CN111146791B - An economical optimization control method for operation and maintenance of virtual supercapacitors - Google Patents

Abstract

The invention relates to an operation and maintenance economic optimization control method of a virtual super capacitor, which comprises the steps of establishing a conversion relation between the rotation kinetic energy of a controllable load and the charging and discharging energy of a direct-current capacitor energy storage element, virtualizing an asynchronous motor with the rotating speed regulation capacity into the super capacitor, comprehensively analyzing the load economic loss and the energy storage life depreciation yield in the virtual super capacitor control process, establishing an economic operation and maintenance evaluation model of the virtual super capacitor, solving by utilizing a particle swarm algorithm, providing a calculation basis for a virtual capacitance value, and realizing the economic and stable operation of a system.

Description

A kind of operation and maintenance economic optimization control method of virtual supercapacitor

technical field

The invention relates to the technical field of power grid control, in particular to an operation and maintenance economic optimization control method of a virtual supercapacitor.

Background technique

In the prior art, in order to reduce damage to the environment, more and more attention has been paid to new energy power generation. However, the intermittent and fluctuating wind power, photovoltaic and other new energy sources connected to the power grid will threaten the safe and stable operation of the power grid system. In order to improve the stability of power grid operation, energy storage devices that can stabilize unbalanced power have received a lot of attention. However, the investment in energy storage devices such as batteries and supercapacitors will inevitably increase the initial construction cost and later operation and maintenance costs of the power grid.

In a DC microgrid, the load side actively participates in system power regulation, which can theoretically significantly reduce the capacity configuration of energy storage devices, and their life-span aging and loss during charging and discharging, saving operation and maintenance costs, and becoming an economical way to improve system operation. one of the feasible options. At present, the management of the load demand side mainly focuses on changing the power of the temperature-controlled load to cooperate with the traditional power supply to maintain the dynamic balance of the system power, without considering the loss of revenue after load adjustment. In addition, the operating cost of energy storage after changing the charging and discharging method is rarely involved in existing research.

SUMMARY OF THE INVENTION

In view of this, the object of the present invention is to overcome the deficiencies of the prior art and provide an economically optimized control method for operation and maintenance of a virtual supercapacitor.

To achieve the above object, the present invention adopts the following technical solutions:

A method for economically optimized control of operation and maintenance of a virtual supercapacitor, wherein the power grid system includes a storage battery, a supercapacitor and a controllable load, the method comprising:

According to the conversion relationship between the rotational kinetic energy of the controllable load and the charging and discharging energy of the supercapacitor, the expression of the virtual capacitance value and the expression of the virtual state of charge are determined;

According to the expression of the virtual capacitance value and the expression of the virtual state of charge, the load economic loss model under the control of the virtual capacitance is established;

Determining a system operation and maintenance economic evaluation model including a virtual capacitance value according to the load economic loss model;

Based on the particle swarm optimization algorithm, the system operation and maintenance economic evaluation model is solved to obtain the virtual capacitance value corresponding to the maximum benefit;

The operating condition of the controllable load is controlled according to the virtual capacitance value.

Optionally, the expression of the virtual capacitance value and the expression of the virtual state of charge are determined according to the conversion relationship between the rotational kinetic energy of the controllable load and the charging and discharging energy of the supercapacitor, specifically including:

Establish the energy relationship between mechanical kinetic energy and capacitor energy storage as

Among them, E _Cvir is the virtual energy of the virtual energy storage device; C _vir is the virtual capacitance value of the virtual energy storage device; J _s , ω _r , p _n are the rotor moment of inertia, electrical angular velocity and number of pole pairs of the motor; U _C is the supercapacitor voltage;

The virtual capacitance C _vir (t) of period t can be expressed as

Based on the energy angle, the state of charge SOC _vir of the virtual energy storage device is defined as

Optionally, the expression of the system operation and maintenance economic evaluation model is:

In the formula: F is the total economic benefit of the system; F _in (t) is the system benefit in the t period; F _out (t) is the life loss of the battery in the t period.

