CN117249537A - Virtual power plant scheduling and control system and method based on central air conditioner - Google Patents
Virtual power plant scheduling and control system and method based on central air conditioner Download PDFInfo
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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
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- F24F11/00—Control or safety arrangements
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- F24F11/46—Improving electric energy efficiency or saving
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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
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/54—Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
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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
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Abstract
The invention discloses a virtual power plant dispatching and controlling system and method based on a central air conditioner, which combines a plurality of building central air conditioning systems to form a virtual power plant, collects real-time data of the building central air conditioning systems based on data collection equipment, carries out data cleaning and processing, and when the demand of a power grid or the price of power changes, a dispatching control center uses an optimization algorithm to calculate the optimal running state according to the running data of each building central air conditioning system and the demand of the power grid; then, the dispatching control center sends the states to the control module of each building central air conditioning system through the communication module; finally, the control module adjusts the running state of the building central air conditioning system according to the received instruction; meanwhile, the energy storage module provides energy storage and release functions for each building central air conditioning system so as to optimize the power cost and improve the system efficiency, thereby realizing balance of power requirements and optimization of the power cost.
Description
Technical Field
The invention relates to a dispatching and controlling technology of a building central air conditioner, in particular to a dispatching and controlling system and method of a virtual power plant based on a central air conditioner.
Background
The frequency modulation scheduling technology of the building central air conditioning system has been developed remarkably in recent years. On the one hand, the development of the scheduling technology of the modern building central air conditioning system tends to be more intelligent and automatic, and by using AI and machine learning algorithms, the system automatically adjusts the operation parameters thereof, improves the efficiency and reduces the energy consumption, and optimizes in real time according to the energy demand, weather conditions, energy price and other factors of the building. On the other hand, the building central air conditioning system allows remote monitoring and control through an access network, higher flexibility and convenience are provided, and the Internet of things equipment also provides rich data for optimizing and adjusting the operation of the system. Meanwhile, with the improvement of environmental awareness, energy conservation and sustainability have become important consideration factors for the design of a building central air conditioning system.
By adopting a new control strategy and high-efficiency equipment, the energy efficiency of central air conditioning systems of large public buildings and commercial buildings is greatly improved, and more refined and intelligent air conditioning system management and control are realized.
However, in the prior art, building central air conditioning systems are generally only concerned with meeting the temperature requirements inside the building, and neglecting their impact on the power grid. During peak demand, a large number of building central air conditioning systems are running simultaneously, which may create significant pressure on the grid and even result in an insufficient supply of electricity.
Disclosure of Invention
The invention aims to solve the problems that: the utility model provides a virtual power plant dispatching and controlling system and method based on a central air conditioner, which are used for timely optimizing and adjusting the running state of the central air conditioner of a building according to the real-time demand change of a power grid.
The invention adopts the following technical scheme: a virtual power plant dispatching and controlling method based on a central air conditioner combines a plurality of building central air conditioning systems to form a virtual power plant for centralized dispatching and controlling, comprising the following steps:
s1, data collection: based on the data acquisition equipment installed in each building central air conditioning system, acquiring real-time data, including building central air conditioning equipment operation data and environment data, and transmitting the acquired real-time data to a centralized control system through a wired or wireless network;
s2, data processing: after the centralized control system receives the collected real-time data, the data is cleaned and processed, including abnormal value removal, missing value filling and data standardization;
s3, optimizing decision: the energy management system performs optimization calculation according to the electric power market information and based on the collected real-time data to obtain a central air conditioner operation strategy of each building;
s4, control signal transmission: after the optimization decision is completed, the central control system sends a central air-conditioning control signal to each building central air-conditioning system;
s5, demand response: when a power grid operator sends a demand response signal, the central control system adjusts the central air-conditioning operation strategy of each building so as to reduce the load of the power grid;
s6, data analysis and optimization: in the running process of each building central air conditioning system, the central control system continuously collects and analyzes real-time data, the energy management system evaluates the performance of the system, and the data analysis and the machine learning algorithm are used for optimizing the control strategy, so that the economy and the reliability of the building central air conditioning system are improved.
S7, energy recovery and storage: the energy management system also comprises an energy storage module which provides energy storage and release functions for each building central air conditioning system so as to realize optimization of electric power cost and improvement of system efficiency.
