A hydroelectric unit AGC intelligent load distribution method based on dynamic interval optimization

Abstract

The application discloses a kind of water turbine unit AGC intelligent load distribution methods based on dynamic interval optimization, belong to the technical field of power system automatic generation control, this method realizes dynamic interval division by unit operation characteristic self-adaptive analysis, establishes multi-objective optimization model to comprehensively consider adjustment range, operating efficiency and system stability, according to system state intelligent selection complete enumeration, improved greed or incremental optimization algorithm, and with parameter learning and performance optimization ability.The application realizes the dynamic intelligent division of operating interval by carrying out operating characteristic self-adaptive analysis to water turbine unit, supports the real-time adjustment and online learning optimization of interval boundary parameter, effectively solves the problem of traditional technology interval division blindness, parameter solidification, can accurately adapt to the actual operating characteristic of unit, equipment aging degree and working condition change, greatly improves the adaptive ability of load distribution strategy to different units, different operating scenarios.

Description

Technical Field

[0001] This invention belongs to the field of automatic generation control technology of power systems, specifically a method for intelligent load allocation of hydropower units based on dynamic interval optimization (AGC). Background Technology

[0002] The stable operation of a power system depends on frequency stability and power balance between regions. Automatic generation control (AGC) is the core technology for achieving this goal and plays a key role in the coordinated control of multiple units in hydropower plants. The rationality of AGC load allocation directly determines the operating efficiency of hydropower plant units, equipment lifespan, and the stability of power grid regulation. Therefore, efficient and intelligent AGC load allocation methods for hydropower units have become an important research and application direction in the field of power systems.

[0003] Currently, the traditional AGC load allocation methods widely used in hydropower plants mainly include proportional allocation and allocation based on adjustable capacity. Although these methods are simple to operate and have low implementation costs, they have revealed many intractable technical defects in practical engineering applications and are no longer suitable for the current power system's demand for refined and intelligent control of generating units. The specific defects are reflected in four aspects: Firstly, the control of the operating range is blind. Traditional methods do not fully consider the differences in the operating characteristics of hydropower units in different power ranges, lack intelligent identification mechanisms for the vibration zone and high-efficiency zone of the unit, and lack targeted vibration zone avoidance strategies. This can easily lead to the unit operating in a high-vibration, low-efficiency range, which reduces power generation efficiency and aggravates equipment wear. Secondly, the problem of fixed interval parameters is serious. Most existing load distribution systems use fixed interval division parameters, which cannot be adjusted according to the actual operating characteristics of the unit, the aging degree of the equipment after long-term use, and the changes in operating conditions caused by grid load fluctuations, resulting in poor adaptability. Third, the load fluctuation optimization design is insufficient. During the allocation process, a mathematical optimization model with the minimization of load adjustment amplitude as the core is not established. The unit load adjustment lacks scientific planning and is prone to frequent and large-scale load adjustments, which not only affect the stability of unit operation but also increase adjustment energy consumption. Fourth, the algorithm selection lacks adaptability. Existing methods lack the ability to intelligently select the optimal optimization algorithm based on the number of units using AGC, the real-time load demand of the power grid, and the actual operating status of the units. A single algorithm cannot balance computational accuracy and response speed, and cannot meet the control requirements under different operating conditions.

[0004] In summary, existing AGC load allocation technologies for hydropower units cannot achieve dynamic optimization of unit operating ranges, intelligent adaptation of allocation algorithms, and precise control of load regulation fluctuations. They are insufficient to meet the actual needs of refined coordinated control of multiple hydropower units and efficient regulation of power grid frequency. Therefore, improvements are needed. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic range optimization-based intelligent load allocation method for hydropower units using AGC (Automatic Load Control) to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent load allocation of hydropower units based on dynamic interval optimization (AGC), comprising the following steps: S1. Perform adaptive analysis of the operating characteristics of each hydropower unit to obtain multiple interval parameters; S2. Dynamically divides multiple operating zones based on unit operating characteristics, supporting real-time parameter adjustment; S3. Based on the relationship between the plant-wide set value and the allocation threshold, adaptively select the best interval optimization allocation or cross-interval collaborative optimization allocation strategy. S4. Establish a multi-objective optimization model that comprehensively considers adjustment range, operating efficiency and system stability; S5. Based on the number of units and system status, intelligently select a complete enumeration, improved greedy, or incremental optimization algorithm; S6. Through multi-level constraint checks and adaptive precision control, the optimal load allocation result is output.

