CN107947211A - Using WAVELET PACKET DECOMPOSITION and meter and the isolated island type micro-capacitance sensor energy storage Optimal Configuration Method of frequency response - Google Patents
Using WAVELET PACKET DECOMPOSITION and meter and the isolated island type micro-capacitance sensor energy storage Optimal Configuration Method of frequency response Download PDFInfo
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Abstract
The invention discloses a kind of using WAVELET PACKET DECOMPOSITION and meter and the isolated island type micro-capacitance sensor energy storage Optimal Configuration Method of frequency response, choose dividing method of the adaptive WAVELET PACKET DECOMPOSITION as imbalance power, the characteristics of considering energy-storage battery, super capacitor and system frequency deviation digestion capability, imbalance power is divided into low frequency component, intermediate frequency component and high fdrequency component three parts, stabilized respectively by three, hybrid energy-storing is established into the model of this year value, the allocation optimum result of energy storage is determined on the basis of expense is most economical.The foundation of the isolated island micro-capacitance sensor cost model of the present invention only expense of meter and energy-storage system body expenditure, and configure and abandon eolian and auxiliary frequency modulation income etc. of stored energy capacitance have very big relation, for this reason, how to build more fully accurate energy storage cost model becomes the emphasis further studied.
Description
Technical Field
The invention relates to an island-type microgrid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response.
Background
In recent years, environmental pollution and energy crisis have attracted more and more attention, and wind power is widely applied to power systems as a representative of pollution-free renewable energy. However, for the power grid, especially for the islanded micro-grid, due to the randomness and instability of wind power, the large access of the power grid can create great challenges for the reliable operation and the frequency stability of the power system. For this reason, necessary measures are required to reduce the negative impact of grid-connected wind power on the grid.
The energy storage device has the capacity of energy transfer in a space-time range, wind power fluctuation can be effectively stabilized by using an energy storage technology, a dispatching plan of a power grid is tracked, and the grid connection capacity of wind power is improved. Energy storage technologies are mainly divided into two main categories: energy type and power type. Each type of energy storage technology has its own advantages and disadvantages. The unit cost of energy storage is generally lower than that of power storage, but the service life of the energy storage is seriously damaged by the fact that the energy storage absorbs excessive high-frequency fluctuation power due to the characteristics of the energy storage. The power type energy storage has the advantages that the high-frequency low-amplitude fluctuation in unbalanced power can be quickly captured by means of the high power density and the quick charge-discharge conversion capacity of the power type energy storage, but the capacity of the power type energy storage is not easy to select too large due to price factors, so that the long-time charge-discharge performance is poor. Thus, hybrid energy storage combines the advantages of both types of energy storage technologies, resulting in better power and energy performance. However, the cost of the energy storage system still accounts for a large proportion of the overall cost of the energy storage power station, and how to effectively solve the problem of energy storage optimization configuration is still urgent. The prior art realizes the segmented smooth power output adaptive to the power grid dispatching decision, establishes an energy storage battery capacity optimization decision model with the optimal economic benefit as the target and solves the problem by a particle swarm algorithm; the fluctuation probability characteristic distribution of the wind power is analyzed, and the wind-storage hybrid power station using the double-battery energy storage topological structure is provided for stabilizing the wind power fluctuation.
The prior art does not consider the characteristic that the optimum operation time scale of a single energy storage mode is narrow, can not treat power fluctuation of different frequency bands differently, can not give full play to the advantages of different types of energy storage, and has the problems of high cost and short energy storage service life.
In the prior art, the unbalanced supply and demand power of the microgrid is decomposed into a daily component and an hour component, the optimal division breakpoint and the energy storage configuration result are obtained through cost model calculation, and the method completely decomposes two types of energy storage and independently performs optimal configuration. Meanwhile, the literature also proposes that the power distribution of the hybrid energy storage is determined by a method of combining a low-pass filter and a sliding average value, and an energy storage battery and a super capacitor respectively stabilize the trend and the fast fluctuation component in the wind power. In other documents, energy storage capacity configuration and real-time tracking performance are further optimized through variable first-order filtering time constant control, and a maximum power limit control strategy is applied to maintain the energy storage SOC within a normal operation range. However, the hybrid energy storage stabilizing wind power fluctuation proposed at present does not relate to the problem of system self frequency fluctuation caused by active imbalance, the frequency deviation allowed by the island-type micro-grid corresponds to high-frequency low-amplitude power fluctuation, part of high-frequency fluctuation power is processed by the self absorption capacity of the system, and the configuration result of the hybrid energy storage capacity, particularly the configuration result of the power type energy storage, can be optimized again. In addition, the traditional filtering methods such as low-pass filtering, sliding average value method and the like have the defects of poor positioning characteristics and difficulty in scientific quantification of parameters.
Disclosure of Invention
The invention provides an island-type microgrid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response.
In order to achieve the purpose, the invention adopts the following technical scheme:
an island-type microgrid energy storage optimal configuration method adopting wavelet packet decomposition and considering frequency response selects self-adaptive wavelet packet decomposition as a frequency division method of unbalanced power, comprehensively considers the characteristics of energy storage batteries, super capacitors and system frequency deviation absorption capacity, divides the unbalanced power into three parts of low-frequency components, medium-frequency components and high-frequency components, respectively stabilizes the three parts, establishes a model of forming the annual value of hybrid energy storage, and determines the optimal configuration result of the energy storage on the basis of the most economic cost.
Furthermore, two fuzzy controllers are adopted to carry out self-adaptive control on the energy storage SOC, and the running allowance of the energy storage battery is fully utilized to share the pressure of the super capacitor, so that the purpose of secondarily optimizing the energy storage state in real time is achieved.
And respectively establishing fuzzy controllers for the energy storage battery and the super capacitor, wherein the two fuzzy controllers both belong to a two-dimensional control structure, the primarily distributed charging and discharging power instructions and the real-time SOC of the energy storage battery and the super capacitor are used as input quantities of fuzzy reasoning, and the output of the fuzzy controllers is respectively an energy storage battery power regulating coefficient and a super capacitor power primary regulating coefficient.
Further, the basis of the optimal configuration of the energy storage system is a target value of the output power of the wind power plant, the target value needs to meet the fluctuation characteristic of the wind power plant and is required to adapt to the scheduling plan arrangement of the power grid, according to the scheduling requirement, the set time is selected as the size of a time window, and the expected value of the wind power in each hour is obtained and used as the output target power P of the wind power plantref。
Further, if the unbalanced power is decomposed by a Lay layer wavelet packet to obtain a wavelet packet component, a frequency dividing point n is usedLAnd nHThe wavelet packet is divided into three parts.
