Hydropower station multi-unit load distribution method, system, device and readable storage medium

Abstract

A method, system, equipment, and readable storage medium for load allocation across multiple units in a hydropower station are disclosed. This invention relates to the field of hydropower generation. Under the constraint of the total active power output of the entire station, an initial population is constructed within a safe range satisfying a preset unit vibration zone. Each individual in the initial population includes a set of load allocation schemes. For each individual, under preset rigidity and objective constraints, Pareto dominance iteration is performed to obtain a Pareto optimal solution set. The objective constraints include minimizing the total station's water consumption for power generation, the lifespan loss of unit equipment, the unit vibration zone crossing time, and the grid peak-shaving command response speed. For each candidate solution in the Pareto optimal solution set, a comprehensive score is determined based on the objective parameter value and preset weight coefficients. Based on the comprehensive score, a target load allocation scheme for the multiple units of the hydropower station is determined from all candidate solutions. This application ensures the safe operation of unit equipment while responding in real-time to changes in renewable energy output and meeting grid peak-shaving requirements.

Description

Technical Field

[0001] This application relates to the field of hydropower technology, specifically to a method, system, equipment, and readable storage medium for load sharing among multiple units in a hydropower station. Background Technology

[0002] With the advancement of the construction of new power systems, wind power, photovoltaic and other new energy sources are being connected to the grid on a large scale. Their output is highly intermittent, random and fluctuating. The power grid has put forward higher requirements for the peak shaving, frequency regulation and AGC (automatic generation control) rapid response capabilities of hydropower stations. Hydropower stations need to operate with frequent and wide-range load changes, and traditional load allocation methods are no longer able to meet the current operating conditions.

[0003] In related technologies, the mainstream method for multi-unit load distribution is the equal incremental rate method. Its core is to distribute the load according to the principle that the unit's water consumption rate and incremental rate are equal. It only takes the minimum water consumption rate of the entire station as the single objective, without considering the wear and tear of the unit equipment and the constraints of vibration zone operation. This makes it easy for the units to frequently cross the vibration zone and the equipment wear to be aggravated when the grid is shaving. Moreover, due to the lack of optimization consideration for load response speed, it is unable to respond to the rapid changes in the output power of new energy power generation such as wind power and photovoltaic in real time, which makes it difficult to meet the grid's peak shaving needs.

[0004] Therefore, how to provide a load distribution method for multiple units in a hydropower station to respond in real time to changes in new energy output and meet the peak-shaving requirements of the power grid while ensuring the safe operation of the unit equipment is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, system, equipment, and readable storage medium for load sharing among multiple units in a hydropower station, which can respond in real time to changes in new energy output and meet the peak-shaving requirements of the power grid while ensuring the safe operation of the unit equipment.

[0006] In a first aspect, embodiments of this application provide a method for load sharing among multiple generating units in a hydropower station, including: Under the constraint of total active power output of the entire station, an initial population is constructed within a safe range that meets the preset unit vibration zone range. Each individual in the initial population includes a set of load allocation schemes. For each individual, under the preset rigid constraints and target constraints, Pareto dominance iteration is performed to obtain the Pareto optimal solution set. The target constraints include minimizing the target parameter values, which include the total power generation water consumption, the unit equipment life loss, the unit vibration zone crossing time, and the grid peak shaving command response speed. For each candidate solution in the Pareto optimal solution set, a comprehensive score is determined based on the target parameter value and the preset weight coefficient. The target load allocation scheme for multiple units of the hydropower station is determined from all candidate solutions based on the comprehensive score.

[0007] In conjunction with the first aspect, in one implementation method, the method for determining the target parameter value is as follows: The target parameter values ​​are determined based on the number of units, loss parameters, output parameters, and duration parameters.

[0008] In conjunction with the first aspect, in one implementation, determining the target parameter value based on the number of generating units, loss parameters, output parameters, and duration parameters includes: The total water consumption for power generation and the response speed of the power grid peak shaving command were determined based on the number of generating units, output parameters, and duration parameters. The lifespan loss value of the unit equipment is determined based on the loss parameters; The duration of the vibration zone crossing is determined based on the duration parameter.

[0009] In conjunction with the first aspect, in one implementation, the output parameters include the active power output of the generating units, the total active power output of the entire station, and the target output; the duration parameters include the command response duration; and the determination of the total station power generation water consumption and the grid peak-shaving command response speed value based on the number of generating units, output parameters, and duration parameters includes: The total water consumption for power generation at the station is determined based on the number of generating units and their active power output. The power grid peak-shaving command response speed value is determined based on the total active power output, target output, and command response time of the entire station.

[0010] In conjunction with the first aspect, in one embodiment, the loss parameters include unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss. Determining the unit equipment life loss value based on the loss parameters includes: The life loss value of the unit equipment is determined based on the unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss.

[0011] In conjunction with the first aspect, in one embodiment, the duration parameter includes the operating time in the prohibited vibration zone and the operating time in the restricted vibration zone, and determining the unit's vibration zone crossing time value based on the duration parameter includes: The vibration zone travel time of the unit is determined based on the operating time in the prohibited vibration zone and the operating time in the restricted vibration zone.

[0012] In conjunction with the first aspect, in one implementation method, determining the target load allocation scheme for multiple generating units of a hydropower station based on a comprehensive score includes: The candidate solution with the highest comprehensive score is used as the target allocation scheme for the load of multiple units in the hydropower station.

[0013] Secondly, embodiments of this application provide a multi-unit load distribution system for a hydropower station, the multi-unit load distribution system for a hydropower station comprising: The first processing module is used to construct an initial population within a safe range that meets the preset unit vibration zone range under the constraint of the total active power output of the entire station. Each individual in the initial population includes a set of load allocation schemes. The second processing module is used to perform Pareto domination iteration on each individual under preset rigid constraints and target constraints to obtain the Pareto optimal solution set. The target constraints include minimizing the target parameter values, which include the total power generation water consumption value, the unit equipment life loss value, the unit vibration zone crossing time value, and the grid peak shaving command response speed value. The third processing module is used to determine the comprehensive score value for each candidate solution in the Pareto optimal solution set based on the target parameter value and the preset weight coefficient. The fourth processing module is used to determine the target load allocation scheme for multiple units of the hydropower station from all candidate solutions based on the comprehensive score value.

[0014] Thirdly, embodiments of this application provide a hydropower station multi-unit load distribution device, the hydropower station multi-unit load distribution device including a processor, a memory, and a hydropower station multi-unit load distribution program stored in the memory and executable by the processor, wherein when the hydropower station multi-unit load distribution program is executed by the processor, it implements the steps of the hydropower station multi-unit load distribution method as described in any of the preceding claims.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing a multi-unit load allocation program for a hydropower station, wherein when the multi-unit load allocation program for a hydropower station is executed by a processor, it implements the steps of the multi-unit load allocation method for a hydropower station as described in any of the preceding claims.

