An improved swarm intelligence fusion-based method for optimal configuration of water and electricity sharing resources

Abstract

The application discloses a kind of water and electricity sharing resource optimization configuration methods based on improved swarm intelligence fusion comprising: the physical information of reservoir, the relevant information of hydropower station and environmental condition information are collected, and reservoir hydropower station electric power resource model is constructed;Based on the relevant information obtained and electric power resource model, the water and electricity sharing resource optimization configuration model is constructed;Through the improvement of crow search algorithm by fusing Levy flight strategy, tabu strategy and special amnesty law, the solution of water and electricity sharing resource optimization configuration model is completed by using improved crow search algorithm.The water and electricity sharing resource optimization configuration method based on improved swarm intelligence fusion provided in the application retains the ability of algorithm to solve high-dimensional problems while improving the local and global search capability of the algorithm, solves the problem of poor optimization speed and accuracy when solving complex optimization problems by existing methods.At the same time, the solution of the improved algorithm provides effective reference for the decision of reservoir hydropower station operator, and promotes the intelligent development of reservoir hydropower station.

Description

Technical Field

[0001] This invention relates to the field of hydropower resource scheduling and optimization technology, specifically to a method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion. Background Technology

[0002] With the increasing severity of extreme weather events and global warming, water resources are dwindling, leading to a decrease in reservoir water levels and consequently affecting the fulfillment of people's water and electricity needs. Therefore, the rational allocation and utilization of hydropower resources has gradually become a research hotspot in recent years. Rational allocation of hydropower resources can meet the diverse water and electricity demands of different regions, serving as a fundamental guarantee for regional development. However, balancing the allocation of water and electricity resources to maximize resource utilization is a key challenge and development focus for reservoir operators.

[0003] The optimal allocation of shared hydropower resources mainly includes two aspects: optimization of reservoir water use and power generation scheduling. While ensuring the basic water needs of each region, the goal is to maximize the efficiency of water resource utilization and guarantee the water and electricity rights of each region. Furthermore, the scheduling of shared hydropower resources is greatly constrained by environmental impacts. Therefore, this optimization model needs to fully consider the differences in demand types and quantities among the service areas, as well as the impact of weather patterns. These factors undoubtedly constitute the key and challenging aspects of determining the optimal allocation of shared hydropower resources.

[0004] Unlike general resource optimization, the optimization of shared hydropower resources involves diverse water and electricity demands across multiple regions, often encompassing multiple scheduling objectives and complex heterogeneous demand constraints. Solving this model is mathematically an NP-hard problem, making it difficult and inefficient to solve using typical exact algorithms. In contrast, swarm intelligence algorithms, which have emerged in recent years, are derived from the stochastic processes of biological populations. They can quickly escape local optima and find optimal solutions in a broader optimization space. Their robustness also makes them widely applicable in complex engineering and operations research problems. However, as problem complexity increases, single swarm intelligence algorithms suffer from drawbacks such as poor convergence speed and low optimization results.

[0005] Therefore, to improve the speed and accuracy of the algorithm's local and global solutions, an improved hydropower shared resource optimization allocation model based on swarm intelligence fusion was developed. By incorporating the Levy flight strategy and tabu strategy into the traditional crow search algorithm, the algorithm's search space and search mechanism were expanded while retaining its ability to solve high-dimensional problems. This significantly improved the algorithm's solution speed and accuracy, enabling it to effectively solve the hydropower shared resource optimization allocation model, thereby ensuring the needs of different regions and maximizing benefits at the reservoir scheduling decision-making level, providing a blueprint for intelligent reservoir construction. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by this invention is that existing resource optimization allocation methods suffer from problems such as high difficulty in solving complex optimization problems, low solution accuracy, and poor convergence speed.

[0009] To address the aforementioned technical problems, this invention provides the following technical solution: a method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion, comprising: collecting physical information of reservoirs, relevant information of hydropower stations, and environmental condition information to construct a power resource model of reservoirs and hydropower stations; constructing an optimized allocation model of shared hydropower resources based on the relevant information of the hydropower stations and the power resource model of reservoirs and hydropower stations; and improving the crow search algorithm by fusing Levy flight strategy, tabu strategy, and amnesty rule, and using the improved crow search algorithm to solve the optimized allocation model of shared hydropower resources.

