CN110867852A - Microgrid energy storage optimization configuration method and device considering whole life cycle cost - Google Patents

Microgrid energy storage optimization configuration method and device considering whole life cycle cost Download PDF

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CN110867852A
CN110867852A CN201911165802.7A CN201911165802A CN110867852A CN 110867852 A CN110867852 A CN 110867852A CN 201911165802 A CN201911165802 A CN 201911165802A CN 110867852 A CN110867852 A CN 110867852A
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energy storage
optimization model
storage system
cost
layer optimization
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CN110867852B (en
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李涛
许苑
陈健
王珂
陈坤
岑海凤
林琳
徐辉
孙开元
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Guangzhou Power Supply Bureau of Guangdong Power Grid 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • 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

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Abstract

The application relates to a micro-grid energy storage optimal configuration method and device considering life cycle cost. The microgrid energy storage optimization configuration method considering the life cycle cost comprises the following steps: acquiring data of an energy storage system to be configured to obtain target data; acquiring a pre-established energy storage optimization model, wherein the energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model; according to the target data, solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm to obtain an optimal configuration result, wherein the optimal configuration result at least comprises the rated power, the rated capacity and the economic operation power; and configuring the energy storage system to be configured according to the optimized configuration result. The method provided by the application can be used for minimizing the cost of the energy storage system in the whole period and maximizing the benefit.

Description

Microgrid energy storage optimization configuration method and device considering whole life cycle cost
Technical Field
The application relates to the technical field of microgrid energy storage, in particular to a microgrid energy storage optimal configuration method and device considering life cycle cost.
Background
With the increasing prominence of energy crisis and environmental pollution problems, grid-connected generation (DG) is an important research direction in the power industry of the 21 st century. The distributed power generation is connected to a power grid in a large scale, and a series of influences are generated on the voltage, the power quality, the dispatching and the operation of the power grid. An Energy Storage System (ESS) is an important component of a microgrid, and can effectively solve the problems and maintain the stability of the power grid.
Currently, the major obstacles to the large-scale application of energy storage systems are the relatively low service life and high cost. Therefore, optimal configuration of ESS capabilities has attracted a wide range of attention worldwide.
Research and development personnel carry out a great deal of research work on the energy storage optimization configuration method of the micro-grid, and research on energy storage planning mainly focuses on two aspects: reducing costs and maximizing revenue. However, in the conventional technology, there are few researches on the two problems of cost reduction and achievement of maximum profit in a full cycle.
Disclosure of Invention
In view of the above, there is a need to provide a method, an apparatus, a computer device and a readable storage medium for optimizing configuration of microgrid energy storage, which take into account life cycle costs.
A microgrid energy storage optimization configuration method taking full life cycle costs into account, the method comprising:
acquiring data of an energy storage system to be configured to obtain target data;
the method comprises the steps of obtaining a pre-established energy storage optimization model, wherein the energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, an objective function of the outer layer optimization model is used for outputting rated capacity and full life cycle cost of an energy storage system according to the inner layer optimization model, improving voltage distribution of the energy storage system by optimizing economic operation power of the energy storage system and minimizing the annual average net cost of the energy storage system, and an objective function of the inner layer optimization model is used for optimizing the capacity of the energy storage system according to the economic operation power and the rated power of the energy storage system output by the outer layer optimization model and by minimizing the full life cycle cost;
according to the target data, solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm to obtain an optimal configuration result, wherein the optimal configuration result at least comprises the rated power, the rated capacity and the economic operation power;
and configuring the energy storage system to be configured according to the optimized configuration result.
In one embodiment, the objective function of the inner layer optimization model is specifically used for:
determining the minimum configuration capacity of the energy storage system according to the economic operation power and the rated power;
expanding the minimum configuration capacity to obtain the capacity of the energy storage system;
determining a minimized full life cycle cost of the energy storage system corresponding to a capacity of the energy storage system.
In one embodiment, the process of establishing the outer layer optimization model and the inner layer optimization model includes:
determining an energy storage life cycle cost model, a profit model and a battery model of the energy storage system;
establishing an objective function of the outer-layer optimization model and an objective function of the inner-layer optimization model according to the energy storage life cycle cost model and the profit model;
and respectively determining an objective function of the outer layer optimization model and a constraint condition corresponding to the inner layer optimization model according to the battery model to obtain the constraint condition of the outer layer optimization model and the constraint condition of the inner layer optimization model.
In one embodiment, the objective function of the outer optimization model is:
f1＝min(cost-profit+punishvalue)；
wherein f1 represents the objective function of the outer optimization model, cost represents the life-cycle cost model, and cost is C1+C2+C3+C4-C5Profit stands for yield model, profit ═ I1+I2+I3Punishvalue stands for a penalty value, C1Representing the initial investment cost, C2Representing replacement costs, C3Representing the operation and maintenance cost, C4Representing the cost of the treatment, C5Represents the recovery value, I1Representing the profit of energy storage, I2Representing a return on government incentives, I3Representing the benefits of the associated environment.
