A pumped storage power station water energy parameter calculation method based on a particle swarm algorithm

Abstract

The present application relates to pumped storage technology field, disclose a kind of pumped storage power station water energy parameter calculation method based on particle swarm algorithm, comprising: obtaining the position and speed of each particle in this round iteration, the individual optimal position of last round iteration, obtain the global optimal position of last round iteration of particle swarm;When not satisfying the head ratio constraint, correct position;Calculate fitness;When pumped storage power station does not satisfy the reserve storage capacity symmetry constraint, local optimization is carried out;After optimization, fitness is calculated again, and individual optimal position and global optimal position are updated;The speed and position of each particle in this round iteration are updated;Repeat the above iteration process until the iteration stop condition is reached, and the global optimal position based on the last round iteration determines target water energy parameter.The present application is suitable for the water energy parameter calculation problem of strong nonlinearity by particle swarm algorithm and multi-condition constraint, efficiently and accurately searches global optimal solution, improves calculation accuracy, and provides reliable basis for power station design and operation.

Description

Technical Field

[0001] This invention relates to the field of pumped storage technology, specifically to a method for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization. Background Technology

[0002] The hydroelectric parameters of a pumped storage power station include the normal water level, dead water level, regulating capacity of the upper and lower reservoirs, and the head and energy storage capacity of the power station. These hydroelectric parameters are the core basis for defining the scale of the power station and directly determine the scientific validity and feasibility of the power station design. Therefore, how to calculate these hydroelectric parameters is a problem that needs to be solved.

[0003] The calculation of hydroelectric parameters for pumped storage power stations is a nonlinear, multi-constraint coupled optimization problem. It requires the simultaneous satisfaction of multiple interrelated constraints, which are not independent but coupled, further increasing the computational complexity. Due to these characteristics, existing methods struggle to quickly identify feasible solutions that satisfy all constraints. Some solutions even require iterative calculations to verify constraint compliance, leading to redundant and time-consuming computational processes. Furthermore, the hydroelectric parameters calculated by existing methods may only satisfy local optimum conditions, rather than the global optimum, resulting in poor computational accuracy. Summary of the Invention

[0004] In view of this, the present invention provides a method for calculating hydropower parameters of pumped storage power stations based on particle swarm optimization algorithm, so as to solve the problems of redundant calculation process, long calculation time and poor calculation accuracy of existing hydropower parameter calculation methods.

[0005] In a first aspect, the present invention provides a method for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization, the method comprising:

[0006] The position and velocity of each particle in the particle swarm in the current iteration and the individual optimal position obtained in the previous iteration are obtained. The global optimal position of the particle swarm obtained in the previous iteration is also obtained. The particle swarm is generated based on multiple sets of hydropower parameters, and the position represents multiple water level dimensions of the hydropower parameters.

[0007] When the position of the particle in the current iteration does not meet the head-to-water ratio constraint, the position of the particle in the current iteration is corrected to obtain the corrected position of the particle.

[0008] The fitness of the particles is calculated based on the power station parameters of the pumped storage power station and the corrected position of the particles.

[0009] When the pumped storage power station does not meet the symmetric constraint of the backup reservoir capacity, local optimization is performed based on the power station parameters, the fitness of the particles, and the corrected position to obtain the optimized position of the particles.

[0010] The fitness is recalculated based on the optimized position of the particles. The individual optimal position obtained in the previous iteration is updated based on the fitness to obtain the individual optimal position of the particles in this iteration. Based on the individual optimal positions of all particles in this iteration, the global optimal position of the particle swarm obtained in the previous iteration is updated to obtain the global optimal position of the particle swarm in this iteration.

[0011] Based on the individual optimal position of each particle in this iteration and the global optimal position of the particle swarm in this iteration, update the velocity and position of each particle in this iteration to obtain the velocity and position of each particle in the next iteration.

[0012] Repeat the above iterative process until the iteration stops. Based on the global optimal position obtained in the last iteration, determine the target hydropower parameters of the pumped storage power station.

[0013] This invention maps multiple sets of hydropower parameters to particles and obtains particle swarm iteration parameters, providing a directional basis for subsequent convergence to the optimal solution. It corrects the positions of particles that do not meet the head-to-head ratio constraint, preventing invalid positions that violate the constraint from entering subsequent calculations. This ensures that subsequent iterations always proceed within the effective solution space, avoiding invalid solutions interfering with the optimization direction and improving the algorithm's convergence stability. The fitness of the corrected particles is calculated, transforming the abstract quality of hydropower parameters into quantifiable fitness values. Local optimization is performed when the power station does not meet the symmetric constraint of reserve capacity to reduce the reserve capacity deviation. Based on the optimized particles, the individual optimal position and the global optimal position are updated, ensuring that each iteration captures a better parameter combination, solving the problem that traditional methods cannot track the optimal solution in real time. The velocity and position of the particles are updated for the next iteration, and the iteration is repeated to avoid insufficient solution space coverage in a single iteration, ensuring that the final parameters have both the highest fitness and satisfy all constraints. Through the particle swarm algorithm and multiple constraints, it is suitable for hydropower parameter calculation problems with strong nonlinearity, efficiently and accurately searching for the global optimal solution, shortening computation time, improving computational accuracy, and providing a reliable basis for power station design and operation.

[0014] In one alternative implementation, the locations include the normal water level of the upper reservoir, the dead water level of the upper reservoir, the normal water level of the lower reservoir, and the dead water level of the lower reservoir of the pumped storage power station.

[0015] Correcting the particle's position in this iteration yields the corrected position, including:

[0016] For the normal water level of the upper reservoir and the normal water level of the lower reservoir in the position of the particle in this iteration, the first upper limit corresponding to the normal water level of the upper reservoir and the second upper limit corresponding to the normal water level of the lower reservoir are calculated based on multiple other water levels and the maximum head-to-head ratio.

[0017] For the dead water levels of the upper and lower reservoirs in the position of the particle in this iteration, the first lower limit corresponding to the dead water level of the upper reservoir and the second lower limit corresponding to the dead water level of the lower reservoir are calculated based on multiple other water levels and the maximum head-to-head ratio.

[0018] The first upper limit and the first preset lower limit corresponding to the normal water level of the upper reservoir are used to generate corresponding constraint conditions. The first preset upper limit and the first lower limit corresponding to the dead water level of the upper reservoir are used to generate corresponding constraint conditions. The second upper limit and the second preset lower limit corresponding to the normal water level of the lower reservoir are used to generate corresponding constraint conditions. The second preset upper limit and the second lower limit corresponding to the dead water level of the lower reservoir are used to form corresponding constraint conditions.

[0019] For each water level in the position of the particle in this iteration, a corrected water level is randomly generated in the constraints corresponding to the water level, and the corrected position of the particle is obtained.

[0020] This embodiment corrects the position by compressing the range, which solves the problem that the original particle position does not meet the head-to-water ratio constraint, avoids the particle swarm getting trapped in the local solution space, and improves the overall optimization efficiency.

[0021] In one optional implementation, the power plant parameters include the reserve capacity of the upper reservoir, the reserve capacity of the lower reservoir, the overall power output coefficient, and the power generation capacity.

[0022] Fitness is calculated using the following formula:

[0023]

[0024] In the formula, Indices representing particles; Indicates the first The fitness of each particle; Indicates the first The energy stored in each particle; Indicates the penalty coefficient; This indicates the reserve capacity of the upper reservoir; This indicates the reserve capacity of the reservoir.

[0025] Energy storage is calculated using the following formula:

[0026]

[0027] In the formula, Indicates the overall power output coefficient of the power plant; Indicates the reservoir capacity for power generation; Represents the time constant; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level of the upper reservoir at the position of each particle; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level in the reservoir at the position of each particle.

[0028] This embodiment calculates fitness to quantify the merits of a combination of hydropower parameters, providing a basis for subsequent optimization.

[0029] In one optional implementation, local optimization is performed based on power plant parameters, particle fitness, and corrected position to obtain the optimized position of the particle, including:

[0030] For any water level in the corrected position of the particle, if the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir, the reservoir capacity is expanded outward based on the power station parameters to obtain multiple first virtual particles.

[0031] When the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir, the reservoir capacity is reduced inward based on the power station parameters to obtain multiple second virtual particles.

[0032] Remove the first virtual particle that does not meet the head-to-head ratio constraint, or remove the second virtual particle that does not meet the head-to-head ratio constraint;

[0033] Calculate the fitness of each first virtual particle after removal, or calculate the fitness of each second virtual particle after removal;

[0034] The target virtual particle with the highest fitness is determined from all first virtual particles or all second virtual particles. When the fitness of the target virtual particle is greater than that of the particle, the target virtual particle replaces the particle to complete the local optimization of the water level.

[0035] Repeat the above process of local optimization of water level until the local optimization of all water levels in the corrected position of the particle is completed, thus completing this round of optimization and obtaining the position of the particle in this round of optimization.

[0036] Repeat the above optimization process until the optimization stopping condition is met, and take the position obtained in the last round of optimization as the optimized position of the particle.

