Multi-unit parallel energy system group control method based on double engine drive control architecture

Abstract

The invention relates to the field of energy system intelligent optimization control, in particular to a multi-unit parallel energy system group control method based on a double-engine drive control framework. The method comprises the following steps: collecting on-site historical data and carrying out multiple data processing; pLC + intelligent control platform double-engine driving optimization control is implemented; equipment in the units of the multi-unit parallel energy system is fixed, a unit model can be quickly built, and certain control parameters in the units are irrelevant to system characteristics, so that unit-level intelligent optimization can be quickly realized; the system structure of the multi-unit parallel energy system is ever-changing, a corresponding model needs to be built according to system characteristics, and therefore system-level characteristic intelligent optimization is achieved, and system-level optimization needs a certain time. According to the method, unit-level optimization and system-level optimization are ingeniously separated, unit-level energy saving of the multi-unit parallel energy system can be rapidly achieved, system-level characteristic energy saving is further achieved according to the system structure, the generalization ability is high, and rapid energy saving can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent optimization control of energy systems, and in particular to a group control method for a multi-unit parallel energy system based on a dual-engine drive control architecture. Background Art

[0002] The multi-unit parallel energy system involves all aspects of life, such as large-scale refrigeration systems with multiple chillers in parallel, large-scale power generation systems with multiple generators in parallel, etc. Taking the refrigeration system with multiple chillers in parallel as an example, the multi-chiller parallel system provides chilled water for the building air conditioning system to improve indoor comfort, and its energy consumption accounts for about 25%-40% of the total energy consumption of the building. Therefore, the multi-unit parallel energy system consumes a lot of energy, and it is very important to optimize the multi-unit parallel energy system and improve its operating efficiency.

[0003] Generally speaking, the system optimization process is to collect data-level data processing, establish models, forecast demand and optimize and control, among which the most important and time-consuming step is to establish models. There are usually two methods for modeling multi-unit parallel energy systems. One is to directly establish a system model, and the other is to first establish a device model and then connect the device models into a system model. However, both methods have shortcomings. The method of directly establishing a system model has weak generalization ability, and different multi-unit parallel energy systems must be re-modeled, which is time-consuming and labor-intensive. The method of first establishing a device model and then connecting each device into a system model has a long optimization cycle. It is necessary to establish a system model based on the characteristics of different multi-unit parallel energy systems before optimizing and intelligently controlling. Therefore, the present invention proposes a highly generalized multi-unit parallel energy system intelligent group control method based on a dual-engine drive control architecture. Summary of the invention

[0004] In order to overcome the problems in the prior art, the present invention proposes a group control method for a multi-unit parallel energy system based on a dual-engine drive control architecture, which separates the unit-level intelligent regulation with strong generalization capability and the system-level intelligent regulation with weak generalization capability, and can quickly achieve unit-level partial energy saving in the multi-unit parallel energy system, while having the advantages of strong generalization capability and rapid energy saving.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a multi-unit parallel energy system group control method based on a dual-engine drive control architecture, comprising the following steps:

[0007] The multi-unit parallel energy system is provided with a variety of sensors, through which the thermodynamic parameter data in the multi-unit parallel energy system are obtained in real time;

[0008] Process the acquired thermodynamic parameter data; classify and store the processed data in the database, continuously collect and process the on-site thermodynamic parameter data, and continuously update the database;

[0009] Construct a PLC+intelligent control platform dual-engine drive control architecture and implement PLC+intelligent control platform dual-engine drive optimization control; wherein, the PLC+intelligent control platform dual-engine drive optimization control includes unit-level optimization control and system-level optimization control;

[0010] The unit-level optimization control includes: constructing a model library of each device in the unit, building a unit model, predicting and optimizing the unit load, forming a unit optimization correlation and sending it to the PLC, and realizing intelligent energy-saving optimization at the unit level;

[0011] The system-level optimization control includes: constructing a model library of equipment outside the unit, and building a system-level model, predicting and optimizing the system load, forming a system optimization correlation and sending it to the PLC, to achieve system-level energy-saving tuning and intelligent optimization.