In one embodiment, the constraint conditions of the outer layer optimization model include energy storage system constraint conditions and grid system constraint conditions, where the energy storage system optimization constraint conditions are:
Figure BDA0002287417850000031
ηDfor discharge power, ηCFor charging power, η is the overall charge-discharge efficiency, SOCmaxAt the upper limit of the state of charge, SOCminSOC (t) is the state of charge at time t;
the power grid system constraint conditions are as follows:
Figure BDA0002287417850000032
PDG,PS,PESS,PL,PLOSSrespectively active power of distributed power supply, main power networkActive power, active power of an energy storage system, active power of a load, and active power of line loss; pijminAnd PijmaxRespectively, the limit condition, V, for the transmission active power of the line ijmaxand VminRespectively, the voltage amplitude constraint, PijAnd ViThe actual active power on line ij and the actual voltage at node i, respectively.
In one embodiment, the objective function of the inner layer optimization model is: f2 min cos t, where f2 represents the objective function of the inner layer optimization model, cost represents the life-cycle cost model, and cost C1+C2+C3+C4-C5，C1Representing the initial investment cost, C2Representing replacement costs, C3Representing the operation and maintenance cost, C4Representing the cost of the treatment, C5Represents the recovery value.
In one embodiment, the constraint condition of the inner layer optimization model is SOCmin≤SOC(t)≤SOCmaxWherein, SOCmaxAt the upper limit of the state of charge, SOCminSOC (t), which is the lower limit of the state of charge, is the state of charge at time t.
In one embodiment, the obtaining an optimal configuration result by solving an optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm according to the target data includes:
according to the target data, encoding the output power of the energy storage system to be configured every hour by adopting a genetic algorithm, and initializing a population;
carrying out load flow calculation on each individual of the population according to the target data to obtain a penalty value of each individual due to an unsolved voltage problem;
if the penalty value is zero, optimizing the output power of the energy storage system to be configured represented by the individual in each hour in one day so as to enable the charging and discharging amounts of the energy storage system to be configured to be equal in one charging and discharging period;
calculating the profit according to the profit model, and determining the rated power;
calculating the rated capacity and the full-period cost of the energy storage system to be configured through the inner layer optimization model, and outputting the rated capacity and the full-period cost to the outer layer optimization model;
if the punishment value is not zero, setting each income and the whole life cycle cost of the energy storage system to be configured represented by the individual to be zero;
determining a fitness function of the genetic algorithm according to the penalty value, the rated capacity and the life cycle cost, and calculating the fitness of each individual in the genetic algorithm according to the fitness function;
screening individuals with higher fitness through a roulette method, and reserving the individuals with the highest fitness to update the population to obtain an outer new population;
carrying out simulated annealing operation on the outer layer new population to obtain an outer layer annealing new population;
performing crossing and mutation operations on the outer annealing new population, and judging whether the last generation is performed;
if the last generation is not executed, returning to the execution step, and performing load flow calculation on each individual of the population according to the target data to obtain a penalty value of each individual due to the unsolved voltage problem;
and if the last generation is executed, sequentially outputting the rated capacity and the rated power with the minimum running cost in consideration of the income.
In one embodiment, the obtaining of the rated capacity and the full-cycle cost of the energy storage system to be configured through calculation of the inner layer optimization model includes:
generating an initial population;
calculating the charge state, the battery life and the whole-cycle life cost of the energy storage system to be configured according to the output power value and the rated power of the energy storage system to be configured in each time period, which are output by the outer layer optimization model;
according to the target function of the inner layer optimization model, a genetic algorithm fitness function is selected, and the fitness of each individual in the genetic algorithm is calculated;
screening individuals with higher fitness through a roulette method, and reserving the individuals with the highest fitness to update the population to obtain an inner-layer new population;
carrying out simulated annealing operation on the inner layer new population to obtain an inner layer annealing population;
performing crossing and mutation operations on the inner layer annealing population, and judging whether the last generation is performed;
if the last generation is not executed, returning to the step of executing, and calculating the state of charge, the battery life and the full-cycle life cost of the energy storage system to be configured according to the output power value and the rated power of the energy storage system to be configured in each time period output by the outer layer optimization model;
and if the last generation is executed, outputting the rated capacity and the full-cycle life cost of the energy storage system to be configured.
A microgrid energy storage optimization configuration apparatus that accounts for full life cycle costs, the apparatus comprising:
the target data acquisition module is used for acquiring data of the energy storage system to be configured to obtain target data;
the energy storage optimization model obtaining module is used for obtaining a pre-established energy storage optimization model, wherein the energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, an objective function of the outer layer optimization model is used for outputting rated capacity and full life cycle cost of an energy storage system according to the inner layer optimization model, improving voltage distribution of the energy storage system by optimizing economic operation power of the energy storage system and minimizing the annual net cost of the energy storage system, and an objective function of the inner layer optimization model is used for optimizing the capacity of the energy storage system according to the economic operation power and the rated power of the energy storage system output by the outer layer optimization model and minimizing the full life cycle cost;
the solving module is used for solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm according to the target data to obtain an optimized configuration result, and the optimized configuration result at least comprises the rated power, the rated capacity and the economic operation power;
and the configuration module is used for configuring the energy storage system to be configured according to the optimized configuration result.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as claimed in any one of the above when the computer program is executed.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of the preceding claims.
