A method for evaluating the regulation potential of thermal power generating units under high penetration of new energy based on thermodynamic-electric coupling

Abstract

This invention discloses a method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under conditions of high renewable energy penetration, belonging to the field of power system operation and control technology. It involves collecting multi-dimensional data from thermal power units, constructing a three-dimensional phase space, and dynamically segmenting the operating conditions; constructing thermodynamic time-delay constraint characteristics and performing multi-scale frequency domain deconstruction of the renewable energy net load prediction error; synthesizing thermodynamic-electrical coupling state vectors, and calculating the nonlinear regulation elasticity coefficient, dynamic regulation potential, and regional spatiotemporal margin of the phase coupling; and identifying bottleneck types and matching improvement technology paths through multi-dimensional sensitivity matrix analysis. This invention solves the problems of insufficient consideration of thermodynamic constraints, unclear regulation potential boundaries, and inaccurate matching of technology transformation paths in traditional thermal power regulation potential evaluation methods, providing a quantitative evaluation basis for the flexible regulation of thermal power units under conditions of high renewable energy integration.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and control technology, specifically relating to a method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under the high penetration of new energy sources. Background Technology

[0002] With the rapid increase in the installed capacity of new energy sources such as wind power and photovoltaics in the power system, the net load of the power system is showing stronger randomness, volatility, and anti-peak-shaving characteristics. Thermal power units are gradually shifting from traditional base load operation to multiple operation modes, including deep peak shaving, rapid ramping, frequency regulation support, and backup protection. Their safe, economical, and flexible operation capabilities directly affect the capacity for new energy absorption and the level of grid security and stability.

[0003] In reality, a thermal power unit is a multi-physics coupled system composed of a boiler, turbine, generator, auxiliary systems, environmental protection systems, and grid interfaces. Rapid upward or downward adjustments on the electrical side trigger a series of dynamic responses on the thermal side, including delayed release of boiler heat storage, fluctuations in main steam pressure, changes in turbine rotor temperature difference, low-cycle fatigue accumulation, reduced combustion stability at low loads, and constraints on denitrification inlet flue gas temperature. Ignoring these underlying physical processes and relying solely on static capacity addition or linear margin calculations may result in situations where the regulation potential is sufficient but actual safe execution is impossible.

[0004] Furthermore, at the regional thermal power cluster level, even if each thermal power unit possesses individual regulation capabilities, its regulation power is still constrained by its node location, power flow constraints across transmission sections, and network power transfer sensitivity. Traditional regional flexibility assessment methods typically ignore topological constraints and struggle to identify spatial differences in which regulation capabilities are weakened by power flow congestion. Therefore, there is an urgent need for a method to assess the regulation potential of thermal power units that can uniformly incorporate thermodynamic dynamic constraints, multi-band regulation requirements on the electrical side, unit fatigue losses, and regional grid topological constraints into a unified computational framework. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the regulation potential of thermal power units based on thermodynamic-electric coupling under the high penetration of new energy sources. This method addresses the problem that existing evaluation methods conceptualize, staticize, and linearize key flexibility indicators, leading to a disconnect between the physical safety boundary on the thermal side and the dynamic regulation requirements on the electrical side, and resulting in insufficient accuracy of the evaluation results.

[0006] To achieve the above objectives, the present invention provides the following solution: a method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration, comprising the following steps: S1: Collect multi-source high-dimensional operational data and perform standardization and time-series segmentation on the multi-source high-dimensional operational data to obtain standardized operational data; S2: Based on the standardized operating data, the thermodynamic time-delay constraint characteristics and the frequency domain deconstructed electrical regulation demand characteristics are obtained; S3: Weighted fusion of the thermodynamic time delay constraint characteristics and the frequency domain deconstructed electrical regulation demand characteristics is performed to obtain a thermodynamic-electrical coupled state vector; S4: Based on the thermodynamic-electric coupling state vector, the nonlinear adjustment elasticity coefficient, dynamic adjustment potential, and regional spatiotemporal margin are obtained; S5: Based on the nonlinear adjustment elasticity coefficient, the dynamic adjustment potential, and the regional spatiotemporal margin, the bottleneck type is obtained; and the path is improved based on the bottleneck type matching technology. S6: Construct a set of uncertain scenarios for handling new energy sources, and based on the aforementioned technology improvement path and the set of uncertain scenarios for handling new energy sources, obtain the expected value of the flexibility shortage risk of thermal power units, and then obtain a comprehensive evaluation report.

