A low-carbon resource regulation performance measurement method, system, device and medium

Abstract

The present application relates to a kind of low-carbon resource regulating performance actual measurement method, system, equipment and medium, the method includes in response to target down-regulation power instruction, obtain the compound power drop curve data of low-carbon building in high-frequency acquisition period;According to low-carbon resource thermodynamics inertia and electronic transient physical characteristics, determine inertia response base function and step response base function;Extract the step anchor point time and the climbing end time of compound power drop curve, and respectively constrain step response base function and inertia response base function;The two kinds of base functions after constraint are carried out full-time domain joint fitting, obtain the independent power drop trajectory of different low-carbon resources;Based on independent power drop trajectory generation reconstruction compound curve, according to reconstruction residual updates independent power drop trajectory, and combined with target down-regulation power instruction output independent regulating performance numerical value.The present application has the effect of improving evaluation accuracy.

Description

Technical Field

[0001] This invention belongs to the technical field of power measurement and control, and in particular relates to a method, system, equipment and medium for measuring the performance of low-carbon resource regulation. Background Technology

[0002] Currently, low-carbon buildings, acting as distributed and flexible resource aggregation nodes, often aggregate low-carbon resources with different power response characteristics to participate in grid demand response. After the target power reduction command is triggered, testers typically conduct offline measurements at the main power inlet of the low-carbon building to obtain composite power decline curve data within a high-frequency acquisition cycle, in order to evaluate the actual performance of low-carbon resources in power regulation.

[0003] Existing offline measurements typically analyze the composite power decline curve collected at the total power inlet as the overall response result. However, low-carbon resources exhibit both slowly varying responses with thermodynamic inertial physical characteristics and step responses with electronic transient physical characteristics. The power regulation processes corresponding to different physical characteristics are intertwined in the high-frequency time domain, causing the total curve to mask the true regulation contribution of individual resources, resulting in inaccurate evaluation results of regulation contribution and regulation accuracy. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, equipment and medium for measuring the regulation performance of low-carbon resources, so as to solve the technical problem that existing offline measurement methods distort the true regulation contribution by evaluating the superimposed response of multiple types of low-carbon resources as the overall result.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for measuring the performance of low-carbon resource regulation, the method comprising: In response to the triggering of the target power reduction command, the composite power reduction curve data of low-carbon buildings is acquired during the high-frequency acquisition cycle; Based on the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources, the inertial response basis function and the step response basis function are determined respectively. Curve features were extracted from the composite power decrease curve data to obtain the step anchor point time and the climb end time; Based on the step anchor point time, the step response basis function is parameter-constrained, and based on the ramp end time, the inertial response basis function is parameter-constrained. The parameter-constrained inertial response basis function and the step response basis function are then jointly fitted in the entire time domain to obtain the independent power decline trajectories of different low-carbon resources. A reconstructed composite curve is generated based on the independent power decline trajectory. The independent power decline trajectory is updated according to the residual between the reconstructed composite curve and the composite power decline curve data. The independent adjustment performance value is output according to the updated independent power decline trajectory and the target power reduction command.

[0006] By adopting the above technical solution, and by acquiring composite power decline curve data of low-carbon buildings within a high-frequency acquisition cycle in response to the target power reduction command, the total change trajectory of low-carbon resources during the power reduction process can be completely recorded, thus providing a data foundation for subsequent analysis of the adjustment contribution of different low-carbon resources. By determining the inertial response basis function and the step response basis function according to the thermodynamic inertial physical characteristics and the electronic transient physical characteristics, respectively, the power decline characteristics of different low-carbon resources can be described with different response forms, thereby improving the specificity of composite power curve decoupling. By extracting curve features from the composite power decline curve data, the step anchor point time and the ramp-up can be obtained. At the end of the process, the location of the transient response and the stabilization point of the inertial response can be pinpointed, providing a reliable time point basis for subsequent parameter constraints. By constraining the step response basis function and the inertial response basis function based on the step anchor point and the end of the ramp and performing full-time domain joint fitting, the composite power decline curve can be decomposed into independent power decline trajectories of different low-carbon resources, thereby accurately identifying the true regulation contribution of different low-carbon resources. By generating a reconstructed composite curve based on the independent power decline trajectory and updating the independent power decline trajectory according to the residual, the impact of abnormal fluctuations on the decoupling results can be reduced, thereby improving the accuracy of the independent regulation performance values.

[0007] In one example, the present invention can be further configured as follows: In response to the triggering of a target power reduction command, acquiring composite power reduction curve data of low-carbon buildings within a high-frequency acquisition cycle includes: Parse the target power reduction command to obtain the command issuance timestamp; Based on the timestamp of the instruction issuance, obtain the total active power data of the low-carbon building during the corresponding time period before and after the target power reduction instruction is triggered; The total active power data is sorted according to the acquisition time, and the curve segment covering the power decline process is retained to obtain the composite power decline curve data.

[0008] By adopting the above technical solution, the time base of the power reduction process can be determined by parsing the target power reduction command and obtaining the command issuance timestamp, thereby improving the accuracy of curve truncation and response process alignment. By obtaining the total active power data within the corresponding time period before and after the target power reduction command is triggered based on the command issuance timestamp, the complete change process before, during and after the power reduction can be covered, thereby avoiding the loss of key response stages in the power reduction curve data. By sorting the total active power data according to the acquisition time and retaining the curve segments covering the power reduction process, continuous and complete composite power reduction curve data can be formed, thereby providing stable input for subsequent feature extraction and full-time domain joint fitting.

[0009] In one example, the present invention can be further configured as follows: determining the inertial response basis function and the step response basis function according to the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources, respectively, includes: Obtain historical power decline curves for low-carbon resources with thermodynamic inertial physical characteristics, and historical power decline curves for low-carbon resources with electronic transient physical characteristics; Based on the historical power decline curves of low-carbon resources with thermodynamic inertial physical characteristics, the inertial time constant parameter and steady-state decline amplitude parameter used to characterize the slowly varying power decline process are determined. Based on the historical power decline curves of low-carbon resources with electronic transient physical characteristics, the step amplitude parameter and step response delay parameter used to characterize the transient power decline process are determined. The inertial response basis function is determined based on the inertial time constant parameter and the steady-state descent amplitude parameter, and the step response basis function is determined based on the step amplitude parameter and the step response delay parameter.

[0010] By employing the above technical solutions, and by acquiring historical power decline curves of low-carbon resources with thermodynamic inertial and electronic transient physical characteristics, response samples of different low-carbon resources acting individually can be obtained, thus providing a reference for determining basis function parameters. By determining the inertial time constant parameter and steady-state decline amplitude parameter based on the historical power decline curves of thermodynamic inertial low-carbon resources, the strength and magnitude of inertia in the slowly varying power decline process can be characterized, thereby improving the accuracy of the inertial response basis function in describing this type of low-carbon resource. By determining the step amplitude parameter and step response delay parameter based on the historical power decline curves of electronic transient low-carbon resources, the jump amplitude and response delay of the transient power decline process can be characterized, thereby improving the accuracy of the step response basis function in describing this type of low-carbon resource. By determining the inertial response basis function and step response basis function based on the above parameters, a response model matching different physical characteristics can be formed, thus providing physical priors for the joint fitting and decoupling of composite power decline curves.

