Hybrid energy storage capacity optimization method and system based on improved particle swarm optimization algorithm

Abstract

The present disclosure provides a hybrid energy storage capacity optimization method and system based on an improved particle swarm optimization algorithm, which comprises obtaining the number, unit price, operation coefficient, maintenance coefficient, power shortage rate threshold and energy storage capacity threshold of the equipment in the hybrid energy storage system; constructing a target function based on the number, unit price, operation coefficient and maintenance coefficient of the equipment; constructing a constraint condition based on the load power shortage rate, hybrid energy storage system energy storage capacity, power, power shortage rate threshold and energy storage capacity threshold; when the constraint condition is satisfied, solving the target function by using the improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and controlling the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity, wherein the improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using a dynamic inertia weight and a dynamic learning factor. According to the method of the present disclosure, the optimization ability and speed of the particle swarm optimization algorithm can be improved, the cost of hybrid energy storage can be better reduced, and the capacity configuration can be better performed.

Description

Technical Field

[0001] This disclosure relates to the field of hybrid energy storage capacity optimization technology, and in particular to a hybrid energy storage capacity optimization method and system based on an improved particle swarm optimization algorithm. Background Technology

[0002] Currently, energy storage technology plays a significant role in power systems across the "source, grid, load, and consumption" domains. Traditional energy storage technologies primarily utilize lithium-ion batteries; however, lithium-ion batteries suffer from short cycle life, poor safety performance, and low power density, severely impacting the quality and economic viability of energy storage projects. Compared to lithium-ion batteries, supercapacitors offer advantages such as fast charging and discharging speeds, high power density, long cycle life, and high safety performance, making them a promising new option for power frequency regulation technology. Hybrid energy storage systems combining supercapacitors and batteries have garnered significant attention from the power industry. However, hybrid energy storage systems involving supercapacitors and batteries face the challenge of capacity optimization. Current methods for hybrid energy storage capacity optimization largely employ existing particle swarm optimization (PSO) algorithms to study capacity optimization configuration problems. While these algorithms offer fast convergence, they are prone to local optima during iteration, making it difficult to escape these local optima. Furthermore, the optimal position in the PSO optimization algorithm is related to the particle velocity; this velocity limitation restricts the search space for each iteration to a finite region, preventing the search range from expanding to the entire feasible solution space and thus failing to guarantee finding the globally optimal solution. Therefore, while existing technologies can optimize capacity configuration to a certain extent, their optimization capabilities still need to be improved. Summary of the Invention

[0003] This disclosure provides a hybrid energy storage capacity optimization method and system based on an improved particle swarm optimization algorithm. The main purpose is to improve the optimization capability and speed of the particle swarm optimization algorithm, better reduce the cost of hybrid energy storage, and better configure the capacity.

[0004] According to a first aspect of this disclosure, a method for optimizing hybrid energy storage capacity based on an improved particle swarm optimization algorithm is provided, comprising:

[0005] Obtain the quantity, unit price, operating coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system;

[0006] Construct an objective function based on the quantity, unit price, operating coefficient, and maintenance coefficient of the equipment;

[0007] Constraints are constructed based on the load power shortage rate, the energy stored in the hybrid energy storage system, the amount of electricity, the power shortage rate threshold, and the energy storage threshold.

[0008] When the constraints are met, the objective function is solved using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity. The capacity of the hybrid energy storage is then controlled based on the optimal hybrid energy storage capacity. The improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors.

[0009] In one embodiment of this disclosure, the step of optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors to obtain an improved particle swarm optimization algorithm includes: obtaining dynamic inertia weights based on the initial value of the inertia weights, the inertia weights in the later stages of iteration, the maximum number of iterations, a first random value, and a second random value; using the dynamic inertia weights as the improved inertia weights for the particle swarm optimization algorithm, and combining them with the dynamic learning factor to obtain the improved particle swarm optimization algorithm.

[0010] In one embodiment of this disclosure, the step of using dynamic inertia weight as the inertia weight for improving the particle swarm optimization algorithm and combining it with a dynamic learning factor to obtain an improved particle swarm optimization algorithm includes: obtaining a dynamic learning factor based on the dynamic inertia weight, a first random value, and the dynamic inertia weight; using the dynamic inertia weight as the inertia weight for improving the particle swarm optimization algorithm and using the dynamic learning factor as the learning factor for improving the particle swarm optimization algorithm, thereby obtaining an improved particle swarm optimization algorithm.