Dividing the low-frequency-division point into two parts of low-frequency and medium-frequency fluctuation power by using the low-frequency-division point, respectively determining the charging and discharging power of the energy storage battery and the super capacitor, realizing the purpose of smoothing the unbalanced power of the microgrid of the island, and dividing the low-frequency-division point nLThe determination needs to be within the set range while the lowest cost principle needs to be followed.
On the premise of meeting the requirement of certain frequency deviation accuracy, the minimum decomposition layer number and the corresponding breaking point which are separated from the high-frequency low-amplitude fluctuation power absorbed by the system from the original unbalanced power are the optimal wavelet packet decomposition layer number and the optimal high frequency dividing point.
According to the characteristics of energy type and power type energy storage, different kinds of energy storage are used for stabilizing different fluctuation compositions.
Further, for n<nLThe low-frequency high-energy fluctuation is suitable for being stabilized by an energy storage battery; for nL≤n≤nHThe medium-frequency power fluctuation is suitable for being stabilized by a super capacitor; for n>nHThe high-frequency power fluctuation of (2) is not processed, but absorbed by the frequency deviation absorbing capability of the micro-grid itself.
Further, unbalanced power Pim(t) is regarded as a time domain discrete signal with the number of sampling points N, then the unbalanced power P isim(t) is regarded as one period NTsThe fundamental frequency of the signal is 1/(NT)s) The unbalanced power is converted into a form of the sum of a direct current component, a fundamental frequency periodic component and a frequency multiplication periodic component through DFT, and the form is the expression form of the unbalanced power in a frequency domain.
Further, the method for calculating the frequency deviation caused by the power imbalance comprises the following steps: by means of the pair of unbalanced powers PimAnd performing DFT to obtain a power spectral density function of the frequency deviation, obtaining a spectral density function of the frequency deviation of the micro-grid based on the power spectral density function, and performing IDFT on the spectral density function to obtain time domain distribution of the frequency deviation of the micro-grid.
Furthermore, the energy E stored in the stored energy is the sum of the integral of the charging and discharging power P in time and the original initial energy of the stored energy, so that the rated capacities of the energy storage battery and the super capacitor are determined.
Furthermore, the integral of the fluctuation power over time is the energy absorbed or released by the energy storage accumulation, the numerical value of the fluctuation power changes continuously with the operation process, and in the operation process, the energy stored in the energy storage must meet the SOC constraint condition of the energy storage system, that is, when the accumulated energy of the energy storage system by the unbalanced power sequence is the maximum, the SOC of the energy storage system must not exceed the maximum limit, and when the accumulated energy is the minimum, the SOC of the energy storage system must not be lower than the minimum limit.
Compared with the prior art, the invention has the beneficial effects that:
1) the invention utilizes the hybrid energy storage composed of the energy storage battery and the super capacitor to stabilize the wind power fluctuation in the island-type micro-grid, considers the frequency characteristic of the micro-grid and the self ability of absorbing unbalanced power, carries out three-frequency division processing on the unbalanced power according to the frequency height, the middle and the low frequency division based on the self-adaptive wavelet packet decomposition algorithm and the determination principle of the frequency division point, establishes a cost annual value model taking the hybrid energy storage configuration result and the low frequency division point as independent variables on the basis of the life models of the energy storage battery and the super capacitor, and realizes the cooperative optimization of the hybrid energy storage configuration and the power distribution. The two fuzzy controllers are used for controlling the hybrid energy storage power instruction step by step, so that the real-time SOC operation is ensured to be in a reasonable range, and the energy storage battery and the super capacitor are matched for use;
2) the single energy storage system of the invention needs to be provided with a high-capacity high-rated-power energy storage battery or a super capacitor and auxiliary facilities capable of coping with frequent switching conditions due to the consideration of short-term power fluctuation and long-term charge and discharge requirements, and compared with the single energy storage, the hybrid energy storage system of the invention separates and controls unbalanced power according to different energy storage time spans, thereby obviously reducing the cost. The consideration of the system absorption frequency fluctuation capability further reduces the configuration requirement of the super capacitor and improves the running economy of the hybrid energy storage system. After the fuzzy self-adaptive controller is added, the real-time SOC constraint condition is met, and the long-term effective operation of the control strategy is ensured;
3) the cost model of the island microgrid is established only by considering the expense of the energy storage system body, and the configuration of the energy storage capacity has a great relationship with the wind curtailment cost, the auxiliary frequency modulation benefit and the like, so that how to establish a more comprehensive and accurate energy storage cost model becomes the key point of further research.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of determining a high frequency division point according to the present invention;
FIG. 2 is a flow chart of determining a low frequency division point according to the present invention;
FIG. 3 is a schematic diagram of the daily output power and the unbalanced power of the wind farm according to the present invention;
FIG. 4 is a schematic diagram of the high frequency ripple power dissipated by the microgrid itself according to the present invention;
FIG. 5 is a schematic view of a cost of the hybrid energy storage system of the present invention;
FIG. 6 is a diagram of the low and medium frequency components of the unbalanced power of the present invention;
FIG. 7 is a schematic diagram of the SOC of the energy storage battery before and after fuzzy optimization when the initial SOC is out of limit;
FIG. 8 is a schematic diagram of a cycle life curve of an energy storage battery according to the present invention;
FIG. 9 shows α (t), β (t), and k according to the present inventionbatA schematic diagram of the membership function of (1);
FIG. 10 shows γ (t), δ (t) and k according to the present inventionucA schematic diagram of the membership function of (1);
FIG. 11 shows the output variable k according to the present inventionsA schematic diagram of the membership function of (1);
FIG. 12 is a schematic diagram of energy storage power and SOC prior to fuzzy optimization in accordance with the present invention;
FIG. 13 is a schematic diagram of the energy storage power and SOC after fuzzy optimization according to the present invention;
the specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the present invention, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only terms of relationships determined for convenience of describing structural relationships of the parts or elements of the present invention, and are not intended to refer to any parts or elements of the present invention, and are not to be construed as limiting the present invention.
In the present invention, terms such as "fixedly connected", "connected", and the like are to be understood in a broad sense, and mean either a fixed connection or an integrally connected or detachable connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be determined according to specific situations by persons skilled in the relevant scientific or technical field, and are not to be construed as limiting the present invention.
As introduced in the background art, the traditional filtering methods such as low-pass filtering, sliding average value method and the like in the prior art have the defects of poor positioning characteristics and difficulty in scientific quantification of parameters, and in order to solve the technical problems, the application provides an island-type microgrid energy storage optimization configuration method which adopts wavelet packet decomposition and takes frequency response into account.
