CN110311396B - Hybrid energy storage capacity optimization configuration method for AC/DC hybrid micro-grid - Google Patents
Hybrid energy storage capacity optimization configuration method for AC/DC hybrid micro-grid Download PDFInfo
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- 238000004146 energy storage Methods 0.000 title claims abstract description 119
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/02—Circuit arrangements for ac mains or ac distribution networks using a single network for simultaneous distribution of power at different frequencies; using a single network for simultaneous distribution of ac power and of dc power
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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]
Abstract
An optimal configuration method for hybrid energy storage capacity of an AC/DC hybrid micro-grid belongs to the field of AC/DC hybrid micro-grids, and comprises the following steps: establishing a double-layer optimal configuration model of the hybrid energy storage capacity; power allocation verification based on aggregate empirical mode decomposition; configuration of the capacity of the hybrid energy storage system. The power instructions of the lithium battery and the super capacitor are obtained through the filtering order, rated power and rated capacity of the lithium battery and the super capacitor are used as optimization variables, the annual comprehensive cost of the system is used as a target, the energy storage life and the converter loss are comprehensively considered, and the adaptive particle swarm algorithm is adopted for optimization solving, so that the optimal annual comprehensive cost of the system and the rated power and rated capacity of the corresponding lithium battery and super capacitor are obtained. The invention solves the problem of tie line power fluctuation caused by wind-light output fluctuation and load fluctuation in an AC/DC hybrid micro-grid.
Description
Technical Field
The invention relates to the field of AC/DC hybrid micro-grids, in particular to a method for optimally configuring the hybrid energy storage capacity of an AC/DC hybrid micro-grid.
Background
The AC/DC hybrid micro-grid combines the advantages of the AC micro-grid and the DC micro-grid, omits a plurality of conversion links, reduces the running loss of the system and ensures that the micro-grid is more flexible to control. However, renewable energy power generation such as wind and light has the defects of volatility, uncontrollability and the like, and the adverse effect caused by renewable energy power generation can be reduced by configuring certain energy storage in the AC/DC hybrid micro-grid. The hybrid energy storage system is a more efficient and economical energy storage system integrating the characteristics of energy storage and power storage. The lithium battery is used as energy type energy storage, can provide long-time power shortage, and the supercapacitor is used as the power type energy storage element which is most widely applied at present and is responsible for stabilizing short-time frequent power fluctuation, so that the service life of the energy storage system is prolonged, and the overall performance of the energy storage system is improved.
Because the system structure of the AC/DC hybrid micro-grid is greatly different from that of a single AC micro-grid, the existing micro-grid energy storage optimization configuration method cannot be directly applied to the AC/DC hybrid micro-grid. Compared with the traditional AC micro-grid, the AC/DC hybrid micro-grid energy storage optimal configuration also needs to consider the power interaction problem between the AC sub-grid and the DC sub-grid. Therefore, the method has important significance for configuring the mixed energy storage system with proper capacity for the AC/DC mixed micro-grid.
Disclosure of Invention
The invention establishes an AC/DC hybrid micro-grid hybrid energy storage capacity double-layer optimal configuration model based on ensemble empirical mode decomposition in order to solve the problem of unbalanced power in the AC/DC hybrid micro-grid caused by wind-light output fluctuation and load fluctuation.
A mixed energy storage capacity optimizing configuration method of an AC/DC mixed micro-grid comprises the establishment of a mixed energy storage capacity double-layer optimizing configuration model; power allocation verification based on aggregate empirical mode decomposition; configuration of the capacity of the hybrid energy storage system.
1. Establishing a hybrid energy storage capacity double-layer optimization configuration model:
(1) The energy storage life and the converter loss are comprehensively considered, and the aim of the capacity optimization configuration model of the hybrid energy storage system of the AC/DC hybrid micro-grid is that the annual comprehensive cost of the system is minimum:
min C 0 ＝min{C B +C C +C IC }
wherein C is 0 The comprehensive cost is the annual cost of the system; c (C) B 、C C 、C IC The investment operation cost of the lithium battery, the investment operation cost of the super capacitor and the loss cost of the converter (which are converted into equal annual values) are respectively calculated.
