WO2014132370A1 - Negawatt transaction assistance system - Google Patents

Abstract

[Problem] To provide a system whereby it is possible to assist with negawatt transactions. [Solution] The present invention is a negawatt transaction assistance system, comprising a control device and a storage device. The storage device further comprises a demand forecasting information storage region which stores energy demand forecasting information, and a bidding information storage region which stores bidding forecasting information with respect to a negawatt transaction of the energy. The control device creates a demand response scenario with respect to the energy on the basis of the demand forecasting information, formulates a transaction plan of the negawatt on the basis of the created scenario and the bidding forecasting information, and outputs the formulated transaction plan.

Description

Negawatt trading support system

The present invention relates to a system that supports the buying and selling of negawatts that are created by a consumer suppressing the use of electric power.

Demand response (hereinafter referred to as “DR”) is designed to allow consumers to temporarily suppress the use of electricity by changing electricity charges or paying compensation when the wholesale market price rises or the system reliability decreases. It is to change the power consumption pattern. In the power trading market such as the United States, negawatts created by DR are traded in the same way as the power generated at power plants.

In addition, aggregators that gather negawatts of small-scale customers and trade with electric power companies and grid operators have appeared. In the future, due to the expansion of power generation by renewable energy, there are concerns about unstable power supply and demand balance and increase in electricity price volatility. In that sense, the effective use of negawatts compiled by an aggregator is expected.

Patent Document 1 predicts a power transaction balance by a simulation using a model that predicts risk factors such as fuel price, power demand, and power price in response to the problem of enabling risk management in power trading. An apparatus for calculating a risk index from the distribution of power transaction balance and occurrence probability predicted by the simulation of the number of times is disclosed.

According to Patent Document 2, the output of wind power generation is unstable due to wind, and it is not possible to obtain planned power generation output for each time zone, and the power exchange cannot deal with the power generated by wind power generation. The means to predict the power that can be supplied from past power generation results, the means to bid on the power exchange based on the predicted supply amount and obtain the contracted power amount, and the power generation amount of multiple wind power generators And a wind power generation management unit that charges and discharges a surplus or deficiency to a storage battery by charge / discharge control.

Patent document 3 calculates the predicted cumulative amount of hot water supply demand based on the past actual data of hot water supply demand in response to the problem of realizing economical operation of an HP (heat pump) type water heater, Calculates the predicted cumulative amount of power demand based on the actual data of the power generation, calculates the predicted value of power generation output based on the past actual power output data and the weather data, the predicted cumulative amount of hot water supply demand, the predicted cumulative power demand An apparatus is disclosed that calculates a heat storage amount of a water heater to be maintained based on the amount and a predicted value of the power generation output, and controls the operation of the water heater so that the calculated heat storage amount is maintained.

JP 2008-84095 A JP 2006-280020 A JP 2013-2794 A

However, since Patent Document 1 relates to a system for managing the balance of electric power sales and risks and the risk of power generation by ordinary power generation, the factors that influence the balance and risk of power generation are significantly different from the factors that affect the balance and risk of negative wattage, Patent Document 1 cannot be applied to negawatt trading.

Patent Document 2 adjusts the imbalance between the contracted result and the output result based on the output prediction of wind power generation by charging and discharging the storage battery, and the main body having the wind power generation and the main body having the storage battery are the same. Because it is assumed that there is, it is not applicable to the trading of negawatts.

Patent Document 3 operates a heat pump type hot water heater in accordance with output prediction by solar power generation. Like Patent Document 2, a main body having solar power generation and a main body having a heat pump water heater are the same. Therefore, it is not applicable to buying and selling negawatts.

The technology described in the above-mentioned technical literature can be one of the individual elemental technologies required to evaluate Negawatt's balance and risk. Not applicable to trading.

As described above, although the effective use of negawatts is expected, the conventional system does not reach the level where the tools necessary for buying and selling negawatts are sufficiently prepared.

The present invention aims to provide a system capable of supporting the trading of negawatts and a negawatt and a trading support method in order to solve the above-described problems.

In order to achieve the object, the present invention includes a control device and a storage device, and the storage device stores a demand prediction information storage area for storing energy demand prediction information, and bid prediction for buying and selling of the energy negative watts. A bid information storage area for storing information, the control device creates a demand response scenario for the energy based on the demand prediction information, and based on the created scenario and the bid prediction information It is a negawatt trading support system that formulates a negawatt trading plan and outputs the developed trading plan.

According to the present invention, since a negawatt trading plan can be formulated in an optimal form, it is possible to provide a negative wattage trading support system and a negawatt trading support method that can promote effective use of negawatts.

It is a figure which shows the structure of a negawatt trading support system. It is a block diagram which shows the structure of a network of a negative wattage buying and selling support system and an energy management system of each consumer. It is a figure which shows the structure of contract performance DB. It is a figure which shows the structure of bid information DB. It is a figure which shows the structure of scenario DB. It is a figure which shows the structure of customer DB. It is a figure which shows the structure of demand forecast DB. It is a figure which shows the structure of heat storage plan DB. It is a figure which shows the structure of apparatus characteristic DB. It is an example of the graph which shows an apparatus characteristic (relationship between power consumption and generated heat amount). It is a figure which shows the structure of operation performance DB. It is a figure which shows the structure of the hardware of a negawatt trading support system. It is a figure which shows the network structure of a market operator's terminal, a retailer's terminal, an aggregator's terminal, and a consumer's terminal. It is a figure which shows the structure of the electric equipment of the consumer of the 1st Example of this invention. It is a flowchart which shows the process of a sales plan formulation part. It is an example of the screen output from a sales plan output part. It is a flowchart which shows the process of a scenario preparation part. It is a flowchart which shows the process of DR scenario preparation of the demand shift of a scenario preparation part. It is a flowchart which shows the DR scenario creation process of the demand suppression of a scenario creation part. It is a flowchart which shows the process of a bid estimation part. It is a flowchart which shows the process of a thermal storage plan part. It is a flowchart which shows the whole process of a negawatt trading support system. It is a flowchart which shows the process of a risk evaluation part. It is a figure which shows the structure of the electric equipment of the consumer of the 2nd Example of this invention. It is a block diagram which shows the structure of the virtual market system of the 3rd Example of this invention. It is a figure which shows the structure of bid performance DB of the 3rd Example of this invention. It is a figure which shows the structure of bid estimation DB of the 3rd Example of this invention. It is a flowchart which shows the process of the about quantitative calculation part of the 3rd Example of this invention. It is a flowchart which shows the process of the bid estimation part of the 3rd Example of this invention. It is an example of the screen which a consumer's energy management system displays.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. First, the electricity trading market and negawatt trading will be explained. The power trading market (hereinafter referred to as the market) is a place where the delivery price and quantity are contracted by matching the demand and supply of power. There are spot markets for trading electricity delivered the next day, futures and forward markets for trading electricity delivered the next day, and an imbalanced market for trading electricity delivered on the day.

