Capacity configuration method and device of composite power generation system and electronic equipment

Abstract

The application discloses a capacity configuration method and device of a composite power generation system and electronic equipment, and the method comprises the following steps: initializing capacity configuration parameters of the composite power generation system; initializing output condition parameters of the composite power generation system based on the capacity configuration parameters; taking the maximum annual income as a first optimization target, optimizing and adjusting the output condition parameters based on preset ladder carbon price information to obtain the maximum expected annual income; calculating the investment recovery time based on the maximum expected annual income and the capacity configuration parameters, and taking the shortest investment recovery time as a second optimization target to optimize and adjust the capacity configuration parameters to obtain new capacity configuration parameters; and iteratively optimizing based on the new capacity configuration parameters until a preset stopping iteration condition is reached to obtain the best new capacity configuration parameters. The technical scheme provided by the application realizes optimal capacity configuration considering the ladder carbon price mechanism for the composite power generation system composed of wind power, photovoltaic power, thermal power and pumped storage-battery energy storage hybrid energy storage.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more specifically to a capacity configuration method, apparatus, and electronic equipment for a composite power generation system. Background Technology

[0002] New energy power generation is characterized by randomness and uncertainty, which can have a certain impact on the safety and stability of the power system. At present, it is encouraged to coordinate various power sources such as wind power, photovoltaic power, thermal power, and energy storage, and actively implement the "integrated wind, solar, thermal and energy storage" of existing power sources.

[0003] For existing thermal power projects, efforts should be made to expand the scale of bundled new energy power near the power plants, taking into account the development conditions, output characteristics, and absorption capacity of the receiving system in the vicinity of the power transmission end. The coordinated operation of energy storage, wind power, and photovoltaics is one of the effective ways to optimize existing thermal power and achieve low-carbon energy in the system.

[0004] Each energy storage technology has its advantages and disadvantages. Some scholars have proposed using hybrid energy storage to overcome the limitations of single energy storage methods and improve system efficiency. Considering large-scale charging and discharging, pumped hydro storage remains a better choice, and hybrid pumped hydro storage-battery storage systems have been applied. In these systems, batteries are used only for lower energy shortages, while pumped hydro storage serves as the primary storage for high energy demands. However, current technologies for hybrid pumped hydro storage-battery storage systems only focus on maximizing economic benefits in capacity optimization analysis, without considering the system's carbon emission costs. Furthermore, current research mainly focuses on the capacity configuration of composite energy storage under a unified carbon price carbon trading mechanism, and has not yet proposed capacity configuration schemes that consider tiered carbon pricing mechanisms for composite power generation systems composed of wind power, photovoltaic power, thermal power, and hybrid pumped hydro storage-battery storage. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a capacity configuration method, apparatus and electronic equipment for a hybrid power generation system, thereby achieving optimal capacity configuration considering the tiered carbon pricing mechanism for a hybrid power generation system composed of wind power, photovoltaic, thermal power and pumped storage-battery energy storage.

[0006] According to a first aspect, embodiments of the present invention provide a capacity configuration method for a hybrid power generation system. The method includes: initializing capacity configuration parameters for wind power, photovoltaic power, and hybrid energy storage in the hybrid power generation system, wherein the hybrid energy storage includes pumped hydro storage and battery energy storage; initializing output parameters of the hybrid power generation system based on the capacity configuration parameters; optimizing and adjusting the output parameters based on preset tiered carbon price information with the maximum annual return of the hybrid power generation system as a first optimization objective, and calculating the maximum expected annual return corresponding to the optimized output parameters; calculating the investment payback period of the hybrid power generation system based on the maximum expected annual return and the capacity configuration parameters, and optimizing and adjusting the capacity configuration parameters with the shortest investment payback period as a second optimization objective to obtain new capacity configuration parameters; using the new capacity configuration parameters as the capacity configuration parameters, returning to the step of initializing the output parameters of the hybrid power generation system based on the capacity configuration parameters for iterative calculation until a preset stopping iteration condition is reached, and using the new capacity configuration parameters obtained at the time of stopping iteration to configure the capacity of wind power, photovoltaic power, and hybrid energy storage.

[0007] Optionally, the initialization of the capacity configuration parameters of the wind power, photovoltaic, and hybrid energy storage of the hybrid power generation system includes: initializing an initial capacity configuration parameter containing multiple individuals using a chaotic Tent mapping mechanism; generating an individual with an opposing position for each individual of the initial capacity configuration parameter using an opposition learning mechanism; calculating the fitness of each individual and its opposing individual, deleting individuals with lower fitness in each group of individuals, and using the remaining individuals as the capacity configuration parameter.

[0008] Optionally, the initialization of the output parameters of the composite power generation system based on the capacity configuration parameters includes: sampling output scenario samples of wind power and photovoltaic power using the Latin hypercube sampling algorithm, and reducing the sampling results using the synchronous back substitution method to obtain typical output scenarios of wind power and photovoltaic power; initializing initial output parameters containing multiple individuals using the chaotic Tent mapping mechanism according to the capacity configuration parameters and the typical output scenarios; generating individuals with opposite positions for each individual of the initial output parameters using the opposite learning mechanism; calculating the fitness of each individual and its opposite individual, and deleting individuals with lower fitness in each group of individuals, and using the remaining individuals as the output parameters.

[0009] Optionally, the step of optimizing the power output parameters based on preset tiered carbon price information, with the first optimization objective being to maximize the annual return of the hybrid power generation system, and calculating the maximum expected annual return corresponding to the optimized power output parameters, includes: establishing an annual return optimization function corresponding to the first optimization objective based on preset tiered carbon price information; inputting the power output parameters into the annual return optimization function for calculation, and outputting the expected annual return corresponding to each individual in the power output parameters; calculating the return ratio of each individual to the total expected annual return of all individuals; comparing the return ratio corresponding to each individual with a preset return ratio threshold; if the return ratio of the current individual is less than the preset return ratio threshold... If the threshold is not less than the preset threshold, the current individual is updated based on the differential evolution algorithm. If the current individual's profit ratio is not less than the preset profit ratio threshold, the current individual is updated based on the gray wolf optimization algorithm. The first perturbation operator of each individual is calculated based on the Euclidean distance between the updated individuals, and the corresponding first perturbation operators are multiplied by the individuals to obtain the optimized new output parameters. The new output parameters are used as the output parameters, and the process returns to the step of inputting the output parameters into the annual profit optimization function for calculation, until the preset stop optimization condition is reached. When the optimization stops, the maximum expected annual profit corresponding to the new output parameters is output through the annual profit optimization function.

