CN113835351B - Intelligent household electricity optimization control system and method based on multi-terminal cooperative architecture - Google Patents
Intelligent household electricity optimization control system and method based on multi-terminal cooperative architecture Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
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- G—PHYSICS
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention discloses an intelligent household electricity optimization control system based on a multi-terminal cooperative framework, which is characterized by comprising a cloud management and control platform, intelligent household clusters and a Web application server, wherein the intelligent household clusters are communicated with the cloud management and control platform, and each intelligent household cluster is connected with a plurality of intelligent household and environment data collectors. The invention also provides a corresponding method, and by implementing the embodiment of the invention, the electricity consumption optimization control of the intelligent home can be realized, the electricity consumption comfort feeling of a user is enhanced, the interactivity is improved, and the electricity fee can be saved.
Description
Technical Field
The invention belongs to the technical field of digital home intelligent home, and particularly relates to an intelligent home electricity optimization control system and method based on a multi-terminal cooperative framework.
Background
Today, the economy is vigorously developed, people pursue higher and higher self life quality, and intelligent household equipment is integrated into each household. More and more consumers interact with home appliances through smart APP for home control, such as controlling a switch, selecting an operation mode, and the like. In order to save electricity, a green low-carbon society is constructed, and the step electricity price and the time-sharing electricity price are adopted in many ways. At present, consumers want to know the electricity charge generally only by monthly bills. Under the conditions of step electricity price and time-sharing electricity price, consumers cannot well predict electricity consumption conditions, and the electricity consumption in one day cannot be planned better. Some intelligent home APP can control the switch of an electric appliance, but no perfect strategy combines electricity price to accurately plan electricity consumption. With the rise and development of home energy management systems, different optimization strategies have been formulated, but none of these strategies provides a platform for consumers to use.
Meanwhile, most of the existing smart home is interconnected in the same Wi-Fi, if each smart home is directly subjected to unified regulation and control, the information access cost can be greatly increased due to the action, the cloud computing is also faced with the difficulty of dimension disaster, and privacy protection of users is also difficult to guarantee.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the intelligent household electricity optimization control system and the intelligent household electricity optimization control method based on the multi-terminal cooperative framework, which can realize the electricity optimization control of the intelligent household, enhance the electricity comfort feeling of users, improve interactivity and save electricity charge.
In order to solve the technical problems, in one aspect of the invention, an intelligent household electricity optimization control system based on a multi-terminal cooperative architecture is provided, which comprises a cloud management and control platform, intelligent household clusters communicated with the cloud management and control platform and a Web application server, wherein each intelligent household cluster is connected with a plurality of intelligent household and environmental data collectors; wherein:
the cloud management and control platform is used for receiving the aggregation model from each intelligent home cluster, calculating the aggregation model by combining time-of-use electricity price information and current environment data after receiving a setting requirement for the selected intelligent home sent by a user through the Web application server, obtaining an optimal control scheme for the intelligent home, generating a regulation and control instruction for each intelligent home, and issuing the regulation and control instruction through the intelligent home cluster;
the intelligent home cluster is used for constructing an aggregation model for each intelligent home in the cluster, uploading the aggregation model to the cloud management and control platform, and simultaneously responding to the regulation and control instruction issued by the cloud management and control platform on line and decomposing the regulation and control instruction to each intelligent home in the cluster; the cloud management and control platform is used for collecting current environmental data through the environmental data collector and uploading the current environmental data to the cloud management and control platform;
the Web application server is used for receiving setting requirements of various intelligent households from users, uploading the setting requirements to the cloud control platform, receiving an optimal control scheme of the cloud control platform and displaying the optimal control scheme.
Preferably, the smart home comprises: at least one of an air conditioner, a refrigerator, a washing machine, an electric lamp, and a water heater; the environmental data includes: at least one of infrared sensing data, temperature and humidity data, photosensitive data, and room position data.
