Hydropower station unit load distribution method and system based on power system

Abstract

The invention belongs to the technical field of power systems, and relates to a hydropower station unit load distribution method and system based on a power system. The method comprises the following steps: acquiring operation state data of a hydropower station in real time; establishing a mapping relation model of output power and flow, and determining an operation constraint condition; establishing a mathematical model taking the minimum total water consumption rate of the power station as an optimization target; solving the mathematical model by adopting a firefly algorithm; calculating the brightness of each firefly in the population; random disturbance is introduced in the moving process, a solution falling into a vibration area range is punished and corrected in the iteration process, and a global optimal power distribution scheme is output; according to the method, a firefly algorithm is used for mining tiny efficiency differences among different units, maximization of water energy utilization is achieved, and an optimal power distribution scheme is output by monitoring operation condition changes in real time; according to the invention, the maximization of water energy utilization is realized, and the risk that a unit enters a vibration area by mistake to operate under a low-load working condition is effectively avoided.

Description

A method and system for load allocation of hydropower station units based on power system Technical Field

[0001] This invention belongs to the field of power system technology, and more specifically, relates to a method and system for load allocation of hydropower station units based on a power system. Background Technology

[0002] In traditional power systems, hydropower stations typically operate at maximum power output or full load. However, in the context of new power systems, the operating mode of hydropower stations has undergone profound changes:

[0003] The regulatory role is shifting from simple power regulation to capacity regulation and frequency support. The power grid increasingly values ​​the rotational inertia and rapid climbing ability provided by hydropower stations to smooth out fluctuations in wind and solar power output.

[0004] Low-load operation is becoming the norm: Due to the priority given to wind and solar power for grid connection, hydropower stations often need to operate under partial or even low-load conditions for extended periods, requiring multiple units to be connected to the grid to provide sufficient spinning reserve capacity. Frequent load adjustments make it easier for units to cross or fall into vibration zones (such as the draft tube vortex zone and blade passage vortex zone), posing a significant threat to equipment lifespan and safety. Traditional in-plant economic operation methods typically employ the constant incremental rate method or a simple uniform distribution method. The constant incremental rate method can theoretically achieve optimal performance, but it requires the unit's consumption characteristic curve to be a convex function with a monotonically increasing incremental rate. However, the actual turbine characteristic curve is often non-convex and discontinuous due to the influence of vibration zones, causing this method to fail. Although the uniform distribution method is simple to implement, it ignores the performance differences between different units (even units of the same model will have different wear and efficiency characteristics after years of operation) and the distribution of vibration zones, which can easily lead to units operating in low-efficiency zones, wasting water resources, and even forcing units to operate in vibration zones for extended periods, causing mechanical failures.

[0005] To address the aforementioned issues, using artificial intelligence algorithms to solve nonlinear, discontinuous, and multi-constraint optimization problems has become a research hotspot. Genetic algorithms and particle swarm optimization algorithms have been applied in this field. However, genetic algorithms suffer from slow convergence speed and premature convergence, while particle swarm optimization algorithms are prone to getting trapped in local optima. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for load allocation of hydropower station units based on a power system.

[0007] In a first aspect, the present invention provides a method for load allocation of hydropower station units based on a power system, comprising: real-time acquisition of hydropower station operating status data; the operating status data including the total load demand of the power station, the number of units requiring grid connection, the current real-time head of the power station, and the vibration zone range to be avoided by each turbine generator unit; establishing a mapping relationship model between the output power and flow rate of each turbine generator unit under different heads, and determining the operating constraints of each turbine generator unit; the constraints at least include the vibration zone range to be avoided by the power allocation of each turbine generator unit; establishing a mathematical model with the minimum total water consumption rate of the power station as the optimization objective, the objective function being to minimize the total flow rate of all operating turbine generator units while meeting the total load demand of the power station; and adopting... The firefly algorithm solves the mathematical model, including: initializing the firefly population, setting the population size, maximum number of iterations, attraction coefficient, and step size factor, with each firefly's position representing a unit power allocation scheme; calculating the brightness of each firefly in the population; the brightness of the fireflies is negatively correlated with the objective function value; based on the differences in firefly brightness, guiding each firefly to move towards the brighter firefly, introducing random perturbations during the movement, penalizing and correcting solutions that fall into the vibration zone during the iteration process, until the maximum number of iterations or the convergence condition is reached, and outputting the globally optimal power allocation scheme; distributing the optimal power allocation scheme to the local control unit for execution, monitoring changes in operating conditions in real time, and re-outputting a new load allocation scheme when the trigger condition is met.

