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

Optical storage capacity optimal configuration method and system based on sub-band mixed energy storage Download PDF

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CN111817313B
CN111817313B CN202010672667.1A CN202010672667A CN111817313B CN 111817313 B CN111817313 B CN 111817313B CN 202010672667 A CN202010672667 A CN 202010672667A CN 111817313 B CN111817313 B CN 111817313B
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energy storage
storage device
power
capacity
frequency
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CN111817313A (en
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张兴友
张元鹏
李俊恩
袁帅
张用
于芃
魏大钧
李广磊
王士柏
滕玮
程艳
孙树敏
史洁
程新功
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Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
State Grid Shandong Electric Power Co Ltd
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State Grid Shandong Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

A method and a system for optimal configuration of optical storage capacity based on mixed energy storage of frequency bands are provided, the method comprises: decomposing the photovoltaic output data based on Hilbert-Huang transformation, and decomposing the photovoltaic output data into high-frequency components and low-frequency components; randomly generating the capacity of an energy storage device as a variable; setting upper and lower limits of variables; inputting the generated initial energy storage device capacity into a fitness function, and calculating a target function in the fitness function; carrying out genetic variation on the current population so as to form a next generation population; detecting whether the genetic algebra reaches a set maximum genetic algebra, selecting to continue or finish the calculation according to the detection result, and taking the individual with the highest fitness of the last generation as a final calculation result; the optimal capacities of the super capacitor and the storage battery are respectively determined by using the energy storage optimization results of the high-frequency component and the low-frequency component, so that the defects of low photovoltaic dispersity, low energy density and intermittence are overcome, and the cost is greatly reduced.

Description

Optical storage capacity optimal configuration method and system based on sub-band mixed energy storage
Technical Field
The invention belongs to the field of photovoltaic power generation, and particularly relates to an optimal configuration method and system for optical storage capacity based on sub-band mixed energy storage.
Background
Photovoltaic power generation is one of the most important ways to utilize solar energy, and is also one of the fields with the most mature technology and the most commercial prospect in energy development and utilization in the world. Solar energy naturally has many defects, such as strong fluctuation, strong intermittency, obvious seasonal change and the like, and causes the problems of strong fluctuation of photovoltaic output, low stability, difficult control and the like, so that the power generation amount is limited. Configuring the photovoltaic with appropriate energy storage is one of the most efficient ways of photovoltaic has been long developed, where supercapacitors have been widely and successfully applied. Aiming at the characteristics of photovoltaic power generation, the requirements on the energy storage technology are high response speed and large storage capacity, and comprehensive consideration is taken, the invention provides a hybrid energy storage mode which is matched with photovoltaic output to operate so as to achieve the effect of optimizing an ultra-short-term grid-connected plan.
For photovoltaic-energy storage combined optimization operation and capacity configuration, many researches are already carried out at home and abroad, but the researches on how to select and design a hybrid energy storage mode are still less. The optimization design of the installed capacity involves many factors, such as the geographical location of the super capacitor, the development conditions, the unit investment and the like. When the capacity of the super capacitor is configured, if the configured capacity is large, the influence of the fluctuation of the photovoltaic can be eliminated better, but the investment of the corresponding capacity and the land is more, and if the configured capacity is small, the initial investment is naturally reduced, but the effect of solving the fluctuation of the photovoltaic is also small.
However, for photovoltaic development, the capacity configuration of the super capacitor and the storage battery with the optimal capacity is the most reasonable today, and the method has important significance in terms of construction investment and power grid operation.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a light storage capacity configuration method based on Hilbert-Huang transform and a genetic algorithm, which can provide the optimal capacity of an energy storage device corresponding to a photovoltaic system with a certain capacity, and has the advantages of simple calculation method and strong practicability.
The invention adopts the following technical scheme. An optical storage capacity optimal configuration method based on sub-band mixed energy storage is characterized by comprising the following steps:
step 1, acquiring historical photovoltaic power generation data within a set time span;
step 2, using HHT to decompose and process the historical photovoltaic power generation data obtained in the step 1 to obtain high-frequency power generation data and low-frequency power generation data;
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 initialized GEN is 0;
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;
step 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 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;
wherein HHT refers to the Hilbert-Huang transform.