Optionally, use the following formula to calculate the life loss of the battery during the period t of the battery:

Among them, W _total is the purchase price of the battery, SOH(t) is the state of health value of the battery at time t, which is defined as the ratio of the maximum available capacity E _Bmax (t) of the battery to the rated capacity E _Bnom in the period t; SOH _min is the battery life at the end of time health status value.

Optionally, the maximum available capacity E _Bmax (t) of the storage battery during the period t is calculated by the following formula:

Among them, E _B is the available capacity of the battery.

Optionally, the expression of the load economic loss model is:

U_C为超级电容器电压。Among them, F _in (t) is the load gain F _in (t), C _vir (t) is the virtual capacitance value,

U _C is the supercapacitor voltage.

Optionally, the system operation and maintenance economic evaluation model is solved based on the particle swarm optimization algorithm, specifically including:

Step 1: Generate an initial group, set the position of the particle and the update speed, the position of the particle i is the virtual capacitance value of the period t: C _vir (t) _i ;

Step: 2: Calculate the system income corresponding to C _vir (t) _i according to the objective function;

Step 3: For each particle, judge whether the system income of C _vir (t) _i is greater than its historical maximum system income, if so, update C _vir (t) _i ;

Step 4: judge whether the system income corresponding to the C _vir (t) _i of each particle is greater than the system income of the global optimal position, if so, update the global optimal position C _vir (t);

Step 5: update the position C _vir (t) _i and velocity of each particle;

Step 6: If the convergence accuracy is satisfied or the number of iterations is reached, stop the algorithm and output the virtual capacitance value corresponding to the highest system total revenue, otherwise return to step 2 and continue the iteration.

Technical effect of the present invention is as follows:

1. The present invention enables the load to have operating parameters similar to capacitors by introducing virtual capacitance and virtual state of charge parameters, so that the DC power grid can obtain additional energy storage backup from the load side.

2. The present invention evaluates the load loss after realizing the virtual capacitor and the battery damage cost, and proposes an economical operation method when the controllable load is equivalent to a supercapacitor energy storage device participating in system power regulation, which can not only improve system reliability , and can reduce system operation and maintenance costs.

3. The system operation and maintenance economic optimization control method with virtual capacitors proposed by the present invention can provide calculation basis for the optimal value of virtual capacitors, effectively reduce the investment of energy storage devices, and improve the economical efficiency of system operation.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

Fig. 1 is a schematic flow chart of an operation and maintenance economic optimization control method for a virtual supercapacitor of the present invention;

Fig. 2 is the power grid system simulation topological diagram of the present invention;

Fig. 3 is the virtual capacitance value in different periods obtained by the present invention;

Fig. 4 is a wind power prediction data map;

Figure 5 is a graph showing the change curve of load power and income before/after putting into virtual energy storage;

Fig. 6 is the change curve of PC, SOC _C , _{P B} , SOC _B and battery life loss cost before/after putting into virtual energy storage;

Figure 7 is a comparison chart of total system revenue before and after virtual energy storage is put into use.

Detailed ways

In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other implementations obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

The controllability of the virtual capacitor can become a feasible solution to improve the system operation capacity on the load side, but the adjustment of the virtual capacitor will affect the load income. Therefore, there must be an optimal control problem of virtual capacitance in the running process. According to the virtual capacitance model derived from the controllable load, the invention considers the load income and battery life loss, establishes a system economic operation and maintenance evaluation model including the virtual capacitance, and analyzes the relationship between the virtual capacitance value and the total income of the system. Comprehensively analyze the economic loss in the controllable load speed regulation process and the energy storage loss income in the virtual energy storage control process, use the particle swarm optimization algorithm to solve the economic operation and maintenance evaluation model, provide calculation basis for the virtual capacitance value, and propose a virtual super Operation and maintenance economic optimization control method for capacitors.

