CN115545275A - Energy storage capacity optimal configuration method for stabilizing photovoltaic output by using Butterse Wo Lvbo method - Google Patents
Energy storage capacity optimal configuration method for stabilizing photovoltaic output by using Butterse Wo Lvbo method Download PDFInfo
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Abstract
The disclosure provides an energy storage capacity optimal configuration method for stabilizing photovoltaic output by a Butterse Wo Lvbo method, and relates to the field of photovoltaics. The method comprises the following specific steps: acquiring original output data of a photovoltaic power station; filtering the original output data according to a Butterse Wo Ditong filter to obtain filtered data; constructing a genetic algorithm model, and determining algorithm parameters and a fitness function of the genetic algorithm; and performing optimization operation based on the genetic algorithm, the filtering data and the fitness function to obtain recommended energy storage capacity, and constructing an energy storage device according to the recommended energy storage capacity. According to the method and the device, the recommended energy storage capacity is obtained by performing optimization operation according to the genetic algorithm, the filtering data and the fitness function, the energy storage capacity of the energy storage device is optimized, the energy waste of a photovoltaic power station is avoided, and the operating efficiency of the energy storage hybrid system is improved.
Description
Technical Field
The disclosure relates to the field of new energy, in particular to an energy storage capacity optimal configuration method for stabilizing photovoltaic output by a Butterse Wo Lvbo method.
Background
In recent years, with the rapid development of renewable energy power generation technology, under the background of a large amount of new energy grid-connected power generation, an energy configuration structure is reasonably improved, and the safe and reliable operation of a power network is ensured, so that the method becomes an important research direction for the development of a power system. The high randomness and the fluctuation of the output of the photovoltaic power station can bring challenges to the safe operation and peak regulation scheduling of a power grid, and limit the consumption of renewable energy to a greater extent.
A hydraulic power generation and thermal generator set in a traditional power grid are used as main peak-load and frequency modulation power supplies, and the output of the generator set is continuously changed according to the frequency change of a system by methods such as primary frequency modulation and secondary frequency modulation. However, for a photovoltaic power station, the photovoltaic power station is limited by the self power generation characteristics, and the traditional frequency modulation method has certain limitation, and may affect the adjustment quality of the power grid frequency and even the safe and stable operation. Therefore, in order to adapt to the trend that the machine capacity occupation ratio of the photovoltaic energy in the power system is continuously expanded and reduce the impact of large-scale grid connection on the power system, the configuration of the energy storage device on the power grid side is an effective means for improving the wind and light energy consumption capability. In the configuration process of the energy storage capacity of the energy storage hybrid system, the performance and the economy need to be balanced, if the balance is not good, the energy of the photovoltaic power station is wasted, and the operation efficiency of the energy storage hybrid system is reduced.
Disclosure of Invention
The invention provides an energy storage capacity optimal configuration method for stabilizing photovoltaic output by a Butterse Wo Lvbo method, which at least solves the problem that the operation efficiency of a photovoltaic power station is reduced due to improper energy storage capacity configuration in the related technology. The technical scheme of the disclosure is as follows:
according to a first aspect of the embodiments of the present disclosure, there is provided an energy storage capacity optimal configuration method for stabilizing photovoltaic output by a barth Wo Lvbo method, including:
acquiring original output data of a photovoltaic power station;
filtering the original output data according to a Butterse Wo Ditong filter to obtain filtered data;
constructing a genetic algorithm model, and determining algorithm parameters and a fitness function of the genetic algorithm;
and performing optimization operation based on the genetic algorithm, the filtering data and the fitness function to obtain recommended energy storage capacity, and constructing an energy storage device according to the recommended energy storage capacity.
Optionally, the formulation of the transfer function in the butterworth low-pass filter is expressed as:
where n is the order of the Butterworth low-pass filter, D 0 D (u, v) is the distance of the point (u, v) from the origin, and H is the amplitude.
Optionally, the cutoff frequency of the butterworth low-pass filter is a frequency point attenuated by 3 dB.
Optionally, the algorithm parameters specifically include:
the population scale Q, the cross probability Pe and the mutation probability Pm.