(2) Constraint satisfied in each period: and the residual capacity constraint of the energy storage system, the charge and discharge power constraint and the energy deviation constraint of the energy storage system.
(3) Dividing the model into two layers, wherein the first layer is continuous optimization, and solving the energy storage system capacity optimization configuration model by adopting a self-adaptive particle swarm algorithm under the condition of determining the filter order to obtain the system annual comprehensive cost under each configuration; the second layer is integer optimization, and the filtering order and the energy storage optimization configuration scheme with the minimum system annual comprehensive cost are finally determined by sequencing the system annual comprehensive costs corresponding to different filtering orders.
2. Power allocation verification based on ensemble empirical mode decomposition
(1) Taking typical daily operation data of an AC/DC hybrid micro-grid as an example, sampling intervals are 1min, 1440 sampling points are taken in a day, the net load power of the system is calculated, the link protocol power is determined according to the average value of the net load power of the system in 60min when no energy is stored, the upper limit and the lower limit of the link transmission power are corrected, the total power of the hybrid energy storage system is calculated according to the net load power of the system and the link protocol power, and the total power of the hybrid energy storage system is decomposed through ensemble empirical mode decomposition;
(2) The IMF component is utilized to design a new space-time filter, the total power of the hybrid energy storage system is decomposed into two parts by selecting a proper filtering order d, the sum of the IMF components with the filtering order less than or equal to d is a high-frequency part, and the sum of the IMF components with the filtering order greater than d is a low-frequency part. According to the characteristics of the lithium battery and the super capacitor, the low-frequency fluctuation part in the total power of the hybrid energy storage system is stabilized by the lithium battery, and the high-frequency fluctuation part is stabilized by the super capacitor, so that the power distribution of the hybrid energy storage system is realized;
(3) Obtaining power instructions of the lithium battery and the supercapacitor under the condition of determining the filter order, substituting the power instructions into a hybrid energy storage capacity double-layer optimization configuration model which takes rated power and rated capacity of the lithium battery and the supercapacitor as optimization variables and aims at the annual comprehensive cost of the system, comprehensively considering the energy storage life and the converter loss, and carrying out optimization solution by adopting a self-adaptive particle swarm algorithm to obtain the optimal annual comprehensive cost of the system and the rated power and rated capacity of the corresponding lithium battery and supercapacitor;
(4) Sequencing the system annual comprehensive cost corresponding to different filter orders, and finally determining the filter order with the minimum system annual comprehensive cost and the corresponding hybrid energy storage system optimal configuration scheme;
(5) Solving according to the established model: lithium battery charge and discharge power, lithium battery SOC value, supercapacitor charge and discharge power, supercapacitor SOC value, system annual comprehensive cost, a hybrid energy storage system configuration scheme and the like.
3. Configuration of capacity of hybrid energy storage system
(1) According to historical monitoring values of annual photovoltaic power generation output, fan power generation output, alternating current load and direct current load in the region, selecting 12-month typical daily operation data, and optimally configuring the alternating current-direct current hybrid micro-grid hybrid energy storage system;
(2) Because the wind-solar power generation and the load condition have stronger seasonality, a typical day is selected for operation in 4 seasons respectively, and the reliability of the configuration result is verified;
(3) And analyzing the influence of the filtering order on the configuration result of the hybrid energy storage system.
The typical day of 12 months is the same day of each month, such as 1 month 3 days, 2 months 3 days, 3 months 3 days, 4 months 3 days, 5 months 3 days, 6 months 3 days, 7 months 3 days, 8 months 3 days, 9 months 3 days, 10 months 3 days, 11 months 3 days, 12 months 3 days.