Below in conjunction with accompanying drawing, the optional implementation mode of this application is described in further detail:

Referring to Fig. 1, the specific process of an operation and maintenance economic optimization control method for a virtual supercapacitor provided by the embodiment of the present application is as follows:

Step 101: According to the conversion relationship between the rotational kinetic energy of the controllable load and the charge and discharge energy of the supercapacitor, determine the expression of the virtual capacitance value and the expression of the virtual state of charge; the asynchronous motor with speed adjustment capability can be virtualized as a supercapacitor.

Step 102: Establishing a load economic loss model under the control of virtual capacitance according to the expression of the virtual capacitance value and the expression of the virtual state of charge;

Step 103: Determine the system operation and maintenance economic evaluation model including the virtual capacitance value according to the load economic loss model;

Step 104: Solve the system operation and maintenance economic evaluation model based on the particle swarm optimization algorithm to obtain the virtual capacitance value corresponding to the maximum benefit;

Step 105: Control the operating condition of the controllable load according to the virtual capacitance value.

The method in Figure 1, by establishing the conversion relationship between the rotational kinetic energy of the controllable load and the charge and discharge energy of the DC capacitor energy storage element, virtualizes the asynchronous motor with speed adjustment capability as a super capacitor, and comprehensively analyzes the economic loss of the controllable load speed regulation process Based on the energy storage depreciation income in the virtual energy storage control process, the system economic operation and maintenance evaluation model is established, and the particle swarm algorithm is used to solve the problem, which provides the calculation basis for the virtual capacitance value and realizes the stable economic operation of the system.

In step 101, the asynchronous motor is used as a load unit. When the rotor speed changes, the electromagnetic power also changes. By analogy with the charging and discharging process of a supercapacitor, the load can be regarded as a virtual energy storage device, sharing The unbalanced power of the system borne by the energy storage device. Establish the energy relationship between mechanical kinetic energy and capacitive energy storage as:

In the formula: E _Cvir is the virtual energy of the virtual energy storage; C _vir is the capacitance value of the virtual energy storage; J _s , ω _r , p _n are the rotor moment of inertia, electrical angular velocity and number of pole pairs of the motor; U _C is the super capacitor voltage.

The virtual capacitance C _vir (t) of period t can be expressed as:

The virtual capacitance value of the controllable load at any time can be obtained from formula (2), so as to coordinate the power and load changes of the energy storage device and realize the improvement of system operation economy.

Referring to the definition of the state of charge of the energy storage element, the state of charge SOC _vir of the virtual energy storage device can be defined as

In step 104, the steps to solve the virtual capacitance value at different times by using the particle swarm optimization algorithm are as follows:

Step 1: Generate an initial group, set the position of the particle and the update speed, the position of the particle i is the virtual capacitance value of the period t: C _vir (t) _i ;

Step: 2: Calculate the system income corresponding to C _vir (t) _i according to the objective function;

Step 3: For each particle, judge whether the system income of C _vir (t) _i is greater than its historical maximum system income, if so, update C _vir (t) _i ;

Step 4: judge whether the system income corresponding to the C _vir (t) _i of each particle is greater than the system income of the global optimal position, if so, update the global optimal position C _vir (t);

Step 5: update the position C _vir (t) _i and velocity of each particle;

Step 6: If the convergence accuracy is satisfied or the number of iterations is reached, stop the algorithm and output the virtual capacitance value corresponding to the highest system total revenue, otherwise return to step 2 and continue the iteration.

In the example of the present invention, the seawater desalination device is used as the controllable load, and the main economic benefit of the system comes from fresh water production. The life loss of supercapacitors is negligible, and the life loss system caused by battery charging and discharging is taken as the main operation and maintenance cost of the system. Therefore, the objective function of the economic evaluation model for DC microgrid operation and maintenance with virtual energy storage can be expressed as

In the formula: F is the total economic benefit of the system; F _in (t) is the water production benefit of the system during the t period; F _out (t) is the life loss of the battery during the t period.

