a curve that is a mirror image of the curve.

Japanese Patent Application Publication No. 2015-014876

However, in the conventional demand adjustment technology, it is necessary to install a new sensor to obtain information on the amount of charge and discharge of a storage battery, and to install new communication equipment to obtain information from the sensor. Information collection routes using new communication equipment are routes other than route A, which connects power transmission and distribution companies and smart meters, and route C, which connects power transmission and distribution companies and aggregators. When installing new equipment in this way, a lot of work is required to obtain the charging and discharging amount of storage batteries for each customer, and the cost is also high, making it difficult to realize accurate demand adjustment.

The disclosed technology has been made in view of the above, and aims to provide an output estimation device, an output estimation program, and an output estimation method that accurately perform demand adjustment with a simple configuration.

In one aspect of the output estimation device, output estimation program, and output estimation method disclosed in the present application, the data acquisition unit collects the active power amount and reactive power amount of a plurality of consumers. The load characteristic calculation unit calculates a load characteristic using the active power amount and the reactive power amount of a non-possessing customer that does not have a charging/discharging device among the plurality of customers. The estimating unit calculates the charge/discharge amount of the charge/discharge device of the estimated target consumer based on the active power amount and the reactive power amount of the estimated target consumer who owns the charge/discharge device, and the load characteristics. presume.

In one aspect, the present invention can accurately adjust demand with a simple configuration.

FIG. 1 is a schematic diagram of a power management system for consumers. FIG. 2 is a block diagram of the output estimation device and the demand response generation device. FIG. 3 is a diagram for explaining calculation of load characteristics. FIG. 4 is a diagram for explaining estimation processing when the rated output of the storage battery is known. FIG. 5 is a diagram for explaining estimation processing when the rated output of the storage battery is not known. FIG. 6 is a diagram illustrating an overview of the output estimation process of the estimation target consumer. FIG. 7 is a flowchart of the demand response command generation process by the aggregator system. FIG. 8 is a flowchart of the process of estimating the charge/discharge amount and actual load of the storage battery of the estimated target consumer by the output estimating device. FIG. 9 is a hardware configuration diagram of the output estimation device.

Embodiments of the output estimation device, output estimation program, and output estimation method disclosed in the present application will be described in detail below based on the drawings. Note that the following examples do not limit the output estimation device, output estimation program, and output estimation method disclosed in the present application.

FIG. 1 is a schematic diagram of a power management system for consumers. The power management system includes an aggregator system 1 that manages power of consumers 2 and a power transmission and distribution company system 3.

The power transmission and distribution company system 3 is a system managed by a company that delivers electricity generated at a power plant to a point where a consumer 2 uses the electricity via a power transmission line, a power distribution line, or the like. The power transmission and distribution company system 3 is connected to a smart meter 22 installed at each consumer 2 via a network. The power transmission and distribution company system 3 receives information measured by the smart meter 22. This route for sending the information measured by the smart meter 22 to the power transmission and distribution company system 3 is called the A route. The power transmission and distribution company system 3 transmits information measured by the smart meter 22 of each consumer 2 to the aggregator system 1.

The aggregator system 1 is a system managed by an aggregator, a retail electricity company, or the like. The aggregator system 1 receives smart meter data including information measured by the smart meters 22 of each consumer 2 from the power transmission and distribution company system 3. This route for sending the information measured by the smart meter 22 to the aggregator system 1 via the power transmission and distribution company system 3 is called the C route. In other words, Route C is a transmission route for transmitting data measured by the smart meter 22 of the consumer 2 to the aggregator system 1 owned by the retailer via the power transmission and distribution company 3 (power company). 3 and the aggregator system 1.

The aggregator system 1 uses the smart meter data transmitted from the smart meter 22 to instruct each consumer 2 to charge or discharge. For example, the aggregator system 1 issues a demand response command to the storage battery 23 owned by the consumer 2, and causes the consumer 2 to charge and discharge the storage battery 23. In this embodiment, the aggregator system 1 also collects information from smart meters 22 installed at consumers 2 that do not have charging/discharging devices such as storage batteries 23.

Consumers 2 are businesses and households that use electricity supplied from the power transmission and distribution company system 3. The consumer 2 includes, for example, a consumer terminal device 21 and a smart meter 22. Furthermore, the consumer 2 has load devices that use electricity supplied from the power transmission and distribution company system 3, such as an air conditioner 24 and an electric water heater 25 such as EcoCute (registered trademark). Furthermore, the consumer 2 may have a charging/discharging device such as a storage battery 23. Here, the charging/discharging device also includes a power generation device such as a solar power generation system.

Below, the storage battery 23 will be explained as an example of a charging/discharging device. Here, in FIG. 1, the consumer 2 that owns the storage battery 23 is illustrated as an example, but the consumer 2 does not need to own the storage battery 23. However, the power management by the aggregator system 1 described below is targeted at the consumer 2 who owns the storage battery 23. Here, the consumer 2 that owns the storage battery 23 is referred to as a consumer that owns a storage battery, and the consumer 2 that does not own a storage battery 23 is referred to as a consumer that does not own a storage battery.

