A method and system for constructing a load sharing chain of a generator group unit

Abstract

The application discloses a method and system for constructing a load sharing chain of a generator group unit, and relates to the field of data processing specially used for administrative, commercial, financial, management, supervision or prediction purposes. The method comprises the following steps: calculating a comprehensive response capability value of each power plant according to operation parameters and transaction parameters, arranging the comprehensive response capability values in descending order according to the numerical values to form a response sequence, calculating the regulation capability matching degree between adjacent power plants, the difference between the regulation capability matching degrees of each adjacent response section being less than a preset matching degree threshold, calculating the output regulation range and regulation rate of each generator unit, and determining the load distribution coefficient of each response section according to the output regulation range and regulation rate; determining the regulation sequence of the units in all response sections based on the load distribution coefficient, and calculating the maximum regulation amount that can be borne by each unit, and constructing the load sharing chain according to the regulation sequence and the maximum regulation amount. By implementing the method, the efficiency of task regulation of the units of the power plant can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing specifically applicable to administrative, commercial, financial, management, supervisory, or forecasting purposes, and in particular to a method and system for constructing a load-sharing chain for generator groups. Background Technology

[0002] As the power system continues to expand and the proportion of renewable energy connected to the grid continues to increase, grid frequency fluctuations are intensifying, placing higher demands on the frequency regulation capabilities of generating units. To ensure the safe and stable operation of the power grid, multiple generating units need to work together to share the burden of system load fluctuations.

[0003] Currently, the power system commonly uses a method of allocating load regulation tasks according to the size of the generating units. Specifically, the dispatch center allocates regulation tasks proportionally to the rated capacity of each unit, with larger units undertaking more regulation tasks and smaller units undertaking fewer. This method is simple to operate and easy to implement in practice.

[0004] However, in actual operation, this simple capacity allocation method can lead to a mismatch between the allocation of regulation tasks and the actual regulation characteristics of the units. Since different types of units under different operating conditions have different dynamic response characteristics, considering only capacity factors may cause slower-responding units to bear too much regulation task, while faster-responding units are not fully utilized, resulting in low overall regulation efficiency. Summary of the Invention

[0005] This application provides a method and system for constructing a load-sharing chain for generator group units, which can improve the efficiency of task regulation of power plant units.

[0006] Firstly, this application provides a method for constructing a load-sharing chain for generator groups, applied to a generator group load-sharing chain construction system. The method includes: acquiring the operating parameters and transaction parameters of the power plant where each generator unit is located; the operating parameters include the unit's ramp rate, regulation range, maximum / minimum output, and response time; the transaction parameters include the unit's quoted price and contracted electricity volume; calculating the comprehensive response capability value of each power plant based on the operating parameters and transaction parameters, and arranging the comprehensive response capability values ​​in descending order of numerical value to form a response sequence; and calculating the regulation capability matching degree between adjacent power plants based on the response sequence. The capacity matching degree is a weighted combination of the ratio of the ramp rate and the ratio of the regulating capacity of adjacent power plants. Based on the regulating capacity matching degree, the power plants are divided into fast response, medium response, and slow response segments, and the difference in regulating capacity matching degree between each adjacent response segment is less than a preset matching degree threshold. Within each response segment, the output regulation range and regulation rate of each generator unit are calculated, and the load distribution coefficient of each response segment is determined based on the output regulation range and regulation rate. Based on the load distribution coefficient, the regulation order of the units in all response segments is determined, and the maximum regulation amount that each unit can undertake is calculated. A load sharing chain is constructed based on the regulation order and the maximum regulation amount.

[0007] In the above embodiments, a comprehensive response capability value is calculated based on operating parameters and transaction parameters to form a response sequence, enabling a comprehensive evaluation of the dynamic and static characteristics of the unit. The power plant is divided into fast, medium, and slow response segments according to the matching degree of regulation capacity, establishing a hierarchical regulation mechanism adapted to the unit performance. Based on this, the regulation sequence is determined by the load allocation coefficient, and a load-sharing chain is constructed, achieving precise allocation of regulation tasks. Ultimately, while ensuring the system's rapid response capability, this avoids wasting the unit's regulation capacity and improves overall frequency regulation efficiency.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the comprehensive response capability value of each power plant based on operating parameters and transaction parameters, and arranging the comprehensive response capability values ​​in descending order to form a response sequence, specifically includes: calculating the basic regulation capability of the power plant based on the unit ramp rate and regulation range; calculating the time response coefficient of the power plant based on the response time, which is inversely proportional to the response time; calculating the cost coefficient of the power plant based on the unit bid price, which is inversely proportional to the unit bid price; calculating the adjustable capacity coefficient of the power plant based on the contracted electricity volume, which is the ratio of the actual adjustable electricity volume to the rated capacity; taking the weighted product of the basic regulation capability, the time response coefficient, the cost coefficient, and the adjustable capacity coefficient as the comprehensive response capability value of the power plant; and sorting the comprehensive response capability values ​​from largest to smallest to obtain the response sequence.

[0009] In the above embodiments, the basic adjustment capabilities of the unit's ramp rate and adjustment range are weighted and combined with the time response coefficient, cost coefficient, and adjustable capacity coefficient to construct a multi-dimensional performance evaluation system. The time response coefficient reflects dynamic characteristics, the cost coefficient reflects economic efficiency, and the adjustable capacity coefficient characterizes availability. The response capability value obtained through this comprehensive evaluation method is more objective and accurate. The resulting response sequence fully reflects the actual adjustment performance of the unit, laying a scientific foundation for subsequent tiered allocation.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the regulation capacity matching degree between adjacent power plants based on the response sequence, wherein the regulation capacity matching degree is a weighted combination of the ramp rate ratio and the regulation capacity ratio of adjacent power plants, specifically includes: obtaining the unit ramp rates of two adjacent power plants in the response sequence and calculating the ramp rate ratio of the two power plants; obtaining the regulation capacity of two adjacent power plants in the response sequence, wherein the regulation capacity is the difference between the maximum output and the minimum output, and calculating the regulation capacity ratio of the two power plants; and using the sum of the product of the ramp rate ratio and a first preset weight and the product of the regulation capacity ratio and a second preset weight as the regulation capacity matching degree.

[0011] In the above embodiments, the ratio of ramp rates of adjacent power plants to the ratio of regulating capacity are weighted according to preset weights to establish a quantitative index for evaluating the coordination of regulation between adjacent units. The ramp rate ratio reflects the degree of dynamic matching, while the regulating capacity ratio reflects the static matching relationship. The weighted combination of the two comprehensively characterizes the matching degree of regulation capabilities among units. This ensures the consistency of unit regulation characteristics within each response segment and improves the execution effect of the hierarchical regulation strategy.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the output adjustment range and adjustment rate of each generator set within each response segment, and determining the load allocation coefficient of each response segment based on the output adjustment range and adjustment rate, specifically includes: obtaining the adjustment rate by multiplying the computer set ramp rate by the response time; calculating the output adjustment range based on the maximum and minimum output; for each response segment among the fast response segment, medium-speed response segment, and slow response segment, calculating the rate weighting coefficient based on the adjustment rate of each generator set within the response segment, and calculating the capacity weighting coefficient based on the output adjustment range of each generator set within the response segment; and using the weighted combination of the rate weighting coefficient and the capacity weighting coefficient as the load allocation coefficient for the response segment.

[0013] In the above embodiments, the regulation rate is obtained by multiplying the unit ramp rate by the response time, quantifying the dynamic response characteristics into specific indicators. By combining the output regulation range to calculate the rate weighting coefficient and the capacity weighting coefficient, an evaluation standard for unit performance within the response segment is established. The weighted combination of the two weighting coefficients forms the load allocation coefficient, making the allocation of regulation tasks within each response segment more balanced and reasonable, and improving the execution accuracy of the hierarchical regulation strategy.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of determining the adjustment order of generator units within all response segments based on the load allocation coefficient, calculating the maximum adjustment amount that each generator unit can undertake, and constructing a load-sharing chain based on the adjustment order and the maximum adjustment amount specifically includes: sorting the generator units within the fast response segment, medium-speed response segment, and slow response segment according to the load allocation coefficient to obtain the adjustment order of the generator units within each response segment; calculating the maximum adjustment amount of each generator unit per unit time based on the unit ramp rate and adjustment range; and connecting the generator units within each response segment sequentially according to their maximum adjustment amount to construct a load-sharing chain, wherein the difference in the maximum adjustment amount of adjacent generator units in the load-sharing chain is less than a preset adjustment amount threshold.

[0015] In the above embodiments, the generating units within the response segment are sorted based on the load allocation coefficient, establishing a clear regulation priority. The maximum regulation amount is calculated by the unit ramp rate and regulation range, quantifying the actual regulation capacity of the units. By controlling the difference in the maximum regulation amount between adjacent units when constructing the load sharing chain, a smooth transition of regulation tasks between units is ensured, enhancing the operational stability of the load sharing chain.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after determining the adjustment sequence of all units within all response segments based on the load allocation coefficient, calculating the maximum adjustment amount that each unit can undertake, and constructing a load-sharing chain based on the adjustment sequence and the maximum adjustment amount, the method further includes: acquiring production index data of the generator group, which includes unit load rate, fuel consumption rate, and unit efficiency parameters; constructing a set of unit combination optimization constraints based on the production index data; compiling monthly and weekly start-up and shutdown plans for the units in the load-sharing chain based on the constraint set, which includes calculating the unit combination plan for each time period, generating a unit start-up and shutdown sequence table, and determining the transition condition adjustment strategy; performing a matching analysis between the unit combination plan and the adjustment sequence of the load-sharing chain, and adjusting the unit start-up and shutdown sequence table in the start-up and shutdown plan when the unit combination plan and the adjustment sequence do not match.