[0016] The beneficial effects of the technical solutions provided in this application include: Under the constraint of total active power output of the entire station, an initial population is constructed within a safe range that meets the preset unit vibration zone. Each individual in the initial population includes a set of load allocation schemes. For each individual, under preset rigid constraints and objective constraints including minimizing the total station power generation water consumption, unit equipment life loss, unit vibration zone travel time, and grid peak-shaving command response speed, Pareto dominance iteration is performed to obtain the Pareto optimal solution set. By quantifying equipment safety and response performance as a co-optimization objective, this solves the problem that the traditional constant incremental rate method only takes minimizing water consumption rate as the single objective and does not consider unit equipment life loss and vibration zone operation constraints, thus meeting the grid peak-shaving requirements. While effectively avoiding frequent vibration zones and excessive wear of equipment, the system ensures the safe operation of the generating units. It also utilizes the optimal solution set to provide a set of best trade-off solutions between different objective parameters, ensuring that all candidate solutions meet safety constraints and possess diversity. For each candidate solution in the Pareto optimal solution set, a comprehensive score is determined based on the objective parameter values ​​and preset weight coefficients. Based on this comprehensive score, a target load allocation scheme for multiple generating units in the hydropower station is determined. Through weight adjustment, the system adapts to peak-shaving demands under different operating conditions, solving the problem that traditional methods cannot respond in real-time to changes in renewable energy output and meet grid peak-shaving requirements while ensuring equipment safety. Ultimately, it achieves a globally optimal balance between economy, safety, and response speed. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the hydropower station multi-unit load allocation method of this application; Figure 2 This is a flowchart illustrating the adaptive correction of unit health status in the multi-unit load allocation method for hydropower stations in this application. Figure 3 This is a flowchart illustrating the multi-objective rolling optimization process in the multi-unit load allocation method for hydropower stations presented in this application. Figure 4 This is a functional module diagram of an embodiment of the multi-unit load distribution system for hydropower stations in this application; Figure 5 This is a schematic diagram of the hardware structure of the multi-unit load distribution equipment for a hydropower station involved in the embodiments of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0020] In a first aspect, embodiments of this application provide a method for load sharing among multiple generating units in a hydropower station.

[0021] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the hydropower station multi-unit load sharing method of this application. Figure 1 As shown, the load distribution method for multiple generating units in a hydropower station includes: Step S10: Under the constraint of total active power output of the entire station, construct an initial population within a safe range that meets the preset unit vibration zone range. Each individual in the initial population includes a set of load allocation schemes.

[0022] As an example, in the embodiments of this application, before constructing the initial population, full-dimensional data of the existing system of the hydropower station are collected, and after preprocessing, a standardized database updated on a minute-by-minute basis is formed to provide a data foundation for subsequent optimization.

[0023] (1) Classification of collected data:

[0024] (2) Data preprocessing: Complete data cleaning (removing outliers and wild values), missing value completion (linear interpolation / historical mean completion), data normalization and time-series alignment of multi-source data to form a standardized dataset, ensuring data consistency and availability.

[0025] It should be understood that the total active power output constraint of the entire station refers to the overall power generation target that the hydropower station is required to achieve under the grid dispatch instructions. Its function is to ensure that the load allocation scheme meets the power balance requirements of the grid. The preset unit vibration zone range refers to the specific output range of the turbine unit that is prone to strong vibration and cavitation due to the hydraulic mechanical characteristics. The specific range can be determined according to actual needs and is not limited here. Its function is to define the dangerous operating area that needs to be avoided. The safe zone refers to the output range of the unit that is allowed to operate stably after deducting the vibration zone range. Its function is to ensure the physical safety of the unit equipment. The initial population refers to the set of feasible solutions at the beginning of the iterative search. Its function is to provide the initial search space for the algorithm. Individual refers to the basic constituent unit in the initial population. Each individual corresponds to a potential optimal solution. The load allocation scheme refers to the combination sequence of active power output values ​​of each operating unit in the hydropower station that the individual specifically undertakes. Its function is to clarify the specific task undertaken by each unit.

[0026] Specifically, the total active power output command issued by the power grid is first used as a constraint, and the preset vibration zone range of each operating unit is obtained to determine the safe output range for each unit to avoid the unit entering the high-risk area of ​​mechanical vibration. Then, within the safe output range, multiple candidate individuals are generated using a preset sampling method (such as fine-tuning after proportional allocation, assigning values ​​one by one after random sorting, etc.). Each individual includes a set of load allocation schemes. During the generation process, it is verified in real time whether the output value of each unit falls within the prohibited range of the vibration zone and whether the sum of the output of all units is equal to the total active power output command of the entire station. Finally, an initial population is formed that satisfies the power grid load balance requirements and avoids the high-risk area of ​​mechanical vibration of the units, so as to ensure the convergence speed and the effectiveness of the solution in the subsequent optimization iteration process.

[0027] Step S20: For each individual, under the preset rigid constraints and target constraints, Pareto dominance iteration is performed on the individual to obtain the Pareto optimal solution set. The target constraints include minimizing the target parameter values, which include the total power generation water consumption value, the unit equipment life loss value, the unit vibration zone crossing time value, and the grid peak shaving command response speed value.

[0028] In this exemplary embodiment, Pareto dominance iteration refers to the algorithmic evolution process of eliminating dominated solutions by comparing the superiority or inferiority of different individuals on various objective parameter values; the Pareto optimal solution set refers to the set of non-dominated solutions retained after iteration convergence that cannot be further optimized on any single objective without sacrificing the performance of other objectives; the objective constraint condition refers to the performance evaluation index that needs to tend to the extreme value during the optimization process, and the objective constraint condition includes minimizing the objective parameter value, which means that the optimization direction is set so that the smaller the value, the better the performance; among them, the total power generation water consumption (economic core) value The parameters reflect the economic efficiency of water resource utilization in hydropower station operation; the value of unit equipment life loss (equipment safety core) characterizes the degree of mechanical wear and fatigue accumulation of the unit, which is related to the long-term durability of the equipment; the value of unit vibration zone crossing time (operational stability core) measures the unit's operational stability and ability to avoid hydraulic vibration risks; and the value of grid peak-shaving command response speed (new energy adaptation core) reflects the hydropower station's agility and compliance in tracking grid dispatch commands. The above parameters together constitute the evaluation dimensions of multi-objective optimization, aiming to obtain a comprehensive optimal load allocation strategy set that takes into account economy, safety, stability and responsiveness through iterative optimization.

