JP7475202B2 - Power system control support system and power system control support method - Google Patents

Description

The present invention relates to a power system control support system and a power system control support method that support power adjustment in a power system.

In recent years, due to mismatches in power supply and demand and local voltage fluctuations in power transmission and distribution systems (electric power grids), aggregation businesses that aggregate power from DERs (Distributed Energy Resources) such as electric vehicles and storage batteries owned by consumers and provide it to the power transmission and distribution grid are becoming more widespread.

In particular, with the increase in solar power generation systems, local voltage fluctuations will necessitate local power adjustments.

A power suppression control system has been proposed that allows consumers to assist in such localized power adjustments and evaluates incentives for consumers to adjust loads when consumers cooperate with load adjustments.

Background technology in this technical field is disclosed in Japanese Patent Publication No. 5998046 (hereinafter referred to as "Patent Document 1").

Patent Document 1 also describes a database that stores system information of a distribution substation, and equipment information and operation information of distributed power sources, storage batteries, and loads connected to this system; a first allocation calculation device that calculates a load suppression amount consisting of the discharge of the storage battery, the power generation of the distributed power source, and the load suppression of the load, and a load creation amount that is created by charging the storage battery and starting up a load that can be shifted in time, in order to control the load adjustment amount of the distribution substation at each specified time, and allocates the load adjustment amount required of the distribution substation to each feeder of the distribution substation based on the calculated load suppression amount and load creation amount; and a second allocation calculation device that calculates the load suppression amount for each feeder and the load creation amount for each feeder in order to control the load adjustment amount for each feeder at each specified time. A power suppression control system is described that has a second distribution calculation device that calculates a load suppression amount for each pole transformer and distributes the load adjustment amount transmitted from the first distribution calculation device based on the calculated load suppression amount for each feeder and the load creation amount for each feeder, and a third distribution calculation device that calculates a load suppression amount for each pole transformer and a load creation amount index for each pole transformer in order to control the load adjustment amount for each pole transformer at a predetermined time, and calculates the contribution of each consumer to the load adjustment amount using the consumer's load adjustment amount and the load amount of the distribution substation based on the calculated load suppression amount for each pole transformer and the load creation amount for each pole transformer for the load adjustment amount transmitted from the second distribution calculation device (see paragraph 0018).

Japanese Patent No. 5998046

Patent document 1 describes a power suppression control system that can appropriately evaluate the incentive for the amount of load adjustment.

In other words, the power suppression control system described in Patent Document 1 allocates the necessary adjustment amount to the target equipment of a contracted consumer in response to the consumer's load adjustment, and calculates the contribution level according to the allocated adjustment amount.

However, Patent Document 1 does not mention that when encouraging consumers to temporarily secure flexible adjustment capacity through voluntary cooperation in response to local power adjustments based on local voltage fluctuations, the level of compensation to be awarded to encourage such securing of adjustment capacity should be determined in advance.

The present invention provides a power system control support system and a power system control support method that determine in advance the level of reward (granted reward) to be temporarily awarded to consumers who own target equipment in order to encourage them to ensure adjustment capacity.

In order to solve the above-mentioned problems, the power system control support system of the present invention is characterized by having an adjustment capacity-related action designation unit that designates an action related to adjustment capacity, an adjustment capacity-related action learning unit that links actions stored in a consumer action database that stores consumer actions with actions designated by the adjustment capacity-related action designation unit, learns whether or not there is a corresponding action, and generates an adjustment capacity-related action learning model, a reward sensitivity generation unit that generates a reward sensitivity of a consumer from information stored in a granted reward + result action database that stores a granted reward granted to a consumer and a resulting action, and an adjustment capacity provision reward level calculation unit that calculates an appropriate granted reward to be granted when a consumer performs an action related to adjustment capacity using the consumer's reward sensitivity generated by the reward sensitivity generation unit, the adjustment capacity-related action learning model generated by the adjustment capacity-related action learning unit, and information stored in a adjustment capacity database that stores the adjustment capacity owned by the power transmission and distribution company and the location and time period when an action related to adjustment capacity corresponding to an action for securing adjustment capacity was performed, and notifies the power transmission and distribution company of the calculated granted reward.