The smart meter 22 measures the amount of active power and the amount of reactive power at the consumer 2 every predetermined period. The smart meter 22 adds the amount of power actually used by load devices such as the air conditioner 24 and the electric water heater 25 and the amount of power charged in the storage battery 23 as the effective power amount, and calculates the amount of power discharged from the storage battery 23. The effective power amount is obtained by subtracting the calculated power amount. However, although the smart meter 22 measures the overall amount of power used by the consumer 2, it does not measure the active power amount and the reactive power amount for individual devices such as the storage battery 23 and the air conditioner 24. Then, the smart meter 22 sends the measured active power amount and reactive power amount to the power transmission and distribution company system 3 as smart meter data.

The customer terminal device 21 is an information terminal device such as a personal computer used by the customer 2. A consumer terminal device 21 owned by a consumer who owns a storage battery receives a demand response command issued from the aggregator system 1. Then, the consumer terminal device 21 displays the demand response command on a monitor or the like and provides it to the consumer 2. The consumer 2 performs charging and discharging of the storage battery 23 based on the demand response command.

FIG. 2 is a block diagram of the output estimation device and the demand response generation device. The aggregator system 1 includes an output estimation device 10 and a demand response generation device 11.

The output estimation device 10 estimates the output of the storage battery 23 owned by the storage battery-owning consumer, that is, the amount of charge and discharge of the storage battery 23 . Furthermore, the output estimation device 10 estimates the actual load of a consumer who owns a storage battery. The details of the output estimation device 10 will be explained below. As shown in FIG. 2, the output estimation device 10 includes a smart meter data acquisition section 101, a load characteristic calculation section 102, a data storage section 103, a customer information storage section 104, and an estimation section 105.

The customer information storage unit 104 also stores the rated output of the storage battery 23 whose rated output is known among the storage batteries 23 owned by the customer 2 . The customer information storage unit 104 may store information input from the administrator of the aggregator system 1, or may store information about each customer 2 sent from the power transmission and distribution company system 3. .

The smart meter data acquisition unit 101 acquires smart meter data including the active power amount and reactive power amount output from the smart meters 22 placed at each customer 2 via the power transmission and distribution company system 3 via route C. do. Then, the smart meter data acquisition unit 101 accumulates and holds the smart meter data of each customer 2. At this time, information indicating whether each consumer 2 is a consumer with a storage battery or a consumer without a storage battery is added to the smart meter data.

The load characteristic calculation unit 102 acquires, from the smart meter data acquisition unit 101, smart meter data of a consumer who does not own a storage battery from among the smart meter data held by the smart meter data acquisition unit 101. Then, the load characteristic calculation unit 102 creates a PQ correlation diagram for a consumer who does not own a storage battery using the active energy amount and the reactive energy amount included in the smart meter data.

FIG. 3 is a diagram for explaining calculation of load characteristics. The horizontal axis in FIG. 3 represents active power amount, and the vertical axis represents reactive power amount. For example, as shown in FIG. 3, the load characteristic calculation unit 102 converts each point 201 indicating the active energy amount and reactive energy amount at each time included in the smart meter data of each customer without a storage battery into the active energy amount. and reactive power amount, respectively, are plotted on a two-dimensional coordinate plane with coordinate axes. As a result, the load characteristic calculation unit 102 creates a PQ correlation diagram for consumers who do not own storage batteries.

Next, the load characteristic calculation unit 102 calculates an approximate curve of each plotted point 201 to obtain a load characteristic 202. Here, the load characteristic calculation unit 102 calculated the load characteristic 202 by linear approximation, but the load characteristic calculation unit 102 is not limited to this, and may calculate the load characteristic 202 using other approximation methods such as nonlinear approximation. Thereafter, the load characteristic calculation unit 102 stores information on the calculated load characteristic 202 and the PQ correlation diagram of the consumer who does not have a storage battery in the data storage unit 103.

The estimating unit 105 acquires information on storage battery-owning consumers whose output is to be estimated. For example, the estimating unit 105 may receive input from the administrator of the aggregator system 1, using an input device, of information on storage battery-owning consumers whose output is to be estimated. In addition, the estimating unit 105 may obtain information on the storage battery-owning consumers to be output estimated from an external database that stores information on a plurality of storage battery-owning consumers to be output estimated. Hereinafter, the storage battery-owning consumers targeted for output estimation will be referred to as "estimation target consumers."

Next, the estimating unit 105 obtains smart meter data of the consumer to be estimated from the smart meter data obtaining unit 101 . Then, the estimating unit 105 creates a PQ correlation diagram of the estimated customer. Next, the estimation unit 105 checks the information of the estimation target consumer stored in the consumer information storage unit 104, and determines whether the rated output of the storage battery 23 of the estimation target consumer is known.

FIG. 4 is a diagram for explaining estimation processing when the rated output of the storage battery is known. The horizontal axis in FIG. 4 represents active power amount, and the vertical axis represents reactive power amount. The diagram shown in FIG. 4 in which each point 210 is plotted on a two-dimensional coordinate plane representing the active energy amount and the reactive energy amount is a PQ correlation diagram of the estimation target customer generated by the estimation unit 105.

