CN118868186A - A photovoltaic energy storage coordinated control system based on retired battery reorganization secondary energy storage technology - Google Patents

Abstract

The present invention discloses a photovoltaic and storage coordinated control system based on the technology of secondary energy storage of reorganized retired batteries. The system is based on new technologies such as cloud computing, secondary energy storage of reorganized retired batteries and photovoltaic and storage coordination to construct a photovoltaic and storage coordinated control system. Retired batteries are reorganized and SOH is estimated using a variety of methods. At the same time, a secondary energy storage economic model is established to calculate the economic benefits of the utilization of secondary energy storage of reorganized retired batteries. Through the cloud platform and combined with the improved crown porcupine optimization algorithm (CPO), data collection and data analysis are performed on SOH data and the economic benefits of the utilization of secondary energy storage of reorganized retired batteries. Iterative optimization is used to obtain the optimal operation plan of the system, and the secondary energy storage of reorganized retired batteries is used as the power generation module in the photovoltaic and storage coordinated system to drive the photovoltaic and storage coordinated operation. At the same time, the photovoltaic and storage coordinated system transmits the operation data to the cloud platform module in real time, so as to maximize the economic benefits of the photovoltaic and storage coordinated system utilizing the secondary energy storage of reorganized retired batteries.

Description

A photovoltaic energy storage coordinated control system based on retired battery reorganization secondary energy storage technology

Technical Field

The present invention relates to a photovoltaic storage coordinated control system based on retired battery reorganization secondary energy storage technology, belonging to the field of retired battery reorganization and cloud computing.

Background Art

First of all, the cascade utilization of retired batteries is a technology that has gradually emerged in recent years with the popularization of products such as electric vehicles. During the life cycle of products such as electric vehicles, battery performance will gradually decline. When the battery performance declines to a certain extent, it is no longer suitable for the original application scenarios, but these batteries still have a certain energy storage capacity. Therefore, through necessary inspection, classification, repair or reorganization, these retired batteries can be reused in other fields, such as energy storage systems. This cascade utilization method can not only effectively reduce resource waste, but also reduce the cost of energy storage systems, which has important economic and environmental significance.

Secondly, the development of energy storage technology is an important factor in promoting the development of photovoltaic and energy storage coordinated control systems. With the rapid development of renewable energy such as solar energy, how to effectively store and utilize these energies has become an important issue. Energy storage technologies include many types, such as electrochemical energy storage, compressed air energy storage, flywheel energy storage, etc. Among them, electrochemical energy storage technology has become one of the most widely used energy storage technologies due to its advantages such as high energy density and fast response speed. The cascade utilization of retired batteries is an important direction in electrochemical energy storage technology.

Finally, photovoltaic-storage coordinated control technology is a key technology that combines power generation and energy storage technologies to achieve efficient utilization and stable supply of energy. In photovoltaic-storage coordinated power generation systems, due to the instability of solar energy resources and the volatility of load demand, traditional photovoltaic-storage coordinated power generation systems have certain limitations. Therefore, there is an urgent need for a photovoltaic-storage coordinated control system based on retired battery reorganization secondary energy storage technology to achieve the coordinated work of the three links of power generation, energy storage and charging through intelligent control technology, which can maximize energy utilization efficiency, reduce energy waste and achieve sustainable development.

Summary of the invention

Purpose of the invention: In view of the problem of unstable power generation in the existing photovoltaic and energy storage cooperative system, the present invention provides a photovoltaic and energy storage cooperative control system based on the secondary energy storage technology of reorganizing retired batteries, which improves the stable power generation for the photovoltaic and energy storage cooperative control technology, thereby realizing the coordinated work of the three links of system power generation, energy storage and charging, which can maximize the energy utilization efficiency, reduce energy waste and achieve sustainable development.

Technical solution: The present invention discloses a photovoltaic storage coordinated control system based on retired battery reorganization secondary energy storage technology, including a retired battery reorganization module, a SOH estimation module, a secondary energy storage economic simulation module, a photovoltaic storage coordination module and a cloud platform module;

The retired battery reorganization module recycles and reorganizes retired batteries, estimates the SOH of reorganized batteries, and combines batteries with similar SOH values;

The secondary energy storage economic simulation module establishes a secondary energy storage economic model, simulates and calculates the economic benefits of secondary energy storage of retired battery reorganization, takes the lowest total system cost as the objective function, collects and analyzes the SOH data of the reorganized batteries and the economic benefits of secondary energy storage of retired batteries through the cloud platform module, combines the crown porcupine optimization algorithm, iteratively optimizes and obtains the optimal operation plan of the system and drives the operation of the photovoltaic storage coordination module. At the same time, the photovoltaic storage coordination module feeds back the operation data to the cloud platform module in real time to realize real-time regulation of the photovoltaic storage coordination system.

