CN117081128A - Micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition - Google Patents
Micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition Download PDFInfo
- Publication number
- CN117081128A CN117081128A CN202311077293.9A CN202311077293A CN117081128A CN 117081128 A CN117081128 A CN 117081128A CN 202311077293 A CN202311077293 A CN 202311077293A CN 117081128 A CN117081128 A CN 117081128A
- Authority
- CN
- China
- Prior art keywords
- modal
- power
- frequency
- energy storage
- decomposition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 140
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 112
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000013178 mathematical model Methods 0.000 claims abstract description 18
- 239000003990 capacitor Substances 0.000 claims description 90
- 238000010248 power generation Methods 0.000 claims description 19
- 238000007599 discharging Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 12
- 230000009466 transformation Effects 0.000 claims description 11
- 238000012423 maintenance Methods 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 7
- 238000011084 recovery Methods 0.000 claims description 3
- 230000000295 complement effect Effects 0.000 abstract description 9
- 238000005457 optimization Methods 0.000 description 16
- 238000010586 diagram Methods 0.000 description 10
- 230000000087 stabilizing effect Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002035 prolonged effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010187 selection method Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 239000003034 coal gas Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007728 cost analysis Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J15/00—Systems for storing electric energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J2207/50—Charging of capacitors, supercapacitors, ultra-capacitors or double layer capacitors
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Power Engineering (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition, which comprises the following steps: reading a micro-grid hybrid energy storage system parameter value, and acquiring unbalanced power through the micro-grid hybrid energy storage system parameter value; constructing a variational modal decomposition VMD mathematical model, selecting the decomposition layer number of the variational modal decomposition VMD mathematical model, decomposing the unbalanced power based on the variational modal decomposition VMD mathematical model after determining the decomposition layer number, and obtaining a plurality of modal components; and determining rated power and rated capacity of the micro-grid hybrid energy storage system through a plurality of modal components, acquiring annual comprehensive cost, and determining an optimal configuration scheme. The invention not only can realize the complementary advantages of the power type energy storage and the energy type energy storage, but also can improve the service life of the storage battery and the economical efficiency of the hybrid energy storage system.
Description
Technical Field
The invention relates to the technical field of energy storage optimal configuration, in particular to a micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition.
Background
With the aggravation of global fossil energy crisis, the generation permeability of new energy such as wind, light and the like is continuously improved. The global renewable energy power generation trend is shown in figure 1. New energy power generation represented by wind energy and solar energy has been developed extremely rapidly since the 20 th century. The rapid development of renewable energy sources means that the proportion of renewable energy sources in global power generation will rise from 29% in 2002 to 35% in 2025, while the proportion of coal and natural gas power generation will drop. The large-scale use of renewable energy accelerates the processes of energy structure transformation and carbon neutralization. However, the inherent randomness and volatility of renewable energy sources can lead to low energy utilization, affecting the stability and power quality of the grid. Under the background, micro Grid (MG) technology is gradually developed, and the flexible, modularized and localized characteristics of the micro grid are utilized, so that the consumption of new energy is improved, and the challenges of large-scale new energy grid connection to the power grid are reduced.
In the micro-grid, unbalanced power fluctuation between a source and a load can influence the safe and stable operation of the micro-grid. The energy storage system can stabilize power fluctuation through flexible charge and discharge, and becomes a key for maintaining the power balance of the micro-grid. Therefore, the capacity optimization configuration of the energy storage system is an important point of research. The prior art uses particle swarm optimization algorithms to evaluate the optimal capacity of a battery energy storage system in an island micro-grid. In the prior art, an optimal configuration model of a photovoltaic and battery energy storage system taking energy storage capacity as a variable is established, and the model is solved by utilizing a genetic algorithm. The prior art proposes to use incremental cost methods to verify the economics of the battery cells and to determine the optimal capacity to minimize the cost of operating the battery energy storage system. These studies use only a single material, i.e. a battery, as a carrier for the energy storage system, without taking into account the fluctuating character of renewable energy sources on different time scales. The prior art applies supercapacitors and batteries to trams, supercapacitors handle peak demand consumption, while batteries are primarily used to provide backup long-term storage capability. The prior art has investigated the role of battery and supercapacitor based hybrid energy storage systems in increasing the revenue of photovoltaic home users. The battery is an energy type energy storage element with high energy density and is suitable for suppressing low-frequency fluctuation; the super capacitor is a power type energy storage element with high power density and is suitable for stabilizing high-frequency fluctuation. The hybrid energy storage system (Hybrid Energy Storage System, HESS) formed by the complementary advantages of the two energy storage elements can effectively stabilize renewable energy source power fluctuation on different time scales and improve micro-grid stability. Therefore, it is important to select an appropriate power distribution algorithm to reasonably configure the capacity of the hybrid energy storage system in the microgrid.
The power distribution algorithm and capacity allocation of the hybrid energy storage system are researched by students at home and abroad. In the prior art, low-Pass Filtering (LPF) is adopted to decompose unbalanced power of high frequency and Low frequency in a micro-grid, so that power fluctuation can be well restrained. However, low pass filtering has a time delay that can result in excessive capacity of the hybrid energy storage system configuration. The prior art proposes a discrete fourier transform (Discrete Fourier Transformation, DFT) to optimize the capacity of the hybrid energy storage system. However, discrete fourier transforms are susceptible to noise, complicating spectral analysis. The prior art uses wavelet decomposition (Wavelet Decomposition, WD) to decompose the load sequence to reduce non-stationary load sequences. The prior art uses wavelet packet decomposition (Wavelet Packet Decomposition, WPD) to decompose the payload power in the time dimension. Based on wavelet packet decomposition, the prior art proposes to adaptively control the State of Charge (SOC) of a power energy storage element by using fuzzy control, so as to realize optimal allocation of power, but the optimal allocation is limited by selection of a basis function, and has an influence on an energy storage capacity configuration result. The prior art provides empirical mode decomposition (Empirical Mode Decomposition, EMD) for decomposing wind power and establishing a wind power time sequence prediction model. However, empirical mode decomposition is prone to modal aliasing. The prior art effectively solves the problem of modal aliasing by adopting ensemble empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD). However, boundary effects still affect the outcome of the power decomposition.
Disclosure of Invention
The invention aims to provide a micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition, which can obtain economic and reasonable energy storage capacity configuration results.
In order to achieve the above object, the present invention provides the following solutions:
a micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition comprises the following steps:
reading a micro-grid hybrid energy storage system parameter value, and acquiring unbalanced power through the micro-grid hybrid energy storage system parameter value;
constructing a variational modal decomposition VMD mathematical model, selecting the decomposition layer number of the variational modal decomposition VMD mathematical model, decomposing the unbalanced power based on the variational modal decomposition VMD mathematical model after determining the decomposition layer number, and obtaining a plurality of modal components;
and determining rated power and rated capacity of the micro-grid hybrid energy storage system through a plurality of modal components, acquiring annual comprehensive cost, and determining an optimal configuration scheme.