Further, in step S1, the device operation data, which is acquired based on the sensor and the smart meter on each building central air conditioning system device, includes: the operating state, the power consumption, the cooling or heating capacity of the central air conditioning system.
The environmental data, based on indoor and outdoor environmental sensors and network service acquisition, comprises: environmental parameters such as indoor temperature, humidity and the like, and weather forecast data.
Further, in step S2, based on the Python database, the centralized control system performs data cleaning and processing on the received real-time data, identifies missing values in the real-time data, deletes repeated items, and converts the repeated items into usable formats or standardized scales, so as to obtain real-time data with consistent data structures for analysis and optimization.
Further, in step S3, the electric power market information includes electric power market data, grid demand data, and weather forecast data; electric power market data, acquired through network services of an electric power market, comprising: real-time price, predicted price and transaction rules of the electric power market;
grid demand data: network service acquisition by a grid operator, comprising: real-time demand of the power grid, predicted demand, demand response signal.
When the electric power market data or the electric network demand data change, the energy management system performs optimization calculation to obtain and compare the air conditioner operation strategy of each building, and the optimization calculation method comprises linear programming and/or dynamic programming:
s3.1, linear programming: determining an operation strategy of each building central air conditioning system through linear programming, and minimizing the electric power purchase cost and/or maximizing the electric power sales income while meeting the electric network demand; defining a coefficient A for maximizing the electric sales income c and the constraint condition and a boundary value b of the constraint condition, and solving a linear programming by using a linprog (c, A, b) function;
s3.2, dynamic programming: based on optimization of time sequence factors, an air conditioner operation strategy of each time period is determined according to the predicted price of the electric power market and the power grid demand, dynamic programming solution is carried out, and electric power sales income, electric power demand and total electric power output capacity of each time period are calculated.
Further, in step S4, the centralized control system sends a control signal to the air conditioning system of each building through a wired or wireless network or through the building automation system BAS; the control signal is a control signal for each building air conditioning system, and is generated by a centralized control system, and comprises: a startup and shutdown instruction and a set temperature instruction.
Further, in step S5, the central control system establishes a real-time communication link with the building central air conditioning system, and adjusts the air conditioning operation policy according to the requirements sent by the grid operators, and sends the air conditioning operation policy to the building central air conditioning system.
Further, in step S6, the data analysis and machine learning algorithm comprises regression analysis, cluster analysis and neural network; the optimization results are generated by the energy management system for the generation of control signals and the evaluation of system performance, comprising: air conditioning operation strategy, expected power consumption and cost for each building.
Further, in step S7, the energy storage module is a battery energy storage system or a thermal energy storage system, and integrates the energy storage module, so as to provide energy storage and release for the system, and be used for optimizing the electric power cost and improving the efficiency of the building central air conditioning system;
the battery energy storage system directly stores electric energy through the battery system and releases the electric energy back to a power grid or for energy requirements of a building when needed; the heat energy storage system is used for generating and storing heat energy by using redundant electric power when the electric power price is low, and then converting the stored heat energy back into electric energy or directly using the electric energy to meet the heat energy requirement of a building when the electric power price is high;
the integration of the energy storage module comprises the following steps: data integration, integrating operation data of the energy storage module, including energy storage energy and charge/discharge states; the control integration, dispatch control center sends instruction to control module, control the charge and discharge operation of energy storage module; and optimizing and integrating, namely optimizing an air conditioner operation strategy and an electric power transaction strategy of each building based on the state and the capacity of the energy storage module in an optimization algorithm.
The technical scheme of the invention also comprises a virtual power plant dispatching and controlling system based on the central air conditioner, wherein the dispatching and controlling method of any virtual power plant is used for dispatching and controlling the building central air conditioning system, and the dispatching and controlling system comprises the following steps:
a data collection module for collecting operational data of each building central air conditioning system including, but not limited to, temperature, humidity, power consumption, etc., which will be used to determine the operational status of each system;
the communication module is used for sending the data collected by the data collection module to the dispatching control center and receiving an instruction of the dispatching control center, and the communication module establishes communication by using a wireless communication technology, wherein the communication module comprises Wi-Fi or 4G/5G;
a scheduling control center: the core of the whole system uses a cloud computing technology to provide enough computing resources, and determines the running state of each building central air conditioning system, including a central control system and the like, by using an optimization algorithm according to the received data and the requirements of a power grid;
the energy storage module provides energy storage and release functions for each building central air conditioning system so as to realize energy recovery and storage;
and the control module is used for: a PLC or other industrial control device is used to control the operation of each building central air conditioning system according to the instructions of the dispatch control center.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
1. according to the invention, a virtual power plant is formed by combining a plurality of building central air conditioning systems, and the running state of each system can be flexibly adjusted according to the requirements of a power grid, so that the balance of power requirements is realized.
