CN116841268A - Urban large-scale energy station centralized control method and system based on multi-station data fusion - Google Patents
Urban large-scale energy station centralized control method and system based on multi-station data fusion Download PDFInfo
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Abstract
The invention belongs to the field of ground source heat regulation and control, and relates to a centralized control method for urban large-scale energy stations. The method of the invention comprises the following steps: the sensor module of the energy station collects the operation parameters of the large-scale ground source heat pump system and submits the operation parameters to the data storage module i through the digital controller; the data is then presented to a data fusion module of the central control platform; the data fusion module presents the data to a data storage module Z of the central control platform; the data storage module Z receives data submitted by the data fusion module and meteorological data collected by the meteorological acquisition module, performs data interaction with the load prediction module and the optimization control module of the central control platform, sends an obtained instruction to the data storage module i of the energy station, and simultaneously transmits the obtained instruction to the information display module for displaying data. The invention can fuse the data of a plurality of ground source heat pumps in the city, predict and optimally control the load and the energy consumption, reduce the operation and maintenance cost of the energy station and improve the accuracy of the prediction and the control of the energy station system.
Description
Technical Field
The invention belongs to the field of ground source heat regulation and control, and relates to a centralized control method for urban large-scale energy stations. In particular, the invention relates to a centralized control method for urban large energy stations based on multi-station data fusion, a corresponding centralized control system for urban large energy stations based on multi-station data fusion and application thereof.
Background
The ground source heat pump technology belongs to a heating ventilation air conditioning technology in the field of renewable energy sources. Because the system performance coefficient of the ground source heat pump system is high, compared with other forms of cold and heat source systems, such as an air source heat pump system, the ground source heat pump system has larger energy saving and carbon reduction potential, and can be widely applied to cities to greatly reduce the energy consumption and carbon emission of the cooling and heating of buildings in the city range.
And for the city scale building group, a large-scale ground source heat pump system is adopted for cooling and heating, and as the building group is unevenly distributed and the distance between the building group and the building group is possibly large, the distance between the building group and the building group is too long, and only one oversized energy station is difficult to use in a city for cooling and heating the whole city, and the dispersed regional oversized energy stations are required to be adopted for cooling and heating different city areas.
For the dispersed regional large-scale energy stations which all adopt the large-scale ground source heat pump technology, the cooling and heating schemes adopted by the energy stations are basically the same. In a conventional energy station operation and maintenance scheme, each energy station needs to be locally controlled and monitored by an independent operation and maintenance team, operation data of the energy station is only stored locally, and temperature difference control and PID adjustment are adopted by a control method of the system. This approach has several drawbacks: 1) The operation and maintenance personnel need more cost; 2) Due to the characteristics of large time lag, multiple parameters and nonlinearity of a large-scale heating and ventilation system, the conventional control method is not beneficial to energy-saving and carbon-reduction operation; 3) Because the operation data is only stored in the local energy station, when an optimization control and fault diagnosis algorithm based on machine learning is adopted, the data quantity for model training and optimization is smaller, and the model accuracy is lower.
Disclosure of Invention
The invention aims to provide a centralized control method for large-scale energy stations in cities, which integrates and fuses the energy station control systems which are originally independent in cities and adopt a large-scale ground source heat pump system, predicts and optimally controls load and energy consumption, reduces the operation and maintenance cost of the energy stations and improves the accuracy of the prediction and control of the energy station systems.
It is a further object of the invention to provide a corresponding control system.
The invention comprises the following aspects:
(1) The data fusion centralized control method and system are suitable for a plurality of large-scale ground source heat pump system energy stations;
(2) The system and the module of the energy station are formed and the system and the module of the central control platform are formed;
(3) A data fusion method of a plurality of energy stations;
(4) The optimization control method based on load prediction adopted by the central control platform comprises an energy station load prediction method and an optimization control method.
The invention provides a centralized control method for urban large-scale energy stations based on multi-station data fusion, which comprises the following steps: the sensor module of the energy station collects the operation parameters of the large-scale ground source heat pump system and submits the operation parameters to the data storage module i through the digital controller; the data storage module i presents the data to a data fusion module of the central control platform; the data fusion module presents the data to a data storage module Z of the central control platform; the data storage module Z receives data submitted by the data fusion module and meteorological data collected by the meteorological acquisition module, performs data interaction with the load prediction module and the optimization control module of the central control platform, sends an obtained instruction to the data storage module i of the energy station, and simultaneously transmits the obtained instruction to the information display module for displaying data.
Preferably, the system operating parameters monitored by the sensors in the sensor module are transmitted to the digital controller via the data transmission cable.
Preferably, the operation parameters include, but are not limited to, the frequency of the circulating water pump, the set temperatures of the evaporator and the condenser of the ground source heat pump unit, and the number of the ground source heat pump units.
Preferably, the sensor module comprises: temperature sensor, flow sensor, pressure sensor. The temperature sensor is used for monitoring the water temperature at the outlet of the evaporator, the water temperature at the inlet and the water temperature at the outlet of the condenser of the ground source heat pump unit in the ground source heat pump system, and the soil temperatures of different areas of the ground buried pipe group; the flow sensor is used for monitoring the load side circulation flow and the ground source side circulation flow of the ground source heat pump unit in the ground source heat pump system; the pressure sensor is used for monitoring the load side circulating pipeline pressure and the ground source side circulating pipeline pressure of the ground source heat pump unit in the ground source heat pump system.
Preferably, the ground source heat pump unit is internally provided with a power monitoring module for the power load of the unit, and the circulating water pump is internally provided with a frequency monitoring module for monitoring the frequency of the variable-frequency water pump.
Preferably, the digital controller receives the operating parameters from the sensors and transmits the operating parameters to the data storage module, receives the control parameters from the data storage module, and transmits the control commands to the execution modules.
Preferably, the execution module executes control commands from the digital controller to control the operating parameters of the devices within the system.
Preferably, when the network communication between the local energy station and the central control platform is interrupted and the control instruction from the central control platform cannot be received, the calculation module performs load prediction and optimization control calculation by using the load prediction-based optimization control model in the data storage module and local operation data, and transmits the calculation result to the data storage module.
Preferably, the data storage module receives the system operation parameters from the numerical controller, and simultaneously stores and transmits the system operation parameters to the central control platform; receiving and storing parameters of an optimal control model based on load prediction from a central control platform, and transmitting the parameters to a local calculation module when network communication with the central control platform is interrupted; and receiving system regulation parameters from the central control platform, storing the regulation parameters and transmitting the regulation parameters to the local digital controller.