Compared with the prior research institute, the invention has the following beneficial effects:
(1) The exchange power of the AC/DC hybrid micro-grid and the large power grid and the exchange power of the AC sub-grid and the DC sub-grid are comprehensively considered, the mixed energy storage capacity of the AC/DC hybrid micro-grid is optimally configured by taking the system annual comprehensive cost including the investment running cost of the lithium battery and the super capacitor, the converter loss cost and the like as an objective function according to the annual photovoltaic power generation output, the fan power generation output, the historical monitoring values of the AC load and the DC load of the area, and the energy storage configuration of the micro-grid is more accurate;
(2) The invention adopts the integrated empirical mode decomposition to distribute the total power of the hybrid energy storage system, distributes the low-frequency component with larger energy in the total power of the hybrid energy storage system to the lithium battery, distributes the high-frequency component containing more frequent reciprocating power to the super capacitor, fully plays the role of the super capacitor in stabilizing the high-frequency fluctuation power, improves the service life of the lithium battery by utilizing the complementary advantages between the super capacitor and the lithium battery, and reduces the annual comprehensive cost of the system;
(3) The invention provides a lithium battery/super capacitor hybrid energy storage system with proper capacity for an AC/DC hybrid micro-grid, which reduces peak-valley difference of micro-grid-connected link power, reduces fluctuation of micro-grid-connected link power and reduces influence of micro-grid on a large power grid.
Drawings
Fig. 1 is a structural diagram of an ac/dc hybrid micro-grid system according to the present invention;
FIG. 2 is a flow chart of a double-layer capacity optimization configuration of the hybrid energy storage system according to the invention;
FIG. 3 is a graph of typical solar volts, fan output and load curves in accordance with the present invention;
FIG. 4 is a graph of typical daily system payload power, link protocol power, and hybrid energy storage system total power in accordance with the present invention;
FIG. 5 is a graph of IMF components and remainder after aggregate empirical mode decomposition of total power of a typical day hybrid energy storage system in accordance with the present invention;
FIG. 6 is a graph of charge and discharge power of a typical day hybrid energy storage system in accordance with the present invention;
FIG. 7 is a graph of an exemplary day hybrid energy storage system SOC according to the present invention;
FIG. 8 is a plot of the stabilizing effect of a typical day hybrid energy storage system in accordance with the present invention;
FIG. 9 is a plot of the stabilizing effect of a spring typical day hybrid energy storage system in accordance with the present invention;
FIG. 10 is a plot of the stabilizing effect of a typical day summer hybrid energy storage system in accordance with the present invention;
FIG. 11 is a plot of the stabilizing effect of an autumn typical day hybrid energy storage system according to the present invention;
FIG. 12 is a plot of the stabilizing effect of a winter typical day hybrid energy storage system in accordance with the present invention;
FIG. 13 is a graph of configuration versus filter order for an embodiment of the present invention;
figure 14 is a graph of annual composite cost versus filter order for a system in accordance with the present invention;
fig. 15 is a graph of the annual composite cost growth rate versus filter order for a system in accordance with the present invention.
Detailed Description
As shown in fig. 1, the ac/dc hybrid micro grid system includes an ac sub-network and a dc sub-network. The distributed power supply at the alternating current side is a fan, and the distributed power supply at the direct current side is a photovoltaic and hybrid energy storage system.
The micro-grid comprises an alternating current bus and a direct current bus, and can directly supply power to an alternating current load and a direct current load, the alternating current bus and the direct current bus are connected through a bidirectional interconnection converter, and the alternating current-direct current hybrid micro-grid is connected with a large power grid through a connecting wire.