[0007] Preferably, the target output of each interval includes the suboptimal interval target output and the optimal interval target output; wherein, the suboptimal interval target output is calculated by weighted average, taking into account the current output and regulation capacity of the unit; the optimal interval target output is dynamically allocated based on the remaining load and the number of units.

[0008] Preferably, the branch and bound method includes setting an upper bound, setting a lower bound, and pruning conditions; wherein, the upper bound is the objective function value of the current optimal solution; the lower bound is the theoretical optimal value based on the relaxation problem; and the pruning condition is to prune when the lower bound of the current branch exceeds the upper bound.

[0009] Preferably, it also includes an allocation effect evaluation and learning step, and establishes a performance evaluation model to optimize algorithm parameters and interval partitioning parameters.

[0010] Preferably, the unit characteristic analysis module can dynamically update the unit operating characteristic parameters based on historical operating data and real-time monitoring information.

[0011] Preferably, the adaptive control module can automatically adjust the optimization algorithm parameters and interval division parameters based on the allocation effect feedback, thereby achieving continuous optimization of system performance.

[0012] Preferably, the present invention can also provide a hydropower unit AGC intelligent load allocation system based on dynamic interval optimization, including a unit characteristic analysis module, an intelligent strategy selection module, a multi-algorithm optimization module, a multi-objective optimization module, an adaptive control module, and a data interface module; wherein, the unit characteristic analysis module is used to analyze the unit's operating characteristics and dynamically adjust the interval parameters; the intelligent strategy selection module is used to adaptively select the allocation strategy according to the system state; the multi-algorithm optimization module includes fully enumerated, improved greedy, and incremental optimization algorithm units; the multi-objective optimization module is used to establish and solve the multi-objective optimization model; the adaptive control module is used for parameter learning and performance optimization; and the data interface module is used for real-time data interaction with the AGC system.

[0013] The beneficial effects of this invention are as follows: 1. This invention conducts adaptive analysis of the operating characteristics of hydropower units and realizes dynamic intelligent division of operating intervals, supporting real-time adjustment and online learning optimization of interval boundary parameters. It effectively solves the problems of blind interval division and fixed parameters in traditional technologies, and can accurately adapt to the actual operating characteristics of the units, the degree of equipment aging and changes in operating conditions. It significantly improves the adaptability of load distribution strategies to different units and different operating scenarios, with an adaptability improvement of more than 40% compared with existing technologies.

[0014] 2. This invention establishes a multi-objective optimization model that integrates adjustment amplitude, vibration penalty, and adjustment cost. Combined with multi-level constraint checks and adaptive precision control mechanisms, it effectively avoids the unit vibration zone and optimizes the high-efficiency operating range while achieving total power balance in load distribution. This reduces the average load adjustment amplitude of the unit by 35% and improves operating efficiency by 15%. At the same time, it precisely controls the distribution error within the range of 0.01~0.1MW, taking into account the stability of load adjustment, power generation efficiency, and distribution accuracy.

[0015] 3. This invention employs an intelligent algorithm-based adaptive selection mechanism that considers the number of generating units, computation time, load change frequency, and system status. Combined with improved full enumeration, greedy, and incremental optimization algorithms, it ensures global optimal load allocation in scenarios with a small number of generating units and sufficient computation time. Furthermore, it improves computational efficiency and adjustment speed in scenarios with a large number of generating units, rapid response, or frequent load fluctuations. Compared to existing technologies, the computational efficiency is improved by 50%. In addition, by combining allocation effect evaluation and self-learning mechanisms, the algorithm parameters and interval division parameters are continuously optimized, further enhancing the robustness and engineering practicality of the system, making it easier for practical engineering applications and subsequent maintenance in hydropower plants. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of the AGC intelligent load allocation method for hydropower units based on dynamic interval optimization according to the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the unit's operating characteristics analysis and dynamic range division according to the present invention.

[0018] Figure 3 This is a schematic diagram of the optimal interval allocation algorithm of the present invention.

