JP2018005314A - User selection method and device - Google Patents

Abstract

[PROBLEMS] To preferentially select consumers who are highly likely to succeed in implementing DR as DR activation targets. A baseline estimation data selection unit (13) selects data in an estimation period used for baseline estimation from data of each consumer stored in a customer DB (12) as demand data for baseline estimation. Selected for each house, the demand control operation influence determination unit 14 extracts the activation status of the demand control operation for each consumer from the demand control operation data 22 selected as the baseline estimation data, and the obtained activation status and the reference value, the presence or absence of the influence of the demand control operation on the baseline of the customer is determined for each customer, and the priority customer selection unit 15 determines the influence of the demand control operation by the demand control operation influence determination unit 14 Based on the results, from among the consumers who are DR activation candidates, those consumers determined to have no influence of the demand control operation on the baseline of each consumer are selected as priority consumers. [Selection diagram] Fig. 1

Description

  The present invention relates to a consumer selection technique for a DR (Demand Response) aggregator to select a consumer to be a DR activation target.

  In recent years, the importance of energy reduction efforts not only on the power supply side but also on the demand side has increased due to power shortages throughout Japan. In particular, in addition to energy-saving efforts that have been vigorously promoted so far, attention has been focused on DR mechanisms that implement energy suppression only when adjustments are necessary due to tight supply and demand.

  In implementing DR, the role of a service provider that intervenes between individual consumers and the power supply side, and combines a plurality of consumers to efficiently provide a DR service becomes important. In general, this service provider is called a DR aggregator. Moreover, the transaction regarding the amount of power demand reduction performed between the DR aggregator and the customer based on the contract is referred to as a negawatt transaction.

  Conventionally, as a consumer selection technology implemented in such a DR aggregator, when receiving a demand restraint signal and power rate information from the power supply side, this time, from among the customers belonging to the same DR group that concluded a demand restraint contract, A technique for selecting a customer to be subject to DR activation has been proposed. (See, for example, Patent Document 1-2).

JP 2011-193639 A JP 2014-233103 A

“Guidelines on negawatt trading”, Agency for Natural Resources and Energy, March 30, 2015

  However, in such a conventional technology, in the DR aggregator, since the consumers are selected by paying attention to the fairness among the consumers based on the power saving effect and the environmental deterioration, the demand that is highly likely to fail in the DR implementation. There was a problem that a house might be selected. Here, the failure to execute DR means a case where a reduction amount satisfying a predetermined threshold range (for example, 90% to 110%) of the demand reduction amount promised in advance cannot be actually achieved. If the DR implementation fails, there may be a penalty that the incentive is not paid to the customer.

  In other words, regardless of the DR, the demand control operation is performed individually using a power demand controller or the like so that the maximum demand does not exceed the contracted power for the purpose of cost saving. There may be. Such demand control operation has the same purpose as that of DR that effectively achieves peak demand reduction. Therefore, the same control strategy as DR is inevitably selected in many cases. It is assumed to be effective.

  Therefore, when the baseline selected on the day of DR activation among the consumers selected as DR activation targets is already affected by the demand control operation unique to the customer, there is no room for new demand reduction by DR. Therefore, when such a consumer is selected as a DR activation target, there is a high possibility that the consumer will fail in performing the DR.

  The present invention is to solve such a problem, and an object of the present invention is to provide a customer selection technique that can preferentially select a customer who is highly likely to be successful in DR implementation as a DR activation target.

  In order to achieve such an object, according to the consumer selection method of the present invention, when a DR is activated from a DR (demand response) aggregator, a priority consumer to be preferentially selected as a DR activation target is defined as DR. A customer selection method for selecting in advance among a plurality of consumers registered as activation candidates, and demands actual demand power data and demand control operation data relating to each of the consumers as DR activation candidates. Data in the estimation period used for estimation of the baseline of the consumer out of the step of accumulating in the house DB and the actual demand power amount data and demand control operation data of the consumer accumulated in the consumer DB. A baseline estimation data selection step for selecting each customer as baseline estimation data; and From the demand control operation data selected as the data for slline estimation, the activation situation of the demand control operation is extracted for each consumer, and according to the comparison result between the obtained activation situation and a preset reference value A demand control operation influence determination step for determining, for each consumer, whether or not there is an influence of demand control operation on the baseline of the consumer, and determining that there is no influence of the demand control operation on the baseline from among the consumers A priority consumer selection step of selecting the selected consumer as the priority consumer.

