JP2013247848A - Apparatus and method for predicting demand value - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a demand value prediction apparatus and a demand value prediction method which are capable of suppressing repetition of an alarm on state and an alarm off state.SOLUTION: A demand value prediction apparatus (demand monitoring unit 330) includes: a first power obtaining section for obtaining electric power supplied from a system; and a prediction section that obtains, as a sample value, electric energy obtained by the first power obtaining section, and predicts an integral power consumption on the basis of the obtained sample value. The number of the sample values used for predicting the integral power consumption, or an interval to obtain the sample value used for predicting the integral power consumption is changed.

Description

  The present invention relates to a demand value prediction apparatus and a demand value prediction method for predicting an integrated power amount supplied from a system during a predetermined period.

  In recent years, awareness of environmental considerations has increased, and techniques for reducing power consumption of loads have been proposed.

  By the way, although it greatly depends on the power situation in each country, for example, in Japan, the total power charge of a high-voltage power receiver is determined by a basic charge and a power charge. The basic charge is determined based on the integrated power amount (peak power amount) supplied from the system in the past predetermined period (for example, 30 minutes). On the other hand, the electric energy charge is determined based on the electric power consumption in the calculation target period. Therefore, it is preferable to control the power consumption of each load so that the integrated power amount does not exceed the predetermined power amount.

  Here, a technique has been proposed in which an alarm indicating that power consumption should be suppressed is presented to the user so that the integrated power amount supplied from the system in a predetermined period does not exceed the predetermined power amount. Specifically, based on the amount by which the amount of power supplied from the system increases every unit time (hereinafter, “unit time increase amount”), the integrated power amount at the end of the predetermined period is predicted, and the predicted integration When the amount of power exceeds a predetermined amount of power, an alarm indicating that power consumption should be suppressed is presented to the user (for example, Patent Document 1).

JP-A-10-198875

  In the above-described technique, the integrated power amount at the end of the predetermined period is predicted based on the unit time increase amount. That is, the difference between the integrated power amounts at two timings (unit time increase amount) is used. Therefore, when the fluctuation of the unit time increase amount is large, the state where the alarm is presented (alarm on state) and the state where the alarm is released (alarm off state) are repeated.

  Therefore, the present invention has been made to solve the above-described problem, and provides a demand value prediction device and a demand value prediction method that can suppress repetition of an alarm-on state and an alarm-off state. For the purpose.

  The demand value prediction apparatus according to the first feature predicts the integrated power amount supplied from the system during a predetermined period. The demand value prediction device acquires, as a sample value, a first power acquisition unit that acquires power supplied from the grid and a power amount acquired by the first power acquisition unit, and based on the acquired sample value And a predicting unit for predicting the integrated power amount. The prediction unit changes the number of sample values or an interval for acquiring the sample values.

  In the first feature, the prediction unit changes the number of sample values or the interval for acquiring the sample values based on the type of load connected to the system.

  In the first feature, the demand value prediction apparatus includes a second power acquisition unit that acquires power consumed by a load connected to the system. The prediction unit changes the number of the sample values or the interval for acquiring the sample values based on the fluctuation of the power acquired by the second power acquisition unit.

  In the first feature, the prediction unit predicts the integrated power amount by using three or more sample values as the sample value. The prediction unit specifies a slope indicating a change in the integrated power amount based on a least square method or a moving average method.

  The demand value prediction method according to the second feature is a method of predicting the integrated power amount supplied from the system during a predetermined period. The demand value prediction method includes step A for acquiring electric power supplied from the grid, acquiring the electric energy acquired in step A as a sample value, and calculating the integrated electric energy based on the acquired sample value. And B for changing the number of sample values or the interval for obtaining the sample values.

  According to the present invention, it is possible to provide a demand value prediction device and a demand value prediction method that can suppress repetition of an alarm on state and an alarm off state.

FIG. 1 is a diagram illustrating an energy management system 100 according to the first embodiment. FIG. 2 is a diagram illustrating the customer 10 according to the first embodiment. FIG. 3 is a diagram for explaining an application scene of the first embodiment. FIG. 4 is a diagram illustrating the EMS 200 according to the first embodiment. FIG. 5 is a diagram showing the presentation information 400 according to the first embodiment. FIG. 6 is a diagram for explaining the normal prediction mode according to the first embodiment. FIG. 7 is a diagram for explaining the normal prediction mode according to the first embodiment. FIG. 8 is a diagram for explaining the relaxation prediction mode according to the first embodiment. FIG. 9 is a diagram for explaining the relaxation prediction mode according to the first embodiment. FIG. 10 is a flowchart showing the demand value prediction method according to the first embodiment. FIG. 11 is a flowchart illustrating the demand value prediction method according to the first modification.

  Hereinafter, a demand value prediction apparatus and a demand value prediction method according to an embodiment of the present invention will be described with reference to the drawings. In the following description of the drawings, the same or similar parts are denoted by the same or similar reference numerals.

  However, it should be noted that the drawings are schematic and ratios of dimensions and the like are different from actual ones. Therefore, specific dimensions and the like should be determined in consideration of the following description. Moreover, it is a matter of course that portions having different dimensional relationships and ratios are included between the drawings.

[Outline of Embodiment]
The demand value prediction apparatus according to the embodiment predicts the integrated power amount supplied from the system in a predetermined period. The demand value prediction device acquires, as a sample value, a first power acquisition unit that acquires power supplied from the grid and a power amount acquired by the first power acquisition unit, and based on the acquired sample value And a predicting unit for predicting the integrated power amount. The prediction unit changes the number of sample values or an interval for acquiring the sample values.

  In the embodiment, the prediction unit changes the number of sample values used for prediction of the integrated power amount or the interval for acquiring the sample values used for prediction of the integrated power amount. By appropriately controlling the number of sample values or the interval at which the sample values are acquired, repetition of the alarm-on state and the alarm-off state is suppressed.

[First Embodiment]
(Energy management system)
Hereinafter, the energy management system according to the first embodiment will be described. FIG. 1 is a diagram illustrating an energy management system 100 according to the first embodiment.

  As shown in FIG. 1, the energy management system 100 includes a customer 10, a CEMS 20, a substation 30, a smart server 40, and a power plant 50. The customer 10, the CEMS 20, the substation 30 and the smart server 40 are connected by a network 60.