Preferably, in step 1, the time span is one year, and the data sampling period is 15 min.
Preferably, in step 1, historical photovoltaic power generation power P within a set time span is obtainedpvAnd historical photovoltaic planned power Pplan(ii) a Wherein, Ppv＝{Ppv(i)}i＝1,2,…,n，Pplan＝{Pplan(i)}i＝1,2,…nThe sequence number i corresponds to the historical time.
Preferably, in step 2, the historical photovoltaic power generation power P in the set time span is obtained for step 1pvAnd historical photovoltaic planned power PplanThe data are decomposed by HHT, and the photovoltaic power generation data are decomposed into high-frequency historical power Phf_pv＝{Phf_pv(i)}i＝1,2,…,nLow frequency historical power Plf_pv＝{Plf_pv(i)}i＝1,2,…,nHigh frequency planned power Phf_plan＝{Phf_plan(i)}i＝1,2,…,nAnd low frequency planned power Plf_plan＝{Plf_plan(i)}i＝1,2,…,n。
Preferably, step 2 specifically comprises:
step 2.1, decomposing historical photovoltaic power generation power P by using EMDpvAnd historical photovoltaic planned power Pplan；
Figure BDA0002582903170000031
In the formula:
s (t) represents photovoltaic power generation historical data, namely P acquired in the step 1pvAnd Pplan，
ck(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 spectrum by using the HSA;
Figure BDA0002582903170000032
Figure BDA0002582903170000033
Figure BDA0002582903170000034
in the formula:
re represents a real part;
ak(t) represents the instantaneous amplitude of each IMF component;
ω (t) represents the instantaneous frequency;
theta (t) represents the instantaneous phase,
Figure BDA0002582903170000035
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;
preferably, wherein EMD refers to empirical mode decomposition; HSA refers to hilbert spectral analysis and IMF refers to intrinsic mode functions.
Preferably, step 3 specifically comprises:
defining independent variable super capacitor capacity x1And battery capacity x2，x1_min≤x1≤x1_max，x1_minAnd x1_maxRespectively a super capacitor capacity lower limit constraint and an upper limit constraint,
Figure BDA0002582903170000041
and x2_maxRespectively 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 devicep＝{Pp(i)i＝1,2,…,n}，Ppmin≤Pp(i)≤Ppmax，PpminAnd PpmaxRespectively a charging power lower limit constraint and an upper limit constraint;
defining the discharge power P of an energy storage deviceh＝{Ph(i)i＝1,2,…,n}，PhminAnd PhmaxRespectively 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 SOCmaxLower limit of state of charge SOCmin，SOC(i)＝SOCmaxThe energy storage device can not be charged continuously, and SOC (i) ═ SOCminWhen the battery is charged, the energy storage device can not continue to discharge, and the initial SOC (1) is 100%;
and defining the investment cost of the energy storage device with unit capacity.
Preferably, 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.
Preferably, the operation policy calculation includes:
calculating P of the energy storage device at each moment by the following formulaextro(i)i＝1,2,…nWherein
Pextro(i)＝Pplan(i)-Ppv(i)，
If P isextro(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 Ph(i)＝0；
If P isextro(i)>0, the energy storage device is in a discharge state at the moment i, and the discharge power of the energy storage device is zero, namely Pp(i)＝0。
Preferably, the energy storage device state calculation comprises: calculating the state of charge (SOC (i)) of the energy storage device according to the formulai＝1,2,…,nWherein SOC (1) ═ 100%,
if P isextro(i) Less than or equal to 0, the energy storage device is in a charging state at the moment i,
if P isextro(i)>0, indicating that the energy storage device is in a discharged 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.
Preferably, the energy storage device state calculation comprises: calculating the charge-discharge power P according to the following formulap(i)i＝1,2,…,nAnd Ph(i)i＝1,2,…,n，
If P isextro(i) Less than or equal to 0, indicating that the energy storage device is in a charging state at time i, Ph(i) P is calculated as follows when P is 0p(i)，
If P isextro(i)>0, indicating that the energy storage device is in a discharge state at time i, Pp(i) P is calculated as follows when P is 0h(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.