Preferably, a Web end interaction interface is adopted to interact between a user and a Web application server; in the Web-side interactive interface, the input part at least comprises: an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a history inquiry item; the display part at least comprises a three-dimensional histogram of the temperature before and after optimization, a power consumption line graph corresponding to time, a 24-hour power ranking of a pie chart, an integration time, indoor and outdoor temperature, power consumption and cost.
Preferably, in the smart home cluster, the aggregate model of the air conditioner is built by the following formula:
the discretized operation model of energy consumption and temperature in the room is expressed as:
wherein t=1, 2,. -%, T; t is the total time period, and DeltaT is the scheduling time interval;the indoor temperature in the j house at the t moment; />The thermal power consumption in the j-th house at the time t; c (C) M Is the thermal mass specific heat capacity; g j Is the thermal mass in the j-th house;
and->Respectively representing the heat loss, the osmotic heat loss and the ventilation heat loss of the building enclosure structure, and is specifically defined as:
and->The heat loss coefficients corresponding to the heat consumption, the infiltration heat consumption and the ventilation heat consumption of the building enclosure structure are respectively; />The outdoor ambient temperature at time t can be measured by a temperature sensor;
the heat power in the house is limited by the rated heat power range and the climbing limit of the radiator by adopting formulas (4. A) and (4. B) respectively, and the indoor temperature is limited by adopting formula (4. C); finally, a discretization operation model in the j house in the formula (1) is obtained as shown in the formula (4. D):
wherein COP is the energy efficiency ratio of the corresponding air conditioner;the heating value (refrigerating capacity) of the j-th air conditioner at the time t; />The thermal power of the jth air conditioner at the t moment; at->The minimum heat power and the maximum heat power of the jth air conditioner;is the lowest and highest indoor temperature acceptable to the user in the j-th house; />Representing a maximum thermal power ramp rate of a j-th house heat sink (TCR); s is S use The use state of the air conditioner is represented, 1 represents use, and 0 represents non-use; />Is the indoor initial temperature; alpha j ，β j ，γ j And the equivalent coefficient parameter of the j-th house.
Correspondingly, the invention also provides an intelligent household electricity optimization control method based on the multi-terminal cooperative framework, which is realized by the system, and is characterized by comprising the following steps:
step S10, a Web application server receives setting requirements of various intelligent home furnishings from a user and uploads the setting requirements to a cloud management and control platform;
step S11, after receiving a setting requirement for a selected intelligent home sent by a user through a Web application server, a cloud management and control platform calls a prestored aggregation model for the intelligent home, calculates by combining time-of-use electricity price information and current environment data, obtains an optimal control scheme for the intelligent home, generates a corresponding regulation and control instruction, and issues the intelligent home through an intelligent home cluster;
step S12, the intelligent home cluster responds to the regulation and control instruction issued by the cloud management and control platform online and decomposes the regulation and control instruction into all intelligent home in the cluster;
and step S13, the Web application server receives the optimized control scheme from the cloud control platform and displays the optimized control scheme to a user.
Preferably, the method further comprises:
step S20, the intelligent home cluster builds an aggregation model for each intelligent home in the cluster in advance and uploads the aggregation model to the cloud management and control platform;
and S21, uploading real-time environment data collected by the environment data collector to a cloud management and control platform by the intelligent home cluster.
Preferably, the method further comprises:
the user interacts with the Web application server through a Web-side interaction interface, and specifically, in the Web-side interaction interface, the user performs input operations of an air-conditioner selection item, a use time range input item, a comfort temperature input item, an air-conditioner mode selection item and a history inquiry item; and performing display of the following information in a display portion thereof: the three-dimensional histogram of the temperature before and after optimization, the power consumption line graph corresponding to the time, the cake graph which is a table of 24-hour power ranking, integration time, indoor and outdoor temperature, power consumption and cost.