  The consumer 10 includes, for example, a power generation device and a power storage device. The power generation device is a device that outputs electric power using fuel gas, such as a fuel cell. The power storage device is a device that stores electric power, such as a secondary battery.

  The consumer 10 is a store such as a convenience store or a supermarket. The consumer 10 may be a single-family house, an apartment house such as a condominium, a commercial facility such as a building, or a factory.

  In the first embodiment, a customer group 10 </ b> A and a customer group 10 </ b> B are configured by a plurality of consumers 10. The consumer group 10A and the consumer group 10B are classified by, for example, a geographical area.

  The CEMS 20 controls interconnection between the plurality of consumers 10 and the power system. The CEMS 20 may be referred to as a CEMS (Cluster / Community Energy Management System) in order to manage a plurality of consumers 10. Specifically, the CEMS 20 disconnects between the plurality of consumers 10 and the power system at the time of a power failure or the like. On the other hand, the CEMS 20 interconnects the plurality of consumers 10 and the power system when power is restored.

  In the first embodiment, a CEMS 20A and a CEMS 20B are provided. For example, the CEMS 20A controls interconnection between the customer 10 included in the customer group 10A and the power system. For example, the CEMS 20B controls interconnection between the customer 10 included in the customer group 10B and the power system.

  The substation 30 supplies electric power to the plurality of consumers 10 via the distribution line 31. Specifically, the substation 30 steps down the voltage received from the power plant 50.

  In the first embodiment, a substation 30A and a substation 30B are provided. For example, the substation 30A supplies power to the consumers 10 included in the consumer group 10A via the distribution line 31A. For example, the substation 30B supplies power to the consumers 10 included in the consumer group 10B via the distribution line 31B.

  The smart server 40 manages a plurality of CEMSs 20 (here, CEMS 20A and CEMS 20B). The smart server 40 also manages a plurality of substations 30 (here, the substation 30A and the substation 30B). In other words, the smart server 40 comprehensively manages the customers 10 included in the customer group 10A and the customer group 10B. For example, the smart server 40 has a function of balancing the power to be supplied to the consumer group 10A and the power to be supplied to the consumer group 10B.

  The power plant 50 generates power using thermal power, wind power, hydraulic power, nuclear power, and the like. The power plant 50 supplies power to the plurality of substations 30 (here, the substation 30A and the substation 30B) via the power transmission line 51.

  The network 60 is connected to each device via a signal line. The network 60 is, for example, the Internet, a wide area network, a narrow area network, a mobile phone network, or the like.

(Customer)
Below, the consumer which concerns on 1st Embodiment is demonstrated. FIG. 2 is a diagram illustrating details of the customer 10 according to the first embodiment.

  As shown in FIG. 2, the customer 10 includes a distribution board 110, a load 120, a PV unit 130, a storage battery unit 140, a fuel cell unit 150, a hot water storage unit 160, and an EMS 200.

  Distribution board 110 is connected to distribution line 31 (system). Distribution board 110 is connected to load 120, PV unit 130, storage battery unit 140, and fuel cell unit 150 via a power line.

  The load 120 is a device that consumes power supplied via a power line. For example, the load 120 includes devices such as a refrigerator, a freezer, lighting, and an air conditioner.

  The PV unit 130 includes a PV 131 and a PCS 132. The PV 131 is an example of a power generation device, and is a solar power generation device that generates power in response to reception of sunlight. The PV 131 outputs the generated DC power. The amount of power generated by the PV 131 changes according to the amount of solar radiation applied to the PV 131. The PCS 132 is a device (Power Conditioning System) that converts DC power output from the PV 131 into AC power. The PCS 132 outputs AC power to the distribution board 110 via the power line.

  In the first embodiment, the PV unit 130 may have a pyranometer that measures the amount of solar radiation irradiated on the PV 131.

  The PV unit 130 is controlled by an MPPT (Maximum Power Point Tracking) method. Specifically, the PV unit 130 optimizes the operating point (a point determined by the operating point voltage value and the power value, or a point determined by the operating point voltage value and the current value) of the PV 131.

  The storage battery unit 140 includes a storage battery 141 and a PCS 142. The storage battery 141 is a device that stores electric power. The PCS 142 is a device (Power Conditioning System) that converts AC power supplied from the distribution line 31 (system) into DC power. Further, the PCS 142 converts DC power output from the storage battery 141 into AC power.

  The fuel cell unit 150 includes a fuel cell 151 and a PCS 152. The fuel cell 151 is an example of a power generation device, and is a device that outputs electric power using fuel gas. The PCS 152 is a device (Power Conditioning System) that converts DC power output from the fuel cell 151 into AC power.

  The fuel cell unit 150 operates by load following control. Specifically, the fuel cell unit 150 controls the fuel cell 151 so that the power output from the fuel cell 151 becomes the target power for load following control.

  The hot water storage unit 160 is an example of a heat storage device that converts electric power into heat, accumulates heat, and stores heat generated by a cogeneration device such as the fuel cell unit 150 as hot water. Specifically, the hot water storage unit 160 has a hot water storage tank, and warms water supplied from the hot water storage tank by exhaust heat generated by the operation (power generation) of the fuel cell 151. Specifically, the hot water storage unit 160 warms the water supplied from the hot water storage tank and returns the warmed hot water to the hot water storage tank.

  The EMS 200 is an apparatus (Energy Management System) that controls the PV unit 130, the storage battery unit 140, the fuel cell unit 150, and the hot water storage unit 160. Specifically, the EMS 200 is connected to the PV unit 130, the storage battery unit 140, the fuel cell unit 150, and the hot water storage unit 160 via signal lines, and the PV unit 130, the storage battery unit 140, the fuel cell unit 150, and the hot water storage unit. 160 is controlled. The EMS 200 controls the power consumption of the load 120 by controlling the operation mode of the load 120.

  The EMS 200 is connected to various servers via the network 60. Various servers store, for example, information (hereinafter referred to as energy charge information) such as the unit price of power supplied from the grid, the unit price of power received from the grid, and the unit price of fuel gas.

  Or various servers store the information (henceforth energy consumption prediction information) for predicting the power consumption of the load 120, for example. The energy consumption prediction information may be generated based on, for example, the past power consumption actual value of the load 120. Alternatively, the energy consumption prediction information may be a model of power consumption of the load 120.