Optionally, the method for acquiring the unbalanced power is as follows:
P un (t)＝P l (t)-P w (t)-P v (t)
wherein P is un (t) is unbalanced power, P l (t) is load power, P w (t) is the output power of wind power generation, P v And (t) is photovoltaic power generation output power.
Optionally, constructing the variational modal decomposition VMD mathematical model includes:
Determining an original signal, constructing a variation model with constraint conditions according to the original signal, introducing a secondary penalty factor and a Lagrange multiplier, converting the variation model into an unconstrained variation model, setting an iteration stop condition, updating parameters in the unconstrained variation model through the iteration stop condition, and obtaining the variation modal decomposition VMD mathematical model.
Optionally, the method for setting the iteration stop condition is as follows:
wherein,respectively->Corresponding Fourier transformation, K is the number of modal components obtained by decomposition, and e is a convergence parameter;
the method for obtaining the variational modal decomposition VMD mathematical model comprises the following steps:
wherein,for the updated modal component, +.>For the center frequency +.>And->Respectively P hess (t)、u i Fourier transform results corresponding to (t) and λ (t); />For the center frequency of the current sub-mode function, K is the number of mode components obtained by decomposition, alpha is a secondary penalty factor, and omega is the mode componentCenter frequency omega k For the center frequency of the kth modal component, < +.>Is u k (ω) the corresponding fourier transform.
Optionally, selecting the number of decomposition layers of the variational modal decomposition VMD mathematical model includes:
performing Hilbert transformation on the updated modal component, constructing an analysis signal of the updated modal component in a polar coordinate form, acquiring an amplitude function and a phase function of the analysis signal, and acquiring the instantaneous frequency of the updated modal component through the analysis signal;
Performing Hilbert transformation on the instantaneous frequency to obtain a normalized frequency-time curve, obtaining overlapped modal components through the normalized frequency-time curve, mapping the overlapped modal components to a time domain, and calculating to determine high-frequency energy and low-frequency energy;
setting the decomposition layer number, obtaining total energy of a plurality of mode aliasing by adding the high-frequency energy and the low-frequency energy, and determining the decomposition layer number by taking the principle that the total energy of the mode aliasing is minimum.
Optionally, the method for obtaining the instantaneous frequency of the updated modal component is as follows:
wherein f i (t) is the instantaneous frequency of the modal component,imf of a shape of imf i (t) phase function in polar form, d is p +.>Differentiating, t is time;
the method for acquiring the total energy of the aliasing of the plurality of modes comprises the following steps:
wherein E is s E is the sum of the energies of all the modal component aliases s,j For the total energy of modal aliasing existing in any modal component, K is the number of modal components obtained by decomposition, i= (f, g) is the period when the modal component exists low-frequency aliasing energy, i= (m, n) is the period when the modal component exists high-frequency aliasing energy, g is the last time node of the period when the modal component exists low-frequency aliasing energy, imf j (t) is the j-th modal component, Δt is the time interval, and n is the last time node in which the modal component has a high frequency aliased energy period.
Optionally, determining the optimal configuration scheme includes:
step 1, selecting a modal component frequency demarcation point, and dividing a plurality of modal components into a high-frequency fluctuation part and a low-frequency fluctuation part according to the modal component frequency demarcation point;
step 2, determining a charging and discharging power instruction of the micro-grid hybrid energy storage system through the high-frequency fluctuation part and the low-frequency fluctuation part, and determining rated power and rated capacity of the micro-grid hybrid energy storage system through the charging and discharging power instruction to obtain annual comprehensive cost;
step 3, judging whether the modal component frequency demarcation point is smaller than the decomposition layer number, if the modal component frequency demarcation point is smaller than the decomposition layer number, repeating the step 1 and the step 2 to obtain a plurality of annual comprehensive costs, and if the modal component frequency demarcation point is larger than the decomposition layer number, outputting a plurality of annual comprehensive costs;
and 4, comparing the annual comprehensive cost under each modal component frequency demarcation point to obtain the optimal annual comprehensive cost, and determining an optimal configuration scheme according to the optimal annual comprehensive cost.
Optionally, the method for determining the charge-discharge power instruction of the micro-grid hybrid energy storage system is as follows:
wherein p is sc (t) and p bat (t) is the charge-discharge power instruction of the super capacitor and the storage battery, x H (t) is a high-frequency fluctuation portion, x L (t) is a low-frequency fluctuation part, N is a mode component high-low frequency demarcation point, imf j (t) is the j-th modal component.
Optionally, the rated power and rated capacity of the micro-grid hybrid energy storage system comprises: rated power of the super capacitor, rated capacity of the super capacitor, rated power of the storage battery and rated capacity of the storage battery;
the method for determining the rated power of the super capacitor comprises the following steps:
wherein P is SCN Rated power of super capacitor, p sc (t) is a charge-discharge power command of the super capacitor, eta sc,c 、η sc,d The charging and discharging efficiencies of the super capacitor are respectively;
the method for determining the rated capacity of the super capacitor comprises the following steps:
wherein E is SCN Is the rated capacity of the super capacitor, T is the total charge and discharge time of the super capacitor, and p C (t) is a power reference value of the super capacitor after the charge and discharge efficiency is considered, delta t is a charge and discharge power instruction time interval, and SOC sc-max Upper limit of super-capacitor charge state, SOC sc0 For the initial value of the charge state of the super capacitor, SOC sc-min Is the lower limit of the super-capacitance state of charge;
the method for determining the rated power of the storage battery comprises the following steps:
wherein P is BN For rated power of accumulator, p bat (t) is a charge/discharge power command of the storage battery, eta bat,c 、η bat,d The charging and discharging efficiencies of the storage battery are respectively;
the method for determining the rated capacity of the storage battery comprises the following steps:
wherein E is BN Is rated capacity of the storage battery, T is total charge and discharge time of the storage battery, and p B (t) is a power reference value of the storage battery after the charge and discharge efficiency is considered, deltat is a charge and discharge power instruction time interval, and SOC bat-max For the upper limit of the charge state of the storage battery, SOC bat0 For initial value of charge state of storage battery, SOC bat-min Is the lower limit of the charge state of the storage battery.
Optionally, obtaining the annual comprehensive cost includes:
C tol ＝C inv +C main +C rep
C inv ＝(c Pbat P BN +c Ebat E BN +c Psc P SCN +c Esc E SCN )γ
C main ＝c bat,m E BN +c sc,m E SCN
C rep ＝γ(c Pbat P BN +c Ebat E BN )n b
wherein C is tol For annual comprehensive cost, C inv For initial investment cost, C main To run and maintain costs, C rep To update the replacement cost, c Pbat Power per battery, c Ebat Investment cost per unit capacity of battery c Psc Power per super capacitor, P SCN Investment cost per capacity of super capacitor, gamma is capital recovery coefficient, c bat,m Annual operating maintenance costs for unit capacity of battery c sc,m Annual operation maintenance cost for unit capacity of super capacitor, n b Update the number of replacement times P for the battery BN For rated power of accumulator, E BN C is the rated capacity of the accumulator Esc Investment cost per capacity of super capacitor E SCN Is the rated capacity of the super capacitor.