In the embodiment, the cost coefficient of unit power of the lithium battery is 9000 yuan/kW, the cost coefficient of unit capacity is 2500 yuan/kWh, the operation and maintenance cost coefficient is 0.05 yuan/kWh, the charge and discharge efficiency is 90%, the initial capacity is 0.6 times of the rated capacity, the upper limit of the residual capacity is 0.8 times of the rated capacity, the lower limit of the residual capacity is 0.2 times of the rated capacity, and the cycle service life is 1000 times; the cost coefficient of the unit power of the super capacitor is 1000 yuan/kW, the cost coefficient of the unit capacity is 100000 yuan/kWh, the cost coefficient of operation and maintenance is 0.05 yuan/kWh, the charge and discharge efficiency is 95%, the initial capacity is 0.6 times of the rated capacity, the upper limit of the residual capacity is 0.9 times of the rated capacity, the lower limit of the residual capacity is 0.1 times of the rated capacity, and the operation period is 20 years; the paste rate is 6%; the converter has a conversion efficiency of 90% and a annual loss cost coefficient of 0.4 yuan/kWh; the maximum energy root mean square deviation is 0.1; the link transmission power limit is 5kW.
Establishing a hybrid energy storage capacity double-layer optimization configuration model:
(1) Considering the investment running cost of the hybrid energy storage system and the loss cost of the converter, the capacity optimization configuration model of the hybrid energy storage system of the AC/DC hybrid micro-grid aims at the minimum annual comprehensive cost of the system and can be expressed as follows:
min C 0 ＝min{C B +C C +C IC }
wherein C is 0 The comprehensive cost is the annual cost of the system; c (C) B 、C C 、C IC The investment operation cost of the lithium battery, the investment operation cost of the super capacitor and the loss cost of the converter (which are converted into equal annual values) are respectively calculated.
a. Investment and operation cost of lithium battery
The service life of the lithium battery is closely related to the working mode, and is mainly influenced by the depth of discharge and the recycling times of the lithium battery. The depth of discharge (depth of discharge, DOD) of a lithium battery is the ratio of the amount of discharge of the lithium battery to its rated capacity. According to the corresponding relation between the discharge depth of the lithium battery and the service life of the energy storage cycle, the functional relation can be fitted as follows:
wherein D is b The reference depth of discharge of the storage battery; n (N) B (D i ) Depth of discharge of D i And the service life of the corresponding storage battery.
Thereby obtaining a depth of discharge of D i The equivalent cycle number of the ith cycle is as follows:
the actual operating years of the lithium battery are as follows:
wherein N is j (D i ) Equivalent cycle times corresponding to the ith cycle on the jth day; l is the charge and discharge times in 1 d; p is the number of days of work run in 1 a.
The investment running cost of a lithium battery can be expressed as:
wherein P is BN Rated power of the lithium battery; e (E) BN Rated capacity of the lithium battery; y is Y B The service life of the lithium battery is prolonged; k (k) BP The unit power cost coefficient of the lithium battery; k (k) BE The cost coefficient is the unit capacity of the lithium battery; k (k) BY Maintaining a cost coefficient for operation of the lithium battery; lambda is the discount rate.
b. Investment and operation cost of super capacitor
Wherein P is CN Rated power for the super capacitor; e (E) CN Rated capacity of the super capacitor; y is Y C The service life of the super capacitor is usually set to be a fixed value; k (k) CP The unit power cost coefficient of the super capacitor; k (k) CE The cost coefficient is the unit capacity of the super capacitor; k (k) CY A cost factor is maintained for the operation of the supercapacitor.
c. Annual loss cost of converter
C IC ＝k ac/dc ·(1-η ac/dc )∫ 1 T (P AC (t)+P DC (t))
Wherein k is ac/dc Cost system for annual loss of current converterA number; t is the study period.
(2) In order to ensure safe and reliable operation of the micro-grid, certain constraint conditions need to be met in each period of time, including:
a. energy storage system remaining capacity constraint:
in order to ensure the normal operation of the energy storage system and prolong the service life of the energy storage system, the residual capacity of the energy storage system at any moment should meet the following constraint conditions:
wherein E is B (t)、E B (t-delta t) is the residual electric quantity of the lithium battery at the moment t and the moment t-delta t respectively; e (E) C (t)、E C (t-deltat) is the residual capacity of the super capacitor at the moment t and the moment t-deltat respectively; e (E) Bmin 、E Bmax The lower limit and the upper limit of the residual capacity of the lithium battery are respectively; e (E) Cmin 、E Cmax The lower limit and the upper limit of the residual capacity of the super capacitor are respectively; p (P) B (t)、P C The (t) is the actual charge and discharge power (the discharge is taken as the positive charge and the charge is taken as the negative charge) of the lithium battery and the super capacitor at the moment t; η (eta) B 、η C The charge and discharge efficiencies of the lithium battery and the super capacitor are respectively; Δt is the calculated step length, taking 1min.
