JP2015056976A - Smoothing and adjustment system and information communication system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a smoothing and adjustment system and an information communication system capable of adjusting smoothing of the power from outer power supply based on the power which is generated by unstable, uncertain distributed power sources such as solar power generation, wind-power generation, etc.SOLUTION: A smoothing and adjustment system Y is a system in which a management server 1 connected, in a communicable manner, to a home power generation control system V which controls the home-generated power using distributed power sources adjusts smoothing of the power. The home power generation control system V performs a recurrence analysis using a record of previous facility usage to extract load apparatuses 4 operable on a power amount generated by the distributed power sources on an estimation day, and changes a priority order of apparatuses and conditions of sale and purchase of power according to action information. The management server 1 estimates time to come back home from the action information to imply going out, and activate each of the load apparatuses 4 at a different time based on the time to come back home.

Description

  The present invention relates to a leveling adjustment system and an information communication system.

  Conventionally, in demand response (DR), there is known a technique that receives a power reduction amount previously presented by an electric power company / aggregator, etc., and calculates and controls the amount of the range that can be reduced in light of the supply and demand of the customer system (Patent Document 1). It has been. Further, a storage battery is controlled by looking at the surplus power (Patent Document 2), a surplus power destination is controlled by power prediction (Patent Document 3), and a buying and selling price is determined by looking at the congestion status of the store. There existed a thing (patent document 4), a thing (patent document 5) etc. which adjust an incentive.

JP 2012-65407 A JP 2012-249476 A International Publication No. 11/152021 International Publication No. 12/160666 International Publication No. 12/098729

  According to national policy, about 1 million solar panels were installed in 2012 over 10 years. In the future, it is expected that installation will proceed several times due to mandatory energy conservation efforts such as the revised Energy Conservation Law, but if the installation increases, the reverse power flow to the power system will increase, and the high-price purchase in the old system may end There is. If this happens, the current power control of solar power generation (in-house power generation) based on purchase must be based on self-consumption control.

  However, there is no problem if all the household power can be covered by self-consumption, but the power of photovoltaic power generation (PV) depends on the weather and the amount of solar radiation. At this time, even if the power shift is performed using the storage battery, there is a problem in some cases. That is, if the power is not enough for demand, it will purchase power from the power company, and if it is more than the demand (for example, when going out), it will want to sell it. It is desirable that such control be automated, but for self-consumption of power using distributed power sources such as photovoltaic (PV) and wind power generation, the adjustment of self-consumption is coordinated with local power in cooperation with the power company. It is not possible to procure power cheaper by smoothing the power of the entire region.

  Such a technique is generally called demand response, but most demand response aims at power leveling in external power supply (= operational control of expensive power generation facilities). In other words, each home consumes power generated by a distributed power source installed in the dwelling unit of each household while making effective use of its own power, and that leveles the power on the external power supply side. There was nothing to contribute to. In addition, although it is a problem to grasp the schedule of humans in the home with effective power consumption, schedule control including automation of home appliance control based on in-house power generation amount prediction and a combination of devices has not been considered.

  The present invention is a leveling adjustment capable of adjusting power leveling on the external power supply side based on power generated by a self-generated power source such as photovoltaic power generation or wind power generation that is unstable or uncertain. It is an object to provide a system and an information communication system.

  A leveling adjustment system according to an embodiment of the present invention is a leveling adjustment system in which a management server that is communicably connected to a self-generated power control system that controls power generated in-house by a distributed power source adjusts power leveling. It is. Here, the private power generation power control system performs regression analysis using past device usage history, extracts load devices that can operate with the power generation amount of the distributed power source on the estimation date, and matches the behavior information. Change the priority order of equipment and the conditions for buying and selling electricity. In addition, the management server estimates a return time from behavior information when it is regarded as going out, and moves each of the load devices with a time difference based on the return time.

  The management server may select each time difference at random.

  The management server may select each time difference from a conditional time slot.

  Further, the management server may select each time difference so that the power consumption peaks of the homes overlap each other when the overall power consumption is large.

