CN111049246A - Capacity configuration method for hybrid energy storage system - Google Patents

Capacity configuration method for hybrid energy storage system Download PDF

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Publication number
CN111049246A
CN111049246A CN201911353280.3A CN201911353280A CN111049246A CN 111049246 A CN111049246 A CN 111049246A CN 201911353280 A CN201911353280 A CN 201911353280A CN 111049246 A CN111049246 A CN 111049246A
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energy storage
power
storage system
value
hybrid energy
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何斌
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Zhejiang Hozon New Energy Automobile Co Ltd
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Zhejiang Hozon New Energy Automobile Co Ltd
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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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention discloses a capacity configuration method of a hybrid energy storage system, which considers the real hybrid energy storage system constraint through various constraint modes and optimizes the capacity based on an improved artificial bee colony algorithm. The invention can well optimize the configuration process of the energy storage capacity, realizes the minimization of the investment cost and simultaneously comprehensively considers various factors, thereby ensuring that the whole system has low cost and the energy storage capacity is maximized.

Description

Capacity configuration method for hybrid energy storage system
Technical Field
The invention relates to the field of energy control of hybrid energy storage systems, in particular to a capacity configuration method of a hybrid energy storage system.
Background
The defect of high cost of the energy storage device has been an obstacle to popularization and application for a long time, and although the cost of the energy storage device is in a descending trend along with the increasing maturity of energy storage technology, the proportion of the energy storage cost is still higher in the investment of a renewable energy power generation system. In the process of optimizing and configuring the energy storage capacity, while the investment cost is minimized, various factors, such as the geographical environment and meteorological conditions of the installation location, the investment cost for selecting the energy storage unit, the later maintenance cost and the like, need to be considered comprehensively. Therefore, the energy storage capacity is configured on the basis of maximally reducing the investment cost of energy storage while meeting the system requirements, which becomes a key difficulty in optimizing the problem.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a capacity configuration method of a hybrid energy storage system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a capacity configuration method for a hybrid energy storage system sets the output power of a photovoltaic array toP pv The output power of the wind turbine isP wind The load consumes power ofP L The power absorbed or discharged by the storage battery and the super capacitor is respectivelyP bat AndP sc when the wind-solar energy storage micro-grid stably runs, the difference between the power generated by the distributed power supply and the load power consumption is stabilized by the hybrid energy storage system, and the power relationship in the system is as follows:
Figure DEST_PATH_IMAGE001
；
when in useP batAndP sc when the numerical value is positive, the storage battery and the super capacitor are in a charging state and absorb energy; when in useP bat AndPscwhen the numerical value is negative, the storage battery and the super capacitor are in a discharging state, and energy is released outwards;
wherein the hybrid energy storage powerP hess The expression is as follows:
Figure 761654DEST_PATH_IMAGE002
；
the respective output power of the wind power and the photovoltaic reaches the maximum value, the output energy efficiency of the distributed power generation system is highest, and the power loss distributed by the hybrid energy storage system reaches the minimum value, so that the economy of the energy storage system is optimal:
Figure 52696DEST_PATH_IMAGE003
；
wherein the content of the first and second substances,Pfor the investment cost value of the whole hybrid energy storage system,n 1 the number of supercapacitors;n 2 the number of storage batteries;M sc is the cost value of a single supercapacitor;M bat is the cost value of a single storage battery.
Further, during operation, wind power generationE wind And solar power generationE pv The generated energy is equal to the required energy of the loadE load And hybrid energy storageE bat Release energyE sc And adding the sum, wherein the corresponding expression is as follows:
Figure 832433DEST_PATH_IMAGE004
。
further, when the generated energy of the wind turbine generator and the photovoltaic array is sufficient, the load requirement is met, redundant energy exists in the system and is stored by the hybrid energy storage system, and the first assumption is thatj（1≤j≤12）Surplus energy of monthly microgrid system reaches maximum valueE（j）Then the average surplus electric energy per day isE（j）/n，nIs as followsiDays of the month; in order to avoid waste caused by energy loss, the total capacity value of the hybrid energy storage system is less than or equal ton 1 E（j）/n,n 1 For the self-recovery time of the energy storage system, the whole constraint expression is as follows:
Figure DEST_PATH_IMAGE005
。
further, when the system has impact load in short time, and the output power of the fan and the photovoltaic cell is 0, the output power value of the whole hybrid energy storage system is greater than or equal to the maximum power value of the impact load, and the constraint expression is as follows:
Figure 103009DEST_PATH_IMAGE006
；
Figure DEST_PATH_IMAGE007
；
wherein the content of the first and second substances,P Lmax the impact load power value is obtained.
Further, the system adopts an improved bee colony algorithm to configure the capacity, and the specific steps are as follows:
step 1: initializing parameters;
initializing all food source vectors
Figure 294212DEST_PATH_IMAGE008
(m =1,2, … SN, SN representing the number of food sources). Since each food source is a solution vector, each solution vector contains i variables, the following expression is an initialization formula:
Figure 100002_DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 116674DEST_PATH_IMAGE010
represents the interval [0,1]The random number of (2) is greater than,
Figure 100002_DEST_PATH_IMAGE011
and
Figure 259074DEST_PATH_IMAGE012
are respectively
Figure DEST_PATH_IMAGE013
A maximum boundary value and a minimum boundary value.
Step 2: establishing an initialization population by using a reverse learning method;
employing bees to find food sources by random search
Figure 457974DEST_PATH_IMAGE014
. To make the quality of the food source better, the hiring bee continues to search for new food sources at the already found food sources. Discovery of new food sources
Figure DEST_PATH_IMAGE015
The expression is as follows:
Figure 500754DEST_PATH_IMAGE016
Figure DEST_PATH_IMAGE017
wherein the content of the first and second substances,
Figure 369484DEST_PATH_IMAGE018
in the case of a randomly selected food source,
Figure DEST_PATH_IMAGE019
is between the interval [ -1,1 [ ]]After a new food source is found, the fitness of the food source is calculated, and a greedy algorithm is used for selecting new and old foods, wherein the fitness calculation formula is as follows:
Figure 500645DEST_PATH_IMAGE020
and step 3: food sources found by hiring bees
Figure DEST_PATH_IMAGE021
Continuously searching for new food sources nearby to find new food sources
Figure 237656DEST_PATH_IMAGE022
Is updated to
Figure DEST_PATH_IMAGE023
；
And 4, step 4: hiring bees according to a formula
Figure 636408DEST_PATH_IMAGE024
After the food source is selected, continuously searching for a new food source around the food source;
and 5: when the reconnaissance bees determine that the quality of the food source found by the hiring bees meets the condition, ending; if the food source does not meet the condition, the food source is abandoned, and the food is searched by using the formula step 1 again until the food source with the best quality is found.
Figure DEST_PATH_IMAGE025
Is an algorithm in
Figure 66252DEST_PATH_IMAGE026
The adaptive weights of the sub-iterations are,
Figure DEST_PATH_IMAGE027
the size of the artificial bee colony algorithm has great influence on the optimization capability of the artificial bee colony algorithm, an overlarge weight coefficient is favorable for global search, a smaller weight coefficient is favorable for local search, generally, the algorithm is often subjected to global search in the initial stage and local search in the later stage, wherein,
Figure 681779DEST_PATH_IMAGE028
the initial weight, the weight value is 1,
Figure DEST_PATH_IMAGE029
the weight degradation coefficient is 0.99.
By adopting the technical scheme of the invention, the invention has the beneficial effects that: compared with the prior art, the method can well optimize the energy storage capacity in the process of configuring the energy storage capacity, minimize the investment cost and comprehensively consider various factors at the same time, so that the overall system cost is low and the energy storage capacity is maximized.
Drawings
Fig. 1 is a power distribution diagram of a wind-solar energy storage microgrid in a capacity configuration method of a hybrid energy storage system provided by the invention;
fig. 2 is a flow chart of capacity optimization based on an improved artificial bee colony algorithm of the capacity allocation method of the hybrid energy storage system provided by the invention.
Detailed Description
Specific embodiments of the present invention will be further described with reference to the accompanying drawings.
