Improved multi-target Harris eagle wind storage capacity configuration method and system considering two detailed rules

Abstract

The invention discloses an improved multi-target Harris eagle wind storage capacity configuration method and system considering two detailed rules, and belongs to capacity configuration and control of a wind power grid-connected energy storage system. Through collaborative design of the improved Harris eagle algorithm and the two-stage control strategy, the limitation of an existing wind storage capacity configuration technology is overcome, and the problems that convergence is slow, local optimum is likely to be achieved when a traditional algorithm processes wind power volatility and energy storage nonlinear constraint, and prediction error penalty is not sufficiently considered are solved. Multi-objective function design is adopted to balance economical efficiency and technical performance, adaptive weight is introduced into an improved Harris eagle algorithm to improve solution space exploration efficiency so as to avoid local optimum, prediction deviation is rapidly corrected by combining a two-stage strategy of prediction error compensation and model prediction control, power fluctuation is stabilized, and the energy storage charge state is stabilized. Finally, the method is superior to the prior art in optimization efficiency, target balance and engineering applicability, and a more economical and efficient configuration scheme is provided for the wind storage system.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, specifically involving an improved multi-objective Harris Hawk wind-storage capacity configuration method and system that takes into account two detailed rules. Background Technology

[0002] As wind power penetration in power systems continues to increase, the volatility and intermittency of wind power are increasingly impacting grid stability. Configuring energy storage systems has become an effective means of addressing wind power grid integration issues; however, the capacity configuration of these systems requires a trade-off between economic efficiency and technical effectiveness. Traditional capacity configuration methods largely rely on experience or simple optimization algorithms, making it difficult to achieve optimal configuration in complex wind power scenarios. Summary of the Invention

[0003] The purpose of this invention is to provide an improved multi-objective Harris Hawk wind-storage capacity configuration method and system that considers two detailed rules. The optimal capacity parameters of the energy storage system are determined through an intelligent optimization algorithm. The objective function not only considers the volatility of wind power, but also introduces the assessment penalty for wind power prediction error. A two-stage control strategy is adopted to achieve efficient operation of the energy storage system, thereby improving the wind power absorption capacity and system economy.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] Consider the two detailed rules for an improved multi-objective Harris Hawk wind storage capacity configuration approach, including the following steps:

[0006] Step 1: Data Preprocessing

[0007] Historical wind power data is collected to obtain the historical predicted wind power and actual grid-connected power outside the power curtailment period. Missing data and outliers are repaired and used as input for a multi-objective optimization model.

[0008] Step 2: Setting parameters for the multi-objective optimization model

[0009] Set basic parameters, including the unit power cost Cp, unit energy cost Ce, residual value rate Csv, allowable range of prediction error, power fluctuation limit and penalty coefficient λ of the energy storage system;

[0010] Step 3: Solving the multi-objective optimization model

[0011] The preprocessed data and the set basic parameters are input into a multi-objective optimization model, and the multi-objective Harris-Eagle algorithm is used to optimize and solve for the maximum power P of the energy storage system. max and maximum capacity E max To ensure that the economic and performance objectives meet the requirements, the Pareto solution set is obtained;

[0012] Step 4: Output optimization results

[0013] The final optimal configuration method is selected from the Pareto solution set.

[0014] A further improvement of this invention is that, in step 1, historical wind power data is collected to obtain the historical predicted wind power and actual grid-connected power outside the power curtailment period, and missing data and outliers are repaired, as follows:

[0015]

[0016] Among them, t i Let N be the true value of historical wind power at time i, and N be the number of samples. This represents the correction value for the missing data at time i. This is the average of historical wind power data. ξ and ζ are weighting coefficients.

[0017] A further improvement of this invention is that, in step 3, solving the multi-objective optimization model includes:

[0018] Prediction error compensation:

[0019] The prediction error boundary is calculated as shown in equation (2):

[0020]

[0021] The compensation power of the energy storage system is determined by comparing the actual power with the predicted error range.