Preferably, the data fusion module constructs a data structure containing all the energy station data, and transmits the fused complete data to the data storage module: firstly, constructing different data rows of the received data of each energy station according to time labels, and constructing different data columns according to names and categories of operation parameters; secondly, adding the received data of each energy station into a data column containing the system characteristics of the energy station; after the fusion data structure is built, the data is transmitted to the data storage module.
Preferably, the weather acquisition module acquires weather data for the current day of the city from the local weather station and transmits the data to the data storage module.
Preferably, the load prediction module predicts the cooling and heating load intensity borne by each energy station: firstly, the module acquires historical operation data after data fusion from a data storage module, corrects and updates a pre-deployed BP neural network load prediction model, and increases the accuracy of model prediction; secondly, predicting the cooling and heating load intensity of each energy station in the time step delta tau based on the operation data of each energy station at the current moment tau and the time-by-time meteorological data of the same day; and then, transmitting the corrected model parameters and the calculated load result to a data storage module.
Preferably, the optimizing control module optimizes and regulates the operation parameters of each device of the energy station to achieve the aim of meeting the load demand and optimizing the economical efficiency; through an established simulation optimizing model of the TRNSYS platform and the MATLAB platform, through a particle swarm optimizing algorithm, taking a cold load and equipment operation parameters as input parameters, taking a system cold or heat supply quantity meeting a system cold load as a constraint condition, optimizing the equipment parameters in a system in a time step delta tau, and obtaining the value and the adjusting moment of the equipment operation parameters in the time step delta tau, wherein the optimizing objective function is the minimum system operation cost; and then, the system parameter regulation information of each energy station is sent to a data storage module.
Preferably, the data storage module stores the complete data constructed by the data fusion module, stores the meteorological data acquired by the meteorological acquisition module, stores the model parameters and calculated load results in the load prediction module, transmits the model parameters and calculated load results to each energy station through the network transmission module, and stores the system parameter regulation information of each energy station in the optimization control module, and transmits the system parameter regulation information to each energy station through the network transmission module.
Preferably, constructing different data rows according to the time labels from the received data of each energy station, and constructing different data columns according to the names and categories of the operation parameters means that:
each item of operation data of the energy station 1 at the moment tau is stored in columns 1-n of row 1,
each item of operation data of the energy station 2 at the time τ is stored in columns (n+1) - (2 n) of row 1,
each item of operation data of the energy station 1 at the time tau + delta tau is stored in columns 1-n of row 2,
each item of operation data of the energy station 2 at the time tau+delta tau is stored in columns (n+1) - (2 n) of the 2 nd row;
and so on.
The data of the system characteristics of the energy station itself include, but are not limited to: the number of the ground source heat pump units, the rated power of the ground source heat pump units, the number of the buried pipes, the depth of the buried pipes and the number of the subareas of the buried pipe group.
Preferably, the equipment parameters in the system in the optimizing time step delta tau include, but are not limited to, circulating pump frequency, set temperatures of an evaporator and a condenser of the ground source heat pump unit, the number of the ground source heat pump unit operation and the ground buried pipe operation partition.
Preferably, the centralized control method for the urban large-scale energy stations based on multi-station data fusion comprises the following steps:
1) Various sensors in each energy station collect the operation parameters of the large-scale ground source heat pump system and transmit the data to a digital controller of the local energy station through a data transmission cable;
2) The digital controller in each energy station transmits data to a local data storage module through a network communication module;
3) The data storage module stores the data in the step 2) and transmits the data to the central control platform through the network communication module;
4) The data fusion module of the central control platform carries out data fusion on data from different energy stations, constructs a data structure containing data of all the energy stations, and then transmits the fused data structure to the data storage module;
4) The weather acquisition module of the central control platform acquires weather data of the urban day from a local weather station and transmits the data to the data storage module;
5) The load prediction module of the central control platform acquires historical operation data after data fusion from the data storage module, corrects and updates a pre-deployed BP neural network load prediction model, predicts the cooling and heating load intensity of each energy station in a time step delta tau based on the operation data of each energy station at the current moment tau and the time-by-time meteorological data of the day, and transmits the updated prediction model parameters and load intensity data to the data storage module;
6) The optimizing control module of the central control platform optimizes the equipment parameters in the time step delta tau by using a particle swarm optimizing algorithm and taking the cold and hot load and the equipment operation parameters as input parameters and taking the cold and hot load of the system as constraint conditions, wherein the optimizing target function is minimum in system operation cost, acquires the value of the equipment operation parameters and the adjusting moment in the time step delta tau, and transmits the system parameter regulation information of each energy station to the data storage module;
7) The data storage module of the central control platform transmits the updated load prediction model parameters, load intensity data and system parameter regulation and control information to each energy station through the network communication module;
8) Each energy station receives data from the central control platform and stores the data and the data storage module;
9) The data storage module of each energy station transmits the system parameter regulation and control information to the digital controller;
10 The digital controller of each energy station converts the system parameter regulation and control information into a regulation and control command and transmits the regulation and control command to each execution module of the system to regulate and control the equipment parameters.
Preferably, the simulated optimizing model in step 6) is formed by coupling a TRNSYS platform with a MATLAB platform.
The invention also provides a centralized control system of the urban large-scale energy stations based on multi-station data fusion, which comprises a central control platform and one or more energy stations;
each energy station comprises a sensor module, a digital controller, a data storage module i, an equipment layer, an execution module and a calculation module; the sensor module collects the energy station operation parameters measured by the sensor and presents the energy station operation parameters to the digital controller, the digital controller receives the operation parameters presented by the sensor module and interacts with the information of the data storage module i, the operation parameters are output to the equipment layer through the execution module, and after the equipment layer operates, the sensor module collects the updated energy station operation parameters measured by the sensor and presents the updated energy station operation parameters to the digital controller, and the operation parameters are reciprocated;
The central control platform comprises a weather acquisition module, a load prediction module, an optimization control module, an information display module, a data storage module Z and a data fusion module; the weather acquisition module presents weather information to the data storage module Z; the data storage module Z receives meteorological information submitted by the data storage module Z, data interacted with the load prediction module and the optimization control module, and energy station data submitted by the data fusion module, and then outputs the meteorological information and the data to the information display module for displaying data.