The beneficial effects of the invention are as follows:
the invention fully utilizes the energy storage characteristics of the storage battery and the super capacitor, and uses the VMD to decompose the source and the unbalanced load power in the micro-grid. The decomposition layer number selection method based on the principle of minimum modal aliasing total energy can effectively avoid the influence of subjective setting of the decomposition layer number on the energy storage configuration result, and remarkably improves the applicability of the VMD.
The invention takes the annual comprehensive cost of the hybrid energy storage system as an objective function, takes the high-frequency and low-frequency demarcation points of unbalanced power as optimization variables, establishes a capacity optimization configuration model of the hybrid energy storage system, decouples each modal component of the unbalanced power after VMD decomposition into two parts of low frequency and high frequency, and respectively takes the two parts as charge and discharge power instructions of a storage battery and a super capacitor to obtain economic and reasonable energy storage capacity configuration results.
The VMD-based hybrid energy storage capacity optimization configuration model provided by the invention not only can realize the complementary advantages of power type energy storage and energy type energy storage, but also can improve the service life of the storage battery and the economical efficiency of the hybrid energy storage system.
Drawings
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 schematic diagram of renewable energy power generation according to an embodiment of the present invention;
fig. 2 is a schematic view of a micro-grid structure according to an embodiment of the present invention;
FIG. 3 is a graph of normalized frequency versus time for each IMF in accordance with an embodiment of the present invention;
FIG. 4 is a diagram of an embodiment imf of the invention 2 A frequency-time domain map of (t);
fig. 5 is a flowchart of a method for optimizing and configuring the hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to an embodiment of the invention;
FIG. 6 is a graph illustrating typical solar wind power generation, photovoltaic power generation and load demand according to an embodiment of the present invention;
FIG. 7 is a graph showing an unbalanced power curve according to an embodiment of the present invention;
FIG. 8 is a normalized mode aliasing energy diagram for K2 through 35 according to an embodiment of the present invention;
FIG. 9 is a diagram showing the decomposition results of VMD according to an embodiment of the present invention;
FIG. 10 is a diagram of annual total cost at different frequency demarcation points according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of the power rating versus capacity rating of a hybrid energy storage system at different frequency demarcation points according to an embodiment of the present invention;
fig. 12 is a schematic diagram of charge and discharge power curves of an EMD-based battery and a supercapacitor according to an embodiment of the invention;
fig. 13 is a schematic diagram of charge and discharge power curves of a storage battery and a super capacitor based on VMD according to an embodiment of the present invention;
fig. 14 is a schematic diagram of states of charge of an EMD-based battery and a supercapacitor according to an embodiment of the invention;
fig. 15 is a schematic diagram of the state of charge of the VMD-based battery and the supercapacitor according to an embodiment of the 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 order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Energy storage systems play a critical role in micro-grids. Different energy storage elements have different energy storage characteristics. Hybrid energy storage systems combine high energy density and high power density with better performance than single energy storage systems. Common power decomposition algorithms such as low-pass filtering, discrete fourier transform, wavelet packet decomposition, empirical mode decomposition, and integrated empirical mode decomposition can be affected by time delay, poor accuracy, boundary effects, and mode aliasing. The variant mode decomposition (Variational Mode Decomposition, VMD) has advantages in terms of frequency extraction and mode separation, and can better handle non-stationary signals and smooth renewable energy power fluctuations.
Therefore, the embodiment establishes a capacity optimization configuration model of the hybrid energy storage system consisting of the battery and the super capacitor based on variation modal decomposition, and realizes the optimization configuration of the energy storage capacity of the wind-solar complementary island micro-grid. As shown in fig. 5, the invention discloses a micro-grid hybrid energy storage capacity optimization configuration method based on variation modal decomposition, which comprises the following steps: firstly, reading a micro-grid hybrid energy storage system parameter value, and acquiring unbalanced power through the micro-grid hybrid energy storage system parameter value; based on the energy mapping relation between the frequency domain and the time domain, determining the decomposition layer number of the variational mode decomposition by taking the total mode aliasing energy as the principle of minimum; then, considering the stable fluctuation characteristics of different energy storage elements, selecting a high-frequency and low-frequency demarcation point of source and load unbalanced power in the micro-grid, and distributing charge and discharge power instructions for the battery and the super capacitor; and finally, establishing a hybrid energy storage capacity optimal configuration model by taking the annual comprehensive cost of the hybrid energy storage system as an objective function, and solving the model by taking a high-low frequency demarcation point of unbalanced power as an optimal variable to obtain an optimal configuration result. Example research results show that the method has more economy and feasibility than the existing energy storage configuration method.
Micro-grid structure
The wind-solar complementary island type direct current micro-grid is a small power generation and distribution system which is composed of a wind-light distributed energy system, a conventional load, a hybrid energy storage system, a power distribution device, a central controller and the like, and can realize self-operation, regulation and control, and the structure of the wind-solar complementary island type direct current micro-grid is shown in figure 2.
In this system, each unit is connected to a dc bus via a converter. In order to ensure power balance and improve electric energy quality, unbalanced power between renewable energy sources and load demands in the micro-grid is stabilized through a hybrid energy storage system controlled by a central controller. When the load demand is higher than the energy generation capacity of renewable energy sources, the hybrid energy storage system works in a discharging state so as to meet the load demand; when the renewable energy power generation is above the load demand, the hybrid energy storage system operates in a charged state to absorb more power. Hybrid energy storage system power signal P HESS (t) with source, charge imbalance power P in micro-grid un (t) is a reference, and is represented by formula (1).
P un (t)＝P l (t)-P w (t)-P v (t) (1)
Wherein: p (P) un (t) is unbalanced power; p (P) l (t) is the load power; p (P) w (t) is wind power generation output power; p (P) v And (t) is photovoltaic power generation output power.
VMD-based hybrid energy storage capacity configuration
The core idea of the VMD is to adaptively decompose the original signal into K bandwidth-limited eigen-mode functions (Intrinsic Mode Function, IMF) by varying the constraint conditions, iteratively search for the optimal solution of the variational model using an alternating direction multiplier method (Alternate Direction Method of Multipliers, ADMM), while minimizing the sum of the bandwidths of all the mode components so that the IMFs overlap to reproduce the original signal. Wherein the number of modal components is the same as the number of decomposition layers.
Mathematical model of VMD
In this embodiment, the hybrid energy storage system power is taken as an original signal (the original signal refers to a decomposed signal, namely, unbalanced power in a historical micro-grid), and a variation problem (variation model) with constraint conditions is constructed as follows:
wherein: { u k }＝{u 1 ,u 2 ,...,u K K IMFs obtained by decomposing the original signals; { omega k }＝{ω 1 ,ω 2 ,...,ω K The center frequency corresponding to each IMF;representing a bias derivative of time t; delta (t) is impulse function, K is the number of decomposition layers of VMD, i.e. the number of modal components obtained by decomposition, j is imaginary unit, pi is angle, t is time, u k (t) is the kth modal component obtained by decomposing the original signal, e is a natural constant, s.t. is a constraint condition, P hess And (t) is a hybrid energy storage system power signal.