[0022] Upgrade the SOC of the energy storage system:

[0023] E a (k)=E a (k-1)+P a (k)·Δt (4)

[0024] Calculate the compensated wind power:

[0025] P bg (k)=P s (k)-P a (k) (5)

[0026] Calculate the prediction error penalty:

[0027]

[0028] Among them, P w1 (k) represents the upper limit of the prediction error, P w2 (k) represents the lower bound of the prediction error, P s (k) represents the actual power value;

[0029] MPC control of energy storage system:

[0030] MPC objective function:

[0031]

[0032] Among them, u i For energy storage control input, SOC i The energy storage state of charge, N is the prediction step size;

[0033] The constraints are considered as follows:

[0034]

[0035] Solve the optimization problem to obtain the optimal control sequence u. * Take the first control input:

[0036]

[0037] Objective function:

[0038]

[0039] A further improvement of this invention lies in the use of the Harris Eagle optimization algorithm to solve the two stages of prediction error compensation and energy storage MPC control:

[0040] Initialize the Harris Eagle population, with each individual representing a set of energy storage configuration schemes [Pamax,Eamax]. Then calculate the fitness value of each individual. The final value is obtained by iteratively determining the current optimal solution, updating the Harris Eagle position according to the prey position, and simulating the Harris Eagle's hunting strategy. Equation (12) is the simulated Harris Eagle hunting strategy:

[0041]

[0042] Where X(t) is the current position of the Harris Hawk, X prey (t) represents the prey's location, X rand (t) is the randomly selected position of the Harris Hawk, r1, r2, r3, r4, q are random numbers, E is the energy factor, J is the jump factor, and LB and UB are the lower and upper bounds of the variable.

[0043] To prevent premature convergence of the optimization process, an adaptive energy factor E and an exploration weight α are introduced:

[0044]

[0045] Consider two detailed rules for the improved multi-objective Harris Hawk wind storage capacity configuration system, including:

[0046] The data preprocessing unit collects historical wind power data, obtains the historical wind power prediction and actual grid-connected power outside the power curtailment period, and repairs missing data and outliers as input to the multi-objective optimization model.

[0047] The multi-objective optimization model parameter setting unit sets basic parameters, including the unit power cost Cp, unit energy cost Ce, residual value rate Csv, allowable range of prediction error, power fluctuation limit value, and penalty coefficient λ of the energy storage system.

[0048] The multi-objective optimization model solver unit inputs the preprocessed data and set basic parameters into the multi-objective optimization model, and uses the multi-objective Harris-Eagle algorithm to optimize and solve for the maximum power P of the energy storage system. max and maximum capacity E max To ensure that the economic and performance objectives meet the requirements, the Pareto solution set is obtained;

[0049] Output the optimization result unit and select the final optimization configuration method from the Pareto solution set.

[0050] A further improvement of this invention is that, in the data preprocessing unit, historical wind power data is collected to obtain the historical predicted wind power and actual grid-connected power outside the power curtailment period, and missing data and outliers are repaired, as follows:

[0051]

[0052] Among them, t i Let N be the true value of historical wind power at time i, and N be the number of samples. This represents the correction value for the missing data at time i. This is the average of historical wind power data. ξ and ζ are weighting coefficients.

[0053] A further improvement of this invention is that, in the multi-objective optimization model solving unit, the multi-objective optimization model solving includes:

[0054] Prediction error compensation:

[0055] The prediction error boundary is calculated as shown in equation (2):

[0056]

[0057] The compensation power of the energy storage system is determined by comparing the actual power with the predicted error range.

[0058]

[0059] Upgrade the SOC of the energy storage system:

[0060] E a (k)=E a (k-1)+P a (k)·Δt (4)

[0061] Calculate the compensated wind power:

[0062] P bg (k)=P s (k)-P a (k) (5)

[0063] Calculate the prediction error penalty:

[0064]

[0065] Among them, P w1 (k) represents the upper limit of the prediction error, P w2 (k) represents the lower bound of the prediction error, P s (k) represents the actual power value;

[0066] MPC control of energy storage system:

[0067] MPC objective function:

[0068]

[0069] Among them, u i For energy storage control input, SOC i The energy storage state of charge, N is the prediction step size;

[0070] The constraints are considered as follows:

[0071]

[0072] Solve the optimization problem to obtain the optimal control sequence u. * Take the first control input:

[0073]

[0074] Objective function:

[0075]