Preferably, the equipment layer of the large-scale ground source heat pump system comprises: the system comprises a ground source heat pump unit, a buried pipe group, a water separator, a water collector and a variable-frequency circulating water pump;
the monitoring control layer of the energy station further comprises a module for monitoring and controlling operation parameters: the system comprises a sensor module network communication module, a digital controller, an execution module, a calculation module and a data storage module;
the system operation parameters monitored by each sensor in the sensor module are transmitted to the digital controller through a data transmission cable, wherein the sensor module comprises: the system comprises a temperature sensor, a flow sensor and a pressure sensor, wherein the temperature sensor is used for monitoring the outlet water temperature of an evaporator of a ground source heat pump unit in a ground source heat pump system, the inlet water temperature, the outlet water temperature of a condenser, the inlet water temperature and the soil temperature of different subareas of a ground buried pipe group; the flow sensor is used for monitoring the load side circulation flow and the ground source side circulation flow of the ground source heat pump unit in the ground source heat pump system; the pressure sensor is used for monitoring the load side circulating pipeline pressure and the ground source side circulating pipeline pressure of the ground source heat pump unit in the ground source heat pump system;
The ground source heat pump unit is internally provided with a power monitoring module for the power load of the unit, and the circulating water pump is internally provided with a frequency monitoring module for monitoring the frequency of the variable-frequency water pump.
Preferably, the network communication module is a carrier and a channel for communication and data transmission between the local energy station and the central control platform.
Preferably, the digital controller is configured to receive the operating parameters from the sensors and transmit the operating parameters to the data storage module, receive the control parameters from the data storage module, and transmit the control parameters to the execution modules.
Preferably, the execution module is used for executing a control command from the digital controller to control the operation parameters of each device in the system, such as the frequency of the circulating water pump, the set temperature of the evaporator and the condenser of the ground source heat pump unit, the number of the ground source heat pump units, and the like.
Preferably, the calculation module is used for carrying out load prediction and optimization control calculation by utilizing the load prediction-based optimization control model and local operation data in the data storage module when the network communication between the local energy station and the central control platform is interrupted and the control instruction from the central control platform cannot be received, and transmitting the calculation result to the data storage module.
Preferably, the data storage module is used for receiving the system operation parameters from the numerical controller, and simultaneously storing and sending the system operation parameters to the central control platform; receiving and storing parameters of an optimal control model based on load prediction from a central control platform, and transmitting the parameters to a local calculation module when network communication with the central control platform is interrupted; and receiving system regulation parameters from the central control platform, storing the regulation parameters and transmitting the regulation parameters to the local digital controller.
Preferably, the central control platform for centralized control of a plurality of energy stations is characterized by comprising: the system comprises a network communication module, a data fusion module, a data storage module, a weather acquisition module, a load prediction module, an optimization control module and an information display module.
The network communication module is a carrier and a channel for communication and data transmission between the central control platform and each independent energy station.
The data fusion module is used for constructing a data structure containing all the energy station data and transmitting the fused complete data to the data storage module: firstly, constructing different data rows of the received data of each energy station according to time labels, and constructing different data columns according to names and categories of operation parameters; secondly, adding the received data of each energy station into a data column containing the system characteristics of the energy station; after the fusion data structure is built, the data is transmitted to the data storage module.
The weather acquisition module is used for acquiring weather data of the urban day from the local weather station and transmitting the data to the data storage module.
The load prediction module is used for predicting the cooling and heating load intensity born by each energy station: firstly, the module acquires historical operation data after data fusion from a data storage module, corrects and updates a pre-deployed BP neural network load prediction model, and increases the accuracy of model prediction; secondly, predicting the cooling and heating load intensity of each energy station in the time step delta tau based on the operation data of each energy station at the current moment tau and the time-by-time meteorological data of the same day; and then, transmitting the corrected model parameters and the calculated load result to a data storage module.
Preferably, the BP neural network model has 37 hidden layer neurons, the transfer function between the input layer and the hidden layer is Sigmoid, and the transfer function between the hidden layer and the output layer is Purelin.
The optimizing control module is used for optimizing and regulating the operation parameters of each device of the energy station to achieve the aim of meeting load demands and optimizing economy, by using a built simulation optimizing model of coupling a TRNSYS platform and a MATLAB platform, using a cold and hot load and the operation parameters of the device as input parameters through a particle swarm optimizing algorithm, using the cold and hot load of the system as constraint conditions when the cooling or heating quantity of the system meets the cold and hot load of the system, optimizing the operation parameters of the device in the system in a time step delta tau, and obtaining the value and the adjusting moment of the operation parameters of the device in a time step delta tau, wherein the optimizing objective function is the minimum operation cost of the system; and then, the system parameter regulation information of each energy station is sent to a data storage module.
Taking the operation parameters of all devices in the system as variables, such as the frequency value f of a circulating pump, the opening alpha of each valve, the adjusting time tau and the set outlet temperature value T of a unit set The method comprises the steps of carrying out a first treatment on the surface of the With the running cost W of the system within one hour at the current moment h The minimum is the function of the object to be measured,
wherein W is 1 For the operating cost of the heat pump unit in the ground source heat pump system,
W 2 for the running cost of the load side circulating pump in the ground source heat pump system,
W 3 the operation cost of a ground source side circulating pump in the ground source heat pump system is set;
the constraint conditions are as follows:
Q≥Q h ，
wherein Q is h For the amount of cooling or heat required for the building,
q is the cooling capacity or the heating capacity of the ground source heat pump.
The data storage module is used for storing the complete data constructed by the data fusion module, storing the meteorological data acquired by the meteorological acquisition module, storing the model parameters and the calculated load results in the load prediction module, sending the model parameters and the calculated load results to each energy station through the network transmission module, storing the system parameter regulation and control information of each energy station in the optimization control module, and sending the system parameter regulation and control information to each energy station through the network transmission module.
The information display module acquires basic information and operation data of each energy station from the data storage module and displays interfaces, wherein the basic information and operation data comprise addresses of the energy stations, the number of ground source heat pump units of the energy stations, the number of ground pipes of the energy stations, information of each temperature sensor, information of flow sensors, information of pressure sensors, circulating pump frequencies of the energy stations, set temperatures of evaporators and condensers of the ground source heat pump units, power loads of the ground source heat pump units and regional operation conditions of the ground pipes, and the information comprises but is not limited to historical data, real-time data and data curves.
In a plurality of energy stations in a conventional city, because the energy stations are mutually independent, the operation and maintenance of the energy stations are independent, each energy station needs to be provided with a control system, a platform and operation maintenance personnel, system operation data are stored in a local data server, and an energy consumption prediction and optimization control algorithm can only utilize the operation data of the current energy station as a basis to carry out model training and construction.
