CN115247864B - Building flexible load control method, system, electronic device and medium - Google Patents

Building flexible load control method, system, electronic device and medium Download PDF

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CN115247864B
CN115247864B CN202211156294.8A CN202211156294A CN115247864B CN 115247864 B CN115247864 B CN 115247864B CN 202211156294 A CN202211156294 A CN 202211156294A CN 115247864 B CN115247864 B CN 115247864B
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stage
load
central air
power demand
predicting
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CN115247864A (en
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程奇
马杰
孙日近
李文华
吴杰文
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Suzhou Sicui Integrated Infrastructure Technology Research Institute Co ltd
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Suzhou Sicui Integrated Infrastructure Technology Research Institute Co ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/88—Electrical aspects, e.g. circuits
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/50—Load
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
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  • Air Conditioning Control Device (AREA)

Abstract

The invention provides a building flexible load control method based on power demand control, a device, equipment and a medium, wherein the method comprises the steps of predicting central air-conditioning load; predicting the upper limit of the number of running central air conditioners in the starting stage and the running stage based on the power demand; predicting flexible load control quantity of the central air conditioner in a starting-up stage and an operation stage based on the power demand; the method comprises the steps of predicting the operation time of a starting-up stage and the operation time of an operation stage based on the power demand, controlling the operation strategy of the starting-up stage by adopting a first control mode, and controlling the operation strategy of the operation stage by adopting a second mode.

Description

Building flexible load control method, system, electronic device and medium
Technical Field
The invention relates to the field of power, in particular to a building flexible load control method and system based on power demand control, electronic equipment and a medium.
Background
The empirical regulation and control of the central air conditioner are carried out on intelligent office buildings. On the premise of not influencing the comfort level of a human body basically, the regulation curve and the load reduction effect of a building central air conditioning system are verified by optimizing various modes such as an air conditioner host operation mode, an operation mode, a field operation parameter change, an operation state change, boundary condition setting and the like, and actual data show that a proper air conditioner regulation strategy is adopted, so that the peak load can be obviously reduced, the influence on the comfort level of the human body can be reduced to the minimum, the load reduction amount can reach 5-15% under the general condition, and the load reduction amount can reach 60% under the special condition.
Therefore, the flexible load in the building is controlled, so that the whole power load tends to be more stable, and the service life of the equipment is prolonged.
In the prior art, when a central air conditioner is controlled, the difference between the power loads in the starting stage and the running stage of the central air conditioner is large, so that the fall of the power loads is large, and the power loads are not stable enough.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, it is an object of the present invention to provide a method, a system, an electronic device, and a medium for building flexible load control based on power demand control.
To achieve the above and other related objects, the present invention provides a method for controlling a building flexible load based on power demand control, comprising:
predicting the load of the central air conditioner;
predicting the upper limit of the number of running central air conditioners in a starting-up stage and a running stage based on the power demand;
predicting flexible load control quantity of the central air conditioner in a starting-up stage and an operation stage based on the power demand;
and predicting the operation time of the starting-up stage and the operation time of the operation stage based on the power demand, and controlling the operation strategy of the starting-up stage by adopting a first control mode and controlling the operation strategy of the operation stage by adopting a second control mode.
The predicting central air conditioning compliance comprises:
predicting the coincidence of the central air conditioner according to historical data, processing historical operation data of the building, screening effective data, and predicting the load of the central air conditioner through the effective data;
the screened effective data comprises screening effective running time and screening parameter limit values;
the screening effective running time comprises data within a preset time period after the startup execution and data after the shutdown execution are filtered;
the screening parameter limit values comprise data of a filtering water chilling unit, a freezing pump, a cooling pump and a cooling tower which operate outside a preset Hertz range, and data of chilled water supply temperature, chilled water return temperature, chilled water supply and return temperature difference and chilled water supply and return temperature difference outside a set value range are filtered;
the step of predicting the load of the central air conditioner through the effective data comprises the steps of carrying out normal distribution on the effective data, filtering data which is 3 times of standard deviation to obtain standard data, and calculating the standard data by adopting an ARIMA regression algorithm to obtain the load of the central air conditioner.