[0011] In one embodiment of this disclosure, the first random value is a random number between 0 and 1, and the second random value is a random number between 1.5 and 2.

[0012] In one embodiment of this disclosure, the constraints include load shortage rate constraints, hybrid energy storage system energy storage constraints, and power constraints.

[0013] According to a second aspect embodiment of this disclosure, a hybrid energy storage capacity optimization system based on an improved particle swarm optimization algorithm is also provided, comprising:

[0014] The acquisition module is used to acquire the quantity, unit price, operating coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system.

[0015] The first construction module is used to construct an objective function based on the quantity, unit price, operating coefficient, and maintenance coefficient of the equipment.

[0016] The second construction module is used to construct constraints based on the load power shortage rate, the energy stored in the hybrid energy storage system, the amount of electricity, the power shortage rate threshold, and the energy storage threshold.

[0017] The processing and control module is used to solve the objective function using an improved particle swarm optimization algorithm when the constraints are met, to obtain the optimal hybrid energy storage capacity, and to control the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity. The improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors.

[0018] In one embodiment of this disclosure, the processing control module is specifically used to: obtain dynamic inertial weights based on the initial value of the inertial weights, the inertial weights in the later stages of iteration, the maximum number of iterations, the first random value, and the second random value; use the dynamic inertial weights as the improved inertial weights for the particle swarm optimization algorithm, and combine them with the dynamic learning factor to obtain the improved particle swarm optimization algorithm.

[0019] In one embodiment of this disclosure, the processing control module is specifically used to: obtain a dynamic learning factor based on the dynamic inertia weight, the first random value, and the dynamic inertia weight; use the dynamic inertia weight as the improved inertia weight of the particle swarm optimization algorithm, and use the dynamic learning factor as the improved learning factor of the particle swarm optimization algorithm, thereby obtaining an improved particle swarm optimization algorithm.

[0020] In one embodiment of this disclosure, the first random value is a random number between 0 and 1, and the second random value is a random number between 1.5 and 2.

[0021] According to a third aspect of the present disclosure, a hybrid energy storage capacity optimization device based on an improved particle swarm optimization algorithm is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the hybrid energy storage capacity optimization method based on an improved particle swarm optimization algorithm proposed in the first aspect of the present disclosure.

[0022] In one or more embodiments of this disclosure, the quantity, unit price, operating coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system are obtained. An objective function is constructed based on the quantity, unit price, operating coefficient, and maintenance coefficient. Constraints are constructed based on the load power shortage rate, the energy storage capacity of the hybrid energy storage system, the amount of electricity stored, the power shortage rate threshold, and the energy storage threshold. When the constraints are satisfied, an improved particle swarm optimization algorithm is used to solve the objective function to obtain the optimal hybrid energy storage capacity. The capacity of the hybrid energy storage is then controlled based on the optimal hybrid energy storage capacity. The improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors. In this case, the improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors. The improved particle swarm optimization algorithm is then used to solve the objective function to obtain the optimal hybrid energy storage capacity, thereby controlling the capacity of the hybrid energy storage. This improves the optimization capability and speed of the particle swarm optimization algorithm, better reduces the cost of hybrid energy storage, and better configures the capacity.

[0023] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0025] Figure 1 This diagram illustrates a flow chart of a hybrid energy storage capacity optimization method based on an improved particle swarm optimization algorithm provided in an embodiment of this disclosure.

[0026] Figure 2 This diagram illustrates a block diagram of a hybrid energy storage capacity optimization system based on an improved particle swarm optimization algorithm, according to an embodiment of this disclosure.

[0027] Figure 3 This is a block diagram of a hybrid energy storage capacity optimization device based on an improved particle swarm optimization algorithm, used to implement the hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm of the embodiments of this disclosure. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this disclosure as detailed in the appended claims.

[0029] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0030] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined. It should also be understood that the term "and / or" as used in this disclosure refers to and includes any or all possible combinations of one or more associated listed items.

[0031] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0032] This disclosure provides a hybrid energy storage capacity optimization method and system based on an improved particle swarm optimization algorithm. The main purpose is to improve the optimization capability and speed of the particle swarm optimization algorithm, better reduce the cost of hybrid energy storage, and better configure the capacity.

[0033] In the first embodiment, Figure 1 This diagram illustrates a flowchart of a hybrid energy storage capacity optimization method based on an improved particle swarm optimization algorithm, provided in an embodiment of this disclosure. Figure 1 As shown, specifically, the hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm includes:

[0034] Step S11: Obtain the quantity, unit price, operating coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system.

