A heat network stable boundary evaluation method and system considering network frequency random fluctuation

Abstract

The application discloses a kind of considering network frequency random fluctuation's heat network stable boundary evaluation method and system, belong to steam turbine technology, thermal system performance test and energy efficiency analysis technical field, to the measured power grid frequency deviation data is preprocessed to obtain clean sequence, residual sequence is obtained by daytime standardization processing;Standardized disturbance library is constructed using moving block bootstrap method, and the parameterization description model and mapping relationship of heat source heat disturbance are established;A heat network dynamic simulation model is constructed, and simulation is carried out by extracting multiple disturbance paths, and the system compliance probability under each group of parameters is calculated;According to compliance probability threshold value, the allowable parameter region is determined in parameter space, and the heat network stable boundary is obtained by extracting outer envelope line.

Description

Technical Field

[0001] This invention belongs to the fields of steam turbine technology, thermal system performance testing and energy efficiency analysis technology, and specifically relates to a method and system for evaluating the stability boundary of a heating network that considers random fluctuations in grid frequency. Background Technology

[0002] District heating systems are considered an important resource for supporting the flexible operation of the power system, especially for absorbing renewable energy, due to their thermal inertia.

[0003] Existing research assessing the ability of district heating systems to cope with heat flow fluctuations on the source side typically assumes that heat source disturbances follow an ideal Gaussian distribution (normal distribution). However, disturbances in actual operation (such as grid frequency fluctuations) often exhibit non-Gaussianity (such as spikes, thick tails, or low kurtosis) and time dependence (such as low-frequency oscillations).

[0004] Furthermore, traditional assessment methods often rely on a single random simulation to determine system feasibility, resulting in sensitivity to random seeds and a lack of statistical robustness. Therefore, it is necessary to construct more realistic random disturbance models based on actual data and employ probabilistic criteria to improve the robustness and accuracy of assessing the acceptable range of heat source fluctuations in heating networks. Summary of the Invention

[0005] The purpose of this invention is to overcome the problem that traditional heating network stability boundary assessment only considers the heating balance and ignores the random fluctuation interference of the source-side heating units participating in grid frequency regulation. A heating network stability boundary assessment method and system that considers random fluctuations in grid frequency is proposed.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for assessing the stability boundary of a heating network considering random fluctuations in network frequency, comprising the following steps: Preprocessing the measured power grid frequency deviation data yields a clean frequency deviation sequence. The clean frequency deviation sequence is subjected to daytime normalization to obtain the daytime normalized residual sequence; The moving block bootstrapping method is used to divide the daytime standardized residual sequence into blocks, perform sampling with replacement and path-level restandardization to generate multiple standardized perturbation paths and construct a standardized perturbation library. Establish a parameterized description model of heat source heat flow disturbance and determine the mapping relationship from the standardized disturbance path to the heat source heat flow disturbance sequence; A dynamic simulation model of the heating network is constructed. For each set of heat source heat flow disturbance parameters, multiple standardized disturbance paths are extracted from the standardized disturbance library. Based on the mapping relationship, heat source heat flow disturbance sequences are generated and input into the dynamic simulation model of the heating network for simulation to obtain system operation data under each standardized disturbance path. Based on the preset system operation constraints and system operation data, the system operation compliance under each standardized disturbance path is determined, and the system compliance probability under each set of heat source heat flow disturbance parameters is calculated; Based on the system compliance probability under each set of heat source heat flow disturbance parameters, and according to the preset compliance probability threshold, the allowable parameter region is determined in the parameter space of heat source heat flow disturbance, and the outer envelope of the allowable parameter region is extracted to obtain the heat network stability boundary.

[0007] Furthermore, the measured power grid frequency deviation data is preprocessed, specifically as follows: Perform timeline auditing on the timestamps of measured power grid frequency deviation data to identify out-of-order samples, duplicate timestamps, and missing intervals; Conservative cleaning was performed on the measured power grid frequency deviation data to identify and remove outliers and short-time pulse artifacts. Linear interpolation is used to fill in the abnormal sample points identified by timeline audit and conservative cleaning, as well as the points to be filled in the missing intervals, and outputs a clean frequency deviation sequence.