[0012] Furthermore, the dual-engine drive control architecture includes an intelligent control platform, an edge control layer and a device layer; the device layer includes sensors and various devices; the edge control layer includes edge control devices, and the edge control devices include PLCs; the intelligent control computing platform is used for data storage and management, model building, load forecasting, optimization calculation and control, and fault diagnosis;

[0013] The optimization results of the intelligent control platform are sent to the PLC in the edge control layer. The PLC in the edge control layer automatically controls the equipment in the equipment layer according to the sent optimization results. The PLC in the edge control layer collects thermodynamic parameter data in the equipment layer and uploads the thermodynamic parameter data to the intelligent control platform for subsequent optimization calculations.

[0014] Furthermore, the calculation and optimization functions of the system-level optimization control and the unit-level optimization control are both realized by relying on the intelligent control platform; the control functions of the system-level optimization control module and the unit-level optimization control module are both realized by relying on PLC.

[0015] Furthermore, in the unit-level optimization control, a model library of each device in the unit is constructed, and a unit model is built to predict and optimize the unit load, form a unit optimization correlation and send it to the PLC to achieve unit-level intelligent energy-saving optimization, specifically including:

[0016] For molding unit products with relatively fixed structures, a model library for each device in the unit is constructed;

[0017] According to the processed data, the corresponding models in the model library of each device are established. By comparing the prediction accuracy of different device models, the device model with the highest accuracy is selected, and the unit model is built;

[0018] Predict the unit load; optimize and adjust the control parameters within the unit based on the load prediction results to achieve the goal of energy saving and consumption reduction;

[0019] By analyzing the relationship between the control parameters in the unit and the external factors, a unit optimization correlation formula is formed, wherein the input variables of the unit optimization correlation formula are the external factors, and the output of the optimization correlation formula is the optimal value of the corresponding control parameter;

[0020] The unit optimization association is sent to the PLC so that the PLC can automatically adjust the control parameters within the unit according to changes in external factors to achieve unit-level energy-saving tuning.

[0021] Furthermore, in the system-level optimization control, a model library of equipment outside the unit is constructed, and a system-level model is built to predict and optimize the system load, form a system optimization correlation and send it to the PLC to achieve system-level energy-saving tuning and intelligent optimization, specifically including:

[0022] Establish models of other equipment outside the unit, and store the established models of other equipment outside the unit in the model library;

[0023] Build a system-level model based on the structure of different multi-unit parallel energy systems and the unit models built;

[0024] Predict system load; optimize and adjust system-level control parameters outside the unit based on load prediction results to achieve the goal of energy saving and consumption reduction;

[0025] Analyze the relationship between the system-level control parameters outside the unit and the external factors to form a system-level optimization correlation formula, wherein the input variables of the system-level optimization correlation formula are the external factors, and the output of the system-level optimization correlation formula is the optimal value of the corresponding control parameter;

[0026] The system-level optimization correlation formula is directly sent to the PLC so that the PLC can automatically adjust the system-level control parameters according to the changes in external factors; the system performance is monitored and evaluated in real time, and the system-level correlation formula is continuously optimized and adjusted according to the feedback results to achieve system-level energy-saving tuning.

[0027] Furthermore, the data processing includes data noise reduction, data filtering, data repair, and data reduction.

[0028] Compared with the prior art, the present invention has the following technical effects:

[0029] The intelligent control method of the present invention separates the unit-level intelligent regulation with strong generalization ability from the system-level intelligent regulation with weak generalization ability, which can quickly realize partial energy saving at the unit level of a multi-unit parallel energy system, and has the advantages of strong generalization ability and rapid energy saving. For a new multi-unit parallel energy system, the parallel unit structure is relatively fixed and has strong generalization ability. It can be quickly modeled and optimized based on past experience to quickly realize unit-level energy saving optimization. The system model outside the parallel unit has weak generalization ability, so the system and the unit are separated to realize rapid energy saving of the unit part, and the energy saving optimization of the system part will be implemented after modeling according to the system characteristics. In short, the present invention cleverly separates the unit and system optimization, utilizes the parallel characteristics of the unit, improves the generalization ability of the optimization regulation of the multi-unit parallel energy system, and quickly realizes partial energy saving of the multi-unit parallel energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A schematic diagram of a typical multi-chilled water unit parallel system according to an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of the internal structure of a chiller according to an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of a dual-engine drive control architecture according to an embodiment of the present invention;

[0034] Figure 4 This is a flow chart of a group control method for a multi-unit parallel energy system based on a dual-engine drive control architecture according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the technical solutions proposed by the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. The specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by technicians in the technical field of the present invention.