2. According to the invention, through the dispatching and control of the virtual power plants, the running state of each building central air conditioning system can be optimized according to the change of the electric power price, so that the optimization of the electric power cost is realized.
3. The invention can improve the energy efficiency of the system by optimizing the running state of each building central air-conditioning system, thereby saving energy.
4. The invention can reduce the pressure of the power grid in the peak period of the power demand by balancing the power demand and optimizing the power cost, thereby improving the stability of the power grid.
5. The invention can reduce the influence on the comfort level of the user as much as possible through the optimization algorithm, thereby improving the user experience, and simultaneously, can reduce the carbon emission through improving the energy efficiency and saving the energy, thereby contributing to the environmental protection.
Drawings
FIG. 1 is a schematic diagram of a virtual power plant architecture of the present invention;
FIG. 2 is a flow chart of a method for scheduling and controlling a virtual power plant according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the application will be further elaborated in conjunction with the accompanying drawings, and the described embodiments are only a part of the embodiments to which the present invention relates. All non-innovative embodiments in this example by others skilled in the art are intended to be within the scope of the invention. Meanwhile, the step numbers in the embodiments of the present invention are set for convenience of illustration, the order between the steps is not limited, and the execution order of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.
The terms of related art terms in the present invention are explained as follows:
building central air conditioning system: this is a system for providing cooling and heating to a building and generally includes a central air conditioning unit and a set of air treatment facilities distributed throughout the various areas of the building.
Virtual power plant (Virtual Power Plant): this is a concept that forms a virtual power plant that can be flexibly scheduled by combining multiple distributed energy sources (e.g., wind generators, solar panels, energy storage devices, etc.). The virtual power plant can flexibly adjust the running state of each energy resource according to the change of the power grid demand and the power price, so that the balance of the power demand and the optimization of the power cost are realized.
Scheduling (Scheduling): this is a process of determining the order and time of task execution based on some optimization objective. In the invention, scheduling refers to determining the running state of each building central air conditioning system according to the change of the power grid demand and the power price.
Control (Control): this is a process by which the output of the system is brought to the desired destination by adjusting the input to the system. In the invention, control means that the running state of each building central air conditioning system is adjusted according to the dispatching result.
Optimization algorithm (Optimization Algorithm): this is an algorithm for finding the optimal solution. In the invention, the optimization algorithm is used for determining the optimal running state of each building central air conditioning system so as to realize balance of power demand and optimization of power cost.
Communication module (Communication Module): this is a device or software for data transmission. In the invention, the communication module is used for sending the data collected by the data collection module to the dispatching control center and receiving the instruction of the dispatching control center.
Data collection module (Data Collection Module): this is a device or software for collecting and processing data. In the invention, the data collection module is responsible for collecting the operation data of each building central air conditioning system.
PLC (Programmable Logic Controller): this is an industrial computer for implementing automation control. In the present invention, a PLC may be used to implement control of a building central air conditioning system.
Cloud Computing (Cloud Computing): this is a technique for providing computing resources over the internet. In the present invention, cloud computing may be used to provide computing resources required by a dispatch control center.
Wireless communication (Wireless Communication): this is a technique for data transmission over radio waves or other wireless media. In the present invention, wireless communication may be used to enable the transmission of data and instructions.
Grid Demand (Grid Demand): this refers to the amount of power that the grid needs at a particular time. In the present invention, the grid demand is an important factor in determining the operating status of each building central air conditioning system.
Price of electricity (Electricity Price): this refers to the price per unit of power. In the present invention, the variation of the price of electricity affects the operation state of each building central air conditioning system.
User Experience (User Experience): this refers to the perception of the user during use of the product or service. In the invention, although the running state of the building central air conditioning system needs to be adjusted according to the requirements of the power grid, the influence on the user experience can be reduced as much as possible through an optimization algorithm.