[0029] The preset rigid constraints include power balance constraints, unit output constraints, load change rate constraints, reservoir operation constraints, flow constraints, equipment health constraints, and power grid safety constraints; as detailed below: (1) Power balance constraint: The total output of the entire station meets the requirements of the power grid's AGC / peak shaving commands, and the deviation is within the allowable range of the power grid. Let represent the active power output of the i-th generating unit at time t. This represents the total active power output command issued by the power grid at time t, and N represents the total number of generating units. (2) Unit output constraints: ≤ ≤ The upper and lower limits of power output are dynamically adjusted based on the unit's head and health status. This represents the minimum allowable output of the i-th generating unit at time t, considering head and health status. This represents the maximum allowable output of the i-th generating unit at time t, considering head and health status. (3) Variable load rate constraint: The load change rate shall not exceed the maximum allowable value of the unit, and the limit shall be dynamically lowered when the health status is abnormal. Let represent the active power output of the i-th generating unit at time t-1, and Δt represent the control time step. This represents the maximum allowable load change rate for the i-th generating unit; (4) Reservoir operation constraints: ≤ ≤ The reservoir water level shall not be lower than the dead water level or higher than the flood control limit water level (flood season) / normal high water level (non-flood season). Indicates the dead water level of the reservoir. This represents the real-time water level of the reservoir at time t. This indicates the upper limit of water level for reservoir operation, which is either the flood control limit during the flood season or the normal high water level outside the flood season. (5) Flow constraints: ≤ ≤ The outflow shall not be lower than the ecological flow limit and not higher than the maximum allowable flood control flow of the river. This indicates the lower limit of ecological flow in the downstream river channel. This represents the total discharge flow of the entire station at time t. This indicates the maximum allowable flow rate for flood control in the downstream river channel; (6) Equipment health constraints: For units with serious defects, the output range and load change rate are limited, and frequent start-stop is prohibited; (7) Power grid safety constraints: After the output is adjusted, the power flow and voltage stability of the power grid line must meet the limit requirements.

[0030] Specifically, firstly, based on preset rigid constraints, the feasibility of the load allocation scheme represented by each individual is verified, eliminating infeasible solutions that do not meet power balance and equipment safety limits to ensure the physical realizability of the solution, thereby defining the boundary of the feasible region for optimization search; then, for the individuals that pass the verification, four target parameter values ​​are calculated: total station power generation water consumption, unit equipment life loss, unit vibration zone crossing time, and grid peak-shaving command response speed. Minimizing these target parameter values ​​is set as the optimization search direction to reflect the comprehensive performance requirements of economy, safety, stability, and responsiveness; then... Based on the Pareto dominance principle, individuals within the population are sorted and compared for non-dominated status. That is, an individual is considered a non-dominated solution when it is not inferior to other individuals in all objective parameters and is superior to other individuals in at least one objective parameter. Then, the population is driven to evolve iteratively through selection, crossover, and mutation operations of a genetic algorithm. In each generation, non-dominated solutions are retained and dominated solutions are removed to guide the population to move towards the optimal frontier. Finally, a set of Pareto optimal solutions that cannot be further optimized in any objective parameter without sacrificing the performance of other objective parameters is obtained, thereby realizing comprehensive optimal decision support under multi-objective conflict.

[0031] Step S30: For each candidate solution in the Pareto optimal solution set, determine the comprehensive score based on the target parameter value and the preset weight coefficient.

[0032] In this exemplary embodiment, the preset weighting coefficient refers to the numerical allocation that characterizes the relative importance of each target parameter in the current operating scenario. Its function is to quantify decision preferences to distinguish the priorities of different optimization objectives, thereby adapting to the strategy emphasis under different operating conditions. The specific value can be determined according to the actual operating conditions and is not limited here. For example, the weighting coefficient for normal operating conditions is as follows: =0.35, =0.25, =0.2, =0.2; Weighting coefficient for high-fluctuation operating conditions of new energy sources: =0.2, =0.2, =0.2, =0.4; Low-water option weighting coefficient: =0.5, =0.2, =0.2, =0.1; Weighting coefficient for high volatility in new energy sources + dry season adaptation: =0.35, =0.25, =0.2, =0.2, where, This is the weighting coefficient for the total water consumption for power generation at the station. This is a weighting coefficient for the lifespan loss value of the unit equipment. This is a weighting coefficient for the lifespan loss value of the unit equipment. The weighting coefficient is the response speed value of the power grid peak-shaving command. The comprehensive score value is a single-dimensional evaluation index obtained by weighting the multi-dimensional objective parameter values ​​corresponding to each candidate solution. Its function is to eliminate the incomparability between non-dominated solutions in the Pareto optimal solution set so as to facilitate the final selection of the optimal solution.

[0033] Specifically, for each candidate solution in the Pareto optimal solution set, the objective parameter value is normalized to obtain the normalized objective parameter value (i.e., normalized membership degree). The value is then mapped to the interval [0,1], with smaller values ​​representing higher membership degrees. The larger the value, the better the solution; then, based on the normalized value of the target parameter and its corresponding preset weight coefficient, the comprehensive score value is determined, where the formula for calculating the comprehensive score value is:

[0034] In the formula, The normalized membership degree of the total water consumption for power generation at the station; Normalized membership degree for the life loss value of unit equipment; The normalized membership degree of the unit's vibration zone travel time value; Normalized membership degree for the response speed value of power grid peak-shaving command; This is the overall score.

[0035] Step S40: Determine the target load allocation scheme for the multi-unit hydropower station from all candidate solutions based on the comprehensive score value.

[0036] In an exemplary embodiment of this application, the comprehensive score values ​​of each candidate solution in the Pareto optimal solution set are first compared and sorted. Based on the preset optimization direction, the candidate solution with the best comprehensive score value is selected as the best individual. The active power output allocation sequence of each unit carried by the best individual is determined as the target allocation scheme for the multi-unit load of the hydropower station. By using the comprehensive score value, the multi-dimensional target conflict is transformed into a single-dimensional judgment of superiority and inferiority, ensuring that the final output target allocation scheme not only conforms to the global optimal characteristics of multi-objective collaborative optimization, but also has the operability to be directly issued to the power station control system for load regulation.

[0037] It should be noted that after determining the target allocation scheme, considering the potential dynamic changes in the real-time health status of the units, the scheme can be further adjusted to better align with the actual operational constraints of the on-site equipment, and adaptively modified based on the real-time health status of the units; for details, refer to... Figure 2As shown, based on real-time operating data and defect records of the units, a fuzzy comprehensive evaluation method (the principle of which is common knowledge in this field and will not be elaborated here for the sake of brevity) is used to assess the health status of the units in real time into five levels: Level I (healthy), Level II (good), Level III (average), Level IV (poor), and Level V (faulty). Subsequently, adaptive correction rules are executed according to the assessment level: for Level I-II healthy units, the normal parameters of the target allocation scheme are used for optimization, and frequent peak-shaving loads are prioritized for allocation; for Level III healthy units, the upper and lower limits of output are narrowed and the maximum load change rate is lowered, and stable base loads are prioritized for allocation; for Level IV healthy units, the output range and load change rate are strictly limited, participation in frequent AGC adjustments is prohibited, and the weight of their life loss sub-target is increased to reduce their load priority; for Level V faulty units, no load is allocated directly; in addition, an emergency correction trigger mechanism is established. When a sudden abnormality such as a sudden rise in bearing temperature or excessive vibration is detected in the unit, an emergency re-optimization is immediately triggered to transfer the load of the abnormal unit to the healthy unit to avoid the fault from escalating.