[0011] In one example, the present invention can be further configured as follows: the step feature extraction of the composite power decline curve data to obtain the step anchor point time and the ramp-up end time includes: The composite power decrease curve data is discretized and preprocessed to obtain the power change rate and power extreme points of adjacent sampling points; Based on the power change rate of the adjacent acquisition points, a set of abrupt change feature points is selected from the composite power decrease curve data; Based on the set of mutation feature points and the power extreme points, a local feature vector sequence of the composite power decline curve data is constructed; Based on the local feature vector sequence, the step anchor point time and the climbing end time are identified.

[0012] By employing the above technical solutions, the power change rate and power extreme points of adjacent acquisition points are obtained through discretization preprocessing of the composite power decline curve data. This allows for the extraction of fundamental features reflecting the rate of change and local morphology in the curve, thereby improving the reliability of subsequent abrupt change identification and stabilization identification. By filtering the set of abrupt change feature points based on the power change rate of adjacent acquisition points, rapidly changing regions in the composite power decline curve can be identified, providing a basis for step response location. By constructing a local feature vector sequence based on the set of abrupt change feature points and power extreme points, the amplitude, rate, and fluctuation characteristics of the local curve can be comprehensively expressed, thereby enhancing the ability to distinguish different power response patterns. By identifying the step anchor point and the end of the ramp based on the local feature vector sequence, transient response nodes and inertial response stabilization nodes can be determined respectively, providing accurate node parameters for parameter constraints and joint fitting.

[0013] In one example, the present invention can be further configured as follows: identifying the step anchor point time and the climbing end time based on the local feature vector sequence includes: Extract feature vectors reflecting the transient power decrease pattern from the local feature vector sequence, and determine candidate step nodes based on the feature vectors reflecting the transient power decrease pattern; Extract feature vectors reflecting the power stabilization pattern from the local feature vector sequence, and determine candidate ramp-end nodes based on the feature vectors reflecting the power stabilization pattern; The step anchor point time is determined based on the corresponding time of the candidate step node in the composite power decrease curve data; The ramp-end time is determined based on the corresponding time of the candidate ramp-end node in the composite power decrease curve data.

[0014] By employing the above technical solutions, and by extracting feature vectors reflecting the transient power decrease pattern from the local feature vector sequence and determining candidate step nodes, the local pattern corresponding to the electronic transient response can be identified from the composite curve, thereby improving the accuracy of determining the step anchor point time. Furthermore, by extracting feature vectors reflecting the power stabilization pattern from the local feature vector sequence and determining candidate ramp-up end nodes, the local pattern of the inertial response tending towards stability can be identified from the composite curve, thereby improving the accuracy of determining the ramp-up end time. By determining the step anchor point time based on the corresponding time of the candidate step nodes in the composite power decrease curve data, the location of the step response on the time axis can be clearly identified, thereby improving the effectiveness of the step response basis function parameter constraints. Finally, by determining the ramp-up end time based on the corresponding time of the candidate ramp-up end nodes in the composite power decrease curve data, the location of the basically stable inertial response can be clearly identified, thereby improving the effectiveness of the inertial response basis function parameter constraints.

[0015] In one example, the present invention can be further configured as follows: The step response basis function is parameter-constrained based on the step anchor point time, and the inertial response basis function is parameter-constrained based on the ramp-end time. The parameter-constrained inertial response basis function and the step response basis function are then jointly fitted across the entire time domain to obtain independent power decline trajectories for different low-carbon resources, including: Based on the step anchor point time, determine the initial positioning range of the step response delay parameter in the step response basis function; Based on the time when the climb ends, determine the initial positioning range of the inertial time constant parameter in the inertial response basis function; Based on the initial positioning range of the step response delay parameter and the initial positioning range of the inertial time constant parameter, the inertial response basis function and the step response basis function are jointly solved in the full time domain of the composite power decrease curve data to obtain the basis function solution parameters. Based on the parameters obtained from the basis functions, the fitted power curves corresponding to low-carbon resources with different physical characteristics are reconstructed respectively, and the fitted power curves are used as the independent power decline trajectories.

[0016] By adopting the above technical solutions, the initial positioning range of the step response delay parameter can be narrowed by determining the initial positioning range of the step response delay parameter based on the step anchor point time, thereby improving the convergence stability of the full-time domain joint fitting. Similarly, the initial positioning range of the inertial time constant parameter can be narrowed by determining the initial positioning range of the inertial time constant parameter in the inertial response basis function, thus reducing the risk of inertial response trajectory fitting deviation. Furthermore, by jointly solving the inertial response basis function and the step response basis function based on the initial positioning range of the step response delay parameter and the inertial time constant parameter, different response components can be estimated simultaneously in the full-time domain of the composite power decline curve, thereby improving the accuracy of separating the power contributions of different low-carbon resources. Finally, by reconstructing the fitted power curves corresponding to low-carbon resources with different physical characteristics based on the parameters solved by the basis functions and using them as independent power decline trajectories, independent power decline process expressions for various low-carbon resources can be formed, thus providing a quantifiable basis for evaluating independent regulation performance.

[0017] In one example, the present invention can be further configured as follows: generating a reconstructed composite curve based on the independent power decline trajectory, updating the independent power decline trajectory according to the residual between the reconstructed composite curve and the composite power decline curve data, and outputting an independent adjustment performance value according to the updated independent power decline trajectory and the target power reduction command, including: The independent power decrease trajectories are summed to generate the reconstructed composite curve; Calculate the residual between the reconstructed composite curve and the composite power decrease curve data, and locate the abnormal fluctuation time series segment based on the residual; The composite power decline curve data within the abnormal fluctuation time series segment is masked to obtain the clean power decline curve; Based on the clean power decrease curve, the inertial response basis function and the step response basis function are refitted in the full time domain to obtain the updated independent power decrease trajectory; The actual power reduction for different low-carbon resources is calculated based on the updated independent power reduction trajectory, and the independent regulation performance value is output based on the actual power reduction and the target power reduction value in the target power reduction command.

[0018] By employing the above technical solutions, and generating a reconstructed composite curve through summing the independent power decline trajectories, the decoupled component trajectories can be recombined into a total curve, thus providing a basis for verifying the consistency between the decoupling results and measured data. By calculating the residual between the reconstructed composite curve and the composite power decline curve data and locating the time series segments of abnormal fluctuations, abnormal power fluctuations affecting the fitting accuracy can be identified, thereby improving the targeting of subsequent trajectory updates. By masking the composite power decline curve data within the time series segments of abnormal fluctuations to obtain a clean power decline curve, the impact of abnormal interference data on joint fitting can be reduced, thereby improving the credibility of the updated independent power decline trajectory. By refitting the inertial response basis function and the step response basis function in the full time domain based on the clean power decline curve, an independent power decline trajectory that more closely resembles the actual response process can be obtained, thereby improving the accuracy of the measured results of low-carbon resource regulation performance. By calculating the actual down-regulation power of different low-carbon resources based on the updated independent power decline trajectory and combining it with the target down-regulation power value to output the independent regulation performance value, the actual regulation contribution and regulation completion status of different low-carbon resources can be quantified, thus providing an accurate basis for evaluating the regulation performance of low-carbon resources.