[0037] This embodiment optimizes each water level of the particle separately when the power station does not meet the symmetry constraint of the backup reservoir capacity, so as to reduce the reservoir capacity deviation and make the optimized position satisfy multiple constraints and have the highest fitness.

[0038] In one optional implementation, the power plant parameters also include the reservoir capacity curve and the power plant's reserve reservoir capacity;

[0039] When the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir, the reservoir capacity is expanded outward based on the power station parameters, resulting in multiple first virtual particles, including:

[0040] When the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir, the first maximum capacity and the first minimum capacity corresponding to the water level are determined based on the power station parameters.

[0041] Based on the first maximum storage capacity and the first minimum storage capacity, calculate multiple discrete points of the first storage capacity;

[0042] Map each discrete point of the first reservoir capacity to the reservoir capacity curve to obtain the corresponding first target water level;

[0043] Replace the water level with the first target water level, fix the other multiple water levels in the corrected position of the particle, and obtain the first virtual particle corresponding to the particle.

[0044] This embodiment expands the reservoir capacity outward when the reserve capacity of the reservoir corresponding to the water level is smaller than that of another reservoir. This can effectively reduce the deviation between the reserve capacity of the two reservoirs, generate the first virtual particle to fully cover the high-quality solution space, facilitate the accurate mining of local optimal solutions, and help to advance towards the global optimal solution.

[0045] In one optional implementation, determining the first maximum reservoir capacity and the first minimum reservoir capacity corresponding to the water level based on power station parameters includes:

[0046] When the water level is at the normal storage level of the upper reservoir or the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the normal storage level of the upper reservoir or the lower reservoir is taken as the first minimum reservoir capacity, and the first maximum reservoir capacity is calculated by the following formula:

[0047]

[0048] In the formula, Indicates the first maximum storage capacity; This indicates the first minimum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir;

[0049] When the water level is at the dead water level of the upper or lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the dead water level of the upper or lower reservoir is taken as the first maximum reservoir capacity, and the first minimum reservoir capacity is calculated using the following formula:

[0050]

[0051] In the formula, This indicates the first minimum storage capacity; Indicates the first maximum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0052] This embodiment designs the reservoir capacity boundary calculation logic differently for different water levels, ensuring that the reservoir capacity boundary can guide subsequent expansion operations to reduce the deviation between the two reservoirs' backup capacity, laying a reliable foundation for generating compliant and high-quality first virtual particles, and further improving the efficiency and accuracy of local optimization.

[0053] In one optional implementation, when the reserve capacity of the reservoir corresponding to a certain water level is greater than the reserve capacity of another reservoir, the reservoir capacity is reduced inward based on power station parameters to obtain multiple second virtual particles, including:

[0054] When the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir, the second maximum capacity and the second minimum capacity corresponding to the water level are determined based on the power station parameters.

[0055] Based on the second maximum storage capacity and the second minimum storage capacity, calculate multiple discrete points of the second storage capacity;

[0056] Map each discrete point of the second reservoir capacity to the reservoir capacity curve to obtain the corresponding second target water level;

[0057] Replace the water level with the second target water level, fix the other multiple water levels in the corrected position of the particle, and obtain the second virtual particle corresponding to the particle.

[0058] This embodiment effectively reduces the discrepancy between the reserve capacity of the two reservoirs by shrinking the reservoir capacity inward when the reserve capacity of the reservoir corresponding to the water level is greater than that of the other reservoir. This generates a second virtual particle that fully covers the high-quality solution space, which facilitates the accurate discovery of local optimal solutions and helps to advance towards the global optimal solution.

[0059] In one optional implementation, determining the second maximum reservoir capacity and the second minimum reservoir capacity corresponding to the water level based on power station parameters includes:

[0060] When the water level is at the normal storage level of the upper reservoir or the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the normal storage level of the upper reservoir or the lower reservoir is taken as the second maximum reservoir capacity, and the second minimum reservoir capacity is calculated by the following formula:

[0061]

[0062] In the formula, Indicates the second minimum storage capacity; Indicates the second largest storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir;

[0063] When the water level is at the dead water level of the upper or lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the dead water level of the upper or lower reservoir is taken as the second minimum reservoir capacity, and the second maximum reservoir capacity is calculated using the following formula:

[0064]

[0065] In the formula, Indicates the second largest storage capacity; Indicates the second minimum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0066] This embodiment designs the reservoir capacity boundary calculation logic differently for different water levels, ensuring that the reservoir capacity boundary can guide subsequent expansion operations to reduce the deviation between the two reservoirs' backup capacity, laying a reliable foundation for generating compliant and high-quality second virtual particles, and further improving the efficiency and accuracy of local optimization.

[0067] In an optional implementation, before correcting the particle's position in the current iteration when the particle's position does not satisfy the head-to-water ratio constraint, the method further includes:

[0068] Calculate the head-to-lift ratio based on the particle's position in this iteration;

[0069] When the head-to-water ratio is greater than a preset threshold, the position of the particle in this iteration is determined to satisfy the head-to-water ratio constraint.

[0070] When the head-to-water ratio is not greater than the preset threshold, it is determined that the position of the particle in this iteration does not meet the head-to-water ratio constraint.

[0071] When a pumped storage power station does not meet the symmetric constraint of reserve capacity, before performing local optimization based on power station parameters, particle fitness, and corrected position to obtain the optimized position of the particle, the method further includes:

[0072] Calculate the absolute value of the difference between the reserve capacity of the upper reservoir and the reserve capacity of the lower reservoir, and use it as the reservoir capacity difference;

[0073] Calculate the ratio of the power plant's reserve capacity to the preset value, and calculate the product of the ratio and the reserve capacity symmetry deviation threshold;

[0074] When the difference in storage capacity is greater than the product, it is determined that the symmetric constraint of the spare storage capacity is not satisfied.

[0075] When the difference in storage capacity is no greater than the product, the symmetric constraint of the spare storage capacity is determined to be satisfied.

[0076] This embodiment determines whether the head-head ratio constraint is met by calculating the head-head ratio, and determines whether the symmetry constraint of the spare reservoir capacity is met by using the spare reservoir capacity, so as to find the optimal solution under the condition of satisfying multiple constraint coupling.

[0077] In one alternative implementation, the velocity of each particle in this iteration is updated using the following formula:

[0078]

[0079] In the formula, Indicates the updated number The first particle The speed of each water level dimension in the next iteration; Indicates inertia weight; Indicates the first The first particle The speed of each water level dimension in this iteration; and Indicates the learning factor; This represents the position of the globally optimal position obtained in this iteration. Each water level dimension; Indicates the first The first particle The position of each water level dimension in this iteration; This indicates the result of the current iteration. The particle in the first The optimal position of an individual at each water level dimension; Indicates the first The particle in the first The regulating reservoir capacity gradient in each water level dimension; This represents the gradient term weight.

[0080] This embodiment further optimizes the search direction by introducing gradient term weights when updating the speed, ensuring that the particles can quickly explore the high-quality solution space, improving computational efficiency, and also conforming to the engineering characteristics of the hydropower parameters of pumped storage power stations.

[0081] Secondly, the present invention provides a device for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization algorithm, the device comprising:

[0082] The acquisition module is used to acquire the position and velocity of each particle in the particle swarm in the current iteration and the individual optimal position obtained in the previous iteration, and to acquire the global optimal position of the particle swarm obtained in the previous iteration. The particle swarm is generated based on multiple sets of hydro-energy parameters, and the position represents multiple water level dimensions of the hydro-energy parameters.

[0083] The correction module is used to correct the position of the particle in the current iteration when the position of the particle does not meet the head-to-water ratio constraint, so as to obtain the corrected position of the particle.

[0084] The first calculation module is used to calculate the fitness of the particles based on the power station parameters of the pumped storage power station and the corrected position of the particles.

[0085] The optimization module is used to perform local optimization based on power station parameters, particle fitness, and corrected position when the pumped storage power station does not meet the symmetry constraint of the backup reservoir capacity, so as to obtain the optimized position of the particle.

[0086] The first update module is used to recalculate the fitness based on the optimized position of the particles, update the individual optimal position obtained in the previous iteration based on the fitness, obtain the individual optimal position of the particles in the current iteration, and update the global optimal position of the particle swarm obtained in the previous iteration based on the individual optimal positions of all particles in the current iteration, obtain the global optimal position of the particle swarm in the current iteration.

[0087] The second update module is used to update the velocity and position of each particle in the current iteration based on the individual optimal position of each particle in the current iteration and the global optimal position of the particle swarm in the current iteration, so as to obtain the velocity and position of each particle in the next iteration.

[0088] The first determining module is used to repeat the above iterative process until the iteration stops. Based on the global optimal position obtained in the last iteration, the target hydropower parameters of the pumped storage power station are determined.

[0089] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-described method for calculating hydropower parameters of a pumped storage power station based on particle swarm optimization algorithm, as described in the first aspect or any corresponding embodiment.

[0090] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for calculating hydroelectric parameters of a pumped storage power station based on the particle swarm optimization algorithm described in the first aspect or any corresponding embodiment. Attached Figure Description

[0091] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0092] Figure 1 This is a flowchart illustrating the method for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization according to an embodiment of the present invention.

