CN117540767A - PSO-DP coupling nesting algorithm for solving short-term multi-target scheduling model of water, wind and solar energy storage - Google Patents
PSO-DP coupling nesting algorithm for solving short-term multi-target scheduling model of water, wind and solar energy storage Download PDFInfo
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Abstract
The invention provides a PSO-DP coupling nested algorithm for solving a water-wind-light-storage short-term multi-target scheduling model, which combines the high-dimensional nonlinear solving problem of the water-wind-light-storage short-term multi-target scheduling model, solves the scheduling scheme of a system by taking the maximum energy storage increment of a cascade hydropower station as an objective function through providing a double-layer nested coupling solving algorithm, adopts a particle swarm algorithm to solve the scheduling scheme of the system, and embeds a calculation unit for allocating cascade hydropower load tasks at the inner layer so as to realize the objective function with the minimum source load difference, so as to generate an initial population meeting the requirements and constraint conditions of a complementary system output process, and performs optimization calculation by utilizing a dynamic programming algorithm, thereby realizing the dimension reduction processing of the multi-target complex high-dimensional problem, greatly reducing the time and difficulty of model calculation and realizing the efficient solving of the water-wind-light-storage short-term multi-target scheduling model.
Description
Technical Field
The invention relates to the field of water-wind-light-storage complementary operation, in particular to a PSO-DP coupling nesting algorithm for solving a short-term multi-target scheduling model of water-wind-storage.
Background
Clean low-carbon renewable energy sources such as wind power, photovoltaic and the like are rapidly developed, but wind-light power generation output has obvious randomness, volatility and intermittence due to the influence of meteorological factors, so that large-scale grid-connected operation of the wind-light power generation output is restricted. In order to promote grid-connected digestion of wind power and photovoltaic power stations, the wind power generation output is complementarily regulated by utilizing the water power station and the pumped storage power station which are flexible to start and rapid to regulate, so that the water power station, the wind power station and the energy storage power station can complementarily dispatch and operate, the grid-connected digestion level of wind power and solar energy clean energy resources is improved, and the wind power station, the wind power station and the energy storage power station become important grippers for novel power system construction. However, due to the water-wind-light-storage multi-energy complementary power generation scheduling problem, the number of related power stations is large, different targets are difficult to coordinate, influence factors are large and complex, the method belongs to the optimization solving problem of discontinuous multi-feasible domains, and the requirements on the calculation precision and the solving efficiency of a model solving algorithm are high. Therefore, in order to realize short-term multi-target scheduling operation of the water-wind-solar-energy-storage multi-energy complementary system, promote large-scale development and absorption of new energy and build a boosting novel power system, research on a PSO-DP coupling nesting algorithm for solving the water-wind-solar-energy-storage short-term multi-target scheduling model is urgently needed, efficient solving of the water-wind-solar-energy-storage multi-target short-term scheduling model is realized, and important technical support is provided for integrated operation of the water-wind-solar-energy-storage multi-energy complementary system and the clean energy base.
Disclosure of Invention
The invention aims to provide a PSO-DP coupling nesting algorithm for solving a short-term multi-target scheduling model of water, wind and light storage, which is used for solving the technical problems existing in the prior art, and aiming at the high-dimensional nonlinear problem faced by the short-term scheduling model of a water, wind and light storage multi-energy complementary system, an inner-outer layer nesting algorithm for PSO-DP coupling is provided, so that the convergence speed and the solving efficiency of the algorithm are improved, and the efficient solving of the short-term multi-target scheduling model of water, wind and light storage is realized.
The method combines the high-dimensional nonlinear solving problem of the water, wind and solar energy storage short-term multi-objective scheduling model, solves the scheduling scheme of the system by taking the maximum energy storage increment of the cascade hydropower station as an objective function through a double-layer nested coupling solving algorithm, adopts a particle swarm algorithm to solve the scheduling scheme of the system, realizes the objective function with the minimum source load difference by embedding calculation units distributed by cascade hydropower load tasks at the inner layer, is used for generating an initial population meeting the output process requirement and constraint condition of the complementary system, performs optimization calculation by utilizing a dynamic programming algorithm, realizes the dimension reduction processing of the multi-objective complex high-dimensional problem, greatly reduces the calculation time and difficulty of the model, and realizes the efficient solving of the water, wind and solar energy storage short-term multi-objective scheduling model.
The invention adopts the following technical scheme:
a PSO-DP coupling nesting algorithm for solving a water-wind-light-storage short-term multi-target scheduling model is characterized in that:
aiming at the difficult problem of high-dimensional nonlinear solution of a water-wind-light-storage short-term multi-target scheduling model, the solution is carried out by a double-layer nested coupling solution algorithm:
the outer layer model adopts a particle swarm algorithm to solve a scheduling scheme of the system by taking the maximum energy storage increment of the cascade hydropower station as an objective function;
and a calculation unit for allocating the water and electricity load in the inner layer nested step is used for generating an initial population meeting the requirements and constraint conditions of the output process of the complementary system through an objective function with the minimum source load difference, and carrying out optimization calculation by utilizing a dynamic programming algorithm so as to realize dimension reduction treatment on the multi-target complex high-dimension problem and finally realize efficient solution of the water, wind and light storage short-term multi-target scheduling model.
Further, the method comprises the following steps:
s1, setting basic parameters of an outer layer PSO algorithm according to a water, wind and solar energy storage short-term multi-target scheduling model with minimum source load difference and maximum step hydropower energy storage increment as targets;
s2, calculating a cascade hydropower residual load task curve according to a power grid load demand curve and a wind-light output curve, and optimally solving an objective function with the minimum source-load difference by adopting an inner layer DP algorithm;
s3, initializing a particle population, and determining optimal individuals of the initial population and optimal fitness values of the initial population in an outer layer PSO algorithm according to the positions of individuals of the initial population;
s4, determining state update parameters of the population according to the determined optimal positions of the individuals and the positions of the individuals with the optimal population, and iterating according to a speed and position formula to obtain new speeds and positions of all the individuals
S5, calculating an updated fitness value of each individual position according to an objective function calculation formula with the largest step hydroelectric energy storage increment according to the updated position of each individual, comparing the fitness value with the optimal fitness value of the individual, and determining a new optimal fitness value and an optimal position of the current individual;
s6, comparing the new optimal fitness value of each individual with the optimal fitness value of the population, and determining the new optimal fitness value of the population and the optimal individuals of the population;
s7, judging whether iteration termination conditions are met, and if so, outputting an optimized calculation result of the water-wind-light-storage short-term multi-target scheduling model; otherwise, returning to the step S4, and entering the next iteration until the iteration termination condition is met.
Further, the step S1 specifically includes the following steps:
s11, setting a population scale and basic parameters comprising the variable number of each individual, the population iteration times, inertia weight factors and learning factors;
s12, setting boundary values of constraint conditions in the water-wind-light-storage short-term multi-target scheduling model, and setting iteration termination conditions of a solving algorithm.
Further, the step S2 specifically includes the following steps:
s21, dividing a scheduling period into T time periods according to time scales, and dispersing initial and final water levels of different time periods of each reservoir;
s22, setting each stage of cascade hydropower station as a stage, wherein the stage variable is the stage number of the cascade hydropower station, and distributing the residual load tasks of each period to each hydropower station;
s23, setting the absolute value of the difference value between the total output of the 1 st-stage power station and the i-stage power station and the residual load of the corresponding period as a state variable of the end of the i-stage of the current period;
s24, taking the output of each stage of power station as a decision variable, wherein an objective function is that the absolute value of the difference between the total output of step hydropower and the residual load in each period of scheduling period is minimum, and a calculation formula is shown as a formula (1):
wherein: beta represents the minimum value of the sum of the endogenous-load differences in the scheduling period, T represents the total number of calculation periods, T represents the number of calculation periods, N represents the total number of hydropower stations, i represents the number of power stations, N i,t Represents the power generation output of the ith power station in t period, NP t Representing a residual load task at time t;
s25, calculating the reverse thrust leakage flow from the 1 st stage power station in the 1 st period, namely assuming that the power generation flow of the hydropower station in the current period is Q 1,1 Calculating the water level and the reservoir capacity state at the end of the period and the output N of the period through a power generation output formula and a water balance formula of the hydropower station unit 1,1 If the trial calculation does not meet the constraint condition, returning to the step S22 to redistribute the residual load tasks, and if the constraint condition is met, executing the step S26;
s26, calculating from the upstream to the downstream by adopting the same method as the step S25, checking the water level, the reservoir capacity and the output obtained by trial calculation step by step, returning to the step S22 to redistribute the residual load tasks if the constraint condition is not met, and executing the step S27 if the constraint condition is met;
s27, after trial calculation of all hydropower stations is completed, error judgment is carried out in a period, total output of the step hydropower stations in the period obtained by trial calculation is compared with the residual load task in the corresponding period, if the error is larger than a specified range epsilon, as shown in a formula (2), the step hydropower load distribution in the period is unreasonable, the step S22 is returned to redistribute the residual load task, and if the error is smaller than the specified range, the step S28 is executed:
and S28, distributing loads in time intervals according to the steps S21-S27, recording the water level and the storage capacity state of each power station in each time interval, traversing all the time intervals and all the power stations to obtain a group of feasible step hydropower station residual load task curve distribution schemes and the scheduling operation process of each hydropower station, and feeding back the results as initial individual particle swarms to an outer layer PSO algorithm.
Further, in step S27, the adjustment capacity of the pumped-storage power station is selected as the upper limit of the prescribed range epsilon.
Further, the step S3 specifically includes the following steps:
s31, randomly generating an initial population in a variable threshold, namely generating a plurality of individuals;
s32, determining the fitness value of each individual according to an objective function calculation formula with the largest step hydroelectric energy storage increment as shown in formula (3), and taking the fitness value as the optimal fitness value of the initial individual:
wherein: e (E) 0 Representing the maximum value of the energy storage increment of the cascade hydropower station, T represents the total number of calculation time periods, T represents the number of calculation time periods, N represents the total number of the hydropower station, i and j both represent the number of the power station, E i,t Represents the energy accumulation and increment of the ith power station in the t period, Q i,t Represents the warehouse-in flow of the ith power station in the period t, q i,t Represents the let-down flow rate, K of the ith power station in the period t j,t Representing the output coefficient of a j-th power station in t period, H j,t The power generation water head of the j-th power station in the t period is represented, and deltat represents the time length of the unit period;
s33, sequentially comparing fitness values of all individuals, and determining the optimal fitness value of the initial population and the optimal individuals of the initial population.
Further, in step S4, determining a state update parameter of the population, and iterating according to a speed and position formula to obtain new speeds and positions of all individuals, where the specific calculation formulas are shown in formulas (4) and (5):
wherein:representing the j-th dimensional speed of the ith particle in the t-th iteration process, wherein i is the serial number of the particle, n is the population number of the particle, j is the dimensional serial number, d is the spatial dimension of the particle, ω is the inertia weight factor, c 1 To recognize learning factors c 2 R is a social learning factor 1 、r 2 Is [0,1]Random numbers subject to uniform distribution, p i t ,j For the j-th dimensional coordinate of the individual optimum position of the i-th particle during the t-th iteration,/->For the j-th dimensional coordinates of the globally optimal position of all particles during the t-th iteration +.>Representing the j-th dimensional coordinates of the ith particle during t iterations.
Compared with the prior art, the method and the device have the advantages that the high-dimensional nonlinear solving problem of the water, wind and light storage short-term multi-objective scheduling model is combined, the double-layer nested coupling solving algorithm is provided, the outer-layer model uses the maximum energy storage increment of the cascade hydropower station as an objective function, the particle swarm algorithm is adopted to solve the scheduling scheme of the system, the computing unit distributed by the task of the inner-layer nested cascade hydropower load is used for realizing the objective function with the minimum source load difference and generating the initial population meeting the output process requirement and constraint condition of the complementary system, the dynamic planning algorithm is used for optimizing calculation, the dimension reduction processing of the multi-objective complex high-dimensional problem is realized, the time and difficulty of model calculation are greatly reduced, and the high-efficiency solving of the water, wind and light storage short-term multi-objective scheduling model is realized.
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The invention is described in further detail below with reference to the attached drawings and detailed description:
FIG. 1 is a PSO-DP coupling nesting algorithm calculation flow chart for solving a water-wind-light-accumulation short-term multi-target scheduling model in an embodiment of the invention.
Detailed Description
Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings, and according to these detailed descriptions, those skilled in the art can clearly understand the present application and can practice the present application. Features from various embodiments may be combined to obtain new implementations or to replace certain features from certain embodiments to obtain other preferred implementations without departing from the principles of the present application.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
In order to make the features and advantages of the present patent more comprehensible, embodiments accompanied with figures are described in detail below:
it is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
The problems of large number of power stations, large coordination difficulty of different targets, large and complex influence factors, and the optimization solving problem of discontinuous multiple feasible areas are considered, so that the requirements on the calculation accuracy and the solving efficiency of a model solving algorithm are high. Therefore, in order to realize short-term multi-target scheduling operation of the water-wind-solar-energy-storage multi-energy complementary system, promote large-scale development and absorption of new energy, promote construction of a novel power system and realization of a 'double-carbon' strategic target, research on a PSO-DP coupling nesting algorithm for solving a water-wind-solar-storage short-term multi-target scheduling model is urgently needed, efficient solving of the water-wind-solar-storage multi-target short-term scheduling model is realized, and important technical support is provided for integrated operation of the water-wind-solar-storage multi-energy complementary and clean energy base.
As shown in fig. 1, the embodiment of the invention provides a PSO-DP coupling nesting algorithm for solving a water-wind-light-storage short-term multi-target scheduling model, combines the high-dimensional nonlinear solving problem of the water-wind-light-storage short-term multi-target scheduling model, solves the scheduling scheme of a system by taking the maximum energy storage increment of a cascade hydropower station as an objective function through providing a double-layer nesting coupling solving algorithm, adopts a particle swarm algorithm to solve the scheduling scheme of the system, realizes the objective function with the minimum source-load difference in an inner nested cascade hydropower load task allocation, is used for generating an initial population meeting the requirements and constraint conditions of a complementary system output process, performs optimization calculation by utilizing a dynamic programming algorithm, realizes the dimension reduction processing of the multi-target complex high-dimensional problem, greatly reduces the time and difficulty of model calculation, and realizes the high-efficiency solving of the water-storage short-term multi-target scheduling model.
The specific implementation process comprises the following steps:
s1, setting basic parameters of an outer layer PSO algorithm according to a water-wind-solar-energy-storage short-term multi-target scheduling model with the minimum source load difference and the maximum step hydropower energy storage increment as targets, and executing a step S2;
s2, calculating a cascade hydropower residual load task curve according to a power grid load demand curve and a wind-light output curve, adopting an inner layer DP algorithm to perform optimization solution on an objective function with the minimum source load difference, and executing a step S3;
s3, initializing a particle population, determining optimal individuals of the initial population and optimal fitness values of the initial population in an outer layer PSO algorithm according to the positions of the individuals of the initial population, and executing a step S4;
s4, determining state updating parameters of the population according to the determined optimal positions of the individuals and the positions of the optimal individuals of the population, iterating according to a speed and position formula to obtain new speeds and positions of all the individuals, and executing a step S5;
s5, calculating an updated fitness value of each individual position according to an objective function calculation formula with the largest step hydroelectric energy storage increment according to the updated position of each individual, comparing the fitness value with the optimal fitness value of the individual, determining a new optimal fitness value and an optimal position of the current individual, and executing the step S6;
s6, comparing the new optimal fitness value of each individual with the optimal fitness value of the population, determining the new optimal fitness value of the population and the optimal individuals of the population, and executing the step S7;
s7, judging whether iteration termination conditions are met, and if so, outputting an optimized calculation result of the water-wind-light-storage short-term multi-target scheduling model; otherwise, returning to the step S4, and entering the next iteration until the iteration termination condition is met.
As one preferable mode of this embodiment, in step S1, according to a water-wind-solar-storage short-term multi-target scheduling model with the minimum source load difference and the maximum step hydropower energy storage increment as targets, basic parameters of an outer layer PSO algorithm are set, including the following steps:
s11, setting basic parameters such as population scale, variable number of each individual, population iteration times, inertia weight factors, learning factors and the like, and executing the step S12;
s12, setting boundary values of constraint conditions in the water-wind-light-storage short-term multi-target scheduling model, and setting iteration termination conditions of a solving algorithm.
As a preferable mode of this embodiment, in step S2, an objective function with the smallest source load difference is optimized and solved by adopting an inner layer DP algorithm, which includes the following steps:
s21, dividing a scheduling period into T time periods according to the time scale requirement, dispersing initial and final water levels of different time periods of each reservoir, and executing a step S22;
s22, setting each stage of cascade hydropower station as a stage, wherein the stage variable is the stage number of the cascade hydropower station, distributing the residual load tasks of each period to each hydropower station, and executing the step S23;
s23, setting the absolute value of the difference value between the total output of the 1 st-stage power station and the i-stage power station and the residual load of the corresponding period as a state variable at the end of the i-stage of the current period, and executing step S24;
s24, taking the output of each stage of power station as a decision variable, wherein an objective function is that the absolute value of the difference between the total output of the step hydropower and the residual load in each period in the scheduling period is minimum, a calculation formula is shown as a formula (1), and the step S25 is executed;
wherein: beta represents the minimum value of the sum of the endogenous-load differences in the scheduling period, T represents the total number of calculation periods, T represents the number of calculation periods, N represents the total number of hydropower stations, i represents the number of power stations, N i,t Represents the power generation output of the ith power station in t period, NP t Representing the residual load task at time t.
S25, the 1 st stage power station in the 1 st period calculates the reverse thrust leakage flow in trial, namely, the power generation flow of the hydropower station in the current period is assumed to be Q 1,1 Calculating the water level and the reservoir capacity state at the end of the period and the output N of the period through a power generation output formula and a water balance formula of the hydropower station unit 1,1 If the trial calculation does not meet the constraint condition, returning to the step S22 to redistribute the residual load tasks, and if the constraint condition is met, executing the step S26;
s26, calculating from the upstream to the downstream power stations one by one, checking the water level, the reservoir capacity and the output by one, if the constraint condition is not met, returning to the step S22 to redistribute the residual load tasks, and if the constraint condition is met, executing the step S27;
s27, after trial calculation of all hydropower stations is completed, error judgment is carried out in a period, total output of the step hydropower stations in the period obtained by trial calculation is compared with the residual load task in the corresponding period, if the error is larger than a specified range epsilon, as shown in a formula (2), the step hydropower load distribution in the period is unreasonable, the step S22 is returned to redistribute the residual load task, and if the error is smaller than the specified range, the step S28 is executed;
s28, with reference to the steps, load is distributed time by time, the water level and the storage capacity state of each power station in each time period are recorded, all the time periods and all the power stations are traversed, a set of feasible cascade hydropower station residual load task curve distribution schemes are obtained, the hydropower stations are scheduled to operate, and the results are fed back to an outer layer PSO algorithm as initial individual particle swarms.
As one preferable mode of this embodiment, in step S3, a particle population is initialized, and an optimal individual of the initial population and an optimal fitness value of the initial population in the outer layer PSO algorithm are determined according to the positions of the individuals of the initial population, including the following steps:
s31, randomly generating an initial population in a variable threshold, namely generating a plurality of individuals, and executing a step S32;
s32, determining the fitness value of each individual according to an objective function calculation formula with the largest step hydroelectric energy storage increment as shown in formula (3), and executing step S33 as the initial individual optimal fitness value;
wherein: e (E) 0 Representing the maximum value of the energy storage increment of the cascade hydropower station, T represents the total number of calculation time periods, T represents the number of calculation time periods, N represents the total number of the hydropower station, i and j both represent the number of the power station, E i,t Represents the energy accumulation and increment of the ith power station in the t period, Q i,t Represents the warehouse-in flow of the ith power station in the period t, q i,t Represents the let-down flow rate, K of the ith power station in the period t j,t Output system for j-th power station in t periodNumber, H j,t The power generation water head of the j-th power station in the t period is represented, and deltat represents the time length of the unit period.
S33, sequentially comparing fitness values of all individuals, and determining the optimal fitness value of the initial population and the optimal individuals of the initial population.
In step S4, the state update parameters of the population are determined, and new speeds and positions of all individuals are obtained by iteration according to the speed and position formulas, wherein the specific calculation formulas are shown in formulas (4) and (5).
Wherein:representing the j-th dimensional speed of the ith particle in the t-th iteration process, wherein i is the serial number of the particle, n is the population number of the particle, j is the dimensional serial number, d is the spatial dimension of the particle, ω is the inertia weight factor, c 1 To recognize learning factors c 2 R is a social learning factor 1 、r 2 Is [0,1]Random numbers subject to uniform distribution among them, +.>For the j-th dimensional coordinate of the individual optimum position of the i-th particle during the t-th iteration,/->For the j-th dimensional coordinates of the globally optimal position of all particles during the t-th iteration +.>Representing the j-th dimensional coordinates of the ith particle during t iterations.
In step S27, the total output of the step hydropower station in the period is compared with the residual load task in the corresponding period, and if the error is greater than the specified range epsilon, the adjustment capacity of the pumped storage power station is generally selected as the upper limit of the specified range epsilon during calculation to satisfy the objective function with the minimum source load difference.
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. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, and yet fall within the scope of the invention.
The above system and method provided in this embodiment may be stored in a computer readable storage medium in a coded form, implemented in a computer program, and input basic parameter information required for calculation through computer hardware, and output a calculation result.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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, and the like) having computer-usable program code embodied therein.
The present 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 flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
The PSO-DP coupling nesting algorithm for solving the short-term multi-target scheduling model of water, wind and light accumulation can be obtained by any person under the teaching of the patent without being limited to the best embodiment, and all equivalent changes and modifications made according to the scope of the application of the invention are covered by the patent.
Claims (7)
1. A PSO-DP coupling nesting algorithm for solving a water-wind-light-storage short-term multi-target scheduling model is characterized in that:
aiming at the difficult problem of high-dimensional nonlinear solution of a water-wind-light-storage short-term multi-target scheduling model, the solution is carried out by a double-layer nested coupling solution algorithm:
the outer layer model adopts a particle swarm algorithm to solve a scheduling scheme of the system by taking the maximum energy storage increment of the cascade hydropower station as an objective function;
and a calculation unit for allocating the water and electricity load in the inner layer nested step is used for generating an initial population meeting the requirements and constraint conditions of the output process of the complementary system through an objective function with the minimum source load difference, and carrying out optimization calculation by utilizing a dynamic programming algorithm so as to realize dimension reduction treatment on the multi-target complex high-dimension problem and finally realize efficient solution of the water, wind and light storage short-term multi-target scheduling model.
2. The PSO-DP coupling nesting algorithm for solving a water-wind-light-accumulation short-term multi-objective scheduling model according to claim 1, which is characterized in that: the method comprises the following steps:
s1, setting basic parameters of an outer layer PSO algorithm according to a water, wind and solar energy storage short-term multi-target scheduling model with minimum source load difference and maximum step hydropower energy storage increment as targets;
s2, calculating a cascade hydropower residual load task curve according to a power grid load demand curve and a wind-light output curve, and optimally solving an objective function with the minimum source-load difference by adopting an inner layer DP algorithm;
s3, initializing a particle population, and determining optimal individuals of the initial population and optimal fitness values of the initial population in an outer layer PSO algorithm according to the positions of individuals of the initial population;
s4, determining state update parameters of the population according to the determined optimal positions of the individuals and the positions of the individuals with the optimal population, and iterating according to a speed and position formula to obtain new speeds and positions of all the individuals
S5, calculating an updated fitness value of each individual position according to an objective function calculation formula with the largest step hydroelectric energy storage increment according to the updated position of each individual, comparing the fitness value with the optimal fitness value of the individual, and determining a new optimal fitness value and an optimal position of the current individual;
s6, comparing the new optimal fitness value of each individual with the optimal fitness value of the population, and determining the new optimal fitness value of the population and the optimal individuals of the population;
s7, judging whether iteration termination conditions are met, and if so, outputting an optimized calculation result of the water-wind-light-storage short-term multi-target scheduling model; otherwise, returning to the step S4, and entering the next iteration until the iteration termination condition is met.
3. The PSO-DP coupling nesting algorithm for solving a water-wind-light-accumulation short-term multi-objective scheduling model according to claim 2, which is characterized in that:
the step S1 specifically comprises the following steps:
s11, setting a population scale and basic parameters comprising the variable number of each individual, the population iteration times, inertia weight factors and learning factors;
s12, setting boundary values of constraint conditions in the water-wind-light-storage short-term multi-target scheduling model, and setting iteration termination conditions of a solving algorithm.
4. A PSO-DP coupled nesting algorithm for a short-term multi-objective scheduling model solution for water, wind and solar energy storage according to claim 3, wherein:
the step S2 specifically comprises the following steps:
s21, dividing a scheduling period into T time periods according to time scales, and dispersing initial and final water levels of different time periods of each reservoir;
s22, setting each stage of cascade hydropower station as a stage, wherein the stage variable is the stage number of the cascade hydropower station, and distributing the residual load tasks of each period to each hydropower station;
s23, setting the absolute value of the difference value between the total output of the 1 st-stage power station and the i-stage power station and the residual load of the corresponding period as a state variable of the end of the i-stage of the current period;
s24, taking the output of each stage of power station as a decision variable, wherein an objective function is that the absolute value of the difference between the total output of step hydropower and the residual load in each period of scheduling period is minimum, and a calculation formula is shown as a formula (1):
wherein: beta represents the minimum value of the sum of the endogenous-load differences in the scheduling period, T represents the total number of calculation periods, T represents the number of calculation periods, N represents the total number of hydropower stations, i represents the number of power stations, N i,t Represents the power generation output of the ith power station in t period, NP t Representing a residual load task at time t;
s25, calculating the reverse thrust leakage flow from the 1 st stage power station in the 1 st period, namely assuming that the power generation flow of the hydropower station in the current period is Q 1,1 Calculating the water level and the reservoir capacity state at the end of the period and the output N of the period through a power generation output formula and a water balance formula of the hydropower station unit 1,1 If the trial calculation does not meet the constraint condition, returning to the step S22 to redistribute the residual load tasks, and if the constraint condition is met, executing the step S26;
s26, calculating from the upstream to the downstream by adopting the same method as the step S25, checking the water level, the reservoir capacity and the output obtained by trial calculation step by step, returning to the step S22 to redistribute the residual load tasks if the constraint condition is not met, and executing the step S27 if the constraint condition is met;
s27, after trial calculation of all hydropower stations is completed, error judgment is carried out in a period, total output of the step hydropower stations in the period obtained by trial calculation is compared with the residual load task in the corresponding period, if the error is larger than a specified range epsilon, as shown in a formula (2), the step hydropower load distribution in the period is unreasonable, the step S22 is returned to redistribute the residual load task, and if the error is smaller than the specified range, the step S28 is executed:
and S28, distributing loads in time intervals according to the steps S21-S27, recording the water level and the storage capacity state of each power station in each time interval, traversing all the time intervals and all the power stations to obtain a group of feasible step hydropower station residual load task curve distribution schemes and the scheduling operation process of each hydropower station, and feeding back the results as initial individual particle swarms to an outer layer PSO algorithm.
5. The PSO-DP coupling nesting algorithm for solving a short-term multi-objective scheduling model for water, wind and solar energy storage according to claim 4, which is characterized in that: in step S27, the regulating capacity of the pumped-storage power station is selected as the upper limit of the defined range epsilon.
6. The PSO-DP coupling nesting algorithm for solving a short-term multi-objective scheduling model for water, wind and solar energy storage according to claim 4, which is characterized in that:
the step S3 specifically comprises the following steps:
s31, randomly generating an initial population in a variable threshold, namely generating a plurality of individuals;
s32, determining the fitness value of each individual according to an objective function calculation formula with the largest step hydroelectric energy storage increment as shown in formula (3), and taking the fitness value as the optimal fitness value of the initial individual:
wherein: e (E) 0 Representing the maximum value of the energy storage increment of the cascade hydropower station, T represents the total number of calculation time periods, T represents the number of calculation time periods, N represents the total number of the hydropower station, i and j both represent the number of the power station, E i,t Represents the energy accumulation and increment of the ith power station in the t period, Q i,t Represents the warehouse-in flow of the ith power station in the period t, q i,t Represents the let-down flow rate, K of the ith power station in the period t j,t Representing the output coefficient of a j-th power station in t period, H j,t The power generation water head of the j-th power station in the t period is represented, and deltat represents the time length of the unit period;
s33, sequentially comparing fitness values of all individuals, and determining the optimal fitness value of the initial population and the optimal individuals of the initial population.
7. The PSO-DP coupling nesting algorithm for solving a short-term multi-objective scheduling model for water, wind and solar energy storage according to claim 6, wherein the PSO-DP coupling nesting algorithm is characterized in that:
in step S4, determining state update parameters of the population, and iterating according to a speed and position formula to obtain new speeds and positions of all individuals, wherein the specific calculation formulas are shown in the formulas (4) and (5):
wherein:representing the j-th dimensional speed of the ith particle in the t-th iteration process, wherein i is the serial number of the particle, n is the population number of the particle, j is the dimensional serial number, d is the spatial dimension of the particle, ω is the inertia weight factor, c 1 To recognize learning factors c 2 R is a social learning factor 1 、r 2 Is [0,1]Random numbers subject to uniform distribution among them, +.>For the j-th dimensional coordinate of the individual optimum position of the i-th particle during the t-th iteration,/->For the j-th dimensional coordinates of the globally optimal position of all particles during the t-th iteration +.>Representing the j-th dimensional coordinates of the ith particle during t iterations.
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| CN118378873A (en) * | 2024-06-27 | 2024-07-23 | 河海大学 | Day-ahead optimal scheduling method for cascade multi-main-body hybrid pumped storage power station |
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| CN118316121A (en) * | 2024-04-09 | 2024-07-09 | 中国长江三峡集团有限公司 | Method, device, equipment and medium for generating water-light complementary scheduling scheme |
| CN118378873A (en) * | 2024-06-27 | 2024-07-23 | 河海大学 | Day-ahead optimal scheduling method for cascade multi-main-body hybrid pumped storage power station |
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