[0036] In order to enhance the generalization capability of multi-unit parallel energy system modeling and achieve rapid energy saving, the present invention proposes a multi-unit parallel energy system group control method based on a dual-engine drive control architecture, including:

[0037] Step 100: A plurality of sensors are provided in the multi-unit parallel energy system, and the thermodynamic parameter data in the multi-unit parallel energy system are obtained in real time through the sensors.

[0038] The multi-unit parallel energy system includes all multi-unit parallel systems in the fields of heat, electricity, building HVAC, and industrial refrigeration. The equipment in the multi-unit parallel energy system is fixed. For example, the chiller must include a compressor, condenser, throttling device, and evaporator. Therefore, the unit model can be quickly built with strong generalization ability, and some control parameters in the unit are independent of system characteristics, so unit-level intelligent optimization can be quickly achieved. A variety of sensors are provided in the multi-unit parallel energy system, and the thermodynamic parameter data in the multi-unit parallel energy system are obtained in real time through the sensors.

[0039] Step 200: Process the acquired thermodynamic parameter data; classify and store the processed data in a database, continuously collect field thermodynamic parameter data and perform multiple data processing, and continuously update the database.

[0040] Multiple data processing includes data noise reduction, data filtering, data repair, and data reduction. Specifically, the multiple data processing methods include data noise reduction methods such as mean filtering, wavelet transform, and Wiener filtering, data filtering methods based on threshold filtering, low-pass filtering, and high-pass filtering, data repair methods such as interpolation, regression filling, and K-nearest neighbor filling, and data reduction methods such as principal component analysis (PCA), feature selection, and Z-score standardization to improve data quality. Multiple data processing obtains high-quality data and improves the accuracy of subsequent modeling and optimization effects.

[0041] Step 300: Construct a PLC+intelligent control platform dual-engine drive control architecture, and implement PLC+intelligent control platform dual-engine drive optimization control; wherein, the PLC+intelligent control platform dual-engine drive optimization control includes unit-level optimization control and system-level optimization control.

[0042] The dual-engine drive control architecture includes an intelligent control platform, an edge control layer, and an equipment layer; the equipment layer includes sensors and various devices, such as temperature, pressure, flow and other sensors; the edge control layer includes edge control devices, such as PLC, etc.; the intelligent control computing platform includes data storage and management, model building, load forecasting, optimization calculation and control, fault diagnosis and other functions.

[0043] The optimization results of the intelligent control platform are sent to the PLC in the edge control layer. The PLC in the edge control layer automatically controls the equipment in the equipment layer according to the sent optimization results. The PLC in the edge control layer collects thermodynamic parameter data in the equipment layer and uploads the thermodynamic parameter data to the intelligent control platform for subsequent optimization calculations.

[0044] The PLC+intelligent control platform dual-engine drive optimization control includes unit-level optimization control and system-level optimization control; the calculation and optimization functions of the system-level optimization control and unit-level optimization control are both realized by the intelligent control platform; the control functions of the system-level optimization control and unit-level optimization control are both realized by PLC.

[0045] Step 400: The unit-level optimization control includes: constructing a model library for each device in the unit, and building a unit model, predicting and optimizing the unit load, forming a unit optimization correlation and sending it to the PLC, so as to realize intelligent energy-saving optimization at the unit level.

[0046] As an example, this step may include the following steps:

[0047] Step 401: For a molding unit product with a relatively fixed structure, a model library of each device in the unit is constructed.

[0048] For formed unit products with relatively fixed structures, such as chillers, their internal equipment is usually fixed, including compressors, condensers, throttling devices and evaporators, etc., and a model library for each device in the unit is constructed.