[0038] It should be understood that after the scheme is adaptively corrected based on the real-time health status of the units, the final optimized scheme will be pushed to the central control room duty operation terminal and the power plant AGC system in real time. The scheme includes: target output of each unit, load change rate, start-up and shutdown suggestions, execution time nodes, risk warnings and handling suggestions, etc. At the same time, the actual output of the units, the actual output of renewable energy, and changes in grid commands are tracked in real time. When the following situations occur, emergency re-optimization will be triggered immediately: a) The actual output of new energy differs from the predicted output by more than or equal to the preset deviation threshold (preferably 10%). b) The magnitude of the power grid AGC command change is greater than or equal to the preset magnitude threshold (preferably 20% of the total installed capacity of the station). c) A sudden change occurs in the unit's health status; d) A sudden change occurs in the reservoir's inflow / water level; In addition, after each implementation of the plan, the implementation effect, water consumption rate, equipment operation data and power grid response are stored in the historical database. The weight coefficients are self-learned and corrected using a preset reinforcement learning algorithm to continuously improve the optimization effect.

[0039] This application constructs an initial population within a safe range that satisfies the total active power output constraint of the entire station and meets the preset unit vibration zone range. Each individual in the initial population includes a set of load allocation schemes. For each individual, under preset rigid constraints and target parameter values ​​including minimizing the total station power generation water consumption, unit equipment life loss, unit vibration zone crossing time, and grid peak-shaving command response speed, Pareto-dominated iterative processing is performed to obtain the Pareto optimal solution set. By quantifying equipment safety and response performance as co-optimization objectives, this application solves the problem that the traditional constant incremental rate method only takes minimizing water consumption rate as a single objective and does not consider unit equipment life loss and vibration zone operation constraints, thus satisfying the grid peak-shaving command response speed. While meeting peak demand, it effectively avoids frequent vibration zones and excessive wear of equipment, ensuring the safe operation of the units. At the same time, it utilizes the optimal solution set to provide a set of best trade-off solutions between different objective parameters, ensuring that all candidate solutions meet the safety operation constraints and have diversity. For each candidate solution in the Pareto optimal solution set, a comprehensive score value is determined based on the objective parameter value and preset weight coefficients. Based on the comprehensive score value, a target load allocation scheme for multiple units of the hydropower station is determined. By adjusting the weights, it adapts to the peak-shaving demand under different operating conditions, solving the problem that traditional methods cannot respond to changes in new energy output and meet the peak-shaving requirements of the power grid in real time while ensuring equipment safety. Ultimately, it achieves a global optimal balance between economy, safety and response speed.

[0040] Furthermore, in one embodiment, the method for determining the target parameter value is as follows: The target parameter values ​​are determined based on the number of units, loss parameters, output parameters, and duration parameters.

[0041] In this exemplary embodiment, the number of generating units refers to the total number of hydro-generator units participating in the optimization calculation; the loss parameter refers to the basic data reflecting the degree of mechanical wear and fatigue accumulation of the generating units; the output parameter refers to the active power value of each generating unit; the duration parameter refers to the duration of the generating unit's operating state; and the economic, safety, stability, and responsiveness evaluation indicators (i.e., target parameter values) that can be calculated based on the above input parameters provide a specific quantitative comparison benchmark for subsequent Pareto-dominated iterative processing.

[0042] Furthermore, in one embodiment, determining the target parameter value based on the number of units, loss parameters, output parameters, and duration parameters includes: The total water consumption for power generation and the response speed of the power grid peak shaving command were determined based on the number of generating units, output parameters, and duration parameters. The lifespan loss value of the unit equipment is determined based on the loss parameters; The duration of the vibration zone crossing is determined based on the duration parameter.

[0043] In this exemplary embodiment, the number of generating units is used to define the range of generating units involved in the calculation. By combining the power value represented by the output parameter and the time span represented by the duration parameter, the total power generation water consumption and the grid peak shaving command response speed can be determined respectively through these three parameters. Based on the equipment wear data contained in the loss parameter, the life loss value of the generating unit equipment is determined through quantitative calculation. The time segment of the unit in the vibration zone operation state is extracted from the duration parameter to determine the unit vibration zone crossing time value.

[0044] Further, in one embodiment, the output parameters include the active power output of the generating unit, the total active power output of the entire station, and the target output; the duration parameters include the command response duration; and the determination of the total power generation water consumption and the grid peak-shaving command response speed value based on the number of generating units, output parameters, and duration parameters includes: The total water consumption for power generation at the station is determined based on the number of generating units and their active power output. The power grid peak-shaving command response speed value is determined based on the total active power output, target output, and command response time of the entire station.

[0045] In this exemplary embodiment, the output parameters include the active power output of the generating unit, the total active power output of the entire station, and the target output; the duration parameters include the command response duration; the number of generating units refers to the total number of hydro-generator units participating in load allocation optimization; the active power output of the generating unit refers to the actual power generation of each generating unit at a specific moment; and the total water consumption for power generation of the entire station refers to the total amount of water resources consumed by all operating generating units in completing the specified output task. Specifically, the total water consumption for power generation of the entire station is obtained by substituting the number of generating units and the active power output of the generating units into the following calculation formula:

[0046] In the formula: n is the number of generating units. For the active power output of the i-th unit, Let be the water consumption rate fitting formula for the i-th unit, in m³ / s·MW; where the fitting formula is a mathematical approximation expression established by regression analysis based on the unit's factory efficiency curve or historical operating data, and its function is to accurately quantify and characterize the hydraulic conversion efficiency characteristics of the unit under different load conditions. This represents the total water consumption for power generation at the entire station.

[0047] It should be noted that the total active power output of the entire hydropower station refers to the sum of the real-time output of all generating units, the target output refers to the expected power command value issued by the power grid dispatch, and the command response time refers to the time interval required for the power station to adjust from its current output to the target output. The power grid peak shaving command response speed value is an evaluation index calculated based on the total active power output, target output, and command response time. Its function is to quantitatively evaluate the hydropower station's agility and compliance in tracking power grid peak shaving commands. The above parameters together construct the calculation logic of economic efficiency and responsiveness objectives, ensuring that the optimization scheme takes into account both water resource conservation and power grid dispatch requirements. Specifically, the power grid peak shaving command response speed value is obtained by substituting the total active power output, target output, and command response time into the following calculation formula:

[0048] In the formula: The entire site contributes to the effort at time t. The target output of the power grid AGC at time t is Δt, where Δt is the command response time. This represents the response speed value for power grid peak shaving commands.