[0035] In step S11, the equipment of the hybrid energy storage system includes batteries and supercapacitors, and the number of batteries and supercapacitors can be multiple.

[0036] Step S12: Construct an objective function based on the number of equipment, unit price, operating coefficient, and maintenance coefficient.

[0037] Specifically, in step S12, a full life cycle cost model is constructed based on the number of equipment, unit price, operating coefficient, and maintenance coefficient, and the objective function is obtained with the goal of minimizing the full life cycle cost.

[0038] The lifecycle cost model satisfies:

[0039] L cc =C o +C P +C M +C D

[0040] The objective function satisfies:

[0041] min L cc =C O +C P +C M +C D

[0042] =(1+f ob +f mb +f db )N b P b +(1+f oc +f dc )N c P c

[0043] In the formula, L cc Indicates total lifecycle cost; C O Indicates the operating cost of the equipment; C P Indicates the purchase cost of the equipment; C M Indicates the cost of equipment maintenance; C D Indicates the processing cost of the equipment; f ob f oc These represent the operating coefficients of the storage battery and the supercapacitor, respectively; f mb The maintenance factor, f, represents the battery's maintenance coefficient. db f dc N represents the processing coefficient of the battery and the supercapacitor, respectively. b N c These represent the number of batteries and supercapacitors, respectively; P b P c These represent the unit price of the storage battery and the supercapacitor, respectively.

[0044] Step S13: Construct constraints based on load power shortage rate, energy storage capacity of hybrid energy storage system, power quantity, power shortage rate threshold, and energy storage threshold.

[0045] In step S13, the constraints include load power shortage rate constraints, hybrid energy storage system energy storage constraints, and power constraints.

[0046] In some embodiments, the power outage rate threshold is the maximum allowable power outage rate of the load. A load power outage rate constraint is constructed based on the maximum allowable power outage rate and the load power outage rate. The load power outage rate constraint satisfies the following:

[0047] f LPSP ≤f LPSPmax

[0048] In the formula, f LPSP f is the load power shortage rate. LPSPmax This represents the maximum allowable power shortage rate for the load.

[0049] In some embodiments, the energy storage threshold includes the maximum and minimum energy storage of the supercapacitor bank, the rated energy storage of the battery, and the minimum remaining energy storage of the battery. Based on the energy storage of the hybrid energy storage system and the energy storage threshold, energy storage constraints for the hybrid energy storage system are constructed, and these constraints satisfy the following:

[0050]

[0051] In the formula, E b (k) represents the remaining energy stored in the battery, E c (k) represents the energy stored in the supercapacitor bank, E bmin For the minimum remaining energy storage of the battery, E cmax E cmin E represents the maximum and minimum stored energy of the supercapacitor bank, respectively. bn This refers to the rated energy storage capacity of the battery.

[0052] In some embodiments, the minimum remaining energy storage capacity of the battery satisfies:

[0053] E bmin =N b C b U b (1-DOD) / 10 6

[0054] In the formula, U b This indicates the rated voltage of the battery (unit: V); C b The rated capacity of the battery is indicated by Ah; DOD indicates the maximum depth of discharge.

[0055] In some embodiments, the maximum energy storage capacity of the supercapacitor bank satisfies:

[0056]

[0057] The minimum energy storage requirement of a supercapacitor bank is:

[0058]

[0059] In the formula, U cmax U is the maximum terminal voltage of the supercapacitor. cmin C is the minimum terminal voltage of the supercapacitor. c This indicates the capacitance value of the supercapacitor.

[0060] In some embodiments, the power constraint condition is satisfied as follows:

[0061] E b (k)≤μΔE

[0062] ΔE=(E w (k)+E pv (k))η c -E L (k)

[0063] In the formula, μ represents the proportionality coefficient, and E w (k), E pv (k), E L (k) represents the electricity generated by wind power, solar energy, and load at time k, respectively; η c It refers to the power conversion efficiency of the inverter.

[0064] In this embodiment, a capacity optimization configuration model for the hybrid energy storage system is formed based on the objective function of step S12 and the constraints of step S13. The hybrid energy storage system is a hybrid energy storage device that utilizes batteries and supercapacitors as a wind-solar complementary system. The capacity optimization configuration model aims to minimize the total lifecycle cost, with constraints including load shedding rate, energy storage capacity, and electricity generation of the energy storage system.