[0076] A further improvement of this invention lies in that, in the multi-objective optimization model solution unit, the Harris Eagle optimization algorithm is used to solve the two stages of prediction error compensation and energy storage MPC control:

[0077] Initialize the Harris Eagle population, with each individual representing a set of energy storage configuration schemes [Pamax,Eamax]. Then calculate the fitness value of each individual. The final value is obtained by iteratively determining the current optimal solution, updating the Harris Eagle position according to the prey position, and simulating the Harris Eagle's hunting strategy. Equation (12) is the simulated Harris Eagle hunting strategy:

[0078]

[0079] Where X(t) is the current position of the Harris Hawk, X prey (t) represents the prey's location, X rand (t) is the randomly selected position of the Harris Hawk, r1, r2, r3, r4, q are random numbers, E is the energy factor, J is the jump factor, and LB and UB are the lower and upper bounds of the variable.

[0080] To prevent premature convergence of the optimization process, an adaptive energy factor E and an exploration weight α are introduced:

[0081]

[0082]

[0083] An electronic device includes: a processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of the improved multi-objective Harris Hawk wind storage capacity configuration method considering two details.

[0084] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the improved multi-objective Harris Hawk wind storage capacity configuration method considering two specific rules.

[0085] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0086] This invention provides an improved multi-objective Harris Eagle wind-storage capacity configuration method and system that considers two specific rules. Through the collaborative design of the improved Harris Eagle algorithm and a two-stage control strategy, it effectively overcomes the limitations of existing wind-storage capacity configuration technologies. In existing technologies, traditional optimization algorithms such as genetic algorithms and particle swarm optimization often suffer from slow convergence speeds and a tendency to get trapped in local optima when dealing with the volatility of wind power and the nonlinear constraints of energy storage systems, making it difficult to find a configuration scheme that balances economic efficiency and technical performance. Furthermore, existing research does not adequately consider the prediction error penalties in the two specific rules. Therefore, this invention, through multi-objective function design rather than simple single-objective optimization, can better balance the economic efficiency and technical performance of the energy storage system. The improved Harris Eagle algorithm, by introducing adaptive weights to balance global search and local optimization, can explore the solution space more efficiently than traditional algorithms, avoiding local optima and thus finding a better energy storage capacity configuration scheme. The two-stage strategy of prediction error compensation and multi-objective optimization model predictive control can quickly correct wind power prediction deviations, reduce grid connection penalties, and smooth power fluctuations and maintain stable energy storage state of charge through multi-step prediction, thus achieving efficient operation of the energy storage system under dynamic conditions. In summary, this invention outperforms existing technologies in terms of optimization efficiency, objective balancing capability, and engineering applicability, providing a more economical and efficient configuration scheme for wind-storage systems. Attached Figure Description

[0087] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0088] Figure 1 This is a flowchart of the improved multi-objective Harris Hawk wind storage capacity configuration method considering two detailed rules of the present invention.

[0089] Figure 2 This is a schematic diagram illustrating the solution set obtained from the embodiment.

[0090] Figure 3 This is a structural block diagram of the improved multi-objective Harris Hawk wind storage capacity configuration system considering two details of the present invention. Detailed Implementation

[0091] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0092] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0093] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0094] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0095] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0096] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0097] Example 1

[0098] Reference Figure 1 The improved multi-objective Harris Hawk wind storage capacity configuration method considering two detailed rules provided by this invention is implemented according to the following steps:

[0099] Step 1: Data Preprocessing

[0100] Historical wind power forecasts and actual grid-connected power outside of curtailment periods were collected, and missing data and outliers in the historical wind power data were repaired, as follows:

[0101]

[0102] Among them, t i Let N be the true value of historical wind power at time i, and N be the number of samples. This represents the correction value for the missing data at time i. This is the average of historical wind power data. ξ and ζ are weighting coefficients.

[0103] Step 2: Setting parameters for the multi-objective optimization model

[0104] Set basic parameters, including the unit power cost C of the energy storage system. p Unit energy cost C e Residual value rate C sv The allowable range of prediction error, the power fluctuation limit, and the penalty coefficient λ;

[0105] Step 3: Solving the multi-objective optimization model

[0106] Solving multi-objective optimization models, including:

[0107] Prediction error compensation:

[0108] The prediction error boundary is calculated as shown in equation (2):

[0109]

[0110] The compensation power of the energy storage system is determined by comparing the actual power with the predicted error range.