The quadratic penalty factor alpha and the Lagrangian multiplier lambda (t) are introduced, and the above problem of unconstrained variation (unconstrained variation model) is converted into:
wherein L is a Lagrangian function, and lambda (t) are Lagrangian multipliers; alpha is a quadratic penalty factor.
Iterative updating using alternate direction multiplier methodAnd lambda (lambda) n+1 Finally, the updated modal component and the corresponding center frequency thereof are obtained by utilizing Fourier transformation:
wherein,for the updated modal component, +. >For the center frequency +.>And->Respectively P hess (t)、u i Fourier transform results corresponding to (t) and λ (t); />K is the number of decomposition layers of the VMD, namely the number of modal components obtained by decomposition, and is the center frequency of the current sub-modal function; alpha is a secondary penalty factor; omega is the center frequency of the modal component; omega k Is the center frequency of the kth modal component; />Is u k (ω) the corresponding fourier transform.
The iteration stop condition of the above process is as shown in formula (6):
wherein,respectively->A corresponding fourier transform; k is the number of the modal components obtained by decomposition, namely the number of the modal components obtained by decomposition, and e is a convergence parameter.
Selection of VMD decomposition level K
The choice of the VMD decomposition layer number K is unreasonable, and in the decomposition process, false components are generated due to over decomposition or the IMFs are not separated due to under decomposition, which causes modal mixing among modal components to further influence the configuration result.
For updated modal components imf i (t) performing a hilbert transform:
wherein H is Hilbert transform; imf i (t) is the ith modal component; pi is the angle; t and τ are time variables;
construction imf in the form of polar coordinates i Analytical signal of (t):
wherein z is i The amplitude function of (t) is omega i (t)，z i (t) the phase function isj is an imaginary unit; omega i Is an amplitude function; />In polar form, the phase difference is represented.
Instantaneous frequency of modal components:
wherein f i (t) is the instantaneous frequency of the modal component,imf of a shape of imf i (t) phase function in polar form, d is p +.>Differentiation, t is time.
After decomposing the unbalanced power in the micro-grid, the VMD obtains a normalized frequency-time curve of each IMF by utilizing Hilbert transformation, namely, performs Hilbert transformation on the instantaneous frequency to obtain a normalized frequency-time curve of each IMF, as shown in FIG. 3.
As can be seen from the figure, each IMF is at a certain frequency ω i (t) presenting a narrower band for the center, the instantaneous frequency at each instant fluctuates up and down around the center frequency. The overlapping of the modal components in the frequency domain, and the mapping to the time domain can generate modal aliasing. 3 modal components overlapping in the frequency domain are taken and mapped to the time domain computing modal aliasing energy. imf 2 (t) a map from the frequency domain to the time domain is shown in fig. 4.
imf 2 Instantaneous frequency f of (2) 2 (t) is lower than imf in the (a, b) period 3 Instantaneous frequency f of (2) 3 (t) mapping to time domain representation imf 2 There is low frequency aliasing energy in this period. If the products of power and time are directly summed, then the sum can be within the (a, b) time period The energy generated by the positive and negative power can be offset, so that the actual energy aliasing condition is difficult to determine, and the absolute value of the power is used for calculation. Determination imf using (12) 2 Low frequency energy aliased during (a, b) time periods.
Wherein E is low,2 Imf of a shape of imf 2 Low frequency energy aliased during (a, b); a is a starting time node; b is the last time node; Δt is the time interval; i is the time point.
Similarly, imf 2 Instantaneous frequency f of (2) 2 (t) higher than imf in (c, d) period 1 Instantaneous frequency f of (2) 1 (t) mapping to time domain representation imf 2 There is high frequency aliasing energy in this period. Determination imf using equation (13) 2 High frequency energy aliased during (c, d).
Wherein E is high,2 Imf of a shape of imf 2 High frequency energy aliased during (c, d); c is a starting time node; d is the last time node.
Thus imf 2 The total modal aliasing energy at each time period is:
E s,2 ＝E low,2 +E high,2 (14)
without loss of generality, for any of the modal components imf j The total energy of modal aliasing present is:
wherein i= (f, g) is a period in which the low-frequency aliasing energy exists in the modal component, i= (m, n) is a period in which the high-frequency aliasing energy exists in the modal component, g is a last time node of the period in which the low-frequency aliasing energy exists in the modal component, and imf j (t) is the j-th modal component, Δt is the time interval, and n is the last time node in which the modal component has a high frequency aliased energy period.
Assuming that the number of VMD decomposition layers is K, the sum of the energies of all modal component aliases at this decomposition layer number is calculated according to equation (16) as:
wherein E is s E is the sum of the energies of all the modal component aliases s,j The total energy of modal aliasing existing for any modal component is K, which is the number of modal components obtained by decomposition.
Based on the principle of the minimum modal aliasing total energy, the number of decomposition layers of the VMD can be determined by combining the formula.
Rated power and rated capacity
The unbalanced power is decomposed into K modal components by using the VMD, and the frequency of each modal component is reduced from high to low in sequence. Selecting proper modal component frequency demarcation point N to reconstruct the initial signal into two parts, high-frequency fluctuation part x H (t) is the sum of the front N-order modal components, the low frequency fluctuation portion x L (t) is the sum of modal components greater than the order N. According to the characteristic that the super capacitor is suitable for stabilizing high-frequency fluctuation and the storage battery is suitable for stabilizing low-frequency fluctuation, a charge and discharge power instruction of the energy storage element can be obtained, as shown in a formula (17):
wherein p is sc (t) and p bat (t) is the charge-discharge power instruction of the super capacitor and the storage battery, x H (t) is the high frequency fluctuation part, i.e. the sum of the front N-order modal components, x L (t) is the low frequency fluctuation part, i.e. the sum of modal components larger than N-order, N is the high-low frequency demarcation point of the modal components, imf j (t) is the j-th modal component and Δt is the time interval.
In order to ensure the power balance in the micro-grid, the rated power of the super capacitor and the storage battery is configured to stabilize the maximum source and load unbalanced power in the micro-grid. And the charging and discharging efficiency of the energy storage element is considered, so that rated power of the super capacitor and the storage battery can be obtained:
wherein P is SCN Rated power of super capacitor, p sc (t) is the charge-discharge power command eta of the super capacitor sc,c 、η sc,d The charging and discharging efficiencies of the super capacitor are respectively; η (eta) bat,c 、η bat,d Respectively the charge and discharge efficiency of the storage battery, P BN For rated power of accumulator, p bat (t) is a charge/discharge power command of the storage battery, eta bat,c 、η bat,d The charging and discharging efficiencies of the storage battery are respectively.