In addition, in order to solve the above-mentioned problems, the power system control support method of the present invention is characterized in that an adjustment capacity-related action designation unit designates an action related to adjustment capacity, an adjustment capacity-related action learning unit links actions stored in a consumer action database that stores consumer actions with the action designated by the adjustment capacity-related action designation unit to learn whether or not a corresponding action has occurred, and generates an adjustment capacity-related action learning model, a reward sensitivity generation unit generates a reward sensitivity of the consumer from information stored in an granted reward + result action database that stores the granted reward granted to the consumer and the resulting action, and an adjustment capacity provision reward level calculation unit calculates an appropriate granted reward to be granted when the consumer performs an action related to adjustment capacity using the consumer's reward sensitivity generated by the reward sensitivity generation unit, the adjustment capacity-related action learning model generated by the adjustment capacity-related action learning unit, and information stored in an adjustment capacity database that stores the adjustment capacity owned by the power transmission and distribution company and the location and time period when the action related to adjustment capacity corresponding to the action for securing the adjustment capacity was performed, and notifies the power transmission and distribution company of the calculated granted reward.

The present invention provides a power system control support system and a power system control support method that determine in advance the remuneration level (remuneration to be granted) to be granted temporarily to consumers who own target equipment in order to encourage them to ensure adjustment capacity.

Note that issues, configurations and effects other than those mentioned above will become clearer in the explanation of the examples below.

FIG. 1 is an explanatory diagram illustrating a power system control support system 101 according to a first embodiment. FIG. 2 is an explanatory diagram for explaining a consumer behavior database 103 described in the first embodiment. FIG. 11 is an explanatory diagram illustrating reward sensitivity (the relationship between the given reward and the behavior execution rate) described in the first embodiment. 1 is an explanatory diagram illustrating a processing flow in the reward sensitivity generation unit 106 described in the first embodiment. FIG. 11 is an explanatory diagram illustrating a processing flow in the adjustment capability provision remuneration level calculation unit 109 described in the first embodiment. FIG. 13 is an explanatory diagram for explaining adjustment of reward sensitivity in relation to the granted reward and the behavior execution ratio described in the first embodiment. FIG. 11 is an explanatory diagram illustrating a power system control support system 101 according to a second embodiment. FIG. 11 is an explanatory diagram illustrating a power system control support system 101 according to a third embodiment.

The following describes an embodiment of the present invention with reference to the drawings. Note that the same reference numerals are used to designate substantially identical or similar configurations, and where explanations are redundant, they may be omitted.

First, the power system control support system 101 described in Example 1 will be described.

Figure 1 is an explanatory diagram illustrating the power system control support system 101 described in the first embodiment.

The power system control support system 101 described in the first embodiment has the following configuration.
(1) a regulation capability related action designation unit 102 that designates an action (information) related to regulation capability;
(2) A regulation capacity related behavior learning unit 104 that links the behavior (information) stored in a consumer behavior database (hereinafter referred to as a "consumer behavior DB") 103 that stores various behaviors (information) of consumers such as households that are owners of target devices with the behavior specified by the regulation capacity related behavior specifying unit 102, learns the presence or absence of a corresponding behavior and the frequency of the behavior, and generates a regulation capacity related behavior learning model;
(3) a reward sensitivity generating unit 106 that generates a reward sensitivity of a consumer from information stored in a given reward + result behavior database (hereinafter referred to as the “given reward + result behavior DB”) 105 that stores a given reward (reward level) given to a consumer and a resultant behavior;
(4) An adjustment power provision remuneration level calculation unit 109 that calculates an appropriate remuneration (remuneration level) to be granted when a consumer performs an action related to adjustment power using the consumer's remuneration sensitivity generated by the remuneration sensitivity generation unit 106, the adjustment power-related action learning model generated by the adjustment power-related action learning unit 104, and adjustment power information stored in an adjustment power database (hereinafter referred to as the "adjustment power DB") 108 that stores the adjustment power owned by the power transmission and distribution company 107 and the location and time period (including date and time, the same applies hereinafter) when actions related to adjustment power corresponding to actions for securing adjustment power were performed, and notifies the power transmission and distribution company 107 of the calculated remuneration.

In this way, the power system control support system 101 helps ensure the necessary adjustment capacity for power adjustment in the power system.

Note that adjustment capacity is the adjustable power of target equipment that can be controlled by consumers.

This makes it possible to determine in advance the compensation to be paid to consumers in order to encourage them to take temporary action to secure balancing capacity (load adjustment).