In the market, electricity delivered in one day is divided into times such as every 30 minutes (hereinafter referred to as “frames”), and transactions are made for each of 48 frames. A market participant designates a price and quantity for one or more pieces, and bids to buy or sell electricity. Bids for buying and selling are executed using the Zaraba method or the boarding method. By executing the contract, unless the counter-trading is done, the power seller will supply the contracted amount of electricity during the delivery period, and the power buyer will be obliged to pay the seller the price of the contracted amount multiplied by the contracted price. Occurs.

The price contracted in the market (hereinafter referred to as “electricity price”) fluctuates from moment to moment depending on the supply and demand situation of electricity. Usually, the daytime is high on weekdays and cheap on holidays and nights. Moreover, the midsummer and the midwinter, when demand for cooling and heating is large, are high. Electricity prices also rise when the prices of oil, coal and natural gas required for power generation rise. In the case of sudden maintenance or accidents at the power plant, the power price will be high. Moreover, when the ratio of wind power generation or solar power generation is large, the power price fluctuates with respect to wind strength and weather prediction.

In order to cope with such fluctuations in power prices, negative watts created by DR are attracting attention. As an example, assume that a certain customer purchases power through a long-term contract, and that one day, due to a strong cold wave (the next day's demand surge is expected), the price of next-day delivery will soar. At this time, a consumer who has a long-term contract can purchase power at a contract price. However, for example, by using an oil stove instead of an air conditioner, the power consumption can be suppressed more than the contracted amount. This reduced amount of power can be sold to the market at a high price, and a large profit can be obtained.

As another DR example, it is assumed that there is a customer having a heat pump water heater with a hot water storage tank (hereinafter referred to as an HP water heater). Since the HP water heater has a heat storage function, it is possible to adjust the operating time to some extent without impairing convenience. In general, the HP water heater is operated at night when the power price is low, to prepare for the demand for hot water during the daytime. For example, one day, a strong wind is expected in the daytime of the next day, and the price of power delivered in the daytime of the next day (due to a sudden increase in supply capacity) falls drastically.

At this time, the consumer suppresses the operation of the HP water heater at night of the day, sells the amount of power to be suppressed, and operates the HP water heater in the daytime day of the next day when the power price has dropped drastically instead. Power can be procured at a lower cost by purchasing power for operation. It is also possible to procure the power for the night operation on the next day during the day. In other words, either the suppression time zone or the operation time zone may be first. On the other hand, even if the power price of the night rises due to sudden maintenance of the power plant, it is possible to avoid procurement with high power by shifting the operation time to the next day. In summary, if the electricity price in the time zone where the HP water heater was originally to be operated is higher than the electricity price in other time zones, the operation of the HP water heater during the time zone that was originally supposed to be operated is suppressed. (Selling power) and shifting to a time zone where the power price is cheap to operate (purchase power) can make the power procurement price advantageous.

The first example above is a simple demand-suppressed DR example, and the second example is a demand-shifted DR example. The increase in negative wattage trading due to demand restraint or demand shift DR as described above will eventually improve the supply-demand balance, suppress excessive fluctuations in power prices, and thus contribute to improving the reliability of the power system. It is considered.

In general, the consumer does not directly interact with the market, but the aggregator that collects the consumers performs the above-mentioned buying and selling according to the daily power price, and the consumer's electrical equipment is directly or ( It is often controlled indirectly (by contacting the customer by telephone, etc.). The aggregator makes profits and pays consumers.

Also, the role of the aggregator is to position the outsourcing of the business of retailers. Retailers sell electricity by contracting with consumers. Retailers can lead the DR themselves, but in order to buy and sell negawatts in the market, it is necessary to secure a certain number of consumers (collective amount of negawatts). In addition, since it is not always easy to appropriately control electric appliances of a large number of customers and to carry out appropriate transactions in the market, it is often considered to be entrusted to an aggregator. The aggregator leads DR while exchanging necessary information with the retailer.

In order for an aggregator to properly buy and sell negawatts, it is necessary to capture revenue opportunities according to the ever-changing electricity prices, while creating negawatts taking into account the convenience of consumers, etc. It is also important. For example, in the case of a demand shift by an HP water heater, quantitatively grasp the quantitative potential and temporal flexibility that can shift the demand, and formulate an optimal trading plan according to the power price that changes from moment to moment The technology for the negawatt trading system is required.

Hereinafter, the details of the technique of the present invention will be described with reference to individual drawings. FIG. 1 is a block diagram of a negawatt trading support system 100 according to the present invention. The negawatt trading support system 100 includes a storage unit 102 and a processing unit 104.

The storage unit 102 includes a plurality of databases (DB). The database includes a contract performance DB 102A, a bid information DB 102B, a scenario DB 102C, a customer DB 102D, a device characteristic DB 102E, a demand prediction DB 102F, and a heat storage plan DB 102G. The storage unit 102 and the processing unit 104 are realized by a combination of computer hardware resources and software resources.

The execution result DB 102A is a DB that stores information on prices and quantities executed in the past in the market, and includes a date, a frame, an execution price, and an execution amount. Data is obtained from the market operator's terminal.

The bid information DB 102B is a DB indicating information on buying or selling that is bid in the market, and is configured to include a top, a bid type, a bid price, and a bid amount. The data is acquired from the market operator's terminal or created by the bid prediction unit 104A.

Scenario DB102C is DB which shows the information of negawatt to buy and sell, customer ID, equipment ID, DR type, suppression start time, suppression continuation time, suppression amount, earliest wake-up start time, latest wake-up start time, wake-up duration , Configured with an arousal amount. The data is created by the scenario creation unit 104D or acquired from the energy management system 200 of the customer.

