CN113792911A - Double-layer collaborative optimization configuration method and system for energy storage capacity of optical storage system - Google Patents

Double-layer collaborative optimization configuration method and system for energy storage capacity of optical storage system Download PDF

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CN113792911A
CN113792911A CN202110942544.XA CN202110942544A CN113792911A CN 113792911 A CN113792911 A CN 113792911A CN 202110942544 A CN202110942544 A CN 202110942544A CN 113792911 A CN113792911 A CN 113792911A
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陆超
刘杰
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Abstract

The invention provides a double-layer collaborative optimization configuration method and a double-layer collaborative optimization configuration system for energy storage capacity of an optical storage system, wherein the method comprises the following steps: acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening out sample data of energy storage capacity optimization based on fluctuation analysis; constructing an energy storage capacity double-layer multi-target cooperative optimization configuration model including an economic optimization layer model and an operation optimization layer model; and inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain the optimal configuration result of the energy storage capacity. The invention realizes the reasonable and effective configuration of the energy storage capacity of the optical storage system under the influence of double uncertainties.

Description

Double-layer collaborative optimization configuration method and system for energy storage capacity of optical storage system
Technical Field
The invention relates to the technical field of energy storage capacity configuration of an optical storage system, in particular to a double-layer collaborative optimization configuration method and system for energy storage capacity of the optical storage system.
Background
In the existing research of the energy storage capacity optimization problem of new energy power generation, a single angle in the target of meeting the system operation performance requirement or optimizing the system economy is selected to be considered mostly based on a certain determined operation strategy, and the coupling relation existing between energy storage capacity planning and system optimization operation is not considered. In addition, the existing energy storage capacity optimization method does not fully consider the influence of uncertainty factors on the energy storage capacity requirement, is not suitable for complex scenes with various uncertainty coupling, is inconvenient to analyze the corresponding capacity of configuration schemes on the influence of the uncertainty factors, and is difficult to coordinate the contradiction between robustness and economy.
For the optical storage system, the capacity optimization problem of the stored energy is more complicated. On one hand, the application function of the stored energy is diversified, and a series of new energy grid-connected standards formulated by the state provide clear requirements for the power fluctuation level, the power prediction error, the electric energy quality and the like of new energy power generation, so that the stored energy needs to both stabilize the power fluctuation and compensate the prediction error. On the other hand, the uncertain factors in the optical storage system are more, the uncertainty existing in photovoltaic power prediction needs to be considered besides the random uncertainty objectively existing in photovoltaic output under the influence of weather, and the optimization of the capacity of the stored energy has great challenges due to the characteristic of double uncertain coupling. Therefore, an energy storage capacity optimization method which can fully consider the influence of uncertainty factors and can also consider the coupling relation between capacity planning and system optimization operation is urgently needed.
Disclosure of Invention
The embodiment of the invention provides a double-layer collaborative optimization method and system for energy storage capacity of an optical storage system, which are used for solving the problem that the energy storage capacity of the optical storage system cannot be reasonably and effectively configured under the influence of double uncertainty at present.
In a first aspect, an embodiment of the present invention provides a double-layer collaborative optimization configuration method for energy storage capacity of an optical storage system, including:
acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening out sample data of energy storage capacity optimization based on fluctuation analysis;
constructing an energy storage capacity double-layer multi-target collaborative optimization configuration model including an economic optimization layer model and an operation optimization layer model;
and inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain the optimal configuration result of the energy storage capacity.
Further, the screening of the sample data for optimizing the energy storage capacity based on the fluctuation analysis comprises the following steps:
forming photovoltaic power data of the d-th operation day of the photovoltaic power data of the photovoltaic power station to be configured into a photovoltaic power sequence P according to the time sequenced＝(Pd，1，Pd，2，...，Pd，m) (ii) a Wherein m is the data number of the photovoltaic power sequence;
sample entropy E from the photovoltaic power seriesSEAnd a predetermined sequence complexity decision reference value E* SECarrying out wave momentum analysis to screen out sample data of energy storage capacity optimization: if ESE＞E* SEIf so, the photovoltaic power sequence is a complex meteorological power sequence, and the fluctuation quantity analysis is finished;
otherwise, counting the out-of-limit times N of elements in the first-order difference sequence of the photovoltaic power sequenceD: if N is presentD＞N* DIf the photovoltaic power sequence is a complex meteorological power sequence, otherwise, the photovoltaic power sequence is a simple meteorological power sequence; wherein N is* DThe method is a preset threshold value for judging the out-of-limit times of the photovoltaic power fluctuation of the complex weather.
Further, the counting of the number N of times of out-of-limit of elements in the first-order difference sequence of the photovoltaic power sequenceDThe method comprises the following steps: statistics are such that the inequality Δ pd，j＞βDΔ p for which m-1 holdsd，jThe number of (2); wherein, Δ pd，j＝|pi，j+1-pi，j|，j＝1，2，...，m-1，βDIs a reference value for measuring the power fluctuation amplitude of adjacent time instants.
Further, the building of the energy storage capacity double-layer multi-objective collaborative optimization configuration model including the economic optimization layer model and the operation optimization layer model comprises the following steps:
constructing an economic optimization layer model comprising a target function and constraint conditions on the basis of taking the daily average net gain of the light storage system and the energy balance capability index of the energy storage system as optimization targets and taking the rated power and the rated capacity of the stored energy as decision variables; the objective function of the economic optimization layer model comprises assessment punishment cost generated when the active power change value of the photovoltaic power station exceeds an assessment limit value;
and constructing an operation optimization layer model including an objective function and constraint conditions on the basis of taking the assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds the assessment limit value in the operation process of the light storage system as an optimization objective and taking the photovoltaic output at each moment of energy storage as a decision variable.
Further, the objective function of the economic optimization layer model is represented as follows:
Figure BDA0003215656090000041
wherein, CNIThe equivalent daily average net gain of the light storage system in the operation process is obtained; csalObtaining electricity selling income for the system online electricity quantity; c'LCCThe corresponding conversion cost of the energy storage life cycle cost in the operation process is calculated; cpenThe assessment penalty cost is generated when the active power change value of the photovoltaic power station exceeds the assessment limit value; b isoThe energy balance capability index of the energy storage system is obtained; po down(t) is the lower limit value of the probability that the energy storage system is in the energy balance state at the moment t; t is the number of moments contained in each operation day;
the objective function of the run optimization layer model is represented as follows:
Figure BDA0003215656090000042
Figure BDA0003215656090000043
Figure BDA0003215656090000044
wherein, betapenThe evaluation unit price is corresponding to the evaluation electric quantity; delta PU d，iThe output power change value at the moment i in the d operating day is obtained; pb d，iThe output power of the stored energy at the moment i in the d operating day; plimIs an allowable limit value of power variation; ppv d，iThe photovoltaic output power is the photovoltaic output power at the ith operating day time; t isdOperating days contained in sample data optimized for energy storage capacity; n is a radical ofTThe number of moments included in the illumination period in each operation day.