The key point of the reliable operation of the island-type microgrid is that the power balance between a power generation side and a user side is ensured at any time, so that the maintenance of wind power output as a required value of a dispatching plan has important significance for the frequency stability and the cost saving of a system. The basis of the optimal configuration of the energy storage system is a target value of the output power of the wind power plant, and the target value not only needs to meet the fluctuation characteristic of the wind power plant but also needs to adapt to the scheduling plan arrangement of a power grid. According to the method, 1h is selected as the size of a time window according to the scheduling requirement, and the expected value of the wind power in each hour is obtained and used as the output target power P of the wind power plantref。
The present invention defines: unbalanced power PimThe difference value of the actual output of the microgrid wind power plant and the target power, namely the power fluctuation which is expected to be stabilized by the energy storage system and the self-absorption capacity of the system.
Wavelet packet decomposition is a time scale analysis method of signals, has the characteristic of multi-resolution analysis, has the capability of representing local characteristics of the signals in both time-frequency domains, and is a time-frequency localization analysis method with changeable time windows and frequency windows.
If Lay layer wavelet packet decomposition is carried out on the unbalanced power (Lay is more than or equal to 1), m is 2LayWavelet packet components, represented as:
in the formula: gnWavelet packet components decomposed for the Lay layer; t corresponds to time, t equals t0+NTs. Wherein,t0as an initial time, TsAnd N is the number of sampling points. The nth wavelet packet component consists of [ nFs/2Lay+1,(n+1)Fs/2Lay+1]Composition of fluctuations in frequency band, FsIs the sampling frequency.
After the unbalanced power is decomposed by the wavelet packet, the frequency dividing point n is usedLAnd nHThe wavelet packet is divided into three parts. According to the characteristics of energy type and power type energy storage, different kinds of energy storage are used for stabilizing different fluctuation compositions. For n<nLThe low-frequency high-energy fluctuation is suitable for being stabilized by an energy storage battery; for nL≤n≤nHThe medium-frequency power fluctuation of the super capacitor is suitable for being stabilized by the super capacitor. Since the network allows a small range of frequency deviations, which correspond to high-frequency unbalanced power in the microgrid, for n>nHThe high-frequency power fluctuation of (2) can be absorbed by the frequency deviation absorbing capability of the micro-grid itself without processing the high-frequency power fluctuation. Thus, the energy storage battery is charged with:
the super capacitor bears the following power:
the power consumed by the micro-grid is as follows:
DFT and Inverse Discrete Fourier Transform (IDFT) are mathematical transform methods that are widely used, and mainly function to perform inter-transformation between time domain and frequency domain on a function, and achieve the purpose of function decomposition by using methods such as separation, filtering or interception.
The unbalanced power pim (t) can be regarded as a time domain discrete signal with N sampling points, and pim (t) can be regarded as a signal with a period NTs, the fundamental frequency of the signal is 1/(NTs), and the unbalanced power can be converted into a form of the sum of a direct current component, a fundamental frequency periodic component and a frequency multiplication periodic component through DFT, that is, the form of the unbalanced power expressed in the frequency domain.
Discrete fourier transform analysis formula:
discrete fourier transform synthesis formula:
the change of the rotating speed of the generator in the island-type micro-grid, namely the change of the frequency of the power system, is closely related to the unbalanced power between the power supply side and the demand side, and when the generated power is equal to the power load, the frequency of the power grid is maintained at a rated value; when the generated power is greater than the power load, the frequency of the power grid is increased; when the generated power is less than the electrical load, the grid frequency is reduced. In an island-type microgrid, a method for calculating frequency deviation caused by power imbalance comprises the following steps:
obtaining a power spectral density function sim (f) by performing DFT on the unbalanced power Pim;
obtaining a spectrum density function of the frequency deviation of the microgrid according to a formula S Δ fgrid (f) | h (f) | 2sim (f), where | h (f) | is an amplitude of a frequency response associated with the microgrid system itself, and can be obtained by the following formula:
|H(f)|＝|G(s)|＝|G(j2πf)| (7)
wherein G(s) is a function related to the frequency response characteristic of the microgrid.
And performing IDFT on the result S delta fgrid (f) to obtain a time domain distribution delta fgrid (t) of the microgrid frequency deviation.
Energy storage charging and discharging model
The rated power of the super capacitor and the energy storage battery can be solved by infinite norm of power time sequence, and the influence of charge and discharge efficiency is considered, namely:
in the formula, P + bat and P-bat are respectively the charging rated power and the discharging rated power of the energy storage battery, P + uc and P-uc are respectively the charging rated power and the discharging rated power of the super capacitor, P '+ bat and P' -bat are component sets of which the power sequence of the energy storage battery is more than zero and less than zero, P '+ uc and P' -uc are component sets of which the power sequence of the super capacitor is more than zero and less than zero, η batc and η batd are the charging and discharging efficiency of the energy storage battery, η ucc and η ucd are the charging and discharging efficiency of the super capacitor, therefore, the rated power of the energy storage battery and the super capacitor is as follows:
the determination of the rated capacity of the hybrid energy storage can be realized by the following two steps:
the energy E stored in the stored energy is the sum of the integral of the charging and discharging power P in time and the original initial energy of the stored energy, and is represented by the following formula:
ebat0 and Euc0 are the energy stored at the initial moment of the energy storage battery and the super capacitor respectively; ebat (t) and Euc (t) are energy stored by the energy storage battery and the super capacitor at the time t; t0+ iTs is the time of the ith sample point.
Therefore, the rated capacities of the energy storage battery and the super capacitor are as follows:
in the formula: ebatr and Eucr are rated capacities of the energy storage battery and the super capacitor respectively; socbatup and Socbattow are the upper limit and the lower limit of the SOC of the energy storage battery, and the SOC is 0.9 and 0.2; socucup and Socuclow are the upper limit and the lower limit of the SOC of the super capacitor, and the values are 1 and 0.
Determining a constrained range for an initial state of charge Soc0
In a research time interval, the integral of the fluctuation power in time is the energy absorbed or released by the accumulated energy storage, the numerical value of the integral changes with the running process, and the calculation formula is as follows:
therefore, the real-time SOC value of the stored energy is determined by the following equation:
during operation, the energy stored in the energy storage system must meet its own SOC constraint condition, that is, when the accumulated energy of the unbalanced power sequence to the energy storage system is maximum, the SOC of the energy storage system must not exceed the maximum limit value, and when the accumulated energy is minimum, the SOC of the energy storage system must not be lower than the minimum limit value:
when the above equations are transformed and combined, the initial SOC of the energy storage system is within the range specified by the following equation:
the constraint condition is guaranteed on the premise that the unbalanced power can be completely stabilized by stored energy, and when the constraint condition is applied in practical engineering, the initial SOC is required to be kept within a constraint interval at any time with certain difficulty. Therefore, some auxiliary control strategies, such as feedback control, fuzzy control, etc., are required to ensure that this condition is met.
