Energy-saving and carbon-reducing control system and method for energy equipment based on SCADA

Abstract

The invention discloses an energy-saving and carbon-reducing control system and method for energy equipment based on SCADA (Supervisory Control And Data Acquisition), and relates to the technical field of energy-saving and carbon-reducing control. The system comprises a historical data acquisition and analysis module, a correlation analysis and optimization model module, a real-time data acquisition and analysis module and a control strategy matching module. The historical data acquisition and analysis module acquires and analyzes historical operation and carbon emission data of the energy equipment, extracts feature vectors and performs energy efficiency and carbon emission evaluation; the association analysis and optimization model module obtains an energy-saving and carbon-reducing control strategy by associating historical energy efficiency with a carbon emission evaluation index; the real-time data acquisition and analysis module acquires real-time operation and carbon emission data and calculates a real-time energy efficiency and carbon emission evaluation index; and the control strategy matching module analyzes the real-time energy efficiency, the historical energy efficiency and the carbon emission evaluation index, and matches a corresponding energy-saving and carbon-reducing control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving and carbon-reducing control, and particularly to an energy-saving and carbon-reducing control system and method for energy equipment based on SCADA. Background Technique

[0002] With the continuous growth of global energy demand and the increasing pressure of environmental protection, energy conservation and carbon reduction have become important goals of governments and enterprises in various countries. As the core of energy production and consumption, the energy efficiency and carbon emission status of energy equipment directly affect the effect of energy conservation and carbon reduction. Therefore, how to monitor the energy efficiency and carbon emissions of energy equipment in real time, evaluate its energy-saving and carbon-reducing effect, and take corresponding optimization measures has become a technical problem to be solved urgently in the field of energy management. As an automated control and monitoring system, the SCADA system is widely used in the fields of energy production, transmission, and consumption. Through real-time data acquisition, transmission, and analysis of energy equipment, the SCADA system can provide accurate monitoring of the operating status of equipment. However, existing SCADA systems mainly focus on the status monitoring and operation control of equipment, and lack dedicated functional modules for energy efficiency monitoring and carbon emission calculation and management, resulting in the underutilization of their application potential in energy conservation and carbon reduction.

[0003] Therefore, how to integrate energy efficiency monitoring and carbon emission management in the SCADA system, track the energy efficiency and carbon emissions of equipment operation in real time, and conduct correlation analysis based on the monitoring data has become a key technical problem for improving the efficiency of the energy management system and achieving the goal of energy conservation and carbon reduction. Although there are also energy efficiency management solutions based on data analysis in the prior art, most of these solutions do not combine real-time equipment operation data with carbon emission information and lack dynamic adjustment and refined management functions. Summary of the Invention

[0004] The purpose of the present invention is to provide an energy-saving and carbon-reducing control system and method for energy equipment based on SCADA to solve the problems raised in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] An energy-saving and carbon-reducing control method for energy equipment based on SCADA includes the following steps:

[0007] Step S100. Obtain the historical operation data of energy equipment from the database, analyze the historical operation data, extract the equipment energy efficiency feature vector from the historical operation data, and conduct an energy efficiency assessment of the energy equipment based on the equipment energy efficiency feature vector to obtain the historical energy efficiency assessment index;

[0008] Step S200. Obtain the historical carbon emission data of the energy equipment from the database, and conduct a carbon emission trend analysis based on the historical carbon emission data; according to the results of the carbon emission trend analysis, extract the equipment carbon emission feature vectors, and calculate the historical carbon emission evaluation index based on the equipment carbon emission feature vectors;

[0009] Step S300. Correspond the historical operation data of the energy equipment with the historical carbon emission data in chronological order, and conduct a correlation analysis on the corresponding historical energy efficiency evaluation index and historical carbon emission evaluation index, so as to obtain the energy-saving and carbon-reducing control strategy of the energy equipment;

[0010] Step S400. Configure the distributed data acquisition terminal of the SCADA system, collect and analyze the real-time operation data and real-time carbon emission data of the energy equipment to obtain the corresponding real-time energy efficiency evaluation index and real-time carbon emission evaluation index; analyze the real-time energy efficiency evaluation index and real-time carbon emission evaluation index with the historical energy efficiency evaluation index and historical carbon emission evaluation index, and match the corresponding energy-saving and carbon-reducing control strategy of the energy equipment according to the analysis results.

[0011] Further, step S100 includes:

[0012] S101. Obtain the historical operation data of the energy equipment from the database, preprocess the historical operation data, and divide the historical operation data according to a preset period, so as to divide the historical operation data into several data segments, and construct a historical operation data set A according to the division results, and A = {a1, a2,..., an}, where a1 represents the historical operation data segment corresponding to the 1st preset period, a2 represents the historical operation data segment corresponding to the 2nd preset period, and so on, an represents the historical operation data segment corresponding to the nth preset period, and n represents the number of historical operation data segments; for each element in the historical operation data set A, obtain the time series data of the load L(t) and efficiency η(t) of the energy equipment within the preset period, and conduct a relationship fitting of the load and efficiency according to the time series data of the load L(t) and efficiency η(t), so as to obtain the load rate - efficiency curve η load (t);

[0013] S102. For each element in the historical operation data set A, calculate the energy consumption increment P start-stop of the equipment start-stop within the preset period of the energy equipment, and where P on (t) represents the power when the energy equipment starts, P base represents the power of the energy equipment in the standby state, t0 represents the time point when the energy equipment starts from the standby state or the shutdown state, and t1 represents the moment when the energy equipment is fully started and operates stably; calculate the energy consumption increment ΔE of the working condition switching of the energy equipment within the preset periodmode , and where P(t) represents the real-time power of the energy device during the switching process, tm and tm+1 respectively represent the starting time and the ending time of the operating condition switching of the energy device; calculate the steady-state duration T of the energy device within a preset period stabilize , and T stabilize = t stabilize_end - t stabilize_start , where t stabilize_start represents the time when the energy device starts to be stable, and t stabilize_end represents the time when the stable state of the device is maintained within a stable range;