[0093] Figure 2 This is a structural block diagram of a pumped storage power station hydropower parameter calculation device based on particle swarm optimization algorithm according to an embodiment of the present invention.

[0094] Figure 3 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0095] 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0096] According to an embodiment of the present invention, a method for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization algorithm is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0097] This embodiment provides a method for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization, which can be used on a server, including a main control server and multiple computing servers.

[0098] Figure 1 This is a flowchart of a method for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps:

[0099] Step S101: Obtain the position and velocity of each particle in the particle swarm in this iteration and the individual optimal position obtained in the previous iteration, and obtain the global optimal position of the particle swarm obtained in the previous iteration. The particle swarm is generated based on multiple sets of hydro-energy parameters, and the position represents multiple water level dimensions of the hydro-energy parameters.

[0100] Specifically, the calculation of hydroelectric parameters for pumped storage power stations is a nonlinear, multi-constraint coupled optimization problem, and related technologies suffer from redundant calculation processes, long computation times, and poor accuracy. To address this, this invention maps each set of hydroelectric parameters to a particle, forming a four-dimensional particle swarm with a population size of N. .in, , , Indicates the first The normal water level and dead water level of the reservoir at the position of each particle. , Indicates the first The normal and dead water levels of the reservoir are determined at the positions of individual particles. An improved particle swarm optimization algorithm is employed, which is more accurately adapted to complex nonlinear optimization scenarios, thereby effectively enhancing the search capability for the global optimum. It can quickly select feasible solutions that satisfy all constraints while avoiding getting trapped in local optima. While shortening the computation time and simplifying the calculation process, it significantly improves the accuracy of hydropower parameter calculations, providing more reliable parameter data for the design and operation of pumped storage power stations.

[0101] Step S102: When the position of the particle in this iteration does not meet the head-to-water ratio constraint, the position of the particle in this iteration is corrected to obtain the corrected position of the particle.

[0102] Specifically, the head-to-lift ratio is a key technical indicator for the operation of pumped-storage power station units. This indicator is calculated by combining the dead water level and normal water level of the upper and lower reservoirs. From the perspective of safe operation, its value must be less than or equal to a pre-determined limit based on the power station's head conditions. If this limit is exceeded, the unit is prone to problems such as excessive vibration, sudden drop in efficiency, or even mechanical failure, failing to meet the basic design and operation requirements of the power station. Therefore, when the head-to-lift ratio calculated from the particle's position in this iteration exceeds the above constraint, that particle position is an invalid solution. Substituting it into subsequent calculations not only has no practical engineering significance but also consumes computational resources, prolongs the overall iteration time, and hinders the algorithm from converging to the optimal solution. Based on this, the position of the particle in this iteration is corrected to ensure that the corrected position satisfies the head-to-lift ratio constraint.

[0103] Step S103: Calculate the fitness of the particle based on the power station parameters of the pumped storage power station and the corrected position of the particle.

[0104] Specifically, fitness is the core indicator for quantifying the quality of particle positions (i.e., a combination of hydropower parameters), providing a basis for subsequent optimization.

[0105] Step S104: When the pumped storage power station does not meet the symmetric constraint of the backup reservoir capacity, local optimization is performed based on the power station parameters, the fitness of the particles, and the corrected position to obtain the optimized position of the particles.

[0106] Specifically, reserve capacity is the storage margin reserved by a pumped-storage power station to cope with special operating conditions. In principle, the reserve capacity of the upper and lower reservoirs of the power station should be symmetrical. This constraint is crucial to ensuring the operational stability of the power station. If the reserve capacity is severely asymmetrical, one reservoir may be unable to cope with special operating conditions due to insufficient reserve capacity, affecting the power station's energy storage and power generation functions. When a pumped-storage power station does not meet the symmetry constraint of reserve capacity, local optimization of particle positions is performed to reduce the capacity deviation. This ensures compliance with the symmetry constraint of reserve capacity while quickly converging to the global optimum, helping to simplify the calculation process and improve computational efficiency and accuracy.

[0107] Step S105: Calculate the fitness again based on the optimized position of the particles, update the individual optimal position obtained in the previous iteration based on the fitness, obtain the individual optimal position of the particles in this iteration, and update the global optimal position of the particle swarm obtained in the previous iteration based on the individual optimal positions of all particles in this iteration, obtain the global optimal position of the particle swarm in this iteration.

[0108] Specifically, the individual optimal position refers to the position with the highest fitness among all positions of a single particle since the start of the iteration (i.e., the corresponding optimal set of hydro-energy parameters); the global optimal position is the position with the highest fitness among all positions of the entire particle swarm since the start of the iteration (i.e., the globally optimal set of hydro-energy parameters). Since the position of a particle changes after local optimization, and this change in position leads to a change in fitness, it is necessary to recalculate the fitness based on the optimized position of the particle to ensure that the fitness perfectly matches the current position. This provides an accurate basis for updating the optimal position and ensures that the individual optimal and global optimal positions in each iteration are always the best combination of parameters available at the time.

[0109] Step S106: Based on the individual optimal position of each particle in this iteration and the global optimal position of the particle swarm in this iteration, update the velocity and position of each particle in this iteration to obtain the velocity and position of each particle in the next iteration.

[0110] Specifically, after each iteration, particles need to adjust their speed and position based on their own historical best group global optimum in order to gradually approach the global optimum solution and avoid low optimization efficiency or getting trapped in local optima.

[0111] Step S107: Repeat the above iterative process until the iteration stop condition is met. Based on the global optimal position obtained in the last iteration, determine the target hydropower parameters of the pumped storage power station.

[0112] Specifically, the optimization process of the particle swarm optimization algorithm requires multiple iterations to gradually approach the global optimum. Therefore, steps S101-S106 need to be repeated until the iteration stopping condition is met, ensuring that the final output hydropower parameters reach the overall optimality. The iteration stopping condition can be reaching a preset number of iterations, or the global optimum position of the particle swarm no longer changing. The iteration stopping condition can also be adjusted according to actual needs. The target hydropower parameters are determined based on the global optimum position obtained in the last iteration. This position represents the parameter combination with the highest fitness and satisfying all constraints after multiple rounds of optimization by the entire particle swarm. Based on this, the target hydropower parameters can be accurately determined, providing a valid basis for the design, construction, and operation of pumped storage power stations.

[0113] This invention maps multiple sets of hydropower parameters to particles and obtains particle swarm iteration parameters, providing a directional basis for subsequent convergence to the optimal solution. It corrects the positions of particles that do not meet the head-to-head ratio constraint, preventing invalid positions that violate the constraint from entering subsequent calculations. This ensures that subsequent iterations always proceed within the effective solution space, avoiding invalid solutions interfering with the optimization direction and improving the algorithm's convergence stability. The fitness of the corrected particles is calculated, transforming the abstract quality of hydropower parameters into quantifiable fitness values. Local optimization is performed when the power station does not meet the symmetric constraint of reserve capacity to reduce the reserve capacity deviation. Based on the optimized particles, the individual optimal position and the global optimal position are updated, ensuring that each iteration captures a better parameter combination, solving the problem that traditional methods cannot track the optimal solution in real time. The velocity and position of the particles are updated for the next iteration, and the iteration is repeated to avoid insufficient solution space coverage in a single iteration, ensuring that the final parameters have both the highest fitness and satisfy all constraints. Through the particle swarm algorithm and multiple constraints, it is suitable for hydropower parameter calculation problems with strong nonlinearity, efficiently and accurately searching for the global optimal solution, shortening computation time, improving computational accuracy, and providing a reliable basis for power station design and operation.

[0114] This embodiment provides a method for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization, which can be used in the aforementioned server. The method specifically includes the following steps:

[0115] Step S201: Obtain the position and velocity of each particle in the particle swarm in this iteration, as well as the individual optimal position obtained in the previous iteration. Also obtain the global optimal position of the particle swarm obtained in the previous iteration. The particle swarm is generated based on multiple sets of hydroelectric parameters. The position represents multiple water level dimensions of the hydroelectric parameters, including the normal water level of the upper reservoir, the dead water level of the upper reservoir, the normal water level of the lower reservoir, and the dead water level of the lower reservoir of the pumped storage power station. For details, please refer to [link to details]. Figure 1Step S101 of the illustrated embodiment will not be described again here.

[0116] In some optional implementations, the original constraints on the normal water level of the upper reservoir of the power station are an upper limit predetermined based on topography, geology, and inundation impact requirements, and a lower limit for the dead water level of the upper reservoir. Similarly, the original constraints on the normal water level of the lower reservoir can be obtained. The original constraints on the dead water level of the upper reservoir are the normal water level of the upper reservoir as the upper limit and the reservoir bottom elevation as the lower limit. If this iteration is the first iteration, compared to the traditional particle swarm optimization algorithm which randomly generates the initial positions of particles, this embodiment of the invention randomly generates the initial positions within the original constraints corresponding to each water level.

[0117] Step S202: Calculate the head-to-lift ratio based on the position of the particle in this iteration.

[0118] Specifically, the head-to-head ratio corresponding to the current hydropower parameters is calculated using the following formula (1).