Furthermore, the retired battery reorganization module includes recycling classification, disassembly, screening and reorganization, performance testing, residual energy testing and performance verification. It adopts a degradation processing method and an equal capacity-based reorganization strategy, and groups the batteries according to every 5% change to balance the number of batteries in each group. The retired power batteries are charged and discharged in order of their available capacity.

Furthermore, the SOH estimation module estimates the battery SOH value according to five methods: table calculation based on battery cycle number, calculation based on charging capacity, direct discharge method, equivalent circuit model, and data driven method, and takes the average value after removing the maximum and minimum values.

Furthermore, the total system cost of the secondary energy storage economic model includes recovery cost, equipment cost, integration cost, replacement cost and operation and maintenance cost:

O＝C ₁ +C ₂ +C ₃ +C ₄ +C ₅

In the formula, O is the total system cost, _C1 is the recovery cost, _C2 is the equipment cost, _C3 is the integration cost, _C4 is the replacement cost, and _C5 is the operation and maintenance cost;

_C1 ＝C _B · _EN1

_C2 ＝ _CP · _PN1 + _CM · _EN1

_C3 ＝ _Cs · _EN1

C ₄ =n ₁ ·(C _B +C _M )·E _N1

_C5 ＝ _CW · _EN1

In the formula, _CB represents the unit price of recycled batteries; _EN1 represents the recycled rated capacity; _CP represents the unit price of power converters; _CM represents the unit price of management systems; _PN1 represents the recycled rated power; _CS represents the unit battery integration cost; M represents the project cycle, T represents the number of operating days per year, m represents the number of cycles per day, k represents the number of remaining cycles of the power battery, _n1 represents the number of replacements; and _CV represents the annual operation and maintenance unit price.

Furthermore, the crown porcupine optimization algorithm CPO is used to iteratively optimize the optimal operation plan of the system, which specifically includes the following steps:

Step 1: Initialization stage, the calculation formula for initializing the position of the crested porcupine population is as follows:

O _i = L + r × (UL) i = 1, 2 ..., N

Where O _i is the position of individual i, that is, the system strategy generated by initialization, that is, the relevant cost parameter corresponding to the initial system cost, L and U are the lower and upper limits of the search range, r is a random number between 0 and 1, and N is the number of individuals;

Step 2: Cyclic population reduction phase: Using the cyclic population reduction technique CPR, some CPs are obtained from the population during the optimization process to speed up the convergence, and they are reintroduced into the population to improve the population diversity and avoid falling into local minima. The loop is based on the loop variable T to determine the number of times the process is executed during the optimization process. Its mathematical expression is as follows:

Where T is the variable that determines the number of loops, x is the current function evaluation, % represents the remainder or modulo operator, T _max is the maximum number of function evaluations, and N _min is the minimum number of individuals in the newly generated population, so the population size cannot be less than N _min ;

Step 3: In the first defense stage, when the recovery cost is fixed, find the local optimal solution with the lowest system cost. The mathematical expression is as follows:

In the formula, To evaluate the optimal solution of function x, is the vector generated between the current CP and the CP randomly selected from the population in the first defense stage, indicating the position of the predator at iteration t, that is, seeking a better solution with the lowest cost, τ ₁ is a random number based on the normal distribution, and τ ₂ is a random number between 0 and 1; It is the local optimal solution with the lowest cost after t+1 iterations when the recovery cost of the first defense stage is fixed;

In the formula, r is a random number between [1, N], It is the local optimal solution with the lowest cost after t iterations when the recovery cost of the first defense stage is fixed. The maximum value of the equipment cost within the allowable error range for t iterations when the recovery cost of the first defense stage is fixed;

Step 4: In the second defense stage, when the recovery cost is fixed and the equipment cost is the lowest, find the local optimal solution with the lowest integration cost. The mathematical expression is as follows:

Where r1 and r2 are random numbers between [1, N], τ ₃ is a random number between 0 and 1, It is the local optimal solution with the lowest integrated cost after t+1 iterations in the second defense phase when the recovery cost is fixed and the equipment cost is the lowest; In the second defense stage, when the recovery cost is fixed and the equipment cost is the lowest, the maximum value of the integration cost of iteration t within the allowable error range is: In the second defense stage, when the recovery cost is fixed and the equipment cost is the lowest, the integration cost of iteration t is the minimum value within the allowable error range. is the optimal solution with fixed recovery cost and lowest equipment cost in the second defense phase, and U is the upper limit of the search range;

Step 5: In the third defense stage, when the recovery cost is fixed, the equipment cost is the lowest, and the integration cost is the lowest, find the local optimal solution with the lowest replacement cost. The mathematical expression is as follows:

Where r3 is a random number between [1, N], δ is a parameter used to control the search direction, is the position of the i-th individual at iteration t of the third defense phase, They represent the maximum and minimum values of the replacement cost within the allowable error range when the recovery cost is fixed, the equipment cost is the lowest, and the integration cost is the lowest in the third defense stage. _γt is the defense factor. is the odor diffusion factor, It is the local optimal solution with the lowest replacement cost at iteration t+1 in the third defense stage under the condition of fixed recovery cost, lowest equipment cost and lowest integration cost;

In the formula, is the objective function value of the ith individual at iteration t, ε is a small value to avoid division by zero, rand is a variable containing randomly generated numbers between 0 and 1, t is the current iteration number, and t _max is the maximum iteration number;

Step 6: In the fourth defense stage, when the recovery cost is fixed, the equipment cost is the lowest, the integration cost is the lowest, and the replacement cost is the lowest, find the local optimal solution with the lowest operation and maintenance cost. The mathematical expression is as follows:

In the formula, is the position of the ith individual at iteration t, represents the predator at that position, α is the convergence speed factor, τ ₄ is a random value between 0 and 1, is the average force that affects the CP of the i-th predator, that is, the local optimal solution with the lowest operation and maintenance cost after t iterations in the fourth defense stage when the recovery cost is fixed, the equipment cost is the lowest, the integration cost is the lowest, and the replacement cost is the lowest. It is the local optimal solution with the lowest operation and maintenance cost at iteration t+1 in the fourth defense stage when the recovery cost is fixed, the equipment cost is the lowest, the integration cost is the lowest, and the replacement cost is the lowest, that is, the global optimal solution with the lowest total system cost;

In the formula, _mi is the mass of the ith individual at iteration t, f() represents the objective function, is the final velocity of the ith individual at the next iteration t+1 and is allocated based on the selection of random solutions from the current population, is the speed of the ith individual at iteration t, t is the number of current iterations, τ ₆ is a vector of random values generated between 0 and 1, represents the position of the kth individual at iteration t, k = 1, 2, 3, ..., N;

The final output That is, it is the optimal operation plan of the system in the iteration results, that is, the system operation plan with the lowest recovery cost, the lowest equipment cost, the lowest integration cost, the lowest replacement cost and the lowest operation and maintenance cost.

Furthermore, the photovoltaic storage collaborative module includes a power generation module, an energy storage module, an energy management module, a control module and a monitoring and communication module. The secondary energy storage reorganized from retired batteries is utilized as a power generation module, and retired batteries are reused. The corresponding power generation module operation mode under the lowest system operating cost is determined according to the system operation plan optimized by the secondary energy storage economic simulation module.

Beneficial effects:

1. Compared with the traditional secondary utilization system of retired batteries, the present invention reorganizes retired batteries and combines batteries with similar SOH values. While ensuring the capacity of the reorganized batteries, it maximizes the battery life, improves the utilization rate of the reorganized batteries, and makes full use of the retired batteries.

2. Compared with the traditional photovoltaic storage coordinated system, the present invention utilizes retired battery reorganization secondary energy storage technology to replace traditional photovoltaic power generation, combined with the improved crown porcupine optimization algorithm (CPO), and optimizes the equipment cost, integration cost, replacement cost and operation and maintenance cost under the condition of fixed recovery cost, and finally obtains the global optimal solution with the lowest system cost, realizes stable power generation of the system, and improves the system operation efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a diagram showing the structure of the system equipment of the present invention;

Fig. 2 is a flow chart of the algorithm of the present invention;

Figure 3 is a comparison of the total system cost before and after optimization;

Figure 4 is a comparison chart of system operation benefits before and after optimization.