The customer DB 102D is a DB indicating information belonging to the customer, and is configured to include a customer ID, a device ID, a device type, a unit price of suppression amount, a DR type, a suppression time zone, and a suppression coefficient. The data is acquired from the consumer's energy management system 200 or the retailer's terminal.

The demand prediction DB 102F is a DB that indicates a prediction of consumer power consumption and the like, and includes a consumer ID, a device ID, a prediction start time, a prediction end time, a predicted heat consumption, and a predicted power consumption. Data is created by the demand prediction unit 104E.

The heat storage plan DB 102G is a DB that indicates a heat storage plan of the hot water storage tank of the HP water heater, and is configured to include a customer ID, a device ID, a plan start time, a plan end time, a plan generated heat amount, and a plan heat storage amount. Data is created by the demand prediction unit 104E.

The device characteristic DB 102E is a DB indicating the device characteristics of an electric device, and is configured to include a device ID, a generated heat amount, power consumption, a lower limit heat storage amount, and an upper limit heat storage amount. The data is obtained from a consumer energy management system or a retailer terminal.

The processing unit 104 includes a bid prediction unit 104A, a sales meter output unit 104B, a sales plan formulation unit 104C, a scenario creation unit 104D, a demand prediction unit 104E, and a risk evaluation unit 104F.

The bid prediction unit 104A predicts the price and quantity of sell or buy that will be bid on the market. The sales plan formulation unit 104C formulates a sales plan and passes the sales plan to the sales plan output unit 104B. The sales plan output unit 104B displays the sales plan formulated by the sales plan formulation unit 104C on a screen or the like.

The scenario creation unit 104D creates a DR scenario and records it in the scenario DB 102C. The demand prediction unit 104E creates a demand prediction and records it in the demand prediction DB 102F. Moreover, a heat storage plan is created and recorded in the heat storage plan DB 102G. The risk evaluation unit 104F repeatedly processes the plurality of demand predictions and bid predictions to calculate the balance, and evaluates the risk of negawatt trading. Details of each processing block of the processing unit 104 and each DB of the storage unit 102 will be described later.

FIG. 2 is a block diagram showing the relationship between the negawatt trading support system 100, the consumer energy management system 200A, and the energy management system 200B.

The operation result DB 204A is a DB that shows the result of the operation status of the electrical equipment of the customer, and includes the customer ID, device ID, date, time, temperature, actual heat consumption, and actual power consumption.

The energy management unit 202A stores the operating status of the consumer's electrical equipment in the operating performance DB 204A. In addition, the energy management unit 202A can transmit the operation status record data stored in the operation record DB 204A and data input by the customer on the screen or the like to the negawatt trading support system 100.

FIG. 30 is an example of a screen displayed by the energy management system 200A. The consumer inputs the configuration information of the DR scenario, such as the electrical device, the implementation condition, and the DR type that are the target of the DR. The target equipment area 3000 is an area for inputting information on the electrical equipment of the customer. The execution condition area 3002 is an area for inputting conditions (day of the week, reward unit price) for the customer to perform DR. The demand response type area 3004 is an area for inputting a DR type to be executed.

The configuration / function of the energy management system 200A is the same as the energy management system 202B of another customer. Hereinafter, in order to simplify the description, the description regarding the energy management system 202A also applies to the energy management system 200B, and the description regarding the energy management system 200B is omitted.

Moreover, although the structure regarding two energy management systems was shown in FIG. 2, one may be sufficient and three or more may be sufficient. Further, the processing blocks and DB configurations of the negative wattage trading support system 100 and the energy management system 200 are not limited to those described above. For example, the demand prediction unit 104E and the demand prediction DB 102F may be constituent elements of the consumer energy management system 200 instead of the constituent elements of the negative power trading support system 100.

FIG. 3 is an example of a configuration table of the contract performance DB 102. The contract price and contract quantity are recorded for each date and frame.

FIG. 4 is an example of a configuration table of the bid information DB 102B. Bid type (buy, sell), bid price, and bid amount are recorded for each date and frame.

FIG. 5 is an example of a configuration table of the scenario DB 102C. For each customer ID and device ID, the DR type, suppression start time, suppression duration, suppression amount, earliest arousal start time, latest awakening start time arousal duration, arousal duration, and arousal amount are recorded.

FIG. 6 is an example of a configuration table of the customer DB 102D. Device type, suppression unit price, DR type, suppression time zone, and suppression coefficient are recorded for each customer ID and device ID.

FIG. 7 is an example of a configuration table of the demand forecast DB 102F. The prediction start time, the predicted heat consumption, and the predicted power consumption are recorded for each customer ID and device ID.

FIG. 8 is an example of a configuration table of the heat storage plan DB 102G. The planned start time, planned end time, planned generated heat amount, and planned heat storage amount are recorded for each customer ID and device ID.

FIG. 9 is a diagram showing a configuration of the device characteristic DB 102E. The amount of generated heat, power consumption, the lower limit heat storage amount, and the upper limit heat storage amount are recorded for each device ID. The device characteristics are determined from the relationship between the power consumption of the device and the amount of generated heat, as shown in FIG.

FIG. 11 is an example of a configuration table of the operation result DB 204. The time, temperature, actual heat consumption, and actual power consumption are recorded for each customer ID and device ID.

FIG. 12 is a hardware configuration diagram of an aggregator terminal 1200 for realizing the negawatt trading support system 100. The basic configuration as an information processing device such as a CPU 1202, an input device 1204, an output device 1206, a communication device 1208, and a storage device 1210 is shown. It has a configuration. Each processing block of the processing unit 104 is stored in the storage device 1210 as a program, and is realized by the CPU 1202 executing the program.

The data of each DB in the storage unit 102 is stored in the storage device 1210 in the form of a relational database table or the like, used for processing of a program executed by the CPU 1202, and the processing result data is stored in the storage device 1210. Each processing block and DB can also be realized by hardware such as an integrated circuit. Each processing block and DB may be stored in advance in a storage device in the computer, but a removable storage medium or communication medium (wired, wireless, optical network, or carrier wave or digital signal on the network) It may be introduced to the external storage device when necessary. The same applies to the hardware configuration of the consumer energy management system.

