Parallel steam turbine generator unit load distribution method and device

Abstract

The invention discloses a parallel steam turbine generator unit load distribution method and device. The method comprises the following steps: acquiring a total power generation instruction issued by a power grid, operation parameters of each steam turbine generator unit and power grid working condition parameters; determining a total load rate based on the total task load and the weighted average value of the rated power of each steam turbine generator unit; according to the total load rate, the real-time energy efficiency deviation value of each steam turbine generator unit and the peak regulation response aging requirement of the power grid, determining the operation mode of at least two steam turbine generator units operating in parallel; determining the load distribution proportion of each steam turbine generator unit according to the determined operation mode, the real-time energy efficiency deviation value of each steam turbine generator unit, the load margin, the equipment state and the power grid working condition parameter, and distributing the total task load to each steam turbine generator unit according to the load distribution proportion, thereby achieving the optimal comprehensive energy efficiency under different working conditions.

Description

Technical Field

[0001] This application relates to the field of control technology, and in particular to a load distribution method and equipment for parallel steam turbine generator sets. Background Technology

[0002] In the operation and control of steam turbine generator sets, existing methods often employ an independent control system for each unit, achieving load control through information interaction between this control system and the power grid. Specifically, each unit's control system monitors its own operating parameters, such as real-time load, steam parameters, and equipment temperature, and adjusts the unit's output according to preset control logic. The power grid load dispatching department, based on the actual load of each unit and the power grid's load demand, calculates the load share each unit should bear using its internal algorithm, generates new load commands, and directly issues these commands to the units through the information link between the power grid and the unit control systems. On the power grid dispatching side, command generation typically focuses solely on the current load capacity of each unit, simply dividing the total workload according to the number of units within the grid and the upper and lower limits of each unit's load, ensuring that the load allocated to each unit does not exceed its current carrying capacity, ultimately achieving the distribution of the total workload.

[0003] Given that the power generation efficiency of generating units decreases rapidly with the decrease in load factor, the existing methods for coordinating the load of each unit on the grid side only aim to avoid overload, essentially resulting in an average distribution across units without an energy efficiency-oriented allocation logic. When the overall load factor of the grid is low, all units are in the low-load-factor inefficiency zone, causing significant losses. If the load is artificially distributed across different units, although some units become less efficient due to the lower load factor, others become more efficient due to the increased load factor. Furthermore, the efficient units undertake more power generation capacity, thus increasing the overall energy efficiency level and ultimately achieving optimal comprehensive energy efficiency under different operating conditions. Summary of the Invention

[0004] In view of the above problems, this application provides a load distribution method and equipment for parallel steam turbine generator sets.

[0005] To solve the above-mentioned technical problems, this application proposes the following solution: Firstly, this application provides a load sharing method for parallel steam turbine generator sets. The method includes: acquiring a total generation command issued by the power grid, operating parameters of each steam turbine generator set, and power grid operating condition parameters. The total generation command is used to indicate the total workload of at least two parallel-operating steam turbine generator sets. The operating parameters include the rated power of the steam turbine generator sets and real-time equipment operating condition parameters. The power grid operating condition parameters include the power grid peak-shaving response time requirements. The total load factor is determined based on a weighted average of the total workload and the rated power of each steam turbine generator set. The load factor is then calculated based on the real-time energy efficiency deviation of each steam turbine generator set. In accordance with the grid peak-shaving response time requirements, the operating mode of at least two parallel steam turbine generator units is determined. The operating mode is used to enable the unit cluster to achieve optimal comprehensive energy efficiency by adapting to different load distribution strategies during the process of completing the total power generation task assigned by the grid. The real-time energy efficiency deviation value is determined based on the real-time equipment operating parameters. Based on the determined operating mode, the real-time energy efficiency deviation value of each steam turbine generator unit, the load margin, the equipment status, and the grid operating parameters, the load distribution ratio of each steam turbine generator unit is determined, and the total task is distributed to each steam turbine generator unit according to the load distribution ratio.

[0006] Secondly, this application provides a load distribution device for parallel steam turbine generator sets, the load distribution device for parallel steam turbine generator sets comprising: The acquisition module is used to acquire the total power generation command issued by the power grid, the operating parameters of each steam turbine generator set and the power grid operating condition parameters. The total power generation command is used to indicate the total workload of at least two steam turbine generator sets operating in parallel. The operating parameters include the rated power of the steam turbine generator sets and the real-time equipment operating condition parameters. The power grid operating condition parameters include the power grid peak shaving response time requirements. The first determining module is used to determine the total load factor based on the weighted average of the total workload and the rated power of each steam turbine generator set; The second determining module is used to determine the operating mode of at least two steam turbine generator units operating in parallel based on the total load factor, the real-time energy efficiency deviation value of each steam turbine generator unit and the grid peak-shaving response time requirements. The operating mode is used to enable the unit cluster to achieve the optimal comprehensive energy efficiency of the unit cluster by adapting to the load distribution strategy under different operating conditions in the process of completing the total power generation task assigned by the grid. The real-time energy efficiency deviation value is determined based on the real-time equipment operating parameters. The third determining module is used to determine the load allocation ratio of each turbine generator unit based on the determined operating mode, the real-time energy efficiency deviation value of each turbine generator unit, the load margin, the equipment status and the power grid operating parameters, and to allocate the total workload to each turbine generator unit according to the load allocation ratio.

[0007] To achieve the above objectives, according to a third aspect of this application, a storage medium is provided, the storage medium including a stored program, wherein, when the program is executed, the device where the storage medium is located is controlled to perform the parallel turbine generator set load distribution method of the first aspect described above.

[0008] To achieve the above objectives, according to a fourth aspect of this application, an electronic device is provided, the device including at least one processor, and at least one memory and bus connected to the processor; wherein the processor and memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the parallel turbine generator set load sharing method of the first aspect described above.

[0009] By employing the above-described technical solution, the technical solution provided in this application has at least the following advantages: This application breaks down the information barriers of single-unit control systems by uniformly acquiring the total power generation command, operating parameters of each unit, and grid operating condition parameters. This ensures that the decision-making process can comprehensively grasp key information such as the total workload, rated power of each unit, real-time equipment operating conditions, and grid peak-shaving response time requirements, avoiding allocation deviations caused by partial information. Based on this, the total load factor is determined by a weighted average of the total workload and the rated power of each unit. Weighting highlights the actual impact of different units within the cluster, enabling the total load factor to accurately reflect the matching relationship between overall load pressure and the cluster's carrying capacity, providing a quantitative benchmark for subsequent mode selection. Furthermore, by combining the total load factor, real-time energy efficiency deviation values ​​of each unit, and grid peak-shaving response time requirements, an appropriate scheme is selected from three operating modes, achieving precise matching between the load allocation strategy and overall operating conditions. Finally, based on the selected mode, the allocation ratio is determined by comprehensively considering the real-time energy efficiency deviation, load margin, equipment status, and grid operating conditions of each unit. This ensures that the load allocation can meet the total workload requirements while adapting to the real-time status of each unit and the grid demand. From a global perspective, the scientific and coordinated nature of the load allocation is achieved, effectively improving the stability of the response to the grid. At the same time, by allowing the more energy-efficient units to undertake a more reasonable load ratio, the overall energy consumption is reduced, and the optimal comprehensive energy efficiency of the unit cluster under different operating conditions is achieved.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic flowchart of a load distribution method for parallel steam turbine generator sets provided in an embodiment of this application is shown; Figure 2 This paper shows a schematic diagram of the structure of a load distribution device for a parallel steam turbine generator set according to an embodiment of this application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0012] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0013] In the embodiments of this application, the terms "first," "second," etc., do not have a logical or temporal dependency, nor do they limit the quantity or execution order. It should also be understood that although the following description uses the terms "first," "second," etc., to describe various elements, these elements should not be limited by the terms. These terms are merely used to distinguish one element from another.

[0014] In this application, the term "at least one" means one or more, and the term "multiple" means two or more.

[0015] It should also be understood that the term “if” can be interpreted as “when” or “upon”, or “in response to determination” or “in response to detection”. Similarly, depending on the context, the phrase “if determination…” or “if detection [the stated condition or event]” can be interpreted as “when determination…” or “in response to determination…” or “when detection [the stated condition or event]” or “in response to detection [the stated condition or event]”.

[0016] This application provides a load distribution method for parallel steam turbine generator sets. The load distribution method for parallel steam turbine generator sets will be described in detail below with reference to the accompanying drawings. Figure 1 A flowchart illustrating a load distribution method for parallel steam turbine generator sets provided in this application. Specifically, it includes the following steps: Step 110: Obtain the total power generation command issued by the power grid, the operating parameters of each steam turbine generator set, and the power grid operating parameters.