Optionally, the fitness function is formulated as:
wherein LCC is the total operating cost of the energy storage device, and ICC is the initial cost of the energy storage device; n is the life of the energy storage device; n is the nth year of operation of the energy storage device; d is a radical of n Depreciation in the nth year; i is interest rate; tr is the tax rate; a is a n The maintenance and operation cost of the energy storage device in the nth year; r is the r-th replacement part; r is the total replacement times in the operation period of the energy storage device; ICCC is the cost of the part to be replaced; lc is the lifetime of the c-th part to be replaced; s is the residual value (element); the penalty is the electric quantity which does not reach the ideal grid-connected value in one year; m is punishment electricity price; wherein the total number of replacements R is a function of the lifetime of the part to be replaced.
Optionally, the performing optimization operation based on the genetic algorithm, the filtering data, and the fitness function to obtain the recommended energy storage capacity specifically includes:
randomly generating Q energy storage capacity values, and taking the energy storage capacity values as individuals to construct a population X (t);
acquiring fitness function values corresponding to individuals in the population according to the filtering data;
and carrying out evolution processing on the population X (t) until the fitness function value of the individual in the population meets the termination optimization condition, wherein t is the evolution frequency of the population.
Optionally, the evolving the population X (t) includes:
selecting Y/2 pairs of parent bodies from X (t) by using a preset selection operator, wherein Y is greater than or equal to Q;
for the found Y/2 pairs of parents, determining a target parent pair of cross operation according to the cross probability Pe and performing cross operation to obtain Y individuals;
carrying out variation on the obtained Y individuals according to the variation probability Pm to generate Y varied individuals;
and (4) screening Q individuals from the generated Y variant individuals according to the corresponding fitness function values to generate a next generation population X (t + 1).
Optionally, the evolving the population X (t) until the fitness function value of an individual in the population meets a termination optimization condition, where the evolving includes:
if the fitness function value of the individual in the population X (t + 1) meets the termination optimization condition, outputting the individual with the maximum fitness function value in the population X (t + 1) as an optimal solution, and stopping evolution processing;
otherwise, continuing to carry out evolution treatment on the population.
According to a second aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the energy storage capacity optimization configuration method for stabilizing photovoltaic output according to the barts Wo Lvbo method as described in any of the first aspect above.
According to a third aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, wherein instructions, when executed by a processor of an electronic device, enable the electronic device to perform the energy storage capacity optimization configuration method for stabilizing photovoltaic output according to bartess Wo Lvbo method as described in any one of the first aspect above.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
the recommended energy storage capacity is obtained by performing optimization operation according to the genetic algorithm, the filtering data and the fitness function, so that the energy storage capacity of the energy storage device is optimized, the energy waste of a photovoltaic power station is avoided, and the operating efficiency of the energy storage hybrid system is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.
Fig. 1 is a flow chart illustrating a method for optimally configuring energy storage capacity to suppress photovoltaic contribution according to the barts Wo Lvbo method, according to an exemplary embodiment.
Fig. 2 is a schematic diagram illustrating a filter frequency response according to an exemplary embodiment.
FIG. 3 is a schematic illustration of photovoltaic power plant raw output data and filtered data, according to an exemplary embodiment.
Fig. 4 is a flow chart illustrating a method for optimally configuring energy storage capacity to stabilize photovoltaic output with a barth Wo Lvbo method, according to an exemplary embodiment.
Fig. 5 is a flow chart illustrating a method for optimally configuring energy storage capacity to stabilize photovoltaic output with a barth Wo Lvbo method, according to an exemplary embodiment.
FIG. 6 is a diagram illustrating an arrangement of genetic algorithms after optimization according to an exemplary embodiment.
FIG. 7 is a photovoltaic power plant force diagram shown in accordance with an exemplary embodiment.
FIG. 8 is a graph illustrating a total cost of operation and a light rejection ratio of an energy storage device according to an exemplary embodiment.
FIG. 9 is a block diagram illustrating an apparatus in accordance with an example embodiment.
FIG. 10 is a block diagram illustrating an apparatus in accordance with an example embodiment.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as recited in the claims appended hereto.
In recent years, with the rapid development of renewable energy power generation technology, under the background of a large amount of new energy grid-connected power generation, an energy configuration structure is reasonably improved, and the safe and reliable operation of a power network is ensured, so that the method becomes an important research direction for the development of a power system. The high randomness and the fluctuation of the output of the photovoltaic power station can bring challenges to the safe operation and peak regulation scheduling of a power grid, and limit the consumption of renewable energy to a greater extent.
