CN111817313B - Optical storage capacity optimal configuration method and system based on sub-band mixed energy storage - Google Patents

Abstract

A method and system for optimal configuration of optical storage capacity based on sub-band hybrid energy storage, the method includes: decomposing photovoltaic output data based on Hilbert-Huang transform, and decomposing it into high-frequency components and low-frequency components; randomly generating the capacity of an energy storage device as a variable; set the upper and lower limits of the variable; input the generated initial energy storage device capacity into the fitness function to calculate the objective function in the fitness function; genetically mutate the current population to form the next generation population; detect the genetic algebra Whether the set maximum genetic algebra is reached, choose to continue the calculation or end the calculation according to the test results, and take the individual with the highest fitness in the last generation as the final calculation result; use the energy storage optimization results of the high-frequency components and low-frequency components to determine the supercapacitor respectively. And the optimal capacity of the battery is beneficial to overcome the shortcomings of photovoltaic dispersion, low energy density, and intermittent, and achieve a significant reduction in cost.

Description

A method and system for optimal configuration of optical storage capacity based on sub-band hybrid energy storage

technical field

The invention belongs to the field of photovoltaic power generation, and more particularly, relates to a method and a system for optimizing the configuration of optical storage capacity based on sub-band hybrid energy storage.

Background technique

Photovoltaic power generation is one of the most important ways to utilize solar energy, and it is also one of the fields with the most mature technology and the most commercial prospects in energy development and utilization in the world. Solar energy naturally has many shortcomings, such as strong volatility, strong intermittency, and significant seasonal changes, which lead to problems such as strong photovoltaic output volatility, low stability, and difficulty in control, so power generation is limited. Configuring suitable energy storage for photovoltaics is one of the most effective ways for the long-term development of photovoltaics, among which supercapacitors have been widely and successfully applied. According to the characteristics of photovoltaic power generation, the requirements for energy storage technology should be fast response speed and large storage capacity. Considering comprehensively, the present invention proposes a hybrid energy storage method, which cooperates with photovoltaic output to achieve the effect of optimizing the ultra-short-term grid connection plan. .

There have been many studies at home and abroad on the optimal operation and capacity allocation of photovoltaic-energy storage, but there are still few studies on how to select and design hybrid energy storage methods. The optimal design of installed capacity involves many factors, such as the geographical location of supercapacitors, development conditions, unit investment and so on. When configuring the supercapacitor capacity, if the configured capacity is larger, although the impact of photovoltaic fluctuations can be better eliminated, the corresponding capacity and land investment will be more. If the configuration is smaller, the initial investment will naturally be Reduced, but the effect on solving the volatility of photovoltaics is also small.

However, for the rapid development of photovoltaics today, the configuration of supercapacitors and battery capacity with the best capacity is the most reasonable, which is of great significance in terms of construction investment and power grid operation.

SUMMARY OF THE INVENTION

In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide an optical storage capacity configuration method based on Hilbert-Huang transform and genetic algorithm, which can provide the energy storage device corresponding to a photovoltaic system with a certain capacity. The best capacity, and the calculation method is simple and practical.

The present invention adopts the following technical solutions. A method for optimizing the configuration of optical storage capacity based on frequency-frequency hybrid energy storage, characterized in that it includes the following steps:

Step 1: Obtain historical photovoltaic power generation data within a set time span;

Step 2, using the HHT decomposition to process the historical photovoltaic power generation data obtained in step 1 to obtain high-frequency power generation data and low-frequency power generation data;

Step 3, initialize the genetic algorithm parameters, take the supercapacitor capacity and the battery capacity as independent variables, randomly generate an initial population, the total population is a constant N, the genetic algebra is GEN, the maximum genetic algebra is M, and the initialization GEN=0;

Step 4: Input the low-frequency power generation data and the high-frequency power generation data respectively, calculate the fitness function of the individual population in step 3, take the total life cycle cost of the energy storage device as the first objective function, and take the number of net power greater than zero as the first objective function. two objective functions;

Step 5: Perform genetic variation on the current population to form the next generation population; use the fitness function values of different individuals in the current population to perform operations on the population; perform crossover operations on the feasible populations generated by the selection operation to generate new individuals; The individual is brought into the fitness function for calculation, and compared with the excellence of the previous generation objective function;

Step 6: Judge whether the genetic variation has reached the maximum genetic algebra. If the genetic variation has not reached the maximum genetic algebra, return to step 5. If the maximum genetic algebra M is reached, the calculation is over, and the last generation of population with the highest fitness is the final result, and output the maximum genetic algebra. The best energy storage device capacity, in which the output result of inputting low-frequency power generation data is the best battery capacity, and the output result of inputting high-frequency power generation data is the best supercapacitor capacity;

where HHT refers to the Hilbert-Huang transform.

