CN115764849A - A hybrid energy storage capacity optimization configuration method and its configuration system - Google Patents

Abstract

The invention discloses a hybrid energy storage capacity optimal configuration method, wherein a wind power generation system and a solar power generation system supply power for a direct current bus, power supply parameters on the direct current bus are obtained, a super capacitor system provides high-frequency components required by load fluctuation power, the impact of sudden load change on the bus is inhibited, the high-frequency components which fluctuate frequently in a supply power shortage delta E are responded, a battery system is charged and discharged at rated power, and basic parts except the high-frequency components which fluctuate frequently in the supply power shortage delta E are supplied. The method has certain competitiveness and superiority by utilizing the northern eagle algorithm. The method has higher reference value for capacity optimization configuration and system economic benefit of the hybrid energy storage device in the new energy wind and light power generation system.

Description

A hybrid energy storage capacity optimization configuration method and its configuration system

technical field

The invention relates to the field of electric power technology.

Background technique

In recent years, the energy crisis and environmental problems have become increasingly serious. Faced with the increasing shortage of traditional non-renewable energy sources, renewable new energy sources have emerged. Among them, wind energy and solar energy have good development and application prospects due to their wide distribution of resources and low pollution. national promotion. Among them, the wind-solar hybrid power generation system is an important way of renewable energy power generation, but due to weather and climate reasons, the output power of the wind-solar hybrid power generation system fluctuates and is uncertain.

The introduction of energy storage devices can effectively smooth the random fluctuations of unbalanced power caused by the uncertainty of source load, and the joint use of two types of energy storage with different characteristics can more effectively deal with the high and low frequency components in power fluctuations. Therefore, it is of great significance to study the capacity optimization configuration in the wind-solar hybrid system to reduce the economic cost of the system and improve the reliability of power supply.

At present, the capacity optimization configuration research methods of wind and solar power generation systems mainly include single-objective optimization and multi-objective optimization. Solve the optimal solution; multi-objective optimization generally considers system cost, load shortage rate, energy waste rate, etc., and combines constraints to perform multi-objective solution to obtain a reasonable configuration of energy storage devices. The use of intelligent optimization algorithms has brought great convenience to power system planning, but at the same time there is also the question of whether the optimization ability of intelligent algorithms to solve models can be optimal.

Aiming at the shortcomings of the traditional particle swarm optimization algorithm, such as easy "premature" and slow convergence speed, an improved particle swarm optimization algorithm is proposed to solve the wind and solar power generation system model with the minimum energy storage cost as the goal and the load shortage rate as the constraint. For the wind power generation system, based on the hybrid energy storage energy distribution strategy, an adaptive cuckoo algorithm is proposed to solve the objective function optimization. The improved genetic algorithm is used to solve the wind and wind power generation system with three objectives: system cost, load shortage rate and energy waste rate.

Traditional intelligent optimization algorithms generally have problems such as insufficient search ability, which leads to suboptimal solutions instead of optimal solutions, and the realization of the algorithm is related to multiple parameters, and the selection of parameters will affect the performance of the algorithm.

Contents of the invention

The technical problem to be solved by the present invention is to realize a more reasonable and reliable hybrid energy storage capacity optimization configuration method.

In order to achieve the above purpose, the technical solution adopted by the present invention is: a hybrid energy storage capacity optimization configuration method, the wind power generation system and the solar power generation system supply power to the DC bus, obtain the power supply parameters on the DC bus, and the supercapacitor system provides the load fluctuation power. The high-frequency components required to suppress the impact of sudden load changes on the busbar are to respond to the high-frequency components that frequently fluctuate in the supply power gap ΔE. The battery system is charged and discharged at rated power, and the high-frequency components that frequently fluctuate in the supply power gap ΔE other basic parts.

Configuration steps:

1) Initialize the model and input the annual operation data of wind power generation system, solar power generation system and load;

2) Initialize algorithm parameters and population: set the northern goshawk population size to 200, the maximum number of iterations to 400, and set i=0, N=0;

3) Establish the objective function and constraints and perform calculations;

4) The northern goshawk selects, attacks, and hunts down prey, constantly updates the location information and saves the current optimal solution and fitness value, and calculates the load shortage rate;

5) Judging whether the termination condition is satisfied, if not satisfied, continue to cyclically update, if satisfied, output the optimal solution, and end the optimization process.

The load power shortage rate of the wind power generation system and the solar power generation system is the ratio between the load power shortage E _LPS and the total load demand E _L , the expression:

In the formula, E _w (k), E _s (k) and E _L (k) are the wind energy, solar energy and load power at time k, respectively.