A hydraulic power generation and thermal generator set in a traditional power grid are used as main peak-load and frequency modulation power supplies, and the output of the generator set is continuously changed according to the frequency change of a system by methods such as primary frequency modulation and secondary frequency modulation. However, for a photovoltaic power station, the photovoltaic power station is limited by the self power generation characteristics, and the traditional frequency modulation method has certain limitation, and may affect the adjustment quality of the power grid frequency and even the safe and stable operation. Therefore, in order to adapt to the trend that the machine capacity of the photovoltaic energy in the electric power system is continuously expanded, and reduce the impact of large-scale grid connection on the electric power system, the arrangement of the energy storage device on the electric network side is an effective means for improving the light energy consumption capability. And in the configuration process of the energy storage capacity of the energy storage hybrid system, the performance and the economy need to be balanced, if the balance is not good, the energy of the photovoltaic power station is wasted, and the operation efficiency of the energy storage hybrid system is reduced.
Fig. 1 is a flow chart illustrating a method for optimally configuring energy storage capacity to suppress photovoltaic contribution according to the barts Wo Lvbo method, according to an exemplary embodiment. As shown in fig. 1, the method includes:
In the embodiment of the application, because the data of exerting oneself of photovoltaic power plant is influenced by the meteorological data around the power plant, the meteorological data possess randomness, intermittent type nature and fluctuating characteristics, the data of exerting oneself of photovoltaic power plant also possesses these characteristics, can't control the processing data of photovoltaic power plant through the manual work, so when the recommendation energy storage capacity of analysis suitable energy storage hybrid system, need carry out the analysis through the original data of exerting oneself of photovoltaic power plant in the past, the original data of exerting oneself is the active power of photovoltaic power plant output, the step length of the original data of exerting oneself is 1h (hour).
And 102, filtering the original output data according to a Butterse Wo Ditong filter to obtain filtered data.
In the embodiment of the application, the original output data comprises some interference data, and in order to filter the interference data in the original output data, reduce noise in the data and improve the accuracy of the recommended energy storage capacity obtained after the subsequent genetic algorithm is optimized, the original output data is filtered by using a Butterse Wo Ditong filter to obtain filtered data.
The butterworth filter is characterized by a frequency response curve in the pass band that is maximally flat with no fluctuations, and gradually drops to zero in the stop band. On the bode plot of the logarithm of the amplitude against the angular frequency, starting from a certain boundary angular frequency, the amplitude decreases gradually with increasing angular frequency, tending to minus infinity. The attenuation ratio of the first order butterworth filter is 6 db per octave and 20 db per decade. The second order butterworth filter has an attenuation rate of 12 db per octave, the third order butterworth filter has an attenuation rate of 18 db per octave, and so on. The amplitude versus the diagonal frequency of the butterworth filter decreases monotonically and is also the only filter in which the amplitude versus the diagonal frequency curve maintains the same shape regardless of order. Except that the higher the filter order, the faster the amplitude attenuation speed in the stop band. Other filters have different shapes for the amplitude versus angular frequency plot of the higher order and the amplitude versus angular frequency of the lower order numbers.
Fig. 2 is a schematic diagram illustrating a filter frequency response according to an exemplary embodiment. As shown in fig. 2, a bode graph, which is a frequency response diagram of a filter, is a semilogarithmic graph of a transfer function of a linear time-invariant system to frequency, and the frequency response of the system can be seen by using the bode graph. dB is a ratio, a numerical value, a pure counting method, without any unit labels. Fig. 2-a is a schematic diagram of the frequency response of a butterworth filter, fig. 2-B is a schematic diagram of the frequency response of a first type of chebyshev filter of the same order as the butterworth filter, fig. 2-C is a schematic diagram of the frequency response of a second type of chebyshev filter of the same order as the butterworth filter, and fig. 2-D is a schematic diagram of the frequency response of an elliptic function filter of the same order as the butterworth filter. It can be seen that the signal passing through the butterworth filter is the flattest, so the butterworth filter construction model is selected in this embodiment.
And 103, constructing a genetic algorithm model, and determining algorithm parameters and a fitness function of the genetic algorithm.