The seawater desalination load income depends on the water flow rate, and the expression of the water flow rate Q(t) is

Q(t)＝2.741-2.408cos[0.1216P _vir (t)]+1.324sin[0.1216P _vir (t)] (5)

The revenue of seawater desalination device in time period t can be expressed as

F _in (t) = kQ (t) Δt (6)

In the formula: k is the unit price of fresh water per ton; Q(t) is the flow rate of produced water in the period t; Δt is the time interval.

From formula (2), it can be obtained that the electrical angular velocity ω _r (t) of the asynchronous motor at time t

Charging and discharging power of virtual energy storage in time period t

From equations (7) and (8), it can be seen that adjusting the charging and discharging power by virtual energy storage will change the electrical angular velocity ω _r (t) of the asynchronous motor, so that the seawater desalination load exhibits the energy regulation characteristics of a supercapacitor with variable capacitance. Combining formulas (5) and (6), the load economic loss model under virtual capacitor control can be established

In the formula:

System operation and maintenance cost mainly depends on battery life loss F _out (t)

In the formula: W _total is the battery purchase investment; SOH(t) is the health status of the battery at time t, which is defined as the ratio of the maximum available capacity E _Bmax (t) of the battery to the rated capacity E _Bnom in the time period t; SOH _min is the battery life at the end of time The health status value of , preferably 0.8.

The maximum available capacity E _Bmax (t) of the battery during the period t can be expressed as

In the formula: A is the linear aging coefficient of the battery.

During the charging and discharging process, the storage capacity of the battery will change, and E _B (t) can be expressed as

In the formula: P _B (t) is the charging and discharging power of the battery in the t period; η _C and η _D represent the charging and discharging efficiency of the battery, respectively.

The power balance of the system should meet the following conditions

P _B (t) = P _vir (t) - P _W (t) - P _C (t) (13)

Equation (8) determines the relationship between virtual energy storage charging and discharging power and virtual capacitance value, and it can be concluded from equation (13) that virtual capacitance value will affect battery power. Furthermore, the power change of the virtual capacitor will affect the battery life loss cost F _out (t).

To sum up, after the virtual energy storage is connected to the system at different times, the load income and operating cost will become the two key factors to evaluate the system economy. Therefore, to maximize the total revenue of the system, it is also necessary to analyze the optimal solution of the system economic evaluation model after accessing virtual energy storage.

The present invention builds a simulation model as shown in Figure 2, including a wind power generation module, a storage battery and a supercapacitor hybrid energy storage module, and a controllable load module. The hybrid energy storage module composed of batteries and supercapacitors is used to stabilize the power fluctuation of the system, and the controllable load is used to share the charge and discharge pressure of the hybrid energy storage module. The permanent magnet direct drive wind turbine is connected to the DC bus through a converter (WVSC) as a wind power generation unit. The battery and supercapacitor are respectively connected to the DC bus through a bidirectional DC/DC converter (BVSC, CVSC). The asynchronous motor is used as a controllable The load module is connected to the DC bus through a DC/AC converter (LVSC).

Figure 3 shows the virtual supercapacitor value corresponding to the maximum system benefit. Fig. 4 shows the wind power fluctuation data collected by the present invention, and Fig. 5 is a comparison chart of load power and income before and after putting into virtual energy storage, which can be seen in combination with Fig. 4 and Fig. 5 . Before the virtual energy storage is put into use, the load power is constant at 16kW, and the water production income shows a uniform upward trend. After 1 hour, the water production income is about 26.22 yuan. After the virtual energy storage is put into use, the load power is no longer constant, but adjusted according to the fluctuation of wind power. The maximum load is about 18.2kW, and the minimum load is close to 9kW. Because the load power is no longer constant, the water production income is no longer the origin. In the straight line of 40s, the fan output power gradually decreases, the load power also decreases, and the growth rate of the water yield curve slows down. After the simulation, the water production income is about 23.22 yuan, which is lower than when the virtual energy storage is not used.