[0111]

[0112] Upgrade the SOC of the energy storage system:

[0113] E a (k)=E a (k-1)+P a (k)·Δt (4)

[0114] Calculate the compensated wind power:

[0115] P bg (k)=P s (k)-P a (k) (5)

[0116] Calculate the prediction error penalty:

[0117]

[0118] Among them, P w1 (k) represents the upper limit of the prediction error, P w2 (k) represents the lower bound of the prediction error, P s (k) represents the actual power value;

[0119] MPC control of energy storage system:

[0120] MPC objective function:

[0121]

[0122] Among them, u i For energy storage control input, SOC i The energy storage state of charge, N is the prediction step size;

[0123] The constraints are considered as follows:

[0124]

[0125] Solve the optimization problem to obtain the optimal control sequence u. * Take the first control input:

[0126]

[0127] Objective function:

[0128]

[0129] The Harris Eagle optimization algorithm is used to solve the two stages of prediction error compensation and energy storage MPC control:

[0130] Initialize the Harris Eagle population, with each individual representing a set of energy storage configuration schemes [Pamax,Eamax]. Then calculate the fitness value of each individual. The final value is obtained by iteratively determining the current optimal solution, updating the Harris Eagle position according to the prey position, and simulating the Harris Eagle's hunting strategy. Equation (12) is the simulated Harris Eagle hunting strategy:

[0131]

[0132] Where X(t) is the current position of the Harris Hawk, X prey (t) represents the prey's location, X rand (t) is the randomly selected position of the Harris Hawk, r1, r2, r3, r4, q are random numbers, E is the energy factor, J is the jump factor, and LB and UB are the lower and upper bounds of the variable.

[0133] To prevent premature convergence of the optimization process, an adaptive energy factor E and an exploration weight α are introduced:

[0134]

[0135] The processed data and the set basic parameters are input into the above multi-objective optimization model. The multi-objective Harris Eagle algorithm is used to optimize and solve the maximum power Pmax and maximum capacity Emax of the energy storage system, ensuring that the economic and performance objectives are minimized globally, and the desired solution set is obtained.

[0136] Step 4: Output the optimization results.

[0137] Example 2

[0138] The improved multi-objective Harris Hawk wind storage capacity configuration method considering two detailed rules provided by this invention includes the following steps:

[0139] Step 1: Data Preprocessing

[0140] Historical wind power forecasts and actual grid-connected power outside of curtailment periods were collected, and missing data and outliers in the historical wind power data were repaired, as follows:

[0141]

[0142] Among them, t i Let N be the true value of historical wind power at time i, and N be the number of samples. This represents the correction value for the missing data at time i. This is the average of historical wind power data. ξ and ζ are weighting coefficients.

[0143] Step 2: Setting parameters for the multi-objective optimization model

[0144] Set basic parameters, energy storage system unit power cost C p = 2500 yuan / kW, unit energy cost C e =700 yuan / kWh, residual value rate C sv =0.05, Prediction error allowable range: (0.65-1.35)P pre Power fluctuation limit: 5MW / 15min, penalty coefficient λ=100, SOC_min=0.1, SOC_max=0.9, initial state of charge (SOC): 0.5, optimization target range: 1-50.

[0145] Step 3: Solving the multi-objective optimization model

[0146] A multi-objective optimization model is constructed based on formulas (2)-(14). The processed data and the set basic parameters are input into the multi-objective optimization model, and the multi-objective Harris Eagle algorithm is used to optimize and solve the maximum power P of the energy storage system. max and maximum capacity E max This ensures that the economic and performance objectives are minimized globally, thus obtaining the desired solution set.

[0147] Step 4: Output optimization results

[0148] Figure 2 For the solution set obtained in the example, when P max =7.7, E max When the value is 45.9, the performance is optimal, but the economy is worst; conversely, there is another extreme solution. Using the distance method, a solution that balances both factors is selected: P max =3.9, Emax =19.80.