The adjustment capacity-related behavior designation unit 102 is a calculation unit (e.g., a CPU) that designates behavior related to adjustment capacity.

In particular, in Example 1, the target is individual consumers with home solar power generation systems, etc., so electricity generated by large generators and whose frequency is adjusted does not qualify as adjustment capacity.

The action for securing the adjustment capability in the first embodiment is, for example, to maintain the supply and demand balance.
(1) Negawatt (action) - when it is expected that the amount of electricity demanded during the daytime in the summer when it is extremely hot exceeds the amount of electricity that can be supplied, consumers are asked to reduce their electricity consumption.
(2) Posiwatts (actions) that ask consumers to increase their electricity consumption in order to avoid suppressing power generation from renewable energy systems when there is a surplus of electricity generated by wind power generation systems during times when electricity consumption is low (e.g., during the night) or solar power generation systems during the daytime in intermediate seasons (seasons between summer and winter),
and so on.

In addition, in the future, if the amount of electricity generated by solar power generation systems increases and local voltage fluctuations occur, Posiwatt is considering asking customers not only to increase their power consumption but also to output reactive power from inverter devices installed in electric vehicles and homes in order to suppress local voltage fluctuations.

In addition, the action related to the regulation capability in the first embodiment is linked to the action for securing the regulation capability described above, for example,
(1) In response to negawatts, people go outside during the daytime in summer, even during the hottest hours.
(2-1) Increase the amount of power consumed during nighttime hours, etc., compared to posiwatts.
(2-2) In contrast to Posiwatt, people are at home during the day on weekdays during the mid-term period.
(2-3) For posiwatts, charging electric vehicles at charging stations in specific locations to suppress local voltage fluctuations;
Actions related to each adjustment capacity correspond to actions to secure each adjustment capacity.

For nighttime hours, a detailed time period such as 1:00 to 4:00 may be specified. For a specific location, a range (for example, within ² km2) may be specified, without being limited to a specific area (location). For a specific location, a facility name such as a specific shopping center may be specified, without being limited to a location.

In addition, behavior related to adjustment capacity may be any behavior other than those described here as long as it is closely related to ensuring adjustment capacity.

In other words, actions related to adjustment capacity are actions taken by consumers, such as requesting that the amount of power they use be increased or decreased during the time period in which they use it in response to local voltage fluctuations.

The consumer behavior DB103 is a database that stores various consumer behaviors.

Here, we explain the consumer behavior DB103 described in Example 1.

Figure 2 is an explanatory diagram explaining the consumer behavior DB 103 described in Example 1.

The consumer behavior DB 103 described in Example 1 is a relational database in tabular format, but is not necessarily limited to this format and various formats can be used.

The consumer behavior DB 103 stores, for each consumer and for each date, the time period of the behavior, whether the behavior was performed, and the duration of the behavior for each consumer's various behaviors. For example, detailed information such as behavior A can be stored, including the time period (AM), whether the behavior was performed (yes: yes), and the duration (1 h), or, for behavior B, information can be stored that only includes whether the behavior was performed (yes: yes) and whether the behavior was performed on that day.

The actions stored in the consumer action DB103 are not limited to this. For example, if the action content is charging an electric vehicle at a charging station in a specific location, specific numerical values such as the amount of power charged by the electric vehicle (amount of charge) may be stored.

The adjustment ability-related behavior learning unit 104 is a calculation unit (e.g., a CPU) that generates an adjustment ability-related behavior learning model.

Then, the adjustment capacity related action learning unit 104 links the actions stored in the consumer action DB 103 with the actions specified by the adjustment capacity related action designation unit 102, searches for actions stored in the consumer action DB 103 that are the same as the actions specified by the adjustment capacity related action designation unit 102 (presence or absence of a corresponding action and the frequency of that action), and extracts the action.

The adjustment capacity related behavior learning model is a tabular relational database in which the behavior related to adjustment capacity is recorded for each consumer, but it is not necessarily limited to this format and various formats can be used.

In other words, the adjustment capacity related behavior learning model is behavior that is searched for and extracted from the behavior stored in the consumer behavior DB 103 and the behavior specified by the adjustment capacity related behavior specification unit 102.

The method for searching for identical actions involves selecting actions that are considered to be identical to the actions specified by the adjustment capacity related action specification unit 102 from the consumer action DB 103 through syntax analysis.