According to the microgrid energy storage optimization configuration method and device, the computer equipment and the readable storage medium, which are provided by the embodiment of the application and take the life cycle cost into consideration, the optimal solution of the energy storage optimization model is solved according to the target data to obtain the optimization configuration result, and the energy storage system to be configured is configured according to the optimization configuration result. The energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, the objective function of the outer layer optimization model aims to improve the voltage distribution of the energy storage system by optimizing the economic operation power of the energy storage system according to the rated capacity and the full life cycle cost output by the inner layer optimization model, and the annual average net cost of the energy storage system is minimized. The objective function of the inner optimization model aims to optimize the capacity of the energy storage system by minimizing the life cycle cost according to the economic operating power and rated power output by the outer optimization model. The inner layer optimization model and the outer layer optimization model are restricted with each other, so that the optimal solution of the inner layer optimization model and the outer layer optimization model is solved, and the energy storage capacity configuration result with the minimum whole period cost can be obtained when the voltage distribution and the annual average income of the energy storage system are simultaneously improved. According to the method provided by the embodiment of the application, the minimum full-period cost output by the inner-layer optimization model is input into the outer-layer optimization model, so that the cost minimization and the benefit maximization in the full period of the energy storage system are realized.
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Fig. 1 is an application environment diagram of a microgrid energy storage optimization configuration method considering life cycle cost according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a microgrid energy storage optimization configuration method considering a life cycle cost according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a microgrid energy storage optimization configuration method considering a life cycle cost according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a microgrid energy storage optimization configuration method considering a life cycle cost according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a microgrid energy storage optimization configuration method considering a life cycle cost according to an embodiment of the present application;
fig. 6 is a schematic flowchart of a microgrid energy storage optimization configuration method considering a life cycle cost according to an embodiment of the present application;
fig. 7 is a block diagram of a high-coupling reactor modeling apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, the microgrid energy storage optimization configuration method considering the life cycle cost may be applied to a computer device, and an internal structure diagram of the computer device may be as shown in fig. 1. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a microgrid energy storage optimization configuration method that accounts for full life cycle costs.
Referring to fig. 2, an embodiment of the present application provides a microgrid energy storage optimization configuration method for accounting for life cycle cost, including the following steps:
and S10, acquiring data of the energy storage system to be configured to obtain target data.
The energy storage system to be configured refers to a micro-grid energy storage system needing to be optimally configured. The target data refers to data information in the microgrid related to the optimized configuration, and includes, but is not limited to, a unit capacity price, a maintenance cost, a recovery rate of the energy storage system, position information of a distributed power bus in the microgrid, the energy storage system and a load point, parameters of each element in the microgrid system, and the like.
And S20, obtaining a pre-established energy storage optimization model, wherein the energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, an objective function of the outer layer optimization model is used for guiding the economic operation of the energy storage system according to the rated capacity and the full life cycle cost of the energy storage system output by the inner layer optimization model, the voltage distribution of the energy storage system is improved by optimizing the economic operation power of the energy storage system, the annual net cost of the energy storage system is minimized, and an objective function of the inner layer optimization model is used for optimizing the capacity of the energy storage system according to the economic operation power and the rated power of the energy storage system output by the outer layer optimization model and the full life cycle cost.
The energy storage optimization model can be pre-established and stored, and can be called as required. The energy storage optimization model comprises an inner layer optimization model and an outer layer optimization model. The inner layer optimization model and the outer layer optimization model both comprise an objective function and a constraint condition. The outer layer optimization model is used for optimizing the operating power of the energy storage system, and the inner layer optimization model is used for optimizing the capacity of the energy storage system. And the outer optimization model outputs the economic operation power and rated power of the energy storage system. The inner layer optimization model outputs the rated capacity and the full life cycle cost of the energy storage system. The outer layer optimization model inputs the output economic operation power and rated power into the inner layer optimization model, and the inner layer model outputs the output rated capacity and annual life cycle cost to the outer layer optimization model. The inner layer optimization model and the outer layer optimization model are restricted with each other, so that the voltage distribution of the energy storage system can be improved, and the cost of the whole period can be minimized.
And S30, solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm according to the target data to obtain an optimal configuration result, wherein the optimal configuration result at least comprises rated power, rated capacity and economic operation power.
And substituting target data of the energy storage system to be configured into the outer layer optimization model and the inner layer optimization model, and solving the outer layer optimization model and the inner layer optimization model based on a genetic algorithm and a simulated degradation algorithm to obtain rated power, rated capacity, economic operation power and the like. Because the outer layer optimization model inputs the output economic operation power and rated power into the inner layer optimization model, the inner layer model outputs the output rated capacity and annual full life cycle cost to the outer layer optimization model, and the inner layer optimization model and the outer layer optimization model are restricted with each other, the optimal solution of the inner layer optimization model and the outer layer optimization model is solved, and the energy storage capacity configuration result with the minimum full cycle cost when the voltage distribution and the annual average income of the energy storage system are simultaneously improved can be obtained.
And S40, configuring the energy storage system to be configured according to the optimized configuration result.
And configuring the energy storage system to be configured according to the obtained parameters such as rated power, rated capacity, economic operation power and the like, so that the cost of the energy storage system to be configured in the whole period is minimum, and the benefit is maximum.
In this embodiment, according to the target data, the optimal solution of the energy storage optimization model is solved to obtain an optimized configuration result, and the energy storage system to be configured is configured according to the optimized configuration result. The energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, the objective function of the outer layer optimization model aims to improve the voltage distribution of the energy storage system by optimizing the economic operation power of the energy storage system according to the rated capacity and the full life cycle cost output by the inner layer optimization model, and the annual average net cost of the energy storage system is minimized. The objective function of the inner optimization model aims to optimize the capacity of the energy storage system by minimizing the life cycle cost according to the economic operating power and rated power output by the outer optimization model. The inner layer optimization model and the outer layer optimization model are restricted with each other, so that the optimal solution of the inner layer optimization model and the outer layer optimization model is solved, and the energy storage capacity configuration result with the minimum whole period cost can be obtained when the voltage distribution and the annual average income of the energy storage system are simultaneously improved. According to the method provided by the embodiment of the application, the minimum full-period cost output by the inner-layer optimization model is input into the outer-layer optimization model, so that the cost minimization and the benefit maximization in the full period of the energy storage system are realized.
Referring to fig. 3, the present embodiment relates to a specific optimization process of an objective function of an inner-layer optimization model, where the objective function of the inner-layer optimization model is specifically used for:
s210, determining the minimum configuration capacity of the energy storage system according to the economic operation power and the rated power;
s220, expanding the minimum configuration capacity to obtain the capacity of the energy storage system;
and S230, determining the minimized full life cycle cost of the energy storage system corresponding to the configuration capacity.
The inner-layer optimization model determines a minimum configuration capacity based on the economic operation power and the rated power input by the outer-layer optimization model, and expands the capacity according to a preset rule. For example, the minimum configuration capacity is expanded by 1.2 times, resulting in the capacity of the energy storage system. Further, a minimum life cycle cost of the energy storage system at this capacity is determined.
Referring to fig. 4, this embodiment relates to a possible implementation manner of the process for establishing the outer layer optimization model and the inner layer optimization model, and the process for establishing the outer layer optimization model and the inner layer optimization model may include the following steps:
s201, determining an energy storage life cycle cost model, a profit model and a battery model of an energy storage system;
s202, establishing an objective function of an outer-layer optimization model and an objective function of an inner-layer optimization model according to the energy storage life cycle cost model and the profit model;
and S203, respectively determining the objective function of the outer layer optimization model and the constraint condition corresponding to the inner layer optimization model according to the battery model to obtain the constraint condition of the outer layer optimization model and the constraint condition of the inner layer optimization model.
Specifically, each model, objective function, and constraint condition are as follows:
1) the energy storage life cycle cost model, i.e. the energy storage cost function, is as follows:
cost＝C1+C2+C3+C4-C5(1)
Figure BDA0002287417850000111
Figure BDA0002287417850000112
C3＝cfPrate(4)
Figure BDA0002287417850000113
C5＝cres(C1+C2) (6)
formula (1), C1Representing the initial investment cost, C can be obtained using the formula (2)1Wherein c isE(￥/kW.h) represents the price per unit capacity of the energy storage system, cP(￥/kW) represents the price per unit power of the energy storage system, cB(￥/kW.h) represents the kW.h price of the subsidiary facility unit, ErateAnd PrateRespectively representing the rated capacity and the rated power of the energy storage system, wherein Y is the project period (year) and sigma is the conversion rate (%).
C2Representing the replacement cost, k in equation (3) is the total number of times of battery replacement ((rounded up, (k ═ Y/n-1)), n is the battery life, epsilon is the replacement sequence, β is the average annual rate of reduction in the investment cost of the energy storage system, and the life of the energy storage converter can be considered to be 10 years fixed.
C3Representing operation and maintenance costs, by labor costs and managementCost component, related to rated power, c in formula (4)fMaintenance costs for every kilowatt of operation;
C4representing the cost of the treatment, c in formula (5)d(￥/kW) represents a specific treatment cost for the energy storage system;
C5represents a recovery value, c in formula (6)resIs a recovery rate, which can be 3% to 5%.
2) The revenue model, i.e., the revenue function, is as follows:
profit＝I1+I2+I3(7)
Figure BDA0002287417850000121
Figure BDA0002287417850000122
Figure BDA0002287417850000123
formula (7), I1Representing the profit of energy storage and arbitrage, A1Earnings for energy storage in one day, y is number of years, D is number of days of energy storage system operation in one year, Cc(t) is the electricity price at time t, Pdis(t) is the discharge capacity at time t, Pch(t) is the amount of charge at time t, μchAnd mudisFor charge-discharge indication of the energy storage system, mu, when the energy storage system is chargedchIs 1 and when the energy storage system is discharged, mudisIs 1.
I2Representing a return on government incentives, A2Is a daily subsidy of electricity prices, Ce,FTTIs a power price subsidy;
I3representing benefits of the associated environment, Cau(￥/(MW h)) is the emission cost of unit energy produced by thermal power generating units, Es,outThe unit is MW · h for the discharge capacity of the energy storage system participating in the auxiliary service.
3) The cell model is as follows:
ηDand ηCRespectively, the discharge power and the charge power of the battery, η is the overall charge-discharge efficiency, SOC, of the batterymaxAnd SOCminUpper and lower limits of state of charge, respectively, and soc (t) is the state of charge at time t. The SOC (t) calculation method is shown as a formula (12), and the initial state of the SOC is shown as a formula (13);
Figure BDA0002287417850000131
Figure BDA0002287417850000132
e (t) is the energy fluctuation at time t relative to the initial state of the energy storage system, and equation (14) can be obtained, where E (0) is 0.
Figure BDA0002287417850000133
Battery life is generally defined as the cycle life corresponding to degradation to 80% of rated capacity. The capacity of the battery is decreased mainly due to the decrease in the solution concentration and the increase in the internal resistance of the battery, which are related to the charge and discharge power, the depth of discharge, the SOC fluctuation, the number of cycles, and the operating temperature of the battery. The remaining value of the lithium ion battery can be determined by the state of health (SOH), which changes from 1 to 0 when the capacity decreases from the rated capacity to 80%, see equation (17). Capacity fade rate and average SOC (SOC)avg,m) And deviation from SOC (SOC)dev,m) Related to, SOCavg,mOr SOCdev,mThe higher the capacity fading rate, the higher the capacity fading exponential see equation (15).
Figure BDA0002287417850000134
The parameter gamma can be selected as x1is-4.092X 10-4,x2Is-2.167, x3Is-1.408 x 10-5,x46.130, Ea 78.06kmol/J, R8.314J/(mol x K), sampling period m 24 hr, tausumAs total time, Ahm、Tm、SOCavg,mAnd SOCdev,mRespectively representing charging energy, energy storage system temperature, average SOC and SOC deviation, TrefFor reference temperature, 25 ℃ is usually set. T ismSee formula (16), RthIs an empirical constant.
Figure BDA0002287417850000141
Figure BDA0002287417850000142
In one specific embodiment, the upper and lower limits of SOC are 90% and 10%, respectively, η is 95%, the project period is 20 years, regardless of the annual average battery installation cost reduction rate, cEIs 3224 ￥/kW.h, cpIs 1085 ￥/kW, cfIs 155 ￥/kW year, cdIs 1582 ￥/kW, cres5%, because the lithium cell does not need complicated corollary equipment, so do not consider BOP, thermal power unit production unit energy emission cost is 230 ￥/(MW · h), and the price of electricity of T subsidy is 18.6 ￥/MW · h, in addition, pastes the rate of occurrence and decides 10%.
4) The objective function of the outer optimization model is:
f1＝min(cost-profit+punishvalue) (18)
in equation (18), the cost function and the profit function are expressed as equation (1) and equation (7), respectively. The cost value is calculated in the internal optimization model and the profit value is calculated in the external optimization model, and the penalty value is the penalty cost required by the unresolved voltage problem.
The optimization cost of the outer model is determined by the constraints on the system, in addition to the constraints on the energy storage system, see equation (11), see equation (19)
Figure BDA0002287417850000143
In the formula (19), PDG,PS,PESS,PL,PLOSSActive power of a distributed power supply, a main power grid, an energy storage system, a load and line loss are respectively provided; pijminAnd PijmaxIs the limiting condition of the transmission active power of the line ij, Vmaxand VminIs a constraint of the voltage amplitude, PijAnd ViThe actual active power on line ij and the actual voltage at node i, respectively.
5) The objective function of the inner optimization model aims to optimize the capacity of the energy storage system by minimizing the life cycle cost according to the economic operating power and the rated power output by the outer optimization model, and return the rated capacity and the annual life cycle cost to the outer optimization, and the objective function of the inner optimization model is as follows (20):
f2＝min cost (20)
by increasing battery capacity and extending battery life, replacement costs may be reduced. The energy storage system output power and the energy fluctuation E (t) obtained according to the outer optimization model can be obtained by the formula (14). According to the energy fluctuation of one day, the minimum capacity meeting the output of the energy storage system can be obtained by considering the limit of the SOC;
the constraint conditions of the inner layer are SOC constraint: SOCmin≤SOC(t)≤SOCmax。
Referring to fig. 5, the present embodiment relates to a possible implementation manner of solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm according to the objective to obtain the optimal configuration result, that is, S40 includes:
and S310, encoding the output power of the energy storage system to be configured in each hour by adopting a genetic algorithm according to the target data, and initializing the population.
The target data comprise positions of a distributed power bus, an energy storage system, a load node and the like in the microgrid. When the output power of the energy storage system to be configured per hour is coded, the output power of the energy storage system to be configured per hour is coded by four gene positions, and the output power of the energy storage system to be configured per day can be represented by 96 gene positions.
And S320, performing load flow calculation on each individual of the population according to the target data to obtain a penalty value of each individual due to the unsolved voltage problem.
Specifically, load flow calculation can be performed on each individual of the population according to parameters of each element in the microgrid to obtain a penalty value.
And S331, judging whether the penalty value is zero or not.
If the penalty value is zero, step S332 is executed.
If the penalty value is not zero, go to step S335.
And S332, optimizing the output power of each hour of the energy storage system to be configured in one day so as to enable the charging and discharging amount of the energy storage system to be configured to be equal in one charging and discharging period.
The battery of the energy storage system can be recycled, the energy storage system can meet energy conservation in a charging and discharging period, and the output power of the energy storage system is further optimized to ensure that the charging and discharging amounts of the stored energy in the charging and discharging period are equal.
And S333, calculating the profit according to the profit model and determining the rated power.
The yield model is as in equation (7).
And S334, calculating the rated capacity and the full-period cost of the energy storage system to be configured through the inner-layer optimization model, and outputting the rated capacity and the full-period cost to the outer-layer optimization model.
And S335, setting the profits and the life cycle cost of the energy storage system to be configured represented by the individual to be zero.
And S336, calculating the fitness of each individual in the genetic algorithm according to the target function and the genetic algorithm fitness function of the outer layer optimization model and the fitness function.
The genetic algorithm fitness function may be taken as equation (21):
Figure BDA0002287417850000161
s340, screening individuals with higher fitness through a roulette method, reserving the individuals with the highest fitness to update the population, and obtaining an outer new population.
And S350, carrying out simulated annealing operation on the outer layer new population to obtain the outer layer annealing new population.
And carrying out simulated annealing operation on the outer new population, wherein the simulated annealing operation comprises simulated annealing action and selection of new individuals after simulated annealing. Wherein the annealing act comprises:
1) randomly selecting one stored energy for each individual and turning off the stored energy;
2) randomly opening an energy storage for each individual and randomly setting the power of the energy storage;
selection of new individuals after simulated annealing included:
the individuals formed by annealing are accepted if the fitness of the individuals is higher than that of the previous generation population;
if the fitness decreases, the fitness is accepted with a probability decreasing from round to round according to a probability acceptance function, equation (22),
Figure BDA0002287417850000171
in the formula, P1 is the probability of new individual acceptance of the first-stage annealing algorithm, Fit 'is the population individual fitness before the first-stage annealing, Fitnew' is the newly formed individual fitness after the first-stage annealing, α is a temperature reduction coefficient, k is the annealing times, and T is the annealing initial temperature.
And S360, performing crossing and mutation operations on the outer layer annealing new population.
S370, determine whether to execute the last generation.
If not, the process returns to step S320.
If the last generation is executed, S370 is executed.
And S370, sequentially outputting the rated capacity and the rated power with the minimum running cost in consideration of the profit.
Referring to fig. 6, the present embodiment relates to a possible implementation manner of calculating the rated capacity and the full-cycle cost of the energy storage system to be configured through the inner-layer optimization model, that is, S335 includes:
and S3351, generating an initial population.
S3352, calculating the charge state, the battery life and the whole-cycle life cost of the energy storage system to be configured according to the output power value and the rated power of the energy storage system to be configured in each time period output by the outer layer optimization model.
S3353, taking a genetic algorithm fitness function according to the objective function of the inner layer optimization model, and calculating the fitness of each individual in the genetic algorithm.
Here, the fitness function of the genetic algorithm is taken as
Figure BDA0002287417850000172
S3354, screening individuals with higher fitness by a roulette method, reserving the individuals with the highest fitness to update the population, and obtaining an inner-layer new population.
S3355, carrying out simulated annealing operation on the inner layer new population to obtain an inner layer annealing population.
S3356, performing crossover and mutation operations on the inner annealing population.
S3357, determine whether to execute to the last generation.
If not, the process returns to step S3352.
If the last generation is reached, step S3358 is executed.
And S3358, outputting the rated capacity and the full-cycle life cost of the energy storage system to be configured.
It should be understood that, although the steps in the flowchart are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 7, there is provided a microgrid energy storage optimization configuration apparatus 100 that accounts for life cycle costs, comprising: the target data obtaining module 110, the energy storage optimization model obtaining module 120, the solving module 130 and the configuration module 140, wherein:
the target data acquisition module 110 is configured to acquire data of an energy storage system to be configured to obtain target data;
an energy storage optimization model obtaining module 120, configured to obtain a pre-established energy storage optimization model, where the energy storage optimization model includes an outer layer optimization model and an inner layer optimization model, an objective function of the outer layer optimization model is configured to output a rated capacity and a full life cycle cost of an energy storage system according to the inner layer optimization model, improve voltage distribution of the energy storage system by optimizing economic operation power of the energy storage system, and minimize an annual net cost of the energy storage system, and an objective function of the inner layer optimization model is configured to optimize a capacity of the energy storage system by minimizing the full life cycle cost according to the economic operation power and the rated power of the energy storage system output by the outer layer optimization model;
a solving module 130, configured to solve an optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm according to the target data to obtain an optimal configuration result, where the optimal configuration result at least includes the rated power, the rated capacity, and the economic operation power;
and the configuration module 140 is configured to configure the energy storage system to be configured according to the optimal configuration result.
In one embodiment, the objective function of the inner layer optimization model is specifically configured to: determining the minimum configuration capacity of the energy storage system according to the economic operation power and the rated power; expanding the minimum configuration capacity to obtain the capacity of the energy storage system; determining a minimized full life cycle cost of the energy storage system corresponding to a capacity of the energy storage system.
In one embodiment, the microgrid energy storage optimization configuration apparatus 100 considering life cycle cost further includes a model building module 150, and the model building module 140 is configured to determine an energy storage life cycle cost model, a profit model and a battery model of the energy storage system; establishing an objective function of the outer-layer optimization model and an objective function of the inner-layer optimization model according to the energy storage life cycle cost model and the profit model; and respectively determining an objective function of the outer layer optimization model and a constraint condition corresponding to the inner layer optimization model according to the battery model to obtain the constraint condition of the outer layer optimization model and the constraint condition of the inner layer optimization model.
In one embodiment, the objective function of the outer optimization model is: f1 ═ min (cost-fit + punishvalue); wherein f1 represents the objective function of the outer optimization model, cost represents the life-cycle cost model, and cost is C1+C2+C3+C4-C5Profit stands for yield model, profit ═ I1+I2+I3Punishvalue stands for a penalty value, C1Representing the initial investment cost, C2Representing replacement costs, C3Representing the operation and maintenance cost, C4Representing the cost of the treatment, C5Represents the recovery value, I1Representing the profit of energy storage, I2Representing a return on government incentives, I3Representing the benefits of the associated environment.
In one embodiment, the constraint conditions of the outer layer optimization model include energy storage system constraint conditions and grid system constraint conditions, where the energy storage system optimization constraint conditions are:
Figure BDA0002287417850000201
ηDfor discharge power, ηCFor charging power, η is the overall charge-discharge efficiency, SOCmaxAt the upper limit of the state of charge, SOCminSOC (t) is the state of charge at time t;
the power grid system constraint conditions are as follows:
Figure BDA0002287417850000202
PDG,PS,PESS,PL,PLOSSthe active power of the distributed power supply, the active power of the main power grid, the active power of the energy storage system, the active power of the load and the active power of the line loss are respectively; pijminAnd PijmaxRespectively, the limit condition, V, for the transmission active power of the line ijmaxand VminRespectively, the voltage amplitude constraint, PijAnd ViThe actual active power on line ij and the actual voltage at node i, respectively.
In one embodiment, the objective function of the inner layer optimization model is: f2 ═ min cost, where f2 represents the objective function of the inner optimization model, cost represents the full life cycle cost model, and cost ═ C1+C2+C3+C4-C5，C1Representing the initial investment cost, C2Representing replacement costs, C3Representing the operation and maintenance cost, C4Representing the cost of the treatment, C5Represents the recovery value.
In one embodiment, the constraint condition of the inner layer optimization model is SOCmin≤SOC(t)≤SOCmaxWherein, SOCmaxAt the upper limit of the state of charge, SOCminSOC (t), which is the lower limit of the state of charge, is the state of charge at time t.
In one embodiment, the solving module 130 is specifically configured to: according to the target data, encoding the output power of the energy storage system to be configured every hour by adopting a genetic algorithm, and initializing a population; carrying out load flow calculation on each individual of the population according to the target data to obtain a penalty value of each individual due to an unsolved voltage problem; if the penalty value is zero, optimizing the output power of the energy storage system to be configured represented by the individual in each hour in one day so as to enable the charging and discharging amounts of the energy storage system to be configured to be equal in one charging and discharging period; calculating the profit according to the profit model, and determining the rated power; calculating the rated capacity and the full-period cost of the energy storage system to be configured through the inner layer optimization model, and outputting the rated capacity and the full-period cost to the outer layer optimization model; if the punishment value is not zero, setting each income and the whole life cycle cost of the energy storage system to be configured represented by the individual to be zero; determining a fitness function of the genetic algorithm according to the penalty value, the rated capacity and the life cycle cost, and calculating the fitness of each individual in the genetic algorithm according to the fitness function; screening individuals with higher fitness through a roulette method, and reserving the individuals with the highest fitness to update the population to obtain an outer new population; carrying out simulated annealing operation on the outer layer new population to obtain an outer layer annealing new population; performing crossing and mutation operations on the outer annealing new population, and judging whether the last generation is performed; if the last generation is not executed, returning to the execution step, and performing load flow calculation on each individual of the population according to the target data to obtain a penalty value of each individual due to the unsolved voltage problem; and if the last generation is executed, sequentially outputting the rated capacity and the rated power with the minimum running cost in consideration of the income.
In one embodiment, the solving module 130 is specifically configured to: generating an initial population; calculating the charge state, the battery life and the whole-cycle life cost of the energy storage system to be configured according to the output power value and the rated power of the energy storage system to be configured in each time period, which are output by the outer layer optimization model; according to the target function of the inner layer optimization model, a genetic algorithm fitness function is selected, and the fitness of each individual in the genetic algorithm is calculated; screening individuals with higher fitness through a roulette method, and reserving the individuals with the highest fitness to update the population to obtain an inner-layer new population; carrying out simulated annealing operation on the inner layer new population to obtain an inner layer annealing population; performing crossing and mutation operations on the inner layer annealing population, and judging whether the last generation is performed; if the last generation is not executed, returning to the step of executing, and calculating the state of charge, the battery life and the full-cycle life cost of the energy storage system to be configured according to the output power value and the rated power of the energy storage system to be configured in each time period output by the outer layer optimization model; and if the last generation is executed, outputting the rated capacity and the full-cycle life cost of the energy storage system to be configured.
For specific limitations of the microgrid energy storage optimization configuration 100 related to the life cycle cost, reference may be made to the above limitations of the microgrid energy storage optimization configuration method related to the life cycle cost, and details thereof are not described herein again. The various modules in the microgrid energy storage optimization configuration 100 described above in view of full life cycle costs may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring data of an energy storage system to be configured to obtain target data;
the method comprises the steps of obtaining a pre-established energy storage optimization model, wherein the energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, an objective function of the outer layer optimization model is used for outputting rated capacity and full life cycle cost of an energy storage system according to the inner layer optimization model, improving voltage distribution of the energy storage system by optimizing economic operation power of the energy storage system and minimizing the annual average net cost of the energy storage system, and an objective function of the inner layer optimization model is used for optimizing the capacity of the energy storage system according to the economic operation power and the rated power of the energy storage system output by the outer layer optimization model and by minimizing the full life cycle cost;
according to the target data, solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm to obtain an optimal configuration result, wherein the optimal configuration result at least comprises the rated power, the rated capacity and the economic operation power;
and configuring the energy storage system to be configured according to the optimized configuration result.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when executed by a processor, performs the steps of:
acquiring data of an energy storage system to be configured to obtain target data;
the method comprises the steps of obtaining a pre-established energy storage optimization model, wherein the energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, an objective function of the outer layer optimization model is used for outputting rated capacity and full life cycle cost of an energy storage system according to the inner layer optimization model, improving voltage distribution of the energy storage system by optimizing economic operation power of the energy storage system and minimizing the annual average net cost of the energy storage system, and an objective function of the inner layer optimization model is used for optimizing the capacity of the energy storage system according to the economic operation power and the rated power of the energy storage system output by the outer layer optimization model and by minimizing the full life cycle cost;
according to the target data, solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm to obtain an optimal configuration result, wherein the optimal configuration result at least comprises the rated power, the rated capacity and the economic operation power;
and configuring the energy storage system to be configured according to the optimized configuration result.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A microgrid energy storage optimization configuration method considering life cycle cost is characterized by comprising the following steps:
acquiring data of an energy storage system to be configured to obtain target data;
the method comprises the steps of obtaining a pre-established energy storage optimization model, wherein the energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, an objective function of the outer layer optimization model is used for outputting rated capacity and full life cycle cost of an energy storage system according to the inner layer optimization model, improving voltage distribution of the energy storage system by optimizing economic operation power of the energy storage system and minimizing the annual average net cost of the energy storage system, and an objective function of the inner layer optimization model is used for optimizing the capacity of the energy storage system according to the economic operation power and the rated power of the energy storage system output by the outer layer optimization model and by minimizing the full life cycle cost;
according to the target data, solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm to obtain an optimal configuration result, wherein the optimal configuration result at least comprises the rated power, the rated capacity and the economic operation power;
and configuring the energy storage system to be configured according to the optimized configuration result.
2. The method according to claim 1, wherein the objective function of the inner layer optimization model is specifically configured to:
determining the minimum configuration capacity of the energy storage system according to the economic operation power and the rated power;
expanding the minimum configuration capacity to obtain the capacity of the energy storage system;
determining a minimized full life cycle cost of the energy storage system corresponding to a capacity of the energy storage system.
3. The method of claim 1, wherein the outer optimization model and the inner optimization model are established by:
determining an energy storage life cycle cost model, a profit model and a battery model of the energy storage system;
establishing an objective function of the outer-layer optimization model and an objective function of the inner-layer optimization model according to the energy storage life cycle cost model and the profit model;
and respectively determining an objective function of the outer layer optimization model and a constraint condition corresponding to the inner layer optimization model according to the battery model to obtain the constraint condition of the outer layer optimization model and the constraint condition of the inner layer optimization model.
4. The method of claim 3, wherein the objective function of the outer optimization model is:
f1＝min(cost-profit+punishvalue)；
wherein f1 represents the objective function of the outer optimization model, cost represents the life-cycle cost model, and cost is C1+C2+C3+C4-C5Profit stands for yield model, profit ═ I1+I2+I3Punishvalue stands for a penalty value, C1Representing the initial investment cost, C2Representing replacement costs, C3Representing the operation and maintenance cost, C4Representing the cost of the treatment, C5Represents the recovery value, I1Representing the profit of energy storage, I2Representing a return on government incentives, I3Representing the benefits of the associated environment.
5. The method according to claim 4, wherein the constraints of the outer optimization model comprise energy storage system constraints and power grid system constraints, wherein the energy storage system optimization constraints are:
Figure FDA0002287417840000021
ηDfor discharge power, ηCFor charging power, η is the overall charge-discharge efficiency, SOCmaxAt the upper limit of the state of charge, SOCminSOC (t) is the state of charge at time t;
the power grid system constraint conditions are as follows:
Figure FDA0002287417840000031
PDG,PS,PESS,PL,PLOSSthe active power of the distributed power supply, the active power of the main power grid, the active power of the energy storage system, the active power of the load and the active power of the line loss are respectively; pijminAnd PijmaxRespectively, the limit condition, V, for the transmission active power of the line ijmaxand VminRespectively, the voltage amplitude constraint, PijAnd ViThe actual active power on line ij and the actual voltage at node i, respectively.
6. The method of claim 3, wherein the objective function of the inner layer optimization model is: f2 mincost, where f2 represents the objective function of the inner layer optimization model, cost represents the life-cycle cost model, and cost C1+C2+C3+C4-C5，C1Representing the initial investment cost, C2Representing replacement costs, C3Representing the operation and maintenance cost, C4Representing the cost of the treatment, C5Represents the recovery value.
7. The method of claim 6, wherein the constraint of the inner layer optimization model is SOCmin≤SOC(t)≤SOCmaxWherein, SOCmaxAt the upper limit of the state of charge, SOCminSOC (t), which is the lower limit of the state of charge, is the state of charge at time t.
8. A microgrid energy storage optimization configuration apparatus taking into account life cycle costs, the apparatus comprising:
the target data acquisition module is used for acquiring data of the energy storage system to be configured to obtain target data;
the energy storage optimization model obtaining module is used for obtaining a pre-established energy storage optimization model, wherein the energy storage optimization model comprises an outer layer optimization model and an inner layer optimization model, an objective function of the outer layer optimization model is used for outputting rated capacity and full life cycle cost of an energy storage system according to the inner layer optimization model, improving voltage distribution of the energy storage system by optimizing economic operation power of the energy storage system and minimizing the annual net cost of the energy storage system, and an objective function of the inner layer optimization model is used for optimizing the capacity of the energy storage system according to the economic operation power and the rated power of the energy storage system output by the outer layer optimization model and minimizing the full life cycle cost;
the solving module is used for solving the optimal solution of the energy storage optimization model through a genetic algorithm and a simulated annealing algorithm according to the target data to obtain an optimized configuration result, and the optimized configuration result at least comprises the rated power, the rated capacity and the economic operation power;
and the configuration module is used for configuring the energy storage system to be configured according to the optimized configuration result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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