[0017] The total power generation command is a core instruction generated and issued by the power grid dispatch center based on the overall operating status of the power system, load demand forecasts, and energy regulation objectives. Its core function is to define the total power generation capacity that at least two parallel-operating steam turbine generator units, working as a coordinated unit, must undertake. This command is typically quantified in megawatts (MW). For example, when a regional power grid faces a 300MW power shortage during peak hours, and there are two parallel-operating steam turbine generator units in the region that can collaboratively supply power, the power grid dispatch center will issue a 300MW total power generation command to these two units. This means that the two units need to cooperate and, through reasonable load allocation, ultimately achieve a total output power of 300MW to meet the power grid's demand.

[0018] The operating parameters of each steam turbine generator unit are a key set of data reflecting the unit's performance, real-time operating status, and potential operating capacity. These parameters mainly include rated power and real-time equipment operating condition parameters. Rated power is the maximum output power that each steam turbine generator unit can achieve under design conditions, enabling long-term stable operation. It is a fundamental parameter for measuring the unit's power generation capacity. For example, if the rated power of a steam turbine generator unit is set at 200MW, this means that under ideal operating conditions, the unit can continuously output 200MW of electricity. Rated power is not only an important basis for calculating the total load factor but also a core parameter for determining the initial allocation ratio in multi-unit load balancing modes.

[0019] Real-time equipment operating parameters are dynamically changing, encompassing various real-time monitoring data during unit operation. These data reflect the unit's energy efficiency level, load regulation potential, and operational safety in real time. Among these, real-time energy efficiency-related parameters, such as real-time heat rate and steam rate, are key inputs for calculating real-time energy efficiency deviation values. By comparing these parameters with the standard values ​​of the unit under rated operating conditions, the real-time energy efficiency deviation value can be obtained. This deviation value directly affects the direction and magnitude of the load distribution ratio correction. Equipment status parameters include the temperature field distribution of key components (such as turbine rotors, high-pressure cylinders, and low-pressure cylinders), cumulative operating time, and maintenance cycles. For example, by monitoring the temperature field distribution of the turbine rotor, its thermal stress state can be determined, preventing excessive load distribution from causing rotor thermal stress to exceed safe limits. Load-related parameters, such as the current actual load and load margin (i.e., the difference between the unit's current actual load and maximum continuous output), reflect the unit's current load bearing capacity and adjustment space, providing a basis for determining the upper limit of the load that can be allocated to each unit.

[0020] Power grid operating parameters primarily reflect the overall operating status of the power grid and the control requirements for generating units. Among these, the peak-shaving response time requirement is a particularly critical parameter. The peak-shaving response time requirement reflects the power grid's demand for the speed of load changes in generating units, and is typically categorized into low, medium, and high response rates based on the severity of load fluctuations. When the power grid operates stably with minimal load fluctuations, such as during the night when electricity demand is stable, the peak-shaving response time requirement is low, allowing generating units to adopt a more stable load distribution mode. When the power grid load experiences moderate fluctuations, such as during weekday load changes, the peak-shaving response time requirement is medium, requiring generating units to possess a certain degree of adjustment flexibility while ensuring stability. When the power grid encounters sudden events, such as a sharp drop in renewable energy generation output or the sudden start-up of large electrical equipment causing drastic load fluctuations, the peak-shaving response time requirement is high, requiring generating units to quickly adjust their output power to ensure the stability of the power grid's frequency and voltage. In addition, power grid operating parameters may also include the real-time voltage fluctuation range of the power grid and the power transmission limit of transmission lines. These parameters together constitute the external constraints of load allocation, ensuring that the load allocation results of each unit can not only meet the total workload requirements, but also adapt to the overall operating status of the power grid, avoiding problems such as line overload or voltage instability caused by excessive local load.

[0021] In practical applications, the acquisition of the aforementioned commands and parameters is achieved through various sensors, data acquisition terminals, and communication systems deployed in the generating units and the power grid. Sensors monitor the operating status of the generating units and the operating conditions of the power grid in real time. The data acquisition terminals perform preliminary processing and aggregation of the monitoring data, and then transmit the overall power generation command from the power grid dispatch center to the generating unit control system via a reliable communication system, while also feeding back the operating parameters of each generating unit to the control system. The control system integrates and verifies this data to ensure its accuracy and completeness, providing a solid data foundation for subsequent calculations of the total load factor, determination of the operating mode, and calculation of load allocation ratios.

[0022] Step 120: Determine the total load factor based on the weighted average of the total workload and the rated power of each turbine generator set.

[0023] The total load factor is a parameter used to quantify the proportion of the overall power generation task relative to the total power generation capacity of the parallel-operating steam turbine generator unit cluster. Its core function is to reflect the matching relationship between the current total task and the rated load capacity of the unit cluster in numerical form, providing a quantitative basis for determining the operating mode.

[0024] The calculation of the total load factor uses the total load issued by the power grid and the rated power of each generating unit as the core inputs, where the total load is the value indicated by the total generation command (denoted as P). 总For example, when the power grid requires three parallel generating units to jointly undertake a power generation task of 280MW, the rated power of each unit (denoted as Pn1, Pn2, ..., Pn) k , where k is the number of units and k≥2) is the maximum output power of each unit under long-term stable operation under design conditions. For example, the rated power of three parallel units is 150MW, 150MW and 200MW respectively. These parameters are the basic indicators for measuring the inherent power generation capacity of the units, which are determined by the design specifications of the units and remain fixed during operation.

[0025] When calculating the total load factor, the first step is to determine the weighted average of the rated power of each turbine generator unit. Weighting here refers to adjusting the rated power of each unit based on its actual influence within the parallel-operation system. The weighting must comprehensively consider factors such as the unit's installed capacity ratio, historical operational reliability, and maintenance costs. For example, for units with significantly different installed capacities, the larger capacity unit has a more significant impact on the overall system's power generation capacity, and its weight can be set to a higher value. If a unit has lower reliability due to equipment aging, its weight can be appropriately reduced during the weighted calculation. Specific weight values ​​(denoted as w1, w2, ..., w...) are calculated as follows. k And satisfying w1+w2+…+w k =1) The weight can be determined through expert evaluation or fitting of historical operating data. For example, in the case of the three units mentioned above, if the weights are set according to the proportion of installed capacity, and the total installed capacity is 150+150+200=500MW, then the weight of the first unit is w1=150 / 500=0.3, the weight of the second unit is w2=150 / 500=0.3, and the weight of the third unit is w3=200 / 500=0.4.

[0026] Based on the above weights, the weighted average of the rated power of each unit (denoted as P) 加权平均 The formula for calculating P is: 加权平均 =w1×Pn1+w2×Pn2+…+w k ×Pn k Continuing with the above example, then P 加权平均 =0.3×150+0.3×150+0.4×200=170MW, which comprehensively reflects the equivalent rated power generation capacity of the parallel unit cluster.

[0027] The formula for calculating the total load factor (denoted as λ) is: λ = P 总 / P 加权平均 ×100%. Using the case study again, when the total task volume P... 总 When the total capacity is 280MW, λ = 280 / 170 × 100% ≈ 164.7%, indicating that the current total workload has exceeded the equivalent rated carrying capacity of the unit cluster. If P 总=100MW, then λ=100 / 170×100%≈58.8%, indicating that the total workload is at a medium level of the cluster's carrying capacity.

[0028] It is important to note that the introduction of the weighted average makes the calculation of the total load factor more closely reflect actual operating scenarios. Compared to simply using the arithmetic mean of rated power (i.e., the sum of the rated power of all units divided by the number of units), the weighted calculation highlights the impact of key units on the system and avoids distortion of the total load factor assessment due to performance differences of individual units. For example, if a unit has a high rated power but operates at low load for a long time, reducing its weight allows the weighted average to more accurately reflect the actual available generating capacity of the system, thus making the calculated total load factor more valuable.

[0029] In practical applications, the total load factor is calculated automatically by the data processing module in the unit control system. This module receives the total power generation command and the rated power parameters of each unit in real time, calls the pre-stored weighting coefficients to perform a weighted average calculation, and then substitutes the result into the total load factor formula. This result is then transmitted in real time to the operation mode determination module, serving as one of the core criteria for determining whether a single-unit concentrated load mode, a multi-unit balanced load mode, or a dynamic priority load mode is being used. For example, when the calculated total load factor is ≤ the first threshold (e.g., 50%), it can be determined as a single-unit concentrated load mode based on other conditions; when the total load factor is between the first and second thresholds (e.g., 50%-80%), it can be used as one of the criteria for determining a multi-unit balanced load mode, thereby achieving a precise match between the load allocation strategy and the actual carrying capacity of the system.

[0030] Step 130: Determine the operating mode of at least two steam turbine generator sets operating in parallel based on the total load factor, the real-time energy efficiency deviation of each steam turbine generator set, and the grid peak-shaving response time requirements.