[0065] Step S14: When the constraints are met, the objective function is solved using the improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity. The capacity of the hybrid energy storage is controlled based on the optimal hybrid energy storage capacity. The improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weight and dynamic learning factor.

[0066] In step S14, when the constraints are met, the objective function is solved using the improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, that is, the optimal hybrid energy storage capacity is obtained by solving the capacity optimization configuration model.

[0067] In step S14, it is easy to understand that Particle Swarm Optimization (PSO) is a swarm optimization algorithm and an iterative optimization tool. Compared with other intelligent algorithms, its advantages are simplicity, ease of implementation, good robustness, high accuracy, and fast convergence, making it suitable for application in engineering practice.

[0068] In this particle swarm optimization algorithm, particles in the search space change their positions based on their own experience and the experience of other particles. By continuously updating their positions, they eventually find the optimal point. PSO uses a "velocity-displacement" search model. The PSO algorithm is a method that simulates birds hunting. Individuals in the particle swarm optimization algorithm are called "particles" and are distributed in a multi-dimensional search space. The changes of particles in the search space are based on the social psychology concept of individuals imitating the successful experiences of others. The swarm consists of a group of particles, each of which is a potential solution. The position change of each particle is determined by its own experience and the experience of its neighboring particles. This represents the particle's position; the particle's position changes by adding the particle's velocity to its current position. To improve the convergence performance of the basic PSO algorithm, an inertia weight ω is introduced into the velocity equation, resulting in the standard PSO algorithm with the following velocity update formula:

[0069]

[0070] In the formula, ω represents the inertia weight; C1 is the first acceleration factor, C2 is the second acceleration factor, r1 and r2 represent random numbers distributed in the interval [0, 1]; i is the current iteration number. Let be the velocity of the particle at the i-th iteration number. Let i be the position of the particle at the i-th iteration number. Let ω represent the individual extreme value and the group extreme value (i.e., the optimal position), respectively. If the inertia weight ω is set to decrease linearly, the approximate position of the optimal solution can be determined relatively quickly at the beginning of the search. As the inertia weight ω gradually decreases, the particle speed will also slow down, which is beneficial for fine-grained local search and improves the performance of the algorithm.

[0071] Considering that in particle swarm optimization (PSO) with linearly decreasing inertia weights, the algorithm is prone to premature convergence to local optima in the later stages of the search, resulting in a lack of diversity in particle positions, an improved PSO algorithm is developed by using dynamic inertia weights and a dynamic learning factor. This allows particles to traverse the entire search space as much as possible in the early stages of the search, achieving particle diversity and thus avoiding premature convergence to local optima.

[0072] In step S14, the particle swarm optimization algorithm is optimized using dynamic inertia weights and a dynamic learning factor to obtain an improved particle swarm optimization algorithm. This includes: obtaining dynamic inertia weights based on the initial inertia weight value, the inertia weight value in the later stage of iteration, the maximum number of iterations, the first random value, and the second random value; using the dynamic inertia weights as the improved inertia weights for the particle swarm optimization algorithm, and combining them with the dynamic learning factor to obtain the improved particle swarm optimization algorithm. The improved inertia weights (i.e., dynamic inertia weights) satisfy:

[0073]

[0074] In the formula, i is the current iteration number of the particle, and ω start ω is the initial value of the weights at the start of the iteration (i.e., the initial value of the inertia weights). end Here, T represents the weight value in the later stage of iteration (i.e., the inertia weight value in the later stage of iteration), rand(0,1) is the first random value, which means randomly selecting data between [0,1] (i.e., the first random value is a random number between 0 and 1), and rand(1.5,2) is the second random value, which means randomly selecting data between [1.5,2] (i.e., the second random value is a random number between 1.5 and 2).

[0075] In step S14, the dynamic inertia weight is used as the improved inertia weight for the particle swarm optimization algorithm. Combined with the dynamic learning factor, an improved particle swarm optimization algorithm is obtained. This includes: obtaining the dynamic learning factor based on the dynamic inertia weight, the first random value, and the dynamic inertia weight; using the dynamic inertia weight as the improved inertia weight for the particle swarm optimization algorithm; and using the dynamic learning factor as the improved learning factor for the particle swarm optimization algorithm, thus obtaining the improved particle swarm optimization algorithm. In this case, the inertia weight is improved, and the acceleration factor is optimized based on the improved inertia weight, thus improving the particle swarm optimization algorithm, reducing the total lifecycle cost of the hybrid energy storage system, and accelerating the convergence speed of the system to the optimal value. This avoids the problem of getting trapped in local optima like in ordinary particle swarm optimization algorithms when the objective function is complex, as well as the slow convergence speed and decreased accuracy of the entire algorithm in the later stages.