As shown in FIG. 13, the execution system for negawatt trading includes a terminal 1304 equipped with an aggregator negawatt trading support system, a terminal 1306 equipped with a consumer energy management system, a consumer electric device 1308, a market An operator terminal 1300, a retailer terminal 1302, and a network 1310 such as the Internet that connects these terminals to each other are configured. These terminals can transmit and receive data to and from each other via the network 1310. The customer terminal 1306 and the electric device 1308 transmit and receive data via a wireless or wired communication line.

FIG. 14 shows an example of the configuration of a consumer's electrical equipment. Electricity is supplied to the heat pump 1406 and the air conditioner 1404 through a power receiving facility 1402 connected to the electric wire 1400. The heat pump 1406 warms the water in the hot water storage tank 1408 by circulation of the heat medium, and the hot water storage tank 1408 sends the warmed water to the panel heater 1410. Water in the water storage tank 1408 is supplied from a water pipe 1412.

FIG. 15 is a flowchart showing the processing of the sales plan formulation unit 104C. First, the sales plan formulation unit 104C acquires all bid information from the bid information DB 102B (S1500).

Next, the sales plan formulation unit 104C acquires all the DR scenarios from the scenario DB 102C, and selects a DR scenario with a predetermined standard (for example, a unit price of the payment amount to the consumer having the smallest restraint unit price). (Select)), one DR scenario is selected (S1502). The control unit price is obtained from the customer DB 102D for the corresponding consumer and electrical equipment. Hereinafter, in order to simplify the description, first, the process will be described when the DR type of the selected DR scenario is “demand shift”, and the difference from the case where the DR type is “demand suppression” is summarized. Will be described later.

Next, the sales plan formulation unit 104C provisionally determines one sales pattern from the DR scenario (S1504). The buying / selling pattern is a combination of a selling bid frame, a selling bid amount, a buying bid frame, and a buying bid amount.

The selling bid frame is a frame in the time period from “suppression start time” to “suppression start time + suppression duration”. The frames of the buying bid are frames from the “calling start time” to “calling start time + calling duration”. The awakening start time is any time between the earliest awakening start time and the latest awakening start time. The amount of bids sold is the same as the amount of restraint. The amount of bids bought is the same as the amount of arousal. The sell bid frame and the buy bid frame do not include frames of the same time.

Next, the sales plan formulation unit 104C estimates a sale contract amount, a sell price, a buy contract amount, and a buy contract price from the sales pattern and bid information for each frame (S1506).

Approximate sales amount is the smaller of the amount of selling bids in the buying and selling pattern and the amount of buying bids in the bid information (the total amount when there are multiple buying bids in the same frame). The buy purchase fixed quantity is the smaller of the buy bid amount of the buying and selling pattern and the sell bid amount of the bid information (the total amount when there are a plurality of sell bids in the same frame).

The sale contract price is the highest price among the bid prices of the bid information. However, when contracting with a plurality of buy bids in the bid information, it is a weighted average of contract quantitative values for each price of the bid bid to be executed.

The purchase contract price is the lowest price among selling bid prices in the bid information. However, when contracting with a plurality of selling bids in the bid information, it is a weighted average of contracted amounts for each selling bid price to be contracted. It is assumed that selling bids for bid information are preferentially executed in descending order of price, and buying bids for bid information are preferentially executed in order of high price.

Next, for each frame, the sales plan formulating unit 104C subtracts the payment amount obtained by multiplying the purchase contract amount by the purchase contract price from the receipt amount obtained by multiplying the sell contract amount by the sale contract price, and the payment amount to the consumer is obtained. Then, the balance of each frame is calculated, and when the balance of all the frames is calculated, the balance of all the frames is summed (S1508). The amount paid to the consumer of each frame is calculated by multiplying the selling amount by the unit price of the restraint for each consumer / electric equipment.

Next, the buying and selling plan formulation unit 104C checks whether or not the balance has been calculated for all possible buying and selling patterns (actually the calling start time) (S1510). If all the trading patterns have been calculated, the process proceeds to the next step (S1512), and if there is any trading pattern remaining, the process returns to the provisional determination of the trading pattern (S1504).

Next, the trading plan formulation unit 104C detects a trading pattern having the maximum balance among the calculated trading pattern balances (S1512).

Next, the sales plan formulating unit 104C updates the bid information by subtracting the sold bid and the bid to be bid from the bid information for each frame regarding the trading pattern with the maximum balance (S1514).

Next, the sales plan formulation unit 104C checks whether all the DR scenarios have been calculated (S1516). If all the DR scenarios have been calculated, the process proceeds to the next step (S1518). If any DR scenario remains, the process returns to DR scenario selection (S1502), and one DR scenario is selected from the DR scenarios that have not been calculated yet. select.

Next, the trading plan formulation unit 104C passes the trading pattern having the maximum balance among all the DR scenarios calculated above to the trading plan output unit 104B and outputs it to the screen (S1518).

The above is the description of the processing of the sales plan formulation unit 104C. However, the processing is not limited to the description of the above flowchart, and it is changed within the scope of the purpose for the purpose of speeding up and sophisticating the processing. May be. For example, in the above, in the buying and selling pattern, only the time of the buying bid (that is, the awakening start time) is used as a parameter, but the amount of buying bid, selling bid, and selling bid may be used as parameters. In other words, the bid bid amount (that is, the arousal amount) and the sell bid amount (that is, the suppression amount) that gives a range rather than a fixed value for the time of the sell bid frame (that is, the suppression start time). Instead of a fixed value, give it a width. Further, for example, instead of selecting a trading pattern with the largest balance, a trading pattern may be selected in consideration of balance distribution calculated by the risk evaluation unit 104F.

Next, processing when the DR type of the DR scenario is “demand suppression” will be described. When the DR type of the DR scenario is “demand restraint”, the buying / selling pattern is a combination of the selling bid frame and the selling bid amount, and does not include the buying bid frame and the buying bid amount. In addition, it is not necessary to estimate the purchase contract quantity and the purchase contract price. Further, the balance of each frame is calculated by subtracting the payment to the customer from the received money obtained by multiplying the sale contract amount by the selling contract price. In addition, it is not necessary to deduct the buying bid when updating the bid information. Other processing is the same as in the case of “demand shift”.

FIG. 16 is an example of a screen output by the sales plan output unit 104B. The sales plan area 1600 shows a sales pattern with the largest balance. The balance area 1602 is a graph showing the probability distribution of the balance, and the calculation method is described in the description section of the risk evaluation unit 104F.

