JP2015056977A - Apparatus operation analysis system and information communication system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an apparatus operation analysis system and an information communication system capable of determining whether a household is convenient for demand response using a smart meter value.SOLUTION: An apparatus operation analysis system Z is a system for analyzing the operation of apparatuses using a home power generation control system V that controls the power home-generated using distributed power sources and a management server 1 communicatively connected therewith. The management server 1 extracts sets of power consumption values from a record of previous power consumption values of a smart meter 7. Assuming that the extracted number of sets as the number of apparatuses, the management server 1 determines whether demand response is controllable on the basis of the possible distribution status of the sets.

Description

  The present invention relates to a device operation analysis system and an information communication system.

  Conventionally, a system for estimating an action from a meter value such as a smart meter is known. For example, Non-Patent Document 1 discloses a non-intrusive monitoring system that grasps the behavior of an elderly person using a power value obtained from a meter.

Katsuhisa Yumoto, Yoshiteru Amano, Yukio Nakano, “Non-intrusive monitoring system (estimated household power consumption)”, Central Research Institute of Electric Power, search on August 22, 2013, Internet <http: //criepi.denken.or. jp / jp / kenkikaku / report / download / Vuz8tSgvcgkA4mtzlL3n0Wra6l3qqrBr / report.pdf>

  However, in the prior art of Non-Patent Document 1, an aggregator or electric power company that performs demand response (DR) obtains necessary information from a meter for the purpose of selecting a target residence for the service. There was no use of information. That is, it was not possible to extract only homes that are convenient for demand response using the power value obtained from the meter.

  An object of the present invention is to provide an apparatus operation analysis system and an information communication system that can determine whether a household is convenient for demand response using a smart meter value.

  An apparatus operation analysis system according to an embodiment of the present invention is an apparatus operation analysis system in which a management server that is communicably connected to an in-house generated power control system that controls electric power generated in-house by a distributed power source analyzes the operation of the apparatus. is there. The management server extracts a set of power consumption values from the history of power consumption values of past smart meters, assumes the extracted number of sets as the number of devices, and determines the demand response according to the distribution state that the set can take. It is determined whether or not it is easy to control.

  The management server may determine that the load that can be controlled is suitable for demand response control by determining that the controllable load has a large load from the possible distribution of the set.

  Further, the management server may determine that it is suitable for demand response control by determining that the load is large and the power consumption of the load is equal from the possible distribution of the set. .

  In addition, the management server may determine that the load is suitable for demand response control by determining that the number of loads is small and the load that can be controlled is large from the distribution that the set can take. .

  The distributed power source may be a solar power generation device, and the management server may analyze the operation of the device based on the surplus power prediction of the solar power generation device.

  An information communication system according to an embodiment of the present invention includes a management server, a home load device, a HEMS device, a distributed power source, and a smart meter. The management server performs any of the above processes. The HEMS device controls the load device. The distributed power source is a power source other than the system power source. The smart meter is communicably connected to the management server and the HEMS device.

  ADVANTAGE OF THE INVENTION According to this invention, it becomes possible to provide the apparatus operation | movement analysis system and information communication system which can determine whether the household is convenient for demand response using a smart meter value.