Preferably, in step 1, the time span is one year, and the data sampling period is 15 minutes.

Preferably, in step 1, the historical photovoltaic power generation power P _pv and the historical photovoltaic planned power P _plan within the set time span are obtained; wherein, P _pv ={P _pv (i)} _i=1,2,...,n , P _plan ={P _plan (i)} _{i=1, 2, . . . n} , and the sequence number i corresponds to a historical moment.

Preferably, in step 2, the historical photovoltaic power generation power P _pv and historical photovoltaic plan power P _plan data obtained in step 1 within the set time span are decomposed using HHT, and the photovoltaic power generation data is decomposed into high-frequency historical power P _{hf_pv} = {P _{hf_pv} (i)} _i=1,2,...,n , low frequency historical power P _{lf_pv} ={P _{lf_pv} (i)} _i=1,2,...,n , high frequency plan power P _{hf_plan} ={P _{hf_plan} (i)} _i=1,2,...,n and low frequency plan power P _{lf_plan} ={P _{lf_plan} (i)} _i=1,2,...,n .

Preferably, step 2 specifically includes:

Step 2.1, using EMD to decompose historical photovoltaic power P _pv and historical photovoltaic plan power P _plan ;

where:

s(t) represents the historical data of photovoltaic power generation, namely P _pv and P _plan obtained in step 1,

c _k (t) represents the IMF component, that is, c ₁ (t) represents the high-frequency component, c ₂ (t) represents the low-frequency component,

r(t) represents the residual function;

Step 2.2, use HSA to obtain the frequency spectrum of the historical PV output data;

where:

Re means to take the real part;

a _k (t) represents the instantaneous amplitude of each IMF component;

ω(t) represents the instantaneous frequency;

θ(t) represents the instantaneous phase,

H(ω,t) represents the distribution of the instantaneous amplitude in the time and frequency planes;

H(ω) represents the distribution of the instantaneous amplitude in the frequency plane;

Preferably, wherein, EMD refers to empirical mode decomposition; HSA refers to Hilbert spectral analysis, and IMF refers to intrinsic mode function.

Preferably, step 3 specifically includes:

和x_{2_max}分别为蓄电池容量下限约束和上限约束；Define the independent variables supercapacitor capacity x ₁ and battery capacity x ₂ , x _{1_min ≤x 1 ≤x 1_max} , x _{1_min} and x _{1_max} are the lower limit and upper limit constraints of the super capacitor capacity, respectively,

and x _{2_max} are the lower limit and upper limit constraints of the battery capacity, respectively;

_Define the _charging power _{P p} = _{{ P p} (i) _{i = 1, 2, .} and upper bound;

Define the discharge power of the energy storage device P _h = {P _h (i) _{i = 1, 2, ..., n} }, and P _hmin and P _hmax are the lower and upper limit constraints of the discharge power, respectively;

Define the state of charge SOC={SOC(i) _i=1,2,...,n }, the upper limit of the state of charge SOC _max , the lower limit of the state of charge SOC _min , the energy storage device cannot continue to charge when SOC(i)=SOC _max , when SOC(i)=SOC _min , the energy storage device cannot continue to discharge, and the initialization SOC(1)=100%;

Define the investment cost of energy storage device per unit capacity.

Preferably, the fitness function in step 4 includes: operation strategy calculation, energy storage device state calculation and objective function calculation.

Preferably, running the policy calculation includes:

Calculate the P _extro (i) _i=1,2,...n of the energy storage device at each moment with the following formula, where

P _extro (i)=P _plan (i)-P _pv (i),

If P _extro (i)≤0, the energy storage device is in the charging state at time i, and the discharge power of the energy storage device is zero, that is, P _h (i)=0;

If P _extro (i)>0, the energy storage device is in a discharge state at time i, and the discharge power of the energy storage device is zero, that is, P _p (i)=0.

Preferably, the calculation of the state of the energy storage device includes: calculating the state of charge of the energy storage device SOC(i) _{i=1, 2, . .} .

If P _extro (i)≤0, the energy storage device is in the charging state at time i,

If P _extro (i)>0, it means that the energy storage device is in the discharge state at time i,

where:

x represents the independent variable supercapacitor capacity x ₁ and battery capacity x ₂ ,

eta represents the inverter efficiency,

σ represents the self-discharge loss of the energy storage device.

