Distributed energy storage aggregator shared transaction method based on improved particle swarm optimization

Abstract

The invention discloses a distributed energy storage aggregator shared transaction method based on an improved particle swarm algorithm, and relates to the technical field of power systems, comprising: establishing an operation mode in which a distributed energy storage aggregator participates in demand response; establishing an energy storage model by considering the relationship between the charging and discharging times and the actual capacity of the battery; considering a power grid power balance constraint and a battery electric quantity constraint, and establishing a target function with the maximum daily income of the load aggregator; and solving by using an improved particle swarm algorithm. According to the distributed energy storage aggregator shared transaction method based on the improved particle swarm algorithm provided by the invention, a global optimal solution or a near-optimal solution can be found in a complex search space, so that a scheduling strategy of distributed energy storage equipment can be optimized more effectively; the profit of the aggregator is maximized or the effect of participating in the demand response transaction is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm. Background Art

[0002] With the increasing severity of environmental pollution and fossil energy shortages, new energy sources such as wind power and photovoltaics have been connected to the power grid on a large scale, resulting in supply and demand imbalances in the power system, such as peak load regulation, frequency regulation, and insufficient backup capacity. As a flexible demand response resource, with technological advancement and the rapid growth of installed capacity, distributed energy storage at the user end can effectively promote the coordinated and stable operation of resources on both sides of power supply and demand. However, due to the decentralized access, poor controllability, and small individual capacity of distributed energy storage, it is difficult to meet the minimum requirements for participating in grid demand response. As a new market entity, "distributed energy storage aggregators" can integrate idle energy storage resources at the user end through scientific aggregation management, act as agents to participate in the power market in a large-scale manner, and obtain economic compensation. This can not only reduce the peak load demand, line losses, and system operating costs of the power system, but also improve the utilization rate of energy storage resources and the economic benefits of energy storage users. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: how to reduce the peak load demand, line loss and system operating cost of the power system, and improve the utilization rate of energy storage resources and the economic benefits of energy storage users.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm, comprising:

[0006] Establishing an operating model for distributed energy storage aggregators to participate in demand response;

[0007] Constructing the operation mode, establishing an energy storage model by considering the relationship between the number of charge and discharge times and the actual capacity of the battery;

[0008] Based on the energy storage model, considering the power balance constraint of the power grid and the battery power constraint, an objective function with the goal of maximizing the daily profit of the load aggregator is established;

[0009] An improved particle swarm algorithm is used for solving the problem.

[0010] As a preferred solution of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in the present invention, wherein: the operation mode includes obtaining batch time-of-use electricity price discounts and setting internal electricity prices;

[0011] The internal electricity price satisfies the constraint condition, which is expressed as:

[0012]

[0013] in, is the grid-connected electricity price of distributed energy, and are the internal electricity purchase and sales prices set by energy storage aggregators for users, The price at which individual users purchase electricity from the power grid.

[0014] As a preferred solution of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in the present invention, the relationship between the number of charge and discharge times and the actual capacity is expressed as:

[0015]

[0016] Among them, N f Indicates the number of cycles of the energy storage battery, N r DOD is the rated number of energy storage discharges r Indicates rated depth of discharge, DOD a Indicates the discharge depth of the energy storage battery in actual use. δ1 and δ2 are both the battery deep cycle discharge coefficients.

[0017] As a preferred solution of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in the present invention, wherein: the energy storage model is expressed as:

[0018]

[0019] Among them, SOC i (t) represents the battery energy state of the distributed energy storage battery at time t; P i chr / dis represents the charging and discharging power of energy storage battery i at time t; η chr / dis It is the charge and discharge efficiency of the battery.

[0020] As a preferred solution of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in the present invention, the objective function is expressed as:

[0021] maxF=F1-F2

[0022]

[0023] Among them, F1 represents the profit obtained by the aggregator through electricity price arbitrage, F2 represents the compensation cost of battery loss, n represents the number of energy storage individuals of the dispatching user, α represents the profit distribution ratio between the aggregator and the energy storage user, and P chr(t) represents the charging power of the energy storage at time t, P dis (t) represents the discharge power of the energy storage at time t, C w represents the user's energy storage battery cost, β represents the proportion of energy storage batteries given to aggregators for dispatch, and DOD d Indicates the discharge depth of the energy storage battery.