[0008] Furthermore, the clean frequency deviation sequence is subjected to daytime normalization, specifically as follows: The clean frequency deviation sequence is divided into natural days to obtain multiple sets of daily frequency deviation sequences; Calculate the mean and standard deviation of each group of daily frequency deviation sequences, and perform a standardization transformation on each group of daily frequency deviation sequences; By concatenating all the daily frequency deviation sequences that have undergone standardization in chronological order, we obtain the inter-day standardized residual sequence.

[0009] Furthermore, a standardized perturbation library is constructed using the moving block self-help method, specifically as follows: The block length is optimized for the daytime standardized residual sequence, and multiple sets of overlapping data blocks are generated by sliding within the daytime standardized residual sequence; Sampling with replacement is performed on all overlapping data blocks, and the sampled data blocks are spliced ​​together in the sampling order to generate a perturbation path. Each generated perturbation path is subjected to path-level re-standardization to obtain multiple standardized perturbation paths, which are then combined to form a standardized perturbation library.

[0010] Furthermore, the parameterized description model of heat source heat flow disturbance includes mean parameter and variance parameter; The mapping relationship is based on the mean and variance parameters, which converts the standardized perturbation path into a heat source heat flow perturbation sequence.

[0011] Furthermore, the system's success probability is calculated and the outer envelope is extracted, specifically as follows: For each set of heat source heat flow disturbance parameters, multiple standardized disturbance paths are extracted and simulated separately. The number of paths that meet the system operation constraints is counted to obtain the system compliance probability. In the parameter space of heat source heat flow disturbance, a non-uniform stepping method is used to scan parameters and obtain the maximum variance parameter that meets the compliance probability threshold under each mean parameter level. Connect the points formed by the mean parameter and the maximum variance parameter of each group in the order of the mean parameter values ​​to form the outer envelope of the allowable parameter region.

[0012] Furthermore, the dynamic simulation model of the heating network is composed of a pipeline temperature transfer model, a heat exchanger model, and a user-side indoor temperature model coupled together. During the simulation, the system's operational compliance is determined based on preset indoor temperature constraints and water supply temperature constraints.

[0013] Secondly, the present invention provides a heating network stability boundary assessment system that considers random fluctuations in grid frequency, comprising: The deviation data preprocessing module is used to preprocess the measured power grid frequency deviation data to obtain a clean frequency deviation sequence; The standardization module is used to perform daytime standardization on the clean frequency deviation sequence to obtain the daytime standardized residual sequence. The perturbation path generation module is used to divide the daytime standardized residual sequence into blocks, perform sampling with replacement and splicing, and restandardize the path level using the moving block bootstrapping method to generate multiple standardized perturbation paths and build a standardized perturbation library. The description model building module is used to build a parameterized description model of heat source heat flow disturbance and determine the mapping relationship from the standardized disturbance path to the heat source heat flow disturbance sequence. The simulation model building module is used to build a dynamic simulation model of the heating network. For each set of heat source heat flow disturbance parameters, multiple standardized disturbance paths are extracted from the standardized disturbance library. Based on the mapping relationship, heat source heat flow disturbance sequences are generated and input into the dynamic simulation model of the heating network for simulation to obtain system operation data under each standardized disturbance path. The compliance probability calculation module is used to determine the compliance of system operation under each standardized disturbance path based on the preset system operation constraints and system operation data, and to calculate the system compliance probability under each set of heat source heat flow disturbance parameters. The outer envelope extraction module is used to determine the allowable parameter region in the parameter space of the heat source heat flow disturbance based on the system compliance probability under each set of heat source heat flow disturbance parameters and according to the preset compliance probability threshold, and extract the outer envelope of the allowable parameter region to obtain the heat network stability boundary.

[0014] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for assessing the stability boundary of a thermal network that takes into account random fluctuations in grid frequency.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for assessing the stability boundary of a heating network that takes into account random fluctuations in grid frequency.