When the rated output of the storage battery 23 of the estimated target customer is known, the estimation unit 105 acquires the rated output of the storage battery 23 of the estimated target customer from the customer information storage unit 104. The rated output of the storage battery 23 includes the amount of charge and the amount of discharge. Here, the rated output of the amount of charge of the storage battery 23 is expressed as P (charge), and the rated output of the amount of discharge of the storage battery 23 is expressed as P (discharge).

Furthermore, the estimating unit 105 acquires the load characteristic 202 from the data storage unit 103, and arranges the load characteristic 202 in the PQ correlation diagram of the consumer to be estimated, as shown in FIG.

Here, the electricity usage status of each consumer 2 is generally similar. That is, the actual load obtained by subtracting the amount of charging and discharging of the storage battery 23 from the amount of power used by the consumer to be estimated approximately corresponds to the amount of power used by each general customer. From this, it is considered that the actual load of the estimated target consumer approximates the load characteristic 202. That is, the amount of charging and discharging when the value obtained by subtracting the amount of charging and discharging of the storage battery from the value of the effective power amount of the consumer to be estimated most closely approximates the load characteristic 202 most accurately represents the charging and discharging power of the storage battery 23. Then, if the difference is greater than half of the rated output for charging and discharging, the rated output is removed from the active energy of the smart meter data, so that the entire amount of electricity used by the target consumer is most approximated to the load characteristic 202. be able to. From this, the estimating unit 105 uses P (charging)/2 and P (discharging)/2, which are one half of the rated power for charging and discharging the storage battery 23, as threshold values.

The estimation unit 105 generates the charging threshold 203 by adding the active power amount of P (charging)/2 to the load characteristic 202 in the PQ correlation diagram of the estimated customer. That is, the charge threshold value 203 is a line obtained by shifting the load characteristic 202 to the right by P (charge)/2. Furthermore, the estimating unit 105 generates the discharge threshold 204 by subtracting the active power amount of P (discharge)/2 from the load characteristic 202 in the PQ correlation diagram of the consumer to be estimated. That is, the discharge threshold value 204 is a line obtained by moving the load characteristic 202 in parallel to the left by P (discharge)/2.

A case where the active power amount deviates greatly from the load characteristic 202 in the direction of more active power or less reactive power is a case where the storage battery 23 is being charged. Therefore, the estimation unit 105 determines that the storage battery 23 is charged at a point 210 plotted below the charging threshold 203 on the coordinate plane representing the active energy and the reactive energy. Then, the estimation unit 105 calculates the actual load at the time corresponding to each point 210 plotted below the charging threshold 203 by subtracting P (charging) from the active power amount of the smart meter data at that time. . Furthermore, the estimation unit 105 calculates the amount of charge of the storage battery 23 per day by integrating the amount of charge of the storage battery 23 at each time.

Further, a case where the active power amount deviates greatly from the load characteristic 202 in the direction where the active power amount is small or the reactive power amount is large is a case where the storage battery 23 is discharging. Therefore, the estimating unit 105 determines that the storage battery 23 is discharged at a point 210 plotted above the discharge threshold 204 on the coordinate plane representing the active energy and the reactive energy. Then, the estimation unit 105 calculates the actual load at the time corresponding to each point 210 plotted above the discharge threshold 204 by adding P (discharge) from the active power amount of the smart meter data at that time. Furthermore, the estimation unit 105 calculates the discharge amount of the storage battery 23 per day by integrating the discharge amount of the storage battery 23 at each time.

On the other hand, if the rated output of the storage battery 23 of the consumer to be estimated is not known, the estimation unit 105 performs the following process. FIG. 5 is a diagram for explaining estimation processing when the rated output of the storage battery is not known. The horizontal axes of graphs 221 and 222 in FIG. 5 represent active energy, and the vertical axes represent reactive energy.

The estimating unit 105 acquires information on the PQ correlation diagram and load characteristics 202 of the consumer who does not own a storage battery from the data storage unit 103. Then, the estimating unit 105 calculates a charging threshold 205 and a discharging threshold 206 that include points 210 representing the smart meter data of the PQ correlation diagram of the consumer who does not own a storage battery between the number threshold or more.

For example, the estimation unit 105 determines the value of σ such that the range of ±2σ is between the charging threshold 205 and the discharging threshold 206 for the load characteristic 202, as shown in the graph 221. That is, the estimating unit 105 calculates, in the PQ correlation diagram of the consumer to be estimated, a charging threshold 205 that is calculated by adding 2σ active energy to the load characteristic 202 and a discharge threshold value 205 that is calculated by subtracting 2σ active electric energy from the load characteristic 202. σ is calculated so that a predetermined total number of points 210 or points 210 that are close to the total number of points 210 or more than a threshold value are included between the threshold value 206 and the predetermined total number of points 210 . The number threshold can be set to, for example, 95% or more of the total number of points 210. However, the load characteristic calculation unit 102 may calculate the charging threshold 205 and the discharging threshold 206 using not only linear approximation but also other approximation methods such as nonlinear approximation.