[0007] More preferably, in S2, the thermodynamic time delay constraint characteristics include: boiler heat storage transient margin, boiler thermal response time delay constant, turbine rotor transient thermal stress margin, rotor low-cycle fatigue damage degree, low-load pulverizing system combustion stability boundary, and wide-load denitrification flue gas temperature constraint boundary. The frequency domain deconstructed electrical regulation requirements include: transient up / down flexibility requirements, dynamic ramping requirements, and steady-state standby requirements.

[0008] More preferably, the transient thermal storage margin of the boiler is obtained based on a nonlinear first-order inertial model with pure time delay; The nonlinear first-order inertial model with pure time delay includes: ; In the formula, This indicates the boiler's output steam thermal power; Indicates after pure time delay The thermal power input to the coal feeder; Indicates the boiler heat exchange efficiency; This represents the boiler thermal response time delay constant; The transient thermal storage margin of the boiler is obtained by combining the main steam parameters of the unit and the load change rate. .

[0009] More preferably, the rotor low-cycle fatigue damage degree include: ; In the formula, Indicates the first Number of cycles under the stress amplitude; This indicates the number of cycles the material can withstand at the corresponding stress amplitude.

[0010] More preferably, the thermodynamic-electric coupling state vector includes: ; In the formula, , , , These represent the characteristic weights of thermodynamic time delay constraints, the normalized characteristic vector of thermodynamic time delay constraints, the characteristic weights of frequency domain deconstructed electrical regulation demand, and the characteristic vector of frequency domain deconstructed electrical regulation demand, respectively.

[0011] More preferably, the nonlinear adjustment elastic coefficient include: ; In the formula, and These represent the dynamic upward adjustment capability and the dynamic downward adjustment potential, respectively. Indicates the rated capacity of the unit; This represents the correction function that introduces the boiler thermal storage hysteresis time constant; This represents the nonlinear thermal stress penalty factor based on rotor temperature difference.

[0012] More preferably, the dynamic adjustment potential includes: dynamic upward adjustment potential and dynamic downward adjustment potential; The dynamic upward adjustment potential include: ; In the formula, This indicates the maximum safe ramp rate allowed for the unit under current operating conditions; express Transient margin of boiler thermal storage at all times; Indicates the characteristic time of boiler heat storage and release; This represents the safety correction factor for the main steam pressure; express The upward adjustment penalty constraint obtained by converting the rotor thermal stress margin at any time; Indicates a time interval; The dynamic adjustment potential include: ; In the formula, This indicates the minimum technical output of the generator unit; This indicates the lower limit of output corresponding to the combustion stability constraint of the low-load pulverizing system. This indicates the lower limit of output corresponding to the inlet flue gas temperature constraint of the denitrification system; This indicates the lower limit of output corresponding to the safety operation constraints of auxiliary equipment; This indicates the lower limit of output corresponding to environmental emission constraints; This indicates the unit's real-time output.

[0013] More preferably, an index for adjusting the severity of the bottleneck is used. Bottleneck type identified: ; In the formula, Indicates key physical constraint variables; This represents the combined weight of the difficulty and economic cost of technological transformation corresponding to key physical constraint variables; This indicates a reduction in the flexibility margin for regional thermal power clusters; This indicates that the flexibility margin of regional thermal power clusters has been increased; , and These are the sensitivity weights for the nonlinear adjustment elasticity coefficient, dynamic adjustment potential, and regional spatiotemporal margin, respectively. .

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention incorporates boiler heat storage hysteresis, turbine rotor thermal stress, low-cycle fatigue, low-load stable combustion, denitrification flue gas temperature and grid topology constraints into the thermal power unit regulation potential assessment process, avoiding overestimation of potential caused by estimating regulation capacity solely based on static capacity or linear ramp rate.

[0015] 2. This invention, through dynamic segmentation of three-dimensional phase space operating conditions, frequency domain deconstruction-type electrical regulation demand extraction, and improved entropy weight-DTW fusion, can uniformly characterize the thermal safety boundary and electrical regulation demand under different operating conditions and multiple time scales, thereby improving the interpretability and traceability of the evaluation results.