[0076] Specifically, in particle swarm optimization (PSO) algorithms, the first acceleration factor C1 and the second acceleration factor C2 significantly influence the search results. Existing technologies typically set the acceleration factors to fixed values, which has limitations. Considering that the main parameters affecting the efficiency and accuracy of PSO algorithms are the inertia weight ω, the first acceleration factor (also known as the individual cognitive factor) C1, and the second acceleration factor (also known as the group cognitive factor) C2, a larger inertia weight ω results in better global search capability; a smaller first acceleration factor C1 and a larger second acceleration factor C2 result in better local search capability. Therefore, this disclosure uses a linear decreasing strategy to improve the inertia weight ω, giving it good global search capability in the initial iteration stage, and as the iteration progresses, the particle velocity gradually decreases, resulting in better local search capability. To control the first acceleration factor C1 to have a larger value and the second acceleration factor C2 to have a smaller value in the initial stage, thereby enhancing the global search capability, and to enhance the local search capability in the final iteration stage, sine and cosine functions are used to control the first acceleration factor C1 and the second acceleration factor C2. This allows the value of the first acceleration factor C1 to decrease non-linearly and the value of the second acceleration factor C2 to increase non-linearly, thus enhancing the convergence capability of the particle's global optimum. The optimized first acceleration factor C1 and the second acceleration factor C2 satisfy:

[0077]

[0078]

[0079] In the formula, C1 is the first acceleration factor and C2 is the second acceleration factor.

[0080] In step S14, the objective function is solved using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity. Based on the optimal hybrid energy storage capacity, the capacity of the hybrid energy storage is controlled. The optimal hybrid energy storage capacity includes the optimal battery energy storage capacity and the optimal supercapacitor energy storage capacity.

[0081] To verify the hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm, simulation verification was conducted.

[0082] During verification, the iteration begins with the initial weight value ω. start Take 1.1, the weight value ω in the later stage of the weight iteration. end Using a value of 0.4, the maximum number of iterations was set to 100. After 100 iterations, it was found that in the early stages of the overall search, ω had a higher probability of taking a large value, indicating enhanced global search capability. In the later stages, ω had a higher probability of taking a small value, indicating enhanced local development capability. This improved population diversity and enhanced local search capability. Simulations verified the feasibility and effectiveness of the proposed method in the optimal configuration of hybrid energy storage capacity.

[0083] In the hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm of this disclosure embodiment, the quantity, unit price, operation coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system are obtained; an objective function is constructed based on the quantity, unit price, operation coefficient, and maintenance coefficient of the equipment; constraints are constructed based on the load power shortage rate, the energy storage capacity of the hybrid energy storage system, the amount of electricity, the power shortage rate threshold, and the energy storage threshold; when the constraints are satisfied, the improved particle swarm optimization algorithm is used to solve the objective function to obtain the optimal hybrid energy storage capacity; and the capacity of the hybrid energy storage is controlled based on the optimal hybrid energy storage capacity. The improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors. In this case, the improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors, and the optimal hybrid energy storage capacity is obtained by solving the objective function using the improved particle swarm optimization algorithm, thereby controlling the capacity of the hybrid energy storage. This improves the optimization ability and speed of the particle swarm optimization algorithm, better reduces the cost of hybrid energy storage, and better configures the capacity. This disclosure leverages the optimization characteristics of the Particle Swarm Optimization (PSO) algorithm, employing an improved inertia weight calculation method. Simultaneously, it optimizes the acceleration factor based on this improved inertia weight, thereby enhancing the PSO algorithm's local search capability, global optimization capability, and speed, ultimately reducing the total lifecycle cost of the hybrid energy storage system. Firstly, considering the significant impact of inertia weight on the PSO algorithm, an improved inertia weight is proposed. A dynamic inertia weight is introduced, allowing particles to traverse the entire search space as much as possible in the early stages of the search, achieving particle diversity and preventing premature convergence to local extrema. This improves the PSO optimization capability and the number of iterations, reducing the total lifecycle cost of the hybrid energy storage system. Secondly, based on the improved inertia weight, considering the impact of the acceleration factor on PSO optimization, the first and second acceleration factors are changed from fixed values ​​to dynamic acceleration factors, accelerating the convergence speed and further reducing the total lifecycle cost of the hybrid energy storage system, thus speeding up the convergence rate to the optimal value. The method disclosed herein is a hybrid energy storage capacity optimization method based on a particle swarm optimization algorithm with improved inertia weights and learning factors (i.e., acceleration factors). It can be used for hybrid energy storage capacity optimization configuration to smooth out wind power fluctuations, improve the power quality of wind-storage combined power generation systems, reduce the fluctuation of wind power grid-connected power, and improve the stability and economy of system operation.