FIG. 1 is an explanatory diagram outlining an information communication system according to an embodiment of the present invention. FIG. 2 is an overall configuration diagram of the information communication system according to the embodiment of the present invention. FIG. 3 is a functional block diagram of the information communication system according to the embodiment of the present invention. FIG. 4 is a functional block diagram of the information communication system according to the embodiment of the present invention. FIG. 5 is a functional block diagram of the information communication system according to the embodiment of the present invention. FIG. 6 is a power supply / demand configuration diagram in the information communication system according to the embodiment of the present invention. FIG. 7 is a control flowchart of the private power generation control system according to the embodiment of the present invention. FIG. 8 is an explanatory diagram of a method of calculating multiple regression analysis using HEMS. FIG. 9 is an explanatory diagram of a method of calculating in the cloud using HEMS. FIG. 10 is an explanatory diagram of a method for calculating a multiple regression analysis on a PC. FIG. 11 is an internal configuration diagram of Table 1. FIG. 12 is a flowchart of the priority map process. FIG. 13 is a flowchart of power purchase / power sale selection. FIG. 14 is an internal configuration diagram of Table 2. FIG. 15 is a diagram illustrating formulas used in the information communication system according to the embodiment of the present invention. FIG. 16 is a flowchart of power consumption prediction. FIG. 17 is a flowchart of power generation amount prediction. FIG. 18 is a flowchart of power consumption prediction. FIG. 19 is an explanatory diagram of the time zone distribution of the predicted power generation amount. FIG. 20 is a diagram illustrating formulas used in the information communication system according to the embodiment of the present invention. FIG. 21 is a flowchart of control device candidate extraction. FIG. 22 is a diagram illustrating an example of multiple regression analysis of (explanatory variable x ₁ = temperature). FIG. 23 is a diagram illustrating an example of (explanatory variable x ₁ = temperature). FIG. 24 is a diagram illustrating an example of (explanatory variable x ₁ = temperature, humidity, amount of solar radiation, weather information). FIG. 25 is a diagram illustrating Data (past history). FIG. 26 is a diagram illustrating formulas used in the information communication system according to the embodiment of the present invention. FIG. 27 is a diagram illustrating equations used in the information communication system according to the embodiment of the present invention. FIG. 28 is a diagram illustrating formulas used in the information communication system according to the embodiment of the present invention. FIG. 29 is a diagram showing formulas used in the information communication system according to the embodiment of the present invention. FIG. 30 is a diagram illustrating formulas used in the information communication system according to the embodiment of the present invention. FIG. 31 is an explanatory diagram of device operation analysis with a smart meter. FIG. 32 is an explanatory diagram of device operation analysis by the smart meter.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Note that similar components are included in the following embodiments. Therefore, in the following, common reference numerals are given to those similar components, and redundant description is omitted.

"Overview"
FIG. 1 is an explanatory diagram outlining an information communication system according to an embodiment of the present invention. Functionally, this information communication system is functionally self-generated power control system V, self-generated power scheduling Q, behavior determination X by device operation, device operation analysis W in ECHONET-Lite, and DR leveling adjustment Y And a device operation analysis Z with a smart meter. As shown in this figure, private power generation scheduling Q, action determination X based on device operation, and device operation analysis W in ECHONET-Lite are realized as a part of functions of private power generation control system V.

(V: Outline of private power generation control system)
The purpose of the self-generated power control system V is to effectively utilize power generated by a self-generated power source (for example, surplus power of a solar power generation device). Therefore, based on the PV power generation amount prediction and the power consumption history, the power generated by the PV power generation is effectively used while controlling the household load (the storage battery is also used). Thereby, it becomes possible to set the priority according to the action state.

(Q: Overview of self-generated power scheduling)
The purpose of the private power generation scheduling Q is to adjust a control schedule for load formation including human requests when there are a plurality of devices. Therefore, priority rules are determined on the schedule, and the schedule is adjusted accordingly, including human hopes. As a result, it is possible to perform optimal control in consideration of the person's wishes and load formation according to the adjustment rules.

(X: Outline of action determination by device operation)
The action determination X based on the operation of the device is intended to predict the action from the operation of the device (this leads to demand response control or the like). Therefore, in accordance with the behavioral state, the priority order of the devices and the conditions for buying and selling power are changed. Thereby, it becomes possible to utilize surplus electric power, suppressing purchase of electric power.

(W: Outline of equipment operation analysis in ECHONET-Lite)
The device operation analysis W in ECHONET-Lite is intended to easily set a temperature (peak cut) when power usage is dominant in a certain time zone. Therefore, using the function after multiple regression analysis with the temperature, humidity, and set temperature of a certain device as explanatory variables, the set temperature candidates that are suppressed by the predicted power usage are extracted. Thereby, when the power of a certain device (or device group) occupies most of the time period, it is possible to estimate the set temperature of the past device.

(Y: Overview of DR leveling adjustment (backoff))
The purpose of the DR leveling adjustment Y is to smooth the power (demand response) using the predicted behavior such as the time to go home. Therefore, the home time is estimated from the time of going out based on the detailed time of the time zone when it is regarded as outing information (for example, in units of 30 minutes), and the timer device selected by the control information is determined from the returning time by a certain time difference from the management server. Move with. Thereby, it is possible to execute a demand response.