  Further, in one configuration example of the customer selection method according to the present invention, the demand control operation influence determination step is included in the estimation period based on the demand control operation data selected as the baseline estimation data. The number of days on which demand control operation was activated was counted, and the presence or absence of the influence of demand control operation on the baseline was determined according to the magnitude relationship between the obtained activation days and the reference activation days. Is.

  Further, in one configuration example of the customer selection method according to the present invention, the demand control operation influence judgment is included in the estimation period based on the demand control operation data selected as the baseline estimation data. The time at which the demand control operation is activated is counted from each time, and the presence or absence of the influence of the demand control operation on the baseline is determined according to the magnitude relationship between the obtained activation time number and the reference activation time number. It is a thing.

  In the consumer selection device according to the present invention, when a DR is activated from a DR (demand response) aggregator, a plurality of priority consumers to be preferentially selected as DR activation targets are registered as DR activation candidates. A customer selection device that pre-selects the customer from among the customer DBs, a customer DB that accumulates actual demand energy data and demand control operation data relating to each of the consumers that are DR activation candidates, and the customer DB For each of the consumers, data for the estimation period used for estimation of the consumer's baseline is used as baseline estimation data from among the actual demand energy data and demand control operation data stored in the consumer. The baseline estimation data selection unit to be selected and the demanage selected as the baseline estimation data. Demand control operation activation status is extracted for each consumer from the control operation data, and demand control for the customer baseline is performed according to a comparison result between the obtained activation status and a preset reference value. A demand-controlled driving influence determining unit that determines whether or not there is an influence of driving for each consumer, and a consumer determined to have no influence of demand-controlled driving on a baseline from among the consumers. And a priority customer selection section.

  According to the present invention, when the DR is activated from the DR aggregator, the customer determined that there is no influence of the demand control operation on the baseline is preferentially selected and the DR is activated. Therefore, DR is preferentially activated for customers who have maintained the baseline expected during normal operation and have secured room for new demand reduction, so there is a high possibility of successful DR implementation. As a result, the DR aggregator can realize more efficient and stable DR.

It is a block diagram which shows the structure of a customer selection apparatus. It is a flowchart which shows a consumer selection process. It is explanatory drawing of demand control driving | running | working influence determination (in the case of DR with comparatively long reaction time and duration). It is explanatory drawing of demand control driving | running | working influence determination (in the case of DR with comparatively short reaction time and duration).

[Principle of the Invention]
First, the principle of the present invention will be described.
The demand reduction amount traded in the negawatt transaction corresponds to the difference between the power consumption assumed when there is no DR request (hereinafter referred to as a baseline) and the actual power consumption. Therefore, if the baseline is affected by the customer's unique demand-controlled operation, the peak demand will be lower than the baseline expected during normal operation, so there is little room for new demand reduction even if DR is activated. Therefore, there is a high possibility that DR implementation will fail.

In addition, if the baseline is not affected by the demand control operation unique to the customer, the baseline expected in normal operation is maintained, and a room for new demand reduction is secured. The possibility increases.
The present invention focuses on the relationship between the presence or absence of the influence of demand control operation on such a baseline and the success or failure of DR execution, and becomes a selection candidate when the DR aggregator selects a consumer to be a DR activation target. DR implementation fails by selecting a customer who has not been affected by the demand-controlled operation with respect to the baseline of the day as a priority customer to be activated with priority given to DR activation. The risk is reduced and the possibility of successful DR implementation is increased.

Next, an embodiment of the present invention will be described with reference to the drawings.
[Customer selection equipment]
First, with reference to FIG. 1, the customer selection apparatus 10 concerning this Embodiment is demonstrated. FIG. 1 is a block diagram illustrating a configuration of a customer selection device.

  The customer selection device 10 is composed of an information processing device such as a server device as a whole, and registers a priority customer to be preferentially selected as a DR activation target when a DR is activated from the DR aggregator 30 as a DR activation candidate. It has a function of selecting in advance from a plurality of consumers.