[0049] Further, in one embodiment, the loss parameters include unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss. Determining the unit equipment life loss value based on the loss parameters includes: The life loss value of the unit equipment is determined based on the unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss.

[0050] In this exemplary embodiment, the loss parameters include unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss. Unit start-up and shutdown life loss refers to the quantitative value of equipment life reduction caused by sudden changes in mechanical stress and temperature fluctuations during the start-up and shutdown of the hydro-generator unit. Load adjustment fatigue loss refers to the quantitative value of fatigue damage caused by changes in guide vane opening and cyclic accumulation of rotor stress during load adjustment. Vibration zone crossing additional loss refers to the quantitative value of additional equipment damage caused by severe vibration and cavitation effects when the unit's operating output enters or passes through a hydraulically unstable vibration zone. The unit equipment life loss value is the total life consumption evaluation index calculated by combining the above three loss components, providing key data support for balancing economy and equipment safety in multi-objective optimization. Specifically, the unit equipment life loss value is obtained by substituting the unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss into the following calculation formula:

[0051] In the formula: Let represent the start-up and shutdown life loss of the i-th unit at time t (a fixed loss coefficient corresponding to a single start-up and shutdown). The load adjustment fatigue loss of the i-th unit at time t (positively correlated with the magnitude and rate of load change). The additional loss of the i-th unit during vibration zone crossing at time t (positively correlated with crossing time and vibration zone risk level). This represents the lifespan loss value of the unit's equipment.

[0052] Further, in one embodiment, the duration parameter includes the operating time in the prohibited vibration zone and the operating time in the restricted vibration zone, and determining the unit's vibration zone travel time value based on the duration parameter includes: The vibration zone travel time of the unit is determined based on the operating time in the prohibited vibration zone and the operating time in the restricted vibration zone.

[0053] In an exemplary embodiment of this application, the duration parameter includes the operating time in the prohibited vibration zone and the operating time in the restricted vibration zone; the operating time in the prohibited vibration zone and its corresponding first preset coefficient, and the operating time in the restricted vibration zone and its corresponding second preset coefficient are used to determine the unit's vibration zone crossing time value, and the calculation formula is as follows:

[0054] In the formula, the specific values ​​of the first and second preset coefficients can be determined according to actual needs, as long as they meet the following requirements. > Anything is acceptable; no specific restrictions are imposed here. Let be the operating time of the i-th unit in the vibration-prohibited zone at time t. Let be the operating time of the i-th unit in the restricted vibration zone at time t; This represents the time it takes for the unit to pass through the vibration zone.

[0055] Furthermore, in one embodiment, determining the target load allocation scheme for multiple generating units of the hydropower station based on the comprehensive score includes: The candidate solution with the highest comprehensive score is used as the target allocation scheme for the load of multiple units in the hydropower station.

[0056] As an example, in the embodiments of this application, the comprehensive score values ​​of each candidate solution in the Pareto optimal solution set are compared and sorted. Based on the preset optimization direction, the candidate solution with the best comprehensive score value is selected as the best individual. The active power output allocation sequence of each unit carried by the best individual is determined as the target allocation scheme of the multi-unit load of the hydropower station.

[0057] This embodiment takes a large hydropower station in a power grid in a region with a high proportion of new energy sources in China as the implementation object. The power station has four mixed-flow turbine generator units with a single unit capacity of 300MW, with a total installed capacity of 1200MW. It usually undertakes the main peak-shaving and frequency regulation tasks of the power grid. The installed capacity of new energy sources in the region accounts for more than 40%, and the power output fluctuates frequently.

[0058] I. Preparations before implementation (1) Complete data interface docking: dock with the power plant’s existing SCADA (Supervisory Control and Data Acquisition) system, hydrological monitoring system, power grid AGC system and equipment online monitoring system to achieve real-time acquisition of all data, and the acquisition cycle meets the requirements; (2) Complete the basic characteristics of the units: Complete the water consumption rate curves, vibration zone range (preferably set 2 prohibited operation zones for each unit: 50-80MW, 180-200MW; 1 restricted operation zone: 100-130MW), start-up and shutdown loss coefficients and life loss coefficients of the 4 units under full operating conditions. (3) Parameter initialization: To meet the real-time requirements of new energy fluctuations, MPC (Model Predictive Control) optimization is adopted to replace traditional static optimization, achieving minute-level scheme updates. The preferred settings are a prediction time domain of 2 hours, a control time domain of 10 minutes, and a rolling step size of 5 minutes, meaning that optimization is re-executed every 1-5 minutes to update the load allocation scheme; weighting coefficients for normal operating conditions: =0.35, =0.25, =0.2, =0.2; Weighting coefficient for high-fluctuation operating conditions of new energy sources: =0.2, =0.2, =0.2, =0.4; Low-water option weighting coefficient: =0.5, =0.2, =0.2, =0.1; Weighting coefficient for high volatility in new energy sources + dry season adaptation: =0.35, =0.25, =0.2, =0.2.

[0059] II. Specific Implementation Steps (1) Data acquisition and preprocessing: Real-time acquisition of regional wind power / photovoltaic power output forecast for the next 2 hours (5-minute resolution), real-time grid AGC commands, real-time reservoir water level and inflow data, real-time operation data and health status of 4 generating units, and data preprocessing is completed and stored in the basic database, which is fully updated every 5 minutes.

[0060] (2) Working condition matching and model setting: The day is the dry season, the water supply is tight, and the power output of new energy sources fluctuates by 25% during the morning peak period of the power grid. Switch to new energy high fluctuation + dry season adaptation weight coefficient, refresh rigid constraints and target preset conditions.

[0061] (3) Rolling optimization solution: The power grid AGC command is issued to the station with a total active power target of 900MW. The optimization solution is started and the Pareto optimal solution set is obtained by using the NSGA-III algorithm. The initial allocation scheme is selected: Unit 1 250MW, Unit 2 230MW, Unit 3 220MW, Unit 4 200MW. The NSGA-III algorithm refers to the third generation of non-dominated sorting genetic algorithm based on reference points. It is an evolutionary calculation method for solving multi-objective optimization problems. The core principle is to introduce a pre-set structured reference point mechanism to select the next generation of individuals on the basis of retaining the non-dominated sorting hierarchical mechanism. It can effectively find a balance solution among multiple conflicting objectives to generate a Pareto optimal solution set.

[0062] In summary, referring to Figure 3 As shown, by inputting ultra-short-term power output forecast data of new energy sources, real-time AGC commands / peak-shaving plans of the power grid, and real-time water conditions / reservoir operation data, and then optimizing the prediction time domain to 15 min to 4 h, the control time domain to 5 min to 15 min, and the rolling step size to 1 min to 5 min; then, by considering the target condition constraints and multi-dimensional rigid constraints, and using the NSGA-III algorithm to solve the Pareto optimal solution set, and performing optimal scheme screening, the target load allocation scheme of multiple units is finally output.