FIG. 17 is a flowchart showing the processing of the scenario creation unit 104D. First, the scenario creation unit 104D acquires all customer information from the customer DB 102D, and then acquires the demand prediction of the corresponding customer and electrical equipment from the demand prediction DB 102F (S1700).

Next, the scenario creation unit 104D repeats the process for each customer and device from step S1702 to step S1710. In the iterative process, the scenario creating unit 104D first determines whether the DR type of the customer information is a demand shift or a demand suppression (S1704). If the DR type is a demand shift, a process for creating a DR scenario for demand shift is performed (S1708). If the DR type is a demand suppression, a process for creating a DR scenario for demand suppression is performed (S1706). The process for creating the demand shift DR scenario and the process for creating the demand suppression DR scenario will be described with reference to different flowcharts.

Next, the scenario creation unit 104D returns to the processing of the next consumer and equipment (S1704) when the consumer and the electrical equipment that have not been processed remain, and the processing of all the consumers and electrical equipment is completed. If so, the process ends (S1710).

The above is the description of the process of the scenario creation unit 104D. However, the process is not limited to the above-described flowchart, and can be changed within a range that does not depart from the gist for the purpose of speeding up and sophisticating the process. Good. For example, in the above, a DR scenario is created for each consumer and electrical device, but one DR scenario may be created for a plurality of customers or electrical devices.

For example, in a phase where the electrical equipment is actually controlled directly or indirectly, a consumer who starts controlling the electrical equipment immediately after issuing an instruction from the aggregator, and a demand for starting the restraint after some time has passed. When there is a house, it is conceivable to stabilize the responsiveness of demand control as a whole by appropriately combining these consumers.

In addition, especially when the consumer's electrical equipment is indirectly controlled, the consumer does not always perform demand suppression according to the instructions of the aggregator, and therefore, the amount of suppression is likely to fluctuate. Thus, by creating a single DR scenario by gathering a plurality of consumers or devices, it is possible to reduce fluctuation in the amount of suppression of the DR scenario (relative variation with respect to the amount of suppression).

FIG. 18 is a flowchart showing details of the process (S1708 in FIG. 17) of creating a DR scenario for demand shift by the scenario creating unit 104D. Hereinafter, in order to simplify the description, the process will be described first in the case where the DR type of the customer information is “demand shift”. Further, assuming that a DR scenario for 24 hours is created at 0:00, “time” means one frame obtained by dividing one day into frames, and one frame = 1 hour. In addition, the suppression time zone, the arousal time zone, the reduced heat amount, and the arousal generated heat amount represent variables that store values used in the processing.

First, the scenario creation unit 104D acquires the corresponding customer and device heat storage plans from the heat storage plan DB 102G, and acquires the device characteristics of the corresponding electrical device from the device characteristics DB 102E (S1800).

Next, the scenario creation unit 104D sets a time zone from the plan start time to the plan end time of the heat storage plan as a suppression time zone (S1802).

Next, the scenario creation unit 104D sets the reduced heat amount to the same value as the planned generated heat amount of the heat storage plan (S1804). In order to shift the demand of the heat storage plan, the scenario creation unit 104D sets the time zone from the plan start time to the plan end time of the heat storage plan as the suppression time zone, and sets the planned heat generation amount of the heat storage plan to the reduced heat amount.

Next, the scenario creation unit 104D sets a calling pattern (S1806). The arousal pattern is the arousal time zone and the amount of heat generated. The awakening time zone does not overlap with the suppression time zone, and is set in the range from 0:00 to 24:00 with the same length as the suppression time zone. The amount of generated heat is set to the same value as the reduced amount of heat.

Next, the scenario creation unit 104D calculates a heat storage amount by the following formula (S1808). In the following equation, t is a time from 0:00 to 24:00. However, it is assumed that the heat storage amount at 0:00 is equal to the planned heat storage amount at 0:00.
Heat storage amount at time (t) =
Heat storage amount at time (t-1)-Predicted heat consumption at time (t) + Planned heat generation at time (t)-Reduction heat amount (however, it is zero except for the suppression time zone) + Arousal generation heat amount (However, arousal time zone Other than zero)

Next, the scenario creation unit 104D checks whether all possible arousing patterns have been calculated (S1810). If the calculation has been completed for all the awakening patterns, the process proceeds to the next step (S1812). Otherwise, the process returns to the setting of the awakening pattern (S1806).

Next, the scenario creation unit 104D checks whether or not the time series of the heat storage amount is within the range of the lower limit heat storage amount and the upper limit heat storage amount of the device characteristics at all times for each awakening pattern ( S1812). Hereinafter, at all times, the arousal pattern in which the heat storage amount is within the range of the lower limit heat storage amount and the upper limit heat storage amount is referred to as an appropriate arousal pattern.

Next, the scenario creation unit 104D checks whether there is one or more appropriate calling patterns (S1814). If there is 1 or more, the process proceeds to the next step (S1816). If it is 0, the process returns to the setting of reduced heat quantity (S1804), the reduced heat quantity is set to a smaller value, and the process is repeated.

Next, the scenario creation unit 104D calculates the suppression amount and the arousal amount according to the following formula from the device characteristics (relationship between the generated heat amount and the power consumption) for the appropriate arousal pattern (S1816). P in the following equation is a function for obtaining power consumption necessary for generating heat.
Suppression amount = P (predicted heat generation amount at time t) −P (predicted heat generation amount at time t−reduction heat amount)
Arousal amount = P (arousal generation heat quantity at time t)

Next, the scenario creation unit 104D determines the suppression amount and the arousal amount calculated in the previous step, the first time of the suppression time zone (suppression start time), the length of the suppression time zone (suppression duration), and the appropriate arousal pattern. The first time of the earliest arousal time zone (earliest awakening start time), the first time of the latest arousal time zone (latest arousal start time) of the appropriate arousal patterns, the length of the arousal time zone of the appropriate arousal pattern Is stored in the scenario DB 102C (S1818).

The above is the description of the process of creating the demand shift DR scenario by the scenario creation unit 104D. However, the process is not limited to the description of the above flowchart, but for the purpose of speeding up and sophisticating the process. It may be changed without departing from the spirit of the invention. For example, in the above, the suppression time zone is set to be the same as the time zone from the plan start time to the plan end time, but may be a partial time zone.