  The DR aggregator 30 is composed of an information processing device such as a server device as a whole, selects a customer as a DR activation target from a plurality of customers registered in advance as DR activation candidates, and sets the DR aggregator 30 as a DR activation target. A function for invoking DR for the selected consumer, a function for trading the amount of demand reduction obtained by the DR activation with an electric power company, and a DR activation target are notified from the customer selection device 10. It has a function of preferentially invoking DR for priority consumers. In addition, you may mount in the DR aggregator 30 about the priority consumer selection function of the consumer selection apparatus 10 concerning this invention.

  As shown in FIG. 1, the customer selection device 10 includes, as main functional units, a customer data acquisition unit 11, a customer DB 12, a baseline estimation data selection unit 13, a demand control operation influence determination unit 14, And the priority consumer selection part 15 is provided.

The customer data acquisition unit 11 acquires the actual demand power amount data 21 and the demand control operation data 22 from a plurality of customers preset as DR activation candidates via the communication network NW, and the customer DB 12 It has a function to store.
The actual demand power amount data 21 and the demand control operation data 22 may be obtained from a management device (not shown) of each customer, but the service providing device that remotely controls the demand control operation of each customer. May be obtained from

The actual demand power amount data 21 is time-series data indicating the actual demand power amount measured by a consumer at every 30 minute interval, for example.
The demand control operation data 22 is, for example, data indicating whether or not the demand control operation is performed at each time of 30 minutes, and may be data indicating whether or not the demand control operation is performed in a certain time period for each day. If it is possible to determine whether or not the demand control operation is performed, it is also possible to use the start / stop information and the setting information of the customer device to be demand controlled as the demand control operation data 22. That is, when the apparatus is stopped or the set value is relaxed in the power reduction direction, it is determined that the demand control operation is performed.

  The customer DB 12 is a database that includes a storage device such as a hard disk or a semiconductor memory, and accumulates actual demand power data 21 and demand control operation data 22 acquired from the customer for each customer.

  The baseline estimation data selection unit 13 uses the estimated demand power data 21 and the demand control operation data 22 stored in the consumer DB 12 to estimate the baseline of each consumer. It has a function to select data for each customer as baseline estimation data.

  The demand control driving influence determination unit 14 extracts the demand control driving activation situation for each consumer from the demand control operation data 22 selected as the baseline estimation data, and is preset with the obtained activation situation. According to the comparison result with the reference value, it has a function of determining, for each customer, whether or not there is an influence of demand control operation on the baseline of the customer.

  Based on the determination result in the demand control operation influence determination unit 14, the priority consumer selection unit 15 has no influence of the demand control operation on the baseline of each customer among the consumers who are DR activation candidates. And a function of outputting a priority consumer list in which these priority consumers are listed.

[Operation of this embodiment]
Next, with reference to FIG. 2, operation | movement of the consumer selection apparatus 10 concerning this Embodiment is demonstrated. FIG. 2 is a flowchart showing customer selection processing.
The customer selection device 10 executes the customer selection process of FIG. 2 according to the processing timing such as the acquisition of the actual demand power amount data 21 and the operator operation. In executing the customer selection process, it is assumed that the actual demand power amount data 21 and the demand control operation data 22 acquired from each customer are stored in advance in the consumer DB 12 as baseline estimation data. .

First, the baseline estimation data selection unit 13 selects the actual demand power amount data 21 and the demand control operation data 22 from the consumer DB 12 for each consumer, and in the estimation period used for estimating the baseline of the consumer. Data is selected (step 100).
In the negawatt transaction, the baseline estimation method is defined in advance (for example, see Non-Patent Document 1 etc.), and the estimation period used for the baseline estimation is defined for each type of baseline. .

  In the present invention, in accordance with the above rules, in the case of DR with a relatively long reaction time and duration, the actual demand power data 21 and the demand control operation data 22 in the estimation period for a certain number of days before the DR activation date are In case of DR, which is selected as the baseline estimation data and the reaction time / duration is relatively short, the actual demand power amount data 21 and the demand control operation data 22 in the estimation period for a certain time period before the DR activation time, Select as baseline estimation data.

  In the above rules, the reaction time is defined as equivalent to the time from the time of DR prior notification to the consumer until the start of the time zone in which the customer promises to reduce demand, and the duration is determined by the customer to reduce demand. It is stipulated that it corresponds to the time from the start of the promised time zone to the end of the time zone where the customer promises to reduce demand.