(Z: Overview of device operation analysis with smart meter)
The device operation analysis Z with a smart meter aims to determine whether or not a household that wants to make a contract using the smart meter value has a high reduction effect by DR. Therefore, by extracting a set of power consumption values from the history of power consumption values of the past smart meter, assuming that the number of sets is the number of devices, depending on the distribution status that the set can take, demand response control It is determined whether or not the power consumption reduction effect is high. As a result, only the homes that are convenient for demand response can be extracted, and a DR control aggregate can be configured, thereby enabling efficient aggregation control.

"Example"
Hereinafter, the apparatus operation | movement analysis system concerning embodiment of this invention is demonstrated in detail. Since the device operation analysis system is a system corresponding to the device operation analysis Z in the smart meter, in the following description, the same reference numeral may be used to refer to the “device operation analysis system Z”. Moreover, since the private power generation power control system V is a function deeply related to the device operation analysis system Z, the private power generation power control system V will also be described in detail.

(Device connection configuration)
FIG. 2 is an overall configuration diagram of the information communication system according to the embodiment of the present invention. 3 to 5 are functional block diagrams of the information communication system according to the embodiment of the present invention.

  As shown in these drawings, the management server 1 is connected to a control network 300 (A route) and a control network 600. The smart meter 7 is connected to the control network 300 and the control network 100 (B route). A HEMS (HEMS device) 5 is connected to a control network 100, a control network 200 (HAN), and a control network 700. The router 9 is connected to the control network 700 and the control network 500. The mobile terminal 6 is connected to the control network 200 and the control network 400. The storage battery 3, the photovoltaic power generation device (PV) 2, and the load device group are connected to the control network 200. PV2 may not be connected if it is a creation-saving cooperation that shares a power conditioner with storage battery 3. The control networks 400, 500, and 600 are the Internet network or the mobile terminal network 8 (C route).

(Server side configuration)
The management server 1 communicates with the mobile terminal 6 via the C route. The management server 1 communicates with the HEMS 5 via the C route and the router 9. Further, the management server 1 communicates with the smart meter 7 via the A route.

(Home side composition)
The smart meter 7 communicates with the HEMS 5 via the B route. The HEMS 5 communicates with the storage battery 3, PV 2, portable terminal 6, and load device group. Specifically, the HEMS 5 manages the power consumption history, predicts the PV power generation amount, controls charging / discharging of the storage battery 3, and controls the load device 4. If it is the creation-saving cooperation which shares a power conditioner with the storage battery 3, HEMS5 does not need to communicate with PV2. The HEMS 5 communicates with the mobile terminal 6 (remote communication) and the management server 1 via the C route. PV2 is a solar power generation device and is an example of a distributed power source. The distributed power source is a power source other than the system power source, and includes a wind power generator and the like in addition to a solar power generator. The load device 4 can be operated remotely.

(Power wiring)
FIG. 6 is a power supply / demand configuration diagram in the information communication system according to the embodiment of the present invention. As shown in FIG. 6, the smart meter 7 is connected to one or a plurality of load devices 4, the mobile terminal 6, and the HEMS 5 via the distribution board 10 to supply power. The PV 2 stores electricity obtained by solar power generation in the storage battery 3. The storage battery 3 is connected to the smart meter 7, one or a plurality of load devices 4, the portable terminal 6, and the HEMS 5 via the distribution board 10 in accordance with an instruction from the information wiring of the HEMS 5 to supply power. Supplying power to the system side via the smart meter 7 is called “reverse power flow to the system side”.

(Relationship between storage battery, PV, and power conditioner)
The storage battery 3 and the PV 2 have different power conditioners, and there are an independent type in which electric power is exchanged by alternating current, and a direct current distribution (creation and cooperation type) in which electric power is delivered by direct current under the same power conditioner. Here, a case of direct current distribution is shown. In the case of a stand-alone type in which power is exchanged by alternating current, control from the HEMS 5 to the single PV 2 is performed. In the case of direct current distribution, control of PV2 is performed from a single power conditioner belonging to storage battery 3 or PV2. In the case of a single case, as shown in FIG. 6, the power supply to the distribution board 10 can be controlled by controlling the power conditioner belonging to the storage battery 3 side.