[0010] As a preferred embodiment of the improved swarm intelligence fusion-based hydropower resource optimization allocation method described in this invention, the physical information of the reservoir includes the reservoir surface area, average reservoir depth, upstream and downstream flow velocities of the reservoir dam, upstream and downstream water levels of the reservoir dam, tailwater level of the reservoir, and cross-sectional area of ​​the reservoir.

[0011] The relevant information of the hydropower station includes the cross-sectional area of ​​the turbine inlet and the distance between the turbine inlet and the upstream water level of the reservoir;

[0012] The environmental condition information includes infiltration loss coefficient, precipitation time, precipitation intensity, and ambient temperature;

[0013] Water-related information for each region includes the type of water used in each region, as well as the corresponding water costs and demand.

[0014] Electricity-related information for each region includes the type of electricity used, the corresponding electricity costs, and the demand.

[0015] As a preferred embodiment of the improved swarm intelligence fusion-based hydropower shared resource optimization allocation method described in this invention, the acquisition of the reservoir hydropower station power resource model includes:

[0016] The calculation of power generation by a reservoir hydropower station includes,

[0017] E a =n×P a ×t

[0018] in,

[0019]

[0020] H a =Wl a -DS a -WD a

[0021]

[0022] V r = (1-β)×V r0 +ΔV r

[0023]

[0024]

[0025]

[0026] Where n represents the number of turbine units in the reservoir hydropower station, P a The t represents the operating efficiency of a single turbine, t represents the operating time of a single turbine, and γ represents the specific gravity of water (γ = 9.81 kN / m³). 3 ), δ represents the degree of effective utilization of the turbine's work capacity by the water flow, H a This indicates the height difference between the turbine's inlet and outlet. This represents the upstream flow velocity of dam a. The inlet cross-sectional area of ​​the turbine is represented by r, the reservoir is represented by e, and Wl is represented by t. a DS represents the water level upstream of dam a. a Indicates the distance from the turbine outlet to the reference plane, WD a V represents the water intake error, i.e., the distance from the turbine inlet to the upstream water level. r / S r Indicates reservoir depth, US a V represents the distance from the bottom of the reservoir to a reference plane, which is generally taken as the reference plane. β represents the seepage loss coefficient. r0 Let ΔV represent the initial capacity of reservoir r. r A represents the change in the capacity of reservoir r. r Let q represent the cross-sectional area of ​​reservoir r. r Rt represents the outflow velocity from the reservoir, RI represents the precipitation intensity, and Rt represents the precipitation intensity. r VE represents the precipitation time of reservoir r.λ VD represents the evaporation rate at temperature λ. r This represents the amount of water drawn from reservoir r. Let g represent the downstream velocity of dam a, and g represent the acceleration due to gravity.

[0027] As a preferred embodiment of the hydropower shared resource optimization allocation method based on improved swarm intelligence fusion described in this invention, the optimization objectives of the hydropower shared resource optimization allocation model include hydropower resource supply costs and the time required for hydropower resource supply.

[0028] The cost of hydropower resources supply includes water usage costs and electricity usage costs;

[0029] The calculation of water and electricity costs includes,

[0030] SC = CW + CE

[0031]

[0032]

[0033] Where SC represents the cost of supplying hydropower resources, CW represents the cost of water use, CE represents the cost of electricity use, and w q P indicates the priority of water demand. i q This represents the cost of the q-th water type in the i-th region. P represents the actual amount of water of the qth type supplied by the r-th reservoir to the i-th region. i WE This represents the cost of electricity generated by hydropower in the i-th region. This represents the electricity cost for the i-th region using power generation methods other than hydropower, (1-ψ i HE i N represents the electricity demand for other power generation methods. ai This represents the actual amount of electricity supplied by the a-th dam to the i-th region;

[0034] The time required for the supply of hydropower resources includes the time required for water supply and the time required for power supply;

[0035] The calculation of the time required for water supply and power supply includes,

[0036] ST = TE + TW

[0037]

[0038]

[0039] Where ST represents the time required for hydropower supply, TE represents the time required for power supply, and TW represents the time required for water supply. Let r represent the upstream flow velocity of reservoir r. Formula (1-1) is the formula for calculating the water supply time when precipitation is present, and formula (1-2) is the formula for calculating the water supply time when precipitation is absent.