The centralized control method and the control system for the urban large-scale energy stations integrate and fuse the energy station control systems which are originally independent in the city and adopt the large-scale ground source heat pump system, greatly expand the available data quantity, utilize the fused data to predict and optimally control the load and the energy consumption, reduce the operation and maintenance cost of the energy stations and improve the accuracy of the prediction and the control of the energy station systems.
The beneficial effects of the invention include:
(1) A plurality of large-scale ground source heat pump system energy stations are adopted in the city to perform cooling and heating, and the system energy efficiency is high;
(2) A data fusion method of a plurality of energy stations is provided;
(3) The method and the system for centralized control of the urban large-scale energy stations based on data fusion of a plurality of energy stations are provided;
(4) In the centralized control method of the urban large-scale energy station, the central control platform performs optimization control calculation based on load prediction based on the fused data.
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In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that, for some embodiments of the present application, each drawing in the following description can be further obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the connection between a central control platform and each energy station.
Fig. 2 is a schematic diagram of data transmission between the central control platform and each energy station.
Detailed Description
The application provides a centralized control method and a corresponding system for large-scale energy stations of a city based on data fusion of a plurality of energy stations, which are used for constructing a central control platform of the city, integrating and data fusion of the energy station control systems which are originally independent in the city and adopt a large-scale ground source heat pump system, greatly expanding the available data quantity, carrying out load and energy consumption prediction and optimization control by utilizing the fused data, reducing the operation and maintenance cost of the energy stations and improving the accuracy of the prediction and control of the energy station systems.
The technical solutions will be clearly and completely described below by means of embodiments of the present application, it being apparent that the described embodiments are only some of the preferred embodiments of the present application, but not all embodiments. All other embodiments, which are obtained by persons skilled in the art without creative efforts, are included in the protection scope of the present application based on the embodiments of the present application.
The application provides a centralized control method and a centralized control system for large-scale energy stations in a city based on data fusion of a plurality of energy stations, which are used for constructing a central control platform in the city, integrating and data fusion of the energy station control systems which are originally independent in the city and adopt a large-scale ground source heat pump system, and carrying out load and energy consumption prediction and optimization control by utilizing the fused data, thereby reducing the operation and maintenance cost of the energy stations and improving the prediction and control accuracy of the energy station systems.
The large-scale urban energy station adopts a large-scale ground source heat pump system as a cold source to supply cold and heat for building groups in the area. The large-scale ground source heat pump system comprises an equipment layer and a monitoring control layer, wherein the equipment layer comprises: ground source heat pump unit, buried pipe group, water separator and water collector, variable frequency circulating water pump and water supplementing equipment, pipeline, electromagnetic valve, etc. According to the scale and the load of the building group, the large-scale ground source heat pump system adopts a plurality of ground source heat pump units to be connected in parallel, so that the high-efficiency operation of each unit is more beneficial to be maintained under different load rates; the ground buried pipes are partitioned and connected in parallel according to the positions in the ground pipe group, so that partitioned heat exchange is conveniently performed according to the load size and the soil temperature, and the high-efficiency operation of the ground source heat pump system is ensured; the electromagnetic valve is used for adjusting the flow, the pressure and the like of the circulating pipeline by adjusting the opening of the valve; .
The monitoring control layer of the energy station further comprises a module for monitoring and controlling operation parameters: the system comprises a sensor module network communication module, a digital controller, an execution module, a calculation module, a data storage module and the like.
The system operation parameters monitored by each sensor in the sensor module are transmitted to the digital controller through a data transmission cable, wherein the sensor module comprises: the system comprises a temperature sensor, a flow sensor and a pressure sensor, wherein the temperature sensor is used for monitoring the outlet water temperature of an evaporator of a ground source heat pump unit in a ground source heat pump system, the inlet water temperature, the outlet water temperature of a condenser, the inlet water temperature and the soil temperature of different subareas of a ground buried pipe group; the flow sensor is used for monitoring the load side circulation flow and the ground source side circulation flow of the ground source heat pump unit in the ground source heat pump system; the pressure sensor is used for monitoring the load side circulating pipeline pressure and the ground source side circulating pipeline pressure of the ground source heat pump unit in the ground source heat pump system; meanwhile, a power monitoring module is arranged in the ground source heat pump unit and used for monitoring the power load of the unit, and a frequency monitoring module is arranged in the circulating water pump and used for monitoring the frequency of the variable-frequency water pump;
the network communication module is a carrier and a channel for communication and data transmission between the local energy station and the central control platform;
The digital controller is used for receiving the operation parameters from each sensor, transmitting the operation parameters to the data storage module, receiving the control parameters from the data storage module, and transmitting the control parameters converted into control commands to each execution module;
the execution module is used for executing a control command from the digital controller and controlling the operation parameters of all equipment in the system, such as the frequency of a circulating water pump, the set temperature of an evaporator and a condenser of the ground source heat pump unit, the number of the ground source heat pump unit and the like;
the calculation module is used for carrying out load prediction and optimization control calculation by utilizing the optimization control model based on load prediction in the data storage module and local operation data when the network communication between the local energy station and the central control platform is interrupted and the control instruction from the central control platform cannot be received, and transmitting the calculation result to the data storage module;
the data storage module is used for receiving the system operation parameters from the numerical controller, and simultaneously storing and transmitting the system operation parameters to the central control platform; receiving and storing parameters of an optimal control model based on load prediction from a central control platform, and transmitting the parameters to a local calculation module when network communication with the central control platform is interrupted; receiving system regulation parameters from a central control platform, storing the regulation parameters and transmitting the regulation parameters to a local digital controller;
The invention relates to a central control platform for centralized control of a plurality of energy stations, which comprises: the system comprises a network communication module, a data fusion module, a data storage module, a weather acquisition module, a load prediction module, an optimization control module and an information display module.
