CN111753471B - Optimized operation method of energy storage battery - Google Patents

Optimized operation method of energy storage battery Download PDF

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CN111753471B
CN111753471B CN202010644364.9A CN202010644364A CN111753471B CN 111753471 B CN111753471 B CN 111753471B CN 202010644364 A CN202010644364 A CN 202010644364A CN 111753471 B CN111753471 B CN 111753471B
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energy storage
storage battery
discharge
energy
objective function
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CN111753471A (en
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胡国伟
郭莉
谈健
吴晨
南开辉
牛文娟
陈琛
薛贵元
吴垠
邹盛
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State Grid Jiangsu Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses an optimized operation method of an energy storage battery, which comprises the steps of establishing a multi-objective function comprising a maximum objective function of charge-discharge income of the energy storage battery and a minimum objective function of operation cost of the energy storage battery, setting constraint conditions of the multi-objective function, and solving the multi-objective function according to a particle swarm optimization algorithm to obtain the charge power and the discharge power of the energy storage battery at each time interval.

Description

Optimized operation method of energy storage battery
The technical field is as follows:
the invention belongs to the field of electric power, and particularly relates to an optimal operation method of an energy storage battery.
Background art:
the energy storage battery technology is used as an important branch of electric energy storage, and has the advantages of flexible and convenient configuration, high response speed and suitability for mass production and large-scale application.
The operation cost of the energy storage battery has a great relationship with the operation age of the energy storage battery, the annual operation and maintenance cost of the energy storage battery and the initial investment cost of the energy storage battery. The operation age of the energy storage battery is greatly related to the discharge depth and the ambient temperature of the battery during each charge-discharge cycle, and the temperature control system is arranged on the energy storage battery to control the ambient temperature in a proper range, so that the operation age of the energy storage battery is mainly related to the discharge depth of the battery during operation. Therefore, the method for obtaining the operation plan of the energy storage power station is established by considering the operation cost of the energy storage battery, and is particularly necessary for improving the operation income of the energy storage power station.
The invention content is as follows:
in order to solve the problems, the invention provides an optimized operation method of an energy storage battery, which is suitable for an energy storage power station which operates independently, and the technical scheme is as follows:
an optimized operation method of an energy storage battery comprises the following steps:
1) establishing a multi-objective function of the energy storage battery, wherein the multi-objective function comprises an objective function with the maximum charge-discharge benefit of the energy storage battery and an objective function with the minimum operation cost of the energy storage battery;
the maximum objective function of the charge and discharge benefits of the energy storage battery is as follows:
Figure BDA0002572589230000011
Pch(t)＝E(d(t-1)-d(t))/η1lc(t)
Pdis(t)＝Ed(t)-(d(t-1))η2ld(t)；
the minimum objective function of the operation cost of the energy storage battery is as follows:
Figure BDA0002572589230000012
Figure BDA0002572589230000013
in the above formula, T is the total number of the daily periods; s (t) is the electricity price of the t time period; pch(t) and Pdis(t) charging power and discharging power of the energy storage battery for the t-th time period, respectively; e is the rated capacity of the energy storage battery; d (t) is the discharge depth of the energy storage battery in the t period; d (t-1) is the discharge depth of the energy storage battery in the t-1 th time interval; eta1Charging efficiency coefficients for the energy storage battery; eta2The discharge efficiency coefficient of the energy storage battery is obtained; lc(t) the charging logic variable of the energy storage battery in the tth time interval is taken as 0 or 1; ld(t) a discharge logic variable of the energy storage battery in the tth time period is taken as 0 or 1; ce(t) the operating cost of the energy storage battery for the tth time period; f (d) is the unit charge and discharge cost of the energy storage battery when the discharge depth is d;
2) setting constraint conditions of the multi-objective function, wherein the constraint conditions comprise energy continuity constraint of the energy storage battery, charging and discharging power constraint of the energy storage battery, stored energy constraint of the energy storage battery, charging and discharging logic constraint of the energy storage battery and initial energy constraint before optimization of the energy storage battery;
3) solving the multi-objective function by adopting a particle swarm optimization algorithm to obtain the charging power P of the energy storage battery in each time intervalch(t) and discharge Power Pdis(t)。
Preferably, the energy continuity constraints of the energy storage cell are:
Figure BDA0002572589230000021
the energy storage battery is subjected to charge and discharge power constraints as follows:
0≤pch(t)≤lc(t)Pch,max
0≤pdis(t)≤ld(t)Pdis,max
the energy storage battery stores energy and restricts:
E(t)≤E
the charge and discharge logic constraint of the energy storage battery is as follows:
lc(t)+ld(t)≤1
initial energy constraint before optimization of the energy storage battery:
E(0)＝E0
in the formula, E (t +1) is the energy of the energy storage battery in a period of t; e (t) is the energy of the energy storage battery in the period of t; pch,maxAnd Pdis,maxRespectively the maximum charging power and the maximum discharging power of the energy storage battery; e0Setting the energy value before optimizing the energy storage battery; e (0) is the energy of the energy storage battery before optimization.