  Or various servers store the information (henceforth PV power generation amount prediction information) for predicting the power generation amount of PV131, for example. The PV power generation prediction information may be a predicted value of the amount of solar radiation irradiated on the PV 131. Alternatively, the PV power generation prediction information may be weather forecast, season, sunshine time, and the like.

(Applicable scene)
Below, the application scene of 1st Embodiment is demonstrated. FIG. 3 is a diagram for explaining an application scene of the first embodiment. In FIG. 3, the flow of information in the customer 10 will be mainly described.

  As shown in FIG. 3, the customer 10 includes a system power meter 310, a demand measurement unit 320, a demand monitoring unit 330, a load power meter 340, a smart sensor 350, and a hub 360. As described above, the customer 10 has the EMS 200.

  The system power meter 310 measures the power supplied from the distribution line 31 (system). Specifically, the grid power meter 310 is provided on the distribution line 31 (grid) side of the distribution board 110 and measures the power supplied to the entire consumer 10.

  The demand measurement unit 320 integrates the power measured by the system power meter 310 in a predetermined period (for example, 30 minutes). In other words, the demand measurement unit 320 integrates the electric power measured by the system wattmeter 310 from the start of the predetermined period until the expiration of the predetermined period. That is, the demand measurement unit 320 resets the integrated value (integrated power amount) every predetermined period.

  The demand monitoring unit 330 acquires the integrated value (integrated electric energy) acquired from the demand measuring unit 320 as a sample value, and based on the acquired sample value, the predicted value of the integrated electric energy at the expiration time of the predetermined period is obtained. It is monitored whether or not a predetermined amount of power is exceeded. Specifically, the demand monitoring unit 330 acquires the power amount measured by the system power meter 310 as a sample value, and predicts the integrated power amount based on the actual value of the acquired sample value. Or the demand monitoring unit 330 acquires the electric energy measured by the system power meter 310 as a sample value, and predicts the integrated electric energy based on the obtained fluctuation amount of the sample value. That is, in the first embodiment, the demand monitoring unit 330 constitutes a first power acquisition unit and a prediction unit.

  Here, in the first half (first timing) of the predetermined period, the demand monitoring unit 330 acquires the electric energy measured by the system wattmeter 310 as a sample value, and integrates it based on the actual value of the acquired sample value. It is preferable to predict the amount of power. The demand monitoring unit 330 acquires the electric energy measured by the system wattmeter 310 as a sample value in the second half of the predetermined period (second timing), and calculates the integrated electric energy based on the obtained fluctuation amount of the sample value. It is preferable to predict.

  For example, the first half of the predetermined period is a period from the start of the predetermined period to ½ of the predetermined period, and the second half of the predetermined period is a period from ½ of the predetermined period to the expiration of the predetermined period. Alternatively, the first half of the predetermined period is a period from the start of the predetermined period to 2/3 of the predetermined period, and the second half of the predetermined period is a period from 2/3 of the predetermined period to the expiration of the predetermined period. The timing for dividing the first half and the second half may be any timing within a predetermined period.

  In the first embodiment, the demand monitoring unit 330 changes the number of sample values used for prediction of the integrated power amount at the end of the predetermined period. Alternatively, the demand monitoring unit 330 changes the interval at which the sample value used for prediction of the integrated power amount at the end of the predetermined period is acquired.

  Specifically, the demand monitoring unit 330 changes the number of sample values or the interval at which sample values are acquired based on the type of load connected to the grid. Specifically, when the demand monitoring unit 330 determines that the variation in the power consumption of the load connected to the grid is larger than a predetermined variation based on the type of load connected to the grid, the demand monitoring unit 330 Increase the number. For example, the demand monitoring unit 330 increases the number of sample values when the proportion of the load connected to the grid with the load whose power consumption fluctuation amount exceeds a predetermined amount is larger than the predetermined proportion. The demand monitoring unit 330 may increase the number of sample values when the average value or variance value of the fluctuation amount of the power consumption of the load connected to the grid is larger than a predetermined ratio. Alternatively, the demand monitoring unit 330 acquires the sample value when it is determined that the fluctuation of the power consumption of the load connected to the grid is larger than the predetermined fluctuation based on the type of the load connected to the grid. Increase. For example, the demand monitoring unit 330 increases the interval at which the sample value is acquired when the ratio of the load connected to the grid to the load whose power consumption fluctuation amount exceeds a predetermined amount is larger than the predetermined ratio. The demand monitoring unit 330 may increase the interval for acquiring the sample value when the average value or the variance value of the fluctuation amount of the power consumption of the load connected to the grid is larger than a predetermined ratio.

  Alternatively, the demand monitoring unit 330 uses three or more sample values as sample values to predict the integrated power amount at the end of the predetermined period. The demand monitoring unit 330 specifies an inclination indicating a change in the integrated power amount based on the least square method or the moving average method. For example, when the fluctuation in power consumption of a load connected to the grid is smaller than a predetermined fluctuation, the demand monitoring unit 330 specifies the slope indicating the change in the integrated power amount by the least square method using three sample values. On the other hand, when the fluctuation of the power consumption of the load connected to the system is larger than the predetermined fluctuation, the demand monitoring unit 330 specifies the slope indicating the change in the integrated power amount by the least square method using five sample values. .

  The demand monitoring unit 330 transmits information indicating that the predicted value of the integrated power amount exceeds the predetermined power amount to the EMS 200 when the predicted value of the integrated power amount at the end of the predetermined period exceeds the predetermined power amount.

The load power meter 340 is provided along with each load 120 and measures the power consumed by each load 120. In the first embodiment, as the load power meter 340, a second power meter 340A ₁ ~340A _n and the second power system 340B ₁ ~340B _n are provided. The second power meter 340A ₁ ~340A _n, are connected to the power line A provided under the circuit breaker A distribution board 110, a second power system 340B ₁ ~340B _n is the distribution panel 110 of the breaker B It is connected to a power line B provided thereunder.

The smart sensor 350 collects power measured by a load power meter 340 provided under the smart sensor 350. In the first embodiment, as the smart sensor 350, a smart sensor 350A and a smart sensor 350B are provided. Smart sensors 350A collects the power measured by the second power meter 340A ₁ ~340A _n. The smart sensor 350B collects power measured by the second power meters 340B _{1 to} 340B _n .