The network communication module is a carrier and a channel for communication and data transmission between the central control platform and each independent energy station;
the data fusion module is used for constructing a data structure containing all the energy station data and transmitting the integrated data to the data storage module. Firstly, constructing different data rows according to received data of each energy station according to time labels, and constructing different data columns according to names and categories of operation parameters, for example, each item of operation data of the energy station 1 at a moment tau is stored in columns 1-i of the 1 st row, each item of operation data of the energy station 2 at the moment tau is stored in columns 1-i of the 2 nd row, and each item of operation data of the energy station 1 at a moment tau+delta tau is stored in columns 1-i of the 3 rd row; secondly, adding the received data of each energy station to a data sequence containing the system characteristics of the energy station, for example, the number of ground source heat pump units, the rated power of the ground source heat pump units, the number of ground buried pipes, the depth of the ground buried pipes, the partition number of the ground buried pipe groups and the like; after the fusion data structure is built, transmitting the data to a data storage module;
The weather acquisition module is used for acquiring weather data of the urban day from the local weather station and transmitting the data to the data storage module;
the load prediction module is used for predicting the cooling and heating load intensity born by each energy station. Firstly, the module acquires historical operation data after data fusion from the data storage module, corrects and updates a pre-deployed BP neural network load prediction model, and increases the accuracy of model prediction. Secondly, predicting the cooling and heating load intensity of each energy station in the time step delta tau based on the operation data of each energy station at the current moment tau and the time-by-time meteorological data of the same day; then, the corrected model parameters and the calculated load result are transmitted to a data storage module;
the optimization control module is used for optimizing and controlling the operation parameters of each device of the energy station to achieve the aim of meeting the load demand and optimizing the economical efficiency. Through an established simulation optimizing model of coupling a TRNSYS platform and a MATLAB platform, a particle swarm optimizing algorithm is adopted, a cold load and equipment operation parameters are taken as input parameters, the cold load or the heat supply of a system meets the constraint condition of the cold load of the system, equipment parameters in the system in a time step delta tau are optimized, such as the frequency of a circulating pump, the set temperatures of an evaporator and a condenser of a ground source heat pump unit, the number of operating stations of the ground source heat pump unit, the operating partition of a ground buried pipe and the like, an optimizing objective function is that the operating cost of the system is minimum, and the value and the adjusting moment of the equipment operation parameters in the time step delta tau are obtained; and then, the system parameter regulation information of each energy station is sent to a data storage module.
Taking the operation parameters of all devices in the system as variables, such as the frequency value f of a circulating pump, the opening alpha of each valve, the adjusting time tau and the set outlet temperature value T of a unit set The method comprises the steps of carrying out a first treatment on the surface of the With the running cost W of the system within one hour at the current moment h The minimum is the function of the object to be measured,
wherein W is 1 For the operating cost of the heat pump unit in the ground source heat pump system,
W 2 for the running cost of the load side circulating pump in the ground source heat pump system,
W 3 the operation cost of a ground source side circulating pump in the ground source heat pump system is set;
the constraint conditions are as follows:
Q≥Q h ，
wherein Q is h For the amount of cooling or heat required for the building,
q is the cooling capacity or the heating capacity of the ground source heat pump.
The data storage module is used for storing the complete data constructed by the data fusion module, storing the meteorological data acquired by the meteorological acquisition module, storing the model parameters and the calculated load results in the load prediction module, sending the model parameters and the calculated load results to each energy station through the network transmission module, storing the system parameter regulation and control information of each energy station in the optimization control module, and sending the system parameter regulation and control information to each energy station through the network transmission module.
The information display module acquires basic information and operation data of each energy station from the data storage module and displays an interface, wherein the basic information and operation data comprise addresses of the energy stations, the number of ground source heat pump units of the energy stations, the number of ground pipes of the energy stations, information of each temperature sensor, information of flow sensors, information of pressure sensors, frequency of a circulating pump of the energy stations, set temperatures of an evaporator and a condenser of the ground source heat pump units, power loads of the ground source heat pump units, regional operation conditions of the ground pipes, and the information comprises historical data, real-time data, data curves and the like.
The workflow of the system of the invention is as follows:
1) Various sensors in each energy station collect the operation parameters of the large-scale ground source heat pump system and transmit the data to a digital controller of the local energy station through a data transmission cable;
2) The digital controller in each energy station transmits data to a local data storage module through a network communication module;
3) The data storage module stores the data in the step 2) and transmits the data to the central control platform through the network communication module;
4) The data fusion module of the central control platform carries out data fusion on data from different energy stations, constructs a data structure containing data of all the energy stations, and then transmits the fused data structure to the data storage module;
4) The weather acquisition module of the central control platform acquires weather data of the urban day from a local weather station and transmits the data to the data storage module;
5) The load prediction module of the central control platform acquires historical operation data after data fusion from the data storage module, corrects and updates a pre-deployed BP neural network load prediction model, predicts the cooling and heating load intensity of each energy station in a time step delta tau based on the operation data of each energy station at the current moment tau and the time-by-time meteorological data of the day, and transmits the updated prediction model parameters and load intensity data to the data storage module;
6) The optimization control module of the central control platform utilizes a particle swarm optimization algorithm, takes cold and hot loads and equipment operation parameters as input parameters, takes the cold and hot loads of a system as constraint conditions, optimizes the equipment parameters in the system in a time step delta tau, such as the frequency of a circulating pump, the set temperatures of an evaporator and a condenser of a ground source heat pump unit, the operation partition of a ground buried pipe and the like, and obtains the value and the adjustment moment of the equipment operation parameters in the time step delta tau by taking the optimized objective function as the minimum operation cost of the system, and transmits the system parameter regulation information of each energy station to the data storage module.
7) The data storage module of the central control platform transmits the updated load prediction model parameters, load intensity data and system parameter regulation and control information to each energy station through the network communication module;
8) Each energy station receives data from the central control platform and stores the data and the data storage module;
9) The data storage module of each energy station transmits the system parameter regulation and control information to the digital controller;
10 The digital controller of each energy station converts the system parameter regulation and control information into a regulation and control command and transmits the regulation and control command to each execution module of the system to regulate and control the equipment parameters.
Each item of operation data of the energy station 1 at the moment tau is stored in columns 1-n of row 1,
each item of operation data of the energy station 2 at the time τ is stored in columns (n+1) - (2 n) of row 1,
each item of operation data of the energy station 1 at the time tau + delta tau is stored in columns 1-n of row 2,
each item of operation data of the energy station 2 at the time tau+delta tau is stored in columns (n+1) - (2 n) of the 2 nd row;
the data structure after data fusion is completed is schematically shown in table 1.
TABLE 1 energy station data fusion schematic
The above-described embodiments are merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be suggested to one skilled in the art without inventive effort are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims in the present application.