[0016] 3. This invention utilizes comprehensive sensitivity analysis to identify regulation bottlenecks and matches operation optimization, equipment modification, or system coupling improvement paths according to the bottleneck type. At the same time, it combines the uncertainty of new energy output scenarios to calculate the flexibility shortage risk and the flexibility value after risk adjustment, providing a quantitative basis for the flexibility modification, capacity compensation, and cost reduction of thermal power units. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1A business execution flowchart of a method for assessing the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration is provided in an embodiment of the present invention. Figure 2 A flowchart of thermodynamic-electric coupling state vector construction and weight fusion processing based on phase space evolution trajectory and state sensitivity provided in this embodiment of the invention; Figure 3 A flowchart for generating suggestions on flexibility value assessment, capacity compensation, and cost mitigation driven by uncertain scenarios provided in embodiments of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1: like Figure 1 As shown, this embodiment provides a method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high penetration of new energy sources, including the following steps: S1: Collect multi-source high-dimensional operational data and perform standardization and time-series segmentation on the multi-source high-dimensional operational data to obtain standardized operational data.

[0022] Multi-level delayed operation data of thermal power units, millisecond-level scheduling data of electrical units, multi-scale prediction data of new energy sources, and market cost data for flexibility value assessment are collected. The data is then cleaned, time-aligned, and dynamically segmented into multi-dimensional phase space operating conditions to obtain a standardized operation dataset.

[0023] Data interfaces are established in the DCS, SIS, AGC / EMS systems of thermal power plants, new energy power prediction systems, and spot market or ancillary service market settlement systems. Compared with conventional methods, the data collected in this embodiment has multi-timescale characteristics: on the thermal side, data with multi-level time delay characteristics such as coal feed rate, main steam temperature, main steam pressure, reheat steam parameters, turbine rotor temperature, auxiliary equipment load, and denitrification inlet flue gas temperature are collected; on the electrical side, data from milliseconds to seconds of real-time unit output, dispatching instructions, AGC response, ramp rate, and cross-sectional power flow are collected; new energy prediction data includes multi-scale wind and solar power prediction output and net load curves; and cost data includes flexibility retrofitting and peak-shaving incremental costs.

[0024] The collected data undergoes cleaning processing to remove data from communication interruptions and sensor distortions. To address the issue of inconsistent sampling rates across different systems, time scale alignment is performed, followed by dynamic segmentation of operating conditions, unlike conventional methods that only divide based on load intervals. This embodiment uses the unit's real-time output. Load change rate and the rate of change of main steam pressure Construct a three-dimensional phase space for the coordinate axes: ; By identifying the convergence, divergence, oscillation, and abrupt change characteristics of the phase space trajectory, the operating samples are divided into startup, rapid ramp-up, steady-state high-load operation, deep peak shaving low-load stable combustion, emergency load reduction, and shutdown conditions. The standardized operating dataset obtained through this phase space processing allows subsequent analysis to be performed under the same dynamic trajectory, thus avoiding interference caused by inconsistent dimensions from different data sources and the mixing of dissimilar operating conditions.

[0025] In this embodiment, the specific method for dividing the dynamic working condition segments includes: using a sliding time window to... Processing is performed to calculate the trajectory velocity within the window. trajectory curvature Mean of output change rate Average rate of change of main steam pressure Trajectory dispersion and mutation coefficient ;when and The time is divided into steady-state operating conditions, when When the main steam pressure change rate does not exceed the safety limit, it is classified as a rapid ramp operation. and The time is divided into emergency load reduction conditions, when the trajectory repeatedly crosses the same neighborhood in the phase space and Periodic changes are classified as oscillation conditions; when the unit output approaches the minimum technical output and the pulverizing system or denitrification flue gas temperature constraint is triggered, it is classified as deep peak shaving low-load stable combustion conditions; when the output gradually increases from zero to the minimum grid load, it is classified as start-up conditions; when the output gradually decreases from low load to zero, it is classified as shutdown conditions. Threshold , and Determined based on the quantiles of historical stable samples of the unit or the equipment safety limits.

[0026] S2: Based on standardized operating data, thermodynamic time delay constraint characteristics and frequency domain deconstruction-type electrical regulation demand characteristics are obtained.

[0027] In this embodiment, thermodynamic constraint characteristics are used to reflect the physical baseline for the thermal power unit to sustainably participate in regulation under current operating conditions. This embodiment does not directly use data from a single measuring point, but rather transforms the operating data into constraint quantities characterizing the unit's regulation capability based on the operating boundaries of the boiler, turbine, and environmental protection equipment.