[0119] (1)

[0120] In the formula, Indicates the first The head-to-lift ratio corresponding to the position of each particle; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level of the upper reservoir at the position of each particle; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level in the reservoir at the position of each particle; This indicates the head increase value corresponding to the preset maximum head. This indicates the head loss corresponding to the preset minimum head.

[0121] Step S203: When the head-to-water ratio is greater than a preset threshold, determine that the position of the particle in this iteration satisfies the head-to-water ratio constraint.

[0122] Specifically, the preset threshold can be set according to the power station's head conditions. When the head-to-head ratio is greater than the preset threshold, it indicates that the hydropower parameters corresponding to the current particle position can maintain a reasonable match between the unit's head and the pump head, and there will be no problems such as excessive vibration, sudden drop in efficiency, or mechanical failure. Therefore, it can be determined that the particle's position in this iteration satisfies the head-to-head ratio constraint.

[0123] Step S204: When the head-to-water ratio is not greater than a preset threshold, determine that the position of the particle in this iteration does not meet the head-to-water ratio constraint.

[0124] Specifically, when the head-to-head ratio is less than or equal to a preset threshold, it indicates that the hydropower parameters corresponding to the current particle position have violated the head-to-head ratio constraint. In this case, the unit will experience increased vibration and a significant decrease in power generation efficiency due to the imbalance between head and water head. In severe cases, it may cause mechanical failures such as bearing wear and blade damage, failing to meet the safety requirements of power plant design and operation. Therefore, correction is necessary. By using the head-to-head ratio constraint, feasible solutions that meet the unit's safe operation requirements can be quickly selected, avoiding invalid solutions from entering subsequent calculations, reducing unnecessary computational resource consumption, and improving overall optimization efficiency and the practicality and accuracy of the final parameters.

[0125] Step S205: When the position of the particle in this iteration does not meet the head-to-water ratio constraint, the position of the particle in this iteration is corrected to obtain the corrected position of the particle.

[0126] Specifically, step S205 includes:

[0127] Step S2051: For the normal water level of the upper reservoir and the normal water level of the lower reservoir in the position of the particle in this iteration, calculate the first upper limit corresponding to the normal water level of the upper reservoir and the second upper limit corresponding to the normal water level of the lower reservoir based on multiple other water levels and the maximum head-to-water ratio.

[0128] Specifically, the core of particle position is the four-dimensional water level dimension: the normal water level of the upper reservoir, the dead water level of the upper reservoir, the normal water level of the lower reservoir, and the dead water level of the lower reservoir. The head-to-lift ratio is determined by these four water levels. When a particle needs to be corrected because it does not meet the head-to-lift ratio constraint, the value range of each water level needs to be compressed separately to ensure that the corrected position meets the constraint. Since the upper limit in the original constraint condition of the normal water level is predetermined, it may be too wide, causing the randomly generated water level to easily break the head-to-lift ratio constraint. Therefore, a more precise upper limit needs to be determined to narrow the value range. More specifically, for the normal water level of the upper reservoir, the other three water levels are fixed. After fixing, the interference of other water levels on the head-to-lift ratio can be eliminated, and the influence of the normal water level of the upper reservoir can be focused. Substituting the three fixed water levels and the preset maximum head-to-lift ratio into the above formula (1), the first upper limit of the normal water level of the upper reservoir can be derived. The calculation process of the second upper limit corresponding to the normal water level of the lower reservoir is similar and will not be repeated here.

[0129] Step S2052: For the upper and lower dead water levels of the reservoir at the position of the particle in this iteration, calculate the first lower limit corresponding to the upper dead water level and the second lower limit corresponding to the lower dead water level based on multiple other water levels and the maximum head-to-water ratio.

[0130] Specifically, since an excessively low dead water level may cause the head-to-lift ratio to exceed the threshold, a more reasonable lower limit needs to be determined to ensure that the dead water level value does not cause constraint failure. The calculation process for determining the lower limit is the same as in step S2051, and will not be repeated here.

[0131] Step S2053: Generate corresponding constraint conditions based on the first upper limit and the first preset lower limit corresponding to the normal water level of the upper reservoir; generate corresponding constraint conditions based on the first preset upper limit and the first lower limit corresponding to the dead water level of the upper reservoir; generate corresponding constraint conditions based on the second upper limit and the second preset lower limit corresponding to the normal water level of the lower reservoir; and form corresponding constraint conditions based on the second preset upper limit and the second lower limit corresponding to the dead water level of the lower reservoir.

[0132] Specifically, for the normal water level of the upper reservoir, the first upper limit calculated in step S2051 replaces the upper limit in its original constraint condition, thereby compressing the value range and generating new constraint conditions. The other three water levels are also compressed in the same way. This ensures that the compressed range will not cause the head-to-water ratio constraint to be violated, while retaining a reasonable value space, thus solving the problem that the original constraint range is too wide and easily leads to constraint violation.

[0133] Step S2054: For each water level in the position of the particle in this iteration, a corrected water level is randomly generated in the constraints corresponding to the water level to obtain the corrected position of the particle.

[0134] Specifically, each water level is randomly selected within its compressed range. Combining four randomly generated water levels yields the corrected position of the particle. By correcting the position through compression, the problem of the original particle position not satisfying the head-to-water ratio constraint is solved, preventing the particle swarm from getting trapped in the local solution space and improving the overall optimization efficiency.

[0135] Step S206: Based on the power station parameters of the pumped storage power station and the corrected position of the particles, calculate the fitness of the particles. The power station parameters include the reserve capacity of the upper reservoir, the reserve capacity of the lower reservoir, the overall power output coefficient of the power station, the power generation capacity, the capacity curve, and the reserve capacity of the power station.

[0136] Specifically, fitness is calculated using the following formula (2) to measure the quality of the hydroelectric parameters corresponding to the position of the particle.

[0137] (2)

[0138] In the formula, Indices representing particles; Indicates the first The fitness of each particle; Indicates the first The energy stored in each particle is calculated using the following formula (3); Indicates the penalty coefficient; This indicates the reserve capacity of the upper reservoir; This indicates the reserve capacity of the reservoir.

[0139] (3)

[0140] In the formula, Indicates the overall power output coefficient of the power plant; The power generation reservoir capacity can be calculated using the following formula (4); This represents the time constant, which can be 3600. Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level of the upper reservoir at the position of each particle; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level in the reservoir at the position of each particle.

[0141] (4)

[0142] In the formula, Indicates the reservoir capacity for power generation; This indicates the regulating capacity of the upper reservoir; This indicates the regulating capacity of the lower reservoir; This indicates the power plant's reserve storage capacity.

[0143] Step S207: Calculate the absolute value of the difference between the reserve capacity of the upper reservoir and the reserve capacity of the lower reservoir, and use it as the capacity difference.

[0144] Step S208: Calculate the ratio of the power plant's reserve capacity to the preset value, and calculate the product of the ratio and the reserve capacity symmetry deviation threshold.

[0145] Step S209: When the difference in storage capacity is greater than the product, it is determined that the symmetric constraint of the spare storage capacity is not satisfied.

[0146] Specifically, assuming a preset value of 2, when the storage capacity difference is greater than the product, that is... , This represents the threshold for symmetry deviation of the reserve capacity, which can be selected based on experience, for example, 5%. At this point, the deviation between the reserve capacity of the two reservoirs is large, failing to meet the symmetry constraint of the reserve capacity, and local optimization is required to reduce the capacity deviation.

[0147] Step S210: When the difference in storage capacity is not greater than the product, it is determined that the symmetric constraint of the spare storage capacity is satisfied.

[0148] Specifically, when the difference in storage capacity is no greater than the product, it means that the reserve storage capacity of the two storage facilities is symmetrical and no optimization is required.

[0149] Step S211: When the pumped storage power station does not meet the symmetric constraint of the backup reservoir capacity, local optimization is performed based on the power station parameters, the fitness of the particles, and the corrected position to obtain the optimized position of the particles.

[0150] Specifically, step S211 includes:

[0151] Step S2111: For any water level in the corrected position of the particle, if the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir, the reservoir capacity is expanded outward based on the power station parameters to obtain multiple first virtual particles.

[0152] In some optional implementations, step S2111 above includes:

[0153] Step A1: When the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir, determine the first maximum capacity and the first minimum capacity corresponding to the water level based on the power station parameters.

[0154] In some alternative implementations, step A1 above includes:

[0155] Step a1: When the water level is the normal storage level of the upper reservoir or the normal storage level of the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the normal storage level of the upper reservoir or the normal storage level of the lower reservoir is taken as the first minimum reservoir capacity, and the first maximum reservoir capacity is calculated by the following formula (5):

[0156] (5)

[0157] In the formula, Indicates the first maximum storage capacity; This indicates the first minimum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0158] Specifically, for any water level in the corrected position of the particle, such as the normal storage level of the upper reservoir, which belongs to the upper reservoir, if the reserve capacity of the upper reservoir is less than the reserve capacity of the lower reservoir, the capacity is expanded outward through steps A1-A4, thereby reducing the deviation between it and the reserve capacity of the other reservoir. More specifically, the storage capacity curve is used to describe the relationship between storage capacity and water level in a pumped storage power station. The storage capacity corresponding to the normal storage level of the upper reservoir is determined from the storage capacity curve and taken as the first minimum storage capacity, which is the actual storage capacity under the current water level, as the starting benchmark for expansion, ensuring that the expansion range fits the current water level conditions, and the first maximum storage capacity is determined according to the above formula (5). Similarly, the first minimum storage capacity and the first maximum storage capacity corresponding to the normal storage level of the lower reservoir are calculated.