  The smart sensor 350 transmits information indicating the power measured by each load power meter 340 to the EMS 200 together with the identifier of each load power meter 340. Alternatively, the smart sensor 350 transmits information indicating the total value of power measured by the load power meter 340 to the EMS 200.

  The hub 360 is connected to the EMS 200, the demand monitoring unit 330, and the smart sensor 350 via signal lines. The hub 360 relays information output from the demand monitoring unit 330 and the smart sensor 350 to the EMS 200.

(Configuration of EMS)
Hereinafter, the EMS of the first embodiment will be described. FIG. 4 is a block diagram showing the EMS 200 of the first embodiment.

  As illustrated in FIG. 4, the EMS 200 includes a reception unit 210, a transmission unit 220, a control unit 230, and a presentation unit 240.

  The receiving unit 210 receives various signals from a device connected via a signal line. For example, the receiving unit 210 receives information indicating that the predicted value of the integrated power amount exceeds a predetermined power amount from the demand monitoring unit 330. The receiving unit 210 receives information indicating the power measured by each load power meter 340 from the smart sensor 350 together with the identifier of each load power meter 340. Alternatively, the reception unit 210 may receive information indicating the power collected by the smart sensor 350 from the smart sensor 350.

  In the first embodiment, the receiving unit 210 may receive information indicating the power generation amount of the PV 131 from the PV unit 130. The receiving unit 210 may receive information indicating the storage amount of the storage battery 141 from the storage battery unit 140. The receiving unit 210 may receive information indicating the power generation amount of the fuel cell 151 from the fuel cell unit 150. The receiving unit 210 may receive information indicating the amount of hot water stored in the hot water storage unit 160 from the hot water storage unit 160.

  In the first embodiment, the reception unit 210 may receive energy charge information, energy consumption prediction information, and PV power generation amount prediction information from various servers via the network 60. However, the energy fee information, the energy consumption prediction information, and the PV power generation amount prediction information may be stored in the EMS 200 in advance.

  The transmission unit 220 transmits various signals to a device connected via a signal line. For example, the transmission part 220 transmits the signal for controlling the PV unit 130, the storage battery unit 140, the fuel cell unit 150, and the hot water storage unit 160 to each apparatus. The transmission unit 220 transmits a control signal for controlling the load 120 to the load 120.

  The control unit 230 controls the load 120, the PV unit 130, the storage battery unit 140, the fuel cell unit 150, and the hot water storage unit 160.

  In the first embodiment, the control unit 230 generates a load list including the power consumption of the load. The list of loads may be a list that is regularly presented, or a list that is presented when the predicted value of the integrated power amount exceeds a predetermined power amount.

  Specifically, the control unit 230 generates a load list based on the power measured by each load wattmeter 340. The list of loads includes, for example, at least a load name and load power consumption. The list of loads may include the amount of power consumption variation in addition to these pieces of information.

  For example, the control unit 230 displays a list of loads whose power consumption should be suppressed when the predicted value of the integrated power amount predicted based on the actual power value measured by the system power meter 310 exceeds a predetermined power amount. Generated in the first mode. On the other hand, when the predicted value of the integrated power amount predicted based on the fluctuation amount of power measured by the system power meter 310 exceeds the predetermined power amount, the control unit 230 is a list of loads whose power consumption should be suppressed. Is generated in a second manner.

  The first mode is a mode in which a list of loads is presented in descending order of the actual power value acquired by the load power meter 340. The second mode is a mode in which a list of loads is presented in descending order of the amount of power fluctuation acquired by the load wattmeter 340.

  Here, the control unit 230 generates the list so that the replacement frequency of the loads included in the list is low.

  For example, the control unit 230 generates a list including a predetermined number of loads in descending order of power consumption, or a list including a predetermined number of loads in order of increasing power consumption among the loads connected to the system. May be generated.

  Alternatively, the control unit 230 generates a list in which a predetermined number of loads are highlighted in descending order of the power consumption among the loads connected to the system, or increases the power consumption among the loads connected to the system. A list may be generated in which a predetermined number of loads are highlighted in descending order.

  Alternatively, the list update interval in the first half (first period) of the predetermined period may be longer than the list update interval in the second half (second period) of the predetermined period.

  The presentation unit 240 presents various information to the user. Specifically, the presentation unit 240 is a display that displays each piece of information. However, the presentation unit 240 may be a speaker that outputs each information as a sound.

  Here, when presenting the list, the presenting unit 240 may present the list with an application or browser that acquires the power consumption of the load 120.

  In the first embodiment, the presentation unit 240 displays, for example, the presentation information 400 illustrated in FIG. The presentation information 400 includes date and time information 410, state summary information 420, state detailed information 430, state legend information 440, link information 450, equipment fluctuation list 460, and energy saving action list 470.

  The date information 410 is information indicating the current date.

  The state summary information 420 is information indicating a summary of the state of power supplied from the system during the current predetermined period. The state summary information 420 is represented by, for example, four levels (margin, caution, warning, danger).

  The detailed state information 430 is information indicating details of the state of power received from the system during the current predetermined period. The detailed state information 430 includes, for example, a target demand value, a predicted demand value, and excess power. The target demand value is a target value of electric power supplied from the system during a predetermined period. The predicted demand value is a predicted value of the integrated power amount predicted by the demand monitoring unit 330 described above. Excess power is the amount of power for which the predicted demand value exceeds the target demand value. The unit of the demand value is kW / h.

  The state legend information 440 is information indicating the legend of the state summary information 420. The state legend information 440 includes, for example, a threshold value of each stage (margin, caution, warning, danger), a color expressing each stage, and the like.

  The link information 450 is information indicating various types of information that can be switched from the presentation information 400 (demand monitoring graph, demand recording / day, demand recording / month, facility power visualization TOP). The “demand monitoring graph” is, for example, a graph shown in FIG. “Demand recording / day” and “demand recording / month” are the total results of past histories. “Facility power visualization TOP” is a top page corresponding to the highest hierarchy of information that can be presented by the presentation information 400. By selecting (clicking) the link information 450, the information presented by the presentation unit 240 is switched to the selected information.

  The equipment fluctuation list 460 is a list of loads that are regularly presented. The equipment fluctuation list 460 includes, for example, the name of the load and the power consumption of the load.