[0028] Thermodynamic time-delay constraint characteristics include: boiler thermal storage transient margin extracted based on the nonlinear first-order inertial element of coal input heat power and steam output heat power; boiler thermal response time-delay constant; turbine rotor transient thermal stress margin calculated based on rotor transient heat conduction equation; rotor low-cycle fatigue damage degree accumulated based on fatigue life loss model; low-load pulverizing system stable combustion boundary; and wide-load denitrification flue gas temperature constraint boundary. Frequency domain deconstruction-type electrical regulation demand characteristics include: transient up / down flexibility requirements, dynamic ramping requirements, and steady-state standby requirements separated based on empirical mode decomposition or wavelet packet decomposition.

[0029] Specifically, considering the boiler-side constraints, and taking into account the heat transfer hysteresis and heat storage release process between changes in coal feed rate and changes in steam thermal power, a nonlinear first-order inertial model with pure time delay is established: ; In the formula, To output steam heat power to the boiler; After pure time delay The thermal power input to the coal feeder; For boiler heat exchange efficiency; This is the boiler thermal response time delay constant.

[0030] Based on the above relationships, and combined with the unit's main steam parameters and load change rate, the boiler's transient thermal storage margin can be calculated. This is used to characterize the actual thermal capacity of the boiler to continue supporting regulation under the current variable load command.

[0031] In this embodiment, the boiler thermal storage transient margin The solution process is as follows: based on historical steady-state samples... , and Least squares identification is performed, and the predicted steam thermal power is obtained using a nonlinear first-order inertial model with pure time delay; based on the lower limit of the main steam pressure... Main steam temperature lower limit and safe climbing limits Structural pressure margin Temperature margin and climbing margin , ;in, , , If any margin is less than zero, then zero is taken; where, P ms ( t ), P ms,ref ( t ), T ms ( t ), T ms,ref ( t ), R safe These represent the current main steam pressure, the reference value of the main steam pressure under the current operating condition, the current main steam temperature, the reference value of the main steam temperature under the current operating condition, and the allowable safe ramp-up limit under the current operating condition, respectively.

[0032] To prevent the boiler's heat storage state from exceeding the safety boundary, the transient margin and response inertia characteristics of the boiler's heat storage are extracted. When the heat storage capacity approaches the lower limit or its rate of change approaches the extreme value, the unit's transient regulation capability is locked by thermal-side inertia.

[0033] In this embodiment, the extraction process of boiler response inertia characteristics is as follows: within a sliding time window, the cross-correlation function between the change in coal input heat power and the change in steam output heat power is calculated, and the time difference corresponding to the peak value of the cross-correlation is taken as the pure time delay. The initial value is then substituted into the nonlinear first-order inertial model, and the recursive least squares method is used to update the value. and From this, we obtain , and Together they serve as time-delay constraint characteristics on the boiler side.

[0034] Steam turbine rotors experience alternating thermal stress and accumulate low-cycle fatigue damage during frequent load increases and decreases. This embodiment incorporates a rotor life loss model into the constraint features to calculate the rotor's low-cycle fatigue damage degree. : ; In the formula, For the first Number of cycles under the stress amplitude; This indicates the number of cycles the material can withstand at the corresponding stress amplitude.

[0035] The temperature field distribution inside the rotor satisfies the transient heat conduction equation: ; In the formula, , , These represent the rotor material density, specific heat capacity, and thermal conductivity, respectively. Represents radial coordinates;T This represents the radial temperature field of the rotor.

[0036] The temperature difference between the rotor center and surface is calculated by combining the steam convection heat transfer boundary conditions, and the transient thermal stress margin of the turbine rotor is extracted accordingly.

[0037] In this embodiment, the process of extracting the transient thermal stress margin of the turbine rotor is as follows: using the steam temperature on the rotor surface and the steam convection heat transfer coefficient as boundary conditions, the radial temperature field of the rotor is solved. To obtain the rotor surface temperature and core temperature Calculate the temperature difference Then, calculate the rotor thermal stress based on the thermoelastic relationship. and according to The transient thermal stress margin of the turbine rotor is obtained, where, Allowable thermal stress for the material.

[0038] The smaller the transient thermal stress margin of the turbine rotor, the closer the turbine is to the safety boundary. Meanwhile, when the unit is under extremely low load, combustion stability and environmental constraints are particularly pronounced. This embodiment combines factors such as the operating status of the pulverizing system and the lower limit of the denitrification inlet flue gas temperature to extract the combustion stability boundary of the low-load pulverizing system and the denitrification flue gas temperature constraint boundary over wide loads.