In the above, the amount of generated heat is set to the same value as the reduced amount of heat, but it may be set freely within the range allowed by the specifications of the electrical equipment. Although it was set to the same length as the length of the suppression time zone, it may be set to a different length. In the above description, the reduced heat amount and the arousal generated heat amount are implicitly assumed to be constant over time, but may be values that change with time.

In addition, in the above description, the relationship between the generated heat amount and power consumption of the device characteristic is stored in the device characteristic DB 102E. However, since this device characteristic changes depending on the temperature, the temperature of water entering the hot water storage tank, etc. Data such as air temperature and incoming water temperature may be acquired from the management system 200 or the like, and the power consumption may be calculated using more accurate device characteristics.

FIG. 19 is a flowchart showing details of the process (S1706 in FIG. 17) of creating a DR scenario for demand suppression by the scenario creation unit 104D. First, the scenario creation unit 104D calculates a common time zone between the time zone from the forecast start time of the demand forecast to the forecast end time and the time zone in which the customer information can be suppressed, and sets this as the time zone that can be suppressed. (S1900).

Next, the scenario creation unit 104D calculates the suppression amount by multiplying the predicted power consumption of the demand prediction by the suppression coefficient of the customer information (S1902).

Next, the scenario creation unit 104D stores the first time of the suppression possible time zone (suppression start time), the length of the suppression possible time zone (suppression duration), and the suppression amount in the scenario DB 102C.

The above is the description of the process of creating the demand-suppressing DR scenario by the scenario creation unit 104D. However, the process is not limited to the above flowchart, and the purpose is to increase the speed and sophistication of the process. You may change in the range which does not deviate from.

For example, the suppression time zone is not a common time zone between the forecast start time of the demand forecast and the prediction end time and the suppression time zone of the customer information, but as a part of the time zone Also good.

FIG. 20 is a flowchart showing the processing of the bid prediction unit 104A. First, the bid prediction unit 104A checks whether bid information can be acquired (S2000). For example, bid information cannot be obtained at a stage where the market of the target piece is not open. In addition, the tender status may not be disclosed by the boarding method. If so, the process ends. If not, the process proceeds to the next step (S2002).

Next, the bid prediction unit 104A acquires execution result data from the execution result DB 102A (S2002). Next, the bid prediction unit 104A predicts the bid price and bid amount of the selling bid in the market and the bid price and bid amount of the purchase bid (S2004). For example, it is assumed that the bid price and the bid amount of a certain frame on a certain day of a certain year are equal to the contract price and contract amount of the same frame on the same day of the previous year. Alternatively, the bid price and the bid amount may be predicted by a method such as regression analysis in consideration of the past fuel prices such as oil and gas and the operating status of the power plant. Such electric power price prediction logic is abundant in conventional techniques such as Patent Document 1, and therefore further description is omitted.

Next, the bid prediction unit 104A stores the predicted bid information in the bid information DB 102B (S2006).

FIG. 21 is a flowchart showing processing of the demand prediction unit 104E. The demand prediction unit 104E repeats the processing from step S2100 to step S2114 for each customer and electrical device.

First, the demand prediction unit 104E acquires operation result data from the energy management system 200 of the customer (S2102).

Next, the demand prediction unit 104E predicts the demand (prediction start time, prediction end time, predicted heat consumption, predicted power consumption) of the consumer's electrical equipment (S2104). For example, it is assumed that the predicted heat consumption and predicted power consumption at a certain time on a certain day have the same values as the predicted heat consumption and predicted power consumption at the same time on the same day of the week one week ago. Alternatively, the predicted heat consumption and the predicted power consumption may be predicted by a method such as regression analysis in consideration of weather conditions such as temperature and weather. Such demand prediction logic is abundant in conventional techniques such as Patent Document 1, and therefore, further description is omitted.

Next, the demand prediction unit 104E stores the demand prediction in the demand prediction DB 102F (S2106).

Next, the demand prediction unit 104E checks whether the device type is an HP water heater (S2108). If the device type is an HP water heater, the process proceeds to the next step (S2110), and if not, the process proceeds to the next consumer and device (S2114).

Next, the demand prediction unit 104E creates a heat storage plan (plan start time, plan end time, plan generation heat amount, plan heat storage amount) from the predicted heat consumption (S2110). For example, the heat storage amount is planned so that the heat storage amount of the hot water storage tank is minimized within the restriction range of the specifications of the electrical equipment. The logic for creating such a heat storage plan is abundant in conventional techniques such as Patent Document 3, and therefore further description is omitted.

Next, the demand prediction unit 104E stores the created heat storage plan in the heat storage plan DB 102G (S2112). Next, the demand prediction unit 104E moves to processing of the next consumer and device (S2100). When the process is performed for all customers and devices, the process ends.

FIG. 22 is a flowchart showing the overall processing of the negawatt trading support system 100. These processes are performed as a whole by the cooperation of the processing blocks. The processing from step S2200 to step S2212 is repeated at regular intervals.

In the repeated process, the process from step 2202 to step 2204 and the process of step 2206 are executed in parallel by branching into two processes. In the first branch process, the demand prediction unit 104E predicts the demand of the consumer (S2202). Next, the scenario creation unit 104D creates a DR scenario (S2204).

In the second branching process, the bid prediction unit 104A performs bid prediction (S2206). This is the branching process.

Next, the sales plan formulation unit 104C formulates a sales plan based on the DR scenario and the bid prediction (S2208). Next, the sales plan output unit 104B outputs the formulated sales plan (S2210). After a certain time, the process is repeated from steps S2202 and S2206 (S2212).

FIG. 23 is a flowchart showing processing of the risk evaluation unit 104F. However, the processes of the broken line portion 230 and the broken line portion 232 are performed in cooperation with other processing blocks.

The risk evaluation unit 104F repeats the processing from step 2302 to step 2310 a predetermined number of times (for example, 100 times) (S2300). In the iterative process, first, the risk evaluation unit 104F generates a random number and passes the random number to the demand prediction unit 104E and the bid prediction unit 104A (S2302).

Next, the bid prediction unit 104A predicts a bid using the random number passed from the risk evaluation unit 104F (S2308). For example, a random number is added to the bid price or the bid amount.