Figure 6 is _a comparison diagram of the dynamic response of the output power P _C of the supercapacitor bank, the state of charge of the supercapacitor SOCC, the output power of the battery pack _PB , the state of charge of the battery pack SOC _B , and the battery life depreciation cost before and after virtual energy storage is put into use. Combined with the wind power data in Figure 4, it can be concluded that when no virtual energy storage is used, the system power fluctuations are mainly stabilized by the supercapacitor. For example, at 26 minutes, the state of charge of the supercapacitor reaches 84.3%. After that, the output power of the wind turbine continues to increase. In order to prevent the supercapacitor from being overcharged, the capacitor is out of operation, and the excess wind power is absorbed by the battery. 36-60min, the wind power drops, the supercapacitor discharges, and the discharge power is large, the state of charge of the supercapacitor drops sharply, and drops to 11.6% at 40min, and the supercapacitor stops running. Since then, the output power of the wind turbine continued to decline. In order to ensure the load power supply, the output power of the battery was increased, and the output power was close to 8kW. The slope of the battery state of charge curve increased, and the downward trend was obvious. During this process, the output power of the battery increases, resulting in an obvious upward trend in the cost of battery life loss, and the operating cost of the system is relatively high. After the simulation, the cost of battery loss is about 8.92 yuan. On the contrary, after the virtual energy storage is put into use, the load adjusts its own power according to the change of wind power, the output of the battery is more stable, and the maximum charging and discharging power does not exceed 1kW during the whole simulation process, thereby reducing the aging cost of the battery. During the period of 40-60 minutes, the output power of wind power showed an obvious downward trend, but the virtual energy storage adjusted the power demand of the load and greatly reduced the change in the output power of the battery. Slowing down, the aging cost of battery life is also significantly reduced. After the simulation, the cost of battery damage is about 0.16 yuan.

Figure 7 compares the changes in total system revenue before and after adopting the economic optimization control method for system operation and maintenance with virtual capacitors. When the controllable load is virtualized as a supercapacitor, although the load income is reduced due to participation in power regulation, the cost of battery life loss will be greatly reduced at the same time. It can be seen from the figure that after the seawater desalination load is controlled by virtual energy storage, the total income of the system is higher than that of no-load response. While the system obtains more benefits, the safe operation level of the system is also improved under the joint participation of source-load-storage power regulation.

The controllable load has virtual energy storage capability. By establishing the conversion relationship between the rotational kinetic energy of the controllable load and the charge and discharge energy of the DC capacitor energy storage element, the virtual capacitance value can be virtualized, which can share the charge and discharge power of the traditional energy storage element and stabilize the power in time. fluctuation.

The present invention studies the economic optimization control method of system operation and maintenance with virtual capacitors, uses controllable loads to assemble virtual energy storage devices for the system, and through economic analysis, makes them cooperate with hybrid energy storage to improve the economical and stable operation level of the system. It has a significant effect on relieving the power regulation pressure of energy storage equipment and reducing the number of charging and discharging of battery packs, and has the potential to reduce system operation and maintenance costs. Through the comparative analysis of the economics before and after the virtual energy storage is put into use, the virtual energy storage can not only relieve the adjustment pressure of the energy storage device, but also make the system operation obtain better overall benefits.

It can be understood that, the same or similar parts in the above embodiments can be referred to each other, and the content that is not described in detail in some embodiments can be referred to the same or similar content in other embodiments.

It should be noted that, in the description of the present invention, the terms "first", "second" and so on are only used for description purposes, and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" means at least two.

Any process or method descriptions described in flowcharts or otherwise herein may be understood as representing a module, segment or portion of code comprising one or more executable instructions for implementing specific logical functions or steps of the process , and the scope of preferred embodiments of the invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including in substantially simultaneous fashion or in reverse order depending on the functions involved, which shall It is understood by those skilled in the art to which the embodiments of the present invention pertain.