Preferably, the objective function calculation comprises: 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+Fpenalty·n
in the formula:
F1representing the total cost of the supercapacitor over the life cycle,
F2indicating battery life cycleThe total cost of the process is reduced to a total cost,
Fpenaltyrepresents 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 satisfiedextro(i) Is less than or equal to 0, and power _ grid is Ppv(i)-Pplan(i)-Ph(i)>0, time i.
Preferably, step 4, the low-frequency historical power P is usedlf_pv＝{Plf_pv(i)}i＝1,2,…,nAnd low frequency planned power Plf_plan＝{Plf_plan(i)}i＝1,2,…,nAs low frequency group data, high frequency historical power Phf_pv＝{Phf_pv(i)}i＝1,2,…,nAnd high frequency planning power Phf_plan＝{Phf_plan(i)}i＝1,2,…,nAs 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.
Preferably, in steps 1-6, the storage battery is replaced by a conventional hydropower station, and the super capacitor is replaced by a pumped storage power station.
The invention also provides an optical storage capacity optimal configuration system based on the sub-band mixed energy storage based on the optical storage capacity optimal configuration method, which is characterized by comprising the following steps:
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 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.
Preferably, the energy storage device is a super capacitor and a storage battery, or a conventional hydropower station and a pumped storage power station.
Compared with the prior art, the photovoltaic power generation fluctuation optimization method has the beneficial effects that the optimal configuration energy storage capacity is finally obtained by carrying out genetic variation on the result of the previous generation based on Hilbert-Huang transformation and a genetic algorithm, so that the defect of photovoltaic power generation fluctuation is better solved. And (3) adopting subsection treatment during capacity design, and respectively carrying out design of the capacity of the energy storage equipment and optimization determination of a power generation plan according to the low-frequency power component and the high-frequency power component.
The method is used for solving the problem of difficulty in large-scale centralized grid connection of the photovoltaic system, lays a foundation for the energy storage system to play a better role in electric power, and has obvious social value and economic value.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a fitness function workflow diagram;
FIG. 3 is a graph of low stability combining system output power and projected power;
FIG. 4 is a high stability joint system output power and projected power;
FIG. 5 is a comparison graph of charge and discharge power of an energy storage device of the high-stability combined system and the low-temperature qualitative combined system.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the present invention provides an optical storage capacity optimization configuration method based on sub-band hybrid energy storage, which includes the following steps:
step 1, obtaining historical photovoltaic power generation power P in a set time spanpvAnd historical photovoltaic planned power Pplan(ii) a Wherein, Ppv＝{Ppv(i)}i＝1,2,…,n，Pplan＝{Pplan(i)}i＝1,2,…nThe sequence number i corresponds to the historical time.
The skilled person can set the time span and the interval time of data sampling at will, as a non-restrictive optimization, the data time interval is 15 minutes, the data time span is at least 1 year, and the change rule of low frequency and high frequency can be captured more scientifically and intuitively by adopting more than one year of operation data to carry out time-frequency transformation.
Step 2, obtaining the historical photovoltaic power generation power P in the set time spanpvAnd historical photovoltaic planned power PplanThe data are decomposed, the photovoltaic power generation data are divided into high-frequency data and low-frequency data, the high-frequency output data are large in fluctuation up and down, the low-frequency output data are slow in fluctuation, for the low-frequency output, due to slow fluctuation, the corresponding transformation needing charging and discharging of the energy storage device is not so rapid, and the storage battery can process the low-frequency output; correspondingly, for high-frequency output data, the required charge and discharge change is obvious, so that a super capacitor is required for processing. Or the conventional hydropower station is used for replacing a storage battery to process low-frequency output data, and the pumped storage station is used for replacing a super capacitor to process high-frequency output data.