Preferably, in step S20, the smart home cluster builds an aggregate model of the air conditioner by:
step S200, determining a discretization operation model of energy consumption and temperature in a room as the following formula:
wherein t=1, 2,. -%, T; t is the total time period, and DeltaT is the scheduling time interval;the indoor temperature in the j house at the t moment; />The thermal power consumption in the j-th house at the time t; c (C) M Is the thermal mass specific heat capacity; g j Is the thermal mass in the j-th house;
step S201, respectively obtaining heat consumption of the building enclosure structure by adopting the following formulasOsmotic heat loss->And ventilation heat loss->
Wherein,and->The heat loss coefficients corresponding to the heat consumption, the infiltration heat consumption and the ventilation heat consumption of the building enclosure structure are respectively; />The outdoor ambient temperature at time t can be measured by a temperature sensor;
step S202, respectively adopting formulas (4. A) and (4. B) to limit the heat power in the house by the rated heat power range and the climbing limit of the radiator, and adopting formula (4. C) to limit the indoor temperature; finally, a discretization operation model in the j house in the formula (1) is obtained as shown in the formula (4. D):
wherein COP is the energy efficiency ratio of the corresponding air conditioner;the heating value (refrigerating capacity) of the j-th air conditioner at the time t; />The thermal power of the jth air conditioner at the t moment; at->The minimum heat power and the maximum heat power of the jth air conditioner;is the lowest and highest indoor temperature acceptable to the user in the j-th house; />Representing a maximum thermal power ramp rate of a j-th house heat sink (TCR); s is S use The use state of the air conditioner is represented, 1 represents use, and 0 represents non-use; />Is the indoor initial temperature; alpha j ，β j ，γ j And the equivalent coefficient parameter of the j-th house.
The implementation of the invention has the following beneficial effects:
the invention provides an intelligent household electricity optimization control system and method based on a multi-terminal cooperative framework, which are used for controlling corresponding electric appliances to reach a desired range value by constructing a cloud-group-terminal framework with stronger fusion interactivity and layered expansibility and relying on a Web terminal interaction platform according to self requirements of users, such as comfort temperature, service time and the like and combining electricity prices for calculation and optimization. The intelligent household power consumption optimization control can be realized, the power consumption comfort feeling of a user is enhanced, the interactivity is improved, and the electric charge can be saved.
Meanwhile, in the embodiment of the invention, a platform for intuitively knowing the electricity consumption and environmental conditions of the electric appliance is provided for the user by contacting the user with the electric appliance in the home. After the user inputs subjective feelings, the subjective feelings can be visually displayed through Web, so that the user can know electricity consumption conditions at any time, and future electricity consumption can be planned. Therefore, the method can further promote the electricity-saving behavior of the user and respond to the call of national energy-saving and emission-reducing and low-carbon life.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that it is within the scope of the invention to one skilled in the art to obtain other drawings from these drawings without inventive faculty.
Fig. 1 is a schematic structural diagram of an embodiment of an intelligent household electricity optimization control system based on a multi-terminal cooperative architecture;
FIG. 2 is a schematic diagram of a main flow of an embodiment of an intelligent household electricity optimization control system based on a multi-terminal cooperative architecture provided by the present invention;
FIG. 3 is a schematic illustration of an initial interface in one example of the Web-side interactive interface referred to in FIG. 2;
FIG. 4 is a schematic diagram of an example of the Web-side interface of FIG. 2 after loading data;
FIG. 5 is a schematic diagram of an interface after optimization calculation in one example of the Web-side interactive interface in FIG. 2;
fig. 6 is an interface diagram of a history information display in an example of the Web-side interactive interface referred to in fig. 2.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a schematic structural diagram of an embodiment of an intelligent household electricity optimization control system based on a multi-terminal cooperative architecture is shown; in this embodiment, the multi-terminal collaboration architecture is a cloud-group-terminal architecture. Specifically, the system includes: the cloud management and control platform 1, a plurality of intelligent home clusters 2 communicated with the cloud management and control platform 1 and a Web application server 4, wherein each intelligent home cluster 2 is connected with a plurality of intelligent home 3 and an environmental data collector 5; wherein:
the cloud management and control platform 1 is used for receiving an aggregation model from each intelligent home cluster 2, calculating the aggregation model by combining time-of-use electricity price information and current environment data after receiving a setting requirement for a selected intelligent home sent by a user through the WEB application server 4, obtaining an optimal control scheme for the intelligent home, generating a regulation and control instruction for each intelligent home 3, and issuing the regulation and control instruction through the intelligent home clusters 2; it can be understood that the cloud management and control platform located at the cloud end is a decision center of the whole architecture, and the cloud management and control platform can dynamically aggregate intelligent households. Analyzing various intelligent home furnishings in the cluster on line to obtain an optimal control scheme, and generating a regulation and control instruction for issuing; the time-sharing electricity price signal can be obtained from a power system transaction platform connected with the time-sharing electricity price signal.