  The distributed power source may be a solar power generation device, and the management server may adjust leveling of power supplied from an electric power company based on a 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 that is communicably connected to the management server and the HEMS device. Prepare. One of the above processes is performed with the management server. 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.

  According to the present invention, the leveling of the power on the external power supply side can be adjusted based on the power generated by a self-generated power source such as photovoltaic power generation or wind power generation that is unstable or uncertain. It is possible to provide a computerized adjustment system and an information communication system.

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 behavior / apparatus operation frequency determination. FIG. 32 is an explanatory diagram of behavior / apparatus operation frequency determination. FIG. 33 is an explanatory diagram of priority map adjustment. FIG. 34 is an explanatory diagram of DR leveling adjustment. FIG. 35 is an explanatory diagram of DR leveling adjustment. FIG. 36 is an explanatory diagram of DR leveling adjustment.

  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 leveling adjustment system according to the embodiment of the present invention will be described in detail. Since the leveling adjustment system is a system corresponding to the DR leveling adjustment Y, in the following description, the same reference numeral may be used to refer to the “leveling adjustment system Y”. Moreover, since the private power generation power control system V and the action determination X by device operation are functions deeply related to the leveling adjustment system Y, these 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.

(X: Details of action determination by device operation)
Next, action determination X based on device operation will be described with reference to FIGS. The action determination X based on the device operation can be realized by the HEMS 5 itself having a calculation resource for each processing X1 described below or by placing it on the cloud of the management server 1 (or a PC or the like).

(X1: Action / device operation map)
1. In the behavior / apparatus operation map X001, it is determined whether or not the user is going home, and it is determined whether or not there is a tendency for the estimated date on which the demand response is to be performed. The determination result is notified from the HEMS 5 to the management server 1. The message notified to the management server 1 is given {ID of HEMS5, estimated return time}.

2. In the behavior / apparatus operation map X001, the priority map V010 is operated.

(X1: Behavior / equipment operation frequency determination)
In the action / apparatus operation frequency determination X002, as shown in FIG. 31, a table composed of {behavior state, digitized ID, apparatus condition, other condition} is included, and a power usage strategy is set according to the apparatus condition of the action state. Adjust a priority map. The action state (past) is a table that can be set and input by operating the device priority setting V021. By default, it can be set as in the example of FIG. It is also possible to add a frequency reference.

  In the lower table of FIG. 31, the actual operation frequency is calculated from the device timer setting V008, the device temperature setting V009, the smart meter 7, the device operation history information obtained by the device load information pattern analysis V020, and the like. Here, the frequency is calculated as 25% (1/4) if the operation is confirmed in any time zone among all time zones {morning, noon, evening, night}. If the value of the operating smart meter 7 is within the margin set from the minimum value periodically observed, it is regarded as the base power. Base power refers to the lowest power usage. Here, assuming that the operation of a device or the observation of the smart meter 7 is performed exceeding the set frequency standard, it is assumed that the action in the correspondence table is performed. For example, in this example, the state of the base power exceeds 70%, so it is determined that the user has gone out.

(X1: Behavior / equipment operation frequency determination)
If it is determined that the user is going out, the priority map, which is a power utilization strategy, is adjusted as shown in FIG. At the same time, the behavior / device operation map X001 is instructed to send a behavior message.

  When adjusting the priority map, as shown in FIG. 33, several {power sale, power purchase} patterns are prepared. For example, in the case of going out, the priority map can be changed as shown in FIG. In this way, it is possible to automatically buy and sell power without waste while consuming private power without waste according to the life scene.

(Y: Details of DR leveling adjustment)
Next, the DR leveling adjustment Y will be described with reference to FIGS. The DR leveling adjustment Y can be realized by the HEMS 5 itself having a calculation resource for each processing Y1 described below or by placing it on the cloud of the management server 1 (or a PC or the like).