Claims (10)
1. A centralized control method of urban large-scale energy stations based on multi-station data fusion is characterized in that a sensor module of the energy stations collects operation parameters of a large-scale ground source heat pump system and presents the operation parameters to a data storage module i through a digital controller; the data storage module i presents the data to a data fusion module of the central control platform; the data fusion module presents the data to a data storage module Z of the central control platform; the data storage module Z receives data submitted by the data fusion module and meteorological data collected by the meteorological acquisition module, performs data interaction with the load prediction module and the optimization control module of the central control platform, sends an obtained instruction to the data storage module i of the energy station, and simultaneously transmits the obtained instruction to the information display module for displaying data.
2. The centralized control method for the urban large energy station based on multi-station data fusion according to claim 1, wherein the system operation parameters monitored by each sensor in the sensor module are transmitted to the digital controller through a data transmission cable;
the operation parameters include, but are not limited to, the frequency of the circulating water pump, the set temperatures of the evaporator and the condenser of the ground source heat pump unit, and the number of the ground source heat pump units;
the sensor module comprises: a temperature sensor, a flow sensor, and a pressure sensor;
the temperature sensor is used for monitoring the water temperature at the outlet of the evaporator, the water temperature at the inlet and the water temperature at the outlet of the condenser of the ground source heat pump unit in the ground source heat pump system, and the soil temperatures of different areas of the ground buried pipe group;
the flow sensor is used for monitoring the load side circulation flow and the ground source side circulation flow of the ground source heat pump unit in the ground source heat pump system;
the pressure sensor is used for monitoring the load side circulating pipeline pressure and the ground source side circulating pipeline pressure of the ground source heat pump unit in the ground source heat pump system;
the ground source heat pump unit is internally provided with a power monitoring module for the power load of the unit, and the circulating water pump is internally provided with a frequency monitoring module for monitoring the frequency of the variable-frequency water pump.
3. The centralized control method for the urban large energy station based on multi-station data fusion according to claim 1, wherein the digital controller receives the operation parameters from each sensor and transmits the operation parameters to the data storage module, receives the control parameters from the data storage module and transmits the control commands to each execution module;
the execution module executes a control command from the digital controller to control the operation parameters of all devices in the system;
when the network communication between the local energy station and the central control platform is interrupted and a control instruction from the central control platform cannot be received, the calculation module performs load prediction and optimization control calculation by utilizing the load prediction-based optimization control model in the data storage module and local operation data, and transmits a calculation result to the data storage module;
the data storage module receives the system operation parameters from the numerical controller, and simultaneously stores and transmits the system operation parameters to the central control platform; receiving and storing parameters of an optimal control model based on load prediction from a central control platform, and transmitting the parameters to a local calculation module when network communication with the central control platform is interrupted; receiving system regulation parameters from a central control platform, storing the regulation parameters and transmitting the regulation parameters to a local digital controller;
The data fusion module constructs a data structure containing all the energy station data and transmits the integrated data to the data storage module: firstly, constructing different data rows of the received data of each energy station according to time labels, and constructing different data columns according to names and categories of operation parameters; secondly, adding the received data of each energy station into a data column containing the system characteristics of the energy station; after the fusion data structure is built, transmitting the data to a data storage module;
the weather acquisition module acquires weather data of the urban day from the local weather station and transmits the data to the data storage module;
the load prediction module predicts the cooling and heating load intensity born by each energy station: firstly, the module acquires historical operation data after data fusion from a data storage module, corrects and updates a pre-deployed BP neural network load prediction model, and increases the accuracy of model prediction; secondly, predicting the cooling and heating load intensity of each energy station in the time step delta tau based on the operation data of each energy station at the current moment tau and the time-by-time meteorological data of the same day; then, the corrected model parameters and the calculated load result are transmitted to a data storage module;
The optimizing control module optimizes and regulates the operation parameters of each device of the energy station to achieve the aim of meeting the load demand and optimizing the economical efficiency; through an established simulation optimizing model of the TRNSYS platform and the MATLAB platform, through a particle swarm optimizing algorithm, taking a cold load and equipment operation parameters as input parameters, taking a system cold or heat supply quantity meeting a system cold load as a constraint condition, optimizing the equipment parameters in a system in a time step delta tau, and obtaining the value and the adjusting moment of the equipment operation parameters in the time step delta tau, wherein the optimizing objective function is the minimum system operation cost; afterwards, system parameter regulation information of each energy station is sent to a data storage module;
the data storage module stores the complete data constructed by the data fusion module, stores the meteorological data acquired by the meteorological acquisition module, stores the model parameters and calculated load results in the load prediction module, transmits the model parameters and calculated load results to each energy station through the network transmission module, stores the system parameter regulation information of each energy station in the optimization control module, and transmits the system parameter regulation information to each energy station through the network transmission module.
4. The method for centralized control of large energy stations in a city based on multi-station data fusion according to claim 3, wherein the construction of different data rows from the received data of each energy station according to the time labels and the construction of different data columns according to the names and categories of the operation parameters means:
Each item of operation data of the energy station 1 at the moment tau is stored in columns 1-n of row 1,
each item of operation data of the energy station 2 at the time τ is stored in columns (n+1) - (2 n) of row 1,
each item of operation data of the energy station 1 at the time tau + delta tau is stored in columns 1-n of row 2,
each item of operation data of the energy station 2 at the time tau+delta tau is stored in columns (n+1) - (2 n) of the 2 nd row;
the schematic diagram is shown in Table 1
The data of the system characteristics of the energy station itself include, but are not limited to: the number of the ground source heat pump units, the rated power of the ground source heat pump units, the number of the ground buried pipes, the depth of the ground buried pipes and the partition number of the ground buried pipe groups;
the equipment parameters in the optimizing time step delta tau system comprise, but are not limited to, circulating pump frequency, set temperatures of an evaporator and a condenser of the ground source heat pump unit, the number of running ground source heat pump units and the running partition of a ground buried pipe.
