CN117578533A - Electro-hydrogen fusion collaborative optimization configuration method oriented to electro-hydrogen supply capability improvement - Google Patents
Electro-hydrogen fusion collaborative optimization configuration method oriented to electro-hydrogen supply capability improvement Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention provides an electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability, which belongs to the technical field of circuit devices or systems for power supply or distribution, and comprises the following steps: s1, collecting data, dividing a compensation frequency band through frequency domain transformation, and calculating to obtain one constraint condition. And S2, carrying out clustering calculation based on the data to obtain a typical daily scene. S3, constructing an electro-hydrogen energy storage collaborative optimization configuration model with constraint conditions based on a typical day scene, solving the model, and obtaining a target configuration scheme with minimum cost. According to the invention, reasonable frequency sections are divided by adopting time domain-frequency domain transformation, so that the boundary conditions of model solving are more real and reasonable; the method of combining clustering keeps the time sequence of the original data without losing generality, so that the energy storage configuration result has higher credibility; and then, through the built configuration model, the energy storage configuration result has good electro-hydrogen energy supply capacity, and is beneficial to large-area popularization and application.
Description
Technical Field
The invention relates to the technical field of circuit devices or systems for power supply or distribution, in particular to an electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability.
Background
At present, the renewable new energy duty ratio of the power system in China is continuously improved, but the power system is extremely susceptible to the condition that the output loss and even large-scale shutdown occur due to the influence of weather factors, so that the minimum guarantee output level is low, the long-time scale power supply capacity is insufficient and the difficulty in guaranteeing the power supply and the hydrogen supply is high in extreme and turning weather. In addition, hydrogen energy is an important secondary energy source for promoting energy transformation and low carbon development, has excellent characteristics of zero carbon, high quality energy density, long-term storage and the like, can be converted into various energy forms of electricity, heat, gas and the like, and is considered as a 'final energy source' in the 21 st century. The technology for producing hydrogen by electrolyzing water can convert the abundant new energy source electric power into hydrogen energy, and the stored hydrogen can be continuously supplied with power under extreme scenes through a hydrogen fuel cell/a hydrogen gas turbine and can be directly utilized for multi-way absorption in terminal coupling transportation and industrial systems.
Electrochemical energy storage is an energy storage technology with high efficiency, high energy density and rapid charge and discharge capability, and has been widely applied to the fields of electric vehicles, solar energy storage, smart grids and the like. Meanwhile, with the successful application of distributed hydrogen energy production and storage and hydrogen fuel cells in transportation and distribution networks, electric hydrogen interaction becomes a current hot spot for the research of novel electric power systems. The electrochemical energy storage and hydrogen energy storage device is connected to the distribution network side, so that renewable new energy output can be stored in a space-time translation mode and a long time mode, and the distribution network side is stable and continuously powered and supplied with hydrogen when a renewable power supply is not output in extreme weather, so that the method has important significance for interconnection and intercommunication and complementary reciprocity of electric hydrogen energy, efficient absorption and power supply capacity of the renewable new energy and improvement of hydrogen supply capacity.
Defects and deficiencies of the prior art:
at present, renewable new energy power generation is extremely easily influenced by extreme and turning weather factors such as sand storm, chill, heavy rain, continuous rainfall and the like, and the output has the characteristics of uncertainty and variability, so that the power generation is difficult to meet the sustainability and stability of energy demands. In order to solve the problem, electrochemical energy storage is reasonably configured on a distribution network side, the flexible regulation function of a power supply side is enhanced, and the hour-level stable fluctuation and the force-output tracking plan are realized. However, electrochemical energy storage cannot meet the requirement of large-scale energy storage under a long time scale, and the problem that the power supply capacity of a distribution network side is weak under extreme weather conditions cannot be effectively solved. Although the foregoing problems can be solved in combination with a hydrogen storage device, the development of the distributed hydrogen energy project is limited due to the long equipment investment recovery period and high production cost of the hydrogen energy project. In the off-peak period of hydrogen load, the hydrogen energy power station does not fully exert the energy storage capacity of the color, and the flexibility of the hydrogen energy power station for improving the power supply and hydrogen supply capacity of the distribution network side in multiple time scales is often neglected.
On the other hand, the large-scale rapid fluctuation of the renewable new energy electrolysis power can lead to the dissolution of an electrode catalyst, the oxidation corrosion of a bipolar plate and the mechanical damage of a diaphragm, thereby obviously reducing the service life of an electrolytic cell and the hydrogen production performance of the electrolytic cell.
Therefore, the electrochemical energy storage mainly solves the problem of short-time power balance, the hydrogen storage system can support continuous and stable power supply and hydrogen supply in a long time scale, and the problem of power supply fluctuation can be solved to a certain extent by combining the two.
However, on the basis of the above, an optimal configuration method of the electro-hydrogen multi-element energy storage capacity for achieving the electro-hydrogen multi-element energy storage capacity with the power supply capability, the hydrogen supply capability and the operation life of the hydrogen production device under the fluctuation operation condition of the electrolysis power of the renewable energy source is not clear.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an electro-hydrogen fusion collaborative optimization configuration method oriented to improving the electro-hydrogen supply capability.
In order to achieve the above purpose, the invention provides an electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability, which is used for a regional power distribution network with new energy power supply, wherein an electro-hydrogen energy storage system is configured in the regional power distribution network;
the configuration method comprises the following steps:
s1, collecting new energy output data of a region and load data of the region within a period of time, and calculating balance power; performing frequency domain transformation on the balance power, and dividing a compensation frequency band; performing time domain transformation on the compensation frequency band, and calculating to obtain the initial capacity of the electro-hydrogen energy storage system;
s2, carrying out clustering calculation based on the new energy output data and the load data to obtain a typical day scene and the frequency of the typical day scene;
s3, constructing a configuration model based on the cost model, and acquiring constraint conditions;
the constraint conditions include: a maximum capacity condition determined by the initial capacity;
and obtaining at least one configuration scheme based on the typical day scene, the frequency of the typical day scene, the constraint condition and the configuration model, and taking the configuration scheme with the minimum cost as a target configuration scheme.
Optionally, the step of calculating the balance power in S1 includes:
and the power difference between the load data of the region and the new energy output data of the region is the balance power.
Optionally, the electro-hydrogen energy storage system comprises an electrochemical energy storage system and a hydrogen energy storage system;
the compensation frequency band is divided into a high-frequency band and a low-frequency band;
the output power of the electrochemical energy storage system corresponds to the high-frequency band; the output power of the hydrogen energy storage system corresponds to the low frequency band.
Optionally, the maximum value of the power curve integral of the sampling time period after the high-frequency period is subjected to time domain transformation is the initial capacity of the electrochemical energy storage system;
and the maximum value of the power curve integral of the sampling time period after the time domain transformation of the low-frequency band is the initial capacity of the hydrogen energy storage system.
Optionally, in the step S2, the new energy output data includes photovoltaic output data, and the load data includes electric load data;
carrying out ordered clustering on the photovoltaic output data and the power load data, and finding out a dividing point according to a contour coefficient;
merging, selecting and deleting the dividing points;
and dividing and partitioning the photovoltaic output data and the power load data based on the dividing points.
Optionally, in the step S2, the new energy output data further includes wind power output data, and the load data further includes hydrogen load data;
combining the wind power output data with the hydrogen load data, and solving through a K-means clustering algorithm to obtain a cluster map in a partition;
and according to the characteristics of the new energy output data and the load data, calculating the average value in each partition so as to obtain the typical daily scene of each double-source double charge and the frequency of the typical daily scene.
Optionally, the K-means clustering algorithm adopts an improved K-means clustering algorithm;
and preprocessing the combined wind power output data and the hydrogen load data through a watershed algorithm, determining a K value, and then carrying out a K-means clustering algorithm.
Optionally, in the step S3, the cost model includes equivalent annual construction cost, operation and maintenance cost, wind and light discarding punishment cost, energy shortage punishment cost, storage battery discharge life loss cost and electrolysis power fluctuation loss cost.
Optionally, in the step S3, the constraint condition further includes a power balance constraint, a line transmission channel constraint, a power supply reliability constraint, a stable power supply capability constraint, and a peak power supply capability constraint;
the constraint condition further comprises a node installation capacity constraint and an installation total capacity constraint of the electro-hydrogen energy storage system;
the total capacity constraint of the installation is: the total installed capacity of the region is less than or equal to the initial capacity of the electrical hydrogen storage system in S1.
Optionally, the electro-hydrogen energy storage system comprises an electrochemical energy storage system and a hydrogen energy storage system;
the constraints further include hydrogen supply capacity constraints, hydrogen storage system operating constraints, and electrical storage system operating constraints.
The invention adopts the electro-hydrogen fusion collaborative optimization configuration method facing the improvement of the electro-hydrogen supply capability, and has the following beneficial effects compared with the prior art:
(1) According to the invention, the balance power is decomposed on the frequency domain by adopting time domain-frequency domain transformation, and reasonable frequency sections are divided according to the characteristics of power type and energy type energy storage equipment, so that the obtained initial capacity of the energy storage equipment is used as a boundary condition for model solving, and the boundary condition is more real and reasonable; the clustering method is adopted, so that the typical scene is extracted more abundantly, the time sequence of the original data is reserved without losing generality, and the energy storage configuration result is more credible;
(2) The configuration model is built, so that a comprehensive evaluation index system for the power supply capacity of the distribution network side electricity-hydrogen fusion is provided, and the power supply capacity, the hydrogen supply capacity and the operation economy of the distribution network side under deterministic electricity-hydrogen storage configuration are quantitatively analyzed; converting the evaluation system into corresponding constraint conditions and adding the constraint conditions into the constructed electro-hydrogen energy storage collaborative optimization configuration model, so that an energy storage configuration result has good electro-hydrogen energy capacity;
furthermore, different influencing factors, particularly fluctuation of new energy output and a service life degradation mechanism of the electrolytic cell can be fully considered by combining multiple constraint conditions, and a service life degradation model of the electrolytic cell is established to quantify the service life degradation influence of electrolytic power fluctuation on the hydrogen production electrolytic cell, so that an electric hydrogen energy storage collaborative optimization configuration model comprehensively considering electric hydrogen energy capacity improvement and new energy consumption is established.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a watershed algorithm in accordance with an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the specific embodiment, when the numerical values such as i, j, n, m are not specifically described in the respective formulas, the meaning of the formulas should be understood.
In the following embodiments, the preset (preset) coefficients related to each formula may be obtained by means of historical data collection or test measurement, or may be obtained by data fitting, or may be reversely deduced according to the result generated by the formula to obtain a more suitable coefficient, which is not described in detail.
The invention provides an electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability, which is used for a regional power distribution network with new energy power supply, and an electro-hydrogen energy storage system is configured in the regional power distribution network. Referring to fig. 1, the specific steps of the embodiment of the present invention are as follows:
s1, calculating balance power by taking annual new energy output data (including wind power output data and photovoltaic output data) and load data (including electric load data and hydrogen load data) of a distribution network side of a certain area as samples; performing discrete Fourier transform on the balance power, converting a time sequence curve into a series of frequencies and corresponding amplitudes under amplitude-frequency characteristics, and dividing frequency compensation frequency bands of the electro-hydrogen energy storage system according to the characteristics of the electro-hydrogen energy storage system; and converting the compensation result of the electric hydrogen energy storage system into a time domain through Fourier inverse transformation to obtain the initial capacity of the electric hydrogen energy storage system.
Optionally, the step further includes:
s11, calculating balance power. Defining the total output power P at the time t in the renewable new energy output data accessed by the distribution network side RE (t) renewable new energy sources include wind power generation and photovoltaic power generation. The total output power in the new energy output data has the following formula:
in omega w The method comprises the steps of collecting wind turbine units; omega shape pv Is a photovoltaic array set; p (P) w,i (t) is the output power of wind power generation at the ith node at the moment t, P pv,j And (t) is the output power of the photovoltaic power generation at the j-th node at the time t.
Defining the total power of loads in load data of a distribution network side at t time as P L (t); the power difference formed by the load data and the new energy output data is the balance power P of the distribution network side B (t) the formula is as follows:
in omega Node Is a node set at the distribution network side; p (P) load,i And (t) is the load of the ith node at the moment t.
S12, spectrum analysis. It is known from the nyquist sampling theorem that when discrete sampling is performed on a band-limited signal, only if the sampling frequency is 2 times higher than the highest frequency (i.e., at least 2 points are taken in one period), the original band-limited signal can be recovered from the sampled signal only and correctly. The 2 times the highest frequency is referred to herein as the nyquist frequency. If the sampling frequency does not meet the condition, the spectrum aliasing phenomenon is generated on the spectrum of the original signal, so that the original signal cannot be correctly recovered. The smaller the sampling period, the more sampling points and the more accurate the spectrum analysis range. The sampling period adopted by the invention is 36s, namely the sampling point number per hour is 100; the 1200 multiplication frequency is 1/(2×36) Hz, so the reference frequency is 1/(2×36×1200) Hz.
For sample data P B ={P B (1), …, P B (n), …, P B After discrete fourier transform, the different frequencies and their corresponding amplitudes are:
in the formula, DFT (P B ) For balancing power data P on the side of a distribution network B Performing discrete Fourier transform, f B Is a frequency set, S B Is a corresponding set of magnitudes; specifically S B (n) the n-th frequency f after Fourier transform for the balanced power B (n) the corresponding amplitude; n is the total number of balanced power samples; wherein N is a positive integer representing an ordinal number, and 1 < N < N.
S13, power distribution. According to the invention, the lithium battery is selected as an energy storage device of the electrochemical energy storage system, and the lithium battery has the advantages of high energy density and short charge and discharge period, and is suitable for compensating high-frequency fluctuation, so that the lithium battery can be regarded as a power type energy storage unit; compared with a lithium battery, the hydrogen energy storage system has better energy density and more stable continuous power supply capacity, so the hydrogen energy storage system can be regarded as an energy type energy storage unit. And the power distribution among the electro-hydrogen energy storage systems is reasonably distributed according to the response characteristics of different energy storage devices, so that the power supply reliability of the distribution network side is ensured.
According to the invention, 600 times frequency is selected as a division point, the whole frequency spectrum is divided into two sections, 0,600 times frequency is used as a low-frequency band, and hydrogen energy storage is used for compensation; [600,1200] frequency multiplication is used as a high-frequency band, and compensation is performed by using a lithium battery. According to the divided compensation frequency bands, the amplitude frequency results of the lithium battery and the hydrogen energy storage compensation frequency bands can be converted into the time domain by utilizing Fourier inverse transformation, and then the power compensation result of the electro-hydrogen energy storage system can be obtained.
Let f LJ ∪f LJ1 Compensating the frequency band for lithium batteries, wherein f LJ1 Is S B The Nyquist frequency is usedThe highest resolution frequency of the spectral analysis results, i.e. 1/2 of the sampling frequency) is the sum f of the symmetry axis LJ Symmetrical frequency band, where f LJ =[f LJmin ，f LJmax ]，f LJmin 、f LJmax Respectively compensating frequency band f for lithium battery LJ Is defined by the endpoints of (a). Thus using S L ={S Li (n L ), …, S Li (N L ) The frequency spectrum analysis result is represented by the amplitude corresponding to the compensation frequency band of the lithium battery energy storage unit, wherein n L 、N L All representing the corresponding ordinal number. The corresponding amplitude of the uncompensated frequency band can be set to 0, and the amplitude of the compensated frequency band is kept unchanged so as to achieve the purpose of simplifying calculation, and the formula is as follows:
in the formula, "0+j0" corresponds to a complex function expression (cos θ+j·sin θ) of a conventional magnitude, taking 0. Compensating frequency band S for lithium battery energy storage device L Performing Fourier inverse transformation, and converting a result of the spectrum analysis into a time domain to obtain the compensation power of the lithium battery energy storage device in the electrochemical energy storage system, wherein the compensation power is as follows:
in which ordinal number n L Represented as nth of frequency domain division L Frequency. And similarly, the power compensation result of the hydrogen energy storage system can be obtained. And the electrochemical energy storage system and the hydrogen energy storage system in the electric hydrogen energy storage system respectively take the maximum value of the time domain power compensation result as initial capacity. Specifically, after inverse fourier transform, the power compensation of each energy storage system is still discrete data, each acquisition time period is multiplied by the corresponding power (specifically, the integral of the corresponding power curve in the acquisition time period over time), so as to obtain corresponding discrete data, and the maximum value is found out as the initial capacity, so that the capacity of the energy storage system can cover the power supply requirement in unit time (acquisition time period).
S2, respectively carrying out ordered clustering on photovoltaic output data and power load data, and finding out an optimal dividing point according to the profile coefficient; merging the dividing points obtained by the photovoltaic output data and the power load data, taking difference, and deleting the approximate dividing points; dividing the photovoltaic output data and the power load data by using the obtained dividing points; combining the wind power output data with the hydrogen load data, and solving the wind power output data and the hydrogen load data at one time by utilizing an improved K-means clustering algorithm to obtain a cluster map in the partition; and respectively calculating average values in the subareas according to the characteristics of the new energy output data and the load data, so as to obtain each typical day scene and the frequency of the typical day scene.
The method specifically comprises the following steps:
s21, orderly clustering. Selecting annual new energy output data (including wind power output data and photovoltaic output data) and load data (including electric load data and hydrogen load data) of a regional distribution network side as samples, wherein the data time interval is 1 hour. Under the time scale of sampling, the photovoltaic output and the power load have obvious change trend along with time, so the method is suitable for ordered clustering; the wind power output and the hydrogen load have smaller change trend along with the time, so the method is suitable for improving K-means clustering.
Taking power load data as an example for orderly clustering, the 8760-hour power load data L= { L 1 ，L 2 ，…，L m The per-unit is performed to simplify the calculation, m is ordinal number. The range of the number p of the division points is determined to be 1,]an integer therebetween. Then, respectively selecting different numbers of the dividing points, taking the minimum sum of squares of deviations as an objective function, and optimizing by using a particle swarm optimization algorithm (PSO algorithm) to obtain a dividing point set P= { P 1 , P 2 ，…，P p Sample L is divided into p+1 scene sets. Assuming now that the number of annual power load scenario samples is W, and each sample W (w=1, 2, …, W) has data for T moments, the sum of squares of the dispersion is calculated as follows:
wherein mu is i,t Centroid at time t for the ith centroid sample; w (w) i The number of samples corresponding to the ith scene set; p (P) ij,t The power value of the jth sample at the t moment is concentrated for the ith scene; e is the sum of squares of the deviations; x is x ij,t Sample data of a jth sample at a t moment in the ith scene set; k is the number of centroid samples; v i Is the number of samples in the ith scene.
And determining the optimal division point number by adopting the improved contour coefficient, and taking the expected value of the divided load scene set as a typical power load scene. Wherein, the contour coefficient range is [ -1,1], the larger the value is, the better the clustering effect is. The contour coefficient calculation formula is as follows:
wherein d (c, e) is the Euclidean distance between samples c and e; q is the latitude of the sample; c i 、e i The ith attribute value of samples c, e, respectively; a, a i The average value of Euclidean distance from the ith sample to other points in the class to which the ith sample belongs; n is n r Is C r The number of samples in the class; d (i, j) is the Euclidean distance between samples i and j; b i The average Euclidean distance minimum value from the ith sample to all points in the class adjacent to the class to which the ith sample belongs in the time period; n is n s Is C s The number of samples in the class; c is a contour coefficient; m is the number of samples.
Similarly, the photovoltaic output data can also be solved according to the steps to obtain the corresponding optimal division points. Combining the photovoltaic output data with the dividing points obtained by the power load data, taking difference, and deleting the approximate dividing points. And dividing and partitioning the photovoltaic output data and the power load data by using the obtained dividing points to finish ordered clustering.
S22, improving K-means clustering. The K-means algorithm is a clustering algorithm based on division, and Euclidean distance is used as an index for measuring the similarity between data objects, the similarity is inversely proportional to the distance between the data objects, and the larger the similarity is, the smaller the distance is. The algorithm needs to pre-designate the initial cluster number K and randomly select K initial cluster centers C from the data before calculation i (1 is more than or equal to i is more than or equal to K), and calculating the rest data objects and the clustering center C i Finding the nearest cluster center C from the target data object i And assign data objects to cluster centers C i In the corresponding cluster. And then calculating the average value of the data objects in each cluster as a new cluster center, and carrying out the next iteration. The error square sum (Sum of Squared Error, SSE) of the cluster is continuously reduced, and when the SSE is not changed or the cluster center is not changed, the clustering is ended, so that a final result can be obtained. Wherein the size of SSE represents the quality of the clustering result, and the calculation formula is as follows:
wherein y is a group C i Is a sample of (a).
However, the method is sensitive to the initial value and is easy to sink into a local optimal solution, which is a well-known disadvantage of the K-means algorithm, so that the method adopts a watershed algorithm to divide the original data obtained by combining wind power output data and hydrogen load data into a plurality of areas so as to determine the optimal cluster number K. The improved algorithm firstly needs to preprocess the data, and calculates the density of each data object in the data by using the following formula:
wherein Z is i Representing data object Y i Is a degree of density of (3); gamma is an empirical value and needs to be preset; m is M data The number of data objects in the data; y is Y j Is the j-th data object.
In fig. 2, a curve Line1 represents a two-dimensional image of a gray-scale image, and a broken Line2 is a water Line. Ordering the density of data objects in the data from small to large as an ordinate, and the abscissa as a data point corresponding to the density, wherein the water line is from Y 0 To Y 2 In the process of (2), A, B, C areas are generated, the center of each area is selected to be the initial clustering center of the K-means algorithm, the optimal cluster number K is the number of the areas, and then K-means iteration is carried out to obtain a final clustering result.
Firstly, 8760 hours data of wind power output data and hydrogen load data are converted into daily output and daily load data, namely 365 multiplied by 24 hours data, then the data are combined into 365 multiplied by 24 multiplied by 2 hours data, and the typical daily scene of wind power output and hydrogen load in each partition in S2 is obtained by solving through the improved K-means algorithm. And finally, respectively solving the average value in the subareas according to the characteristics of the wind power output data and the photovoltaic output data, the electric power load data and the hydrogen load data, so as to obtain the typical daily scene of each double-source double charge and the frequency of the typical daily scene.
According to the method, a typical daily scene is constructed, and samples of annual wind power output data, photovoltaic output data, power load data and hydrogen load data on a distribution network side are effectively reduced, so that the subsequent calculation cost is reduced.
S3, based on the typical day scene and the frequency of the typical day scene in S2, the comprehensive cost including investment, maintenance, operation, wind and light abandoning punishment cost, energy shortage punishment cost, storage battery charge and discharge life loss cost and electrolysis power fluctuation loss cost is taken as a target, and hydrogen energy conversion equipment, energy storage equipment, hydrogen energy, power balance and hydrogen fusion energy supply capacity are taken as constraints, so that an electric hydrogen fusion collaborative optimization configuration model for energy supply capacity improvement and new energy consumption is constructed. The method comprises the steps of bringing a typical day scene and the frequency of the typical day scene into a configuration model, adding the acquired constraint conditions into the configuration model, solving the constraint conditions to obtain at least one configuration scheme, and screening out the configuration scheme with the minimum cost as a target configuration scheme.
The method specifically further comprises the following steps (parameters are set to be used continuously and are not repeatedly described in the step):
s31, an objective function. According to the invention, through quantifying the influence of electrolytic power fluctuation on the service life degradation of the hydrogen production electrolytic cell, an electro-hydrogen energy storage collaborative optimization configuration model is established, wherein the electro-hydrogen energy capacity improvement and new energy consumption are comprehensively considered. The configuration model is based on the minimum annual comprehensive COST COST of the distribution network side distributed electric hydrogen energy storage system as a COST model, and specifically comprises the equivalent annual construction COST C inv Cost of operation and maintenance C om Wind and light discarding punishment cost C loss Cost of energy deficiency punishment C short Cost of battery discharge life loss C cd Cost of electrolytic power fluctuation loss C fluct . The calculation formula of the minimum cost model is as follows:
wherein, each cost is calculated as follows:
s311, equating annual construction cost of the distributed electric hydrogen energy storage system. The total construction cost of the system is folded into equivalent annual construction cost.
Wherein C is BESS,inv The equivalent annual construction cost of the electric energy storage system is C HESS,inv The construction cost is equivalent to the annual construction cost of the hydrogen energy storage system; n (N) BESS For the installation quantity of the electric energy storage system, N HESS The number of hydrogen storage systems installed; erate BESS, i is the rated capacity of the accumulator, erate hst, i is the rated capacity of the hydrogen storage tank, prate el, i is the rated power of the electrolytic cell, prate fc, i is the rated power of the fuel cell; cost BESS Cost for investment cost of unit capacity of storage battery hst Cost is investment cost for unit capacity of hydrogen storage tank el Cost for investment of unit power of electrolytic cell fc Investment cost per unit power for fuel cell; m is m BESS Is the discount rate of the electric energy storage system, m HESS Is the discount rate of the hydrogen energy storage system; y is BESS For life expectancy of an electrical energy storage system, y HESS Is the expected lifetime of the hydrogen storage system.
S312, calculating the operation and maintenance cost of the distributed electric hydrogen energy storage system as follows:
wherein C is BESS,om For the operation and maintenance cost of the electric energy storage system, C HESS,om The operation and maintenance cost of the hydrogen energy storage system is reduced; lambda (lambda) BESS For maintaining cost coefficient lambda of accumulator hst As a maintenance cost coefficient of the hydrogen storage tank lambda el Lambda is the maintenance cost coefficient of the electrolytic cell fc A maintenance cost factor for the fuel cell; the other parameters are defined as in the foregoing formulas.
S313, wind and light discarding punishment cost. If the generated energy output by the photovoltaic power and the wind power in the new energy cannot be completely consumed, the resource waste is caused, and therefore the consumed part of electric energy forms wind discarding and light discarding quantity; therefore, punishment is performed on the wind and light discarding phenomenon, and the calculation formula is as follows:
wherein T is s Is the total time scale, in hours; c w Cost coefficients punished for preset wind discarding and light discarding units; p (P) waste,i (t) the wind and light quantity is abandoned at the time t for the ith node; Δt is the time interval (1 hour); the other parameters are defined as in the foregoing formulas.
Solving the abandoned wind and the abandoned light quantity which are not consumed by the system in unit time in a sampling time period to obtain the output generated energy of the new energy which is not consumed in the sampling time period, multiplying the output generated energy by a cost coefficient, and summing up based on a total time scale and distribution nodes to obtain the total abandoned wind and abandoned light punishment cost.
S314, energy deficiency punishment cost. If the output power generation capacity of the distribution network side cannot meet the load requirements (the load comprises an electric load and a hydrogen load), the electric load and the hydrogen load are required to be reduced, so that node power loss and hydrogen yield are reduced; therefore, it is penalized as follows:
wherein: c e Cost coefficient for a predetermined unit loss penalty, c h A cost coefficient of hydrogen penalty is cut for a preset unit; p (P) short,i (t) is the power loss of the ith node at the moment t, H cut,i (t) is the hydrogen cutting amount of the ith node at the moment t; the other parameters are defined as in the foregoing formulas.
The power required by the power generation amount which is output by the distribution network side in unit time and is not satisfied with the load is displayed in the mode of power loss of the nodes and reduction of the hydrogen production amount, the power loss and the reduction of the hydrogen production amount are respectively multiplied by preset cost coefficients, the power loss and the reduction of the hydrogen production amount are solved in a sampling time period, and the total energy shortage punishment cost is obtained based on the total time scale and the distribution nodes.
S315, the discharge life loss cost of the storage battery. Because the storage battery is charged and discharged frequently, the aging process is unavoidable, and therefore, the service life degradation cost of the storage battery is quantified by adopting a service life degradation model of the storage battery, and the calculation formula of the model is as follows:
in omega BESS For installing an electrical energy storage node set; x-shaped articles BESS Rated throughput for the battery; η (eta) BESS The back and forth efficiency of the storage battery is achieved; η (eta) ch (t) is the battery charging efficiency, eta at time t dch (t) is the battery discharge efficiency at time t; p (P) ch,i (t) is the charging power of the storage battery at the ith node at the time t, P dch,i (t) is the discharge power of the storage battery at the ith node at the moment t; the other parameters are defined as in the foregoing formulas.
S316, electrolysis power fluctuation loss cost. Because of the intermittence of renewable new energy output, when the renewable new energy output is incorporated into a power distribution network, larger power fluctuation can be generated, and certain bipolar plate oxidation corrosion, proton exchange membrane mechanical abrasion and electrolysis potential lifting can be caused, so that the service life of the electrolytic cell is degraded. The calculation formula of the electrolytic power fluctuation loss cost is as follows:
in omega HESS A node set for installing a hydrogen energy storage system; c fluct,i (t) is the loss cost caused by the fluctuation of the electrolytic power of the ith node at the moment t; p (P) el,i (t) is the electrolytic power of the electrolytic cell at the ith node at the time t, P el,i (t-1) is the electrolytic power of the electrolytic cell at the ith node at the time t-1;θ 1 is the oxidation corrosion punishment coefficient of the bipolar plate, theta 2 The method is characterized in that the method is a proton exchange membrane mechanical abrasion punishment coefficient, beta is an electrolysis potential lifting punishment coefficient, and all three coefficients can be obtained through actual tests and are preset according to measurement results; the other parameters are defined as in the foregoing formulas.
S32, constraint conditions. Because the configuration capacity and the installation position of the electric hydrogen energy storage system are planned on the distribution network side, the topological structure constraint of the distribution network side needs to be considered. Therefore, the constraint conditions of the configuration model comprise system constraint, electro-hydrogen fusion energy capability constraint, equipment operation constraint and the like. The specific constraint conditions are as follows:
s3201, the expression of the power balance constraint condition is as follows:
wherein P is energy,i (t) is the sum of wind power output and photovoltaic output of the ith node at the moment t; p (P) fc,i (t) is the output power of the fuel cell at the ith node at time t; omega shape Line The method is a set of all lines on a distribution network side; p (P) ij (t) is the line transmission power between the buses i and j at the moment t; the other parameters are defined as in the foregoing formulas.
S3202, line transmission channel constraint.
Wherein B is ij A parameter which is susceptance of the transmission line i, j; θ ij (t) is the phase angle difference of the lines i, j at time t; p (P) ij,max Maximum transmission power of the transmission lines i and j; the other parameters are defined as in the foregoing formulas.
S3203, the expression set of the installation capacity constraint condition of the electric hydrogen energy storage candidate node is as follows:
wherein Emax BESS, i is the maximum allowable configuration capacity of the electric energy storage at the inode, emax hst, i is the maximum allowable configuration capacity of the hydrogen energy storage at the inode; b (B) BESS，i And B is connected with HESS，i All 0-1 variables, 1 indicating that the energy storage device is installed there, and 0 not installed, the constraint indicating that the electrical energy storage and hydrogen storage devices are not allowed to be installed at the same node at the same time; the other parameters are defined as in the foregoing formulas.
S3204, the expression set of the total capacity constraint condition of the electric hydrogen energy storage installation is as follows:
wherein the initial capacity of the electrochemical energy storage system and the initial capacity of the hydrogen energy storage system as determined in S1 are used as maximum capacity conditions to limit the total capacity of the electro-hydrogen energy storage installation, in particular E BESS，up For initial capacity of electrochemical energy storage system E hst，up Is the initial capacity of the hydrogen storage system; the other parameters are defined as in the foregoing formulas.
S3205, power supply reliability constraints. Load loss of power (LOLP) is the ratio of the power that is lacking to the total demand of the load when the power generated by the system cannot meet the load demand, and the constraint condition expression is as follows:
in the formula, LOLP set Setting a value for the power loss rate of the load; the other parameters are defined as in the foregoing formulas.
S3206, steady power supply capability constraint. Definition: the renewable new energy and the distributed electro-hydrogen energy storage system can always deliver the sum of power (P) to the distribution network within 15 consecutive days sum ) If the probability of (a) is not less than alpha, the maximum power satisfying the condition is called stable transmission power, P α The calculation formula of (2) is as follows:
considering the confidence probability constraint of the renewable new energy output power, the renewable new energy and the distributed electro-hydrogen energy storage output power should satisfy the following formula:
every 15 days, the alpha stable conveying power is larger than or equal to the set value
Wherein, alpha is a numerical value (0 < alpha < 1) representing probability; z l 0-1 variable, 0 means that the renewable new energy source and the distributed electric hydrogen energy storage output are required to be maintained at P all day after the first day α When the power is 1, no requirement is given to the renewable new energy source and the distributed electric hydrogen energy storage output on the first day; zeta is a constant for matching z l Relaxation constraints, i.e. by z on one side of the inequality l And the product of zeta and the constraint condition is relaxed to a certain extent, so that the original problem becomes easier to solve. The above expression shows that renewable new energy and distributed electric hydrogen energy are stored for 15 daysThe stable output power P can be ensured in 15. Alpha. Days α ；
P α，set A power setting value is stably transmitted for alpha; i. j, m and n each represent an object ordinal number in the corresponding set; the other parameters are defined as in the foregoing formulas.
S3207, peak power capability constraint. When the power grid operates in the daytime, a power utilization peak usually appears at the golden moment (19-21 points) at night, and at the moment, a power gap is large. Therefore, in order to ensure the peak load power demand, the renewable new energy and the distributed electric hydrogen energy storage are required to have certain peak power supply capacity. However, when the energy storage capacity is configured, the energy storage capacity cannot be infinitely configured to ensure the normal use of electricity for some tip loads. The invention considers that the renewable new energy and the distributed electric hydrogen energy storage can meet the set proportion S at the time of electricity consumption peak within one day e The peak load of (2) represents a peak power supply capability, and the constraint condition is expressed as follows:
in the formula, the proportion coefficient S e Is a preset value.
S3208, hydrogen supply capacity constraint. The hydrogen amount supplied by the hydrogen energy storage at any moment should not be lower than a certain proportion S h Hydrogen loading of (c) in the gas turbine.
Wherein H is hst,i (t) represents the hydrogen quantity which is externally transmitted by the hydrogen storage tank at the ith node at the moment t, H el,i (t) represents the hydrogen amount produced by the electrolytic cell at the ith node at the time t; h load,j (t) represents the hydrogen load of the j-th node at time t; in the formula, the proportion coefficient S h Is a preset value.
S3209, hydrogen energy storage system operation constraint. The hydrogen energy storage system comprises an electrolytic cell, a fuel cell and a hydrogen storage tank, and all 3 devices are required to meet respective operation constraints.
Wherein, the electrolytic cell and the fuel cell are both hydrogen-electricity energy conversion equipment and can not be in working state at the same time, thus obtaining the expression group of the operation constraint condition as follows:
wherein B is el,i (t)、B fc,i (t) are all 0-1 variables, when 1 is the condition that the electrolytic cell or the fuel cell is in an operating state at the time t, and when 0 is the condition that the electrolytic cell or the fuel cell is not put into operation at the time t; η (eta) el For the electrolytic efficiency of the electrolytic cell, eta fc Energy conversion efficiency of the fuel cell; h fc,i (t) is the hydrogen consumption of the fuel cell at the ith node at time t; the other parameters are defined as in the foregoing formulas.
In addition, the hydrogen energy balance constraint needs to be met between the hydrogen quantity and the hydrogen load, which are produced or consumed by the electrolytic cell and the fuel cell, and the hydrogen quantity and the hydrogen load supplied by the hydrogen storage tank, and meanwhile, the hydrogen storage quantity of the hydrogen storage tank needs to meet the upper limit constraint and the lower limit constraint, and the expression set is as follows:
wherein E is hst,i (t) is the hydrogen storage capacity of the hydrogen storage tank at the ith node at the time t, E hst,i (t+1) is the hydrogen reserves of the hydrogen storage tank at the ith node at the time t+1; the other parameters are defined as in the foregoing formulas.
S3210, the operation constraint of the electric energy storage system. The battery charge-discharge process is generally described using a parameter state of charge (SOC) as follows:
in SOC i (t) is the charge rate of the storage battery at the ith node at the time t; τ is the self-discharge rate of the storage battery; the other parameters are defined as in the foregoing formulas.
The ratio of the rated charge/discharge power of the electric storage to the capacity thereof is generally about 0.5. In order to ensure the service life and the working stability of the storage battery, the storage battery can only be in a charging or discharging state at the same time. The charge rate and charge-discharge power thereof need to satisfy the following expression set:
wherein Prate BESS, i is rated charge and discharge power of the storage battery at the ith node; b (B) ch,i (t)、B dch,i (t) are all 0-1 variables, which indicate that the storage battery at the ith node is in a charging state or a discharging state at the moment t, and 0 indicates that the storage battery is not in a corresponding working state; SOC (State of Charge) i,min Representing the lower limit of the charge rate of the storage battery at the ith node, SOC i,max An upper limit representing the battery charge rate at the i-th node; the other parameters are defined as in the foregoing formulas.
The objective function based on the cost model and the corresponding constraint conditions form an electro-hydrogen fusion collaborative optimization configuration model, and a typical day scene and the frequency of the typical day scene are input to simulate the operation of the distribution network in the region, so that a plurality of configuration schemes are obtained; and solving the model by using a solver (Gurobi), selecting a configuration scheme with the minimum cost as a target configuration scheme, and outputting the electrochemical energy storage rated capacity, the hydrogen energy storage rated capacity, the electrolytic cell rated power and the fuel cell rated power of the ground distribution network side in the target configuration scheme.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In summary, the present description should not be construed as limiting the invention.
Claims (10)
1. An electro-hydrogen fusion collaborative optimization configuration method oriented to the improvement of electro-hydrogen supply capability is used for a regional power distribution network with new energy power supply, and an electro-hydrogen energy storage system is configured in the regional power distribution network;
the configuration method is characterized by comprising the following steps:
s1, collecting new energy output data of a region and load data of the region within a period of time, and calculating balance power; performing frequency domain transformation on the balance power, and dividing a compensation frequency band; performing time domain transformation on the compensation frequency band, and calculating to obtain the initial capacity of the electro-hydrogen energy storage system;
s2, carrying out clustering calculation based on the new energy output data and the load data to obtain a typical day scene and the frequency of the typical day scene;
s3, constructing a configuration model based on the cost model, and acquiring constraint conditions;
the constraint conditions include: a maximum capacity condition determined by the initial capacity;
and obtaining at least one configuration scheme based on the typical day scene, the frequency of the typical day scene, the constraint condition and the configuration model, and taking the configuration scheme with the minimum cost as a target configuration scheme.
2. The electro-hydrogen fusion collaborative optimization configuration method for electro-hydrogen supply capability promotion according to claim 1, wherein the step of calculating the balance power in S1 comprises:
and the power difference between the load data of the region and the new energy output data of the region is the balance power.
3. The electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability according to claim 1, wherein the electro-hydrogen energy storage system comprises an electrochemical energy storage system and a hydrogen energy storage system;
the compensation frequency band is divided into a high-frequency band and a low-frequency band;
the output power of the electrochemical energy storage system corresponds to the high-frequency band; the output power of the hydrogen energy storage system corresponds to the low frequency band.
4. The electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability according to claim 3, wherein the maximum value of the power curve integral of the sampling time period after the time domain transformation of the high-frequency period is the initial capacity of the electrochemical energy storage system;
and the maximum value of the power curve integral of the sampling time period after the time domain transformation of the low-frequency band is the initial capacity of the hydrogen energy storage system.
5. The electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability according to claim 1, wherein in S2, the new energy output data comprises photovoltaic output data, and the load data comprises electric load data;
carrying out ordered clustering on the photovoltaic output data and the power load data, and finding out a dividing point according to a contour coefficient;
merging, selecting and deleting the dividing points;
and dividing and partitioning the photovoltaic output data and the power load data based on the dividing points.
6. The method for collaborative optimization configuration of electro-hydrogen fusion for improving electro-hydrogen supply capability according to claim 5, wherein in S2, the new energy output data further comprises wind power output data, and the load data further comprises hydrogen load data;
combining the wind power output data with the hydrogen load data, and solving through a K-means clustering algorithm to obtain a cluster map in a partition;
and according to the characteristics of the new energy output data and the load data, calculating the average value in each partition so as to obtain the typical daily scene of each double-source double charge and the frequency of the typical daily scene.
7. The electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability according to claim 6, wherein the K-means clustering algorithm adopts an improved K-means clustering algorithm;
and preprocessing the combined wind power output data and the hydrogen load data through a watershed algorithm, determining a K value, and then carrying out a K-means clustering algorithm.
8. The electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability according to claim 1, wherein in S3, the cost model comprises equivalent annual construction cost, operation and maintenance cost, abandoned wind and abandoned light punishment cost, energy deficiency punishment cost, storage battery discharge life loss cost and electrolysis power fluctuation loss cost.
9. The electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability according to claim 1 or 8, wherein in S3, the constraint conditions further include a power balance constraint, a line transmission channel constraint, a power supply reliability constraint, a stable power supply capability constraint, and a spike power supply capability constraint;
the constraint condition further comprises a node installation capacity constraint and an installation total capacity constraint of the electro-hydrogen energy storage system;
the total capacity constraint of the installation is: the total installed capacity of the region is less than or equal to the initial capacity of the electrical hydrogen storage system in S1.
10. The electro-hydrogen fusion collaborative optimization configuration method for improving electro-hydrogen supply capability according to claim 9, wherein the electro-hydrogen energy storage system comprises an electrochemical energy storage system and a hydrogen energy storage system;
the constraints further include hydrogen supply capacity constraints, hydrogen storage system operating constraints, and electrical storage system operating constraints.
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