[0014] S103. Aggregate the dynamic fitting values η load (t), the start-stop energy consumption increment P start-stop , the energy consumption increment ΔE mode and the steady-state duration T stabilize corresponding to each element in the historical operation data set A, and perform normalization processing, and respectively obtain the dynamic fitting value η load (t), the start-stop energy consumption increment P start-stop , the energy consumption increment ΔE mode and the steady-state duration T stabilize within a preset period, so as to construct the corresponding device energy efficiency characteristic vector E, and E = (η, P, ΔE, T); according to the device energy efficiency characteristic vector E of each element, calculate the corresponding historical energy efficiency evaluation index HEEI, and HEEI = w1·η - w2·P - w3·ΔE + w4·T, where w1, w2, w3 and w4 represent the weights of the corresponding features of the device energy efficiency characteristic vector E, and w1 + w2 + w3 + w4 = 1.

[0015] Further, step S200 includes:

[0016] S201. Obtain the historical carbon emission data of the energy device from the database, preprocess the historical carbon emission data, and divide the preprocessed historical carbon emission data according to the preset period corresponding to the historical operation data, so as to construct the historical carbon emission data set B, and B = {b1, b2,..., bn}, where b1 represents the historical carbon emission data segment corresponding to the 1st preset period, b2 represents the historical carbon emission data segment corresponding to the 2nd preset period, and so on, bn represents the historical carbon emission data segment corresponding to the 1st preset period; for each element in the historical carbon emission data set B, adopt the regression analysis method to construct the trend model of the carbon emission data, and the trend model expression is: C trend (t) = β0 + β1t + β2t 2+...; where β0, β1, β2,... are regression coefficients, and t represents the time variable; through Fourier transform, analyze the periodic carbon emission fluctuations in the frequency domain, and the corresponding calculation formula is: where f represents the frequency, C(t) represents the time series data of the corresponding element, and m represents the total number of data points;

[0017] S202. Extract the carbon emission characteristics of the energy equipment according to the trend model and the periodic fluctuation analysis results. The carbon emission characteristics are the trend characteristics and periodic characteristics of the historical carbon emission data, and normalize the carbon emission characteristics of the energy equipment; construct the equipment carbon emission characteristic vector C according to the normalized carbon emission characteristics, and C = [c1, c2], where c1 represents the trend characteristic of the historical carbon emission data of the corresponding element, and c2 represents the periodic characteristic of the historical carbon emission data of the corresponding element; calculate the corresponding historical carbon emission evaluation index HCEI according to the equipment carbon emission characteristic vector C corresponding to each element, and HCEI = α1 × c1 + α2 × c2, where α1 and α2 represent the weight coefficients of the trend characteristic and the periodic characteristic respectively, and α1 + α2 = 1.

[0018] Further, step S300 includes:

[0019] S301. One-to-one correspond each element in the historical operation dataset A and the historical carbon emission dataset B of the energy equipment in chronological order, that is, ai corresponds to bi, and i takes values from 1 to n; according to the corresponding relationship between the elements in the historical operation dataset A and the historical carbon emission dataset B, obtain the corresponding historical energy efficiency evaluation index HEEI and historical carbon emission evaluation index HCEI, so as to form the associated data pair D, and D = (HEEI, HCEI); summarize all the associated data pairs D, and construct a multi-objective optimization model including energy efficiency and carbon emission. The objective function is: Objective = k1 × HEEI - k2 × HCEI, where k1 and k2 represent the weights of energy efficiency and carbon emission respectively, and k1 + k2 = 1;

[0020] S302. According to the multi-objective optimization model, obtain the objective value Objective corresponding to all the associated data pairs D. According to the preset objective threshold R, and the corresponding energy efficiency evaluation threshold EEI and carbon emission evaluation threshold CEI, screen out the associated data pairs D that meet the following conditions, and the conditions are: Objective > R and HEEI > EEI and HCEI < CEI; according to the screened associated data pairs D, find the corresponding energy equipment energy-saving and carbon-reduction control strategies in the corresponding historical operation data and historical carbon emission data respectively, and the associated data pair D and the energy equipment energy-saving and carbon-reduction control strategy are in a one-to-one correspondence relationship.

[0021] Further, step S400 includes:

[0022] S401. Configure the distributed data acquisition terminals of the SCADA system to collect the real-time operation data and real-time carbon emission data of energy equipment, analyze the real-time operation data and real-time carbon emission data in the same way as the analysis of historical operation data and historical carbon emission data, so as to obtain the real-time energy efficiency evaluation index REEI and the real-time carbon emission evaluation index RCEI, and form a real-time associated data pair RD, and RD = (REEI, RCEI); calculate the differences between the real-time energy efficiency evaluation index REEI and the real-time carbon emission evaluation index RCEI in the real-time associated data pair RD and the historical energy efficiency evaluation index HEEI and the historical carbon emission evaluation index HCEI in the associated data pair D in sequence, and select the associated data pair D with the smallest sum of the two differences as the matching result;

[0023] S402. If the matching result is unique, use the energy equipment energy-saving and carbon-reducing control strategy corresponding to the matching result as the current real-time energy-saving and carbon-reducing control strategy of the energy equipment; if the matching result is not unique, obtain all the energy equipment energy-saving and carbon-reducing control strategies, arrange them in descending order according to the corresponding target values, so as to obtain the list of the current real-time energy-saving and carbon-reducing control strategies of the energy equipment.