[0024] As a preferred solution of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in the present invention, when the distributed energy storage is connected to the power grid, the constraint conditions are set, which are expressed as:

[0025] P grid (t)+P dis (t)+P dist (t) = P load (t)+P chr (t)

[0026] SOC i min ≤SOC i ≤SOC i max

[0027] SOC0=SOC 24

[0028] P i chr P i dis =0

[0029] Among them, P res (t) represents the purchase of electricity from the grid at time t, P dist (t) represents the distributed energy generation power, P load (t) represents the load size of the user at time t, P chr (t) represents the charging power of the energy storage at time t, P dis (t) represents the discharge power of the energy storage at time t, SOC i min and SOC i max They are the minimum and maximum values ​​of the battery state, SOC0 represents the initial capacity of energy storage, SOC 24 Indicates the final charge after charging and discharging.

[0030] As a preferred solution of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in the present invention, wherein: the improved particle swarm algorithm is expressed as:

[0031]

[0032] in, represents the speed of the i-th particle in the d-dimensional space at the k-th iteration, ω is the weight coefficient, c is the learning factor, and r is a random number between (0,1). represents the individual extreme value in dimension d at the kth iteration, represents the global extreme value of the k-th iteration in dimension d, represents the position of the i-th particle in the d dimension at the k-th iteration, b is the deviation, which indicates the degree of deviation between a single particle and the optimal particle in the group, and fit i is the fitness of the current particle, fit gbest is the fitness of the optimal particle, fit m is the dividing value between the fitness of superior particles and inferior particles, n is the coefficient, which ensures that ω is continuous at the function segmentation point, ω a is the upper limit of the inertia weight of the dominant particle.

[0033] As a preferred solution of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in the present invention, wherein: the improved particle swarm algorithm performs the solution including inputting the user's average daily load data and renewable energy data;

[0034] Initialize the population size, output the maximum number of iterations, learning factor, and assign a random initial speed and position to each particle;

[0035] For each particle evaluation, evaluate the fitness of the d-dimensional optimization function and find the individual optimal position and the global optimal position of each particle;

[0036] If the particle fitness is better than the individual extreme point, the individual best position of the particle will be updated to the position of the current particle; if the particle fitness is better than the global extreme point, the global best position of the particle will be updated to the position of the current particle;

[0037] Update the particle's speed and position, and determine whether it exceeds the boundary value after the update. If it exceeds the boundary value, take its upper or lower limit;

[0038] The iteration is terminated when the global fitness is satisfied or the maximum number of iterations is reached.

[0039] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm are implemented as described above.

[0040] A computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm as described above.

[0041] Beneficial effects of the present invention: The distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm provided by the present invention simulates group behavior through the particle swarm algorithm, and can find the global optimal solution or a near-optimal solution in a complex search space, so it can more effectively optimize the dispatching strategy of the distributed energy storage equipment to maximize the profit of the aggregator or the effect of participating in demand response transactions. The improved particle swarm algorithm usually has better adaptability and robustness, and can handle nonlinear, non-convex and dynamically changing optimization problems. It is suitable for actual demand response environments, because market prices and load demands may change. The optimization results show that the improved algorithm can improve the degree of optimization and maximize benefits in terms of results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 An overall flow chart of a distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm provided for one embodiment of the present invention.

[0044] Figure 2 A time-of-use electricity price curve diagram of a distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm provided in one embodiment of the present invention.

[0045] Figure 3 An improved particle swarm algorithm flow chart of a distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm provided for one embodiment of the present invention.

[0046] Figure 4 A diagram of energy storage charge state changes in each time period of a distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm provided in one embodiment of the present invention.

[0047] Figure 5 A total net load diagram before and after algorithm improvement of a distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Example 1, reference Figure 1-Figure 3 , which is an embodiment of the present invention, provides a distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm, including:

[0051] Step (1): Construct an operating model for distributed energy storage aggregators to participate in demand response. Distributed energy storage aggregators determine the actual response of energy storage users by collecting electricity consumption information, and reward or punish them in profit distribution according to the effect of demand response. Since users have different response capabilities under different incentive electricity prices, aggregators need to negotiate with energy storage users to determine the response compensation price. Aggregators also need to formulate penalty rules to limit the default behavior of energy storage users during the contract period to avoid excessive defaults and improve the economic efficiency of market participation and the reliability of power system operation. If the energy storage user fails to respond to the regulation instruction in accordance with the contract, the aggregator can charge a penalty fee to protect its own economic benefits. In the example, there are 50 distributed energy storage users, with individual installed capacity of 15kW / 30kWh and an initial battery state of charge of 0.1.