5. The urban large-scale energy station centralized control method based on multi-station data fusion according to claim 1, comprising the following steps:
1) Various sensors in each energy station collect the operation parameters of the large-scale ground source heat pump system and transmit the data to a digital controller of the local energy station through a data transmission cable;
2) The digital controller in each energy station transmits data to a local data storage module through a network communication module;
3) The data storage module stores the data in the step 2) and transmits the data to the central control platform through the network communication module;
4) The data fusion module of the central control platform carries out data fusion on data from different energy stations, constructs a data structure containing data of all the energy stations, and then transmits the fused data structure to the data storage module;
4) The weather acquisition module of the central control platform acquires weather data of the urban day from a local weather station and transmits the data to the data storage module;
5) The load prediction module of the central control platform acquires historical operation data after data fusion from the data storage module, corrects and updates a pre-deployed BP neural network load prediction model, predicts the cooling and heating load intensity of each energy station in a time step delta tau based on the operation data of each energy station at the current moment tau and the time-by-time meteorological data of the day, and transmits the updated prediction model parameters and load intensity data to the data storage module;
6) The optimizing control module of the central control platform optimizes the equipment parameters in the time step delta tau by using a particle swarm optimizing algorithm and taking the cold and hot load and the equipment operation parameters as input parameters and taking the cold and hot load of the system as constraint conditions, wherein the optimizing target function is minimum in system operation cost, acquires the value of the equipment operation parameters and the adjusting moment in the time step delta tau, and transmits the system parameter regulation information of each energy station to the data storage module;
7) The data storage module of the central control platform transmits the updated load prediction model parameters, load intensity data and system parameter regulation and control information to each energy station through the network communication module;
8) Each energy station receives data from the central control platform and stores the data and the data storage module;
9) The data storage module of each energy station transmits the system parameter regulation and control information to the digital controller;
10 The digital controller of each energy station converts the system parameter regulation and control information into a regulation and control command and transmits the regulation and control command to each execution module of the system to regulate and control the equipment parameters.
6. The method for centralized control of a large energy station in a city based on multi-station data fusion according to claim 5, wherein the simulated optimizing model in step 6) is formed by coupling a TRNSYS platform and a MATLAB platform.
7. A centralized control system of urban large energy stations based on multi-station data fusion is characterized by comprising a central control platform and one or more energy stations;
each energy station comprises a sensor module, a digital controller, a data storage module i, an equipment layer, an execution module and a calculation module; the sensor module collects the energy station operation parameters measured by the sensor and presents the energy station operation parameters to the digital controller, the digital controller receives the operation parameters presented by the sensor module and interacts with the information of the data storage module i, the operation parameters are output to the equipment layer through the execution module, and after the equipment layer operates, the sensor module collects the updated energy station operation parameters measured by the sensor and presents the updated energy station operation parameters to the digital controller, and the operation parameters are reciprocated;
The central control platform comprises a weather acquisition module, a load prediction module, an optimization control module, an information display module, a data storage module Z and a data fusion module; the weather acquisition module presents weather information to the data storage module Z; the data storage module Z receives meteorological information submitted by the data storage module Z, data interacted with the load prediction module and the optimization control module, and energy station data submitted by the data fusion module, and then outputs the meteorological information and the data to the information display module for displaying data.
8. The urban large-scale energy resource station centralized control system based on multi-station data fusion according to claim 7, wherein,
the equipment layer of the large-scale ground source heat pump system comprises: the system comprises a ground source heat pump unit, a buried pipe group, a water separator, a water collector and a variable-frequency circulating water pump;
the monitoring control layer of the energy station further comprises a module for monitoring and controlling operation parameters: the system comprises a sensor module network communication module, a digital controller, an execution module, a calculation module and a data storage module;
the system operation parameters monitored by each sensor in the sensor module are transmitted to the digital controller through a data transmission cable, wherein the sensor module comprises: the system comprises a temperature sensor, a flow sensor and a pressure sensor, wherein the temperature sensor is used for monitoring the outlet water temperature of an evaporator of a ground source heat pump unit in a ground source heat pump system, the inlet water temperature, the outlet water temperature of a condenser, the inlet water temperature and the soil temperature of different subareas of a ground buried pipe group; the flow sensor is used for monitoring the load side circulation flow and the ground source side circulation flow of the ground source heat pump unit in the ground source heat pump system; the pressure sensor is used for monitoring the load side circulating pipeline pressure and the ground source side circulating pipeline pressure of the ground source heat pump unit in the ground source heat pump system;
The ground source heat pump unit is internally provided with a power monitoring module for a power load of the unit, and the circulating water pump is internally provided with a frequency monitoring module for monitoring the frequency of the variable-frequency water pump;
the network communication module is a carrier and a channel for communication and data transmission between the local energy station and the central control platform;
the digital controller is used for receiving the operation parameters from each sensor, transmitting the operation parameters to the data storage module, receiving the control parameters from the data storage module, and transmitting the control parameters converted into control commands to each execution module;
the execution module is used for executing a control command from the digital controller and controlling the operation parameters of all equipment in the system, such as the frequency of a circulating water pump, the set temperature of an evaporator and a condenser of the ground source heat pump unit, the number of the ground source heat pump unit and the like;
the calculation module is used for carrying out load prediction and optimization control calculation by utilizing the optimization control model based on load prediction in the data storage module and local operation data when the network communication between the local energy station and the central control platform is interrupted and the control instruction from the central control platform cannot be received, and transmitting the calculation result to the data storage module;
the data storage module is used for receiving the system operation parameters from the numerical controller, and simultaneously storing and transmitting the system operation parameters to the central control platform; receiving and storing parameters of an optimal control model based on load prediction from a central control platform, and transmitting the parameters to a local calculation module when network communication with the central control platform is interrupted; receiving system regulation parameters from a central control platform, storing the regulation parameters and transmitting the regulation parameters to a local digital controller;
The central control platform for centralized control of a plurality of energy stations is characterized by comprising: the system comprises a network communication module, a data fusion module, a data storage module, a weather acquisition module, a load prediction module, an optimization control module and an information display module;
the network communication module is a carrier and a channel for communication and data transmission between the central control platform and each independent energy station;
the data fusion module is used for constructing a data structure containing all the energy station data and transmitting the fused complete data to the data storage module: firstly, constructing different data rows of the received data of each energy station according to time labels, and constructing different data columns according to names and categories of operation parameters; secondly, adding the received data of each energy station into a data column containing the system characteristics of the energy station; after the fusion data structure is built, transmitting the data to a data storage module;
the weather acquisition module is used for acquiring weather data of the urban day from the local weather station and transmitting the data to the data storage module;
the load prediction module is used for predicting the cooling and heating load intensity born by each energy station: firstly, the module acquires historical operation data after data fusion from a data storage module, corrects and updates a pre-deployed BP neural network load prediction model, and increases the accuracy of model prediction; secondly, predicting the cooling and heating load intensity of each energy station in the time step delta tau based on the operation data of each energy station at the current moment tau and the time-by-time meteorological data of the same day; then, the corrected model parameters and the calculated load result are transmitted to a data storage module;
The optimizing control module is used for optimizing and regulating the operation parameters of each device of the energy station to achieve the aim of meeting load demands and optimizing economy, by using a built simulation optimizing model of coupling a TRNSYS platform and a MATLAB platform, using a cold and hot load and the operation parameters of the device as input parameters through a particle swarm optimizing algorithm, using the cold and hot load of the system as constraint conditions when the cooling or heating quantity of the system meets the cold and hot load of the system, optimizing the operation parameters of the device in the system in a time step delta tau, and obtaining the value and the adjusting moment of the operation parameters of the device in a time step delta tau, wherein the optimizing objective function is the minimum operation cost of the system; afterwards, system parameter regulation information of each energy station is sent to a data storage module;
the data storage module is used for storing the complete data constructed by the data fusion module, storing the meteorological data acquired by the meteorological acquisition module, storing the model parameters and the calculated load results in the load prediction module, sending the model parameters and the calculated load results to each energy station through the network transmission module, storing the system parameter regulation and control information of each energy station in the optimization control module, and sending the system parameter regulation and control information to each energy station through the network transmission module;
the information display module acquires basic information and operation data of each energy station from the data storage module and displays interfaces, wherein the basic information and operation data comprise addresses of the energy stations, the number of ground source heat pump units of the energy stations, the number of ground pipes of the energy stations, information of each temperature sensor, information of flow sensors, information of pressure sensors, circulating pump frequencies of the energy stations, set temperatures of evaporators and condensers of the ground source heat pump units, power loads of the ground source heat pump units and regional operation conditions of the ground pipes, and the information comprises but is not limited to historical data, real-time data and data curves.