The construction of the energy storage power station consists of an energy storage body, an energy conversion device and necessary auxiliary facilities. In the total cost of the energy storage power station, the body cost of the energy storage battery and the super capacitor accounts for a considerable proportion, wherein the body cost of the energy storage battery can be expressed as:
Cbat＝CbatEEbatr+CbatPPbatr(17)
in the formula: cbatEPrice per unit capacity of the energy storage cell body, CbatPIs the price per unit power of the energy storage battery body.
Thus, the cost annual value of an energy storage battery body can be expressed as:
in the formula: i is the discount rate; lifebatLife in years for energy storage batteriesbatAnd service Life Life of super capacitorucThe method of (3).
The life of the energy storage battery can be expressed as the sum of the effective throughputs available for the energy storage battery, and the effective throughputs can be obtained by correcting the actual throughputs by using a conversion formula. And when the accumulated effective throughput reaches the rated service life of the energy storage battery, the energy storage battery is scrapped. The rated available throughput of the energy storage battery is
Thbat＝LRDREbat(19)
In the formula EbatThe rated capacity of the energy storage battery; dRThe rated discharge depth is used for determining the rated cycle life; l isRTo a rated depth of discharge DRAnd a rated cycle life at a rated discharge power. A large number of experimental analyses prove that the service life of the energy storage battery mainly has three influence factors: depth of discharge, state of charge, and charge-discharge power.
Influence of the depth of discharge of the energy storage cell: the influence of the depth of discharge on the life of the energy storage battery is calculated for each discharge cycle, and then the relation between the depth of discharge and the actual cycle life of the energy storage battery can be obtained by fitting experimental data, as shown in fig. 8.
The calculation formula of the cycle life of the energy storage battery is
The relation between the effective throughput of the energy storage battery and the charge and discharge energy under the rated charge and discharge power is
In the formula: lbat is the actual cycle life of the energy storage battery; LR is the cycle life at the rated depth of discharge DR; u0 and u1 are fitting parameters; eeff (DA) is the effective throughput corresponding to the charge-discharge depth DA under the rated charge-discharge power.
Influence of energy storage battery SOC: in order to deal with the fact that in the actual use process, the charge-discharge cycle does not always enter the discharge process after full charge as in the experiment, but is charged or discharged between two arbitrary SOC levels, the invention replaces the arbitrary charge-discharge process with the difference value of the two standard experiment cycle processes. For example, if the energy storage battery is charged from a SOC of 0.2 to a SOC of 0.8, then this random event may consist of two standard charge and discharge events from a SOC of 0.2 to a state of full SOC and from a SOC discharge to a SOC of 0.8. The effective throughput of the energy storage battery is equal to 0.5| eff (0.8) | -eff (0.2) |, and 0.5 indicates that each charge and discharge cycle process is composed of two equivalent charge processes and discharge processes.
Influence of charging and discharging power of the energy storage battery: the Raygon curve is a relation curve of power density and energy density of the energy storage device, describes the power output capacity and the energy output capacity of the energy storage device and the optimal working area of the energy storage device, and is an effective means for evaluating the energy storage device. And obtaining the actual capacity of the energy storage battery under any charge and discharge power according to the Ragong curve. Therefore, the proportional relationship between the equivalent charge-discharge energy at rated power and the charge-discharge energy during a certain actual operation is
In the formula: ER is equivalent charge-discharge energy of the energy storage battery under rated charge-discharge power, Eactual is actual charge-discharge energy, EA is actual capacity of the energy storage battery under the actual charge-discharge power, and the equivalent charge-discharge energy can be obtained from a pull-out curve of the energy storage battery.
The energy storage battery has experienced n charge and discharge events within the study time interval T, and the service life of the energy storage battery is then
For the super capacitor, the invention takes the throughput which can be borne in the service life of the super capacitor as the basis for service life calculation. The throughput achieved by the supercapacitor during the study time interval T is:
Es＝∑|P'uc|Ts(24)
therefore, under a certain use mode, the service life of the super capacitor in the actual use process is as follows
The bulk cost of the supercapacitor can be expressed as:
Cuc＝CucEEucr+CucPPucr(26)
in the formula: cucEPrice per unit capacity of super capacitor body, CucPIs the unit power price of the super capacitor body.
The cost year value of the super capacitor body can be expressed as:
the total annual cost of a hybrid energy storage system can be expressed as:
CHESS_y＝Cbat_y+Cuc_y(28)
in the invention, on the premise of meeting the requirement of certain frequency deviation accuracy, the minimum decomposition layer number and the corresponding breaking point which can separate the high-frequency low-amplitude fluctuation power absorbed by the system from the original unbalanced power are the optimal wavelet packet decomposition layer number and the high frequency dividing point. And the application of the nH value is equivalent to that the original unbalanced power is firstly subjected to low-pass filtering before entering the hybrid energy storage stabilization, and the filtered relatively low-frequency fluctuation power is stabilized by the hybrid energy storage system. Compared with the method which does not take the frequency fluctuation absorbing capacity of the system into consideration, the method of the invention reduces the requirements on the rated capacity and the rated power of the energy storage, particularly the super capacitor. The determination of the high divider point value nH is illustrated in the flow chart of fig. 1.
After the high frequency dividing point nH is determined, a total power sequence of hybrid energy storage can be obtained, the low frequency dividing point is used for dividing the total power sequence into a low frequency fluctuation power part and a medium frequency fluctuation power part, the charging and discharging power of the energy storage battery and the super capacitor can be respectively determined, and the purpose of smoothing the unbalanced power of the micro grid of the island is achieved. The value of the nL value of the low frequency dividing point directly determines the distribution condition of the low frequency fluctuation power and the medium frequency fluctuation power, thereby further influencing the configuration result and the energy storage cost. Therefore, the determination of the low dividing point nL in the present invention needs to follow two principles:
the value of nL at low frequency dividing point must not be too large or too small
Although the stabilization task of the super capacitor can be reduced to a certain extent by the excessively large nL value, the low-frequency fluctuation part borne by the energy storage battery has more high-frequency fluctuation power, the energy storage battery is required to frequently switch the charging and discharging states in the running process, the loss of auxiliary equipment and the aging of the battery are accelerated, and the annual value of the energy storage battery is sharply increased. The nL value is too small, so that the super capacitor can stabilize too much low-frequency high-amplitude fluctuation power which should be absorbed by the energy storage battery, and the requirement on the long-time charging and discharging energy performance of the energy storage element is higher, so that the super capacitor with a large capacity value needs to be configured, and unnecessary cost expenditure of the super capacitor is inevitably caused.