[0040] As a preferred embodiment of the hydropower shared resource optimization allocation method based on improved swarm intelligence fusion described in this invention, the calculation of the objective function O of the hydropower shared resource optimization allocation model includes,

[0041] O = [minSC, minST] T .

[0042] As a preferred embodiment of the hydropower shared resource optimization allocation method based on improved swarm intelligence fusion described in this invention, the constraints of the hydropower shared resource optimization allocation model include the minimum demand guarantee constraints of each region and the supply capacity constraints of reservoirs and hydropower stations.

[0043] The calculation of the minimum demand guarantee constraints for each region includes,

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] in, This represents the water demand for the q-th water use type in the i-th region;

[0050] The calculation of the supply capacity constraint of the reservoir hydropower station includes,

[0051]

[0052] As a preferred embodiment of the improved swarm intelligence fusion-based hydropower shared resource optimization allocation method described in this invention, it includes:

[0053] Given the crow population size N and the crow search space dimension Dim, the perceptual probability AP i,iter Initial position x i,iter Initial memory position m i,iter Perform initialization;

[0054] Individual crows are randomly selected from the objective function of the hydropower shared resource optimization allocation model, and their initial fitness values ​​are calculated.

[0055] The improved tracking phase position update is performed using the Levy flight strategy, and a fitness value for the new position is obtained. The improved tracking phase position update includes:

[0056]

[0057]

[0058]

[0059]

[0060] Among them, rand i This represents a random number between 0 and 1, which is randomly generated each time levy flies to the i-th position. i,iter Indicates the step size for each flight;

[0061] The updating of the memory location of the individual crow includes,

[0062]

[0063] As a preferred embodiment of the hydropower shared resource optimization allocation method based on improved swarm intelligence fusion described in this invention, the taboo strategy includes taboo length adjustment and candidate set set;

[0064] The tabu length adjustment includes: a shorter tabu length results in a larger upper limit for the search space, but the actual search space is smaller, which can easily lead to cyclic searches; conversely, an excessively long tabu length leads to excessively long computation time. Therefore, the calculation using adaptive parameter adjustment includes...

[0065]

[0066] Where TabuL represents the adaptive tabu length, N represents the crow population size, BestL represents the current best distance solution, and BestCa represents the best solution in the neighborhood solution;

[0067] The acquisition of the candidate set includes: the size of the candidate set. If the size is too large, it will increase the computation memory and computation time. If the size is too small, it will fall into local optima too early. The selection of the candidate set is generally composed of neighborhood solutions. All neighborhood solutions can be selected, or the better-performing neighborhood solutions can be selected, or several neighborhood solutions can be randomly selected.

[0068] The candidate set is calculated as follows:

[0069] CaNum = zeros(Ca, N)

[0070] Where CaNum represents the candidate set, and Ca represents the number of initial neighborhood solutions.

[0071] As a preferred embodiment of the hydropower shared resource optimization allocation method based on improved swarm intelligence fusion described in this invention, the determination of the amnesty criterion includes:

[0072] Based on the rules of the fitness function, if the objective value of the neighborhood solution is better than any of the previous best candidate solutions, it can be exempted;

[0073] Based on the principle that when the crow searches for Levy's flight update location, if all objects are taboo, the solution with the smallest evaluation value is pardoned;

[0074] Rules based on influence can grant amnesty to objects that have a significant impact on the target value.

[0075] As a preferred embodiment of the improved swarm intelligence fusion-based hydropower shared resource optimization allocation method described in this invention, it further includes:

[0076] If the maximum number of iterations is reached, the optimal solution is output; otherwise, the selection and update process continues until the maximum number of iterations is reached.

[0077] The beneficial effects of this invention are as follows: Based on the operational requirements of reservoirs and hydropower stations and the demand characteristics of various regions, this invention designs an improved crow search algorithm that integrates Levy flight strategy, tabu strategy, and special interpretation criterion. While retaining the algorithm's ability to solve high-dimensional problems, it improves the algorithm's local and global search capabilities, solving the problem of poor optimization speed and accuracy of traditional swarm intelligence algorithms when solving complex optimization problems. Furthermore, the improved crow search algorithm is used to solve the hydropower shared resource optimization allocation model, providing effective reference for reservoir and hydropower station operators' decision-making and promoting the intelligent development of reservoirs and hydropower stations. Attached Figure Description