Here, the actual load of the consumer to be estimated is considered to approximate the load characteristic 202. Therefore, the case where the value obtained by subtracting the charging and discharging amount of the storage battery from the value of the effective power amount of the consumer to be estimated is the most approximate to the load characteristic 202 is considered to be the accurate charging and discharging amount.

When the amount of active power deviates significantly from the load characteristic 202 in the direction of increasing the amount of active power or decreasing the amount of reactive power, it is considered that the storage battery 23 is being charged. Therefore, the estimation unit 105 determines that the storage battery 23 is charged at a point 210 plotted below the charging threshold 205 on the coordinate plane representing the active energy and the reactive energy.

Next, the estimating unit 105 sets the charging amount, which is the rated output for charging the storage battery 23, to P (charging α). Next, the estimation unit 105 sets P (charging α) to an initial value. For example, the estimation unit 105 sets the rated output of the lowest charge amount of a general storage battery as the initial value of P (charge α). Specifically, the estimation unit 105 can set the initial value of P (charging α) to 2 kW or the like.

Then, the estimating unit 105 obtains a point by subtracting P (charging α) from the active energy at a point 210 plotted below the charging threshold 205 on the coordinate plane representing the active energy and the reactive energy. That is, the estimation unit 105 horizontally moves the point 210 plotted below the charging threshold 205 on the coordinate plane representing the active energy and the reactive energy in the left direction by P (charging α). Then, the estimation unit 105 subtracts the active power amount at the load characteristic 202 when the reactive power amount is the same from the active power amount at the point 210 after the movement. That is, the estimating unit 105 calculates the horizontal distance from the point 210 after the movement to the load characteristic 202, and if the point 210 after the movement is on the left side of the load characteristic 202, the sign is negative, and if the point 210 after the movement is on the right side of the load characteristic 202, the sign is negative. The sign is positive. Next, the estimating unit 105 calculates the charging time, which is a set of charging times t, by subtracting the active power amount at the load characteristic 202 when the reactive power amount is the same from the active power amount at the point 210 after the movement. An integrated value Σ _t∈T L(t) in time period T is calculated. That is, the integrated value Σ _t∈T L(t) is the integrated value obtained by subtracting the set charging amount from the active energy measured in the charging time period T and further subtracting the active energy in the load characteristic 202. be.

Next, the estimation unit 105 similarly calculates Σ _tεT L(t) by changing P (charge α) by one predetermined step. For example, the estimation unit 105 changes P (charge α) in 0.1 W increments. Then, the estimation unit 105 repeats the calculation of Σ _t∈T L(t) while changing P (charge α) up to a specific upper limit value. For example, the estimation unit 105 sets the rated output of the highest charging amount of a general storage battery as the upper limit value of P (charging α). Specifically, the estimation unit 105 can set the upper limit value of P (charging α) to 5 kW.

Next, the estimation unit 105 specifies the value of P (charge α) that minimizes Σ _t∈T L(t). Then, the estimating unit 105 subtracts the specified value of P (charging α) from the effective power amount of the smart meter data of the estimated target customer at the charging time t, and calculates the actual load of the estimated target customer in the charging time period T. Calculate the estimated value of That is, when the effective power amount of the smart meter data of the consumer to be estimated at charging time t is Px(t), using the specified P(charging α), the estimating unit 105 calculates Px(t)−P( Let charging α) be the estimated value of the actual load of the estimated target customer in the charging time period T. Furthermore, the estimation unit 105 calculates the amount of charge of the storage battery 23 per day by integrating the amount of charge of the storage battery 23 at each time.

Further, if the load characteristic 202 is far away from the active power amount in the direction where the active power amount is small or the reactive power amount is large, it is considered that the storage battery 23 is discharging. Therefore, the estimation unit 105 determines that the storage battery 23 has been discharged at a point 210 plotted above the discharge threshold 206 on the coordinate plane representing the active energy and the reactive energy.

Next, the estimation unit 105 sets the discharge amount, which is the rated output of discharge of the storage battery 23, to P (discharge β). Next, the estimation unit 105 sets P (discharge β) to an initial value. For example, the estimation unit 105 sets the rated output of the lowest discharge amount of a general storage battery as the initial value of P (discharge β).

Then, the estimating unit 105 calculates a point by adding P (discharge β) from the active energy of the point 210 plotted above the discharge threshold 206 on the coordinate plane representing the active energy and the reactive energy. That is, the estimation unit 105 horizontally moves the point 210 plotted above the discharge threshold 206 on the coordinate plane representing the active energy and the reactive energy in the right direction by P (discharge β). Then, the estimation unit 105 subtracts the active power amount at the load characteristic 202 when the reactive power amount is the same from the active power amount at the point 210 after the movement. That is, the estimating unit 105 calculates the horizontal distance from the point 210 after the movement to the load characteristic 202, and if the point 210 after the movement is on the left side of the load characteristic 202, the sign is negative, and if the point 210 after the movement is on the right side of the load characteristic 202, the sign is negative. The sign is positive. Next, the estimating unit 105 calculates a set of discharge times t' by subtracting the active power amount at the load characteristic 202 when the reactive power is the same from the active power amount at the point 210 after the movement. An integrated value Σ _t'εT' L'(t) in the discharge time period T' is calculated. That is, the integrated value Σ _t'∈T' L'(t') is obtained by adding the set discharge amount to the active energy measured in the discharge time period T', and then calculating the active energy at the load characteristic 202. This is the integrated value of the subtracted values.