As shown in fig. 1, the virtual power plant scheduling and controlling system based on the central air conditioner of the present invention includes:
a data collection module for collecting operational data of each building central air conditioning system including, but not limited to, temperature, humidity, power consumption, etc., which will be used to determine the operational status of each system;
the communication module is used for sending the data collected by the data collection module to the dispatching control center and receiving an instruction of the dispatching control center, and the communication module establishes communication by using a wireless communication technology, wherein the communication module comprises Wi-Fi or 4G/5G;
a scheduling control center: the core of the whole system uses a cloud computing technology to provide enough computing resources, and determines the running state of each building central air conditioning system by using an optimization algorithm according to the received data and the requirements of a power grid;
and the control module is used for: a PLC or other industrial control device is used to control the operation of each building central air conditioning system according to the instructions of the dispatch control center.
When the demand of the power grid or the price of the power changes, the dispatching control center can calculate the optimal running state by using an optimization algorithm according to the running data of each building central air conditioning system and the demand of the power grid. The dispatch control center will then send these conditions to the control module of each building central air conditioning system via the communication module. And the control module adjusts the running state of the building central air conditioning system according to the received instruction, so that the balance of power requirements and the optimization of power cost are realized.
In one embodiment of the present invention, a virtual power plant scheduling and controlling method based on a central air conditioner, as shown in fig. 2, specifically comprises the following steps:
s1, data collection:
first, the air conditioning system of each building needs to install data acquisition devices, such as smart meters and sensors, to acquire real-time data, including building central air conditioning device operation data and environment data, which are transmitted to the centralized control system in real time through a wired or wireless network.
The equipment operation data is acquired based on sensors and intelligent electric meters on equipment of each building central air conditioning system, and comprises the following components: the operating state, power consumption, cooling or heating capacity of the central air conditioning system; the running state comprises a switching-on and switching-off state and a running mode of the central air conditioning system;
environmental data, based on indoor and outdoor environmental sensors and network service acquisition, comprising: environmental parameters such as indoor temperature, humidity and the like, and weather forecast data, wherein the weather forecast data comprises: such as temperature, humidity, wind speed, solar radiation, etc.
S2, data processing: after the centralized control system receives the data, data cleaning and processing are needed, such as removing abnormal values, filling missing values, performing data standardization and the like, so as to facilitate subsequent data analysis and optimization.
In this embodiment, the Pandas library and the NumPy library in Python are used for data cleaning and processing, and the steps are as follows:
a. identifying a missing value: identifying missing values in the data by using isnull () method, which returns a boolean value indicating whether a value is missing;
b. delete duplicates if there are duplicates in the data, delete them using the drop_duplicates () method in Pandas.
c. Filling missing values-filling missing values using the filena () method, one value or method (e.g., forward fill, backward fill) may be specified.
d. Data conversion, in which string operations are performed using methods such as str.replace () to ensure consistency of data.
e. Deleting unnecessary columns or rows by deleting unnecessary columns or rows from the data using a drop () method;
f. after the steps, the data with good and consistent structure is obtained, the problem of missing values in the data is solved, repeated items are deleted, and the repeated items are converted into a more usable format or standardized scales, so that more accurate and effective optimization calculation is promoted in the subsequent steps of the method.
S3, optimizing decision: and the energy management system performs optimization calculation according to the electric power market information and the collected real-time data to obtain an air conditioner operation strategy of each building.
The power market information comprises power market data, power grid demand data and weather forecast data; electric power market data, acquired through network services of an electric power market, comprising: real-time price, predicted price and transaction rules of the electric power market; grid demand data: network service acquisition by a grid operator, comprising: real-time demand of the power grid, predicted demand, demand response signal.
This requires the use of some optimization algorithms, such as linear programming, dynamic programming, etc.
Specifically, when the price of electricity is low, the air conditioning system is operated more than the building to cool or heat the building, and then when the price of electricity is high, the air conditioning operation is reduced, and the heat capacity of the building is utilized to maintain the indoor temperature.