In the embodiment of the application, optimization operation is performed through a Genetic Algorithm to obtain a proper energy storage capacity, the Genetic Algorithm (GA) is originated from computer simulation research on a biological system, and is a random global search optimization method, which simulates phenomena of natural selection and replication, crossing (cross) and mutation (mutation) in heredity, and from any Population (Population), a group of individuals more suitable for the environment are generated through random selection, crossing and mutation operations, so that the Population evolves to a better and better area in a search space, and thus a generation is continuously evolved and finally converged to a group of individuals (Individual) most suitable for the environment, and thus a high-quality solution of the problem is obtained. Firstly, algorithm parameters of a genetic algorithm and a fitness function need to be set, wherein the algorithm parameters are used for controlling the population evolution process of the genetic algorithm, and the fitness function is used for judging whether individuals in a population meet an optimization target or not.
And 104, performing optimization operation based on the genetic algorithm, the filtering data and the fitness function to obtain recommended energy storage capacity, and constructing an energy storage device according to the recommended energy storage capacity.
In the embodiment of the application, a population is generated based on a genetic algorithm, individuals in the population are energy storage capacity, the population is evolved based on filtering data until the population meets the condition of stopping evolution, and at the moment, the fitness function value corresponding to the population individuals meets the preset condition. And acquiring the optimal energy storage capacity corresponding to the individual as the recommended energy storage capacity. And then, an energy storage device can be built according to the recommended energy storage capacity, the built photovoltaic power station meets the optimization target during operation, the economy is met, the operation cost is low, and the power generation capacity of the photovoltaic power station cannot be wasted.
Optionally, the formulation of the transfer function in the butterworth low-pass filter is expressed as:
where n is the order of the Butterworth low-pass filter, D 0 D (u, v) is the distance of the point (u, v) from the origin, and H is the amplitude.
Optionally, the cutoff frequency of the butterworth low-pass filter is a frequency point attenuated by 3 dB.
In the embodiment of the present application, a frequency point attenuated by 3dB, that is, a frequency point corresponding to gain-3 dB, is 10lg2=3 according to the principle of gain operation, that is, power corresponding to 3dB bandwidth is reduced by half. When the amplitude of the input signal is kept unchanged, the frequency is changed to reduce the output signal to 0.707 times of the maximum value, namely, the point of-3 dB is the cut-off frequency expressed by frequency response characteristics, and the cut-off frequency is a special frequency for explaining the frequency characteristic index. The cutoff frequency is the boundary frequency at which the energy of the output signal of a system begins to drop significantly or rise significantly in the band stop filter.
FIG. 3 is a schematic illustration of photovoltaic power plant raw output data and filtered data, according to an exemplary embodiment. As shown in fig. 3, fig. 3-a is a schematic diagram of raw output data, fig. 3-B is a schematic diagram of filtered data, and the raw output data of the photovoltaic power station is converted into a smoother curve after being filtered, so that convenience and possibility are provided for photovoltaic output grid connection. However, after the filtering operation, part of the high-frequency power component is separated and removed, which causes the waste of photovoltaic output and may cause the light rejection in this scenario.
Optionally, the algorithm parameters specifically include:
the population scale Q, the cross probability Pe and the variation probability Pm.
In the embodiment of the application, the population scale is the number of individuals in one population, and one individual corresponds to one energy storage capacity value in the application. The cross operation refers to randomly selecting two individuals from a population as a pair of parents according to a certain cross probability Pe, and transmitting the excellent characteristics of the parents to the substrings through the exchange combination of two chromosomes, so as to generate new individuals, wherein common cross operators comprise single-point cross, two-point cross, multi-point cross, uniform cross, arithmetic cross and the like, and the cross positions are also random. The crossover probability is generally very high, generally between 0.6 and 0.9. The mutation operation is to change a small part of genes into alleles according to a certain mutation probability Pm for each individual in the population. The variation can keep the diversity of the population and prevent the loss of important genes, but the variation probability is not suitable to be too large and is generally 0.001-0.1.
Optionally, the fitness function is formulated as:
wherein LCC is the total operating cost of the energy storage device, and ICC is the initial cost of the energy storage device; n is the life of the energy storage device; n is the nth year of operation of the energy storage device; d n Depreciation in the nth year; i is interest rate; tr is the tax rate; a is a n Maintenance and operation costs for the energy storage device in the nth year; r is the r-th replacement part; rThe total replacement times in the operation period of the energy storage device; ICCC is the cost of the part to be replaced; lc is the lifetime of the c-th part to be replaced; s is a residual value (element); the penalty is the electric quantity which does not reach the ideal grid-connected value in one year; m is punished electricity price; wherein the total number of replacements R is a function of the lifetime of the part to be replaced.