[0017] In the above embodiments, unit operating indicators are obtained and an optimized constraint set is constructed, providing comprehensive constraints for the development of start-up and shutdown plans. Monthly and weekly start-up and shutdown plans are developed based on this constraint set, achieving system optimization of unit combination. By matching and analyzing the unit combination plans with the regulation sequence and dynamically adjusting the start-up and shutdown timing table, the continuity and effectiveness of the load sharing chain at different times are ensured, improving the reliability of the regulation strategy execution.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after determining the adjustment order of all units within all response segments based on the load allocation coefficient, calculating the maximum adjustment amount that each unit can undertake, and constructing a load-sharing chain based on the adjustment order and the maximum adjustment amount, the method further includes: collecting full-process operation data of the units, which includes equipment status data, operating condition data, and adjustment execution data; establishing a unit operation evaluation index system based on the full-process operation data; analyzing the adjustment performance of each generating unit in the load-sharing chain according to the evaluation index system; calculating the dynamic adjustment reliability of the units based on the adjustment performance; reducing the load allocation coefficient of the corresponding unit in its corresponding response segment when the dynamic adjustment reliability is lower than a preset reliability threshold; and updating the adjustment strategy of the load-sharing chain according to the adjusted load allocation coefficient.

[0019] In the above embodiments, real-time monitoring of unit regulation performance is achieved by collecting full-process operation data and establishing an operation evaluation index system. Dynamic regulation reliability is calculated based on regulation performance to promptly identify unit performance fluctuations. When the reliability falls below a threshold, the load allocation coefficient is adjusted and the regulation strategy is updated, enabling the load-sharing chain to have adaptive optimization capabilities. This constructs a closed-loop feedback dynamic regulation mechanism, enhancing the adaptability of the load-sharing chain to changes in unit performance and ensuring continuous optimization of regulation task allocation.

[0020] Secondly, embodiments of this application provide a generator group load sharing chain construction system, which includes one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the generator group load sharing chain construction system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a generator group load-sharing chain construction system, cause the generator group load-sharing chain construction system to execute the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a generator group load-sharing chain construction system, cause the generator group load-sharing chain construction system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the generator group load-sharing chain construction system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application calculates a comprehensive response capability value and forms a response sequence based on operating and trading parameters, enabling a comprehensive evaluation of the dynamic and static characteristics of the generating unit. Based on the matching degree of regulation capacity, the power plant is divided into fast, medium, and slow response segments, establishing a hierarchical regulation mechanism adapted to the unit's performance. On this basis, the regulation sequence is determined by the load allocation coefficient, and a load-sharing chain is constructed, achieving precise allocation of regulation tasks. Ultimately, while ensuring the system's rapid response capability, this avoids wasting the unit's regulation capacity and improves overall frequency regulation efficiency.

[0025] 2. This application constructs a multi-dimensional performance evaluation system by weighting the unit's ramp rate and adjustment range based on its fundamental adjustment capabilities with time response coefficient, cost coefficient, and adjustable capacity coefficient. The time response coefficient reflects dynamic characteristics, the cost coefficient reflects economic efficiency, and the adjustable capacity coefficient characterizes availability. The response capability value obtained through this comprehensive evaluation method is more objective and accurate. The resulting response sequence fully reflects the unit's actual adjustment performance, laying a scientific foundation for subsequent tiered allocation.

[0026] 3. This application establishes a quantitative index for evaluating the coordination of regulation between adjacent power plants by weighting the ratio of their ramp rates to their regulating capacities according to preset weights. The ramp rate ratio reflects the degree of dynamic matching, while the regulating capacity ratio reflects the static matching relationship. The weighted combination of the two comprehensively characterizes the matching degree of regulation capabilities among the units. This ensures the consistency of the regulation characteristics of the units within each response segment and improves the execution effect of the hierarchical regulation strategy. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a method for constructing a load-sharing chain for generator groups in an embodiment of this application. Figure 2This is another flowchart illustrating the method for constructing a load-sharing chain for generator groups in this application embodiment; Figure 3 This is a schematic diagram of a physical device structure of a generator group unit load sharing chain construction system in the embodiments of this application. Detailed Implementation

[0028] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0030] To facilitate understanding, the application scenarios of the embodiments of this application are described below.

[0031] A large power generation group operates multiple thermal power plants with over 20 large thermal power units, totaling more than 6000MW of installed capacity. As the installed capacity of new energy sources in the region continues to expand, the output fluctuations of photovoltaic and wind power generation have intensified, causing a continuous increase in the amplitude of grid frequency fluctuations. Especially during cloudy or rainy weather leading to a sharp drop in photovoltaic power generation, or during periods of strong winds when wind turbines frequently malfunction, the system frequency fluctuation amplitude can even exceed ±0.2Hz. To maintain grid frequency stability, thermal power units are required to respond quickly and undertake system frequency regulation tasks. However, due to differences in the regulation characteristics of each unit, simply allocating frequency regulation tasks according to installed capacity often results in unsatisfactory regulation effects. For example, in one system frequency drop event, a 600MW unit, due to its slow regulation speed, only managed to increase its output by 50MW in 10 minutes, while a 300MW unit completed a 40MW output increase within 5 minutes during the same period. This unreasonable task allocation not only affected system frequency recovery but also wasted the unit's regulation capacity.

[0032] To address the aforementioned issues, a power generation group adopted a simple task allocation scheme based on unit capacity. This scheme allocates system frequency regulation tasks according to the ratio of unit rated capacity, with larger capacity units undertaking more regulation tasks and smaller capacity units undertaking fewer. For example, when an additional 200MW of generating output is needed, the regulation tasks are allocated to two 600MW units and two 300MW units in a 2:2:1:1 ratio, meaning each 600MW unit undertakes 67MW and each 300MW unit undertakes 33MW. While this scheme is easy to implement, it completely ignores the dynamic response characteristics of the units. In actual operation, problems such as slow regulation by large-capacity units and limited regulation by small-capacity units frequently occur. During a system frequency regulation process, a 600MW subcritical unit took 30 minutes to complete a 50MW load increase regulation, while a 300MW supercritical unit completed the same magnitude regulation in just 8 minutes, but due to its smaller allocated capacity, it could not play a greater role. This simple capacity allocation method neither fully utilizes the regulation potential of the units nor guarantees the system's rapid response requirements.

[0033] After adopting the load-sharing chain construction method of this invention, the power generation group optimized the allocation of unit regulation tasks. First, the operating parameters (ramp rate, regulation range, etc.) and transaction parameters (bid price, contracted power volume, etc.) of each unit were collected to calculate the comprehensive response capability value and form a response sequence. Then, the units were divided into three response segments: fast, medium, and slow. Two 300MW supercritical units and one 600MW ultra-supercritical unit entered the fast response segment, two 600MW supercritical units entered the medium response segment, and one 600MW subcritical unit entered the slow response segment. In actual regulation, when the system needed to increase output by 200MW, the units in the fast response segment initially handled 120MW, the medium response segment handled 60MW, and the slow response segment handled 20MW. This hierarchical allocation method based on comprehensive performance ensured both the system's rapid response capability and avoided wasting unit regulation capacity. Actual operation data showed that after adopting this scheme, the system frequency recovery time was shortened by 40%, the unit regulation success rate increased by 15%, and operating costs decreased by 8%. This approach not only fully leverages the regulation strengths of each unit but also achieves overall optimization of the system's regulation performance.

[0034] To facilitate understanding, the method provided in this implementation will be described in detail below, using the above scenario as an example. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a method for constructing a load-sharing chain for generator groups in this application.

[0035] S101. Obtain the operating parameters and transaction parameters of the power plant where each generator unit is located. The operating parameters include the unit's ramp rate, adjustment range, maximum / minimum output, and response time. The transaction parameters include the unit's quoted price and contracted electricity volume.

[0036] Among them, operating parameters represent the real-time operating characteristics of the generator set during operation; the unit ramp rate refers to the rate of change of the unit's output power, measured in MW / min; the adjustment range represents the adjustable output range of the unit; maximum / minimum output represents the maximum and minimum generating power of the unit under safe and stable operating conditions, respectively; and response time refers to the time required for the unit to reach the target output value after receiving the adjustment command. Trading parameters represent the commercial indicators of the generator set participating in the electricity market; the unit price refers to the unit's price level in the electricity market; and the contracted volume represents the generation contracts already signed by the unit.