[0063] The calculation process is as follows: (1) Calculate basic parameters 1.1 Core Operating Conditions and Rolling Optimization Time-Domain Parameters

[0064] 1.2 Core characteristic parameters of a single unit (actual field measurements)

[0065] 1.3 Rigid Constraints (On-site Safety Regulations and Power Grid Requirements) 1. Power balance constraint: =900MW, deviation ≤±18MW; 2. Unit output constraint: 50MW≤ ≤300MW; 3. Variable load rate constraint: ≤30MW (At the current moment, all units are operating at a stable output of 225MW); 4. Vibration zone constraints: Prioritize avoiding prohibited operating zones and minimize operating time in restricted zones; 5. Grid response constraints: After the scheme is implemented, the AGC response time is ≤15s, and there is no reverse regulation.

[0066] 1.4 Core parameters of the NSGA-III algorithm (adapting to real-time requirements) The algorithm parameters for this application are as follows:

[0067] (2) Complete step-by-step calculation process of rolling optimization solution Step N1: Rolling optimization trigger and decision variable initialization 1.1 Optimized triggering The power grid AGC system issued a total active power target of 900MW for the entire station, triggering a rolling optimization process. It simultaneously collected ultra-short-term output forecast data of new energy sources for the next 2 hours, real-time water level of reservoirs, and real-time operation data of generating units, completed data preprocessing, and locked all input parameters.

[0068] 1.2 Definition of Decision Variables With the active power output of 4 units , , , As decision variables, they constitute a 4-dimensional decision vector X=[ , , The value range of each dimension is [50, 300] MW, while satisfying the constraint of a total output of 900 MW.

[0069] 1.3 Initial Population Generation Instead of random generation, a constraint-aware initialization method is used: 1. First, eliminate all output values ​​that fall into the vibration zone, and narrow the decision space of each unit to three safe ranges: [80-100MW], [130-180MW], and [200-300MW]. 2. Based on a preset sampling method (such as Latin hypercube sampling), 80 initial individuals that satisfy the total output constraint of 900MW are generated within the safe interval, ensuring that the initial population is 100% feasible, avoiding invalid search, and greatly improving the convergence speed.

[0070] Example individuals in the initial population: a) Individual 1: [260,240,210,190], total output 900MW, Unit 4 with 190MW falls into the prohibited operation zone and is marked as an infeasible solution; b) Individual 2: [250,230,220,200], total output 900MW, all unit outputs avoid the vibration zone, marked as a feasible solution; c) Individual 3: [280,240,190,190], total output 900MW, units 3 and 4, 190MW, fall into the prohibited vibration zone and are marked as infeasible solutions.

[0071] Step N2: Sub-target value calculation For each feasible individual in the population, four sub-objective values ​​are calculated, all of which are minimization objectives. The calculation process is as follows: Sub-objective 1: Minimize the total water consumption of the station.

[0072]

[0073] In the formula Let be the fitting formula for the water consumption rate of the i-th unit, in m³ / s·MW.

[0074] Example calculation (individual 2: [250,230,220,200]): Unit 1, 250MW: =0.00008×250² 0.045×250+12.6=6.35 m³ / s·MW, water consumption 6.35×250=1587.5 m³ / s; Unit 2, 230MW: =0.00009×230² 0.048×230+12.8=6.449 m³ / s·MW, water consumption 6.449×230=1483.27 m³ / s; Unit 3, 220MW: =0.00007×220² 0.042×220+12.5=6.628 m³ / s·MW, water consumption 6.628×220=1458.16 m³ / s; Unit 4, 200MW: =0.000085×200² 0.046×200+12.7=6.9 m³ / s·MW, water consumption 6.9×200=1380 m³ / s; Total water consumption =1587.5+1483.27+1458.16+1380=5908.93 m³ / s.

[0075] Sub-objective 2: Minimize the lifespan loss of unit equipment.

[0076] Quantitative load adjustment leads to load adjustment fatigue loss, i.e. Therefore, the simplified formula is:

[0077] In the formula, This is the benchmark coefficient for unit life loss (its specific value can be determined according to actual needs, and is not limited here). To optimize the stable output of the upstream units, the preferred method is to select... .

[0078] Example calculation (individual 2): =1.0×|250 225|+1.1×|230 225|+0.95×|220 225|+1.05×|200 225|=25+5.5+4.75+26.25=61.5.

[0079] Sub-objective 3: Minimize the unit's vibration zone travel time (F3) Differentiated weights are assigned to prohibited and restricted areas, using the following formula:

[0080] In the formula , As a preset coefficient, the output of all units in Individual 2 avoids the vibration zone, therefore .

[0081] Sub-objective 4: Fastest AGC response speed in the power grid (F4) The formula for quantifying the output adjustment range and response time is:

[0082] Individual 2 has a total output of 900MW, which perfectly matches the AGC command, therefore F4=0.

[0083] Step N3: Non-dominated sorting and reference point association (NSGA-III core logic) 3.1 Non-dominated sorting Based on the Pareto dominance rule, individuals in the population are stratified: If all sub-objective values ​​of individual A are less than or equal to those of individual B, and at least one sub-objective value is less than that of individual B, then A dominates B. Non-dominated layer 1 (Pareto front): The set of individuals in the population that are not dominated by any other individual; Non-dominated layer 2: The set of individuals dominated only by non-dominated layer 1, and so on.

[0084] By the 30th iteration, the non-dominated layer 1 had generated 32 uniformly distributed Pareto optimal solutions. All solutions satisfied the constraints and could not optimize any one of the sub-objectives without worsening other sub-objectives.

[0085] 3.2 Reference Point Correlation and Elite Retention 1. Normalize the four targets to eliminate dimensional differences; 2. Associate each Pareto solution with 16 preset reference points and calculate the vertical distance from the solution to the reference points; 3. Based on niche counting, retain the elite individuals closest to the reference point to ensure the uniformity and diversity of the Pareto solution set and avoid local convergence.

[0086] Step N4: Iterative convergence and generation of Pareto optimal solution set Repeat the iterative process of "crossover mutation → sub-objective calculation → non-dominated sorting → elite retention" until the maximum number of iterations is reached (60). This ultimately generates a Pareto optimal solution set containing 28 feasible solutions. Some typical solutions are as follows:

[0087] Step N5: Optimal compromise solution selection based on dynamic weights The fuzzy membership degree-weighted summation method is used to select the optimal solution that fits the current working condition from the Pareto solution set. The calculation process is as follows: 1. Perform linear normalization on each sub-target, mapping the value to the interval [0,1]. The smaller the value, the higher the membership degree. The larger the size, the better the solution; 2. Substitute the dynamic weights of the current operating condition ( =0.35、 =0.25、 =0.2、 =0.2), calculate the comprehensive score S for each solution:

[0088] In the formula The normalized membership degree of each sub-objective.