Preferably, the cost per charge and discharge amount f (d) of the energy storage battery operation at the discharge depth d is obtained by performing a linear fit according to a least square method to the following formula:
Figure BDA0002572589230000031
in the formula, CmaIs the annual operating maintenance cost of the energy storage battery; cpvIs the initial investment cost of the energy storage battery; r is the discount rate; n is a radical of hydrogendIs the cycle number of the energy storage battery in the full life cycle when the discharge depth is d, Ndμ (d); e is the rated capacity of the energy storage battery; n is the daily charge-discharge cycle number of the energy storage battery,
Figure BDA0002572589230000032
k (t) is a state variable of price change in the t period; s (t) is the electricity price of the t-th period.
Compared with the prior art, the invention has the following beneficial effects:
according to the optimization operation method, the multi-objective function comprising the maximum objective function of the charge and discharge income of the energy storage battery and the minimum objective function of the operation cost of the energy storage battery is established, the constraint condition of the multi-objective function is set, the multi-objective function is solved according to the particle swarm optimization algorithm, and the charge power and the discharge power of the energy storage battery in each time interval are obtained.
According to the optimization operation method, when the operation cost of the energy storage battery is considered, the relation between the discharge depth and the unit charge and discharge cost of the operation of the energy storage battery is obtained through fitting based on the relation between the discharge depth and the cycle number of the full life cycle of the energy storage battery, the method has the advantages of universality and accuracy in technology, and meanwhile, the method for solving the problem by adopting the particle swarm optimization algorithm has the advantages of simplicity in calculation, high convergence speed, good robustness and the like.
Description of the drawings:
FIG. 1 is a flow chart of an optimized operation method in an embodiment;
FIG. 2 is a graph of daily electricity prices in the examples;
FIG. 3 is a schematic diagram illustrating the relationship between the number of cycles of the energy storage battery in the full life cycle and the depth of discharge in the embodiment;
fig. 4 is a schematic view of a charge/discharge operation plan in the embodiment.
The specific implementation mode is as follows:
the invention is described in detail below with reference to the figures and the specific embodiments.
Example (b):
in this embodiment, the optimal operation method of the energy storage battery is implemented on an energy storage power station in a simulation manner, where the rated capacity E of the energy storage battery of the energy storage power station is 10000 kilowatt-hours, and the annual operation and maintenance cost Cma1.8 ten thousand, initial investment cost Cpv1700 ten thousand yuan, 5% investment discount rate and charging efficiency coefficient eta1And discharge efficiency coefficient η295 percent of the total energy, the maximum charge-discharge power of 2500 kilowatts/hour, and the initial energy given value E before the optimization of the energy storage battery0At 0 kilowatt hour, the daily load curve is divided into 24 time segments at hourly intervals according to the actual operation requirement of the system, i.e., T is 24, and the electricity prices of the time segments are shown in FIG. 2 and Table 1The relationship between the cycle number of the energy storage battery in the full life cycle and the depth of discharge is given by a manufacturer, as shown in fig. 3:
TABLE 1 electric rate table for each time period
Figure BDA0002572589230000041
As shown in fig. 1, the optimized operation method is as follows:
1) establishing a multi-objective function of the energy storage battery, wherein the multi-objective function comprises an objective function with the maximum charge-discharge benefit of the energy storage battery and an objective function with the minimum operation cost of the energy storage battery; wherein, the first and the second end of the pipe are connected with each other,
the maximum objective function of the charge and discharge benefits of the energy storage battery is as follows:
Figure BDA0002572589230000042
Pch(t)＝10000(d(t-1)-d(t))/0.95lc(t)
Pdis(t)＝10000(d(t)-d(t-1))*0.95ld(t)；
the minimum objective function of the operating cost of the energy storage battery is as follows:
Figure BDA0002572589230000043
Figure BDA0002572589230000044
in the above formula, s (t) is the electricity price of the t-th time period; pch(t) and Pdis(t) charging power and discharging power of the energy storage battery at the tth time interval respectively; e is the rated capacity of the energy storage battery; d (t) is the discharge depth of the energy storage battery in the t period; d (t-1) is the discharge depth of the energy storage battery in the t-1 th time period; eta1Charging efficiency coefficients for the energy storage battery; eta2The discharge efficiency coefficient of the energy storage battery is obtained; lc(t) taking the charging logic variable of the energy storage battery in the tth time periodA value of 0 or 1; ld(t) a discharge logic variable of the energy storage battery in the tth time period is taken as 0 or 1; cE(t) is the operating cost of the energy storage battery at the tth time period; f (d) is the unit charge-discharge cost of the energy storage battery when the discharge depth is d; ABS means absolute value.