In addition, if a specific behavior database exists, the behaviors stored in this behavior database may be linked to the behaviors stored in the consumer behavior DB 103, and the contents of the behaviors stored in this behavior database may be combined with the contents of the behaviors specified by the adjustment capacity related behavior specification unit 102, and the behaviors whose contents are the same may be selected.

In addition, when creating a new consumer behavior DB103, only actions related to adjustment capacity may be stored in the consumer behavior DB103.

The granted reward + resulting action DB105 is a database that stores the granted reward granted to consumers and the resulting actions.

The data stored in the granted reward + result behavior DB 105 is data acquired from a general customer management system. The granted reward is, for example, a monetary value such as various coupons that can be used for shopping or various discount services, and the result behavior is the behavior performed by the target consumer in response to the granted coupons or discount services.

In other words, the granted reward + resulting action DB105 is a database that stores the granted reward and the resulting action performed by the target consumer in response to the granted reward.

In Example 1, the data stored in the given reward + result action DB 105 is data obtained from a general customer management system, but the given reward and the result action are not limited to those described above.

The reward sensitivity generation unit 106 is a calculation unit (e.g., a CPU) that generates the consumer's reward sensitivity from the information stored in the granted reward + result behavior DB 105.

Here, we explain the reward sensitivity (the relationship between the reward given and the rate of behavior execution) described in Example 1.

Figure 3 is an explanatory diagram explaining the reward sensitivity (the relationship between the given reward and the rate of action execution) described in Example 1.

The reward sensitivity is given by curve 301, which shows the behavior implementation rate (%) relative to the reward (¥) granted to the consumer.

In other words, reward sensitivity is the rate at which a consumer performs a certain action (degree of action) depending on the size of the reward granted, and indicates the degree to which a consumer is predicted to perform a certain action in response to the reward granted to the consumer. In general, as the reward granted increases, the rate at which a consumer performs a certain action also increases.

Here, we will explain the processing flow of the reward sensitivity generation unit 106 described in Example 1.

Figure 4 is an explanatory diagram explaining the processing flow in the reward sensitivity generation unit 106 described in Example 1.

The reward sensitivity (curve 301) is generated by the following steps.
(1) First, a ratio of actions taken by a consumer with respect to the granted reward is calculated from the history of actions taken by the consumer with respect to the granted reward (S401).
(2) Next, from several points (triangle marks in FIG. 3, four points in Example 1) of behavioral performance ratios for the granted remuneration, interpolation is performed between points (behavior performance ratios of consumers for a certain granted remuneration) and points (behavior performance ratios of consumers for a certain granted remuneration).Then, the behavioral performance ratios of consumers for the granted remuneration are calculated, and remuneration sensitivity (curve 301) is generated (S402).

The interpolation method can be any common method, such as polynomials, exponential functions, or other curve approximations.

The adjustment capacity DB108 is a database that stores the adjustment capacity owned by the power transmission and distribution company 107 and the locations and time periods when actions related to the adjustment capacity were carried out.

The adjustment capacity provision remuneration level calculation unit 109 is a calculation unit (e.g., a CPU) that uses the consumer's remuneration sensitivity generated by the remuneration sensitivity generation unit 106, the adjustment capacity related behavior learning model generated by the adjustment capacity related behavior learning unit 104, and information stored in the adjustment capacity DB 108 (adjustment capacity information input from the power transmission and distribution company 107) to calculate an appropriate remuneration (remuneration level) to be granted when a consumer performs an action related to adjustment capacity.

Then, the adjustment capacity provision remuneration level calculation unit 109 notifies the calculated remuneration to the power transmission and distribution company 107.

The adjustment capacity provision reward level calculation unit 109 also determines whether or not an action related to adjustment capacity exists in the adjustment capacity related action learning model based on the adjustment capacity information input from the power transmission and distribution company 107, and if it exists, increases the reward sensitivity, and if it does not exist, decreases the reward sensitivity to determine the reward sensitivity.

Here, we will explain the processing flow of the adjustment capability provision compensation level calculation unit 109 described in Example 1.

Figure 5 is an explanatory diagram illustrating the processing flow in the adjustment capability provision compensation level calculation unit 109 described in Example 1.