[0008] Secondly, this invention provides a hydropower station unit load allocation system based on a power system, comprising an acquisition unit, a model building unit, an objective function building unit, a solution unit, an iteration unit, and a dynamic update unit. The acquisition unit is used to acquire real-time operating status data of the hydropower station. The operating status data includes the total load demand of the power station, the number of units requiring grid connection, the current real-time head of the power station, and the vibration zone range to be avoided by each turbine generator unit. The model building unit is used to establish a mapping relationship model between the output power and flow rate of each turbine generator unit under different heads, and to determine the operating constraints of each turbine generator unit. The constraints at least include the vibration zone range to be avoided for power allocation of each turbine generator unit. The objective function building unit is used to establish a mathematical model with the minimum total water consumption rate of the power station as the optimization objective. The objective function is to achieve the minimum total water consumption rate of the power station while meeting the total load demand of the power station. The total flow rate of the operating units with hydro-generators is minimized. The solution unit uses the firefly algorithm to solve the mathematical model, including: initializing the firefly population, setting the population size, maximum number of iterations, attraction coefficient, and step size factor; the position of each firefly represents a power allocation scheme for the units. The iteration unit calculates the brightness of each firefly in the population; the brightness of the fireflies is negatively correlated with the objective function value; based on the brightness differences, each firefly is guided to move towards a brighter firefly, and random disturbances are introduced during the movement. Solutions falling into the vibration zone are penalized and corrected during the iteration process until the maximum number of iterations or the convergence condition is reached, outputting the globally optimal power allocation scheme. The dynamic update unit distributes the optimal power allocation scheme to the local control unit for execution, monitors changes in operating conditions in real time, and re-outputs a new load allocation scheme when trigger conditions are met.

[0009] Based on the above technical solution, the present invention can be further improved as follows.

[0010] Furthermore, after real-time acquisition of hydropower station operation status data, outlier removal, smoothing, and normalization preprocessing are performed, outliers are removed using the 3σ criterion, data smoothing is performed using the moving average filtering method, the window size is set, and the data is mapped to the [0,1] interval using the linear normalization method.

[0011] Furthermore, a mapping model of the output power and flow rate of each hydro-generator unit under different heads is established, including: assuming... For the first The output power of the hydro-generator unit, For the corresponding traffic, For the water head, The constant coefficients, For the overall efficiency of the hydro-generator unit, the mapping relationship model is expressed as: .

[0012] Furthermore, the operating constraints of each hydro-generator unit are determined, including: setting... To meet the total load demand of the power plant, For the first The output power of the hydro-generator unit, The number of hydro-generator units that need to be operated. For the first Taiwan hydro-generator set The output power of the point, For the first Taiwan hydro-generator set The output power of the point, Indicates the first Taiwan hydro-generator set Click to In the continuous vibration zone of the point, the total load demand of the power station is a constant and .

[0013] Furthermore, based on the differences in the brightness of fireflies, the fireflies are guided to move towards the brighter fireflies, including: setting... For the first iteration Indicates the first Only fireflies, compared to the first The brighter firefly is indicated as the first one. A firefly, The updated location of the fireflies. For the first The location of the fireflies For the first The location of the fireflies As the attraction coefficient, It's a firefly and fireflies The Euclidean distance between them The distance threshold is used to update the firefly's position as follows: .

[0014] Furthermore, during the iteration process, solutions falling within the vibration zone are penalized, including: setting the penalty function as follows: , For the first Taiwan hydro-generator set The output power of the point, For the first Taiwan hydro-generator set The output power of the point, Indicates the first Taiwan hydro-generator set Click to The continuous vibration zone of the point For safety margin, the first The output power of the hydro-generator unit is When the first Output power of the hydro-generator unit Falling into any vibration zone When the solution is given a fitness maximum, the brightness of the solution in the Firefly algorithm is greater than the fitness maximum.