[0159] Step a2: When the water level is the dead water level of the upper reservoir or the dead water level of the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the dead water level of the upper reservoir or the dead water level of the lower reservoir is taken as the first maximum reservoir capacity, and the first minimum reservoir capacity is calculated by the following formula (6):

[0160] (6)

[0161] In the formula, This indicates the first minimum storage capacity; Indicates the first maximum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0162] Specifically, when expanding the dead water level of the upper reservoir outward, the reservoir capacity corresponding to the dead water level of the upper reservoir is determined from the reservoir capacity curve and taken as the first maximum reservoir capacity. This capacity is the actual reservoir capacity under the current dead water level and serves as the starting benchmark for downward expansion, ensuring that the expansion range matches the current operating conditions. The first minimum reservoir capacity is then determined according to the above formula (6). Similarly, the first minimum reservoir capacity and the first maximum reservoir capacity corresponding to the dead water level of the lower reservoir are calculated.

[0163] Step A2: Calculate multiple discrete points of the first storage capacity based on the first maximum storage capacity and the first minimum storage capacity.

[0164] Specifically, the change between every two first storage capacity discrete points is calculated by the following formula (7), and each storage capacity discrete point is obtained by the following formula (8), thereby realizing the transformation of the continuous storage capacity expansion range into discrete and verifiable storage capacity points.

[0165] (7)

[0166] In the formula, This represents the change in the first discrete point of the storage capacity; Indicates the first maximum storage capacity; This indicates the first minimum storage capacity; This indicates the number of preset first storage capacity discrete points.

[0167] (8)

[0168] In the formula, Indicates the first The first discrete point of the storage capacity; This indicates the first minimum storage capacity; This represents the change in the first discrete point of the storage capacity.

[0169] Step A3: Map each discrete point of the first reservoir capacity to the reservoir capacity curve to obtain the corresponding first target water level.

[0170] Step A4 involves replacing the water level with each first target water level, fixing the other multiple water levels in the corrected position of the particle, and obtaining the first virtual particle corresponding to the particle.

[0171] Specifically, suppose we expand the normal water level at the top of the reservoir from the particle's position outwards, replacing the value of the normal water level at the top with the determined first target water level, while keeping the values ​​of the other three water levels unchanged, thus obtaining a first virtual particle. Each replacement of the first target water level yields a first virtual particle; each particle differs only in its normal water level at the top, while the other water levels remain consistent with the particle's corrected position. Similarly, we can expand other water levels to obtain corresponding first virtual particles. By using multiple virtual particles to cover different water level conditions within the expanded range, we can ensure that the optimization direction remains focused, accelerating the algorithm's convergence to the global optimum.

[0172] In step S2112, when the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir, the reservoir capacity is reduced inward based on the power station parameters to obtain multiple second virtual particles.

[0173] In some optional implementations, step S2112 above includes:

[0174] Step B1: When the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir, determine the second maximum capacity and the second minimum capacity corresponding to the water level based on the power station parameters.

[0175] In some optional implementations, step B1 above includes:

[0176] Step b1: When the water level is the normal storage level of the upper reservoir or the normal storage level of the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the normal storage level of the upper reservoir or the normal storage level of the lower reservoir is taken as the second maximum reservoir capacity, and the second minimum reservoir capacity is calculated by the following formula (9):

[0177] (9)

[0178] In the formula, Indicates the second minimum storage capacity; Indicates the second largest storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0179] Specifically, for any water level in the corrected position of the particle, such as the normal storage level of the upper reservoir, which belongs to the upper reservoir, if the reserve capacity of the upper reservoir is greater than the reserve capacity of the lower reservoir, the reservoir capacity is contracted inward through steps B1-B4, thereby reducing the deviation between it and the reserve capacity of the other reservoir. More specifically, the reservoir capacity corresponding to the normal storage level of the upper reservoir is determined from the reservoir capacity curve, and it is taken as the second maximum reservoir capacity, which is the actual reservoir capacity under the current water level, and serves as the upper limit benchmark for inward contraction, ensuring that the contraction range fits the water level conditions of the current iteration, avoiding blind adjustment that deviates from the actual parameters, and the second minimum reservoir capacity is determined according to the above formula (9). Similarly, the second minimum reservoir capacity and the second maximum reservoir capacity corresponding to the normal storage level of the lower reservoir are calculated.

[0180] Step b2: When the water level is the dead water level of the upper reservoir or the dead water level of the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the dead water level of the upper reservoir or the dead water level of the lower reservoir is taken as the second minimum reservoir capacity, and the second maximum reservoir capacity is calculated by the following formula (10):

[0181] (10)

[0182] In the formula, Indicates the second largest storage capacity; Indicates the second minimum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0183] Specifically, when the reservoir capacity is reduced inward from the dead water level of the upper reservoir, the reservoir capacity corresponding to the dead water level of the upper reservoir is determined from the reservoir capacity curve and used as the second minimum reservoir capacity, which is the actual reservoir capacity under the current dead water level. This serves as the starting benchmark for upward reduction, ensuring that the reduction range matches the current operating conditions. The second maximum reservoir capacity is then determined according to the above formula (10). Similarly, the second minimum reservoir capacity and the second maximum reservoir capacity corresponding to the dead water level of the lower reservoir are calculated.

[0184] Step B2 involves calculating multiple discrete points for the second storage capacity based on the second maximum and second minimum storage capacities. For details, please refer to step A2; these will not be repeated here.

[0185] Step B3 involves mapping each discrete point of the second reservoir capacity to the reservoir capacity curve to obtain the corresponding second target water level. For details, please refer to step A3, which will not be repeated here.

[0186] Step B4 involves replacing the water level with the second target water level and fixing multiple other water levels in the corrected position of the particle to obtain the second virtual particle corresponding to the particle. For details, please refer to step A4, which will not be repeated here.

[0187] Step S2113: Remove the first virtual particle that does not meet the head-to-head ratio constraint, or remove the second virtual particle that does not meet the head-to-head ratio constraint.

[0188] Specifically, the specific water level value in the first virtual particle or the second virtual particle is substituted into the above formula (1) to obtain the corresponding head-to-water ratio, and virtual particles with a head-to-water ratio ≤ a preset threshold are removed.

[0189] Step S2114: Calculate the fitness of each first virtual particle after removal, or calculate the fitness of each second virtual particle after removal.

[0190] Specifically, each virtual particle after being removed is substituted into the above equation (2) to calculate the corresponding fitness.

[0191] Step S2115: Determine the target virtual particle with the highest fitness from all first virtual particles or all second virtual particles. When the fitness of the target virtual particle is greater than that of the particle, replace the particle with the target virtual particle to complete the local optimization of the water level.

[0192] Specifically, assuming a local optimization is performed on the normal water level of the upper reservoir for the particle, if the reserve capacity of the upper reservoir is less than that of the lower reservoir, then the target virtual particle with the highest fitness is determined from all the first virtual particles. Alternatively, if the reserve capacity of the upper reservoir is greater than that of the lower reservoir, then the target virtual particle with the highest fitness is determined from all the second virtual particles. As can be seen from the above formula (2), the selected target virtual particle has the largest energy storage capacity and satisfies the head-to-lift ratio constraint and the symmetry constraint of the reserve capacity. If the fitness of the target virtual particle is greater than the fitness calculated in step S206, it indicates that its corresponding combination of hydropower parameters is better. The original particle is replaced with the target virtual particle to complete the local optimization of the normal water level of the upper reservoir for the particle.

[0193] Step S2116: Repeat the above process of local optimization of water level until the local optimization of all water levels in the corrected position of the particle is completed, thus completing this round of optimization and obtaining the position of the particle in this round of optimization.

[0194] Specifically, returning to step S2111, any water level for the particle is selected again for local optimization until all four water levels for the particle have been locally optimized, at which point one round of optimization is considered complete. Optimizing only a single water level cannot achieve the optimal overall parameters of the particle. It is possible that the symmetry constraint of the backup reservoir capacity is not satisfied due to the failure to optimize a certain water level, or that the energy storage is not optimal. Therefore, the local optimization process needs to be repeated to ensure that all water levels are optimized, thereby achieving a comprehensive improvement in the particle's position.

[0195] Step S2117: Repeat the above optimization process until the optimization stopping condition is met, and take the position obtained in the last round of optimization as the optimized position of the particle.