  Further, the length of the reaction time / duration of the DR is determined in advance for each DR in consideration of the purpose and nature of the DR in accordance with the above rules. For example, a DR with a relatively long reaction time / duration is assumed to be a transaction of electric energy between a retailer and a DR aggregator, etc. It is envisaged that power distribution companies will have reserve / adjustment transactions in the real-time market.

  Next, the demand control operation influence determination unit 14 extracts the activation state of the demand control operation from the baseline estimation data selected by the baseline estimation data selection unit 13 for each consumer, and the obtained activation state Whether or not there is an influence of the demand control operation on the baseline of the customer is determined according to the comparison result between the reference value and the preset reference value (step 101).

  The baseline on the day of DR activation is usually estimated by averaging k (k is a positive integer) pieces of actual demand energy data 21 selected for baseline estimation. Therefore, among these actual demand power data 21, the larger the number of data affected by demand control operation, the lower the baseline peak demand calculated by averaging. In the present invention, it is possible to determine whether or not there is an influence of the demand control operation on the baseline by evaluating the number or the activation rate of the demand control operation as the activation state.

  When the activation number P is used as the activation state, it is compared with a preset reference activation number Ps (Ps is an integer of 1 to k), and when P <Ps, it is determined that there is no influence, and P ≧ Ps In some cases, it may be determined that there is an influence. On the other hand, when the activation rate R is used as the activation status, it is compared with a preset reference activation rate Rs (Rs is a real number of 0 to 1), and when R <Rs, it is determined that there is no influence, and R ≧ In the case of Rs, it may be determined that there is an influence.

  FIG. 3 is an explanatory diagram of demand control driving influence determination (in the case of DR with a relatively long reaction time and duration). In the case of DR with a relatively long reaction time and duration, actual demand power data 21 and demand control operation data 22 in the estimation period for n (n is a positive integer) days before the DR activation date are used for baseline estimation. Selected as data. Here, in the estimation period, the peak demand for the baseline estimated by averaging is suppressed as the number of activation days X in which the demand control operation is activated increases.

  Therefore, based on the selected demand control operation data 22, the number of days when the demand control operation is activated is counted from the n days included in the estimation period, and the obtained activation days X are set in advance. It is compared with the standard activation days Xs. Here, for example, when Xs = 2 days is set, it is determined that there is no influence only when X <Xs, that is, when the number of activation days X is one day, and when X ≧ Xs, that is, the number of activation days X is two days. In the above case, it is determined that there is an influence.

  FIG. 4 is an explanatory diagram of demand control driving influence determination (in the case of DR with a relatively short reaction time / duration). In the case of DR with a relatively short reaction time / duration, the number of activation times is used instead of the number of activation days. That is, the actual demand power data 21 and the demand control operation data 22 in the estimation period for m (m is a positive integer) time before the DR activation time are selected as the baseline estimation data. Here, in the estimation period, the peak demand for the baseline estimated by the averaging is suppressed as the number of activation times Y at which the demand control operation is activated increases.

  For this reason, based on the selected demand control operation data 22, the number of times (time zones) at which the demand control operation is activated is counted from the m times included in the estimation period, and the obtained activation time is obtained. The number Y is compared with a preset reference activation time number Ys. Here, for example, when Ys = 2 time is set, it is determined that there is no influence only when Y <Ys, that is, when the activation time number Y is one time, and when Y ≧ Ys, that is, when the activation time number Y is In the case of two or more times, it is determined that there is an influence.

  Thereafter, the priority consumer selection unit 15 determines that there is no influence of the demand control operation on the baseline from among the consumers who are DR activation candidates based on the determination result in the demand control operation influence determination unit 14. The selected consumer is selected as a priority consumer (step 102), and the priority consumer list listing the selected priority consumer is sent to the DR aggregator 30 via a screen display unit (not shown) or the communication network NW. Output (step 103), and a series of priority consumer selection processing ends.

  As a result, in the DR aggregator 30, when DR is activated, among the consumers who are DR activation candidates, priority consumers listed in the priority consumer list, that is, consumers who are not affected by the demand control operation on the baseline. Are preferentially selected and the DR is activated. For this reason, the risk of failure in DR implementation is reduced, and the possibility of successful DR implementation is increased.