[0010] Optionally, the annual return optimization function is:

[0011] In the formula, E[ ] represents the mathematical expectation. It is an annual return function. Let be the probability of the s-th power output scenario for wind and solar power. M s It refers to the number of output scenarios, which meets the requirements. , Let T represent the power exchanged between the combined power generation system and the power grid at time t, where T represents the time duration. Let t be the thermal power output. The electricity price of the combined power generation system at time t is the same as that sold by the grid. For the carbon emission costs of system operation, For the cost of thermal power generation, Indicates the interval between times, where

[0012]

[0013] In the formula, This represents the power generation cost coefficient for thermal power units. Indicates the interval between moments. The carbon emission coefficient for thermal power generation. This represents the total carbon emissions from thermal power units. This represents the total carbon quota for the system. The preset tiered carbon price information.

[0014] Optionally, the step of calculating the investment payback time of the hybrid power generation system based on the maximum expected annual return and the capacity configuration parameters, and using the shortest investment payback time as the second optimization objective, and optimizing the capacity configuration parameters to obtain new capacity configuration parameters, includes: creating an investment payback time function corresponding to the second optimization objective; inputting the capacity configuration parameters and the maximum expected annual return into the investment payback time function for calculation, and outputting the investment payback time corresponding to each individual in the capacity configuration parameters; calculating the time ratio of each individual's investment payback time to the total investment payback time of all individuals; comparing the time ratio corresponding to each individual with a preset time ratio threshold; if the time ratio of the current individual is less than the preset time ratio threshold, updating the current individual based on the differential evolution algorithm; if the time ratio of the current individual is not less than the preset time ratio threshold, updating the current individual based on the gray wolf optimization algorithm; calculating the second perturbation operator of each individual based on the Euclidean distance between the updated individuals, and multiplying the corresponding second perturbation operators with the individuals to obtain the optimized new capacity configuration parameters.

[0015] Optionally, the investment payback period function is:

[0016] In the formula, For the investment payback period of the combined power generation system, The expected annual return that the system can obtain, where It is an annual return function. Let t be the power exchanged between the combined power generation system and the power grid. Let t be the thermal power output at time t; The total investment cost of the system, Total system maintenance cost; in

[0017]

[0018] In the formula, , , , These are the unit construction costs for wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems, respectively. , , , These refer to the configuration capacity of wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems, respectively. , , , The service life of wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems. For the discount rate, , , , , The unit operation and maintenance costs for thermal power, wind power, photovoltaic, pumped storage reversible pump turbines and battery energy storage.

[0019] According to a second aspect, embodiments of the present invention provide a capacity configuration device for a hybrid power generation system. The device includes: a capacity initialization module for initializing capacity configuration parameters of wind power, photovoltaic, and hybrid energy storage in the hybrid power generation system, wherein the hybrid energy storage includes pumped hydro storage and battery energy storage; an output status initialization module for initializing output status parameters of the hybrid power generation system based on the capacity configuration parameters; an annual return module for optimizing and adjusting the output status parameters based on preset tiered carbon price information with the maximum annual return of the hybrid power generation system as a first optimization objective, and calculating the maximum expected annual return corresponding to the optimized output status parameters; a capacity parameter adjustment module for calculating the investment payback period of the hybrid power generation system based on the maximum expected annual return and the capacity configuration parameters, and optimizing and adjusting the capacity configuration parameters with the shortest investment payback period as a second optimization objective to obtain new capacity configuration parameters; and an iterative optimization module for using the new capacity configuration parameters as the capacity configuration parameters, returning to the step of initializing the output status parameters of the hybrid power generation system based on the capacity configuration parameters for iterative calculation until a preset stop iteration condition is reached, and configuring the capacity of wind power, photovoltaic, and hybrid energy storage based on the new capacity configuration parameters obtained at the stop iteration.

[0020] According to a third aspect, embodiments of the present invention provide an electronic device, including: 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 method described in the first aspect, or any optional embodiment of the first aspect.

[0021] According to a fourth aspect, embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect, or any optional embodiment of the first aspect.

[0022] The technical solution provided in this application has the following advantages: The technical solution provided in this application proposes a two-layer optimization model, including an upper layer and a lower layer, to optimize the capacity parameters of a hybrid power generation system. First, the upper-layer optimization unit initializes the capacity configuration parameters for wind power, photovoltaic (PV), and hybrid energy storage in the hybrid power generation system. Then, it moves to the lower-layer optimization unit to initialize the output parameters of the hybrid power generation system based on the capacity configuration parameters. In the lower-layer optimization unit, maximizing the annual return of the hybrid power generation system is the first optimization objective. Based on preset tiered carbon price information, the output parameters are optimized and adjusted so that the optimized output parameters correspond to the calculated maximum expected annual return. Then, it moves back to the upper-layer optimization unit to calculate the investment payback period of the hybrid power generation system based on the obtained maximum expected annual return and the initialized capacity configuration parameters. In the upper-layer optimization unit, minimizing the investment payback period is the second optimization objective, and the capacity configuration parameters are optimized and adjusted to obtain new capacity configuration parameters. Then, it continues to move to the lower-layer optimization unit, initializing the output parameters of the hybrid power generation system based on the new capacity configuration parameters and calculating a new maximum expected annual return. This process is iterated repeatedly until a preset stopping condition is reached, and the new capacity configuration parameters obtained at the stopping iteration are used to configure the capacity of wind power, PV, and hybrid energy storage. This results in a capacity configuration scheme that satisfies the tiered carbon pricing mechanism and enables the fastest investment recovery for the composite power system. Attached Figure Description

[0023] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings: Figure 1 This diagram illustrates the steps of a capacity configuration method for a hybrid power generation system according to one embodiment of the present invention. Figure 2 A flowchart illustrating a capacity configuration method for a hybrid power generation system according to one embodiment of the present invention is shown. Figure 3 Another schematic flowchart of a capacity configuration method for a hybrid power generation system according to one embodiment of the present invention is shown; Figure 4 A schematic diagram of the capacity configuration device of a composite power generation system according to one embodiment of the present invention is shown. Figure 5 A schematic diagram of an electronic device according to one embodiment of the present invention is shown. Detailed Implementation

[0024] 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 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 are within the scope of protection of the present invention.

[0025] Please see Figure 1 and Figure 2 In one embodiment, a capacity configuration method for a hybrid power generation system specifically includes the following steps: Step S101: Initialize the capacity configuration parameters of the hybrid power generation system, including wind power, photovoltaic power, and hybrid energy storage. Hybrid energy storage includes pumped hydro storage and battery energy storage.

[0026] Step S102: Initialize the output parameters of the hybrid power generation system based on the capacity configuration parameters; Step S103: Taking the maximization of the annual revenue of the hybrid power generation system as the primary optimization objective, optimize and adjust the output parameters based on the preset tiered carbon price information, and calculate the maximum expected annual revenue corresponding to the optimized output parameters. Step S104: Calculate the payback period of the hybrid power generation system based on the maximum expected annual return and capacity configuration parameters, and optimize and adjust the capacity configuration parameters with the shortest payback period as the second optimization objective to obtain new capacity configuration parameters; Step S105: Using the new capacity configuration parameters as the capacity configuration parameters, return to the step of initializing the output parameters of the hybrid power generation system based on the capacity configuration parameters for iterative calculation until the preset stop iteration condition is reached, and use the new capacity configuration parameters obtained when the iteration stops to configure the capacity of wind power, photovoltaic and hybrid energy storage.