In order to ensure safe and stable operation of the hybrid energy storage system, the charge states of the super capacitor and the storage battery also need to be considered. The SOC of the super capacitor at the time t is as follows:
in SOC sc0 The initial SOC value of the super capacitor; Δt is the charge-discharge power instruction time interval; e (E) SCN The rated capacity of the super capacitor; p (P) C And (t) is a power reference value of the super capacitor after the charge and discharge efficiency is considered, and the power reference value is shown in a formula (21):
taking into account the constraint of SOC, i.e. SOC min ≤SOC sc ≤SOC max The method can obtain:
the rated capacity of the super capacitor can be obtained by combining the formula (20) and the formula (22), as shown in the formula (23).
Wherein E is SCN Is the rated capacity of the super capacitor, T is the total charge and discharge time of the super capacitor, and p C (t) is a power reference value of the super capacitor after the charge and discharge efficiency is considered, delta t is a charge and discharge power instruction time interval, and SOC sc-max Upper limit of super-capacitor charge state, SOC sc0 For the initial value of the charge state of the super capacitor, SOC sc-min Is the super-capacitor state of charge lower limit.
Similarly, the rated capacity E of the storage battery can be obtained BN 。
Wherein E is BN Is rated capacity of the storage battery, T is total charge and discharge time of the storage battery, and p B (t) is a power reference value of the storage battery after the charge and discharge efficiency is considered, deltat is a charge and discharge power instruction time interval, and SOC bat-max For the upper limit of the charge state of the storage battery, SOC bat0 For initial value of charge state of storage battery, SOC bat-min Is the lower limit of the charge state of the storage battery;
capacity optimization configuration model of hybrid energy storage system
The present embodiment comprehensively considers the initial investment cost C of the hybrid energy storage system inv Cost of operation and maintenance C main Update replacement cost C rep And (3) taking annual comprehensive cost of the hybrid energy storage system as a target, taking high-low frequency demarcation points of source and load unbalanced power as optimization variables, and establishing a capacity optimization configuration model of the hybrid energy storage system.
Objective function
The annual total cost of the hybrid energy storage system is shown in formula (24):
C tol ＝C inv +C main +C rep (24)
(1) Initial investment cost
C inv ＝(c Pbat P BN +c Ebat E BN +c Psc P SCN +c Esc E SCN )γ (25)
Wherein, c Pbat 、c Ebat Investment cost of unit power and unit capacity of the storage battery respectively; c Psc 、c Esc Investment cost of the super capacitor per unit power and unit capacity is respectively; gamma is the capital recovery coefficient, P BN Is the rated power of the storage battery; e (E) BN Is the rated capacity of the storage battery; c Esc Investment cost for the unit capacity of the super capacitor; e (E) SCN Is the rated capacity of the super capacitor. As shown in formula (26):
wherein r is investment discount rate; t (T) N The rated service life of the energy storage system is prolonged.
(2) Cost of operation and maintenance
C main ＝c bat,m E BN +c sc,m E SCN (27)
Wherein, c bat,m 、c sc,m The annual operation maintenance cost of the storage battery and the super capacitor unit capacity is respectively.
(3) Update replacement cost
C rep ＝γ(c Pbat P BN +c Ebat E BN )n b (28)
Wherein, c Pbat 、c Ebat The replacement cost of the unit power and the unit capacity of the storage battery respectively; because of the longer service life of the super capacitor, the update replacement cost is not considered here. n is n b Renewing the replacement times for the batteryThe number is represented by formula (29):
in the method, in the process of the invention,to round up the function, n b The replacement times for the storage battery are updated, T N For the rated service life of the storage battery, T life The service life of the storage battery is prolonged.
The constraint conditions are as follows:
(1) Power balance constraint
P hess (t)＝P l (t)-P w (t)-P v (t) (30)
Wherein P is hess (t) is hybrid energy storage system power; p (P) l (t) is the load power; p (P) w (t) is wind power generation output power; p (P) v And (t) is photovoltaic power generation output power.
(2) Energy storage element charge-discharge power constraint
Wherein P is SCN Rated power of the super capacitor; p (P) C (t) is a power reference value of the super capacitor after charge and discharge efficiency is considered; p (P) BN Rated power for the storage battery de; p (P) B And (t) is a power reference value of the storage battery after the charge and discharge efficiency is considered.
(3) Energy storage element SOC constraint
In SOC sc (t) is the state of charge of the super capacitor at time t;respectively is superbThe upper and lower limits of the state of charge of the stage capacitor; SOC (State of Charge) bat (t) is the state of charge of the battery at time t; />The upper limit and the lower limit of the charge state of the storage battery are respectively.
Calculation case analysis
And selecting typical calendar history data of the micro-grid in a certain area for carrying out calculation analysis so as to verify the feasibility of the model in the embodiment. The sampling time span is 1 day, the sampling time interval is 5min, and the number of sampling points is 288. Typical solar wind power generation, photovoltaic power generation and load demand data are shown in fig. 6.
The unbalanced power in the micro-grid is the difference between the load power and the renewable energy source generation power of each sampling point. The power curve may be unbalanced according to equation (1), as shown in fig. 7:
the relevant parameters of the hybrid energy storage system are shown in table 1, and table 1 is the relevant parameters of the hybrid energy storage system:
TABLE 1
VMD decomposition layer number selection analysis
According to the mapping relation between the frequency domain and the time domain, the decomposition layer number K of the VMD is selected by taking the mode aliasing total energy as the principle of minimum. The modal aliasing energy values under different decomposition mode numbers are shown in table 2, and table 2 is the modal aliasing energy values under different decomposition mode numbers;
TABLE 2
As can be seen from the table, the modal aliasing energy is minimal when k=6. Therefore, with k=6 as a reference value, the mode aliasing energy under different decomposition levels is normalized, and the normalized mode aliasing total energy curve corresponding to the decomposition levels under 2 to 35 is shown in fig. 8:
the K is underdecomposed from 2 to 5 layers, the IMFs are mutually overlapped, the number of the decomposed IMFs is increased along with the increase of the K value, the separation of the modal components is obvious, and the total energy of modal aliasing generally shows a decreasing trend; when K=6, the IMFs of unbalanced power can be obviously separated, and the total energy of modal aliasing reaches the minimum value; the K value continues to be increased, and each IMF has over-decomposition condition so as to generate false components, and the total energy of the modal aliasing has irregular up-and-down fluctuation trend.
Therefore, k=6 is taken as the optimal decomposition level, and the decomposition result is shown in fig. 9:
from the decomposition results, it can be seen that from IMF1 to IMF6, the frequency of each modal component gradually decreases and the amplitude gradually increases. At this time, the modal aliasing energy is minimum, and each IMF can more completely reflect the fluctuation characteristic of the original unbalanced power.