[0015] Furthermore, during the iteration process, solutions falling within the vibration zone are corrected, including: when the... Output power of the hydro-generator unit When falling within the vibration zone, the boundary correction method is used to adjust the first... Output power of the hydro-generator unit Corrected to the boundary value of the non-vibration zone closest to the boundary of the vibration zone; the boundary value of the non-vibration zone is... or .

[0016] Furthermore, the operational status data also includes grid frequency regulation commands; the total load demand is dynamically synthesized by superimposing the day-ahead generation plan curve with real-time primary and secondary frequency regulation commands.

[0017] Furthermore, when the total output power or head change of all hydro-generator units exceeds a preset threshold, a globally optimal power allocation scheme is regenerated.

[0018] The beneficial effects of this invention are: (1) This invention breaks the traditional uniform distribution mode and uses the firefly algorithm to explore the small efficiency differences between different units, thereby maximizing the utilization of water energy and achieving more power generation under the same water volume or less water consumption under the same power volume; (2) By using the range of avoiding the vibration zone as a constraint for model solving, the risk of the unit accidentally entering the vibration zone under low load conditions is effectively eliminated, the safety is greatly improved, the overhaul cycle of the unit is extended, and the equipment maintenance cost is reduced; (3) This invention can flexibly meet the grid's demand for multi-unit grid connection and wide load regulation, especially in the wind-solar complementary scenario, it can quickly calculate the optimal solution under non-standard operating conditions, thereby improving the hydropower station's support capability for the grid; (4) Compared with the traditional gradient descent method, the firefly algorithm used in this invention does not depend on the continuity and differentiability of the objective function, and is very suitable for handling optimization problems of water turbines with complex non-convex characteristic curves, and has good robustness. Attached Figure Description

[0019] Figure 1 is a schematic diagram of a hydropower station unit load distribution method based on a power system provided in Embodiment 1 of the present invention; Figure 2 is a schematic diagram of a hydropower station unit load distribution system based on a power system provided in Embodiment 2 of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Example 1, as shown in Figure 1, provides a method for load allocation of hydropower station units based on a power system to solve the above-mentioned technical problems. The method includes the following steps: Step 110: Real-time acquisition of hydropower station operation status data; the operation status data includes the total load demand of the power station, the number of units that need to be connected to the grid, the current real-time head of the power station, and the vibration zone range that each turbine generator unit needs to avoid; in some optional embodiments, the system first acquires real-time hydropower station operation status data from the SCADA system (Supervisory Control And Data Acquisition) and the power grid dispatch center.

[0022] The number of operating generating units is determined by the automatic power generation control system or start-up and shutdown optimization program. To ensure system inertia, the number of operating units is often required to be maintained at a high level, even if the load of a single unit is low. The current head of the power station is the real-time difference between the upstream and downstream water levels minus head losses.

[0023] Obtain vibration zone maps based on the power range of each unit under different water heads (such as the air replenishment zone and the strong vibration zone) determined by on-site actual machine tests.

[0024] The total load demand of the power station and the number of generating units that need to be operated are the grid demand, taking 4 turbine units as an example; the current head of the power station is the real-time measurement data of the power station, with each meter as a new input; the vibration zone range that each turbine needs to avoid is the actual data that the power station has measured.

[0025] In practical applications, the real-time operating status data of hydropower stations is preprocessed by outlier removal, smoothing, and normalization. Outliers are removed using the 3σ criterion, and the data is smoothed using the moving average filtering method. The window size is set, and the data is mapped to the [0,1] interval using the linear normalization method.

[0026] Step 120: Establish a mapping model between the output power and flow rate of each hydro-generator unit under different water heads, and determine the operating constraints of each hydro-generator unit; the constraints should at least include ensuring that the power distribution of each hydro-generator unit avoids the vibration zone; specifically, establish a mapping model between the output power and flow rate of each hydro-generator unit under different water heads, including: setting For the first The output power of the hydro-generator unit, For the first The flow rate corresponding to the turbine generator unit. For the water head, The constant coefficients, For the overall efficiency of the hydro-generator unit, the mapping relationship model is expressed as: ; .