In an embodiment, the predicting the upper limit of the number of operating central air conditioners in the startup phase and the operating phase based on the power demand includes:
Figure GDA0003953209000000021
wherein N is CHL The upper limit number of the water chilling units, N CHWP The upper limit of the number of the refrigerating pumps, N CWP Upper limit number of cooling pumps, N COT The total number of the cooling towers; q CHL,rated Nominal load of chiller, Q CHWP,rated Nominal load of the refrigerating pump, Q CWP,rated For nominal load of the cooling pump, Q COT,rated Nominal load of the cooling tower;
upper limit of system load is Q SYS Upper load limit of the system Q SYS Less than power demand Q ER ；
Q SYS ＝N CHL *Q CHL，rrated +N CHWP *Q CHWP，rated +N CWP *Q CWP，rated +N COT *Q COTrated 。
In the start-up phase, the first,
Figure GDA0003953209000000031
wherein
Q open The control quantity of the flexible load of the central air conditioner at the starting-up stage;
Figure GDA0003953209000000032
the central air-conditioning flexible load is the predicted starting stage;
Figure GDA0003953209000000033
a predicted power demand load at a startup phase;
in the course of the operating phase,
Figure GDA0003953209000000034
wherein
Q opearting The control quantity of the flexible load of the central air conditioner in the operation stage;
Figure GDA0003953209000000035
the central air conditioner flexible load is a predicted operation stage;
Figure GDA0003953209000000036
the predicted operating stage power demand load.
In an embodiment, the predicting the boot stage operation time and the operation stage operation time based on the power demand includes controlling a boot stage operation policy by using a first control method, and controlling an operation stage operation policy by using a second control method includes:
calculating the running time of the boot stage:
Figure GDA0003953209000000037
the first control mode is to control Q open The flexible load is arranged in the normal starting time in advance of T open，ER Time of (D) turn on N CHL Platform water chilling unit, N CHWP Platform refrigeration pump, N CWP Platform cooling pump, N COT A stage cooling tower;
in the operation stage, predicting the upper limit Q of the energy consumption of the operation of the central air-conditioning system MAX Comprises the following steps:
Q MAX ＝N CHL *Q CHL，rated *I MAX +N CHWP *Q CHWP，rated *(FRQ CHWP /50) c +N CWP *Q CWP，rated *(FRQ CWP /50) c +N COT *Q COT，rated *(FRQ COT /50) c ；
c is a constant;
control quantity based on flexible load of central air conditioner
Figure GDA0003953209000000041
Running time T of the running phase opearting，ER Comprises the following steps:
Figure GDA0003953209000000042
the second control mode is as follows: advance T opearting，ER Time setting water chilling unit current load rate upper limit I under power demand control mode MAX Frequency operation upper limit FRQ of refrigeration pump, cooling pump and cooling tower CHWP 、FRQ CWP 、FRQ COT ；
Raising the indoor temperature set value to the upper limit of the confidence interval T indoor，MAX The temperature of the chilled water supply is adjusted up to the upper limit T of the confidence interval CHWS，MAX 。
In one embodiment, c ranges from 2.5 to 3.
In one embodiment, the preset time period is 30min;
the preset Hertz section is 30-50 Hz.
A building flexible load control system comprising:
the load prediction module is used for predicting the load of the central air conditioner;
the central air conditioner number prediction module is used for predicting the upper limit of the number of the central air conditioners in a starting stage and an operation stage under the condition of power demand;
the control quantity prediction module is used for predicting the flexible load control quantity of the central air conditioner in a starting-up stage and an operation stage under the condition of power demand;
and the strategy module is used for predicting the operation time of the startup phase and the operation phase under the condition of power demand, controlling the operation strategy of the startup phase through a first control mode and controlling the operation strategy of the operation phase through a second control mode.
An electronic device, comprising:
at least one processor; and
a memory in communication with the at least one processor, wherein
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the control method described above.
A medium stores computer instructions for causing a computer to execute the control method described above.
As described above, the building flexible load control method based on power demand control according to the present invention has the following advantages:
the central air-conditioning system is controlled to realize real-time management of the load of the central air-conditioning system, control and management of the power demand are realized, and sustainable operation is stable.
Drawings
Fig. 1 is a flow chart of a control method according to the present invention.
Fig. 2 is a system diagram illustrating a control method according to the present invention.
Detailed Description
The present invention is further illustrated below with reference to specific examples, which are only intended to illustrate the invention and are not intended to limit the scope of the invention.
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.
Referring to fig. 1, a method for controlling a building flexible load based on power demand control includes:
in step 100, central air conditioning load is predicted;
in step 200, predicting the upper limit of the number of the running central air conditioners in the starting stage and the running stage based on the power demand;
in step 300, predicting flexible load control quantity of the central air conditioner in a starting-up stage and an operation stage based on the power demand;
in step 400, the boot phase operating time and the operating phase operating time are predicted based on the power demand, and the boot phase operating strategy is controlled using the first control method and the operating phase operating strategy is controlled using the second control method.
The air conditioner, especially a central air conditioner, is divided into two stages, wherein one stage is a starting stage and is controlled in a first mode, and the operation stage is controlled in a second control mode, so that the air conditioner can be controlled separately according to different time, and the power load is adjusted according to the power demand, and the stable operation of power equipment is ensured.
In one embodiment, the predicting central air conditioning compliance comprises:
predicting the coincidence of the central air conditioner according to historical data, processing historical operation data of the building, screening effective data, and predicting the load of the central air conditioner through the effective data;
the screened effective data comprises screening effective running time and screening parameter limit values;
the screening effective running time comprises data within a preset time period after the startup execution and data after the shutdown execution are filtered;
the screening parameter limit value comprises data that a filtering water chilling unit, a freezing pump, a cooling pump and a cooling tower run outside a preset Hertz range, and data that the chilled water supply temperature, the chilled water return temperature, the chilled water supply and return temperature difference and the chilled water supply and return temperature difference are outside a set value range are filtered;
the step of predicting the central air-conditioning load through the effective data comprises the steps of carrying out normal distribution on the effective data, filtering data which is 3 times of standard deviation to obtain standard data, and calculating the standard data by adopting an ARIMA regression algorithm to obtain the central air-conditioning load.
The ARIMA Model is called an Autoregressive Moving Average Model (ARIMA, autoregensive Integrated Moving Average Model). Also known as ARIMA (p, d, q), is the most common model among statistical models (statistical models) for time series prediction.
The ARIMA model is adopted to predict the time series data, which is required to be stable, and if the data is unstable, the regularity cannot be captured. The reason why the stock data cannot be predicted by ARIMA, for example, is that the stock data is unstable and often fluctuates due to the influence of policy and news. And the relevance of the power model and the time is very large, so that the ARIMA model is adopted for prediction, and the result is accurate.
And (3) screening effective time and parameter limit values through the predicted data, and screening running time: the operation of the central air-conditioning system can be divided into a starting-up stage, an operating stage and a shutdown stage, and due to the unstable operation of the starting-up stage and the shutdown stage, data within 30min after the execution of a starting-up strategy and data after the execution of a shutdown strategy are filtered;
screening parameter limit values: due to the upper and lower boundary constraints of system operation parameters, data of a water chilling unit, a freezing pump, a cooling pump and a cooling tower which operate outside the range of 30-50 Hz are filtered; filtering out data of a chilled water supply temperature TCHWS, a cooling water return temperature TCWR, a chilled water supply and return water temperature difference delta TCHW and a cooling water supply and return water temperature difference delta TCHW which are outside the range of the upper limit and the lower limit of a set value;
PauTa criterion: because the dispersion of the monitored parameters is large and the measurement error of the sensor exists, discretizing the parameters and performing normal distribution, and filtering data beyond 3 standard deviations;
predicting flexible loads such as air conditioner loads and the like by using an ARIMA regression algorithm according to screened historical data and the like FC 。
In an embodiment, the predicting the upper limit of the number of operating central air conditioners in the startup phase and the operation phase based on the power demand includes:
Figure GDA0003953209000000081
wherein N is CHL The upper limit number of the water chilling units, N CHWP The upper limit of the number of the refrigerating pumps, N CWP Upper limit number of cooling pumps, N COT The total number of the cooling towers; q CHL，rated Nominal load of chiller, Q CHWP，rated Nominal load of the refrigerating pump, Q CWP，rated Nominal load of the cooling pump, Q COT，rated Nominal load of the cooling tower;
upper limit of system load is Q SYS Upper limit of load Q of the system SYS Less than power demand Q ER ；
Q SYS ＝N CHL *Q CHL，rated +N CHWP *Q CHWP，rated +N CWP *Q CWP，rated +N COT *Q COT，rated 。
In one embodiment, during the boot-up phase,
Figure GDA0003953209000000082
wherein
Q open The control quantity of the flexible load of the central air conditioner at the starting-up stage;
Figure GDA0003953209000000083
the central air-conditioning flexible load is the predicted starting stage;
Figure GDA0003953209000000084
load for predicted power demand at start-up;
in the course of the operating phase,
Figure GDA0003953209000000085
wherein
Q opearting The control quantity of the flexible load of the central air conditioner in the operation stage;
Figure GDA0003953209000000086
the central air-conditioning flexible load is a predicted operation stage;
Figure GDA0003953209000000091
is the predicted operating phase power demand load.
In an embodiment, the predicting the boot phase operating time and the operating phase operating time based on the power demand includes controlling a boot phase operating policy in a first control manner, and controlling an operating policy in an operating phase in a second control manner includes:
calculating the running time of the boot stage:
Figure GDA0003953209000000092
the first control mode is to control Q open The flexible load is arranged in the normal starting time in advance of T open，ER Time of (2) on N CHL Table water chiller, N CHWP Platform refrigeration pump, N CWP Platform cooling pump, N COT A platform cooling tower;
in the operation stage, predicting the upper limit Q of the energy consumption of the operation of the central air-conditioning system MAX Comprises the following steps:
Q MAX ＝N CHL *Q CHL，rated *I MAX +N CHWP *Q CHWP，rated *(FRQ CHWP /50) c +N CWP *Q CWP，rated *(FRQ CWP /50) c +N COT *Q COT，rated *(FRQ COT /50) c ；
control quantity of flexible load based on central air conditioner
Figure GDA0003953209000000093
Operating time T of the operating phase opearting，ER Comprises the following steps:
Figure GDA0003953209000000094
the second control mode is as follows: advance T opearting，ER Time setting water chilling unit current load rate upper limit I under power demand control mode MAX Frequency operation upper limit FRQ of refrigeration pump, cooling pump and cooling tower CHWP 、FRQ CWP 、FRQ COT ；
Raising the indoor temperature set value to the confidence interval upper limit T indoor，MAX The temperature of the chilled water supply is adjusted up to the upper limit T of the confidence interval CHWS,MAX 。
In one embodiment, c ranges from 2.5 to 3.
In one embodiment, the preset time period is 30min;
the preset Hertz section is 30-50 Hz.
The building flexible load control method based on the power demand control realizes real-time management of the central air-conditioning load and control and management of the power demand through the control of the central air-conditioning system, and realizes sustainable and stable operation.
In practical implementation, for example, in a business building, the power is often turned on more often during the business hours, between 8 and 9 am, and the power load is increased sharply in this time period, but by adopting the method, the power can be turned on in advance through the change of the power load, for example, between 7 and 7:30, the power-on peak period can be avoided, so that the whole power system can run more stably, and the stability and the safety of the power system are improved.
Referring to fig. 2, a building flexible load control system includes:
the load prediction module is used for predicting the load of the central air conditioner;
the central air conditioner running number prediction module is used for predicting the central air conditioner running number upper limit in the starting stage and the running stage under the condition of power demand;
the control quantity prediction module is used for predicting the flexible load control quantity of the central air conditioner in a starting-up stage and an operation stage under the condition of power demand;
and the strategy module is used for predicting the operation time of the startup phase and the operation phase under the condition of power demand, controlling the operation strategy of the startup phase through a first control mode and controlling the operation strategy of the operation phase through a second control mode.
An electronic device, comprising:
at least one processor; and
a memory in communication with the at least one processor, wherein
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the control method described above.
A medium stores computer instructions for causing a computer to execute the control method described above.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be made by those skilled in the art without departing from the spirit and scope of the present invention as defined in the appended claims.
In the description herein, numerous specific details are provided, such as examples of components and/or methods, to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that an embodiment of the invention can be practiced without one or more of the specific details, or with other apparatus, systems, assemblies, methods, components, materials, parts, and/or the like. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of embodiments of the invention.
Reference throughout this specification to "one embodiment," "an embodiment," or "a specific embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment, and not necessarily in all embodiments, of the present invention. Thus, appearances of the phrases "in one embodiment," "in an embodiment," or "in a specific embodiment" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics of any specific embodiment of the present invention may be combined in any suitable manner with one or more other embodiments. It is to be understood that other variations and modifications of the embodiments of the invention described and illustrated herein are possible in light of the teachings herein and are to be considered as part of the spirit and scope of the present invention.
It will also be appreciated that one or more of the elements shown in the figures can also be implemented in a more separated or integrated manner, or even removed for inoperability in some circumstances or provided for usefulness in accordance with a particular application.
Additionally, any reference arrows in the drawings/figures should be considered only as exemplary, and not limiting, unless otherwise expressly specified. Further, as used herein, the term "or" is generally intended to mean "and/or" unless otherwise indicated. Combinations of components or steps will also be considered as being noted where terminology is foreseen as rendering the ability to separate or combine is unclear.
As used in the description herein and throughout the claims that follow, "a", "an", and "the" include plural references unless otherwise specified. Also, as used in the description herein and throughout the claims that follow, the meaning of "in \823030; includes" in 8230; and "in 8230; unless otherwise indicated.
The above description of illustrated embodiments of the invention, including what is described in the abstract, is not intended to be exhaustive or to limit the invention to the precise forms disclosed herein. While specific embodiments of, and examples for, the invention are described herein for illustrative purposes only, various equivalent modifications are possible within the spirit and scope of the present invention, as those skilled in the relevant art will recognize and appreciate. As indicated, these modifications may be made to the present invention in light of the foregoing description of illustrated embodiments of the present invention and are to be included within the spirit and scope of the present invention.
The systems and methods have been described herein in general terms as the details aid in understanding the invention. Furthermore, various specific details have been given to provide a general understanding of the embodiments of the invention. One skilled in the relevant art will recognize, however, that an embodiment of the invention can be practiced without one or more of the specific details, or with other apparatus, systems, assemblies, methods, components, materials, parts, and/or the like. In other instances, well-known structures, materials, and/or operations are not specifically shown or described in detail to avoid obscuring aspects of embodiments of the invention.
Thus, although the present invention has been described herein with reference to particular embodiments thereof, a latitude of modification, various changes and substitutions are intended in the foregoing disclosures, and it will be appreciated that in some instances some features of the invention will be employed without a corresponding use of other features without departing from the scope and spirit of the invention as set forth. Accordingly, many modifications may be made to adapt a particular situation or material to the essential scope and spirit of the present invention. It is intended that the invention not be limited to the particular terms used in following claims and/or to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include any and all embodiments and equivalents falling within the scope of the appended claims. Accordingly, the scope of the invention is to be determined solely by the appended claims.

Claims (8)

1. A building flexible load control method based on power demand control is characterized by comprising the following steps:
predicting the load of the central air conditioner;
predicting the upper limit of the number of running central air conditioners in the starting stage and the running stage based on the power demand;
predicting flexible load control quantity of the central air conditioner in a starting-up stage and an operation stage based on the power demand;
predicting the operation time of a starting-up stage and the operation time of an operation stage based on the power demand, controlling an operation strategy of the starting-up stage by adopting a first control mode, and controlling an operation strategy of the operation stage by adopting a second control mode;
the predicting the central air conditioning load includes:
forecasting the load of the central air conditioner according to the historical data, processing the historical operation data of the building, screening out effective data, and forecasting the load of the central air conditioner through the effective data;
the screened effective data comprises screening effective running time and screening parameter limit values;
the screening effective running time comprises data within a preset time period after the startup execution and data after the shutdown execution are filtered;
the screening parameter limit values comprise data of a filtering water chilling unit, a freezing pump, a cooling pump and a cooling tower which operate outside a preset Hertz range, and data of chilled water supply temperature, chilled water return temperature, chilled water supply and return temperature difference and chilled water supply and return temperature difference outside a set value range are filtered;
the step of predicting the load of the central air conditioner through the effective data comprises the steps of carrying out normal distribution on the effective data, filtering data which is 3 times of standard deviation to obtain standard data, and calculating the standard data by adopting an ARIMA regression algorithm to obtain the load of the central air conditioner;
the upper limit of the number of the running central air conditioners in the starting-up stage and the running stage based on the power demand prediction comprises the following steps:
Figure 399556DEST_PATH_IMAGE001
；
wherein the content of the first and second substances,N CHL is the upper limit number of the water chilling units,N CHWP the upper limit of the number of the freezing pumps,N CWP in order to have an upper limit of the number of the cooling pumps,N COT the total number of the cooling towers;Q CHL,rated is the nominal load of the water chiller,Q CHWP,rated in order to be the nominal load of the freeze pump,Q CWP,rated in order to be the nominal load of the cooling pump,Q COT,rated nominal load of the cooling tower;
upper limit of system load isQ SYS Upper limit of the load of the systemQ SYS Less than power demandQ ER ；
Figure 959851DEST_PATH_IMAGE002
。
2. The building flexible load control method according to claim 1, characterized in that: the method for predicting the flexible load control quantity of the central air conditioner in the starting-up stage and the running stage based on the power demand comprises the following steps:
in the start-up phase, the first,
Figure 777503DEST_PATH_IMAGE003
(ii) a Wherein
Q open The control quantity of the flexible load of the central air conditioner at the starting-up stage;
Figure 790458DEST_PATH_IMAGE004
the central air-conditioning flexible load is the predicted starting stage;
Figure 407384DEST_PATH_IMAGE005
load for predicted power demand at start-up;
in the course of the operating phase,
Figure 307338DEST_PATH_IMAGE006
(ii) a Wherein
Q opearting The control quantity of the flexible load of the central air conditioner in the operation stage;
Figure 312204DEST_PATH_IMAGE007
the central air-conditioning flexible load is a predicted operation stage;
Figure 64652DEST_PATH_IMAGE008
is the predicted operating phase power demand load.