To ensure the periodicity of continuous operation of the system, the storage capacity of the energy storage system is kept consistent at the beginning and the end of each research period, namely
Wherein E is B (0)、E C (0) Initial capacities of lithium battery and supercapacitor, respectively.
b. Energy storage system charge-discharge power constraint:
the energy storage device charge-discharge power constraint at time t can be expressed as:
when the residual capacity of the lithium battery and the supercapacitor cannot meet the required discharge power, the discharge power of the lithium battery and the supercapacitor needs to be adjusted as follows:
when the residual capacity of the lithium battery and the supercapacitor cannot meet the required charging power, the charging power of the lithium battery and the supercapacitor needs to be adjusted as follows:
c. energy deviation constraint:
the energy root mean square deviation is used for representing the stabilizing effect after energy storage, and the net load power after energy storage stabilization and the link protocol power energy root mean square deviation R are required to be not greater than a certain threshold value, namely
Wherein R is max Is the maximum root mean square deviation value.
(3) Considering different filtering orders, the low-frequency component and the high-frequency component obtained after the total power of the hybrid energy storage system is decomposed are different, and the obtained hybrid energy storage configuration result is also different, namely the model is divided into two layers, wherein the first layer is continuously optimized, and under the condition of determining the filtering orders, the energy storage system capacity optimization configuration model is solved by adopting a self-adaptive particle swarm algorithm, so that the system year comprehensive cost under each configuration is obtained; the second layer is integer optimization, and the filtering order and the energy storage optimization configuration scheme with the minimum system annual comprehensive cost are finally determined by sequencing the system annual comprehensive costs corresponding to different filtering orders. The specific optimal configuration flow is shown in fig. 2.
Power allocation verification based on aggregate empirical mode decomposition:
(1) Taking typical daily operation data of an AC/DC hybrid micro-grid as an example, wherein the typical daily operation data is shown in FIG. 3, the sampling interval is 1min, and 1440 sampling points are taken in one day;
(2) The direct current side net load power and the alternating current side system net load power are calculated respectively, and the calculation formula can be expressed as:
P J-ac (t)＝P L-ac (t)-P WT (t)
P J-dc (t)＝P L-dc (t)-P PV (t)
wherein T is time, and T is more than or equal to 1 and less than or equal to T; p (P) WT (t) is an ac side fan output; p (P) PV (t) is a dc-side optical output; p (P) L-ac (t)、P L-dc (t) consuming power for the ac side, dc side loads, respectively; p (P) J-ac (t)、P J-dc (t) is the ac side and dc side payload power, respectively.
(3) Calculating the net load power of the AC-DC hybrid micro-grid system according to the net load power of the DC side and the net load power of the AC side, wherein the calculated power can be expressed as:
wherein eta ac/dc Is the commutation efficiency of the AC/DC converter.
(4) Determining the protocol power of the interconnecting line by considering the limitation of the power of the interconnecting line and the average value of the net load power of the micro-grid system in 60 minutes when no energy is stored;
(5) Calculating the total power of the hybrid energy storage system according to the tie-line protocol power and the system payload power, wherein the calculated power can be expressed as:
wherein: p (P) H (t) is the total power of the hybrid energy storage system; p (P) J (t) is the system payload power; p (P) A And (t) is the power of the link protocol. The total power, the net load power and the link protocol power of the hybrid energy storage system are shown in figure 4
(6) The total power of the hybrid energy storage system is decomposed through ensemble empirical mode decomposition, and the decomposed results are shown as follows:
wherein: p (P) H (t) is the total power of the hybrid energy storage system; h is a i (t) is the ith IMF component of the total power of the hybrid energy storage system; r is (r) n And (t) is the decomposition allowance. The result of the decomposition is shown in FIG. 5.