As shown in the figure, the first and second,
a capacity configuration method for a hybrid energy storage system sets the output power of a photovoltaic array toP pv The output power of the wind turbine isP wind The load consumes power ofP L The power absorbed or discharged by the storage battery and the super capacitor is respectivelyP bat AndP sc when the wind-solar energy storage micro-grid stably runs, the difference between the power generated by the distributed power supply and the load power consumption is stabilized by the hybrid energy storage system, and the power relationship in the system is as follows:
Figure 956903DEST_PATH_IMAGE030
；
when in useP batAndP sc when the numerical value is positive, the storage battery and the super capacitor are in a charging state and absorb energy; when in useP bat AndPscwhen the numerical value is negative, the storage battery and the super capacitor are in a discharging state, and energy is released outwards;
wherein the hybrid energy storage powerP hess The expression is as follows:
Figure DEST_PATH_IMAGE031
；
the respective output power of the wind power and the photovoltaic reaches the maximum value, the output energy efficiency of the distributed power generation system is highest, and the power loss distributed by the hybrid energy storage system reaches the minimum value, so that the economy of the energy storage system is optimal:
Figure 475740DEST_PATH_IMAGE032
；
wherein the content of the first and second substances,Pfor the investment cost value of the whole hybrid energy storage system,n 1 the number of supercapacitors;n 2 the number of storage batteries;M sc is the cost value of a single supercapacitor;M bat is the cost value of a single storage battery.
During operation, wind power generationE wind And solar power generationE pv The generated energy is equal to the required energy of the loadE load And hybrid energy storageE bat Release energyE sc And adding the sum, wherein the corresponding expression is as follows:
Figure DEST_PATH_IMAGE033
。
when the generated energy of the wind turbine generator and the photovoltaic array is sufficient, the load requirement is met, redundant energy in the system is stored by the hybrid energy storage system, and the first assumption is thatj（1≤j≤12）Surplus energy of monthly microgrid system reaches maximum valueE（j）Then the average surplus electric energy per day isE（j）/n，nIs as followsiDays of the month; in order to avoid waste caused by energy loss, the total capacity value of the hybrid energy storage system is less than or equal ton 1 E（j）/n,n 1 For the self-recovery time of the energy storage system, the whole constraint expression is as follows:
Figure 810906DEST_PATH_IMAGE034
。
when the system has impact load in a short time, the hybrid energy storage system generally takes the storage battery as a main part and the super capacitor as an auxiliary part, the storage battery is largely used in the energy storage system due to the characteristic of large energy storage, the super capacitor can only be used as an auxiliary energy storage element due to low energy density, but the super capacitor has strong response capability and short action time when the power fluctuation is large. Under the worst condition, when the output power of the fan and the photovoltaic cell is 0, the output power value of the whole hybrid energy storage system is greater than or equal to the maximum power value of the impact load, and the constraint expression is as follows:
Figure DEST_PATH_IMAGE035
；
Figure 916659DEST_PATH_IMAGE036
；
wherein the content of the first and second substances,P Lmax the impact load power value is obtained.
The traditional bee colony algorithm adopts a random initialization method, which limits the optimization efficiency of the algorithm to a certain extent. In order to improve the quality of the initial honey source of the algorithm, the invention carries out initialization operation by means of reverse learning in the improved bee colony algorithm. And constructing a random initial population and a reverse population thereof, and selecting excellent individuals in the random initial population to construct an initial population. Given the number of employed bee colonies as
Figure DEST_PATH_IMAGE037
Problem dimension of
Figure 995473DEST_PATH_IMAGE038
And the value range of each dimension independent variable
Figure DEST_PATH_IMAGE039
The specific steps of the reverse learning initialization operation are as follows:
step 1, order
Figure 368817DEST_PATH_IMAGE040
，
Figure DEST_PATH_IMAGE041
Turning to the step 2;
step 2, if
Figure 514365DEST_PATH_IMAGE042
Turning to the step 3, otherwise, turning to the step 7;
step 3, if
Figure DEST_PATH_IMAGE043
Turning to the step 4, otherwise, turning to the step 5;
step 4, order
Figure 465004DEST_PATH_IMAGE044
，
Figure DEST_PATH_IMAGE045
And turning to step 5;
step 5, order
Figure 222875DEST_PATH_IMAGE046
If, if
Figure DEST_PATH_IMAGE047
Turning to the step 6, otherwise, turning to the step 3;
step 6, order
Figure 575359DEST_PATH_IMAGE048
Turning to the step 2;
step 7 selecting a set
Figure DEST_PATH_IMAGE049
Combined check
Figure 894738DEST_PATH_IMAGE050
The first S excellent individuals construct an initial population.