The demand prediction unit 104E predicts demand using the random number passed from the risk evaluation unit 104F (S2304). For example, a random number is added to the predicted heat consumption or the predicted start time.

Next, the scenario creation unit 104D creates a DR scenario based on the demand predicted by the demand prediction unit 104E (S2306). Note that steps S2304 and S2306, and step S2308 are two parallel processes.

Next, the sales plan formulation unit 104C formulates a sales plan based on the bid prediction and the DR scenario, and passes it to the risk evaluation unit 104F (S2310).

Next, the process returns to step 2302 and the process is repeated (S2312). When the process reaches the predetermined number of times, the risk evaluation unit 104F passes the established sales plan for the predetermined number of times to the sales plan output unit 104B, and proceeds to the next step (S2314).

Next, the sales plan output unit 104B creates a probability distribution of the sales plan balance for the predetermined number of sales plans, and outputs a graph to the screen (S2314). The above is the description of the first embodiment of the present invention.

The second embodiment of the present invention relates to negawatt buying and selling combined with gas equipment. FIG. 24 is a configuration example of consumer electric equipment and gas equipment according to the second embodiment of the present invention. The heat pump 1406 may be operated not only by the electricity supplied from the electric wire 1400 via the power receiving facility 1402 but also by the power of the gas engine 2402 that drives the gas supplied from the gas pipe 2400 as fuel. The fan heater 2404 is operated by the gas supplied from the gas pipe 2400. Other electrical devices have the same configuration as that of FIG.

In the case of negawatt buying and selling that uses both electric equipment and gas equipment, it is necessary to formulate a plan for trading in the gas market. However, since the system configuration and the like are basically the same as in the case of power, detailed description is omitted. Arbitrage transactions using the price difference between the electric power market and the gas market are also possible, and an optimal trading plan may be selected by combining both trading plans. The part of the inter-market transaction of different energy is already known and can be realized simply by combining with the system of the present invention, so detailed description will be omitted.

Also, it is possible to combine trading of other energy (for example, coal), but since it is repeated, detailed description is omitted. The above is the description of the second embodiment of the present invention.

The third embodiment of the present invention relates to the virtual market system 2500 of FIG. The virtual market system 2500 is used as a means for creating contract information or bid information that is an input of the negawatt trading support system 100 of the present invention, or as a means for executing a trading plan that is the output of the negawatt trading support system 100 of the present invention. .

One of the purposes of the virtual market system 2500 is to estimate a power trading market under a predetermined condition and to make a profit forecast of negawatt buying and selling. The predetermined condition is, for example, a case where the ratio of wind power generation is increased. Another purpose of the virtual market system 2500 is for the training of traders who place orders for negawatt buying and selling on the market.

FIG. 25 is a block diagram showing the configuration of the virtual market system 2500. The storage unit 2504 includes a bid performance DB 2504A, a bid estimation DB 2504B, and an environment information DB 2504C. The processing unit 2502 includes a bid estimation unit 2502A, a bid input unit 2502B, an approximate quantitative calculation (estimation) unit 2502C, and an approximate quantitative output unit 2502D.

The bid input unit 2502B executes a process of inputting a bid for negawatt buying and selling. The bid input unit 2502B can acquire negawatt trading information from the negawatt trading support system 100. Also, the bid input unit 2502B allows a trader to input negawatt buying and selling.

The bid estimation unit 2502A performs a process of estimating a virtual market bid. The bid estimation unit 2502A can output the estimated bid to the negawatt trading support system 100. Using the virtual bid information estimated by the virtual market system as an input, the negawatt trading support system can formulate a trading plan.

The approximate quantitative calculation unit 2502C performs a process of calculating an approximate quantitative value between the bid for negawatt buying and selling and the actual market bid or the virtual market bid.

The approximate quantitative output unit 2502D is a process for outputting the approximate quantitative value calculated by the approximate quantitative calculation unit 2502C.

The hardware configuration of the virtual market system 2500 is the same as the hardware configuration of the negawatt trading support system 100. In addition, data can be transmitted to and received from the negawatt trading support system 100 via a communication line 1310 such as the Internet.

FIG. 26 is an example of a configuration table of the bid performance DB 2504A, in which the delivery date, delivery frame, bid type, bid price, and bid amount are recorded for each bid date and bid time.

FIG. 27 is an example of a configuration table of the bid estimation DB 2504B, in which delivery date, delivery frame, bid type, bid price, and bid amount are recorded for each bid date and bid time.

FIG. 28 is a flowchart showing the processing of the approximately quantitative calculation unit 2502C. The approximately quantitative calculation unit 2502C repeatedly performs the processing from step S2800 to step S2812 at regular intervals. In the first iteration, the uncommitted bid (bidding time, delivery time, price, quantity) is initialized to zero.

In the iterative process, first, the approximately quantitative calculation unit 2502C branches the process of step S2802 and the process of step S2804 in parallel.

In Step 2802, the approximately quantitative calculation unit 2502C obtains the input bid information (bid time, delivery time, price, amount) input from the negawatt trading support system 100 or the trader from the bid input unit 2502B.

In step S2804, the approximate quantitative calculation unit 2502C acquires market bid information (bid time, delivery time, price, amount) from the bid performance DB 2504A or the bid estimation DB 2504B.

Next, the approximately quantitative calculation unit 2502C repeats the process of step S2808 for each bid of each input bid information and market bid information. In the iterative process, the contracted amount calculation unit 2502C calculates the contracted amount and the contract price 2502C from the uncommitted bid and the input bid information or the market bid information, subtracts the contracted bid from the uncommitted bid, The bid is updated (S2808). These calculations are the same as the normal Zaraba method. The fixed amount and the contract price are passed to the fixed amount output unit 2502D.

Next, the approximately quantitative calculation unit 2502C proceeds to the next step (SS2810) when all the bid processing is completed, and returns to another bid processing (S2806) if not yet completed (S2810).

Next, the approximately quantitative calculation unit 2502C repeatedly performs processing from the initial processing (S2800) after a predetermined time (S2812).

FIG. 29 is a flowchart showing the processing of the bid amount estimation unit 2502A. First, the bid amount estimation unit 2502A acquires environmental information (past weather conditions, power plant operation results, energy price, exchange price, etc.) from the environmental information DB 2504C (S2900).

Next, the bid amount estimation unit 2502A acquires a bid result (bid time, delivery time, price, amount) from the bid result DB 2504A (S2902).