[0027] Specifically, for existing thermal power projects, considering the development conditions, output characteristics, and absorption capacity of new energy sources in the vicinity of the power transmission end and the receiving end system, energy storage, wind power, and photovoltaic capacity are configured near existing thermal power projects. The coordinated operation of these new energy sources can optimize existing thermal power and achieve system energy decarbonization. To effectively configure the capacity of wind power, photovoltaic, and hybrid energy storage near thermal power plants, this invention proposes a two-layer capacity optimization configuration model for composite power generation systems. This model proposes two optimization units, an upper layer and a lower layer, for wind power, photovoltaic, and hybrid energy storage. The upper-layer optimization unit is used to adjust capacity configuration parameters, while the lower-layer optimization unit is used to calculate the maximum expected annual return of the power generation system. The upper-layer optimization unit needs to use the maximum expected annual return output by the lower-layer optimization unit to calculate the investment payback time of the power generation system, thereby adjusting to obtain the optimal capacity configuration parameters with the shortest investment payback time as the upper-layer optimization objective.

[0028] First, the upper-level optimization unit initializes the capacity configuration parameters for the wind power, photovoltaic (PV), and hybrid energy storage components of the hybrid power generation system. These parameters include various capacity configuration schemes, specifically the number of wind turbines, the number of PV cells in the PV power station, the reservoir volume and installed capacity of the pumped storage power station, and the battery storage capacity. The upper-level optimization unit needs to use these capacity configuration parameters to calculate the investment cost and, based on the investment cost and the maximum expected annual return, calculate the payback period before performing the optimization calculation that minimizes the payback period. Therefore, the upper-level optimization unit first transmits the initialized capacity configuration parameters to the lower-level optimization unit. The lower-level optimization unit then initializes the operating output of each type of unit based on these parameters, i.e., the output parameters of the hybrid power generation system, specifically including different output schemes composed of various power outputs of each type of unit at different times. The lower-level optimization unit prioritizes maximizing the annual revenue of the hybrid power generation system. It fully considers the tiered carbon pricing mechanism, calculating the annual revenue value using preset tiered carbon price information and output parameters. By adjusting the output parameters to increase the annual revenue value, and after multiple iterations, the optimal output parameters are obtained, and the corresponding maximum expected annual revenue is calculated. The upper-level optimization unit then receives the maximum expected annual revenue returned by the lower-level unit and calculates the investment payback period using the initialized capacity configuration parameters. With minimizing the investment payback period as its second optimization objective, the upper-level unit adjusts the capacity configuration parameters to decrease the payback period, obtaining the adjusted new capacity configuration parameters. The upper-level unit then transmits the new capacity configuration parameters back to the lower-level unit, allowing the lower-level unit to calculate the new maximum expected annual revenue. This process is repeated iteratively until a preset stopping condition is met. Finally, the new capacity configuration parameters obtained at the stopping point are used to configure the capacity for wind power, solar power, and hybrid energy storage. This achieves a capacity configuration scheme that satisfies the tiered carbon pricing mechanism and maximizes the investment recovery of the hybrid power system.

[0029] Specifically, in this embodiment, a hybrid energy storage system consisting of pumped hydro storage and battery energy storage is selected. Pumped hydro storage is responsible for absorbing and releasing the system's larger power output, while battery energy storage is responsible for absorbing and releasing the system's smaller power output. Pumped hydro storage is used to smooth out fluctuations in wind and solar power output and improve system efficiency. Compared with other types of energy storage, it has advantages such as long service life, lower cost per kilowatt-hour, and clean and low-carbon operation. When the electricity price is low or the transmission channel capacity reaches its limit, and the system needs to store electricity, pumped hydro storage can use a reversible pump-turbine to pump water from the lower reservoir to the upper reservoir, converting electrical energy into potential energy to complete time-limited energy storage. When the electricity price is high and the transmission channel capacity is still sufficient, and the system needs to discharge pumped hydro storage, the water stored in the upper reservoir is released to generate electricity, thereby improving the system's economic efficiency. However, the reversible pump-turbine configured in the pumped hydro power station has a certain threshold for pumping and generating electricity, typically requiring about 15% of the pump unit capacity to pump / generate electricity, which will have a certain impact on the system's economic efficiency. The embodiments of the present invention can reduce the system's charge and discharge threshold by configuring battery energy storage, absorb / release small power that pumped hydro storage cannot absorb, improve system flexibility, and help the system increase the proportion of new energy output.

[0030] The mathematical model for pumped storage can be expressed as follows:

[0031]

[0032]

[0033] In the formula, , The reservoir capacity is the capacity of the upper and lower pumped storage reservoirs at time t. , for t A pump-storage reversible water pump-turbine system continuously pumps water and generates electricity. , This refers to the efficiency of reversible pump-turbine systems for pumping water and generating electricity. , This is a 0-1 variable, representing the operating conditions of a reversible pump-turbine. A value of 1 indicates the pumping and power generation states of the reversible pump-turbine, respectively. , Let t be the amount of electricity stored in the reservoir. The conversion coefficient between water volume and power output. For time intervals.

[0034] The mathematical model for battery energy storage can be expressed as:

[0035] In the formula, This refers to the battery's state of charge. For battery energy storage self-loss rate, , These are 0-1 variables; a value of 1 represents the battery's charging and discharging states, respectively. . , To improve the charging and discharging efficiency of battery energy storage, For battery capacity, , Let t be the battery's energy storage charging and discharging power at time t.

[0036] Specifically, carbon trading is essentially a trading mechanism that achieves carbon emission reduction through the buying and selling of carbon emission allowances. Relevant departments allocate a certain amount of carbon emission allowances to power generation companies with carbon emissions, either paid or free. When the actual carbon emissions of a power generation company are less than the allowance allocated by the government, it can sell the excess allowances to generate revenue; conversely, the power generation company must purchase carbon emission allowances to compensate for the excess emissions. In this embodiment of the invention, the thermal power units of the proposed hybrid power generation system account for the highest proportion of carbon emissions, while the carbon emissions of other units during operation and construction are negligible. The actual carbon emissions of the system are calculated as follows:

[0037] In the formula, This refers to the carbon emissions from thermal power units. This represents the carbon emission coefficient of thermal power units. Let t be the thermal power output at time t.

[0038] In this embodiment of the invention, only thermal power units are eligible for carbon allowances. The system carbon allowance is calculated as follows:

[0039] In the formula, This represents the total carbon quota for the system. Carbon quotas for power generation per unit of generating unit.