[0024] The energy equipment energy-saving and carbon-reducing control system based on SCADA includes: a historical data acquisition and analysis module, an association analysis and optimization model module, a real-time data acquisition and analysis module, and a control strategy matching module;

[0025] The historical data acquisition and analysis module obtains the historical operation data of energy equipment from the database, analyzes the historical operation data, extracts the equipment energy efficiency feature vector from the historical operation data, and conducts energy efficiency evaluation on the energy equipment according to the equipment energy efficiency feature vector, so as to obtain the historical energy efficiency evaluation index; obtains the historical carbon emission data of energy equipment from the database, conducts carbon emission trend analysis based on the historical carbon emission data; extracts the equipment carbon emission feature vector according to the carbon emission trend analysis result, and calculates the historical carbon emission evaluation index according to the equipment carbon emission feature vector;

[0026] The association analysis and optimization model module corresponds the historical operation data and historical carbon emission data of energy equipment in chronological order, conducts association analysis on the corresponding historical energy efficiency evaluation index and historical carbon emission evaluation index, so as to obtain the energy equipment energy-saving and carbon-reducing control strategy;

[0027] The real-time data acquisition and analysis module configures the distributed data acquisition terminals of the SCADA system, collects and analyzes the real-time operation data and real-time carbon emission data of energy equipment, and obtains the corresponding real-time energy efficiency evaluation index and real-time carbon emission evaluation index;

[0028] The control strategy matching module analyzes the real-time energy efficiency evaluation index and the real-time carbon emission evaluation index with the historical energy efficiency evaluation index and the historical carbon emission evaluation index, and matches the corresponding energy equipment energy-saving and carbon-reducing control strategies according to the analysis results.

[0029] Furthermore, the historical data collection and analysis module includes a historical data acquisition unit and a historical data analysis unit;

[0030] The historical data acquisition unit obtains the historical operation data and historical carbon emission data of the energy equipment from the database, preprocesses the historical operation data and historical carbon emission data respectively, and constructs a historical operation data set and a historical carbon emission data set; The historical data analysis unit performs feature extraction based on the historical operation data set and the historical carbon emission data set, constructs an equipment energy efficiency feature vector and an equipment carbon emission feature vector, and obtains the corresponding historical energy efficiency evaluation index and historical carbon emission evaluation index according to the equipment energy efficiency feature vector and the equipment carbon emission feature vector.

[0031] Furthermore, the correlation analysis and optimization model module includes a correlation analysis unit and an optimization model construction unit;

[0032] The correlation analysis unit corresponds the historical operation data and historical carbon emission data of the energy equipment in chronological order to obtain the corresponding correlation data pairs; The optimization model construction unit constructs a multi-objective optimization model based on the correlation data pairs.

[0033] Furthermore, the real-time data collection and analysis module includes a real-time data acquisition unit and a real-time data analysis unit;

[0034] The real-time data acquisition unit configures the distributed data acquisition terminal of the SCADA system to collect and analyze the real-time operation data and real-time carbon emission data of the energy equipment; The real-time data analysis unit analyzes the real-time operation data and real-time carbon emission data in the same way as the historical operation data and historical carbon emission data to obtain the corresponding real-time energy efficiency evaluation index and real-time carbon emission evaluation index.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: By integrating energy efficiency monitoring and carbon emission management, it is not only possible to monitor the energy efficiency status of energy equipment in real time, but also to track its carbon emission situation in real time; By combining the analysis of historical data and real-time data, accurate energy efficiency assessment and carbon emission assessment can be provided, thereby comprehensively improving the energy-saving and carbon-reduction effects of energy equipment. Through multi-dimensional analysis of historical data and real-time data, combined with the correlation between energy efficiency and carbon emissions, a multi-objective optimization model is constructed; This optimization model can adjust the operation strategy of energy equipment in a dynamically changing environment according to different evaluation indexes (such as HEEI and HCEI), ensuring effective reduction of carbon emissions while maximizing energy efficiency, and realizing refined management of energy-saving and carbon-reduction goals. Different from the static management methods in the prior art, the present invention adopts real-time energy efficiency assessment and carbon emission assessment indexes, and dynamically adjusts in combination with the equipment operation status; Through the real-time data acquisition terminal of the SCADA system, the energy efficiency and carbon emission assessment results can be fed back in real time during the equipment operation process, quickly respond to changes, and ensure the timely and effective implementation of energy-saving and carbon-reduction measures. The present invention not only relies on real-time data, but also deeply explores the potential optimization space in equipment operation through trend analysis and periodic fluctuation analysis of historical data; By constructing evaluation indexes for historical energy efficiency and carbon emissions, it is possible to predict and optimize the long-term operation of equipment, avoiding potential problems that may be overlooked by simply relying on real-time data. Through the multi-objective optimization model and correlation analysis, the present invention can accurately screen out the energy-saving and carbon-reduction control strategies that best match the current operation status of the equipment; Through the correlation with historical data, the accuracy of strategy selection is ensured, and it can cope with real-time adjustments when the equipment status changes, further improving the energy-saving and carbon-reduction effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings:

[0037] Figure 1 is a schematic diagram of the modules of the energy-saving and carbon-reduction control system for energy equipment based on SCADA of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Please refer to Figure 1 , the present invention provides the following technical solutions:

[0040] SCADA-based energy equipment energy-saving and carbon-reduction control system, including: historical data acquisition and analysis module, correlation analysis and optimization model module, real-time data acquisition and analysis module, and control strategy matching module;

