A PSO-DP coupling nested calculation method for solving a short-term multi-objective scheduling model of hydro-thermal power storage

Abstract

The application provides a PSO-DP coupling nested algorithm for solving a water, wind, light, storage and short-term multi-objective scheduling model, and in combination with the high-dimensional nonlinear solving difficulty of the water, wind, light, storage and short-term multi-objective scheduling model, a double-layer nested coupling solving algorithm is provided, the outer model takes the maximum increment of energy storage of cascade hydropower stations as an objective function, a particle swarm algorithm is used to solve the scheduling scheme of the system, in the calculation unit of the inner layer nested cascade hydropower load task allocation, the objective function of the minimum source-load difference is realized, an initial population satisfying the requirements and constraint conditions of the complementary system output process is generated, and a dynamic programming algorithm is used for optimization calculation, so that the dimension reduction of the multi-objective complex high-dimensional problem is realized, the time and difficulty of model calculation are greatly reduced, and the efficient solving of the water, wind, light, storage and short-term multi-objective scheduling model is realized.

Description

Technical Field

[0001] This invention relates to the field of hydro-wind-solar-storage complementary operation technology, and in particular to a PSO-DP coupled nested algorithm for solving a short-term multi-objective scheduling model of hydro-wind-solar-storage. Background Technology

[0002] With the introduction of the "dual-carbon" strategic goal, clean and low-carbon renewable energy sources such as wind power and photovoltaics have developed rapidly. However, due to the influence of meteorological factors, the output of wind and solar power generation exhibits significant randomness, fluctuation, and intermittency, which restricts their large-scale grid connection. To promote the grid connection and absorption of wind and solar power plants, utilizing hydropower and pumped storage power plants, which are flexible in start-up and quick in adjustment, to complementarily regulate the output of wind and solar power generation, and to achieve multi-energy complementary scheduling operation of hydropower, wind power, solar power, and storage, thereby improving the grid connection and absorption level of clean energy resources, has become an important approach to the construction of new power systems. However, the multi-energy complementary power generation scheduling problem of hydropower, wind power, solar power, and storage involves a large number of power plants, is difficult to coordinate with different objectives, and has many complex influencing factors. It belongs to the optimization problem of discontinuous multiple feasible regions, which requires high computational accuracy and efficiency of the model solution algorithm. Therefore, in order to achieve short-term multi-objective scheduling operation of hydro-wind-solar-storage multi-energy complementary systems, promote the large-scale development and consumption of new energy sources, and boost the construction of new power systems and the realization of the "dual-carbon" strategic goals, it is urgent to study a PSO-DP coupled nested algorithm for solving the short-term multi-objective scheduling model of hydro-wind-solar-storage systems. This algorithm will enable efficient solution of the short-term multi-objective scheduling model of hydro-wind-solar-storage systems and provide important technical support for the integrated operation of multi-energy complementary systems and clean energy bases. Summary of the Invention

[0003] The purpose of this invention is to provide a PSO-DP coupled nested algorithm for solving short-term multi-objective scheduling models of hydropower, wind power, solar power, and energy storage systems. This algorithm addresses the technical problems existing in the prior art. For the high-dimensional nonlinear problems faced in solving short-term scheduling models of hydropower, wind power, solar power, and energy storage systems, a PSO-DP coupled inner and outer nested algorithm is proposed to improve the convergence speed and solution efficiency of the algorithm, thereby achieving efficient solution of short-term multi-objective scheduling models of hydropower, wind power, solar power, and energy storage systems.

[0004] Combining the high-dimensional nonlinear solution challenge of short-term multi-objective scheduling models for hydropower, wind power, solar power, and energy storage, this paper proposes a double-layer nested coupled solution algorithm. The outer layer model takes maximizing the energy storage increment of cascade hydropower stations as the objective function and uses particle swarm optimization to solve the system's scheduling scheme. The inner layer nests a calculation unit for cascade hydropower load task allocation, realizing the objective function of minimizing source-load differences. This is used to generate an initial population that meets the requirements and constraints of the complementary system's output process. Dynamic programming is then used for optimization calculations, achieving dimensionality reduction of complex high-dimensional multi-objective problems. This significantly reduces the model's computation time and difficulty, enabling efficient solutions to short-term multi-objective scheduling models for hydropower, wind power, solar power, and energy storage.