Next, the estimation unit 105 changes P (discharge β) by one predetermined step and similarly calculates Σ _t'∈T' L'(t'). For example, the estimation unit 105 changes P (discharge β) in 0.1W increments. Then, the estimation unit 105 repeats the calculation of Σ _t'εT' L'(t') while changing P (discharge β) up to a specific upper limit value. For example, the estimation unit 105 sets the rated output of the highest discharge amount of a general storage battery as the upper limit value of P (discharge β).

Next, the estimation unit 105 specifies the value of P (discharge β) that minimizes Σ _t'∈T' L'(t'). Then, the estimating unit 105 adds the value of P (discharge β) specified from the active energy of the smart meter data of the estimated target consumer at the discharge time t', and Calculate the estimated actual load. That is, when the effective power amount of the smart meter data of the estimated target customer at discharge time t' is Px'(t'), using the specified P (discharge β), the estimation unit 105 calculates Px'(t'). ')+P (discharge β) is the estimated value of the actual load of the estimated customer in the discharge time period T'. Furthermore, the estimation unit 105 calculates the discharge amount of the storage battery 23 per day by integrating the discharge amount of the storage battery 23 at each time.

Here, in this embodiment, the estimating unit 105 determines P (charging α) and P (discharging β) using a brute force method, but is not limited to this, and can also determine other factors such as solving a quadratic programming problem. It is also possible to determine P (charge α) and P (discharge β) using a method.

Thereafter, the estimating unit 105 transmits the estimated value of the actual load of the consumer to be estimated to the demand response generation device 11. Further, the estimating unit 105 may also transmit information on the estimated charging/discharging time period of the storage battery 23 of the estimated target consumer, an estimated value of the rated output of the storage battery 23, etc. to the demand response generation device 11.

The demand response generation device 11 includes a transmission section 111 and a demand response generation section 112.

The demand response generation unit 112 receives the estimated value of the actual load of the consumer to be estimated from the estimation unit 105 of the output estimation device 10 . Next, the demand response generation unit 112 generates a demand response command for the estimation target consumer based on the transition of the overall power usage of the consumer 2 and the estimated value of the actual load of the estimation target consumer. Then, the demand response generation unit 112 outputs the generated demand response command to the transmission unit 111.

The transmitting unit 111 receives input of a demand response command for the estimation target customer from the demand response generating unit 112. Then, the transmitter 111 transmits the obtained demand response command for the estimated target consumer to the consumer terminal device 21 of the estimated target consumer, and notifies the estimated target consumer of the demand response command.

FIG. 6 is a diagram illustrating an overview of the output estimation process of the estimation target consumer. Next, with reference to FIG. 6, the output estimation process of the estimation target customer by the output estimation device 10 will be collectively explained.

The load characteristic calculation unit 102 acquires smart meter data of a consumer who does not own a storage battery from among the smart meter data collected by the smart meter data acquisition unit 101 (step S1).

Next, the load characteristic calculation unit 102 creates a PQ correlation diagram for the consumer who does not own a storage battery using the smart meter data of the consumer who does not own a storage battery (step S2).

Next, the load characteristic calculation unit 102 calculates the load characteristic 202 from the created PQ correlation diagram of the consumer who does not own a storage battery (step S3). The load characteristic calculation unit 102 stores the PQ correlation diagram and the load characteristic 202 of the consumer who does not own a storage battery in the data storage unit 103.

Next, the estimating unit 105 acquires smart meter data of the consumer to be estimated from among the smart meter data collected by the smart meter data acquiring unit 101 (step S4).

Furthermore, the load characteristic calculation unit 102 creates a PQ correlation diagram of the estimated customer using the smart meter data of the estimated customer (step S5).

Next, the load characteristic calculation unit 102 compares the P-Q correlation diagram of the estimated target consumer with the P-Q correlation diagram of the consumer who does not own a storage battery based on the load characteristic 202, and calculates the charging/discharging of the storage battery 23. The time period is estimated (step S6).

Then, the estimating unit 105 estimates the charging/discharging amount and the actual load of the storage battery 23 in the estimation target consumer according to the estimated charging time period and discharging time period (step S7).

After that, the estimating unit 105 transmits the estimation results, which are the charging/discharging amount of the storage battery 23 and the actual load at the estimation target consumer, to the demand response generation device 11 (step S8).

FIG. 7 is a flowchart of the demand response command generation process by the aggregator system. Next, with reference to FIG. 7, the flow of the demand response command generation process by the aggregator system 1 will be described.

The smart meter data acquisition unit 101 collects smart meter data of both consumers who own storage batteries and consumers who do not own storage batteries. The load characteristic calculation unit 102 acquires smart meter data of a consumer who does not own a storage battery from among the smart meter data collected by the smart meter data acquisition unit 101 (step S101).