[0027] Under the condition that the total power remains constant in different head sections, we seek to optimize the power distribution of each unit so that the total flow of all operating turbine generator units is minimized, i.e., the water consumption rate is minimized.

[0028] Optionally, determine the operating constraints for each hydro-generator unit, including: setting... To meet the total load demand of the power plant, For the first The output power of the hydro-generator unit, The number of hydro-generator units that need to be operated. For the first Taiwan hydro-generator set The output power of the point, For the first Taiwan hydro-generator set The output power of the point, Indicates the first Taiwan hydro-generator set Click to In the continuous vibration zone of the point, the total load demand of the power station is a constant and .

[0029] Step 130: Establish a mathematical model with the goal of minimizing the total water consumption rate of the power station. The objective function is to minimize the total flow rate of all operating turbine generator units while meeting the total load demand of the power station. Let the total flow rate of all operating turbine generator units be... , Given the number of hydro-generator units that need to be operated, the objective function is expressed as: .

[0030] Step 140: Solve the mathematical model using the firefly algorithm, including: initializing the firefly population, setting the population size, maximum number of iterations, attraction coefficient and step size factor, and the position of each firefly representing a unit power allocation scheme; the attraction between fireflies is related to the distance between fireflies and the brightness, and the brightness is determined by the objective function value.

[0031] Let the population size be ,For example That is, 30 fireflies, representing 30 allocation schemes; the maximum number of iterations is 100; let the attraction coefficient be... ,For example: ; Randomly generate 30 initial solutions that sum to 500. For example, the position vector of firefly 1 is... The position vector of firefly 2 is (If the vibration zone is not considered, and 100MW is in the vibration zone, a correction is required). Correction mechanism: During initialization, if the generated random solution falls into the vibration zone, it is pushed towards the nearest boundary. For example, 100MW is forcibly corrected to 110MW or 80MW, and the power of other units is rebalanced to maintain a total of 500MW.

[0032] Step 150: Calculate the brightness of each firefly in the population; the brightness of the fireflies is negatively correlated with the objective function value; based on the difference in the brightness of the fireflies, guide each firefly to move towards the firefly with higher brightness, and introduce random perturbation during the movement process. During the iteration process, the solutions that fall into the vibration zone are penalized and corrected until the maximum number of iterations or the convergence condition is reached, and the globally optimal power allocation scheme is output.

[0033] Optionally, during the iteration process, solutions falling within the vibration zone are penalized, including: setting the penalty function as follows: , For the first Taiwan hydro-generator set The output power of the point, For the first Taiwan hydro-generator set The output power of the point, Indicates the first Taiwan hydro-generator set Click to The continuous vibration zone of the point For safety margin, the first The output power of the hydro-generator unit is When the first Output power of the hydro-generator unit Falling into any vibration zone When the solution is given a fitness maximum, the brightness of the solution in the Firefly algorithm is greater than the fitness maximum.

[0034] Optionally, solutions falling within the vibration zone range can be corrected during the iteration process, including: when the... Output power of the hydro-generator unit When falling within the vibration zone, the boundary correction method is used to adjust the first... Output power of the hydro-generator unit Corrected to the boundary value of the non-vibration zone closest to the boundary of the vibration zone; the boundary value of the non-vibration zone is... or .

[0035] Optionally, based on the difference in the brightness of the fireflies, guide the fireflies to move towards the brighter fireflies, including: setting For the first iteration Indicates the first Only fireflies, compared to the first The brighter firefly is indicated as the first one. A firefly, The updated location of the fireflies. For the first The location of the fireflies For the first The location of the fireflies As the attraction coefficient, It's a firefly and fireflies The Euclidean distance between them The distance threshold is used to update the firefly's position as follows: .

[0036] firefly and The Euclidean distance between them is expressed as: .

[0037] When the maximum number of iterations is reached or the objective function value converges, the iteration stops and the optimal power allocation scheme is output.

[0038] Step 160: Send the optimal power allocation scheme to the local control unit for execution, monitor changes in operating conditions in real time, and output a new load allocation scheme when the triggering conditions are met.

[0039] This invention breaks away from the traditional uniform distribution model and uses the firefly algorithm to exploit the minute efficiency differences between different units, thereby maximizing the utilization of hydropower and enabling more power generation with the same amount of water, or less water consumption with the same amount of power.

[0040] This invention uses the range of the vibration zone as a constraint for model solving, effectively eliminating the risk of the unit accidentally entering the vibration zone under low load conditions, greatly improving safety, extending the unit's overhaul cycle, and reducing equipment maintenance costs.

[0041] This invention can flexibly meet the grid's needs for multi-unit grid connection and wide load regulation. Especially in the wind-solar hybrid scenario, it can quickly calculate the optimal solution under non-standard operating conditions, thereby improving the hydropower station's support capability for the grid.

[0042] Compared with the traditional gradient descent method, the firefly algorithm used in this invention does not depend on the continuity and differentiability of the objective function, making it very suitable for handling optimization problems of water turbines with complex non-convex characteristic curves and exhibiting good robustness.

[0043] Optionally, the operating status data also includes grid frequency regulation commands; the total load demand is dynamically synthesized by superimposing the day-ahead generation plan curve with real-time primary and secondary frequency regulation commands.

[0044] Optionally, when the total output power or head change of all hydro-generator units exceeds a preset threshold, a globally optimal power allocation scheme is regenerated.

[0045] Example 2, based on the same principle as the method shown in Example 1 of the present invention, as shown in Figure 2, also provides a hydropower station unit load allocation system based on a power system, including an acquisition unit, a model building unit, an objective function building unit, a solution unit, an iteration unit, and a dynamic update unit. The acquisition unit is used to acquire real-time operating status data of the hydropower station. The operating status data includes the total load demand of the power station, the number of units requiring grid connection, the current real-time head of the power station, and the vibration zone range to be avoided by each turbine generator unit. The model building unit is used to establish a mapping relationship model between the output power and flow rate of each turbine generator unit under different heads, and to determine the operating constraints of each turbine generator unit. The constraints at least include the power allocation of each turbine generator unit to avoid the vibration zone range. The objective function building unit is used to establish a mathematical model with the minimum total water consumption rate of the power station as the optimization objective. To minimize the total flow of all operating turbine generator units while meeting the total load demand of the power station, the following steps are taken: A solution unit uses the firefly algorithm to solve the mathematical model, including: initializing the firefly population, setting the population size, maximum number of iterations, attraction coefficient, and step size factor; the position of each firefly represents a power allocation scheme for a generator unit; an iteration unit calculates the brightness of each firefly in the population; the brightness of the fireflies is negatively correlated with the objective function value; based on the brightness differences, each firefly is guided to move towards a brighter firefly, and random disturbances are introduced during the movement; solutions falling into the vibration zone are penalized and corrected during the iteration process until the maximum number of iterations or the convergence condition is reached, outputting the globally optimal power allocation scheme; a dynamic update unit distributes the optimal power allocation scheme to the local control unit for execution, monitors changes in operating conditions in real time, and re-outputs a new load allocation scheme when trigger conditions are met.

[0046] Optionally, after real-time acquisition of hydropower station operation status data is preprocessed by outlier removal, smoothing, and normalization, outliers are removed using the 3σ criterion, data is smoothed using the moving average filtering method, the window size is set, and the data is mapped to the [0,1] interval using the linear normalization method.

[0047] Optionally, establish a mapping model between the output power and flow rate of each hydro-generator unit under different heads, including: setting For the first The output power of the hydro-generator unit, For the corresponding traffic, For the water head, The constant coefficients, For the overall efficiency of the hydro-generator unit, the mapping relationship model is expressed as: .

[0048] Optionally, determine the operating constraints for each hydro-generator unit, including: setting... To meet the total load demand of the power plant, For the first The output power of the hydro-generator unit, The number of hydro-generator units that need to be operated. For the first Taiwan hydro-generator set The output power of the point, For the first Taiwan hydro-generator set The output power of the point, Indicates the first Taiwan hydro-generator set Click to In the continuous vibration zone of the point, the total load demand of the power station is a constant and .

[0049] Optionally, based on the difference in the brightness of the fireflies, guide the fireflies to move towards the brighter fireflies, including: setting For the first iteration Indicates the first Only fireflies, compared to the first The brighter firefly is indicated as the first one. A firefly, The updated location of the fireflies. For the first The location of the fireflies For the first The location of the fireflies As the attraction coefficient, It's a firefly and fireflies The Euclidean distance between them The distance threshold is used to update the firefly's position as follows: .

[0050] Optionally, during the iteration process, solutions falling within the vibration zone are penalized, including: setting the penalty function as follows: , For the first Taiwan hydro-generator set The output power of the point, For the first Taiwan hydro-generator set The output power of the point, Indicates the first Taiwan hydro-generator set Click to The continuous vibration zone of the point For safety margin, the first The output power of the hydro-generator unit is When the first Output power of the hydro-generator unit Falling into any vibration zone When the solution is given a fitness maximum, the brightness of the solution in the Firefly algorithm is greater than the fitness maximum.

[0051] Optionally, solutions falling within the vibration zone range can be corrected during the iteration process, including: when the... Output power of the hydro-generator unit When falling within the vibration zone, the boundary correction method is used to adjust the first... Output power of the hydro-generator unit Corrected to the boundary value of the non-vibration zone closest to the boundary of the vibration zone; the boundary value of the non-vibration zone is... or .

[0052] Optionally, the operating status data also includes grid frequency regulation commands; the total load demand is dynamically synthesized by superimposing the day-ahead generation plan curve with real-time primary and secondary frequency regulation commands.

[0053] Optionally, when the total output power or head change of all hydro-generator units exceeds a preset threshold, a globally optimal power allocation scheme is regenerated.

[0054] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for load allocation of hydropower station units based on a power system, characterized in that, include: Real-time acquisition of hydropower station operation status data; operation status data includes the total load demand of the power station, the number of generating units that need to be connected to the grid, the current real-time head of the power station, and the vibration zone range that each turbine generator unit needs to avoid; establish a mapping relationship model between the output power and flow rate of each turbine generator unit under different heads, and determine the operation constraints of each turbine generator unit; the constraints should at least include the range of vibration zone range that each turbine generator unit should avoid in power allocation; establish a mathematical model with the minimum total water consumption rate of the power station as the optimization objective, the objective function being to minimize the total flow rate of all operating turbine generator units while meeting the total load demand of the power station; use the firefly algorithm to solve the mathematical model, including: initializing the firefly population, setting the population size, maximum number of iterations, attraction coefficient and step size factor, and the position of each firefly representing a unit power allocation scheme; The brightness of each firefly in the population is calculated; the brightness of the fireflies is negatively correlated with the objective function value; based on the difference in brightness, each firefly is guided to move towards the brighter firefly, and random perturbation is introduced during the movement; during the iteration process, solutions that fall into the vibration zone are penalized and corrected until the maximum number of iterations or the convergence condition is reached, and the globally optimal power allocation scheme is output; the optimal power allocation scheme is sent to the local control unit for execution, and changes in operating conditions are monitored in real time. When the trigger condition is met, a new load allocation scheme is output again.

2. The method for load allocation of hydropower station units based on a power system according to claim 1, characterized in that, The real-time operational status data of the hydropower station is preprocessed by outlier removal, smoothing, and normalization. Outliers are removed using the 3σ criterion, and the data is smoothed using the moving average filtering method. The window size is set, and the data is mapped to the [0,1] interval using the linear normalization method.

3. The method for load allocation of hydropower station units based on a power system according to claim 1, characterized in that, Establish a mapping model between the output power and flow rate of each hydro-generator unit under different water heads, including: setting... For the first The output power of the hydro-generator unit, For the corresponding traffic, For the water head, The constant coefficients, For the overall efficiency of the hydro-generator unit, the mapping relationship model is expressed as: 。 4. The method for load allocation of hydropower station units based on a power system according to claim 1, characterized in that, Determine the operating constraints for each hydro-generator unit, including: setting To meet the total load demand of the power plant, For the first The output power of the hydro-generator unit, The number of hydro-generator units that need to be operated. For the first Taiwan hydro-generator set The output power of the point, For the first Taiwan hydro-generator set The output power of the point, Indicates the first Taiwan hydro-generator set Click to In the continuous vibration zone of the point, the total load demand of the power station is a constant and 。 5. The method for load allocation of hydropower station units based on a power system according to claim 1, characterized in that, Based on the differences in the brightness of fireflies, guide fireflies to move towards fireflies with higher brightness, including: setting... For the first iteration Indicates the first Only fireflies, compared to the first The brighter firefly is indicated as the first one. A firefly, The updated location of the fireflies. For the first The location of the fireflies For the first The location of the fireflies As the attraction coefficient, Fireflies and fireflies The Euclidean distance between them The distance threshold is used to update the firefly's position as follows: 。 6. The method for load allocation of hydropower station units based on a power system according to claim 1, characterized in that, During the iteration process, solutions falling within the vibration zone are penalized, including: setting the penalty function as follows: ， For the first Taiwan hydro-generator set The output power of the point, For the first Taiwan hydro-generator set The output power of the point, Indicates the first Taiwan hydro-generator set Click to The continuous vibration zone of the point For safety margin, the first The output power of the hydro-generator unit is When the first Output power of the hydro-generator unit Falling into any vibration zone When the solution is given a fitness maximum, the brightness of the solution in the Firefly algorithm is greater than the fitness maximum.

7. The method for load allocation of hydropower station units based on a power system according to claim 1, characterized in that, During the iteration process, solutions falling within the vibration zone are corrected, including: when the... Output power of the hydro-generator unit When falling within the vibration zone, the boundary correction method is used to adjust the first... Output power of the hydro-generator unit Corrected to the boundary value of the non-vibration zone closest to the boundary of the vibration zone; the boundary value of the non-vibration zone is... or 。 8. The method for load allocation of hydropower station units based on a power system according to claim 1, characterized in that, The operational status data also includes grid frequency regulation commands; the total load demand is dynamically synthesized by superimposing the day-ahead generation plan curve with real-time primary and secondary frequency regulation commands.

9. The method for load allocation of hydropower station units based on a power system according to claim 1, characterized in that, When the total output power or head change of all hydro-generator units exceeds a preset threshold, a globally optimal power allocation scheme is regenerated.

10. A hydropower station unit load distribution system based on a power system according to claim 1, characterized in that, It includes an acquisition unit, a model building unit, an objective function building unit, a solution unit, an iteration unit, and a dynamic update unit; the acquisition unit is used to acquire the operating status data of the hydropower station in real time; the operating status data includes the total load demand of the power station, the number of generating units that need to be connected to the grid, the current real-time head of the power station, and the vibration zone range that each turbine generator unit needs to avoid during operation; The model building unit is used to establish a mapping relationship model between the output power and flow rate of each hydro-generator unit under different water heads, and to determine the operating constraints of each hydro-generator unit; the constraints include at least the power distribution of each hydro-generator unit avoiding the vibration zone. The objective function establishment unit is used to establish a mathematical model with the goal of minimizing the total water consumption rate of the power station. The objective function is to minimize the total flow of all operating units of the hydro-turbine generator set while meeting the total load demand of the power station. The solution unit is used to solve the mathematical model using the firefly algorithm, including: initializing the firefly population, setting the population size, maximum number of iterations, attraction coefficient, and step size factor, with each firefly's position representing a unit power allocation scheme; the iteration unit is used to calculate the brightness of each firefly in the population; the brightness of the fireflies is negatively correlated with the objective function value; based on the differences in the brightness of the fireflies, each firefly is guided to move towards the brighter firefly, and random perturbations are introduced during the movement; during the iteration process, solutions falling into the vibration zone are penalized and corrected until the maximum number of iterations or the convergence condition is reached, and the globally optimal power allocation scheme is output; the dynamic update unit is used to distribute the optimal power allocation scheme to the local control unit for execution, monitor changes in operating conditions in real time, and re-output a new load allocation scheme when the trigger condition is met.

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