Preferably, the calculation of the state of the energy storage device includes: calculating the charging and discharging power P _p (i) _i=1,2,...,n and P _h (i) _i=1,2,...,n by the following formulas,

If P _extro (i)≤0, it means that the energy storage device is in the charging state at time i, and P _h (i)=0, and P _p (i) is calculated by the following formula,

If P _extro (i)>0, it means that the energy storage device is in the discharge state at time i, P _p (i)=0, and P _h (i) is calculated by the following formula,

where:

x represents the independent variable supercapacitor capacity x ₁ and battery capacity x ₂ ,

eta represents the inverter efficiency,

σ represents the self-discharge loss of the energy storage device.

Preferably, the calculation of the objective function includes: calculating the total life cycle cost of the energy storage device objective ₁ and the number of times when the planned power is reached objective ₂ ; defining the objective function 1:

objective ₁ =F ₁ +F ₂ +F _penalty n

where:

F ₁ represents the total cost of the supercapacitor in its life cycle,

F ₂ represents the total cost of the battery in the life cycle,

F _penalty represents the penalty fee for photovoltaic power generation that does not reach the planned value within one year,

n represents the life cycle of the energy storage device;

Define objective function 2:

objective ₂ = Reliability

Reliability is the number of times that the net power power_grid is greater than zero, that is, the number of times i when P _extro (i)≤0, and power_grid=P _pv (i)-P _plan (i)-P _h (i)>0 is satisfied.

Preferably, in step 4, the low frequency historical power P _{lf_pv} ={P _{lf_pv} (i)} _i=1,2,...,n and the low frequency plan power P _{lf_plan} ={P _{lf_plan} (i)} _{i=1,2,... ,n} as the low frequency group data, the high frequency historical power P _{hf_pv} ={P _{hf_pv} (i)} _i=1,2,...,n and the high frequency plan power P _{hf_plan} ={P _{hf_plan} (i)} _{i=1, 2 , .} two objective functions.

Preferably, in steps 1-6, the battery is replaced by a conventional hydropower station, and the super capacitor is replaced by a pumped storage power station.

The present invention also provides an optimal configuration system for optical storage capacity based on the hybrid energy storage in sub-frequency bands based on the method for optimal configuration of optical storage capacity, characterized in that the optimal configuration system for optical storage capacity includes:

The data acquisition module is used to obtain the historical photovoltaic power generation data within the set time span;

The data conversion module has a built-in HHT decomposition unit, which decomposes the historical photovoltaic power generation data obtained by the data acquisition module into high-frequency power generation data and low-frequency power generation data;

The optimization module, with built-in genetic algorithm unit, inputs the high-frequency power generation data and low-frequency power generation data obtained by the data conversion module, randomly generates an initial population, takes the supercapacitor capacity and battery capacity as independent variables, and obtains the optimal energy storage device capacity,

The display output module is used to visually display the data used and obtained by the data acquisition module, the data transformation module and the optimization module.

Preferably, the energy storage devices are supercapacitors and batteries, or conventional hydropower stations and pumped storage power stations.

The beneficial effect of the present invention is that, compared with the prior art, the present invention is based on Hilbert-Huang transform and genetic algorithm, by genetically mutating the results of the previous generation to find the optimal solution, and finally obtain the optimal configuration energy storage capacity , so as to better solve the defects of photovoltaic power generation fluctuations. In the capacity design, segmental processing is adopted, and the capacity design of the energy storage equipment and the optimization of the power generation plan are carried out for the low-frequency power components and the high-frequency power components respectively.

The invention analyzes and calculates based on the economy and stability of the combined photovoltaic and storage system, is used to solve the problem of the difficulty of large-scale centralized photovoltaic grid connection, lays a foundation for the energy storage system to play a better role in electric power, and has significant social benefits. value and economic value.

Description of drawings

Fig. 1 is the overall flow chart of the present invention;

Fig. 2 is the work flow chart of fitness function;

Figure 3 shows the low stability combined system output power and planned power;

Figure 4 shows the output power and planned power of the high-stability combined system;

Figure 5 is a comparison chart of the charging and discharging power of the energy storage device of the high-stability combined system and the low-temperature qualitative combined system.

Detailed ways

The present application will be further described below with reference to the accompanying drawings. The following examples are only used to more clearly illustrate the technical solutions of the present invention, and cannot be used to limit the protection scope of the present application.

As shown in FIG. 1 , the present invention provides an optimal configuration method for optical storage capacity based on sub-band hybrid energy storage, including the following steps:

Step 1: Obtain the historical photovoltaic power generation power P _pv and the historical photovoltaic planned power P _plan within the set time span; wherein, P _pv ={P _pv (i)} _{i = 1,2,...,n} , P _plan ={ P _plan (i)} _{i=1, 2, . . . n} , and the serial number i corresponds to a historical moment.