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: This invention proposes a method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency. Based on measured grid frequency deviation data, a standardized disturbance library is constructed, which fully preserves the non-Gaussian distribution characteristics and time autocorrelation properties of actual grid frequency fluctuations. This overcomes the limitations of the traditional Gaussian distribution assumption, making the heat source disturbance model more closely reflect the actual operating characteristics of grid frequency regulation, effectively improving the authenticity and accuracy of the heating network stability boundary assessment results. A standardized disturbance path is generated using the moving block bootstrap method, expanding the disturbance sample while preserving the intraday fluctuation patterns of the original data, providing reliable data support for batch simulation assessments. A parametric description model is used to establish the mapping relationship between standardized disturbances and heat source heat flow disturbances, achieving a unified quantitative description of heat source disturbances under different fluctuation intensities. Based on multi-path simulation calculations of the system's compliance probability, a probability threshold is used to determine the permissible operating area, replacing the traditional single-path determination method. This reduces the impact of random samples on the assessment results, enhances the statistical robustness of the boundary assessment results, and the finally extracted stability boundary can provide a quantitative reference for the safe operation and scheduling optimization of the electro-thermal coupled system. Attached Figure Description

[0017] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. Furthermore, the shapes and proportions of the components in the drawings are merely schematic to aid in understanding the invention and do not specifically limit the shapes and proportions of the components. In the drawings: Figure 1 This is a flowchart of a method for assessing the stability boundary of a heating network that considers random fluctuations in grid frequency, according to the present invention.

[0018] Figure 2 This is a simplified structural diagram of a heating network stability boundary assessment system that considers random fluctuations in grid frequency, according to the present invention.

[0019] Figure 3 This is an electronic device diagram of a thermal network stability boundary assessment method considering random fluctuations in grid frequency, according to the present invention.

[0020] Figure 4This is a flowchart illustrating the overall process of a heating network stability boundary assessment method considering random fluctuations in grid frequency, as described in an embodiment of the present invention.

[0021] Figure 5 This represents the water temperature distribution inside the pipe at time t in an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] Example 1 See Figure 1 A method for assessing the stability boundary of a heating network that considers random fluctuations in grid frequency includes the following steps: Preprocessing the measured power grid frequency deviation data yields a clean frequency deviation sequence. The clean frequency deviation sequence is subjected to daytime normalization to obtain the daytime normalized residual sequence; The moving block bootstrapping method is used to divide the daytime standardized residual sequence into blocks, perform sampling with replacement and path-level restandardization to generate multiple standardized perturbation paths and construct a standardized perturbation library. Establish a parameterized description model of heat source heat flow disturbance and determine the mapping relationship from the standardized disturbance path to the heat source heat flow disturbance sequence; A dynamic simulation model of the heating network is constructed. For each set of heat source heat flow disturbance parameters, multiple standardized disturbance paths are extracted from the standardized disturbance library. Based on the mapping relationship, heat source heat flow disturbance sequences are generated and input into the dynamic simulation model of the heating network for simulation to obtain system operation data under each standardized disturbance path. Based on the preset system operation constraints and system operation data, the system operation compliance under each standardized disturbance path is determined, and the system compliance probability under each set of heat source heat flow disturbance parameters is calculated; Based on the system compliance probability under each set of heat source heat flow disturbance parameters, and according to the preset compliance probability threshold, the allowable parameter region is determined in the parameter space of heat source heat flow disturbance, and the outer envelope of the allowable parameter region is extracted to obtain the heat network stability boundary.