In the embodiment of the application, the fitness function is constructed according to the operation cost of the energy storage device, the ICC is the initial cost of the energy storage device, namely the cost required by the energy storage device is purchased and installed, the ICC is in direct proportion to the energy storage capacity of the energy storage device, and the larger the energy storage capacity is, the higher the required initial cost is.
Energy storage devices are subject to depreciation every year, and the depreciated price can reduce the total operating cost.
Reflecting the costs spent in the first to nth years in maintaining and operating the energy storage device. During the operating cycle of the energy storage device, the life of some of the components is short, requiring periodic replacement of the components. The total replacement times of each component to be replaced in the operation cycle of the energy storage device are related to the service life of the component to be replaced, and the specific formula is as follows:
floor is a Matlab function used to round a number to the next small integer, and subtracting 1 to avoid the last replacement of the energy storage device in view of the residual value, so as to reduce the operating cost of the energy storage device.
Reflecting the residual value of the energy storage means, i.e. the residual value expected to be recovered at the expiry of the use of an asset, i.e. at a fixed assetThe price that can be collected by the disposal assets when the production and use period is expired is larger, and the larger N is, the smaller is the residual value. In the power grid, if the energy storage device does not reach the electric quantity of an ideal grid-connected value in one year, the penalty is received, and the penalty M reflects the penalty in an operation period.
Fig. 4 is a flow chart illustrating a method for optimally configuring energy storage capacity to stabilize photovoltaic output with a barth Wo Lvbo method, according to an exemplary embodiment. As shown in fig. 4, step 104 in fig. 1 specifically includes:
in the embodiment of the application, in the optimization process of the genetic algorithm, firstly, a population and individuals in the population need to be generated, Q energy storage capacity values, namely the individuals, are randomly generated according to the set population scale Q, and the population X (0) is formed according to the individuals. X (0) is the initial population, and 0 in X (0) represents that the population has not undergone evolution, namely 0 times of evolution.
in the embodiment of the present application, the fitness function is a mathematical function used for evaluating the quality of the individual, and is generally mapped to the fitness function by the objective function, or the fitness of the individual is directly represented by the objective function.
And 403, performing evolution processing on the population X (0) to obtain a population X (t) until the fitness function value of the individual in the population meets a termination optimization condition, wherein t is the evolution frequency of the population.
In the embodiment of the application, the genetic algorithm uses Darwin's biological evolution theory and Mendel's genetic law for reference, and uses the principle of survival of the fittest to successively generate a solution approximate to the optimal solution in the potential solutions. In each generation of the genetic algorithm, selection is carried out according to the fitness value of the individual, and a new generation of the individual is generated according to the genetic rule. In the process, the individual fitness of the population is continuously enhanced, and the obtained solution is also continuously close to the optimal solution. After X (0) is generated, evolution treatment needs to be carried out on the population X (0) to obtain a population X (t), and through continuous iterative evolution, fitness functions of individuals in the population are enabled to be closer to a preset target.
Fig. 5 is a flow chart illustrating a method for optimally configuring energy storage capacity to stabilize photovoltaic output with a barth Wo Lvbo method, according to an exemplary embodiment. As shown in fig. 5, the step of performing evolution processing on the population specifically includes:
and step 501, selecting Y/2 pairs of matrixes from X (t) by using a preset selection operator, wherein Y is greater than or equal to Q.