[0031] The determination of a single-unit concentrated load mode is applicable in scenarios where the total load demand is low, the unit energy efficiency is relatively consistent, and the grid's response speed requirements are not high. Specifically, when the total load factor is less than or equal to the first threshold, it means that the current total power generation task is at a low level relative to the rated power generation capacity of the unit cluster. In this case, it is not necessary for all units to participate at full load to complete the task. The setting of the first threshold needs to be determined in combination with the unit type, operating characteristics, and grid dispatching experience. For example, for a thermal power unit cluster, the first threshold can be set to 50%, that is, when the total load factor is ≤50%, the total load factor condition of this mode is met. Meanwhile, the maximum difference in real-time energy efficiency deviation values ​​among all turbine generator units must be less than a preset deviation threshold. Real-time energy efficiency deviation refers to the difference between the actual energy efficiency of each unit and the standard energy efficiency under rated operating conditions, reflecting the real-time operational efficiency differences among the units. The maximum difference is the difference between the maximum and minimum values ​​of this deviation among all units. The preset deviation threshold is usually set according to the energy efficiency stability requirements of the units, such as 5%. When the maximum difference is <5%, it indicates that the energy efficiency status of each unit is relatively similar, and there is no significant efficiency difference. Furthermore, the grid peak-shaving response time requirement is a low response rate, meaning that the current grid load fluctuation is small, the speed and flexibility requirements for unit load adjustment are low, and more emphasis is placed on operational stability and energy efficiency. Only when these three conditions are simultaneously met will the system determine it to be in a single-subunit centralized load mode. In this case, the overall operational energy efficiency can be maximized by centrally utilizing the single unit with the best efficiency to bear the main load, while the remaining units are on standby.

[0032] The criteria for determining the multi-unit load balancing mode are applicable to scenarios with moderate total load demand, stable unit energy efficiency, and certain grid response speed requirements. This mode requires the total load factor to be between a first threshold and a second threshold, where the first threshold is less than the second threshold. For example, the first threshold is 50% and the second threshold is 80%, meaning the total load factor is between 50% and 80%. In this case, the total workload requires multiple units to work together, but it hasn't reached the point where full-load operation is required. Simultaneously, the real-time energy efficiency deviation of each turbine generator unit must be within the normal deviation range. The normal deviation range refers to the unit's energy efficiency fluctuating within the design allowable range, such as ±3%. This indicates that all units are operating stably and efficiently, and there are no individual units exhibiting abnormal energy efficiency. Furthermore, the grid peak-shaving response time requirement is a medium response rate, meaning that the grid load fluctuates to some extent, requiring the units to have a moderate level of load adjustment capability to adapt to load changes. When these three conditions are met simultaneously, the system determines that it is in a multi-unit load balancing mode. By allowing multiple units to share the load proportionally, it can ensure the completion of the total task, avoid the energy efficiency decline caused by excessive load on a single unit, and meet the grid's moderate requirements for response speed.

[0033] The dynamic priority load mode is applicable to complex scenarios with high total load demand, differences in unit energy efficiency, or high grid response speed requirements. This mode uses an OR logic, meaning it is triggered when any of the following conditions are met: First, the total load factor is greater than or equal to the second threshold, for example, ≥80%. At this point, the total workload is close to or has reached the rated generating capacity limit of the unit cluster, requiring priority to mobilize the maximum potential of each unit. Second, there is a real-time energy efficiency deviation value of the turbine generator units exceeding the normal deviation range, meaning that the energy efficiency of some units deviates from the design standard. In this case, load allocation needs to be adjusted according to the actual status of the units to avoid units with low energy efficiency bearing excessive loads. Third, the grid peak-shaving response time requirement is high, meaning the grid faces sudden load fluctuations, such as a sudden drop in renewable energy generation output or the sudden commissioning of large electrical equipment, requiring units to quickly adjust load output to maintain grid frequency and voltage stability. When any of the above conditions are met, the system determines that it is in dynamic priority load mode. By establishing a priority evaluation system, the units with better status and faster response will be allowed to take on more load, ensuring the smooth completion of the total task and the stable operation of the power grid under complex operating conditions.

[0034] In practical applications, the specific values ​​of the first threshold, second threshold, preset deviation threshold, and normal deviation range in the above-mentioned judgment conditions can be dynamically adjusted according to the unit's model, operating years, and actual grid dispatch requirements, and are pre-stored in the judgment module of the unit control system. This module receives parameters such as the total load factor, the real-time energy efficiency deviation value of each unit, and the grid peak-shaving response time requirements in real time, performs condition judgments according to the above logic, and transmits the judgment results to the load allocation ratio calculation module in real time, providing clear mode guidance for subsequent load allocation, thereby realizing adaptive switching of load allocation strategies under different operating scenarios.

[0035] Step 140: Determine the load allocation ratio of each turbine generator set based on the determined operating mode, real-time energy efficiency deviation value, load margin, equipment status and grid operating parameters, and allocate the total workload to each turbine generator set according to the load allocation ratio.

[0036] When the system determines that it is in a single-subgroup centralized load mode, it means that the total load demand is low, the energy efficiency differences among the units are small, and the grid's requirements for peak-shaving response rate are not high. In this case, a centralized load allocation strategy is adopted. By selecting the unit with the best overall efficiency to undertake the main load, and keeping the remaining units in standby mode, the overall operating energy efficiency can be maximized and equipment losses reduced while ensuring the completion of the total workload. The load allocation process in this mode is described in detail below.

[0037] First, constructing a multi-dimensional evaluation matrix is ​​the foundation for selecting the main operating units. This matrix uses the real-time energy efficiency deviation, current load margin, cumulative operating time, maintenance cycle, and temperature field distribution characteristics of key components of each turbine generator unit as core dimensions, comprehensively covering the unit's energy efficiency level, load regulation potential, equipment aging degree, and operational safety. The real-time energy efficiency deviation is calculated by comparing the unit's current heat rate, steam consumption rate, and other parameters with the standard values ​​under rated operating conditions. For example, if a unit's real-time heat rate is 2% higher than the rated value, its energy efficiency deviation is +2%. The smaller this value, the higher the unit's current operating efficiency. The current load margin is the difference between the unit's maximum continuous output and the actual load. For example, if a unit's maximum continuous output is 200MW and the current actual load is 50MW, the load margin is 150MW. A larger margin indicates more sufficient load capacity. The cumulative operating time reflects the unit's aging degree. The closer the operating time is to the upper limit of the maintenance cycle, the higher the risk of equipment failure, and its weight should be appropriately reduced in the evaluation. The maintenance cycle is linked to the planned maintenance time of the equipment. If a unit is about to enter its maintenance cycle, its priority for undertaking high loads will decrease accordingly. The temperature field distribution characteristics of key components are obtained through infrared thermography or built-in sensors, such as the temperature distribution uniformity of components like turbine rotors and high-pressure cylinders. The more stable the temperature field, the lower the thermal stress of the component and the higher the operational safety. When constructing the matrix, the parameters of each dimension need to be standardized. For example, quantitative indicators such as energy efficiency deviation and load margin are converted into a scoring range of 0-10 points to ensure that parameters of different dimensions can be compared horizontally, forming a matrix table containing the scores of all units on each evaluation dimension.

[0038] Based on a multidimensional evaluation matrix, the analytic hierarchy process (AHP) is used for comprehensive performance scoring. The AHP decomposes the complex evaluation problem into a target layer (optimal comprehensive performance), a criterion layer (the five evaluation dimensions mentioned above), and a scheme layer (each turbine generator unit). A judgment matrix is ​​constructed to determine the weight of each criterion layer. For example, in low-load scenarios, real-time energy efficiency deviation has the greatest impact on overall performance and is assigned a weight of 30%; the temperature field distribution characteristics of key components are related to operational safety and are assigned a weight of 25%; current load margin, cumulative equipment operating time, and maintenance cycle are assigned weights of 20%, 15%, and 10%, respectively (the total weight is 100%). Subsequently, the comprehensive performance score is obtained by calculating the weighted sum of the scores of each unit at the criterion layer. For example, if a unit scores 9 points in energy efficiency deviation, 8 points in load margin, 9 points in temperature field distribution, 7 points in cumulative operating time, and 8 points in maintenance cycle, it can be calculated as 9×30%+8×20%+9×25%+7×15%+8×10%=8.4 points according to the above weights. This score directly reflects the overall operating status of the unit, and the unit with the highest score is the main unit with the best overall efficiency.