When the energy generated by the wind power generation system and the solar power generation system meets the load demand ΔE>0, the power shortage E _LPS =0, where ΔE=(E _w (k)+E _s (k))η _c -E _L (k) , to charge the hybrid energy storage system;

When the power generation of the wind power generation system and the solar power generation system cannot meet the demand ΔE<0, the hybrid energy storage system discharges to supplement the shortfall energy of the power supply and load. At this time, ΔE=-ΔE, then

E _lps ＝E _L (k)-(E _w (k)+E _s (k))η _c

In the formula, η _c is the power conversion efficiency of the inverter.

The battery system is a battery pack composed of N _b storage batteries, the rated voltage is U _b (V), the rated capacitance is C _b (Ah), the rated total energy storage is E _bn (MWh), and the rated output power is P _bn ,

E _bn ＝N _b C _b U _b /10 ⁶

P _bn = N _b C _b U _b /10 ⁷

The relative expression of the minimum remaining storage energy E _bmin of the battery system and the maximum discharge depth DOD:

E _bmin =N _b C _b U _b (1-DOD)/10 ⁶ .

The terminal voltage of the supercapacitor system is U _c , the capacitance value is C _c , and the terminal voltage of the supercapacitor system is kept at U _cmin ~ U _cmax during the working process;

The maximum storage energy E _cmax of the supercapacitor system:

The minimum storage energy E _cmin of the supercapacitor system:

The load power shortage rate is controlled at f _LPSP ≤ f _LPSPmax

Where f _LPSPmax is the maximum power shortage rate allowed by the system load power shortage rate;

Reserve constraints of the battery system and supercapacitor system:

E _{b min} < E _b (k) < E _bn

E _cmin < E _c (k) < E _cmax

Among them, ΔE includes the basic part of low-frequency fluctuation and the part of high-frequency fluctuation;

The battery system satisfies E _b (k)≤μΔE, where μ represents a proportionality coefficient.

A hybrid energy storage capacity optimization configuration system. The power supply end of the system is connected to the DC bus through a DC converter, the DC load is connected to the DC bus through a DC/DC converter, and the AC load is connected to the DC bus through a DC/AC converter. The system executes the described Hybrid energy storage capacity optimization configuration method

The power supply end includes part or all of the wind power generation system, solar power generation system, battery system, and supercapacitor system. The control end of each DC converter is connected to the control device of the configuration system through a signal line, and is adjusted by the control device. The input, output, and input-output power of each power supply.

The present invention uses the northern goshawk algorithm to solve the problem and has certain competitiveness and superiority. The method has a high reference value for the optimal configuration of the capacity of the hybrid energy storage device in the new energy wind power generation system and the economic benefits of the system.

Description of drawings

The following is a brief description of the content expressed by each piece of accompanying drawing in the description of the present invention:

Figure 1 is a block diagram of the hybrid energy storage capacity optimization configuration system.

Figure 2 shows the monthly electricity consumption of wind and solar loads;

Fig. 3 is four kinds of algorithm optimization process curves;

Figure 4 shows the comparison curves of the optimal solutions of the four algorithms.

Detailed ways

Referring to the accompanying drawings, through the description of the embodiments, the specific embodiments of the present invention include the shape, structure, mutual position and connection relationship of each part, the function and working principle of each part, and the manufacturing process of the various components involved. And the method of operation and use, etc., are described in further detail to help those skilled in the art have a more complete, accurate and in-depth understanding of the inventive concepts and technical solutions of the present invention.

In 2021, Mohammad Dehghani et al. proposed a novel swarm intelligence optimization algorithm that simulates the hunting process behavior of the northern goshawk - the northern goshawk algorithm. The algorithm has the advantages of fast convergence speed, good stability, small amount of calculation, and few parameters to be adjusted, etc., and some literatures have applied it to the engineering field. The present invention is based on the optimal configuration of hybrid energy storage capacity based on the northern goshawk algorithm, and uses the northern goshawk algorithm to solve the problem with the goal of minimizing the cost of the entire life cycle, the system load shortage rate and the energy constraints of the energy storage system. Large configuration of optimized hybrid energy storage capacity.