3. The building flexible load control method according to claim 2, wherein the predicting the operation time of the startup phase and the operation time of the operation phase based on the power demand, and the controlling the operation strategy of the startup phase by using the first control method and the operation strategy of the operation phase by using the second control method comprises:
calculating the running time of the boot stage:
Figure 344324DEST_PATH_IMAGE009
；
the first control mode is to control the motor to rotate
Figure 364364DEST_PATH_IMAGE010
The flexible load is arranged in the normal starting time in advanceT open,ER Time of openingN CHL A water chilling unit,N CHWP A table freezing pump,N CWP A platform cooling pump,N COT A platform cooling tower;
in the operation stage, the upper limit of energy consumption for operating the central air-conditioning system is predictedQ MAX Comprises the following steps:
Figure 540130DEST_PATH_IMAGE011
;
c is a constant;
control quantity of flexible load based on central air conditioner
Figure 776945DEST_PATH_IMAGE012
；
Run time of run phaseT opearting,ER Comprises the following steps:
Figure 63570DEST_PATH_IMAGE013
；
the second control mode is as follows: advance the timeT opearting,ER Time-set water chilling unit current load rate upper limit in power demand control modeI MAX Upper limit of frequency operation of a refrigerating pump, a cooling pump, and a cooling towerFRQ CHWP 、FRQ CWP 、FRQ COT ；
Raising the indoor temperature setpoint to the confidence interval upper limitT indoor,MAX The temperature of the chilled water supply is adjusted up to the upper limit of the confidence intervalT CHWS,MAX 。
4. The building flexible load control method according to claim 3, wherein the value range of c is 2.5 to 3.
5. The building flexible load control method according to claim 4, further comprising:
the preset time period is 30min;
the preset Hertz section is 30 to 50Hz.
6. A building flexible load control system incorporating the building flexible load control method of any one of claims 1-5, comprising:
the load prediction module is used for predicting the load of the central air conditioner;
the central air conditioner running number prediction module is used for predicting the central air conditioner running number upper limit in the starting stage and the running stage under the condition of power demand;
the control quantity prediction module is used for predicting the flexible load control quantity of the central air conditioner in a starting-up stage and an operation stage under the condition of power demand;
and the strategy module is used for predicting the operation time of the startup phase and the operation phase under the condition of power demand, controlling the operation strategy of the startup phase through a first control mode and controlling the operation strategy of the operation phase through a second control mode.
7. An electronic device, comprising:
at least one processor; and
a memory in communication with the at least one processor, wherein
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the control method of any one of claims 1-5.
8. A medium characterized by storing computer instructions for causing a computer to execute the control method of any one of claims 1 to 5.
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Publication number Priority date Publication date Assignee Title
CN108954679A (en) * 2018-06-28 2018-12-07 湖南湖大瑞格能源科技有限公司 A kind of energy-saving control method and system of sewage source heat pump system
CN109059195A (en) * 2018-06-08 2018-12-21 肖永建 For cutting down the control method and control system of the central air-conditioning of network load peak value
CN113418228A (en) * 2020-12-14 2021-09-21 建科环能科技有限公司 Air source heat pump return difference changing water temperature control method and system based on supply and demand matching
CN113469412A (en) * 2021-06-02 2021-10-01 国核电力规划设计研究院有限公司 Real-time operation strategy optimization method and system for comprehensive energy system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109059195A (en) * 2018-06-08 2018-12-21 肖永建 For cutting down the control method and control system of the central air-conditioning of network load peak value
CN108954679A (en) * 2018-06-28 2018-12-07 湖南湖大瑞格能源科技有限公司 A kind of energy-saving control method and system of sewage source heat pump system
CN113418228A (en) * 2020-12-14 2021-09-21 建科环能科技有限公司 Air source heat pump return difference changing water temperature control method and system based on supply and demand matching
CN113469412A (en) * 2021-06-02 2021-10-01 国核电力规划设计研究院有限公司 Real-time operation strategy optimization method and system for comprehensive energy system

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