[0039] In this embodiment, the extraction process of the stable combustion boundary of the low-load pulverizing system is as follows: using the minimum number of coal mills in operation, the minimum coal quantity per coal mill, the furnace flame intensity, oxygen content, and primary air volume as constraint variables, the minimum equivalent output that can maintain stable combustion is calculated. The process of extracting the wide-load denitrification flue gas temperature constraint boundary is as follows: establish the mapping relationship between unit output, flue gas flow rate and denitrification inlet flue gas temperature; when the predicted denitrification inlet flue gas temperature is lower than the lower limit of the catalyst's safe operating temperature, back-calculate the corresponding minimum allowable output. And it serves as the environmental boundary in the calculation of downregulation potential.

[0040] It should be noted that the minimum number of coal mills in operation, the minimum coal output per mill, the furnace flame intensity, oxygen content, primary air volume, and the lower limit of the denitrification inlet flue gas temperature, among other individual parameters, can be determined by the unit design parameters, operating procedures, or historical stable operating samples. This invention does not treat these individual parameters themselves as improvement points, but rather converts them into a lower limit of adjustable output that varies with operating conditions. and It is then combined with boiler heat storage, rotor thermal stress, electrical frequency domain requirements and regional cross-sectional constraints to enter the dynamic adjustment potential calculation, thus forming a thermodynamic-electrical coupled evaluation process for scenarios with a high proportion of new energy penetration.

[0041] For the electrical side, instead of directly using the net load error as a conventional approach, this embodiment employs wavelet packet decomposition or empirical mode decomposition (EMD) to analyze the net load prediction error sequence of new energy sources. Perform multi-scale frequency domain deconstruction: ; In the formula, Indicates the first The eigenmode function or the eigenfunction Each frequency band component; This represents the residual trend term that is not characterized by the individual eigenmode functions after decomposition.

[0042] The deconstructed high-frequency bands are mapped to the system's transient up / down flexibility requirements, the mid-frequency bands are mapped to the dynamic ramp-up requirements, and the low-frequency bands are mapped to the steady-state backup requirements.

[0043] In this embodiment, the high-frequency, mid-frequency, and low-frequency bands are divided as follows: for each Or calculate the center frequency of wavelet packet nodes and characteristic period ;Will Not greater than the AGC response time threshold The components are classified into high-frequency bands, and The components are classified as mid-frequency band, and The components are classified as low-frequency bands, among which The response cycle is set according to the frequency modulation or AGC command. The rolling cycle is set according to the standby call or scheduling plan. During mapping, the quantile value or maximum envelope of the positive and negative error components of each band is calculated to obtain the transient up / down flexibility requirements; the slope of the net load change per unit time for the mid-frequency band is calculated to obtain the dynamic ramp-up requirements; and the continuous deviation of the low-frequency trend term and low-frequency components is calculated to obtain the steady-state standby requirements.

[0044] S3: Weighted fusion of thermodynamic time delay constraint characteristics and frequency domain deconstruction-type electrical regulation demand characteristics is performed to obtain a thermodynamic-electrical coupled state vector.

[0045] like Figure 2 As shown, since the thermodynamic time-delay constraint characteristics and the frequency domain deconstructed electrical regulation demand characteristics have different dimensions and time delays, this invention employs an improved entropy weight-dynamic time warping (DTW) fusion algorithm. DTW is used to capture the dynamic time-delay similarity between the thermodynamic and electrical sequences, and then for feature selection and nonlinear scale normalization.

[0046] During the integration process, an information entropy weighting method and an analytical hierarchical process are used to construct a subjective-objective combined weight model. Based on the dynamic operating conditions of the units in the three-dimensional phase space, the transient sensitivity of the thermal safety boundary to regulation actions is analyzed, and the weight ratio is adjusted in real time. When there is an emergency load reduction and the denitrification flue gas temperature is close to the lower limit, the environmental penalty weight on the thermal side is automatically increased; when the high-frequency fluctuations of the power grid intensify, the demand weight on the electrical side is increased.

[0047] In this embodiment, the specific process of adjusting the weight ratio in real time is as follows: first, the objective weight is obtained by the information entropy weighting method. Subjective weights are obtained from the hierarchical analysis process. ,according to Initial combined weights are formed; then, based on the constraint margins... With safety threshold Distance to construct state sensitivity coefficient , and according to Normalization is performed to obtain the dynamic weights under the current operating conditions.