The determination of the nL value needs to follow the least cost principle
The difference of nL values of the low frequency dividing points can cause the change of the rated power and capacity value of the energy storage and the operation life of the energy storage, and the cost annual value of the energy storage system is changed, so that the configuration result corresponding to the lowest point of the cost annual value is the optimal solution of the island microgrid energy storage configuration problem. The method for determining the low frequency dividing point nL is shown in the flowchart of fig. 2, and the invention searches for the lowest cost point by an exhaustion method, updates the values of nL and CHESS _ y in a rolling manner, compares the cost year value CHESS _ y corresponding to the optimal frequency dividing point nL in the past search process with the CHESS _ y0 corresponding to the latest frequency dividing point nL0 along with the progress of a new search, updates nL and CHESS _ y, and outputs the optimal frequency dividing point and the optimal cost value until all potential frequency dividing points are searched and compared. At this time, the optimum configuration result of the hybrid energy storage can also be obtained.
The energy storage configuration result obtained by power sample data of one day is lack of universality, so that a plurality of typical days are taken from the operating data of the wind power plant in one year as research objects, the influence of data uncertainty on the energy storage configuration is comprehensively considered, and the mathematical expectation of the typical day configuration result is calculated as the final configuration result of the wind power plant. The proposed control strategy is realized on the basis of solar wind power, the initial SOC requirement at the moment is easy to meet, and in the actual long-term operation process, due to the randomness and the volatility of the output of the fan and the limit of the capacity of the energy storage device, the problems that the initial SOC exceeds the constraint and the power regulation capacity is insufficient can occur, and further economic problems such as wind curtailment, power imbalance, frequency fluctuation and the like are caused. Therefore, the hybrid energy storage fuzzy controller is used for controlling the energy storage SOC in real time and secondarily distributing the charging and discharging power of the energy storage battery and the super capacitor on the basis of the SOC control, so that the super capacitor has higher high-frequency fluctuation stabilizing capability at any time.
The control process of the fuzzy optimization controller is realized in two steps, and comprises two sub fuzzy controllers:
1) energy storage SOC control
The first step of design work of the fuzzy controller is mainly to control the energy storage SOC in real time, and the design principle is as follows: when the SOC of the energy storage battery or the super capacitor is higher, the charging power constraint is reduced, and the discharging power is not subjected to additional constraint except the power limit constraint of the discharging power; when the SOC is low, the discharge power constraint is narrowed, and no additional constraint other than the self power limit constraint is imposed on the charge power. Therefore, the energy storage can be prevented from being in an overcharge and overdischarge state for a long time, the energy storage service life loss is reduced, the smooth output of wind energy storage combined power is facilitated, and the power distortion and the economic waste caused by insufficient capacity are reduced.
The invention respectively establishes fuzzy controllers for the energy storage battery and the super capacitor, the two fuzzy controllers both belong to a two-dimensional control structure, the charging and discharging power instructions of the energy storage battery and the super capacitor which are primarily distributed and the real-time SOC are used as the input quantity of fuzzy reasoning, and the output of the fuzzy controllers is respectively the power regulating coefficient k of the energy storage batterybatAnd the primary power regulation coefficient k of the super capacitoruc. Membership functions of input and output variables and fuzzy rules.
Charging and discharging power P 'of energy storage battery'batObtaining an input variable α (t) of the SOC fuzzy controller after normalization, wherein the fuzzy domain is { -1, -0.5, 0, 0.5, 1}, the corresponding fuzzy subset is { NB, NS, Z, PS, PB }, which respectively represents negative large, negative small, zero, positive small, positive large, and real-time SOC (state of charge)batThe normalized input variable is β (t), the ambiguity domain is {0.2, 0.4, 0.5, 0, 7, 0.9}, the corresponding ambiguity subset is { VS, S, M, B, VB }, which respectively represents minimum, small, medium, large and maximum, and the output variable k isbatThe fuzzy universe of (1), (0, 0.2, 0.4, 0.6, 0.8, 1) and the corresponding fuzzy subsets (VS, S, MS, MB, B, VB) respectively represent minimum, small, medium, large and maximum, and membership functions of α (t), β (t) and output variables and fuzzy control rules are shown in FIG. 9 and Table C1.
TABLE C1 fuzzy control rule for energy storage battery
Charging and discharging power P 'of super capacitor'ucObtaining an input variable gamma (t) of the SOC fuzzy controller after normalization, wherein the fuzzy domain is { -1, -0.5, 0, 0.5, 1}, the corresponding fuzzy subset is { NB, NS, Z, PS, PB }, and the corresponding fuzzy subset is divided intoRespectively indicates the real-time SOC of the large negative, small negative, zero, small positive and large positiveucThe normalized input variable is δ (t), the ambiguity domain is {0, 0.3, 0.5, 0.7, 1}, and the corresponding ambiguity subset is { VS, S, M, B, VB }, which respectively represents minimum, small, medium, large, and maximum. Output variable kucThe ambiguity domain of (1) is {0, 0.3, 0.5, 0.7, 1}, and the corresponding ambiguity subset is { VS, S, M, B, VB }, which respectively represents minimum, small, medium, large, and maximum. Five language variables are selected to describe the output variable of the super capacitor, and six language variables are selected to describe the output variable of the energy storage battery because the charging and discharging conversion capability of the super capacitor is higher than that of the energy storage battery, the SOC level has great influence on the service life of the energy storage battery, and the SOC control of the energy storage battery needs to be more precise. The membership functions for γ (t) and δ (t) and output variables and the fuzzy control rules are shown in fig. 10 and table C2.
TABLE C2 ultracapacitor fuzzy control rule
For output variable ambiguity resolution, the method of gravity center is adopted to convert the ambiguity into accurate quantity, thus obtaining the power adjustment coefficient value k of the stored energybatAnd kucTherefore, after the adjustment by the SOC fuzzy controller, the real-time power values of the energy storage battery and the super capacitor are as follows:
energy storage internal power secondary distribution
The response speed of the energy storage battery can reach the second level required by the power of the compensation super capacitor, so that when the capacity of the energy storage battery is still surplus under the condition that the capacity of the energy storage battery meets the requirement, the charge and discharge power of the energy storage battery can be properly increased, the working pressure of the super capacitor is reduced, and the continuous stabilizing capability of the energy storage system on high-frequency power fluctuation is improved. The design principle is as follows: when the super capacitor SOC is higher, the energy storage battery SOC is not high and is in low power charging or even discharging state, the energy storage battery shares the charging pressure of the super capacitor, and when the capacitor SOC is lower, the energy storage battery SOC is not high and is in low power discharging or even discharging state, the energy storage battery shares the discharging pressure of the super capacitor.
The input variable of the fuzzy controller for secondary distribution of the internal power of the energy storage is the real-time SOC of the energy storage battery and the super capacitor, and the output variable is the secondary regulation coefficient k of the super capacitors. Membership functions of input and output variables and fuzzy rules.
