CN110752630A - Light storage charging station capacity optimization simulation modeling method considering battery echelon utilization - Google Patents
Light storage charging station capacity optimization simulation modeling method considering battery echelon utilization Download PDFInfo
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Abstract
The invention relates to the technical field of capacity allocation of an optical storage charging station of an electric automobile, and discloses a capacity allocation simulation modeling method of the optical storage charging station, which takes battery echelon utilization into consideration. On the basis of calculating the service life of the echelon battery energy storage system, the method estimates the cost of the echelon battery energy storage system through a double balance subtraction method; the method comprises the steps that the maximum annual net income of the optical storage charging station is an objective function, the real-time power balance of the charging station, the state of charge constraint of an energy storage system and the like are taken as constraint conditions, and an optical storage capacity configuration model of the optical storage charging station considering the utilization of battery echelons is established; and solving the model by adopting a teaching and learning algorithm to obtain the optimal photovoltaic capacity and energy storage capacity.
Description
Technical Field
The invention relates to the technical field of battery energy storage, in particular to a capacity optimization simulation modeling method for an optical storage charging station, which takes battery echelon utilization into consideration.
Background
With the wide development of electric vehicles in various countries of the world, the planning and construction problems of charging infrastructures have received more attention from governments of China. Because solar energy has the characteristics of green, clean and never failing, the construction of the photovoltaic charging station for the electric automobile can effectively improve the utilization rate of clean energy, reduce the dependence on fossil fuel, improve the primary energy structure of China and improve the energy-saving and emission-reducing efficiency. However, the photovoltaic system can only meet the charging requirement of the electric automobile in the daytime, and the power grid is required to charge the electric automobile at night.
If an energy storage system is configured in the photovoltaic charging station system to store surplus photovoltaic power generation in the daytime, the energy storage system discharges to charge the electric automobile at night, the photovoltaic utilization rate can be improved, and the dependence of the charging station on a large power grid can be reduced. Due to the sudden and violent increase of the sales volume of the electric automobiles in recent years, the scrapping of the power batteries for the automobiles is gradually scaled. The China automobile technology research center predicts that the accumulated scrappage of the power batteries of pure electric (including plug-in type) passenger vehicles and hybrid power passenger vehicles in China will reach 12-17 ten thousand tons by 2020 years, and how to dispose retired power batteries is a major topic influencing the development of new energy automobiles. Therefore, the method is very necessary for the research of the retired power battery in the field of energy storage, and has very important practical significance.
Disclosure of Invention
The invention aims to solve the problems mentioned in the technical background, and provides a capacity optimization simulation modeling method for an optical storage charging station, which takes battery echelon utilization into consideration. The method uses a double balance subtraction method for estimating the unit price of the capacity of the retired power battery, so that the initial investment cost of a echelon battery energy storage system is calculated, the annual net income maximization of the optical storage charging station is used as an objective function, the real-time power balance, the state of charge of the energy storage system and the like are used as constraint conditions, a capacity optimization configuration model of the optical storage charging station is constructed, and a teaching and learning algorithm (TLBO) is adopted to solve the calculation example. The double balance subtraction method realizes reasonable estimation of initial investment cost of the echelon battery energy storage system, and a teaching and learning algorithm (TLBO) performs optimized calculation on a model to obtain optimal photovoltaic capacity and echelon energy storage capacity.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an optical storage charging station capacity optimization simulation modeling method considering battery echelon utilization, the method comprising the steps of:
step 1: collecting relevant data of the retired power battery and the echelon energy storage battery and peak load times of charging load of the electric automobile every day;
step 2: estimating initial investment cost of the echelon battery energy storage system by using a double balance subtraction method;
and step 3: establishing an optical storage charging station capacity optimization configuration model considering battery echelon utilization by taking the maximum annual net income of the optical storage charging station system as an objective function;
and 4, step 4: and (3) implementing optimal configuration of the capacity of the optical storage charging station based on echelon battery utilization by adopting a teaching and learning algorithm (TLBO).
In the step 2, the specific process of estimating the cost of the echelon battery energy storage system is as follows:
① estimating service life n of echelon battery energy storage system2；
In the formula: n is2The service life of the echelon battery energy storage system is prolonged; n is the daily peak load times of the charging load of the electric automobile; x is the number of1、x2The cycle times correspond to the capacity retention rate (75%) of the retired power battery at the beginning of the echelon utilization and the capacity retention rate (65%) at the end of the echelon utilization respectively;
y＝-2.6043×10-5x+0.8340
in the formula: y is a capacity retention ratio; and x is the number of charge and discharge cycles.