[0041] The historical data acquisition and analysis module obtains the historical operation data of energy equipment from the database, analyzes the historical operation data, extracts the equipment energy efficiency feature vector from the historical operation data, and conducts an energy efficiency assessment of the energy equipment based on the equipment energy efficiency feature vector, thereby obtaining the historical energy efficiency assessment index; obtains the historical carbon emission data of energy equipment from the database, conducts a carbon emission trend analysis based on the historical carbon emission data; extracts the equipment carbon emission feature vector according to the carbon emission trend analysis result, and calculates the historical carbon emission assessment index according to the equipment carbon emission feature vector;

[0042] The correlation analysis and optimization model module corresponds the historical operation data and historical carbon emission data of energy equipment in chronological order, and conducts a correlation analysis on the corresponding historical energy efficiency assessment index and historical carbon emission assessment index, thereby obtaining the energy equipment energy-saving and carbon-reduction control strategy;

[0043] The real-time data acquisition and analysis module configures the distributed data acquisition terminal of the SCADA system, collects and analyzes the real-time operation data and real-time carbon emission data of energy equipment, and obtains the corresponding real-time energy efficiency assessment index and real-time carbon emission assessment index;

[0044] The control strategy matching module analyzes the real-time energy efficiency assessment index and real-time carbon emission assessment index with the historical energy efficiency assessment index and historical carbon emission assessment index, and matches the corresponding energy equipment energy-saving and carbon-reduction control strategy according to the analysis result.

[0045] The historical data acquisition and analysis module includes a historical data acquisition unit and a historical data analysis unit;

[0046] The historical data acquisition unit obtains the historical operation data and historical carbon emission data of energy equipment from the database, preprocesses the historical operation data and historical carbon emission data respectively, and constructs a historical operation data set and a historical carbon emission data set; the historical data analysis unit conducts feature extraction based on the historical operation data set and historical carbon emission data set, constructs the equipment energy efficiency feature vector and equipment carbon emission feature vector, and obtains the corresponding historical energy efficiency assessment index and historical carbon emission assessment index according to the equipment energy efficiency feature vector and equipment carbon emission feature vector.

[0047] The correlation analysis and optimization model module includes a correlation analysis unit and an optimization model construction unit;

[0048] The association analysis unit corresponds the historical operation data and historical carbon emission data of the energy equipment in chronological order to obtain corresponding associated data pairs; the optimization model construction unit constructs a multi-objective optimization model based on the associated data pairs.

[0049] The real-time data acquisition and analysis module includes a real-time data acquisition unit and a real-time data analysis unit;

[0050] The real-time data acquisition unit configures the distributed data acquisition terminal of the SCADA system to collect and analyze the real-time operation data and real-time carbon emission data of the energy equipment; the real-time data analysis unit analyzes the real-time operation data and real-time carbon emission data in the same way as the analysis of the historical operation data and historical carbon emission data to obtain the corresponding real-time energy efficiency evaluation index and real-time carbon emission evaluation index.

[0051] The energy equipment energy-saving and carbon-reducing control method based on SCADA includes the following steps:

[0052] Step S100. Obtain the historical operation data of the energy equipment from the database, analyze the historical operation data, extract the equipment energy efficiency feature vector from the historical operation data, and perform energy efficiency evaluation on the energy equipment according to the equipment energy efficiency feature vector to obtain the historical energy efficiency evaluation index;

[0053] Step S200. Obtain the historical carbon emission data of the energy equipment from the database, perform carbon emission trend analysis based on the historical carbon emission data; according to the carbon emission trend analysis result, extract the equipment carbon emission feature vector, and calculate the historical carbon emission evaluation index according to the equipment carbon emission feature vector;

[0054] Step S300. Correspond the historical operation data and historical carbon emission data of the energy equipment in chronological order, perform association analysis on the corresponding historical energy efficiency evaluation index and historical carbon emission evaluation index, so as to obtain the energy equipment energy-saving and carbon-reducing control strategy;

[0055] Step S400. Configure the distributed data acquisition terminal of the SCADA system to collect and analyze the real-time operation data and real-time carbon emission data of the energy equipment to obtain the corresponding real-time energy efficiency evaluation index and real-time carbon emission evaluation index; analyze the real-time energy efficiency evaluation index and real-time carbon emission evaluation index with the historical energy efficiency evaluation index and historical carbon emission evaluation index, and match the corresponding energy equipment energy-saving and carbon-reducing control strategy according to the analysis result.

[0056] Step S100 includes:

[0057] S101. Obtain the historical operation data of the energy device from the database, preprocess the historical operation data, and divide the historical operation data according to a preset period, so as to divide the historical operation data into several data segments, and construct a historical operation data set A according to the division result, and A = {a1, a2,..., an}, where a1 represents the historical operation data segment corresponding to the 1st preset period, a2 represents the historical operation data segment corresponding to the 2nd preset period, and so on, an represents the historical operation data segment corresponding to the nth preset period, and n represents the number of historical operation data segments; for each element in the historical operation data set A, obtain the time series data of the load L(t) and efficiency η(t) of the energy device within the preset period, and perform a relationship fitting between the load and the efficiency according to the time series data of the load L(t) and efficiency η(t), so as to obtain the load rate - efficiency curve η load (t);

[0058] In this embodiment, each data segment ai contains the historical operation data of the energy device, including the load (usually the percentage of device operation) and the corresponding power output (for example, the power consumption per unit time). There is usually a certain relationship between the load and the power, and this relationship can be analyzed through the following steps:

[0059] Load definition: The load L(t) of the device is usually defined as the ratio between the current actual output power P(t) of the device and the maximum rated power Pmax of the device: L(t) = P(t) / Pmax; where P(t) is the instantaneous power output of the device at the time moment, and Pmax is the maximum rated power of the device.