(Y1: Return home information processing)
In the return home information processing Y001, information indicating when to go home is obtained from the behavior / apparatus operation map X001. That is, in the behavior / apparatus operation map X001, it is determined whether or not the user is going home, and it is determined whether or not there is a tendency for the estimated date on which the demand response is to be performed. The determination result is notified from the HEMS 5 to the management server 1. The message notified to the management server 1 is given {ID of HEMS5, estimated return time}.

  Alternatively, in the return home information processing Y001, information indicating when to go home may be obtained from the location information of the mobile terminal. That is, the mobile terminal records the distance difference between the GPS information of the house registered by the terminal and the GPS information of the current position in time series. Then, after the distance difference becomes large, the inflection point when it becomes small is observed, and when the difference exceeds a certain set value, it is determined that the head is going home and the message is sent to the HEMS 5 or the management server 1. Notice. The message notified to the management server 1 is given {ID of HEMS5, estimated return time}.

  Note that the HEMS 5 uses the return home random method Y003 when the estimated date is determined to be returned from the behaviour / apparatus operation map X001 or when a return message is received from the mobile terminal. On the other hand, the management server 1 uses the return home slot method Y002 or the return home random method Y003 when receiving a return home message from the behavior / apparatus operation map X001 or the mobile terminal.

(Y1: return home slot method)
In the return home slot method Y002, information indicating when to return is obtained from the action / apparatus operation map X001 or the location information of the mobile terminal. That is, the management server 1 obtains {HEMS5 ID, estimated return time} from the return message. Further, the ID of the HEMS 5 corresponding to the time at which the power is desired to be leveled is extracted from the statistical distribution (standard deviation or the like) of the expected return time. Further, a message for specifying time is transmitted to the timer device of the load devices 4, and a message for specifying to decrease the time temperature is transmitted to the HEMS device associated with the ID of the HEMS 5 for a device capable of setting the temperature.

  At the same time, the management server 1 sends a coupon issuance or a sales price list on an estimated date (DR day). For the sales price list, we send a price list by time zone such as time designation (morning, noon, evening, night). The HEMS 5 processes automatically or semi-automatically (change on a mobile terminal or the like) whether or not to execute in the slot. Issuance of coupon and sale price adjustment are processed by coupon issue Y004 and sale price adjustment Y005, respectively, and a message is transmitted to HEMS5.

(Y1: Return home random system)
In the return home random method Y003, information indicating when to return is obtained from the action / apparatus operation map X001 or the location information of the mobile terminal. That is, the management server 1 obtains {HEMS5 ID, estimated return time} from the return message. Further, the ID of the HEMS 5 corresponding to the time at which the power is desired to be leveled is extracted from the statistical distribution (standard deviation or the like) of the expected return time. Further, a message for specifying time is transmitted to the timer device of the load devices 4, and a message for specifying to decrease the time temperature is transmitted to the HEMS device associated with the ID of the HEMS 5 for a device capable of setting the temperature.

  At this time, the time designation range is randomly performed for each ID of the HEMS 5. Thereby, leveling of demand is performed. It is also possible to call coupon issue Y004 or trade price adjustment Y005 as provision of incentives.

(Y1: Coupon issued)
In coupon issue Y004, when called from the return home slot method Y002 or the return home random method Y003, coupon information is issued, and the discount rate is displayed on the HEMS device linked to the ID of HEMS5.

(Y1: Sales price adjustment)
In the sale price adjustment Y005, the price is adjusted by a certain ratio x with respect to the time zone specified in the return home slot method (the time zone in which congestion is expected). For example, when returning home is congested at night, the night price in Table 2 is increased by x%, and vice versa. Provide congestion level y, set the level of congestion level y to {small, medium, large}, determine which level the congestion level y is, and use coupons during that time according to the level of congestion level y Judge whether or not. Such a determination can be freely determined within a setting range.

  When executing the return home random method Y003 in HEMS5, the coupon issue Y004 and the sale price adjustment Y005 are not performed.