As shown in Figure 1, the hybrid energy storage capacity optimization configuration system consists of a wind power generation system, a solar power generation system, a battery system, a supercapacitor system, a converter, and a load. The wind power generation system and the solar power generation system supply power to the load. When the power supply energy is insufficient, the energy storage device stores electricity to supply power to the system. When the power supply meets the demand and there is a surplus, the energy storage device stores electricity to improve the reliability of the power supply. The power supply end is the energy storage device used to store electricity, including the battery system and Supercapacitor system, wind power generation system, solar power generation system, battery system, supercapacitor system, each power supply end is equipped with an independent DC converter, wind power generation system, solar power generation system, battery system, supercapacitor system are all through DC conversion The inverter is connected to the DC bus, the DC load is connected to the DC bus through a DC/DC converter, and the AC load is connected to the DC bus through a DC/AC converter.

Each DC converter has the functions of controlling input, output on-off, and adjusting input power and output power. Therefore, the control terminal of each DC converter is connected to the control device through a signal line, and the control device controls each DC converter. Adjustment control is carried out to control the working status and working efficiency of each power supply terminal. The specific control method adopts the optimal configuration method of hybrid energy storage capacity based on the Northern Goshawk algorithm. This method aims at the power fluctuation caused by uncertain factors such as wind and wind power generation will have a negative impact on the power quality of the power grid, and in order to improve the economy of the system, the capacity optimization configuration method of hybrid energy storage in the wind and wind hybrid power generation system is realized. First, , with the minimum cost of the whole life cycle as the objective function, and the load shortage rate and energy storage energy constraints as the constraints to establish a model; then, the model is solved by the North Goshawk intelligent optimization algorithm. The method is simulated by a MATLAB example. The experimental results show that the capacity allocation scheme solved by the North Goshawk algorithm not only reduces the system cost, improves the convergence speed, but also narrows the optimization fluctuation range of the optimal value, making the optimization stability improved, thus proving the correctness of the algorithm. In addition, the method can also verify the correctness of the algorithm to solve the model by comparing and analyzing the optimization results of the three optimization algorithms: particle swarm optimization algorithm, whale optimization algorithm and sparrow search algorithm.

Solar power generation system (battery device) model,

Assuming that the rated voltage of a single storage battery is U _b (V) and the rated capacitance is C _b (Ah), then the rated energy storage E _bn (MWh) of the battery pack composed of N _b batteries and the rated output power of the battery pack Such as formula (1). And in order to prolong the service life of the battery, the rated power is usually used for charging and discharging.

E _bn ＝N _b C _b U _b /10 ⁶

P _bn ＝N _b C _b U _b /10 ⁷ (1)

The minimum remaining storage energy of the battery is related to the maximum discharge depth DOD, and the related expression is expressed by formula (2):

E _bmin ＝N _b C _b U _b (1-DOD)/10 ⁶ (2)

Supercapacitor System Model

Assume that the terminal voltage of a single supercapacitor is U _c , and the capacitance value is C _c . However, in the actual operation situation, the terminal voltage of the supercapacitor is kept within a voltage range, which is recorded as U _cmin ~ U _cmax , then the maximum storage energy E _cmax of the actual supercapacitor is:

The minimum storage energy E _cmin of the supercapacitor is:

The calculation process of the load shortage rate includes the calculation process of the energy allocation strategy and the load shortage rate;

As an important operating index of the wind-solar hybrid power generation system, the load power shortage rate reflects the stable operation status of the system, and the load power shortage rate is defined as the ratio between the load power shortage E _LPS and the total load demand E _L , the expression is as follows (5):

In the formula, E _w (k), E _s (k) and E _L (k) are the wind energy, solar energy and load power at time k, respectively.

Calculation process of load power shortage rate, when the energy of wind-solar hybrid power generation meets the load demand ΔE>0, the power shortage E _LPS =0, where ΔE=(E _w (k)+E _s (k))η _c -E _L (k), charge the hybrid energy storage system; when the wind-solar hybrid power generation cannot meet the demand ΔE<0, the hybrid energy storage system discharges to supplement the shortfall energy of the power supply and load, at this time, ΔE=-ΔE, namely:

E _lps ＝E _L (k)-(E _w (k)+E _s (k))η _c (6)

In the formula, η _c is the power conversion efficiency of the inverter, for example, the value is 0.95.