[0039] After determining the primary generating unit, it is necessary to further clarify its maximum allocable load ratio. Determining this ratio requires comprehensive consideration of the real-time load regulation rate of the main generating unit, the minimum stable operating load, and the sensitivity to grid frequency fluctuations. That is... ,in, It is responsible for the maximum continuous output of the main generating unit. The main load to be borne by the unit is the current actual load. Let r be the total workload, t be the real-time load regulation rate of the main generating unit, t be the grid peak-shaving response time requirement, and k be the grid frequency fluctuation sensitivity coefficient. The real-time load regulation rate refers to the amount of load that a generating unit can increase per unit time. For example, if a generating unit can increase its load by 10MW per minute, then when the total workload requires rapid allocation, its regulation rate determines the upper limit of the maximum load allocation. The minimum stable operating load is the minimum output power required for the generating unit to maintain stable operation, for example, through formula E. min =0.95*DLP min *η+E ss Among them, DLP min The minimum cooling steam quantity for the low-pressure cylinder is E, where η is the cooling steam utilization rate. ss For plant power consumption, the minimum stable operating load of a certain generating unit is calculated to be 30MW, meaning that the load allocated to this unit must be higher than this value. Grid frequency fluctuation sensitivity reflects the degree to which changes in unit load affect the grid frequency; the lower the sensitivity, the higher the proportion of load the unit can handle. By substituting the above parameters into a preset algorithm, for example, using the minimum stable operating load as the lower limit and (maximum continuous output - current actual load) as the upper limit, combined with the adjustment rate and frequency sensitivity, a dynamic range is determined. The maximum value within this range is then taken as the maximum load proportion of the main generating unit. For example, if this proportion is calculated to be 90%, then the main generating unit can be allocated 90% of the total workload.

[0040] Finally, the actual load is allocated based on the maximum load ratio. Assuming a total workload of 100MW and the main unit's maximum load ratio is 90%, then 90MW is allocated to that unit. The remaining units (if there are two standby units) are allocated only the minimum load necessary to maintain their basic operation. This minimum load is typically equal to their minimum stable operating load (e.g., 30MW each), but it must be ensured that the total load of all units does not exceed a reasonable range between the total workload and the standby load. In this mode, the main unit operates at full load and efficiently, while the standby units maintain a minimum load to quickly respond to possible load fluctuations. This avoids energy efficiency losses caused by multiple units operating at low loads, improves overall operational economy through load concentration, and simultaneously meets the stability requirements under low grid response rates.

[0041] In practical applications, the above process is automatically executed by the load allocation module in the unit control system. This module collects parameters from various dimensions in real time, calls the analytic hierarchy process (AHP) algorithm for comprehensive scoring, selects the main load-bearing unit, calculates the maximum load ratio based on its regulation performance and grid characteristics, and finally generates load allocation instructions and sends them to the actuators of each unit, realizing automated and precise load allocation in the single-subunit centralized load mode.

[0042] When the system is determined to be in a multi-unit load balancing mode, it indicates that the total load factor is at a moderate level, the energy efficiency of each unit is stable, and the power grid has a moderate requirement for peak-shaving response rate. In this case, multiple units need to work together to share the load, achieving a balance between optimal energy efficiency and stable operation while ensuring the completion of the total workload. The load allocation process in this mode involves multiple steps of precise calculation and constraint calibration to ensure the scientific and rational allocation of load to each unit, which will be explained in detail below.

[0043] First, the initial load allocation ratio is determined based on the rated power percentage of each turbine generator unit. This serves as the fundamental reference for load allocation. Rated power, as the maximum stable output determined during unit design, directly reflects the unit's inherent generating capacity. Using this as a basis for initial allocation ensures a preliminary balance of load among the units. The rated power percentage is calculated as the ratio of the rated power of a single turbine generator unit to the sum of the rated power of all parallel-operating turbine generator units. For example, if there are three units operating in parallel with rated powers of 300MW, 400MW, and 300MW respectively, the total rated power is 300 + 400 + 300 = 1000MW, corresponding to rated power percentages of 300 / 1000 = 30%, 400 / 1000 = 40%, and 300 / 1000 = 30%. If the total workload is 800MW, according to this initial allocation ratio, the initial allocated loads for each unit are 800×30%=240MW, 800×40%=320MW, and 800×30%=240MW, respectively. This initial allocation method is simple and intuitive, and can quickly achieve the initial distribution of load among the units, while matching the inherent capacity of the units, laying the foundation for subsequent accurate calibration.

[0044] Next, the deviation correction coefficient is calculated based on the real-time energy efficiency deviation values ​​of each steam turbine generator unit. Ki ,Right now Ki =1+ α ×(−Δ η total i ),in, α Δ is the adjustment factor for the correction coefficient. η total iThe comprehensive real-time energy efficiency deviation value of the i-th unit is used for dynamic calibration of the initial allocation ratio. The real-time energy efficiency deviation value is the difference between the unit's current real-time equipment operating parameters (such as heat rate, steam rate, main steam parameters, etc.) and the standard parameters under rated operating conditions. It reflects the degree of deviation between the unit's actual operating efficiency and the ideal state. For example, if a unit's real-time heat rate is 2% lower than the rated value, it indicates that its current operating efficiency is higher than the standard state, and the real-time energy efficiency deviation value is -2%; if another unit's real-time heat rate is 3% higher than the rated value, then the real-time energy efficiency deviation value is +3%. The deviation correction coefficient is calculated based on the real-time energy efficiency deviation value, following the principle that the higher the energy efficiency, the larger the correction coefficient. That is, for units with a negative energy efficiency deviation value (efficiency higher than the standard), a correction coefficient greater than 1 is assigned, allowing them to bear more load; for units with a positive energy efficiency deviation value (efficiency lower than the standard), a correction coefficient less than 1 is assigned, appropriately reducing their load share. The specific value of the correction coefficient can be calculated using a linear or nonlinear function. For example, the correction coefficient K is set to 1 - 0.02 × ε (where ε is the real-time energy efficiency deviation value, expressed as a decimal). When ε = -0.02 (i.e. -2%), K = 1 - 0.02 × (-0.02) = 1.0004; when ε = 0.03 (i.e. +3%), K = 1 - 0.02 × 0.03 = 0.9994. In this way, the initial allocation ratio can be finely adjusted to tilt the load towards high-efficiency units.

[0045] After obtaining the deviation correction factor, it needs to be applied to the initial allocation ratio to obtain the calibrated allocation ratio. Specifically, the initial allocation ratio of each unit is multiplied by the corresponding deviation correction factor, and then the calibrated ratios of all units are normalized (i.e., ensuring that the sum of all ratios is 100%) to guarantee the integrity of the total load allocation. Continuing with the above example, if the initial allocation ratios of the three units are 30%, 40%, and 30%, and the corresponding deviation correction factors are 1.0004, 0.9994, and 1.0001, respectively, then the calibrated temporary ratios are 30% × 1.0004 = 30.012%, 40% × 0.9994 = 39.976%, and 30% × 1.0001 = 30.003%, respectively. After normalization, the calibrated allocation ratios are approximately 30.01%, 39.98%, and 30.01%. If the total workload remains at 800MW, the calibrated allocated loads will be approximately 240.08MW (800×30.01%), 319.84MW (800×39.98%), and 240.08MW (800×30.01%) respectively. Compared to the initial allocation, the load of high-efficiency units has increased slightly, while the load of low-efficiency units has decreased slightly, achieving dynamic optimization based on real-time energy efficiency.

[0046] Subsequently, based on the real-time voltage fluctuation range of the power grid, the power transmission limit of transmission lines, and the distribution characteristics of regional load centers, boundary conditions are imposed on the calibration allocation ratio to ensure that the load allocation results are compatible with the power grid operating status. The real-time voltage fluctuation range of the power grid reflects the current voltage stability of the grid. If the voltage in a certain area is low, the load increase of units in that area needs to be limited to prevent further voltage drops due to excessive power output. The power transmission limit of transmission lines refers to the maximum power that a line can carry. If the transmission line limit for a certain unit is 280MW, the load allocated to that unit must not exceed this value to prevent line overload. The distribution characteristics of regional load centers are related to the economics of power transmission; units near load centers can be allocated more load to reduce transmission losses. For example, in the example above, the transmission line limit corresponding to the second unit is 300MW, while its calibrated load allocation is 319.84MW, which exceeds the limit. At this time, its load needs to be reduced to 300MW, and the excess 19.84MW needs to be distributed to other units proportionally. At the same time, it is ensured that the adjusted load does not violate the voltage fluctuation constraints and regional distribution requirements, and finally the constrained calibration allocation ratio is obtained.