The fuzzy controller design of the secondary distribution of the internal power of the energy storage is divided into the following two conditions, the definitions of an input variable fuzzy domain and a fuzzy subset are consistent with the related definition of the first step, and the secondary regulation coefficient k of the output variable super capacitorsThe ambiguity domain of (1) is {0, 0.3, 0.5, 0.7, 1}, and the corresponding ambiguity subset is { VS, S, M, B, VB }, which respectively represents minimum, small, medium, large, and maximum. The membership function is shown in D1, and the fuzzy control rules corresponding to the super capacitor in the charging and discharging states are shown in tables D1 and D2.
TABLE D1 fuzzy control rules for supercapacitor charging
TABLE D2 fuzzy control rules for discharge state of supercapacitor
Energy storage battery power P after two-step fuzzy control optimizationbat(t) and super-capacitor Power PucThe formula for calculation of (t) is:
Puc(t)＝ksP″uc(t)＝kskucP'uc(t) (31)
the method takes an island type micro-grid in China as an example to carry out the optimal configuration of the hybrid energy storage capacity, and verifies the economy and practicability of the scheme of the invention. The wind field data of the invention is the annual output of a 30MW wind power plant in an island microgrid, the sampling period is 5s, and the output data of 10 typical days in different periods in one year are selected for calculation. The charge-discharge efficiency of the energy storage battery is 90%, and the charge-discharge efficiency of the super capacitor is 95%. The allowed frequency operation range of the micro-grid is delta fmax∈[49.3，50.5]Hz, determining the optimal number of decomposition layers of the wavelet packet and nLThe frequency precision value required by the value is 0.01Hz, and the discount rate is 8%.
A typical day of data was selected for simulation analysis, and the curve of the function of the original wind power and the smoothing target is shown in FIG. 3. The smoothed power curve is a constant value in each hour, and can be matched with a day-ahead dispatching plan of a wind power plant, so that a foundation is laid for effective connection of an energy storage system and the existing power grid dispatching operation mode. Adopting the flow method of FIG. 1, selecting db6 wavelet to carry out data decomposition, using 8-layer wavelet packet decomposition result with high frequency power fluctuation separation and optimal operation time as analysis object, decomposing into 256 wavelet packets, and high frequency dividing point nHThe value of (b) is 199, and the high frequency power fluctuates as shown in fig. 4.
As shown in Table 1, the configuration results of only using a single energy storage (scheme 1: energy storage battery, scheme 2: super capacitor) are shown, and comparison shows that for the same wind power plant, the annual value of cost of only using the single energy storage is far higher than that of hybrid energy storage under the same stabilizing effect.
TABLE 1 Single energy storage configuration results
The method provided by the invention utilizes the frequency deviation characteristic allowed by the power grid to absorb the high-frequency low-amplitude fluctuation in the unbalanced power, and the residual low-and-medium-frequency power fluctuation is stabilized by hybrid energy storage. If the unbalanced power is completely stabilized by the hybrid energy storage system according to the conventional theory, the energy storage configuration result at the moment can be changed. In order to compare and account for the difference of the configuration results before and after the grid frequency characteristic, a scheme 3 is set: the high frequency dividing point is not calculated, and is directly determined as a wavelet packet component value corresponding to the frequency of 0.1Hz (the highest resolution of a frequency domain, namely half of the sampling frequency value); scheme 4: the energy storage configuration was performed according to the method of the present invention and the comparison results are shown in table 2.
Table 2 microgrid digestion capability consideration front and back configuration results
By comparing the energy storage optimization configuration results under the 4 schemes, the following conclusions can be drawn:
under the same stabilizing effect, the economical efficiency of the schemes 1 and 2 is far lower than that of the scheme 4, and the capacity and power configuration result is obviously higher than that of the scheme 4, because when a single energy storage mode is adopted, the high-frequency power fluctuation needs to be considered in the energy storage configuration, at the moment, the energy storage system is required to track the rapid high-amplitude power fluctuation, and the long-time large-amplitude SOC change needs to be met, so that the rated power of a single energy storage battery and the rated capacity of a super capacitor are increased.
Scheme 4 divides unbalanced power into two parts of medium-frequency fluctuation and low-frequency fluctuation, power distribution between hybrid energy storage is completed according to performance characteristics of energy type and power type energy storage, advantages of different types of energy storage are fully utilized, fluctuation frequency is relatively large, medium-frequency fluctuation with low fluctuation amplitude is taken as energy buffering and is taken charge of by the super capacitor, compared with scheme 2, requirements for capacity configuration of the super capacitor are greatly reduced, and cost values are reduced accordingly. Energy storage battery stabilizes the low frequency fluctuation that the fluctuation cycle is long, the amplitude is big, compares with scheme 1, at the actual operation in-process, because the power fluctuation of intermediate frequency is absorbed by super capacitor, and energy storage battery's charge-discharge state conversion cycle is prolonged to minute level or even hour level by second level, and the number of times of conversion significantly reduces, has prolonged energy storage battery body and auxiliary assembly's operating life, has reduced energy storage system's cost annual value.
Comparing schemes 3 and 4, it is found that the cost of energy storage can be further reduced by considering the system absorption capacity, and the method is mainly embodied in that the configuration value of the super capacitor is lower than that of the traditional method, because the power fluctuation allowed by the system is high-frequency and low-amplitude, part of the power is stabilized by the super capacitor in the scheme 3, the application of the scheme 4 method not only reduces the requirements on the fast tracking and continuous charging and discharging capacity of the super capacitor, the power and capacity configuration values are more optimized, but also the super capacitor can be switched to a charging and discharging state for one time after being continuously charged and discharged for a longer time compared with the scheme 3, and the charging and discharging switching frequency is reduced. Although the switching frequency in the service life cycle of the super capacitor can reach thousands of times, the reduction of the switching frequency can still prolong the service life of the super capacitor to a certain extent, and the running economy of the energy storage system is improved.
The fuzzy controller is used for ensuring the effective execution of the stabilizing strategy of the invention when the initial SOC of the day does not satisfy the constraint condition of the formula (15). This example discusses the effect of the conditioning action of the fuzzy controller on the change in energy storage SOC when both the initial SOC constraint (example 1) and the initial SOC constraint (example 2) are satisfied.
Variations in energy storage power and SOC before and after fuzzy optimization in example 1.