DETAILED DESCRIPTION

The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the protection scope of the present invention.

The present invention discloses a photovoltaic-storage coordinated control system based on retired battery reorganization secondary energy storage technology, which mainly includes a retired battery reorganization module, a SOH estimation module, a secondary energy storage economic simulation module, a photovoltaic-storage coordinated module and a cloud platform module.

The system will recycle and reorganize retired batteries, estimate the SOH of the reorganized batteries, establish a secondary energy storage economic model, simulate and calculate the economic benefits of reorganizing retired batteries for secondary energy storage, collect and analyze the SOH data of the reorganized batteries and the economic benefits of reorganizing retired batteries for secondary energy storage through the cloud platform module, and combine with the improved optimization algorithm to iteratively optimize and obtain the optimal operation plan of the system and drive the operation of the photovoltaic and storage collaborative module. At the same time, the photovoltaic and storage collaborative module will feed back the operation data to the cloud platform module in real time to realize real-time regulation of the photovoltaic and storage collaborative system.

The retired battery reorganization module mainly includes the steps of recycling classification, disassembly, screening and reorganization, performance testing, residual energy testing and performance verification. It adopts a degradation processing method and an equal capacity-based reorganization strategy, and groups the batteries according to every 5% change to make the number of batteries in each group as balanced as possible. The retired power batteries are charged and discharged in sequence according to their available capacity, which effectively reduces the number of times low-capacity retired batteries are used and increases their service life.

SOH is the abbreviation of battery health state, which is used to indicate the ratio of the current performance state of the battery to its performance in the original or new state. The SOH estimation module mainly estimates the battery SOH value by looking up the table according to the number of battery cycles, calculating according to the charging capacity, direct discharge method, equivalent circuit model and data-driven species method. It estimates SOH by using multiple methods to improve the accuracy of SOH estimation and takes the average value after removing the maximum and minimum values.

Obtain the SOH value of the battery, combine batteries with similar SOH values, and improve the utilization rate of the reorganized battery. When recycling batteries, the SOH value of the battery is fixed, and batteries with similar SOH values are combined to ensure the battery capacity and maximum utilization of the reorganized battery. This step is performed by the screening and reorganization function in the retired battery reorganization module, and the recycling cost is fixed.

The secondary energy storage economic simulation module establishes a secondary energy storage scenario model and collects and processes data such as recovery cost, equipment cost, integration cost, replacement cost and operation and maintenance cost to simulate and calculate the economic benefits of reorganizing retired batteries into secondary energy storage.

The photovoltaic and storage collaborative module mainly includes a power generation module, an energy storage module, an energy management module, a control module and a monitoring and communication module. The secondary energy storage reorganized from retired batteries is used as a power generation module, and retired batteries are reused. The corresponding power generation module operation mode under the lowest system operation cost is determined according to the system operation plan optimized by the secondary energy storage economic simulation module. After reaching the scrap stage, battery metal and other resources are recycled to improve the economy of the power battery throughout its life cycle.

The optimal operation strategy of the system, that is, the lowest total system cost, including the lowest recovery cost, the lowest equipment cost, the lowest integration cost, the lowest replacement cost and the lowest operation and maintenance cost, is controlled by the improved crown porcupine optimization algorithm (CPO), which mainly includes the following steps:

The optimal operation plan of the system is obtained by optimizing the objective function through the improved crown porcupine optimization algorithm (CPO). The objective function is the total cost of the system, including the lowest recovery cost, the lowest equipment cost, the lowest integration cost, the lowest replacement cost and the lowest operation and maintenance cost.

O＝C ₁ +C ₂ +C ₃ +C ₄ +C ₅

Where O is the total system cost, _C1 is the recovery cost, _C2 is the equipment cost, _C3 is the integration cost, _C4 is the replacement cost, and _C5 is the operation and maintenance cost.

_C1 ＝C _B · _EN1

_C2 ＝ _CP · _PN1 + _CM · _EN1

_C3 ＝ _Cs · _EN1

C ₄ =n ₁ ·(C _B +C _M )·E _N1

_C5 ＝ _CW · _EN1

In the formula, _CB represents the unit price of recycled batteries, in yuan/Wh; _EN1 represents the recycled rated capacity, in kWh; _CP represents the unit price of power converter, in yuan/Wh; _CM represents the unit price of management system, in yuan/Wh; _PN1 represents the recycled rated power, in kW; _CS represents the unit battery integration cost, in yuan/Wh; M represents the project cycle, T represents the number of operating days per year, m represents the number of cycles per day, k represents the remaining number of cycles of the power battery, _n1 represents the number of replacements; _GW represents the annual operation and maintenance unit price, in yuan/Wh.