(Smart meter management)
The management server 1 is managed by an electric power company that operates the power plant. At least one smart meter 7 and HEMS 5 are installed in each household. The HEMS 5 is allowed to periodically communicate with the smart meter 7. However, in some cases, a home consumer owns the smart meter 7 and may cause the electric power company to perform power meter reading.

(V: Details of private power generation control system)
FIG. 7 is a control flowchart of the private power generation control system V according to the embodiment of the present invention. The private power generation control system V can be realized by the HEMS 5 itself having calculation resources for each processing V1 described below or by placing it on the cloud of the management server 1 (or a PC or the like). For example, as shown in FIG. 8, not only control HEMS but also calculation of multiple regression analysis may be performed by HEMS5. Further, as shown in FIG. 9, the calculation of control HEMS and multiple regression analysis may be performed in the cloud. Furthermore, as shown in FIG. 10, the calculation of the control HEMS and the multiple regression analysis may be performed by a PC. 8 to 10, W, D, and S indicate the amount of solar radiation, the device ID, and the integrated power consumption of the smart meter 7 in the time zone in FIG. 16, respectively. The coefficient and residual of the objective function can be extracted by calculating the multiple regression analysis using the calculation resources shown in FIGS. In FIG. 16, power consumption prediction is performed, and in FIG.

(V1: Power consumption prediction, power generation amount prediction, power storage amount prediction)
1. A power consumption prediction V001, a power generation amount prediction V002, and a power storage amount prediction V003 are performed.

(V1: Extraction of control device candidates)
2. In the control device candidate V004, a load device whose operation is predicted from the past history so as to be within the available amount of power derived from private power generation predicted in the time zone (morning, noon, evening, night) of the day to be predicted Extract groups.

(V1: Sifting by device priority)
3. In the device priority selection V005, if a predetermined candidate selection priority is set for the load device 4, candidates for the load device 4 to be operated are narrowed down.

(V1: Selection of operating equipment completed)
4). In the operating device selection V006, the load device 4 that is to be operated with privately generated power is determined.

(V1: Confirmation of operating equipment selection and adjustment of operation schedule)
5. In the operation schedule V007, the load device group is extracted based on the predicted power generation amount of the private power generation and the past operation history in the time zone (morning, noon, evening, night) of the predicted day, and these are operated automatically. Scheduled driving in a certain sense. Therefore, here, it is also possible for a person at the dwelling unit to check and adjust the load device group selected by the HEMS 5 and the schedule in which the operation is set using a mobile terminal or the like. Although it is possible to adjust only the operation time (time slot) for the device selected by the HEMS 5, as an option, the setter directly rewrites the priority map V010 for selecting the device by setting the screen of the mobile terminal. Is also possible.

(V1: Timer setting of operating equipment)
6). In the device timer setting V008, since the HEMS 5 and the load device group are connected via the control network, the operation time timer can be set by communication processing. In the case of a thermal device, parameters such as a set temperature can be set. For example, if it is desired to move the air conditioner using privately generated power in a certain operating time (evening), the HEMS 5 can set a timer by communication control. The HEMS 5 uses the preset function as much as possible when the load device 4 has a preset function such as a schedule function, but remembers the time zone when the load device 4 does not have the preset function. It is possible to wait until that time and control directly.

(V1: Temperature setting of operating equipment)
7). In the device temperature setting V009, since the HEMS 5 and the load device group are connected via a control network, it is possible to set parameters such as temperature setting, dehumidification, air blowing, etc., particularly to a heat source device such as an air conditioner by communication processing. For example, if it is desired to move the air conditioner using privately generated power in a certain operating time (evening), the HEMS 5 can set the temperature and the like by communication control. The HEMS 5 uses the preset function as much as possible when the load device 4 has a preset function such as a schedule function, but remembers the time zone when the load device 4 does not have the preset function. It is possible to wait until that time and control directly.

(V1: Priority map)
8). As shown in Table 1 (see FIG. 11), the priority map V010 has a table of device names (may be device IDs) and their maximum or average power consumption (kW). A priority can be set for this device name (device ID) using a mobile terminal or the like in the device priority setting V021. The load device group extracted by the device priority selection V005 is further selected by this priority map (see FIGS. 12 and 13).