[0024] Traditional methods determine the stability boundary of a heating network solely based on heat supply balance, neglecting the impact of random fluctuations in grid frequency regulation caused by the participation of source-side heating units, leading to biases in boundary assessment. This invention provides a method for assessing the stability boundary of a heating network that considers random grid frequency fluctuations, belonging to the field of integrated electric-thermal energy systems. First, this invention proposes a parameterized description method for the stability boundary of a heating network considering random grid frequency fluctuations, providing the determination and assessment process for each parameter. Second, combining the dead-zone mechanism (±2 rpm) of heating unit frequency regulation and statistical data analysis, a bimodal distribution probability model of actual random grid frequency fluctuations is constructed. Finally, based on this probability distribution, Monte Carlo simulation is used for dynamic simulation assessment to obtain the specific numerical range of the heating network stability boundary. This invention can more accurately reflect the impact of real grid frequency fluctuations on heating network stability, improving the assessment accuracy of the safe operation boundary of electric-thermal coupled systems.

[0025] See Figure 4 This invention presents a flowchart illustrating the overall technical solution implementation process. The flowchart shows the complete technical path from raw data acquisition to final boundary output, including four layers: data layer, modeling layer, simulation layer, and evaluation layer. The data-driven stage transforms grid frequency data into a standardized disturbance library, and the probabilistic evaluation stage uses Monte Carlo simulation in the parameter space (…). μ , σ 2 Extract acceptable boundaries from ).

[0026] This invention's method for constructing random disturbances in the heat source is based on measured power grid frequency data. According to power grid dispatching specifications, the power grid frequency regulation involving heating units has a clear dead zone (usually set at ±2 rpm, corresponding to approximately ±0.022 Hz for a 50 Hz power frequency system). When the grid frequency deviation is within the dead zone, the unit does not affect frequency changes, and the heat source output remains basically stable. Only when the grid frequency deviation exceeds the dead zone does the unit perform active power regulation, causing fluctuations in the bleed air heat source heat flow. Due to the dead zone mechanism, the steady-state probability distribution of the actual grid frequency deviation no longer exhibits an ideal Gaussian distribution, but rather a bimodal distribution: the two peaks are located near the dead zone boundary, with a relatively low probability density in the middle region (near zero frequency difference). The formation mechanism of this bimodal distribution can be explained by the following physical process. The power grid frequency dynamics with a primary frequency regulation dead zone can be simplified as follows:

[0027] in, Let be the system's inertial constant. The damping coefficient is... This is due to random load fluctuations (which can be considered as random excitation). The regulating power provided to the primary frequency control unit. When the absolute value of the frequency deviation... Located in a dead zone Within the time, The system relies solely on inertia and damping to resist load disturbances, lacking strong restoring force within this frequency range, exhibiting a "free drift" characteristic; once... Once the dead zone boundary is crossed, the unit's primary frequency regulation function is activated. and The sudden, proportional intervention creates a strong "pullback" restoring torque, quickly suppressing further frequency deviation and "pushing" it back in the opposite direction.

[0028] The aforementioned dynamic characteristics determine the dwell behavior of the frequency trajectory in the time domain: within the dead zone, due to weak restoring force, the frequency is easily pushed beyond the dead zone by random disturbances; however, once it touches the dead zone boundary and crosses the boundary, the strong restoring force causes the frequency to quickly turn back. Therefore, the frequency trajectory repeatedly undergoes a "braking-turning" process near the dead zone boundary, with a significantly longer dwell time in this region than at other locations. From the perspective of probability density, the cumulative effect of this dwell time manifests as two peaks in probability density near the dead zone boundary, while the center of the dead zone (near zero frequency difference) becomes the trough of probability density due to the dwell time being diverted by the two side boundaries.

[0029] Mechanistically, this phenomenon can be further confirmed by comparing it with the case without dead zones (where the probability density reaches a single-peak maximum at zero frequency difference): the introduction of dead zones does not reduce the probability density inside the dead zone to zero, but rather lowers it from its original peak to a valley between two peaks, while simultaneously migrating the probability density towards the dead zone boundaries on both sides, thus forming a bimodal shape. Statistical analysis of measured power grid frequency data also fully supports the above mechanism deduction—the empirical probability density accumulates into a peak near the dead zone boundary, with a relatively low density in the central region, which is significantly different from the normal distribution assumption in the case without dead zones.