[0084] The following are system embodiments of this disclosure, which can be used to execute the method embodiments of this disclosure. For details not disclosed in the system embodiments of this disclosure, please refer to the method embodiments of this disclosure.

[0085] Please see Figure 2 , Figure 2This diagram illustrates a block diagram of a hybrid energy storage capacity optimization system based on an improved particle swarm optimization algorithm, according to an embodiment of this disclosure. The hybrid energy storage capacity optimization system 10 based on the improved particle swarm optimization algorithm includes an acquisition module 11, a first construction module 12, a second construction module 13, and a processing control module 14, wherein:

[0086] Module 11 is used to acquire the quantity, unit price, operating coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system.

[0087] The first construction module 12 is used to construct an objective function based on the number of equipment, unit price, operating coefficient, and maintenance coefficient;

[0088] The second construction module 13 is used to construct constraints based on the load power shortage rate, the energy stored in the hybrid energy storage system, the amount of electricity, the power shortage rate threshold, and the energy storage threshold.

[0089] The processing control module 14 is used to solve the objective function using an improved particle swarm optimization algorithm when the constraints are met, to obtain the optimal hybrid energy storage capacity, and to control the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity. The improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors.

[0090] Optionally, the processing control module 14 is specifically used to: obtain dynamic inertial weight based on the initial value of inertial weight, the inertial weight value in the later stage of iteration, the maximum number of iterations, the first random value, and the second random value; use the dynamic inertial weight as the improved inertial weight of the particle swarm optimization algorithm, and combine it with the dynamic learning factor to obtain the improved particle swarm optimization algorithm.

[0091] Optionally, the processing control module 14 is specifically used to: obtain a dynamic learning factor based on the dynamic inertia weight, the first random value, and the dynamic inertia weight; use the dynamic inertia weight as the improved inertia weight of the particle swarm optimization algorithm, and use the dynamic learning factor as the improved learning factor of the particle swarm optimization algorithm, thereby obtaining the improved particle swarm optimization algorithm.

[0092] Optionally, the first random value is a random number between 0 and 1, and the second random value is a random number between 1.5 and 2.

[0093] It should be noted that the foregoing explanation of the embodiment of the hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm also applies to the hybrid energy storage capacity optimization system based on the improved particle swarm optimization algorithm in this embodiment, and will not be repeated here.

[0094] In the hybrid energy storage capacity optimization system based on the improved particle swarm optimization algorithm of this embodiment, the acquisition module acquires the number, unit price, operation coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system; the first construction module constructs an objective function based on the number, unit price, operation coefficient, and maintenance coefficient of the equipment; the second construction module constructs constraints based on the load power shortage rate, the energy storage of the hybrid energy storage system, the amount of electricity, the power shortage rate threshold, and the energy storage threshold; when the constraints are satisfied, the processing and control module solves the objective function using the improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity, and controls the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity. The improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors. In this case, the improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors, and the optimal hybrid energy storage capacity is obtained by solving the objective function using the improved particle swarm optimization algorithm, thereby controlling the capacity of the hybrid energy storage. This improves the optimization ability and speed of the particle swarm optimization algorithm, better reduces the cost of hybrid energy storage, and better configures the capacity. This disclosure leverages the optimization characteristics of the Particle Swarm Optimization (PSO) algorithm, employing an improved inertia weight calculation method. Simultaneously, it optimizes the acceleration factor based on this improved inertia weight, thereby enhancing the PSO algorithm's local search capability, global optimization capability, and speed, ultimately reducing the total lifecycle cost of the hybrid energy storage system. Firstly, considering the significant impact of inertia weight on the PSO algorithm, an improved inertia weight is proposed. A dynamic inertia weight is introduced, allowing particles to traverse the entire search space as much as possible in the early stages of the search, achieving particle diversity and preventing premature convergence to local extrema. This improves the PSO optimization capability and the number of iterations, reducing the total lifecycle cost of the hybrid energy storage system. Secondly, based on the improved inertia weight, considering the impact of the acceleration factor on PSO optimization, the first and second acceleration factors are changed from fixed values ​​to dynamic acceleration factors, accelerating the convergence speed and further reducing the total lifecycle cost of the hybrid energy storage system, thus speeding up the convergence rate to the optimal value. The system disclosed herein is a hybrid energy storage capacity optimization system based on a particle swarm optimization algorithm with improved inertia weights and learning factors. It can be used for hybrid energy storage capacity optimization configuration to smooth out wind power fluctuations, improve the power quality of wind-storage combined power generation systems, reduce fluctuations in wind power grid-connected power, and improve the stability and economy of system operation.