In this embodiment, the optimization algorithm used is as follows:
a. linear programming (Linear Programming, LP):
through linear programming, an operating strategy of each building central air conditioning system is determined to minimize power purchase costs and/or maximize power sales revenue while meeting grid requirements. The linprog package introduced into Python and defining parameters includes: maximizing the electric sales income c, the coefficient A of the constraint condition and the boundary value b of the constraint condition, and solving the linear programming by using a linprog (c, A, b) function;
b. based on optimization of time series factors, determining an air conditioner operation strategy of each time period according to the predicted price of the electric power market and the power grid demand;
and obtaining and comparing the air conditioner operation strategies of each building based on the two methods.
S4, control signal transmission: after the optimization decision is completed, the centralized control system sends control signals to the air conditioning systems of each building, either through a wired or wireless network, or through a Building Automation System (BAS).
The control signal is a control signal for each building air conditioning system, and is generated by a centralized control system, and comprises: a startup and shutdown instruction, a set temperature instruction and the like.
S5, demand response: when the power grid operator sends out a demand response signal, the centralized control system needs to respond quickly, and the operation strategy of the air conditioner is adjusted so as to reduce the load of the power grid. This may require a real-time communication link, as well as a set of fast-response control strategies.
In this embodiment, adjusting an operation policy of an air conditioner includes:
a. and (3) setting the temperature: according to the price of the electric market and the demand of the power grid, the set temperature of the building central air conditioning system is dynamically adjusted so as to reduce the electric power consumption or increase the electric power sales;
b. on/off strategy: determining when to turn on or off a building central air conditioning system according to the power requirements and market conditions;
c. demand response: when the power grid operator sends a demand response signal, quickly adjusting an air conditioner operation strategy, for example, reducing the power grid load by reducing the refrigeration/heating load;
d. energy storage and release: the energy storage capacity of the building is utilized, energy is stored when the price of the electric power is low, and the energy is released when the price of the electric power is high;
e. priority scheduling: scheduling operation of the air conditioning system according to the priority of the building to ensure comfort of the important area;
f. prediction and optimization: according to weather forecast data and electric power market forecast, an air conditioner operation strategy is adjusted in advance so as to achieve higher energy efficiency and economic benefit;
g. data monitoring and analysis: monitoring and analyzing operation data of the air conditioning system in real time, including energy consumption, temperature, humidity and the like, so as to optimize an air conditioning operation strategy in real time;
h. fault detection and maintenance: the running state of the air conditioning system is monitored in real time, so that problems are found and repaired in time, and the normal running and energy efficiency of the air conditioning system are ensured;
i. user feedback and adjustment: and adjusting the operation strategy of the air conditioner according to the feedback and the requirements of the user so as to improve the satisfaction and the comfort of the user.
S6, data analysis and optimization: in the running process of the system, data are required to be continuously collected and analyzed to evaluate the performance of the system, optimize the control strategy, improve the economy and reliability of the system, and apply data analysis and machine learning algorithms such as regression analysis, cluster analysis, neural networks and the like.
In this embodiment, the data analysis and machine learning algorithm is specifically as follows:
a. regression analysis (Regression Analysis) regression analysis is a statistical method used to study the relationship between variables. Generally, it involves identifying how one or more independent variables affect a dependent variable. For example, explore how energy consumption of a building is affected by external temperature and activity levels within the building, collect data and use regression analysis to build a model that predicts energy consumption at different external temperatures and activity levels.
b. Cluster Analysis (Cluster Analysis) is an unsupervised machine learning method that groups data points into clusters such that the similarity between data points in the same Cluster is greatest and the similarity between data points in different clusters is smallest. Different buildings are grouped according to daytime and nighttime energy consumption patterns, cluster analysis is applied to identify similar energy consumption patterns, and a cluster label is allocated to each building.
c. Neural Networks (Neural Networks) are a machine learning model that mimics the working mechanism of the human brain. It consists of a number of processing units (or neurons) connected together by connecting weights to implement a complex nonlinear model, predicting future energy consumption from past data.
A neural network model is created and trained to capture patterns in the data and use it to predict future energy consumption.
The optimization result is generated by an energy management system and used for generating control signals and evaluating system performance, and the optimization result comprises the following steps: air conditioning operation strategy, expected power consumption and cost for each building.
S7, energy recovery and storage: as an important component, the system is provided with energy storage and release functions to optimize power costs and improve system efficiency.
It should be noted that the virtual power plant scheduling and control system based on the central air conditioner further comprises an energy storage module.