Next, the bid amount estimation unit 2502A performs clustering (classification by class) on the acquired bid results based on the correlation between the environmental information and the bid results (S2904). This is a process of estimating which bid is made by which power generation entity. Since there are various conventional techniques for the clustering method itself, further description is omitted.

Next, the bid amount estimation unit 2502A adjusts bids based on the clustered result (S2906). For example, the number of bids of a class considered to be wind power generation among the clustered bid performance classes is increased.

Next, the bid amount estimation unit 2502A stores the adjusted bid in the bid estimation DB 2502A (S2908). The above is the description of the third embodiment of the present invention.

As described above, the negawatt buying and selling support system according to the present invention can predict profits and evaluate risks when buying and selling negawatts, formulate an optimum negawatt buying and selling plan, and support negawatt buying and selling. can do. In addition, it is possible to support negawatt trading including multiple energy sources such as gas. In addition, it is possible to estimate the power trading market under conditions such as when the ratio of wind power generation rises and make a profit forecast of negawatt buying and selling, as well as training of traders who place orders for negawatt buying and selling on the market It is possible to further support negawatt buying and selling such that it can be used for various purposes.

100: Negawatt trading support system, 102: Storage unit, 102A: Contract performance DB, 102C: Scenario DB, 102D: Customer DB, 102E: Equipment characteristic DB, 102E: Demand characteristic DB, 102F: Demand prediction DB, 102G: Heat storage Plan DB, 104: processing unit, 104A: bid prediction unit, 104B: sales plan output unit, 104D: scenario creation unit, 104E: demand prediction unit, 104F: risk evaluation unit

Claims (13)

1. A control device;
   A storage device,
   The storage device
   A demand forecast information storage area for storing energy demand forecast information;
   A bid information storage area for storing bid prediction information for the trading of negawatts of the energy;
   With
   The controller is
   Create a demand response scenario for the energy based on the demand forecast information,
   Formulate the negawatt trading plan based on the created scenario and the bid prediction information,
   Output the developed sales plan,
   Negawatt trading support system.
2. The controller is
   From the energy management system having the energy use device, obtain the operation result of the device, perform the energy demand prediction from the operation result, record the demand predicted information in the demand prediction information storage area,
   Based on the actual performance data of the negawatt trading, the bid information of the negawatt trading is predicted, and the predicted bid information is recorded in the bid information storage means.
   The negawatt trading support system according to claim 1.
3. The demand response scenario includes a type that suppresses energy demand and a type that shifts energy demand.
   For the type that shifts the energy demand, the control device
   Based on the energy use plan of the energy management system, set the energy suppression pattern of the energy management system,
   Set the energy awakening pattern of the energy management system,
   Based on the energy suppression pattern and the energy stimulation pattern, determine whether the current energy storage state of the energy management system is within a target range,
   The negawatt buying and selling support system according to claim 2, wherein the demand response scenario is created based on the energy suppression pattern and the energy arousing pattern for which the determination is affirmed.
4. The controller is
   The suppression pattern is set to include a suppression amount of energy and a suppression time period for suppressing the use of energy,
   The arousal pattern is set to include an arousal time zone that arouses the use of energy and an energy arousal amount,
   Set the time zone to be evoked so as not to overlap the time zone to be suppressed and the same length as the time zone to be suppressed,
   The negawatt buying and selling support system according to claim 3, wherein the arousal amount is set to the same value as the suppression amount.
5. The control device sets a plurality of the arousal patterns, selects an arousal pattern whose current energy storage state of the energy management system is within a target range, and creates a demand response scenario based on the arousal pattern The negawatt buying and selling support system according to claim 3 or 4.
6. The controller is
   Based on the demand response scenario and the bid prediction information, determine a plurality of negawatt trading patterns,
   For each trading pattern, estimate the contractual form of negawatt trading and calculate the balance of the contractual form,
   Determining the highest profit trading pattern having the highest profit among the plurality of trading patterns;
   Update the bid forecast information based on the contract form of the highest profit trading pattern,
   When there are a plurality of demand response scenarios, the determination of the highest profit trading pattern and the updating of the bid prediction information are sequentially repeated for the plurality of demand response scenarios based on the updated bid prediction information. ,
   2. The negawatt trading support system according to claim 1, wherein a specific trading pattern is selected for the plurality of demand response scenarios, the trading pattern is created as a trading plan, and the trading plan is output.
7. The negawatt buying and selling support system according to claim 6, wherein the control device sets a buying and selling pattern having a maximum balance as the specific buying and selling pattern.
8. The controller is
   Repeat the demand forecast and the bid information forecast multiple times,
   Create a demand response scenario based on each of multiple demand forecasts, create a sales plan based on each demand response scenario and each bid forecast information,
   The negawatt trading support system according to claim 2, wherein a balance distribution is created for the plurality of trading plans created to evaluate the risk of the trading plan.
9. The negative wattage buying and selling support system according to claim 8, wherein each of the generation of the demand forecast and the bid forecast is generated based on a generated random number.
10. Connected with a virtual market system for negawatt buying and selling under virtual conditions,
    2. The negawatt trading support system according to claim 1, wherein the control device receives bid prediction information estimated by the virtual market system and formulates the trading plan based on the received bid prediction information.
11. 11. The bid prediction information of the storage device is input to the virtual market system, and the virtual market system estimates a negotiated amount of negawatts and sales in the virtual market system based on the input bid prediction information. Negawatt trading support system.
12. Information constituting a demand response scenario for the energy is input to the energy management system,
    The control device acquires information constituting the inputted scenario from the energy management system,
    Formulate the negawatt trading plan based on the information constituting the acquired scenario and the bid prediction information,
    Output the developed sales plan,
    The negawatt buying and selling support system according to claim 2.
13. A control device, and a storage device, wherein the storage device stores a demand prediction information storage region for storing energy demand prediction information, and a bid information storage region for storing bid prediction information for the negawatt trading of the energy, A method for supporting transactions when a plurality of consumers' negawatts are collected and sold in the negawatt buying and selling market using a computer having
    The control device creates a demand response scenario for the energy based on the demand forecast information, formulates the negawatt trading plan based on the created scenario and the bid forecast information, And a step for outputting the developed sales plan, and a negative wattage sales support method.
    

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