[0030] After determining the bimodal shape through mechanistic analysis, statistical modeling was further performed using measured power grid frequencies to determine the specific parameters of the bimodal distribution. The specific modeling process includes the following steps: Timeline audit: Collect measured power grid frequency deviation data and its corresponding timestamp Calculate the difference between adjacent timestamps. Through analysis Empirical distribution determines the dominant sampling interval .like If it is determined to be a random sample; If it is determined to be a duplicate timestamp; It was determined to be a missing interval.

[0031] Conservative cleaning: A conservative cleaning strategy was adopted, including: Hard threshold filtering: Removes those that meet the following conditions The sample, of which The preset frequency difference upper limit is set (preferably 0.5Hz). Local robust detection: for each sample Take its local neighborhood (Optimal length is 51 points), calculate the median. and median absolute deviation .like (in The robust threshold coefficient is preferably 3.0, and the length of consecutive anomalies does not exceed a preset upper limit. (If 5 points are selected, they are identified as short-time pulse artifacts and removed.) Linear interpolation repair: For threshold outliers, short-time pulse artifacts, and points to be repaired in missing intervals, linear interpolation is used for filling. After the above processing, a clean frequency deviation sequence is obtained. Experimental data shows that the final interpolation ratio of this method is only 0.254%, effectively eliminating outliers while preserving the original fluctuation structure.

[0032] Daytime standardization: The clean frequency deviation sequence is divided by natural day. For the first... Calculate the average value for that day. and standard deviation Then, perform a standardization transformation on each sample for that day:

[0033] By concatenating the standardized samples from all days in chronological order, we obtain the diurnal standardized residual sequence. This method eliminates the slow diurnal drift (i.e., the variation in mean and variance between different dates) while preserving the shape characteristics of intraday fluctuations.

[0034] Building a standardized perturbation library using the moving block self-help method: Based on the intraday standardized residual sequences, a standardized perturbation library is constructed using the moving fast bootstrap method, including: Block length optimization: Optimize candidate block length Calculate the root mean square error of the autocorrelation function for each second. and histogram distance Select the one that achieves the optimal balance between the two. Value (3600 seconds is the optimal value based on actual testing); Effective overlapping block generation: Within each natural day, the sliding generation length is Valid overlapping blocks, blocks can overlap with each other; Sampling with replacement and splicing: Randomly select blocks with replacement from the valid block pool of all days, and splice them first in the order of selection until the target path length is reached (preferably 24 hours). Path-level restandardization: For each stitched path, calculate the mean and standard deviation, and perform... The calculation ensures the generation of standardized perturbation paths. It has zero mean and unit variance.

[0035] Repeat the above process Next, a standardized perturbation library is obtained. This perturbation library preserves the non-Gaussianity (skewness, kurtosis) and temporal correlation (autocorrelation function, power spectral density) of the measured data.

[0036] A unified description method for heat source fluctuations based on parameter space: This invention proposes a method in parameterized space A unified method for describing heat source fluctuations. This involves perturbing the heat flow on the source side. Represented as:

[0037] in: This is a heat flux mean parameter, representing the base load level of the heat source (dimensionless). The variance parameter represents the intensity (dimensionless) of the heat source fluctuation. For a standardized stochastic process, satisfying .

[0038] when When derived from a Gaussian process, the description degenerates into the Gaussian perturbation model in the prior art; when When derived from the constructed data-driven perturbation library, this description constitutes the data-driven perturbation model of this invention. This unified parameterization framework enables fair comparison of the evaluation results of Gaussian perturbations and data-driven perturbations within the same space.

[0039] The probabilistic acceptable boundary evaluation method of this invention is based on Monte Carlo simulation to overcome the statistical instability of traditional single-path criteria.

[0040] Monte Carlo feasibility probability calculation: For each pair of parameters Extract from the standardized disturbance library The disturbance path. For the first A path is generated to produce source-side heat flow input. The data is input into the dynamic model of the heating network for simulation, and the maximum heating duration under this path is recorded. .

[0041] Define a path-level feasibility indicator function:

[0042] in, To evaluate the window length.