Next, the load characteristic calculation unit 102 uses the acquired smart meter data to create a PQ correlation diagram for consumers who do not own storage batteries (step S102).

Next, the load characteristic calculation unit 102 calculates the load characteristic 202 from the PQ correlation diagram of the consumer who does not own a storage battery (step S103). After that, the load characteristic calculation unit 102 stores the PQ correlation diagram and the load characteristic 202 of the consumer who does not have a storage battery in the data storage unit 103.

Next, the estimating unit 105 acquires smart meter data of the consumer to be estimated from among the smart meter data collected by the smart meter data acquiring unit 101 (step S104).

Next, the estimating unit 105 uses the acquired smart meter data to create a PQ correlation diagram of the estimated customer (step S105).

Next, the estimation unit 105 acquires the PQ correlation diagram and load characteristics 202 of the consumer who does not own a storage battery from the data storage unit 103. Then, based on the load characteristics 202, the estimating unit 105 compares the P-Q correlation diagram of the consumer who does not own a storage battery with the P-Q correlation diagram of the estimation target consumer, and calculates the charge of the storage battery 23 at the estimation target consumer. The discharge amount and actual load are estimated (step S106).

After that, the estimating unit 105 transmits and notifies the demand response generation device 11 of the estimation results, which are the amount of charging and discharging of the storage battery 23 and the actual load at the estimation target consumer (step S107).

The demand response generation unit 112 receives the estimated value of the charging/discharging amount of the storage battery 23 and the actual load of the consumer to be estimated from the estimation unit 105 of the output estimation device 10 . Next, the demand response generation unit 112 generates a response for the estimated target consumer based on the trends in the overall power usage of the consumer 2, the charge/discharge amount of the storage battery 23 of the estimated target consumer, and the estimated value of the actual load. Determine the content of the demand response and generate a demand response command. Then, the demand response generation unit 112 outputs the generated demand response command to the transmission unit 111. The transmitting unit 111 receives input of a demand response command for the estimation target customer from the demand response generating unit 112. Then, the transmitting unit 111 transmits the obtained demand response command for the estimated target consumer to the consumer terminal device 21 of the estimated target consumer, and notifies the estimated target consumer of the content of the demand response (step S108 ).

FIG. 8 is a flowchart of the process of estimating the charge/discharge amount and actual load of the storage battery of the estimated target consumer by the output estimating device. Next, with reference to FIG. 8, the flow of the process of estimating the charge/discharge amount and actual load of the storage battery 23 of the estimation target customer by the output estimating device 10 will be described. Each process in the flow of FIG. 8 corresponds to an example of the process executed in step S106 in the flow of FIG.

The estimating unit 105 checks the information stored in the consumer information storage unit 104 and determines whether information on the rated output of the storage battery 23 of the estimated target consumer exists (step S201).

If information on the rated output of the storage battery 23 of the estimation target consumer exists (step S201: affirmative), the estimation unit 105 acquires the load characteristic 202 from the data storage unit 103 (step S202).

Next, the estimation unit 105 calculates the charging threshold value 203 by adding P (charge)/2, which is the rated output of the charging amount of the storage battery 23, to the load characteristic 202 (step S203).

Furthermore, the estimation unit 105 calculates the discharge threshold value 204 by subtracting P (discharge)/2, which is the rated output of the discharge amount of the storage battery 23, from the load characteristic 202 (step S204).

Next, the estimating unit 105 subtracts P (charging) from the active power amount of the smart meter data at a point below the charging threshold 203 to calculate the actual load of the consumer to be estimated (step S205).

Next, the estimating unit 105 calculates the actual load of the consumer to be estimated by adding P (discharge) to the active power amount of the smart meter data at a point above the discharge threshold 204 (step S206).

On the other hand, if there is no information on the rated output of the storage battery 23 of the consumer to be estimated (step S201: negative), the estimation unit 105 uses the P-Q correlation diagram and load characteristics 202 of the customer who does not own a storage battery as data. It is acquired from the storage unit 103 (step S207).

Next, the estimating unit 105 determines the charging threshold 205 and the discharging threshold 206 such that points that are equal to or greater than the threshold for the number of points in the PQ correlation diagram of the consumer who does not own a storage battery are sandwiched therebetween (step S208).

Next, the estimating unit 105 sets the rated output of the charging amount of the storage battery 23 to an initial value as P (charging α) (step S209).

Next, the estimation unit 105 subtracts P (charging α) from the active energy amount of the smart meter data at a point below the charging threshold 205, and further calculates the load characteristic 202 when the reactive energy amount is the same as that point. Calculate the integrated value of the value obtained by subtracting the active power amount. That is, the estimation unit 105 calculates Σ _tεT L(t) (step S210). Here, the time t is the time when the storage battery 23 at a point below the charging threshold 205 is charged, and the time period that summarizes the time t is the charging time period T for the storage battery 23. Furthermore, L(t) is obtained by subtracting P (charging α) from the active energy of the smart meter data at time t, and further subtracting the active energy at the load characteristic 202 when the reactive energy is the same as that point. It is a value.