  As described above, in the leveling adjustment system Y 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 power generated in-house by the distributed power supply is leveled. It is a system to adjust the conversion. Here, the private power generation power control system V performs regression analysis using the past device usage history, extracts load devices 4 that can operate with the power generation amount of the distributed power source on the estimation date, and matches the behavior information. Change the priority order of equipment and the conditions for buying and selling electricity. In addition, the management server 1 estimates the return time from the behavior information when it is regarded as going out, and moves each of the load devices 4 with a time difference based on the return time. For example, the home time is estimated from going out by the detailed time (30 minutes unit) of the time zone when regarded as outing information, and the timer device selected by the control information from the returning time is each given a time difference by an instruction from the management server 1 Move with. This adjusts the power leveling on the external power supply side based on the power generated by the self-generated power from unstable or uncertain distributed power sources (ie, DR is executed). It is possible to provide a leveling adjustment system Y that can perform the above.

  Moreover, the management server 1 may select each time difference at random. This makes it possible to execute a demand response regardless of an instruction from the management server 1.

  Further, the management server 1 may select each time difference from a conditional time slot. For example, a certain time difference is selected from a time slot with a condition such as a purchase price difference from the return time instructed from the management server 1 and a coupon issue. As a result, a demand response is executed, but there is room for the returnee to select a time zone (see FIG. 34). In this case, there is an effect that the specified time can be guided (pricing) so as to be cheap (see FIG. 35). For example, the earlier the return time, the more control is available, so the price may be reduced. Alternatively, the price may be reduced as the timer period is longer. Further, a charge may be displayed simultaneously with the washing button. However, remote control is not possible unless clothing is set in the washing machine, so there may be a control of “shaking randomly” without using the cloud in each DR.

  Moreover, the management server 1 may select each time difference so that the power consumption peak of each household overlaps when the whole power consumption is large. Thereby, since the device operation after returning home is adjusted, it is possible to set the place where the income of the electric power company is high by the high power smoothing in the demand response. In addition, since a peak is likely to occur when a device that consumes a lot of electric power is clogged, if shaping is desired, the basis is to adjust the overlap (see FIG. 36). If shaping can be made into “overlap” and “power consumption” with good furnishings, the possibility of smoothing power increases.

  Further, the distributed power source is a solar power generation device, and the management server 1 may adjust the leveling of the power supplied from the power company based on the surplus power prediction of the solar power generation device. This makes it possible to adjust the leveling of the power on the external power supply side based on the surplus power of the solar power generation device.

  In addition, 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 supply, and a smart that is communicably connected to the management server 1 and the HEMS device 5. Meter 7 is provided. 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, an information communication system capable of adjusting the leveling of the power on the external power supply side based on the power generated in-house by the unstable and uncertain distributed power supply, such as solar power generation and wind power generation. It becomes possible to provide.

  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.

Y leveling adjustment system V Private power generation control system 1 Management server 4 Load equipment 5 HEMS equipment (HEMS)
7 Smart meter

Claims (6)

A management server that is communicably connected to a self-generated power control system that controls self-generated power by a distributed power source is a leveling adjustment system that adjusts power leveling,
The private power generation power control system performs regression analysis using past device usage history, extracts load devices that can operate with the power generation amount of the distributed power source on the estimation date, and prioritizes devices according to behavior information. Change the rank of the degree, the conditions of buying and selling electricity,
The leveling adjustment system, wherein the management server estimates a return time from behavior information when it is regarded as going out, and moves each of the load devices with a time difference based on the return time.   The leveling adjustment system according to claim 1, wherein the management server randomly selects each time difference.   The leveling adjustment system according to claim 1, wherein the management server selects each time difference from a conditional time slot.   2. The leveling adjustment system according to claim 1, wherein the management server selects each time difference so that power consumption peaks of each household overlap when the overall power consumption is large. The distributed power source is a solar power generation device,
The leveling according to any one of claims 1 to 4, wherein the management server adjusts leveling of power supplied from an electric power company based on a surplus power forecast of the photovoltaic power generation apparatus. Adjustment system. 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.