[0043] Calculate the empirical feasibility probability:

[0044] Acceptable region definition and boundary extraction: Given a feasibility probability threshold The acceptable region is defined as:

[0045] exist On the parameter plane, for each mean level Take the largest value in the acceptable area. The value as that Corresponding boundary values:

[0046] All Connecting the points forms the outer envelope, which represents the acceptable range boundary for heat source fluctuations.

[0047] Technical measures to reduce simulation: Considering the large computational load of dynamic model simulation for heating networks, this invention adopts the following measures to reduce computational costs while ensuring the reliability of the evaluation: Path count probability threshold That is, a path is considered acceptable if at least two of the four paths satisfy the constraint (majority rule). Parametric scanning employs non-uniform stepping: increasing the scanning density in the region near the boundary and using coarse scanning in the region far from the boundary.

[0048] The dynamic model of the heating network used in this invention includes three coupled levels to ensure accurate simulation of the response to heat source disturbances: Pipeline temperature transfer model, such as Figure 5 As shown.

[0049] The dynamic changes in water temperature within the pipe are described using a one-dimensional transient temperature transfer equation:

[0050] in: The average flow velocity; is the axial thermal diffusivity of water; This is the equivalent heat transfer coefficient between the pipeline and the environment; This is the equivalent heat transfer area; , and These are the density, control volume, and specific heat capacity of water, respectively. The ambient temperature.

[0051] The left side of the equation includes local storage terms and axial convection terms, while the right side represents axial heat conduction and heat loss to the environment.

[0052] Heat exchanger model: use Method for calculating heat exchange between primary and secondary networks:

[0053] Among them, heat loss item From the heat transfer unit coefficient and heat capacity flow rate ratio Decide; User-side indoor temperature model: The indoor temperature response is described using an energy balance equation:

[0054] Among them, heat loss item .

[0055] Constraints: The following constraints are monitored in real time during the simulation: Indoor temperature constraints:

[0056] Safe water supply temperature: and .

[0057] This invention more realistically reflects the frequency disturbance characteristics of actual power grids, improves the statistical robustness of the evaluation results, reveals the quantitative impact of disturbance statistical characteristics on the boundary, and has generalizability and reproducibility; specifically: This invention constructs a disturbance model based on measured power grid frequency data, which can preserve the non-Gaussian distribution of the original data (such as the bimodal distribution caused by the frequency regulation dead zone) and significant time autocorrelation (integral correlation time reaches 803 seconds). Compared with traditional Gaussian disturbance models, the method of this invention can more accurately reflect the impact of real power grid frequency fluctuations on the stability of the heating network.

[0058] This invention extends the traditional single-path feasibility assessment to a Monte Carlo probability criterion, employing multiple perturbation paths (preferably 4) for simulation and using a majority pass rule ( This serves as an acceptable criterion. Compared to the single-path criterion, this method significantly reduces the sensitivity of the evaluation results to random seeds and specific sample paths, making the extracted boundaries probabilistic and statistically robust. By the same parameterization space By comparing the evaluation results of Gaussian perturbations and data-driven perturbations, this invention quantitatively reveals the impact of non-Gaussianity and temporal correlation on the acceptable boundary.

[0059] The data-driven method of this invention does not rely on a specific heating network structure or specific power grid frequency data. As long as measured power grid frequency data (or any random signal reflecting the external regulation environment) is available, a corresponding standardized disturbance library can be constructed and the acceptable range of the heating network can be evaluated according to the technical solution of this invention (data cleaning → daytime standardization → moving block self-service → Monte Carlo assessment). This method can be extended to application scenarios with different regions, seasons, and heating network structures.

[0060] The moving block bootstrap method used in this invention has a controllable computational load (200 24-hour paths), and the Monte Carlo evaluation uses... The single path and majority rule significantly reduce the number of simulations while ensuring the reliability of the evaluation (compared to...). The classic Monte Carlo method (which reduces computation by 250 times) makes engineering applications possible.