The energy storage power and SOC change curves before and after fuzzy optimization are shown in fig. 12 and fig. 13, and it can be seen from the power curve diagram that after the fuzzy optimization controller is added, the unbalanced power is still distributed according to the performance characteristics of power type and energy type energy storage, the medium-frequency fluctuation is borne by the super capacitor, the low-frequency fluctuation is borne by the energy storage battery, and it can be seen from the SOC curve diagram that because the initial SOC of the hybrid energy storage is within the constraint condition before the fuzzy optimization controller is added, the SOC of the hybrid energy storage before and after the fuzzy controller is added is operated within the safe range, and the phenomena of overcharge, overdischarge and power overrun are not generated. The all-day SOC running curve of the energy storage battery under the fuzzy optimization controller is closer to the SOC middle value straight line, and compared with the energy storage battery with a common control strategy, the energy storage battery runs in a shallow charging and shallow discharging state more, the chemical energy storage substances in the battery can be damaged due to deep charging and discharging, the service life of the battery is shortened, the charging and discharging depth of the energy storage battery is effectively reduced through the fuzzy optimization control strategy, and the service life of the energy storage battery is prolonged.
Example 2: the daily initial SOC of the energy storage battery is set to be 0.9 in a full charge state exceeding the constraint, the SOC change curves of the energy storage battery under a common control strategy and a fuzzy optimization control strategy are shown in FIG. 7, the SOC change range of the energy storage battery under the common control strategy is large, multiple overcharge and overdischarge phenomena exist, the minimum SOC value under the fuzzy optimization control is 0.3033, and except for the extreme condition of the initial time period, the SOC value in the remaining time is mostly in a reasonable interval of [0.3,0.8 ]. Therefore, the addition of the fuzzy controller can ensure that the stored energy can still be adjusted to a reasonable SOC operation range as soon as possible in an extreme state, and the service life damage of the energy storage equipment is reduced.
The wind power fluctuation in an island-type micro-grid is stabilized by using hybrid energy storage composed of an energy storage battery and a super capacitor, the frequency characteristic of the micro-grid and the self ability of absorbing unbalanced power are considered, the unbalanced power is subjected to three-frequency division processing according to the frequency height, the middle and the low on the basis of an adaptive wavelet packet decomposition algorithm and a frequency division point determining principle, a cost annual value model with hybrid energy storage configuration results and low frequency division points as independent variables is established on the basis of an energy storage battery and super capacitor service life model, and the model realizes the collaborative optimization of hybrid energy storage configuration and power distribution. The two fuzzy controllers are used for controlling the hybrid energy storage power instruction step by step, so that the real-time SOC operation is ensured within a reasonable range, and the energy storage battery and the super capacitor are matched for use.
The simulation result taking the actual island microgrid as an example shows that: the single energy storage is because compromise the power fluctuation of short-term and the charge-discharge requirement of long-term, and energy storage system need be equipped with the high rated power's of large capacity energy storage battery or super capacitor and the auxiliary facilities that can deal with the frequent switch condition, and mixed energy storage for single energy storage, separately controls after decomposing unbalanced power according to the difference of all kinds of energy storage time span, and the cost is showing and is reducing. The consideration of the system absorption frequency fluctuation capability further reduces the configuration requirement of the super capacitor and improves the running economy of the hybrid energy storage system. After the fuzzy self-adaptive controller is added, the real-time SOC constraint condition is met, and the long-term effective operation of the control strategy is ensured.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. An island-type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response is characterized by comprising the following steps: the method comprises the steps of selecting self-adaptive wavelet packet decomposition as a frequency division method of unbalanced power, comprehensively considering the characteristics of an energy storage battery, a super capacitor and system frequency deviation absorption capacity, dividing the unbalanced power into three parts, namely a low-frequency component, a medium-frequency component and a high-frequency component, stabilizing the three parts respectively, establishing a model of the year value of hybrid energy storage cost, and determining the optimal configuration result of the energy storage on the basis of the most economical cost.
2. The island type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response as claimed in claim 1, characterized by: two fuzzy controllers are adopted to carry out self-adaptive control on the energy storage SOC, and the running allowance of the energy storage battery is fully utilized to share the pressure of the super capacitor, so that the purpose of optimizing the energy storage state secondarily in real time is achieved.
3. The island type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response as claimed in claim 1, characterized by: and respectively establishing fuzzy controllers for the energy storage battery and the super capacitor, wherein the two fuzzy controllers both belong to a two-dimensional control structure, the primarily distributed charging and discharging power instructions and the real-time SOC of the energy storage battery and the super capacitor are used as input quantities of fuzzy reasoning, and the output of the fuzzy controllers is respectively an energy storage battery power regulating coefficient and a super capacitor power primary regulating coefficient.
4. The island type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response as claimed in claim 1, characterized by: the basis of the optimal configuration of the energy storage system is a target value of the output power of the wind power plant, the target value needs to meet the fluctuation characteristics of the wind power plant and is required to adapt to the scheduling plan arrangement of a power grid, according to the target value, the scheduling requirement selects set time as the size of a time window, and the expected value of the wind power in each hour is obtained and used as the output target power P of the wind power plantref。
5. The island type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response as claimed in claim 1, characterized by: if the unbalanced power is decomposed by a Lay layer wavelet packet to obtain a wavelet packet component, a frequency dividing point n is usedLAnd nHDividing the wavelet packet into three parts;
for n<nLThe low-frequency high-energy fluctuation is suitable for being stabilized by an energy storage battery; for nL≤n≤nHThe medium-frequency power fluctuation is suitable for being stabilized by a super capacitor; for n>nHThe high-frequency power fluctuation of (2) is not processed, but absorbed by the frequency deviation absorbing capability of the micro-grid itself.
6. The island type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response as claimed in claim 1, characterized by: dividing the low-frequency-division point into two parts of low-frequency and medium-frequency fluctuation power by using the low-frequency-division point, respectively determining the charging and discharging power of the energy storage battery and the super capacitor, realizing the purpose of smoothing the unbalanced power of the microgrid of the island, and dividing the low-frequency-division point nLThe determination needs to be within the set range while the lowest cost principle needs to be followed.
7. The island type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response as claimed in claim 1, characterized by: on the premise of meeting the requirement of certain frequency deviation accuracy, the minimum decomposition layer number and the corresponding breaking point which are separated from the high-frequency low-amplitude fluctuation power absorbed by the system from the original unbalanced power are the optimal wavelet packet decomposition layer number and the optimal high frequency dividing point.
8. The island type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response as claimed in claim 1, characterized by: will unbalance the power Pim(t) is regarded as a time domain discrete signal with the number of sampling points N, then the unbalanced power P isim(t) is regarded as one period NTsThe fundamental frequency of the signal is 1/(NT)s) The unbalanced power is converted into a form of the sum of a direct current component, a fundamental frequency periodic component and a frequency multiplication periodic component through DFT, and the form is the expression form of the unbalanced power in a frequency domain.