[0049] In order to build a model library for the internal equipment of the chiller, various types of models can be considered, including data models, semi-empirical models, pure mechanism models, and dual-driven models of mechanism and data fusion.

[0050] Data models include models constructed using methods such as ANN (Artificial Neural Network), SVM (Support Vector Machine), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit); semi-empirical models can be models written based on the operating principle of the equipment and experimental data, combined with certain assumptions and simplifications; pure mechanism models can be models that are completely based on the physical and chemical principles of the equipment; and the mechanism and data fusion dual-driven model can combine the advantages of the mechanism model and the data model, providing a physical background through the mechanism model and performing parameter optimization and uncertainty analysis through the data model.

[0051] Step 402: Based on the multi-processed data, establish the corresponding model in each equipment model library, select the equipment model with the highest accuracy by comparing the prediction accuracy of different equipment models, and build the unit model.

[0052] Step 403: predicting the load of the unit; optimizing and regulating the control parameters in the unit according to the load prediction result to achieve the goal of energy saving and consumption reduction;

[0053] Among them, the algorithms used to predict unit load include machine learning algorithms, deep learning algorithms, etc.

[0054] Step 404: by analyzing the relationship between the control parameters in the unit and the external factors, a unit-level optimization correlation formula is formed, wherein the input variables of the unit-level optimization correlation formula are the external factors, and the output of the optimization correlation formula is the optimal value of the corresponding control parameter;

[0055] Among them, external factors include ambient temperature, relative humidity, load, etc.

[0056] Step 405: Send the unit-level optimization correlation formula to the PLC so that the PLC can automatically adjust the control parameters in the unit according to the changes in external factors to achieve unit-level energy-saving tuning.

[0057] Since the equipment in the unit is fixed and the structure is relatively simple, the process of building the unit model can be faster and more efficient. Some control parameters in the unit are independent of the system characteristics, so the unit-level intelligent optimization can be performed independently of the system-level optimization. By continuously accumulating and optimizing the models in the model library, the generalization ability and optimization effect of the unit-level optimization control module can be further improved.

[0058] Step 500: The system-level optimization control includes: constructing a model library of equipment outside the unit, and building a system-level model, predicting and optimizing the system load, forming a system optimization correlation and sending it to the PLC, to achieve system-level energy-saving tuning and intelligent optimization.

[0059] As an example, this step may include the following steps:

[0060] Step 501: Establish models of other equipment outside the unit, and store the established models of other equipment outside the unit in a model library.

[0061] Models of other equipment outside the unit also include data models, semi-empirical models and pure mechanism models, semi-empirical models and pure mechanism models; the high-precision models of other equipment outside the unit are stored in the model library after being written, so that they can be quickly called when processing the same type of multi-unit parallel energy system, thereby enhancing the system-level modeling generalization capability.

[0062] Step 502: Build a system-level model according to the structure of different multi-unit parallel energy systems and the built unit models.

[0063] Since different multi-unit parallel energy systems have different numbers of units, unit structures, and types of external equipment, it takes a certain amount of time to build a system-level high-precision model.

[0064] Step 503: predicting the system load; optimizing and regulating the system-level control parameters outside the unit according to the load prediction result to achieve the goal of energy saving and consumption reduction.

[0065] Advanced algorithms are used to achieve load forecasting, including machine learning algorithms, deep learning algorithms, etc.

[0066] Step 504: Analyze the relationship between the system-level control parameters outside the unit and the external factors to form a system-level optimization correlation formula, wherein the input variables of the system-level optimization correlation formula are external factors, and the output of the system-level optimization correlation formula is the optimal value of the corresponding control parameter; the external factors include ambient temperature, relative humidity, load and other external factors.

[0067] Step 505: Send the system-level optimization correlation directly to the PLC so that the PLC can automatically adjust the system-level control parameters according to the changes in external factors; monitor and evaluate the system performance in real time, and continuously optimize and adjust the system-level correlation according to the feedback results to achieve system-level energy-saving tuning.

[0068] The system structure of a multi-unit parallel energy system is ever-changing. For example, the number of units, unit types, external energy storage devices, etc. of different systems are all different. The system model has weak generalization capabilities, and it is necessary to build corresponding models based on the system characteristics to achieve system-level intelligent optimization of characteristics.