[0037] In the operation of a power system, in order to achieve precise regulation and reasonable load allocation of generator units, it is necessary to first obtain the basic operating status and market transaction information of the units. Specifically, the system acquires the operating parameter data of each generator unit through a real-time acquisition system or database interface, including dynamic parameters such as ramp rate and adjustment range collected from the DCS system, as well as market data such as quotations and contracts obtained from the power trading platform. This data is then organized and categorized according to the power plant dimension to form a complete set of unit parameter information.

[0038] In some embodiments, operating parameters and trading parameters can be obtained in multiple ways: Optionally, unit operating data can be directly collected through the power plant automation system to establish a real-time data channel for obtaining operating parameters, while trading parameters can be automatically obtained through the data interface of the power trading platform, achieving automatic data collection and updating; Optionally, unit parameter information forms, including unit operating parameters and trading parameters, can be manually filled out by power plant operators on a regular basis, and then entered into the system database after review. It is understood that other methods can also be used to obtain parameters, such as through historical data analysis or expert experience estimation, which are not limited here.

[0039] S102. Calculate the comprehensive response capability value of each power plant based on the operating parameters and transaction parameters, and arrange the comprehensive response capability values ​​in descending order of numerical value to form a response sequence.

[0040] The comprehensive response capability value represents the overall regulation performance index of the generator set after considering multiple factors, and is a comprehensive evaluation value for measuring the unit's ability to participate in system regulation. The response sequence refers to the sequence formed by sorting each power plant according to its comprehensive response capability value, which is used for subsequent segmentation.

[0041] After obtaining the unit parameters, the regulation capacity of each power plant needs to be quantitatively evaluated. Specifically, the system first standardizes the operating parameters and trading parameters to eliminate the influence of different dimensions. Then, a weighted summation method is used to calculate the comprehensive response capacity value, where the operating parameters have a weight of 0.7 and the trading parameters have a weight of 0.3. The larger the calculated comprehensive response capacity value, the better the overall regulation performance of the power plant. Finally, the comprehensive response capacity values ​​of all power plants are arranged in descending order to form a response sequence.

[0042] In some embodiments, the comprehensive response capability value can be calculated in several ways: Optionally, a fuzzy comprehensive evaluation method can be used to establish an evaluation index system and obtain the comprehensive evaluation value through fuzzy matrix operations; alternatively, an analytic hierarchy process (AHP) can be used to establish a hierarchical structure model and obtain the comprehensive response capability value by calculating the weight vector; alternatively, a neural network method can be used to establish an evaluation model by training historical data to achieve automatic evaluation of the comprehensive response capability. It is understood that other methods can also be used to calculate the comprehensive response capability value, such as grey relational analysis and data envelopment analysis, which are not limited here.

[0043] S103. Based on the response sequence, calculate the matching degree of regulation capacity between adjacent power plants. The matching degree of regulation capacity is a weighted combination of the ratio of the ramp rate and the ratio of the regulation capacity of adjacent power plants.

[0044] Among them, the regulation capacity matching degree represents the degree of coordination of regulation characteristics between adjacent power plants, and is used to measure the cooperation effect of two power plants in the load regulation process. The ramp rate ratio refers to the ratio of the ramp rates of two adjacent power plants, and is used to characterize their matching relationship in regulation speed. The regulation capacity ratio represents the ratio of the regulation capacity of two adjacent power plants, and is used to reflect their matching degree in regulation quantity. Weighted combination refers to linearly combining the ramp rate ratio and the regulation capacity ratio according to different weights.

[0045] After obtaining the response sequence, it is necessary to assess the regulation coordination between adjacent power plants to facilitate reasonable segmentation in the subsequent process. Specifically, the system first extracts two adjacent power plants sequentially from the response sequence and calculates their ramp rate ratio, which is the larger ramp rate divided by the smaller one. Then, it calculates the regulation capacity ratio, which is the larger regulation capacity divided by the smaller one. Finally, the ramp rate ratio is assigned a weight of 0.6, and the regulation capacity ratio is assigned a weight of 0.4. The final regulation capacity matching degree is obtained by weighted summation. The closer the matching degree is to 1, the more coordinated the regulation characteristics of the two power plants are.

[0046] In some embodiments, the matching degree of regulation capacity can be calculated in several ways: Optionally, firstly, feature vectors of adjacent power plants are constructed, including parameters such as ramp rate and regulation capacity; then, the Euclidean distance of the feature vectors is calculated; next, the similarity is calculated based on the distance value; finally, the matching degree is obtained through normalization. Optionally, firstly, a fuzzy evaluation index system is established, including indicators such as speed matching degree and capacity matching degree; then, the membership degree of each indicator is calculated; next, the indicator weights are determined; finally, the matching degree is obtained through fuzzy comprehensive evaluation. It is understood that other methods can also be used to calculate the matching degree, such as grey relational analysis, hierarchical analysis, etc., which are not limited here.

[0047] S104. Based on the regulation capacity matching degree, the power plant is divided into a fast response segment, a medium-speed response segment, and a slow response segment, and the difference in regulation capacity matching degree between each adjacent response segment is less than the preset matching degree threshold.

[0048] The fast response segment represents a power plant combination with high regulation speed and flexibility, the medium response segment represents a power plant combination with medium regulation performance, and the slow response segment represents a power plant combination with relatively slow regulation speed. The preset matching degree threshold refers to the maximum allowable difference in matching degree between adjacent response segments, used to ensure the rationality and smoothness of the segmentation.

[0049] After calculating the matching degree of regulation capacity between adjacent power plants, the power plants need to be divided into different response segments. Specifically, the system first determines a preset matching degree threshold, usually set between 0.2 and 0.3. Then, starting from the beginning of the response sequence, the matching degree difference between adjacent power plants is calculated sequentially. When the matching degree difference first exceeds the threshold, this position is used as the boundary between the fast response segment and the medium-speed response segment. Continuing to traverse forward, when the matching degree difference exceeds the threshold again, this position is used as the boundary between the medium-speed response segment and the slow response segment. In this way, all power plants are divided into three response segments.

[0050] In some embodiments, response segments can be divided in several ways: Optionally, cluster analysis is first used to group power plants with similar regulation characteristics into one class, then the response segment to which each class belongs is determined based on its average response speed, and finally, the matching degree difference between adjacent response segments is checked to see if it meets the threshold requirement; Optionally, a segmentation optimization model is first established with the goal of minimizing intra-segment differences and maximizing inter-segment differences, then a dynamic programming algorithm is used to solve for the optimal segmentation scheme, and finally, the segmentation results are adjusted according to a preset threshold. It is understood that other methods can also be used to divide response segments, such as fuzzy classification, neural networks, etc., which are not limited here.

[0051] S105. Calculate the output adjustment range and adjustment rate of each generator set within each response segment, and determine the load distribution coefficient of each response segment based on the output adjustment range and adjustment rate.

[0052] The output adjustment range represents the adjustable power range of the unit under safety constraints, and is the difference between the maximum and minimum output. The adjustment rate refers to the speed at which the unit responds to load changes, expressed as the product of the ramp rate and the response time. The load allocation coefficient represents the weighting index of the unit's adjustment task within its corresponding response range, used to determine the load allocation ratio. The rate weighting coefficient is a normalized weight calculated based on the adjustment rate. The capacity weighting coefficient is a normalized weight calculated based on the adjustment range.

[0053] After completing the power plant segmentation, it is necessary to determine the specific regulation capacity indicators of the units within each response segment. Specifically, the system first calculates the output regulation range of each unit, i.e., the maximum output minus the minimum output. Then, the unit's ramp rate is multiplied by the response time to obtain the regulation rate; the larger this value, the stronger the unit's dynamic response capability. For each response segment, the regulation rate of the units within the segment is normalized to obtain a rate weighting coefficient, and the regulation range is normalized to obtain a capacity weighting coefficient. Finally, the rate weighting coefficient is assigned a weight of 0.7, and the capacity weighting coefficient is assigned a weight of 0.3, and the load allocation coefficient is obtained by weighted summation.

[0054] In some embodiments, the load allocation coefficient can be determined in several ways: Optionally, firstly, an evaluation system for the unit's regulation characteristics is established, including two dimensions: dynamic response characteristics and static capacity characteristics. Then, the weight of each indicator is calculated using the entropy weight method. Next, the indicators are dimensionless, and finally, the comprehensive score is calculated using the TOPSIS method as the allocation coefficient. Optionally, firstly, a fuzzy comprehensive evaluation matrix for the unit is constructed, and evaluation levels for rate and capacity indicators are set. Then, the membership function of each indicator is determined. Next, the weight vector of each indicator is calculated, and finally, the allocation coefficient is obtained through fuzzy comprehensive evaluation. It is understood that other methods can also be used to determine the allocation coefficient, such as principal component analysis, data envelopment analysis, etc., which are not limited here.

[0055] S106. Determine the adjustment order of all units within the response segment based on the load distribution coefficient, calculate the maximum adjustment amount that each unit can undertake, and construct a load sharing chain based on the adjustment order and the maximum adjustment amount.

[0056] Here, the adjustment order indicates the priority order in which generating units within each response segment participate in system regulation. Maximum adjustment amount refers to the maximum power adjustment that a unit can provide per unit time. The load-sharing chain refers to the adjustment sequence formed by connecting generating units within each response segment in series according to certain rules. The preset adjustment threshold represents the maximum allowable difference in adjustment between adjacent generating units, used to ensure the uniformity of load sharing.