Hybrid energy storage system optimization model

Objective function: Life cycle cost (LCC) refers to the sum of all expenses in the process of equipment manufacturing cost, installation, use, maintenance, disposal, etc. within the life cycle of the equipment, calculated by the annual cost of equipment purchase C ₁ , Operating cost C _o , maintenance cost C _M and processing cost C _D are composed, namely:

LCC＝C ₁ +C _O +C _M +C _D (7)

The objective function is to minimize the cost of the whole life cycle, as shown in formula (8):

minC＝C ₁ +C _O +C _M +C _D

＝(1+f _ob +f _mb +f _db )N _b P _b +(1+f _oc +f _mc +f _dc )N _c P _c (8)

In the formula, N _b , N _c are the number of batteries and supercapacitors respectively; P _b , P _c are the purchase unit prices of batteries and supercapacitors respectively; f _ob , f _oc are the operating coefficients of batteries and supercapacitors respectively; f _mb , f _mc are maintenance coefficients of storage battery and supercapacitor, and supercapacitors are generally maintenance-free, so f _mc = 0; _fdb , _fdc are treatment coefficients of storage battery and supercapacitor, respectively.

Constraints include:

(1) Power supply reliability

The power supply reliability of the system is usually characterized by the load shortage rate, which should be kept within a certain range, namely:

f _LPSP ≤f _LPSPmax (9)

In the formula, f _LPSPmax is the maximum power shortage rate allowed by the system load power shortage rate, for example, the value is 0.05.

(2) Reserve constraints of the energy storage system

E _{b min} < E _b (k) < E _bn

E _cmin < E _c (k) < E _cmax (10)

(3) ΔE is mainly composed of the basic part of low-frequency fluctuations and the part of high-frequency fluctuations, and the battery is mainly responsible for the basic part of low-frequency fluctuations, which must satisfy formula (11):

E _b (k)≤μΔE (11)

In the formula, μ represents a proportionality coefficient, for example, the value is 0.7.

The northern goshawk optimization algorithm (Northern Goshawk Optimization, GO) simulates the behavior of the northern goshawk in the predation process. The predation process is divided into two stages: prey identification and attack (reconnaissance stage) and chase and escape (development stage).

Prey identification and attack (reconnaissance stage)

During the first session of the hunt, the northern goshawk randomly selects a prey item and quickly attacks it. The behavioral mathematical expressions of the northern goshawk at this stage are as follows (12)-(14):

P _i =X _k , i=1,2,...,N; k=1,2,...i-1,...N (12)

是第i个北方苍鹰的新位置；

是第i个北方苍鹰的第j维的新位置；F_i ^new,p1是与之对应的目标函数值；r是[0,1]范围内的随机数；I为1或2的随机整数。In the formula, P _i is the position of the prey selected by the i-th northern goshawk; F _pi is the objective function value of the position of the i-th northern goshawk's prey; k is a number belonging to [1,N]N=200;

is the new position of the i-th northern goshawk;

is the new position of the i-th northern goshawk in the j-th dimension; F _i ^{new, p1} is the corresponding objective function value; r is a random number in the range of [0,1]; I is a random integer of 1 or 2 .

Chase and Escape (in development)

After the northern goshawk attacks the prey, the prey will try to escape. Therefore, in the process of chasing prey, the northern goshawk is extremely fast and can catch prey under any circumstances. The simulation of this behavior improves the local search ability of the algorithm for the search space. Suppose the hunting activity is close to an attack location with radius R. The mathematical expression of the second stage is as formula (15)～(17):

是在第二个狩猎阶段的第i个北方苍鹰的新位置；

是是第二个狩猎阶段的i个北方苍鹰的第j维的新位置；F_i ^new,p2是新状态下对应的目标函数值。In the formula, t is the current number of iterations; T=400 generations;

is the new position of the i-th northern goshawk in the second hunting phase;

F _i ^{new, p2} is the corresponding objective function value in the new state.

The capacity optimization configuration process of the energy storage system is as follows:

1: Initialize the model and input the annual operation data of wind, solar and load;

Two: Initialize the algorithm parameters and population, set the northern goshawk population size to 200, the maximum number of iterations to 400, and set i=0, N=0;

Three: Establish the objective function and constraints and perform calculations;

Four: The northern goshawk selects, attacks, and hunts down prey, constantly updates location information and saves the current optimal solution and fitness value, and calculates the load shortage rate;

Five: Judging whether the termination condition is satisfied, if not satisfied, continue to update circularly, if satisfied, output the optimal solution, and end the optimization process.

When setting parameters, a case analysis is carried out with the annual operation data of a wind-solar hybrid power generation system. Figure 2 shows the annual wind power, photovoltaic power generation and load consumption of the wind and solar hybrid system. Inverter efficiency η _c is 0.95, and the maximum value of load power shortage rate f _LPSPmax is 0.05. The parameters set in the battery and supercapacitor in the example analysis are shown in Table 1.