[0095] According to embodiments of this disclosure, this disclosure also provides a hybrid energy storage capacity optimization device based on an improved particle swarm optimization algorithm, a readable storage medium, and a computer program product.

[0096] Figure 3This is a block diagram of a hybrid energy storage capacity optimization device based on an improved particle swarm optimization algorithm, used to implement the hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm of the embodiments of this disclosure. The hybrid energy storage capacity optimization device based on the improved particle swarm optimization algorithm is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The hybrid energy storage capacity optimization device based on the improved particle swarm optimization algorithm can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components, connections and relationships of components, and functions of components shown in this disclosure are merely examples and are not intended to limit the implementation of the disclosure described and / or claimed herein.

[0097] like Figure 3 As shown, the hybrid energy storage capacity optimization device 20 based on the improved particle swarm optimization algorithm includes a computing unit 21, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 22 or a computer program loaded from a storage unit 28 into a random access memory (RAM) 23. The RAM 23 can also store various programs and data required for the operation of the hybrid energy storage capacity optimization device 20 based on the improved particle swarm optimization algorithm. The computing unit 21, ROM 22, and RAM 23 are interconnected via a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0098] The hybrid energy storage capacity optimization device 20 based on the improved particle swarm optimization algorithm has multiple components connected to an I / O interface 25, including: an input unit 26, such as a keyboard or mouse; an output unit 27, such as various types of displays or speakers; a storage unit 28, such as a disk or optical disk, which is communicatively connected to a computing unit 21; and a communication unit 29, such as a network interface card (NIC), modem, or wireless transceiver. The communication unit 29 allows the hybrid energy storage capacity optimization device 20 based on the improved particle swarm optimization algorithm to exchange information / data with other hybrid energy storage capacity optimization devices based on the improved particle swarm optimization algorithm through computer networks such as the Internet and / or various telecommunications networks.

[0099] The computing unit 21 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 21 performs the various methods and processes described above, such as performing a hybrid energy storage capacity optimization method based on an improved particle swarm optimization algorithm. For example, in some embodiments, the hybrid energy storage capacity optimization method based on an improved particle swarm optimization algorithm can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 28. In some embodiments, part or all of the computer program can be loaded and / or installed on the hybrid energy storage capacity optimization device 20 based on an improved particle swarm optimization algorithm via ROM 22 and / or communication unit 29. When the computer program is loaded into RAM 23 and executed by the computing unit 21, one or more steps of the hybrid energy storage capacity optimization method based on an improved particle swarm optimization algorithm described above can be performed. Alternatively, in other embodiments, computing unit 21 may be configured by any other suitable means (e.g., by means of firmware) to perform a hybrid energy storage capacity optimization method based on an improved particle swarm optimization algorithm.

[0100] Various embodiments of the systems and techniques described above in this disclosure can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), hybrid energy storage capacity optimization devices (CPLDs) with loaded programmable logic based on an improved particle swarm optimization algorithm, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0102] In this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or hybrid energy storage capacity optimization device based on an improved particle swarm optimization algorithm. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or hybrid energy storage capacity optimization devices based on improved particle swarm optimization algorithms, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage hybrid energy storage capacity optimization devices based on improved particle swarm optimization algorithms, magnetic storage hybrid energy storage capacity optimization devices based on improved particle swarm optimization algorithms, or any suitable combination of the foregoing.

[0103] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0104] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0105] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0106] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this disclosure does not impose any limitations herein.