The intelligent home cluster 2 is used for constructing an aggregation model for each intelligent home 3 in the cluster, uploading the aggregation model to the cloud management and control platform 1, and simultaneously responding to the regulation and control instruction issued by the cloud management and control platform 1 on line and decomposing the regulation and control instruction to each intelligent home 3 in the cluster; the cloud management and control platform 1 is used for collecting current environmental data through the environmental data collector 5 and uploading the current environmental data to the cloud management and control platform 1; it can be understood that the smart home cluster 2 is a "group" hierarchy formed by the cluster control logic layers of the smart home, and plays a role of being responsible for on-line aggregation of various resources within the cluster in the whole control framework.
The Web application server 4 is configured to receive setting requirements of the user on various smart home 3, upload the setting requirements to the cloud management and control platform 1, and receive an optimal control scheme of the cloud management and control platform 1 for display.
Wherein, smart home includes: at least one of an air conditioner, a refrigerator, a washing machine, an electric lamp, and a water heater; the environmental data includes: at least one of infrared sensing data, temperature and humidity data, photosensitive data, and room position data. It can be understood that the intelligent home and environmental data sensor arranged at the end side is the bottommost layer of the whole control framework and is responsible for collecting, sensing and communicating real-time operation information with the upper layer of various intelligent home, and automatically receiving the regulation and control instruction issued by the response group level (intelligent product cluster).
The method comprises the steps that interaction is carried out between a user and a Web application server by adopting a Web end interaction interface; in the Web-side interactive interface, the input part at least comprises: an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a history inquiry item; the display part at least comprises a three-dimensional histogram of the temperature before and after optimization, a power consumption line graph corresponding to time, a 24-hour power ranking of a pie chart, an integration time, indoor and outdoor temperature, power consumption and cost.
It can be appreciated that an aggregation model of each smart home needs to be constructed in advance in the smart home cluster. In one specific example, an aggregate model of an air conditioner may be constructed by the following formula:
the discretized operation model of energy consumption and temperature in the room is expressed as:
wherein t=1, 2,. -%, T; t is the total time period, and DeltaT is the scheduling time interval;the indoor temperature in the j house at the t moment; />The thermal power consumption in the j-th house at the time t; c (C) M Is the thermal mass specific heat capacity; g j Is the thermal mass in the j-th house;
and->Respectively representing the heat loss, the osmotic heat loss and the ventilation heat loss of the building enclosure structure, and is specifically defined as:
and->Heat loss corresponding to heat loss, penetration heat loss and ventilation heat loss of building enclosure structure respectivelyLosing coefficients; />The outdoor ambient temperature at time t can be measured by a temperature sensor;
the heat power in the house is limited by the rated heat power range and the climbing limit of the radiator by adopting formulas (4. A) and (4. B) respectively, and the indoor temperature is limited by adopting formula (4. C); finally, a discretization operation model in the j house in the formula (1) is obtained as shown in the formula (4. D):
wherein COP is the energy efficiency ratio of the corresponding air conditioner;the heating value (refrigerating capacity) of the j-th air conditioner at the time t; />The thermal power of the jth air conditioner at the t moment; at->The minimum heat power and the maximum heat power of the jth air conditioner;is the lowest and highest indoor temperature acceptable to the user in the j-th house; />Representing the maximum thermal power ramp rate of the j-th house heat sink (thermal control resident, TCR); s is S use The use state of the air conditioner is represented, 1 represents use, and 0 represents non-use; />Is the indoor initial temperature; alpha j ，β j ，γ j And the equivalent coefficient parameter of the j-th house.