9. The isolated island type micro-grid energy storage device adopting wavelet packet decomposition and taking frequency response into account as claimed in claim 1The configuration method is characterized in that: the method for calculating the frequency deviation caused by the power imbalance comprises the following steps: by means of the pair of unbalanced powers PimDFT is carried out to obtain a power spectral density function of the micro-grid frequency deviation, a spectrum density function of the micro-grid frequency deviation is obtained based on the power spectral density function, and IDFT is carried out on the spectrum density function to obtain time domain distribution of the micro-grid frequency deviation; and determining the rated capacities of the energy storage battery and the super capacitor by using the energy E stored in the energy storage as the sum of the integral of the charging and discharging power P in time and the original initial energy of the energy storage.
10. The island type micro-grid energy storage optimization configuration method adopting wavelet packet decomposition and considering frequency response as claimed in claim 1, characterized by: the integral of the fluctuation power in time is the energy absorbed or released by the energy storage accumulation, the numerical value of the fluctuation power is constantly changed along with the operation process, in the operation process, the energy stored in the energy storage must meet the SOC constraint condition of the energy storage, namely when the accumulated energy of the energy storage system by the unbalanced power sequence is the maximum, the SOC of the energy storage system must not exceed the highest limit value, and when the accumulated energy is the minimum, the SOC of the energy storage system must not be lower than the lowest limit value.
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|---|---|---|---|---|
| CN109088421A (en) * | 2018-08-28 | 2018-12-25 | 河海大学 | Mixed energy storage system capacity configuration optimizing method based on FDM |
| CN109713703A (en) * | 2019-03-06 | 2019-05-03 | 华北电力大学 | A kind of energy-storage system dispatching method based on battery rotation |
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| CN110783955A (en) * | 2019-11-01 | 2020-02-11 | 国网河北省电力有限公司沧州供电分公司 | Photovoltaic single-phase access capacity configuration method beneficial to reducing three-phase imbalance of transformer area |
| CN112003311A (en) * | 2020-08-20 | 2020-11-27 | 内蒙古工业大学 | Wind power system power fluctuation stabilizing method and device and wind power system |
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| CN113162080A (en) * | 2021-04-23 | 2021-07-23 | 安徽信息工程学院 | Capacity configuration method and system for hybrid energy storage system |
| CN113193568A (en) * | 2021-02-22 | 2021-07-30 | 北京凌阳伟业科技有限公司 | Method, system and device for configuration optimization based on power spectral density |
| CN113516306A (en) * | 2021-07-05 | 2021-10-19 | 内蒙古工业大学 | Power configuration method, device, medium and electronic equipment of flywheel energy storage system |
| CN114186398A (en) * | 2021-11-24 | 2022-03-15 | 浙江大学 | Optimal frequency selection method for offshore wind power low-frequency sending-out system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105406498A (en) * | 2016-01-13 | 2016-03-16 | 沈阳工业大学 | Control method of wind power generation hybrid energy storage system |
| CN106410870A (en) * | 2016-12-02 | 2017-02-15 | 浙江大学 | Frequency control method of offshore wind power integration multi-terminal flexible DC transmission system |
| US20170229866A1 (en) * | 2016-02-10 | 2017-08-10 | Eguana Technologies | Seamless transitions between control modes |
-
2017
- 2017-12-06 CN CN201711277213.9A patent/CN107947211A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105406498A (en) * | 2016-01-13 | 2016-03-16 | 沈阳工业大学 | Control method of wind power generation hybrid energy storage system |
| US20170229866A1 (en) * | 2016-02-10 | 2017-08-10 | Eguana Technologies | Seamless transitions between control modes |
| CN106410870A (en) * | 2016-12-02 | 2017-02-15 | 浙江大学 | Frequency control method of offshore wind power integration multi-terminal flexible DC transmission system |
Non-Patent Citations (3)
| Title |
|---|
| YI LIU，等: "Sizing a Hybrid Energy Storage System for Maintaining Power Balance of an Isolated System With High Penetration of Wind Generation", 《IEEE TRANSACTIONS ON POWER SYSTEMS》 * |
| 吴杰，等: "采用自适应小波包分解的混合储能平抑风电波动控制策略", 《电力系统自动化》 * |
| 李龙云，等: "基于离散傅里叶变换的孤岛型微电网混合储能优化配置", 《电力系统自动化》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109088421A (en) * | 2018-08-28 | 2018-12-25 | 河海大学 | Mixed energy storage system capacity configuration optimizing method based on FDM |
| CN109713703A (en) * | 2019-03-06 | 2019-05-03 | 华北电力大学 | A kind of energy-storage system dispatching method based on battery rotation |
| CN110011334A (en) * | 2019-05-24 | 2019-07-12 | 深圳赛诺凡尔纳能源有限公司 | A kind of auto-adjustment control method and system for overcritical thermal power generation unit |
| CN110783955A (en) * | 2019-11-01 | 2020-02-11 | 国网河北省电力有限公司沧州供电分公司 | Photovoltaic single-phase access capacity configuration method beneficial to reducing three-phase imbalance of transformer area |
| CN112003311A (en) * | 2020-08-20 | 2020-11-27 | 内蒙古工业大学 | Wind power system power fluctuation stabilizing method and device and wind power system |
| CN112531744A (en) * | 2020-12-11 | 2021-03-19 | 福州大学 | Wind power fluctuation stabilizing method based on variational modal decomposition |
| CN113193568A (en) * | 2021-02-22 | 2021-07-30 | 北京凌阳伟业科技有限公司 | Method, system and device for configuration optimization based on power spectral density |
| CN112952878A (en) * | 2021-03-08 | 2021-06-11 | 阳光电源股份有限公司 | Multi-direct-current coupling system and control method thereof |
| CN112952878B (en) * | 2021-03-08 | 2024-05-14 | 阳光电源股份有限公司 | Multi-direct-current coupling system and control method thereof |
| CN113011101A (en) * | 2021-03-29 | 2021-06-22 | 广东电网有限责任公司电力调度控制中心 | Control method and system for energy storage participating in frequency modulation auxiliary service optimization |
| CN113011101B (en) * | 2021-03-29 | 2024-01-23 | 广东电网有限责任公司电力调度控制中心 | Control method and system for energy storage to participate in frequency modulation auxiliary service optimization |
| CN113162080A (en) * | 2021-04-23 | 2021-07-23 | 安徽信息工程学院 | Capacity configuration method and system for hybrid energy storage system |
| CN113516306A (en) * | 2021-07-05 | 2021-10-19 | 内蒙古工业大学 | Power configuration method, device, medium and electronic equipment of flywheel energy storage system |
| CN114186398A (en) * | 2021-11-24 | 2022-03-15 | 浙江大学 | Optimal frequency selection method for offshore wind power low-frequency sending-out system |
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