Those skilled in the art can arbitrarily set the time span and the data sampling interval. As a non-limiting preference, the data time interval is 15 minutes, the data time span is at least 1 year, and the time-frequency data is carried out using the operation data of more than one year. The transformation can capture the variation law of low frequency and high frequency more scientifically and intuitively.

Step 2, decompose the acquired historical photovoltaic power generation power P _pv and historical photovoltaic planned power P _plan data within the set time span, and divide the photovoltaic power generation data into high-frequency and low-frequency data, and the high-frequency output data fluctuates greatly. The low-frequency output data fluctuates slowly. For low-frequency output, due to the slow fluctuation, the corresponding change of charge and discharge of the energy storage device is not so fast, and the battery can handle it; correspondingly, for the high-frequency output data, the required charge and discharge changes are relatively Obviously, supercapacitors are therefore needed for processing. Or use conventional hydropower stations instead of batteries to process low-frequency output data, and use pumped-storage power stations instead of super capacitors to process high-frequency output data.

Specifically, using EMD (Empirical Mode Decomposition, Empirical Mode Decomposition) to decompose P _pv and P _plan obtained in step 1, the non-stationary data can be stabilized, and then HAS (Hilbert Spectrum Analysis, Hilbert Spectrum Analysis) Obtain the spectrogram when Hilbert, that is, use the HHT to process the P _pv and P _plan obtained in step 1, and obtain

High-frequency historical power P _{hf_pv} , P _{hf_pv} ={P _{hf_pv} (i)} _i=1,2,...,n ,

Low frequency historical power P _{lf_pv} , P _{lf_pv} ={P _{lf_pv} (i)} _i=1,2,...,n ,

High frequency plan power P _{hf_plan} , P _{hf_plan} ={P _{hf_plan} (i)} _i=1,2,...,n ,

Low frequency plan power P _{lf_plan} , P _{lf_plan} ={P _{lf_plan} (i)} _i=1,2,...,n .

More specifically, the P _pv and P _plan obtained in step 1 are processed using the following HHT formula:

where:

s(t) represents the historical data of photovoltaic power generation, namely P _pv and P _plan obtained in step 1;

c _k (t) represents the IMF (Intrinsic Mode Function, intrinsic mode function) component, that is, c ₁ (t) represents the high-frequency component, and c ₂ (t) represents the low-frequency component; they respectively contain the components of the characteristic scale at different times of the signal. .

r(t) represents the residual function, which represents the average trend of the signal;

Re means to take the real part;

a _k (t) represents the amplitude of each IMF component;

ω(t) represents the instantaneous frequency;

θ(t) represents the instantaneous phase,

H(ω,t) represents the distribution of the instantaneous amplitude in the time and frequency planes;

H(ω) represents the distribution of the instantaneous amplitude in the frequency plane.

Step 3: Initialize genetic algorithm parameters, including:

x_{2_min}和x_{2_max}分别为蓄电池容量下限约束和上限约束；Define the independent variables supercapacitor capacity x ₁ and battery capacity x ₂ , x _{1_min ≤x 1 ≤x 1_max} , x _{1_min} and x _{1_max} are the lower limit and upper limit constraints of the super capacitor capacity, respectively,

x _{2_min} and x _{2_max} are the lower limit and upper limit constraints of the battery capacity, respectively;

_Define the _charging power _{P p} = _{{ P p} (i) _{i = 1, 2, .} and upper bound;

Define the discharge power of the energy storage device P _h = {P _h (i) _{i = 1, 2, ..., n} }, and P _hmin and P _hmax are the lower and upper limit constraints of the discharge power, respectively;

Define the state of charge SOC={SOC(i) _i=1,2,...,n }, the upper limit of the state of charge SOC _max , the lower limit of the state of charge SOC _min , the energy storage device cannot continue to charge when SOC(i)=SOC _max , when SOC(i)=SOC _min , the energy storage device cannot continue to discharge, and initializes SOC(1)=100%.

Step 4: Input the high-frequency power generation data and the low-frequency power generation data respectively, calculate the fitness function of the individual population in step 3, take the total life cycle cost of the energy storage device as the first objective function, and take the number of net power greater than zero as the first objective function. two objective functions.

As shown in Figure 2, the fitness function includes system operation strategy, stability calculation, and total life cycle cost calculation, and two objective functions are defined.

Operation strategy: input the low-frequency power of photovoltaic output, the low-frequency planned power of photovoltaics and the capacity of the energy storage device, and output the net power at each moment, the total life cycle cost under the capacity of the energy storage device, and the charging and discharging power of the energy storage device at each moment. The net power that has not reached the planned power moment is combined with the high-frequency power, and the low-frequency power generation power perfectly matches the planned power through the processing of high-frequency data.