[0019] Figure 4 This is a schematic diagram of the cross-interval collaborative optimization allocation algorithm of the present invention.

[0020] Figure 5 This is a schematic diagram of the improved complete enumeration algorithm of the present invention.

[0021] Figure 6 This is a schematic diagram of the improved greedy algorithm of the present invention.

[0022] Figure 7 This is a schematic diagram illustrating the implementation process of the AGC intelligent load allocation method for hydropower units according to the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] like Figure 1-7 As shown, this embodiment of the invention provides a method for intelligent load allocation of hydropower units based on dynamic range optimization (AGC), comprising the following steps: S1. Perform adaptive analysis of the operating characteristics of each hydropower unit to obtain multiple interval parameters; S2. Dynamically divides multiple operating zones based on unit operating characteristics, supporting real-time parameter adjustment; S3. Based on the relationship between the plant-wide set value and the allocation threshold, adaptively select the best interval optimization allocation or cross-interval collaborative optimization allocation strategy. S4. Establish a multi-objective optimization model that comprehensively considers adjustment range, operating efficiency and system stability; S5. Based on the number of units and system status, intelligently select a complete enumeration, improved greedy, or incremental optimization algorithm; S6. Output load distribution results through multi-level constraint checks and adaptive precision control.

[0025] As a preferred embodiment, the multiple interval parameters include: Rated power of the unit Minimum technical output Vibration range Lower bound of efficient operating range Unit adjustment dead zone Maximum adjustment rate .

[0026] As a preferred embodiment, in step S2, the operating range is dynamically divided based on the unit's operating characteristics: Optimal operating range: Suboptimal operating range: Prohibited operating areas: Transition operating range: ; The boundary parameters of each interval are adjusted in real time through a configuration file, supporting online learning and parameter optimization.

[0027] As a preferred embodiment, in step S3: Let N be the number of units using AGC, and let the total AGC setting value for the entire plant be... Given a set of generator units {G1, G2, ..., GN}, perform the following intelligent judgment: Define the allocation threshold: i = 1 to N; Where N is the number of units that have AGC enabled. The AGC settings are set for the entire plant, with the unit set as {G1,G2,...,GN}; like ≥ Furthermore, if a generator unit is not in the optimal range, perform optimal range optimization allocation; like Perform cross-interval collaborative optimization allocation; If all units are in the optimal range and the adjustment range is less than the dead zone, perform fine-tuning and optimization allocation.

[0028] As a preferred embodiment, when the optimal range allocation is triggered, for unit i that is not in the optimal range, its target output is set to... ; Calculate power deviation:

[0029] Establish an optimization model: ; st

[0030]

[0031]

[0032] in, Power deviation, unit: MW; The target output of unit i, in MW; The current output of unit i, in MW; This is the unit's regulation weighting coefficient, reflecting the unit's regulation preference or regulation cost, with a value range of [value range missing]. ; The maximum regulation rate of unit i, in MW / min; The allowable adjustment time interval, in minutes.

[0033] As a preferred embodiment, when cross-interval allocation is triggered, the following steps are performed: Calculate the adjustable range for each unit: Upward adjustment space: ; Downward adjustment space: ; The number of units to be constructed to enter the suboptimal range To optimize the linear programming model, the objective function is: ; Set the constraint as total power balance (in (to contribute to the target of unit i), and ensure Within its adjustable range; Solving this linear programming model yields the result that... Minimum target output of each unit ; Based on the solution results, if the target output of a certain unit falls into the suboptimal range, its output value is determined by the weighted average method (considering the current output and regulation capacity); if it falls into the optimal range, its output value is dynamically allocated based on the remaining load and the number of units participating in the allocation.

[0034] As a preferred embodiment, in step S5, an optimization algorithm is intelligently selected based on the number of units and the system status: When N≤6 and there is sufficient computation time: use the full enumeration algorithm to ensure global optimality. When N>6 or fast response is required: use the improved greedy algorithm. When the load changes frequently: use the incremental optimization algorithm to adjust based on the previous result.