Next, the estimation unit 105 determines whether P (charge α) is an upper limit value (step S211). If P (charge α) is not the upper limit value (step S211: negative), the estimation unit 105 increases P (charge α) by one step (step S212). After that, the estimation unit 105 returns to step S210.

On the other hand, if P (charge α) is the upper limit value (step S211: affirmative), the estimation unit 105 selects the value of P (charge α) that minimizes Σ _t∈T L(t) ( Step S213).

Then, the estimation unit 105 calculates the actual load of the estimated customer by subtracting the selected P (charging α) from the active power amount of the smart meter data in the charging time period T (step S214).

Next, the estimation unit 105 sets the discharge amount P (discharge β) of the storage battery 23 to an initial value (step S215).

Next, the estimation unit 105 adds P (discharge β) from the active energy of the smart meter data at a point above the discharge threshold 206, and further calculates the value of the load characteristic 202 when the reactive energy is the same as that point. Calculate the integrated value of the value obtained by subtracting the active power amount. That is, the estimation unit 105 calculates Σ _t'εT' L'(t') (step S216). Here, time t' is the time at which the storage battery 23 at a point above the discharge threshold 206 is discharged, and the time period in which the time t' is summarized is the discharge time period T' of the storage battery 23. Furthermore, L(t') adds P (discharge β) to the active energy of the smart meter data at time t', and then calculates the active energy at the load characteristic 202 when the reactive energy is the same as that point. This is the subtracted value.

Next, the estimation unit 105 determines whether P (discharge β) is an upper limit value (step S217). If P (discharge β) is not the upper limit value (step S217: negative), the estimation unit 105 increases P (discharge β) by one step (step S218). After that, the estimation unit 105 returns to step S216.

On the other hand, if P (discharge β) is the upper limit value (step S217: affirmative), the estimation unit 105 determines the value of P (discharge β) that minimizes Σ _t'∈T' L'(t'). is selected (step S219).

Then, the estimating unit 105 calculates the actual load of the consumer to be estimated by adding the selected P (discharge β) to the active power amount of the smart meter data in the discharge time period T' (step S220).

As explained above, the output estimation device according to the present embodiment uses the smart meter data of the consumers who do not own storage batteries obtained through route C to generate a P-Q correlation diagram and load characteristics of the consumers who do not own storage batteries. Calculate. Then, the output estimating device compares the P-Q correlation diagram of the consumer who does not own a storage battery with the P-Q correlation diagram of the estimation target consumer based on the load characteristics, and calculates the charging/discharging of the storage battery of the estimation target consumer. Estimate the amount and actual load.

Thereby, it is possible to estimate the amount of charging and discharging of the storage battery and the actual load of the consumer to be estimated, without increasing the number of sensors that measure the amount of charging and discharging of electricity supply equipment such as storage batteries and new communication routes. That is, by using smart meter data via route C, demand adjustment can be performed with a simple configuration. Furthermore, since estimation is performed using smart meter data that can be collected from each consumer, it is possible to easily estimate the charge/discharge amount and actual load of each consumer's storage battery. Therefore, it becomes possible to perform accurate demand adjustment while keeping costs down with a simple configuration.

Next, Example 2 will be explained. The load curve per customer changes almost every day. Therefore, when estimation is performed using daily smart meter data for each customer, there is a risk that the estimation accuracy will decrease. Therefore, the output estimation device according to the present embodiment performs estimation using an average value over a certain period. The output estimating device 10 according to this embodiment is also represented by the block diagram in FIG. 2. In the following description, descriptions of the operations of the same parts as in the first embodiment will be omitted.

The estimating unit 105 acquires smart meter data for a certain period of time, such as one week, regarding the estimation target consumer from the smart meter data acquiring unit 101. Then, the estimating unit 105 calculates the average of the smart meter data at each time on each day during a certain period.

The estimation unit 105 estimates the charging time period and charging amount, as well as the discharging time period and the discharge amount of the storage battery 23 of the estimation target customer, using the average of the smart meter data at each time during a certain period of the estimation target customer. do.

As described above, the output estimating device according to the present embodiment executes the process of estimating the charge/discharge amount and the actual load using the average value of smart meter data over a certain period. This makes it possible to absorb daily changes in the load curve, improve estimation accuracy, and enable more accurate demand adjustment.

Here, in each of the above embodiments, the output estimation device 10 and the demand response generation device 11 have been described as separate devices, but the functions of the demand response generation device 11 can be incorporated into the output estimation device 10 to form a single device. It is also possible to do so.

FIG. 9 is a hardware configuration diagram of the output estimation device. The output estimating device 10 described in each of the above embodiments has, for example, a hardware configuration as shown in FIG. That is, the output estimation device 10 includes a CPU (Central Processing Unit) 91, a memory 92, a hard disk 93, and a network interface 94. The CPU 91 is connected to a memory 92, a hard disk 93, and a network interface 94 via a bus.

The network interface 94 is a communication interface between the output estimation device 10 and an external device. The network interface 94 relays communication between the power transmission and distribution company system 3 or the demand response generation device 11 and the CPU 91, for example.