[0060] Efficiency definition: The efficiency η(t) of the device is the ratio between the output power and the input power of the device at time t. Assuming the input power is Pin(t), the efficiency can be expressed as: η(t) = Pout(t) / Pin(t), where Pout(t) = P(t);

[0061] Therefore, the relationship between the efficiency and the load of the device can be described by the load rate - efficiency curve η load (t), and usually the efficiency will change with the increase of the load.

[0062] In order to extract the dynamic fitting value η load (t) of the load rate - efficiency curve from the data segment ai, the following steps can be used for dynamic fitting:

[0063] Data preprocessing: Extract the time series data of the load L(t) and efficiency η(t) from the data segment ai. Assume that L(t) and η(t) are discrete time series data, where t represents time, L(t) represents the load, and η(t) represents the efficiency.

[0064] Select the fitting model: Select an appropriate fitting model based on the operating characteristics of the equipment. Usually, the relationship between load and efficiency can be described by models such as polynomial fitting, power function fitting, exponential function fitting, etc. For example, it can be assumed that the relationship between load and efficiency is a power function: η(t) = αL(t) β ;

[0065] Among them, α and β are parameters that need to be obtained through fitting calculation, and appropriate α and β parameters can be fitted by methods such as the least squares method.

[0066] Dynamics of the fitting curve: Since the equipment load and power output will change during actual operation, there may be nonlinear fluctuations. Therefore, the fitting process needs to consider the dynamics of equipment load changes. For example, the moving window method can be used to perform local fitting on the data in each time period to adapt to load fluctuations and changes in equipment status.

[0067] For example, in each time period t1 to t2, where the time period t1 to t2 represents a preset period, local fitting is performed to obtain the fitting curve for each time period:

[0068] η load (t) = α local (t)L(t) βlocal(t) ;

[0069] Among them, α local (t) and β local (t) is the parameter obtained by local fitting based on the time period [t1, t2].

[0070] S102. For each element in the historical operation data set A, calculate the energy consumption increment P of the energy equipment during the preset period. start-stop ,and Where P on (t) represents the power when the energy equipment starts, P base Indicates the power of the energy device in standby state, t0 indicates the time point when the energy device starts from standby state or shutdown state, and t1 indicates the time when the energy device is fully started and runs stably; calculate the energy consumption increment ΔE of the energy device when the working condition switches within the preset period mode ,and Where P(t) represents the real-time power of the energy device during the switching process, tm and tm+1 represent the start and end time of the working condition switching of the energy device respectively; calculate the steady-state duration T of the energy device within the preset period stabilize , and T stabilize =t stabilize_end -t stabilize_start , where t stabilize_startIndicates the time when the energy device begins to stabilize, t stabilize_end Indicates the moment when the equipment's stable state is maintained within a stable range;

[0071] S103. Summarize the dynamic fitting value η of the load rate-efficiency curve corresponding to each element in the historical operation data set A load (t), incremental energy consumption of equipment start-up and shutdown P start-stop , energy consumption increment ΔE mode And the steady-state duration T stabilize , and normalize it, and calculate the dynamic fitting value η of the load rate-efficiency curve respectively load (t), incremental energy consumption of equipment start-up and shutdown P start-stop , energy consumption increment ΔE mode And the steady-state duration T stabilize The average value within the preset period is used to construct the corresponding equipment energy efficiency feature vector E, and E = (η, P, ΔE, T); according to the equipment energy efficiency feature vector E of each element, the corresponding historical energy efficiency evaluation index HEEI is calculated, and HEEI = w1·η-w2·P-w3·ΔE+w4·T, where w1, w2, w3 and w4 represent the weights of the corresponding features of the equipment energy efficiency feature vector E, and w1+w2+w3+w4=1.

[0072] Step S200 includes:

[0073] S201. Obtain historical carbon emission data of energy equipment from the database, pre-process the historical carbon emission data, and divide the pre-processed historical carbon emission data according to the preset period corresponding to the historical operation data, so as to construct a historical carbon emission data set B, and B = {b1, b2, ..., bn}, wherein b1 represents the historical carbon emission data segment corresponding to the first preset period, b2 represents the historical carbon emission data segment corresponding to the second preset period, and so on, bn represents the historical carbon emission data segment corresponding to the first preset period; for each element in the historical carbon emission data set B, a regression analysis method is used to construct a trend model of carbon emission data, and the trend model expression is: C trend (t) = β0 + β1t + β2t 2 +...; where β0, β1, β2, ... are regression coefficients, and t represents the time variable; through Fourier transform, the periodic carbon emission fluctuations are analyzed in the frequency domain, and the corresponding calculation formula is: Where f represents the frequency, C(t) represents the time series data of the corresponding element, and m represents the total number of data points;

[0074] S202. Extract the carbon emission characteristics of energy equipment based on the trend model and the analysis results of periodic fluctuations. The carbon emission characteristics are the trend characteristics and periodic characteristics of historical carbon emission data, and normalize the carbon emission characteristics of energy equipment. Construct an equipment carbon emission characteristic vector C based on the normalized carbon emission characteristics, and C = [c1, c2], where c1 represents the trend characteristic of the historical carbon emission data of the corresponding element, and c2 represents the periodic characteristic of the historical carbon emission data of the corresponding element. Calculate the corresponding historical carbon emission evaluation index HCEI according to the equipment carbon emission characteristic vector C corresponding to each element, and HCEI = α1×c1 + α2×c2, where α1 and α2 represent the weight coefficients of the trend characteristic and the periodic characteristic respectively, and α1 + α2 = 1.