[0057] After obtaining the load allocation coefficient, a complete load-sharing chain needs to be constructed. Specifically, the system first sorts the generating units within each response segment according to the load allocation coefficient, with units having higher allocation coefficients having higher adjustment priority. Then, the maximum adjustment amount for each unit is calculated, which is the smaller value between the product of the unit's ramp rate and its standard response time and the adjustment range. Finally, the generating units within each segment are connected sequentially according to the adjustment order, from fast response segment to medium response segment to slow response segment, ensuring that the difference in the maximum adjustment amount between adjacent units does not exceed a preset threshold, thereby constructing a complete load-sharing chain.

[0058] In some embodiments, the load-sharing chain can be constructed in several ways: Optionally, a multi-objective optimization model is first established with the objectives of maximizing system response speed, minimizing regulation cost, and optimizing load distribution. Then, a genetic algorithm is used to solve for the optimal regulation order. Next, the regulation limits of each unit are calculated. Finally, the final load-sharing chain structure is adjusted and determined according to the constraints. Optionally, a hierarchical analysis model is first constructed, using factors such as response speed, regulation cost, and operational stability as evaluation indicators. Then, the weights of each indicator are calculated. Next, the units are comprehensively scored and ranked. Finally, a dynamic programming algorithm is used to optimize the regulation order and form the load-sharing chain. It is understood that other methods can also be used to construct the load-sharing chain, such as intelligent optimization methods like ant colony optimization and particle swarm optimization. This is not limited here.

[0059] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the method for constructing a load-sharing chain for generator groups in this application.

[0060] S201. Obtain the operating parameters and transaction parameters of the power plant where each generator unit is located. The operating parameters include the unit's ramp rate, adjustment range, maximum / minimum output, and response time. The transaction parameters include the unit's quoted price and contracted electricity volume.

[0061] Among them, operating parameters represent the real-time operating conditions and performance indicators of the generator set during operation. The unit's ramp rate refers to the percentage increase or decrease in the generator set's rated power per minute. The adjustment range indicates the adjustable output range of the unit, determined by the difference between the unit's maximum and minimum output. Response time refers to the time required from receiving an adjustment command to reaching the target output value. Trading parameters represent the commercial indicators for the unit's participation in the electricity market, including the unit's market bid level and the amount of electricity already under contract.

[0062] By establishing data communication links with the power plant's operation control system and the power trading platform, operational and trading data of the generating units are collected. For operational data, parameters such as unit ramp rate and maximum / minimum output are obtained from the distributed control system (DCS), and response time is determined using historical data recorded by the AGC system. For trading data, real-time bidding information of the generating units is obtained from the data interface of the power market trading platform, and monthly and annual contract power data of the units are extracted from the contract management system. The collected data is then categorized and organized according to generating units and power plants to form a complete parameter information table.

[0063] S202. The basic regulation capacity of the power plant is calculated based on the unit's ramp rate and regulation range.

[0064] Among them, basic regulation capacity represents the basic regulation performance index of power plant units in both static and dynamic aspects. This index is calculated by multiplying the unit's ramp rate and regulation range, reflecting the unit's basic capability level during load regulation.

[0065] The calculation of basic regulation capacity adopts a standardized method. First, the unit's ramp rate is converted to standard units (MW / min), and then the regulation range value (MW) is calculated. Next, the ramp rate is multiplied by the regulation range to obtain the initial basic regulation capacity value. To facilitate comparison between units of different capacities, the initial value is normalized by dividing by the unit's rated capacity, finally obtaining the power plant's basic regulation capacity index. For example, if a 300MW unit has a ramp rate of 2% / min and a regulation range of 150MW, then its basic regulation capacity is (300×2%×150) / 300=1MW² / min.

[0066] S203. The time response coefficient of the power plant is calculated based on the response time, and the time response coefficient is inversely proportional to the response time.

[0067] The time response coefficient represents how quickly a power plant unit responds to control commands. This coefficient is calculated by taking the reciprocal of the response time, reflecting the timeliness of the unit's execution of control commands.

[0068] The calculation method for the time response coefficient is as follows: First, determine the standard response time baseline value, which is usually taken as 60 seconds. Then, take the reciprocal of the ratio of the actual response time of the unit to the baseline value to obtain the initial response coefficient. To ensure the reasonableness of the calculation results, the initial response coefficient is normalized so that its value range falls between 0 and 1. The shorter the response time, the larger the time response coefficient, indicating that the dynamic response performance of the unit is better. For example, if the response time of a unit is 30 seconds, its time response coefficient is (60 / 30) × 0.5 = 1; if the response time is 120 seconds, the time response coefficient is (60 / 120) × 0.5 = 0.25.

[0069] S204. The cost coefficient of the power plant is calculated based on the unit price, and the cost coefficient is inversely proportional to the unit price.

[0070] The cost coefficient represents the economic efficiency of power plant units participating in regulation. Unit pricing refers to the unit's pricing level in the electricity market, including electricity price and frequency regulation service price. The cost coefficient is calculated using the normalized reciprocal of the unit pricing and reflects the cost competitiveness of the unit participating in regulation.

[0071] The calculation of the cost coefficient involves several steps. First, the electricity price and frequency regulation service price of the generating unit are weighted and combined in a 3:7 ratio to obtain a comprehensive price. Then, a benchmark price is determined, taking the average of the comprehensive prices of all generating units as the benchmark. Next, the ratio of each generating unit's comprehensive price to the benchmark price is calculated, and the reciprocal of this ratio is normalized to obtain the cost coefficient. For example, if a generating unit's electricity price is 300 yuan / MWh and its frequency regulation service price is 50 yuan / MW·h, then the comprehensive price is 300×0.3+50×0.7=125 yuan / MWh. If the benchmark price is 100 yuan / MWh, then the cost coefficient of this generating unit is (100 / 125)×0.8=0.64.

[0072] S205. The adjustable capacity coefficient of the power plant is calculated based on the contracted electricity volume. The adjustable capacity coefficient is the ratio of the actual adjustable electricity volume to the rated capacity.

[0073] The adjustable capacity factor represents the degree of capacity availability for the unit to participate in regulation. Actual adjustable power refers to the amount of electricity generated that the unit can regulate while meeting contractual power requirements. Rated capacity refers to the nameplate capacity of the unit. The adjustable capacity factor reflects the unit's reserve regulation capacity after fulfilling the basic power supply contract.

[0074] The calculation of the adjustable capacity factor needs to consider several factors. First, the monthly and annual contracted electricity volume of the generating unit is statistically analyzed and converted into average hourly generating load. Then, the contracted load is subtracted from the rated capacity of the unit to obtain the theoretical adjustable capacity. Next, the theoretical adjustable capacity is divided by the rated capacity and normalized to obtain the adjustable capacity factor. For example, if the monthly contracted electricity volume of a 300MW unit is 130,000 MWh, which is converted into an average load of 180MW, then its theoretical adjustable capacity is 300 - 180 = 120MW, and the adjustable capacity factor is 120 / 300 = 0.4.

[0075] S206. The weighted product of the basic regulation capacity, time response coefficient, cost coefficient, and adjustable capacity coefficient shall be used as the comprehensive response capacity value of the power plant.

[0076] The comprehensive response capability value represents the overall regulation performance index of the power plant unit. The weighted product refers to the result of multiplying various performance coefficients according to different weights. This value reflects the unit's comprehensive performance in multiple aspects such as technical performance, response speed, economy, and capacity reserve.

[0077] The comprehensive response capability value is calculated using a multi-factor weighted product method. First, the weights of each coefficient are determined: the basic regulation capability has a weight of 0.4, the time response coefficient has a weight of 0.3, the cost coefficient has a weight of 0.2, and the adjustable capacity coefficient has a weight of 0.1. Then, each coefficient is multiplied by its corresponding weight raised to the power of its weight, yielding the weighted product. Finally, the calculation result is normalized to obtain the final comprehensive response capability value. For example, if a unit has a basic regulation capability of 0.8, a time response coefficient of 0.7, a cost coefficient of 0.6, and an adjustable capacity coefficient of 0.5, then its comprehensive response capability value is 0.8^0.4 × 0.7^0.3 × 0.6^0.2 × 0.5^0.1 = 0.688.

[0078] S207. Sort the comprehensive response capability values ​​from largest to smallest to obtain the response sequence.

[0079] The response sequence represents a sequence of power plants arranged according to their overall response capabilities. Sorting from largest to smallest refers to arranging the overall response capability values ​​in descending order of numerical value. The sorted sequence reflects the relative performance of each power plant in terms of overall regulation.

[0080] The response sequence is generated using a bidirectional bubble sort algorithm. First, a power plant index array is created to store the power plant number and its corresponding comprehensive response capability value. Then, starting from the beginning of the array, the comprehensive response capability values ​​of adjacent power plants are compared, and the larger value is moved forward. Simultaneously, starting from the end of the array, the comprehensive response capability values ​​of adjacent power plants are compared, and the smaller value is moved backward. This process is repeated until the array is completely sorted. Finally, a response sequence table containing the power plant number, comprehensive response capability value, and sorting position is generated. For example, if the comprehensive response capability values ​​of five power plants are 0.85, 0.92, 0.78, 0.88, and 0.83, the sorted response sequence is: 0.92 (Power Plant No. 2), 0.88 (Power Plant No. 4), 0.85 (Power Plant No. 1), 0.83 (Power Plant No. 5), and 0.78 (Power Plant No. 3).