9. The urban large-scale energy resource station centralized control system based on multi-station data fusion according to claim 8, wherein,
taking the operation parameters of all devices in the system as variables, such as the frequency value f of a circulating pump, the opening alpha of each valve, the adjusting time tau and the set outlet temperature value T of a unit set The method comprises the steps of carrying out a first treatment on the surface of the With the running cost W of the system within one hour at the current moment h The minimum is the function of the object to be measured,
wherein W is 1 For the operating cost of the heat pump unit in the ground source heat pump system,
W 2 is a ground source heat pumpThe running cost of the load side circulating pump in the system,
W 3 the operation cost of a ground source side circulating pump in the ground source heat pump system is set;
the constraint conditions are as follows:
Q≥Q h ，
wherein Q is h For the amount of cooling or heat required for the building,
q is the cooling capacity or the heating capacity of the ground source heat pump.
10. The application of the multi-station data fusion-based urban large-scale energy station centralized control system as claimed in any one of claims 7 to 9, which is characterized in that the energy station control system which is originally independent in the city and adopts a large-scale ground source heat pump system is integrated and data fused, the available data volume is greatly expanded, the fused data is utilized for load and energy consumption prediction and optimization control, the operation and maintenance cost of the energy station is reduced, and the accuracy of the prediction and control of the energy station system is improved.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102305451A (en) * | 2011-07-04 | 2012-01-04 | 徐坚 | Mixed ground source heat pump monitoring system and method based on health assessment technology |
| US20120010758A1 (en) * | 2010-07-09 | 2012-01-12 | Emerson Process Management Power & Water Solutions, Inc. | Optimization system using an iteratively coupled expert engine |
| CN102968111A (en) * | 2012-12-14 | 2013-03-13 | 新奥科技发展有限公司 | Method and system for controlling distributive energy system |
| CN104571068A (en) * | 2015-01-30 | 2015-04-29 | 中国华电集团科学技术研究总院有限公司 | Optimized operation control method and system of distributed energy system |
| CN106448193A (en) * | 2016-10-28 | 2017-02-22 | 杭州利艾智能科技有限公司 | Urban traffic control system and method |
| CN108416472A (en) * | 2018-02-27 | 2018-08-17 | 天津大学 | A kind of Regional And Multi-source cold and heat supply system optimization dispatching method |
| CN108800378A (en) * | 2018-05-30 | 2018-11-13 | 安徽华冶新能源科技有限公司 | A kind of geothermal heat pump air-conditioning system based on technology of Internet of things |
| CN111650871A (en) * | 2019-12-13 | 2020-09-11 | 河北省科学院能源研究所 | Remote communication monitoring system for operation of distributed ground source heat pump machine room |
| CN112968522A (en) * | 2021-02-20 | 2021-06-15 | 国网重庆市电力公司电力科学研究院 | Multi-load direct regulation and control system and method thereof |
| CN116151535A (en) * | 2022-10-25 | 2023-05-23 | 英集动力科技(山东)有限公司 | Multi-energy coupling ground source heat pump building energy supply system scheduling method based on reinforcement learning |
-
2023
- 2023-07-24 CN CN202310906401.2A patent/CN116841268A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120010758A1 (en) * | 2010-07-09 | 2012-01-12 | Emerson Process Management Power & Water Solutions, Inc. | Optimization system using an iteratively coupled expert engine |
| CN102305451A (en) * | 2011-07-04 | 2012-01-04 | 徐坚 | Mixed ground source heat pump monitoring system and method based on health assessment technology |
| CN102968111A (en) * | 2012-12-14 | 2013-03-13 | 新奥科技发展有限公司 | Method and system for controlling distributive energy system |
| CN104571068A (en) * | 2015-01-30 | 2015-04-29 | 中国华电集团科学技术研究总院有限公司 | Optimized operation control method and system of distributed energy system |
| CN106448193A (en) * | 2016-10-28 | 2017-02-22 | 杭州利艾智能科技有限公司 | Urban traffic control system and method |
| CN108416472A (en) * | 2018-02-27 | 2018-08-17 | 天津大学 | A kind of Regional And Multi-source cold and heat supply system optimization dispatching method |
| CN108800378A (en) * | 2018-05-30 | 2018-11-13 | 安徽华冶新能源科技有限公司 | A kind of geothermal heat pump air-conditioning system based on technology of Internet of things |
| CN111650871A (en) * | 2019-12-13 | 2020-09-11 | 河北省科学院能源研究所 | Remote communication monitoring system for operation of distributed ground source heat pump machine room |
| CN112968522A (en) * | 2021-02-20 | 2021-06-15 | 国网重庆市电力公司电力科学研究院 | Multi-load direct regulation and control system and method thereof |
| CN116151535A (en) * | 2022-10-25 | 2023-05-23 | 英集动力科技(山东)有限公司 | Multi-energy coupling ground source heat pump building energy supply system scheduling method based on reinforcement learning |
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