[0061] Example 2 See Figure 2 A system for assessing the stability boundary of a heating network that considers random fluctuations in network frequency, comprising: The deviation data preprocessing module is used to preprocess the measured power grid frequency deviation data to obtain a clean frequency deviation sequence; The standardization module is used to perform daytime standardization on the clean frequency deviation sequence to obtain the daytime standardized residual sequence. The perturbation path generation module is used to divide the daytime standardized residual sequence into blocks, perform sampling with replacement and splicing, and restandardize the path level using the moving block bootstrapping method to generate multiple standardized perturbation paths and build a standardized perturbation library. The description model building module is used to build a parameterized description model of heat source heat flow disturbance and determine the mapping relationship from the standardized disturbance path to the heat source heat flow disturbance sequence. The simulation model building module is used to build a dynamic simulation model of the heating network. For each set of heat source heat flow disturbance parameters, multiple standardized disturbance paths are extracted from the standardized disturbance library. Based on the mapping relationship, heat source heat flow disturbance sequences are generated and input into the dynamic simulation model of the heating network for simulation to obtain system operation data under each standardized disturbance path. The compliance probability calculation module is used to determine the compliance of system operation under each standardized disturbance path based on the preset system operation constraints and system operation data, and to calculate the system compliance probability under each set of heat source heat flow disturbance parameters. The outer envelope extraction module is used to determine the allowable parameter region in the parameter space of the heat source heat flow disturbance based on the system compliance probability under each set of heat source heat flow disturbance parameters and according to the preset compliance probability threshold, and extract the outer envelope of the allowable parameter region to obtain the heat network stability boundary.

[0062] Example 3 See Figure 3 An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for assessing the stability boundary of a thermal network that takes into account random fluctuations in grid frequency.

[0063] Example 4 A computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for assessing the stability boundary of a thermal network that takes into account random fluctuations in grid frequency.

[0064] 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, read-only optical discs, optical storage, etc.) containing computer-usable program code.

[0065] 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 1 A device that provides the functions specified in one or more boxes.

[0066] 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 instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] 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.

[0068] 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 protection scope of the present invention.

Claims

1. A method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency, characterized in that, Includes the following steps: Preprocessing the measured power grid frequency deviation data yields a clean frequency deviation sequence. The clean frequency deviation sequence is subjected to daytime normalization to obtain the daytime normalized residual sequence; The daytime standardized residual sequence is divided into blocks, sampled with replacement and spliced, and re-standardized at the path level using the moving block bootstrapping method to generate multiple standardized perturbation paths and construct a standardized perturbation library. Establish a parameterized description model of heat source heat flow disturbance, and determine the mapping relationship from the standardized disturbance path to the heat source heat flow disturbance sequence; A dynamic simulation model of the heating network is constructed. For each set of heat source heat flow disturbance parameters, multiple standardized disturbance paths are extracted from the standardized disturbance library. Based on the mapping relationship, a heat source heat flow disturbance sequence is generated and input into the dynamic simulation model of the heating network for simulation to obtain system operation data under each standardized disturbance path. Based on the preset system operation constraints and the system operation data, the system operation compliance under each standardized disturbance path is determined, and the system compliance probability under each set of heat source heat flow disturbance parameters is calculated; Based on the system compliance probability under each set of heat source heat flow disturbance parameters, and according to the preset compliance probability threshold, the allowable parameter region is determined in the parameter space of the heat source heat flow disturbance, and the outer envelope of the allowable parameter region is extracted to obtain the heat network stability boundary.

2. The method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency as described in claim 1, characterized in that, The preprocessing of the measured power grid frequency deviation data specifically includes: Time axis auditing is performed on the timestamps of the measured power grid frequency deviation data to identify out-of-order samples, duplicate timestamps, and missing intervals; The measured power grid frequency deviation data is conservatively cleaned to identify and remove outliers and short-time pulse artifacts; Linear interpolation is used to fill in the abnormal sample points identified by timeline audit and conservative cleaning, as well as the points to be filled in the missing interval, and the clean frequency deviation sequence is output.