In this embodiment, the energy storage module is used to provide the energy storage and release functions for the system, so as to optimize the power cost and improve the system efficiency, and the specific application is as follows:
a. and the power cost is optimized by storing redundant energy when the power price is low and then releasing the stored energy when the power price is high, so that the power cost is reduced.
b. The energy efficiency is improved by storing the redundant energy generated by the central air conditioning system in the off-peak period and releasing the redundant energy in the peak period so as to meet the requirement of a power grid, thereby improving the energy efficiency of the whole system.
c. The stability and the reliability of the system are enhanced, namely, the energy storage module provides temporary energy support when the power grid needs to be in a peak period or power interruption occurs, so that the stable operation of the system is ensured.
d. The utilization of renewable energy sources is facilitated by the energy storage module storing electricity from renewable energy sources (such as solar or wind energy) for later use, thereby facilitating the utilization of renewable energy sources.
The method and principle of the energy storage module are as follows:
a. battery energy storage-storing electrical energy by a battery system, the most common of which is a lithium ion battery. The battery energy storage system directly stores electrical energy and releases the electrical energy back to the grid or for energy demand of the building when needed.
b. Thermal energy storage-thermal energy storage systems utilize excess electrical power to generate thermal energy and store it when the price of the electrical power is low, such as by means of electric water heaters or thermal storage materials. And then when the price of the electric power is high, the stored heat energy is converted back into electric energy or is directly used for meeting the heat energy requirement of the building.
The integration of the energy storage module is as follows:
a. and data integration, namely integrating operation data (such as energy storage energy, charge/discharge states and the like) of the energy storage module into a data collection module so as to enable a dispatching control center to make decisions according to the real-time energy storage data.
b. And the control integration is that the dispatching control center sends an instruction to the control module according to the condition of the electric power market and the requirement of the power grid so as to control the charging and discharging operation of the energy storage module.
c. And optimizing integration, namely taking the state and the capacity of the energy storage module into consideration in an optimization algorithm to further optimize the air conditioner operation strategy and the electric power transaction strategy of each building.
It should be specifically noted that, in the communication process of this embodiment, data that needs to be acquired and interacted is as follows:
device operation data: including the operating status (e.g., on/off, operational mode, etc.), power consumption, cooling or heating capacity, etc. of each building air conditioning system. These data are obtained mainly by sensors and smart meters on the device.
Environmental data: including environmental parameters such as indoor temperature, humidity, etc., and weather forecast data (e.g., temperature, humidity, wind speed, solar radiation, etc.). These data are obtained mainly through indoor and outdoor environmental sensors and network services.
Electric market data: including real-time prices for electricity markets, forecasted prices, trading rules, and the like. These data are obtained mainly through network services of the power market.
Grid demand data: including real-time demand for the grid, predicted demand, demand response signals, etc. These data are mainly obtained through network services of the grid operators.
Control signal: including control signals for each building air conditioning system, such as power on/off commands, set temperature commands, etc. These signals are generated primarily by the centralized control system and transmitted through a network or Building Automation System (BAS).
Optimizing the result: including the results of each optimization calculation such as the air conditioning operating strategy, expected power consumption and cost for each building, etc. These results are generated primarily by the energy management system and are used for control signal generation and system performance assessment.
Through the interaction and communication control of the data, a plurality of building central air conditioning systems are combined to form a virtual power plant, when the demand of a power grid or the price of the power changes, a dispatching control center uses an optimization algorithm, and an optimal running state is calculated according to the running data of each building central air conditioning system and the demand of the power grid; then, the dispatching control center sends the states to the control module of each building central air conditioning system through the communication module; and the control module adjusts the running state of the building central air conditioning system according to the received instruction, so that the balance of power requirements and the optimization of power cost are realized.
Specifically, the embodiment researches a central air conditioner virtual unit based on the data of a commercial building in a large city of east China, and uses a centrifugal water chiller to perform experiments, so that the relation between the water temperature and steady-state power and COP is analyzed.