[0075] In this embodiment, for each data segment bi (such as the carbon emission data of each day), construct a trend model through regression analysis:

[0076] For example, assume that the regression analysis result of data segment b1 is: C trend (t) = β0 + β1t + β2t 2

[0077] Assume that the regression coefficients are: β0 = 100, β1 = 5, β2 = -0.2; then, the trend model is: C trend (t) = 100 + 5t - 0.2t 2 ;

[0078] The trend characteristic c1 usually adopts the coefficients of the regression model or the overall representation of the data trend based on this model. For example, based on the output of the model, calculate the trend characteristic c1 of the data segment:

[0079] The trend characteristic c1 can be the output of the regression model value at a certain moment, or the expression of a certain coefficient (β0). Assume that: c1 = 100 represents the trend characteristic of the data.

[0080] Perform periodic analysis. Then, for each data segment bi, analyze its periodic fluctuations through Fourier transform. The Fourier transform formula is: For a certain data segment b1, the result of Fourier transform may show a periodic fluctuation. Assume that the frequency f is an annual cycle (such as 1 / 365). When calculating the periodic characteristic c2, assume that the frequency domain analysis shows that there is a certain periodic fluctuation, and the result may be: the periodic characteristic c2; for example, it is the amplitude of the periodic fluctuation, the frequency component, etc. Assume that: c2 = 0.15 (indicating the intensity of the periodic fluctuation);

[0081] Feature normalization:

[0082] Normalize the trend characteristic c1 and the periodic characteristic c2. For example, use min-max normalization:

[0083] Suppose the normalized results are: c1 = 0.85, c2 = 0.75;

[0084] Device carbon emission feature vector C:

[0085] Construct the device carbon emission feature vector C according to the normalized trend feature c1 and periodic feature c2:

[0086] C = [c1, c2] = [0.85, 0.75];

[0087] Historical carbon emission evaluation index (HCEI):

[0088] Calculate the historical carbon emission evaluation index HCEI according to the given weight coefficients α1 and α2. Suppose: α1 = 0.6, α2 = 0.4; then:

[0089] HCEI = 0.6×0.85 + 0.4×0.75 = 0.81.

[0090] Step S300 includes:

[0091] S301. One-to-one correspond each element in the historical operation data set A of the energy device and the historical carbon emission data set B in chronological order, that is, ai corresponds to bi, and i ranges from 1 to n; according to the corresponding relationship between the elements in the historical operation data set A and the historical carbon emission data set B, obtain the corresponding historical energy efficiency evaluation index HEEI and historical carbon emission evaluation index HCEI, so as to form the associated data pair D, and D = (HEEI, HCEI); summarize all the associated data pairs D, and construct a multi-objective optimization model including energy efficiency and carbon emission. The objective function is: Objective = k1×HEEI - k2×HCEI, where k1 and k2 respectively represent the weights of energy efficiency and carbon emission, and k1 + k2 = 1;

[0092] S302. According to the multi-objective optimization model, obtain the objective value Objective corresponding to all the associated data pairs D. According to the preset objective threshold R, and the corresponding energy efficiency evaluation threshold EEI and carbon emission evaluation threshold CEI, screen out the associated data pairs D that meet the following conditions, and the conditions are: Objective > R and HEEI > EEI and HCEI < CEI; according to the screened associated data pairs D, find the corresponding energy device energy-saving and carbon-reduction control strategies in the corresponding historical operation data and historical carbon emission data respectively, and the associated data pair D and the energy device energy-saving and carbon-reduction control strategy are in a one-to-one correspondence relationship.

[0093] Step S400 includes:

[0094] S401. Configure the distributed data acquisition terminals of the SCADA system to collect the real-time operation data and real-time carbon emission data of energy equipment, analyze the real-time operation data and real-time carbon emission data in the same way as the historical operation data and historical carbon emission data, so as to obtain the real-time energy efficiency evaluation index REEI and the real-time carbon emission evaluation index RCEI, and form a real-time correlation data pair RD, and RD = (REEI, RCEI); calculate the differences between the real-time energy efficiency evaluation index REEI and the real-time carbon emission evaluation index RCEI in the real-time correlation data pair RD and the historical energy efficiency evaluation index HEEI and the historical carbon emission evaluation index HCEI in the correlation data pair D in sequence, and select the correlation data pair D with the smallest sum of the two differences as the matching result;

[0095] S402. If the matching result is unique, use the energy equipment energy-saving and carbon-reducing control strategy corresponding to the matching result as the current energy equipment real-time energy-saving and carbon-reducing control strategy; if the matching result is not unique, obtain all the energy equipment energy-saving and carbon-reducing control strategies, and arrange them in descending order according to the corresponding target values to obtain the current energy equipment real-time energy-saving and carbon-reducing control strategy list.

[0096] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0097] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A SCADA-based energy equipment energy saving and carbon reduction control method, characterized by: The method comprises the following steps: Step S100. Obtain historical operation data of energy equipment from a database, analyze the historical operation data, extract equipment energy efficiency feature vectors from the historical operation data, and evaluate the energy efficiency of the energy equipment according to the equipment energy efficiency feature vectors, thereby obtaining a historical energy efficiency evaluation index; Step S200. Obtain historical carbon emission data of energy equipment from a database, and perform carbon emission trend analysis based on the historical carbon emission data; extract equipment carbon emission feature vectors according to the carbon emission trend analysis results, and calculate a historical carbon emission assessment index according to the equipment carbon emission feature vectors; Step S300. According to the time series relationship, the historical operation data of the energy equipment is matched with the historical carbon emission data, and the corresponding historical energy efficiency evaluation index and historical carbon emission evaluation index are combined for correlation analysis, so as to obtain the energy equipment energy saving and carbon reduction control strategy; Step S400. Configure the distributed data acquisition terminal of the SCADA system to collect and analyze the real-time operation data and real-time carbon emission data of the energy equipment to obtain the corresponding real-time energy efficiency evaluation index and real-time carbon emission evaluation index; analyze the real-time energy efficiency evaluation index and real-time carbon emission evaluation index with the historical energy efficiency evaluation index and historical carbon emission evaluation index, and match the corresponding energy equipment energy-saving and carbon reduction control strategy according to the analysis results.