[0107] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for optimizing hybrid energy storage capacity based on an improved particle swarm optimization algorithm, characterized in that, include: Obtain the quantity, unit price, operating coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system; Construct an objective function based on the quantity, unit price, operating coefficient, and maintenance coefficient of the equipment; Constraints are constructed based on the load power shortage rate, the energy stored in the hybrid energy storage system, the amount of electricity, the power shortage rate threshold, and the energy storage threshold. When the constraints are met, the objective function is solved using an improved particle swarm optimization algorithm to obtain the optimal hybrid energy storage capacity. The capacity of the hybrid energy storage is then controlled based on the optimal hybrid energy storage capacity. The improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors. The improved particle swarm optimization algorithm obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors includes: The dynamic inertial weight is obtained based on the initial value of the inertial weight, the inertial weight value in the later stage of the iteration, the maximum number of iterations, the first random value, and the second random value. The dynamic inertia weight is used as the inertia weight for the improvement of the particle swarm optimization algorithm, and the improved particle swarm optimization algorithm is obtained by combining the dynamic learning factor. The step of using dynamic inertia weights as inertia weights for improving the particle swarm optimization algorithm, and combining them with a dynamic learning factor to obtain an improved particle swarm optimization algorithm, includes: The dynamic learning factor is obtained based on the dynamic inertia weight, the first random value, and the dynamic inertia weight. The dynamic inertia weight is used as the inertia weight for the improvement of the particle swarm optimization algorithm, and the dynamic learning factor is used as the learning factor for the improvement of the particle swarm optimization algorithm, thus obtaining the improved particle swarm optimization algorithm. Wherein, the inertial weight satisfy: Where i is the current iteration number of the particle. ω start This is the initial value for the iterative inertia weight. ω end Here, T represents the inertia weight value in the later stage of the iteration, T represents the maximum number of iterations, rand(0,1) is the first random value, and rand(1.5,2) is the second random value.

2. The hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm as described in claim 1, characterized in that, The first random value is a random number between 0 and 1, and the second random value is a random number between 1.5 and 2.

3. The hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm as described in claim 1, characterized in that, The constraints include load power shortage rate constraints, hybrid energy storage system energy storage constraints, and power consumption constraints.

4. A hybrid energy storage capacity optimization system based on an improved particle swarm optimization algorithm, characterized in that, include: The acquisition module is used to acquire the quantity, unit price, operating coefficient, maintenance coefficient, power shortage rate threshold, and energy storage threshold of the equipment in the hybrid energy storage system. The first construction module is used to construct an objective function based on the quantity, unit price, operating coefficient, and maintenance coefficient of the equipment. The second construction module is used to construct constraints based on the load power shortage rate, the energy stored in the hybrid energy storage system, the amount of electricity, the power shortage rate threshold, and the energy storage threshold. The processing and control module is used to solve the objective function using an improved particle swarm optimization algorithm when the constraints are met, to obtain the optimal hybrid energy storage capacity, and to control the capacity of the hybrid energy storage based on the optimal hybrid energy storage capacity, wherein the improved particle swarm optimization algorithm is obtained by optimizing the particle swarm optimization algorithm using dynamic inertia weights and dynamic learning factors. Specifically, the processing control module is used for: The dynamic inertial weight is obtained based on the initial value of the inertial weight, the inertial weight value in the later stage of the iteration, the maximum number of iterations, the first random value, and the second random value; the dynamic inertial weight is used as the inertial weight for the improvement of the particle swarm optimization algorithm, and the improved particle swarm optimization algorithm is obtained by combining the dynamic learning factor; Specifically, the processing control module is used for: The dynamic learning factor is obtained based on the dynamic inertia weight, the first random value, and the dynamic inertia weight; the dynamic inertia weight is used as the inertia weight for the improved particle swarm optimization algorithm, and the dynamic learning factor is used as the learning factor for the improved particle swarm optimization algorithm, thus obtaining the improved particle swarm optimization algorithm. Wherein, the inertial weight satisfy: Where i is the current iteration number of the particle. ω start This is the initial value for the iterative inertia weight. ω end Here, T represents the inertia weight value in the later stage of the iteration, T represents the maximum number of iterations, rand(0,1) is the first random value, and rand(1.5,2) is the second random value.

5. The hybrid energy storage capacity optimization system based on the improved particle swarm optimization algorithm as described in claim 4, characterized in that, The first random value is a random number between 0 and 1, and the second random value is a random number between 1.5 and 2.

6. A hybrid energy storage capacity optimization device based on an improved particle swarm optimization algorithm, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the hybrid energy storage capacity optimization method based on the improved particle swarm optimization algorithm as described in any one of claims 1-3.

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