[0019] In a second aspect, the present invention provides a system for measuring the performance of low-carbon resource regulation, the system comprising: The curve acquisition module is used to acquire composite power reduction curve data of low-carbon buildings in the high-frequency acquisition cycle in response to the triggering of the target power reduction command. The basis function module is used to determine the inertial response basis function and the step response basis function according to the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources, respectively. The feature extraction module is used to extract curve features from the composite power decline curve data to obtain the step anchor point time and the climbing end time. The joint fitting module is used to constrain the parameters of the step response basis function based on the step anchor point time, and constrain the parameters of the inertial response basis function based on the climbing end time. The parameter-constrained inertial response basis function and the step response basis function are then jointly fitted in the entire time domain to obtain the independent power decline trajectories of different low-carbon resources. The performance output module is used to generate a reconstructed composite curve based on the independent power decline trajectory, update the independent power decline trajectory according to the residual between the reconstructed composite curve and the composite power decline curve data, and output an independent adjustment performance value according to the updated independent power decline trajectory and the target power reduction command.

[0020] By adopting the above technical solution, and by acquiring composite power decline curve data of low-carbon buildings within a high-frequency acquisition cycle in response to the target power reduction command, the total change trajectory of low-carbon resources during the power reduction process can be completely recorded, thus providing a data foundation for subsequent analysis of the adjustment contribution of different low-carbon resources. By determining the inertial response basis function and the step response basis function according to the thermodynamic inertial physical characteristics and the electronic transient physical characteristics, respectively, the power decline characteristics of different low-carbon resources can be described with different response forms, thereby improving the specificity of composite power curve decoupling. By extracting curve features from the composite power decline curve data, the step anchor point time and the ramp-up can be obtained. At the end of the process, the location of the transient response and the stabilization point of the inertial response can be pinpointed, providing a reliable time point basis for subsequent parameter constraints. By constraining the step response basis function and the inertial response basis function based on the step anchor point and the end of the ramp and performing full-time domain joint fitting, the composite power decline curve can be decomposed into independent power decline trajectories of different low-carbon resources, thereby accurately identifying the true regulation contribution of different low-carbon resources. By generating a reconstructed composite curve based on the independent power decline trajectory and updating the independent power decline trajectory according to the residual, the impact of abnormal fluctuations on the decoupling results can be reduced, thereby improving the accuracy of the independent regulation performance values.

[0021] In a third aspect, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for measuring the performance of low-carbon resource regulation.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for measuring the performance of low-carbon resource regulation. Attached Figure Description

[0023] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of a method for measuring the low-carbon resource regulation performance in an embodiment of the present invention; Figure 2 This is a structural block diagram of the low-carbon resource regulation performance measurement system in an embodiment of the present invention; Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0025] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0026] Example 1 like Figure 1 As shown, this invention discloses a method for measuring the performance of low-carbon resource regulation, which specifically includes the following steps: S10: In response to the triggering of the target power reduction command, acquire the composite power reduction curve data of the low-carbon building during the high-frequency acquisition cycle.

[0027] Specifically, after the target power reduction command is triggered, high-frequency power sampling data arranged in time is acquired around the power change process at the total power entrance of the low-carbon building. The high-frequency power sampling data covers the initial steady-state period before the target power reduction command is triggered, the power decline period after the trigger is triggered, and the steady-state period after the power stabilizes. The composite power decline curve data is used to characterize the superimposed power decline trajectory of low-carbon resources with thermodynamic inertial physical characteristics and low-carbon resources with electronic transient physical characteristics in the same power reduction process.

[0028] S20: Determine the inertial response basis function and the step response basis function respectively according to the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources.

[0029] Specifically, based on the differences in the physical responses of low-carbon resources during power reduction, the power reduction process with thermodynamic inertial physical characteristics is characterized as a slow response, and the power reduction process with electronic transient physical characteristics is characterized as a step response. Using historical independent action curves as the basis for parameter determination, inertial response basis functions for describing the slow power reduction process and step response basis functions for describing the transient power reduction process are formed respectively. The inertial response basis functions and the step response basis functions together serve as the physical prior expressions for the subsequent decoupling of the composite power reduction curve.

[0030] S30: Extract curve features from the composite power decline curve data to obtain the step anchor point time and the end time of the climb.

[0031] Specifically, the curve morphology is analyzed based on the abrupt change and stabilization characteristics of the composite power decrease curve data in the time domain. The step anchor point time, which is used to characterize the location of the step response, is determined from the local drastic change region during the power decrease process. The ramp-up end time, which is used to characterize the basic stable position of the inertial response, is determined from the stabilization region in the later stage of the power decrease process. The step anchor point time and the ramp-up end time serve as the time nodes for subsequent parameter constraints and full-time domain joint fitting.

[0032] S40: Based on the step anchor point time, the parameters of the step response basis function are constrained, and the parameters of the inertial response basis function are constrained according to the end time of the climb. The parameter-constrained inertial response basis function and the step response basis function are jointly fitted in the entire time domain to obtain the independent power decline trajectory of different low-carbon resources.

[0033] Specifically, the step anchor point time is used to limit the parameters related to response delay in the step response basis function, and the ramp-up end time is used to limit the parameters related to the rate of inertial decay in the inertial response basis function. Within the complete time domain of the composite power decline curve data, the two types of basis functions are used to fit the measured curve together. By solving the parameter values ​​of the two types of basis functions, the corresponding power decline curves are reconstructed, thereby decomposing the composite power decline curve into independent power decline trajectories of different low-carbon resources.

[0034] S50: Generates a reconstructed composite curve based on the independent power drop trajectory, updates the independent power drop trajectory based on the residual between the reconstructed composite curve and the composite power drop curve data, and outputs the independent adjustment performance value based on the updated independent power drop trajectory and the target power reduction command.

[0035] Specifically, the independent power decline trajectories of different low-carbon resources are superimposed at the same acquisition time to obtain a reconstructed composite curve. The reconstructed composite curve and the composite power decline curve data are then checked for consistency. If there is an abnormal residual between the two, the time series segment corresponding to the abnormal residual is processed and the independent power decline trajectory is obtained again. Then, based on the updated independent power decline trajectory, independent regulation performance values ​​such as actual down-adjustment power, response delay, regulation rate and regulation accuracy are extracted.

[0036] In one embodiment, in step S10, i.e., in response to the triggering of the target power reduction command, the composite power reduction curve data of the low-carbon building during the high-frequency acquisition cycle is acquired, including: S11: Parse the target power reduction command and obtain the command issuance timestamp.

[0037] Specifically, after receiving the target power reduction command, the time stamp in the target power reduction command is parsed, and the time when the command is issued or arrives at the low-carbon building side is recorded as the command issuance timestamp. The command issuance timestamp serves as the time reference for data extraction of the composite power decline curve and subsequent adjustment performance calculation. If there is a time deviation between the local clock and the command source side, the power acquisition time is aligned with the command issuance timestamp according to the pre-completed clock synchronization relationship to ensure that the time axis of the power decline curve corresponds to the trigger time of the target power reduction command.

[0038] S12: Based on the command issuance timestamp, obtain the total active power data of the low-carbon building during the corresponding time period before and after the target power reduction command is triggered.

[0039] Specifically, the corresponding time periods before and after the command issuance timestamp are selected, and the instantaneous values ​​of three-phase voltage and three-phase current are synchronously collected at the main power inlet of the low-carbon building. The instantaneous value of the total active power of the three phases is calculated according to the collection time to form the total active power data. The collection frequency of the high-frequency collection cycle can be set to no less than 1kHz to fully capture the dynamic power changes of low-carbon resources on a second-level time scale. The collection period includes the initial power reference segment before the command is triggered, the power decline segment after the command is triggered, and the stable segment after the power decline, so that the total active power data can fully cover the entire process of low-carbon resources entering the power reduction state from the original operating state.

[0040] S13: Sort the total active power data according to the acquisition time and retain the curve segment covering the power decline process to obtain composite power decline curve data.