2. The SCADA-based energy equipment energy saving and carbon reduction control method according to claim 1, characterized in that: The step S100 includes: S101. Obtain historical operation data of energy equipment from a database, preprocess the historical operation data, and divide the historical operation data according to a preset period, thereby dividing the historical operation data into several data segments, and construct a historical operation data set A according to the division result, and A = {a1, a2, ..., an}, wherein a1 represents the historical operation data segment corresponding to the first preset period, a2 represents the historical operation data segment corresponding to the second preset period, and so on, an represents the historical operation data segment corresponding to the nth preset period, and n represents the number of historical operation data segments; for each element in the historical operation data set A, obtain the time series data corresponding to the load L(t) and efficiency η(t) of the energy equipment within the preset period, and fit the relationship between the load and the efficiency according to the time series data corresponding to the load L(t) and the efficiency η(t), so as to obtain the load rate-efficiency curve η load (t); S102. For each element in the historical operation data set A, calculate the energy consumption increment P of the energy equipment during the preset period. start-stop , and P start-stop =∫ t t0 1 (P on (t)-P base )dt, where P on (t) represents the power when the energy equipment starts, P base Indicates the power of the energy device in standby state, t0 indicates the time point when the energy device starts from standby state or shutdown state, and t1 indicates the time when the energy device is fully started and runs stably; calculate the energy consumption increment ΔE of the energy device when the working condition switches within the preset period mode ,and Where P(t) represents the real-time power of the energy device during the switching process, tm and tm+1 represent the start and end time of the working condition switching of the energy device respectively; calculate the steady-state duration T of the energy device within the preset period stabilize , and T stabilize =t stabilize_end -t stabilize_start , where t stabilize_start Indicates the time when the energy device begins to stabilize, t stabilize_end Indicates the moment when the equipment's stable state is maintained within a stable range; S103. Summarize the dynamic fitting value η of the load rate-efficiency curve corresponding to each element in the historical operation data set A load (t), incremental energy consumption of equipment start-up and shutdown P start-stop , energy consumption increment ΔE mode and the steady-state duration T stabilize , and normalize it, and calculate the dynamic fitting value η of the load rate-efficiency curve respectively load (t), incremental energy consumption of equipment start-up and shutdown P start-stop , energy consumption increment ΔE mode and the steady-state duration T stabilize The average value within the preset period is used to construct the corresponding equipment energy efficiency feature vector E, and E = (η, P, ΔE, T); according to the equipment energy efficiency feature vector E of each element, the corresponding historical energy efficiency evaluation index HEEI is calculated, and HEEI = w1·η-w2·P-w3·ΔE+w4·T, where w1, w2, w3 and w4 represent the weights of the corresponding features of the equipment energy efficiency feature vector E, and w1+w2+w3+w4=1.

3. The SCADA-based energy equipment energy saving and carbon reduction control method according to claim 2, characterized in that: The step S200 includes: S201. Obtain historical carbon emission data of energy equipment from the database, pre-process the historical carbon emission data, and divide the pre-processed historical carbon emission data according to the preset period corresponding to the historical operation data, so as to construct a historical carbon emission data set B, and B = {b1, b2, ..., bn}, wherein b1 represents the historical carbon emission data segment corresponding to the first preset period, b2 represents the historical carbon emission data segment corresponding to the second preset period, and so on, bn represents the historical carbon emission data segment corresponding to the first preset period; for each element in the historical carbon emission data set B, a regression analysis method is used to construct a trend model of carbon emission data, and the trend model expression is: C trend (t) = β0 + β1t + β2t 2 +...; where β0, β1, β2, ... are regression coefficients, and t represents the time variable; through Fourier transform, the periodic carbon emission fluctuations are analyzed in the frequency domain, and the corresponding calculation formula is: Where f represents the frequency, C(t) represents the time series data of the corresponding element, and m represents the total number of data points; S202. According to the trend model and the periodic fluctuation analysis results, the carbon emission characteristics of the energy equipment are extracted, and the carbon emission characteristics are the trend characteristics and periodic characteristics of the historical carbon emission data, and the carbon emission characteristics of the energy equipment are normalized; the equipment carbon emission characteristic vector C is constructed according to the normalized carbon emission characteristics, and C = [c1, c2], where c1 represents the trend characteristics of the historical carbon emission data of the corresponding element, and c2 represents the periodic characteristics of the historical carbon emission data of the corresponding element; according to the equipment carbon emission characteristic vector C corresponding to each element, the corresponding historical carbon emission evaluation index HCEI is calculated, and HCEI = α1×c1+α2×c2, where α1 and α2 represent the weight coefficients of the trend characteristics and the periodic characteristics, respectively, and α1+α2=1.