It can be appreciated that the modeling process of other smart home is similar to this, and will not be described in detail.
Fig. 2 is a schematic flow chart of an embodiment of an intelligent household electricity optimization control method based on a multi-terminal collaboration architecture. In this embodiment, the system described in fig. 1 is used to implement the method, which includes the following steps:
step S10, a Web application server receives setting requirements of various intelligent home furnishings from a user and uploads the setting requirements to a cloud management and control platform;
step S11, after receiving a setting requirement for a selected intelligent home sent by a user through a Web application server, a cloud management and control platform calls a prestored aggregation model for the intelligent home, calculates by combining time-of-use electricity price information and current environment data, obtains an optimal control scheme for the intelligent home, generates a corresponding regulation and control instruction, and issues the intelligent home through an intelligent home cluster;
step S12, the intelligent home cluster responds to the regulation and control instruction issued by the cloud management and control platform online and decomposes the regulation and control instruction into all intelligent home in the cluster;
and step S13, the Web application server receives the optimized control scheme from the cloud control platform and displays the optimized control scheme to a user.
More specifically, further comprising:
step S20, the intelligent home cluster builds an aggregation model for each intelligent home in the cluster in advance and uploads the aggregation model to the cloud management and control platform;
and S21, uploading real-time environment data collected by the environment data collector to a cloud management and control platform by the intelligent home cluster.
Preferably, in step S20, the smart home cluster builds an aggregate model of the air conditioner by:
step S200, determining a discretization operation model of energy consumption and temperature in a room as the following formula:
wherein t=1, 2,. -%, T; t is the total time period, and DeltaT is the scheduling time interval;the indoor temperature in the j house at the t moment; />The thermal power consumption in the j-th house at the time t; c (C) M Is the thermal mass specific heat capacity; g j Is the thermal mass in the j-th house;
step S201, respectively obtaining heat consumption of the building enclosure structure by adopting the following formulasOsmotic heat loss->And ventilation heat loss->
Wherein,and->The heat loss coefficients corresponding to the heat consumption, the infiltration heat consumption and the ventilation heat consumption of the building enclosure structure are respectively; />The outdoor ambient temperature at time t can be measured by a temperature sensor;
step S202, respectively adopting formulas (4. A) and (4. B) to limit the heat power in the house by the rated heat power range and the climbing limit of the radiator, and adopting formula (4. C) to limit the indoor temperature; finally, a discretization operation model in the j house in the formula (1) is obtained as shown in the formula (4. D):
wherein COP is the energy efficiency ratio of the corresponding air conditioner;the heating value (refrigerating capacity) of the j-th air conditioner at the time t; />The thermal power of the jth air conditioner at the t moment; at->The minimum heat power and the maximum heat power of the jth air conditioner;is the lowest and highest indoor temperature acceptable to the user in the j-th house; />Representing the maximum heat of the jth house heat dissipation (TCR)Power ramp rate; s is S use The use state of the air conditioner is represented, 1 represents use, and 0 represents non-use; />Is the indoor initial temperature; alpha j ，β j ，γ j And the equivalent coefficient parameter of the j-th house.