[0005] The present invention specifically adopts the following technical solution:

[0006] A PSO-DP coupled nested algorithm for solving a short-term multi-objective scheduling model for hydropower, wind power, solar power, and energy storage is characterized by:

[0007] To address the challenge of solving high-dimensional nonlinear problems in short-term multi-objective scheduling models for hydropower, wind power, solar power, and energy storage, a double-layer nested coupled solution algorithm is employed.

[0008] Among them, the outer layer model takes the maximum energy storage increment of the cascade hydropower stations as the objective function and uses the particle swarm algorithm to solve the system's scheduling scheme;

[0009] In the inner nested calculation unit for cascade hydropower load allocation, an initial population that meets the requirements and constraints of the complementary system output process is generated by using the objective function of minimizing the source-load difference. Dynamic programming algorithm is then used for optimization calculation to achieve dimensionality reduction of complex high-dimensional multi-objective problems, ultimately realizing the efficient solution of the short-term multi-objective scheduling model of hydropower, wind power, solar power and energy storage.

[0010] Furthermore, it includes the following steps:

[0011] S1. Based on the short-term multi-objective scheduling model of hydropower, wind power, solar power and energy storage with the goal of minimizing source-load difference and maximizing the incremental capacity of cascade hydropower storage, the basic parameters of the outer PSO algorithm are set.

[0012] S2. Based on the power grid load demand curve and the wind and solar power output curve, calculate the remaining load task curve of the cascade hydropower, and use the inner layer DP algorithm to optimize the objective function that minimizes the source-load difference.

[0013] S3. Initialize the particle population, and determine the optimal individual and the optimal fitness value of the initial population in the outer PSO algorithm based on the position of the individuals in the initial population.

[0014] S4. Based on the determined optimal positions of individuals and the optimal positions of the population, determine the state update parameters of the population, and iterate according to the velocity and position formulas to obtain the new velocities and positions of all individuals.

[0015] S5. Based on the updated position of each individual, calculate the fitness value of each individual after the position update according to the objective function calculation formula for maximizing the incremental increase of cascade hydropower storage, and compare it with the individual's optimal fitness value to determine the new optimal fitness value and optimal position of the current individual.

[0016] S6. Compare the new optimal fitness value of each individual with the optimal fitness value of the population to determine the new optimal fitness value and the optimal individual of the population.

[0017] S7. Determine whether the iteration termination condition is met. If it is met, output the optimization calculation results of the short-term multi-objective scheduling model of water, wind, solar and storage. Otherwise, return to step S4 and enter the next iteration until the iteration termination condition is met.

[0018] Furthermore, step S1 specifically includes the following steps:

[0019] S11, set the population size and basic parameters including the number of variables for each individual, the number of population iterations, the inertia weight factor, and the learning factor;

[0020] S12 sets the boundary values ​​of each constraint in the short-term multi-objective scheduling model of water, wind, solar and energy storage, and sets the iterative termination condition of the solution algorithm.

[0021] Furthermore, step S2 specifically includes the following steps:

[0022] S21 divides the scheduling cycle into T time periods according to the time scale, and discretizes the initial and final water levels of each reservoir in different time periods;

[0023] S22, set each cascade hydropower station as a stage, the stage variable is the number of the cascade hydropower station, and allocate the remaining load tasks of each time period to each hydropower station.

[0024] S23, from the first-level power station to the second-level power station. The absolute value of the difference between the total output of the first-level power station and the remaining load for the corresponding time period is set as the number of the current time period. State variables at the end of the phase;

[0025] S24, taking the output of each level of power station as the decision variable, the objective function is to minimize the absolute value of the difference between the total output of the cascade hydropower and the remaining load in each time period during the scheduling period. The calculation formula is shown in equation (1):

[0026] (1)

[0027] In the formula: This represents the minimum sum of source-load differences during the scheduling period. This indicates the total number of time periods for calculation. Indicates the calculation period number. This indicates the total number of hydroelectric power stations. Indicates the power station number. Indicates the first Level 1 power station Power generation output during a given period Indicates in The remaining load tasks for the specified time period;

[0028] S25, the downstream flow is calculated backward from the first-stage power station in the first time period, that is, assuming the power generation flow of the hydropower station in the current time period is... The water level, reservoir capacity, and power output at the end of the specified period are calculated using the power generation formula and water balance formula of the hydropower station units. If the trial calculation does not meet the constraints, return to step S22 to redistribute the remaining load tasks; if the constraints are met, proceed to step S26.