4. The SCADA-based energy equipment energy saving and carbon reduction control method according to claim 3 is characterized in that: The step S300 includes: S301. Make one-to-one correspondence between each element in the historical operation data set A of energy equipment and the historical carbon emission data set B in chronological order, that is, ai corresponds to bi, and i ranges from 1 to n; according to the correspondence between the elements in the historical operation data set A and the historical carbon emission data set B, obtain the corresponding historical energy efficiency evaluation index HEEI and the historical carbon emission evaluation index HCEI, thereby forming an associated data pair D, and D = (HEEI, HCEI); summarize all the associated data pairs D, and construct a multi-objective optimization model including energy efficiency and carbon emissions, and the objective function is: Objective = k1×HEEI-k2×HCEI, where k1 and k2 represent the weights of energy efficiency and carbon emissions, respectively, and k1+k2=1; S302. According to the multi-objective optimization model, the target value Objective corresponding to all the associated data pairs D is obtained, and according to the preset target threshold R, and the corresponding energy efficiency assessment threshold EEI and carbon emission assessment threshold CEI, the associated data pairs D that meet the following conditions are screened out, and the conditions are: Objective>R and HEEI>EEI and HCEI<CEI; according to the screened associated data pairs D, the corresponding energy equipment energy-saving and carbon reduction control strategies are found in the corresponding historical operation data and historical carbon emission data, and the associated data pairs D and the energy equipment energy-saving and carbon reduction control strategies are in a one-to-one correspondence.

5. The SCADA-based energy equipment energy saving and carbon reduction control method according to claim 4 is characterized in that: The step S400 includes: S401. Configure the distributed data acquisition terminal of the SCADA system to collect real-time operation data and real-time carbon emission data of energy equipment, analyze the real-time operation data and real-time carbon emission data according to the analysis method of historical operation data and historical carbon emission data, so as to obtain the real-time energy efficiency evaluation index REEI and the real-time carbon emission evaluation index RCEI, and form a real-time associated data pair RD, and RD = (REEI, RCEI); calculate the difference between the real-time energy efficiency evaluation index REEI and the real-time carbon emission evaluation index RCEI in the real-time associated data pair RD and the historical energy efficiency evaluation index HEEI and the historical carbon emission evaluation index HCEI in the associated data pair D, and select the associated data pair D with the smallest sum of the differences as the matching result; S402. If the matching result is unique, the energy equipment energy-saving and carbon-reduction control strategy corresponding to the matching result is used as the real-time energy-saving and carbon-reduction control strategy of the current energy equipment; if the matching result is not unique, all energy equipment energy-saving and carbon-reduction control strategies are obtained and arranged in descending order according to the corresponding target values, so as to obtain a list of real-time energy-saving and carbon-reduction control strategies for the current energy equipment.

6. A SCADA-based energy equipment energy conservation and carbon reduction control system, applied to the SCADA-based energy equipment energy conservation and carbon reduction control method according to any one of claims 1 to 5, characterized in that: The system includes: a historical data collection and analysis module, a correlation analysis and optimization model module, a real-time data collection and analysis module, and a control strategy matching module; The historical data collection and analysis module obtains the historical operation data of the energy equipment from the database, analyzes the historical operation data, extracts the equipment energy efficiency feature vector from the historical operation data, and evaluates the energy efficiency of the energy equipment based on the equipment energy efficiency feature vector, thereby obtaining a historical energy efficiency evaluation index; obtains the historical carbon emission data of the energy equipment from the database, and performs a carbon emission trend analysis based on the historical carbon emission data; according to the carbon emission trend analysis results, extracts the equipment carbon emission feature vector, and calculates the historical carbon emission evaluation index based on the equipment carbon emission feature vector; The correlation analysis and optimization model module matches the historical operation data of energy equipment with the historical carbon emission data in chronological order, and performs correlation analysis on the corresponding historical energy efficiency evaluation index and historical carbon emission evaluation index, so as to obtain the energy equipment energy saving and carbon reduction control strategy; The real-time data acquisition and analysis module is configured with a distributed data acquisition terminal of the SCADA system to collect and analyze the real-time operation data and real-time carbon emission data of energy equipment to obtain the corresponding real-time energy efficiency evaluation index and real-time carbon emission evaluation index; The control strategy matching module analyzes the real-time energy efficiency evaluation index and the real-time carbon emission evaluation index with the historical energy efficiency evaluation index and the historical carbon emission evaluation index, and matches the corresponding energy equipment energy-saving and carbon reduction control strategy according to the analysis results.

7. The SCADA-based energy equipment energy saving and carbon reduction control system according to claim 6 is characterized in that: The historical data collection and analysis module includes a historical data acquisition unit and a historical data analysis unit; The historical data acquisition unit acquires the historical operation data and historical carbon emission data of the energy equipment from the database, preprocesses the historical operation data and historical carbon emission data respectively, and constructs a historical operation data set and a historical carbon emission data set; the historical data analysis unit performs feature extraction and constructs an equipment energy efficiency feature vector and an equipment carbon emission feature vector based on the historical operation data set and the historical carbon emission data set, and obtains the corresponding historical energy efficiency evaluation index and historical carbon emission evaluation index according to the equipment energy efficiency feature vector and the equipment carbon emission feature vector.

8. The SCADA-based energy equipment energy saving and carbon reduction control system according to claim 6 is characterized by: The association analysis and optimization model module includes an association analysis unit and an optimization model building unit; The correlation analysis unit matches the historical operation data of the energy equipment with the historical carbon emission data in chronological order, thereby obtaining corresponding correlation data pairs; the optimization model construction unit constructs a multi-objective optimization model based on the correlation data pairs.

9. The SCADA-based energy equipment energy saving and carbon reduction control system according to claim 6 is characterized in that: The real-time data acquisition and analysis module includes a real-time data acquisition unit and a real-time data analysis unit; The real-time data acquisition unit is configured with a distributed data acquisition terminal of the SCADA system to collect and analyze the real-time operation data and real-time carbon emission data of the energy equipment; the real-time data analysis unit analyzes the real-time operation data and real-time carbon emission data according to the analysis method of historical operation data and historical carbon emission data to obtain the corresponding real-time energy efficiency evaluation index and real-time carbon emission evaluation index.

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