② estimating decommissioned power battery capacity unit price C 'according to double balance subtraction'E；
Assuming that the service life of the power battery is M years, the annual depreciation value C of the previous M-1 years of the power batterynComprises the following steps:
Cn＝CE×(1-r)n-1×r
in the formula: cEThe initial capacity unit price of the power battery; r is the annual aging rate; n-1, 2, …, M-2;
estimation of decommissioned power battery capacity unit price C'EComprises the following steps:
wherein N (N is less than or equal to M-2) is the service life of the power battery for the vehicle;
③ estimating initial investment cost C of echelon battery energy storage system2；
C2＝C21+C22
In the formula: c21Cost to purchase retired power cells; c22The cost for screening, testing and recombining the purchased retired power battery;
in the formula: c'EThe capacity unit price of the retired power battery is yuan/(kW & h); n'2The service life of the echelon battery energy storage system is year; ebRated capacity of the echelon battery energy storage system, (kW & h); pbRated power, kW; cpIs the unit price of power; ctThe cost for screening, testing and recombining the unit capacity retired power battery is yuan/(kW.h); l is the yield; and i is the discount rate.
In step 3, the specific process of establishing the optimal configuration model of the capacity of the optical storage charging station considering the echelon utilization of the battery is as follows:
① taking the annual net profit I of the optical storage charging station system to be the maximum objective function;
maxI＝max(I1+I2-C1-C2-C3-C4)
in the formula: i is1Annual electricity sales revenue for charging electric vehicles; i is2Subsidizing the income for the optical storage charging station; c1Initial investment cost for photovoltaic; c2The initial investment cost of the echelon battery energy storage system is obtained in the step 1; c3The photovoltaic annual operation and maintenance cost is reduced; c4The annual operation and maintenance cost of the echelon battery energy storage system is saved;
C3＝kpvCpvP0
C4＝kb(C'EEb+CPPb)
I2＝0.3(C1+C2+C3+C4)
in the formula: p0,CpvRespectively photovoltaic installed power (kW) and photovoltaic power unit price (yuan/kW); n is1The service life of the photovoltaic system; k is a radical ofpvMaintaining a factor for annual operation of the photovoltaic system; k is a radical ofbMaintaining the annual operation coefficient of the echelon battery energy storage system; q. q.s1The charging price is the charging price of the optical storage charging station, and is yuan/(kW & h); q. q.s2Charging and battery replacement service charge for the optical storage charging station, wherein the charge is unit/(kW & h); pload(t) is the charging load, kW, of the electric vehicle of the optical storage charging station; Δ t is the power data sampling interval; i is the discount rate;
② capacity P is configured in photovoltaic0Capacity E of echelon battery energy storage systembFor decision variables, the model constraints are as follows:
1) power balance constraint
Pload(t)＝Ppv(t)+Pb(t)
The system load demand power keeps balance with the sum of the photovoltaic output power and the charging and discharging power of the energy storage system in the period;
2) energy storage state of charge confinement
SOCmin≤SOC(t)≤SOCmax
In the formula: SOCmax、SOCminUpper and lower limits of SOC;
3) photovoltaic output constraint
Ppv(t)≤P0
In the formula: ppv(t) photovoltaic system output power at time t;
4) decision variable constraints
P0≤P0max
Eb≤Ebmax
In the formula: p0maxThe photovoltaic installed capacity is the upper limit and is restricted by the floor area; ebmaxAnd the upper limit of the rated capacity of the energy storage system is the capacity of the energy storage system calculated by the load peak value of the charging load of the electric automobile at each moment when no photovoltaic system exists.
In the step 4, the concrete process of solving the teaching and learning algorithm is as follows:
① initializing optimization parameters, i.e. determining the number of students N in the class and the maximum number of iterations imaxDesigning the variable quantity D and the upper and lower limits of each variable, and determining a fitness function f (x);
② randomly generating a class matrix P according to the number N of students, the number D of design variables and the upper and lower limits of each variable;
③ teaching stage, calculating score of each student, and selecting the best score as teacher XteacherCalculating the average Mean of each column of the class matrix; difference value Difference and
if it is not
If the fitness value is better, the acceptance is carried out, otherwise, the rejection is carried out;
Difference＝ri×[Xteacher-TF×Mean]
in the formula:
and
respectively representing the values before and after learning of the ith student;
is the average of all students; TFi＝round[1+rand(0,1)]As a teaching factor, riRand (0,1) is a random factor;
④ learning phase, randomly selecting student XiAnd students
j 1,2, N, learning each other to obtain
If it is not
If the fitness value is more optimal, learning is carried out to other students, otherwise, refusing, and the formula is expressed as follows:
in the formula: r isiRand (0,1) is a random factor, f (X)j) The fitness value of the jth student;
⑤, judging whether the maximum iteration number is reached, if yes, ending, otherwise, repeating step ③.