The specific steps of the fitness function of the present invention are as follows:

1) Input the individual population, input the photovoltaic power and the planned power.

2) Calculate the required charging and discharging power of the energy storage device by comparing the photovoltaic power and the planned power.

3) Input the required charging and discharging power into the battery module. Specifically, when calculating by using the state of charge of the energy storage device at the previous moment, it is necessary to use the capacity constraint of the energy storage device at this time, considering the remaining power, self-loss and charge-discharge loss at the previous moment, to calculate the storage capacity at the next moment. The actual charging and discharging power and state of charge of the device can be measured.

4) Input the result obtained in the previous step into the running module, and calculate the objective function through other parameters.

5) After the next group of individual populations are input, repeat step 1.

The beneficial effects of the genetic algorithm adopted in the present invention at least include: the population generated by the selection operation in the genetic algorithm is called the feasible population, and since the value range of the independent variable is set in this method, after the selection operation, it is not necessary to use to determine whether the individual population is Feasible; adopt a way in which both objective functions are better in comparison to preserve this result.

Specifically, the low frequency historical power P _{lf_pv} ={P _{lf_pv} (i)} _i=1,2,...,n and the low frequency plan power P _{lf_plan} ={P _{lf_plan} (i)} _i=1,2,...,n are taken as For the low frequency group data, the high frequency historical power P _{hf_pv} = {P _{hf_pv} (i)} _i=1,2,...,n and the high frequency plan power P _{hf_plan} ={P _{hf_plan} (i)} _{i=1,2,... ,n is} used as high-frequency group data, respectively input into the fitness function for calculation, take the total life cycle cost of the energy storage device objective ₁ as the first objective function, and take the number of net power power_grid greater than zero objective ₂ as the second objective function;

Calculate the P _extro (i) _i=1,2,...n of the energy storage device at each moment with the following formula, where

P _extro (i)=P _plan (i)-P _pv (i);

Calculate the state of charge SOC(i) _i=1,2,...,n of the energy storage device with the following formula, where SOC(1)=100%,

If P _extro (i) ≤ 0, it means that the energy storage device is in the charging state at time i,

where:

eta represents the inverter efficiency;

σ represents the self-discharge loss of the energy storage device;

表示如果储能装置把电量全部吸收，电量会溢出，所以没办法全部吸收电能。in

It means that if the energy storage device absorbs all the electricity, the electricity will overflow, so there is no way to absorb all the electricity.

If P _extro (i)>0, it means that the energy storage device is in the discharge state at time i,

Calculate the charging and discharging power P _p (i) _i=1,2,...,n and P _h (i) _i=1,2,...,n with the following formulas

If P _extro (i)≤0, it means that the energy storage device is in the charging state at time i, and P _h (i)=0, and P _p (i) is calculated by the following formula,

If P _extro (i)>0, it means that the energy storage device is in the discharge state at time i, P _p (i)=0, and P _h (i) is calculated by the following formula,

Calculate the total life cycle cost of the energy storage device objective ₁ and the number of times the planned power is reached objective ₂ ; define the objective function 1:

objective ₁ =F ₁ +F ₂ +F _penalty n

where:

F ₁ represents the total cost of the supercapacitor in its life cycle,

F ₂ represents the total cost of the battery in the life cycle,

F _penalty represents the penalty fee for photovoltaic power generation that does not reach the planned value within one year,

n represents the life cycle of the energy storage device;

Define objective function 2:

objective ₂ = Reliability

Reliability is the number of times that the net power power_grid is greater than zero, that is, the number of times i when P _extro (i)≤0, and power_grid=P _pv (i)-P _plan (i)-P _h (i)>0 is satisfied.

Step 5: Perform genetic variation on the current population to form the next generation population; use the fitness function values of different individuals in the current population to perform operations on the population; perform crossover operations on the feasible populations generated by the selection operation to generate new individuals; The individual is brought into the fitness function for calculation, and compared with the excellence of the previous generation objective function;

Step 6: Judge whether the genetic variation has reached the maximum genetic algebra. If the genetic variation has not reached the maximum genetic algebra, return to step 5. If the maximum genetic algebra M is reached, the calculation is over, and the last generation of population with the highest fitness is the final result, and output the maximum genetic algebra. The optimal energy storage device capacity, in which the output result of inputting high-frequency power generation data is the best super capacitor capacity, and the output result of inputting low-frequency power generation data is the best battery capacity;

In step 6, the battery is replaced by a conventional hydropower station, and the super capacitor is replaced by a pumped storage power station.