[0081] S208. Obtain the ramp-up rates of two adjacent power plants in the response sequence and calculate the ratio of the ramp-up rates of the two power plants.

[0082] The ramp rate ratio indicates the degree of matching between the regulation speeds of adjacent power plant units. The unit ramp rate refers to the rate of change of the unit's output power per unit time. The ratio is calculated by dividing the larger value by the smaller value, and is used to assess the dynamic response coordination between adjacent power plants.

[0083] The calculation process for the ramp rate ratio is as follows: First, the ramp rate data of two adjacent power plants are read sequentially from the response sequence. Then, the two ramp rate values ​​are compared, with the larger value used as the dividend and the smaller value as the divisor. Next, a division operation is performed to obtain the ramp rate ratio. The above steps are repeated to calculate the ramp rate ratio between all pairs of adjacent power plants. For example, if the ramp rates of two adjacent power plants in the response sequence are 6 MW / min and 4 MW / min respectively, then their ramp rate ratio is 6 / 4 = 1.5, indicating that the dynamic response speeds of these two power plants are relatively similar.

[0084] S209. Obtain the regulation capacity of two adjacent power plants in the response sequence. The regulation capacity is the difference between the maximum output and the minimum output. Calculate the ratio of the regulation capacity of the two power plants.

[0085] Among them, regulating capacity represents the output regulation range of the generating unit. Maximum output refers to the maximum generating power of the generating unit under safe and stable operating conditions. Minimum output refers to the minimum generating power of the generating unit under safe and stable operating conditions. The regulating capacity ratio represents the degree of matching between the regulating ranges of adjacent power plants.

[0086] The calculation of the regulation capacity ratio follows these steps: First, obtain the maximum and minimum output data of two adjacent power plants in the response sequence. Then, calculate the regulation capacity of each power plant by subtracting the minimum output from the maximum output. Next, compare the two regulation capacity values ​​and divide the larger value by the smaller value to obtain the regulation capacity ratio. Continue calculating the regulation capacity ratios between other pairs of adjacent power plants. For example, the operating data of two adjacent power plants are: Power Plant A has a maximum output of 300MW and a minimum output of 150MW, while Power Plant B has a maximum output of 250MW and a minimum output of 100MW. Their regulation capacities are 150MW and 150MW respectively, and the regulation capacity ratio is 150 / 150 = 1, indicating that the regulation capacities of these two power plants are perfectly matched.

[0087] S210. The sum of the product of the gradient ratio and the first preset weight and the product of the adjustment capacity ratio and the second preset weight is taken as the adjustment capacity matching degree.

[0088] The first preset weight refers to the weight coefficient of the ramp rate ratio in calculating the regulation capacity matching degree, which is determined to be 0.6 using the analytic hierarchy process. The second preset weight refers to the weight coefficient of the regulation capacity ratio in calculating the regulation capacity matching degree, which is set to 0.4. The regulation capacity matching degree represents the degree of coordination between the dynamic and static characteristics of adjacent power plants; the closer this value is to 1, the more coordinated the regulation characteristics of the two power plants are.

[0089] The calculation of the regulation capacity matching degree adopts a weighted normalization method. First, the ramp rate ratio is processed: ramp rate data of two adjacent power plants are obtained, and the larger ramp rate is divided by the smaller ramp rate to obtain an initial ramp rate ratio. Then, the ratio is normalized using a piecewise function: a value of 0.3 is used when the ratio is greater than 3, a value of 1 is used when the ratio is less than 1, and a normalized value is determined using linear interpolation when the ratio is between 1 and 3. Next, the regulation capacity ratio is processed similarly: regulation capacity data of two adjacent power plants are obtained, and the larger capacity is divided by the smaller capacity to obtain an initial capacity ratio. The same piecewise function as the ramp rate ratio is used for normalization. Finally, the normalized ramp rate ratio is multiplied by a first preset weight of 0.6, and the normalized regulation capacity ratio is multiplied by a second preset weight of 0.4. The sum of the two yields the final regulation capacity matching degree. For example, if the ramp rates of two adjacent power plants are 8MW / min and 4MW / min, and their regulating capacities are 180MW and 120MW, respectively, then the ramp rate ratio is 2 (normalized to 0.65), the capacity ratio is 1.5 (normalized to 0.75), and the final regulating capacity matching degree is 0.65×0.6+0.75×0.4=0.69.

[0090] S211. Based on the regulation capacity matching degree, the power plant is divided into a fast response segment, a medium-speed response segment, and a slow response segment, and the difference in regulation capacity matching degree between each adjacent response segment is less than the preset matching degree threshold.

[0091] The fast response segment represents power plant combinations with high regulation speed and flexibility, whose overall response capability and regulation characteristics are at a high level. The medium response segment represents power plant combinations with moderate regulation performance, whose regulation characteristics are at an intermediate level. The slow response segment represents power plant combinations with relatively slow regulation speed, whose plants mainly bear base load. The preset matching degree threshold represents the maximum allowable difference in regulation capability matching degree between adjacent response segments, which is set to 0.25 based on actual operating experience.

[0092] The response segment division employs a dynamic clustering method. First, the regulation capacity matching degree of all adjacent power plant pairs in the response sequence is traversed, and the matching degree difference for each pair of adjacent power plants is calculated. When a matching degree difference is found to be greater than a preset threshold of 0.25, that location is marked as a potential segmentation point. Then, all potential segmentation points are evaluated: the average matching degree of power plants within the two segments before and after that point is calculated, requiring the ratio of the average matching degree within a segment to the difference in matching degree between segments to be maximized. Based on the evaluation results, two optimal segmentation points are selected, dividing the response sequence into three response segments: fast, medium, and slow. Within each response segment, the regulation capacity matching degree difference of adjacent power plants does not exceed 0.25, ensuring the relative consistency of the regulation characteristics of power plants within the segment. For example, in a certain response sequence, the matching degrees of 6 adjacent power plant pairs are 0.95, 0.88, 0.52, 0.45, 0.32, and 0.28, respectively. By calculation, the first segmentation point can be determined between 0.88 and 0.52, and the second segmentation point can be determined between 0.45 and 0.32. Finally, these power plants are divided into three regulation response segments.

[0093] S212. The adjustment rate is obtained by multiplying the computer group's ramp rate and response time, and the output adjustment range is calculated based on the maximum and minimum output.

[0094] Among them, the regulation rate represents the actual rate of power change of the unit during the regulation process, and this indicator comprehensively considers the effects of ramp-up capability and response delay. The output regulation range represents the actual adjustable power range of the unit after considering various operating constraints, and this indicator reflects the static regulation capability of the unit. These two indicators together constitute a complete description of the unit's regulation characteristics.

[0095] The regulation rate is calculated using a comprehensive weighted method. First, load change data for the most recent 24 hours is obtained from the unit's DCS system, and the base ramp rate is obtained through least squares fitting. Then, response data for recent regulation commands are extracted from the AGC system. For each regulation record, the start-up delay from receiving the command to the start of power change, and the ramp time from the start of change to reaching the target value are calculated. Outlier detection is performed on the statistically obtained delay and ramp time series, removing data points exceeding three standard deviations. A weighted average is calculated for the remaining data, with newer data assigned higher weights. The average start-up delay and average ramp time are added to obtain the comprehensive response time. Finally, the base ramp rate is multiplied by the comprehensive response time to obtain the unit's regulation rate. The calculation of the output regulation range requires consideration of multiple constraints. First, the unit's rated output value is obtained. Based on parameters such as current ambient temperature and atmospheric pressure, the output correction coefficient is obtained from a table. The rated value is multiplied by the correction coefficient to obtain the actual maximum output. Then, the minimum technical output value of the unit is obtained. Considering requirements such as stable boiler combustion and stable turbine operation, an operating margin coefficient is determined. The minimum technical output value is multiplied by the margin coefficient to obtain the actual minimum output value. Subtracting the actual minimum output value from the actual maximum output value yields the basic adjustment range. Finally, the processing capacity constraints of environmental protection equipment such as desulfurization and denitrification, as well as grid safety constraints, need to be checked. If limitations exist, the adjustment range is compressed accordingly. For example, a 300MW unit has a basic ramp rate of 6MW / min under standard operating conditions. The average start-up delay is 30 seconds, and the average ramp time is 150 seconds. Therefore, the comprehensive response time is 3 minutes, and the final adjustment rate is 18MW. The output correction coefficient for this unit at an ambient temperature of 30℃ is 0.98, and the operating margin coefficient is 1.05. The calculated actual maximum output value is 294MW, and the actual minimum output value is 157.5MW. Considering the desulfurization capacity limit of 280MW, the final output adjustment range is 122.5MW.

[0096] S213. For each of the fast response segment, medium-speed response segment, and slow response segment, the rate weighting coefficient is calculated based on the adjustment rate of each generator set within the response segment, and the capacity weighting coefficient is calculated based on the output adjustment range of each generator set within the response segment.