(7) The IMF component is utilized to design a new space-time filter, the total power of the hybrid energy storage system is decomposed into two parts by selecting a proper filtering order d, the sum of the IMF components with the filtering order less than or equal to d is a high-frequency part, and the sum of the IMF components with the filtering order greater than d is a low-frequency part. According to the characteristics of the lithium battery and the super capacitor, the low-frequency fluctuation part in the total power of the hybrid energy storage system is stabilized by the lithium battery, and the high-frequency fluctuation part is stabilized by the super capacitor, so that the power distribution of the hybrid energy storage system is realized. Therefore, power command P for lithium battery B0 (t) and Power command P of supercapacitor C0 (t) are respectively expressed as:
wherein: d is the filtering order, and d is more than or equal to 0 and less than or equal to n.
(8) Obtaining power instructions of the lithium battery and the supercapacitor under the condition of determining the filter order, substituting the power instructions into a hybrid energy storage capacity double-layer optimization configuration model which takes rated power and rated capacity of the lithium battery and the supercapacitor as optimization variables and aims at the annual comprehensive cost of the system, comprehensively considering the energy storage life and the converter loss, and carrying out optimization solution by adopting a self-adaptive particle swarm algorithm to obtain the optimal annual comprehensive cost of the system and the rated power and rated capacity of the corresponding lithium battery and supercapacitor;
(9) Sequencing the system annual comprehensive cost corresponding to different filter orders, and finally determining that the filter order with the minimum system annual comprehensive cost is d=3, the annual comprehensive cost is 2.78 ten thousand yuan, the optimal configuration scheme of the hybrid energy storage system is that the rated power and the rated capacity of the lithium battery are respectively 4.18kW and 8.40kWh, and the rated power and the rated capacity of the super capacitor are respectively 3.16kW and 0.82kWh;
(10) Solving according to the established model: the charge and discharge power of the lithium battery and the super capacitor are shown in fig. 6, the SOC values of the lithium battery and the super capacitor are shown in fig. 7, and the stabilizing effect of the hybrid energy storage system is shown in fig. 8.
Configuration of capacity of the hybrid energy storage system:
(1) According to historical monitoring values of annual photovoltaic power generation output, fan power generation output, alternating current load and direct current load in the region, selecting 12-month typical daily operation data, and optimally configuring the alternating current-direct current hybrid micro-grid hybrid energy storage system, wherein the configuration result is that the rated power and rated capacity of a lithium battery are 5.49kW and 14.28kWh respectively, the rated power and rated capacity of a super capacitor are 4.15kW and 1.24kWh respectively, and the annual comprehensive cost of the system is 4.05 ten thousand yuan;
(2) Because the wind-solar power generation and the load situation have stronger seasonality, a typical day is selected for operation in 4 seasons, the reliability of configuration results is verified, the typical day hybrid energy storage system stabilizing effect curve in spring is shown in fig. 9, the typical day hybrid energy storage system stabilizing effect curve in summer is shown in fig. 10, the typical day hybrid energy storage system stabilizing effect curve in autumn is shown in fig. 11, and the typical day hybrid energy storage system stabilizing effect curve in winter is shown in fig. 12;
(3) The influence of the filtering order on the energy storage configuration result is analyzed, the relation diagram of the configuration scheme and the filtering order is shown in fig. 13, the relation diagram of the system annual comprehensive cost and the filtering order is shown in fig. 14, and the relation diagram of the system annual comprehensive cost increasing rate and the filtering order is shown in fig. 15.