Step 1: Initialization phase. In CPO, the calculation formula for initializing the position of the crested porcupine population is as follows:

O _i = L + r × (UL) i = 1, 2 ..., N

Where _Oi is the position of individual i, that is, any system strategy generated by initialization, L and U are the lower and upper limits of the search range, r is a random number between 0 and 1, and N is the number of individuals.

Step 2: Cyclic population reduction phase.

In order to speed up the convergence and maintain the diversity of the population, the cyclic population reduction technique (CPR) is used to obtain some CPs from the population during the optimization process to speed up the convergence and reintroduce them into the population, thereby improving the diversity of the population and avoiding falling into the local minimum. The loop is based on the loop variable T to determine the number of times the process is executed during the optimization process. Its mathematical expression is as follows:

Where T is the variable that determines the number of loops, x is the current function evaluation, % represents the remainder or modulo operator, T _max is the maximum number of function evaluations, and N _min is the minimum number of individuals in the newly generated population, so the population size cannot be less than N _min .

Step 3: First defense stage. Under the condition of fixed recovery cost, find the local optimal solution with the lowest equipment cost. Its mathematical expression is as follows:

In the formula, To evaluate the optimal solution of function t, is the vector generated between the current CP and the CP randomly selected from the population in the first defense phase, indicating the position of the predator at iteration t, that is, seeking a better solution with the lowest equipment cost, τ ₁ is a random number based on normal distribution, and τ ₂ is a random number between 0 and 1. At this time, It is the local optimal solution with the lowest equipment cost when the recovery cost of the first defense stage is fixed.

In the formula, r is a random number between [1, N], It is the local optimal solution with the lowest cost after t iterations when the recovery cost of the first defense stage is fixed. It is the maximum value of the equipment cost within the allowable error range for t iterations when the recovery cost of the first defense stage is fixed.

Step 4: Second defense stage. Under the condition that the recovery cost is fixed and the equipment cost is the lowest, find the local optimal solution with the lowest integration cost. Its mathematical expression is as follows:

Where r1 and r2 are random numbers between [1, N], τ ₃ is a random number between 0 and 1, It is the local optimal solution with the lowest integrated cost after t+1 iterations in the second defense phase when the recovery cost is fixed and the equipment cost is the lowest; In the second defense stage, when the recovery cost is fixed and the equipment cost is the lowest, the maximum value of the integration cost of iteration t within the allowable error range is: In the second defense stage, when the recovery cost is fixed and the equipment cost is the lowest, the integration cost of iteration t is the minimum value within the allowable error range. It is the optimal solution with fixed recovery cost and lowest equipment cost in the second defense stage, and U is the upper limit of the search range.

Step 5: The third defense stage. Under the condition of fixed recovery cost, lowest equipment cost and lowest integration cost, find the local optimal solution with the lowest replacement cost. Its mathematical expression is as follows:

Where ^r3 is a random number between [1, N], δ is a parameter used to control the search direction, _Oit is the position of the i-th individual at iteration t in the third defense phase, They represent the maximum and minimum values of the replacement cost within the allowable error range when the recovery cost is fixed, the equipment cost is the lowest, and the integration cost is the lowest in the third defense stage. _γt is the defense factor. is the odor diffusion factor, It is the local optimal solution with the lowest replacement cost at iteration t+1 in the third defense stage when the recovery cost is fixed, the equipment cost is lowest, and the integration cost is lowest.

In the formula, is the objective function value of the ith individual at iteration t, ε is a small value to avoid division by zero, rand is a variable containing randomly generated numbers between 0 and 1, t is the current iteration number, and t _max is the maximum iteration number.