More specifically, further comprising:
the user interacts with the Web application server through a Web-side interaction interface, and specifically, in the Web-side interaction interface, the user performs input operations of an air-conditioner selection item, a use time range input item, a comfort temperature input item, an air-conditioner mode selection item and a history inquiry item; and performing display of the following information in a display portion thereof: the three-dimensional histogram of the temperature before and after optimization, the power consumption line graph corresponding to the time, the cake graph which is a table of 24-hour power ranking, integration time, indoor and outdoor temperature, power consumption and cost.
The following describes a Web-side interactive interface according to the present invention by using a specific example:
the main function of the Web-side interactive interface is to provide an interactive platform for users. The manager can publish it on a local area network or a mapping on a wide area network, which the user can access. The Web-side interactive interface is provided with a function of displaying the indoor temperature for 24 hours and the electricity price corresponding to the indoor temperature. After combining the requirements, the user can select the use mode of the electric appliance by himself and input the expected comfort temperature and the use time. And the final prediction calculation result is transmitted back to the WEB terminal interaction interface, so that a user can know the electricity consumption condition in real time, and the use comfort level is improved.
In a specific implementation, a separate desktop or Web application can be created directly based on Matlab Compiler. By MATLAB Web App Server it can support integration with authentication standards like OpenID Connect and LDAP. This allows the designer to directly control access to the Web application. The user can run the Web application directly from the browser without installing any other software. In addition, multiple applications developed using different versions of MATLAB and Simulink may also be hosted and shared on a server.
Taking air conditioning as an example, the input section includes air conditioning selection, use time range input, comfort temperature input, air conditioning mode selection, and the like. Selecting a different date will correspond to displaying the temperature of that date. The display part comprises a three-dimensional histogram of the temperature before and after optimization, a power consumption line graph corresponding to time, a 24-hour power ranking of a pie chart, and a table of integration time, indoor and outdoor temperature, power consumption and cost. A specific pattern is shown in fig. 3.
After the user opens the Web-side interactive interface, the user can select the electric appliance to be used to load data. After the load data key is pressed, the Web terminal interface displays outdoor temperatures at different time points corresponding to the dates, as shown in fig. 4.
After loading the data, the user can customize the appliance requirements. After the user inputs the use time range and the comfort temperature range and selects the use mode of the electric appliance, the background is pressed down to calculate according to the requirement, so as to obtain the optimal value meeting the requirement and display the optimal value in the interface. As shown in FIG. 5, after the temperature is selected to be 23-28 ℃, the use time is set to be 7 a.m. to 12 a.m. and 1 a.m. to 11 a.m. in the evening, and the heating mode is selected, clicking is optimized. The corresponding display interface displays various data, such as an optimized and original temperature map, a 24-hour power consumption ranking cake map, a power consumption graph and various data charts. The larger span part in the electricity utilization graph is the electricity utilization condition when algorithm optimization is not performed, and the smaller span part is the planning result after cloud algorithm optimization is performed. The temperature bar graph shows the outdoor raw temperature, the optimized temperature and the temperature at the time of optimization for 24 hours on the date, respectively.
As shown in fig. 6, the current record is stored in the cloud control terminal, and the Web terminal interactive interface can display the record of the optimized electric energy in the past. Pressing the Week button optimizes the power usage in response to displaying a Week in the power usage graph. For the user to learn about the power usage behavior over a period of time.
The implementation of the invention has the following beneficial effects:
the invention provides an intelligent household electricity optimization control system and method based on a multi-terminal cooperative framework, which are used for controlling corresponding electric appliances to reach a desired range value by constructing a cloud-group-terminal framework with stronger fusion interactivity and layered expansibility and relying on a Web terminal interaction platform according to self requirements of users, such as comfort temperature, service time and the like and combining electricity prices for calculation and optimization. The intelligent household power consumption optimization control can be realized, the power consumption comfort feeling of a user is enhanced, the interactivity is improved, and the electric charge can be saved.