The awarded reward (reward level) is calculated in the following steps.
(1) First, adjustment capacity information is input from the power transmission and distribution company 107 (S501). Note that the adjustment capacity information is information on the adjustment capacity owned by the power transmission and distribution company 107 and the location and time period in which an action related to the adjustment capacity was performed, and is also information on the adjustment capacity that the power transmission and distribution company 107 desires to adjust to a consumer (adjustment capacity to be secured) at a specific location and time period.
(2) Next, for each consumer, adjustment capability information related to the consumer is selected from the adjustment capability information stored in the adjustment capability DB 108 (S502).
(3) Next, the compensation sensitivity of consumers having corresponding adjustment capacity information is increased, and the compensation sensitivity of consumers having no corresponding adjustment capacity information is decreased (S503).
(4) Finally, the remuneration to be offered to each consumer is determined so that the expected value of the adjustment capacity exceeds the conditions and the required remuneration (amount) is minimized (S504).

Based on the above, the compensation to be offered to each consumer in order to have them provide adjustment capacity is determined.

In this way, the reward to be granted is determined by determining whether or not an action related to the adjustment ability exists in the adjustment ability-related action learning model based on the adjustment ability information, and if it does exist, the reward sensitivity is increased, and if it does not exist, the reward sensitivity is decreased, and the ratio of action execution to the granted reward is calculated.

The relationship between the adjustment capacity related behavior learning model and behavior related to adjustment capacity is, for example, information such as whether a consumer who wants to charge an electric vehicle at a charging station in a specific location travels to the vicinity of that specific location, or information such as whether a consumer who wants to increase power consumption during nighttime hours often uses power during nighttime hours. Based on such information and the related adjustment capacity information, the compensation sensitivity is increased or decreased.

In this way, according to the first embodiment, the remuneration to be granted to consumers can be determined in advance in order to prompt them to take action to secure adjustment capacity temporarily.

Here, we explain how to adjust the reward sensitivity in relation to the reward given and the behavior execution rate described in Example 1.

Figure 6 is an explanatory diagram explaining the adjustment of reward sensitivity in relation to the granted reward and the action execution rate described in Example 1.

FIG. 6 shows the increase in reward sensitivity and the decrease in reward sensitivity in S503 shown in FIG.
(1) When a consumer includes an action corresponding to a condition for necessary adjustment capacity, the compensation sensitivity is increased (curve 601).
(2) If the consumer does not include behavior that corresponds to the required adjustment capacity condition, the compensation sensitivity is decreased (curve 602).

In this case, the rate of increase or decrease may be set to a certain fixed value, or different values may be used for each consumer.

Next, the calculation in S504 shown in FIG. 5 will be explained using Equation 1 and Equation 2.

Here, i is the number of each consumer, s(i) is the amount of adjustment capacity provided by each consumer, p(i) is the behavior implementation ratio of each consumer, S is the value of adjustment capacity to be secured, and m(p(i)) is the compensation (amount) to be granted. Note that Equation 1 is a constraint condition, and Equation 2 is an objective function.

In equation 1, the expected value of the adjustment capacity (the left side of equation 1) is made to exceed the value of the adjustment capacity that is desired to be secured (the right side of equation 1). And in equation 2, the granted reward is minimized.

Note that s(i) is derived from the result action performed by the target consumer in response to the given reward, which is stored in the given reward + result action DB 105, and p(i) is generated from the information stored in the given reward + result action DB 105. For example, s(i) is derived based on information that a certain adjustment capacity will be provided if a consumer requests charging of an electric vehicle at a charging station in a certain location, and p(i) is the consumer's action implementation rate related to that information.

Then, m(p(i)) is calculated by the adjustment capacity provision reward level calculation unit 109 based on the consumer's reward sensitivity, the adjustment capacity related behavior learning model, and the information stored in the adjustment capacity DB 108. In other words, it is the reward given for the behavior implementation ratio of the consumer.

Then, the sum of the products of m(p(i)) and p(i) is minimized to calculate the appropriate remuneration (remuneration to be offered to each consumer) to be granted when the consumer takes an action related to adjustment capacity.

Note that for Equation 1, the left or right side may be multiplied by a coefficient to specify how many times the required expected value is compared to the required adjustment capacity.

Furthermore, Mathematical formula 2 is a solution to an optimization problem (optimization variables), and as a nonlinear problem, NLP (Non Linear Programming) may be used, or MILP (Mixed Integer Linear Programming) that linearly divides nonlinear characteristics may be used. Alternatively, a heuristic method such as particle swarm optimization may be used.