Step 6: The fourth defense stage. Under the condition that the recovery cost is fixed, the equipment cost is the lowest, the integration cost is the lowest, and the replacement cost is the lowest, the local optimal solution with the lowest operation and maintenance cost is obtained. The mathematical expression is as follows:

In the formula, is the position of the ith individual at iteration t, represents the predator at that position, α is the convergence speed factor, τ ₄ is a random value between 0 and 1, is the average force that affects the CP of the i-th predator, that is, the local optimal solution with the lowest operation and maintenance cost after t iterations in the fourth defense stage when the recovery cost is fixed, the equipment cost is the lowest, the integration cost is the lowest, and the replacement cost is the lowest. For the fourth defense stage, when the recovery cost is fixed, the equipment cost is the lowest, the integration cost is the lowest, and the replacement cost is the lowest, the local optimal solution with the lowest operation and maintenance cost at iteration t+1, that is, the global optimal solution with the lowest total system cost.

In the formula, _mi is the mass of the i-th individual (predator) at iteration t, f() represents the objective function, is the final velocity of the ith individual at the next iteration t+1 and is allocated based on the selection of random solutions from the current population, is the speed of the ith individual at iteration t, t is the number of current iterations, τ ₆ is a vector of random values generated between 0 and 1, Represents the position of the kth individual at iteration t, k = 1, 2, 3, …, N.

The final output That is, it is the optimal operation plan of the system in the iteration results, that is, the system operation plan with the lowest recovery cost, the lowest equipment cost, the lowest integration cost, the lowest replacement cost and the lowest operation and maintenance cost.

The present invention discloses a photovoltaic and energy storage coordinated control system based on the retired battery reorganization secondary energy storage technology. The following experimental simulation is carried out for the system. The basic experimental environment mainly includes the hardware environment and the software environment. The hardware environment includes retired batteries, energy storage inverters and monitoring systems. A certain number of retired power batteries are selected. After classification, disassembly and screening, battery packs with similar capacity and good consistency are selected to form a power generation system for the experiment. The energy storage inverter adopts a multi-channel energy storage inverter with a DC/DC+DC/AC dual-stage structure to ensure that there is no parallel connection at the battery end to prevent the generation of circulating current between battery packs. The control system includes data acquisition and monitoring equipment, control algorithm development platform, etc., which are used to monitor the system operation status and the execution of control strategies in real time. The software environment includes modeling and simulation software, such as Matlab, Simulink, etc., which are used to build a simulation model of the photovoltaic and energy storage coordinated control system.

The selected comparative experiment is the pre-optimization experiment. The system adopts the traditional charging and discharging strategy, namely the photovoltaic-storage coordinated system. Photovoltaic panels, energy storage converters, inverters and other equipment are integrated into the photovoltaic-storage microgrid system, and connected to the data acquisition and monitoring system. Simulation experiments are carried out on the modeling and simulation software platform to simulate the system operation under different lighting conditions and load requirements, record the system's operating status and data, such as photovoltaic output power, system efficiency, system benefits, etc., and deploy them on the hardware platform for testing and verification.

The embodiment of the present invention is an optimized experiment. The system uses the improved crown porcupine optimization algorithm (CPO) to control the photovoltaic storage cooperative system, and uses the retired battery recombination technology to screen and reorganize the retired batteries to form a power generation system for the experiment, which replaces the unstable photovoltaic power generation system in the traditional photovoltaic storage cooperative system, that is, a photovoltaic storage cooperative control system based on the retired battery recombination secondary energy storage technology. Batteries with output power close to the photovoltaic output power are selected for experimental power generation. In the simulation module, a photovoltaic storage cooperative control system based on the retired battery recombination secondary energy storage technology optimized by the improved crown porcupine optimization algorithm (CPO) is constructed to simulate the operation of the system when the recombinant battery replaces photovoltaic power generation, record the system operation status and data, such as battery output power, battery charge and discharge status, system efficiency, system benefit, etc., and deploy it on the hardware platform for testing and verification.

Comparing the system cost and system benefit before and after optimization, see Figures 3 and 4. The system cost includes recovery cost, equipment cost, integration cost, replacement cost and operation and maintenance cost. The system benefit is the annual profit and loss status of the system or enterprise. The system cost, basic income and saved income after optimization are combined to form the benefit comparison difference. It can be seen from the experimental comparison before and after optimization that the optimized system of the present invention can greatly reduce costs, improve system benefits, increase battery life, and improve the utilization rate of recombinant batteries.