The present invention also provides an optimal configuration system for optical storage capacity based on the hybrid energy storage in sub-frequency bands based on the method for optimal configuration of optical storage capacity. The optimal configuration system for optical storage capacity includes:

The data acquisition module is used to obtain the historical photovoltaic power generation data within the set time span;

The data conversion module has a built-in HHT decomposition unit, which decomposes the historical photovoltaic power generation data obtained by the data acquisition module into high-frequency power generation data and low-frequency power generation data;

The optimization module, with built-in genetic algorithm unit, inputs the high-frequency power generation data and low-frequency power generation data obtained by the data conversion module, randomly generates an initial population, takes the supercapacitor capacity and battery capacity as independent variables, and obtains the optimal energy storage device capacity,

The display output module is used to visually display the data used and obtained by the data acquisition module, the data transformation module and the optimization module.

The energy storage device can be a combination of a super capacitor and a battery, or a combination of a conventional hydropower station and a pumped storage power station.

In order to introduce the technical solutions and beneficial effects of the present invention more clearly, the following calculation example analysis is introduced:

Enter the photovoltaic output data of each 15min in a year in a certain area, and divide the data into high-frequency and low-frequency power generation by Hilbert-Huang transformation. The upper limit of the capacity is 1000kWh. Through the established mathematical model and genetic algorithm, 50 optimal solutions are finally retained.

The capacity of the two energy storage devices corresponding to the highest and lowest total system cost in the optimal solution is compared. The capacity of the corresponding battery with the highest total cost is 2872.52kWh, and the corresponding supercapacitor capacity is 340.38kWh. The battery capacity corresponding to the lowest total cost is 425.659kWh, and the corresponding supercapacitor capacity is 890.9kWh. In the operation strategy section, the power required by the low-frequency photovoltaic power generation does not meet the planned power to be supplemented by the high-frequency part. Because the low-frequency power generation is much larger than the high-frequency power generation, try to make the low-frequency power generation as much as possible. The power input to the grid is more stable. Figure 3 shows the power output of the combined system with higher cost and the planned power in 30 hours. Figure 4 shows the power output of the combined system with low cost and the planned power for 30 hours. Figure 5 shows the higher cost. The comparison chart of charging and discharging of energy storage device with lower cost energy storage device.

For the combined solar-storage system with the same parameters, the total cost of the method using mixed energy is compared with that of the system that does not use mixed energy, as shown in the table below. Obviously, the total cost of the system using mixed energy is lower.

Table 1 Comparison of the total cost of the system with the method of mixed energy and the system without the use of mixed energy

The beneficial effect of the present invention is that, compared with the prior art, the present invention is based on Hilbert-Huang transform and genetic algorithm, by genetically mutating the results of the previous generation to find the optimal solution, and finally obtain the optimal configuration energy storage capacity , so as to better solve the defects of photovoltaic power generation fluctuations. In the capacity design, segmental processing is adopted, and the capacity design of the energy storage equipment and the optimization of the power generation plan are carried out for the low-frequency power components and the high-frequency power components respectively.

The invention analyzes and calculates based on the economy and stability of the combined photovoltaic and storage system, is used to solve the problem of the difficulty of large-scale centralized photovoltaic grid connection, lays a foundation for the energy storage system to play a better role in electric power, and has significant social benefits. value and economic value.

Definition of noun:

HHT: Hilbert-Huang Transform, Hilbert-Huang Transform;

EMD: Empirical Mode Decomposition, empirical mode decomposition

IMF: Intrinsic Mode Function, intrinsic mode function;

HSA: Hilbert Spectrum Analysis, Hilbert Spectrum Analysis.

The applicant of the present invention has described and described the embodiments of the present invention in detail with reference to the accompanying drawings, but those skilled in the art should understand that the above embodiments are only preferred embodiments of the present invention, and the detailed description is only to help readers better It should be understood that the spirit of the present invention is not limited to the protection scope of the present invention. On the contrary, any improvement or modification made based on the spirit of the present invention should fall within the protection scope of the present invention.

Claims (11)

1. An optical storage capacity optimal configuration method based on sub-band mixed energy storage is characterized by comprising the following steps:

step 1, obtaining historical photovoltaic power generation power P in a set time span_pvAnd historical photovoltaic planned power P_plan(ii) a Wherein, P_pv＝{P_pv(i)}_{i＝1,2,…,n}，P_plan＝{P_plan(i)}_i＝1,2,…nThe serial number i correspondingly represents historical time;

step 2, obtaining the historical photovoltaic power generation power P in the set time span from the step 1_pvAnd historical photovoltaic planned power P_planDecomposing the data by using Hilbert-Huang transform to decompose the photovoltaic power generation data into high-frequency historical power P_{hf_pv}＝{P_{hf_pv}(i)}_{i＝1,2,…,n}Low frequency historical power P_{lf_pv}＝{P_{lf_pv}(i)}_{i＝1,2,…,n}High frequency planned power P_{hf_plan}＝{P_{hf_plan}(i)}_{i＝1,2,…,n}And low frequency planned power P_{lf_plan}＝{P_{lf_plan}(i)}_{i＝1,2,…,n}(ii) a The method specifically comprises the following steps: step 2.1, decomposing historical photovoltaic power generation power P by using EMD_pvAnd historical photovoltaic planned power P_plan；