[0029] S26. Using the same method as step S25, calculate for each power station from upstream to downstream, and verify the calculated water level, reservoir capacity and output level by level. If the constraints are not met, return to step S22 to redistribute the remaining load task. If the constraints are met, proceed to step S27.

[0030] S27. After completing the trial calculations for all hydropower stations, determine the error within the time period by comparing the total output of the cascade hydropower stations during that time period with the remaining load task for the corresponding time period. If the error exceeds the specified range... As shown in equation (2), if the distribution of the cascade hydropower load during this period is unreasonable, then return to step S22 to redistribute the remaining load tasks. If the error is less than the specified range, then execute step S28:

[0031] (2)

[0032] S28. Based on steps S21-S27, the load is allocated in time periods, and the water level and reservoir capacity status of each power station are recorded in each time period. By traversing all time periods and all power stations, a set of feasible remaining load task curve allocation schemes for cascade hydropower stations and the scheduling and operation process of each hydropower station are obtained. The results are fed back to the outer PSO algorithm as the initial individuals of the particle swarm.

[0033] Furthermore, in step S27, the regulation capacity of the pumped storage power station is selected as the specified range. The upper limit.

[0034] Furthermore, step S3 specifically includes the following steps:

[0035] S31, randomly generate an initial population within the variable threshold, that is, generate a number of individuals;

[0036] S32, according to the objective function calculation formula for maximizing the incremental energy storage of cascade hydropower, as shown in equation (3), determine the fitness value of each individual and use it as the initial optimal fitness value of the individual:

[0037] (3)

[0038] In the formula: This represents the maximum value of the energy storage increment of a cascade hydropower station. This indicates the total number of time periods for calculation. Indicates the calculation period number. This indicates the total number of hydroelectric power stations. and All of these represent the power station number. Indicates the first Level 1 power station Energy storage value-added over time period Indicates the first Level 1 power station Inbound flow during a specific time period Indicates the first Level 1 power station Downflow during the period Indicates the first Level 1 power station Output coefficient for a given time period Indicates the first Level 1 power station Hydropower head during the period, Indicates the length of a unit of time period;

[0039] S33. Compare the fitness values ​​of all individuals in turn to determine the optimal fitness value and the optimal individual of the initial population.

[0040] Furthermore, in step S4, the state update parameters of the population are determined, and the new velocities and positions of all individuals are obtained iteratively according to the velocity and position formulas. The specific calculation formulas are shown in equations (4) and (5):

[0041] (4)

[0042] (5)

[0043] In the formula: Indicates the first The particle in the first The first iteration in the process of the second iteration Dimensional velocity, where i is the particle index, n is the particle population size, j is the dimensional index, d is the dimension of the space in which the particle resides, and ω is the inertia weighting factor. To understand learning factors, As a social learning factor, , Let be a random number that follows a uniform distribution between [0, 1]. For the first The particle in the first The j-th dimension coordinate of the individual's optimal position during the next iteration. For all particles in the first The j-th dimension coordinate of the globally optimal position during the next iteration. Indicates the first Individual particles The j-th dimension coordinate during the next iteration.

[0044] Compared to existing technologies, this invention and its preferred scheme address the high-dimensional nonlinear problem of solving short-term multi-objective scheduling models for hydropower, wind power, solar power, and energy storage. By proposing a double-layer nested coupled solution algorithm, the outer layer model takes maximizing the energy storage increment of cascade hydropower stations as its objective function and uses particle swarm optimization to solve the system's scheduling scheme. The inner layer nests computational units for cascade hydropower load task allocation, achieving the objective function of minimizing source-load differences. This generates an initial population that satisfies the output process requirements and constraints of the complementary system. Dynamic programming is then used for optimization calculations, achieving dimensionality reduction for complex high-dimensional multi-objective problems. This significantly reduces the computation time and difficulty of the model, enabling efficient solutions to short-term multi-objective scheduling models for hydropower, wind power, solar power, and energy storage. Attached Figure Description

[0045] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0046] Figure 1 The flowchart shows the calculation process of the PSO-DP coupled nested algorithm for solving the short-term multi-objective scheduling model of water, wind, solar and energy storage in an embodiment of the present invention. Detailed Implementation

[0047] In the following, specific embodiments of this application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand and implement this application. Without departing from the principles of this application, features from various embodiments can be combined to obtain new implementations, or certain features from some embodiments can be substituted to obtain other preferred implementations.