The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent transformation or modification made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. The optical storage collaborative regulation and control system based on the retired battery reorganization secondary energy storage technology is characterized by comprising a retired battery reorganization module, an SOH estimation module, a secondary energy storage economic simulation module, an optical storage collaborative module and a cloud platform module;

The retired battery reorganization module is used for recycling retired batteries, SOH estimation is carried out on the reorganized batteries, and batteries with similar SOH values are combined;

The system comprises a secondary energy storage economic simulation module, a secondary energy storage economic model, a cloud platform module, a system optimal operation scheme and an optical storage coordination module, wherein the secondary energy storage economic model is established, the economic benefit of secondary energy storage of recombination of retired batteries is simulated and calculated, SOH data of the recombination batteries and economic benefit of recombination of the secondary energy storage of the retired batteries are collected and analyzed through the cloud platform module, and the system optimal operation scheme is obtained through iterative optimization by combining with a porcupine optimization algorithm, and meanwhile, the optical storage coordination module feeds back operation data to the cloud platform module in real time, so that real-time regulation and control of the optical storage coordination system are realized.

2. The optical storage collaborative regulation and control system based on the retired battery reorganization secondary energy storage technology according to claim 1, further characterized in that the retired battery reorganization module comprises recycling classification, disassembly, screening reorganization, performance detection, residual energy detection and performance verification, a degradation processing method and an isovolumetric reorganization strategy are adopted, grouping is carried out according to 5% of each change, the quantity of each group of batteries is balanced, and charging and discharging are sequentially carried out according to the available capacity of retired power batteries.

3. The light-storage collaborative regulation and control system based on the retired battery recombination secondary energy storage technology according to claim 1, further characterized in that the SOH estimation module estimates the SOH value of the battery according to five methods of battery cycle number table lookup calculation, charge capacity calculation, direct discharge method, equivalent circuit model and data driving method, and takes an average value after removing the maximum value and the minimum value.

4. The optical storage collaborative regulation and control system based on retired battery reorganization secondary energy storage technology according to claim 1, further characterized in that the total system cost of the secondary energy storage economic model includes recovery cost, equipment cost, integration cost, replacement cost and operation and maintenance cost:

O＝C₁+C₂+C₃+C₄+C₅

Wherein O is the total cost of the system, C ₁ is the recovery cost, C ₂ is the equipment cost, C ₃ is the integration cost, C ₄ is the replacement cost, and C ₅ is the operation and maintenance cost;

C₁＝C_B·E_N1

C₂＝C_P·P_N1+C_M·E_N1

C₃＝C_s·E_N1

C₄＝n₁·(C_B+C_M)·E_N1

C₅＝C_W·E_N1

Wherein C _B represents a recovery battery unit price; e _N1 represents a recovery rated capacity; c _P denotes power converter unit price; c _M represents a management system unit price; p _N1 represents the recovery rated power; c _s represents the unit cell integration cost; m represents project period, T represents operation days per year, M represents cycle number per day, k represents residual cycle number of the power battery, and n ₁ represents replacement number; c _W represents annual operation maintenance unit price.

5. The optical storage collaborative regulation and control system based on the retired battery recombination secondary energy storage technology according to claim 4 is further characterized in that a system optimal operation scheme is obtained by iterative optimization through a porcupine optimization algorithm CPO, and the method specifically comprises the following steps:

step one: in the initialization stage, the calculation formula of the position of the initialized porcupine population is as follows:

O_i＝L+r×(U-L)i＝1,2...,N

Wherein O _i is the position of an individual i, namely, initializing a generated system strategy, namely, initializing a related cost parameter corresponding to the system cost, L and U are the lower limit and the upper limit of a search range respectively, r is a random number between 0 and 1, and N is the number of the individual;

step two: a cyclic population reduction stage: some CPR is obtained from the population during the optimization process using the cyclic population reduction technique CPR to increase the convergence rate and reintroduce them into the population to increase population diversity and avoid trapping local minima, the cyclic being based on the cyclic variable T to determine the number of times the process is performed during the optimization process, the mathematical expression of which is as follows:

Wherein T is a variable for determining the number of cycles, x is a current function evaluation,% represents a remainder or modulo operator, T _max is the maximum number of function evaluations, and N _min is the minimum number of individuals in the newly generated population, so the population size cannot be smaller than N _min;

step three: in the first defense stage, under the condition of fixed recovery cost, solving a local optimal solution with the lowest system cost, wherein the mathematical expression is as follows:

In the formula, In order to evaluate the optimal solution of the function x,For a vector generated between a current CP and a CP randomly selected from the population in the first defense phase, representing the position of the predator at iteration t, i.e. seeking a more optimal solution with the lowest cost, τ ₁ is a random number based on normal distribution, and τ ₂ is a random number between 0 and 1; Under the condition that the recovery cost is fixed in the first defense stage, iterating the local optimal solution with the lowest cost for t+1 times;

wherein r is a random number between [1, N ], In the case of fixed recovery costs for the first defense phase, iterating t times the least costly locally optimal solution,Under the condition that the recovery cost is fixed in the first defense stage, iterating the maximum value of the equipment cost for t times within the allowable error range;

Step four: in the second defense stage, under the condition of fixed recovery cost and lowest equipment cost, solving a local optimal solution with the lowest integration cost, wherein the mathematical expression is as follows:

Wherein r1 and r2 are random numbers between [1, N ], τ ₃ is a random number between 0 and 1, For the second defense stage, under the condition that the recovery cost is fixed and the equipment cost is lowest, iterating the local optimal solution with the lowest integration cost for t+1 times; In the case of a fixed recovery cost in the second defense phase and a minimum equipment cost, the integration cost for the iteration t times is the maximum value within the allowable error range, In the case of a fixed recovery cost in the second defense phase and a minimum equipment cost, the minimum integration cost for t iterations is within the allowable error range,The recovery cost is fixed for the second defense stage, the optimal solution with the lowest equipment cost is obtained, and U is the upper limit of the search range;

step five: in the third defense stage, under the conditions of fixed recovery cost, lowest equipment cost and lowest integration cost, solving a local optimal solution with the lowest replacement cost, wherein the mathematical expression is as follows:

Wherein r3 is a random number between [1, N ], delta is a parameter for controlling the search direction, For the position of the ith individual at iteration t of the third defense phase,Respectively represents the maximum value and the minimum value of the replacement cost within the allowable error range under the conditions of fixed recovery cost, lowest equipment cost and lowest integration cost in the third defense stage, gamma _t is a defense factor,As an odor diffusion factor, a natural odor is generated,The method is a local optimal solution with the lowest replacement cost in the iteration t+1 under the condition that the recovery cost is fixed, the equipment cost is lowest and the integration cost is lowest in the third defense stage;

In the formula, For the objective function value of the ith individual at iteration t, ε is a small value that avoids division by zero, rand is a variable that includes randomly generated numbers between 0 and 1, t is the current iteration number, and t _max is the maximum iteration number;

Step six: in the fourth defense stage, under the conditions of fixed recovery cost, lowest equipment cost, lowest integration cost and lowest replacement cost, a local optimal solution with the lowest operation and maintenance cost is obtained, and the mathematical expression is as follows:

In the formula, For the position of the ith individual at iteration t, representing predators at the position, alpha is a convergence speed factor, tau ₄ is a random value between 0 and 1, F _i ^t is an average force affecting CP of the ith predator, namely a local optimal solution with the lowest operation and maintenance cost for iteration t times under the conditions of fixed recovery cost, lowest equipment cost, lowest integration cost and lowest replacement cost in the fourth defense stage,The method is a local optimal solution with the lowest operation and maintenance cost in iteration t+1, namely a global optimal solution with the lowest total system cost under the conditions of fixed recovery cost, lowest equipment cost, lowest integration cost and lowest replacement cost in the fourth defense stage;

Where m _i is the mass of the ith individual at iteration t, f () represents the objective function, For the final speed of the ith individual at the next iteration t +1 and based on the selection of a random solution from the current population,For the phase time velocity of the ith individual at iteration t, t is the number of current iterations, τ ₆ is a vector comprising random values generated between 0 and 1,Representing the position of the kth individual at iteration t, k=1, 2,3, …, N;

Finally output The optimal operation scheme of the system in the iteration result is the system operation scheme with the lowest recovery cost, the lowest equipment cost, the lowest integration cost, the lowest replacement cost and the lowest operation and maintenance cost.

6. The optical storage collaborative regulation and control system based on the retired battery reorganization secondary energy storage technology according to claim 5, further characterized in that the optical storage collaborative module comprises a power generation module, an energy storage module, an energy management module, a control module and a monitoring communication module, the retired battery reorganization secondary energy storage is utilized as the power generation module, the retired battery is utilized for the second time, and the corresponding power generation module operation mode under the lowest system operation cost is determined according to the system operation scheme optimized by the secondary energy storage economic simulation module.