Specifically, P acquired in step 1 is decomposed using EMD (Empirical Mode Decomposition)pvAnd PplanIt is possible to smooth the non-stationary data and then obtain Hilbert spectrograms using HAS (Hilbert Spectrum Analysis), i.e. the P obtained in step 1 is processed by HHTpvAnd PplanObtaining
High frequency historical power Phf_pv，Phf_pv＝{Phf_pv(i)}i＝1,2,…,n，
Low frequency historical power Plf_pv，Plf_pv＝{Plf_pv(i)}i＝1,2,…,n，
High frequency planned power Phf_plan，Phf_plan＝{Phf_plan(i)}i＝1,2,…,n，
Low frequency planned power Plf_plan，Plf_plan＝{Plf_plan(i)}i＝1,2,…,n。
More specifically, the P obtained in step 1 is processed using the HHT formula as followspvAnd Pplan：
Figure BDA0002582903170000081
Figure BDA0002582903170000082
Figure BDA0002582903170000083
Figure BDA0002582903170000084
In the formula:
s (t) represents photovoltaic power generation historical data, namely P acquired in the step 1pvAnd Pplan；
ck(t) represents the IMF (Intrinsic Mode Function) component, i.e., c1(t) represents a high frequency component, c2(t) represents a low frequency component; respectively, contains components of the characteristic scale size at different moments of the signal.
r (t) represents a residual function representing the average trend of the signal;
re represents a real part;
ak(t) represents the amplitude of each IMF component;
ω (t) represents the instantaneous frequency;
theta (t) represents the instantaneous phase,
Figure BDA0002582903170000085
h (ω, t) represents the distribution of the instantaneous amplitude in the time, frequency plane;
h (ω) represents the distribution of the instantaneous amplitude in the frequency plane.
Step 3, initializing genetic algorithm parameters, specifically comprising:
defining independent variable super capacitor capacity x1And battery capacity x2，x1_min≤x1≤x1_max，x1_minAnd x1_maxRespectively a super capacitor capacity lower limit constraint and an upper limit constraint,
Figure BDA0002582903170000086
x2_minand x2_maxRespectively 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 devicep＝{Pp(i)i＝1,2,…,n}，Ppmin≤Pp(i)≤Ppmax，PpminAnd PpmaxRespectively a charging power lower limit constraint and an upper limit constraint;
defining the discharge power P of an energy storage deviceh＝{Ph(i)i＝1,2,…,n}，PhminAnd PhmaxRespectively 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 SOCmaxLower limit of state of charge SOCmin，SOC(i)＝SOCmaxThe energy storage device can not be charged continuously, and SOC (i) ═ SOCminAt this time, the energy storage device cannot continue to discharge, and the initial SOC (1) becomes 100%.
And 4, respectively inputting high-frequency power generation data and low-frequency power generation data, calculating a fitness function of the 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.
As shown in fig. 2, the fitness function includes a system operation policy, a stability calculation, and a lifecycle total cost calculation, and defines two objective functions.
And (3) operating strategies: and inputting the photovoltaic output low-frequency power, the photovoltaic low-frequency planned power and the capacity of the energy storage device, and outputting the net power at each moment, the total life cycle cost under the capacity of the energy storage device and the charge and discharge power of the energy storage device at each moment. And combining the net power and the high-frequency power together when the planned power is not reached, and processing the high-frequency data to achieve the result that the low-frequency generating power perfectly matches the planned power.
The fitness function of the invention comprises the following concrete steps:
1) and inputting population individuals, photovoltaic power and planning power.
2) And calculating the required charge and discharge power of the energy storage device by comparing the photovoltaic power with the planned power.
3) Required charge and discharge power is input into the battery module. Specifically, the actual charge and discharge power and the state of charge of the energy storage device at the next moment are calculated by utilizing the state of charge of the energy storage device at the previous moment, wherein the capacity constraint of the energy storage device is required to be utilized at the moment, and the residual capacity, the self-loss and the charge and discharge loss at the previous moment are considered.
4) And inputting the result obtained in the last step into an operation module, and calculating the target function through other parameters.
5) After the next group of individual populations is entered, step 1 is repeated.
The beneficial effects of the genetic algorithm adopted by the invention at least comprise: the population name generated by the selection operation in the genetic algorithm is a feasible population, and the method does not need to judge whether population individuals are feasible or not after the selection operation because the independent variable value range is set; this result can be retained by using both objective functions in the comparison in a more elegant manner.