[0069] The present invention will be further described below through specific embodiments in conjunction with the accompanying drawings.

[0070] like Figure 1 As shown, a typical multi-unit parallel energy system of an embodiment of the present invention is a refrigeration system with multiple chillers in parallel, and the refrigeration system includes 6 external refrigeration equipment cooling towers, 6 chillers, a cooling tower water pump tank skid, a stratified water tank and multiple circulating water pumps; the external refrigeration equipment cooling tower is used to absorb heat from the system and discharge it into the atmosphere, thereby reducing the temperature in the tower and producing recyclable cooling water; the chiller contains a compressor, a condenser, a throttle valve, and an evaporator, and uses a steam compression refrigeration cycle to generate low-temperature chilled water to cool the food in the cold storage; the cooling tower water pump tank skid is used to store excess cooling water generated by the external refrigeration equipment cooling tower; the stratified water tank is used to store excess chilled water generated by the chiller; the multiple circulating water pumps are used to transport liquids.

[0071] like Figure 2 As shown, the internal structure of the chiller in the embodiment of the present invention is a compressor, a condenser, a throttle valve, and an evaporator; wherein the compressor inhales the refrigerant vapor coming out of the evaporator and compresses it into a high-pressure gas, and since the compressor consumes a certain amount of mechanical energy during the compression process, and the mechanical energy is converted into heat energy in this process, the temperature of the refrigerant vapor increases and the refrigerant vapor is in an overheated state; the high-temperature refrigerant vapor discharged from the compressor releases heat in the condenser and transfers the heat to the surrounding water or air, thereby gradually condensing the refrigerant vapor into liquid; the refrigerant liquid coming out of the condenser is decompressed to the evaporation pressure through the throttle valve; after the liquid refrigerant passes through the throttle valve, due to the reduction in pressure, it begins to boil and vaporize and absorbs heat, thereby achieving the purpose of refrigeration.

[0072] In the above-mentioned refrigeration system with multiple chillers in parallel, the internal structure of the chiller is fixed and must include a compressor, a condenser, a throttle valve, and an evaporator. The condenser side of this system also includes a common cooling tower device, so these units with fixed structures can be quickly modeled. The control parameters of this system, that is, the thermodynamic parameter data, include the condensing pressure of each unit, the load of each unit, and the system supply and return water temperature; among them, the condensing pressure of each unit is used to adjust the cooling tower fan and water pump to maintain the condensing pressure at the set value; the load of each unit is used to adjust the compressor load and start and stop status of each unit to maintain the unit load at the set value; the system supply and return water temperature is used to adjust the water pump speed and water tank status to maintain the system supply and return water temperature at the set value. Among them, the condensing pressure control parameter of the chiller is independent of the system characteristics, and there is an optimal condensing pressure for different unit operating states. Therefore, the unit-level control parameter of this refrigeration system with multiple chillers in parallel is the condensing pressure, which can quickly optimize energy saving. The system-level control parameters are the load of each unit and the system return water temperature. It is necessary to build a system model according to the characteristics of the system and then optimize the control. If the chiller is a cascade system, the unit-level control parameters are the condensing pressure and the intermediate temperature. The unit-level control parameters and system-level control parameters of this system are summarized in Table 1.

[0073] Table 1 Detailed information on the unit-level control parameters and system-level control parameters of this system

[0074]

[0075]

[0076] like Figure 3As shown, the dual-engine drive control architecture of the embodiment of the present invention includes an intelligent control platform, an edge control layer, and a device layer; the intelligent control platform is equivalent to the brain, which is used for data storage and calculation, building a model library of each device in the system, load forecasting and optimization calculation, updating unit-level optimization correlation formulas and system-level optimization correlation formulas, etc.; the edge controller of the edge control layer is equivalent to the nerve, including programmable controllers such as PLC, which are used to collect parameter data of the device layer and upload it to the intelligent control platform on the one hand, and receive control instructions issued by the intelligent control platform and control the device layer according to the control instructions on the other hand; the device layer is equivalent to the limbs, for Figure 1 The described refrigeration system has multiple chillers in parallel. The equipment layer includes sensors such as temperature, pressure, and flow, and equipment such as chillers, water tanks, and cooling towers. On the one hand, the sensors obtain system data and upload it to the edge controller. On the other hand, the equipment receives control instructions from the PLC and generates corresponding actions.