Meanwhile, in the embodiment of the invention, a platform for intuitively knowing the electricity consumption and environmental conditions of the electric appliance is provided for the user by contacting the user with the electric appliance in the home. After the user inputs subjective feelings, the subjective feelings can be visually displayed through Web, so that the user can know electricity consumption conditions at any time, and future electricity consumption can be planned. Therefore, the method can further promote the electricity-saving behavior of the user and respond to the call of national energy-saving and emission-reducing and low-carbon life.
The above disclosure is only a preferred embodiment of the present invention, and it is needless to say that the scope of the invention is not limited thereto, and therefore, the equivalent changes according to the claims of the present invention still fall within the scope of the present invention.
Claims (6)
1. The intelligent household electricity optimization control system based on the multi-terminal cooperative framework is characterized by comprising a cloud management and control platform, intelligent household clusters and a Web application server, wherein the intelligent household clusters are communicated with the cloud management and control platform, and each intelligent household cluster is connected with a plurality of intelligent household and environment data collectors; wherein:
the cloud management and control platform is used for receiving the aggregation model from each intelligent home cluster, calculating the aggregation model by combining time-of-use electricity price information and current environment data after receiving a setting requirement for the selected intelligent home sent by a user through the Web application server, obtaining an optimal control scheme for the intelligent home, generating a regulation and control instruction for each intelligent home, and issuing the regulation and control instruction through the intelligent home cluster;
the intelligent home cluster is used for constructing an aggregation model for each intelligent home in the cluster, uploading the aggregation model to the cloud management and control platform, and simultaneously responding to the regulation and control instruction issued by the cloud management and control platform on line and decomposing the regulation and control instruction to each intelligent home in the cluster; the cloud management and control platform is used for collecting current environmental data through the environmental data collector and uploading the current environmental data to the cloud management and control platform;
the Web application server is used for receiving the setting requirements of the users on various intelligent home furnishings, uploading the setting requirements to the cloud management and control platform, receiving an optimal control scheme of the cloud management and control platform and displaying the optimal control scheme;
in the intelligent home cluster, an aggregation model of the air conditioner is built through the following formula:
the discretized operation model of energy consumption and temperature in the room is expressed as:
wherein t=1, 2,. -%, T; t is the total time period, and DeltaT is the scheduling time interval;the indoor temperature in the j house at the t moment; />The thermal power consumption in the j-th house at the time t; c (C) M Is the thermal mass specific heat capacity; g j Is the thermal mass in the j-th house;
and->Respectively representing the heat loss, the osmotic heat loss and the ventilation heat loss of the building enclosure structure, and is specifically defined as:
and->The heat loss coefficients corresponding to the heat consumption, the infiltration heat consumption and the ventilation heat consumption of the building enclosure structure are respectively;the outdoor ambient temperature at time t can be measured by a temperature sensor;
the heat power in the house is limited by the rated heat power range and the climbing limit of the radiator by adopting formulas (4. A) and (4. B) respectively, and the indoor temperature is limited by adopting formula (4. C); finally, a discretization operation model in the j house in the formula (1) is obtained as shown in the formula (4. D):
wherein COP is the energy efficiency ratio of the corresponding air conditioner;the heating value or the refrigerating output of the j-th air conditioner at the t moment; />The thermal power of the jth air conditioner at the t moment; at->The minimum heat power and the maximum heat power of the jth air conditioner;is the lowest and highest indoor temperature acceptable to the user in the j-th house; />Representing the maximum heat power ramp rate of the j-th house for heat dissipation; s is S use The use state of the air conditioner is represented, 1 represents use, and 0 represents non-use; />Is the indoor initial temperatureA degree; alpha j ，β j ，γ j And the equivalent coefficient parameter of the j-th house.
2. The system of claim 1, wherein the smart home comprises: at least one of an air conditioner, a refrigerator, a washing machine, an electric lamp, and a water heater; the environmental data includes: at least one of infrared sensing data, temperature and humidity data, photosensitive data, and room position data.