[0052] Aggregators cooperate with the power grid on behalf of group users to obtain preferential bulk time-of-use electricity prices and set internal electricity prices accordingly. The specific prices are as follows: Figure 2 The internal electricity price setting is subject to the following constraints:

[0053]

[0054] in, The grid-connected electricity price for distributed energy; The prices for internal power purchase and sales set by energy storage aggregators for users; The price at which individual users purchase electricity from the power grid.

[0055] Step (2): Construct a distributed energy storage model. The energy storage capacity is used to evaluate the energy storage capacity of the energy storage battery, while the state of charge of the distributed energy storage battery indicates the proportion of the remaining battery power to its total capacity under specific conditions. Since the primary purpose of users installing energy storage systems is to meet their own electricity needs, the state of charge level becomes an important factor that restricts users from scheduling charging and discharging and participating in the operation of the power system. The state of charge SOC calculation formula of distributed energy storage is as follows:

[0056]

[0057] Among them, E i,S is the remaining capacity of the battery of distributed energy storage i; E i,E The rated capacity of the battery for distributed energy storage i is 30kWh.

[0058]

[0059] Among them, SOC i (t) represents the battery energy state of the distributed energy storage battery at time t; P i chr / dis represents the charging and discharging power of energy storage battery i at time t; η chr / dis The charge and discharge efficiency of the battery is taken as 0.9.

[0060] Energy storage batteries will gradually degrade during use, and the charge and discharge cycle will affect their lifespan. In order to extend the service life of energy storage batteries, try to avoid deep discharge (DOD). The actual cycle number of energy storage batteries is calculated as follows:

[0061]

[0062] Among them, N f Indicates the number of cycles of the energy storage battery; N r The rated number of energy storage discharges is 3000; DOD r The rated discharge depth is 0.5; DOD a It indicates the discharge depth of the energy storage battery in actual use; δ1 and δ2 are the battery deep cycle discharge coefficients, which are 0.19 and 1.69 respectively.

[0063] Step (3): Establish the objective function based on the maximum daily revenue of the load aggregator

[0064] Aggregators' profits mainly come from electricity price arbitrage and compensation for proxy energy storage users participating in grid demand response. First, proxy users purchase the electricity required by energy storage users in the wholesale market, and then sell it to the grid in the retail market to earn the price difference. Second, proxy energy storage users participate in power system demand response and get compensation by reducing peak load or reducing operating costs.

[0065] maxF=F1-F2

[0066] Among them, F1 represents the profit obtained by the aggregator through electricity price arbitrage; F2 represents the compensation cost of battery loss.

[0067] Under the current time-of-use electricity price system, aggregators purchase electricity from the grid and store it when the electricity price is low; when the electricity price is high, the energy storage device releases electricity to save electricity bills or sells electricity to the grid. The benefits that aggregators obtain through electricity price arbitrage are as follows:

[0068]

[0069] Where n represents the number of individual energy storage users; α is the profit sharing ratio between the aggregator and the energy storage user, which is 2.5%; P chr (t) represents the charging power of the energy storage at time t; P dis (t) represents the discharge power of the energy storage at time t.

[0070] Since energy storage devices will cause battery loss and capacity reduction during the charging and discharging process, when calling energy storage resources, it is not possible to charge and discharge frequently within a day to maximize benefits. It is also necessary to consider the life loss of the energy storage user's battery and bear the corresponding battery loss costs to ensure the economic efficiency of the user's energy storage equipment. The calculation formula for the battery loss fee of energy storage users by aggregators is as follows:

[0071]

[0072] Among them, C w The cost of energy storage battery for users is 100,000 yuan; β represents the proportion of energy storage battery used by aggregators for dispatch; DOD d Indicates the discharge depth of the energy storage battery.