  Here, the equipment fluctuation list 460 may be a list including a predetermined number of loads in descending order of power consumption, and among the loads connected to the grid, the predetermined number of loads are highlighted in descending order of power consumption. It may be a list.

  The energy saving action list 470 is a list that is presented when the predicted value of the integrated power amount exceeds the predetermined power amount. The energy saving action list 470 is an example of an alarm indicating a list of loads whose power consumption should be suppressed.

  In the first embodiment, the energy saving action list 470 is presented in the first mode or the second mode. As described above, the first mode is a mode in which a list of loads is presented in descending order of the actual power value acquired by the load power meter 340. The second mode is a mode in which a list of loads is presented in descending order of the amount of power fluctuation acquired by the load wattmeter 340.

  Here, when the energy saving action list 470 is presented in the first mode, the energy saving action list 470 may be a list including a predetermined number of loads in descending order of power consumption. Of these, a list in which a predetermined number of loads are highlighted in descending order of power consumption may be used. On the other hand, when the energy saving action list 470 is presented in the second mode, the energy saving action list 470 is a list including a predetermined number of loads in order of increasing power consumption among the loads connected to the grid. There may be a list in which, among the loads connected to the grid, a predetermined number of loads are highlighted in descending order of power consumption.

(Normal prediction mode)
Hereinafter, the normal prediction mode according to the first embodiment will be described. FIG. 6 is a diagram for explaining the normal prediction mode according to the first embodiment. FIG. 6 is a diagram illustrating a demand monitoring graph according to the first embodiment. In the normal prediction mode, for example, the integrated power amount is predicted using two sample values.

  As shown in FIG. 6, the demand monitoring graph includes an integrated value (integrated power amount) of power supplied from the system at the current date and time included in a predetermined period (for example, 30 minutes). In detail, the actual value of the integrated electric energy is indicated by a solid line, and the predicted value of the integrated electric energy is indicated by a dotted line.

  The demand monitoring graph includes a target power amount and a limit power amount as predetermined power. The demand monitoring graph may include a target power amount standard line because the integrated power amount becomes the target power amount when the predetermined period expires. The demand monitoring graph may include a limit power amount standard line because the integrated power amount becomes the limit power amount when the predetermined period expires. The demand monitoring graph may include a predicted value (predicted demand value) of the accumulated power amount at the time of expiration of the predetermined period.

Here, a case _where the accumulated power amount is predicted at time tn will be described. The accumulated power amount at time t _n is W _n , and the accumulated power amount at time t _n−1 is W _n−1 .

  In a case where the integrated power amount is predicted based on the actual value, the slope of the predicted value of the integrated power amount is represented by an approximate straight line of the actual value at each timing. The predicted value of the integrated electric energy at the end of the predetermined period is represented by “Y / X × 0.5”. Y / X is the slope of the approximate line.

That is, the demand monitoring unit 330 determines that the predicted value of the integrated power amount exceeds the predetermined power amount when “Y / X × 0.5” exceeds the target power amount (or the limit power amount). Alternatively, the demand monitoring unit 330 determines that the predicted value of the integrated power amount exceeds the predetermined power amount when “W _n ” exceeds the target power amount standard line (or the limit power amount standard line). Also good.

In a case where the integrated power amount is predicted based on the fluctuation amount, the gradient of the predicted value of the integrated power amount is represented by “(W _n −W _n−1 ) / t _n −t _n−1 )”. The predicted value of the integrated electric energy at the end of the predetermined period is “W _n + {(W _n −W _n−1 ) / (t _n −t _n−1 )} × (0.5−t _n )”. expressed.

That is, the demand monitoring unit 330 indicates that “W _n + {(W _n −W _n−1 ) / (t _n −t _n−1 )} × (0.5−t _n )” is the target power amount (or When it exceeds the limit power amount), it is determined that the predicted value of the integrated power amount exceeds the predetermined power amount.

  Here, the predicted demand value is obtained from the predicted value of the integrated power amount predicted by the demand monitoring unit 330 described above. The unit of the predicted demand value is kW / h.

  As described above, in the normal prediction mode, the variation in the power consumption of the load is reflected in real time on the predicted value of the integrated power amount at the end of the predetermined period. Therefore, when the variation in power consumption of the load is large, the alarm on state and the alarm off state are repeated as shown in FIG. For example, in a case where the flyer is repeatedly turned on / off, switching between the alarm on state and the alarm off state frequently occurs.

(Relaxation prediction mode)
Hereinafter, the relaxation prediction mode according to the first embodiment will be described. FIG. 8 is a diagram for explaining the relaxation prediction mode according to the first embodiment. FIG. 8 shows a demand monitoring graph as in FIG. In the relaxation prediction mode, for example, the integrated power amount is predicted using three sample values.

  As shown in FIG. 8, the demand monitoring graph includes an integrated value (integrated power amount) of power supplied from the system at the current date and time included in a predetermined period (for example, 30 minutes). In detail, the actual value of the integrated electric energy is indicated by a solid line, and the predicted value of the integrated electric energy is indicated by a dotted line.

Here, a case _where the accumulated power amount is predicted at time tn will be described. Integral power consumption at time _{t n} is the _{W n,} an integral power consumption is _{W n-1} at time _{t n-1,} the integral power consumption at time _{t n-2} is _{W n-2.}

  In such a case, the gradient of the predicted value of the integrated electric energy is determined as follows when the least square method is used. Specifically, α in the following equation (1) is determined so that the value of “J” shown in the following equation (2) is minimized. α is the slope of the predicted value of the integrated electric energy.

これによって、所定期間の満了時点の積算電力量の予測値は、“Ｗ_ｎ＋α×（０．５−ｔ_ｎ）”で表される。

Thereby, the predicted value of the integrated electric energy at the end of the predetermined period is represented by “W _n + α × (0.5−t _n )”.

That is, when “W _n + α × (0.5−t _n )” exceeds the target power amount (or the limit power amount), the demand monitoring unit 330 causes the predicted value of the integrated power amount to exceed the predetermined power amount. Judge.

  As described above, in the relaxation prediction mode, the fluctuation of the power consumption of the load is gently reflected on the predicted value of the integrated power amount at the end of the predetermined period. Therefore, even when the variation in the power consumption of the load is large, the alarm-on state and the alarm-off state are not repeated as shown in FIG. For example, in a case where the flyer is repeatedly turned on and off, frequent switching between the alarm on state and the alarm off state is suppressed.