[0040] Traditional carbon trading typically uses a uniform carbon price, meaning a fixed price applies regardless of the amount of carbon dioxide emitted. To more strictly constrain system carbon emissions, this embodiment proposes a tiered carbon pricing mechanism, which provides more stringent constraints compared to the uniform carbon trading mechanism, based on the relationship between actual carbon emissions and the free carbon allowances allocated. Building upon the uniform carbon trading model, it calculates carbon trading costs based on emission ranges. When the difference between carbon emissions and carbon allowances exceeds a given range, the price of the excess carbon trading increases. When carbon emissions are lower than carbon allowances, the excess carbon emission rights are sold to generate revenue. A compensation coefficient is introduced to increase the incentive for emission reductions. The tiered carbon pricing information preset in this embodiment is as follows:

[0041] In the formula, K co2 This is tiered carbon price information derived from the actual carbon emission range. α As a reward and punishment factor, h This is the carbon emission range length coefficient, when α When the value is 0, it is a uniform electricity price.

[0042] By incorporating the aforementioned tiered carbon pricing information into a two-layer optimization model, different capacity configuration parameters can be obtained by reasonably setting the reward and penalty coefficients of the tiered carbon pricing mechanism, the carbon trading base price, and the carbon price range coefficient, thereby guiding the system to reduce carbon emissions.

[0043] Specifically, regarding the optimization solutions for the aforementioned upper-level and lower-level optimization units, the Grey Wolf Algorithm is a swarm intelligence algorithm that simulates the life and predation behavior of a grey wolf pack. It has advantages such as low parameter dependence and good optimization performance, and has been widely used in system planning, path planning, and other fields. It is also suitable for solving the two-level model of wind / solar / storage capacity configuration presented in this paper. In the Grey Wolf Algorithm, the α wolf, the group leader, is the manager of the entire pack (the individual with the current optimal capacity configuration parameter or the individual with the current optimal output parameter). The β wolves, γ wolves, and... The wolf ranks decrease sequentially, with lower-ranking wolves obeying higher-ranking wolves. Beta and gamma wolves assist the alpha wolf in decision-making during the hunting process. Led by the alpha wolf, the entire gray wolf pack gradually moves towards the optimal solution, ultimately achieving it.

[0044] However, the traditional Grey Wolf algorithm has some problems that slow down the convergence speed in terms of initial population selection, global and local development, and population diversity. Therefore, this invention improves the traditional Grey Wolf algorithm by introducing a chaotic opposition learning mechanism, differential evolution, and individual perturbation mechanism, thereby improving the optimization ability and convergence speed of the traditional Grey Wolf algorithm.

[0045] (1) Improvements to the population initialization process.

[0046] In the search space, the positions of individual gray wolves are initialized using a chaotic Tent mapping mechanism to form an initial population X. An opposing population X' is generated by an opposing learning mechanism, consisting of opposing positions of the initial population X. The gray wolf individuals with higher fitness between X and X' are retained in the initial population to obtain the final initial gray wolf population.

[0047] In other words, the initial population is first generated using Tent mapping:

[0048] In the formula, i For population size, i =1,2,3,…,N, jThe sequence number is the chaos index. j =1,2,3,…, d r is a random number, and r∈[0,1]. μ For chaotic parameters, μ ∈[0,2], y i,j Let j represent the j-th individual in the i-th population.

[0049] Then, an opposing solution is generated through an opposing learning mechanism:

[0050] In the formula, and elements respectively The lower and upper bounds.

[0051] After generating the complementary solutions, compare the fitness of the original individual and the complementary individual. and The solution with the higher fitness is retained (fitness is calculated using a fitness function; in this embodiment, the fitness function is the objective function corresponding to the investment payback time in the upper-level optimization unit and the objective function corresponding to the maximum expected annual return in the lower-level optimization unit). Thus, through this improvement, the initial parameters have a higher fitness, are closer to the optimal solution, the number of iterations is reduced, and the convergence speed of the Grey Wolf optimization algorithm is improved.

[0052] Specifically, based on the improvements to the population initialization process described above, in one embodiment, step S101 specifically includes the following steps: Step 1: Initialize the initial capacity configuration parameters containing multiple individuals using the chaotic Tent mapping mechanism.

[0053] Step 2: Generate individuals with opposing positions for each individual that has configured parameters for the initial capacity using an opposition learning mechanism; Step 3: Calculate the fitness of each individual and its counterpart, and delete the individuals with lower fitness in each group. Use the remaining individuals as capacity configuration parameters.

[0054] Specifically, the explanation of the principles of steps one to three can be found in the above description of the improvements to the population initialization stage of the gray wolf algorithm, and will not be repeated here.

[0055] Specifically, based on the improvements to the population initialization process described above, in one embodiment, step S102 specifically includes the following steps: Step 4: Sample the output scenarios of wind power and photovoltaic power using the Latin hypercube sampling algorithm, and reduce the sampling results using the synchronous back substitution method to obtain typical output scenarios of wind power and photovoltaic power.

[0056] Step 5: Based on the capacity configuration parameters and typical output scenarios, initialize the initial output parameters containing multiple individuals through the chaotic Tent mapping mechanism.

[0057] Step 6: Generate individuals with opposing positions for each individual of the initial output parameters through an opposition learning mechanism.

[0058] Step 7: Calculate the fitness of each individual and its counterpart, and remove individuals with lower fitness from each group. Use the remaining individuals as parameters for output.

[0059] Specifically, the principles of steps five to seven refer to the aforementioned description of improvements to the population initialization stage of the Gray Wolf algorithm, and will not be repeated here. It is important to note that photovoltaic and wind power outputs are characterized by randomness and uncertainty due to the influence of light intensity and wind speed. Therefore, in step four of this embodiment, a scenario analysis method is used to handle the uncertainty of wind and solar power. Specifically, a large number of wind and photovoltaic power output scenario samples are sampled using the Latin hypercube sampling algorithm to generate scenarios. Then, the scenarios are reduced using a synchronous back-substitution method to obtain several typical power output scenarios with the highest probability, as well as the probability of each typical power output scenario. This reduces the impact of uncertainty in photovoltaic and wind power output. The Latin hypercube scenario generation and synchronous back-substitution scenario reduction methods are existing technologies and will not be described further here.

[0060] (2) Improvements to the individual location update process.

[0061] The traditional gray wolf algorithm updates the positions of all other individuals based on the α, β, and γ wolves with the highest fitness in the initial gray wolf population, thereby hunting (optimizing) the prey (optimal solution).

[0062]

[0063]

[0064]

[0065] In the formula, C and A are coefficient vectors. The gray wolf will gradually decrease the distance between itself and its prey. This distance is determined by parameters A and D, with A gradually decreasing. D α , D β , D γ This represents the distances between the individual to be updated and the α, β, and γ wolves, respectively. X 1. X 2. X 3 represents the movement trend vectors of the individual to be updated towards the three-headed wolf. This represents the maximum number of iterations for the algorithm. tThis represents the current iteration number. , , These represent the positions of the α wolf, β wolf, and γ wolf, respectively. For individual locations to be updated, This is the position of the individual to be updated after the update.