[0048] After normalization and weight adjustment, a thermodynamic-electrical coupled state vector is formed in high-dimensional space: ; In the formula, , , , These represent the characteristic weights of thermodynamic time delay constraints, the normalized characteristic vector of thermodynamic time delay constraints, the characteristic weights of frequency domain deconstructed electrical regulation demand, and the characteristic vector of frequency domain deconstructed electrical regulation demand, respectively.

[0049] Based on the aforementioned dynamic weights, the dynamic weights of each constraint feature on the thermodynamic side are aggregated to obtain... The dynamic weights of the frequency domain demand characteristics of the electrical side are aggregated to obtain and satisfy ;in, Thermodynamic time delay constraint eigenvector after weighted normalization , Feature vector for frequency domain deconstruction of electrical regulation demand after weighted normalization Therefore, in the thermodynamic-electric coupling state vector and All weights are dynamic weights obtained by real-time correction of the sensitivity coefficient under the current operating conditions, rather than fixed constants.

[0050] The thermodynamic-electric coupling state vector avoids the one-sidedness of unilateral evaluation and realizes a deep temporal fusion of physical constraints and grid demands.

[0051] S4: Based on the thermodynamic-electric coupling state vector, the nonlinear regulation elasticity coefficient, dynamic regulation potential, and regional spatiotemporal margin are obtained.

[0052] The nonlinear regulation elasticity coefficient is used to characterize the unit's ability to safely and continuously participate in regulation. To overcome the problem that conventional linear calculations tend to be overly optimistic when approaching physical limits, this embodiment calculates the thermodynamic-electrical temporal coupling nonlinear regulation elasticity coefficient using the following formula. : ; In the formula, and These represent the dynamic upward adjustment potential and the dynamic downward adjustment potential, respectively. Indicates the rated capacity of the unit; This represents the correction function that introduces the boiler thermal storage hysteresis time constant; This represents the nonlinear thermal stress penalty factor based on rotor temperature difference.

[0053] Dynamic adjustment potential Limited by the boiler's heat storage release curve, maximum safe ramp rate, allowable main steam pressure drop rate, and rotor thermal stress: ; In the formula, This indicates the maximum safe ramp rate allowed for the unit under current operating conditions; express Transient margin of boiler thermal storage at all times; Indicates the characteristic time of boiler heat storage and release; This represents the safety correction factor for the main steam pressure; express The upward adjustment penalty constraint obtained by converting the rotor thermal stress margin at any time; Indicates a time interval.

[0054] Dynamic adjustment potential Limited by low-load stable combustion, denitrification flue gas temperature, auxiliary equipment, and environmental protection constraints: ; In the formula, This indicates the minimum technical output of the generator unit; This indicates the lower limit of output corresponding to the combustion stability constraint of the low-load pulverizing system. This indicates the lower limit of output corresponding to the inlet flue gas temperature constraint of the denitrification system; This indicates the lower limit of output corresponding to the safety operation constraints of auxiliary equipment; This indicates the lower limit of output corresponding to environmental emission constraints.

[0055] For including NTo address the issue of "calculated regulation potential but inability to deliver" caused by grid physical congestion in regional thermal power clusters, a generator power transfer distribution factor (PTDF) matrix is ​​introduced to calculate the cluster's effective regulation potential. ; In the formula, This indicates the remaining available transmission capacity for the transmission section.

[0056] By comparing the effective regulation potential of the cluster with the transient up / down flexibility requirements of the frequency domain deconstruction in a spatiotemporal manner, the flexibility margin of the regional thermal power cluster, i.e., the regional spatiotemporal margin, is solved: ; ; In the formula, , These represent the flexibility margins for adjusting regional thermal power clusters upwards and downwards, respectively. , These represent the cluster's effective up-adjustment and down-adjustment potential after taking into account PTDF blocking constraints; , These represent the transient up-modulation and down-modulation flexibility requirements obtained from frequency domain decomposition, respectively.

[0057] This margin metric fully considers both physical congestion and network topology. It unifies the continuous load-changing capacity of the unit group in the time dimension with the network transmission capacity in the spatial node dimension, reflecting the system's true resilience.

[0058] when or If the value is less than the preset safety threshold, the system is deemed to have insufficient dynamic flexibility in a specific spatiotemporal section.

[0059] S5: Based on the nonlinear adjustment elasticity coefficient, the dynamic adjustment potential, and the regional spatiotemporal margin, the bottleneck type is obtained; and the path is improved based on the bottleneck type matching technology.