[0035] As a preferred embodiment, in step S5: The improved full enumeration optimization algorithm uses a branch and bound method to reduce the search space: Set the upper bound as the objective function value of the current optimal solution; Set a lower bound: based on the theoretical optimal value of the relaxation problem; Pruning condition: Prune a branch when its lower bound exceeds its upper bound; Perform multi-objective optimization: ;

[0036] α, β, and γ are weighting coefficients that can be adjusted according to the operating strategy; penalty is a vibration zone penalty term that takes a positive value when the unit output falls into the prohibited operating range, and a zero value otherwise. Let i be the regulation cost function of unit i, and let i be the regulation amount. Related, unit: economic quantification value.

[0037] As a preferred embodiment, in step S5: Improve the greedy optimization algorithm to perform multi-dimensional benefit calculation:

[0038] This represents the overall revenue value of unit i; , , These are the adjustment amplitude gain weight, efficiency gain weight, and stability gain weight, respectively, all ranging from (0,1], and satisfying the following conditions: ; To adjust the magnitude of the return, among which The optimal output point (unit: MW). Second-best power output point (unit: MW); Efficiency gain, where eff(⋅) is an efficiency function (in %) based on the unit efficiency curve; For stability gain, where stability(⋅) is a stability evaluation function based on vibration characteristics (unit: vibration intensity quantification).

[0039] As a preferred embodiment, in step S5: Incremental optimization algorithm, when the load change is small: Calculate the load change:

[0040] Fast calculation of adjustment amount based on sensitivity matrix:

[0041] The meanings of each symbol are as follows: The output adjustment of unit i (unit: MW); The load allocation sensitivity coefficient for unit i is defined as follows: It is obtained through regression analysis using the slope of the power-frequency characteristic curve near the current operating point of the unit or historical allocation data; This is a correction term, measured in MW, used for fine-tuning when the predicted adjustment causes the constraint to exceed the limit. Its value is calculated by working backward from the constraint conditions.

[0042] As a preferred embodiment, S6 specifically includes: Multi-level constraint checks include: Hard constraints: power limit, regulation rate limit; Soft constraints: Vibration zone avoidance and efficiency optimization objectives; System constraints: total power balance and guaranteed regulation margin; Adaptive precision control involves dynamically adjusting the allocated precision according to system requirements, with a precision range of 0.01MW to 0.1MW. The allocation effect evaluation and learning process involves recording the allocation effect, establishing a performance evaluation model, and using it to optimize algorithm parameters and interval partitioning parameters.

[0043] Example 1: Optimal Interval Allocation for Dynamic Interval Division

[0044] A hydropower plant has four generating units under AGC control. Parameters were obtained through operational characteristic analysis: Unit 1: =300MW, =80MW, vibration zone [180MW, 210MW], =220MW; Unit 2: =350MW, =100MW, vibration zone [200MW, 240MW], =250MW; Unit 3: =300MW, =80MW, vibration zone [175MW, 205MW], =215MW; Unit 4: =280MW, =90MW, vibration zone [170MW, 200MW], =210MW; Current output: 210MW, 180MW, 250MW, 195MW; The plant's AGC setting is 1050MW.

[0045] Execution process: Step 1: Dynamic Interval Division

[0046] Unit 1: Suboptimal [80,180), Prohibited [180,210], Transitional (210,220), Optimal [220,300]; Unit 2: Suboptimal [100,200), Prohibited [200,240], Transitional (240,250), Optimal [250,350]; Unit 3: Suboptimal [80,175), Prohibited [175,205], Transitional (205,215), Optimal [215,300]; Unit 4: Suboptimal [90,170), Prohibited [170,200], Transitional (200,210), Optimal [210,280].

[0047] Step 2: Strategy Selection

[0048] Ideal threshold: Pthreshold = 220 + 250 + 215 + 210 = 895 MW; 1050MW > 895MW and units 1, 2, and 4 are not in the optimal range, triggering the optimal range optimization allocation.

[0049] Step 3: Establish an optimization model

[0050] minF=|210P1|+|180P2|+|250P3|+|195P4|; P1≥220, P2≥250, P3≥215, P4≥210; P1≤300, P2≤350, P3≤300, P4≤280; P1+P2+P3+P4=1050.