  Here, as an example of the relaxation prediction mode, a method for specifying the gradient of the predicted value of the integrated power amount based on the least square method using three sample values has been described. However, in the relaxed prediction mode, the slope of the predicted value of the integrated power amount may be specified based on the least square method using four or more sample values. Alternatively, in the relaxed prediction mode, the slope of the predicted value of the integrated power amount may be specified based on a moving average method using three or more sample values. Alternatively, sample values may be acquired in the relaxed prediction mode at longer intervals than in the normal prediction mode.

(Demand value prediction method)
The demand value prediction method according to the first embodiment will be described below. FIG. 10 is a flowchart showing the demand value prediction method according to the first embodiment. The flow shown in FIG. 10 is performed at a list update interval (for example, 1 minute). Here, the list update interval (for example, 5 minutes) in the first half (first period) of the predetermined period may be longer than the list update interval (for example, 1 minute) in the second half (second period) of the predetermined period. preferable. Alternatively, the update interval of the list may be shortened as the predetermined period expires.

  As shown in FIG. 10, in step 10, each load power meter 340 measures the power consumed by the load 120. The smart sensor 350 collects power measured by a load power meter 340 provided under the smart sensor 350. The EMS 200 acquires information indicating the power measured by each load power meter 340 from the smart sensor 350.

  In step 20, the EMS 200 updates the information presented by the presentation unit 240. Specifically, EMS 200 updates information (here, equipment fluctuation list 460) presented by presentation unit 240 based on the power measured by each load wattmeter 340.

  In step 30, the grid power meter 310 measures the power supplied from the distribution line 31 (grid). The demand measurement unit 320 integrates the power measured by the system power meter 310 in a predetermined period (for example, 30 minutes). The demand monitoring unit 330 acquires the integrated value (integrated power amount) from the demand measuring unit 320.

  In step 35, the demand measurement unit 320 determines whether or not the fluctuation of the power consumption of the load connected to the grid is smaller than the predetermined fluctuation. If the variation in power consumption is smaller than the predetermined variation, the process of step 40A is performed (YES). On the other hand, when the fluctuation of the power consumption is larger than the predetermined fluctuation, the process of step 40B is performed (NO).

  In step 40A, the demand monitoring unit 330 applies the normal prediction mode, and predicts the predicted value of the integrated power amount at the expiration of the predetermined period based on the integrated value (integrated power amount) acquired from the demand measurement unit 320. To do.

  In step 40B, the demand monitoring unit 330 applies the relaxation prediction mode and predicts the predicted value of the integrated power amount at the expiration of the predetermined period based on the integrated value (integrated power amount) acquired from the demand measurement unit 320. To do.

  That is, the demand monitoring unit 330 predicts a predicted value of the integrated power amount at the expiration of the predetermined period, using a larger number of sample values than in the normal prediction mode. Alternatively, the demand monitoring unit 330 predicts the predicted value of the integrated power amount at the time when the predetermined period expires, using the sample values acquired at intervals longer than in the normal prediction mode.

  In step 50, the demand monitoring unit 330 determines whether or not the predicted value of the integrated power amount exceeds a predetermined power (target power amount or limit power amount). If the determination result is “YES”, the process of step 60 is performed. If the determination result is “NO”, the process of step 70 is performed.

  In step 60, the EMS 200 updates the information presented by the presentation unit 240. Specifically, the EMS 200 updates the information (here, the energy saving action list 470) presented by the presentation unit 240 based on the predicted value of the integrated power amount (that is, the power measured by the system power meter 310). To do.

  Specifically, the EMS 200 is a list of loads whose power consumption should be suppressed when the predicted value of the integrated power amount predicted based on the actual power value acquired by the system power meter 310 exceeds a predetermined power amount. Is presented in a first manner. For example, the EMS 200 presents a list of loads as the energy saving action list 470 in descending order of the actual power value acquired by the load wattmeter 340 in the first half of the predetermined period (first timing).

  On the other hand, EMS 200 displays a list of loads whose power consumption should be suppressed when the predicted value of the integrated power amount predicted based on the power fluctuation amount acquired by system power meter 310 exceeds a predetermined power amount. Presented in two ways. For example, the EMS 200 presents a list of loads as the energy saving action list 470 in descending order of the amount of power fluctuation acquired by the load wattmeter 340 in the second half of the predetermined period (second timing).

  In step 70, the demand measurement unit 320 determines whether or not a predetermined period has elapsed. If the determination result is “YES”, the process of step 80 is performed. If the determination result is “NO”, the series of processing ends.

  In step 80, the demand measurement unit 320 resets the integrated value (integrated power amount) of the power measured by the grid wattmeter 310.

  As described above, in the first embodiment, the demand value prediction apparatus (demand monitoring unit 330) acquires the number of sample values used for prediction of the integrated power amount or the sample value used for prediction of the integrated power amount. Change the interval. By appropriately controlling the number of sample values or the interval at which the sample values are acquired, repetition of the alarm-on state and the alarm-off state is suppressed. For example, even when the flyer is repeatedly turned on and off, frequent switching between the alarm on state and the alarm off state is suppressed.

  In the first embodiment, the EMS 200 is based on the predicted value of the integrated power amount predicted based on the actual value of the power acquired by the system power meter 310 or the amount of power fluctuation acquired by the system power meter 310. When the predicted value of the integrated power amount predicted in this way reaches a predetermined power amount, an alarm indicating a list of loads whose power consumption should be suppressed is presented. In other words, the demand value prediction apparatus (demand monitoring unit 330) uses different methods for predicting the integrated power amount at the end of the predetermined period. Thereby, repetition of the alarm-on state and the alarm-off state is suppressed.

  For example, the demand value predicting device (demand monitoring unit 330) can calculate the integrated power amount at the end of the predetermined period based on the actual power value acquired by the grid wattmeter 310 in the first half (first timing) of the predetermined period. Predict. Therefore, the alarm-on state and the alarm-off state are not repeated as the power consumption increases or decreases instantaneously.