[0196] Specifically, the optimization stopping condition is that the values ​​of the four water levels do not change before and after a certain round of optimization, or the number of optimization rounds reaches a preset number. Repeat steps S2111-S2116 until the optimization stopping condition is met, and the position obtained in the last round of optimization is taken as the optimized position. The four water levels at this position have undergone multiple rounds of local optimization and constraint verification, satisfying all core requirements such as head-to-lift ratio constraints and symmetric constraints of reserve capacity, while also possessing the highest adaptability. It is the optimal solution that balances engineering feasibility and parameter superiority.

[0197] Step S212: Calculate the fitness again based on the optimized position of the particles, update the individual optimal position obtained in the previous iteration based on the fitness, obtain the individual optimal position of the particles in this iteration, and update the global optimal position of the particle swarm obtained in the previous iteration based on the individual optimal positions of all particles in this iteration, obtain the global optimal position of the particle swarm in this iteration.

[0198] Specifically, based on the optimized position of the particle, substitute it into the above equation (2) and calculate the fitness again. If the fitness of the optimized position is greater than the fitness of the individual's best position obtained in the previous iteration, then the optimized position is taken as the individual's best position in this iteration: If the fitness of the optimized position is less than or equal to the fitness of the individual's optimal position obtained in the previous iteration, the individual's optimal position remains unchanged. Then, the optimal position of the individual with the highest fitness is determined from all particles in the particle swarm. If its fitness is greater than the global optimal position obtained in the previous iteration, it is taken as the global optimal position for this iteration. If the fitness of a particle is less than or equal to the global optimal position obtained in the previous iteration, the global optimal position is maintained. By updating the position based on fitness, the convergence of the entire particle swarm towards the global optimal solution is accelerated.

[0199] Step S213: Based on the individual optimal position of each particle in this iteration and the global optimal position of the particle swarm in this iteration, update the velocity and position of each particle in this iteration to obtain the velocity and position of each particle in the next iteration.

[0200] Specifically, the velocity of each particle in this iteration is updated using the following formula (11):

[0201] (11)

[0202] In the formula, Indicates the updated number The first particle The speed of each water level dimension in the next iteration; Indicates inertia weight; Indicates the first The first particle The speed of each water level dimension in this iteration; and The learning factor can be a random number that varies in the interval [0,1]. This represents the position of the globally optimal position obtained in this iteration. Each water level dimension; Indicates the first The first particle The position of each water level dimension in this iteration; This indicates the result of the current iteration. The particle in the first The optimal position of an individual at each water level dimension; Indicates the first The particle in the first The regulating capacity gradient of the reservoir's regulating capacity along the water level dimension can be considered as the effect of the reservoir's regulating capacity on the first... The partial derivatives of each water level dimension can be calculated using the reservoir capacity curve and numerical difference. The gradient term weight is represented by the following formula (12).

[0203] (12)

[0204] In the formula, Indicates the gradient term weights; This represents a constant coefficient, indicating the numerical magnitude relationship between the water level and reservoir capacity gradient. Represents any two water level values. express The corresponding storage capacity.

[0205] Because the above steps guide the water level to accelerate the search for the optimal solution in the direction of expanding the regulating capacity, it is easier to find the optimal solution. However, as a large number of solutions approach the optimal solution in the later stages, the influence of the symmetric constraint of the reserve capacity becomes more prominent. By setting the weight of the gradient term, its influence is gradually weakened.

[0206] After updating the particle velocity, the position of the current iteration is updated using the following formula (13).

[0207] (13)

[0208] In the formula, Indicates the updated number The first particle The position of each water level dimension in the next iteration; Indicates the first The first particle The position of each water level dimension in this iteration; Indicates the updated number The first particle The speed of each water level dimension in the next iteration.

[0209] By gradually adjusting the speed and position of the particles, they can gradually escape the local solution space and approach the global optimal solution, avoiding the low optimization efficiency caused by undirected random search. Furthermore, the innovative introduction of gradient term weights further optimizes the search direction, ensuring that the particles can quickly explore the high-quality solution space, improving computational efficiency, while also conforming to the engineering characteristics of the hydropower parameters of pumped storage power stations.

[0210] Step S214: Repeat the above iterative process until the iteration stop condition is met. Based on the global optimal position obtained in the last iteration, determine the target hydropower parameters of the pumped storage power station.

[0211] Specifically, after repeating steps S201-S213 until the iteration stopping condition is met, the global optimal position obtained in the last iteration is obtained. This is the parameter combination that simultaneously satisfies the highest fitness and full constraint compliance after multiple iterations of the particle swarm. Substituting it into the above equation (3), the energy storage corresponding to the optimal hydropower parameters is obtained. The normal water level of the upper reservoir, the dead water level of the upper reservoir, the normal water level of the lower reservoir, the dead water level of the lower reservoir, and the energy storage are used together as the target hydropower parameters of the pumped storage power station to provide a reliable basis for the design and operation of the power station.

[0212] In some optional implementations, when the computation involves multiple particles in the particle swarm, the master server first constructs a computation queue, in which multiple computation tasks are queued, and the task status of all computation tasks is initialized to "uncomputed". When a computation server is idle, the master server assigns it an uncomputed task until the computation is completed, at which point its task status is changed to "computed". This process continues until all computation tasks are in the "computed" state, completing the computation process. Since the computation processes for different particles differ—for example, some particles require correction while others do not, and the correction speeds also differ—the dynamic queuing and task assignment mechanism allows for more flexible use of server resources and accelerates computation time.

[0213] This invention maps multiple sets of hydropower parameters to particles and obtains particle swarm iteration parameters, providing a directional basis for subsequent convergence to the optimal solution. It corrects the positions of particles that do not meet the head-to-head ratio constraint, preventing invalid positions that violate the constraint from entering subsequent calculations. This ensures that subsequent iterations always proceed within the effective solution space, avoiding invalid solutions interfering with the optimization direction and improving the algorithm's convergence stability. The fitness of the corrected particles is calculated, transforming the abstract quality of hydropower parameters into quantifiable fitness values. Local optimization is performed when the power station does not meet the symmetric constraint of reserve capacity to reduce the reserve capacity deviation. Based on the optimized particles, the individual optimal position and the global optimal position are updated, ensuring that each iteration captures a better parameter combination, solving the problem that traditional methods cannot track the optimal solution in real time. The velocity and position of the particles are updated for the next iteration, and the iteration is repeated to avoid insufficient solution space coverage in a single iteration, ensuring that the final parameters have both the highest fitness and satisfy all constraints. Through the particle swarm algorithm and multiple constraints, it is suitable for hydropower parameter calculation problems with strong nonlinearity, efficiently and accurately searching for the global optimal solution, shortening computation time, improving computational accuracy, and providing a reliable basis for power station design and operation.

[0214] This embodiment also provides a hydroelectric parameter calculation device for a pumped storage power station based on the particle swarm optimization algorithm. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0215] This embodiment provides a device for calculating hydroelectric parameters of a pumped storage power station based on the particle swarm optimization algorithm, such as... Figure 2 As shown, it includes:

[0216] The acquisition module 201 is used to acquire the position and velocity of each particle in the particle swarm in the current iteration and the individual optimal position obtained in the previous iteration, and to acquire the global optimal position of the particle swarm obtained in the previous iteration. The particle swarm is generated based on multiple sets of hydro-energy parameters, and the position represents multiple water level dimensions of the hydro-energy parameters.

[0217] The correction module 202 is used to correct the position of the particle in the current iteration when the position of the particle does not meet the head-to-water ratio constraint, so as to obtain the corrected position of the particle.

[0218] The first calculation module 203 is used to calculate the fitness of a particle based on the power station parameters of the pumped storage power station and the corrected position of the particle.

[0219] The optimization module 204 is used to perform local optimization based on the power station parameters, the fitness of the particles, and the corrected position when the pumped storage power station does not meet the symmetry constraint of the backup reservoir capacity, so as to obtain the optimized position of the particles.

[0220] The first update module 205 is used to recalculate the fitness based on the optimized position of the particles, update the individual optimal position obtained in the previous iteration based on the fitness, obtain the individual optimal position of the particles in the current iteration, and update the global optimal position of the particle swarm obtained in the previous iteration based on the individual optimal positions of all particles in the current iteration, obtain the global optimal position of the particle swarm in the current iteration.

[0221] The second update module 206 is used to update the velocity and position of each particle in the current iteration based on the individual optimal position of each particle in the current iteration and the global optimal position of the particle swarm in the current iteration, so as to obtain the velocity and position of each particle in the next iteration.

[0222] The first determining module 207 is used to repeat the above iterative process until the iteration stop condition is reached, and to determine the target hydropower parameters of the pumped storage power station based on the global optimal position obtained in the last iteration.

[0223] In some alternative implementations, the locations include the normal water level of the upper reservoir, the dead water level of the upper reservoir, the normal water level of the lower reservoir, and the dead water level of the lower reservoir of the pumped storage power station.

[0224] Correction module 202 includes:

[0225] The first calculation unit is used to calculate the first upper limit corresponding to the normal water level of the upper reservoir and the second upper limit corresponding to the normal water level of the lower reservoir, respectively, based on multiple other water levels and the maximum head-to-water ratio, for the normal water level of the upper reservoir and the normal water level of the lower reservoir at the position of the particle in this iteration.