In the embodiment of the present application, during a single population evolution process, such as the evolution process of X (t), a process of population breeding offspring is simulated, and in the process, the genetic algorithm uses the following three genetic operators: selection operators, crossover operators and mutation operators. Firstly, two parents are selected to propagate (generate) offspring (new individuals), Y/2 pairs of parents are selected from X (t) through a preset selection operator, the new individuals are generated through the parents, each pair of parents generate two new individuals, and considering that the new individuals are screened, the total number of the new individuals is larger than Q, namely Y is larger than or equal to Q. The probability that the individual is selected is related to the fitness function value, and the higher the individual fitness function value is, the higher the probability of being selected is. Taking the roulette method as an example, if the population number is Q, the fitness of the individual i is f i Then the probability that the individual i is selected is:
in the embodiment of the application, the crossover operation refers to that Y/2 pairs of parents are found from a population, and the excellent characteristics of the parents are inherited to offspring through the exchange combination of two chromosomes, so that new excellent individuals are generated. After obtaining good individuals in the population, parts of chromosomes between them are exchanged with a certain probability (genetic probability). The cross operation is controlled by the cross probability, the larger cross probability can enhance the genetic algorithm to open up a new search field, but the solution is more destructive, and the solution is generally 0.25 to 1. And selecting a target parent from the parents according to the cross probability, and performing cross operation through a cross operator.
Wherein the crossover operator comprises:
a) Double-point crossing or multi-point crossing, namely randomly setting two or more crossing points for paired chromosomes, and then carrying out crossing operation to change the gene sequence of the chromosomes.
b) The new gene sequence is formed by uniform crossing, namely, crossing each position on the paired chromosomal gene sequences with equal probability.
c) Arithmetic crossover refers to the crossing of paired chromosomes in a linear combination mode to change the gene sequence of the chromosomes.
d) And a single-point crossing operator, wherein the operator randomly selects a crossing position in the paired chromosomes, and then performs gene locus transformation on the paired chromosomes at the crossing position.
in the present embodiment, each individual in the population changes a small number of genes to alleles with a mutation probability. The variation can keep the diversity of the population and prevent the loss of important genes, but the variation probability is not too large, and is generally 0.001-0.1. And determining which positions of the individual need mutation according to the mutation probability, and performing mutation operation through a mutation operator.
In practical applications, single-point mutation, also called site mutation, is mainly used, i.e. only one site of the individual gene sequence needs to be mutated, taking binary coding as an example, i.e. 0 is changed to 1, and 1 is changed to 0.
And step 504, screening Q individuals from the generated Y variant individuals according to the corresponding fitness function values to generate a next generation population X (t + 1).
After the individuals in X (t) are selected, crossed and mutated, a complete evolutionary process of the population is completed, Q individuals are obtained by screening according to the fitness function value, and the individuals form the next generation population X (t + 1). Each time an evolution is made, the number in X () is incremented by 1.
Optionally, the evolving the population X (0) to obtain the population X (t) until the fitness function value of the individual in the population meets the termination optimization condition includes:
if the fitness function value of the individual in the population X (t + 1) meets the termination optimization condition, outputting the individual with the maximum fitness function value in the population X (t + 1) as an optimal solution, and stopping evolution processing;
otherwise, continuing to carry out evolution treatment on the population.
In the embodiment of the present application, a certain termination optimization condition is set, and if the fitness function is smaller than a certain preset threshold, when the fitness function value of an individual in X (t + 1) is smaller than the preset threshold, it indicates that the operation cost of the energy storage device reaches an expected value, and the evolution can be stopped. Otherwise, the population needs to be subjected to evolution treatment.
FIG. 6 is a diagram illustrating an arrangement of genetic algorithms after optimization according to an exemplary embodiment. As shown in fig. 3, the fitness function includes, in addition to the total operating cost of the energy storage device, a formula related to the light rejection rate:
wherein PV _ a is the light rejection, PV is the power generation amount of the photovoltaic power station, and PV _ plan is the power absorbed by the power grid. The higher the light rejection rate is, the more photovoltaic power generation power is wasted, and the light rejection rate is set to be smaller than a certain preset threshold value.
In order to verify the effectiveness of the method, a 1000 ten thousand kilowatt photovoltaic power station at the position of the cangjiang is selected for analysis. The original output data takes 1h as a time step, 8760 pieces of data are selected from one year of photovoltaic output data, and the original power of the photovoltaic power station is converted into a smoother curve after being filtered, so that convenience and possibility are provided for photovoltaic output grid connection. However, after the filtering operation, part of high-frequency power components are separated and removed, which causes the waste of photovoltaic output, and constitutes the main reason for discarding light in this scenario.