Although the artificial bee colony algorithm has a simple structure at the searching speed, the later searching efficiency is not high. Therefore, the system adopts an improved bee colony algorithm to configure the capacity, and the specific steps are as follows:
step 1: initializing parameters;
initializing all food source vectors
Figure DEST_PATH_IMAGE051
(m =1,2, … SN, SN representing the number of food sources). Since each food source
Figure 332673DEST_PATH_IMAGE052
Is a solution vector, each solution vector contains i variables, and the following expression is an initialization formula:
Figure DEST_PATH_IMAGE053
wherein the content of the first and second substances,
Figure 628656DEST_PATH_IMAGE054
represents the interval [0,1]The random number of (2) is greater than,
Figure DEST_PATH_IMAGE055
and
Figure 101226DEST_PATH_IMAGE056
are respectively
Figure DEST_PATH_IMAGE057
A maximum boundary value and a minimum boundary value.
Step 2: establishing an initialization population by using a reverse learning method;
employing bees to find food sources by random search
Figure 588577DEST_PATH_IMAGE058
. To make the quality of the food source better, the hiring bee continues to search for new food sources at the already found food sources. Discovery of new food sources
Figure DEST_PATH_IMAGE059
The expression is as follows:
Figure 513807DEST_PATH_IMAGE060
Figure DEST_PATH_IMAGE061
wherein the content of the first and second substances,
Figure 613482DEST_PATH_IMAGE062
in the case of a randomly selected food source,
Figure DEST_PATH_IMAGE063
is between the interval [ -1,1 [ ]]After a new food source is found, the fitness of the food source is calculated, and a greedy algorithm is used for selecting new and old foods, wherein the fitness calculation formula is as follows:
Figure 674978DEST_PATH_IMAGE064
and step 3: employmentThe bees continue to search for new food sources near the found food sources and find new food sources
Figure DEST_PATH_IMAGE065
Is updated to
Figure 336160DEST_PATH_IMAGE066
；
And 4, step 4: hiring bees according to a formula
Figure DEST_PATH_IMAGE067
After the food source is selected, continuously searching for a new food source around the food source;
and 5: when the reconnaissance bees determine that the quality of the food source found by the hiring bees meets the condition, ending; if the food source does not meet the condition, the food source is abandoned, and the food is searched by using the formula step 1 again until the food source with the best quality is found.