Table 1 Hybrid energy storage system parameters:

storage battery Super capacitor Rated voltage/V 12 2.7 Rated Capacity 100/Ah 3500/F charging efficiency 0.75 0.98 discharge efficiency 0.85 0.98 depth of discharge 0.4 / operating factor 0.1 0.01 maintenance factor 0.1 0 Cycle life/time 1500 500000 Processing factor 0.08 0.04 Unit price/yuan 400 350

The comparison and analysis of the simulation results of the calculation example adopts the northern goshawk algorithm, and the experiment is carried out in matlab. Set the population size to 200 and the maximum number of iterations to 400. And according to the model selected in the article, the results of the northern goshawk algorithm are compared with the classical particle swarm optimization algorithm, whale optimization algorithm and sparrow algorithm. The experimental results are evaluated from three aspects: optimal cost economy (fitness function value), algorithm efficiency and stability of optimization results.

Table 2 shows the optimal results of solving the model of the present invention by four different algorithms recorded in ten simulation experiments (compared with the minimum objective function). Comparing the objective functions of the four algorithms, that is, the optimal cost economy, it can be seen that the optimal economic cost obtained by the particle swarm optimization algorithm is the highest economic cost obtained by the four algorithms, which is 159826 yuan, and the most obtained by the sparrow algorithm and the northern goshawk algorithm. The optimal economic cost is that the minimum economic cost obtained by the four algorithms is 156645 yuan, the difference between the minimum cost and the maximum cost is 3181 yuan, and the load power shortage rate of the four algorithms is less than the maximum load power shortage rate f _LPSPmax (0.5) , Power supply reliability is also guaranteed.

Table 2 Four kinds of algorithm optimization results:

particle swarm algorithm Whale optimization algorithm sparrow algorithm Northern Goshawk Algorithm Battery/pc 40607 31041 49060 49060 Supercapacitor/pc 5763851 5763987 5545370 5545370 Load shortage rate 0.0379 0.0365 0.0365 0.0365 optimal economic cost 159826 157074 156645 156645

Figure 3 is the iterative curves of the optimization process of the four algorithms, and the number of iterations is set to 400. It can be seen from the figure that the optimal economic cost of the northern goshawk algorithm and the sparrow algorithm are the same, but the optimal solution iteration times of the northern goshawk algorithm is stable at about 20 times, and the optimal solution iteration times of the sparrow algorithm is stable at about 40 times , in contrast, the Northern Goshawk algorithm converges faster.

Figure 4 is a comparison chart of the 10 optimization results of the four algorithms. The results show that the results obtained by the particle swarm algorithm to solve the optimal value have the largest fluctuation range and the solved optimal value is higher than other optimal values, the smallest optimal value solved by the northern goshawk and the smallest optimal value solved by the sparrow algorithm The same, but the northern goshawk solves the optimal value and obtains a stable result of 156645. In contrast, the northern goshawk algorithm solves the optimal value with less fluctuation than the sparrow algorithm, which means that the solution process is more stable and reliable. higher. In addition, it can be seen from the optimization curve that the classical particle swarm optimization algorithm and the whale optimization algorithm obtain the optimal solution more quickly but the optimal value is not ideal, which also confirms that the two algorithms tend to converge prematurely and fall into the local optimal solution.

The invention aims at the minimum cost of the system in the whole cycle, considers the characteristics of the hybrid energy storage device for energy distribution, and takes the system load power shortage rate and other operating indicators as constraints to optimize the capacity of the hybrid energy storage device. And through the experimental simulation results, it also shows that the optimal value solved by the northern goshawk algorithm used in the present invention is the best solution by the sparrow algorithm, and compared with the classic particle swarm optimization algorithm, the system life cycle cost is reduced by 1.99%; The goshawk algorithm has better optimization ability, and the process of solving the optimal value only needs to iterate about 20 times, and the optimal solution of the model using the northern goshawk algorithm remains unchanged, which is better than other algorithms. Stablize. Therefore, using the northern goshawk algorithm to solve the problem has certain competitiveness and advantages.

The present invention has been exemplarily described above in conjunction with the accompanying drawings. Obviously, the specific implementation of the present invention is not limited by the above methods, as long as various insubstantial improvements are adopted in the method concept and technical solutions of the present invention, or there is no improvement Directly applying the conception and technical solutions of the present invention to other occasions falls within the protection scope of the present invention.