Compared with the prior art, the invention has the following beneficial effects: according to the capacity optimization simulation modeling method for the optical storage charging station considering the echelon utilization of the battery, the unit price of the capacity of the retired power battery is estimated by using a double balance decreasing method, so that the reasonable estimation of the initial investment cost of an echelon battery energy storage system is realized, the echelon energy storage is applied to the optical storage charging station, the use value of the power battery is prolonged, and the pressure of large-scale recovery processing of the retired power battery is relieved. The invention can be applied to the capacity configuration of the optical storage charging station.
Drawings
FIG. 1 is a diagram of a light storage and charging station system of an electric vehicle according to the present invention
FIG. 2 is a flow chart of teaching and learning algorithm (TLBO)
Detailed Description
The preferred embodiments are described in detail below. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
Fig. 1 is a structural view of a light storage charging station employed in the present invention. The operation principle of the light storage charging station is that the photovoltaic power generation is used for charging an electric vehicle which is served with priority, and when the generated energy exceeds the charging requirement in the station, surplus generated energy is stored in the echelon battery energy storage system; when the generated energy is insufficient, the echelon battery energy storage system provides the charging electric energy required by the electric automobile. The capacity configuration method for the optical storage charging station provided by the embodiment comprises the following steps:
step 1: collecting relevant data of the retired power battery and the echelon energy storage battery and peak load times of charging load of the electric automobile every day;
step 2: estimating initial investment cost of the echelon battery energy storage system by using a double balance subtraction method;
and step 3: establishing an optical storage charging station capacity optimization configuration model considering battery echelon utilization by taking the maximum annual net income of the optical storage charging station system as an objective function;
and 4, step 4: and (3) implementing optimal configuration of the capacity of the optical storage charging station based on echelon battery utilization by adopting a teaching and learning algorithm (TLBO).
In the step 2, the specific process of estimating the cost of the echelon battery energy storage system is as follows:
① estimating service life n of echelon battery energy storage system2；
In the formula: n is2The service life of the echelon battery energy storage system is prolonged; n is the daily peak load times of the charging load of the electric automobile; x is the number of1、x2The cycle times respectively correspond to the capacity retention rate (75%) of the retired power battery at the beginning of the utilization of the echelon and the capacity retention rate (65%) at the end of the utilization of the echelon；
y＝-2.6043×10-5x+0.8340
In the formula: y is a capacity retention ratio; and x is the number of charge and discharge cycles.
② estimating decommissioned power battery capacity unit price C 'according to double balance subtraction'E；
Assuming that the service life of the power battery is M years, the annual depreciation value C of the previous M-1 years of the power batterynComprises the following steps:
Cn＝CE×(1-r)n-1×r
in the formula: cEThe initial capacity unit price of the power battery; r is the annual aging rate; n-1, 2, …, M-2;
estimation of decommissioned power battery capacity unit price C'EComprises the following steps:
wherein N (N is less than or equal to M-2) is the service life of the power battery for the vehicle;
③ estimating initial investment cost C of echelon battery energy storage system2；
C2＝C21+C22
In the formula: c21Cost to purchase retired power cells; c22The cost for screening, testing and recombining the purchased retired power battery;
in the formula: c'EThe capacity unit price of the retired power battery is yuan/(kW & h); n'2The service life of the echelon battery energy storage system is year; ebRated capacity of the echelon battery energy storage system, (kW & h); pbRated power, kW; cpIs the unit price of power; ctIs taken as a pair unitThe cost for screening, testing and recombining the capacity retired power battery is yuan/(kW.h); l is the yield; and i is the discount rate.