[0089] Typical solution comprehensive score calculation

[0090] Final screening results Solution 1 has the highest overall score (0.986) and is the optimal compromise solution under the current operating conditions. The corresponding power allocation scheme is: Unit 1 250MW, Unit 2 230MW, Unit 3 220MW, and Unit 4 200MW.

[0091] Step N6: Solution Constraint Verification The final selected solutions underwent full constraint validation, and the results are as follows: 1. Power balance: Total output 900MW, perfectly matching AGC commands, deviation 0; 2. Output constraints: The output of all units is within the range of 50-300MW, which meets the requirements; 3. Variable load rate: The maximum output adjustment range of a single unit is 25MW, and the limit is ≤30MW / min, which meets the requirements; 4. Vibration zone constraints: The output of all units avoids the prohibited / restricted vibration zone, which meets the requirements; 5. Response time: The single solution time is approximately 22 seconds, and the AGC command is tracked within 10 seconds, meeting the power grid AGC response time limit requirements.

[0092] The verification passed, and this scheme is output as the target load allocation scheme.

[0093] It should be noted that after obtaining the target load allocation plan, further adaptive adjustments can be made based on the unit health status, specifically as follows: (1) Adaptive correction of health status: If the real-time assessment finds that the temperature of the upper guide bearing of Unit 2 is too high and the vibration swing is close to the limit, the health level of Unit 2 is determined to be Level IV (poor) by fuzzy comprehensive evaluation method, and the real-time health index of Unit 2 is obtained. =0.55, which is lower than the preset threshold of 0.6, triggering an immediate correction: The preferred approach is to lower the upper limit of Unit 2's output to 200MW and the maximum load change rate from 30MW / min to 5MW / min. The correction amount ΔP2 = 230MW × (1 - 0.55 / 0.6) = 230 × 0.0833 ≈ 19.2MW. The corrected load of Unit 2... The load was still above the 200MW limit and was eventually lowered to 200MW. The 30MW of reduced load was allocated to Unit 3, which was in the best health condition, and its load was increased from 220MW to 250MW. The final optimized plan is as follows: Unit 1 250MW, Unit 2 200MW, Unit 3 250MW, Unit 4 200MW. All units are located away from vibration zones, and Unit 2 is allocated a stable base load and will not be involved in frequent adjustments.

[0094]

[0095] (2) Implementation and closed-loop management: The final plan is pushed to the central control room duty terminal and AGC system. The unit adjusts its output according to the plan and tracks the AGC command within 10 seconds, with a 100% response qualification rate. During the execution, the actual output of the new energy is 22% lower than the forecast. The grid AGC command suddenly increases to 1100MW, which immediately triggers emergency re-optimization. A new allocation plan is generated within 30 seconds to adjust the output of each unit to meet the grid requirements. No unit passes through the vibration zone throughout the process.

[0096] (3) Self-learning correction: After the day's operation ends, the load distribution execution data, water consumption rate data, and equipment operation data for the whole day are stored in the historical database to complete the self-learning correction of model parameters and optimize the subsequent operation effect.

[0097] It should be understood that after the power plant adopted the method of this application, the grid AGC response qualification rate increased from 92% to 99.8%, the unit vibration zone crossing time was reduced by 85%, equipment fatigue wear was reduced by 32%, the power generation water consumption rate during the dry season was reduced by 3.2%, and the annual water consumption was reduced by approximately 4.5 million cubic meters, and the power generation was increased by approximately 12 million kWh. At the same time, it significantly reduced the load allocation operation pressure of the shift operators and the risk of equipment failure.

[0098] Secondly, embodiments of this application also provide a multi-unit load distribution system for a hydropower station.

[0099] In one embodiment, reference is made to Figure 4 , Figure 4 This is a functional module diagram of an embodiment of the multi-unit load distribution system for a hydropower station according to this application. Figure 4 As shown, the multi-unit load distribution system of a hydropower station includes: The first processing module is used to construct an initial population within a safe range that meets the preset unit vibration zone range under the constraint of the total active power output of the entire station. Each individual in the initial population includes a set of load allocation schemes. The second processing module is used to perform Pareto domination iteration on each individual under preset rigid constraints and target constraints to obtain the Pareto optimal solution set. The target constraints include minimizing the target parameter values, which include the total power generation water consumption value, the unit equipment life loss value, the unit vibration zone crossing time value, and the grid peak shaving command response speed value. The third processing module is used to determine the comprehensive score value for each candidate solution in the Pareto optimal solution set based on the target parameter value and the preset weight coefficient. The fourth processing module is used to determine the target load allocation scheme for multiple units of the hydropower station from all candidate solutions based on the comprehensive score value.

[0100] Furthermore, in one embodiment, the second processing module is specifically used for: The target parameter values ​​are determined based on the number of units, loss parameters, output parameters, and duration parameters.

[0101] Furthermore, in one embodiment, the second processing module is specifically used for: The total water consumption for power generation and the response speed of the power grid peak shaving command were determined based on the number of generating units, output parameters, and duration parameters. The lifespan loss value of the unit equipment is determined based on the loss parameters; The duration of the vibration zone crossing is determined based on the duration parameter.

[0102] Further, in one embodiment, the output parameters include the active power output of the unit, the total active power output of the entire station, and the target output; the duration parameters include the command response duration; and the second processing module is specifically used for: The total water consumption for power generation at the station is determined based on the number of generating units and their active power output. The power grid peak-shaving command response speed value is determined based on the total active power output, target output, and command response time of the entire station.

[0103] Furthermore, in one embodiment, the loss parameters include unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss, and the second processing module is specifically used for: The life loss value of the unit equipment is determined based on the unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss.

[0104] Furthermore, in one embodiment, the duration parameter includes the duration of operation in the prohibited vibration zone and the duration of operation in the restricted vibration zone, and the second processing module is specifically used for: The vibration zone travel time of the unit is determined based on the operating time in the prohibited vibration zone and the operating time in the restricted vibration zone.

[0105] Furthermore, in one embodiment, the fourth processing module is specifically used for: The candidate solution with the highest comprehensive score is used as the target allocation scheme for the load of multiple units in the hydropower station.

[0106] This application constructs an initial population within a safe range that satisfies the total active power output constraint of the entire station and meets the preset unit vibration zone range. Each individual in the initial population includes a set of load allocation schemes. For each individual, under preset rigid constraints and target parameter values ​​including minimizing the total station power generation water consumption, unit equipment life loss, unit vibration zone crossing time, and grid peak-shaving command response speed, Pareto-dominated iterative processing is performed to obtain the Pareto optimal solution set. By quantifying equipment safety and response performance as co-optimization objectives, this application solves the problem that the traditional constant incremental rate method only takes minimizing water consumption rate as a single objective and does not consider unit equipment life loss and vibration zone operation constraints, thus satisfying the grid peak-shaving command response speed. While meeting peak demand, it effectively avoids frequent vibration zones and excessive wear of equipment, ensuring the safe operation of the units. At the same time, it utilizes the optimal solution set to provide a set of best trade-off solutions between different objective parameters, ensuring that all candidate solutions meet the safety operation constraints and have diversity. For each candidate solution in the Pareto optimal solution set, a comprehensive score value is determined based on the objective parameter value and preset weight coefficients. Based on the comprehensive score value, a target load allocation scheme for multiple units of the hydropower station is determined. By adjusting the weights, it adapts to the peak-shaving demand under different operating conditions, solving the problem that traditional methods cannot respond to changes in new energy output and meet the peak-shaving requirements of the power grid in real time while ensuring equipment safety. Ultimately, it achieves a global optimal balance between economy, safety and response speed.