Modal component frequency demarcation point selection analysis
The hybrid energy storage system takes micro-grid source and unbalanced charge power as reference signals, the storage battery is suitable for suppressing low-frequency fluctuation, and the super capacitor starts to suppress high-frequency fluctuation. If the frequency demarcation points N of the modal components are not reasonably selected, the charge and discharge power instructions of the storage battery and the super capacitor are affected, and then configuration results are affected. In this embodiment, the annual comprehensive cost of the hybrid energy storage system is taken as a target, the frequency demarcation points are taken as optimization variables, and the configuration cost curves under different frequency demarcation points are shown in fig. 10:
as can be seen from the above figures, the annual total cost of the hybrid energy storage system is lowest when n=4. When the frequency demarcation point is selected to be too small, the charge and discharge power instruction of the storage battery contains more high-frequency fluctuation, so that the initial investment cost of the storage battery can be increased, and the service life of the storage battery can be influenced; when the frequency demarcation point is selected to be too large, the charge and discharge power instruction of the super capacitor contains more low-frequency parts, so that the initial investment cost of the super capacitor can be increased.
The rated power and rated capacity of the hybrid energy storage system at different demarcation points can be obtained according to the rated power and rated capacity portions as shown in fig. 11: when N is less than 4, the battery contains high-frequency fluctuation, resulting in an increase in the rated power and rated capacity of the battery; as N increases, the high frequency ripple is gradually stabilized by the supercapacitor and the capacity of the battery is gradually reduced. When N is greater than 4, the super capacitor contains more low-frequency fluctuation, so that the rated power and rated capacity of the super capacitor are higher. Taking n=4, and obtaining an optimal configuration result as shown in a table (3), wherein the table 3 is the optimal configuration result of the hybrid energy storage system;
TABLE 3 Table 3
Configuration cost analysis
Four configuration schemes are selected for comparing the economic advantages of the hybrid energy storage and the single energy storage and the application of the VMD algorithm and the EMD algorithm to the energy storage capacity configuration. Scheme I is single super capacitor energy storage, scheme II is single battery energy storage, scheme III is EMD-based hybrid energy storage, and scheme IV is VMD-based hybrid energy storage. The configuration results are shown in table (4), and table 4 shows the configuration results of four schemes:
TABLE 4 Table 4
From the table, the capacity of the storage battery in scheme II is higher than that of the super capacitor in scheme i from the energy storage capacity configuration. However, the initial investment cost per unit capacity of the super capacitor is far higher than that of the storage battery, resulting in poor economy of scheme i. Compared with scheme III and scheme IV, the configuration result based on the VMD is obviously lower than the configuration result based on the EMD. This is because the VMD effectively avoids modal aliasing and the inability to separate near frequency modal components. Therefore, the rated capacity and rated power of the hybrid energy storage system are lower, and the annual comprehensive cost is further reduced. Compared with EMD, the cost of VMD is reduced by 15.9%.
Overall, hybrid energy storage is preferred over single energy storage and VMD is preferred over EMD. The configuration result of the scheme IV is optimal, and can be used as a recommended scheme of wind-solar complementary island micro-grid energy storage configuration.
Configuration algorithm analysis
When the hybrid energy storage system is used for stabilizing the power fluctuation of renewable energy sources in the micro-grid, the selection of a proper power distribution algorithm is important. The introduction describes a common algorithm for stabilizing power fluctuations, and this section mainly analyzes the advantages of VMD over EMD. EMD is an adaptive time-frequency analysis algorithm that overcomes the limitations of wavelet packet decomposition. However, EMD is still affected by modal aliasing. VMD has advantages in frequency extraction and modal separation, and can well suppress modal aliasing.
Fig. 12 is a charge-discharge power curve of the battery and the super capacitor based on EMD. In the period A of the figure, the charge-discharge power curve P of the super capacitor C (t) floats over a small range, indicating that the supercapacitor is continuously charging and discharging during this period. This is due to the presence of modal aliasing by EMD, resulting in aliasing with low frequency modal components in period a.
Fig. 13 is a graph of the charge/discharge power of the VMD-based battery and the supercapacitor. In the B period of the figure, the super capacitor is in a fast charge and discharge state. This shows that the VMD suppresses modal aliasing, enabling the supercapacitor to effectively smooth out high frequency power fluctuations.
Charging and discharging power curve P of storage battery under two algorithms B And (t) the phase difference is smaller, the low-frequency power fluctuation is stabilized, and the charge and discharge power can reflect the integral power shortage and surplus of unbalanced power.
State of charge (SOC) is an important indicator for measuring safe and stable operation of batteries and supercapacitors. The feasibility of the model proposed by this embodiment can be demonstrated by comparing and analyzing the SOC based on the EMD and VMD. Fig. 14 is an SOC of an EMD-based battery and supercapacitor. Fig. 15 is an SOC of a VMD-based battery and supercapacitor.
SOC (state of charge) of storage battery under two algorithms bat The trend of (t) is substantially uniform and relatively gentle. The storage battery is in a continuous charge and discharge state, and the storage battery is mainly used for suppressing low-frequency power fluctuation as an energy type energy storage element.
Super-power supplyCapacitive state of charge SOC sc The trend of (t) is evident. The super capacitor is in a rapid charge and discharge state, which shows that the super capacitor is used as a power type energy storage element and is mainly used for stabilizing high-frequency power fluctuation. EMD-based supercapacitor SOC (state of charge) sc (t) ratio of VMD-based SOC sc (t) smoother. The reason is that the EMD has modal aliasing, so that the low-frequency modal component and the high-frequency modal component are aliased, and the super capacitor is in a continuous charge and discharge state in certain time periods. Furthermore, as can be seen from FIG. 14, the EMD-based SOC sc (t) out-of-limit conditions exist, and the use of the VMD can effectively avoid out-of-limit states of super-capacitor charges.
Conclusion(s)
Based on VMD, the embodiment establishes a capacity optimization configuration model of the hybrid energy storage system consisting of the storage battery and the super capacitor so as to realize optimal configuration of energy storage capacity in the wind-solar complementary island type direct current micro-grid. The following conclusions were drawn:
in the embodiment, the energy storage characteristics of the storage battery and the super capacitor are fully utilized, and the VMD is used for decomposing the source and the unbalanced charge power in the micro-grid. The decomposition layer number selection method based on the principle of minimum modal aliasing total energy can effectively avoid the influence of subjective setting of the decomposition layer number on the energy storage configuration result, and remarkably improves the applicability of the VMD.
In the embodiment, the annual comprehensive cost of the hybrid energy storage system is taken as an objective function, the high-frequency and low-frequency demarcation points of unbalanced power are taken as optimization variables, a capacity optimization configuration model of the hybrid energy storage system is established, all modal components of the unbalanced power after VMD decomposition are decoupled into a low-frequency part and a high-frequency part, and the low-frequency part and the high-frequency part are respectively taken as charging and discharging power instructions of a storage battery and a super capacitor, so that an economic and reasonable energy storage capacity configuration result is obtained. The service life of the storage battery in the hybrid energy storage system is 124.63% longer than that of a single energy storage battery. The annual comprehensive cost of the hybrid energy storage is 31.68% lower than that of the single super capacitor energy storage and 22.1% lower than that of the single storage battery energy storage. This shows that the VMD-based hybrid energy storage capacity optimization configuration model provided in this embodiment not only can realize the complementary advantages of power-type energy storage and energy-type energy storage, but also can improve the service life of the storage battery and the economical efficiency of the hybrid energy storage system.