By adjusting the water temperature, the adjustment time, the reduction of CAC power, and the time required to reach the upper temperature limit are analyzed. In addition, the study also obtained the total output characteristic by integrating 1000 CAC virtual units with different output characteristics into one Virtual Power Plant (VPP) and superimposing their output characteristics. This study shows that when the outdoor temperature is too high, the power and output time of the virtual unit can be affected to ensure indoor comfort; when the outdoor temperature is too low, the power reduction of the CAC is small due to the low power operation of the CAC, but the long-term power output is maintained.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (10)
1. A virtual power plant dispatching and controlling method based on a central air conditioner is characterized in that a plurality of building central air conditioning systems are combined to form a virtual power plant for centralized dispatching and controlling, and the method comprises the following steps:
s1, data collection: based on the data acquisition equipment installed in each building central air conditioning system, acquiring real-time data, including building central air conditioning equipment operation data and environment data, and transmitting the acquired real-time data to a centralized control system through a wired or wireless network;
s2, data processing: after the centralized control system receives the collected real-time data, the data is cleaned and processed, including abnormal value removal, missing value filling and data standardization;
s3, optimizing decision: the centralized control system performs optimization calculation based on the acquired real-time data according to the electric power market information to obtain a central air conditioner operation strategy of each building;
s4, control signal transmission: after the optimization decision is completed, the central control system sends a central air-conditioning control signal to each building central air-conditioning system;
s5, demand response: when a power grid operator sends a demand response signal, the central control system adjusts the central air-conditioning operation strategy of each building so as to reduce the load of the power grid;
s6, data analysis and optimization: in the running process of each building central air conditioning system, the central control system continuously collects and analyzes real-time data, evaluates the performance of the system, optimizes the control strategy by using data analysis and a machine learning algorithm, and improves the economy and reliability of the building central air conditioning system;
s7, energy recovery and storage: the energy storage module provides energy storage and release functions for each building central air conditioning system so as to optimize the electric power cost and improve the system efficiency.
2. The method for dispatching and controlling a virtual power plant based on a central air conditioner according to claim 1, wherein in step S1, the equipment operation data is obtained based on sensors and smart meters on each building central air conditioner system equipment, and the method comprises: the operating state, power consumption, cooling or heating capacity of the central air conditioning system; the running state comprises a switching-on and switching-off state and a running mode of the central air conditioning system;
the environmental data, based on indoor and outdoor environmental sensors and network service acquisition, comprises: indoor temperature, humidity environmental parameters, and weather forecast data, wherein the weather forecast data comprises: such as temperature, humidity, wind speed, solar radiation.
3. The virtual power plant scheduling and controlling method based on the central air conditioner according to claim 2, wherein in step S2, based on the Python database, the central control system performs data cleaning and processing on the received real-time data, identifies missing values in the real-time data, deletes repeated items, converts the missing values into usable formats or standardized scales, and obtains real-time data with consistent data structures for analysis and optimization, and the data cleaning and processing steps are as follows:
s2.1, identifying a missing value: identifying a missing value in the data, and counting the data of the missing value to represent the degree of the missing data;
s2.2, deleting repeated items, namely deleting the data containing the repeated items by using a Pandas method if the repeated items exist in the data;
s2.3, filling the missing value by using a filena method, wherein the filling of the missing value comprises the following steps: specifying a data value, a forward padding method, or a backward padding method;
s2.4, data conversion: performing character string operation to ensure consistency of data;
s2.5, deleting unnecessary columns or rows, namely deleting the unnecessary columns or rows from the data by using a drop method.
4. The method for dispatching and controlling a virtual power plant based on a central air conditioner according to claim 1, wherein in step S3, the electric power market information includes electric power market data, grid demand data and weather forecast data;
the electric power market data is obtained through network service of the electric power market, and comprises: real-time price, predicted price and transaction rules of the electric power market;
the grid demand data: network service acquisition by a grid operator, comprising: real-time demand of the power grid, predicted demand, demand response signal.
5. The method for dispatching and controlling a virtual power plant based on a central air conditioner according to claim 4, wherein in step S3, when the electric power market data or the electric network demand data change, the central control system performs optimization calculation to obtain and compare the air conditioner operation policy of each building, and the method comprises linear planning and/or dynamic planning:
s3.1, linear programming: determining an operation strategy of each building central air conditioning system through linear programming, and minimizing the electric power purchase cost and/or maximizing the electric power sales income while meeting the electric network demand; defining a coefficient A for maximizing the electric sales income c and the constraint condition and a boundary value b of the constraint condition, and solving a linear programming by using a linprog (c, A, b) function;
s3.2, dynamic programming: based on optimization of time sequence factors, an air conditioner operation strategy of each time period is determined according to the predicted price of the electric power market and the power grid demand, dynamic programming solution is carried out, and electric power sales income, electric power demand and total electric power output capacity of each time period are calculated.