Specifically, the low-frequency historical power Plf_pv＝{Plf_pv(i)}i＝1,2,…,nAnd low frequency planned power Plf_plan＝{Plf_plan(i)}i＝1,2,…,nAs low frequency group data, high frequency historical power Phf_pv＝{Phf_pv(i)}i＝1,2,…,nAnd high frequency planning power Phf_plan＝{Phf_plan(i)}i＝1,2,…,nAs 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 a second objective function;
calculating P of the energy storage device at each moment by the following formulaextro(i)i＝1,2,…nWherein
Pextro(i)＝Pplan(i)-Ppv(i)；
Calculating the state of charge (SOC) (i) of the energy storage device according to the following formulai＝1,2,…,nWherein SOC (1) ═ 100%,
if P isextro(i) Less than or equal to 0, indicating that the energy storage device is in a charging state at the moment i,
Figure BDA0002582903170000101
in the formula:
eta represents the inversion efficiency;
σ represents the energy storage device self-discharge loss;
wherein
Figure BDA0002582903170000102
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 isextro(i)>0, indicating that the energy storage device is in a discharged state at time i,
Figure BDA0002582903170000103
calculating the charge-discharge power P according to the following formulap(i)i＝1,2,…,nAnd Ph(i)i＝1,2,…,n
If P isextro(i) Less than or equal to 0, indicating that the energy storage device is in a charging state at time i, Ph(i) When equal to 0, calculate P as followsp(i)，
Figure BDA0002582903170000111
If P isextro(i)>0, indicating that the energy storage device is in a discharge state at time i, Pp(i) P is calculated as follows when P is 0h(i)，
Figure BDA0002582903170000112
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+Fpenalty·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,
Fpenaltyrepresents 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 satisfiedextro(i) Not more than 0, and power _ grid ═ Ppv(i)-Pplan(i)-Ph(i)>0, time i.
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;
step 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 highest population fitness of the last generation as a final result, and outputting the optimal capacity of the energy storage device, wherein the output result of the input high-frequency power generation data is the optimal super-capacitor capacity, and the output result of the input low-frequency power generation data is the optimal storage battery capacity;
and 6, replacing a storage battery with a conventional hydropower station, and replacing a super capacitor with a pumped storage power station.
The invention also provides an optical storage capacity optimal configuration system based on the sub-band mixed energy storage based on the optical storage capacity optimal configuration method, and 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.
The energy storage device can be a combination of a super capacitor and a storage battery, and can also be a combination of a conventional hydropower station and a water pumping and power storage station.
In order to more clearly introduce the technical scheme and the beneficial effects of the invention, the following introduction is an example analysis:
inputting photovoltaic output data of each 15min in a certain area within one year, dividing the data into high-frequency and low-frequency power generation power through Hilbert-Huang conversion, setting the number of populations to be 50, setting the upper limit of the capacity of a storage battery to be 3000kWh, setting the upper limit of the capacity of a super capacitor to be 1000kWh, and finally reserving 50 optimal solutions through an established mathematical model and a genetic algorithm.
Comparing the capacities of the two energy storage devices corresponding to the highest and lowest total costs of the system in the optimal solution, wherein the capacity of the corresponding battery with the highest total cost is 2872.52kWh and the capacity of the corresponding supercapacitor is 340.38 kWh. The lowest total cost corresponds to a capacity of 425.659kWh for the battery and 890.9kWh for the supercapacitor. In the operation strategy block, the power required at the moment when the photovoltaic low-frequency generating power does not meet the planned power is supplemented by a high-frequency part, and because the low-frequency generating power is much larger than the high-frequency generating power, the power input into the power grid by the low-frequency generating power is more stable as much as possible. The higher cost 30 hours output power to projected power ratio of the combined system is shown in fig. 3, the lower cost 30 hours output power to projected power ratio of the combined system is shown in fig. 4, and fig. 5 is a comparison graph of charging and discharging the higher cost energy storage device and the lower cost energy storage device.
For the combined light and storage system under the same parameters, the total cost of the system using the hybrid energy is obviously lower compared with the total cost of the system without the hybrid energy as shown in the table below.
TABLE 1 method with hybrid energy vs. total cost of a system without hybrid energy
Figure BDA0002582903170000131
Compared with the prior art, the photovoltaic power generation fluctuation optimization method has the beneficial effects that the optimal configuration energy storage capacity is finally obtained by carrying out genetic variation on the result of the previous generation based on Hilbert-Huang transformation and a genetic algorithm, so that the defect of photovoltaic power generation fluctuation is better solved. And (3) adopting segmented processing during capacity design, and respectively carrying out design of the capacity of the energy storage equipment and optimization determination of a power generation plan aiming at the low-frequency power component and the high-frequency power component.