[0226] The second calculation unit is used to calculate the first lower limit corresponding to the upper reservoir dead water level and the second lower limit corresponding to the lower reservoir dead water level, respectively, based on multiple other water levels and the maximum head-to-water ratio, for the position of the particle in this iteration.

[0227] The generation unit is used to generate corresponding constraint conditions based on the first upper limit and the first preset lower limit corresponding to the normal water level of the upper reservoir, generate corresponding constraint conditions based on the first preset upper limit and the first lower limit corresponding to the dead water level of the upper reservoir, generate corresponding constraint conditions based on the second upper limit and the second preset lower limit corresponding to the normal water level of the lower reservoir, and form corresponding constraint conditions based on the second preset upper limit and the second lower limit corresponding to the dead water level of the lower reservoir.

[0228] The correction unit is used to randomly generate a corrected water level for each water level in the current iteration position of the particle, based on the constraints corresponding to the water level, and thus obtain the corrected position of the particle.

[0229] In some alternative implementations, the power plant parameters include the reserve capacity of the upper reservoir, the reserve capacity of the lower reservoir, the overall power output coefficient, and the generating capacity.

[0230] Fitness is calculated using the following formula:

[0231]

[0232] In the formula, Indices representing particles; Indicates the first The fitness of each particle; Indicates the first The energy stored in each particle; Indicates the penalty coefficient; This indicates the reserve capacity of the upper reservoir; This indicates the reserve capacity of the reservoir.

[0233] Energy storage is calculated using the following formula:

[0234]

[0235] In the formula, Indicates the overall power output coefficient of the power plant; Indicates the reservoir capacity for power generation; Represents the time constant; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level of the upper reservoir at the position of each particle; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level in the reservoir at the position of each particle.

[0236] In some alternative implementations, the optimization module 204 includes:

[0237] The expansion unit is used to expand the reservoir capacity outward based on the power station parameters when the reserve capacity of the reservoir corresponding to the corrected position of the particle is less than the reserve capacity of another reservoir, for any water level in the corrected position of the particle. This results in multiple first virtual particles.

[0238] The shrinking unit is used to shrink the reservoir capacity inward based on power station parameters when the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir, thereby obtaining multiple second virtual particles.

[0239] The elimination unit is used to eliminate the first virtual particle that does not meet the head-to-head ratio constraint, or to eliminate the second virtual particle that does not meet the head-to-head ratio constraint.

[0240] The third computational unit is used to calculate the fitness of each first virtual particle after elimination, or to calculate the fitness of each second virtual particle after elimination.

[0241] The replacement unit is used to determine the target virtual particle with the highest fitness from all first virtual particles or all second virtual particles. When the fitness of the target virtual particle is greater than that of the particle, the target virtual particle replaces the particle to complete the local optimization of the water level.

[0242] The first optimization unit is used to repeat the process of local optimization of the water level mentioned above until the local optimization of all water levels in the corrected position of the particle is completed, thus completing this round of optimization and obtaining the position of the particle in this round of optimization.

[0243] The second optimization unit is used to repeat the above optimization process until the optimization stopping condition is met, and the position obtained in the last round of optimization is taken as the optimized position of the particle.

[0244] In some alternative implementations, the power plant parameters also include reservoir capacity curves and the power plant's reserve reservoir capacity;

[0245] The expansion unit includes:

[0246] The first determining subunit is used to determine the first maximum reservoir capacity and the first minimum reservoir capacity corresponding to the water level based on the power station parameters when the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir.

[0247] The first calculation subunit is used to calculate multiple discrete points of the first storage capacity based on the first maximum storage capacity and the first minimum storage capacity.

[0248] The first mapping sub-unit is used to map each discrete point of the first reservoir capacity to the reservoir capacity curve to obtain the corresponding first target water level.

[0249] The second determining subunit is used to replace the water level with each first target water level, fix the other multiple water levels in the corrected position of the particle, and obtain the first virtual particle corresponding to the particle.

[0250] In some alternative implementations, the first determining subunit is specifically used for:

[0251] When the water level is at the normal storage level of the upper reservoir or the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the normal storage level of the upper reservoir or the lower reservoir is taken as the first minimum reservoir capacity, and the first maximum reservoir capacity is calculated by the following formula:

[0252]

[0253] In the formula, Indicates the first maximum storage capacity; This indicates the first minimum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0254] When the water level is at the dead water level of the upper or lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the dead water level of the upper or lower reservoir is taken as the first maximum reservoir capacity, and the first minimum reservoir capacity is calculated using the following formula:

[0255]

[0256] In the formula, This indicates the first minimum storage capacity; Indicates the first maximum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0257] In some alternative implementations, the shrinkage unit includes:

[0258] The third determining subunit is used to determine the second maximum and second minimum reservoir capacity corresponding to the water level based on the power station parameters when the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir.

[0259] The second calculation subunit is used to calculate multiple discrete points of the second storage capacity based on the second maximum storage capacity and the second minimum storage capacity.

[0260] The second mapping sub-unit is used to map each discrete point of the second reservoir capacity to the reservoir capacity curve to obtain the corresponding second target water level.

[0261] The fourth determining sub-unit is used to replace the water level with the second target water level, fix the other multiple water levels in the corrected position of the particle, and obtain the second virtual particle corresponding to the particle.

[0262] In some alternative implementations, the third determining subunit is specifically used for:

[0263] When the water level is at the normal storage level of the upper reservoir or the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the normal storage level of the upper reservoir or the lower reservoir is taken as the second maximum reservoir capacity, and the second minimum reservoir capacity is calculated by the following formula:

[0264]

[0265] In the formula, Indicates the second minimum storage capacity; Indicates the second largest storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0266] When the water level is at the dead water level of the upper or lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the dead water level of the upper or lower reservoir is taken as the second minimum reservoir capacity, and the second maximum reservoir capacity is calculated using the following formula:

[0267]

[0268] In the formula, Indicates the second largest storage capacity; Indicates the second minimum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

[0269] In some alternative embodiments, prior to the correction module 202, the device further includes:

[0270] The second calculation module is used to calculate the head-to-lift ratio based on the position of the particle in this iteration.

[0271] The second determining module is used to determine the position of the particle in this iteration to satisfy the head-to-head ratio constraint when the head-to-head ratio is greater than a preset threshold.

[0272] The third determining module is used to determine whether the position of the particle in this iteration does not meet the head-to-head ratio constraint when the head-to-head ratio is not greater than a preset threshold.

[0273] Prior to the optimization module 204, the device also includes:

[0274] The third calculation module is used to calculate the absolute value of the difference between the reserve capacity of the upper reservoir and the reserve capacity of the lower reservoir, which is taken as the capacity difference.

[0275] The fourth calculation module is used to calculate the ratio of the power plant's reserve capacity to the preset value, and to calculate the product of the ratio and the reserve capacity symmetry deviation threshold.

[0276] The fourth determination module is used to determine whether the symmetric constraint of the spare storage capacity is not met when the storage capacity difference is greater than the product.

[0277] The fifth determination module is used to determine whether the symmetric constraint of the spare storage capacity is satisfied when the storage capacity difference is not greater than the product.

[0278] In some alternative implementations, the velocity of each particle in this iteration is updated using the following formula:

[0279]

[0280] In the formula, Indicates the updated number The first particle The speed of each water level dimension in the next iteration; Indicates inertia weight; Indicates the first The first particle The speed of each water level dimension in this iteration; and Indicates the learning factor; This represents the position of the globally optimal position obtained in this iteration. Each water level dimension; Indicates the first The first particle The position of each water level dimension in this iteration; This indicates the result of the current iteration. The particle in the first The optimal position of an individual at each water level dimension; Indicates the first The particle in the first The regulating reservoir capacity gradient in each water level dimension; This represents the gradient term weight.

[0281] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0282] In this embodiment, the pumped storage power station hydropower parameter calculation device based on particle swarm optimization algorithm is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0283] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 3As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 3 Take a processor 10 as an example.

[0284] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPRS), or any combination thereof. Memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the methods shown in the above embodiments. Communication interface 30 is used for the computer device to communicate with other devices or communication networks.

[0285] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0286] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for calculating hydroelectric parameters of a pumped storage power station based on particle swarm optimization, characterized in that, The method includes: The position and velocity of each particle in the particle swarm in the current iteration and the individual optimal position obtained in the previous iteration are obtained, and the global optimal position of the particle swarm obtained in the previous iteration is obtained. The particle swarm is generated based on multiple sets of hydro-energy parameters, and the position represents multiple water level dimensions of the hydro-energy parameters. When the position of the particle in the current iteration does not meet the head-to-water ratio constraint, the position of the particle in the current iteration is corrected to obtain the corrected position of the particle; The fitness of the particle is calculated based on the power station parameters of the pumped storage power station and the corrected position of the particle. When the pumped storage power station does not meet the symmetry constraint of the backup reservoir capacity, local optimization is performed based on the power station parameters, the fitness of the particle, and the corrected position to obtain the optimized position of the particle; The fitness is recalculated based on the optimized position of the particle. The individual optimal position obtained in the previous iteration is updated based on the fitness to obtain the individual optimal position of the particle in this iteration. Based on the individual optimal positions of all particles in this iteration, the global optimal position of the particle swarm obtained in the previous iteration is updated to obtain the global optimal position of the particle swarm in this iteration. Based on the individual optimal position of each particle in this iteration and the global optimal position of the particle swarm in this iteration, the velocity and position of each particle in this iteration are updated to obtain the velocity and position of each particle in the next iteration. Repeat the above iterative process until the iteration stops. Based on the global optimal position obtained in the last iteration, determine the target hydroelectric parameters of the pumped storage power station.

2. The method according to claim 1, characterized in that, The locations include the normal water level of the upper reservoir, the dead water level of the upper reservoir, the normal water level of the lower reservoir, and the dead water level of the lower reservoir of the pumped storage power station. The process of correcting the position of the particle in this iteration to obtain the corrected position of the particle includes: For the normal water level of the upper reservoir and the normal water level of the lower reservoir at the position of the particle in this iteration, based on multiple other water levels and the maximum head-to-water ratio, calculate the first upper limit corresponding to the normal water level of the upper reservoir and the second upper limit corresponding to the normal water level of the lower reservoir respectively. For the dead water level of the upper reservoir and the dead water level of the lower reservoir in the position of the particle in this iteration, based on multiple other water levels and the maximum head-to-water ratio, the first lower limit corresponding to the dead water level of the upper reservoir and the second lower limit corresponding to the dead water level of the lower reservoir are calculated respectively. Based on the first upper limit and the first preset lower limit corresponding to the normal water level of the upper reservoir, corresponding constraints are generated; based on the first preset upper limit and the first lower limit corresponding to the dead water level of the upper reservoir, corresponding constraints are generated; based on the second upper limit and the second preset lower limit corresponding to the normal water level of the lower reservoir, corresponding constraints are generated; based on the second preset upper limit and the second lower limit corresponding to the dead water level of the lower reservoir, corresponding constraints are formed. For each water level in the position of the particle in this iteration, a corrected water level is randomly generated in the constraints corresponding to the water level to obtain the corrected position of the particle.

3. The method according to claim 2, characterized in that, The power station parameters include the reserve capacity of the upper reservoir, the reserve capacity of the lower reservoir, the overall power output coefficient, and the power generation capacity. Fitness is calculated using the following formula: In the formula, Indices representing particles; Indicates the first The fitness of each particle; Indicates the first The energy stored in each particle; Indicates the penalty coefficient; This indicates the reserve capacity of the upper reservoir; This indicates the reserve capacity of the reservoir. Energy storage is calculated using the following formula: In the formula, Indicates the overall power output coefficient of the power plant; Indicates the reservoir capacity for power generation; Represents the time constant; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level of the upper reservoir at the position of each particle; Indicates the first The normal water level of the reservoir at the position of each particle; Indicates the first The dead water level in the reservoir at the position of each particle.

4. The method according to claim 3, characterized in that, The step of performing local optimization based on the power station parameters, the particle's fitness, and the corrected position to obtain the optimized position of the particle includes: For any water level in the corrected position of the particle, if the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir, the reservoir capacity is expanded outward based on the power station parameters to obtain multiple first virtual particles. When the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir, the reservoir capacity is reduced inward based on the power station parameters to obtain multiple second virtual particles. Remove the first virtual particle that does not meet the head-to-head ratio constraint, or remove the second virtual particle that does not meet the head-to-head ratio constraint; Calculate the fitness of each first virtual particle after removal, or calculate the fitness of each second virtual particle after removal; The target virtual particle with the highest fitness is determined from all first virtual particles or all second virtual particles. When the fitness of the target virtual particle is greater than that of the particle, the target virtual particle replaces the particle, thus completing the local optimization of the water level. Repeat the above process of local optimization of water level until the local optimization of all water levels in the corrected position of the particle is completed, thus completing this round of optimization and obtaining the position of the particle in this round of optimization. Repeat the above optimization process until the optimization stopping condition is met, and take the position obtained in the last round of optimization as the optimized position of the particle.

5. The method according to claim 4, characterized in that, The power plant parameters also include reservoir capacity curves and the power plant's reserve reservoir capacity; When the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir, the reservoir capacity is expanded outward based on the power station parameters to obtain multiple first virtual particles, including: When the reserve capacity of the reservoir corresponding to the water level is less than the reserve capacity of another reservoir, the first maximum capacity and the first minimum capacity corresponding to the water level are determined based on the power station parameters. Based on the first maximum storage capacity and the first minimum storage capacity, calculate multiple discrete points of the first storage capacity; Map each discrete point of the first reservoir capacity to the reservoir capacity curve to obtain the corresponding first target water level; Replace the water level with each first target water level, fix the other multiple water levels in the corrected position of the particle, and obtain the first virtual particle corresponding to the particle.

6. The method according to claim 5, characterized in that, The process of determining the first maximum reservoir capacity and the first minimum reservoir capacity corresponding to the water level based on the power station parameters includes: When the water level is the normal storage level of the upper reservoir or the normal storage level of the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the normal storage level of the upper reservoir or the normal storage level of the lower reservoir is taken as the first minimum reservoir capacity, and the first maximum reservoir capacity is calculated by the following formula: In the formula, Indicates the first maximum storage capacity; This indicates the first minimum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir; When the water level is the dead water level of the upper reservoir or the dead water level of the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the dead water level of the upper reservoir or the dead water level of the lower reservoir is taken as the first maximum reservoir capacity, and the first minimum reservoir capacity is calculated by the following formula: In the formula, This indicates the first minimum storage capacity; Indicates the first maximum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

7. The method according to claim 5, characterized in that, When the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir, the reservoir capacity is reduced inward based on the power station parameters to obtain multiple second virtual particles, including: When the reserve capacity of the reservoir corresponding to the water level is greater than the reserve capacity of another reservoir, the second maximum capacity and the second minimum capacity corresponding to the water level are determined based on the power station parameters. Based on the second maximum storage capacity and the second minimum storage capacity, calculate multiple discrete points of the second storage capacity; Map each discrete point of the second reservoir capacity to the reservoir capacity curve to obtain the corresponding second target water level; Replace the water level with the second target water level, fix the other multiple water levels in the corrected position of the particle, and obtain the second virtual particle corresponding to the particle.

8. The method according to claim 7, characterized in that, The process of determining the second maximum reservoir capacity and the second minimum reservoir capacity corresponding to the water level based on the power station parameters includes: When the water level is the normal storage level of the upper reservoir or the normal storage level of the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the normal storage level of the upper reservoir or the normal storage level of the lower reservoir is taken as the second maximum reservoir capacity, and the second minimum reservoir capacity is calculated by the following formula: In the formula, Indicates the second minimum storage capacity; Indicates the second largest storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir; When the water level is the dead water level of the upper reservoir or the dead water level of the lower reservoir, based on the reservoir capacity curve, the reservoir capacity corresponding to the dead water level of the upper reservoir or the dead water level of the lower reservoir is taken as the second minimum reservoir capacity, and the second maximum reservoir capacity is calculated by the following formula: In the formula, Indicates the second largest storage capacity; Indicates the second minimum storage capacity; Indicates the power plant's reserve storage capacity; This indicates the reserve capacity of the upper reservoir.

9. The method according to claim 5, characterized in that, Before correcting the position of the particle in the current iteration when the position of the particle does not meet the head-to-water ratio constraint, the method further includes: The head-to-lift ratio is calculated based on the position of the particle in this iteration. When the head-to-water ratio is greater than a preset threshold, it is determined that the position of the particle in this iteration satisfies the head-to-water ratio constraint. When the head-to-water ratio is not greater than the preset threshold, it is determined that the position of the particle in this iteration does not satisfy the head-to-water ratio constraint; Before performing local optimization based on the power station parameters, the fitness of the particle, and the corrected position to obtain the optimized position of the particle when the pumped storage power station does not meet the symmetry constraint of the reserve reservoir capacity, the method further includes: Calculate the absolute value of the difference between the reserve capacity of the upper reservoir and the reserve capacity of the lower reservoir, and use it as the capacity difference; Calculate the ratio of the power plant's reserve capacity to a preset value, and calculate the product of the ratio and the reserve capacity symmetry deviation threshold; When the difference in storage capacity is greater than the product, it is determined that the symmetric constraint of the spare storage capacity is not satisfied. When the difference in storage capacity is not greater than the product, it is determined that the symmetric constraint of the spare storage capacity is satisfied.

10. The method according to claim 1, characterized in that, The velocity of each particle in this iteration is updated using the following formula: In the formula, Indicates the updated number The first particle The speed of each water level dimension in the next iteration; Indicates inertia weight; Indicates the first The first particle The speed of each water level dimension in this iteration; and Indicates the learning factor; This represents the position of the globally optimal position obtained in this iteration. Each water level dimension; Indicates the first The first particle The position of each water level dimension in this iteration; This indicates the result of the current iteration. The particle in the first The optimal position of an individual at each water level dimension; Indicates the first The particle in the first The regulating reservoir capacity gradient in each water level dimension; This represents the gradient term weight.

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