[0077] like Figure 4 As shown, the highly generalized intelligent group control method of a multi-unit parallel energy system based on a dual-engine drive control architecture of an embodiment of the present invention includes: a plurality of sensors are provided in the multi-unit parallel energy system, and the thermodynamic parameter data in the system are acquired in real time through the sensors; the acquired data are subjected to multiple data processing, and the multiple data processing includes data noise reduction, data filtering, data repair, data reduction, etc., to obtain high-quality data after multiple data processing, so as to improve the subsequent modeling accuracy and optimization effect; the high-quality data after data processing is classified and stored in a database, and the field data and the multiple processed data are continuously collected, and the data is continuously updated. library; implement PLC+intelligent control platform dual-engine drive optimization control; the equipment in the unit of the multi-unit parallel energy system is fixed, such as the chiller must include a compressor, condenser, throttling device, and evaporator, so the chiller model can be quickly built with strong generalization ability, and the condensing pressure control parameters of the chiller are independent of the system characteristics, so the unit-level intelligent optimization can be quickly realized; the system structure of the multi-unit parallel energy system is ever-changing, such as the number of chillers, chiller models, water tank cold storage devices, etc. in this system can all change, the system model has weak generalization ability, and it is necessary to build a corresponding model according to the system characteristics to realize system-level characteristic intelligent optimization.

[0078] Among them, the unit-level optimization control includes: for the formed chiller products with relatively fixed structures, identify the equipment in the current unit, and build a model library of cooling towers, compressors, condensers, throttle valves, and evaporators in the unit; select a model suitable for the equipment in the current unit from the model library, and establish a corresponding model based on the data after multiple processing, and select the equipment model with the highest accuracy by comparing the prediction accuracy and stability of different equipment models, and quickly build the unit model; predict the unit load and optimize and regulate the control parameters in the unit; form a unit-level optimization correlation formula for the control parameters in the unit, with the input variables being external factors such as ambient temperature, relative humidity, and load, and the output being the optimal value of the condensing pressure; send the unit-level optimization correlation formula to the PLC so that the PLC can automatically adjust the control parameters in the unit according to changes in external factors to achieve unit-level energy-saving tuning.

[0079] Among them, system-level optimization control includes: establishing a high-precision model of other equipment (water tank) outside the unit based on high-quality data after multiple data processing; building a system-level model based on the number of units, connection methods, system structure and previously built unit models of the multi-chilled water unit parallel energy system; due to the different number of units, unit structure and external equipment types, it takes a certain amount of time to build a system-level high-precision model; predicting the system load and optimizing and regulating the system-level control parameters outside the unit; forming a system-level optimization correlation formula for the system-level control parameters outside the unit, whose input variables are external factors such as ambient temperature, relative humidity, and load, and the output is the optimal value of each unit load and the system supply and return water temperature; sending the system-level optimization correlation formula directly to the PLC to achieve system-level energy-saving tuning.

[0080] The unit-level optimization correlation formula and the system-level optimization correlation formula need to be continuously updated. Due to factors such as equipment aging, weather changes, and equipment failures, the optimal values ​​of control parameters change. The intelligent control platform needs to update the relevant control parameter correlation formulas at regular intervals to maximize energy savings.