Claims (1)
1. An optimal configuration method for hybrid energy storage capacity of an AC/DC hybrid micro-grid comprises the following steps: establishing a double-layer optimal configuration model of the hybrid energy storage capacity; power allocation verification based on aggregate empirical mode decomposition; configuring the capacity of the hybrid energy storage system; the method is characterized by comprising the following steps of:
(1) The system year total cost is the smallest, expressed as:
minC 0 ＝min{C B +C C +C IC }
wherein C is 0 The comprehensive cost is the annual cost of the system; c (C) B 、C C 、C IC The investment operation cost of the lithium battery, the investment operation cost of the super capacitor and the loss cost of the converter are respectively;
a. investment and operation cost of lithium battery
P BN Rated power of the lithium battery; e (E) BN Rated capacity of the lithium battery; y is Y B The service life of the lithium battery is prolonged; k (k) BP The unit power cost coefficient of the lithium battery; k (k) BE The cost coefficient is the unit capacity of the lithium battery; k (k) BY Maintaining a cost coefficient for operation of the lithium battery; lambda is the discount rate;
according to the corresponding relation between the discharge depth of the lithium battery and the service life of the energy storage cycle, the functional relation can be fitted as follows:
wherein D is b The reference depth of discharge of the storage battery; n (N) B (D i ) Depth of discharge of D i The service life of the corresponding storage battery; thereby obtaining a depth of discharge of D i The equivalent cycle number of the ith cycle is as follows:
wherein N is j (D i ) Equivalent cycle times corresponding to the ith cycle on the jth day; l is the charge and discharge times in ld; p is the number of days of work operation in 1 a;
b. investment and operation cost of super capacitor
Wherein k is ac/dc The annual loss cost coefficient of the converter is obtained; t is the research period; η (eta) ac/dc The converter efficiency of the AC/DC converter;
(2) Energy storage system remaining capacity constraint:
the remaining capacity of the energy storage system at any moment should satisfy the following constraint conditions:
wherein E is B (t)、E B (t-delta t) is the residual electric quantity of the lithium battery at the moment t and the moment t-delta t respectively; e (E) C (t)、E C (t-deltat) is the residual capacity of the super capacitor at the moment t and the moment t-deltat respectively; e (E) Bmin 、E Bmax The lower limit and the upper limit of the residual capacity of the lithium battery are respectively; e (E) Cmin 、E Cmax The lower limit and the upper limit of the residual capacity of the super capacitor are respectively; p (P) B (t)、P C (t) the actual charge and discharge power of the lithium battery and the supercapacitor at the moment t, the positive charge is taken as discharge, and the negative charge is taken as charge; η (eta) B 、η C The charge and discharge efficiencies of the lithium battery and the super capacitor are respectively; delta t is the calculated step length, and 1min is taken;
the storage capacity of the energy storage system is kept consistent at the beginning and the end of each research period, namely
Wherein E is B (0)、E C (0) Initial capacities of lithium battery and supercapacitor, respectively, E B (T)、E C (T) the capacity at the end of the cycle of the lithium battery and supercapacitor, respectively;
energy storage system charge-discharge power constraint:
the energy storage device charge-discharge power constraint at time t can be expressed as:
wherein P is BN Rated power of the lithium battery; p (P) CN Rated power of the super capacitor;
when the residual capacity of the lithium battery and the supercapacitor cannot meet the required discharge power, the discharge power of the lithium battery and the supercapacitor needs to be adjusted as follows:
when the residual capacity of the lithium battery and the supercapacitor cannot meet the required charging power, the charging power of the lithium battery and the supercapacitor needs to be adjusted as follows:
wherein P is B0 (t) is a charge-discharge power instruction of the lithium battery; p (P) C0 (t) is a charge-discharge power instruction of the supercapacitor;
energy deviation constraint:
the energy root mean square deviation is used for representing the stabilizing effect after energy storage, and the net load power after energy storage stabilization and the link protocol power energy root mean square deviation R are required to be not greater than a certain threshold value, namely
Wherein R is max The maximum root mean square deviation value is represented by T, and the research period is represented by T;