  At this time, the maximum or average power consumption (kW) associated with the device name can be used, and the priority can be selected so as to be within the predicted power generation amount. Since the private power generation amount is assigned in order of priority, when the predicted power generation amount is small, it may not be operated at a lower priority. In this case, the maximum or average power consumption (kW) associated with the device name while assigning in order of priority (converted to kWh by multiplying the time for a predetermined time period) and the remaining power deducted by the assignment The difference from the predicted power generation amount (kWh) is taken, and the value is judged as positive or negative. If it is negative, it will not enter the candidate entry.

(V1: Electricity purchase selection)
9. In the case of including the pre-purchase / selling power selection V031, as shown in Table 1, the item {buy power, sell power} is provided, and processing is performed as in the following 9-1 to 9-4.

9-1. If there is a power purchase mark (O) in the device, in addition to the predicted power generation amount, power is purchased to the extent that the candidate load device 4 can be operated.

9-2. If there is a power purchase mark (Δ) in the device, in addition to the predicted power generation amount, if the price is lower than a certain setting, the power for purchasing the candidate load device 4 is purchased.

9-3. If there is a power sale mark (×) in the device, the power is sold (not operated) if the predicted amount of power generation is such that the load device 4 cannot be operated.

9-4. If the device does not have a power sale mark (), the amount of power is not used if the predicted amount of power generation is such that the load device 4 cannot be operated.

  At this time, whether or not it can be operated is determined by the difference between the maximum or average power consumption (kW) associated with the device name while allocating in order of priority and the predicted power generation amount remaining after the allocation. And determine whether the value is positive or negative. If the power purchase mark is ○ or △ even if it becomes negative, the candidate entry is entered once. If it is negative and the power purchase mark is x, the remaining predicted power generation amount is sold.

  If there is no mark for both buying and selling, power is stored or not used. If the power purchase mark is ○, the power difference between the remaining predicted power generation amount and the maximum or average power consumption (kW) associated with the device name is confirmed, and the load device 4 operates. Be a candidate. If the power purchase mark is Δ, the price table (yen / kWh) and coupon (discount% / kWh) obtained from the power purchase / selling price acquisition V030 in Table 2 (see FIG. 14) and the difference power (kWh) ). If it is determined that the calculated result does not exceed the set amount (yen) input from the device priority setting V021 after converting to the purchased power amount (yen) of the requested power, the load device 4 becomes an operation candidate. . It should be noted that not only the power purchase condition (g) as shown in Table 2, but also a power sale condition (h) of selling power if it is above a certain price.

(V1: Power consumption prediction)
In the power consumption prediction V001, a power consumption objective function (Equation 1) is derived (see FIG. 15). Specifically, using the calculation resource of the HEMS 5 or the management server 1, the history of power consumption of the past smart meter 7 is used as an objective variable, and weather information and past control information in a certain time zone (t) are used as explanatory variables. (See FIG. 16). The weather information includes {temperature, humidity, amount of solar radiation, weather information} and the like. The control information includes {device ID, device state, additional information} and the like. The coefficient can be increased by {number of explanatory variables}.

(V1: Power generation forecast)
In the power generation amount prediction V002, an objective function (formula 2) of the power generation amount is derived (see FIG. 15). Specifically, the calculation resource of the HEMS 5 or the management server 1 is used, the power generation amount of PV2 is an objective variable, and weather information in a certain time zone (t) is an explanatory variable (see FIG. 17). As described above, the weather information includes {temperature, humidity, amount of solar radiation, weather information} and the like. The coefficient can be increased by {number of explanatory variables}.

  Further, when PV power generation and storage battery 3 are integrated and the power generation amount of PV2 is considered as the amount of power including the power supply amount of storage battery 3, it is only necessary to derive the objective function by adding the storage information to the explanatory variables (FIG. 18). The storage information can be {remaining amount of storage battery 3, additional information}. The additional information can include {temperature, use frequency} of the storage battery 3 and the like. The coefficient can be increased by {number of explanatory variables}.