[0041] Specifically, the total active power data corresponding to each acquisition moment is arranged in chronological order. Redundant data segments that are irrelevant to the current target power reduction command are removed, and the continuous curve segment from the initial steady-state power to the decrease and then to the new steady-state power is retained to obtain the composite power decrease curve data. Each sampling point in the composite power decrease curve data includes the acquisition moment and the corresponding total active power value. This curve reflects the trajectory of the total power change after the thermodynamic inertial response and electronic transient response are superimposed in time, and is used for subsequent curve feature extraction and full-time domain joint fitting.

[0042] In one embodiment, step S20, namely, determining the inertial response basis function and the step response basis function according to the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources, includes: S21: Obtain the historical power decline curves of low-carbon resources with thermodynamic inertial physical characteristics, and the historical power decline curves of low-carbon resources with electronic transient physical characteristics.

[0043] Specifically, during the non-target power reduction command execution period, the power reduction process when a low-carbon resource with thermodynamic inertial physical characteristics operates alone and the power reduction process when a low-carbon resource with electronic transient physical characteristics operates alone are selected respectively, and the corresponding historical power reduction curves are recorded. The historical power reduction curves cover the complete process of the corresponding low-carbon resource from receiving the adjustment action to the power stabilizing, and avoid large fluctuations of other low-carbon resources or background loads, so that the historical power reduction curves can reflect the power response characteristics of a single type of low-carbon resource. In practical applications, low-carbon resources with thermodynamic inertial physical characteristics can correspond to central air conditioning temperature control systems, and low-carbon resources with electronic transient physical characteristics can correspond to LED intelligent lighting systems.

[0044] S22: Based on the historical power decline curve of low-carbon resources with thermodynamic inertial physical characteristics, determine the inertial time constant parameter and steady-state decline amplitude parameter used to characterize the slowly changing power decline process.

[0045] Specifically, the initial power value P is identified from the historical power decline curves of low-carbon resources with thermodynamic inertial physical characteristics. init and power stabilization value P standby The difference between the two is used as the steady-state decrease amplitude parameter A. a A a =P init -P standby The sampled power values ​​in the historical power decline curve were normalized according to the power stabilization value and steady-state decline amplitude parameters. The natural logarithm of the normalization result was then fitted to the time series, and the absolute value of the slope of the fitted line corresponded to 1 / τ. a Thus, the inertial time constant parameter τ is determined. a The inertial time constant parameter is used to characterize the rate of inertia during the slowly varying power decrease process, while the steady-state decrease amplitude parameter is used to characterize the total power change during the slowly varying power decrease process. Multiple sets of historical power decrease curves can be used to calculate the corresponding A values. a and τ a The arithmetic mean is then taken as the final parameter value.

[0046] S23: Based on the historical power decline curves of low-carbon resources with electronic transient physical characteristics, determine the step amplitude parameter and step response delay parameter used to characterize the transient power decline process.

[0047] Specifically, the trigger time t0 and the midpoint time t of the power decline edge are identified from the historical power decline curves of low-carbon resources with electronic transient physical characteristics. f Steady-state power P before the drop l_init and the steady-state power P after the decrease l_final The steady-state power P before the decrease l_initWith the steady-state power P after the decrease l_final The difference is used as the step amplitude parameter A. l A l =P l_init -P l_final The power will decrease at the midpoint time t f The difference between the time of action triggering and the time t0 is used as the step response delay parameter t. d , i.e. t d =t f -t0, the step amplitude parameter, is used to characterize the power jump variable during the transient power decrease process. The step response delay parameter is used to characterize the time delay between the trigger action and the start of power decrease. Multiple sets of historical power decrease curves can be used to calculate the corresponding A. l and t d The arithmetic mean is then taken as the final parameter value.

[0048] S24: Determine the inertial response basis function based on the inertial time constant parameter and the steady-state descent amplitude parameter, and determine the step response basis function based on the step amplitude parameter and the step response delay parameter.

[0049] Specifically, the steady-state decrease amplitude parameter A a and the inertial time constant parameter τ a Substitute into the inertial response expression P a (t)=A a ·e -t / τa The inertial response basis function is obtained, where P a (t) represents the change in inertial response power at time t, where t represents the time referenced to the trigger time of the target power reduction command or the trigger time of the independent action. A a τ represents the steady-state decrease magnitude parameter. a The parameter representing the inertial time constant is the step amplitude parameter A. l and step response delay parameter t d Substitute into the step response expression P l (t)=A l ·u(tt d ), thus obtaining the step response basis function, where P l (t) represents the change in step response power at time t, A l The parameter represents the step amplitude, u(·) represents the unit step function, and t d This represents the step response delay parameter.

[0050] In one embodiment, step S30, namely, extracting curve features from the composite power decrease curve data to obtain the step anchor point time and the ramp-up end time, includes: S31: Discretize the composite power decrease curve data to obtain the power change rate and power extreme points of adjacent acquisition points.

[0051] Specifically, the sampling points in the composite power decline curve data are discretized according to the acquisition time, and the original power data is filtered to retain the time-domain variation characteristics of the power decline process. The filtering process can use low-pass filtering, and the cutoff frequency of the low-pass filter can be set to 50Hz to filter out the power grid frequency and its harmonic interference. On the filtered discrete time series, each pair of adjacent sampling points is traversed in time order. The difference between the power value of the later sampling point and the power value of the earlier sampling point is divided by the time interval obtained by the difference between the acquisition time of the later sampling point and the acquisition time of the earlier sampling point to obtain the power change rate of adjacent sampling points. At the same time, local maxima and local minima are identified in the discretized power time series. The local maxima and local minima are regarded as power extrema. The power extrema threshold can be determined according to the power measurement resolution and the fluctuation amplitude of the background load of low-carbon buildings. For example, the standard deviation of the total power during the unregulated steady-state period can be statistically analyzed and a multiple thereof can be taken as the benchmark value of the extrema judgment threshold.

[0052] S32: Based on the power change rate of adjacent acquisition points, select a set of abrupt change feature points from the composite power decrease curve data.

[0053] Specifically, the power change rate of adjacent sampling points is compared with a preset change rate threshold. Sampling points or adjacent sampling point pairs whose power decrease amplitude changes significantly in a short period of time are selected as abrupt change feature points. The preset change rate threshold can be determined by statistically analyzing the power change rate data of historical power decrease curves of low-carbon resources with electronic transient physical characteristics at the moment of cut-off. For example, a certain proportion of the minimum power change rate at the moment of transient cut-off in multiple sets of historical samples is taken as the preset change rate threshold to cover the real transient response and exclude slow fluctuations caused by thermodynamic inertial response. Furthermore, the transient duration condition is combined to exclude power change events whose duration significantly exceeds the electronic transient response time scale, retaining abrupt change feature points that meet the transient decrease pattern, forming a set of abrupt change feature points. Each element in the set of abrupt change feature points includes the time of abrupt change and the corresponding power change rate information, which are used to construct a local feature vector sequence in the future.

[0054] S33: Based on the set of abrupt change feature points and power extreme points, construct a local feature vector sequence for composite power decline curve data.

[0055] Specifically, taking each abrupt change feature point in the set of abrupt change feature points as the center, corresponding local observation windows are extracted from the composite power decline curve data. Within each local observation window, the total power change, the maximum power change rate, the existence of power extrema, the type and value of power extrema, the average slope of the power curve, and the standard deviation of the power change rate are extracted. These feature quantities are arranged in a fixed order to form a local feature vector. Then, the local observation windows are arranged according to their order on the time axis to obtain a local feature vector sequence. The local feature vector sequence encodes the amplitude, rate of change, and volatility of the composite power decline curve within a local time range, providing a morphological feature basis for identifying the step anchor point and the end of the climb.

[0056] S34: Identify the step anchor point and the end of the climb based on the local feature vector sequence.

[0057] Specifically, morphological matching is performed on each local feature vector in the local feature vector sequence. The transient power decrease morphological identification is used to identify the local observation window corresponding to the transient physical characteristics of the electron, and the power stabilization morphological identification is used to identify the stabilization window corresponding to the thermodynamic inertial physical characteristics. The time position corresponding to the transient decrease window is determined as the step anchor point, and the time position corresponding to the power stabilization window is determined as the ramp-up end time. The step anchor point and ramp-up end time are used to subsequently constrain the step response basis function and the inertial response basis function, respectively.

[0058] In one embodiment, step S34, namely identifying the step anchor point time and the climb end time based on the local feature vector sequence, includes: S341: Extract feature vectors reflecting the transient power decrease pattern from the local feature vector sequence, and determine candidate step nodes based on the feature vectors reflecting the transient power decrease pattern.

[0059] Specifically, the local feature vector sequence is traversed, and local feature vectors that are close to the step amplitude parameter, have a power change rate that is significantly higher than that of the adjacent local observation windows, and have a large absolute value of average slope are extracted as feature vectors reflecting the transient power decline pattern. A step judgment score is formed based on the degree of closeness between the total power change and the step amplitude parameter, the ratio of the power change rate to the preset change rate threshold, and the steepness of the local curve decline. The local observation window with the highest step judgment score is selected as a candidate step node.

[0060] S342: Extract feature vectors reflecting the power stabilization pattern from the local feature vector sequence, and determine candidate ramp-end nodes based on the feature vectors reflecting the power stabilization pattern.

[0061] Specifically, starting from the latter part of the local feature vector sequence, local feature vectors that are close to zero in total power change, have a low maximum power change rate, a low standard deviation of power change rate, and a local curve that is close to horizontal are identified as feature vectors reflecting the power stabilization pattern. Based on the absolute value of the total power change, the maximum power change rate, and the standard deviation of the power change rate, a stability judgment score is formed. Local observation windows that meet the stability judgment score and whose subsequent local observation windows continue to maintain a stabilization pattern are selected as candidate ramp-up termination nodes. The stability judgment criteria can be determined based on the high-frequency power data of the unregulated steady-state period of low-carbon buildings. For example, the cumulative distribution of the stability judgment scores of local feature vectors can be statistically analyzed and the corresponding quantile value can be taken as the threshold.

[0062] S343: Determine the step anchor point time based on the corresponding time of the candidate step node in the composite power decrease curve data.

[0063] Specifically, the local observation window corresponding to the candidate step node is mapped back to the time axis of the composite power decrease curve data, and the center time of the local observation window corresponding to the candidate step node is selected as the step anchor time, denoted as t. anchor , t anchor It is used to indicate the location of the step response in the composite power decrease curve data, and subsequently serves as a constraint on the step response delay parameter.

[0064] S344: Determine the ramp-end time based on the corresponding time of the candidate ramp-end node in the composite power decrease curve data.

[0065] Specifically, the local observation window corresponding to the candidate ramp-up end node is mapped back to the time axis of the composite power decrease curve data, and the start time of the local observation window corresponding to the candidate ramp-up end node is selected as the ramp-up end time, denoted as t. end , t end It is used to indicate the position where the inertial response is basically stable in the composite power decrease curve data, and is subsequently used as a constraint basis for the inertial time constant parameter.

[0066] In one embodiment, in step S40, parameter constraints are applied to the step response basis function based on the step anchor point time, and parameter constraints are applied to the inertial response basis function based on the ramp-end time. The parameter-constrained inertial response basis function and the step response basis function are then jointly fitted in the full time domain to obtain the independent power decline trajectories for different low-carbon resources, including: S41: Based on the step anchor point time, determine the initial positioning range of the step response delay parameter in the step response basis function.

[0067] Specifically, taking the step anchor time t anchor As the step response delay parameter t dBased on the positioning foundation, when the recognition reliability of the step anchor point is high, t d Fixed at t anchor Take t nearby or directly d =t anchor When the reliability of the identification at the step anchor point is at a moderate level, t d Limited to t anchor Within the immediate vicinity of the center, when the reliability of identifying the step anchor time is low, t anchor As t d The initial iteration value and allow t d By participating in joint solution over a wider time span, the initial positioning range of the step response delay parameter is used to reduce invalid searches of the step response basis functions in the full-time domain joint fitting. The reliability of identifying the step anchor point can be determined by the ratio r of the highest to the second highest step decision score. s It means that r s The larger the value, the higher the distinction between the candidate step node and other candidate nodes.

[0068] S42: Based on the end time of the climb, determine the initial positioning range of the inertial time constant parameter in the inertial response basis function.

[0069] Specifically, taking the end time t of the climb as an example. end The parameter τ as the inertial time constant a Based on the positioning principle, τ is determined by utilizing the relationship that the inertial response tends to stabilize after several times the inertial time constant. a The initial positioning range, when the recognition reliability at the end of the climb is high, will be τ a The search range is constrained by t end Within a relatively narrow range, when the reliability of the identification at the end of the climb is low, t will be... end Or by t end The calculated time constant is τ a The initial iteration value and allow τ a In a wider range, the initial positioning range of the inertial time constant parameter is used to reduce the parameter offset of the inertial response basis function in the full-time domain joint fitting. The reliability of the identification at the end of the climb can be determined by the number of windows n in which the stability judgment score continuously exceeds the threshold. c It means that n c The larger the value, the stronger the stability of the power output.

[0070] S43: Based on the initial positioning range of the step response delay parameter and the initial positioning range of the inertial time constant parameter, the inertial response basis function and the step response basis function are jointly solved in the full time domain of the composite power decrease curve data to obtain the basis function solution parameters.

[0071] Specifically, a composite power reduction model is constructed by summing the inertial response basis functions and the step response basis functions. , where P total (t) represents the estimated decrease in composite power at time t, A a τ represents the steady-state decrease magnitude parameter. a A represents the inertial time constant parameter. l t represents the step amplitude parameter. d The parameter u(·) represents the step response delay parameter, and u(·) represents the unit step function. The parameter vector to be solved is set in the full time domain of the composite power descent curve data. and with As the joint solution objective, where N represents the number of sampling points involved in the fitting, t i P represents the time of the i-th sampling point. meas (t i P represents the measured power value at the i-th sampling point. total (t i ;θ) represents the estimated composite power decrease at the i-th sampling point under parameter vector θ. The basis function solution parameters are obtained by iteratively solving θ based on the initial positioning range of the step response delay parameter and the initial positioning range of the inertial time constant parameter.

[0072] In some embodiments, the solution can be obtained based on r before joint solution. s and n c Determine the parameter constraint method, which is expressed as follows: , where r s n represents the ratio of the highest score to the second highest score in the step decision, where n is the number of points awarded for a step decision. c This represents the number of windows whose stability score continuously exceeds a threshold. In strict anchoring mode, t d Fixed at t anchor The location does not participate in iterative optimization, τ a The search scope is limited to [t] end / 5,t end Within the interval, under relaxed constraint mode, t d In [t] anchor -0.2,t anchor Participating in optimization within the range of +0.2], τ a The search scope has been broadened to [t] end / 8,2·t end Within the interval, in unconstrained mode, t d and τ a All can be searched freely within the entire domain, but t anchor and t end As t respectively d and τ aThe initial iteration values ​​are substituted into the optimization.

[0073] In some embodiments, the iterative solution process employs the Levenberg-Marquardt algorithm, according to... Update the parameter vector, where J is the Jacobian matrix of the residual vector r with respect to the parameter vector θ, λ is the damping factor, and diag(·) denotes taking the diagonal elements of the matrix to form a diagonal matrix. The elements in the i-th row of the Jacobian matrix are... , where δ(·) is the Dirac function. If the current iteration step causes the joint solution objective to decrease, the damping factor is reduced to accelerate convergence. If the current iteration step fails to cause the joint solution objective to decrease, the damping factor is increased to enhance numerical stability. The iteration termination condition can be that the L2 norm of the parameter change is less than a preset value, the number of iterations reaches a preset number, or the relative change of the joint solution objective continuously satisfies the convergence condition.

[0074] In some embodiments, before each iteration to jointly solve the objective, local outlier detection can be performed on the composite power decline curve data. If the deviation between the power value of a sampling point within the local detection window and the median power within the window exceeds a preset multiple of the absolute median power difference within the window, the sampling point is marked as an outlier, and the weight of the corresponding residual term is reduced in the objective function calculation of the current iteration step. When the iteration converges, if the proportion of the number of sampling points marked as outliers exceeds a preset proportion, a re-acquisition or data reprocessing process is triggered. If the proportion does not exceed the preset proportion, the current parameter vector is output as the basis function solution parameters.

[0075] S44: Based on the basis function, the parameters are solved to reconstruct the fitted power curves corresponding to low-carbon resources with different physical characteristics, and the fitted power curves are used as independent power decline trajectories.

[0076] Specifically, the steady-state decrease magnitude parameter in the basis function solution parameters will be... and the inertial time constant parameter τ a Substitution Reconstruct the fitted power curve corresponding to low-carbon resources with thermodynamic inertial physical characteristics, where P a,fit (t) represents the change in inertial fitting power at time t, and the step amplitude parameter is obtained by solving the basis function. and step response delay parameter t d Substitution Reconstruct the fitted power curve corresponding to low-carbon resources with electronic transient physical characteristics, where P l,fit (t) represents the step-fitted power change at time t. The two fitted power curves maintain the same acquisition period as the composite power decrease curve on the time axis and serve as independent power decrease trajectories for different low-carbon resources.

[0077] In one embodiment, step S50, namely generating a reconstructed composite curve based on the independent power decline trajectory, updating the independent power decline trajectory according to the residual between the reconstructed composite curve and the composite power decline curve data, and outputting an independent adjustment performance value based on the updated independent power decline trajectory and the target power reduction command, includes: S51: Summing the independent power decrease trajectories to generate a reconstructed composite curve.

[0078] Specifically, at each sampling moment, the independent power decrease trajectory corresponding to the low-carbon resource with thermodynamic inertial physical characteristics is summed with the independent power decrease trajectory corresponding to the low-carbon resource with electronic transient physical characteristics to generate a reconstructed composite curve. ,in P represents the reconstructed composite power value at the i-th sampling time. a,fit (t i P represents the change in inertial fitting power at the i-th sampling time. l,fit (t i ) represents the change in step fitted power at the i-th sampling time. The reconstructed composite curve is used for residual comparison with the composite power decrease curve data.

[0079] S52: Calculate the residual between the reconstructed composite curve and the composite power decline curve data, and locate the time series segment of abnormal fluctuations based on the residual.

[0080] Specifically, the absolute value of the residual between the reconstructed composite curve and the composite power descent curve is calculated point by point. , where |e i | represents the absolute value of the residual at the i-th sampling point, P meas (t i ) represents the measured power value at the i-th sampling point. Let represent the reconstructed composite power value at the i-th sampling point, and calculate the sum of squared residuals. If the RSS is greater than the residual tolerance threshold, the sampling points whose absolute residual value exceeds the residual alarm threshold are marked as anomalies, and the anomalies that occur consecutively in time are grouped into anomaly fluctuation time series segments. The anomaly fluctuation time series segments include the corresponding start time and end time. The residual tolerance threshold can be N·(3σn). 2 , where σn represents the standard deviation of power measurement noise of the high-frequency acquisition device, and the residual alarm threshold can be 5σn.

[0081] S53: Mask the composite power decline curve data within the abnormal fluctuation time series segment to obtain the clean power decline curve.

[0082] Specifically, the composite power decrease curve data falling within the abnormal fluctuation time series segment is masked, and interpolation is performed using the adjacent normal sampling points before and after the abnormal fluctuation time series segment. The interpolation expression is as follows: , where P fill (t j ) represents the fill power value of the j-th sampling point within the abnormal fluctuation time series segment, t j t represents the time of the j-th sampling point within the abnormal fluctuation time series segment. pre and t post P represents the time immediately preceding and following the normal sampling point in the time series segment of the abnormal fluctuation, respectively. pre and P post These represent the power values ​​of the preceding and following normal sampling points, respectively. If the abnormal fluctuation time series segment is located at the beginning or end of the curve, the power values ​​of the adjacent normal sampling points are used for extrapolation and filling. After shielding and filling, the clean power decrease curve is obtained.

[0083] S54: Based on the clean power decrease curve, the inertial response basis function and the step response basis function are refitted in the full time domain to obtain the updated independent power decrease trajectory.

[0084] Specifically, the clean power decrease curve is used to replace the original composite power decrease curve data in the full-time domain joint fitting. The initial positioning ranges of the inertial response basis function, step response basis function, step response delay parameter, and inertial time constant parameter are used. The steady-state decrease amplitude parameter, inertial time constant parameter, step amplitude parameter, and step response delay parameter are resolved. The updated steady-state decrease amplitude parameter is then used as the reference. and the updated inertial time constant parameter Substitution The updated step amplitude parameter and the updated step response delay parameters Substitution This yields the updated independent power drop trajectory.

[0085] In some embodiments, after obtaining the updated independent power decline trajectory, the indoor ambient temperature, historical operating water supply temperature, and outdoor ambient temperature of the low-carbon building containing low-carbon resources with thermodynamic inertial physical characteristics can be obtained, and the indoor-outdoor heat transfer temperature difference ΔT can be calculated based on the indoor and outdoor ambient temperatures. amb The water supply temperature difference ΔT is calculated based on the indoor ambient temperature and the historical operating water supply temperature. water Then, an inertial time constant temperature variation model is established using a calibration dataset. Where β0 to β5 are regression coefficients, τ a,refThe baseline inertial time constant is output by the model, and the updated inertial time constant parameters are adjusted according to the piecewise mapping function. The correction is made, and the corrected inertial time constant is denoted as... .

[0086] In some embodiments, the piecewise mapping function includes , where Δ Tamb,ref Δ represents the baseline value of the indoor-outdoor heat transfer temperature difference in the calibration dataset. Twater,ref The calibration dataset represents the baseline value of the water supply temperature difference, α represents the outdoor heat transfer temperature difference correction index, γ represents the water supply temperature difference sensitivity coefficient, and δ represents the indoor-outdoor heat transfer temperature difference linear correction coefficient. The corrected inertial time constant parameter is then used. Substitution This generates a self-adaptive, thermodynamically inertial physical characteristic low-carbon resource independent power decline trajectory.