[0047] Finally, based on the constrained calibration allocation ratio and total workload, the actual load allocation value for each turbine generator unit is determined, ensuring that this value meets the minimum stable operating load, maximum continuous output, and load change rate limits for each unit. For example, if the minimum stable operating load of a unit is 50MW, the actual allocation value must not be lower than 50MW; the maximum continuous output is the maximum load that the unit can operate at for an extended period under its current condition. If, due to equipment limitations, the current maximum continuous output is 350MW, the actual allocation value must not exceed 350MW; the load change rate limit refers to the maximum change in load per unit time (e.g., 20MW / min). If the current load of the unit is 200MW, the actual allocation value is 240MW, and the adjustment needs to be completed within 5 minutes, then the load change rate is (240-200) / 5=8MW / min, which must be less than the limit of 20MW / min to ensure smooth unit adjustment. Through the above multi-dimensional constraint verification, the final determined actual load allocation value not only meets the total workload requirements but also ensures the safe and stable operation of the units, achieving optimal load allocation under the multi-unit balanced load mode.

[0048] In practical applications, the above process is automatically executed by the load distribution module in the unit control system. This module collects the parameters of each unit and the grid data in real time, and sequentially completes the initial ratio calculation, deviation correction, boundary constraints and limit verification. Finally, it generates load distribution instructions and sends them to each unit, ensuring that the entire load distribution process is efficient, accurate and meets all operating requirements.

[0049] When the system determines that it is in dynamic priority load mode, it means that the total load demand is high, some generating units have abnormal energy efficiency, or the grid has high requirements for peak-shaving response rate. At this time, it is necessary to establish a priority evaluation system to allow generating units with better performance and faster response to take on more load, so as to ensure the efficient completion of the total task and the stable operation of the grid. The load allocation process in this mode achieves dynamic matching between load and generating unit capacity through multi-dimensional parameter evaluation, quantitative scoring, and sequence ranking, which will be described in detail below.

[0050] First, establishing a priority assessment system is the core foundation of the dynamic priority load mode. This system encompasses six key dimensions: real-time energy efficiency deviation, current load margin, equipment health status assessment results, historical fault repair records, variable load regulation quality, and environmental adaptability parameters. It comprehensively reflects the unit's operational efficiency, regulation potential, reliability, and environmental adaptability. The real-time energy efficiency deviation is calculated by comparing the unit's current heat rate and steam rate with the rated operating condition standard values. A negative value indicates efficiency higher than the standard (e.g., -3%), while a positive value indicates efficiency lower than the standard (e.g., +2%), directly affecting the economic efficiency of the unit's load capacity. The current load margin is the difference between the unit's maximum continuous output and the actual load (e.g., if a unit has a maximum output of 300MW and a current load of 150MW, the margin is 150MW). A larger margin indicates more sufficient load capacity. Equipment health status assessment results are derived by monitoring parameters such as vibration, temperature, and pressure of key components (e.g., turbine rotor, boiler heating surfaces) and combining them with a degradation trend model. The results are typically expressed as a health index (0-100 points), with scores above 80 indicating good performance and below 60 indicating a warning. Historical fault repair records include the number of faults, average repair time, and fault type (e.g., mechanical faults, control system faults) within the past three months. Units with fewer faults and faster repair times have higher priority. Variable load regulation quality reflects the stability of unit load changes, evaluated by overshoot (e.g., ≤5%), response time (e.g., ≤30 seconds), and fluctuation amplitude (e.g., ≤2%) during regulation. Environmental adaptability parameters reflect the unit's operational stability under extreme weather conditions (e.g., high temperatures, extreme cold) or complex power grid environments (e.g., voltage fluctuations), such as the unit's output retention rate under high-temperature conditions (e.g., ≥95% is excellent). These parameters are acquired in real-time through sensors and data acquisition terminals, and after preprocessing, are input into the assessment system to provide comprehensive data support for subsequent priority calculations.

[0051] Based on the aforementioned priority evaluation system, a fuzzy comprehensive evaluation method is used to calculate the weighted average of each parameter and generate a real-time priority score. The fuzzy comprehensive evaluation method is suitable for handling uncertainties and fuzziness among parameters. By combining qualitative descriptions with quantitative data, it achieves comprehensive quantification of multi-dimensional parameters. Specifically, firstly, an evaluation level (e.g., "Excellent," "Good," "Medium," "Poor") and a corresponding membership function are set for each parameter. For example, the real-time energy efficiency deviation value "Excellent" corresponds to ≤-2%, "Good" to -2% to 0%, "Medium" to 0 to +2%, and "Poor" to ≥+2%. Then, weights are set according to the importance of each parameter in the dynamic priority mode. Variable load regulation quality (responding to high response demands) and equipment health status (ensuring operational safety) have higher weights, set at 25% and 20% respectively. The real-time energy efficiency deviation value (affecting economic efficiency) is set at 18%, the current load margin (related to regulation potential) at 15%, historical fault repair records (reflecting reliability) at 12%, and environmental adaptability parameters (responding to complex environments) at 10% (total weight 100%). Subsequently, fuzzy matrix operations are used to combine the membership degree and weight of each parameter to obtain a comprehensive evaluation vector for each unit, which is ultimately converted into a real-time priority score of 0-100 points. For example, a unit with "Excellent" load regulation quality (membership degree 0.9), "Good" equipment health status (membership degree 0.8), and "Excellent" real-time energy efficiency deviation (membership degree 0.9) receives a priority score of 89 points after weighted calculation. Another unit, due to numerous historical faults and average regulation quality, receives a score of 72 points. The scores directly reflect the comprehensive priority of the units.

[0052] A load allocation priority sequence is established based on real-time priority scores, that is, the generating units are sorted from highest to lowest score to form a priority order for undertaking loads. For example, if four generating units have scores of 92, 89, 78, and 65 respectively, the priority sequence is Unit 1 > Unit 2 > Unit 3 > Unit 4. This sequence clearly defines the order of load allocation, with higher-priority units receiving a larger share of the load to fully utilize their performance advantages. If scores are the same, further fine-tuning can be performed using secondary parameters (such as historical load allocation balance) to avoid excessive losses caused by long-term bias towards a particular generating unit.

[0053] Finally, the load allocation ratio for each generating unit is determined based on the priority sequence, the total workload, and the real-time peak-shaving depth requirements of the power grid. The real-time peak-shaving depth requirements of the power grid refer to the magnitude of load increase or decrease that the generating units need to achieve (e.g., +30% means an increase of 30% in load from the current level), which directly affects the total amount and direction of load allocation. In specific allocation, the maximum load that the highest priority generating unit can handle (not exceeding its maximum continuous output and load margin) is satisfied first, and then the remaining load is allocated to subsequent generating units in sequence until the total workload is completed. For example, if the total load is 500MW, the peak-shaving depth requirement is +20%, and in the priority sequence, unit 1 can handle a maximum of 200MW, unit 2 a maximum of 180MW, unit 3 a maximum of 150MW, and unit 4 is limited to 50MW due to poor health, then unit 1 is allocated 200MW first, leaving 300MW remaining; then unit 2 is allocated 180MW, leaving 120MW remaining; then unit 3 is allocated 120MW (not reaching its maximum capacity). At this point, the total load has reached 500MW, and unit 4 is not allocated any additional load. If the total load is large (e.g., 600MW), then after units 1, 2, and 3 are at full load (200+180+150=530MW), the remaining 70MW is allocated proportionally to the first two units (based on their regulation margin) to ensure that it does not exceed their respective maximum continuous output. Meanwhile, the allocation process must meet the load change rate limits of each unit (e.g., ≤25MW / min). For example, if Unit 1 currently has a load of 100MW and needs to be allocated to 200MW, and the peak shaving time requirement is 5 minutes, then the load change rate is 20MW / min (≤25MW / min), which meets the constraints. This priority-based tiered allocation method can quickly respond to the grid's peak shaving needs while fully utilizing the performance of high-quality units, achieving both high efficiency and security in load allocation.

[0054] In practical applications, the above process is automatically executed by the dynamic allocation module in the unit control system. This module updates parameters and scores every 5 seconds, adjusts the priority sequence in real time, and dynamically calculates the allocation ratio based on the total workload and peak-shaving requirements, generating load commands that are then sent to the actuators of each unit. For example, when the power grid experiences a sudden peak-shaving demand (high response rate), the module completes priority recalculation and load allocation within 10 seconds, ensuring rapid unit response and guaranteeing stable power grid operation.

[0055] After determining the load allocation ratio for each steam turbine generator unit, a comprehensive verification and optimization are needed to further ensure the safety, stability, and economy of the scheme in actual operation. Constructing a digital twin of all physical parameters and combining it with multi-objective optimization algorithms for load allocation correction is a crucial step in ensuring safe operation and optimal efficiency of the units. This step, through a closed-loop mechanism of virtual simulation and reverse optimization, anticipates potential risks and makes dynamic adjustments before the load allocation scheme is implemented, ensuring that the final scheme simultaneously meets multiple objectives of energy efficiency, safety, and equipment lifespan. This will be elaborated in detail below.