The hard disk 93 is an auxiliary storage device. The hard disk 93 can realize the functions of the data storage section 103 and the customer information storage section 104. Further, the hard disk 93 stores various programs including programs for realizing the functions of the smart meter data acquisition section 101, the load characteristic calculation section 102, and the estimation section 105.

Memory 92 is a main storage device. As the memory 92, a DRAM (Dynamic Random Access Memory) or the like can be used.

The CPU 91 reads various programs from the hard disk 93, expands them into the memory 92, and executes them. Thereby, the CPU 91 realizes the functions of the smart meter data acquisition section 101, the load characteristic calculation section 102, and the estimation section 105.

1 Aggregator system 2 Consumer 3 Power transmission and distribution company system 21 Consumer terminal device 22 Smart meter 23 Storage battery 24 Air conditioner 25 Electric water heater 101 Smart meter data acquisition section 102 Load characteristic calculation section 103 Data storage section 104 Consumer information storage section 105 Estimation part

Claims (10)

a data acquisition unit that collects active power amount and reactive power amount of a plurality of consumers;
a load characteristic calculation unit that calculates a load characteristic using the active energy amount and the reactive energy amount of a non-possessing consumer that does not have a charging/discharging device among the plurality of consumers;
an estimating unit that estimates a charge/discharge amount of the charging/discharging device of the estimation target consumer based on the active energy amount and the reactive energy amount of the estimation target consumer who owns the charging/discharging device, and the load characteristics; An output estimation device characterized by comprising: and. The load characteristic calculation unit generates a P-Q correlation diagram of the non-possession consumer based on the active energy amount and the reactive energy amount of the non-possession consumer, and the generated selected non-possession consumer. Calculate the load characteristics based on the P-Q correlation diagram of
The estimating unit generates a P-Q correlation diagram of the estimation target consumer based on the active energy amount and the reactive energy amount of the estimation target consumer, and calculates the load characteristic and the non-possessing consumer's PQ correlation diagram. Output estimation according to claim 1, characterized in that the amount of charge and discharge of the charging and discharging device of the estimated target consumer is estimated based on a -Q correlation diagram and a PQ correlation diagram of the estimated target consumer. Device. The estimation unit is
Calculating a charging threshold and a discharging threshold that define the range of the active energy and the reactive energy in which the charging and discharging device is not charging and discharging based on the rated output of the charging and discharging device,
A P-Q correlation diagram of the estimated target consumer is generated based on the active power amount and the reactive power amount of the estimated target consumer, and the generated PQ correlation diagram of the estimated target consumer, and The output estimating device according to claim 1, wherein the amount of charging and discharging of the charging and discharging device of the estimated target consumer is estimated based on the charging threshold and the discharging threshold. The load characteristic calculation unit generates a P-Q correlation diagram of the non-possession consumer based on the active energy amount and the reactive energy amount of the non-possession consumer, and the generated selected non-possession consumer. Calculate the load characteristics based on the P-Q correlation diagram of
The estimating unit determines a charging threshold and a discharging threshold that define a range of the active energy amount and the reactive energy amount in which the charging/discharging device is not charging or discharging, based on the PQ correlation diagram of the non-possessing consumer. Calculate,
A P-Q correlation diagram of the estimated target consumer is generated based on the active power amount and the reactive power amount of the estimated target consumer, and the generated PQ correlation diagram of the estimated target consumer, and The output estimating device according to claim 1, wherein the amount of charging and discharging of the charging and discharging device of the estimated target consumer is estimated based on the charging threshold and the discharging threshold. The output estimation according to claim 1, wherein the estimating unit calculates the actual load of the estimation target consumer by removing the charge/discharge amount of the charging/discharging device from the active energy amount of the estimation target consumer. Device. The method further includes a demand request notification unit that generates a demand request command for the estimated target consumer based on the charging/discharging amount of the charging/discharging device of the estimated target consumer and notifies the estimated target consumer. The output estimating device according to claim 1. The estimating unit uses an average of the active power amount and the reactive power amount of the estimation target consumer over a certain period as the active power amount and the reactive power amount of the estimation target consumer. 1. The output estimation device according to 1. The data acquisition unit collects the active energy amount and the reactive energy amount measured by smart meters owned by the plurality of consumers via a communication path passing through a power transmission and distribution company. The output estimation device according to item 1. Collects the active energy amount and reactive energy amount of multiple consumers,
Calculating a load characteristic using the active energy amount and the reactive energy amount of a non-possessing consumer that does not have a charging/discharging device among the plurality of consumers;
Estimating the charging and discharging amount of the charging and discharging device of the estimation target consumer based on the active energy amount and the reactive energy amount of the estimation target consumer who owns the charging and discharging device, and the load characteristics. An output estimation program that is executed by a computer. In the output estimation device,
Collect the active power amount and reactive power amount of multiple consumers,
calculating load characteristics using the active energy amount and the reactive energy amount of a non-possessing consumer that does not have a charging/discharging device among the plurality of consumers;
The charging and discharging amount of the charging and discharging device of the estimated target consumer is estimated based on the active power amount and the reactive power amount of the estimated target consumer who owns the charging and discharging device, and the load characteristics. Characteristic output estimation method.

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