The unit charge-discharge capacity cost F (d) of the energy storage battery operation when the discharge depth is d is determined according to the following formula:
Figure BDA0002572589230000051
in the formula, CAPVInvesting in energy storage batteries and annual operating and maintaining capital values; y isd,nIs the operating life of the energy storage battery; qd,nThe total charge and discharge capacity of the energy storage battery in the whole life cycle;
in this embodiment, the annual capital investment and operation and maintenance value C of the energy storage batteryAPVDetermined as follows:
Figure BDA0002572589230000052
in this embodiment, the operating life of the energy storage battery is determined according to the following formula:
Yd,n＝Nd/(365n)
in this embodiment, the total charge/discharge capacity Q of the energy storage battery in the whole life cycled,nDetermined as follows:
Qd,n＝20000Ndd
in the above formula, n is the daily charge-discharge cycle number of the energy storage power station
Figure BDA0002572589230000053
Figure BDA0002572589230000054
Wherein k (t) is a state variable of the change of the electricity price in the t-th time period(ii) a s (t) is the electricity price of the t time period; the electricity price change state variable table is based on the following, in the embodiment
Figure BDA0002572589230000055
TABLE 2 VARIABLE STATE VARIABLE METER OF ELECTRICITY VALUE
t 1 2 3 4 5 6 7 8 9 10 11 12
k(t) 1 1 1 1 1 1 1 1 1 1 1 0
t 13 14 15 16 17 18 19 20 21 22 23 24
k(t) 1 1 1 1 1 1 1 1 0 1 1 -
The cost per charge-discharge capacity of the energy storage battery when the discharge depth is d F (d)
Figure BDA0002572589230000056
Fitting the relation schematic diagram of the cycle times and the depth of discharge of the energy storage battery in the full life cycle provided by the manufacturer to obtain the functional relation N between the cycle times and the depth of discharge of the energy storage battery in the full life cycledWhen mu (d) is combined with the above formula, Table 3 can be obtained
TABLE 3
Figure BDA0002572589230000061
The method adopts a least square method to carry out linear fitting on the data on the table, and utilizes the conventional Matlab mathematical tool to calculate a command polyfit by the least square method to directly calculate linear fitting parameters. Matlab program commands are as follows:
x＝[0.5,0.6,0.7,0.8,0.9]；
y＝[0.2986,0.326,0.3523,0.3757,0.3954]；
p＝polyfit(x,y,1)
the calculation result is as follows:
P＝0.2433 0.1793
so f (d) 0.2433d + 0.1793.
The minimum objective function of the operating cost of the energy storage battery is as follows:
Figure BDA0002572589230000062
2) setting constraint conditions of the multi-objective function, wherein the constraint conditions comprise energy continuity constraint of the energy storage battery, charging and discharging power constraint of the energy storage battery, energy storage constraint of the energy storage battery, charging and discharging logic constraint of the energy storage battery and initial energy constraint of the energy storage battery before optimization;
wherein, the energy continuity constraint of the energy storage battery is as follows:
Figure BDA0002572589230000063
charge and discharge power constraint:
0≤pch(t)≤2500lc(t)；
0≤pdis(t)≤2500ld(t)；
energy storage battery stored energy restraint:
E(t)≤10000；
and (3) charge and discharge logic constraint:
lc(t)+ld(t)≤1；
initial energy constraint before optimization of the energy storage battery:
E(0)＝0；
in the formula, E (t +1) is the energy of the energy storage battery in the period of t; e (t) is the energy of the energy storage battery in the period of t; pch,maxAnd Pdis,maxRespectively the maximum charging power and the maximum discharging power of the energy storage battery; e (0) is the energy of the energy storage battery before optimization.