[0056] Constructing a digital twin of all physical parameters for each steam turbine generator unit is fundamental to achieving virtual operation simulation. The digital twin replicates the physical structure of the unit using 3D modeling technology, encompassing core equipment such as the turbine, boiler, and generator, as well as auxiliary systems like the steam-water system and control system. It integrates massive amounts of real-time physical parameters, including but not limited to material properties (such as elastic modulus and coefficient of thermal expansion), geometric dimensions (such as rotor diameter and blade length), real-time operating parameters (such as steam pressure, temperature, and flow rate), and historical performance data (such as cumulative runtime and fault records). These parameters are collected in real-time by sensors deployed throughout the unit (such as pressure sensors, temperature sensors, and vibration sensors), pre-processed by an edge computing module, and then transmitted to the digital twin platform. This ensures millisecond-level synchronization between the virtual model and the physical unit, achieving real-time mapping between the physical entity and the virtual image. For example, the digital twin of a steam turbine generator unit can accurately reproduce the internal flow field of the high-pressure cylinder, the thermal deformation process of the rotor, and the electromagnetic conversion efficiency of the generator, providing a high-fidelity virtual experimental environment for subsequent simulation analysis.

[0057] The core purpose of inputting the determined load allocation ratio into a digital twin for virtual operation simulation is to predict the unit operating status and grid response characteristics under different load conditions before actual execution. During the simulation, the digital twin, based on the input load allocation ratio (e.g., unit A carries 40% and unit B carries 60%), combines multi-physics coupling algorithms such as thermodynamics, fluid mechanics, and structural mechanics to simulate the dynamic stress distribution of the units, steam-water system pressure fluctuations, and transient stability characteristics of the grid in real time. Specifically, the dynamic stress distribution simulation uses finite element analysis to calculate the thermal and mechanical stresses of key components such as the turbine rotor and blades under different loads. For example, when the load suddenly increases, it determines whether the thermal stress generated by the temperature gradient in the rotor exceeds the material yield limit. The steam-water system pressure fluctuation simulation calculates the flow state of steam in the boiler, pipelines, and turbine, simulating the amplitude and frequency of fluctuations in main steam pressure and reheat steam pressure, such as whether pressure fluctuations exceed a preset range of ±5% during load adjustment. The simulation of power grid transient stability characteristics combines changes in unit output power with parameters such as grid impedance and load distribution to analyze the transient response of frequency and voltage, such as whether sudden changes in load distribution cause grid frequency fluctuations to exceed the safety threshold of ±0.2Hz. The simulation results are output in the form of visual charts (such as stress cloud diagrams and pressure fluctuation curves) and quantitative data, providing an intuitive basis for risk assessment.

[0058] When simulation results reveal risks such as thermal stress exceeding safety thresholds in key unit components, pressure fluctuations in the steam-water system exceeding preset ranges, or insufficient transient stability margins in the power grid, a coupled model based on multi-objective particle swarm optimization and fault mode effect analysis (FMD) needs to be activated to inversely correct the load allocation ratio. This coupled model, by integrating fault risk assessment and intelligent optimization algorithms, achieves a balance between energy efficiency and equipment lifespan while ensuring safety. The specific correction process is as follows: First, optimization objectives and constraints are set. The optimization objectives are clearly defined as optimal energy efficiency and minimum equipment lifespan loss. Optimal energy efficiency is quantified by minimizing the weighted sum of real-time energy efficiency deviations of all units (e.g., total deviation ≤ ±2%). Minimizing equipment lifespan loss is regulated by ensuring that the fatigue life loss rate of key components (e.g., rotor, high-pressure cylinder) is below the allowable upper limit (e.g., annual loss rate ≤ 5%). Constraints cover hard limitations on unit operation, including minimum stable operating load, maximum continuous output (maximum output power under current conditions), load change rate limits (e.g., ≤ 20 MW / min), and grid voltage stability requirements (e.g., voltage fluctuation range ≤ ±5%), ensuring that the revised scheme does not exceed safe operating boundaries.

[0059] Secondly, Failure Mode and Effects Analysis (FMEA) is used to determine risk weights and associated load-sensitive parameters. FMEA analyzes historical failure data and simulated risk points to score failure modes such as excessive thermal stress, excessive pressure fluctuations, and grid transient instability with severity (S), probability of occurrence (O), and detectability (D) scores (1-10 points each), and calculates the Risk Priority Number (RPN = S × O × D). For example, excessive rotor thermal stress may directly damage equipment, with a severity S = 9, probability of occurrence O = 3, detectability D = 2, and an RPN of 54; excessive steam and water pressure fluctuations may affect system stability, with S = 6, O = 4, D = 3, and an RPN of 72. The weight of each risk is determined based on the RPN value (e.g., the risk weight of RPN = 72 is higher than that of 54). Simultaneously, associated load-sensitive parameters are identified, such as excessive thermal stress being positively correlated with the load distribution ratio of unit A (higher load, greater stress), and excessive pressure fluctuations being positively correlated with the load change rate of unit B. These parameters will be used as the adjustment targets for the optimization algorithm.

[0060] Subsequently, risk weights are used as adaptive adjustment coefficients to iteratively optimize the initial load allocation ratio. A multi-objective particle swarm optimization algorithm simulates bird flock foraging behavior, treating each possible load allocation scheme as a particle. The algorithm finds the optimal solution through particle swarm position updates (adjusting the allocation ratio) and velocity optimization (adjusting the iteration step size). In each iteration, the algorithm dynamically adjusts the optimization range based on the risk weights derived from failure mode impact analysis. For example, if the risk weight for "excessive pressure fluctuation" is high, the adjustment range for the load change rate of unit B is narrowed, prioritizing a reduction in its fluctuation amplitude; if the risk of "excessive thermal stress" is prominent, the upper limit of the load allocation for unit A is limited. Simultaneously, the concept of Pareto optimality is introduced to find a balance between energy efficiency and lifespan loss, preventing the optimization of a single objective from leading to the deterioration of other objectives.

[0061] Finally, an iteration termination condition is set. When the iteration result simultaneously satisfies the following conditions: energy efficiency deviation is less than the preset energy efficiency threshold (e.g., ±1.5%), equipment life loss rate is less than the allowable upper limit (e.g., 3% / year), and all safety constraints (e.g., load does not exceed maximum output, voltage fluctuation is within the allowable range), the optimization is terminated, and the current iteration result is determined as the corrected load allocation ratio. For example, under the initial allocation ratio, unit A's rotor thermal stress exceeds the limit due to excessive load. After 50 iterations, the optimization algorithm reduces its load ratio from 45% to 40%, while increasing unit B's load ratio from 55% to 60%. At the same time, the load change rate of unit B is limited from 25MW / min to 18MW / min. The final simulation results show that all risks are eliminated, and the energy efficiency deviation is -1.2% and the life loss rate is 2.8% / year, satisfying all objectives. At this point, the corrected ratio is the final solution.

[0062] In practical applications, the above process is automated through the collaborative operation of a digital twin platform and an optimization algorithm module. The digital twin updates simulation data every 10 seconds. If a risk is detected, the coupled model is immediately triggered, and the optimization algorithm completes iterative calculations and outputs a corrected solution within 2 minutes, ensuring that load allocation achieves optimal balance within the safety boundary. This closed-loop mechanism of "simulation-evaluation-optimization" effectively compensates for the shortcomings of traditional load allocation methods in balancing safety and efficiency, significantly improving the operational reliability and economy of parallel steam turbine generator units.

[0063] It is understood that, in order to achieve the functions in the above embodiments, the computer device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and method steps described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0064] Furthermore, as a response to the above Figure 1 The implementation of the method embodiment shown in this application provides a load distribution device for a parallel steam turbine generator set. The embodiment of this device corresponds to the foregoing method embodiment. For ease of reading, this embodiment will not repeat the details of the foregoing method embodiment, but it should be understood that the device in this embodiment can correspondingly implement all the contents of the foregoing method embodiment. Specifically, as shown... Figure 2 As shown, the load distribution device 200 for parallel steam turbine generator sets includes: The acquisition module 210 is used to acquire the total power generation command issued by the power grid, the operating parameters of each steam turbine generator set and the power grid operating condition parameters. The total power generation command is used to indicate the total workload of at least two steam turbine generator sets operating in parallel. The operating parameters include the rated power of the steam turbine generator sets and the real-time equipment operating condition parameters. The power grid operating condition parameters include the power grid peak shaving response time requirements. The first determining module 220 is used to determine the total load rate based on the weighted average of the total workload and the rated power of each steam turbine generator set; The second determining module 230 is used to determine the operating mode of at least two steam turbine generator units operating in parallel based on the total load factor, the real-time energy efficiency deviation value of each steam turbine generator unit and the grid peak-shaving response time requirements. The operating mode is used to enable the unit cluster to achieve the optimal comprehensive energy efficiency of the unit cluster by adapting to the load distribution strategy under different operating conditions in the process of completing the total power generation task assigned by the grid. The real-time energy efficiency deviation value is determined based on the real-time equipment operating parameters. The third determining module 240 is used to determine the load allocation ratio of each steam turbine generator set based on the determined operating mode, the real-time energy efficiency deviation value of each steam turbine generator set, the load margin, the equipment status and the power grid operating parameters, and to allocate the total workload to each steam turbine generator set according to the load allocation ratio.