[0060] After completing the adjustment potential and deconstruction, a multidimensional sensitivity matrix analysis was performed on the core parameters. Due to the introduction of a nonlinear penalty term, the sensitivity here can accurately capture the deep contradictions of multi-physical boundary coupling constraints.

[0061] A comprehensive sensitivity analysis of the nonlinear regulation elasticity coefficient, dynamic regulation potential, and regional spatiotemporal margin is conducted using the Jacobian sensitivity partial derivative matrix, along with an index of the severity of the regulation bottleneck. : ; In the formula, Indicates key physical constraint variables; This represents the combined weight of the difficulty and economic cost of technological transformation corresponding to key physical constraint variables; This indicates that the flexibility margin of regional thermal power clusters has been increased; , and These represent the sensitivity weights of the nonlinear adjustment elasticity coefficient, dynamic adjustment potential, and regional spatiotemporal margin, respectively. By solving the partial derivative matrix, the dominant factors leading to the precipitous drop in elasticity can be identified.

[0062] The identified bottleneck types include: thermal inertial hysteresis limitation, rotor thermal fatigue limitation, low-load stable combustion and environmental protection limitation, and network topology power flow limitation. Correspondingly, these are matched with operation optimization, equipment modification, and system coupling types. When matching paths, the marginal adjustment capability gain, investment cost, implementation cycle, and technological maturity of each path are systematically evaluated to output a comprehensive modification solution with the best cost-effectiveness, avoiding blind selection.

[0063] In this embodiment, the technology improvement path matching process includes: establishing a candidate path library, which includes coal feeding-combustion control optimization, AGC coordinated control parameter optimization, boiler thermal storage and steam temperature control modification, turbine thermal stress constraint optimization, low-load stable combustion modification, wide-load denitrification modification, auxiliary machine energy-saving modification, and grid power flow reconfiguration or energy storage collaborative configuration; for each candidate path Estimate the reduction in bottleneck index based on sensitivity results. Regional spatiotemporal margin improvement and nonlinear adjustment elasticity increase and in combination with investment costs Implementation period Technology maturity Construction of comprehensive score: .

[0064] In the formula, - All are coefficients.

[0065] The path with the highest overall score and that meets safety constraints is selected as the recommended technology improvement path.

[0066] S6: Construct a set of uncertain scenarios for handling new energy sources, and based on the technology improvement path and the set of uncertain scenarios for handling new energy sources, obtain the expected value of the flexibility shortage risk of thermal power units, and then obtain a comprehensive evaluation report.

[0067] like Figure 3As shown, a set of uncertain scenarios for new energy output is constructed, taking into account the multidimensional joint probability distribution of wind and solar loads. For each scenario, the expected value of flexibility shortage risk is calculated, and based on this, the scarcity value of the flexibility services provided by thermal power units is assessed. Flexibility value consists of energy value, capacity value, and reliability value. Combining conditional risk value theory with the nonlinear adjustment elasticity coefficient of the unit, a risk-adjusted final flexibility market value is formed.

[0068] Flexibility Value It consists of energy value, capacity value, and reliability value. Energy value is calculated based on peak shaving or energy market price differences; capacity value is calculated based on available up / down adjustment capacity and capacity compensation unit price; and reliability value is calculated based on reduced flexibility shortages and reliability loss unit price; combined with conditional risk value. With the nonlinear adjustment elasticity coefficient of the unit To obtain the risk-adjusted final flexibility market value: ; in, Indicates energy value. Indicates capacity value. Indicates reliability value, This refers to risk-punishment technology.

[0069] Based on the initial investment and average annual availability margin of the unit's technical upgrades, capacity compensation parameters and cost mitigation strategies are generated. The final output is a comprehensive evaluation report, which includes: a single unit's nonlinear regulation elasticity curve, a heat map, a ranking matrix of dominant bottlenecks, and a list of recommended technical upgrade paths.