The annual total cost based on VMD is 15.9% lower than that based on EMD. VMD can also solve the modal aliasing and SOC out-of-limit problems present in EMD. By comparing the configuration cost, the charge-discharge power curve and the SOC based on the two algorithms of the EMD and the VMD, the VMD can be obtained to be superior to the EMD.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (10)
1. The micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition is characterized by comprising the following steps of:
reading a micro-grid hybrid energy storage system parameter value, and acquiring unbalanced power through the micro-grid hybrid energy storage system parameter value;
constructing a variational modal decomposition VMD mathematical model, selecting the decomposition layer number of the variational modal decomposition VMD mathematical model, decomposing the unbalanced power based on the variational modal decomposition VMD mathematical model after determining the decomposition layer number, and obtaining a plurality of modal components;
and determining rated power and rated capacity of the micro-grid hybrid energy storage system through a plurality of modal components, acquiring annual comprehensive cost, and determining an optimal configuration scheme.
2. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to claim 1, wherein the method for obtaining the unbalanced power is as follows:
P un (t)＝P l (t)-P w (t)-P v (t)
wherein P is un (t) is unbalanced power, P l (t) is load power, P w (t) is the output power of wind power generation, P v (t) is the output power of photovoltaic power generation。
3. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to claim 1, wherein constructing the variation modal decomposition VMD mathematical model comprises:
determining an original signal, constructing a variation model with constraint conditions according to the original signal, introducing a secondary penalty factor and a Lagrange multiplier, converting the variation model into an unconstrained variation model, setting an iteration stop condition, updating parameters in the unconstrained variation model through the iteration stop condition, and obtaining the variation modal decomposition VMD mathematical model.
4. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to claim 3, wherein the method for setting the iteration stop condition is as follows:
wherein,respectively->Corresponding Fourier transformation, K is the number of modal components obtained by decomposition, and e is a convergence parameter;
The method for obtaining the variational modal decomposition VMD mathematical model comprises the following steps:
wherein,for the updated modal component, +.>For the center frequency +.>And->Respectively P hess (t)、u i Fourier transform results corresponding to (t) and λ (t); />For the center frequency of the current sub-mode function, K is the number of mode components obtained by decomposition, alpha is a quadratic penalty factor, omega is the center frequency of the mode components, omega k For the center frequency of the kth modal component, < +.>Is u k (ω) the corresponding fourier transform.
5. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to claim 4, wherein selecting the number of decomposition layers of the variation modal decomposition VMD mathematical model comprises:
performing Hilbert transformation on the updated modal component, constructing an analysis signal of the updated modal component in a polar coordinate form, acquiring an amplitude function and a phase function of the analysis signal, and acquiring the instantaneous frequency of the updated modal component through the analysis signal;
performing Hilbert transformation on the instantaneous frequency to obtain a normalized frequency-time curve, obtaining overlapped modal components through the normalized frequency-time curve, mapping the overlapped modal components to a time domain, and calculating to determine high-frequency energy and low-frequency energy;
Setting the decomposition layer number, obtaining total energy of a plurality of mode aliasing by adding the high-frequency energy and the low-frequency energy, and determining the decomposition layer number by taking the principle that the total energy of the mode aliasing is minimum.
6. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to claim 4, wherein the method for obtaining the instantaneous frequency of the updated modal component is as follows:
wherein f i (t) is the instantaneous frequency of the modal component,imf of a shape of imf i (t) phase function in polar form, d is the pairDifferentiating, t is time;
the method for acquiring the total energy of the aliasing of the plurality of modes comprises the following steps:
wherein E is s E is the sum of the energies of all the modal component aliases s,j For the total energy of modal aliasing in the presence of any modal component, K is the mode obtained by decompositionThe number of the state components, i= (f, g) is the period when the low-frequency aliasing energy exists in the modal components, i= (m, n) is the period when the high-frequency aliasing energy exists in the modal components, g is the last time node of the period when the low-frequency aliasing energy exists in the modal components, and imf j (t) is the j-th modal component, Δt is the time interval, and n is the last time node in which the modal component has a high frequency aliased energy period.
7. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to claim 1, wherein determining the optimal configuration scheme comprises:
step 1, selecting a modal component frequency demarcation point, and dividing a plurality of modal components into a high-frequency fluctuation part and a low-frequency fluctuation part according to the modal component frequency demarcation point;
step 2, determining a charging and discharging power instruction of the micro-grid hybrid energy storage system through the high-frequency fluctuation part and the low-frequency fluctuation part, and determining rated power and rated capacity of the micro-grid hybrid energy storage system through the charging and discharging power instruction to obtain annual comprehensive cost;
step 3, judging whether the modal component frequency demarcation point is smaller than the decomposition layer number, if the modal component frequency demarcation point is smaller than the decomposition layer number, repeating the step 1 and the step 2 to obtain a plurality of annual comprehensive costs, and if the modal component frequency demarcation point is larger than the decomposition layer number, outputting a plurality of annual comprehensive costs;
and 4, comparing the annual comprehensive cost under each modal component frequency demarcation point to obtain the optimal annual comprehensive cost, and determining an optimal configuration scheme according to the optimal annual comprehensive cost.
8. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to claim 7, wherein the method for determining the charge and discharge power command of the hybrid energy storage system of the micro-grid is as follows:
wherein p is sc (t) and p bat (t) is the charge-discharge power instruction of the super capacitor and the storage battery, x H (t) is a high-frequency fluctuation portion, x L (t) is a low-frequency fluctuation part, N is a mode component high-low frequency demarcation point, imf j (t) is the j-th modal component.
9. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on decomposition of variation modalities according to claim 7, wherein the rated power and rated capacity of the hybrid energy storage system of the micro-grid comprises: rated power of the super capacitor, rated capacity of the super capacitor, rated power of the storage battery and rated capacity of the storage battery;
the method for determining the rated power of the super capacitor comprises the following steps:
wherein P is SCN Rated power of super capacitor, p sc (t) is a charge-discharge power command of the super capacitor, eta sc,c 、η sc,d The charging and discharging efficiencies of the super capacitor are respectively;
the method for determining the rated capacity of the super capacitor comprises the following steps:
wherein E is SCN Is the rated capacity of the super capacitor, T is the total charge and discharge time of the super capacitor, and p C (t) is a power reference value of the super capacitor after the charge and discharge efficiency is considered, delta t is a charge and discharge power instruction time interval, and SOC sc-max Upper limit of super-capacitor charge state, SOC sc0 For the initial value of the charge state of the super capacitor, SOC sc-min Is the lower limit of the super-capacitance state of charge;
the method for determining the rated power of the storage battery comprises the following steps:
wherein P is BN For rated power of accumulator, p bat (t) is a charge/discharge power command of the storage battery, eta bat,c 、η bat,d The charging and discharging efficiencies of the storage battery are respectively;
the method for determining the rated capacity of the storage battery comprises the following steps:
wherein E is BN Is rated capacity of the storage battery, T is total charge and discharge time of the storage battery, and p B (t) is a power reference value of the storage battery after the charge and discharge efficiency is considered, deltat is a charge and discharge power instruction time interval, and SOC bat-max For the upper limit of the charge state of the storage battery, SOC bat0 For initial value of charge state of storage battery, SOC bat-min Is the lower limit of the charge state of the storage battery.