  On the other hand, the demand value prediction device (demand monitoring unit 330), based on the amount of power fluctuation acquired by the grid wattmeter 310 in the second half of the predetermined period (second timing), integrates power at the end of the predetermined period. Predict the amount. Therefore, it is possible to accurately determine whether or not the predicted value of the integrated power amount at the end of the predetermined period exceeds the predetermined power amount.

  In the first embodiment, the EMS 200 is acquired by the load power meter 340 when the predicted value of the integrated power amount predicted based on the actual power value acquired by the system power meter 310 exceeds the predetermined power amount. A list of loads is presented as an energy saving action list 470 in descending order of the actual power value (first mode). Therefore, it is possible to appropriately present a load for which power consumption should be suppressed to the user.

  On the other hand, the EMS 200 determines the fluctuation of the power acquired by the load wattmeter 340 when the predicted value of the integrated power quantity predicted based on the fluctuation amount of the power acquired by the system power meter 310 exceeds the predetermined power amount. A list of loads is presented as an energy saving action list 470 in descending order of quantity (second mode). Therefore, it is possible to appropriately present a load for which power consumption should be suppressed to the user.

  In the first embodiment, the demand value prediction apparatus (EMS 200) presents a list so that the replacement frequency of loads included in the list is low. Thereby, a user can grasp | ascertain effectively the load which should suppress power consumption, suppressing a user's confusion.

  For example, the demand value prediction device (EMS 200) presents a list including a predetermined number of loads in descending order of power consumption, or among the loads connected to the system, a predetermined number of power consumption increases in descending order. Present a list with loads. Thereby, the replacement frequency of the load included in the list decreases.

  Alternatively, the control unit 230 presents a list in which a predetermined number of loads are highlighted in descending order of the power consumption among the loads connected to the system, or the power consumption increases among the loads connected to the system. A list in which a predetermined number of loads are highlighted may be presented in descending order of mass. As a result, the replacement frequency of the cooperatively displayed loads included in the list decreases.

  Alternatively, the list update interval in the first half (first period) of the predetermined period may be longer than the list update interval in the second half (second period) of the predetermined period. As a result, the user's confusion can be suppressed during a period of low urgency for suppressing power consumption (first period). On the other hand, during a period of high urgency for suppressing power consumption (second period), the user can effectively grasp the load for which power consumption should be suppressed.

[Modification 1]
Hereinafter, Modification Example 1 of the first embodiment will be described. In the following, differences from the first embodiment will be mainly described.

  Specifically, in the first embodiment, the interval for acquiring the power consumed by the load 120 and the list update interval are the same. On the other hand, in the first modification, the interval for acquiring the power consumed by the load 120 is different from the list update interval.

  For example, the interval for acquiring the power consumed by the load 120 may be 1 minute, and the list update interval may be a predetermined time interval (for example, 5 minutes). The list update interval may be variable as in the first embodiment.

(Demand value prediction method)
Hereinafter, the demand value prediction method according to the first modification will be described. FIG. 11 is a flowchart illustrating the demand value prediction method according to the first modification. In FIG. 11, step 15 is added between step 10 and step 20 in the flow shown in FIG. 10.

  In step 15, the EMS 200 determines whether a predetermined time interval has elapsed. If the determination result is “YES”, the process of step 20 is performed. If the determination result is “NO”, the process of step 30 is performed.

  As described above, if the predetermined time interval (list update interval) is longer than the interval for acquiring the power consumed by the load 120, the list is frequently updated and the user is prevented from being confused.

[Modification 2]
Hereinafter, Modification Example 2 of the first embodiment will be described. In the following, differences from the first embodiment will be mainly described.

  In the first embodiment, the demand monitoring unit 330 determines whether or not the fluctuation of the power consumption of the load connected to the grid is larger than a predetermined fluctuation based on the type of the load connected to the grid. On the other hand, in the second modification, whether or not the fluctuation of the power consumption of the load connected to the system is larger than the predetermined fluctuation is determined based on the power measured by each load wattmeter 340.

  In the second modification, it should be noted that the main body that determines whether or not the predicted value of the integrated power amount exceeds the predetermined power amount at the end of the predetermined period is the EMS 200. The EMS 200 receives information indicating the accumulated power amount at each timing from the demand monitoring unit 330 as a sample value. As with the demand monitoring unit 330 shown in the first embodiment, the EMS 200 predicts the integrated power amount at the end of the predetermined period and determines whether the predicted value of the integrated power amount exceeds the predetermined power amount. .

[Modification 3]
Hereinafter, Modification 3 of the first embodiment will be described. In the following, differences from the first embodiment will be mainly described.

  Specifically, in the third modification, the EMS 200 pending presentation of the energy saving action list 470 until a predetermined period (for example, 10 minutes) elapses from the start of the predetermined period (for example, 30 minutes). This suppresses repetition of the alarm-on state and the alarm-off state in a state where the prediction accuracy of the integrated power amount at the time of expiration of the predetermined period is low.

[Modification 4]
Hereinafter, Modification 4 of the first embodiment will be described. In the following, differences from the first embodiment will be mainly described.

  Specifically, in the fourth modification, the demand measurement unit 320 is measured by the system wattmeter 310 without resetting the integrated value (integrated power amount) of the power measured by the system wattmeter 310 every predetermined period. The integrated value of power may be managed continuously. That is, the demand measurement unit 320 calculates the integrated power amount at the time of expiration of the current predetermined period based on the power measured by the system wattmeter 310 in the period before the current predetermined period in addition to the current predetermined period. Predict. As a result, immediately after the start of the current predetermined period, a decrease in the prediction accuracy of the integrated power amount at the time of expiration of the current predetermined period is suppressed.

  However, information presented by the EMS 200 (for example, the presentation information 400 and the demand monitoring graph) is, of course, reset every predetermined period.

[Other Embodiments]
Although the present invention has been described with reference to the above-described embodiments, it should not be understood that the descriptions and drawings constituting a part of this disclosure limit the present invention. From this disclosure, various alternative embodiments, examples and operational techniques will be apparent to those skilled in the art.

  In the embodiment, the demand value prediction apparatus is the demand monitoring unit 330. However, the embodiment is not limited to this. The demand value prediction device may be EMS200. Alternatively, the demand value prediction apparatus may be configured by the EMS 200 and the demand monitoring unit 330. Alternatively, the demand value prediction device may be provided in the CEMS 20 or may be provided in the smart server 40. Alternatively, the demand value prediction device may be provided in a Home Energy Management System (HEMS), may be provided in a BEMS (Building Energy Management System), or may be provided in a FEMS (Factor Energy Management). Alternatively, it may be provided in the SEMS (Store Energy Management System).

