CN114362153A - Multi-target capacity optimal configuration method and system for grid-connected wind-solar energy storage system - Google Patents
Multi-target capacity optimal configuration method and system for grid-connected wind-solar energy storage system Download PDFInfo
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Abstract
The invention discloses a multi-target capacity optimal configuration method and a multi-target capacity optimal configuration system for a grid-connected wind-solar energy storage system. The method has the advantages that the economy of project construction and the grid-connected friendliness of the system are both considered, practical factors such as energy storage operation life loss are taken into consideration, the project investment is saved, the output fluctuation of the wind-solar power supply is stabilized, and certain reference and application values are provided in the planning and designing stage of the new energy power station.
Description
Technical Field
The invention relates to the technical field of new energy power supply planning, in particular to a multi-target capacity optimal configuration method and system for a grid-connected wind-solar energy storage system.
Background
Because wind power and photovoltaic self-output have the characteristics of strong randomness, volatility, difficult absorption and the like, in recent years, the development mode of forming a combined power generation system by wind-solar energy storage attracts general attention, and energy storage with certain capacity is configured in the wind-solar power generation system, so that the absorption capacity of wind-solar power generation can be improved, the output fluctuation of the system is also stabilized, and the wind-solar power generation system is more friendly to a large power grid. The grid-connected wind-solar energy storage power generation system is composed of wind power, photovoltaic, energy storage, load and corresponding power electronic converters, and a typical structure of the grid-connected wind-solar energy storage power generation system is shown in the attached figure 2. The wind power, the photovoltaic and the load only perform unidirectional power exchange with the system, the wind power and the photovoltaic inject power into the system, and the load absorbs power from the system. The energy storage and the system perform bidirectional power exchange, when the system is surplus in power, the energy storage is charged, and when the system is in shortage, the energy storage is discharged.
The capacity configuration of various power supplies in the grid-connected wind-solar storage power generation system is a particularly critical problem in the design stage of a power station, and reasonable configuration scale can reduce investment, save construction land, improve new energy consumption and reduce power fluctuation of new energy output, so that the capacity optimization configuration problem of the grid-connected wind-solar storage system becomes one of hot problems concerned by academia and industry in recent years.
Disclosure of Invention
The invention provides a method and a system for optimizing and configuring multiple target capacities of a grid-connected wind-light storage system aiming at the engineering practical problem of power supply capacity planning in the field of new energy power generation, wherein a multiple-target optimization configuration model with the minimum cost-benefit ratio and the minimum system output power fluctuation as target functions is established by taking wind power, photovoltaic and energy storage capacity as decision variables based on the annual 8760h resource output characteristics of the wind power and photovoltaic at the site of project construction, energy storage life loss is calculated under the constraint conditions of considering total project investment, construction land area, system power balance and the like, a multiple-target particle swarm algorithm is adopted for solving to obtain pareto-front solution sets, then the weights of different target functions are quantitatively evaluated by adopting an entropy weight method, and finally a wind-light storage capacity configuration scheme corresponding to the optimal comprehensive target function is determined according to weight coefficients.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-target capacity optimal configuration method for a grid-connected wind-solar energy storage system comprises the following steps:
based on the resource output characteristics of wind power and photovoltaic of the site where the project construction is located, the wind power, photovoltaic and energy storage capacity are used as decision variables, and a multi-objective optimization configuration model with the minimum system cost benefit ratio and the minimum system output power fluctuation as objective functions is established;
under the constraint conditions of considering total system investment, construction land area, system power balance and the like, considering energy storage life loss, and solving a pareto frontier solution set by adopting a multi-target particle swarm algorithm based on a multi-target optimization configuration model;
quantitatively evaluating the weight coefficients corresponding to different sub-objective functions by adopting an entropy weight method, and calculating to obtain a total objective function according to the weight coefficients; and optimally determining a corresponding wind-solar energy storage capacity configuration scheme by using a total objective function in the pareto frontier solution set.
As a further improvement of the invention, under the constraint conditions of considering total system investment, construction land area, system power balance and the like, energy storage life loss is considered, and a multi-target particle swarm algorithm is adopted to solve the pareto frontier solution set, which specifically comprises the following steps:
acquiring required basic data including wind power and photovoltaic 8760h output characteristics of a project location, initial investment of various power supplies, annual operation maintenance rate, residual coefficient of an energy storage power station, discharge depth and maximum cycle number of an energy storage battery and the like;
randomly generating an initial wind-solar energy storage capacity configuration scheme group by taking the total investment of project construction, construction land, maximum energy storage power and maximum duration as constraint conditions;
carrying out production simulation calculation on the power system for 8760h all year round according to the initial wind-solar energy storage capacity configuration scheme group to obtain wind power, photovoltaic energy, energy storage annual energy generation amount and an energy storage charge-discharge SOC curve, and calculating to obtain an equivalent operation life index of the energy storage according to the energy storage SOC curve;
updating the current wind-solar energy storage capacity optimal configuration scheme set by taking the minimum cost-benefit ratio of the wind-solar energy storage system and the minimum fluctuation of the system output power as fitness functions, and carrying out production simulation calculation on the power system for 8760h all the year;
and after repeated iteration, the set precision requirement is met or the maximum iteration times are met, and the wind and light storage capacity optimal configuration pareto front solution set is output.
As a further improvement of the invention, the charge-discharge model of the energy storage battery is as follows:
when [ t-1, t]Period of time, PESS>0, when the energy storage battery is in a charging state, the SOC of the battery at the time t is as follows:
when PESS is less than 0 in the time interval of [ t-1, t ] and the energy storage battery is in a discharging state, the SOC of the battery at the time t is
When [ t-1, t]Period of time, PESSWhen the energy storage battery maintains the original state, the SOC of the battery at time t is:
SOC(t)＝(1-δ)·SOC(t-1) (3)
wherein SOC (t) is the state of charge value of the energy storage battery at the time t, delta is the self-discharge rate of the energy storage battery, and pESS(t) is the charge and discharge power of the battery at time t, positive during charging and negative during discharging; etacAnd ηdEfficiency of charging and discharging, respectively, of the battery, EESSIs the rated capacity of the energy storage battery.
As a further improvement of the invention, the calculation formula of the wind-solar energy storage power generation system with the minimum cost-benefit ratio as the fitness function is as follows:
in the formula, EPVAnd EWTFor annual energy production gains of photovoltaic and wind power, EESSThe energy storage annual energy generation yield is increased; cINFor initial investment costs of the power station, COMAnnual operating maintenance costs of the power station, CREPReplacement costs for stored energy after reaching cycle life, CRECResidual values for the power station after reaching the design life;
the specific calculation formula of the annual cost expense of wind power, photovoltaic and energy storage is as follows:
COM＝μPVCIN-PV+μWTCIN-WT+μESSCIN-ESS (6)
CREP＝δESSCIN-ESS (7)
CREC＝λPVCIN-PV+λWTCIN-WT+λESSCIN-ESS (8)
in the formula (f)DRThe energy storage coefficient is determined after calculation according to the equivalent operating life of the photovoltaic power generation and the wind power generation; cIN-PV、CIN-WT、CIN-ESSRespectively corresponding to the initial investment cost of photovoltaic, wind power and energy storage; mu.sPV、μWT、μESSThe annual operation and maintenance cost coefficients of photovoltaic power, wind power and energy storage are respectively set; deltaESSIs a replacement cost coefficient of the energy storage power station; lambda [ alpha ]PV、λWT、λESSThe residual coefficients of photovoltaic, wind power and energy storage are respectively.
As a further improvement of the present invention, the calculation formula of the system with the minimum fluctuation of the output power as the fitness function is as follows:
in the formula, PWT(t)、PPV(t)、PESS(t) wind power, photovoltaic and energy storage output at the moment t respectively;
the average values of the wind power and photovoltaic output of 8760h all year round are respectively.
As a further improvement of the present invention, the calculation process for calculating the equivalent operating life of the stored energy according to the energy storage SOC curve comprises:
acquiring experimental data between a depth of discharge (DOD) and a maximum cycle number N provided by a battery manufacturer;
according to data measured by experiments, carrying out data fitting by using a Matlab tool box to obtain a functional relation between the fitted charge-discharge depth DOD and the maximum cycle number N;
extracting all effective charge-discharge cycles in the time period T by using a rain flow counting method and calculating the discharge depth DOD corresponding to each cycle;
calculating the maximum cycle number N corresponding to the DOD according to the fitted functional relation between the charge-discharge depth DOD and the maximum cycle number N;
converting N into equivalent cycle times N' under the condition of full charge and discharge, and then accumulating to obtain the equivalent total cycle times Nsum；
And calculating the loss coefficient and the actual operation life of the battery according to the full-charge cycle number N.
As a further improvement of the present invention, the quantitative evaluation of the weighting coefficients corresponding to different objective functions by using the entropy weight method is specifically implemented as follows:
standardizing each sub-target function;
calculating the information entropy of each sub-target function;
determining the weight of each sub-target function according to the calculation result of the information entropy;
calculating a comprehensive objective function according to the weight determined by each objective function;
and optimally screening according to the comprehensive objective function to obtain an optimal solution in the pareto frontier solution set.
As a further improvement of the present invention, the multi-objective optimization configuration model based on the multi-objective particle swarm optimization algorithm for solving the constraint conditions in the pareto frontier solution set is:
and (4) investment constraint:
the upper limit of the total investment amount of the project is CmaxThen wind-powered electricity generation, photovoltaic, energy storage installation restraint do:
wherein, CWTThe unit kilowatt cost of the fan; cPVThe unit kilowatt cost of photovoltaic; cESS-PCost per kilowatt of energy storage battery (power cost), CESS-EThe cost per kilowatt-hour (capacity cost) of the energy storage battery;
building land constraint:
the project construction land area is S, the length is L, the width is W, then the project optimization is to wind-powered electricity generation, photovoltaic, the restraint of energy storage do:
wherein p isWTThe installed capacity of a single fan, and d is the diameter of a wind wheel of the single fan; p is a radical ofPVInstalled capacity, S, for a single photovoltaic arrayPIs the footprint of a single photovoltaic array,
is the shading coefficient; e.g. of the typeESSFor the battery capacity, S, of a single energy-storing containerSThe floor space of a single energy storage container;
energy storage battery SOC constraint
In order to ensure the working life of the energy storage system during working, the battery capacity can be kept within a reasonable range without overshoot or over discharge, and the battery capacity of the energy storage system is measured by adopting the state of charge (SOC) of the battery:
SOCmin≤SOC(t)≤SOCmax (12)
wherein the SOCminIs the SOC minimum limit of the battery, SOCmaxIs the SOC maximum limit of the battery; energy storage power and duration constraints
The power and the energy storage time of different types of energy storage are reasonably limited by the manufacturing process and the technical level:
pESS≤Pmax (13)
TESS≤Tmax (14)
as a further improvement of the invention, the optimizing termination condition is to repeat the processing until reaching the convergence condition of the algorithm or the set iteration number.
A multi-target capacity optimal configuration system of a grid-connected wind-solar energy storage system is characterized by comprising:
the model establishing module is used for establishing a multi-objective optimization configuration model which takes the minimum cost-benefit ratio of the system and the minimum fluctuation of the output power of the system as objective functions by taking the wind power, the photovoltaic and the energy storage capacity as decision variables based on the annual 8760h resource output characteristics of the wind power and the photovoltaic of the project construction location;
the algorithm solving module is used for considering the energy storage life loss under the constraint conditions of considering the total system investment, the construction land area, the system power balance and the like, and solving the pareto front solution set by adopting a multi-target particle swarm algorithm based on a multi-target optimization configuration model;
the scheme determination module is used for quantitatively evaluating the weight coefficients corresponding to different target functions by adopting an entropy weight method and calculating to obtain a total target function according to the weight coefficients; and optimally determining a corresponding wind-solar energy storage capacity configuration scheme by using a total objective function in the pareto frontier solution set.
Compared with the prior art, the invention has the following advantages:
the multi-objective function provided by the invention gives consideration to the project construction economy and the system grid-connected friendliness, and takes into account the practical factors such as energy storage operation life loss and the like, so that the method is closer to the practical engineering application; the method is characterized in that a pareto frontier solution set is solved by adopting a multi-target particle swarm algorithm under actual constraint conditions such as total project investment, construction land area, system power balance and the like, on the basis, the weights of different objective functions are quantitatively evaluated by adopting an entropy weight method, and artificial subjective factors are avoided, so that a wind-light storage capacity construction scheme corresponding to the optimal comprehensive objective function is determined.
Drawings
FIG. 1 is a flow chart of a multi-target capacity optimal configuration method of a grid-connected wind-solar energy storage system of the invention;
FIG. 2 is a typical structure of a grid-connected wind-solar energy storage power generation system according to the invention;
FIG. 3 is the characteristics of the wind power and photovoltaic solar output of the ground where the project is located in the calculation example of the present invention;
FIG. 4 is a characteristic of the cumulative frequency of the wind power and the photovoltaic power of the ground where the project is located in the calculation example of the present invention;
FIG. 5 is a graph of the discharge depth of the energy storage cell as a function of the maximum cycle number in an example of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
As shown in fig. 1, a first object of the present invention is to provide a method for optimizing and configuring multiple target capacities of a grid-connected wind-light storage system, which is based on the annual 8760h resource output characteristics of wind power and photovoltaic power at a project construction site, uses the wind power, photovoltaic power and energy storage capacity as decision variables, establishes a multiple target optimization configuration model with the minimum system cost-benefit ratio and the minimum system output power fluctuation as target functions, considers energy storage life loss under the constraint conditions of considering system total investment, construction land area, system power balance and the like, solves a pareto frontier solution set by using a multiple target particle swarm algorithm, quantitatively evaluates weights of different target functions by using an entropy weight method, and finally determines a wind-light storage capacity configuration scheme corresponding to the optimal comprehensive target function according to a weight coefficient.
The method has the advantages that the economy of project construction and the grid-connected friendliness of the system are both considered, practical factors such as energy storage operation life loss are taken into consideration, the project investment is saved, the output fluctuation of the wind-solar power supply is stabilized, and certain reference and application values are provided in the planning and designing stage of the new energy power station.
The method comprises the following specific steps:
step 1: inputting basic data required by calculation, including experimental data such as wind power and photovoltaic 8760h output characteristics of a project location, initial investment of various power supplies, annual operation maintenance rate, residual coefficient of an energy storage power station, discharge depth and maximum cycle number of an energy storage battery and the like;
step 2: randomly generating an initial wind-solar energy storage capacity configuration scheme group by taking the total investment of project construction, construction land, maximum energy storage power and maximum duration as constraint conditions;
and step 3: according to the initial wind-solar energy storage capacity configuration scheme group, carrying out production simulation calculation on the power system for 8760h all year around to obtain key technical indexes such as wind power, photovoltaic, energy storage annual energy generation amount and energy storage charge-discharge SOC curves, and calculating to obtain an equivalent operation life index of energy storage according to the energy storage SOC curves;
and 4, step 4: updating the current wind-solar energy storage capacity optimal configuration scheme set by taking the minimum cost-benefit ratio of the wind-solar energy storage system and the minimum fluctuation of the system output power as fitness functions, and carrying out production simulation calculation on the power system for 8760h all the year;
and 5: through repeated iteration, the set precision requirement is met or the maximum number of iteration is reached, and a wind-solar energy storage capacity optimal configuration pareto front solution set is output;
step 6: and quantitatively evaluating the weight coefficients corresponding to different objective functions by adopting an entropy weight method, and finally calculating to obtain a total objective function according to the weight coefficients.
And 7: and optimally determining a corresponding wind-solar energy storage capacity configuration scheme by using a total objective function in the pareto frontier solution set.
As a further improvement of the invention, the calculation formula of the minimum objective function of the grid-connected wind-solar energy storage power generation system cost-benefit ratio is as follows:
in the formula, EPVAnd EWTFor annual energy production gains of photovoltaic and wind power, EESSThe energy storage annual energy generation yield is increased; cINFor initial investment costs of the power station, COMAnnual operating maintenance costs of the power station, CREPTo storeReplacement cost after reaching cycle life, CRECThe residual value for the power station after reaching the design life.
As a further improvement of the invention, the specific calculation formula of the annual cost expense of wind power, photovoltaic and energy storage is as follows:
COM＝μPVCIN-PV+μWTCIN-WT+μESSCIN-ESS (3)
CREP＝δESSCIN-ESS (4)
CREC＝λPVCIN-PV+λWTCIN-WT+λESSCIN-ESS (5)
in the formula (f)DRThe energy storage coefficient is determined after calculation according to the equivalent operating life of the photovoltaic power generation and the wind power generation; cIN-PV、CIN-WT、CIN-ESSRespectively corresponding to the initial investment cost of photovoltaic, wind power and energy storage; mu.sPV、μWT、μESSThe annual operation and maintenance cost coefficients of photovoltaic power, wind power and energy storage are respectively set; deltaESSIs a replacement cost coefficient of the energy storage power station; lambda [ alpha ]PV、λWT、λESSThe residual coefficients of photovoltaic, wind power and energy storage are respectively.
As a further improvement of the method, the operation life of the energy storage battery is measured and calculated by adopting an equivalent cycle break algorithm, an energy storage SOC (system on chip) annual operation curve is obtained according to production simulation calculation in the measuring and calculating process, and each actual cycle period is extracted and counted by adopting a rain flow counting method.
And according to experimental data of the maximum cycle times N corresponding to different DODs provided by a battery manufacturer, performing data fitting by using a Matlab tool box to obtain a functional relation between the DOD and the maximum cycle times N.
Extracting all effective charge-discharge cycles in a time period T by using a rain flow counting method, calculating a depth of discharge DOD corresponding to each cycle, calculating a maximum cycle number N corresponding to the DOD according to a functional relation between the depth of discharge DOD and the maximum cycle number N, converting the N to an equivalent cycle number N' under the condition of full charge (DOD is 100%) according to a formula (6), and accumulating to obtain an equivalent total cycle number NsumFinally, as shown in equation (7), the loss factor and the actual operating life of the battery are calculated based on the number of full-charge cycles N (DOD is 100%), as shown in equations (8) and (9).
Wherein, the equivalent cycle coefficient N' is:
total number of cycles N within time period TsumComprises the following steps:
the loss factor alpha of the battery is
When alpha is 1, the battery reaches the service life limit.
When the time period T is one year, the actual operating life m of the battery is calculated as:
as a further improvement of the invention, the constraint conditions of the multi-objective optimization of the wind-solar-energy storage power generation system are as follows:
firstly, investment constraint: the upper limit of the total investment amount of the project is CmaxThe wind power, photovoltaic and energy storage installation are constrained to：
Wherein, CWTThe unit kilowatt cost of the fan; cPVThe unit kilowatt cost of photovoltaic; cESS-PCost per kilowatt of energy storage battery (power cost), CESS-EThe cost per kilowatt-hour (capacity cost) of the energy storage battery is obtained.
Secondly, constructing land constraint: assuming that the area of the project construction land is S, the length is L and the width is W, the constraints of the project optimization on wind power, photovoltaic and energy storage are as follows:
wherein p isWTThe installed capacity of a single fan, and d is the diameter of a wind wheel of the single fan; p is a radical ofPVInstalled capacity, S, for a single photovoltaic arrayPIs the footprint of a single photovoltaic array,
is the shading coefficient; e.g. of the typeESSFor the battery capacity, S, of a single energy-storing containerSIs the floor space of a single energy storage container.
Thirdly, energy storage battery SOC restraint
In order to ensure the working life of the energy storage system during working, the battery capacity should be kept within a reasonable range without overshoot or over discharge, and the battery capacity of the energy storage system is measured by using the State of charge (SOC) of the battery:
SOCmin≤SOC(t)≤SOCmax (12)
wherein the SOCminIs the SOC minimum limit of the battery, SOCmaxIs the SOC maximum limit of the battery.
Energy storage power and duration constraint
The power and the energy storage time of different types of energy storage are reasonably limited by the manufacturing process and the technical level:
pESS≤Pmax (13)
TESS≤Tmax (14)
as a further improvement of the present invention, the objective function of the system with minimum output power fluctuation is:
in the formula, PWT(t)、PPV(t)、PESS(t) wind power, photovoltaic and energy storage output at the moment t respectively;
the average values of the wind power and photovoltaic output of 8760h all year round are respectively.
As a further improvement of the invention, the charge-discharge model of the energy storage battery is as follows:
when [ t-1, t]Period of time, PESS>0, when the energy storage battery is in a charging state, the SOC of the battery at the time t is as follows:
when PESS is less than 0 in the time interval of [ t-1, t ] and the energy storage battery is in a discharging state, the SOC of the battery at the time t is
When [ t-1, t]Period of time, PESSWhen the energy storage battery maintains the original state, the SOC of the battery at time t is:
SOC(t)＝(1-δ)·SOC(t-1) (18)
wherein SOC (t) is the state of charge value of the energy storage battery at the time t, delta is the self-discharge rate of the energy storage battery, and pESS(t) is a battery at time tThe charge/discharge power of (a) is positive during charging and negative during discharging. EtacAnd ηdEfficiency of charging and discharging, respectively, of the battery, EESSIs the rated capacity of the energy storage battery.
As a further improvement of the invention, the weights of different objective functions are quantitatively evaluated by adopting an entropy weight method, and the entropy weight method reflects the importance degree of each sub-objective function by calculating the information entropy of each sub-objective function on the basis of comprehensively considering the information quantity provided by each factor. And determining the objective weight of each sub-target function according to the variability of each sub-target function. If the information entropy of a certain sub-objective function is smaller, it indicates that the information quantity provided by the sub-objective function is larger, the function played in the comprehensive objective function is larger, and the corresponding weight coefficient should be larger. Conversely, the larger the information entropy of a sub-objective function is, the smaller the amount of information provided by the sub-objective function is, the smaller the function that can be played in the integrated objective function is, and the smaller the corresponding weight coefficient should be. The specific implementation process is as follows:
(1) standardizing each sub-objective function, and assuming that n sub-objective functions F are given in the optimization model1,F2,...,FnOptimizing by a particle swarm algorithm to obtain a group of pareto frontier solution sets, wherein Fi＝f1,f2,...,fn. If the sub-targeting function is a forward type target, the normalization processing formula is as follows:
if the sub-targeting function is a negative direction type indicator, the normalization processing formula is as follows:
(2) and calculating the information entropy of each sub-target function, wherein according to the definition of the information entropy in the information theory, the calculation formula of the information entropy is as follows:
wherein
(3) Determining the weight of each objective function according to the calculation result of the information entropy:
(4) and calculating a comprehensive objective function through the weight determined by each sub-objective function:
(5) and optimally determining a corresponding wind-solar energy storage capacity configuration scheme by using a comprehensive objective function in the pareto frontier solution set.
As a further improvement of the invention, the optimizing termination condition is to repeat the processing until reaching the convergence condition of the algorithm or the set iteration number.
The present invention will be described in detail with reference to specific examples.
Examples
As shown in the attached figure 1, the multi-target capacity optimal configuration method of the grid-connected wind-solar storage system comprises the following steps:
1) inputting basic data required by wind, light and storage multi-target capacity optimization calculation, wherein the basic data comprises wind power and photovoltaic 8760h output characteristics of a project location, initial investment of various power supplies, annual operation maintenance rates, residual coefficient reaching a design life, discharge depth of an energy storage battery, maximum cycle number and the like;
2) randomly generating an initial wind-solar energy storage capacity configuration scheme group by taking the total investment of project construction, construction land, maximum energy storage power and maximum duration as constraint conditions;
3) carrying out production simulation calculation on the power system for 8760h all year round according to the generated initialized wind-solar energy storage capacity configuration scheme group to obtain key technical indexes such as wind power, photovoltaic energy, energy storage annual power transmission quantity, an energy storage SOC annual curve and the like, and calculating an equivalent operation life index of the energy storage according to the energy storage SOC curve;
4) aiming at the minimum cost-benefit ratio of the wind-solar energy storage system and the minimum fluctuation of the system output power, updating the current wind-solar energy storage capacity optimal configuration scheme set, and carrying out production simulation calculation on the power system for 8760h all the year round again;
5) repeating the steps 3) to 4), reaching the set precision requirement or reaching the maximum number of iterations, and outputting a group of wind-solar energy storage capacity optimal configuration pareto front solution sets;
6) standardizing each sub-target function according to the pareto frontier solution set;
7) calculating the information entropy of each sub-target function;
8) determining the weight of each sub-target function according to the calculation result of the information entropy;
9) calculating a comprehensive objective function according to the weight determined by each objective function;
10) optimally screening according to the comprehensive objective function to obtain an optimal solution in the pareto frontier solution set;
11) and outputting the optimal wind-solar energy storage capacity planning scheme of the project.
The following description is given with reference to specific examples:
taking a newly built wind-solar energy storage multi-energy complementary project in a certain area of Qinghai as an example, the wind power and photovoltaic solar output characteristics of the area are shown in the attached figure 3, the average 24-hour output value of the wind power in the whole year fluctuates between 0.147 and 0.335, wherein 18h is the largest, and 11h is the smallest; the maximum force output value fluctuates between 0.843 and 0.953, wherein 8h is the maximum, and 2h is the minimum. The maximum value of the photovoltaic 24-hour maximum output is 0.798 and occurs at 14 hours all the year round; the maximum value of the average force was 0.557, occurring at 13; the maximum value of the minimum output is 0.214, occurring at 15; the accumulative frequency characteristics of wind power output and photovoltaic output are shown in the attached figure 4, wherein the wind power output is mainly concentrated in an interval of 0-0.5, the frequency of the wind power output occurring in an interval of 0-0.05 is the highest and reaches 27.08%, and the frequency of the wind power output greater than 0.05 is 72.92%. The photovoltaic output is distributed more intensively between 0.05 and 0.75, and the frequency of the output more than 0.05 is 43.12 percent.
In the project planning stage, the wind power and photovoltaic total installation is proposed to be 500MW, the total investment of the project is estimated to be no more than 32 hundred million yuan, and the construction land is no more than 30 square kilometers; the wind power manufacturing cost is 6500 yuan/kW, the photovoltaic manufacturing cost is 4500 yuan/kW, and the annual operation maintenance cost accounts for 1% of the total investment amount; the energy storage adopts the most applied lithium ion battery at present, the power price of the battery is 1200 yuan/kW, the capacity price is 1600 yuan/kWh, the operation and maintenance rate of the energy storage power station is 0.5%, the comprehensive conversion benefit of the battery is 90%, and the residual value coefficient of the energy storage power station is 0.1; charging and discharging constraint SOC of energy storage batteryminIs 0.1, SOCmaxIs 0.9; the energy storage self-discharge rate is 0.5%/day; the prices of wind power, photovoltaic and energy storage grid-surfing electricity are respectively 0.29 yuan/kWh, 0.4 yuan/kWh and 0.7 yuan/kWh; wind power unit occupies 0.8km of land2The occupied area of the/MW and photovoltaic unit is 0.272km2The occupied area of a/MW energy storage unit is 0.015km2The construction land of/MW and other auxiliary facilities accounts for 5% of the total construction land of the wind-solar energy storage.
According to the experimental data of the maximum cycle number N corresponding to different DODs provided by the battery manufacturer, for example, the data of the maximum cycle number N and the depth of discharge of a certain lithium ion battery measured by the experiment are shown in table 1.
TABLE 1 data on the depth of discharge and maximum cycle number of a lithium ion battery
In the actual operation process of the battery, the DOD value is any number between 0 and 1, data fitting is carried out on the experimentally measured data in the table 1 according to the power function relation, the fitted function relation can be obtained, and the corresponding curve is shown in the attached figure 5.
N＝f(DOD)＝3452.DOD-0.9942-1030
Because the multi-target particle swarm algorithm is the existing mature optimization algorithm, the specific implementation process of the multi-target particle swarm algorithm is not explained in the invention.
Calculation example obtained by adopting implementation process of the inventionThe optimal capacity configuration scheme of the grid-connected wind-solar energy storage system comprises wind power of 310MW, photovoltaic of 190MW, energy storage of 35MW multiplied by 2h, project annual income of 32010.6 ten thousand yuan, annual cost of 28701.47 ten thousand yuan, and sub-objective function F1To 0.8967, the sub-goal function F2At 1.2524, the energy storage battery has an equivalent annual operating life of 9.18 years.
Another objective of the present invention is to provide a multi-objective capacity optimal configuration system for a grid-connected wind-solar energy storage system, which includes:
the model establishing module is used for establishing a multi-objective optimization configuration model taking the minimum cost-benefit ratio of the system and the minimum fluctuation of the output power of the system as objective functions by taking the wind power, the photovoltaic and the energy storage capacity as decision variables based on the annual 8760h resource output characteristics of the wind power and the photovoltaic of the project construction location;
the algorithm solving module is used for considering the energy storage life loss under the constraint conditions of considering the total system investment, the construction land area, the system power balance and the like, and solving the pareto front solution set by adopting a multi-target particle swarm algorithm based on a multi-target optimization configuration model;
the scheme determination module is used for quantitatively evaluating the weight coefficients corresponding to different target functions by adopting an entropy weight method and calculating to obtain a total target function according to the weight coefficients; and optimally determining a corresponding wind-solar energy storage capacity configuration scheme by using a total objective function in the pareto frontier solution set.
The third object of the invention is to provide an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the multi-target capacity optimal configuration method of the grid-connected wind and light storage system when executing the computer program.
A fourth object of the present invention is to provide a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the method for optimally configuring multi-target capacity of the grid-connected wind and light storage system.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A multi-target capacity optimal configuration method for a grid-connected wind-solar energy storage system is characterized by comprising the following steps:
based on the resource output characteristics of wind power and photovoltaic of the site where the project construction is located, the wind power, photovoltaic and energy storage capacity are used as decision variables, and a multi-objective optimization configuration model with the minimum system cost benefit ratio and the minimum system output power fluctuation as objective functions is established;
under the constraint conditions of considering total system investment, construction land area, system power balance and the like, considering energy storage life loss, and solving a pareto frontier solution set by adopting a multi-target particle swarm algorithm based on a multi-target optimization configuration model;
quantitatively evaluating the weight coefficients corresponding to different sub-objective functions by adopting an entropy weight method, and calculating to obtain a total objective function according to the weight coefficients; and optimally determining a corresponding wind-solar energy storage capacity configuration scheme by using a total objective function in the pareto frontier solution set.
2. The multi-target capacity optimal configuration method for the grid-connected wind, photovoltaic and energy storage system according to claim 1, wherein under the constraint conditions of considering total system investment, construction land area, system power balance and the like, energy storage life loss is considered, and a multi-target particle swarm algorithm is adopted to solve the pareto frontier solution set, which specifically comprises the following steps:
acquiring required basic data including wind power and photovoltaic 8760h output characteristics of a project location, initial investment of various power supplies, annual operation maintenance rate, residual coefficient of an energy storage power station, discharge depth and maximum cycle number of an energy storage battery and the like;
randomly generating an initial wind-solar energy storage capacity configuration scheme group by taking the total investment of project construction, construction land, maximum energy storage power and maximum duration as constraint conditions;
carrying out production simulation calculation on the power system for 8760h all year round according to the initial wind-solar energy storage capacity configuration scheme group to obtain wind power, photovoltaic energy, energy storage annual energy generation amount and an energy storage charge-discharge SOC curve, and calculating to obtain an equivalent operation life index of the energy storage according to the energy storage SOC curve;
updating the current wind-solar energy storage capacity optimal configuration scheme set by taking the minimum cost-benefit ratio of the wind-solar energy storage system and the minimum fluctuation of the system output power as fitness functions, and carrying out production simulation calculation on the power system for 8760h all the year;
and after repeated iteration, the set precision requirement is met or the maximum iteration times are met, and the wind and light storage capacity optimal configuration pareto front solution set is output.
3. The multi-target capacity optimal configuration method of the grid-connected wind-solar energy storage system according to claim 2, wherein the charge-discharge model of the energy storage battery is as follows:
when [ t-1, t]Period of time, PESS>0, when the energy storage battery is in a charging state, the SOC of the battery at the time t is as follows:
when PESS is less than 0 in the time interval of [ t-1, t ] and the energy storage battery is in a discharging state, the SOC of the battery at the time t is
When [ t-1, t]Period of time, PESSWhen the energy storage battery maintains the original state, the SOC of the battery at time t is:
SOC(t)＝(1-δ)·SOC(t-1) (3)
wherein SOC (t) is the state of charge value of the energy storage battery at the time t, delta is the self-discharge rate of the energy storage battery, and pESS(t) is the charge and discharge power of the battery at time t, positive during charging and negative during discharging; etacAnd ηdEfficiency of charging and discharging, respectively, of the battery, EESSIs the rated capacity of the energy storage battery.
4. The method for optimizing and configuring the multi-target capacity of the grid-connected wind-solar-energy storage system according to claim 2, wherein the calculation formula with the minimum cost-benefit ratio of the wind-solar-energy storage power generation system as a fitness function is as follows:
in the formula, EPVAnd EWTFor annual energy production gains of photovoltaic and wind power, EESSThe energy storage annual energy generation yield is increased; cINFor initial investment costs of the power station, COMAnnual operating maintenance costs of the power station, CREPReplacement costs for stored energy after reaching cycle life, CRECResidual values for the power station after reaching the design life;
the specific calculation formula of the annual cost expense of wind power, photovoltaic and energy storage is as follows:
COM＝μPVCIN-PV+μWTCIN-WT+μESSCIN-ESS (6)
CREP＝δESSCIN-ESS (7)
CREC＝λPVCIN-PV+λWTCIN-WT+λESSCIN-ESS (8)
in the formula (f)DRFor the annual value coefficient, r is the quasi-baseline discount rate, m is the design operation life of the power supply, the photovoltaic power and the wind power are considered according to the design operation life, the stored energy is considered according to the operation and the likeDetermining after calculating the effective life; cIN-PV、CIN-WT、CIN-ESSRespectively corresponding to the initial investment cost of photovoltaic, wind power and energy storage; mu.sPV、μWT、μESSThe annual operation and maintenance cost coefficients of photovoltaic power, wind power and energy storage are respectively set; deltaESSIs a replacement cost coefficient of the energy storage power station; lambda [ alpha ]PV、λWT、λESSThe residual coefficients of photovoltaic, wind power and energy storage are respectively.
5. The multi-objective capacity optimal configuration method for the grid-connected wind, photovoltaic and energy storage system according to claim 2, wherein the calculation formula of the system output power fluctuation minimum as a fitness function is as follows:
in the formula, PWT(t)、PPV(t)、PESS(t) wind power, photovoltaic and energy storage output at the moment t respectively;
the average values of the wind power and photovoltaic output of 8760h all year round are respectively.
6. The multi-target capacity optimal configuration method of the grid-connected wind-solar-energy storage system according to claim 2, wherein the calculation process of calculating the equivalent operating life of the stored energy according to the energy storage SOC curve comprises the following steps:
acquiring experimental data between a depth of discharge (DOD) and a maximum cycle number N provided by a battery manufacturer;
according to data measured by experiments, carrying out data fitting by using a Matlab tool box to obtain a functional relation between the fitted charge-discharge depth DOD and the maximum cycle number N;
extracting all effective charge-discharge cycles in the time period T by using a rain flow counting method and calculating the discharge depth DOD corresponding to each cycle;
calculating the maximum cycle number N corresponding to the DOD according to the fitted functional relation between the charge-discharge depth DOD and the maximum cycle number N;
converting N into equivalent cycle times N' under the condition of full charge and discharge, and then accumulating to obtain the equivalent total cycle times Nsum；
And calculating the loss coefficient and the actual operation life of the battery according to the full-charge cycle number N.
7. The multi-objective capacity optimal configuration method for the grid-connected wind, photovoltaic and energy storage system according to claim 2, wherein the entropy weight method is adopted to quantitatively evaluate the weight coefficients corresponding to different objective functions, and the specific implementation method is as follows:
standardizing each sub-target function;
calculating the information entropy of each sub-target function;
determining the weight of each sub-target function according to the calculation result of the information entropy;
calculating a comprehensive objective function according to the weight determined by each objective function;
and optimally screening according to the comprehensive objective function to obtain an optimal solution in the pareto frontier solution set.
8. The multi-objective capacity optimal configuration method of the grid-connected wind-solar-energy storage system according to claim 2, wherein the method for solving the constraint conditions in the pareto frontier solution set by adopting a multi-objective particle swarm optimization based on the multi-objective optimal configuration model comprises the following steps:
and (4) investment constraint:
the upper limit of the total investment amount of the project is CmaxThen wind-powered electricity generation, photovoltaic, energy storage installation restraint do:
wherein, CWTThe unit kilowatt cost of the fan; cPVThe unit kilowatt cost of photovoltaic; cESS-PCost per kilowatt for energy-storage batteryThis), C)ESS-EThe cost per kilowatt-hour (capacity cost) of the energy storage battery;
building land constraint:
the project construction land area is S, the length is L, the width is W, then the project optimization is to wind-powered electricity generation, photovoltaic, the restraint of energy storage do:
wherein p isWTThe installed capacity of a single fan, and d is the diameter of a wind wheel of the single fan; p is a radical ofPVInstalled capacity, S, for a single photovoltaic arrayPIs the footprint of a single photovoltaic array,
is the shading coefficient; e.g. of the typeESSFor the battery capacity, S, of a single energy-storing containerSThe floor space of a single energy storage container;
energy storage battery SOC constraint
In order to ensure the working life of the energy storage system during working, the battery capacity can be kept within a reasonable range without overshoot or over discharge, and the battery capacity of the energy storage system is measured by adopting the state of charge (SOC) of the battery:
SOCmin≤SOC(t)≤SOCmax (12)
wherein the SOCminIs the SOC minimum limit of the battery, SOCmaxIs the SOC maximum limit of the battery;
energy storage power and duration constraints
The power and the energy storage time of different types of energy storage are reasonably limited by the manufacturing process and the technical level:
pESS≤Pmax (13)
TESS≤Tmax (14)。
9. the method for optimizing configuration of multi-target capacity of the grid-connected wind, light and storage system according to claim 2, wherein the optimization termination condition is repeated until an algorithm convergence condition is reached or a set iteration number is reached.
10. A multi-target capacity optimal configuration system of a grid-connected wind-solar energy storage system is characterized by comprising:
the model establishing module is used for establishing a multi-objective optimization configuration model which takes the minimum cost-benefit ratio of the system and the minimum fluctuation of the output power of the system as objective functions by taking the wind power, the photovoltaic and the energy storage capacity as decision variables based on the annual 8760h resource output characteristics of the wind power and the photovoltaic of the project construction location;
the algorithm solving module is used for considering the energy storage life loss under the constraint conditions of considering the total system investment, the construction land area, the system power balance and the like, and solving the pareto front solution set by adopting a multi-target particle swarm algorithm based on a multi-target optimization configuration model;
the scheme determination module is used for quantitatively evaluating the weight coefficients corresponding to different target functions by adopting an entropy weight method and calculating to obtain a total target function according to the weight coefficients; and optimally determining a corresponding wind-solar energy storage capacity configuration scheme by using a total objective function in the pareto frontier solution set.
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