[0097] The rate weighting coefficient represents the relative level of the unit's regulation rate within the response range, calculated as the ratio of the unit's regulation rate to the sum of the regulation rates of all units within the response range. The capacity weighting coefficient represents the relative level of the unit's regulation capacity within the response range, calculated as the ratio of the unit's output regulation range to the sum of the output regulation ranges of all units within the response range. Both coefficients reflect the relative regulation capability of the unit within its respective response range.

[0098] The weighting coefficients are calculated separately for each response segment. First, the rate weighting coefficient is processed: regulation rate data for all units within the response segment are collected, and each regulation rate value is standardized by dividing the current value by the maximum regulation rate value within that response segment. Then, the sum of the standardized regulation rates is calculated. Finally, the standardized regulation rate of each unit is divided by the sum to obtain the rate weighting coefficient for that unit. The calculation process for the capacity weighting coefficient is similar: output regulation range data for all units within the response segment is collected and processed using the same standardization method; the sum of the standardized regulation ranges is calculated; the standardized regulation range of each unit is divided by the sum to obtain the capacity weighting coefficient for that unit. For example, if there are three units within the fast response segment with regulation rates of 20MW, 16MW, and 12MW, and output regulation ranges of 140MW, 120MW, and 100MW, respectively, then the rate weighting coefficients for the three units are 0.417, 0.333, and 0.250, and the capacity weighting coefficients are 0.389, 0.333, and 0.278, respectively.

[0099] S214. The weighted combination of the rate weighting coefficient and the capacity weighting coefficient is used as the load allocation coefficient for this response segment.

[0100] The load allocation factor represents the priority of a generator unit in undertaking regulation tasks within its assigned response segment. This factor is calculated by a weighted combination of the rate weighting factor and the capacity weighting factor. The weighting factors used in the weighting combination vary depending on the type of response segment: 0.7 for the fast response segment and 0.3 for the capacity weighting factor; 0.5 for the rate weighting factor and 0.5 for the capacity weighting factor; and 0.3 for the rate weighting factor and 0.7 for the capacity weighting factor.

[0101] The load allocation factor is calculated using an intra-segment differentiated weighting method. For each response segment, the combination coefficients of the rate weight and capacity weight corresponding to that segment are first determined. Then, the rate weight coefficient of each unit is multiplied by the corresponding rate combination coefficient, and the capacity weight coefficient is multiplied by the corresponding capacity combination coefficient. The two are then added together to obtain the load allocation factor for that unit. Continuing the example above, for the three units in the fast response segment, the load allocation factor for the first unit is 0.417×0.7+0.389×0.3=0.409; the load allocation factor for the second unit is 0.333×0.7+0.333×0.3=0.333; and the load allocation factor for the third unit is 0.250×0.7+0.278×0.3=0.258.

[0102] S215. Based on the load distribution coefficient, the generator sets in the fast response section, medium-speed response section and slow response section are sorted to obtain the adjustment order of the generator sets in each response section.

[0103] The regulation order indicates the priority order in which generating units participate in load regulation within each response segment. This order is determined by sorting the load allocation coefficients from largest to smallest. The larger the load allocation coefficient of a generating unit, the better its regulation performance, and the higher its ranking in the regulation sequence.

[0104] The order of adjustment is determined using a segmented sorting method. Each response segment is processed separately: first, the load allocation coefficients of all units within the segment are sorted from largest to smallest; then, the unit numbers are rearranged according to the sorting results, with the unit with the largest allocation coefficient receiving segment number 1, and so on; finally, a segment number is added before the adjustment order of each response segment to distinguish the units in different response segments. For example, a power grid has a total of 9 units, including 3 units in the fast response segment, 4 units in the medium response segment, and 2 units in the slow response segment. The internal sorting results for the fast response segment are: Unit 1 (0.409), Unit 2 (0.333), Unit 3 (0.258); the internal sorting results for the medium-speed response segment are: Unit 4 (0.375), Unit 5 (0.292), Unit 6 (0.208), Unit 7 (0.125); and the internal sorting results for the slow response segment are: Unit 8 (0.583), Unit 9 (0.417). This results in a complete adjustment sequence table, including segment number identifiers and intra-segment numbers.

[0105] S216. Calculate the maximum adjustment amount of each unit per unit time based on the unit's ramp rate and adjustment range.

[0106] The maximum adjustment amount represents the maximum power change that the unit can achieve per unit time. This indicator considers both the dynamic adjustment speed and the static adjustment range constraints of the unit. The unit's ramp rate represents the rate of power change of the unit, measured in MW / min. The adjustment range represents the adjustable output range of the unit, equal to the difference between the maximum and minimum output. The calculation of the maximum adjustment amount requires simultaneous satisfaction of the ramp rate and adjustment range constraints.

[0107] The maximum regulation is calculated using a constraint comparison method. First, the power change achievable by the unit within 15 minutes based on the ramp rate is taken as the dynamic constraint value. This is calculated by multiplying the ramp rate by 15 to obtain the theoretical regulation. Then, the unit's regulation range is taken as the static constraint value, reflecting the maximum physically adjustable range of the unit. Next, the dynamic and static constraint values ​​are compared, and the smaller value is taken as the unit's maximum regulation. A safety margin factor, typically 0.9, is also considered during the calculation. The smaller value obtained from the comparison is multiplied by the safety margin factor to obtain the final maximum regulation. For example, if a unit has a ramp rate of 6 MW / min and a regulation range of 120 MW, its theoretical regulation within 15 minutes based on the ramp rate is 90 MW. Taking the smaller value of 90 MW compared to the regulation range of 120 MW, and considering a safety margin factor of 0.9, the final maximum regulation of this unit is determined to be 81 MW.

[0108] S217. According to the adjustment sequence, the generator sets in each response segment are connected sequentially according to their maximum adjustment capacity to form a load sharing chain. The difference in the maximum adjustment capacity of adjacent generator sets in the load sharing chain is less than the preset adjustment capacity threshold.

[0109] The load-sharing chain represents a coordinated control sequence constructed by generating units within each response segment according to their control order and maximum control amount. The preset control threshold represents the maximum allowable difference in control amount between adjacent generating units in the load-sharing chain; this value is typically set to 10% of the total control capacity. By controlling the difference in control amount between adjacent generating units, the continuity and smoothness of the load-sharing chain are ensured.

[0110] The load-sharing chain is constructed using an iterative comparison method. First, the generating units within each response segment are arranged sequentially according to the established adjustment order. Then, the maximum adjustment difference between adjacent generating units is calculated and compared to a preset threshold. When the adjustment difference between adjacent generating units exceeds the threshold, a transition unit needs to be inserted between these two units. The transition unit is selected from the remaining unassigned generating units, requiring its maximum adjustment to be between the adjustment amounts of the two units to be connected. If no suitable transition unit can be found, the generating unit with the larger adjustment needs to be derated so that its actual available adjustment does not exceed the sum of the adjustment amounts of the adjacent generating units and the threshold. This process is repeated until the adjustment difference between all adjacent generating units in the load-sharing chain is less than the preset threshold. For example, in a response segment, there are three generating units with maximum adjustment amounts of 85MW, 45MW, and 30MW, respectively, and the preset adjustment threshold is 10MW. Since the difference between 85MW and 45MW (40MW) exceeds the threshold, the actual available adjustment of the first generating unit needs to be limited to 55MW (45MW + 10MW). Thus, the actual available regulation capacity of the units in the final load-sharing chain is 55MW, 45MW and 30MW, and the difference in regulation capacity between adjacent units does not exceed 10MW.

[0111] Following step S217, in some embodiments, the following steps may also be included: Obtain production index data for the generator group, including unit load rate, fuel consumption rate, and unit efficiency parameters.

[0112] Among these, production indicators represent a set of economic and efficiency metrics for the generating unit. Unit load factor refers to the ratio of actual output to rated output, reflecting the unit's capacity utilization. Fuel consumption rate refers to the standard fuel consumption per unit of electricity generated, usually expressed in grams of standard coal per kilowatt-hour. Unit efficiency parameters include boiler efficiency, turbine efficiency, and generator efficiency; these parameters collectively determine the unit's energy conversion efficiency.

[0113] The acquisition of production indicator data employs a multi-source data fusion method. First, real-time unit load and rated capacity data are collected from the DCS system to calculate a load rate sequence at 15-minute intervals. Statistical analysis is performed on the daily load rate data (96 points) to generate a daily load rate curve for the units. Then, fuel consumption data is obtained from the online coal consumption monitoring system and combined with power generation data to calculate the hourly fuel consumption rate. A correlation between load rate and fuel consumption rate is established, forming a piecewise linear coal consumption characteristic curve. Next, unit efficiency parameters are extracted from performance test reports, including boiler efficiency, turbine thermal efficiency, and generator efficiency at different load points. Continuous efficiency characteristic curves are generated using interpolation methods. Finally, these data are organized and archived according to unit number and time sequence to form a complete production indicator dataset. For example, a 300MW unit operating at 75% load has a fuel consumption rate of 320 g / kWh, a boiler efficiency of 92%, a turbine efficiency of 45%, and a generator efficiency of 98.5%.