[0073] When distributed energy storage is connected to the power grid, it needs to meet the power constraints of the power system:

[0074] P res (t) = P load (t)+P chr (t)-P dis (t)

[0075] Among them, P res (t) represents the load response of the aggregator at time t; P load (t) represents the load size of the user at time t; P chr (t) represents the charging power of the energy storage at time t; P dis (t) represents the discharge power of the energy storage at time t.

[0076] Considering the service life of energy storage, the battery power is constrained as follows during optimal scheduling:

[0077]

[0078] Among them, SOC i min and SOC i max They are the minimum and maximum values ​​of the battery status respectively; SOC0 represents the initial capacity of energy storage, SOC 24 Indicates the final charge after charging and discharging.

[0079] Distributed storage energy cannot be charged and discharged at the same time. The charging and discharging constraints are as follows:

[0080] P i chr P i dis =0

[0081] Step (4): The solution flow chart of the particle swarm algorithm is as follows: Figure 3 , the formula requirements are as follows:

[0082]

[0083] in, represents the speed of the i-th particle in the d-dimension at the k-th iteration; ω is the weight coefficient; c is the learning factor; r is a random number between (0,1); represents the individual extreme value in dimension d at the kth iteration; represents the global extreme value of the k-th iteration in dimension d; represents the position of the i-th particle in the d dimension at the k-th iteration.

[0084] Embodiment 2, an embodiment of the present invention, is different from the first two embodiments in that:

[0085] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0086] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0087] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0088] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0089] Example 3, reference Figure 4-Figure 5 , which is an embodiment of the present invention, provides a distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0090] In order to improve the optimization ability of the algorithm, we select particles with higher fitness as inferior particles according to the fitness ratio, and use the dynamic calculation inertia weight method. The particle algorithm parameters are shown in Table 1. The specific calculation formula is as follows:

[0091]

[0092]

[0093] Among them, b is the deviation, which indicates the degree of deviation between a single particle and the optimal particle of the group; fit i is the fitness of the current particle; fit gbest is the fitness of the optimal particle; fit m is the dividing value between the fitness of superior particles and inferior particles; n is the coefficient, which ensures that ω is continuous at the function segmentation point; ω a is the upper limit of the inertia weight of the dominant particle. The data comparison before and after optimization is shown in Table 2.

[0094] Table 1 Particle swarm algorithm parameters

[0095]

[0096] Table 2 Comparison of benefits before and after improvement

[0097]

[0098] The battery charging power and state of charge are as follows: Figure 4At 1-7 o'clock, the electricity price is low and the user load is small, so the battery is charged; at 8-12 o'clock, the electricity price increases due to the increase in load, and the energy storage battery is discharged; at 13-17 o'clock, the user's electricity consumption is slightly reduced, and the energy storage battery is charged in order to sell it at a high price during the peak load period of 18-22 o'clock to make a profit.

[0099] Figure 5 This is a comparison chart of the total net load before and after the algorithm improvement. At 1-7 o'clock, due to the low electricity price, the improved algorithm further increases the energy storage at this time; due to the increase in electricity prices, at 8-15 o'clock, the purchase of electricity from the grid decreases and the sale of electricity increases; at 16-24 o'clock, due to renewable energy power generation, the overall energy storage volume is larger.

[0100] In general, the optimization scheme changes the net load of the system to varying degrees in all time periods, and is able to balance the system load, reduce peak load and alleviate load in different time periods.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed energy storage aggregator sharing transaction method based on an improved particle swarm algorithm, characterized in that: include: Establishing an operating model for distributed energy storage aggregators to participate in demand response; Consider the relationship between the number of battery charge and discharge times and actual capacity to establish an energy storage model; Considering the power balance constraints of the power grid and the battery power constraints, an objective function is established to maximize the daily revenue of the load aggregator. An improved particle swarm algorithm is used for solving the problem.

2. The distributed energy storage aggregator sharing transaction method based on improved particle swarm algorithm according to claim 1, characterized in that: The operation model includes obtaining batch time-of-use electricity price discounts and setting internal electricity prices; The internal electricity price satisfies the constraint condition, which is expressed as: in, is the grid-connected electricity price of distributed energy, and are the internal electricity purchase and sales prices set by energy storage aggregators for users. The price at which individual users purchase electricity from the power grid.

3. The distributed energy storage aggregator sharing transaction method based on improved particle swarm algorithm as claimed in claim 2, characterized in that: The relationship between the number of charge and discharge times and the actual capacity is expressed as, Among them, N f Indicates the number of cycles of the energy storage battery, N r DOD is the rated number of energy storage discharges r Indicates rated depth of discharge, DOD a Indicates the discharge depth of the energy storage battery in actual use. δ1 and δ2 are both the battery deep cycle discharge coefficients.

4. The distributed energy storage aggregator sharing transaction method based on improved particle swarm algorithm as claimed in claim 3 is characterized by: The energy storage model is expressed as, Among them, SOC i (t) represents the battery energy state of the distributed energy storage battery at time t; represents the charging and discharging power of energy storage battery i at time t; η chr / dis It is the charge and discharge efficiency of the battery.

5. The distributed energy storage aggregator sharing transaction method based on improved particle swarm algorithm as claimed in claim 4 is characterized by: The objective function is expressed as, maxF=F1-F2 Among them, F1 represents the profit obtained by the aggregator through electricity price arbitrage, F2 represents the compensation cost of battery loss, n represents the number of energy storage individuals of the dispatching user, α represents the profit distribution ratio between the aggregator and the energy storage user, and P chr (t) represents the charging power of the energy storage at time t, P dis (t) represents the discharge power of the energy storage at time t, C w represents the user's energy storage battery cost, β represents the proportion of energy storage batteries given to aggregators for dispatch, and DOD d Indicates the discharge depth of the energy storage battery.

6. The distributed energy storage aggregator sharing transaction method based on improved particle swarm algorithm as claimed in claim 5 is characterized by: When distributed energy storage is connected to the grid, constraints are set, expressed as: P grid (t)+P dis (t)+P dist (t)=P load (t)+P chr (t) SOC0=SOC 24 Among them, P res (t) represents the purchase of electricity from the grid at time t, P dist (t) represents the distributed energy generation power, P load (t) represents the load size of the user at time t, P chr (t) represents the charging power of the energy storage at time t, P dis (t) represents the discharge power of the energy storage at time t, and They are the minimum and maximum values ​​of the battery state, SOC0 represents the initial capacity of energy storage, SOC 24 Indicates the final charge after charging and discharging.

7. The distributed energy storage aggregator sharing transaction method based on improved particle swarm algorithm as claimed in claim 6, characterized in that: The improved particle swarm algorithm is expressed as: in, represents the speed of the i-th particle in the d-dimensional space at the k-th iteration, ω is the weight coefficient, c is the learning factor, and r is a random number between (0,1). represents the individual extreme value in dimension d at the kth iteration, represents the global extreme value of the k-th iteration in dimension d, represents the position of the i-th particle in the d dimension at the k-th iteration, b is the deviation, which indicates the degree of deviation between a single particle and the optimal particle in the group, and fit i is the fitness of the current particle, fit gbest is the fitness of the optimal particle, fit m is the dividing value between the fitness of superior particles and inferior particles, n is the coefficient, which ensures that ω is continuous at the function segmentation point, ω a is the upper limit of the inertia weight of the dominant particle.

8. The distributed energy storage aggregator sharing transaction method based on improved particle swarm algorithm as claimed in claim 7, characterized in that: The improved particle swarm algorithm for solving the problem includes inputting average daily load data of users and renewable energy data; Initialize the population size, output the maximum number of iterations, learning factor, and assign a random initial speed and position to each particle; For each particle evaluation, evaluate the fitness of the d-dimensional optimization function and find the individual optimal position and the global optimal position of each particle; If the particle fitness is better than the individual extreme point, the individual best position of the particle will be updated to the position of the current particle; if the particle fitness is better than the global extreme point, the global best position of the particle will be updated to the position of the current particle; Update the particle's speed and position, and determine whether it exceeds the boundary value after the update. If it exceeds the boundary value, take its upper or lower limit; The iteration is terminated when the global fitness is satisfied or the maximum number of iterations is reached.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed energy storage aggregator sharing transaction method based on the improved particle swarm algorithm described in any one of claims 1 to 8 are implemented.

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