[0048] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0049] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0050] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Considering the complex scheduling problem of multi-energy complementary power generation involving hydropower, wind power, solar power, and energy storage, which involves a large number of power plants, significant coordination challenges due to different objectives, and numerous and complex influencing factors, this problem falls under the category of optimization problems in discontinuous multiple feasible regions. Therefore, it demands high computational accuracy and efficiency from the model solving algorithm. Thus, to achieve short-term multi-objective scheduling operation of multi-energy complementary systems involving hydropower, wind power, solar power, and energy storage, promote the large-scale development and consumption of new energy sources, and contribute to the construction of new power systems and the realization of the "dual-carbon" strategic goals, it is urgently necessary to research a PSO-DP coupled nested algorithm for solving short-term multi-objective scheduling models of hydropower, wind power, solar power, and energy storage. This algorithm would enable efficient solutions to the short-term multi-objective scheduling models of hydropower, wind power, solar power, and energy storage, providing crucial technical support for the integrated operation of multi-energy complementary systems and clean energy bases.

[0052] like Figure 1 As shown in the figure, this invention proposes a PSO-DP coupled nested algorithm for solving a short-term multi-objective scheduling model of hydropower, wind power, solar power, and storage power. Addressing the challenge of solving high-dimensional nonlinear problems in this model, a two-layer nested coupled solution algorithm is proposed. The outer layer model takes maximizing the energy storage increment of cascade hydropower stations as the objective function and uses particle swarm optimization to solve the system's scheduling scheme. The inner layer nests a calculation unit for cascade hydropower load task allocation, achieving the objective function of minimizing source-load differences. This generates an initial population that meets the requirements and constraints of the complementary system's output process. Dynamic programming is then used for optimization calculations, achieving dimensionality reduction for complex high-dimensional multi-objective problems. This significantly reduces the computation time and difficulty of the model, enabling efficient solutions to the short-term multi-objective scheduling model of hydropower, wind power, solar power, and storage power.

[0053] Its specific implementation process includes the following steps:

[0054] S1. Based on the short-term multi-objective scheduling model of hydropower, wind power, solar power and energy storage with the goal of minimizing source-load difference and maximizing the incremental capacity of cascade hydropower storage, set the basic parameters of the outer PSO algorithm and execute step S2.

[0055] S2. Based on the power grid load demand curve and the wind and solar power output curve, calculate the remaining load task curve of the cascade hydropower, and use the inner layer DP algorithm to optimize the objective function that minimizes the source-load difference. Then execute step S3.

[0056] S3. Initialize the particle population, and determine the optimal individual and the optimal fitness value of the initial population in the outer PSO algorithm based on the position of the individuals in the initial population. Then execute step S4.

[0057] S4. Based on the determined optimal position of the individual and the position of the optimal individual in the group, determine the state update parameters of the population, iterate according to the velocity and position formulas to obtain the new velocity and position of all individuals, and execute step S5.

[0058] S5. Based on the updated position of each individual, calculate the fitness value of each individual after the position update according to the objective function calculation formula for maximizing the incremental increase of cascade hydropower storage, and compare it with the individual's optimal fitness value to determine the new optimal fitness value and optimal position of the current individual, and then proceed to step S6.

[0059] S6. Compare the new optimal fitness value of each individual with the optimal fitness value of the population to determine the new optimal fitness value and the optimal individual of the population, and then proceed to step S7.

[0060] S7. Determine whether the iteration termination condition is met. If it is met, output the optimization calculation results of the short-term multi-objective scheduling model of water, wind, solar and storage. Otherwise, return to step S4 and enter the next iteration until the iteration termination condition is met.