[0114] A set of constraints for unit combination optimization is constructed based on production indicator data.

[0115] In this step, the constraint set represents the collection of various restrictions that affect unit combination optimization. These constraints include minimum start-up and shutdown time constraints, ramp rate constraints, regulation capacity constraints, unit efficiency constraints, and fuel consumption constraints, etc., to ensure the feasibility and economy of the unit combination scheme.

[0116] The constraint set was constructed using a hierarchical constraint method. First, a time constraint layer was established, stipulating a minimum continuous operating time of no less than 4 hours and a minimum downtime of no less than 2 hours to avoid frequent start-ups and shutdowns. Next, a technical constraint layer was established, limiting the unit's ramp-up rate to within 90% of its nameplate value and the regulating capacity to within 85% of its actual adjustable range to ensure regulation margin. Then, an efficiency constraint layer was established, requiring the unit to operate in the high-efficiency region (80%-90% load rate) for no less than 60% of its time and in the low-efficiency region (below 50% load rate) for no more than 10% of its time. Finally, an economic constraint layer was established, limiting the unit's comprehensive plant power consumption rate to within 6.5% and its coal consumption for power generation to within 103% of the standard coal consumption. There is a priority relationship among the constraints: safety constraints have the highest priority, followed by efficiency constraints, and economic constraints have the lowest priority. For example, the constraint set of a certain unit includes: minimum continuous operating time of 6 hours, maximum ramp rate of 5MW / min, high-efficiency zone operating time of not less than 15 hours / day, and maximum coal consumption of 330 grams of standard coal / kWh.

[0117] Based on the set of constraints, monthly and weekly start-up and shutdown plans are prepared for the units in the load sharing chain. The preparation of these start-up and shutdown plans includes calculating the unit combination scheme for each time period, generating the unit start-up and shutdown sequence table, and determining the transition condition adjustment strategy.

[0118] In this step, the start-up and shutdown plan refers to the unit's operational schedule within the planned cycle. The unit combination plan for each time period refers to the combination of units participating in operation during different time periods. The start-up and shutdown sequence table refers to the specific execution order and timing of unit start-up and shutdown. The transitional operating condition adjustment strategy refers to the output adjustment plan of the unit during start-up and shutdown.

[0119] The start-up and shutdown plans are developed using a hierarchical optimization approach. First, monthly planning is performed: based on the monthly load forecast curve, the day is divided into three periods: high, medium, and low load. The required unit combinations are calculated for each period, prioritizing units ranked higher in the load-sharing chain; a monthly unit combination plan table is generated. Next, weekly decomposition is performed: the monthly plan is broken down weekly to the daily load curve; considering intraday load fluctuations, specific unit start-up and shutdown combinations for each period are determined; a weekly unit combination plan table is generated. Finally, start-up and shutdown optimization is performed: based on unit start-up and shutdown times, detailed start-up and shutdown sequences are developed; the unit start-up load increase rate and shutdown load decrease rate are defined; and output adjustment strategies are formulated during transition periods. For example, the weekly start-up and shutdown plan for a power plant with 4 generating units is as follows: all 4 generating units are on during peak hours (10:00-20:00), 3 generating units are on during mid-peak hours (06:00-10:00, 20:00-22:00), and 2 generating units are on during off-peak hours (22:00-06:00 the next day); the start-up and shutdown sequence is executed according to the load sharing chain; the transition condition adjustment adopts a uniform load change rate of 3MW / min.

[0120] The unit combination scheme and the adjustment sequence of the load sharing chain are matched and analyzed. When the unit combination scheme and the adjustment sequence do not match, the unit start-up and shutdown sequence table in the start-up and shutdown scheme is adjusted.

[0121] In this step, the matching analysis refers to the quantitative assessment of the suitability of the unit combination scheme and the load sharing chain adjustment sequence. The unit combination scheme represents the selection of units participating in operation during different time periods. The adjustment sequence represents the priority ranking of each unit in the load sharing chain. The unit start-up and shutdown sequence table refers to the execution order and time node arrangement of each unit's start-up and shutdown.

[0122] The matching analysis employs a sequence comparison method. First, a time-unit matrix is ​​established, with the horizontal axis representing a 24-hour timeframe (15-minute intervals) and the vertical axis representing unit numbers. Then, unit combination schemes are converted into 0-1 sequences and filled into the matrix, where 1 indicates a unit is operational during that time period, and 0 indicates a unit is shut down. Next, the adjustment order of the load-sharing chain is compared with the operational unit sequence for each time period in the matrix. The comparison rule is that the operational unit numbers must remain continuous in the load-sharing chain, and skipping selections are not allowed. When a discontinuity is found between the unit combination and the adjustment order for a certain time period, the start-up and shutdown sequence for that time period needs to be adjusted. Adjustment methods include: starting the unit with the earlier adjustment order earlier, delaying the shutdown of the unit with the earlier adjustment order, or swapping the start-up and shutdown order of units in adjacent time periods. For example, if Units 2, 4, and 5 were originally scheduled to be put into operation during a certain period, but the load sharing chain adjustment sequence is 1-2-3-4-5, resulting in a sequence jump, it is necessary to replace Unit 2 with Unit 3, or to add Unit 3 to maintain the continuity of the adjustment sequence.

[0123] Collect full-process operation data of the unit, which includes equipment status data, operating condition data, and regulation execution data.

[0124] In this step, the full-process operation data refers to various real-time data throughout the entire operation of the unit. Equipment status data includes parameters such as the operating status, vibration values, and temperature values ​​of key equipment. Operating condition data includes operating parameters such as unit load, efficiency, and emissions. Regulation execution data includes regulation performance parameters such as AGC response characteristics and primary frequency regulation characteristics. These data comprehensively reflect the unit's operating status and regulation performance.

[0125] The entire process of data acquisition employs a distributed data acquisition method. First, an intelligent sensor network is deployed at the field equipment level to collect state parameters such as equipment vibration, temperature, and pressure, with a sampling period of 1 second. Then, at the control system level, it interfaces with the DCS and DEH systems to collect operational parameters such as unit load, efficiency, and energy consumption, with a sampling period of 5 seconds. Next, at the regulation system level, it interfaces with the AGC and AVC systems to collect regulation parameters such as frequency response and voltage response, with a sampling period of 100 milliseconds. Finally, a data preprocessing unit is set up to handle outliers, complete data, and synchronize time for the collected raw data, forming a standardized data stream. For example, the data acquisition points for a certain unit include: 6 points of turbine vibration, 12 points of temperature, 8 points of pressure, 20 points of boiler parameters, 15 points of electrical parameters, and 10 points of control signals, totaling 71 data measurement points.

[0126] A unit operation evaluation index system is established based on the full-process operation data. Based on the evaluation index system, the adjustment performance of each generator unit in the load sharing chain is analyzed.

[0127] In this step, the evaluation index system refers to a multi-dimensional set of indicators used to assess the unit's regulation performance. This index system constructs a complete evaluation framework from aspects such as dynamic response performance, regulation stability, economy, and reliability. Regulation performance refers to the actual effectiveness of the unit in performing regulation tasks within the load-sharing chain.

[0128] The evaluation index system was established using the analytic hierarchy process (AHP). First, primary indicators were constructed: response timeliness (weight 0.3), regulation accuracy (weight 0.3), operational economy (weight 0.2), and equipment reliability (weight 0.2). Secondary indicators were then refined: response timeliness included AGC response time and frequency regulation response time; regulation accuracy included load control deviation and frequency control deviation; operational economy included changes in coal consumption and plant power consumption during regulation; and equipment reliability included failure rate and equipment vibration changes during regulation. Finally, the calculation method was determined: a sliding analysis was performed on 72 consecutive hours of operating data using a 15-minute time window to calculate the values ​​of each indicator; after normalization, the indicator values ​​were weighted and summed to obtain the unit's comprehensive score. For example, a unit's performance in the load-sharing chain was evaluated as follows: AGC response time 25 seconds, control deviation 1.2%, regulation coal consumption increased by 2.5%, equipment reliability remained stable, and the comprehensive score was 85 points, placing it at a medium level among similar units.

[0129] Based on the dynamic adjustment reliability of the performance computer group, when the dynamic adjustment reliability is lower than the preset reliability threshold, the load allocation coefficient of the corresponding unit in its corresponding response segment is reduced, and the adjustment strategy of the load sharing chain is updated according to the adjusted load allocation coefficient.

[0130] Dynamic regulation reliability refers to the reliable operating level of the unit during the execution of regulation tasks, and is obtained through a comprehensive evaluation of factors such as the completion of regulation tasks, fluctuations in regulation process parameters, and changes in equipment status. The preset reliability threshold represents the minimum reliability standard that the unit needs to maintain, typically set at 0.85. The load allocation coefficient indicates the priority of the unit in undertaking regulation tasks within the response period; this coefficient determines the unit's share of regulation capacity allocation. Regulation strategies include specific implementation plans such as the unit's regulation sequence, capacity allocation, and rate control.