3) Solving the multi-objective function by adopting a particle swarm optimization algorithm to obtain the charging power P of the energy storage battery in each time intervalch(t) and discharge Power Pdis(t), the specific method of the step is as follows:
the energy storage power station optimized operation model considering the service life of the battery is a mixed integer optimization model, and the objective function has the non-convex characteristic and is complex to solve. The solving method of academic research comprises artificial intelligent algorithms such as a heuristic method, a dynamic programming method, a branch and bound method, a genetic algorithm, a particle swarm algorithm and the like. The particle swarm optimization algorithm is adopted to solve the following steps:
3.1) position vector of particle i:
xi＝[xi1,xi2,xij…,xi24]。
xithe discharge of each time interval needs to be solved in the corresponding modelDepth variables [ d (1), d (2), d (t), …, d (24)]。
Two other sets of variables p in the modelch(t) and pdis(t) may be represented by d (t).
pch(t)＝10000/0.95(d(t-1)-d(t)) if d(t-1)≥d(t)
pdis(t)＝0.95×10000(d(t)-d(t-1)) if d(t)＞d(t-1)
3.2) velocity vector of particle i
vi＝[vi1,vi2,vij,…vi24]。
The velocity vector is used to update the position vector in the particle optimization process, i.e. the value of the variable d (t).
3.3) velocity update formula of particles
Figure BDA0002572589230000071
Figure BDA0002572589230000072
3.4) position update formula of particles
Figure BDA0002572589230000081
Figure BDA0002572589230000082
3.5) formula for updating inertial weight of particle
Figure BDA0002572589230000083
3.6) evaluation of the objective function
From x obtained aboveiThe value can be found as pch(t) and pdis(t) value, so that the objective function can be calculated
Figure BDA0002572589230000084
Value of (2) and objective function
Figure BDA0002572589230000085
Figure BDA0002572589230000086
The difference between the two is FHi,k. Namely, it is
Figure BDA0002572589230000087
Figure BDA0002572589230000088
From p obtainedch(t) and pdis(t) value, checking the constraints in the model, and FH if any one of the constraints is not satisfiedi,k＝FHi,k-10000。
3.7) obtaining the optimal objective function value and optimizing the operation mode of the energy storage battery
When x is formed by initialization of 3.1) and 3.2)iAnd viAnd then, repeatedly and circularly iterating the processes from 3.3) to 3.6) until the maximum iteration time is met, so that FH (frequency hopping) is realizedi,kThe particles with the maximum value are the optimal particles, so that p corresponding to the optimal operation mode of the corresponding energy storage battery can be obtainedch(t) and pdis(t) value.
The parameters of the above formulae are illustrated below:
r represents a real number set, Z represents an integer set, P is the size of a particle population, and K is the maximum iteration number;
Figure BDA0002572589230000089
and
Figure BDA00025725892300000810
respectively is a speed value and a position value of the ith particle after the jth dimension and the kth iteration;
Pbesti,jfor the ith particleAn optimal value of the dimension;
Gbestjthe optimal value of the j dimension of the particle is obtained;
c1,c2,ω,ωmax,ωminrespectively is a learning factor, a current inertia weight, a maximum value of the inertia weight and a minimum value of the inertia weight;
iteru,itermaxrespectively the current iteration times and the maximum iteration times;
rand () is a random number uniformly distributed between 0 and 1;
FHi,kand obtaining the difference value between the target function value of the charging and discharging income of the energy storage battery and the target function value of the operation cost of the energy storage battery for the kth iteration of the ith particle.
The values of the parameters involved in the above algorithm are as follows:
the population size of the particles was taken to be 30. The maximum number of iterations is taken to be 20. The learning factor is 0.5. The inertia weight maximum is 1.2. The inertial weight minimum is 0.2.