[0061] As a preferred embodiment, in step S1, based on the short-term multi-objective scheduling model of hydropower, wind power, solar power, and energy storage with the objectives of minimizing source-load differences and maximizing the incremental capacity of cascade hydropower storage, the basic parameters of the outer PSO algorithm are set, including the following steps:

[0062] S11, set the population size and basic parameters such as the number of variables for each individual, the number of population iterations, the inertia weight factor and the learning factor, and execute step S12;

[0063] S12 sets the boundary values ​​of each constraint in the short-term multi-objective scheduling model of water, wind, solar and energy storage, and sets the iterative termination condition of the solution algorithm.

[0064] As a preferred embodiment, step S2 involves using an inner-layer DP algorithm to optimize the objective function that minimizes the source-load difference, including the following steps:

[0065] S21, Divide the scheduling cycle into T time periods according to the time scale requirements, discretize the initial and final water levels of each reservoir in different time periods, and execute step S22;

[0066] S22, set each cascade hydropower station as a stage, the stage variable is the number of the cascade hydropower station, allocate the remaining load tasks of each time period to each hydropower station, and execute step S23.

[0067] S23, from the first-level power station to the second-level power station. The absolute value of the difference between the total output of the first-level power station and the remaining load for the corresponding time period is set as the number of the current time period. At the end of the phase, execute step S24;

[0068] S24, take the output of each level of power station as the decision variable, the objective function is to minimize the absolute value of the difference between the total output of the cascade hydropower and the remaining load in each time period during the scheduling period, the calculation formula is shown in equation (1), and execute step S25.

[0069] (1)

[0070] In the formula: This represents the minimum sum of source-load differences during the scheduling period. This indicates the total number of time periods for calculation. Indicates the calculation period number. This indicates the total number of hydroelectric power stations. Indicates the power station number. Indicates the first Level 1 power station Power generation output during a given period Indicates in The remaining load tasks for the time period.

[0071] S25, the trial calculation of the discharge flow of the first-stage hydropower station in the first time period, that is, assuming the power generation flow of the hydropower station in the current time period is... The water level, reservoir capacity, and power output at the end of the specified period are calculated using the power generation formula and water balance formula of the hydropower station units. If the trial calculation does not meet the constraints, return to step S22 to redistribute the remaining load tasks; if the constraints are met, proceed to step S26.

[0072] S26. Perform calculations for each power station from upstream to downstream, using the same method as step S25. Verify the calculated water level, reservoir capacity, and output level by level. If the constraints are not met, return to step S22 to redistribute the remaining load tasks. If the constraints are met, proceed to step S27.

[0073] S27. After completing the trial calculations for all hydropower stations, determine the error within the time period by comparing the total output of the cascade hydropower stations during that time period with the remaining load task for the corresponding time period. If the error exceeds the specified range... As shown in equation (2), it indicates that the distribution of the cascade hydropower load during this period is unreasonable. Then, return to step S22 to redistribute the remaining load tasks. If the error is less than the specified range, then execute step S28.

[0074] (2)

[0075] S28. Following the steps above, the load is allocated periodically, and the water level and reservoir capacity of each power station are recorded for each period. By traversing all periods and all power stations, a set of feasible remaining load task curve allocation schemes for cascade hydropower stations and the scheduling and operation process of each hydropower station are obtained. The results are fed back to the outer PSO algorithm as the initial individuals of the particle swarm.

[0076] As a preferred embodiment, step S3 involves initializing the particle population and determining the optimal individual and the optimal fitness value of the initial population in the outer PSO algorithm based on the positions of the individuals in the initial population. This includes the following steps:

[0077] S31, randomly generate an initial population within the variable threshold, i.e. generate a number of individuals, and execute step S32;

[0078] S32, according to the objective function calculation formula for maximizing the incremental increase of cascade hydropower storage, as shown in Equation (3), determine the fitness value of each individual and use it as the initial optimal fitness value of the individual, and execute step S33;

[0079] (3)

[0080] In the formula: This represents the maximum value of the energy storage increment of a cascade hydropower station. This indicates the total number of time periods for calculation. Indicates the calculation period number. This indicates the total number of hydroelectric power stations. and All of these represent the power station number. Indicates the first Level 1 power station Energy storage value-added over time period Indicates the first Level 1 power station Inbound flow during a specific time period Indicates the first Level 1 power station Downflow during the period Indicates the first Level 1 power station Output coefficient for a given time period Indicates the first Level 1 power station Hydropower head during the period, Indicates the length of a unit of time period.

[0081] S33. Compare the fitness values ​​of all individuals in turn to determine the optimal fitness value and the optimal individual of the initial population.

[0082] As a preferred embodiment, in step S4, the state update parameters of the population are determined, and the new speed and position of all individuals are obtained by iterative calculation according to the speed and position formulas. The specific calculation formulas are shown in equations (4) and (5).

[0083] (4)

[0084] (5)

[0085] In the formula: Indicates the first The particle in the first The first iteration in the process of the second iteration Dimensional velocity, where i is the particle index, n is the particle population size, j is the dimensional index, d is the dimension of the space in which the particle resides, and ω is the inertia weighting factor. To understand learning factors, As a social learning factor, , Let be a random number that follows a uniform distribution between [0, 1]. For the first The particle in the first The j-th dimension coordinate of the individual's optimal position during the next iteration. For all particles in the first The j-th dimension coordinate of the globally optimal position during the next iteration. Indicates the first Individual particles The j-th dimension coordinate during the next iteration.

[0086] As a preferred embodiment, in step S27, the total output of the cascade hydropower stations during this period is compared with the remaining load task of the corresponding period. If the error is greater than a specified range... To satisfy the objective function of minimizing the source-load difference, the regulation capacity of the pumped storage power station is generally selected as the specified range in the calculation. The upper limit.

[0087] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

[0088] The system and method provided in this embodiment can be stored in a computer-readable storage medium in the form of code, implemented as a computer program, and the basic parameter information required for calculation can be input through computer hardware, and the calculation results can be output.

[0089] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0094] This patent is not limited to the above-described preferred implementation method. Anyone can derive other forms of PSO-DP coupled nested algorithms for solving short-term multi-objective scheduling models of water, wind, solar and energy storage under the guidance of this patent. All equivalent changes and modifications made within the scope of this patent application shall fall within the scope of this patent.

Claims

1. A PSO-DP coupled nested calculation method for solving a short-term multi-objective scheduling model for hydropower and storage, characterized in that, Includes the following steps: S1. Based on the short-term multi-objective scheduling model of hydropower storage with the objectives of minimizing source-load difference and maximizing the incremental capacity of cascade hydropower storage, the basic parameters of the outer PSO algorithm are set. S2. Based on the power grid load demand curve and the wind and solar power output curve, calculate the remaining load task curve of the cascade hydropower, and use the inner layer DP algorithm to optimize the objective function that minimizes the source-load difference. S3. Initialize the particle population, and determine the optimal individual and the optimal fitness value of the initial population in the outer PSO algorithm based on the position of the individuals in the initial population. S4. Based on the determined optimal positions of individuals and the optimal positions of the population, determine the state update parameters of the population, and iterate according to the velocity and position formulas to obtain the new velocities and positions of all individuals. S5. Based on the updated position of each individual, calculate the fitness value of each individual after the position update according to the objective function calculation formula for maximizing the incremental increase of cascade hydropower storage, and compare it with the individual's optimal fitness value to determine the new optimal fitness value and optimal position of the current individual. S6. Compare the new optimal fitness value of each individual with the optimal fitness value of the population to determine the new optimal fitness value and the optimal individual of the population. S7. Determine whether the iteration termination condition is met. If it is met, output the optimization calculation results of the hydropower storage short-term multi-objective scheduling model; otherwise, return to step S4 and enter the next iteration until the iteration termination condition is met. Step S2 specifically includes the following steps: S21 divides the scheduling cycle into T time periods according to the time scale, and discretizes the initial and final water levels of each reservoir in different time periods; S22, set each cascade hydropower station as a stage, the stage variable is the number of the cascade hydropower station, and allocate the remaining load tasks of each time period to each hydropower station. S23, from the first-level power station to the second-level power station. The absolute value of the difference between the total output of the first-level power station and the remaining load for the corresponding time period is set as the number of the current time period. State variables at the end of the phase; S24, taking the output of each level of power station as the decision variable, the objective function is to minimize the absolute value of the difference between the total output of the cascade hydropower and the remaining load in each time period during the scheduling period. The calculation formula is shown in equation (1): （1） In the formula: This represents the minimum sum of source-load differences during the scheduling period. This indicates the total number of time periods for calculation. Indicates the calculation period number. This indicates the total number of hydroelectric power stations. Indicates the power station number. Indicates the first Level 1 power station Power generation output during a given period Indicates in The remaining load tasks for the specified time period; S25, the downstream flow is calculated backward from the first-stage power station in the first time period, that is, assuming the power generation flow of the hydropower station in the current time period is... The water level, reservoir capacity, and power output at the end of the specified period are calculated using the power generation formula and water balance formula of the hydropower station units. If the trial calculation does not meet the constraints, return to step S22 to redistribute the remaining load tasks; if the constraints are met, proceed to step S26. S26. Using the same method as step S25, calculate for each power station from upstream to downstream, and verify the calculated water level, reservoir capacity and output level by level. If the constraints are not met, return to step S22 to redistribute the remaining load task. If the constraints are met, proceed to step S27. S27. After completing the trial calculations for all hydropower stations, determine the error within the time period by comparing the total output of the cascade hydropower stations during that time period with the remaining load task for the corresponding time period. If the error exceeds the specified range... As shown in equation (2), if the distribution of the cascade hydropower load during this period is unreasonable, then return to step S22 to redistribute the remaining load tasks. If the error is less than the specified range, then execute step S28: （2） S28. Based on steps S21-S27, the load is allocated in time periods, and the water level and reservoir capacity status of each power station are recorded in each time period. All time periods and all power stations are traversed to obtain a set of feasible remaining load task curve allocation schemes for cascade hydropower stations, as well as the scheduling and operation process of each hydropower station. The results are fed back to the outer PSO algorithm as the initial individuals of the particle swarm. The specific formula for calculating the objective function that maximizes the incremental energy storage capacity of the cascade hydropower is as follows: （3） In the formula: This represents the maximum value of the energy storage increment of a cascade hydropower station. Indicates the power station number. Indicates the first Level 1 power station Energy storage value-added over time period Indicates the first Level 1 power station Inbound flow during a specific time period Indicates the first Level 1 power station Downflow during the period Indicates the first Level 1 power station Output coefficient for a given time period Indicates the first Level 1 power station Hydropower head during the period, Indicates the length of a unit of time period.