[0131] The complete process employs a combination of dynamic evaluation and adaptive adjustment. First, dynamic adjustment reliability is calculated: adjustment execution data is collected within a 72-hour sliding time window, including adjustment command completion rate, adjustment accuracy, and equipment status data. The completion status of adjustment commands is statistically analyzed to calculate the basic adjustment success rate R1; the output deviation during the adjustment process is analyzed to calculate the accuracy coefficient R2; and the equipment operating status is evaluated to calculate the status coefficient R3. A 4:3:3 weight allocation is used to calculate the overall reliability R = 0.4 × R1 + 0.3 × R2 + 0.3 × R3. When R < 0.85, the load allocation coefficient adjustment process begins: the reliability difference ΔR = R - 0.85 is calculated, the attenuation ratio K = |ΔR| / 0.15 (upper limit 30%) is determined, and the original load allocation coefficient is multiplied by (1-K) to obtain the new allocation coefficient. Then, compensation allocation within the response segment is performed: the reduced allocation amount is redistributed according to the performance ratio of other units. Finally, the adjustment strategy is updated: Based on the new allocation coefficients, the units are reordered, with those at the front receiving larger adjustment capacities and higher ramp rate limits, and those at the back receiving smaller adjustment capacities and lower ramp rate limits. For example, the initial states of three units within a certain response segment are: reliability of 0.82, 0.90, and 0.88, with allocation coefficients of 0.35, 0.40, and 0.25, respectively. The reliability of the first unit is below the threshold, with a calculated attenuation ratio of 20%, and its allocation coefficient is reduced to 0.28. The reduced coefficient of 0.07 is allocated to the other two units in a 2:1 ratio, resulting in final allocation coefficients of 0.28, 0.45, and 0.27. Correspondingly, the upper limits of adjustment capacity are adjusted to 56MW, 90MW, and 54MW, and the ramp rate limits are adjusted to 3MW / min, 6MW / min, and 3MW / min. This dynamic adjustment mechanism ensures the rationality and reliability of the allocation of adjustment tasks.

[0132] The generator group load-sharing chain construction system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of a physical device structure of a generator group unit load sharing chain construction system in an embodiment of this application.

[0133] It should be noted that, Figure 3The structure of the generator group unit load sharing chain construction system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0134] like Figure 3 As shown, the generator group load-sharing chain construction system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0135] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0136] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0137] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0139] Specifically, the generator group load sharing chain construction system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the generator group load sharing chain construction method provided in the above embodiment.

[0140] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the generator group load-sharing chain construction system described in the above embodiments; or it may exist independently and not assembled into the generator group load-sharing chain construction system. The storage medium carries one or more computer programs, which, when executed by a processor of the generator group load-sharing chain construction system, cause the generator group load-sharing chain construction system to implement the generator group load-sharing chain construction method provided in the above embodiments.

[0141] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0142] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0143] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for constructing a load-sharing chain for generator group units, characterized in that, The method, applied to a generator group unit load-sharing chain construction system, includes: Obtain the operating parameters and transaction parameters of the power plant where each generator unit is located. The operating parameters include the unit's ramp rate, unit's adjustment range, maximum / minimum output, and response time. The transaction parameters include the unit's quoted price and contracted electricity volume. The comprehensive response capability value of each generator set is calculated based on the operating parameters and the transaction parameters, and the comprehensive response capability values ​​are arranged in descending order of numerical value to form a response sequence. Based on the response sequence, the regulation capacity matching degree between adjacent generator units is calculated. The regulation capacity matching degree is a weighted combination of the ratio of the ramp rate and the ratio of the regulation capacity of adjacent power plants. Based on the regulation capability matching degree, the generator set is divided into a fast response segment, a medium-speed response segment, and a slow response segment, and the difference in regulation capability matching degree between each adjacent response segment is less than a preset matching degree threshold. Calculating the output adjustment range and adjustment rate of each generator set within each response segment, and determining the load allocation coefficient for each response segment based on the output adjustment range and adjustment rate; the step of calculating the output adjustment range and adjustment rate of each generator set within each response segment, and determining the load allocation coefficient for each response segment based on the output adjustment range and adjustment rate, specifically includes: The adjustment rate is obtained by multiplying the unit's ramp rate by the response time; the output adjustment range is calculated based on the maximum output and the minimum output. For each of the fast response segment, the medium-speed response segment and the slow response segment, a rate weighting coefficient is calculated based on the adjustment rate of each generator set in the response segment, and a capacity weighting coefficient is calculated based on the output adjustment range of each generator set in the response segment. The weighted combination of the rate weighting coefficient and the capacity weighting coefficient is used as the load allocation coefficient for this response segment; The adjustment order of all units within the response segment is determined based on the load allocation coefficient, and the maximum adjustment amount that each unit can undertake is calculated. A load sharing chain is then constructed based on the adjustment order and the maximum adjustment amount.

2. The method according to claim 1, characterized in that, The calculation based on the operating parameters and transaction parameters The steps of calculating the comprehensive response capability value of each power plant and arranging the comprehensive response capability values ​​in descending order to form a response sequence specifically include: The basic regulation capacity of the power plant is calculated based on the unit ramp rate and the regulation range. The time response coefficient of the power plant is calculated based on the response time, and the time response coefficient is inversely proportional to the response time. The cost coefficient of the power plant is calculated based on the unit price, and the cost coefficient is inversely proportional to the unit price. The adjustable capacity coefficient of the power plant is calculated based on the contracted electricity volume, and the adjustable capacity coefficient is the ratio of the actual adjustable electricity volume to the rated capacity. The weighted product of the basic regulation capacity, the time response coefficient, the cost coefficient, and the adjustable capacity coefficient is taken as the comprehensive response capacity value of the power plant. The response sequence is obtained by sorting the comprehensive response capability values ​​from largest to smallest.

3. The method according to claim 1, characterized in that, The step of calculating the regulation capacity matching degree between adjacent power plants based on the response sequence, wherein the regulation capacity matching degree is a weighted combination of the ratio of the ramp rate and the ratio of the regulation capacity of adjacent power plants, specifically includes: Obtain the ramp-up rates of two adjacent power plants in the response sequence, and calculate the ramp-up rate ratio of the two power plants; Obtain the regulation capacity of two adjacent power plants in the response sequence, where the regulation capacity is the difference between the maximum output and the minimum output, and calculate the regulation capacity ratio of the two power plants; The sum of the product of the gradient ratio and the first preset weight and the product of the adjustment capacity ratio and the second preset weight is taken as the adjustment capacity matching degree.

4. The method according to claim 1, characterized in that, The steps of determining the adjustment order of all units within the response segment based on the load allocation coefficient, calculating the maximum adjustment amount that each unit can undertake, and constructing a load-sharing chain based on the adjustment order and the maximum adjustment amount specifically include: The generator sets in the fast response segment, the medium response segment, and the slow response segment are sorted according to the load distribution coefficient to obtain the adjustment order of the generator sets in each response segment; The maximum adjustment amount of each unit per unit time is calculated based on the unit ramp rate and the adjustment range; According to the adjustment sequence, the generator sets in each response segment are connected sequentially according to their maximum adjustment capacity to construct the load sharing chain. The difference in the maximum adjustment capacity between adjacent generator sets in the load sharing chain is less than a preset adjustment capacity threshold.

5. The method according to claim 1, characterized in that, After determining the regulation order of all units within each response segment based on the load allocation coefficient, calculating the maximum regulation capacity that each unit can handle, and constructing a load-sharing chain based on the regulation order and the maximum regulation capacity, the method further includes: Acquire production index data of the generator group, including unit load rate, fuel consumption rate and unit efficiency parameters; A set of constraints for unit combination optimization is constructed based on the aforementioned production indicator data; Based on the set of constraints, monthly and weekly start-up and shutdown plans are prepared for the units in the load sharing chain. The preparation of the start-up and shutdown plans includes calculating the unit combination plan for each time period, generating the unit start-up and shutdown sequence table, and determining the transition condition adjustment strategy. The unit combination scheme is matched and analyzed with the adjustment sequence of the load sharing chain. When the unit combination scheme and the adjustment sequence do not match, the unit start-up and shutdown sequence table in the start-up and shutdown scheme is adjusted.

6. The method according to claim 5, characterized in that, After the steps of determining the regulation order of all units within all response segments based on the load allocation coefficient, calculating the maximum regulation that each unit can undertake, and constructing a load-sharing chain based on the regulation order and the maximum regulation, the method further includes: Collect full-process operation data of the unit, including equipment status data, operating condition data, and regulation execution data; A unit operation evaluation index system is established based on the full-process operation data; the adjustment performance of each generator unit in the load sharing chain is analyzed according to the evaluation index system. Based on the dynamic adjustment reliability of the computer group, when the dynamic adjustment reliability is lower than the preset reliability threshold, the load allocation coefficient of the corresponding unit in its corresponding response segment is reduced; the adjustment strategy of the load sharing chain is updated according to the adjusted load allocation coefficient.

7. A generator group unit load-sharing chain construction system, characterized in that, The generator group load sharing chain construction system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the generator group load sharing chain construction system to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the generator group load sharing chain construction system, the generator group load sharing chain construction system performs the method as described in any one of claims 1-6.

9. A computer program product, characterized in that, When the computer program product is run on the generator group load sharing chain construction system, the generator group load sharing chain construction system performs the method as described in any one of claims 1-6.

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