[0066] This invention employs differential evolution to enhance population diversity through mutation, crossover, and selection operators, thereby improving the aforementioned position update process.

[0067] The mutation operator is represented as follows:

[0068] In the formula, Let i represent the i-th mutated solution. This represents the solutions in the current population. Indicates the current iteration number. N For population size, subscript , , It represents 3 [1, N The three randomly selected independent integers within the range represent three distinct individuals. This represents the variation factor, used to control the difference between two individuals.

[0069] The crossover operator is represented as follows: After individual mutation, the crossover operator can be used to further increase the diversity of the population on the target vector. Differential evolution in the current individual and mutated individuals Using the crossover operator to generate offspring individuals Specifically, it is expressed as

[0070] In the formula, rand represents a random value between [0,1]. This represents the crossover probability of individuals.

[0071] The selection operator is represented as follows: Using a greedy strategy to control the parent individuals and its offspring individuals Comparison is made to achieve the selection of new individuals, specifically...

[0072] In the formula, For vectors In this embodiment of the invention, the fitness function value is the annual return optimization function or the investment payback time function.

[0073] In addition, the embodiments of the present invention define individuals The probability of selection is its The ratio between the fitness of a species and the overall fitness of the population is expressed as:

[0074] The selection probability P determines whether a gray wolf individual updates its position using a differential evolution strategy. If P ≥ a preset threshold, the traditional gray wolf algorithm is used to update the current solution. Perform position updates; if P < preset threshold, then use differential evolution to update the current solution. Perform a location update.

[0075] Finally, the global search capability of the Grey Wolf algorithm is enhanced by individual perturbations, and the perturbation operator is used... D op Defined as:

[0076] In the formula, Represents an individual gray wolf and The Euclidean distance between them Individual The nearest neighbor individual, Representing an interval A uniformly distributed random quantity. According to the definition of the perturbation operator, when two individual gray wolves are relatively close to each other... This will cause the gray wolves to converge toward the initial point. To maintain the diversity of the gray wolf population, the current population is multiplied by the perturbation operator.

[0077] The embodiments of the present invention improve the individual position update step of the gray wolf algorithm by improving the population diversity and global search capability of the algorithm, avoiding getting trapped in local optima, and improving the optimization capability and convergence speed of the traditional gray wolf algorithm.

[0078] Based on the above improvements to the individual location update process, step S103 of this embodiment of the invention specifically includes the following steps: Step 8: Establish the annual return optimization function corresponding to the first optimization objective based on the preset tiered carbon price information.

[0079] Specifically, step eight is equivalent to creating the fitness function. f .

[0080] Step 9: Input the output parameters into the annual return optimization function for calculation, and output the expected annual return for each individual in the output parameters.

[0081] Step 10: Calculate the proportion of each individual's expected annual return to the total expected annual return of all individuals.

[0082] Specifically, steps nine and ten are equivalent to calculating the aforementioned selection probability P.

[0083] Step 11: Compare the profit ratio for each individual with the preset profit ratio threshold.

[0084] Step 12: If the current individual's profit ratio is less than the preset profit ratio threshold, then update the current individual based on the differential evolution algorithm.

[0085] Step 13: If the current individual's profit ratio is not less than the preset profit ratio threshold, then update the current individual based on the Grey Wolf Optimization Algorithm.

[0086] Specifically, steps eleven to thirteen are equivalent to selecting the corresponding individual update method based on the selection probability P, using either the traditional gray wolf algorithm update method or the differential evolution algorithm update method.

[0087] Step Fourteen: Calculate the first perturbation operator for each individual based on the updated Euclidean distance between individuals, and multiply the corresponding first perturbation operators with the individuals to obtain the optimized new output parameters.

[0088] Step 15: Using the new output parameters as the output parameters, return to the step of inputting the output parameters into the annual return optimization function for calculation, until the preset stop optimization condition is reached, and output the maximum expected annual return corresponding to the new output parameters through the annual return optimization function when optimization stops.

[0089] Specifically, the perturbed output parameters are re-inputted into the annual return optimization function for iterative optimization until a preset stopping condition is met, obtaining the optimal output parameters and outputting the corresponding maximum expected annual return. The algorithm principles and beneficial effects of steps eight to fifteen are described in the relevant description of the improvement to the individual position update stage of the Gray Wolf algorithm above, and will not be repeated here.

[0090] Based on the above improvements to the individual location update process, step S104 of this embodiment of the invention specifically includes the following steps: Step 16: Create the investment payback period function corresponding to the second optimization objective.

[0091] Step 17: Input the capacity configuration parameters and the maximum expected annual return into the investment payback time function for calculation, and output the investment payback time corresponding to each individual in the capacity configuration parameters.

[0092] Step 18: Calculate the proportion of each individual's investment payback time to the total investment payback time of all individuals.

[0093] Step 19: Compare the time ratio corresponding to each individual with the preset time ratio threshold.

[0094] Step 20: If the current individual's time proportion is less than the preset time proportion threshold, then update the current individual based on the differential evolution algorithm.

[0095] Step 21: If the current individual's time proportion is not less than the preset time proportion threshold, then update the current individual based on the Grey Wolf Optimization Algorithm.

[0096] Step 22: Calculate the second perturbation operator for each individual based on the updated Euclidean distance between individuals, and multiply the corresponding second perturbation operators with the individuals to obtain the optimized new capacity configuration parameters.

[0097] Specifically, the processing steps 16 to 22 above are the process of updating the capacity configuration parameters individually through the improved Grey Wolf algorithm. For the explanation of the principle and the beneficial effects, please refer to the relevant description of the improvement of the individual position update link of the Grey Wolf algorithm above, and for the relevant description of steps 8 to 15, which will not be repeated here.

[0098] In summary, as Figure 3 As shown, the improved Grey Wolf algorithm is applied to the capacity configuration method provided in this embodiment of the invention, and the specific steps are as follows: 1. Initialize the upper-level population (capacity configuration parameters) using the chaotic Tent opposition mechanism.

[0099] 2. Transfer the upper-level population to the lower-level optimization unit and initialize the lower-level population (output parameters) using the chaotic Tent opposition mechanism.

[0100] 3. Calculate the fitness function value of the expected annual return at the lower level using power output parameters and tiered carbon price information.

[0101] 4. Taking the maximization of expected annual return as the optimization objective, the improved gray wolf algorithm is used to adjust the output parameters until the next iteration number TI1 reaches TI. max When the preset stop optimization condition is met, output the parameters of the optimal output condition and calculate the corresponding maximum expected annual return.

[0102] 5. Return the maximum expected annual return to the upper-level optimization unit and calculate the investment payback period in conjunction with the capacity configuration parameters.