[0065] Furthermore, such as Figure 2 As shown, when the total load factor is less than or equal to the first threshold, and the maximum difference in the real-time energy efficiency deviation of each turbine generator unit is less than the preset deviation threshold, and the grid peak-shaving response time requirement is low response rate, it is determined to be a single-subunit centralized load mode; when the total load factor is between the first threshold and the second threshold, and the real-time energy efficiency deviation of each turbine generator unit is within the normal deviation range, and the grid peak-shaving response time requirement is medium response rate, it is determined to be a multi-unit balanced load mode, and the first threshold is less than the second threshold; when the total load factor is greater than or equal to the second threshold, or there is a turbine generator unit whose real-time energy efficiency deviation exceeds the normal deviation range, or the grid peak-shaving response time requirement is high response rate, it is determined to be a dynamic priority load mode.

[0066] Furthermore, such as Figure 2 As shown, the third determining module 240 is specifically used to construct a multi-dimensional evaluation matrix based on the real-time energy efficiency deviation value, current load margin, cumulative equipment running time, maintenance cycle, and temperature field distribution characteristics of key components of each turbine generator unit when the operating mode is a single subgroup centralized load mode. Based on the multi-dimensional evaluation matrix, the hierarchical analysis method is used to perform a comprehensive performance score on each turbine generator unit. The turbine generator unit with the best comprehensive performance is selected as the main undertaking unit according to the scoring results. The maximum load ratio that can be allocated to the main undertaking unit is determined based on the real-time load adjustment rate, minimum stable operating load, and grid frequency fluctuation sensitivity of the main undertaking unit. The corresponding part of the total task is allocated to the main undertaking unit according to the maximum load ratio, and the remaining units are only allocated the minimum load to maintain their basic operation to remain in standby mode.

[0067] Furthermore, such as Figure 2As shown, the third determining module 240 is specifically used to determine the initial allocation ratio based on the rated power ratio of each turbine generator unit when the operating mode is a multi-unit load balancing mode. The rated power ratio is the ratio of the rated power of a single turbine generator unit to the sum of the rated power of all parallel-operating turbine generator units. It calculates the deviation correction coefficient based on the real-time energy efficiency deviation value of each turbine generator unit; calibrates the initial allocation ratio based on the deviation correction coefficient to obtain the calibrated allocation ratio; imposes boundary condition constraints on the calibrated allocation ratio based on the real-time voltage fluctuation range of the power grid, the power transmission limit of transmission lines, and the distribution characteristics of regional load centers; and determines the actual load allocation value of each turbine generator unit based on the constrained calibrated allocation ratio and the total workload. The actual load allocation value satisfies the minimum stable operating load, maximum continuous output, and load change rate limits of each turbine generator unit.

[0068] Furthermore, such as Figure 2 As shown, the third determining module 240 is specifically used to establish a priority evaluation system based on real-time energy efficiency deviation, current load margin, equipment health status assessment results, historical fault repair records, variable load regulation quality, and environmental adaptability parameters when the operating mode is dynamic priority load mode; based on the priority evaluation system, the fuzzy comprehensive evaluation method is used to perform weighted calculations on each parameter to generate a real-time priority score for each turbine generator unit; a load allocation priority sequence is established based on the real-time priority score; and the load allocation ratio of each turbine generator unit is determined based on the priority sequence, the total workload, and the real-time peak shaving depth requirements of the power grid.

[0069] Furthermore, such as Figure 2 As shown, the third determining module 240 is also used to construct a digital twin of the full physical parameters of each steam turbine generator unit. The determined load allocation ratio is input into the digital twin for virtual operation simulation to simulate the dynamic stress distribution of the unit, the pressure fluctuation of the steam-water system, and the transient stability characteristics of the power grid under different loads in real time. If the thermal stress of the key components of the unit exceeds the safety threshold, the pressure fluctuation of the steam-water system exceeds the preset range, or the transient stability margin of the power grid is insufficient in the simulation results, the load allocation ratio is reversed based on the coupled model of multi-objective particle swarm optimization algorithm and fault mode influence analysis to obtain the optimal solution within the safety boundary that takes into account both energy efficiency and equipment life loss.

[0070] Furthermore, such as Figure 2As shown, the third determining module 240 is specifically used to set the optimization objectives as optimal energy efficiency and minimum equipment life loss. The constraints of the optimization objectives include the minimum stable operating load of the unit, maximum continuous output, load change rate limit, and grid voltage stability requirements. Through failure mode influence analysis, the risk weights and associated load sensitive parameters of the unit's key components thermal stress exceeding the safety threshold, steam-water system pressure fluctuation exceeding the preset range, and insufficient grid transient stability margin are determined. The risk weights are used as adaptive adjustment coefficients to iteratively optimize the initial load allocation ratio. In each iteration, the optimization range is dynamically adjusted according to the failure mode influence analysis results. When the iteration result simultaneously satisfies the conditions that the energy efficiency deviation is less than the preset energy efficiency threshold, the equipment life loss rate is lower than the allowable upper limit, and all safety constraints are met, the optimization is terminated, and the iteration result is determined as the corrected load allocation ratio.

[0071] Optionally, the load distribution device for the parallel steam turbine generator set can be an electronic device with data processing capabilities, or a functional module within the electronic device, without limitation.

[0072] For example, the electronic device can be a server, which can be a single server or a server cluster consisting of multiple servers. As another example, the electronic device can be a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, as well as cellular phone, personal digital assistant (PDA), augmented reality (AR), virtual reality (VR) device, and other terminal devices. Furthermore, the electronic device can also be a recording device, video surveillance device, etc. This application does not impose any special limitations on the specific form of the electronic device.

[0073] The following example uses an electronic load distribution device for a parallel steam turbine generator set. Figure 3 As shown, Figure 3 The hardware structure of an electronic device 300 provided in this application.

[0074] like Figure 3 As shown, the electronic device 300 includes a processor 310, a communication line 320, and a communication interface 330.

[0075] Optionally, the electronic device 300 may also include a memory 340. The processor 310, memory 340, and communication interface 330 can be connected via a communication line 320.

[0076] The processor 310 can be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 310 can also be any other device with processing capabilities, such as a circuit, device, or software module, without limitation.

[0077] In one example, processor 310 may include one or more CPUs, for example Figure 3 CPU0 and CPU1 in the CPU.

[0078] As an optional implementation, the electronic device 300 may include multiple processors, for example, in addition to processor 310, it may also include processor 370. A communication line 320 is used to transmit information between the components included in the electronic device 300.

[0079] Communication interface 330 is used for communication with other devices or other communication networks. These other communication networks can be Ethernet, Radio Access Network (RAN), Wireless Local Area Networks (WLAN), etc. Communication interface 330 can be a module, circuit, transceiver, or any device capable of enabling communication.

[0080] The memory 340 is used to store instructions. These instructions can be computer programs.

[0081] The memory 340 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and / or instructions; it may also be a random access memory (RAM) or other type of dynamic storage device capable of storing information and / or instructions; it may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, etc., without limitation.

[0082] It should be noted that the memory 340 can exist independently of the processor 310, or it can be integrated with the processor 310. The memory 340 can be used to store instructions, program code, or some data, etc. The memory 340 can be located inside or outside the electronic device 300, without restriction.

[0083] The processor 310 is configured to execute instructions stored in the memory 340 to implement the communication method provided in the following embodiments of this application. For example, when the electronic device 300 is a terminal or a chip in a terminal, the processor 310 can execute instructions stored in the memory 340 to implement the steps performed by the sending end in the following embodiments of this application.

[0084] As an optional implementation, the electronic device 300 also includes an output device 350 and an input device 360. The output device 350 can be a display screen, speaker, or other device capable of outputting data from the electronic device 300 to the user. The input device 360 ​​can be a keyboard, mouse, microphone, joystick, or other device capable of inputting data into the electronic device 300.

[0085] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device, except... Figure 3 In addition to the components shown, the electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0086] The parallel turbine generator set load distribution device and application scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of parallel turbine generator set load distribution devices and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0087] This application provides a storage medium storing a program that, when executed by a processor, implements the load distribution method for parallel steam turbine generator sets.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] In a typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input / output interfaces, network interfaces, etc.

[0090] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM, and memory includes at least one memory chip. Memory is an example of computer-readable media.