Smooth output of the photovoltaic power station after Butterworth filtering is used as a target grid-connected value, optimization of a genetic algorithm is carried out, and an optimized arrangement diagram shows that the photovoltaic power station light rejection rate which meets the grid-connected requirement of a system and has the minimum cost is in a range of 0.01% -5.53%. However, in the process of decreasing the light rejection rate, the cost increases. Therefore, different energy storage capacities are selected and should be comprehensively considered under the conditions of stability, economy and light rejection rate. The project selects energy storage capacities which account for 5%,10%,15% and 20% of installed capacity of the photovoltaic power station respectively.
FIG. 7 is a photovoltaic power plant force diagram shown in accordance with an exemplary embodiment. As shown in FIG. 7, successive data point output values are selected, illustrating the actual output of the energy storage system at different energy storage capacities, and compared to the ideal projected value after Butterworth filtering. Fig. 7, 4-1, shows the output and target grid-connected output values under different energy storage capacity ratios, and it can be seen that the grid-connected effect of the configured energy storage is significant, the high-frequency photovoltaic output value remaining from filtering is absorbed, and the coincidence degree of the actual output and the target output value of the optical storage system under the four energy storage capacities is high. If the positions of the three boxes in 4-1 in FIG. 7 are enlarged, they are shown as 4-2 in FIG. 7, 4-3 in FIG. 7, and 4-4 in FIG. 7 in sequence from left to right. Wherein, 4-2 in fig. 7 and 4-3 in fig. 7 are in a complete fit state, and the remaining figures show that the fit of the hybrid energy system is the worst when the energy storage capacity accounts for 5% of the installed capacity of the photovoltaic power station. FIG. 8 is a graph illustrating a total cost of operation and a light rejection ratio of an energy storage device according to an exemplary embodiment. As shown in fig. 8, the photovoltaic power plant exhibits the best hybrid performance when equipped with its installed capacity of 10% and 15%, with the energy storage capacity being boosted from 10% to 15%15%, light rejection rate is improved by about 1%, and cost is increased by 3 x 10 9 A meta. Considering in conjunction with fig. 7, under the conditions of the scenario in fig. 6, equipping a 1000-kilo-kilowatt photovoltaic plant with a 1000MWh energy storage device has the best performance.
Fig. 9 is a block diagram illustrating an apparatus 900 in accordance with an example embodiment. For example, the apparatus 900 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 9, the apparatus 900 may include one or more of the following components: a processing component 902, a memory 904, a power component 906, a multimedia component 908, an audio component 910, an input/output (I/O) interface 912, a sensor component 914, and a communication component 916.
The processing component 902 generally controls overall operation of the device 900, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to perform all or a portion of the steps of the methods described above. Further, processing component 902 may include one or more modules that facilitate interaction between processing component 902 and other components. For example, the processing component 902 can include a multimedia module to facilitate interaction between the multimedia component 908 and the processing component 902.
The memory 904 is configured to store various types of data to support operation at the device 900. Examples of such data include instructions for any application or method operating on device 900, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 904 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 908 comprises a screen providing an output interface between the device 900 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front facing camera and/or a rear facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the device 900 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 910 is configured to output and/or input audio signals. For example, audio component 910 includes a Microphone (MIC) configured to receive external audio signals when apparatus 900 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in memory 904 or transmitted via communications component 916. In some embodiments, audio component 910 also includes a speaker for outputting audio signals.
I/O interface 912 provides an interface between processing unit 902 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor component 914 includes one or more sensors for providing status assessment of various aspects of the apparatus 900. For example, the sensor component 914 may detect an open/closed state of the device 900, the relative positioning of components, such as a display and keypad of the apparatus 900, the sensor component 914 may also detect a change in position of the apparatus 900 or a component of the apparatus 900, the presence or absence of user contact with the apparatus 900, orientation or acceleration/deceleration of the apparatus 900, and a change in temperature of the apparatus 900. The sensor component 914 may include a proximity sensor configured to detect the presence of a nearby object in the absence of any physical contact. The sensor component 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 914 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 916 is configured to facilitate communications between the apparatus 900 and other devices in a wired or wireless manner. The apparatus 900 may access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communications component 916 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 900 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a storage medium comprising instructions, such as memory 904 comprising instructions, executable by processor 920 of device 900 to perform the above-described method is also provided. Alternatively, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Fig. 10 is a block diagram illustrating an apparatus 1000 in accordance with one illustrative embodiment. For example, the apparatus 1000 may be provided as a server. Referring to fig. 10, the apparatus 1000 includes a processing component 1022 that further includes one or more processors and memory resources, represented by memory 1032, for storing instructions, such as applications, that are executable by the processing component 1022. The application programs stored in memory 1032 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1022 is configured to execute instructions to perform the above-described methods.
The device 1000 can also include a power supply component 1026 configured to perform power management for the device 1000, a wired or wireless network interface 1050 configured to connect the device 1000 to a network, and an input/output (I/O) interface 1058. The apparatus 1000 may operate based on an operating system stored in memory 1032, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. An energy storage capacity optimal configuration method for stabilizing photovoltaic output by a Butterse Wo Lvbo method is characterized by comprising the following steps of:
acquiring original output data of a photovoltaic power station;
filtering the original output data according to a Butterse Wo Ditong filter to obtain filtered data;
constructing a genetic algorithm model, and determining algorithm parameters and a fitness function of the genetic algorithm;
and performing optimization operation based on the genetic algorithm, the filtering data and the fitness function to obtain recommended energy storage capacity, and constructing an energy storage device according to the recommended energy storage capacity.
2. The method of claim 1, wherein the formulation of the transfer function in the butterworth low pass filter is expressed as:
where n is the order of the Butterworth low-pass filter, D 0 D (u, v) is the distance of the point (u, v) from the origin, and H is the amplitude.
3. The method of claim 2, wherein the cut-off frequency of the butterworth low-pass filter is the frequency point attenuated by 3 dB.
4. The method according to claim 1, wherein the algorithm parameters specifically include:
the population scale Q, the cross probability Pe and the variation probability Pm.
5. The method of claim 4, wherein the fitness function is formulated as:
wherein LCC is the total operating cost of the energy storage device,ICC is the initial cost of the energy storage device; n is the life of the energy storage device; n is the nth year of operation of the energy storage device; d n Depreciation in the nth year; i is interest rate; tr is the tax rate; a is n The maintenance and operation cost of the energy storage device in the nth year; r is the r-th replacement part; r is the total replacement times in the operation period of the energy storage device; ICCC is the cost of the part to be replaced; lc is the lifetime of the c-th part to be replaced; s is a residual value (element); the penalty is the electric quantity which does not reach the ideal grid-connected value in one year; m is punishment electricity price; wherein the total number of replacements R is a function of the lifetime of the part to be replaced.
6. The method according to claim 5, wherein the performing optimization operations based on the genetic algorithm, the filter data, and the fitness function to obtain the recommended energy storage capacity specifically comprises:
randomly generating Q energy storage capacity values, and taking the energy storage capacity values as individuals to construct a population X (t);
acquiring fitness function values corresponding to individuals in the population according to the filtering data;
and carrying out evolution processing on the population X (t) until the fitness function value of the individual in the population meets the termination optimization condition, wherein t is the evolution frequency of the population.
7. The method of claim 6, wherein the evolving the population X (t) comprises:
selecting Y/2 pairs of parent bodies from X (t) by using a preset selection operator, wherein Y is greater than or equal to Q;
for the found Y/2 pairs of parents, determining a target parent pair of cross operation according to the cross probability Pe and performing cross operation to obtain Y individuals;
carrying out variation on the obtained Y individuals according to the variation probability Pm to generate Y variation individuals;
and (4) screening Q variant individuals according to the corresponding fitness function values from the generated Y variant individuals to generate a next generation population X (t + 1).
8. The method of claim 7, wherein the evolving the population X (t) until the fitness function values of the individuals in the population satisfy a termination optimization condition, comprises:
if the fitness function value of the individual in the population X (t + 1) meets the termination optimization condition, outputting the individual with the maximum fitness function value in the population X (t + 1) as an optimal solution, and stopping evolution processing;
otherwise, continuing to carry out evolution treatment on the population.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the energy storage capacity optimal configuration method of stabilizing photovoltaic output with the barts Wo Lvbo method of any of claims 1 to 8.
10. A computer readable storage medium having instructions that, when executed by a processor of an electronic device, enable the electronic device to perform the method of energy storage capacity optimized configuration for photovoltaic output suppression with the barts Wo Lvbo method of any of claims 1 to 8.
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| CN116924287B (en) * | 2023-09-18 | 2023-12-08 | 临工重机股份有限公司 | Control method, device, equipment and medium of hydraulic compensation leveling mechanism |
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