3. The method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency as described in claim 1, characterized in that, The daily normalization process for the clean frequency deviation sequence specifically involves: The clean frequency deviation sequence is divided into multiple sets of daily frequency deviation sequences by natural days; Calculate the mean and standard deviation of each group of daily frequency deviation sequences, and perform a standardization transformation on each group of daily frequency deviation sequences; The daily frequency deviation sequences that have undergone standardization are concatenated in chronological order to obtain the inter-day standardized residual sequence.

4. The method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency according to claim 1, characterized in that, The construction of the standardized perturbation library using the moving block bootstrapping method is specifically as follows: The block length is optimized and determined for the daytime standardized residual sequence, and multiple sets of overlapping data blocks are generated by sliding within the daytime standardized residual sequence; Sampling with replacement is performed on all the overlapping data blocks, and the sampled data blocks are spliced ​​together in the sampling order to generate a perturbation path. Each generated perturbation path is subjected to path-level re-normalization to obtain multiple standardized perturbation paths, which together form the standardized perturbation library.

5. The method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency according to claim 1, characterized in that, The parameterized description model of the heat source heat flow disturbance includes mean parameter and variance parameter; The mapping relationship is based on the mean parameter and the variance parameter, which converts the standardized perturbation path into the heat source heat flow perturbation sequence.

6. The method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency according to claim 1, characterized in that, The calculation system achieves the target probability and extracts the outer envelope, specifically as follows: For each set of heat source heat flow disturbance parameters, multiple standardized disturbance paths are extracted and simulated separately. The number of paths that meet the system operation constraints is counted to obtain the system compliance probability. Within the parameter space of the heat source heat flow disturbance, a non-uniform stepping method is used to scan the parameters to obtain the maximum variance parameter that satisfies the compliance probability threshold at each mean parameter level; The points formed by the mean parameter and the maximum variance parameter of each group are connected sequentially according to the value order of the mean parameter to form the outer envelope of the allowable parameter region.

7. The method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency according to claim 1, characterized in that, The dynamic simulation model of the heating network is composed of a pipeline temperature transfer model, a heat exchanger model and a user-side indoor temperature model coupled together. During the simulation, the system's operational compliance is determined based on preset indoor temperature constraints and water supply temperature constraints.

8. A system for assessing the stability boundary of a heating network considering random fluctuations in grid frequency, characterized in that, include: The deviation data preprocessing module is used to preprocess the measured power grid frequency deviation data to obtain a clean frequency deviation sequence; The standardization processing module is used to perform daytime standardization processing on the clean frequency deviation sequence to obtain the daytime standardized residual sequence. The perturbation path generation module is used to divide the daytime standardized residual sequence into blocks, perform sampling with replacement and splicing, and perform path-level restandardization using the moving block bootstrapping method to generate multiple standardized perturbation paths and construct a standardized perturbation library. The description model building module is used to build a parameterized description model of the heat source heat flow disturbance and determine the mapping relationship from the standardized disturbance path to the heat source heat flow disturbance sequence. The simulation model construction module is used to construct a dynamic simulation model of the heating network. For each set of heat source heat flow disturbance parameters, multiple standardized disturbance paths are extracted from the standardized disturbance library. Based on the mapping relationship, a heat source heat flow disturbance sequence is generated and input into the dynamic simulation model of the heating network for simulation to obtain system operation data under each standardized disturbance path. The compliance probability calculation module is used to determine the compliance of system operation under each standardized disturbance path based on the preset system operation constraints and the system operation data, and to calculate the system compliance probability under each set of heat source heat flow disturbance parameters. The outer envelope extraction module is used to determine the allowable parameter region in the parameter space of the heat source heat flow disturbance based on the system compliance probability under each set of heat source heat flow disturbance parameters and according to a preset compliance probability threshold, and extract the outer envelope of the allowable parameter region to obtain the heat network stability boundary.

9. An electronic device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements a method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a method for assessing the stability boundary of a heating network considering random fluctuations in grid frequency as described in any one of claims 1-7.

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