[0078] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0079] Figure 1 The flowchart of the improved crow search algorithm designed for this invention;

[0080] Figure 2 The graph shows a comparison of iterative curves of three algorithms for an improved swarm intelligence fusion-based hydropower shared resource optimization allocation method provided in the second embodiment of the present invention. Detailed Implementation

[0081] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0082] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0083] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0084] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0085] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0086] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0087] Example 1

[0088] Reference Figure 1As an embodiment of the present invention, a method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion is provided, comprising:

[0089] S1: Collect physical information about the reservoir, relevant information about the hydropower station, and environmental conditions to construct a power resource model for the reservoir and hydropower station. It should be noted that:

[0090] The physical information of a reservoir includes its surface area, average depth, upstream and downstream flow velocities of the dam, upstream and downstream water levels of the dam, tailrace level, and cross-sectional area. Information about a hydropower station includes the turbine inlet cross-sectional area and the distance between the turbine inlet and the upstream water level of the reservoir. Environmental information includes the infiltration loss coefficient, rainfall duration, rainfall intensity, and ambient temperature.

[0091] Furthermore, the acquisition of the power resource model for reservoir hydropower stations includes,

[0092] The calculation of power generation by a reservoir hydropower station includes,

[0093] E a =n×P a ×t

[0094] in,

[0095]

[0096] H a =Wl a -DS a -WD a

[0097]

[0098] V r = (1-β)×V r0 +ΔV r

[0099]

[0100]

[0101]

[0102] Where n represents the number of turbine units in the reservoir hydropower station, P a The t represents the operating efficiency of a single turbine, t represents the operating time of a single turbine, and γ represents the specific gravity of water (γ = 9.81 kN / m³). 3 ), δ represents the degree of effective utilization of the turbine's work capacity by the water flow, H a This indicates the height difference between the turbine's inlet and outlet. This represents the upstream flow velocity of dam a. The inlet cross-sectional area of ​​the turbine is represented by r, the reservoir is represented by e, and Wl is represented by t. a DS represents the water level upstream of dam a. a Indicates the distance from the turbine outlet to the reference plane, WD a V represents the water intake error, i.e., the distance from the turbine inlet to the upstream water level. r / S r Indicates reservoir depth, US a V represents the distance from the bottom of the reservoir to a reference plane, which is generally taken as the reference plane. β represents the seepage loss coefficient. r0 Let ΔV represent the initial capacity of reservoir r. r A represents the change in the capacity of reservoir r. r Let q represent the cross-sectional area of ​​reservoir r. r Rt represents the outflow velocity from the reservoir, RI represents the precipitation intensity, and Rt represents the precipitation intensity. r VE represents the precipitation time of reservoir r. λ VD represents the evaporation rate at temperature λ. r This represents the amount of water drawn from reservoir r. Let g represent the downstream velocity of dam a, and g represent the acceleration due to gravity.

[0103] It should be noted that reservoir hydropower stations generate electricity by utilizing water resources, primarily by using the falling or rapidly flowing water to pass through turbines, thereby converting potential energy into mechanical energy and ultimately into electrical energy. Based on this power generation mechanism, combined with the physical information of the reservoir, relevant information about the hydropower station, and environmental conditions, a power resource model for reservoir hydropower stations was constructed.

[0104] S2: Based on relevant information about hydropower stations and the power resource model of reservoirs and hydropower stations, construct a hydropower shared resource optimization allocation model. It should be noted that:

[0105] Water-related information for each region includes the type of water used, the corresponding water costs, and the demand; electricity-related information for each region includes the type of electricity used, the corresponding electricity costs, and the demand.

[0106] Furthermore, the hydropower shared resource optimization allocation model includes the optimization objective, objective function, and constraints of the hydropower shared resource optimization allocation model;

[0107] The optimization objectives of the hydropower shared resource optimization allocation model include the cost of hydropower supply and the time required for hydropower supply.

[0108] The cost of hydropower resources includes water usage fees and electricity usage fees;

[0109] The calculation of water and electricity costs includes,

[0110] SC = CW + CE

[0111]

[0112]

[0113] Where SC represents the cost of supplying hydropower resources, CW represents the cost of water use, CE represents the cost of electricity use, and w q P indicates the priority of water demand. i q This represents the cost of the q-th water type in the i-th region. P represents the actual amount of water of the qth type supplied by the r-th reservoir to the i-th region. i WE This represents the cost of electricity generated by hydropower in the i-th region. This represents the electricity cost for the i-th region using power generation methods other than hydropower, (1-ψ i HE i N represents the electricity demand for other power generation methods. ai This represents the actual amount of electricity supplied by the a-th dam to the i-th region;

[0114] The time required for hydropower supply includes the time required for water supply and the time required for electricity supply;

[0115] The calculation of the time required for water supply and electricity supply includes,

[0116] ST = TE + TW

[0117]

[0118]

[0119] Where ST represents the time required for hydropower supply, TE represents the time required for power supply, and TW represents the time required for water supply. Let r represent the upstream flow velocity of reservoir r. Formula (1-1) represents the formula for calculating the water supply time when precipitation is present, and Formula (1-2) represents the formula for calculating the water supply time when precipitation is absent.

[0120] The calculation of the objective function O in the hydropower shared resource optimization allocation model includes:

[0121] O = [minSC, minST] T

[0122] The constraints of the hydropower shared resource optimization allocation model include the minimum demand guarantee constraints of each region and the supply capacity constraints of reservoirs and hydropower stations.

[0123] The calculation of minimum demand guarantee constraints for each region includes,

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] in, This represents the water demand for the q-th water use type in the i-th region;

[0130] The calculation of the supply capacity constraint of a reservoir hydropower station includes,

[0131]

[0132] S3: The Raven Search algorithm is improved by integrating the Levy flight strategy, taboo strategy, and amnesty rule. This improved algorithm is then used to solve the hydropower shared resource optimization allocation model. It should be noted that:

[0133] (1) Given the crow population size N, the crow search space dimension Dim, and the perceptual probability AP i,iter Initial position x i,iter Initial memory position m i,iter Perform initialization;

[0134] (2) Randomly select individual crows from the objective function of the hydropower shared resource optimization allocation model and calculate the initial fitness value;

[0135] (3) Utilize the Levy flight strategy to perform improved position updates during the tracking phase and obtain the fitness value of the new position. The improved position updates during the tracking phase include:

[0136]

[0137]

[0138]

[0139]

[0140] Among them, rand i This represents a random number between 0 and 1, which is randomly generated each time levy flies to the i-th position. i,iter Indicates the step size for each flight;

[0141] (4) The updating of memory locations for individual crows includes,

[0142]

[0143] (5) Update the crow individuals in the current iteration using taboo strategies and record the target fitness function value. Taboo strategies include taboo length adjustment and candidate set.

[0144] Tabu length adjustment includes: a shorter tabu length allows for a larger upper limit to the search space, but the actual search space is smaller, which can easily lead to loop searches; conversely, an excessively long tabu length can result in excessively long computation time. Therefore, adaptive parameter adjustment is used in the computation process.

[0145]

[0146] Where TabuL represents the adaptive tabu length, N represents the crow population size, BestL represents the current best distance solution, and BestCa represents the best solution in the neighborhood solution;

[0147] The acquisition of the candidate set includes: the size of the candidate set; too large a set increases computational memory and time, while too small a set leads to premature local optima. The candidate set is generally composed of neighborhood solutions; it can select all neighborhood solutions, select the best-performing neighborhood solutions, or randomly select a few neighborhood solutions.

[0148] The candidate set is calculated as follows:

[0149] CaNum = zeros(Ca, N)

[0150] Where CaNum represents the candidate set, and Ca represents the number of initial neighborhood solutions;

[0151] (6) Use the special pardon criterion to release a certain number of taboo solutions and return the optimal value among them. The determination of the special pardon criterion includes:

[0152] Based on the fitness function rules, if a neighborhood solution has a better objective value than any of the previous best candidate solutions, it can be exempted from consideration.

[0153] Based on the principle that when the crow searches for Levy's flight update location, if all objects are taboo, the solution with the smallest evaluation value is pardoned;

[0154] Rules based on influence can grant amnesty to objects that have a significant impact on the target value.

[0155] (7) If the maximum number of iterations is reached, the optimal solution is output. If not, continue to execute steps (2) to (5) until the maximum number of iterations is reached.

[0156] This invention, based on the operational requirements of reservoirs and hydropower stations and the specific needs of various regions, designs an improved crow search algorithm that integrates Levy flight strategy, tabu strategy, and special interpretation criterion. While retaining the algorithm's ability to solve high-dimensional problems, it enhances the algorithm's local and global search capabilities, solving the problem of poor optimization speed and accuracy of traditional swarm intelligence algorithms when solving complex optimization problems. Furthermore, the improved crow search algorithm is used to solve the hydropower shared resource optimization allocation model, providing effective reference for reservoir and hydropower station operators' decision-making and promoting the intelligent development of reservoirs and hydropower stations.

[0157] Example 2

[0158] Reference Figure 2 This is the second embodiment of the present invention. Unlike the first embodiment, this embodiment provides a verification test of a method for optimizing the allocation of hydropower shared resources based on improved swarm intelligence fusion. In order to verify and explain the technical effects of the method, this embodiment selects different methods and compares them with the method itself. The test results are compared using scientific demonstration methods to verify the real effect of the method.

[0159] Using data from Glen Canyon Dam and Hoover Dam in the United States, as well as water and electricity demand in Arizona, California, Wyoming, New Mexico, and Colorado, simulation experiments were conducted to compare the improved Raven Search Algorithm (TSCSA), Simulated Annealing Algorithm (SA), and Particle Swarm Optimization Algorithm (PSO) designed in this invention with each other to verify the superiority of the designed algorithms.

[0160] Table 1 shows relevant data on Glen Canyon Dam and Hoover Dam in the United States.

[0161] Table 2 shows the water and electricity demand and related information for Arizona, California, Wyoming, New Mexico, and Colorado.

[0162] Table 1: Relevant parameters of the Ron Canyon Dam and the Hoover Dam.

[0163]

[0164] Table 2: Relevant water and electricity demand in five states.

[0165]

[0166] The simulation results are as follows:

[0167] The iterative curves of the improved Raven Search Algorithm (TSCSA), Simulated Annealing Algorithm (SA), and Particle Swarm Optimization Algorithm (PSO) are shown in the figure below. Figure 2 As shown in Table 3, the optimal solution, worst solution, and average solution obtained by running the three algorithms 50 times are shown in Table 3.

[0168] Table 3: Optimization results of the three algorithms.

[0169]

[0170]

[0171] As shown in Table 3, the TSCSA algorithm designed in this invention achieves the best, worst, and average solutions compared to other optimization algorithms, improving upon the PSO algorithm by 7.11% and the SA algorithm by 36.75%. Figure 2 The iterative curves provide a comparison of the optimized performance of the designed algorithm, as shown in Table 4.

[0172] Table 4: Comparison of the optimization performance of the designed algorithms.

[0173]

[0174] The established model, based on the working mechanism of reservoirs and hydropower stations, possesses strong feasibility and persuasiveness. Furthermore, it incorporates various complex constraints to ensure the model meets practical requirements. The improved crow search algorithm, in solving the hydropower resource allocation optimization problem, demonstrates strong optimization ability, speed, robustness, and uniformity for problems involving multiple optimization objectives and complex constraints. In summary, this invention can provide reservoir and hydropower station operators with sound decision-making references, enabling the rational and effective utilization of resources.

[0175] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion, characterized in that, include: Collect physical information about the reservoir, relevant information about the hydropower station, and environmental conditions to construct a power resource model for the reservoir and hydropower station. Based on the relevant information of the hydropower station and the power resource model of the reservoir hydropower station, a hydropower shared resource optimization allocation model is constructed. The crow search algorithm is improved by integrating the Levy flight strategy, taboo strategy and amnesty rule, and the improved crow search algorithm is used to solve the hydropower shared resource optimization allocation model. For crow population Crow search space dimension Perceived probability Initial position Initial memory location Perform initialization; Individual crows are randomly selected from the objective function of the hydropower shared resource optimization allocation model, and their initial fitness values ​​are calculated. The improved tracking phase position update is performed using the Levy flight strategy, and a fitness value for the new position is obtained. The improved tracking phase position update includes: in, This represents a random number between 0 and 1, which is randomly generated each time levy flies to the i-th position. Indicates the step size for each flight; The updating of the memory location of the individual crow includes, The tabu strategy includes tabu length adjustment and candidate set; The taboo length adjustment includes, in, Indicates the adaptive taboo length. Indicates the population size of crows. This represents the current optimal distance solution. This represents the optimal solution in the neighborhood solution; The acquisition of the candidate set includes, in, Represents the candidate set, Indicates the number of solutions in the initial neighborhood; The criteria for granting amnesty include, Based on the rules of the fitness function, if the objective value of the neighborhood solution is better than any of the previous best candidate solutions, it can be exempted; Based on the principle that when the crow searches for Levy's flight update location, if all objects are taboo, the solution with the smallest evaluation value is pardoned; Rules based on influence can grant amnesty to objects that have a significant impact on the target value; If the maximum number of iterations is reached, the optimal solution is output; otherwise, the selection and update process continues until the maximum number of iterations is reached.

2. The method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion as described in claim 1, characterized in that: The physical information of the reservoir includes the reservoir surface area, average reservoir depth, upstream and downstream flow velocities of the reservoir dam, upstream and downstream water levels of the reservoir dam, tailwater level of the reservoir, and cross-sectional area of ​​the reservoir. The relevant information of the hydropower station includes the cross-sectional area of ​​the turbine inlet and the distance between the turbine inlet and the upstream water level of the reservoir; The environmental condition information includes infiltration loss coefficient, precipitation time, precipitation intensity, and ambient temperature; Water-related information for each region includes the type of water used in each region, as well as the corresponding water costs and demand. Electricity-related information for each region includes the type of electricity used, the corresponding electricity costs, and the demand.

3. The method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion as described in claim 2, characterized in that: The acquisition of the reservoir hydropower station power resource model includes, The calculation of power generation by a reservoir hydropower station includes, in, in, This indicates the number of turbine units in a reservoir hydroelectric power station. This indicates the efficiency of a single turbine. This indicates the operating time of a single turbine. Indicates the specific gravity of water. This indicates the degree to which the turbine effectively utilizes the energy of the water flow for work. This indicates the height difference between the turbine's inlet and outlet. Indicates dam upstream flow velocity, This represents the cross-sectional area of ​​the turbine inlet. Indicates a reservoir. Indicates turbine, Indicates dam The upstream water level, This indicates the distance from the turbine outlet to the reference plane. This indicates the water intake error, which is the distance between the turbine inlet and the upstream water level. Indicates the depth of the reservoir. This indicates the distance from the bottom of the reservoir to the reference plane. Indicates the permeation loss coefficient. Reservoir initial capacity, Reservoir The change in capacity, Reservoir cross-sectional area, Indicates the outflow velocity from the reservoir. Indicates precipitation intensity. Reservoir The timing of precipitation, Indicates temperature as Evaporation rate at that time Indicates from the reservoir The amount of water extracted from it Indicates dam downstream flow velocity, It represents the acceleration due to gravity.

4. The method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion as described in claim 3, characterized in that: The optimization objectives of the hydropower shared resource optimization allocation model include the cost of hydropower supply and the time required for hydropower supply. The cost of hydropower resources supply includes water usage costs and electricity usage costs; The calculation of water and electricity costs includes, in, This indicates the cost of supplying hydropower resources. This indicates the cost of water. This indicates the cost of electricity. Indicates the priority of water demand. Indicates the first The first region Costs for different types of water use Indicates the first The reservoir actually gave the first The supply of the first region Water consumption for different water types Indicates the first The cost of electricity generated by hydropower in a particular region. Indicates the first The cost of electricity used in each region for power generation methods other than hydropower. This indicates the electricity demand for other power generation methods. Indicates the first The dam towards the first The actual electricity supply in each region; The time required for the supply of hydropower resources includes the time required for water supply and the time required for power supply; The calculation of the time required for water supply and power supply includes, in, This indicates the time required for the supply of hydropower resources. Indicates the time required to supply power. Indicates the time required for water supply. Reservoir The upstream flow velocity.

5. The method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion as described in claim 4, characterized in that: The objective function of the hydropower shared resource optimization allocation model O The calculations include, 。 6. The method for optimizing the allocation of shared hydropower resources based on improved swarm intelligence fusion as described in claim 5, characterized in that: The constraints of the hydropower shared resource optimization allocation model include the minimum demand guarantee constraints of each region and the supply capacity constraints of reservoirs and hydropower stations. The calculation of the minimum demand guarantee constraints for each region includes, in, Indicates the first The first region Water demand for each type of water use; The calculation of the supply capacity constraint of the reservoir hydropower station includes, 。

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