[0081] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A multi-unit parallel energy system group control method based on a dual-engine drive control architecture, characterized in that: The following steps are involved: The multi-unit parallel energy system is provided with a variety of sensors, through which the thermodynamic parameter data in the multi-unit parallel energy system are obtained in real time; Process the acquired thermodynamic parameter data, and classify and store the processed data into a database; continuous Collect on-site thermodynamic parameter data, process the data, and continuously update the database; Construct a PLC+intelligent control platform dual-engine drive control architecture and implement PLC+intelligent control platform dual-engine drive optimization control; wherein, the PLC+intelligent control platform dual-engine drive optimization control includes unit-level optimization control and system-level optimization control; The unit-level optimization control includes: constructing a model library of each device in the unit, building a unit model, predicting and optimizing the unit load, forming a unit optimization correlation and sending it to the PLC, and realizing intelligent energy-saving optimization at the unit level; The system-level optimization control includes: constructing a model library of equipment outside the unit, and building a system-level model, predicting and optimizing the system load, forming a system optimization correlation and sending it to the PLC, to achieve system-level energy-saving tuning and intelligent optimization.

2. According to claim 1, a multi-unit parallel energy system group control method based on a dual-engine drive control architecture is characterized in that: The dual-engine drive control architecture includes an intelligent control platform, an edge control layer and a device layer; the device layer includes sensors and various devices; the edge control layer includes edge control devices, and the edge control devices include PLCs; the intelligent control computing platform is used for data storage and management, model building, load forecasting, optimization calculation and control; The optimization results of the intelligent control platform are sent to the PLC of the edge control layer, and the PLC in the edge control layer automatically controls the devices in the device layer according to the sent optimization results; The PLC in the edge control layer collects thermodynamic parameter data in the equipment layer and uploads the thermodynamic parameter data to the intelligent control platform for subsequent optimization calculations.

3. According to claim 2, a multi-unit parallel energy system group control method based on a dual-engine drive control architecture is characterized in that: The calculation and optimization functions of the system-level optimization control and the unit-level optimization control are both realized by the intelligent control platform; the control functions of the system-level optimization control module and the unit-level optimization control module are both realized by PLC.

4. The method for controlling a multi-unit parallel energy system based on a dual-engine drive control architecture according to claim 3 is characterized in that: In the unit-level optimization control, a model library of each device in the unit is constructed, and a unit model is built to predict and optimize the unit load, form a unit optimization correlation and send it to the PLC to achieve unit-level intelligent energy-saving optimization, specifically including: For molding unit products with relatively fixed structures, a model library for each device in the unit is constructed; According to the processed data, the corresponding models in the model library of each device are established. By comparing the prediction accuracy of different device models, the device model with the highest accuracy is selected, and the unit model is built; Predict the unit load; optimize and adjust the control parameters within the unit based on the load prediction results to achieve the goal of energy saving and consumption reduction; By analyzing the relationship between the control parameters in the unit and the external factors, a unit optimization correlation formula is formed, wherein the input variables of the unit optimization correlation formula are the external factors, and the output of the optimization correlation formula is the optimal value of the corresponding control parameter; The unit optimization association is sent to the PLC so that the PLC can automatically adjust the control parameters within the unit according to changes in external factors to achieve unit-level energy-saving tuning.

5. The method for controlling a multi-unit parallel energy system based on a dual-engine drive control architecture according to claim 4 is characterized in that: In the system-level optimization control, a model library of equipment outside the unit is constructed, and a system-level model is built to predict and optimize the system load, form a system optimization correlation and send it to the PLC to achieve system-level energy-saving tuning and intelligent optimization, specifically including: Establish models of other equipment outside the unit, and store the established models of other equipment outside the unit in the model library; Build a system-level model based on the structure of different multi-unit parallel energy systems and the unit models built; Predict system load; optimize and adjust system-level control parameters outside the unit based on load prediction results to achieve the goal of energy saving and consumption reduction; Analyze the relationship between the system-level control parameters outside the unit and the external factors to form a system-level optimization correlation formula, wherein the input variables of the system-level optimization correlation formula are the external factors, and the output of the system-level optimization correlation formula is the optimal value of the corresponding control parameter; The system-level optimization correlation formula is directly sent to the PLC so that the PLC can automatically adjust the system-level control parameters according to the changes in external factors; the system performance is monitored and evaluated in real time, and the system-level correlation formula is continuously optimized and adjusted according to the feedback results to achieve system-level energy-saving tuning.

6. The method for controlling a multi-unit parallel energy system based on a dual-engine drive control architecture according to claim 1, characterized in that: The data processing includes data noise reduction, data filtering, data repair, and data reduction.

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