It should be understood that various parts of the present invention may be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques known in the art: Discrete logic circuits, ASICs with suitable combinational logic gates, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

Those skilled in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, and the program can be stored in a computer-readable storage medium. When executed, one or a combination of the steps of the method embodiment is included.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, and the like.

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present invention have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and should not be construed as limiting the present invention. Embodiments are subject to variations, modifications, substitutions and variations.

Claims (4)

1. An operation, maintenance and economic optimization control method for a virtual super capacitor is characterized in that a power grid system comprises a storage battery, the super capacitor and a controllable load, and the method comprises the following steps:

determining an expression of a virtual capacitance value and an expression of a virtual charge state according to the conversion relation between the rotation kinetic energy of the controllable load and the charge-discharge energy of the super capacitor;

establishing a load economic loss model under the control of a virtual capacitor according to the expression of the virtual capacitor value and the expression of the virtual charge state;

determining a system operation and maintenance economic evaluation model containing a virtual capacitance value according to the load economic loss model;

solving the system operation and maintenance economic evaluation model based on a particle swarm optimization algorithm to obtain a corresponding virtual capacitance value when the profit is the maximum;

controlling the operating condition of the controllable load according to the virtual capacitance value;

the expression of the system operation and maintenance economic evaluation model is as follows:

(4)

in the formula:Fthe total economic benefit of the system;F _in (t) Is composed oftTime interval system revenue;F _out (t) Is composed oftTime-interval storage battery life loss;

calculating the battery by the following formulatTime period battery life loss:

(10)

wherein,W _total the purchase price of the storage battery is set,SOH(t) Is composed oftDefining the health state value of the storage battery at the moment as the storage batterytTime interval maximum available capacityE _Bmax (t) To rated capacityE _Bnom The ratio of (A) to (B);SOH _min the health condition value at the end of the service life of the storage battery;

(9)

wherein,P _vir (t-1) is the load power,C _vir (t) As a virtual capacitance value, a capacitance value,

，U _C is the supercapacitor voltage.

2. The method according to claim 1, wherein the determining the expression of the virtual capacitance value and the expression of the virtual state of charge according to the conversion relationship between the rotational kinetic energy of the controllable load and the charging and discharging energy of the super capacitor comprises:

the energy relationship between the mechanical kinetic energy and the stored energy of the capacitor is established

(1)

Wherein,E _Cvir virtual energy of the virtual energy storage device;C _vir a virtual capacitance value of the virtual energy storage device;J _s 、ω _r 、p _n the rotor rotational inertia, the electrical angular velocity and the pole pair number of the motor are respectively;U _C is the supercapacitor voltage;

tvirtual capacitance of time periodC _vir (t) Is shown as

(2)

Defining a state of charge of a virtual energy storage device based on an energy angleSOC _vir Is composed of

(3)。

3. The method of claim 1, wherein the battery is calculated by the following equationtTime interval maximum available capacityE _Bmax (t)：

(11)

Wherein,E _B is the available capacity of the battery.

4. The method of claim 1, wherein solving the system operation and maintenance economic evaluation model based on a particle swarm optimization algorithm specifically comprises:

step 1: generating an initial population, setting particle positions and update rates, the particlesiIs positioned astTime period virtual capacitance value:C _vir (t) _i ；

step 2: from the objective functionC _vir (t) _i Corresponding system revenue;

and 3, step 3: for each particle, judgeC _vir (t) _i If the system gain is greater than the historical maximum system gain, if so, updatingC _vir (t) _i ；

And 4, step 4: for each particleC _vir (t) _i If the corresponding system gain is larger than the system gain of the global optimal position, updating the global optimal positionC _vir (t)；

And 5: updating the position of each particleC _vir (t) _i And speed;

and 6: and if the convergence precision is met or the iteration times are reached, stopping the algorithm, outputting the corresponding virtual capacitance value when the total yield of the system is highest, otherwise, returning to the step 2, and continuing the iteration.

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