Figure 748687DEST_PATH_IMAGE068
Is an algorithm in
Figure DEST_PATH_IMAGE069
The adaptive weights of the sub-iterations are,
Figure 652052DEST_PATH_IMAGE070
the size of the artificial bee colony algorithm has great influence on the optimization capability of the artificial bee colony algorithm, an overlarge weight coefficient is favorable for global search, a smaller weight coefficient is favorable for local search, generally, the algorithm is often subjected to global search in the initial stage and local search in the later stage, wherein,
Figure DEST_PATH_IMAGE071
the initial weight, the weight value is 1,
Figure 833635DEST_PATH_IMAGE072
the weight degradation coefficient is 0.99. The influence of the magnitude of the weight coefficient on the optimization capability of the artificial bee colony algorithm is large, and the overlarge weight coefficient is beneficial to global searchAnd searching for a smaller weight coefficient is beneficial to local search, and under general conditions, the algorithm usually performs global search in the initial stage and performs local search in the later stage. The change leads the ABC algorithm to be mainly based on global search in the initial stage of iteration and focus on local search in the later stage of iteration.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (5)

1. The capacity configuration method of the hybrid energy storage system is characterized in that the output power of a photovoltaic array is set toP pv The output power of the wind turbine isP wind The load consumes power ofP L The power absorbed or discharged by the storage battery and the super capacitor is respectivelyP bat AndP sc when the wind-solar energy storage micro-grid stably runs, the difference between the power generated by the distributed power supply and the load power consumption is stabilized by the hybrid energy storage system, and the power relationship in the system is as follows:
Figure 894985DEST_PATH_IMAGE001
；
when in useP batAndP sc when the numerical value is positive, the storage battery and the super capacitor are in a charging state and absorb energy; when in useP bat AndPscwhen the numerical value is negative, the storage battery and the super capacitor are in a discharging state, and energy is released outwards;
wherein the hybrid energy storage powerP hess The expression is as follows:
Figure DEST_PATH_IMAGE002
；
the respective output power of the wind power and the photovoltaic reaches the maximum value, the output energy efficiency of the distributed power generation system is highest, and the power loss distributed by the hybrid energy storage system reaches the minimum value, so that the economy of the energy storage system is optimal:
Figure 608863DEST_PATH_IMAGE003
；
wherein the content of the first and second substances,Pfor the investment cost value of the whole hybrid energy storage system,n 1 the number of supercapacitors;n 2 the number of storage batteries;M sc is the cost value of a single supercapacitor;M bat is the cost value of a single storage battery.
2. The method of claim 1, wherein during operation, the wind power generation system is configured to generate powerE wind And solar power generationE pv The generated energy is equal to the required energy of the loadE load And hybrid energy storageE bat Release energyE sc And adding the sum, wherein the corresponding expression is as follows:
Figure DEST_PATH_IMAGE004
。
3. the capacity allocation method of claim 1, wherein when the power generation of the wind turbine and the photovoltaic array is sufficient, the load demand is satisfied, and excess energy in the system is stored in the hybrid energy storage system, assuming the first casej（1≤j≤12）Surplus energy of monthly microgrid system reaches maximum valueE（j）Then averageThe surplus electric energy every day isE （j）/n，nIs as followsiDays of the month; in order to avoid waste caused by energy loss, the total capacity value of the hybrid energy storage system is less than or equal ton 1 E（j）/n,n 1 For the self-recovery time of the energy storage system, the whole constraint expression is as follows:
Figure 919759DEST_PATH_IMAGE005
。
4. the capacity configuration method of the hybrid energy storage system according to claim 1, wherein when the system has an impact load for a short time and the output power of the fan and the photovoltaic cell is both 0, the output power value of the whole hybrid energy storage system is greater than or equal to the maximum power value of the impact load, and the constraint expression is as follows:
Figure DEST_PATH_IMAGE006
；
Figure 846127DEST_PATH_IMAGE007
；
wherein the content of the first and second substances,P Lmax the impact load power value is obtained.
5. The capacity allocation method of the hybrid energy storage system according to claim 1, wherein the capacity allocation system adopts an improved bee colony algorithm to allocate the capacity, and comprises the following specific steps:
step 1: initializing parameters;
step 2: establishing an initialization population by using a reverse learning method;
and step 3: employing bees to continue searching for new food sources in the vicinity of the food sources already found, and the expression for finding new food sources is updated to
Figure DEST_PATH_IMAGE008
；
And 4, step 4: hiring bees according to a formula
Figure DEST_PATH_IMAGE009
After the food source is selected, continuously searching for a new food source around the food source;
Figure DEST_PATH_IMAGE010
is an algorithm in
Figure DEST_PATH_IMAGE011
The adaptive weight of the sub-iteration, wherein the initial weight and the weight value are 1,
Figure DEST_PATH_IMAGE012
the weight degradation coefficient is 0.99;
and 5: when the reconnaissance bees determine that the quality of the food source found by the hiring bees meets the condition, ending; if the food source does not meet the condition, the food source is abandoned, and the step 1 is repeated to find food until the food source with the best quality is found.
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CN116846042A (en) * 2023-09-04 2023-10-03 深圳科力远数智能源技术有限公司 Automatic adjustment method and system for charging and discharging of hybrid energy storage battery

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