[0087] S55: Calculate the actual power reduction for different low-carbon resources based on the updated independent power reduction trajectory, and output the independent regulation performance value based on the actual power reduction and the target power reduction value in the target power reduction command.

[0088] Specifically, the actual down-regulation power of low-carbon resources with thermodynamic inertial physical characteristics and the actual down-regulation power of low-carbon resources with electronic transient physical characteristics are extracted from the updated independent power decline trajectory, and the total down-regulation power is calculated. ,in This parameter represents the steady-state decrease magnitude of low-carbon resources with updated thermodynamic inertial physical characteristics. This represents the updated step amplitude parameter for low-carbon resources exhibiting electronic transient physics characteristics, which will reduce the total power P. total_down The target power reduction value P in the target power reduction command target Compare and calculate the absolute power difference. And calculate the adjustment accuracy value. Meanwhile, based on the updated step response delay parameters Output response delay time value, based on the updated inertial time constant parameter. The updated steady-state drop amplitude parameter Aa_new output power adjustment rate value is used, and the actual power reduction, response delay time, power adjustment rate, and adjustment accuracy are treated as independent adjustment performance values.

[0089] In some embodiments, the initial power change rates of the updated independent power decrease trajectories of low-carbon resources with electronic transient physical characteristics and the updated independent power decrease trajectories of low-carbon resources with thermodynamic inertial physical characteristics during the initial response period can also be obtained, and the two can be summed to obtain the composite initial power change rate. A dominant response percentage value is generated based on the ratio of the initial power change rate of low-carbon resources with electronic transient physical characteristics to the composite initial power change rate. The dominant response percentage value can be expressed as follows: ,in This represents the initial power change rate of low-carbon resources exhibiting transient electronic physical characteristics. The value represents the rate of change of the composite initial power, and the dominant response percentage is used to characterize the contribution of low-carbon resources with electronic transient physical characteristics to the change of composite power during the initial period of the response.

[0090] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a low-carbon resource regulation performance measurement system, comprising: The curve acquisition module is used to acquire composite power reduction curve data of low-carbon buildings in the high-frequency acquisition cycle in response to the triggering of the target power reduction command. The basis function module is used to determine the inertial response basis function and the step response basis function according to the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources, respectively. The feature extraction module is used to extract curve features from the composite power decline curve data to obtain the step anchor point time and the climbing end time. The joint fitting module is used to constrain the parameters of the step response basis function based on the step anchor point time and constrain the parameters of the inertial response basis function based on the climbing end time. The parameter-constrained inertial response basis function and the step response basis function are jointly fitted in the whole time domain to obtain the independent power decline trajectory of different low-carbon resources. The performance output module is used to generate a reconstructed composite curve based on the independent power drop trajectory, update the independent power drop trajectory according to the residual between the reconstructed composite curve and the composite power drop curve data, and output the independent adjustment performance value according to the updated independent power drop trajectory and the target power reduction command.

[0091] Optionally, the curve acquisition module includes: The instruction parsing submodule is used to parse the target power reduction instruction and obtain the instruction issuance timestamp. The power acquisition submodule is used to acquire the total active power data of low-carbon buildings in the corresponding time period before and after the target power reduction command is triggered, based on the command issuance timestamp; The curve generation submodule is used to sort the total active power data according to the acquisition time and retain the curve segment covering the power decline process to obtain composite power decline curve data.

[0092] Optionally, the basis function module includes: The history acquisition submodule is used to acquire the historical power decline curves of low-carbon resources with thermodynamic inertial physical characteristics, as well as the historical power decline curves of low-carbon resources with electronic transient physical characteristics. The inertial parameter submodule is used to determine the inertial time constant parameter and steady-state decrease amplitude parameter to characterize the slowly varying power decrease process based on the historical power decrease curve of low-carbon resources with thermodynamic inertial physical characteristics. The step parameter submodule is used to determine the step amplitude parameter and step response delay parameter to characterize the transient power decline process based on the historical power decline curve of low-carbon resources with electronic transient physical characteristics. The function determination submodule is used to determine the inertial response basis function based on the inertial time constant parameter and the steady-state descent amplitude parameter, and to determine the step response basis function based on the step amplitude parameter and the step response delay parameter.

[0093] Optionally, the feature extraction module includes: The discrete processing submodule is used to perform discretization preprocessing on the composite power decline curve data to obtain the power change rate and power extreme points of adjacent acquisition points; The mutation screening submodule is used to screen a set of mutation feature points from the composite power decrease curve data based on the power change rate of adjacent acquisition points. The vector construction submodule is used to construct a sequence of local feature vectors for composite power decline curve data based on the set of abrupt change feature points and power extreme points; The node identification submodule is used to identify the step anchor point time and the climbing end time based on the local feature vector sequence.

[0094] Optionally, the node identification submodule includes: The step extraction unit is used to extract feature vectors reflecting the transient power decrease pattern from the local feature vector sequence, and to determine candidate step nodes based on the feature vectors reflecting the transient power decrease pattern. The stabilization extraction unit is used to extract feature vectors reflecting the power stabilization pattern from the local feature vector sequence, and to determine candidate ramp-end nodes based on the feature vectors reflecting the power stabilization pattern. Anchor point determination unit is used to determine the step anchor point time based on the corresponding time of the candidate step node in the composite power decrease curve data; The endpoint determination unit is used to determine the ramp-end time based on the corresponding time of the candidate ramp-end node in the composite power decrease curve data.

[0095] Optionally, the joint fitting module includes: The step constraint submodule is used to determine the initial positioning range of the step response delay parameter in the step response basis function based on the step anchor point time. The inertial constraint submodule is used to determine the initial positioning range of the inertial time constant parameter in the inertial response basis function based on the end time of the climb. The joint solution submodule is used to jointly solve the inertial response basis function and the step response basis function in the full time domain of the composite power decrease curve data based on the initial positioning range of the step response delay parameter and the initial positioning range of the inertial time constant parameter, so as to obtain the basis function solution parameters. The trajectory reconstruction submodule is used to reconstruct the fitted power curves corresponding to low-carbon resources with different physical characteristics based on the parameters solved by the basis function, and to use the fitted power curves as independent power decline trajectories.

[0096] Optional, performance output modules include: The curve reconstruction submodule is used to sum the independent power decrease trajectories to generate a reconstructed composite curve; The residual location submodule is used to calculate the residual between the reconstructed composite curve and the composite power decline curve data, and to locate the time series segment of abnormal fluctuations based on the residual. The anomaly shielding submodule is used to shield the composite power decline curve data within the abnormal fluctuation time series segment to obtain the clean power decline curve. The trajectory update submodule is used to refit the inertial response basis function and the step response basis function in the entire time domain based on the clean power decline curve to obtain the updated independent power decline trajectory. The performance calculation submodule is used to calculate the actual power reduction for different low-carbon resources based on the updated independent power reduction trajectory, and output the independent adjustment performance value based on the actual power reduction and the target power reduction value in the target power reduction command.