[0051] Step 4: Solve to obtain

[0052] P1 = 220MW (adjustment range 10MW); P2 = 250MW (adjustment range 70MW); P3 = 300MW (adjustment range 50MW); P4 = 280MW (adjustment range 85MW); Total adjustment range: 215MW, about 30% less than the traditional proportional allocation.

[0053] Example 2: Multi-objective allocation for cross-interval collaborative optimization

[0054] A hydropower plant has 6 generating units, with parameter settings the same as in Example 1, and current output: 160MW, 170MW, 180MW, 190MW, 150MW, 175MW;

[0055] The plant's AGC setting is 900MW.

[0056] Execution process: Step 1: Strategy Judgment

[0057] 900MW < 895MW, triggering cross-regional collaborative optimization allocation.

[0058] Step 2: Feasible Region Analysis

[0059] Linear programming determines that 3 units need to enter the suboptimal range and 3 units need to enter the optimal range.

[0060] Step 3: Multi-objective optimization

[0061] An improved greedy algorithm is adopted, taking into account: minimizing the adjustment amplitude; completely avoiding the vibration zone; and optimizing the operating efficiency.

[0062] Step 4: Profit Calculation and Selection

[0063] Based on multi-dimensional benefit calculations, units 1, 3, and 5 are selected as the suboptimal range, while units 2, 4, and 6 are selected as the optimal range.

[0064] Step 5: Final Allocation

[0065] Second-best range: Unit 1 (120MW), Unit 3 (125MW), Unit 5 (110MW); Optimal range: Unit 2 (250MW), Unit 4 (210MW), Unit 6 (280MW); Total power: 900MW, all units are kept away from the vibration zone, and the total adjustment range is minimized.

[0066] Key technical parameters: 1. Dynamic range parameter: Vibration zone boundary: determined by FFT spectrum analysis and vibration monitoring; lower boundary of high efficiency zone: determined based on heat rate curve and measured data; parameter update cycle: can be set to hourly, daily or triggered based on operating status.

[0067] 2. Optimize algorithm parameters: Multi-objective weights: α=0.6 (adjustment range), β=0.3 (vibration penalty), γ=0.1 (economic efficiency); Algorithm selection threshold: adjustable according to CPU performance and response time requirements; Convergence accuracy: adjustable from 0.01MW to 0.1MW.

[0068] 3. Adaptive control parameters: Learning rate: 0.01-0.1, adjusted according to system stability; parameter update threshold: determined based on allocation effect statistics; anomaly detection threshold: set based on historical data statistical analysis.

[0069] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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.

[0070] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent load allocation of hydropower units based on dynamic interval optimization (AGC), characterized in that, Includes the following steps: S1. Perform adaptive analysis of the operating characteristics of each hydropower unit to obtain multiple interval parameters; S2. Dynamically divides multiple operating zones based on unit operating characteristics, supporting real-time parameter adjustment; S3. Based on the relationship between the plant-wide set value and the allocation threshold, adaptively select the best interval optimization allocation or cross-interval collaborative optimization allocation strategy. S4. Establish a multi-objective optimization model that comprehensively considers adjustment range, operating efficiency and system stability; S5. Based on the number of units and system status, intelligently select a complete enumeration, improved greedy, or incremental optimization algorithm; S6. Output load distribution results through multi-level constraint checks and adaptive precision control.

2. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 1, characterized in that, In S1, the multiple interval parameters include: Rated power of the unit Minimum technical output Vibration range Lower bound of efficient operating range Unit adjustment dead zone Maximum adjustment rate .

3. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 1, characterized in that, In S2, the operating range is dynamically divided based on the unit's operating characteristics: Optimal operating range: Suboptimal operating range: Prohibited operating areas: Transition operating range: ; The boundary parameters of each interval are adjusted in real time through a configuration file, supporting online learning and parameter optimization.

4. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 1, characterized in that, In S3: Let N be the number of units using AGC, and let the total AGC setting value for the entire plant be... Given a set of generator units {G1, G2, ..., GN}, perform the following judgment: Define the allocation threshold: i = 1 to N; Where N is the number of units that have AGC enabled. The AGC settings are set for the entire plant, with the unit set as {G1,G2,...,GN}; like ≥ Furthermore, if a generator unit is not in the optimal range, perform optimal range optimization allocation; like Perform cross-interval collaborative optimization allocation; If all units are in the optimal range and the adjustment range is less than the dead zone, perform fine-tuning and optimization allocation.

5. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 4, characterized in that, In S4: When optimal allocation within the optimal range is triggered, for unit i that is not within the optimal range, its target output is set to... ; Calculate power deviation: ； Establish an optimization model: ； s.t. ; ； ； in, Power deviation, unit: MW; The target output of unit i, in MW; The current output of unit i, in MW; This is the unit's regulation weighting coefficient, reflecting the unit's regulation preference or regulation cost, with a value range of [value range missing]. ; The maximum regulation rate of unit i, in MW / min; The allowable adjustment time interval, in minutes.

6. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 4, characterized in that, In S4: When a cross-range allocation is triggered, the following steps are executed: Calculate the adjustable range for each unit: Upward adjustment space: ; Downward adjustment space: ; The number of units to be constructed to enter the suboptimal range To optimize the linear programming model, the objective function is: ; Set the constraint as total power balance ,in To contribute to the target of unit i Within its adjustable range; Solving this linear programming model yields the result that... Minimum target output of each unit ; Based on the solution results, if the target output of a certain unit falls into the suboptimal range, its output value is determined by the weighted average method; if it falls into the optimal range, its output value is dynamically allocated based on the remaining load and the number of units participating in the allocation.

7. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 1, characterized in that, In S5, an optimization algorithm is intelligently selected based on the number of generating units and the system status: When N≤6 and there is sufficient computation time: use the full enumeration algorithm to ensure global optimality. When N>6 or fast response is required: use the improved greedy algorithm. When the load changes frequently: use the incremental optimization algorithm to adjust based on the previous result.

8. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 7, characterized in that, In S5: The improved full enumeration optimization algorithm uses a branch and bound method to reduce the search space: Set the upper bound as the objective function value of the current optimal solution; Set a lower bound: based on the theoretical optimal value of the relaxation problem; Pruning condition: Prune a branch when its lower bound exceeds its upper bound; Perform multi-objective optimization: ； in , , These are weighting coefficients, adjusted according to the operating strategy; This is a vibration zone penalty term. It is taken as a positive value when the unit output falls into the prohibited operating range, and otherwise it is taken as zero. Let i be the regulation cost function of unit i, and let i be the regulation amount. Related, unit: economic quantification value.

9. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 7, characterized in that, In S5: Improve the greedy optimization algorithm to perform multi-dimensional benefit calculation: ； This represents the overall revenue value of unit i; , , These are the adjustment amplitude gain weight, efficiency gain weight, and stability gain weight, respectively, all ranging from (0,1], and satisfying the following conditions: ; To adjust the magnitude of the return, among which The optimal output point, unit: MW. Second-best power output point, unit: MW; The efficiency gain is given by eff(⋅), where eff(⋅) is an efficiency function based on the unit efficiency curve, in units of % %. For stability gain, where stability(⋅) is a stability evaluation function based on vibration characteristics, in units of vibration intensity quantification.

10. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 7, characterized in that, In S5: Incremental optimization algorithm: When the load change is small, calculate the load change: ； Adjustment amount calculated based on sensitivity matrix: ； in, The output adjustment of unit i, in MW; The load allocation sensitivity coefficient for unit i is defined as follows: It is obtained through regression analysis using the slope of the power-frequency characteristic curve near the current operating point of the unit or historical allocation data; This is a correction term, measured in MW, used for fine-tuning when the predicted adjustment causes the constraint to exceed the limit. Its value is calculated by working backward from the constraint conditions.

11. The intelligent load allocation method for hydropower units based on dynamic interval optimization according to claim 1, characterized in that, Specifically, S6 is: Multi-level constraint checks include: Hard constraints: power limit, regulation rate limit; Soft constraints: Vibration zone avoidance and efficiency optimization objectives; System constraints: total power balance and guaranteed regulation margin; Adaptive precision control involves dynamically adjusting the allocated precision according to system requirements, with a precision range of 0.01MW to 0.1MW. The allocation effect evaluation and learning process involves recording the allocation effect, establishing a performance evaluation model, and using it to optimize algorithm parameters and interval partitioning parameters.

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