It is also possible to provide a margin for the desired adjustment capacity to ensure the necessary adjustment capacity.

In this way, according to the first embodiment, it is possible to determine in advance the remuneration (remuneration level) to be temporarily granted to consumers who own target equipment in order to encourage them to secure adjustment capacity.

Next, the power system control support system 101 described in Example 2 will be described.

Figure 7 is an explanatory diagram illustrating the power system control support system 101 described in Example 2.

In Example 2, the compensation sensitivity adjustment is modified based on the behavior of each consumer.

In other words, the power system control support system 101 described in Example 2 differs from the power system control support system 101 described in Example 1 in that the adjustment power provision remuneration level calculation unit 109 further uses information from each consumer 701.

The power transmission and distribution company 107 notifies each consumer 701 of the remuneration (remuneration level) (the remuneration (remuneration level) to be granted to encourage the securing of adjustment capacity) notified by the adjustment capacity provision remuneration level calculation unit 109, and requests an action related to the adjustment capacity that corresponds to the action to secure the adjustment capacity.

The adjustment capacity provision remuneration level calculation unit 109 inputs information on whether or not each consumer 701 actually responded to a request from the power transmission and distribution company 107.

Then, the adjustment power provision reward level calculation unit 109 changes the degree of adjustment (increase or decrease) of the reward sensitivity based on this information. For example, it multiplies the adjustment of the reward sensitivity by a coefficient, and increases the coefficient if there is a response, and decreases the coefficient if there is no response.

By repeating this process, it is possible to have consumers proactively and voluntarily cooperate to ensure flexible adjustment capacity and take actions related to adjustment capacity.

In this way, according to the second embodiment, it is possible to determine in advance the remuneration to be given to consumers who own the target equipment in order to encourage them to secure adjustment capacity on an ad-hoc basis, and it is also possible to encourage them to proactively take actions related to adjustment capacity.

Next, the power system control support system 101 described in Example 3 will be explained.

Figure 8 is an explanatory diagram illustrating the power system control support system 101 described in Example 3.

In Example 3, incentives provided to each consumer 107 include data such as app game items and characters in addition to (or instead of) monetary incentives such as various coupons and discount services.

In other words, the power system control support system 101 described in Example 3 differs from the power system control support system 101 described in Example 2 in that the adjustment power provision reward level calculation unit 109 further uses information from the application game server 801 owned by the operating company.

In Example 3, an application game server 801 is connected between each consumer 701 and the adjustment power provision reward level calculation unit 109.

The application game server 801 is connected to a personal terminal (such as a smartphone) owned by each consumer 701 via a network. The application game server 801 stores information (the application game usage status of each consumer 701) such as the fact that each consumer 701 is playing the application game and the amount charged for items, characters, etc.

The adjustment capacity provision remuneration level calculation unit 109 inputs this information. Then, the adjustment capacity provision remuneration level calculation unit 109 notifies each consumer 701 via the application game server 801 that all or part of the remuneration to be presented to each consumer, which is calculated separately based on this information, can be obtained as data such as items and characters (application game data equivalent to the remuneration).

The incentive may be a combination of monetary rewards and rewards in the form of app game data, or it may be just one of them.

The adjustment power provision reward level calculation unit 109 may also input location information of each consumer 701 via the application game server 801.

For example, in order to deal with local voltage fluctuations caused by a solar power generation system, if a consumer wants to charge an electric vehicle at a charging station in a specific location, when each consumer 701 charges the electric vehicle at the charging station in that specific location, the consumer 701's location information is input via the application game server 801.

Then, the location information of each consumer 701 may be notified to the electricity transmission company 107 via the application game server 801, and an incentive may be presented including the provision of this location information.

In this way, the adjustment power provision reward level calculation unit 109 provides an incentive to each consumer 701 in a location (specific location) where adjustment power is desired, and if each consumer 701 provides location information of each consumer 701 via the application game server 801, the incentive includes the provision of this location information.

In other words, when each consumer 701 provides a specified amount of adjustment capacity at a specific location, including its location information, the adjustment capacity provision remuneration level calculation unit 109 grants a remuneration including the provision of this location information.

In this way, according to the third embodiment, in addition to (or instead of) monetary incentives, app game data can be provided as an incentive to each consumer 107.