Solving a multi-objective function according to the particle swarm algorithm to obtain the charging power P of the energy storage battery in each time intervalch(t) and discharge Power PdisAnd (t) according to the charging and discharging optimization operation plan of the energy storage battery in the energy storage power station, wherein the result is shown in fig. 4, a positive power value in fig. 4 represents charging power, and a negative power value represents discharging power. The original charging and discharging operation plan is obtained by the existing mode of discharging when the electricity price is high and charging when the electricity price is low, the daily economic benefit of the energy storage power station is 10788 yuan, but after the operation cost of the energy storage battery is considered, the daily comprehensive economic benefit of the energy storage power station is-1248 yuan, the original operation plan arrangement of the energy storage power station can be seen, the comprehensive economic benefit is not good, and by adopting the optimized operation method, the daily comprehensive economic benefit obtained by the energy storage power station is 527 yuan which is improved by 1775 yuan compared with the original operation plan, and the high comprehensive economic benefit is achieved.

Claims (3)

1. An optimal operation method of an energy storage battery is characterized in that: the method comprises the following steps:
1) establishing a multi-objective function of the energy storage battery, wherein the multi-objective function comprises an objective function with the maximum charge-discharge benefit of the energy storage battery and an objective function with the minimum operation cost of the energy storage battery;
the maximum objective function of the charge and discharge benefits of the energy storage battery is as follows:
Figure FDA0002572589220000011
Pch(t)＝E(d(t-1)-d(t))/η1lc(t)
Pdis(t)＝E(d(t)-d(t-1))η2ld(t)；
the minimum objective function of the operation cost of the energy storage battery is as follows:
Figure FDA0002572589220000012
Figure FDA0002572589220000013
in the above formula, T is the total number of the daily periods; s (t) is the electricity price of the t time period; pch(t) and Pdis(t) charging power and discharging power of the energy storage battery at the tth time interval respectively; e is the rated capacity of the energy storage battery; d (t) is the discharge depth of the energy storage battery in the t period; d (t-1) is the discharge depth of the energy storage battery in the t-1 th time period; eta1Charging efficiency coefficients for the energy storage battery; eta2The discharge efficiency coefficient of the energy storage battery is obtained; lc(t) the charging logic variable of the energy storage battery in the tth time interval is taken as 0 or 1; ld(t) the discharge logic variable of the energy storage battery in the tth time interval is taken as 0 or 1; ce(t) is the operating cost of the energy storage battery at the tth time period; f (d) is the unit charge and discharge cost of the energy storage battery when the discharge depth is d;
2) setting constraint conditions of the multi-objective function, wherein the constraint conditions comprise energy continuity constraint of the energy storage battery, charging and discharging power constraint of the energy storage battery, energy storage constraint of the energy storage battery, charging and discharging logic constraint of the energy storage battery and initial energy constraint of the energy storage battery before optimization;
3) solving the multi-objective function by adopting a particle swarm optimization algorithm to obtain the charging power P of the energy storage battery in each time intervalch(t) and discharge Power Pdis(t)。
2. The method for optimizing the operation of an energy storage battery according to claim 1, characterized in that:
the energy continuity constraint of the energy storage battery is as follows:
Figure FDA0002572589220000014
the energy storage battery is characterized in that the charging and discharging power constraint is as follows:
0≤pch(t)≤lc(t)Pch，max
0≤pdis(t)≤ld(t)Pdis，max
the energy storage battery stores energy and restricts as follows:
E(t)≤E
the charge and discharge logic constraint of the energy storage battery is as follows:
lc(t)+ld(t)≤1
initial energy constraint before optimization of the energy storage battery:
E(0)＝E0
in the formula, E (t +1) is the energy of the energy storage battery in the time period of t + 1; e (t) is the energy of the energy storage battery in the period of t; pch，maxAnd Pdis，maxRespectively the maximum charging power and the maximum discharging power of the energy storage battery; e0Setting the energy value before optimizing the energy storage battery; e (0) is the energy of the energy storage battery before optimization.
3. The method for optimizing the operation of an energy storage battery according to claim 2, characterized in that: and when the discharge depth is d, the unit charge-discharge capacity cost F (d) of the energy storage battery operation is obtained by performing linear fitting on the following formula according to a least square method:
Figure FDA0002572589220000021
in the formula, CmaIs the annual operating maintenance cost of the energy storage battery; cpvIs the initial investment cost of the energy storage battery; r is the discount rate; n is a radical ofdIs the cycle number of the energy storage battery in the full life cycle when the discharge depth is d, Ndμ (d); e is the rated capacity of the energy storage battery; n is the daily charge-discharge cycle number of the energy storage battery,
Figure FDA0002572589220000022
k (t) is a state variable of price change in the t period; s (t) is the electricity price for the t-th time period.
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