Claims (9)

1. A hybrid energy storage capacity optimal configuration method is characterized by comprising the following steps: the wind power generation system and the solar power generation system supply power for the direct-current bus to obtain power supply parameters on the direct-current bus, the super-capacitor system provides high-frequency components required by load fluctuation power, the impact of load sudden change on the bus is restrained, the high-frequency component part which is frequently fluctuated in the supply power shortage delta E is responded, the battery system is charged and discharged at rated power, and basic parts except the high-frequency components which are frequently fluctuated in the supply power shortage delta E are supplied.

2. The hybrid energy storage capacity optimal configuration method according to claim 1, characterized in that: a configuration step:

1) Initializing a model, and inputting wind power generation system, solar power generation system and year-round load operation data;

2) Initialization algorithm parameters and population: setting the northern eagle population scale to be 200, the maximum iteration number to be 400, and enabling i =0 and N =0;

3) Establishing an objective function and a constraint condition and calculating;

4) Selecting, attacking and catching preys by the eagles in the north, continuously updating position information, storing the current optimal solution and fitness value, and calculating the load power shortage rate;

5) And judging whether a termination condition is met, if the termination condition is not met, continuing to circularly update, if the termination condition is met, outputting an optimal solution, and ending the optimization process.

3. The hybrid energy storage capacity optimal configuration method according to claim 2, characterized in that: the load power shortage rate of the wind power generation system and the solar power generation system is load power shortage E _LPS And total load demand E _L The ratio between, the expression:

in the formula, E _w (k)、E _s (k) And E _L (k) The electric quantities of wind energy, solar energy and load at the moment k are respectively.

4. The hybrid energy storage capacity optimal configuration method according to claim 3, wherein: when the energy generated by the wind power generation system and the solar power generation system meets the load demand delta E>At 0, the electric quantity is in short _LPS =0, wherein Δ E = (E) _w (k)+E _s (k))η _c -E _L (k) Charging the hybrid energy storage system;

when the generated energy of the wind power generation system and the solar power generation system cannot meet the requirement that the delta E is less than 0, the hybrid energy storage system discharges to supplement the shortage energy of the power supply and the load, and at the moment, the delta E is enabled to be = -delta E, then

E _lps ＝E _L (k)-(E _w (k)+E _s (k))η _c

In the formula eta _c The power conversion efficiency of the inverter.

5. The hybrid energy storage capacity optimal configuration method according to claim 4, wherein: the battery system consists of N _b Accumulator battery composed of single accumulator with rated voltage of U _b (V) rated capacitance ofC _b (Ah) rated total energy storage amount is E _bn (MWh) rated output power P _bn ，

E _bn ＝N _b C _b U _b /10 ⁶

P _bn ＝N _b C _b U _b /10 ⁷

Minimum residual stored energy E of the battery system _bmin Relative expression to the maximum depth of discharge DOD:

E _bmin ＝N _b C _b U _b (1-DOD)/10 ⁶ 。

6. the hybrid energy storage capacity optimal configuration method according to claim 5, wherein: the terminal voltage of the super capacitor system is U _c A capacitance value of C _c And the end voltage is kept at U in the working process of the super capacitor system _cmin ～U _cmax ；

Maximum energy storage E of the super capacitor system _cmax ：

Minimum energy storage E of the supercapacitor system _cmin ：

7. The hybrid energy storage capacity optimal configuration method according to claim 6, wherein: the load power shortage is controlled to be f _LPSP ≤f _LPSPmax

Wherein f is _LPSPmax The maximum power shortage rate allowed by the system load power shortage rate;

reserve constraints of the battery system and the supercapacitor system:

E _bmin <E _b (k)<E _bn

E _cmin <E _c (k)<E _cmax

wherein Δ E comprises a low frequency ripple base portion and a high frequency ripple portion;

the battery system satisfies E _b (k) μ. DELTA.E, where μ represents the proportionality coefficient.

8. A hybrid energy storage capacity optimal configuration system, wherein a power supply end of the system is connected with a direct current bus through a direct current converter, a direct current load is connected with the direct current bus through a DC/DC converter, an alternating current load is connected with the direct current bus through a DC/AC converter, and the system executes the hybrid energy storage capacity optimal configuration method of any one of claims 1 to 7

9. The hybrid energy storage capacity optimal configuration system of claim 8, wherein: the power supply ends comprise part or all of a wind power generation system, a solar power generation system, a battery system and a super capacitor system, the control end of each direct current converter is connected with the control equipment of the configuration system through a signal wire, and the control equipment adjusts the input power, the output power and the input and output power of each power supply end.

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