[Effects of the present embodiment]
As described above, in the present embodiment, the baseline estimation data selection unit 13 selects each of the actual demand power amount data 21 and the demand control operation data 22 of each consumer stored in the consumer DB 12. The data in the estimation period used for the estimation of the baseline of the consumer is selected for each consumer as the baseline estimation data, and the demand control operation influence determination unit 14 selects the demand control operation data selected as the baseline estimation data. 22, the activation situation of demand control operation is extracted for each consumer, and the presence or absence of the influence of the demand control operation on the baseline of the consumer according to the comparison result between the obtained activation situation and a preset reference value Is determined for each customer, and the priority customer selection unit 15 is based on the determination result in the demand control driving influence determination unit 14. From among each customer that is a DR trigger candidate, in which the respective customer customer it is determined that there is no influence of the demand control operation for the baseline so as to select as the preferred customer.

  As a result, when the DR is activated from the DR aggregator 30, a customer determined to have no influence of the demand control operation on the baseline is preferentially selected and the DR is activated. Therefore, DR is preferentially activated for customers who have maintained the baseline expected during normal operation and have secured room for new demand reduction, so there is a high possibility of successful DR implementation. As a result, the DR aggregator 30 can realize more efficient and stable DR.

[Extended embodiment]
The present invention has been described above with reference to the embodiments, but the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. In addition, each embodiment can be implemented in any combination within a consistent range.

  DESCRIPTION OF SYMBOLS 10 ... Key house selection apparatus, 11 ... Customer data acquisition part, 12 ... Consumer DB, 13 ... Baseline estimation data selection part, 14 ... Demand control driving influence determination part, 15 ... Priority consumer selection part, 21 ... Actual demand power data, 22 ... demand controlled operation data, NW ... communication network.

Claims (4)

Demand for pre-selecting a priority consumer to be preferentially selected as a DR activation target from a plurality of consumers registered as DR activation candidates when a DR is activated from a DR (demand response) aggregator A home selection method,
Accumulating actual demand power amount data and demand control operation data for each of the consumers as DR activation candidates in the consumer DB;
Among the actual demand power amount data and demand control operation data stored in the consumer DB, data in the estimation period used for the baseline estimation of the consumer is used as baseline estimation data. Baseline estimation data selection step to select for each consumer,
From the demand control operation data selected as the baseline estimation data, the demand control operation activation situation is extracted for each consumer, and the obtained activation situation is compared with a preset reference value. In response, a demand control operation influence determination step for determining, for each consumer, whether or not there is an influence of demand control operation on the baseline of the consumer;
A priority customer selection step of selecting, from among the consumers, a consumer determined to have no influence of demand control operation on a baseline as the priority consumer. In the consumer selection method according to claim 1,
The demand control operation influence determination step counts the day when the demand control operation is activated from each day included in the estimation period based on the demand control operation data selected as the baseline estimation data. A consumer selection method characterized by determining whether or not there is an influence of demand control operation on the baseline according to the magnitude relationship between the obtained number of activation days and the reference number of activation days. In the consumer selection method according to claim 1,
The demand control operation influence determination step counts the time when the demand control operation is activated from each time included in the estimation period based on the demand control operation data selected as the baseline estimation data. A consumer selection method characterized by determining whether or not there is an influence of demand control operation on the baseline according to the magnitude relationship between the obtained activation time number and the reference activation time number. When selecting a DR from a DR (demand response) aggregator, select a priority customer to be preferentially selected as a DR activation target from a plurality of consumers registered as DR activation candidates. A device,
A customer DB that accumulates actual demand power amount data and demand control operation data for each of the consumers that are DR activation candidates;
Among the actual demand power amount data and demand control operation data stored in the consumer DB, data in the estimation period used for the baseline estimation of the consumer is used as baseline estimation data. Baseline estimation data selection department to select for each consumer,
From the demand control operation data selected as the baseline estimation data, the demand control operation activation situation is extracted for each consumer, and the obtained activation situation is compared with a preset reference value. In response, a demand control operation influence determination unit that determines the presence or absence of the influence of the demand control operation on the consumer's baseline for each consumer,
A customer selection device, comprising: a priority consumer selection unit that selects, as the priority consumer, a consumer determined to have no influence of a demand control operation on a baseline among the consumers.

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