  Although not specifically mentioned in the embodiment, the load wattmeter 340 may be a current sensor or the like.

  In the embodiment, the customer 10 includes a load 120, a PV unit 130, a storage battery unit 140, a fuel cell unit 150, and a hot water storage unit 160. However, the consumer 10 should just have the load 120 at least.

  In the embodiment, in the least square method, α is determined so that the value of “J” is minimized. Here, the value of “J” may be expressed as in Expression (3).

Further, in order to minimize the error sum of squares, the value of “J” may be calculated according to the equation (4) using the weighted sequence a _i .

Here, the values of the weighting sequence a _i may be stored as a predetermined table. Alternatively, the value of the weighted sequence a _i may be represented by “1 / i” (a _i = 1, 1/2, 1/3,...). Alternatively, the value of the weighted sequence a _i may be represented by “1 / i ² ” (a _i = 1, 1/4, 1/8,...). Alternatively, the value of the weighted number sequence a _i may be represented by “1 / i−0.1” (a _i = 1, 0.9, 0.8,...).

  Furthermore, in order to minimize the total sum of error absolute values, the value of “J” may be calculated as in Equation (5) or Equation (6).

  In the embodiment, the list of loads is configured to include power consumption for each load. However, the embodiment is not limited to this. The list of loads may be configured to include power consumption for each group of loads connected to a power line provided under the breaker of distribution board 110. In such a case, the smart sensor 350 preferably transmits information indicating the total value of power measured by the plurality of load power meters 340 to the EMS 200 together with the identifier of the smart sensor 350.

  Although not specifically mentioned in the embodiment, the predicted value of the integrated power amount may be corrected based on the PV power generation prediction information. For example, if the power generation amount of the PV 131 tends to increase, the predicted value of the integrated power amount may be corrected downward. Alternatively, the predicted value of the integrated power amount may be corrected based on the remaining power generation capacity of the fuel cell 151 (a value obtained by subtracting the current power generation amount from the maximum power generation amount). For example, the predicted value of the integrated power amount may be corrected downward as the power generation capacity of the fuel cell 151 increases. Alternatively, the predicted value of the integrated power amount may be corrected based on the remaining amount of power stored in the fuel cell 151. For example, the predicted value of the integrated power amount may be corrected downward as the remaining amount of power stored in the fuel cell 151 increases.

  Although not particularly mentioned in the embodiment, the presentation information 400 may include PV power generation prediction information. Alternatively, the presentation information 400 may include information indicating the remaining power generation capacity of the fuel cell 151. Alternatively, the presentation information 400 may include the remaining amount of power stored in the fuel cell 151.

  Although not specifically mentioned in the embodiment, the EMS 200 controls the PV unit 130, the storage battery unit 140, the fuel cell unit 150, and the hot water storage unit 160 so that the integrated power amount at the end of the predetermined period does not exceed the predetermined power amount. It is preferable.

  Although not specifically mentioned in the embodiment, the basic fee is determined based on, for example, the amount of power in the past predetermined period (for example, 30 minutes). That is, the grid power meter 310 measures the amount of power for 30 minutes (the amount of power used). Then, the average power consumption (kW) for the 30 minutes is calculated. This average power consumption is called the 30 minute demand value. The maximum 30-minute demand value in one month is called the maximum demand power (maximum demand value) for the month. Then, the maximum demand value of the month or the largest value among the maximum demand values during the past year is used for calculation of the basic charge. That is, if a large demand value is generated even once in a month or year, a basic charge using the demand value is applied for the next month or the next year. In this way, the basic fee is determined.

DESCRIPTION OF SYMBOLS 10 ... Consumer, 20 ... CEMS, 30 ... Substation, 31 ... Distribution line, 40 ... Smart server, 50 ... Power plant, 51 ... Transmission line, 60 ... Network, 100 ... Energy management system, 110 ... Distribution board, DESCRIPTION OF SYMBOLS 120 ... Load, 130 ... PV unit, 131 ... PV, 132 ... PCS, 140 ... Storage battery unit, 141 ... Storage battery, 142 ... PCS, 150 ... Fuel cell unit, 151 ... Fuel cell, 152 ... PCS, 160 ... Hot water storage unit, 200 ... EMS, 210 ... receiving unit, 220 ... transmitting unit, 230 ... control unit, 240 ... presenting unit, 310 ... system power meter, 320 ... demand measuring unit, 330 ... demand monitoring unit, 340 ... load power meter, 350 ... Smart sensor 360 ... hub 400 ... presentation information 410 ... date and time information 420 ... status summary information 430 ... status More Information, 440 ... state legend information, 450 ... link information, 460 ... equipment change list, 470 ... energy saving action list

Claims (5)

A demand value prediction device for predicting an integrated power amount supplied from a system in a predetermined period,
A first power acquisition unit for acquiring power supplied from the system;
A prediction unit that acquires the power amount acquired by the first power acquisition unit as a sample value and predicts the integrated power amount based on the acquired sample value;
The prediction unit changes the number of the sample values or the interval for acquiring the sample values.   The demand value prediction according to claim 1, wherein the prediction unit changes the number of the sample values or an interval for acquiring the sample values based on a type of load connected to the system. apparatus. A second power acquisition unit that acquires power consumed by a load connected to the grid;
The said prediction part changes the number of the said sample values, or the space | interval which acquires the said sample value based on the fluctuation | variation of the electric power acquired by the said 2nd electric power acquisition part. Demand value prediction device. The prediction unit predicts the integrated power amount using three or more sample values as the sample value,
The demand value prediction apparatus according to claim 1, wherein the prediction unit specifies a slope indicating a change in the integrated power amount based on a least square method or a moving average method. A demand value prediction method for predicting an integrated power amount supplied from a system in a predetermined period,
Step A for obtaining power supplied from the system;
Acquiring the electric energy acquired in step A as a sample value, and predicting the integrated electric energy based on the acquired sample value; and
A demand value prediction method comprising: a step C of changing a number of the sample values or an interval for acquiring the sample values.

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