[0103] 6. If the number of iterations TI2 in the upper layer does not reach TI... max (With a preset stopping condition), the optimization objective is to maximize the investment recovery time. The improved gray wolf algorithm is used to optimize and adjust the capacity configuration parameters. Then, the optimized capacity configuration parameters are transmitted to the next optimization unit to continue the next optimization.

[0104] 7. If the number of iterations TI2 in the upper layer reaches TI... max(Preset stopping iteration conditions), output optimized capacity configuration parameters, and configure the capacity of wind power, photovoltaic, and hybrid energy storage.

[0105] Specifically, in one embodiment, to verify the advantages of the improved Gray Wolf Algorithm IGWO proposed in this invention, taking a tiered carbon pricing mechanism scenario with a reward / penalty coefficient of 0.3 and a carbon trading base price of 200 as an example, the improved Gray Wolf Algorithm IGWO is compared with the traditional Gray Wolf Algorithm GWO, Particle Swarm Optimization (PSO), improved Particle Swarm Optimization (IPSO), and the fast non-dominated sorting hybrid genetic algorithm NSGA-II. The algorithm parameters are set as follows: initial population size 50, maximum number of iterations 500, and each algorithm is run independently 10 times to obtain the average of the optimal fitness function and running time. The results of the comparison of various algorithms are shown in Table 1.

[0106] From the perspective of algorithm improvement, the improved Grey Wolf algorithm (IGWO) requires fewer iterations than the GWO algorithm. The improved Grey Wolf algorithm also takes less time to solve for the fitness function value compared to the GWO algorithm, and yields better results. Comparison of simulation results shows that the improved Grey Wolf algorithm outperforms the comparative algorithms in terms of optimal fitness function and runtime. Therefore, the improved Grey Wolf algorithm proposed in this embodiment has stronger optimization capabilities and faster convergence speed.

[0107] Table 1 Comparison of simulation results for various algorithms

[0108] Specifically, in one embodiment, preset tiered carbon price information is introduced into the optimization process. The annual revenue optimization function proposed by the lower-level optimization unit in this embodiment of the invention is as follows:

[0109] In the formula, E[ ] represents the mathematical expectation. It is an annual return function. Let be the probability of the s-th power output scenario for wind and solar power. M s It refers to the number of output scenarios, which meets the requirements. , Let T represent the power exchanged between the combined power generation system and the power grid at time t, where T represents the time duration. Let t be the thermal power output. The electricity price of the combined power generation system at time t is the same as that sold by the grid. For the carbon emission costs of system operation, For the cost of thermal power generation, This indicates the interval between moments.

[0110]

[0111] In the formula, This represents the power generation cost coefficient for thermal power units. Indicates the interval between moments. The carbon emission coefficient for thermal power generation. This represents the total carbon emissions from thermal power units. This represents the total carbon quota for the system. This is the preset tiered carbon price information.

[0112] The constraints of the annual return optimization function include: 1) System power balance constraints

[0113] In the formula, , , The power outputs of thermal power, wind power, and photovoltaic power at time t are respectively. Let t be the power exchanged between the large power grid and the system at time t. , Let t be the battery's energy storage charging and discharging power at time t. , The pump-storage reversible pump-turbine pumps and generates power at time t.

[0114] 2) Power constraints between the system and the power grid

[0115] In the formula, , These are the upper and lower limits of the power interaction between the system and the main power grid.

[0116] 3) Constraints of thermal power units ,

[0117] In the formula, , The upper and lower limits of the output of thermal power units. For the installed capacity of thermal power units, This refers to the unit's ramp-up rate.

[0118] 4) Pumped storage capacity constraints

[0119]

[0120]

[0121]

[0122] In the formula, , The reservoir capacity at time t represents the capacity of the upper and lower pumped storage reservoirs. , These are the upper and lower limits of the reservoir's capacity. , These are the upper and lower limits of the reservoir's capacity. , The pumped-storage reversible pump-turbine pump-turbine generates water and electricity at time t. This is the upper limit of the output of a reversible pump-turbine. This is the lower limit of the output of a reversible pump-turbine, which is related to the installed capacity of pumped storage. It limits the daily inflow and outflow of water in the pumped storage reservoir to be the same, meaning the initial water volume of the pumped storage reservoir must be consistent each day. , This is a 0-1 variable, representing the operating conditions of a reversible pump-turbine. A value of 1 indicates the pumping and power generation states of the reversible pump-turbine, respectively. ; , This refers to the efficiency of reversible pump-turbine systems for pumping water and generating electricity.

[0123] 6) Battery energy storage constraints

[0124]

[0125]

[0126] In the formula, , The upper and lower limits of battery energy storage SOC. , The upper and lower limits of the charging and discharging power of the battery energy storage are set to ensure that the charging and discharging amount of the battery energy storage is the same throughout the day, that is, the initial internal energy capacity of the battery energy storage is the same every day. This refers to the battery's state of charge. , These are 0-1 variables; a value of 1 represents the battery's charging and discharging states, respectively. . , To improve the charging and discharging efficiency of battery energy storage, , Let t be the battery's energy storage charging and discharging power at time t.

[0127] Specifically, in one embodiment, the investment payback period function proposed by the upper-level optimization unit in this embodiment of the invention is:

[0128] In the formula, For the system's investment payback period, The expected annual return that the system can obtain, where It is an annual return function. Let t be the power exchanged between the combined power generation system and the power grid. Let t be the thermal power output at time t; The total investment cost of the system, Total system maintenance cost; in

[0129]

[0130] In the formula, , , , These are the unit construction costs for wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems, respectively. , , , These refer to the configuration capacity of wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems, respectively. , , , The service life of wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems. For the discount rate, , , , , The unit operation and maintenance costs for thermal power, wind power, photovoltaic, pumped storage reversible pump turbines and battery energy storage.

[0131] The constraints on the investment payback time function include: 1) Reversible pump-turbine installed capacity constraints

[0132] In the formula, , These are the upper and lower limits of the installed capacity of reversible pump-turbine systems.

[0133] 2) Storage capacity constraints

[0134]

[0135] In the formula, , , , These represent the upper and lower limits of the reservoir capacity, respectively.

[0136] 3) Battery energy storage capacity constraints

[0137] In the formula, , These represent the upper and lower limits of the installed capacity of battery energy storage.

[0138] 4) Wind turbine quantity constraints

[0139] In the formula, , These represent the upper and lower limits of the number of wind turbine units. NW This indicates the number of wind turbine units assembled.

[0140] 5) Constraints on the number of photovoltaic panels

[0141] In the formula, , These represent the upper and lower limits for the number of photovoltaic panels. NP This indicates the number of photovoltaic panels installed.

[0142] By utilizing the investment recovery time function, annual return optimization function, and constraints of the two types of functions provided in this invention embodiment, and fully considering the relevant factors affecting capacity configuration, the accuracy of upper-level optimization and lower-level optimization can be improved, ultimately outputting more accurate capacity configuration parameters.

[0143] Through the above steps, the technical solution provided in this application proposes a method for optimizing the capacity configuration of a hybrid power generation system considering a tiered carbon pricing mechanism. This includes a two-layer optimization model comprising upper and lower layers, thereby optimizing the capacity parameters of the hybrid power generation system. First, the capacity configuration parameters of the hybrid power generation system (HPS), including wind power, solar power, and hybrid energy storage, are initialized in the upper-level optimization unit. Then, the process moves to the lower-level optimization unit, where the output parameters of the HPS are initialized based on the capacity configuration parameters. In the lower-level optimization unit, maximizing the annual return of the HPS is the primary optimization objective. The output parameters are optimized and adjusted based on preset tiered carbon pricing information, resulting in the calculated maximum expected annual return. Next, the process moves back to the upper-level optimization unit, where the investment payback period of the HPS is calculated based on the obtained maximum expected annual return and the initialized capacity configuration parameters. In the upper-level optimization unit, minimizing the investment payback period is the secondary optimization objective, and the capacity configuration parameters are optimized and adjusted to obtain new capacity configuration parameters. The process then continues to move to the lower-level optimization unit, where the output parameters of the HPS are initialized based on the new capacity configuration parameters, and a new maximum expected annual return is calculated. This iterative process continues until a preset stopping condition is met. The new capacity configuration parameters obtained at the stopping point are then used to configure the capacity of wind power, solar power, and hybrid energy storage. This achieves a capacity configuration scheme that satisfies the tiered carbon pricing mechanism and maximizes the investment payback of the HPS. By rationally setting the reward and penalty coefficients for the tiered carbon pricing mechanism, the carbon trading base price, and the carbon price range coefficient, the system can be effectively guided to reduce carbon emissions. Furthermore, the improved Grey Wolf algorithm offers stronger optimization capabilities and can significantly improve the model's solution speed.

[0144] like Figure 4 As shown, this embodiment also provides a capacity configuration device for a hybrid power generation system, the device comprising: The capacity initialization module 101 is used to initialize the capacity configuration parameters of the hybrid power generation system, including wind power, photovoltaic power, and hybrid energy storage. The hybrid energy storage includes pumped hydro storage and battery energy storage. For details, please refer to the relevant description of step S101 in the above method embodiments, which will not be repeated here.

[0145] The output status initialization module 102 is used to initialize the output status parameters of the hybrid power generation system based on the capacity configuration parameters. For details, please refer to the relevant description of step S102 in the above method embodiment, which will not be repeated here.

[0146] The annual revenue module 103 is used to optimize and adjust the output parameters based on preset tiered carbon price information, with the primary optimization objective of maximizing the annual revenue of the hybrid power generation system, and to calculate the maximum expected annual revenue corresponding to the optimized output parameters. For details, please refer to the relevant description of step S103 in the above method embodiment, which will not be repeated here.

[0147] The capacity parameter adjustment module 104 is used to calculate the investment payback period of the hybrid power generation system based on the maximum expected annual return and capacity configuration parameters, and to optimize and adjust the capacity configuration parameters with the shortest possible payback period as the second optimization objective, thereby obtaining new capacity configuration parameters. For details, please refer to the relevant description of step S104 in the above method embodiment, which will not be repeated here.

[0148] The iterative optimization module 105 is used to iteratively calculate the output parameters of the hybrid power generation system based on the new capacity configuration parameters, returning the steps of initializing the output parameters of the hybrid power generation system based on the capacity configuration parameters, until a preset stopping iteration condition is reached, and then configuring the capacity of wind power, photovoltaic, and hybrid energy storage based on the new capacity configuration parameters obtained at the time of stopping iteration. For details, please refer to the relevant description of step S105 in the above method embodiment, which will not be repeated here.

[0149] The capacity configuration device for the hybrid power generation system provided in this embodiment of the invention is used to execute the capacity configuration method for the hybrid power generation system provided in the above embodiment. Its implementation method and principle are the same. For details, please refer to the relevant description of the above method embodiment, which will not be repeated here.

[0150] Figure 5 An electronic device according to an embodiment of the present invention is shown. The device includes a processor 901 and a memory 902, which can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0151] Processor 901 can be a central processing unit (CPU). Processor 901 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0152] The memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the above method embodiments. The processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 902, thereby implementing the methods in the above method embodiments.

[0153] The memory 902 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 901, etc. Furthermore, the memory 902 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 902 may optionally include memory remotely located relative to the processor 901, and these remote memories may be connected to the processor 901 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0154] One or more modules are stored in memory 902, and when executed by processor 901, they perform the methods described in the above method embodiments.

[0155] The specific details of the aforementioned electronic device can be understood by referring to the relevant descriptions and effects in the above method embodiments, and will not be repeated here.

[0156] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The implemented program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0157] 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 capacity configuration method for a hybrid power generation system, characterized in that, The method includes: Initialize the capacity configuration parameters of the hybrid power generation system, which includes wind power, photovoltaic power, and hybrid energy storage, wherein the hybrid energy storage includes pumped hydro storage and battery energy storage; Initialize the output parameters of the hybrid power generation system based on the capacity configuration parameters; the initialization of the output parameters of the hybrid power generation system based on the capacity configuration parameters includes: sampling the output scenario samples of wind power and photovoltaic power using the Latin hypercube sampling algorithm, and reducing the sampling results by the synchronous back substitution method to obtain typical output scenarios of wind power and photovoltaic power; initializing the initial output parameters containing multiple individuals using the chaotic Tent mapping mechanism according to the capacity configuration parameters and the typical output scenarios; generating individuals with opposite positions for each individual of the initial output parameters using the opposite learning mechanism; calculating the fitness of each individual and its opposite individual, and deleting individuals with lower fitness in each group of individuals, and using the remaining individuals as the output parameters; With maximizing the annual revenue of the hybrid power generation system as the primary optimization objective, the output parameters are optimized and adjusted based on preset tiered carbon price information, and the maximum expected annual revenue corresponding to the optimized output parameters is calculated. The calculation of the payback period for the hybrid power generation system based on the maximum expected annual return and the capacity configuration parameters, with the shortest payback period as the second optimization objective, involves optimizing and adjusting the capacity configuration parameters to obtain new capacity configuration parameters. This includes: creating a payback period function corresponding to the second optimization objective; and inputting the capacity configuration parameters and the maximum expected annual return into the payback period function for calculation. The system outputs the investment payback time for each individual in the capacity configuration parameters; calculates the proportion of each individual's investment payback time to the total investment payback time of all individuals; compares the proportion of each individual's time with a preset time proportion threshold; if the current individual's time proportion is less than the preset time proportion threshold, the current individual is updated based on the differential evolution algorithm; if the current individual's time proportion is not less than the preset time proportion threshold, the current individual is updated based on the gray wolf optimization algorithm; calculates the second perturbation operator for each individual based on the Euclidean distance between the updated individuals, and multiplies the corresponding second perturbation operators with the individuals to obtain the optimized new capacity configuration parameters. The new capacity configuration parameters are used as the capacity configuration parameters. The step of initializing the output parameters of the hybrid power generation system based on the capacity configuration parameters is returned for iterative calculation until the preset stop iteration condition is reached. The new capacity configuration parameters obtained when the iteration stops are used to configure the capacity of wind power, photovoltaic and hybrid energy storage.

2. The method according to claim 1, characterized in that, The initialization parameters for the capacity configuration of the wind power, photovoltaic, and hybrid energy storage of the hybrid power generation system include: Initial capacity configuration parameters containing multiple individuals are initialized using a chaotic Tent mapping mechanism; An individual with an opposing position is generated for each individual configured with parameters for the initial capacity through an opposition learning mechanism; Calculate the fitness of each individual and its counterpart, remove individuals with lower fitness from each group, and use the remaining individuals as the capacity configuration parameter.

3. The method according to claim 1, characterized in that, The first optimization objective is to maximize the annual return of the hybrid power generation system. Based on preset tiered carbon price information, the power output parameters are optimized and adjusted, and the maximum expected annual return corresponding to the optimized power output parameters is calculated, including: An annual return optimization function corresponding to the first optimization objective is established based on the preset tiered carbon price information. The output parameters are input into the annual return optimization function for calculation, and the expected annual return corresponding to each individual in the output parameters is output. Calculate the proportion of each individual's expected annual return to the total expected annual return of all individuals; The profit ratio for each individual is compared with the preset profit ratio threshold. If the current individual's profit ratio is less than the preset profit ratio threshold, then the current individual is updated based on the differential evolution algorithm; If the current individual's profit ratio is not less than the preset profit ratio threshold, then the current individual is updated based on the Grey Wolf Optimization Algorithm. The first perturbation operator for each individual is calculated based on the updated Euclidean distance between individuals, and the corresponding first perturbation operators are multiplied by the individual to obtain the optimized new output parameters; The new output parameters are used as the output parameters. The process returns to the step of inputting the output parameters into the annual return optimization function for calculation until the preset stop optimization condition is reached. When the optimization stops, the maximum expected annual return corresponding to the new output parameters is output through the annual return optimization function.

4. The method according to claim 3, characterized in that, The annual return optimization function is: In the formula, E[ ] represents the mathematical expectation. It is an annual return function. Let be the probability of the s-th power output scenario for wind and solar power. M s It refers to the number of output scenarios, which meets the requirements. , Let T represent the power exchanged between the combined power generation system and the power grid at time t, where T represents the time duration. Let t be the thermal power output. The electricity price of the combined power generation system at time t is the same as that sold by the grid. For the carbon emission costs of system operation, For the cost of thermal power generation, Indicates the interval between times, where In the formula, This represents the power generation cost coefficient for thermal power units. Indicates the interval between moments. The carbon emission coefficient for thermal power generation. This represents the total carbon emissions from thermal power units. This represents the total carbon quota for the system. The preset tiered carbon price information.

5. The method according to claim 1, characterized in that, The investment payback time function is: In the formula, For the investment payback period of the combined power generation system, The expected annual return that the system can obtain, where It is an annual return function. Let t be the power exchanged between the combined power generation system and the power grid. Let t be the thermal power output at time t; The total investment cost of the system, Total system maintenance cost; in In the formula, , , , These are the unit construction costs for wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems, respectively. , , , These refer to the configuration capacity of wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems, respectively. , , , The service life of wind turbines, photovoltaic units, reversible water pump turbines, and battery energy storage systems. For the discount rate, , , , , The unit operation and maintenance costs for thermal power, wind power, photovoltaic, pumped storage reversible pump turbines and battery energy storage.

6. A capacity configuration device for a composite power generation system, characterized in that, The device includes: The capacity initialization module is used to initialize the capacity configuration parameters of the wind power, photovoltaic and hybrid energy storage of the hybrid power generation system, wherein the hybrid energy storage includes pumped hydro storage and battery energy storage. The output status initialization module is used to initialize the output status parameters of the hybrid power generation system based on the capacity configuration parameters. The initialization of the output status parameters of the hybrid power generation system based on the capacity configuration parameters includes: sampling output scenario samples of wind power and photovoltaic power using the Latin hypercube sampling algorithm, and reducing the sampling results using a synchronous back-substitution method to obtain typical output scenarios for wind power and photovoltaic power; initializing initial output status parameters containing multiple individuals using a chaotic Tent mapping mechanism based on the capacity configuration parameters and the typical output scenarios; generating individuals with opposite positions for each individual in the initial output status parameters using an opposition learning mechanism; calculating the fitness of each individual and its opposite individual, deleting individuals with lower fitness from each group of individuals, and using the remaining individuals as the output status parameters. The annual revenue module is used to optimize and adjust the output parameters based on preset tiered carbon price information with the primary optimization objective of maximizing the annual revenue of the hybrid power generation system, and to calculate the maximum expected annual revenue corresponding to the optimized output parameters. The capacity parameter adjustment module is used to calculate the payback period of the hybrid power generation system based on the maximum expected annual return and the capacity configuration parameters, and to optimize and adjust the capacity configuration parameters with the shortest payback period as the second optimization objective to obtain new capacity configuration parameters. The step of calculating the payback period of the hybrid power generation system based on the maximum expected annual return and the capacity configuration parameters, and optimizing and adjusting the capacity configuration parameters with the shortest payback period as the second optimization objective to obtain new capacity configuration parameters, includes: creating an payback period function corresponding to the second optimization objective; and inputting the capacity configuration parameters and the maximum expected annual return into the payback period function. The system performs calculations to output the investment payback time for each individual in the capacity configuration parameters; it calculates the proportion of each individual's investment payback time to the total investment payback time of all individuals; it compares the proportion of each individual's time with a preset time proportion threshold; if the current individual's time proportion is less than the preset time proportion threshold, it updates the current individual based on the differential evolution algorithm; if the current individual's time proportion is not less than the preset time proportion threshold, it updates the current individual based on the gray wolf optimization algorithm; it calculates the second perturbation operator for each individual based on the Euclidean distance between the updated individuals, and multiplies the corresponding second perturbation operators with the individuals to obtain the optimized new capacity configuration parameters. The iterative optimization module is used to perform iterative calculations on the step of initializing the output parameters of the hybrid power generation system based on the new capacity configuration parameters, using the new capacity configuration parameters as the capacity configuration parameters, until a preset stop iteration condition is reached, and to configure the capacity of wind power, photovoltaic and hybrid energy storage based on the new capacity configuration parameters obtained when the iteration stops.

7. An electronic device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method as described in any one of claims 1-5.

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