In the formula:

s (t) represents photovoltaic power generation historical data, namely P acquired in the step 1_pvAnd P_plan，

c_k(t) denotes the IMF component, i.e. c₁(t) represents a high frequency component, c₂(t) represents a low-frequency component,

r (t) represents a residual function;

step 2.2, obtaining a photovoltaic output historical data time frequency spectrum by using HSA;

in the formula:

re represents a real part;

a_k(t) represents the instantaneous amplitude of each IMF component;

ω (t) represents the instantaneous frequency;

theta (t) represents the instantaneous phase,

h (ω, t) represents the distribution of instantaneous amplitude in the time, frequency plane;

h (ω) represents the distribution of instantaneous amplitude in the frequency plane;

wherein, EMD refers to empirical mode decomposition; HSA refers to Hilbert spectrum analysis, IMF refers to intrinsic mode function;

step 3, initializing genetic algorithm parameters, randomly generating an initial population by taking the capacity of the super capacitor and the capacity of the storage battery as independent variables, wherein the total number of the population is a constant N, the genetic algebra is GEN, the maximum genetic algebra is M, and the initial GEN is 0; the method specifically comprises the following steps:

defining independent variable super capacitor capacity x₁And battery capacity x₂，x_{1_min}≤x₁≤x_{1_max}，x_{1_min}And x_{1_max}Respectively a super capacitor capacity lower limit constraint and an upper limit constraint, x_{2_min}≤x₂≤x_2max，x_{2_min}And x_{2_max}Respectively a lower limit constraint and an upper limit constraint of the capacity of the storage battery; defining the charging power P of an energy storage device_p＝{P_p(i)_{i＝1,2,…,n}}，P_pmin≤P_p(i)≤P_pmax，P_pminAnd P_pmaxRespectively a charging power lower limit constraint and an upper limit constraint; defining the discharge power P of an energy storage device_h＝{P_h(i)_{i＝1,2,…,n}}，P_hminAnd P_hmaxRespectively a lower limit constraint and an upper limit constraint of the discharge power; define State of Charge SOC ═ { SOC (i)_{i＝1,2,…,n}}, upper limit of state of charge SOC_maxLower limit of state of charge SOC_min，SOC(i)＝SOC_maxThe energy storage device can not be charged continuously, and SOC (i) ═ SOC_minWhen the battery is charged, the energy storage device can not continue to discharge, and the initial SOC (1) is 100%; defining investment cost of an energy storage device with unit capacity;

step 4, respectively inputting low-frequency power generation data and high-frequency power generation data, calculating a fitness function of population individuals in the step 3, taking the total cost of the life cycle of the energy storage device as a first objective function, and taking the number of net powers greater than zero as a second objective function;

step 5, carrying out genetic variation on the current population to form a next generation population; calculating the population by using fitness function values of different individuals in the current population; performing cross operation on feasible populations generated by the selection operation to generate new individuals; the new individual is brought into the fitness function to be calculated, and the superiority of the fitness function is compared with that of the previous generation of objective function;

and 6, judging whether the genetic variation reaches the maximum genetic algebra, if not, returning to the step 5, if so, ending the calculation, taking the individual with the highest population fitness of the last generation as a final result, and outputting the optimal energy storage device capacity, wherein the output result of the input low-frequency power generation data is the optimal storage battery capacity, and the output result of the input high-frequency power generation data is the optimal super-capacitor capacity.

2. The optimal configuration method for optical storage capacity based on sub-band hybrid energy storage according to claim 1, wherein:

in step 1, the time span is one year, and the data sampling period is 15 min.

3. The optimal configuration method for optical storage capacity based on sub-band hybrid energy storage according to claim 1, wherein:

the fitness function in step 4 comprises: the method comprises the steps of operation strategy calculation, energy storage device state calculation and objective function calculation.

