CN106156921B - Electric vehicle photovoltaic charging station energy storage configuration selection method based on Copula theory - Google Patents
Electric vehicle photovoltaic charging station energy storage configuration selection method based on Copula theory Download PDFInfo
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Abstract
The text discloses an electric vehicle photovoltaic charging station energy storage configuration selection method based on a Copula theory, which comprises the following steps: selecting a photovoltaic unit and the charging load output rate of the electric automobile as random variables, normalizing the measured data, and constructing the edge distribution of each variable; based on a Copula theory, Gumbel-Copula and Clayton-Copula are selected to construct a mixed Copula function to describe the correlation of asymmetric tail characteristics among variables; sampling and simulating the annual net load of the photovoltaic charging station on the basis of the combined output probability density function of the photovoltaic charging station and the photovoltaic charging station; under the constraints of conditions such as fluctuation rate, confidence coefficient and the like, establishing an energy storage optimization configuration model taking the minimum annual operation cost of the photovoltaic electric vehicle charging station as an objective function; and carrying out optimization calculation on the optimal energy storage capacity by Matlab programming. The operation performance and the economy are comprehensively considered, the correctness and the feasibility of the integrated system energy storage configuration model with the correlation considered are analyzed, and an optimal configuration scheme is selected, so that the light energy utilization rate and the system economic benefit are improved.
Description
Technical Field
The invention relates to the field of energy storage configuration selection, in particular to an energy storage configuration selection method for an electric vehicle photovoltaic charging station based on a Copula theory.
Background
Energy crisis and environmental problems are becoming more serious, and renewable energy sources and Electric Vehicles (EVs) have great potential in energy conservation and emission reduction. The characteristics of photovoltaic power generation volatility and intermittence make large-scale elimination of the photovoltaic power generation device difficult. The random charging of a large number of electric automobiles can increase the burden of a power grid, and the indirectly generated carbon emission is not dominant, so that the energy and environmental problems are not obviously improved. Under urban environment, the typical integration mode of building the photovoltaic charging station of the electric automobile can realize the local consumption of renewable energy sources and reduce the adverse effect of the charging load of the electric automobile on a power grid, and has certain development prospect and exploration significance.
The energy storage technology has the capability of time migration of power and energy, but the high energy storage cost is always a problem of considerable development of the energy storage technology. The optimal energy storage system can improve the light energy utilization rate of the photovoltaic charging station, reduce the influence on a large power grid and improve the economic benefit. For the capacity configuration of the energy storage system, some research results exist in the aspect of improving the economic stability of the operation of the power grid.
The travel characteristics of electric automobiles are random, but the clustering effect generated by the charging loads of a large number of electric automobiles has statistical characteristics, even if auxiliary services are provided for a power grid through V2G (vehicle to grid) technology, photovoltaic output and the charging loads of the electric automobiles have certain correlation, which is a typical characteristic of an integrated system. At present, the influence of the correlation on the optimal configuration problem of the energy storage system is not considered in the conventional energy storage configuration method.
Disclosure of Invention
The invention aims to provide an energy storage configuration method considering correlation based on Copula theory aiming at a typical integrated system of a photovoltaic electric vehicle charging station so as to improve the light energy utilization rate and the economic efficiency of the system.
In order to achieve the purpose, the invention adopts the technical scheme that:
1) selecting a photovoltaic unit and the charging load output rate of the electric automobile as random variables, carrying out normalization processing on the measured data, and constructing edge distribution of each variable;
2) based on a Copula theory, Gumbel-Copula and Clayton-Copula are selected to construct a mixed Copula function to describe the correlation of photovoltaic output and the tail characteristic of the electric vehicle after the charging load is asymmetric;
3) sampling and simulating the annual net load of the photovoltaic charging station on the basis of the combined output probability density function of the photovoltaic charging station and the photovoltaic charging station;
4) under the constraints of conditions such as power balance, state of charge, fluctuation rate, confidence coefficient and the like, establishing an energy storage optimization configuration model taking the minimum annual operation cost of the photovoltaic electric vehicle charging station as a target function;
5) programming in a matlab environment, and performing optimization calculation on the energy storage capacity.
The alternative configuration scheme specifically includes: a single battery energy storage model that accounts for dependencies, a hybrid energy storage model that accounts for dependencies, a single battery energy storage model that does not account for dependencies, and a hybrid energy storage model that does not account for dependencies.
The technical scheme of the invention has the following beneficial effects:
according to the technical scheme, on the basis of the Copula theory and on the basis of correlation, the feasibility of operation of the integrated system of the photovoltaic electric vehicle charging station is analyzed by comprehensively considering the operation performance and the economic cost, a practical configuration scheme is provided, and therefore the purposes of improving the light energy utilization rate and the economic efficiency are achieved.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a flowchart of a method for selecting an energy storage configuration of a photovoltaic electric vehicle charging station with consideration of correlation according to an embodiment of the present invention
FIG. 2 is a graph comparing a photovoltaic output frequency histogram and a probability density curve
FIG. 3 is a comparison graph of frequency histogram of electric vehicle load output and probability density curve
FIG. 4 is a binary frequency histogram of photovoltaic output and charging load
FIG. 5 is a combined probability density of photovoltaic output and charging load
FIG. 6 shows year net load simulation data of photovoltaic charging station of electric vehicle
Detailed Description
The invention will be described in further detail below with reference to the figures and specific examples.
The electric vehicle charging station is provided with 120 charging piles, and the rated power of each charging pile is 10 kW; the rated capacity of the photovoltaic power generation unit is 800 kW; the energy storage unit is composed of a lead-acid storage battery and a super capacitor, and the element parameters are shown in table 1.
TABLE 1 energy storage device parameters
1) And selecting the photovoltaic unit and the charging load output rate of the electric automobile as random variables, and carrying out normalization processing on the measured data. As can be seen from the frequency histogram and fig. 2 and 3, if the sample data is non-normal distribution, the non-parametric test is used to estimate the edge distribution, and the kernel density estimation method is used herein. Let X1, X2, …, xn be the samples of the random variable X, and the probability density function f (X) of the random variable X is calculated as follows:
2) the dependence between photovoltaic output and charging load has an asymmetric trailing characteristic as shown in fig. 4. And selecting Gumbel-Copula and Clayton-Copula linear combinations related to the asymmetric tail according to the characteristics of each function to construct a mixed Copula function for fitting. The form is as follows:
C(u，v，θ)＝ω1CG(u，v；θ1)+ω2CC(u，v；θ2)
wherein u is FPV(PPV)，v＝FEV(PEV)；ω1，ω2Weight coefficient of a single Copula function, and1+ω2＝1；θ1，θ2are correlation parameters of Gumbel-Copula and Clayton-Copula. Estimating the parameter ω using a maximum Expectation (EM) algorithm1，ω2。
3) The joint probability density function formula is obtained as follows:
h(x，y)＝[ω1cG(u，v；θ1)+ω2cC(u，v；θ2)]fpv(x)fev(y)
ω1＝0.3038，ω2＝0.6962，θ1＝4.2788，θ28.6953. Fig. 5 is a combined output probability density function of the two, and the annual net load capacity of the simulated photovoltaic charging station is sampled on the basis of the function, as shown in fig. 6.
4) And establishing an energy storage optimization configuration model.
A. Establishing the objective function calculates the cost from the following two aspects.
A1 annual average operating cost of energy storage system
C1＝Cin+Cop+Cma+Cdi＝(1+kopba+kmaba+kdiba)·kdebamfba+(1+kopsc+kdisc)kdesnfsc
Wherein C isin、Cop、Cma、CdiFor the cost of purchase, operation, maintenance and disposal, respectively, and for simplicity, this is translated into different scaling factors. k is a radical ofopba、kmaba、kdibaRespectively representing the cost coefficients of the operation, the maintenance and the treatment of the lead-acid storage battery. k is a radical ofopsc、kdiseRespectively representing the running and processing cost coefficients of the super capacitor.
And A2 system year electricity purchase cost.
Wherein pr (t) and Pg(t) respectively representing the interactive electricity price and the average power of the large power grid and the system in the t-th period; Δ t is the duration of the t-th period.
The annual running cost of the whole integrated charging station is the minimum of an objective function, namely:
min C＝min(C1+C2)
constraint of B
B1 system power supply and demand balance.
Pg(t)+Ppv(t)+Pev(t)+Pba(t)+Puc(t)＝0
Exchange power P between integrated charging station and large power gridg(t) photovoltaic output Ppv(t) EVs charging load output Pev(t) energy storage output Pba(t) and Puc(t) should be kept in balance at all times.
B2 power constraint. The output of the battery is zero or rated and the output of the supercapacitor should be less than or equal to its maximum value.
Pba(t) 0 or Pba(t)＝Pban
Psc(t)≤Pscmax
The service life of the energy storage element is reduced by over-charging and over-discharging of B3, and the SOC of the battery and the super capacitor are both
Should be within reasonable limits.
Ebamin≤Eba(t)≤Ebamax
Escmin≤Esc(t)≤Escmax
B4 constraint of grid-connected power fluctuation
α≤αmax
B5 confidence constraint. The performance and the cost of the grid-connected power are mutually restricted and need to be in a certain confidence level
A balance of performance and cost is achieved.
η≥ηmin
Alternative configurations specifically include: a single battery energy storage model that accounts for dependencies, a hybrid energy storage model that accounts for dependencies, a single battery energy storage model that does not account for dependencies, and a hybrid energy storage model that does not account for dependencies.
5) And performing programming calculation in a matlab environment by adopting an improved invasive weed algorithm based on a differential evolution strategy, and selecting an optimal configuration scheme. The results of the optimization calculations with and without correlation are shown in table 2, with a fluctuation ratio limit of 4% and a confidence of 97%.
TABLE 2 energy storage capacity optimization results
Compared with the method without considering the correlation, the method reduces the configuration capacity of the energy storage device on the premise of meeting the actual condition by considering the correlation, and improves the economical efficiency of the system. The hybrid energy storage mode can slow down the depreciation of the battery and reduce the replacement cost of elements, thereby obtaining higher overall economic benefit.
All data in the technical scheme are converted into overall consideration every year
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An energy storage configuration selection method of an electric vehicle photovoltaic charging station based on a Copula theory comprises the following steps:
1) selecting a photovoltaic unit and the charging load output rate of the electric automobile as random variables, carrying out normalization processing on the measured data, and constructing edge distribution of each variable;
2) based on a Copula theory, Gumbel-Copula and Clayton-Copula are selected to construct a mixed Copula function to describe the correlation of photovoltaic output and the tail characteristic of the electric vehicle after the charging load is asymmetric;
3) sampling and simulating the annual net load of the photovoltaic charging station on the basis of the combined output probability density function of the photovoltaic charging station and the photovoltaic charging station;
4) under the constraints of power balance, state of charge, fluctuation rate and confidence coefficient conditions, establishing an energy storage optimization configuration model taking the minimum annual operation cost of the photovoltaic electric vehicle charging station as a target function;
5) and (5) optimally calculating the optimal energy storage capacity by matlab programming.
2. The Copula theory-based energy storage configuration selection method for photovoltaic charging stations of electric vehicles according to claim 1, wherein x in step 1) is1、x2、...、xnFor a sample of the random variable X, the probability density function f (X) of the random variable X is calculated by the formula:
3. the Copula theory-based energy storage configuration selection method for the electric vehicle photovoltaic charging station is characterized in that in the step 2), a hybrid Copula function is constructed by linear combination of Gumbel-Copula and Clayton-Copula related to asymmetric tail according to characteristics of each function and is fitted, and the form is as follows:
C(u，v，θ)＝ω1CG(u，v；θ1)+ω2CC(u，v；θ2)
wherein u is FPV(PPV)，v＝FEV(PEV)；ω1，ω2Weight coefficient of a single Copula function, and1+ω2＝1；θ1，θ2the correlation parameters of Gumbel-Copula and Clayton-Copula; estimating parameters using a maximum expectation algorithmω1，ω2。
4. The Copula theory-based energy storage configuration selection method for the photovoltaic charging station of the electric vehicle according to claim 1, wherein the joint probability density function formula obtained in step 3) is as follows:
h(x，y)＝[ω1cG(u，v；θ1)+ω2cC(u，v；θ2)]fpv(x)fev(y)
5. the Copula theory-based electric vehicle photovoltaic charging station energy storage configuration selection method according to claim 1, characterized in that step 4) of establishing an objective function calculates a cost from the following two aspects,
annual average operating cost of energy storage system
C1＝Cin+Cop+Cma+Cdi＝(1+kopba+kmaba+kdiba)·kdebamfba+(1+kopsc+kdisc)kdescnfsc
Wherein C isin、Cop、Cma、CdiRespectively for the purchase, operation, maintenance and processing costs, simply considered, converted into different proportionality coefficients, kopba、kmaba、kdibaRespectively represents the cost coefficients k of the operation, maintenance and treatment of the lead-acid storage batteryopsc、kdiscRespectively represent the running and processing cost coefficients of the super capacitor,
② the electricity purchasing cost of the system in the year,
wherein pr (t) and Pg(t) respectively representing the interactive electricity price and the average power of the large power grid and the system in the t-th period; at is the duration of the t-th period,
the annual running cost of the whole integrated charging station is the minimum of an objective function, namely:
minC＝min(C1+C2)。
6. the Copula theory-based electric vehicle photovoltaic charging station energy storage configuration selection method according to claim 1, wherein the step 4) constraint conditions include the following five aspects:
the power supply and demand of the system are balanced,
Pg(t)+Ppv(t)+Pev(t)+Pba(t)+Puc(t)＝0
exchange power P between integrated charging station and large power gridg(t) photovoltaic output Ppv(t) EVs charging load output Pev(t) energy storage output Pba(t) and Puc(t) should be kept in balance at the moment,
② power constraint, the output of the battery is zero or rated value and the output of the super capacitor is less than or equal to the maximum value,
Pba(t) 0 or Pba(t)＝Pban
Psc(t)≤Pscmax
The service life of the energy storage element can be reduced due to over-charge and over-discharge, and the SOC of the battery and the SOC of the super capacitor are within a reasonable limit range;
Ebamin≤Eba(t)≤Ebamax
Escmin≤Esc(t)≤Escmax
constraint of grid-connected power fluctuation
α≤αmax
The constraint of confidence, the mutual restriction of grid-connected power performance and cost, the balance of performance and cost needs to be realized under a certain confidence level,
η≥ηmin。
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| CN109814178A (en) * | 2018-12-25 | 2019-05-28 | 河海大学 | Hydrological probability forecasting procedure based on Copula- Model Condition processor |
| CN113435730B (en) * | 2021-06-23 | 2022-03-29 | 中国能源建设集团广东省电力设计研究院有限公司 | Collaborative configuration method, device and system for energy storage capacity of transformer substation |
| CN114123354B (en) * | 2022-01-26 | 2022-05-17 | 湖北工业大学 | Wind storage integrated system optimal scheduling method based on t distribution weed algorithm |
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