[0070] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under conditions of high penetration of new energy sources, characterized in that, Includes the following steps: S1: Collect multi-source high-dimensional operational data and perform standardization and time-series segmentation on the multi-source high-dimensional operational data to obtain standardized operational data; S2: Based on the standardized operating data, the thermodynamic time-delay constraint characteristics and the frequency domain deconstructed electrical regulation demand characteristics are obtained; S3: Weighted fusion of the thermodynamic time delay constraint characteristics and the frequency domain deconstructed electrical regulation demand characteristics is performed to obtain a thermodynamic-electrical coupled state vector; S4: Based on the thermodynamic-electric coupling state vector, the nonlinear adjustment elasticity coefficient, dynamic adjustment potential, and regional spatiotemporal margin are obtained; S5: Based on the nonlinear adjustment elasticity coefficient, the dynamic adjustment potential, and the regional spatiotemporal margin, the bottleneck type is obtained; and the path is improved based on the bottleneck type matching technology. S6: Construct a set of uncertain scenarios for handling new energy sources, and based on the aforementioned technology improvement path and the set of uncertain scenarios for handling new energy sources, obtain the expected value of the flexibility shortage risk of thermal power units, and then obtain a comprehensive evaluation report.

2. The method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration according to claim 1, characterized in that, In S2, the thermodynamic time delay constraint characteristics include: boiler heat storage transient margin, boiler thermal response time delay constant, turbine rotor transient thermal stress margin, rotor low-cycle fatigue damage degree, low-load pulverizing system combustion stability boundary, and wide-load denitrification flue gas temperature constraint boundary. The frequency domain deconstructed electrical regulation requirements include: transient up / down flexibility requirements, dynamic ramping requirements, and steady-state standby requirements.

3. The method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration according to claim 2, characterized in that, The transient thermal storage margin of the boiler is obtained based on a nonlinear first-order inertial model with pure time delay. The nonlinear first-order inertial model with pure time delay includes: ； In the formula, This indicates the boiler's output steam thermal power; Indicates after pure time delay The thermal power input to the coal feeder; Indicates the boiler heat exchange efficiency; This represents the boiler thermal response time delay constant; The transient thermal storage margin of the boiler is obtained by combining the main steam parameters of the unit and the load change rate. .

4. The method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration according to claim 2, characterized in that, The rotor low-cycle fatigue damage include: ； In the formula, Indicates the first Number of cycles under the stress amplitude; This indicates the number of cycles the material can withstand at the corresponding stress amplitude.

5. The method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration according to claim 1, characterized in that, The thermodynamic-electric coupling state vector includes: ； In the formula, , , , These represent the characteristic weights of thermodynamic time delay constraints, the normalized characteristic vector of thermodynamic time delay constraints, the characteristic weights of frequency domain deconstructed electrical regulation demand, and the characteristic vector of frequency domain deconstructed electrical regulation demand, respectively.

6. The method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration according to claim 4, characterized in that, The nonlinear adjustment elastic coefficient include: ； In the formula, and These represent the dynamic upward adjustment capability and the dynamic downward adjustment potential, respectively. Indicates the rated capacity of the unit; This represents the correction function that introduces the boiler thermal storage hysteresis time constant; This represents the nonlinear thermal stress penalty factor based on rotor temperature difference.

7. The method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration according to claim 1, characterized in that, The dynamic adjustment potential includes: dynamic upward adjustment potential and dynamic downward adjustment potential; The dynamic upward adjustment potential include: ； In the formula, This indicates the maximum safe ramp rate allowed for the unit under current operating conditions; express Transient margin of boiler thermal storage at all times; Indicates the characteristic time of heat storage and release in the boiler; This represents the safety correction factor for the main steam pressure; express The upward adjustment penalty constraint obtained by converting the rotor thermal stress margin at any time; Indicates a time interval; The dynamic adjustment potential include: ； In the formula, This indicates the minimum technical output of the generator unit; This indicates the lower limit of output corresponding to the combustion stability constraint of the low-load pulverizing system. This indicates the lower limit of output corresponding to the inlet flue gas temperature constraint of the denitrification system; This indicates the lower limit of output corresponding to the safety operation constraints of auxiliary equipment; This indicates the lower limit of output corresponding to environmental emission constraints; This indicates the unit's real-time output.

8. The method for evaluating the regulation potential of thermal power units based on thermodynamic-electrical coupling under high new energy penetration according to claim 7, characterized in that, Using the bottleneck severity index Bottleneck type identified: ； In the formula, Indicates key physical constraint variables; This represents the combined weight of the difficulty and economic cost of technological transformation corresponding to key physical constraint variables; This indicates a reduction in the flexibility margin for regional thermal power clusters; This indicates that the flexibility margin of regional thermal power clusters has been increased; , and These are the sensitivity weights for the nonlinear adjustment elasticity coefficient, dynamic adjustment potential, and regional spatiotemporal margin, respectively. .

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