[0091] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0092] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0093] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A load distribution method for parallel steam turbine generator sets, applied to control equipment, the control equipment being used to control at least two steam turbine generator sets operating in parallel, the at least two parallel-operating steam turbine generator sets forming a generator set cluster, characterized in that, The method includes: The system obtains the total power generation command issued by the power grid, the operating parameters of each steam turbine generator set, and the power grid operating parameters. The total power generation command is used to indicate the total workload of the generator set cluster. The operating parameters include the rated power of the steam turbine generator set and the real-time equipment operating parameters. The power grid operating parameters include the power grid peak shaving response time requirements. The total load factor is determined based on the weighted average of the total workload and the rated power of each steam turbine generator set; The operating mode of the unit cluster is determined based on the total load factor, the real-time energy efficiency deviation value of each steam turbine generator unit and the grid peak-shaving response time requirement. The operating mode is used to enable the unit cluster to achieve optimal comprehensive energy efficiency by adapting to load distribution strategies under different operating conditions during the process of completing the total power generation task assigned by the grid. The real-time energy efficiency deviation value is determined based on the real-time equipment operating parameters. Based on the determined operating mode, the real-time energy efficiency deviation, load margin, equipment status, and power grid operating parameters of each turbine generator unit are used to determine the load allocation ratio of each turbine generator unit. The total workload is then allocated to each turbine generator unit according to the load allocation ratio.

2. The method according to claim 1, characterized in that, The operating mode of the at least two parallel-operating steam turbine generator units is determined based on the total load factor, the real-time energy efficiency deviation of each steam turbine generator unit, and the grid peak-shaving response time requirements, including: When the total load factor is less than or equal to the first threshold, and the maximum difference in the real-time energy efficiency deviation of each turbine generator unit is less than the preset deviation threshold, and the grid peak-shaving response time requirement is low response rate, it is determined to be a single subunit centralized load mode. When the total load factor is between the first threshold and the second threshold, and the real-time energy efficiency deviation of each turbine generator unit is within the normal deviation range, and the grid peak-shaving response time requirement is medium response rate, it is determined to be a multi-unit load balancing mode, and the first threshold is less than the second threshold. When the total load factor is greater than or equal to the second threshold, or when the real-time energy efficiency deviation of the turbine generator set exceeds the normal deviation range, or when the grid peak-shaving response time requirement is a high response rate, it is determined to be a dynamic priority load mode.

3. The method according to claim 2, characterized in that, The load allocation ratio of each turbine generator unit is determined based on the established operating mode, real-time energy efficiency deviation value of each turbine generator unit, load margin, equipment status, and the aforementioned power grid operating parameters, including: When the operating mode is a single-subgroup centralized load mode, a multi-dimensional evaluation matrix is ​​constructed based on the real-time energy efficiency deviation value, current load margin, cumulative equipment running time, maintenance cycle and temperature field distribution characteristics of key components of each steam turbine generator set. Based on the multi-dimensional evaluation matrix, the analytic hierarchy process is used to score the comprehensive efficiency of each steam turbine generator set. Based on the scoring results, the steam turbine generator set with the best overall performance was selected as the main unit to undertake the task. The maximum load ratio that the main generating unit can be allocated is determined based on the real-time load regulation rate, minimum stable operating load, and grid frequency fluctuation sensitivity of the main generating unit. Based on the maximum load ratio, the corresponding portion of the total workload is allocated to the main undertaking unit, while the remaining units are only allocated the minimum load required to maintain their basic operation in order to remain in standby mode.

4. The method according to claim 2, characterized in that, The load allocation ratio of each turbine generator unit is determined based on the established operating mode, real-time energy efficiency deviation value of each turbine generator unit, load margin, equipment status, and the aforementioned power grid operating parameters, including: When the operating mode is a multi-unit balanced load mode, the initial allocation ratio is determined according to the rated power ratio of each steam turbine generator unit. The rated power ratio is the ratio of the rated power of a single steam turbine generator unit to the total rated power of all parallel-operating steam turbine generator units. Calculate the deviation correction coefficient based on the real-time energy efficiency deviation value of each steam turbine generator set; The initial allocation ratio is calibrated according to the deviation correction coefficient to obtain the calibrated allocation ratio; The calibration allocation ratio is subject to boundary condition constraints based on the real-time voltage fluctuation range of the power grid, the power transmission limit of transmission lines, and the distribution characteristics of regional load centers. The actual load allocation value of each steam turbine generator unit is determined based on the constrained calibration allocation ratio and total workload. The actual load allocation value satisfies the minimum stable operating load, maximum continuous output and load change rate limits of each steam turbine generator unit.

5. The method according to claim 2, characterized in that, The load allocation ratio of each turbine generator unit is determined based on the established operating mode, real-time energy efficiency deviation value of each turbine generator unit, load margin, equipment status, and the aforementioned power grid operating parameters, including: When the operating mode is dynamic priority load mode, a priority evaluation system is established based on real-time energy efficiency deviation value, current load margin, equipment health status assessment results, historical fault repair records, variable load regulation quality and environmental adaptability parameters. Based on the aforementioned priority evaluation system, the fuzzy comprehensive evaluation method is used to calculate the weighted values ​​of each parameter and generate a real-time priority score for each steam turbine generator unit. A load allocation priority sequence is established based on the real-time priority score; The load allocation ratio of each steam turbine generator unit is determined based on the priority sequence, the total workload, and the real-time peak shaving depth requirements of the power grid.

6. The method according to claim 1, characterized in that, The method further includes: A digital twin of the full physical parameters of each steam turbine generator unit is constructed. The determined load distribution ratio is input into the digital twin for virtual operation simulation, so as to simulate the dynamic stress distribution of the unit, the pressure fluctuation of the steam-water system and the transient stability characteristics of the power grid under different loads in real time. If the thermal stress of key components of the unit exceeds the safety threshold, the pressure fluctuation of the steam-water system exceeds the preset range, or the transient stability margin of the power grid is insufficient in the simulation results, the load allocation ratio is reversed based on the coupled model of multi-objective particle swarm optimization algorithm and fault mode influence analysis to obtain the optimal solution within the safety boundary that balances optimal energy efficiency and minimum equipment life loss.

7. The method according to claim 6, characterized in that, The load allocation ratio is inversely corrected based on a coupled model of multi-objective particle swarm optimization algorithm and failure mode effect analysis, including: The optimization objectives are set as optimal energy efficiency and minimum equipment life loss. The constraints of the optimization objectives include minimum stable operating load of the unit, maximum continuous output, load change rate limit and grid voltage stability requirements. Failure Mode and Effects Analysis (FMEA) was used to determine the risk weights and associated load-sensitive parameters of key unit components exceeding safety thresholds, steam-water system pressure fluctuations exceeding preset ranges, and insufficient grid transient stability margins. The risk weights are used as adaptive adjustment coefficients to iteratively optimize the initial load allocation ratio. The optimization range is dynamically adjusted in each iteration based on the failure mode impact analysis results. When the iteration result simultaneously satisfies the following conditions: energy efficiency deviation is less than the preset energy efficiency threshold, equipment life loss rate is lower than the allowable upper limit, and all safety constraints are met, the optimization is terminated and the iteration result is determined as the corrected load allocation ratio.

8. A load distribution device for parallel steam turbine generator sets, characterized in that, The device includes: The acquisition module is used to acquire the total power generation command issued by the power grid, the operating parameters of each steam turbine generator set and the power grid operating condition parameters. The total power generation command is used to indicate the total workload of at least two steam turbine generator sets operating in parallel. The operating parameters include the rated power of the steam turbine generator sets and the real-time equipment operating condition parameters. The power grid operating condition parameters include the power grid peak shaving response time requirements. The first determining module is used to determine the total load rate based on the weighted average of the total workload and the rated power of each steam turbine generator set; The second determining module is used to determine the operating mode of the at least two parallel-operating steam turbine generator units based on the total load factor, the real-time energy efficiency deviation value of each steam turbine generator unit and the grid peak-shaving response time requirement. The operating mode is used to enable the unit cluster to achieve optimal comprehensive energy efficiency by adapting to load distribution strategies under different operating conditions during the process of completing the total power generation task assigned by the grid. The real-time energy efficiency deviation value is determined based on the real-time equipment operating parameters. The third determining module is used to determine the load allocation ratio of each steam turbine generator unit based on the determined operating mode, the real-time energy efficiency deviation value, load margin, equipment status of each steam turbine generator unit, and the power grid operating parameters, and to allocate the total workload to each steam turbine generator unit according to the load allocation ratio.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the load distribution method for parallel steam turbine generator sets as described in any one of claims 1-7.

10. An electronic device, characterized in that, The device includes at least one processor, at least one memory connected to the processor, and a bus; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the load distribution method of the parallel steam turbine generator set as described in any one of claims 1-7.

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