  Further, the distribution ratio of the predicted power generation amount may be changed for each time zone. Multiple regression analysis is performed for each time zone, but the total power generation forecast amount is allocated at {40%, 30%, 20%, 10%} of the ratio of {evening, morning, noon, night} according to the table in FIG. As a result, the distribution of device control also changes. As a result, it is possible to move even a heavily loaded device that moves well during a certain time period.

  The weather information may be the previous {one hour, several weeks, one month}. The unit of {1 hour, several weeks, one month} is set from HEMS5. The {average solar radiation amount (kWh / m 2 · day)} in the past in all weathers may be multiplied by {number of PV panels}. For further correction, {PV panel {azimuth (south), angle (15 degrees)}, {region, time (end of month, etc.), forecast weather (clear weather), forecast temperature (30.2 ° C), forecast humidity (35%), predicted roof temperature, predicted module temperature} may be included. Predicted weather, temperature, and humidity are obtained from weather information from the Japan Meteorological Agency. Predicted roof temperature and predicted module temperature use past data. In {PV power generation prediction}, data provided by the Japan Meteorological Agency or the like may be used, and if it is the latest prediction, a sensor such as a solar radiation meter may be attached and data measured independently in the home may be used. In this case, sensors such as a solar radiation meter, a temperature, and a hygrometer may be attached indoors or outdoors, or a sensor may be attached to PV2, and the sensor data may be transferred to HEMS5 for prediction. The information provided from the weather information service (Sora Eco), weather news, etc. may be acquired by the HEMS 5 from the Internet and used for PV2 power generation amount prediction as it is.

(V1: Power storage amount prediction)
In the storage amount prediction V003, the remaining amount of storage battery 3 and additional information} can be used as an explanatory variable by Equation 3, and the remaining amount of electricity can be predicted using the information in Equations 1 and 2. The additional information can include {temperature, use frequency} of the storage battery 3 and the like. The coefficient can be increased by {number of explanatory variables}.

(V1: Calculation of predicted power consumption of storage battery capacity)
When {PV power generation amount prediction} of a certain household is made (one day unit), the remaining power amount prediction (%) is calculated from {PV power generation prediction} and {power consumption history}. The remaining power storage prediction (%) is obtained by subtracting the predicted power consumption value [kWh] from the PV power generation predicted value [kWh] and the remaining power storage [kWh], and dividing 100 by the maximum storage capacity value [kWh]. Obtained by multiplication (% conversion) (see FIG. 20).

(V1: Control device candidate)
In the control device candidate V004, candidates for the control device are extracted using Equations 1 to 3 of the objective function obtained in the power consumption prediction V001, the power generation amount prediction V002, and the storage amount prediction V003. As shown in FIG. 21, based on a certain weather forecast information X, a condition is selected from the load equipment group x ₂ that satisfies the constraint α ₁ X + β ₁ x ₂ + ε ₁ <y ₂ of the combination of the objective function equations 1 and _2. A set of applicable load devices D is extracted. As a result, when a certain weather condition is predicted, an objective function derived by multiple regression analysis based on past data is used to extract a device list indicating which device was moving against the past case. can do.

(V1: Specific examples of Formula 1 and Formula 1-1)
Hereinafter, Formula 1 and Formula 1-1 will be described more specifically. As already explained, the general forms of Equation 1 and Equation 1-1 are as follows.

Formula 1: (Power consumption) 0 to _t-1 = (Weather information) 0 to _t-1 + (Control information) 0 to _t-1
Formula 1-1: y ₀ = a ₀ + a ₁ x ₁ + a ₂ x ₂ + ... + a _p-1 x _p-1
The weather information is one or a combination of a plurality of {temperature, humidity, solar radiation amount, weather information}. In FIG. 22, for the sake of simplicity, the weather information is one (explanatory variable x ₁ = temperature), and the multiple regression analysis is divided by time zone (morning, noon, evening, night). The contents of FIG. 22 are organized by coefficient and explanatory variable as shown in FIG.

Further, as shown in FIG. 24, when all of {temperature, humidity, solar radiation amount, weather information} are included in the weather information, the explanatory variables of the multiple regression analysis are increased to four. If there are six devices {device 1, device 2, device 3, device 4, device 5, device 6}, when all of these six devices are included in the control information, the explanatory variable of the multiple regression analysis is further increased. Increase by 6. Therefore, specifically, the data shown in Figure 25 into Equation 1, so as to derive a _x of the coefficient and the residual.

  In addition, although the case where a solar power generation device is employed as the distributed power source is illustrated here, a wind power generation device may be employed as the distributed power source. In this case, information such as {wind speed, wind direction, precipitation, temperature, sunshine duration} can be used for the multiple regression analysis.

(V1: Priority condition)
As shown in FIG. 26, the list may be further narrowed down from the extracted device (candidate) list under the restriction of the table indicating the device priority, and the device list may be activated by private power generation. This makes it possible to use privately generated power more effectively by selecting the device list used in the past in order of priority.

(V1: Trading conditions)
As shown in FIG. 27, a device list that is activated with privately generated power may be obtained by using a value obtained by adding the amount of purchased power to the extracted power generation (predicted) amount as a constraint condition (Formula 5). This makes it possible to use privately generated power more effectively by adding power trading conditions and providing flexibility in device operation.

(V1: Priority and trading settings)
As shown in FIG. 28, the list may be further narrowed down from the extracted device (candidate) list under the table showing the device priority and the restrictions on the purchase and sale settings, and the device list may be activated by private power generation (formula 6). Thus, by selecting from the device list used in the past based on the priority order and the trading setting, it becomes possible to use the private power generated more effectively including the trading.

(V1: Action information condition)
As shown in FIG. 29, the device condition (device condition for the action state) and the power state (power consumption in the action state) may be used as explanatory variables. Alternatively, an objective function (expression 7) indicating behavior information (state) may be derived, and an objective function (expression 8) indicating power consumption obtained by adding the behavior information as an explanatory variable to expression 1 may be derived. Furthermore, it is good also as an apparatus list | wrist which starts with self-generated electric power by making into a restriction | limiting condition the value which added the buying and selling electric power amount to the extracted electric power generation (prediction) amount (Formula 9). Identify the behavioral state at a certain amount of power consumption (including when buying and selling) or power generation forecast. This makes it possible to estimate the behavioral state from the power amount information.

(V1: Detailed priority conditions)
As illustrated in FIG. 30, the priority of a device may include a device name, power consumption (kW) of the device, and presence / absence of power purchase / purchase, and the power consumption may fall within a predetermined constraint. Thereby, it becomes possible to use privately generated power more effectively by selecting the devices from the device list used in the past in the priority order for each device.

(Z: Details of device operation analysis with smart meter)
Next, device operation analysis Z with a smart meter will be described with reference to FIGS. 31 and 32.

  In the smart load pattern analysis Z001, a set of power consumption values is extracted from the history of power consumption values of the past smart meter 7, and the state of distribution that the set can assume is assumed assuming that the number of sets is the number of devices. To determine whether it is easy to control demand response. As a result, it is possible to extract and control only households that are convenient for demand response (homes that have a high reduction effect).

  For example, as shown in FIG. 31, the smart meter 7 has an electric energy value W (t) for every time t of 30 minutes. At this time, the set W is collected in a certain time unit (such as one month). Next, the standard deviation of the collected W is taken, the variance of this is averaged with the average of the W of the plurality of HEMS devices collected by the management server 1, and the cluster is classified into three. They are called the uniform type, large / small separation type, and concentrated type in descending order of dispersion. Here, if the variance is large, it is assumed that the power consumption of each load device 4 in the home is gradual (equal type), and if the variance is small, it is assumed that there are many devices that perform the same power consumption (central type), If the dispersion is in the middle, it is assumed that it is a large and small separation type.

  In DR Excellent Home Extraction Z002, it can be known which class of {Destination type, Equal type, Centralized type} the ID of the HEMS device of the target dwelling unit belongs to, {Large size separated type, Equal type, Centralized type} class The priority of demand response is determined in the order of.

  Here, clustering is performed by dispersion. However, if the number N of household devices is known, a value obtained by dividing dispersion by N can be used as a reference. If N is large even in the large and small separation type, it is considered that it is not suitable for demand response, and conversely, in the uniform type, it is considered that the larger N is, the better the demand response is (see FIG. 32). Further, the HEMS 5 notifies the management server 1 of the maximum power consumption of the controllable device, so that the power consumption value is closer to the maximum power value that can be taken by the smart meter 7 (the difference value is smaller), and the demand is further increased. I think that it is suitable for the response.

  The home selection means Z003 adds the HEMS_ID selected by the DR excellent home extraction Z002 as a priority list. In the return home process Y001, DR load leveling is not performed for HEMS devices with HEMS_IDs not in the priority list.

  As described above, in the device operation analysis system Z according to the embodiment of the present invention, the management server 1 that is communicably connected to the self-generated power control system V that controls the self-generated power by the distributed power source operates the device. Is a system for analyzing The management server 1 extracts a set of power consumption values from the history of power consumption values of the past smart meter 7, assumes the extracted number of sets as the number of devices, and demands according to the distribution state that the set can take. Determine whether response control is easy. As a result, it is possible to provide the device operation analysis system Z that can determine whether the household is convenient for demand response using the smart meter value.

  Further, the management server 1 may determine that the controllable load is suitable for demand response control by determining that the controllable load has a large load from the possible distribution of the set. This makes it possible to extract and control only the homes that are convenient for demand response.

  Further, the management server 1 determines from the distribution that the set can have that there is a load with a large load (a large number of N) and a load with a uniform power consumption (a large dispersion). You may determine with it being suitable for control. This makes it possible to extract and control only the homes that are convenient for demand response.

  In addition, the management server 1 may determine that it is suitable for demand response control by determining that the load number is small and the controllable load has a large load from the possible distribution of the set. . This makes it possible to extract and control only the homes that are convenient for demand response.

  Further, the distributed power source is a solar power generation device, and the management server 1 may analyze the operation of the device based on the surplus power prediction of the solar power generation device. As a result, it is possible to extract and control only the homes that are convenient for demand response based on the surplus power of the photovoltaic power generation apparatus.

  The information communication system according to the embodiment of the present invention includes a management server 1, a home load device 4, a HEMS device 5, a distributed power source, and a smart meter 7. The management server 1 performs any of the above processes. The HEMS device 5 controls the load device 4. The distributed power source is a power source other than the system power source. The smart meter 7 is communicably connected to the management server 1 and the HEMS device 5. As a result, it is possible to provide an information communication system that can determine whether the household is convenient for demand response using the smart meter value.

  Here, the device operation analysis system Z analyzes information obtained from the private power generation power control system V, but the analysis target is not limited to this. That is, various power control systems that perform demand response can be analyzed.

  The preferred embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments, and various modifications can be made. For example, although a solar power generation device and a wind power generation device are exemplified as the distributed power source, the same effect can be obtained when other distributed power sources are employed.

Z Equipment operation analysis system V Private power generation power control system 1 Management server 4 Load equipment 5 HEMS equipment (HEMS)
7 Smart meter

Claims (6)

A management server connected so as to be able to communicate with a private power generation control system that controls power generated by a distributed power source is a device operation analysis system that analyzes the operation of the device.
The management server extracts a set of power consumption values from the history of power consumption values of past smart meters, assumes the extracted number of sets as the number of devices, and determines the demand response according to the distribution state that the set can take. Equipment operation analysis system characterized by determining whether it is easy to control.   2. The management server according to claim 1, wherein the management server determines that the controllable load has a large load from the possible distribution of the set, and determines that the management server is suitable for demand response control. Equipment operation analysis system.   The management server determines that it is suitable for demand response control by determining that the load is large and the power consumption of the load has a uniform load from the possible distribution of the set. The apparatus operation | movement analysis system of Claim 1.   The management server determines that it is suitable for demand response control by determining from the distribution that can be taken by the set that the number of loads is small and the load that can be controlled is large. The apparatus operation | movement analysis system of Claim 1. The distributed power source is a solar power generation device,
5. The apparatus operation analysis system according to claim 1, wherein the management server analyzes an operation of the apparatus based on a surplus power prediction of the photovoltaic power generation apparatus. A management server for performing the processing according to any one of claims 1 to 5;
Household load equipment,
A HEMS device for controlling the load device;
A distributed power source that is a power source other than the grid power source,
An information communication system comprising: the management server and a smart meter that is communicably connected to the HEMS device.