The method is used for solving the problem of difficulty in large-scale centralized grid connection of the photovoltaic system, lays a foundation for the energy storage system to play a better role in electric power, and has obvious social value and economic value.
The term is defined as:
HHT: Hilbert-Huang Transform, Hilbert-yellow Transform;
EMD: empical Mode Decomposition, Empirical Mode Decomposition
IMF: intrinsic Mode Function, Intrinsic Mode Function;
HSA: hilbert Spectrum Analysis, Hilbert Spectrum.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the 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 spanpvAnd historical photovoltaic planned power Pplan(ii) a Wherein, Ppv＝{Ppv(i)}i＝1,2,…,n，Pplan＝{Pplan(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 1pvAnd historical photovoltaic planned power PplanDecomposing the data by using Hilbert-Huang transform to decompose the photovoltaic power generation data into high-frequency historical power Phf_pv＝{Phf_pv(i)}i＝1,2,…,nLow frequency historical power Plf_pv＝{Plf_pv(i)}i＝1,2,…,nHigh frequency planned power Phf_plan＝{Phf_plan(i)}i＝1,2,…,nAnd low frequency planned power Plf_plan＝{Plf_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 EMDpvAnd historical photovoltaic planned power Pplan；
Figure FDA0003560812910000011
In the formula:
s (t) represents photovoltaic power generation historical data, namely P acquired in the step 1pvAnd Pplan，
ck(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;
Figure FDA0003560812910000012
Figure FDA0003560812910000013
Figure FDA0003560812910000014
in the formula:
re represents a real part;
ak(t) represents the instantaneous amplitude of each IMF component;
ω (t) represents the instantaneous frequency;
theta (t) represents the instantaneous phase,
Figure FDA0003560812910000021
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，x1_min≤x1≤x1_max，x1_minAnd x1_maxRespectively a super capacitor capacity lower limit constraint and an upper limit constraint, x2_min≤x2≤x2max，x2_minAnd x2_maxRespectively 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 devicep＝{Pp(i)i＝1,2,…,n}，Ppmin≤Pp(i)≤Ppmax，PpminAnd PpmaxRespectively a charging power lower limit constraint and an upper limit constraint; defining the discharge power P of an energy storage deviceh＝{Ph(i)i＝1,2,…,n}，PhminAnd PhmaxRespectively 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 SOCmaxLower limit of state of charge SOCmin，SOC(i)＝SOCmaxThe energy storage device can not be charged continuously, and SOC (i) ═ SOCminWhen 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 formulaextro(i)i＝1,2,…nWherein
Pextro(i)＝Pplan(i)-Ppv(i)，
If P isextro(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 Ph(i)＝0；
If P isextro(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 Pp(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 formulai＝1,2,…,nWherein SOC (1) ═ 100%,
if P isextro(i) Less than or equal to 0, the energy storage device is in a charging state at the moment i,
Figure FDA0003560812910000031
if P isextro(i) Is greater than 0, indicating that the energy storage device is in a discharge state at time i,
Figure FDA0003560812910000041
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 formulap(i)i＝1,2,…,nAnd Ph(i)i＝1,2,…,n，
If P isextro(i) Less than or equal to 0, indicating that the energy storage device is in a charging state at time i, Ph(i) P is calculated as follows when P is 0p(i)，
Figure FDA0003560812910000042
If P isextro(i) Greater than 0, indicating that the energy storage device is in a discharge state at time i, Pp(i) P is calculated as follows when P is 0h(i)，
Figure FDA0003560812910000043
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+Fpenalty·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,
Fpenaltyrepresents 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 satisfiedextro(i) Not more than 0, and power _ grid ═ Ppv(i)-Pplan(i)-Ph(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 Plf_pv＝{Plf_pv(i)}i＝1,2,…,nAnd low frequency planned power Plf_plan＝{Plf_plan(i)}i＝1,2,…,nAs low frequency group data, high frequency historical power Phf_pv＝{Phf_pv(i)}i＝1,2,…,nAnd high frequency planning power Phf_plan＝{Phf_plan(i)}i＝1,2,…,nAs 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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