Further, the constraint conditions of the economic optimization layer model comprise limit constraints of rated power and rated capacity of the energy storage system and limit constraints of investment funds of the energy storage system, and the constraint conditions are expressed as follows:
0≤CLCC≤Cmax；
0≤Prate≤kp，max·Gpv；
0≤Qrate≤kq，max·Prate；
wherein, CLCCThe corresponding conversion cost of the energy storage life cycle cost in the operation process is calculated; cmaxPlanning an allowable limit value of cost for the investment of the energy storage system; gpvPower scaling for photovoltaic power stations; k is a radical ofp，maxThe limit value is the ratio of the rated power of the energy storage system to the installed scale of the photovoltaic power station; k is a radical ofq，maxThe charging and discharging duration can be continued under the rated working condition of the energy storage system.
Further, the constraints of the operation optimization layer model include operation constraints of the energy storage system and opportunity constraints for guaranteeing the performance of prediction error compensation, and the constraints are expressed as follows:
Figure BDA0003215656090000051
Figure BDA0003215656090000052
Figure BDA0003215656090000053
Figure BDA0003215656090000054
Smin≤Sd，i≤Smax；
0.5-Δδ≤Sd，end≤0.5+Δδ；
wherein, P { gamma [ [ gamma ] ])d，jThe probability that the accuracy of the photovoltaic output prediction error meets the assessment requirement is not lower than a certain confidence coefficient alpha;
Figure BDA0003215656090000056
the predicted value of the photovoltaic power at the j moment of the d-th operation day is obtained; gamma rayd，jUltra-short term prediction accuracy for the jth operating day time; l% is the allowable lower limit of prediction accuracy; n is the number of samples selected during the calculation of the ultra-short-term prediction accuracy, and is usually 16; sd，iThe state of charge of energy storage for the d operating day i; smin、SmaxRespectively an upper limit value and a lower limit value allowed by a charge state in the process of energy storage charging and discharging; etad，iThe charge-discharge efficiency of energy storage at the ith operating day i is improved; eta c, etadCharging efficiency and discharging efficiency of stored energy are respectively;
Figure BDA0003215656090000055
the output power of the stored energy at the moment i in the d operating day; prate、QrateRated power and rated capacity of the energy storage system are respectively; delta t is the acquisition interval of the sample data of the energy storage capacity optimization; sd，endThe state of charge value of the stored energy at the end time of the d-th operation day; Δ δ is the allowable fluctuation range of the state of charge.
In a second aspect, an embodiment of the present invention provides a dual-layer collaborative optimization configuration system for energy storage capacity of an optical storage system, including:
the data screening unit is used for acquiring photovoltaic power data of a photovoltaic power station to be configured and screening out sample data of energy storage capacity optimization based on fluctuation analysis;
the model construction unit is used for constructing an energy storage capacity double-layer multi-target collaborative optimization configuration model including an economic optimization layer model and an operation optimization layer model;
and the configuration solving unit is used for inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain the optimal configuration result of the energy storage capacity.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the program, implements the steps of the optical storage system energy storage capacity two-layer cooperative optimization configuration method according to any one of the above-mentioned first aspects.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the dual-layer collaborative optimization configuration method for energy storage capacity of an optical storage system as provided in any one of the above first aspects.
According to the double-layer collaborative optimization configuration method and system for the energy storage capacity of the optical storage system, the operation day data of complex weather with large volatility is screened out through fluctuation analysis to serve as the sample data for optimizing the energy storage capacity, and the effectiveness of sample data selection is guaranteed; and constructing an energy storage capacity planning-operation double-layer multi-target collaborative optimization model of the light storage system through a coupling relation of energy storage capacity planning and system optimization operation, wherein the outer economic optimization layer model ensures the economy of an energy storage configuration scheme, and the inner operation optimization layer model ensures the power stabilizing performance and the prediction error compensation performance of the light storage system. The invention realizes the reasonable and effective configuration of the energy storage capacity of the optical storage system under the influence of double uncertainties.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the following briefly introduces the drawings needed for the embodiments or the prior art descriptions, and obviously, the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a double-layer cooperative optimization configuration method for energy storage capacity of an optical storage system according to the present invention;
FIG. 2 is a schematic diagram of a policy selection process for comprehensive optimal configuration provided by the present invention;
FIG. 3 is a schematic diagram illustrating a trend of an energy balance capability index of the energy storage system provided by the present invention along with a variation of an energy storage rated capacity;
fig. 4 is a schematic structural diagram of a double-layer cooperative optimization configuration system for energy storage capacity of an optical storage system provided by the present invention;
FIG. 5 is a schematic structural diagram of a data screening unit provided by the present invention;
fig. 6 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical idea of the invention is as follows: firstly, screening out operation day data of complex weather with large volatility as sample data for optimizing energy storage capacity through fluctuation amount analysis; secondly, constructing an energy storage capacity double-layer multi-target collaborative optimization model of the optical storage system, wherein the outer layer is an economic optimization layer model, the optimization target is the optimal daily net income of the optical storage system and the optimal energy balance capability index of the energy storage system, and the rated power and the rated capacity of the energy storage are decision variables, so that the economy of an energy storage configuration scheme and the capability of responding to the random uncertainty of the photovoltaic output are ensured; the inner layer is an operation optimization layer model, the lowest assessment penalty cost of the active power change rate in the system operation process is taken as an optimization target, the output condition of each time of energy storage is taken as a decision variable, the power suppression performance and the prediction error compensation performance of the optical storage system are ensured, and meanwhile the influence of the photovoltaic power prediction uncertainty is considered through opportunity constraint; and finally, solving the inner and outer layer optimization models by respectively adopting a multi-target particle swarm algorithm and a CPLEX optimization solver to obtain an optimal configuration scheme of the energy storage. The method can consider the influence of the photovoltaic output random uncertainty and the photovoltaic power prediction uncertainty, can consider the coupling relation between the energy storage capacity planning and the system optimization operation, and can provide theoretical basis and guidance suggestion for the investment construction of the optical storage system.
The following describes a method and a system for configuring the energy storage capacity of an optical storage system in a two-layer cooperative optimization manner according to the present invention with reference to fig. 1 to 6.
The embodiment of the invention provides a double-layer collaborative optimization configuration method for energy storage capacity of an optical storage system. Fig. 1 is a schematic flow chart of a method for configuring energy storage capacity of an optical storage system by double-layer cooperative optimization according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step 110, acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening out sample data of energy storage capacity optimization based on fluctuation analysis;
step 120, constructing an energy storage capacity double-layer multi-target collaborative optimization configuration model including an economic optimization layer model and an operation optimization layer model;
and step 130, inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.
According to the method provided by the embodiment of the invention, the operation day data of the complex weather with large volatility is screened out through fluctuation amount analysis to be used as the sample data of energy storage capacity optimization, the energy storage capacity planning-operation double-layer multi-target collaborative optimization model of the light-emitting and energy storage system is constructed through the coupling relation between the energy storage capacity planning and the system optimization operation, and the double-layer multi-target collaborative optimization model is solved to obtain the optimal configuration result. The invention realizes the reasonable and effective allocation of the energy storage capacity of the optical storage system under the influence of double uncertainties.
Specifically, for the above step 130, an Optimization algorithm of Multi-object Particle Swarm (MOPSO) and a CPLEX Optimization solver are respectively adopted to solve the internal and external Optimization models corresponding to the operation Optimization layer model and the economic Optimization layer model constructed in the step 120, wherein, for the problem that the internal Optimization model solves the problem that the variable scale is too large to solve, a method of piecewise Optimization with day as an Optimization period is adopted to solve, and Optimization processes of the internal and external Optimization models are nested with each other, and a specific solving process is as follows:
1) initializing economic layer (outer layer) parameters: particle X ═ { X ═ X1＝Prate，x2＝Qrate}, determining the size N of the population particlesPSONumber of iterations ImaxInertia factor w, acceleration factor (c)1，c2) Parameters, namely, randomly initializing the position and the speed of each particle, and determining each coefficient in an objective function and a constraint condition of an economic layer;
2) run-level (inner-level) optimization was performed for each particle:
2-1) setting parameters of an operation layer: acquiring photovoltaic output and corresponding power prediction data, setting the rated energy storage power and rated capacity according to current particle parameters, and determining the target function of an operation layer and each coefficient in constraint conditions;
2-2) based on a CPLEX solver, taking the period with illumination in each operation day as an optimization cycle, and solving the optimal operation scheme of each operation day under the current particle parameters one by one;
2-3) combining the optimization results of each operation day and outputting an energy storage output process Pb d，iAnd a penalty cost of assessment Cpen；
3) Respectively calculating the fitness function value C of each particle in the economic layer model population according to the output result of the step 2)NIAnd BoScreening the optimal solution of Parritodur to generate a non-inferior solution set according to the domination relation of the fitness values of the particles;
4) updating individual historical optimal and global optimal positions of each particle based on an optimal compromise solution selection strategy; the specific steps of the optimal compromise solution selection strategy are as follows:
4-1) carrying out normalization processing on the target functions to eliminate the influence of different target functions due to dimension and order difference:
Figure BDA0003215656090000091
wherein, CNI(X)、Bo(X) and C'NI(X)、B’o(X) respectively representing the original value and the normalized value of the target function corresponding to each decision point in the non-inferior solution set; cNI，max、Bo，max、CNI，min、Bo，minIs the extremum of the corresponding objective function;
4-2) calculating each decision point in the non-inferior solution set and the ideal optimal decision point X+And the ideal worst decision point X-The degree of closeness of (c):
Figure BDA0003215656090000101
wherein, X+The number of the (1,1)，X-is (0, 0); cjIs decision point XjThe corresponding closeness;
Figure BDA0003215656090000103
to decide point XjAnd X+Geometric distance of (d)j +＝|XjX+|；dj -Is decision point XjAnd X-Geometric distance of (d)j -＝|XjX-|；d+Is all dj +Minimum value of (d)+＝min{dj +}；d-Is all dj -Maximum value of (d + ═ max { d) }j +}；
4-3) selecting a decision point with the minimum intimacy in the non-inferior solution set as the optimal compromise solution;
5) updating the position and speed of each particle, and resetting the position of the particle exceeding the search space;
6) judging whether the maximum iteration times of the outer layer is reached, if so, executing the step 7), otherwise, turning to the step 2);
7) and selecting the optimal particles in the final pareto frontier based on the optimal compromise solution selection strategy, wherein the corresponding parameters are the optimal configuration scheme of the energy storage synthesis. Fig. 2 shows a schematic diagram of determining a comprehensive optimal configuration scheme by an optimal compromise solution selection strategy in the embodiment of the present invention.
By solving the double-layer multi-objective optimization model in the embodiment through the steps, a comprehensive optimal scheme of the photovoltaic station configuration energy storage can be obtained, as shown in table 1.
TABLE 1 comprehensive optimal configuration scheme
Figure BDA0003215656090000102
Based on any one of the embodiments, the screening of the sample data for optimizing the energy storage capacity based on the fluctuation amount analysis includes the following steps:
the light to be configuredThe photovoltaic power data of the d-th operation day of the photovoltaic power data of the photovoltaic power station form a photovoltaic power sequence P according to the time sequenced＝(Pd，1，Pd，2，...，Pd，m) (ii) a Wherein m is the data number of the photovoltaic power sequence;
sample entropy E from the photovoltaic power seriesSEAnd a predetermined sequence complexity decision reference value E* SECarrying out wave momentum analysis to screen out sample data of energy storage capacity optimization: if ESE＞E* SEIf so, the photovoltaic power sequence is a complex meteorological power sequence, and the fluctuation quantity analysis is finished;
otherwise, counting the out-of-limit times N of elements in the first-order difference sequence of the photovoltaic power sequenceD: if N is presentD＞N* DIf the photovoltaic power sequence is a complex meteorological power sequence, otherwise, the photovoltaic power sequence is a simple meteorological power sequence; wherein N is* DThe method is a preset threshold value for judging the out-of-limit times of the photovoltaic power fluctuation of the complex weather.
Based on any one of the above embodiments, the counting of the number of times of violation N of elements in the first-order difference sequence of the photovoltaic power sequenceDThe method comprises the following steps: statistics are such that the inequality Δ pd，j＞βDΔ p for which m-1 holdsd，jThe number of (2); wherein, Δ pd，j＝|pi，j+1-pi，j|，j＝1，2，...，m-1， βDIs a reference value for measuring the power fluctuation amplitude of adjacent time instants.
Specifically, the method comprises the steps that 1) photovoltaic power data of a photovoltaic power station to be configured are obtained, the photovoltaic power data of each operation day form a power sequence according to time sequence, and the operation day data of complex weather with large volatility is screened out through fluctuation analysis and used as sample data for capacity optimization; photovoltaic power sequence P for the d-th operating dayd＝(Pd，1，Pd，2，...，Pd，m) Wherein, m is the number of data contained in the power sequence, and the fluctuation analysis comprises the following steps:
1) calculating the sample entropy E of the power sequenceSE；
1-2) determining the result of step 1), if ESE＞E* SEIf the power sequence is a complex meteorological power sequence, the fluctuation quantity analysis is finished; otherwise, entering the step 1-3); wherein E is* SEIn the embodiment of the present invention, E is set as a reference value for sequence complexity determination* SE＝0.3；
1-3) calculating a first order difference sequence (Δ p) of the power sequencei，1，Δpi，2，…，Δpi，m-1) Wherein Δ pi，j＝ |pi，j+1-pi，j|，j＝1，2，…，m-1；
1-4) counting the out-of-limit times N of elements in the first-order difference sequence of the power sequence obtained in the step 1-3)DI.e. statistically making the inequality Δ pi，j＞βDΔ p where j is 1, 2, …, and m-1 holdsi，jThe number of (2); then the following is determined:
if N is presentD＞N* DThen the power sequence is complicated with a meteorological power sequence; otherwise the power sequence is a simple meteorological power sequence.
Wherein, betaDFor measuring the reference value of the power fluctuation amplitude of the adjacent time, the beta value in the embodiment of the inventionD＝1.5kW；Δpi，jGreater than betaDWhen it is, then p is consideredi，jThe corresponding time has larger fluctuation; n is a radical of* DFor the judgment threshold value of the photovoltaic power fluctuation out-of-limit times of the complex weather, N in the embodiment of the invention* D＝4。
Based on any one of the above embodiments, the constructing of the energy storage capacity double-layer multi-objective collaborative optimization configuration model including the economic optimization layer model and the operation optimization layer model includes:
constructing an economic optimization layer model comprising a target function and constraint conditions on the basis of taking the daily average net gain of the light storage system and the energy balance capability index of the energy storage system as optimization targets and taking the rated power and the rated capacity of the stored energy as decision variables; the objective function of the economic optimization layer model comprises assessment punishment cost generated when the active power change value of the photovoltaic power station exceeds an assessment limit value;
it should be noted that, with the daily average net profit of the optical storage system and the optimal energy balance capability index of the energy storage system as optimization targets and the rated power and the rated capacity of the energy storage as decision variables, a multi-objective optimization model is constructed to serve as an outer layer model of the energy storage capacity double-layer collaborative optimization model, namely, an economic optimization layer model, so as to ensure the economy of the energy storage configuration scheme. The influence of random uncertainty objectively existing photovoltaic output is considered through the energy balance capability index of the energy storage system.
And constructing an operation optimization layer model including an objective function and constraint conditions on the basis of taking the assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds the assessment limit value in the operation process of the light storage system as an optimization objective and taking the photovoltaic output at each moment of energy storage as a decision variable.
It should be noted that the assessment penalty cost C generated when the active power variation value exceeds the assessment limit value in the operation process of the optical storage systempenThe minimum is an optimization target, the output condition of each time of energy storage is a decision variable, and an operation optimization layer model is constructed to serve as an inner layer model of the energy storage capacity double-layer collaborative optimization model, so that the power stabilizing performance and the prediction error compensation performance of the optical storage system are guaranteed. Wherein the influence of the uncertainty of the photovoltaic power prediction is taken into account by an opportunity constraint,
based on any of the above embodiments, the objective function of the economically optimized layer model is represented as follows:
Figure BDA0003215656090000131
wherein, CNIThe equivalent daily average net gain of the light storage system in the operation process is obtained; csalObtaining electricity selling income for the system online electricity quantity; c'LCCThe corresponding conversion cost of the energy storage life cycle cost in the operation process is calculated; cpenThe active power change value of the photovoltaic power station exceeds the assessment limitThe assessment penalty cost is generated in value; b isoThe energy balance capability index of the energy storage system is obtained; po down(t) is the lower limit value of the probability that the energy storage system is in the energy balance state at the moment t; t is the number of moments contained in each operation day;
specifically, CpenThe photovoltaic power stabilizing performance is related to the photovoltaic power stabilizing performance and is directly influenced by the system optimization operation result, and the photovoltaic power stabilizing performance is used as an objective function of the operation optimization layer model; csalAnd C'LCCThe calculation expression of (a) is as follows:
Figure BDA0003215656090000132
C′LCC＝Ld·CLCC (5)
Figure BDA0003215656090000133
wherein, TdThe number of operation days contained in the selected sample data; n is a radical ofTThe number of times included in the illumination period in each operation day (the time resolution is 1 minute); beta is apvPhotovoltaic internet-surfing electricity price subsidized for policy consideration; pU d，iThe light storage combined output at the ith moment of the ith operating day; cLCCThe total cost corresponding to the total life cycle of the energy storage system; l isd(0≤LdLess than or equal to 1) is a corresponding daily equivalent life loss coefficient in the operation process of the energy storage system, and the equivalent life loss is converted by adopting a method based on a rain flow counting method; n is a radical ofcThe energy storage charging and discharging cycle number is calculated by adopting a rain flow counting method; dDOD，jThe charge-discharge depth is corresponding to the jth charge-discharge cycle period; n is a radical ofctf(DDOD，j) To charge and discharge with a depth DDOD，jThe corresponding functional relation can be obtained according to the performance parameter fitting of the energy storage battery; n is a radical ofctf(DDOD，st) The corresponding cycle life when the charging and discharging depth is 100%; when L isdWhen the value is 1, the storage is consideredThe energy battery life is exhausted;
CLCCthe calculation expression of (a) is as follows:
CLCC＝CI+CoM+Cs (7)
CI＝(1+λbop)·[kqQrate+(kp+kpcs)Prate] (8)
CoM＝λomCI (9)
CS＝(kspPrate+ksqQrate)·γPF (10)
Figure BDA0003215656090000141
NT＝1/365/Ld (12)
wherein, CIThe fixed investment cost is invested for the energy storage system at one time in the initial construction stage; cOMFor operating maintenance costs; cSThe cost generated by the processes of removing and destroying the energy storage battery in the scrapping disposal stage; prate、QrateDividing the power into rated power and rated capacity of an energy storage system; k is a radical ofp、 kqRespectively the power unit price and the capacity unit price of the energy storage system; k is a radical ofpcsUnit price for power conversion equipment; lambda [ alpha ]bopThe ratio of the civil engineering cost to the energy storage system body cost is about 5-15%; lambda [ alpha ]omThe ratio coefficient of the operation and maintenance cost to the fixed investment cost of the system; k is a radical ofsp、ksqThe power processing unit price and the capacity processing unit price of the energy storage system are obtained; gamma rayPFA conversion factor from a final value to a current value; r is depreciation rate; n is a radical ofTThe energy storage system can run for a few years. In the embodiment of the present invention, kp2000 yuan/kW, kq1500 Yuan/kWh, kpcs800 yuan/kW, lambdabop＝10％，λom＝5％，ksp120 yuan/kW, ksq80 yuan/kWh, r 0.08.
It should be noted that, the photovoltaic power fluctuation of the sample data is stabilized based on the kalman filter algorithm, the charge and discharge demand of the stored energy when the photovoltaic power fluctuation is stabilized is obtained, an envelope model for representing the uncertainty of the charge and discharge energy demand of the stored energy is further constructed, and an energy balance capacity index of the energy storage system capable of describing the corresponding capacity of the stored energy to the uncertainty is defined based on a random network calculus theory, and the method specifically comprises the following steps:
1) stabilizing the photovoltaic power fluctuation of the sample data by adopting a conventional Kalman filtering algorithm to obtain target output data meeting the power fluctuation requirement, wherein the deviation of the actual output and the target output of the photovoltaic power station is the charge and discharge requirement of energy storage;
2) an envelope model representing uncertainty of energy storage charging and discharging energy requirements is constructed, and the specific model is as follows:
2-1) energy storage charging energy demand envelope curve representation model
Figure BDA0003215656090000151
Wherein E isc(s, t) is the period of stored energy [ s, t]Cumulative charge energy requirement process in, and Ec(s，t)：Ec(t)-Ec(s)；αc up(·)、αc downThe upper limit function and the lower limit function of the accumulated charging energy demand process are respectively fitted by adopting a multi-order linear function; epsilonc up(x)、εc down(x) Fitting the probability boundary function by adopting an exponential decay function for the corresponding probability boundary function; p is a probability symbol; sup is the supremum operator.
2-2) energy storage discharge energy demand envelope curve representation model
Figure BDA0003215656090000152
Wherein E isd(s, t) is the period of stored energy [ s, t]Accumulated discharge energy requirement process in, and Ed(s，t)＝Ed(t)-Ed(s)；αd up(·)、αd downThe upper limit function and the lower limit function of the accumulated discharge energy demand process are respectively fitted by adopting a multi-order linear function; epsilond up(x)、εd down(x) And fitting the probability boundary function by adopting an exponential decay function.
3) Energy change process of energy storage system is depicted
3-1) description of charging and discharging Process of energy storage System
If the charging energy demand of the energy storage is greater than the discharging energy demand in a certain period of time, the energy storage is the charging state and the charging capacity is the deviation of the two on the whole in the period of time, otherwise, if the discharging energy demand of the energy storage is greater than the charging energy demand in a certain period of time, the energy storage is the discharging state on the whole in the period of time, and the corresponding mathematical description is:
b(t)＝min[Qrate，[b(t-1)+Ec(t-1，t)-Ed(t-1，t)]+] (15)
wherein, b (t) represents the electric quantity stored in the energy storage at the moment t; qrateThe rated capacity of the energy storage system; ec(t-1，t)＝Ec(t)-Ec(t-1)、Ed(t-1，t)＝Ed(t)-Ed(t-1) are periods of time [ t-1, t, respectively]A charging energy requirement and a discharging energy requirement for the internal storage energy.
It can further be derived that the non-recursive form of b (t) is:
Figure BDA0003215656090000161
wherein sup is an operator of supremum; inf is the infimum operator.
3-2) energy shortage state description of energy storage system
If the energy storage system is in a discharge state continuously in a certain period of time and the corresponding deviation of the charge and discharge energy requirements is greater than the stored electric quantity in the stored energy, the discharge energy requirements of the system cannot be met after all the energy is released, the energy storage system is considered to be in an energy shortage state, and the corresponding shortage energy l (t) can be expressed as:
l(t)＝[Ed(t-1，t)-Ec(t-1，t)-b(t-1)]+ (17)
combining the non-recursive form of b (t) in the step 3-1), the non-recursive expression corresponding to l (t) can be obtained as follows:
Figure BDA0003215656090000162
further based on random network calculus theory and E constructed in the above stepsc(t)、Ed(t) an envelope representation model can be used for deducing the probability P that the energy storage system is in an energy shortage state at any moment tL(t) satisfies:
Figure BDA0003215656090000163
it can be seen that this equation determines an upper limit on the probability that the energy storage system is in an energy deficit state.
Wherein the content of the first and second substances,
Figure BDA0003215656090000171
is the minimum convolution operator, which is defined as:
Figure BDA0003215656090000172
3-3) energy storage System energy excess State description
If the stored energy is continuously in a charging state within a certain period of time and the corresponding charging and discharging energy demand deviation is greater than the rated energy storage capacity, the stored energy cannot continuously meet the charging demand after being charged to the rated capacity, the energy storage system is considered to be in an energy excess state, and the corresponding excess energy m (t) can be expressed as:
m(t)＝[Ec(t-1，t)-Ed(t-1，t)+b(t-1)-Qrate]+ (21)
similar procedure3-2), the probability P that the energy storage system is in the energy surplus state at any time t can be deducedM(t) satisfies:
Figure BDA0003215656090000173
similarly, the equation determines an upper limit of the probability of the energy storage system being in an energy surplus state during operation.
4) Energy balance capability evaluation index construction of energy storage system
When the energy storage system does not belong to the two states in the step 3-2) and the step 3-3), the energy storage system can be considered to be in an energy balance state, that is, the configured energy storage capacity can meet the charging and discharging requirements of the system on the energy storage, and the sum of the probabilities of the energy storage system in various energy states at any time is 1, so that the probability P of the energy storage system in the energy balance state at the time t can be obtainedo(t) satisfies:
Figure BDA0003215656090000174
wherein, PM up(t)、PL up(t) the probability upper limit values of the energy storage system in the energy excess state and the energy shortage state at the moment t are respectively set; po downAnd (t) is a lower limit value of the probability that the energy storage system is in the energy balance state at the moment t.
Further, based on Po down(t) constructing an energy balance capability index B of the energy storage systemoMeasuring the comprehensive coping ability of the energy storage capacity configured in the system operation process to the uncertain charging and discharging requirements, BoThe expression is as follows:
Figure BDA0003215656090000181
wherein T is the number of moments contained in each operation day; b isoThe physical meaning of (A) is that the energy is stored in the system under a certain capacity configuration scaleAnd the average probability lower limit value is in an energy balance state in the system operation period. The trend of the energy balance capability index of the energy storage system obtained by the embodiment of the invention along with the change of the rated capacity of the stored energy is shown in figure 3.
The objective function of the run optimization layer model is represented as follows:
Figure BDA0003215656090000182
Figure BDA0003215656090000183
Figure BDA0003215656090000184
wherein, betapenThe evaluation unit price is corresponding to the evaluation electric quantity; delta PU d，iThe output power change value at the moment i in the d operating day is obtained; pb d，iThe output power of the stored energy at the moment i in the d operating day; plimIs an allowable limit value of power variation; ppv d，iThe photovoltaic output power is the photovoltaic output power at the ith operating day time; t isdOperating days contained in sample data optimized for energy storage capacity; n is a radical ofTThe number of moments included in the illumination period in each operation day. Examples of the inventionpen＝1.9 yuan/kWh，Plim＝0.1·Gpv。
Based on any embodiment, the constraint conditions of the economic optimization layer model include limit constraints of rated power and rated capacity of the energy storage system and limit constraints of investment funds of the energy storage system, and the constraint conditions are expressed as follows:
0≤CLCC≤Cmax； (28)
0≤Prate≤kp，max·Gpv； (29)
0≤≤Qrate≤kq，max·Prate； (30)
wherein, CLCCThe corresponding conversion cost of the energy storage life cycle cost in the operation process is calculated; cmaxPlanning an allowable limit value of cost for the investment of the energy storage system; gpvPower scaling for photovoltaic power stations; k is a radical ofp，maxThe limit value is the ratio of the rated power of the energy storage system to the installed scale of the photovoltaic power station; kq，maxThe charging and discharging duration can be continued under the rated working condition of the energy storage system.
In the examples of the present invention, Gpv＝15kW，C max2 ten thousand yuan, kp，max＝60％，k q，max6 hours.
Based on any of the above embodiments, the constraints of the operation optimization layer model include operation constraints of the energy storage system and opportunity constraints for ensuring the prediction error compensation performance, and the constraints are expressed as follows:
Figure BDA0003215656090000191
Figure BDA0003215656090000192
Figure BDA0003215656090000193
Figure BDA0003215656090000194
Smin≤Sd，i≤Smax； (35)
0.5-Δδ≤Sd，end≤0.5+Δδ； (36)
wherein, P { gamma [ [ gamma ] ])d，jThe probability that the accuracy of the photovoltaic output prediction error meets the assessment requirement is not lower than a certain confidenceThe degree alpha;
Figure BDA0003215656090000195
the predicted value of the photovoltaic power at the j moment of the d-th operation day is obtained; gamma rayd，jUltra-short term prediction accuracy for the jth operating day time; l% is the allowable lower limit of prediction accuracy; n is the number of samples selected during the calculation of the ultra-short-term prediction accuracy, and is usually 16; sd，iThe state of charge of energy storage for the d operating day i; smin、SmaxRespectively an upper limit value and a lower limit value allowed by a charge state in the process of energy storage charging and discharging; etad，iThe charge-discharge efficiency of energy storage at the ith operating day i is improved; eta c, etadCharging efficiency and discharging efficiency of stored energy are respectively; pb d，iThe output power of the stored energy at the moment i in the d operating day; prate、QrateRated power and rated capacity of the energy storage system are respectively; delta t is the acquisition interval of the sample data of the energy storage capacity optimization; sd，endThe state of charge value of the stored energy at the end time of the d-th operation day; Δ δ is the allowable fluctuation range of the state of charge. In the embodiment of the invention, alpha is 0.95, L% is 90%, and Smin＝0.9，Smax＝0.1，ηc＝ηd＝95％，Δδ＝0.05。
It should be noted that introducing relaxation variables constrains P { γ } the opportunity to run the optimization layer model in the above stepsd，jMore than or equal to L% } alpha is converted into a general constraint which is convenient for model solution, and the following constraints are corresponded after the conversion:
Figure BDA0003215656090000201
wherein β ═ (1-L%) is an allowable range of prediction error; a. then d，j、Ap d，jPositive and negative relaxation variables (both greater than 0) are introduced and represent the out-of-range amount of the prediction error exceeding the allowable range; a. thed，jThe sum of positive and negative relaxation variables is the total out-of-range amount of the prediction error; a. theoIs Ad，jNumber of all elements equal to 0 in；TdThe number of days included in the selected operation cycle; m is the time number of the predicted power needing to be reported in the period of illumination on each operation day; a. theo/Tdthe/M can represent the confidence level that the power prediction error meets the examination requirement, when AoWhen the number is equal to 0, all the points meet the assessment requirements. In the embodiment of the invention, Td＝40，M＝56。
The following describes a dual-layer collaborative optimization configuration system for energy storage capacity of an optical storage system, and the following description and the above-described dual-layer collaborative optimization configuration method for energy storage capacity of an optical storage system may be referred to correspondingly.
Fig. 4 is a schematic structural diagram of a two-layer collaborative optimization configuration system for energy storage capacity of an optical storage system according to an embodiment of the present invention, and as shown in fig. 4, the system includes a data screening unit 410, a model building unit 420, and a configuration solving unit 430;
the data screening unit 410 is used for acquiring photovoltaic power data of a photovoltaic power station to be configured and screening out sample data of energy storage capacity optimization based on fluctuation analysis;
the model construction unit 420 is used for constructing an energy storage capacity double-layer multi-target collaborative optimization configuration model including an economic optimization layer model and an operation optimization layer model;
and the configuration solving unit 430 is configured to input the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solve the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.
According to the system provided by the embodiment of the invention, the operation day data of complex weather with large volatility is screened out through fluctuation analysis to be used as the sample data of energy storage capacity optimization, the energy storage capacity planning-operation double-layer multi-target collaborative optimization model of the light-emitting and energy storage system is constructed through the coupling relation between the energy storage capacity planning and the system optimization operation, and the double-layer multi-target collaborative optimization model is solved to obtain the optimal configuration result. The invention realizes the reasonable and effective allocation of the energy storage capacity of the optical storage system under the influence of double uncertainties.
According to any of the above embodiments, as shown in fig. 5, the data screening unit includes a sequence building module 510 and a sample screening module 520;
the sequence establishing module 510 is configured to form a photovoltaic power sequence P from the photovoltaic power data of the photovoltaic power station to be configured on the d-th operation day according to the time sequenced＝(Pd，1，Pd，2，...，Pd，m) (ii) a Wherein m is the data number of the photovoltaic power sequence;
the sample screening module 520 is configured to screen the sample entropy E according to the photovoltaic power sequenceSEAnd a predetermined sequence complexity decision reference value E* SECarrying out wave momentum analysis to screen out sample data of energy storage capacity optimization: if ESE＞E* SEIf so, the photovoltaic power sequence is a complex meteorological power sequence, and the fluctuation quantity analysis is finished;
otherwise, counting the out-of-limit times N of elements in the first-order difference sequence of the photovoltaic power sequenceD: if N is presentD＞N* DIf the photovoltaic power sequence is a complex meteorological power sequence, otherwise, the photovoltaic power sequence is a simple meteorological power sequence; wherein N is* DThe method is a preset threshold value for judging the out-of-limit times of the photovoltaic power fluctuation of the complex weather.
Based on any one of the above embodiments, the counting of the number of times of violation N of elements in the first-order difference sequence of the photovoltaic power sequenceDThe method comprises the following steps: statistics are such that the inequality Δ pd，j＞βDΔ p for which m-1 holdsd，jThe number of (2); wherein, Δ pd，j＝|pi，j+1-pi，j|，j＝1，2，...，m-1， βDIs a reference value for measuring the power fluctuation amplitude of adjacent time instants.
Based on any embodiment, the model construction unit comprises an economic optimization layer model construction module and an operation optimization layer model construction module;
the economic optimization layer model building module is used for building an economic optimization layer model comprising an objective function and constraint conditions on the basis of taking the daily net profit of the optical storage system and the energy balance capability index of the energy storage system as optimization targets and taking the rated power and the rated capacity of the energy storage as decision variables; the objective function of the economic optimization layer model comprises assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds an assessment limit value;
the operation optimization layer model building module is used for building an operation optimization layer model including a target function and constraint conditions on the basis of taking the assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds the assessment limit value in the operation process of the light storage system as an optimization target and taking the photovoltaic output at each moment of energy storage as a decision variable.
Based on any of the above embodiments, the objective function of the economically optimized layer model is represented as follows:
Figure BDA0003215656090000221
wherein, CNIThe equivalent daily average net gain of the light storage system in the operation process is obtained; csalObtaining electricity selling income for the system online electricity quantity; c'LCCThe corresponding conversion cost of the energy storage life cycle cost in the operation process is calculated; cpenThe assessment penalty cost is generated when the active power change value of the photovoltaic power station exceeds the assessment limit value; b isoThe energy balance capability index of the energy storage system is obtained; po down(t) is the lower limit value of the probability that the energy storage system is in the energy balance state at the moment t; t is the number of moments contained in each operation day;
the objective function of the run optimization layer model is represented as follows:
Figure BDA0003215656090000222
Figure BDA0003215656090000223
Figure BDA0003215656090000224
wherein, betapenThe evaluation unit price is corresponding to the evaluation electric quantity; delta PU d，iThe output power change value at the moment i in the d operating day is obtained; pb d，iThe output power of the stored energy at the moment i in the d operating day; plimIs an allowable limit value of power variation; ppv d，iThe photovoltaic output power is the photovoltaic output power at the ith operating day time; t isdOperating days contained in sample data optimized for energy storage capacity; n is a radical ofTThe number of moments included in the illumination period in each operation day.
Based on any embodiment, the constraint conditions of the economic optimization layer model include limit constraints of rated power and rated capacity of the energy storage system and limit constraints of investment funds of the energy storage system, and the constraint conditions are expressed as follows:
0≤CLCC≤Cmax； (42)
0≤Prate≤kp，max·Gpv； (43)
0≤Qrate≤kq，max·Prate； (44)
wherein, CLCCThe corresponding conversion cost of the energy storage life cycle cost in the operation process is calculated; cmaxPlanning an allowable limit value of cost for the investment of the energy storage system; gpvPower scaling for photovoltaic power stations; k is a radical ofp，maxThe limit value is the ratio of the rated power of the energy storage system to the installed scale of the photovoltaic power station; k is a radical ofq，maxThe charging and discharging duration can be continued under the rated working condition of the energy storage system.
Based on any of the above embodiments, the constraints of the operation optimization layer model include operation constraints of the energy storage system and opportunity constraints for ensuring the prediction error compensation performance, and the constraints are expressed as follows:
Figure BDA0003215656090000231
Figure BDA0003215656090000232
Figure BDA0003215656090000233
Figure BDA0003215656090000234
Smin≤Sd，i≤Smax； (49)
0.5-Δδ≤Sd，end≤0.5+Δδ； (50)
wherein, P { gamma [ [ gamma ] ])d，iThe probability that the accuracy of the photovoltaic output prediction error meets the assessment requirement is not lower than a certain confidence coefficient alpha;
Figure BDA0003215656090000235
the predicted value of the photovoltaic power at the j moment of the d-th operation day is obtained; gamma rayd，jUltra-short term prediction accuracy for the jth operating day time; l% is the allowable lower limit of prediction accuracy; n is the number of samples selected during the calculation of the ultra-short-term prediction accuracy, and is usually 16; sd，iThe state of charge of energy storage for the d operating day i; smin、SmaxRespectively an upper limit value and a lower limit value allowed by a charge state in the process of energy storage charging and discharging; etad，iThe charge-discharge efficiency of energy storage at the ith operating day i is improved; eta c, etadCharging efficiency and discharging efficiency of stored energy are respectively; pb d，iThe output power of the stored energy at the moment i in the d operating day; prate、QrateAre respectively an energy storage systemRated power and rated capacity of the system; delta t is the acquisition interval of the sample data of the energy storage capacity optimization; sd，endThe state of charge value of the stored energy at the end time of the d-th operation day; Δ δ is the allowable fluctuation range of the state of charge.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, the electronic device may include: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform a dual-layer collaborative optimization configuration method for energy storage capacity of an optical storage system, the method comprising: acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening out sample data of energy storage capacity optimization based on fluctuation analysis; constructing an energy storage capacity double-layer multi-target cooperative optimization configuration model including an economic optimization layer model and an operation optimization layer model; and inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain an optimal configuration result of the energy storage capacity.
Furthermore, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is capable of executing the two-tier collaborative optimization configuration method for energy storage capacity of an optical storage system provided by the foregoing methods, where the method includes: acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening out sample data of energy storage capacity optimization based on fluctuation analysis; constructing an energy storage capacity double-layer multi-target collaborative optimization configuration model including an economic optimization layer model and an operation optimization layer model; and inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain the optimal configuration result of the energy storage capacity.
In yet another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to perform the above-provided two-layer collaborative optimization configuration method for energy storage capacity of an optical storage system, where the method includes: acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening out sample data with optimized energy storage capacity based on fluctuation analysis; constructing an energy storage capacity double-layer multi-target cooperative optimization configuration model including an economic optimization layer model and an operation optimization layer model; and inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain the optimal configuration result of the energy storage capacity.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate components may or may not be physically separate, and components displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiment. One of ordinary skill in the art can understand and implement the present invention without any inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may be modified or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. A double-layer collaborative optimization configuration method for energy storage capacity of an optical storage system is characterized by comprising the following steps:
acquiring photovoltaic power data of a photovoltaic power station to be configured, and screening out sample data of energy storage capacity optimization based on fluctuation analysis;
constructing an energy storage capacity double-layer multi-target cooperative optimization configuration model including an economic optimization layer model and an operation optimization layer model;
and inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain the optimal configuration result of the energy storage capacity.
2. The double-layer collaborative optimization configuration method for the energy storage capacity of the optical storage system according to claim 1, wherein the step of screening out the sample data for optimizing the energy storage capacity based on the fluctuation analysis comprises the following steps:
forming photovoltaic power data of the d-th operation day of the photovoltaic power data of the photovoltaic power station to be configured into a photovoltaic power sequence P according to the time sequenced＝(Pd，1，Pd，2，...，Pd，m) (ii) a Wherein m is the data number of the photovoltaic power sequence;
sample entropy E from the photovoltaic power seriesSEAnd a predetermined sequence complexity decision reference value E* SECarrying out wave momentum analysis to screen out sample data of energy storage capacity optimization: if ESE＞E* SEIf so, the photovoltaic power sequence is a complex meteorological power sequence, and the fluctuation quantity analysis is finished;
otherwise, counting the out-of-limit times N of elements in the first-order difference sequence of the photovoltaic power sequenceD: if N is presentD＞N* DIf the sequence is a complex meteorological power sequence, otherwise, the sequence is a simple meteorological power sequence; wherein N is* DThe method is a preset threshold value for judging the out-of-limit times of the photovoltaic power fluctuation of the complex weather.
3. The double-layer collaborative optimization configuration method for energy storage capacity of light storage system according to claim 2, wherein the statistics of the number of times of violation N of elements in the first-order difference sequence of the photovoltaic power sequenceDThe method comprises the following steps: statistics are such that the inequality Δ pd，j＞βDΔ p for which m-1 holdsd，jThe number of (2); wherein，Δpd，j＝|pi，j+1-pi，j|，j＝1，2，...，m-1，βDIs a reference value for measuring the power fluctuation amplitude of adjacent time instants.
4. The double-layer collaborative optimization configuration method for energy storage capacity of the optical storage system according to claim 1, wherein the constructing of the double-layer multi-objective collaborative optimization configuration model for energy storage capacity, which includes an economic optimization layer model and an operational optimization layer model, comprises:
constructing an economic optimization layer model including an objective function and constraint conditions on the basis of taking the daily average net gain of the light storage system and the energy balance capability index of the energy storage system as optimization targets and taking the rated power and the rated capacity of the stored energy as decision variables; the objective function of the economic optimization layer model comprises assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds an assessment limit value;
and constructing an operation optimization layer model including an objective function and constraint conditions on the basis of taking the assessment penalty cost generated when the active power change value of the photovoltaic power station exceeds the assessment limit value in the operation process of the light storage system as an optimization target and taking the photovoltaic output at each moment of energy storage as a decision variable.
5. The double-layer collaborative optimization configuration method for energy storage capacity of light storage system according to claim 4, wherein the objective function of the economic optimization layer model is expressed as follows:
Figure FDA0003215656080000021
wherein, CNIThe equivalent daily average net gain of the light storage system in the operation process is obtained; csalObtaining electricity selling income for the system online electricity quantity; c'LCCThe corresponding conversion cost of the energy storage life cycle cost in the operation process is calculated; cpenThe assessment penalty cost is generated when the active power change value of the photovoltaic power station exceeds the assessment limit value; b isoFor energy storage systemA system energy balance capability index; po down(t) is the lower limit value of the probability that the energy storage system is in the energy balance state at the moment t; t is the number of moments contained in each operation day;
the objective function of the run optimization layer model is represented as follows:
Figure FDA0003215656080000022
Figure FDA0003215656080000031
Figure FDA0003215656080000032
wherein, betapenThe evaluation unit price is corresponding to the evaluation electric quantity; delta PU d，iThe output power change value at the moment i in the d operating day is obtained; pb d，iThe output power of the stored energy at the moment i in the d operating day; plimIs an allowable limit value of power variation; ppv d，iPhotovoltaic individual output power at the i moment of the d operating day; t isdOperating days contained in sample data optimized for energy storage capacity; n is a radical ofTThe number of moments included in the illumination period in each operation day.
6. The double-layer collaborative optimization configuration method for energy storage capacity of the optical storage system according to claim 4, wherein the constraint conditions of the economic optimization layer model include limit constraints on rated power and rated capacity of the energy storage system and limit constraints on investment capital of the energy storage system, and the constraint conditions are expressed as follows:
0≤CLCC≤Cmax；
0≤Prate≤kp，max·Gpv；
0≤Qrate≤kq，max·Prate；
wherein, CLCCThe corresponding conversion cost of the energy storage life cycle cost in the operation process is calculated; cmaxPlanning an allowable limit value of cost for the investment of the energy storage system; gpvThe power scale of the photovoltaic power station; k is a radical ofp，maxThe limit value is the ratio of the rated power of the energy storage system to the installed scale of the photovoltaic power station; k is a radical ofq，maxThe charging and discharging duration can be continued under the rated working condition of the energy storage system.
7. The double-layer collaborative optimization configuration method for energy storage capacity of an optical storage system according to claim 4, wherein the constraints for running the optimization layer model include running constraints of the energy storage system and opportunity constraints for guaranteeing prediction error compensation performance, and the constraints are expressed as follows:
Figure FDA0003215656080000033
Figure FDA0003215656080000035
Figure FDA0003215656080000034
Figure FDA0003215656080000041
Smin≤Sd，i≤Smax；
0.5-Δδ≤Sd，end≤0.5+Δδ；
wherein, P { gamma [ [ gamma ] ])d，jThe probability that the accuracy of the photovoltaic output prediction error meets the assessment requirement is not lower than a certain confidence coefficient alpha;
Figure FDA0003215656080000042
the predicted value of the photovoltaic power at the j moment of the d-th operation day is obtained; gamma rayd，jUltra-short term prediction accuracy for the jth operating day time; l% is the allowable lower limit of prediction accuracy; n is the number of samples selected during the calculation of the ultra-short-term prediction accuracy, and is usually 16; sd，iThe state of charge of energy storage for the d operating day i; smin、SmaxRespectively an upper limit value and a lower limit value allowed by a charge state in the process of energy storage charging and discharging; etad，iThe charge-discharge efficiency of energy storage at the ith operating day i is improved; eta c, etadCharging efficiency and discharging efficiency of stored energy are respectively;
Figure FDA0003215656080000043
the output power of the stored energy at the moment i in the d operating day; prate、QrateRated power and rated capacity of the energy storage system are respectively; delta t is the acquisition interval of the sample data of the energy storage capacity optimization; sd，endThe state of charge value of the stored energy at the end time of the d-th operation day; Δ δ is the allowable fluctuation range of the state of charge.
8. A two-layer collaborative optimization configuration system for energy storage capacity of an optical storage system is characterized by comprising:
the data screening unit is used for acquiring photovoltaic power data of a photovoltaic power station to be configured and screening out sample data of energy storage capacity optimization based on fluctuation analysis;
the model construction unit is used for constructing an energy storage capacity double-layer multi-target collaborative optimization configuration model including an economic optimization layer model and an operation optimization layer model;
and the configuration solving unit is used for inputting the sample data of the energy storage capacity optimization into the energy storage capacity double-layer multi-target collaborative optimization configuration model, and solving the economic optimization layer model and the operation optimization layer model based on a multi-target particle swarm algorithm and an optimization solver to obtain the optimal configuration result of the energy storage capacity.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the dual-layer co-optimization configuration method of energy storage capacity of an optical storage system according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the dual-layer collaborative optimization configuration method for energy storage capacity of an optical storage system according to any of claims 1 to 7.
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