3. The system of claim 2, wherein the user interacts with the Web application server using a Web-side interactive interface; in the Web-side interactive interface, the input part at least comprises: an air conditioner selection item, a use time range input item, a comfort temperature input item, an air conditioner mode selection item and a history inquiry item; the display part at least comprises a three-dimensional histogram of the temperature before and after optimization, a power consumption line graph corresponding to time, a 24-hour power ranking of a pie chart, an integration time, indoor and outdoor temperature, power consumption and cost.
4. An intelligent household electricity optimization control method based on a multi-terminal cooperative architecture, which is realized by the system of any one of claims 1 to 3, and is characterized in that the method comprises the following steps:
step S10, a Web application server receives setting requirements of various intelligent home furnishings from a user and uploads the setting requirements to a cloud management and control platform;
step S11, after receiving a setting requirement for a selected intelligent home sent by a user through a Web application server, a cloud management and control platform calls a prestored aggregation model for the intelligent home, calculates by combining time-of-use electricity price information and current environment data, obtains an optimal control scheme for the intelligent home, generates a corresponding regulation and control instruction, and issues the intelligent home through an intelligent home cluster;
step S12, the intelligent home cluster responds to the regulation and control instruction issued by the cloud management and control platform online and decomposes the regulation and control instruction into all intelligent home in the cluster;
step S13, the Web application server receives an optimized control scheme from the cloud control platform and displays the optimized control scheme to a user;
in step S20, the smart home cluster builds an aggregation model of the air conditioner by:
step S200, determining a discretization operation model of energy consumption and temperature in a room as the following formula:
wherein t=1, 2,. -%, T; t is the total time period, and DeltaT is the scheduling time interval;the indoor temperature in the j house at the t moment; />The thermal power consumption in the j-th house at the time t; c (C) M Is the thermal mass specific heat capacity; g j Is the thermal mass in the j-th house;
step S201, respectively obtaining heat consumption of the building enclosure structure by adopting the following formulasOsmotic heat loss->And ventilation heat loss->
Wherein,and->The heat loss coefficients corresponding to the heat consumption, the infiltration heat consumption and the ventilation heat consumption of the building enclosure structure are respectively; />The outdoor ambient temperature at time t can be measured by a temperature sensor;
step S202, respectively adopting formulas (4. A) and (4. B) to limit the heat power in the house by the rated heat power range and the climbing limit of the radiator, and adopting formula (4. C) to limit the indoor temperature; finally, a discretization operation model in the j house in the formula (1) is obtained as shown in the formula (4. D):
wherein COP is the energy efficiency ratio of the corresponding air conditioner;the heating value or the refrigerating output of the j-th air conditioner at the t moment; />The thermal power of the jth air conditioner at the t moment; at->The minimum heat power and the maximum heat power of the jth air conditioner;is the lowest and highest indoor temperature acceptable to the user in the j-th house; />Representing the maximum heat power ramp rate of the j-th house for heat dissipation; s is S use The use state of the air conditioner is represented, 1 represents use, and 0 represents non-use; />Is the indoor initial temperature; alpha j ，β j ，γ j Is the firstEquivalent coefficient parameters of j houses.
5. The method as recited in claim 4, further comprising:
step S20, the intelligent home cluster builds an aggregation model for each intelligent home in the cluster in advance and uploads the aggregation model to the cloud management and control platform;
and S21, uploading real-time environment data collected by the environment data collector to a cloud management and control platform by the intelligent home cluster.
6. The method as recited in claim 5, further comprising:
the user interacts with the Web application server through a Web-side interaction interface, and specifically, in the Web-side interaction interface, the user performs input operations of an air-conditioner selection item, a use time range input item, a comfort temperature input item, an air-conditioner mode selection item and a history inquiry item; and performing display of the following information in a display portion thereof: the three-dimensional histogram of the temperature before and after optimization, the power consumption line graph corresponding to the time, the cake graph which is a table of 24-hour power ranking, integration time, indoor and outdoor temperature, power consumption and cost.
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