In step 3, the specific process of establishing the optimal configuration model of the capacity of the optical storage charging station considering the echelon utilization of the battery is as follows:
① taking the annual net profit I of the optical storage charging station system to be the maximum objective function;
maxI＝max(I1+I2-C1-C2-C3-C4)
in the formula: i is1Annual electricity sales revenue for charging electric vehicles; i is2Subsidizing the income for the optical storage charging station; c1Initial investment cost for photovoltaic; c2The initial investment cost of the echelon battery energy storage system is obtained in the step 1; c3The photovoltaic annual operation and maintenance cost is reduced; c4The annual operation and maintenance cost of the echelon battery energy storage system is saved;
C3＝kpvCpvP0
C4＝kb(C'EEb+CPPb)
I2＝0.3(C1+C2+C3+C4)
in the formula: p0,CpvRespectively photovoltaic installed power (kW) and photovoltaic power unit price (yuan/kW); n is1The service life of the photovoltaic system; k is a radical ofpvMaintaining a factor for annual operation of the photovoltaic system; k is a radical ofbMaintaining the annual operation coefficient of the echelon battery energy storage system; q. q.s1The charging price is the charging price of the optical storage charging station, and is yuan/(kW & h); q. q.s2Charging and battery replacement service charge for the optical storage charging station, wherein the charge is unit/(kW & h); pload(t) is the charging load, kW, of the electric vehicle of the optical storage charging station; Δ t is between power data samplesSeparating; i is the discount rate;
② capacity P is configured in photovoltaic0Capacity E of echelon battery energy storage systembFor decision variables, the model constraints are as follows:
1) power balance constraint
Pload(t)＝Ppv(t)+Pb(t)
The system load demand power keeps balance with the sum of the photovoltaic output power and the charging and discharging power of the energy storage system in the period;
2) energy storage state of charge confinement
SOCmin≤SOC(t)≤SOCmax
In the formula: SOCmax、SOCminUpper and lower limits of SOC;
3) photovoltaic output constraint
Ppv(t)≤P0
In the formula: ppv(t) photovoltaic system output power at time t;
4) decision variable constraints
P0≤P0max
Eb≤Ebmax
In the formula: p0maxThe photovoltaic installed capacity is the upper limit and is restricted by the floor area; ebmaxAnd the upper limit of the rated capacity of the energy storage system is the capacity of the energy storage system calculated by the load peak value of the charging load of the electric automobile at each moment when no photovoltaic system exists.
In the step 4, the concrete process of solving the teaching and learning algorithm is as follows:
① initializing optimization parameters, i.e. determining the number of students N in the class and the maximum number of iterations imaxDesigning the variable quantity D and the upper and lower limits of each variable, and determining a fitness function f (x);
② randomly generating a class matrix P according to the number N of students, the number D of design variables and the upper and lower limits of each variable;
③ teaching stage, calculating score of each student, and selecting the best score as teacher XteacherCalculating the average Mean of each column of the class matrix; calculating difference DifferScience and
if it is not
If the fitness value is better, the acceptance is carried out, otherwise, the rejection is carried out;
Difference＝ri×[Xteacher-TF×Mean]
in the formula:
and
respectively representing the values before and after learning of the ith student;
is the average of all students; TFi＝round[1+rand(0,1)]As a teaching factor, riRand (0,1) is a random factor;
④ learning phase, randomly selecting student XiAnd students
j 1,2, N, learning each other to obtainIf it is not
If the fitness value is more optimal, learning is carried out to other students, otherwise, refusing, and the formula is expressed as follows:
in the formula: r isi＝rand(0,1) is a random factor, f (X)j) The fitness value of the jth student;
⑤, judging whether the maximum iteration number is reached, if yes, ending, otherwise, repeating step ③.
Claims (4)
1. A light storage charging station capacity optimization simulation modeling method considering battery echelon utilization is characterized by comprising the following steps:
step 1: collecting relevant data of the retired power battery and the echelon energy storage battery and peak load times of charging load of the electric automobile every day;
step 2: estimating initial investment cost of the echelon battery energy storage system by using a double balance subtraction method;
and step 3: establishing an optical storage charging station capacity optimization configuration model considering battery echelon utilization by taking the maximum annual net income of the optical storage charging station system as an objective function;
and 4, step 4: and (3) implementing optimal configuration of the capacity of the optical storage charging station based on echelon battery utilization by adopting a teaching and learning algorithm (TLBO).
2. The method according to claim 1, wherein in step 2, the specific process of estimating the cost of the battery energy storage system in the echelon is as follows:
① estimating service life n of echelon battery energy storage system2；
In the formula: n is2The service life of the echelon battery energy storage system is prolonged; n is the daily peak load times of the charging load of the electric automobile; x is the number of1、x2The cycle times correspond to the capacity retention rate (75%) of the retired power battery at the beginning of the echelon utilization and the capacity retention rate (65%) at the end of the echelon utilization respectively;
y＝-2.6043×10-5x+0.8340
in the formula: y is a capacity retention ratio; and x is the number of charge and discharge cycles.