(3) The model is divided into two layers, wherein the first layer is to solve the energy storage system capacity optimization configuration model by adopting a self-adaptive particle swarm algorithm under the condition of determining the filtering order, so as to obtain the system annual comprehensive cost under each configuration; the second layer is used for finally determining the filtering order and the energy storage optimal configuration scheme with the minimum system annual comprehensive cost by sequencing the system annual comprehensive cost corresponding to different filtering orders;
the power allocation verification based on the ensemble empirical mode decomposition includes the steps of:
(1) 1440 sampling points in a day, sampling interval is lmin, calculating system payload power,
(2) The direct current side net load power and the alternating current side system net load power are calculated respectively, and the calculation formula can be expressed as:
P J-ac (t)＝P L-ac (t)-P WT (t)
P J-dc (t)＝P L-dc (t)-P PV (t)
wherein T is time, and T is more than or equal to 1 and less than or equal to T; p (P) Pw (t) is an ac side fan output; p (P) PV (t) is a dc-side optical output; p (P) L-ac (t)、P L-dc (t) consuming power for the ac side, dc side loads, respectively; p (P) J-ac (t)、P J-dc (t) ac side, dc side payload power, respectively;
(3) Calculating the net load power of the AC-DC hybrid micro-grid system according to the net load power of the DC side and the net load power of the AC side, wherein the calculated power can be expressed as:
wherein eta ac/dc The converter efficiency of the AC/DC converter;
(4) Determining the power of a link protocol according to a 60-min average value of the net load power of the system when no energy is stored;
(5) Correcting according to the upper limit and the lower limit of the transmission power of the connecting line, and calculating according to the net load power of the system and the protocol power of the connecting line to obtain the total power of the hybrid energy storage system, wherein the calculated power can be expressed as:
wherein: p (P) H (t) is the total power of the hybrid energy storage system; p (P) J (t) is the system payload power; p (P) A (t) is a link protocol power;
(6) The total power of the hybrid energy storage system is decomposed through ensemble empirical mode decomposition, and the decomposed result is as follows:
wherein: p (P) H (t) is the total power of the hybrid energy storage system; h is a i (t) is the ith IMF component of the total power of the hybrid energy storage system; r is (r) n (t) is a decomposition allowance;
(7) Designing a new space-time filter by using IMF components, and decomposing the total power of the hybrid energy storage system into two parts by selecting a proper filtering order d, wherein the sum of the IMF components with the filtering order less than or equal to d is a high-frequency part, and the sum of the IMF components with the filtering order greater than d is a low-frequency part; the low-frequency fluctuation part in the total power of the hybrid energy storage system is stabilized by a lithium battery, and the high-frequency fluctuation part is stabilized by a super capacitor, so that the power distribution of the hybrid energy storage system is realized; power command P for lithium battery B0 (t) and Power command P of supercapacitor C0 (t) are respectively expressed as:
wherein: d is the filtering order, and d is more than or equal to 0 and less than or equal to n;
(8) Obtaining power instructions of the lithium battery and the supercapacitor under the condition of determining the filter order, substituting the power instructions into a hybrid energy storage capacity double-layer optimization configuration model which takes rated power and rated capacity of the lithium battery and the supercapacitor as optimization variables and aims at the annual comprehensive cost of the system, comprehensively considering the energy storage life and the converter loss, and carrying out optimization solution by adopting a self-adaptive particle swarm algorithm to obtain the optimal annual comprehensive cost of the system and the rated power and rated capacity of the corresponding lithium battery and supercapacitor;
(9) Sequencing the system annual comprehensive cost corresponding to different filter orders, and finally determining the filter order with the minimum system annual comprehensive cost and the corresponding hybrid energy storage system optimal configuration scheme;
(10) Solving according to the established model: lithium battery charging and discharging power, lithium battery S0C value, supercapacitor charging and discharging power, supercapacitor SOC value, system annual comprehensive cost and hybrid energy storage system configuration scheme.
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