[0149] This invention effectively overcomes the limitations of existing wind-storage capacity configuration technologies through the collaborative design of an improved Harris Eagle algorithm and a two-stage control strategy. In existing technologies, traditional optimization algorithms such as genetic algorithms and particle swarm optimization often suffer from slow convergence speeds and susceptibility to local optima when dealing with the volatility of wind power and the nonlinear constraints of energy storage systems, making it difficult to find a configuration scheme that balances economic efficiency and technical performance. Furthermore, existing research does not adequately consider the prediction error penalties in the two detailed rules. Therefore, this invention, through multi-objective function design rather than simple single-objective optimization, can better balance the economic efficiency and technical performance of the energy storage system. The improved Harris Eagle algorithm, by introducing adaptive weights to balance global search and local optimization, can explore the solution space more efficiently than traditional algorithms, avoiding local optima and thus finding a better energy storage capacity configuration scheme. The two-stage strategy of prediction error compensation and multi-objective optimization model predictive control can quickly correct wind power prediction deviations, reduce grid connection penalties, and also smooth power fluctuations and maintain stable energy storage state of charge through multi-step prediction, achieving efficient operation of the energy storage system under dynamic conditions. In summary, this invention outperforms existing technologies in terms of optimization efficiency, target balancing capability, and engineering applicability, providing a more economical and efficient configuration scheme for wind storage systems.

[0150] Example 3

[0151] Reference Figure 3 The present invention provides an improved multi-objective Harris Hawk wind storage capacity configuration system considering two details, comprising:

[0152] The data preprocessing unit collects historical wind power data, obtains the historical wind power prediction and actual grid-connected power outside the power curtailment period, and repairs missing data and outliers as input to the multi-objective optimization model.

[0153] The multi-objective optimization model parameter setting unit sets basic parameters, including the unit power cost Cp, unit energy cost Ce, residual value rate Csv, allowable range of prediction error, power fluctuation limit value, and penalty coefficient λ of the energy storage system.

[0154] The multi-objective optimization model solver unit inputs the preprocessed data and set basic parameters into the multi-objective optimization model, and uses the multi-objective Harris-Eagle algorithm to optimize and solve for the maximum power P of the energy storage system. max and maximum capacity E max To ensure that the economic and performance objectives meet the requirements, the Pareto solution set is obtained;

[0155] Output the optimization result unit and select the final optimization configuration method from the Pareto solution set.

[0156] In the data preprocessing unit of this embodiment, historical wind power data is collected to obtain the historical predicted wind power and actual grid-connected power outside the power curtailment period. Missing data and outliers are then repaired, as follows:

[0157]

[0158] Among them, t i Let N be the true value of historical wind power at time i, and N be the number of samples. This represents the correction value for the missing data at time i. This is the average of historical wind power data. ξ and ζ are weighting coefficients.

[0159] In the multi-objective optimization model solving unit of this embodiment, multi-objective optimization model solving includes:

[0160] Prediction error compensation:

[0161] The prediction error boundary is calculated as shown in equation (2):

[0162] The compensation power of the energy storage system is determined by comparing the actual power with the predicted error range.

[0163] Upgrade the SOC of the energy storage system:

[0164] E a (k)=E a (k-1)+P a (k)·Δt (4)

[0165] Calculate the compensated wind power:

[0166] P bg (k)=P s (k)-P a (k) (5)

[0167] Calculate the prediction error penalty:

[0168]

[0169] Among them, P w1 (k) represents the upper limit of the prediction error, P w2 (k) represents the lower bound of the prediction error, P s (k) represents the actual power value;

[0170] MPC control of energy storage system:

[0171] MPC objective function:

[0172]

[0173] Among them, u i For energy storage control input, SOC i The energy storage state of charge, N is the prediction step size;

[0174] The constraints are considered as follows:

[0175]

[0176] Solve the optimization problem to obtain the optimal control sequence u. * Take the first control input:

[0177]

[0178] Objective function:

[0179]

[0180] In the multi-objective optimization model solving unit of this embodiment, the Harris Eagle optimization algorithm is used to solve the two stages of prediction error compensation and energy storage MPC control:

[0181] Initialize the Harris Eagle population, with each individual representing a set of energy storage configuration schemes [Pamax,Eamax]. Then calculate the fitness value of each individual. The final value is obtained by iteratively determining the current optimal solution, updating the Harris Eagle position according to the prey position, and simulating the Harris Eagle's hunting strategy. Equation (12) is the simulated Harris Eagle hunting strategy:

[0182]

[0183] Where X(t) is the current position of the Harris Hawk, X prey (t) represents the prey's location, X rand (t) is the randomly selected position of the Harris Hawk, r1, r2, r3, r4, q are random numbers, E is the energy factor, J is the jump factor, and LB and UB are the lower and upper bounds of the variable.