② estimating decommissioned power battery capacity unit price C 'according to double balance subtraction'E；
Assuming that the service life of the power battery is M years, the annual depreciation value C of the previous M-1 years of the power batterynComprises the following steps:
Cn＝CE×(1-r)n-1×r
in the formula: cEThe initial capacity unit price of the power battery; r is the annual aging rate; n-1, 2, …, M-2;
estimation of decommissioned power battery capacity unit price C'EComprises the following steps:
wherein N (N is less than or equal to M-2) is the service life of the power battery for the vehicle;
③ estimating initial investment cost C of echelon battery energy storage system2；
C2＝C21+C22
In the formula: c21Cost to purchase retired power cells; c22The cost for screening, testing and recombining the purchased retired power battery;
in the formula: c'EThe capacity unit price of the retired power battery is yuan/(kW & h); n'2The service life of the echelon battery energy storage system is year; ebRated capacity of the echelon battery energy storage system, (kW & h); pbRated power, kW; cpIs the unit price of power; ctThe cost for screening, testing and recombining the unit capacity retired power battery is yuan/(kW.h); l is the yield; and i is the discount rate.
3. The method according to claim 1, wherein in step 3, the specific process of establishing the optimal configuration model of the capacity of the optical storage and charging station for considering the battery echelon utilization is as follows:
① taking the annual net profit I of the optical storage charging station system to be the maximum objective function;
maxI＝max(I1+I2-C1-C2-C3-C4)
in the formula: i is1Annual electricity sales revenue for charging electric vehicles; i is2Subsidizing the income for the optical storage charging station; c1Initial investment cost for photovoltaic; c2The initial investment cost of the echelon battery energy storage system is obtained in the step 1; c3The photovoltaic annual operation and maintenance cost is reduced; c4The annual operation and maintenance cost of the echelon battery energy storage system is saved;
C3＝kpvCpvP0
C4＝kb(C'EEb+CPPb)
I2＝0.3(C1+C2+C3+C4)
in the formula: p0,CpvRespectively photovoltaic installed power (kW) and photovoltaic power unit price (yuan/kW); n is1The service life of the photovoltaic system; k is a radical ofpvMaintaining a factor for annual operation of the photovoltaic system; k is a radical ofbMaintaining the annual operation coefficient of the echelon battery energy storage system; q. q.s1The charging price is the charging price of the optical storage charging station, and is yuan/(kW & h); q. q.s2Charging and battery replacement service charge for the optical storage charging station, wherein the charge is unit/(kW & h); pload(t) is the charging load, kW, of the electric vehicle of the optical storage charging station; Δ t is power data acquisitionSample spacing; i is the discount rate;
② capacity P is configured in photovoltaic0Capacity E of echelon battery energy storage systembFor decision variables, the model constraints are as follows:
1) power balance constraint
Pload(t)＝Ppv(t)+Pb(t)
The system load demand power keeps balance with the sum of the photovoltaic output power and the charging and discharging power of the energy storage system in the period;
2) energy storage state of charge confinement
SOCmin≤SOC(t)≤SOCmax
In the formula: SOCmax、SOCminUpper and lower limits of SOC;
3) photovoltaic output constraint
Ppv(t)≤P0
In the formula: ppv(t) photovoltaic system output power at time t;
4) decision variable constraints
P0≤P0max
Eb≤Ebmax
In the formula: p0maxThe photovoltaic installed capacity is the upper limit and is restricted by the floor area; ebmaxAnd the upper limit of the rated capacity of the energy storage system is the capacity of the energy storage system calculated by the load peak value of the charging load of the electric automobile at each moment when no photovoltaic system exists.
4. The method for allocating the capacity of the optical storage and charging station according to claim 1, wherein in the step 4, the specific process of solving the teaching and learning algorithm is as follows:
① initializing optimization parameters, i.e. determining the number of students N in the class and the maximum number of iterations imaxDesigning the variable quantity D and the upper and lower limits of each variable, and determining a fitness function f (x);
② randomly generating a class matrix P according to the number N of students, the number D of design variables and the upper and lower limits of each variable;
③ teaching phase, calculating each student's compositionGet the best achievement as teacher XteacherCalculating the average Mean of each column of the class matrix; difference value Difference and
if it is not
If the fitness value is better, the acceptance is carried out, otherwise, the rejection is carried out;
Difference＝ri×[Xteacher-TF×Mean]
in the formula:
and
respectively representing the values before and after learning of the ith student;
is the average of all students; TFi＝round[1+rand(0,1)]As a teaching factor, riRand (0,1) is a random factor;
④ learning phase, randomly selecting student XiAnd studentsj 1,2, N, learning each other to obtainIf it is not
If the fitness value is more optimal, learning is carried out to other students, otherwise, refusing, and the formula is expressed as follows:
in the formula: r isiRand (0,1) is a random factor, f (X)j) The fitness value of the jth student;
⑤, judging whether the maximum iteration number is reached, if yes, ending, otherwise, repeating step ③.
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