2. The PSO-DP coupled nested calculation method for solving a short-term multi-objective scheduling model for hydropower storage as described in claim 1, characterized in that: Step S1 specifically includes the following steps: S11, set the population size and basic parameters including the number of variables for each individual, the number of population iterations, the inertia weight factor, and the learning factor; S12 sets the boundary values ​​of each constraint in the short-term multi-objective scheduling model of hydropower and storage, and sets the iterative termination condition of the solution algorithm.

3. The PSO-DP coupled nested calculation method for solving a short-term multi-objective scheduling model for hydropower storage as described in claim 1, characterized in that: In step S27, the regulation capacity of the pumped storage power station is selected as the specified range. The upper limit.

4. The PSO-DP coupled nested calculation method for solving a short-term multi-objective scheduling model for hydropower storage as described in claim 1, characterized in that: Step S3 specifically includes the following steps: S31, randomly generate an initial population within the variable threshold, that is, generate a number of individuals; S32. According to the objective function calculation formula for maximizing the incremental energy storage of cascade hydropower, determine the fitness value of each individual and use it as the initial optimal fitness value of the individual. S33. Compare the fitness values ​​of all individuals in turn to determine the optimal fitness value and the optimal individual of the initial population.

5. The PSO-DP coupled nested calculation method for solving a short-term multi-objective scheduling model for hydropower storage as described in claim 4, characterized in that: In step S4, the state update parameters of the population are determined, and the new velocities and positions of all individuals are obtained iteratively according to the velocity and position formulas. The specific calculation formulas are shown in equations (4) and (5): （4） （5） In the formula: Indicates the first The particle in the first The first iteration in the process of the second iteration Dimensional velocity, where i is the particle index, j is the dimension index, and ω is the inertia weighting factor. To understand learning factors, As a social learning factor, , Let be a random number that follows a uniform distribution between [0, 1]. For the first The particle in the first The j-th dimension coordinate of the individual's optimal position during the next iteration. For all particles in the first The j-th dimension coordinate of the globally optimal position during the next iteration. Indicates the first Individual particles The j-th dimension coordinate during the next iteration.

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