6. The method according to claim 5, wherein in step S4, the centralized control system sends the control signal to the air conditioning system of each building through a wired or wireless network or through the building automation system BAS; the control signal is a control signal for each building air conditioning system, and is generated by a centralized control system, and comprises: a startup and shutdown instruction and a set temperature instruction.
7. The virtual power plant dispatching and controlling method based on the central air conditioner as claimed in claim 6, wherein in step S5, the central control system establishes a real-time communication link with the building central air conditioning system, adjusts the operation strategy of the air conditioner according to the requirements sent by the power grid operators, and sends the operation strategy to the building central air conditioning system;
the adjusting the operation strategy of the air conditioner comprises the following steps: temperature setting, on/off control, power grid operator demand response, energy storage and release, priority-based key region scheduling, market prediction and optimization, central air conditioning system data monitoring and analysis, fault detection and maintenance, and central air conditioning system operation control adjustment based on user feedback.
8. The method for dispatching and controlling a virtual power plant based on a central air conditioner according to claim 7, wherein in step S6, the data analysis and machine learning algorithm comprises regression analysis, cluster analysis, neural network;
the regression analysis method is used for researching the relation between variables, establishing a regression analysis model, identifying the influence of the external temperature and the activity level in the building on the energy consumption of the building, and performing data fitting;
the cluster analysis method is used for grouping data points into a plurality of clusters, grouping different buildings according to the energy consumption modes in the daytime and at night, identifying similar energy consumption modes by applying cluster analysis, and distributing a cluster label for each building;
the neural network consists of a plurality of processing units, the processing units are connected together through connection weights, a nonlinear model of the neural network is created and trained, and the mode in the captured data predicts the future energy consumption according to the past data;
the optimization result is generated by an energy management system and used for generating control signals and evaluating system performance, and the optimization result comprises the following steps: air conditioning operation strategy, expected power consumption and cost for each building.
9. The virtual power plant dispatching and controlling method based on the central air conditioner according to claim 8, wherein in step S7, the energy storage module is a battery energy storage system or a thermal energy storage system, the integration of the energy storage module is performed, and energy storage and release are provided for the system, so as to optimize the electric power cost and improve the efficiency of the building central air conditioner system;
the battery energy storage system directly stores electric energy through the battery system and releases the electric energy back to a power grid or for energy requirements of a building when needed;
the heat energy storage system utilizes redundant electric power to generate heat energy and store the heat energy when the electric power price is low, and then converts the stored heat energy back to electric energy or directly uses the electric energy to meet the heat energy requirement of a building when the electric power price is high;
the integration of the energy storage module comprises the following steps: data integration, integrating operation data of the energy storage module, including energy storage energy and charge/discharge states; the control integration, dispatch control center sends instruction to control module, control the charge and discharge operation of energy storage module; and optimizing and integrating, namely optimizing an air conditioner operation strategy and an electric power transaction strategy of each building based on the state and the capacity of the energy storage module in an optimization algorithm.
10. A virtual power plant scheduling and control system based on a central air conditioner, characterized in that the virtual power plant scheduling and control method according to any one of the preceding claims 1 to 9 is used for scheduling and controlling a building central air conditioning system, comprising:
a data collection module for collecting operational data of each building central air conditioning system including, but not limited to, temperature, humidity, power consumption, which data is to be used to determine the operational status of each system;
the communication module is used for sending the data collected by the data collection module to the dispatching control center and receiving an instruction of the dispatching control center, and the communication module establishes communication by using a wireless communication technology, wherein the communication module comprises Wi-Fi or 4G/5G;
the dispatching control center uses cloud computing technology to provide enough computing resources, and the central control system uses an optimization algorithm to determine the running state of each building central air conditioning system according to the received data and the requirements of the power grid;
the energy storage module provides energy storage and release functions for each building central air conditioning system so as to realize energy recovery and storage;
and the control module is used for: a PLC or other industrial control device is used to control the operation of each building central air conditioning system according to the instructions of the dispatch control center.
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