In this way, according to the third embodiment, it is possible to determine in advance the reward (reward level) to be given to consumers who own target equipment in order to encourage them to secure adjustment capacity on an ad-hoc basis, and it is also possible to encourage them to proactively take actions related to adjustment capacity, and it is also possible to provide them with app game data as an incentive.

The present invention is not limited to the above-mentioned embodiment, but includes various modifications. The above-mentioned embodiment is described in detail to explain the present invention in an easy-to-understand manner, and is not necessarily limited to all of the configurations described.

It is also possible to replace part of the configuration of one embodiment with part of the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment.

101: Power system control support system 102: Adjustment capacity related action designation unit 103: Consumer action DB
104: Adjustment ability related behavior learning unit 105: Grant reward + result behavior DB
106: Reward sensitivity generation unit 107: Power transmission and distribution company 108: Adjustment power DB
109: Adjustment power provision reward level calculation unit 301, 601, 602: Curve 701: Each consumer 801: Application game server

Claims (8)

a control capability related action designation unit that designates an action related to control capability;
a control capacity related action learning unit that links actions stored in a consumer action database that stores consumer actions with actions specified by the control capacity related action specifying unit, learns whether or not there is a corresponding action, and generates a control capacity related action learning model;
a reward sensitivity generating unit that generates a reward sensitivity of the consumer from information stored in a reward+result behavior database that stores the reward granted to the consumer and the resulting behavior;
an adjustment capacity provision remuneration level calculation unit that calculates an appropriate remuneration to be granted when the consumer performs an action related to adjustment capacity using the consumer's remuneration sensitivity generated by the remuneration sensitivity generation unit, the adjustment capacity related action learning model generated by the adjustment capacity related action learning unit, and information stored in an adjustment capacity database that stores the adjustment capacity owned by the power transmission and distribution company and the location and time period when an action related to adjustment capacity corresponding to an action for securing adjustment capacity was performed, and notifies the power transmission and distribution company of the calculated remuneration;
A power system control support system comprising: 2. The power system control support system according to claim 1,
The power system control support system is characterized in that the consumer behavior database stores the consumer behavior content, time period, whether or not the consumer behavior was performed, and duration. 2. The power system control support system according to claim 1,
A power system control support system, wherein the reward sensitivity is a ratio of an action execution to a given reward. 2. The power system control support system according to claim 1,
The power system control support system is characterized in that the adjustment capacity provision reward level calculation unit determines whether or not an action related to the adjustment capacity exists in the related action learning model based on the adjustment capacity information input from the power transmission and distribution company, and if the action exists, increases the reward sensitivity, and if the action does not exist, decreases the reward sensitivity, thereby determining the reward sensitivity. 2. The power system control support system according to claim 1,
A power system control support system characterized in that the adjustment capacity provision remuneration level calculation unit changes the degree of adjustment of the remuneration sensitivity based on information on whether or not the consumer actually responded to a request from the power transmission and distribution company. 2. The power system control support system according to claim 1,
The power system control support system is characterized in that the adjustment power provision reward level calculation unit provides app game data as an incentive to the consumer, in addition to or instead of a monetary-based incentive. 7. The power system control support system according to claim 6,
The power system control support system is characterized in that the adjustment power provision reward level calculation unit inputs location information of consumers via an application game server. An action related to adjustment capacity is specified in the adjustment capacity related action specification section;
a control capacity related behavior learning unit links the behavior stored in a consumer behavior database that stores consumer behavior with the behavior specified by the control capacity related behavior specifying unit, learns whether or not there is a corresponding behavior, and generates a control capacity related behavior learning model;
A reward sensitivity generation unit generates a reward sensitivity of the consumer from information stored in a given reward + result behavior database that stores the given reward given to the consumer and the resultant behavior;
an adjustment capacity provision remuneration level calculation unit uses the consumer's remuneration sensitivity generated by the remuneration sensitivity generation unit, the adjustment capacity related behavior learning model generated by the adjustment capacity related behavior learning unit, and information stored in an adjustment capacity database that stores the adjustment capacity owned by the power transmission and distribution company and the location and time period in which an action related to the adjustment capacity corresponding to the action for securing the adjustment capacity was performed to calculate an appropriate remuneration to be granted when the consumer performs an action related to the adjustment capacity, and notifies the power transmission and distribution company of the calculated remuneration;
A power system control support method comprising:

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