[0107] The functions of each module in the above-mentioned hydropower station multi-unit load distribution system correspond to the steps in the above-mentioned hydropower station multi-unit load distribution method embodiment, and their functions and implementation processes will not be described in detail here.

[0108] Thirdly, this application provides a multi-unit load distribution device for a hydropower station. The multi-unit load distribution device for a hydropower station can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0109] Reference Figure 5 , Figure 5 This is a schematic diagram of the hardware structure of a multi-unit load distribution device for a hydropower station involved in an embodiment of this application. In this embodiment, the multi-unit load distribution device for a hydropower station may include a processor, a memory, a communication interface, and a communication bus.

[0110] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0111] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the multi-unit load distribution equipment of a hydropower station, as well as interfaces used for interconnecting the multi-unit load distribution equipment of the hydropower station with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0112] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0113] The processor can be a general-purpose processor, which can call the hydropower station multi-unit load allocation program stored in the memory and execute the hydropower station multi-unit load allocation method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the hydropower station multi-unit load allocation program is called can be referred to the various embodiments of the hydropower station multi-unit load allocation method of this application, and will not be repeated here.

[0114] Those skilled in the art will understand that Figure 5 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0115] Fourthly, embodiments of this application also provide a readable storage medium.

[0116] The present application stores a multi-unit load allocation program for a hydropower station on a readable storage medium, wherein when the multi-unit load allocation program for a hydropower station is executed by a processor, it implements the steps of the multi-unit load allocation method for a hydropower station as described above.

[0117] The method implemented when the multi-unit load allocation procedure of the hydropower station is executed can be referred to in the various embodiments of the multi-unit load allocation method of the hydropower station in this application, and will not be repeated here.

[0118] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0119] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0120] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0121] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0122] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0124] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for load distribution among multiple generating units in a hydropower station, characterized in that, include: Under the constraint of total active power output of the entire station, an initial population is constructed within a safe range that meets the preset unit vibration zone range. Each individual in the initial population includes a set of load allocation schemes. For each individual, under the preset rigid constraints and target constraints, Pareto dominance iteration is performed to obtain the Pareto optimal solution set. The target constraints include minimizing the target parameter values, which include the total power generation water consumption, the unit equipment life loss, the unit vibration zone crossing time, and the grid peak shaving command response speed. For each candidate solution in the Pareto optimal solution set, a comprehensive score is determined based on the target parameter value and the preset weight coefficient. The target load allocation scheme for multiple units of the hydropower station is determined from all candidate solutions based on the comprehensive score.

2. The method for load sharing among multiple generating units in a hydropower station as described in claim 1, characterized in that, The method for determining the target parameter value is as follows: The target parameter values ​​are determined based on the number of units, loss parameters, output parameters, and duration parameters.

3. The method for load sharing among multiple generating units in a hydropower station as described in claim 2, characterized in that, The target parameter values ​​are determined based on the number of generating units, loss parameters, output parameters, and duration parameters, including: The total water consumption for power generation and the response speed of the power grid peak shaving command were determined based on the number of generating units, output parameters, and duration parameters. The lifespan loss value of the unit equipment is determined based on the loss parameters; The vibration zone crossing time of the unit is determined based on the duration parameter.

4. The method for load sharing among multiple generating units in a hydropower station as described in claim 3, characterized in that, The output parameters include the active power output of the generating units, the total active power output of the entire station, and the target output. The duration parameters include the command response duration. The determination of the total station's water consumption for power generation and the grid peak-shaving command response speed based on the number of generating units, output parameters, and duration parameters includes: The total water consumption for power generation at the station is determined based on the number of generating units and their active power output. The power grid peak-shaving command response speed value is determined based on the total active power output, target output, and command response time of the entire station.

5. The method for load sharing among multiple generating units in a hydropower station as described in claim 3, characterized in that, The loss parameters include unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone pass-through additional loss. Determining the unit equipment life loss value based on these loss parameters includes: The life loss value of the unit equipment is determined based on the unit start-up and shutdown life loss, load adjustment fatigue loss, and vibration zone crossing additional loss.

6. The method for load sharing among multiple generating units in a hydropower station as described in claim 3, characterized in that, The duration parameter includes the operating time in the prohibited vibration zone and the operating time in the restricted vibration zone. Determining the unit's vibration zone travel time value based on the duration parameter includes: The vibration zone travel time of the unit is determined based on the operating time in the prohibited vibration zone and the operating time in the restricted vibration zone.

7. The method for load sharing among multiple generating units in a hydropower station as described in claim 1, characterized in that, The target allocation scheme for the load of multiple units of the hydropower station, determined based on a comprehensive score, includes: The candidate solution with the highest comprehensive score is used as the target allocation scheme for the load of multiple units in the hydropower station.

8. A multi-unit load distribution system for a hydropower station, characterized in that, The multi-unit load distribution system of the hydropower station includes: The first processing module is used to construct an initial population within a safe range that meets the preset unit vibration zone range under the constraint of the total active power output of the entire station. Each individual in the initial population includes a set of load allocation schemes. The second processing module is used to perform Pareto domination iteration on each individual under preset rigid constraints and target constraints to obtain the Pareto optimal solution set. The target constraints include minimizing the target parameter values, which include the total power generation water consumption value, the unit equipment life loss value, the unit vibration zone crossing time value, and the grid peak shaving command response speed value. The third processing module is used to determine the comprehensive score value for each candidate solution in the Pareto optimal solution set based on the target parameter value and the preset weight coefficient. The fourth processing module is used to determine the target load allocation scheme for multiple units of the hydropower station from all candidate solutions based on the comprehensive score value.

9. A load distribution device for multiple generating units in a hydropower station, characterized in that, The hydropower station multi-unit load distribution equipment includes a processor, a memory, and a hydropower station multi-unit load distribution program stored in the memory and executable by the processor, wherein when the hydropower station multi-unit load distribution program is executed by the processor, it implements the steps of the hydropower station multi-unit load distribution method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a multi-unit load allocation program for a hydropower station, wherein when the multi-unit load allocation program for a hydropower station is executed by a processor, it implements the steps of the multi-unit load allocation method for a hydropower station as described in any one of claims 1 to 7.

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