[0184] To prevent premature convergence of the optimization process, an adaptive energy factor E and an exploration weight α are introduced:

[0185]

[0186] Example 4

[0187] The present invention provides an electronic device comprising: a processor and a memory coupled to the processor, the memory storing a computer program, which, when executed by the processor, implements the steps of the improved multi-objective Harris Hawk wind storage capacity configuration method considering two details.

[0188] The electronic device may also include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0189] The processor controls the overall operation of the electronic device to complete all or part of the steps in the storage medium sharing method. The memory stores various types of data to support the operation of the electronic device. This data may include, for example, instructions for any application or method operating on the electronic device, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia components may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio components are used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the processor and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical. The communication component is used for wired or wireless communication between the electronic device and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof; therefore, the corresponding communication component may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0190] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing a storage medium sharing method.

[0191] Example 5

[0192] The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the improved multi-objective Harris Hawk wind storage capacity configuration method considering two details.

[0193] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0194] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0195] 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.

[0196] 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.

[0197] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0198] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. An improved multi-objective Harris-Pareto wind capacity allocation method considering two rules, characterized in that, Comprising the following steps: Step 1, data preprocessing Collect historical wind power data to obtain historical wind power prediction and actual on-grid power outside the power limiting period, and repair missing data and outliers in it as input of the multi-objective optimization model; Step 2, parameter setting of multi-objective optimization model Set the basic parameters, including the unit power cost Cp of the energy storage system, the unit energy cost Ce, the residual value rate Csv, the prediction error allowed range, the power fluctuation limit value and the penalty coefficient λ; Step 3, solving the multi-objective optimization model The pretreated data and the set basic parameters are input into a multi-objective optimization model, and a multi-objective Harris eagle algorithm is used for optimization and solution of maximum power P of the energy storage system max and maximum capacity E max , so as to ensure that the economic target and the performance target meet the requirements and obtain a Pareto solution set; Step 4, output optimization result Select the final optimization configuration method from the Pareto solution set.

2. The improved multi-objective Harris-Pareto wind capacity allocation method considering two rules according to claim 1, wherein, In step 1, collect historical wind power data to obtain historical wind power prediction and actual on-grid power outside the power limiting period, and repair missing data and outliers in it as follows: where t i is the true value of the historical wind power data at time i, N is the number of samples, is the corrected value of the missing data at time i, is the mean value of the historical wind power data, ξ and ζ are weight coefficients.

3. The improved multi-objective Harris-Pareto wind capacity allocation method considering two rules according to claim 1, wherein, In step 3, solving the multi-objective optimization model, including: Prediction error compensation: Calculate the prediction error boundary as shown in equation (2): Determine the compensation power of the energy storage system according to the comparison of the actual power and the prediction error range: Update the SOC of the energy storage system: E a (k) = E a (k-1) + P a (k) · Δt (4) Calculate the compensated wind power: P bg (k) = P s (k) - P a (k) (5) Calculate the prediction error penalty: where P w1 (k) represents the upper limit of the prediction error, P w2 (k) represents the lower limit of the prediction error, P s (k) is the actual power value; Energy storage system MPC control: MPC objective function: where u i is the energy storage control input, SOC i is the energy storage state of charge, N is the prediction step; The constraint condition is considered as: Solve the optimization problem to obtain the optimal control sequence u * Take the first control input: Objective function:

4. The improved multi-objective Harris-Pareto wind capacity allocation method considering two rules according to claim 3, characterized in that, The Harris Hawk optimization algorithm is used to solve the two stages of prediction error compensation and energy storage MPC control: Initialize the Harris Hawk population, each individual represents a set of energy storage configuration scheme [Pamax, Eamax], then calculate the fitness value of each individual, and through the steps of determining the current optimal solution, updating the Harris Hawk position according to the prey position, simulating the hunting strategy of Harris Hawk, the final value is obtained by iteration, and equation (12) is the hunting strategy of Harris Hawk simulation: where X(t) is the current Harris hawk position, X prey (t) is the prey position, X rand (t) is the position of a randomly selected Harris hawk, r1, r2, r3, r4, q are random numbers, E is an energy factor, J is a jump factor, LB and UB are lower and upper bounds of the variable; In order to avoid premature convergence in the optimization process, an adaptive energy factor E and an exploration weight α are introduced:

5. An improved multi-objective Harris-Pareto wind capacity allocation system considering two rules, characterized in that, Including: The data preprocessing unit collects historical wind power data to obtain historical wind power prediction and actual on-grid power outside the power limiting period, and repairs missing data and outliers in it as input of the multi-objective optimization model; The multi-objective optimization model parameter setting unit sets the basic parameters, including the unit power cost Cp of the energy storage system, the unit energy cost Ce, the residual value rate Csv, the prediction error allowed range, the power fluctuation limit value and the penalty coefficient λ; The multi-objective optimization model solving unit inputs the preprocessed data and the set basic parameters into the multi-objective optimization model, and uses a multi-objective Harris eagle algorithm to optimize and solve the maximum power P of the energy storage system max and the maximum capacity E max , so as to ensure that the economic target and the performance target meet the requirements and obtain a Pareto solution set. The output optimization result unit selects the final optimization configuration method from the Pareto solution set.

6. The improved multi-objective Harris-Pareto wind capacity allocation method considering two rules according to claim 5, wherein, In the data preprocessing unit, collect historical wind power data to obtain historical wind power prediction and actual on-grid power outside the power limiting period, and repair missing data and outliers in it as follows: where t i is the true value of historical wind power data at time i, N is the number of samples, is the corrected value of missing data at time i, is the mean value of historical wind power data, ξ and ζ are weight coefficients.

7. The improved multi-objective Harris-Pareto wind capacity allocation system considering two rules according to claim 5, wherein, In the multi-objective optimization model solving unit, the multi-objective optimization model is solved, including: Prediction error compensation: Calculate the prediction error boundary as shown in equation (2): Determine the compensation power of the energy storage system according to the comparison of the actual power and the prediction error range: Update the SOC of the energy storage system: E a (k) = E a (k-1) + P a (k) Δt (4) Calculate the compensated wind power: P bg (k) = P s (k) - P a (k) (5) Calculate the prediction error penalty: where P w1 (k) represents the upper limit of the prediction error, P w2 (k) represents the lower limit of the prediction error, P s (k) is the actual power value; Energy storage system MPC control: MPC objective function: where u i is the energy storage control input, SOC i is the energy storage state of charge, and N is the prediction step. The constraint condition is considered as: Solve the optimization problem to get the optimal control sequence u * Take the first control input: Objective function:

8. The improved multi-objective Harris-Pareto wind capacity allocation system considering two rules according to claim 7, wherein, In the multi-objective optimization model solving unit, the Harris hawk optimization algorithm is used to solve the two stages of prediction error compensation and energy storage MPC control: Initialize the Harris hawk population, each individual represents a set of energy storage configuration scheme [Pamax, Eamax], then calculate the fitness value of each individual, through the steps of determining the current optimal solution, updating the Harris hawk position according to the prey position, simulating the hunting strategy of Harris hawk, and the final value is obtained by iteration, formula (12) is the hunting strategy of simulating Harris hawk: where X(t) is the current Harris hawk position, X prey (t) is the prey position, X rand (t) is the position of a randomly selected Harris hawk, r1, r2, r3, r4, q are random numbers, E is an energy factor, J is a jump factor, LB and UB are lower and upper bounds of the variable; In order to avoid premature convergence of the optimization process, an adaptive energy factor E and an exploration weight a are introduced:

9. An electronic device, comprising: It includes: A processor and a memory coupled to the processor, the memory storing a computer program, the computer program being executed by the processor to implement the steps of the improved multi-objective Harris hawk wind and storage capacity configuration method considering two rules in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program implements the steps of the improved multi-objective Harris hawk wind and storage capacity configuration method considering two rules in any one of claims 1-4 when executed by a processor.

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