[0097] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a method for measuring the performance of low-carbon resource regulation; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0098] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for measuring the low-carbon resource regulation performance in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0099] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0100] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0101] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for measuring the performance of low-carbon resource regulation, and the processor 102 can execute multiple instructions to achieve the following: In response to the triggering of the target power reduction command, the composite power reduction curve data of low-carbon buildings is acquired during the high-frequency acquisition cycle; Based on the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources, the inertial response basis function and the step response basis function are determined respectively. Curve features were extracted from the composite power decrease curve data to obtain the step anchor point time and the climb end time. The parameters of the step response basis function are constrained based on the step anchor point time, and the parameters of the inertial response basis function are constrained based on the ramp-end time. The parameter-constrained inertial response basis function and the step response basis function are then jointly fitted in the full time domain to obtain the independent power decline trajectories of different low-carbon resources. A reconstructed composite curve is generated based on the independent power drop trajectory. The independent power drop trajectory is updated based on the residual between the reconstructed composite curve and the composite power drop curve. The independent adjustment performance value is output based on the updated independent power drop trajectory and the target power reduction command.

[0102] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A system that specifies functions in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for measuring the performance of low-carbon resource regulation, characterized in that, The method includes: In response to the triggering of the target power reduction command, the composite power reduction curve data of low-carbon buildings is acquired during the high-frequency acquisition cycle; Based on the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources, the inertial response basis function and the step response basis function are determined respectively. Curve features were extracted from the composite power decrease curve data to obtain the step anchor point time and the climb end time; Based on the step anchor point time, the step response basis function is parameter-constrained, and based on the ramp end time, the inertial response basis function is parameter-constrained. The parameter-constrained inertial response basis function and the step response basis function are then jointly fitted in the entire time domain to obtain the independent power decline trajectories of different low-carbon resources. A reconstructed composite curve is generated based on the independent power decline trajectory. The independent power decline trajectory is updated according to the residual between the reconstructed composite curve and the composite power decline curve data. The independent adjustment performance value is output according to the updated independent power decline trajectory and the target power reduction command.

2. The method for measuring the low-carbon resource regulation performance according to claim 1, characterized in that, The process of acquiring composite power reduction curve data of low-carbon buildings within a high-frequency acquisition cycle in response to a target power reduction command includes: Parse the target power reduction command to obtain the command issuance timestamp; Based on the timestamp of the instruction issuance, obtain the total active power data of the low-carbon building during the corresponding time period before and after the target power reduction instruction is triggered; The total active power data is sorted according to the acquisition time, and the curve segment covering the power decline process is retained to obtain the composite power decline curve data.

3. The method for measuring the low-carbon resource regulation performance according to claim 1, characterized in that, The determination of the inertial response basis function and the step response basis function based on the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources includes: Obtain historical power decline curves for low-carbon resources with thermodynamic inertial physical characteristics, and historical power decline curves for low-carbon resources with electronic transient physical characteristics; Based on the historical power decline curves of low-carbon resources with thermodynamic inertial physical characteristics, the inertial time constant parameter and steady-state decline amplitude parameter used to characterize the slowly varying power decline process are determined. Based on the historical power decline curves of low-carbon resources with electronic transient physical characteristics, the step amplitude parameter and step response delay parameter used to characterize the transient power decline process are determined. The inertial response basis function is determined based on the inertial time constant parameter and the steady-state descent amplitude parameter, and the step response basis function is determined based on the step amplitude parameter and the step response delay parameter.

4. The method for measuring the low-carbon resource regulation performance according to claim 1, characterized in that, The step feature extraction of the composite power decline curve data to obtain the step anchor point time and the climb end time includes: The composite power decrease curve data is discretized and preprocessed to obtain the power change rate and power extreme points of adjacent sampling points; Based on the power change rate of the adjacent acquisition points, a set of abrupt change feature points is selected from the composite power decrease curve data; Based on the set of mutation feature points and the power extreme points, a local feature vector sequence of the composite power decline curve data is constructed; Based on the local feature vector sequence, the step anchor point time and the climbing end time are identified.

5. The method for measuring the low-carbon resource regulation performance according to claim 4, characterized in that, The step of identifying the step anchor point time and the climbing end time based on the local feature vector sequence includes: Extract feature vectors reflecting the transient power decrease pattern from the local feature vector sequence, and determine candidate step nodes based on the feature vectors reflecting the transient power decrease pattern; Extract feature vectors reflecting the power stabilization pattern from the local feature vector sequence, and determine candidate ramp-end nodes based on the feature vectors reflecting the power stabilization pattern; The step anchor point time is determined based on the corresponding time of the candidate step node in the composite power decrease curve data; The ramp-end time is determined based on the corresponding time of the candidate ramp-end node in the composite power decrease curve data.

6. The method for measuring the low-carbon resource regulation performance according to claim 1, characterized in that, The method involves constraining the parameters of the step response basis function based on the step anchor point time, and constraining the parameters of the inertial response basis function based on the ramp-end time. The constrained inertial response basis function is then combined with the step response basis function in the full time domain to obtain independent power decline trajectories for different low-carbon resources, including: Based on the step anchor point time, determine the initial positioning range of the step response delay parameter in the step response basis function; Based on the time when the climb ends, determine the initial positioning range of the inertial time constant parameter in the inertial response basis function; Based on the initial positioning range of the step response delay parameter and the initial positioning range of the inertial time constant parameter, the inertial response basis function and the step response basis function are jointly solved in the full time domain of the composite power decrease curve data to obtain the basis function solution parameters. Based on the parameters obtained from the basis functions, the fitted power curves corresponding to low-carbon resources with different physical characteristics are reconstructed respectively, and the fitted power curves are used as the independent power decline trajectories.

7. The method for measuring the low-carbon resource regulation performance according to claim 1, characterized in that, The process of generating a reconstructed composite curve based on the independent power decline trajectory, updating the independent power decline trajectory according to the residual between the reconstructed composite curve and the composite power decline curve data, and outputting an independent adjustment performance value based on the updated independent power decline trajectory and the target power reduction command includes: The independent power decrease trajectories are summed to generate the reconstructed composite curve; Calculate the residual between the reconstructed composite curve and the composite power decrease curve data, and locate the abnormal fluctuation time series segment based on the residual; The composite power decline curve data within the abnormal fluctuation time series segment is masked to obtain the clean power decline curve; Based on the clean power decrease curve, the inertial response basis function and the step response basis function are refitted in the full time domain to obtain the updated independent power decrease trajectory; The actual power reduction for different low-carbon resources is calculated based on the updated independent power reduction trajectory, and the independent regulation performance value is output based on the actual power reduction and the target power reduction value in the target power reduction command.

8. A system for measuring the performance of low-carbon resource regulation, characterized in that, The system includes: The curve acquisition module is used to acquire composite power reduction curve data of low-carbon buildings in the high-frequency acquisition cycle in response to the triggering of the target power reduction command. The basis function module is used to determine the inertial response basis function and the step response basis function according to the thermodynamic inertial physical characteristics and electronic transient physical characteristics of low-carbon resources, respectively. The feature extraction module is used to extract curve features from the composite power decline curve data to obtain the step anchor point time and the climbing end time. The joint fitting module is used to constrain the parameters of the step response basis function based on the step anchor point time, and constrain the parameters of the inertial response basis function based on the climbing end time. The parameter-constrained inertial response basis function and the step response basis function are then jointly fitted in the entire time domain to obtain the independent power decline trajectories of different low-carbon resources. The performance output module is used to generate a reconstructed composite curve based on the independent power decline trajectory, update the independent power decline trajectory according to the residual between the reconstructed composite curve and the composite power decline curve data, and output an independent adjustment performance value according to the updated independent power decline trajectory and the target power reduction command.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the method for measuring the low-carbon resource regulation performance as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the method for measuring the low-carbon resource regulation performance as described in any one of claims 1 to 7.

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