4. The optimal configuration method for optical storage capacity based on sub-band hybrid energy storage according to claim 2, wherein:

the operation strategy calculation comprises the following steps:

calculating P of the energy storage device at each moment by the following formula_extro(i)_i＝1,2,…nWherein

P_extro(i)＝P_plan(i)-P_pv(i)，

If P is_extro(i) Less than or equal to 0, the energy storage device is in a charging state at the moment i, and the discharge power of the energy storage device is zero, namely P_h(i)＝0；

If P is_extro(i) When the voltage is higher than 0, the energy storage device is in a discharge state at the moment i, and the charging power of the energy storage device is zero, namely P_p(i)＝0。

5. The optimal configuration method for optical storage capacity based on sub-band hybrid energy storage according to claim 3, wherein:

the energy storage device state calculation comprises: calculating the state of charge (SOC (i)) of the energy storage device according to the formula_{i＝1,2,…,n}Wherein SOC (1) ═ 100%,

if P is_extro(i) Less than or equal to 0, the energy storage device is in a charging state at the moment i,

if P is_extro(i) Is greater than 0, indicating that the energy storage device is in a discharge state at time i,

in the formula:

x represents the independent variable super capacitor capacity x₁And battery capacity x₂，

eta represents the efficiency of the inversion,

σ represents the energy storage device self-discharge loss.

6. The method of claim 4 or 5, wherein the method further comprises:

the energy storage device state calculation comprises: calculating the charge-discharge power P according to the following formula_p(i)_{i＝1,2,…,n}And P_h(i)_{i＝1,2,…,n}，

If P is_extro(i) Less than or equal to 0, indicating that the energy storage device is in a charging state at time i, P_h(i) P is calculated as follows when P is 0_p(i)，

If P is_extro(i) Greater than 0, indicating that the energy storage device is in a discharge state at time i, P_p(i) P is calculated as follows when P is 0_h(i)，

In the formula:

x represents the independent variable super capacitor capacity x₁And battery capacity x₂，

eta represents the efficiency of the inversion,

σ represents the energy storage device self-discharge loss.

7. The optimal configuration method for optical storage capacity based on sub-band hybrid energy storage according to claim 6, wherein:

the objective function calculation includes: calculating total cost objective of life cycle of energy storage device₁And the number of objective times to reach the planned power₂(ii) a Defining an objective function 1:

objective₁＝F₁+F₂+F_penalty·n

in the formula:

F₁representing the total cost of the supercapacitor over the life cycle,

F₂represents the total cost of the battery over the life cycle,

F_penaltyrepresents the penalty cost of the photovoltaic power generation power not reaching the planned value within one year,

n represents the energy storage device lifecycle;

defining an objective function 2:

objective₂＝Reliability

reliabilitity is the amount of net power _ grid greater than zero, i.e., P is satisfied_extro(i) Not more than 0, and power _ grid ═ P_pv(i)-P_plan(i)-P_h(i) Number of time instants i > 0.

8. The optimal configuration method for optical storage capacity based on sub-band hybrid energy storage according to claim 7, wherein:

step 4, low-frequency historical power P_{lf_pv}＝{P_{lf_pv}(i)}_{i＝1,2,…,n}And low frequency planned power P_{lf_plan}＝{P_{lf_plan}(i)}_{i＝1,2,…,n}As low frequency group data, high frequency historical power P_{hf_pv}＝{P_{hf_pv}(i)}_{i＝1,2,…,n}And high frequency planning power P_{hf_plan}＝{P_{hf_plan}(i)}_{i＝1,2,…,n}As high-frequency group data, the high-frequency group data are respectively input into a fitness function for calculation so as to obtain the total cost objective of the life cycle of the energy storage device₁As the first objective function, the number objective with net power _ grid larger than zero₂Is the second objective function.

9. The optimal configuration method for optical storage capacity based on sub-band hybrid energy storage according to any one of claims 1 to 5, wherein:

in the steps 1-6, a conventional hydropower station is used for replacing a storage battery, and a pumped storage power station is used for replacing a super capacitor.

10. An optical storage capacity optimal configuration system based on sub-band hybrid energy storage based on the optical storage capacity optimal configuration method of any one of claims 1 to 9, wherein the optical storage capacity optimal configuration system comprises:

the data acquisition module is used for acquiring historical photovoltaic power generation data within a set time span;

the data transformation module is internally provided with an HHT decomposition unit and is used for decomposing the historical photovoltaic power generation data obtained by the data acquisition module into high-frequency power generation data and low-frequency power generation data;

an optimization module, a built-in genetic algorithm unit, which inputs the high-frequency power generation data and the low-frequency power generation data obtained by the data transformation module, randomly generates an initial population, takes the capacity of the super capacitor and the capacity of the storage battery as independent variables, and obtains the optimal capacity of the energy storage device,

and the display output module is used for visually displaying the data used and obtained by the data acquisition module, the data transformation module and the optimization module.

11. The system for optimized configuration of optical storage capacity based on sub-band hybrid energy storage according to claim 10,

the energy storage device is a super capacitor and a storage battery, or a conventional hydropower station and a water pumping and power storage station.

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