10. The method for optimizing configuration of hybrid energy storage capacity of a micro-grid based on variation modal decomposition according to claim 7, wherein obtaining the annual comprehensive cost comprises:
C tol ＝C inv +C main +C rep
C inv ＝(c Pbat P BN +c Ebat E BN +c Psc P SCN +c Esc E SCN )γ
C main ＝c bat,m E BN +c sc,m E SCN
C rep ＝γ(c Pbat P BN +c Ebat E BN )n b
wherein C is tol For annual comprehensive cost, C inv For initial investment cost, C main To run and maintain costs, C rep To update the replacement cost, c Pbat Power per battery, c Ebat Investment cost per unit capacity of battery c Psc Power per super capacitor, P SCN Investment cost per capacity of super capacitor, gamma is capital recovery coefficient, c bat,m Annual operating maintenance costs for unit capacity of battery c sc,m Annual operation maintenance cost for unit capacity of super capacitor, n b Update the number of replacement times P for the battery BN For rated power of accumulator, E BN C is the rated capacity of the accumulator Esc Investment cost per capacity of super capacitor E SCN Is the rated capacity of the super capacitor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311077293.9A CN117081128A (en) | 2023-08-25 | 2023-08-25 | Micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311077293.9A CN117081128A (en) | 2023-08-25 | 2023-08-25 | Micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117081128A true CN117081128A (en) | 2023-11-17 |
Family
ID=88701944
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311077293.9A Pending CN117081128A (en) | 2023-08-25 | 2023-08-25 | Micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117081128A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117674198A (en) * | 2024-02-01 | 2024-03-08 | 西安热工研究院有限公司 | Novel frequency modulation method and system for super-capacitor coupled lithium battery |
| CN117691630A (en) * | 2024-02-04 | 2024-03-12 | 西安热工研究院有限公司 | Novel power system frequency modulation method and system based on VMD-CEEMD |
| CN117713144A (en) * | 2024-02-06 | 2024-03-15 | 西安热工研究院有限公司 | Thermal power generating unit frequency modulation method and system based on molten salt energy storage |
| CN117878973A (en) * | 2024-03-13 | 2024-04-12 | 西安热工研究院有限公司 | Frequency modulation method and system for fused salt coupling thermal power generating unit |
-
2023
- 2023-08-25 CN CN202311077293.9A patent/CN117081128A/en active Pending
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117674198A (en) * | 2024-02-01 | 2024-03-08 | 西安热工研究院有限公司 | Novel frequency modulation method and system for super-capacitor coupled lithium battery |
| CN117674198B (en) * | 2024-02-01 | 2024-04-26 | 西安热工研究院有限公司 | Frequency modulation method and system for super-capacitor coupling lithium battery |
| CN117691630A (en) * | 2024-02-04 | 2024-03-12 | 西安热工研究院有限公司 | Novel power system frequency modulation method and system based on VMD-CEEMD |
| CN117691630B (en) * | 2024-02-04 | 2024-04-30 | 西安热工研究院有限公司 | VMD-CEEMD-based power system frequency modulation method and system |
| CN117713144A (en) * | 2024-02-06 | 2024-03-15 | 西安热工研究院有限公司 | Thermal power generating unit frequency modulation method and system based on molten salt energy storage |
| CN117713144B (en) * | 2024-02-06 | 2024-04-30 | 西安热工研究院有限公司 | Thermal power generating unit frequency modulation method and system based on molten salt energy storage |
| CN117878973A (en) * | 2024-03-13 | 2024-04-12 | 西安热工研究院有限公司 | Frequency modulation method and system for fused salt coupling thermal power generating unit |
| CN117878973B (en) * | 2024-03-13 | 2024-06-11 | 西安热工研究院有限公司 | Frequency modulation method and system for fused salt coupling thermal power generating unit |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117081128A (en) | Micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition | |
| WO2018196433A1 (en) | Multi-type energy storage multi-level control method | |
| CN110676870B (en) | Hybrid energy storage capacity configuration method suitable for wind power grid connection | |
| Hu et al. | Optimization of battery energy storage system with super-capacitor for renewable energy applications | |
| CN109510234B (en) | Hybrid energy storage capacity optimal configuration method and device for micro-grid energy storage power station | |
| CN104377724B (en) | Improve the coordinating and optimizing control method of wind-powered electricity generation/photovoltaic mixed energy storage system economy | |
| CN110718940B (en) | Multi-energy ship intelligent power distribution method and device based on load prediction | |
| CN104779630A (en) | Capacity allocation method for hybrid energy storage system capable of restraining wind power output power fluctuation | |
| Wu et al. | A study on use of hybrid energy storage system along with variable filter time constant to smooth DC power fluctuation in microgrid | |
| CN110797896A (en) | Hybrid energy storage capacity optimal configuration method in micro-grid wind and solar energy storage system | |
| CN106340892B (en) | For stabilizing the control equipment of the energy-storage system of wind power output power | |
| CN107947211A (en) | Using WAVELET PACKET DECOMPOSITION and meter and the isolated island type micro-capacitance sensor energy storage Optimal Configuration Method of frequency response | |
| CN112636367A (en) | Hybrid energy storage capacity optimal configuration method for stabilizing wind power fluctuation | |
| CN111628558B (en) | System and method for optimizing energy management and capacity configuration of hybrid energy storage system | |
| CN110460075B (en) | Hybrid energy storage output control method and system for stabilizing peak-valley difference of power grid | |
| CN113410900B (en) | Micro-grid HESS optimization configuration method and system based on self-adaptive difference whale optimization | |
| CN107370176A (en) | A kind of method stabilized randomness power supply and go out fluctuation | |
| CN114201916A (en) | Residual power decomposition method for micro-grid hybrid energy storage system based on parameter optimization | |
| CN111224432A (en) | Microgrid optimization scheduling method and device | |
| CN106451510A (en) | Energy storage station capacity and charge-discharge power configuring method adaptive to peak clipping and valley filling | |
| CN115986794A (en) | Hybrid energy storage capacity optimal configuration method for absorption wind and solar energy | |
| CN103401260A (en) | Advanced optimization progressive control method for composite energy storage | |
| CN114204562B (en) | Hybrid energy storage optimization method and system considering electric automobile and photovoltaic | |
| CN110460108A (en) | A kind of smooth new energy goes out the energy storage Optimal Configuration Method of fluctuation | |
| CN113555901A (en) | Hybrid energy storage capacity optimization method based on improved S-shaped function particle swarm optimization algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |