CN110745030B - Wide-area-distribution electric vehicle charging method and system - Google Patents
Wide-area-distribution electric vehicle charging method and system Download PDFInfo
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Abstract
A wide-area distributed large-scale electric vehicle charging strategy and system comprises: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a secondary control center optimization model for calculation; substituting the calculation result and the predicted output of the new energy power generation into a two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center; formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve; and the electric automobile is charged according to the charging load following the guide curve. The strategy of the electric automobile ordered charging load following can be embedded into a layered control strategy, and the write cooperative control problem of wide-area distributed large-scale electric automobile charging and clean energy power generation can be solved through the layered control strategy, so that the charging behavior of the electric automobile can be better matched with intermittent new energy power generation, and economic benefits and social benefits are created for society, power grids and electric automobile operators.
Description
Technical Field
The invention relates to the fields of electric vehicle charging, new energy consumption, computer technology and the like, in particular to a method and a system for charging electric vehicles in wide area distribution.
Background
The charging requirements of the electric automobile have certain controllability and certain randomness. In addition, the clean energy power generation mainly based on wind power generation and photovoltaic power generation is limited by natural conditions, and the output of the clean energy power generation is random and intermittent. The method solves the problems of complex structure, large impact on a power grid and a battery and the like of the conventional high-power wired charging equipment by considering the charging requirement of the electric automobile and the uncertainty of the generated output of the clean energy, realizes the ordered charging control suitable for the cooperation of the large-scale electric automobile and the clean energy generation, can realize the cleanness of the electric automobile under the cooperative charging strategy, and is one of the difficulties of the current research in the industry.
Aiming at the aspect of a clean energy collaborative charging strategy such as electric vehicles, wind energy, solar energy and the like, an evaluation index is formulated based on an electric vehicle and power grid interaction platform framework, and the effect of the electric vehicle on absorbing new energy fluctuation under different interaction intentions is analyzed. For example, some research researches develop a collaborative optimization scheduling model of EV and distributed energy under different time scales, and verify that the charging load is scheduled under the model, so that the equivalent load of a power grid is stabilized by using the charging load. Although the research comprehensively considers the combined operation optimization of the electric automobile and the distributed power supply and the energy storage system, the research is basically carried out from the angle of regulation and control of the electric automobile, and the influence on the power demand, the load characteristic and the gasoline consumption cannot be well solved.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a wide-area distributed large-scale electric vehicle charging strategy and system.
The technical scheme provided by the invention is as follows:
based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation;
substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center;
the secondary control center formulates a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center;
the electric automobile is charged along the guide curve according to the charging load;
the battery of the electric automobile is as follows: a lithium ion battery with high storage performance.
Wherein the battery information of the electric vehicle includes: power, capacity and battery state of charge, SOC;
the charging time includes: and charging start-stop time.
Preferably, the two-stage optimization model includes:
and a first stage objective function taking the peak clipping and valley filling of the total load as an objective.
And the first-stage objective function is corresponding to the constraint condition.
Second stage objective function and method aimed at stabilizing desired total load curve fluctuations at each secondary control center
And the constraint condition corresponding to the objective function of the second stage.
Wherein the first stage objective function is calculated as follows:
in the formula (f)1A first stage objective function taking total load peak clipping and valley filling as targets; gi,tGuiding the load of the electric automobile at the moment t of the ith secondary control center control area; di,tControlling the total conventional load of the area t moment for the ith secondary control center; ri,tIs the ith sub-controlAnd (3) the new energy output of the control area t moment is made, omega is the set of the secondary control centers, and tau is the optimization time interval.
Specifically, the constraint conditions corresponding to the first-stage objective function include:
Ri,t≤Gi,t+Di,t
in the formula (I), the compound is shown in the specification,
P i,trespectively setting the upper limit and the lower limit of the total charging power of the electric automobile at the moment t of the ith secondary control center;
Ei,trespectively setting the upper limit and the lower limit of the total charging energy of the electric automobile at the moment t of the ith secondary control center; Δ t is the time interval;
R i,trespectively corresponding upper and lower limits of new energy output at the moment t of the ith secondary control center; and lambda is the set upper limit of the power abandoning proportion of the new energy.
Preferably, the calculation formula of the second stage objective function is as follows:
in the formula (f)2The load value is directed for time t.
The corresponding constraint conditions of the second stage objective function comprise:
in the formula (I), the compound is shown in the specification,
in order to be the weight coefficient,
is the minimum of the first stage objective function.
Preferably, the step of substituting the calculation results of the optimization models of all the secondary control centers and the predicted output of the new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers includes:
substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a two-stage optimization model, and solving the first-stage objective function to obtain a minimum value;
setting a weight coefficient;
and substituting the minimum value of the first-stage objective function and the weight coefficient into the second-stage objective function to solve to obtain a charging load guidance curve and new energy output of each secondary control center:
wherein the weighting factor is greater than 1.
Wherein the secondary control center optimization model comprises:
and the secondary control objective function and the corresponding secondary control constraint condition take the minimum Euclidean distance between the total charging load curve and the load guide curve of the electric automobile as a target.
Specifically, the secondary control objective function is as follows:
in the formula, PtIs the total charging load of the electric automobile at the time t, GtThe component of the load guidance curve at time t that the main control center is required to follow is given.
Specifically, the secondary control constraints include:
in the formula (I), the compound is shown in the specification,
P tthe upper limit and the lower limit of the total charging power of the electric automobile at the moment t; etFor the total energy that the electric vehicle has been charged at time t,
E tthe upper limit and the lower limit of the total charging energy of the electric automobile at the moment t;
wherein, the
P tSatisfies the following conditions:
P t＝0
in the formula, τjIs the chargeable period of the jth electric vehicle, andj＝[τbegin,j,τend,j]；
the sum of rated charging power of the electric automobile at the time t is contained in all chargeable periods; plimitIs the total power upper limit;
the above-mentioned
E tSatisfies the following conditions:
in the formula (I), the compound is shown in the specification,
the energy required by the jth electric automobile.
Preferably, the bringing the battery information and the charging time of the electric vehicle into a pre-constructed secondary control center optimization model for calculation based on each secondary control center includes:
the solution is carried out by using a direct solution method, a distributed algorithm or a probability transfer matrix method.
Preferably, the secondary control center formulates a charging load following guidance curve of each electric vehicle under the secondary control center based on the load guidance curve of the secondary control center, and the method includes: and setting the charging power of each electric automobile at each moment in the time range of the load guidance curve.
Preferably, the electric vehicle is charged according to the charging load following a guidance curve, and includes: the electric automobile adjusts the charging power according to the charging time and the charging power set at the charging time.
Preferably, the positive electrode of the lithium ion battery comprises a current collector, an active material layer and a polymer conducting layer which are arranged in sequence;
the active material layer includes a first active material layer, a second active material layer, and a third active material layer; the first active material layer is made of lithium cobaltate particles, a binder and a thickener, the second active material layer is made of lithium cobaltate particles, lithium nickel cobalt manganese oxide particles, a binder and a thickener, and the third active material layer is made of lithium nickel cobalt manganese oxide particles, a binder and a thickener.
The polymer conducting layer is made of conducting polymer, inorganic filler and binder, and the conducting polymer is selected from polyaniline, polythiophene or polypyrrole.
The binders in the first active material layer, the second active material layer, the third active material layer and the polymer conducting layer are all made of PVDF.
The conductive polymer is polyaniline.
The inorganic filler is selected from titanium dioxide, zirconium dioxide or silicon dioxide, preferably silicon dioxide.
The invention also provides a large-scale electric vehicle charging strategy and system based on the same invention concept, which are distributed in a wide area, and the strategy comprises the following steps: the secondary control center module, the main control center module and the electric vehicle charging calculation module;
the secondary control center computing module is configured to: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation, and uploading the calculation result to a control center calculation module; the system is also used for formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center and issuing the curve to the corresponding electric automobile;
the control center calculation module: substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers, and sending the load guidance curves of the secondary control centers to corresponding secondary control center modules;
electric automobile calculation module that charges: and the electric automobile is charged according to the charging load following the guide curve.
Compared with the prior art, the invention has the beneficial effects that:
1. the electric automobile cooperative charging hierarchical control strategy has good expandability, and strategies for realizing the ordered charging load following of the electric automobile can be embedded into the hierarchical control strategy. By means of the hierarchical control strategy, the write cooperative control problem of wide-area distributed large-scale electric automobile charging and clean energy power generation can be well solved, so that the electric automobile charging behavior can be better matched with intermittent new energy power generation, and therefore, the society, the power grid and the electric automobile operator create economic benefits and social benefits.
2. By using the lithium ion battery with high storage performance, the polymer conducting layer is present, so that the side reaction between the active substance and the electrolyte is relieved, and the decomposition of the electrolyte is avoided; meanwhile, lithium cobaltate provides high energy density and rate capability, and the nickel cobalt lithium manganate is stable in performance and provides high cycle life performance.
Drawings
FIG. 1 is a three-level architecture of a layered control model according to the present invention;
FIG. 2 is a flow chart of a hierarchical control method of the present invention;
FIG. 3 illustrates the conventional load and predicted wind power output of the secondary control center of the present invention;
FIG. 4 is a diagram of a secondary control center normal load versus a desired load for the present invention;
FIG. 5 is a total conventional load curve versus a total desired load curve (three regions) for the present invention;
FIG. 6 is a predicted wind power output versus an expected wind power output of the present invention;
fig. 7 shows the normal load and the desired load of the secondary control center according to the present invention.
Detailed Description
The basic idea of hierarchical control is to divide a control object into different hierarchies, and each hierarchy carries out control activities relatively independently on the basis of obeying the overall goal. The idea of layered control is clear, the expansion is easy, and the method is suitable for the optimization control of large-scale electric vehicles. Most of the layered control assumes that the charging situations and modes of all electric vehicles are consistent, and actually, the charging situations of the electric vehicles governed by different control centers are different, and the ordered charging control mode is also suitable according to local conditions. For example, electric vehicles corresponding to the jurisdiction are mainly charged in a centralized charging/converting station, and centralized control is suitable for the charging; if the electric automobile corresponding to the district is mainly charged in the widely distributed and sparse charging pile, distributed control is suitable to be adopted.
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1: an electric automobile ordered charging hierarchical control strategy with good expandability. As shown in fig. 1, the hierarchical strategy proposed by the present invention is divided into: the main control center, the secondary control center and the electric automobile are arranged in three layers.
Each layer develops control activities of each layer relatively independently on the basis of obeying a main control target in a cooperative and complementary mode, and flexibility and independence are provided for the own control activities of each layer on the basis of meeting the overall control target.
The main control center establishes a two-stage optimization model by taking peak clipping and valley filling as targets according to the electric automobile energy, power boundary and the like given by the secondary control centers and the output prediction given by the new energy power generation, abandon the new energy rate and the like, calculates the electric automobile charging load guidance curve and the new energy output of each secondary control center and issues the electric automobile charging load guidance curve and the new energy output. The main control center part constraint conditions (upper and lower limits of power and energy) are obtained by the secondary control center through calculation and uploading.
The energy boundary and the like are used as constraints and are uploaded to ensure that a load guide curve issued by the main control center can be followed; and each secondary control center controls the total charging load of the electric automobile group to follow the guide curve through an ordered charging control strategy.
Each secondary control center can select a centralized/distributed control strategy according to actual conditions to realize the following of the charging load of the electric automobile. Different secondary control centers can select different control strategies according to the type of the electric vehicle and the charging situation, so that the expandability of the method is greatly improved.
Meanwhile, the power of the electric automobile, the energy boundary constraint, the new energy electricity abandoning proportion constraint, the reverse power transmission constraint and the like are considered in the strategy, so that the high-level consumption of the new energy capacity is ensured.
1. A main control center:
and acquiring related constraints of the electric automobile and new energy power generation, and solving an electric automobile ordered charging optimization model containing new energy access. The optimal solution of the model is the electric vehicle charging load guidance curve and the new energy output which are issued to each secondary control center. And the charging load guide curve of the electric automobile is sent to each secondary control center.
The optimization model of the main control center is targeted to peak clipping and valley filling, so that a two-stage optimization model is established, and the total load curve fluctuation of each secondary control center is reduced, thereby being a multi-objective optimization problem. The optimization target of the first stage is total load peak clipping and valley filling, and the optimization target of the second stage is used for stabilizing the expected total load curve fluctuation of each secondary control center.
1.1 the optimization objective of the first stage is the peak clipping and valley filling of the total load, the objective function f1Comprises the following steps:
in the formula Gi,t.Di,t.Ri,tRespectively guiding loads, total conventional loads and new energy of the electric automobile at the moment t of the ith secondary control center control areaThe output, Ω, represents the set of secondary control centers, and τ is the optimization period, where the optimization period is a settable time range, such as 7: 00 to 9: 00. Typically, a phase of optimization problem has multiple optimal solutions.
The constraint includes: the electric automobile power, the energy boundary constraint, the new energy electricity abandonment proportion constraint, the reverse power transmission constraint and the like of each secondary control center. The upper and lower energy limits are uploaded by each secondary control center to obtain:
Ri,t≤Gi,t+Di,t(6)
in the formula (I), the compound is shown in the specification,
P i,tthe upper limit and the lower limit of the total charging power of the electric automobile at the moment t of the ith secondary control center are set;
E i,tthe upper limit and the lower limit of the total charging energy of the electric automobile at the moment t of the ith secondary control center are set; Δ t is the time interval.
R i,tAnd the output of the new energy is the upper and lower limits corresponding to the ith secondary control center at the moment t.
The predicted output at the time t of the new energy can be set,R i,tcan be set to zero; and lambda is the set upper limit of the power abandoning proportion of the new energy.
1.2 second stage aimed at smoothing the expected Total load Curve fluctuations of the Secondary control centers, the objective function f2Comprises the following steps:
in the formula (f)2Directing load values for time t based on said objective function f2A time load value curve is formed within the period tau.
In the second stage optimization model, in addition to the constraints appearing in the first stage, the following constraints are added:
in the formula
For the weighting factor, a value slightly larger than 1 may be set,
is the minimum value of the formula (1) after the optimization of the first stage is finished.
By solving the two-stage optimization model, the main control center can obtain the charging load guidance curve and the new energy output of each secondary control center.
2. The secondary control center:
the secondary control center can select a centralized or distributed control method according to actual situations to realize that the charging load of the electric automobile follows a guide curve and a target function f3The Euclidean distance between a total charging load curve and a load guide curve of the electric automobile is the minimum, namely:
in the formula PtIs the total charging load of the electric automobile at the time t, GtThe component of the load guidance curve at time t that the main control center is required to follow is given.
The constraint includes a power energy boundary constraint, as follows:
in the formula (I), the compound is shown in the specification,
P tthe upper limit and the lower limit of the total charging power of the electric automobile at the moment t; etFor the total energy that the electric vehicle has been charged at time t,
E tthe upper and lower limits of the total charging energy of the electric automobile at the moment t. Formula (12) is total charging energy E of the electric vehicle at the moment t given by definitiontIs described in (1).
Upper and lower power limits of distributed control model of secondary control center
P tSatisfies the following conditions:
P t＝0 (13)
in the formula, τjIs the chargeable period of the jth electric vehicle, andj＝[τbegin,j,τend,j]；
the sum of rated charging power of the electric automobile at the time t is contained in all chargeable periods; plimitIs the total power upper limit;
the above-mentioned
E tSatisfies the following conditions:
in the formula (I), the compound is shown in the specification,
the energy required by the jth electric automobile.
Equation (15) represents that the lower limit of the total charging energy of the electric vehicle at the time t is all the electric vehicles which have finished charging at the time t (the time t is greater than or equal to the upper limit tau of the charging interval of the electric vehicles at the time t)end,i) The total energy required.
Equation (16) represents an electric vehicle in which the upper limit of the total charging energy of the electric vehicle at time t is set to the upper limit of the charging period τ of the electric vehicle which has started or completed charging at time t (time t is equal to or greater than the lower limit τ of the charging period τ of the electric vehicle at time t)begin,i) The total energy required.
The load following model of the secondary control center can be solved by adopting different optimization algorithms such as a direct solution (centralized algorithm), an ODC (optimal centralized changing) algorithm (distributed algorithm), a probability transfer matrix method (distributed algorithm) and the like.
The direct solution method (centralized algorithm) is suitable for scenes such as a centralized charging/converting station and the like in which the number of controlled electric vehicles is not large and communication is relatively convenient, electric vehicles corresponding to the jurisdiction are mainly charged in the centralized charging/converting station, and the target function is the minimum Euclidean distance between a total charging load curve and a load guide curve of the electric vehicle.
An ODC (optimal localized charging) algorithm (distributed algorithm) is suitable for scenes that controlled electric vehicles such as dispersed charging piles are large in quantity and relatively inconvenient in communication, electric vehicles in corresponding jurisdictions are mainly charged in the widely distributed and sparse charging piles, in the algorithm, a secondary control center collects charging plans of the electric vehicles, control signals are calculated and broadcast to the electric vehicles, the electric vehicles locally solve optimization problems according to the control signals, the charging plans of the electric vehicles are corrected and fed back, and iteration is carried out until a control target is achieved.
According to the probability transfer matrix method (distributed algorithm), the basic flow is consistent with the ODC algorithm, electric vehicles corresponding to jurisdictions are mainly charged in widely distributed and sparse charging piles, but control signals are changed into a probability transfer matrix, meanwhile, the solution of an optimization problem is avoided locally, the requirement on equipment at the electric vehicle end is lower, and the calculation speed is higher.
The positive electrode of the lithium ion battery of the electric automobile comprises a current collector, an active material layer and a polymer conducting layer which are sequentially arranged;
the active material layer includes a first active material layer, a second active material layer, and a third active material layer; the first active material layer is made of lithium cobaltate particles, a binder and a thickener, the second active material layer is made of lithium cobaltate particles, lithium nickel cobalt manganese oxide particles, a binder and a thickener, and the third active material layer is made of lithium nickel cobalt manganese oxide particles, a binder and a thickener.
The polymer conducting layer is made of conducting polymer, inorganic filler and binder, and the conducting polymer is selected from polyaniline, polythiophene or polypyrrole.
The binders in the first active material layer, the second active material layer, the third active material layer and the polymer conducting layer are all made of PVDF.
The conductive polymer is polyaniline.
The inorganic filler is selected from titanium dioxide, zirconium dioxide or silicon dioxide, preferably silicon dioxide.
1) Providing nickel cobalt lithium manganate particles with the average particle size D50 of 200nm, wherein the particle size distribution (D90-D10)/D50 of the nickel cobalt lithium manganate particles is 0.8, mixing the nickel cobalt lithium manganate particles, PVDF and sodium carboxymethylcellulose in a mass ratio of 100:8:4, dispersing in NMP (N-methyl pyrrolidone) consisting of 80% of deionized water and 20% of isopropanol, and stirring for 4 hours to obtain a first slurry, wherein the solid content of the first slurry is 40%;
2) providing lithium cobaltate particles having an average particle diameter D50 of 2 μm and a particle size distribution (D90-D10)/D50 of 0.4, mixing the lithium cobaltate particles, PVDF and sodium carboxymethylcellulose in a mass ratio of 100:4:8, and dispersing in NMP; stirring for 6 hours to obtain a second slurry, wherein the solid content of the second slurry is 70%;
3) mixing the first slurry and the second slurry, and stirring for 1h to obtain a third slurry, wherein the mass ratio of the nickel cobalt lithium manganate particles to the lithium cobaltate particles in the third slurry is 20: 80;
4) providing an Al foil current collector;
5) coating the second slurry on the current collector, and drying to obtain a lithium cobaltate layer with the thickness of 10 microns, namely a first active material layer;
6) coating the third slurry on the lithium cobaltate layer, and drying to obtain a mixed active material layer with the thickness of 20 microns, namely a second active material layer;
7) coating the first slurry on the mixed active material layer, and drying to obtain a lithium nickel cobalt manganese oxide layer with the thickness of 3 mu m, namely a third active material layer;
8) mixing polyaniline and PVDF with a ratio of 4: 6: 1, dispersing in MNP, stirring for 6 hours to obtain inorganic particle slurry, coating the inorganic particle slurry on a nickel-cobalt lithium manganate layer, and drying to obtain a conductive polymer layer with the thickness of 2 mu m;
9) hot pressing to obtain the positive electrode.
By using the lithium ion battery with high storage performance, the polymer conducting layer is present, so that the side reaction between the active substance and the electrolyte is relieved, and the decomposition of the electrolyte is avoided; conjugated electronic bonds in the conductive polymer can capture transition metal elements overflowing from the active substances, so that the storage performance of the battery is improved; active substance particles with different particle sizes are respectively mixed to prepare slurry, and PVDF and thickening agents with different contents are added according to different particle size ranges, so that the dispersibility of the slurry is improved; the particles with two particle size distributions are mixed to prepare the anode, and the small particles are filled in gaps of the large particles, so that the energy density is improved; the lithium cobaltate layer close to the current collector layer selects large-particle particles so as to obtain larger pores in the layer, improve the infiltration degree of the electrolyte and obtain better rate performance. The nickel cobalt lithium manganate layer far away from the current collector adopts small-particle particles and contains PVDF with higher content, so that the surface layer is more compact, the separation of the positive active substance from the positive electrode is avoided, the stability of the positive electrode is improved, and the cycle life is prolonged.
Example 2: based on the same inventive concept, the invention also provides an algorithm of the electric automobile ordered charging layered control model, and the flow of the algorithm is shown in fig. 2.
Firstly, the electric automobile uploads information such as power, capacity, SOC (State of Charge), Charge start-stop time and the like to each secondary control center; each secondary control center calculates the electric automobile constraint and uploads the electric automobile constraint and the like to the main control center; the main control center solves a two-stage optimization model according to the information such as the constraint uploaded by the secondary control centers and the predicted output uploaded by the new energy power generation, and obtains a load guidance curve and the new energy output of each secondary control center; the secondary control center guides the charging load of the electric automobile to follow the guide curve through a centralized/distributed control strategy; and the electric automobile adjusts the charging plan according to the instruction issued by the secondary control center, so as to realize ordered charging.
Example 3: example simulation of the procedure of example 2 above using simulation
1. Emulation setting
A.B.C three secondary control centers are arranged under a main control center, the peak value of the daily load is 3715kW.4000kW.4500kW respectively, and the upper limit of the power is 4000kW.4500kW.5000kW respectively. The regional load of the distribution network corresponding to the A.C secondary center is mainly residential life load, the regional load corresponding to the B secondary center is mainly industrial and commercial load, and new energy power generation mainly based on wind power generation exists in the B region. The conventional load curve and the predicted wind power output (wind power output data from a wind power plant in a certain area in north China) of the corresponding area of the tertiary level center are shown in fig. 3.
Here, the rated charging power of the electric vehicle was 7kW, and the battery capacity was 32 kWh. The distribution of parameters such as the number of electric vehicles governed by the tertiary control center, the arrival time, the departure time, the SOC and the like is shown in table 1. It can be seen that the electric vehicle charging load of the a.c secondary control center is concentrated on the night, and the electric vehicle charging load of the B secondary control center is concentrated on the day.
TABLE 1 basic controlled parameters of electric vehicles
In the aspect of control strategies, the secondary center A controls the electric vehicle under jurisdiction by adopting a direct solution method, the secondary center B controls the electric vehicle under jurisdiction by adopting an ODC algorithm, and the secondary center C controls the electric vehicle under jurisdiction by adopting a probability transfer matrix method.
2 simulation results
Fig. 4 and fig. 5 show the optimization results of the main control center according to the two-stage optimization model. For the sake of comparison, the conventional load curve D and the expected total load curve L of the corresponding region of each secondary control center are actually shown in FIG. 4*(conventional load curve D plus load guidance curve G minus new energy output R). Because the optimization goal of the second stage of the model is to stabilize the fluctuation of the expected total load curve of each secondary control center, the expected total load curve of each region is relatively smooth and has small fluctuation. Given in FIG. 5 is a total normal load curve (three regions) versus a total desired load curve (three regions), which is relatively smooth due to the first stage model optimization objective of minimizing the total load variance, substantially implementing electric steam in a hierarchical control strategyAnd (4) controlling peak clipping and valley filling of the vehicle charging load. The relationship between the predicted wind power output and the optimized expected wind power output is shown in fig. 6. It can be found that the wind abandoning is needed at the time of the part load valley, limited by the new energy electricity abandoning proportion, the reverse power transmission constraint and the like.
Fig. 7(a). (b). (c) shows the load following situation of the a.b.c tertiary control center, respectively. It can be found that although different load following strategies are used, the charging load T of the electric vehicle in each secondary control center basically follows the load guidance curve G, and the actual total load curve L '(the conventional load curve D plus the charging load T of the electric vehicle minus the new energy output R) does not greatly deviate from the expected total load curve L'. As can be seen from fig. 7(d), through the sequential charging hierarchical control of the electric vehicle, the total charging load curve of the electric vehicle substantially follows the total expected load curve, and the control target of peak clipping and valley filling is achieved.
Table 2 quantitative analysis of load following and peak and valley clipping effects of the hierarchical control strategy was performed. The average relative error alpha of the actual total load curve and the expected total load curve is used for measuring the load following effect:
TABLE 2 analysis of load following and load shifting effects
The peak clipping and valley filling effects are measured by the peak-valley difference reduction ratio beta:
according to the definition, the smaller the alpha is, the higher the goodness of fit between the actual total load curve and the expected total load curve is, and the better the load following effect is; the larger beta is, the larger the reduction degree of the peak-to-valley difference of the layered optimization control relative to the peak-to-valley difference of the original conventional load curve is, and the better the peak clipping and valley filling effects are. As can be seen from the observation table 2, the secondary control center a solves the charging load of the electric vehicle by using a direct solution, so that the load following effect is the best and is superior to that of the secondary control center b.c using a distributed algorithm requiring iteration and multiple convergence; the chargeable time period of the electric automobile of the secondary control center B is basically coincident with the peak time period of the load, and the charging load of the electric automobile is difficult to transfer to the valley time period, so that the peak clipping and valley filling effects of the charging load of the electric automobile of the secondary control center B are poor, and partial peak load is reduced only through new energy power generation; the electric vehicle governed by the A.C secondary control center is mainly an electric vehicle for resident life commuting, the charging period covers the load low valley period at night, the electric vehicle is a main application scene of electric vehicle charging load peak clipping and valley filling, and the peak clipping and valley filling effects are good. In general, the proposed electric vehicle ordered charging hierarchical control strategy realizes the load following of the secondary control center and the peak clipping and valley filling control target of the main control center.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. 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.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. The computer program instructions may be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks and/or flowchart block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only exemplary of the invention and is not intended to limit the invention, which may be modified, substituted, modified, etc. within the spirit and scope of the appended claims.
Claims (18)
1. A wide-area distributed electric vehicle charging method is characterized by comprising the following steps:
based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation;
substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center;
the secondary control center formulates a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center;
the electric automobile is charged along the guide curve according to the charging load;
the battery of the electric automobile is as follows: a lithium ion battery.
2. The method of charging an electric vehicle according to claim 1,
the battery information of the electric vehicle includes: power, capacity, and battery state of charge, SOC;
the charging time includes: and charging start-stop time.
3. The electric vehicle charging method of claim 1, wherein the two-stage optimization model comprises:
a first stage objective function taking total load peak clipping and valley filling as targets;
the constraint condition corresponding to the first-stage objective function is as follows:
second stage objective function and method aimed at stabilizing desired total load curve fluctuations at each secondary control center
And the constraint condition corresponding to the objective function of the second stage.
4. The method of charging an electric vehicle of claim 3, wherein the first stage objective function is calculated as follows:
in the formula (f)1A first stage objective function taking total load peak clipping and valley filling as targets; gi,tGuiding the load of the electric automobile at the moment t of the ith secondary control center control area; di,tControlling the total conventional load of the area t moment for the ith secondary control center; ri,tIs as followsi, the new energy output of the secondary control center at the time t is controlled, wherein omega is the set of the secondary control centers, and tau is the optimization time interval.
5. The method of claim 4, wherein the constraints associated with the first-stage objective function include:
Ri,t≤Gi,t+Di,t
in the formula (I), the compound is shown in the specification,
P i,trespectively setting the upper limit and the lower limit of the total charging power of the electric automobile at the moment t of the ith secondary control center;
Ei,trespectively setting the upper limit and the lower limit of the total charging energy of the electric automobile at the moment t of the ith secondary control center; Δ t is the time interval;
R i,trespectively corresponding upper and lower limits of new energy output at the moment t of the ith secondary control center; lambda is a set new energyAnd (4) an upper limit of the power abandonment proportion.
6. The method of charging an electric vehicle according to claim 4, wherein the second stage objective function is calculated as follows:
in the formula (f)2The load value is directed for time t.
7. The method according to claim 6, wherein the constraints associated with the second stage objective function include:
in the formula (I), the compound is shown in the specification,
in order to be the weight coefficient,
is the minimum of the first stage objective function.
8. The electric vehicle charging method according to claim 3, wherein the step of bringing the calculation results of the optimization models of all the secondary control centers and the predicted output of the new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of the secondary control centers comprises the steps of:
substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a two-stage optimization model, and solving the first-stage objective function to obtain a minimum value;
setting a weight coefficient;
and substituting the minimum value of the first-stage objective function and the weight coefficient into the second-stage objective function to solve to obtain a charging load guidance curve and new energy output of each secondary control center:
wherein the weighting factor is greater than 1.
9. The electric vehicle charging method of claim 1, wherein the secondary control center optimization model comprises:
and the secondary control objective function and the corresponding secondary control constraint condition take the minimum Euclidean distance between the total charging load curve and the load guide curve of the electric automobile as a target.
10. The method of charging an electric vehicle of claim 9, wherein the secondary control objective function is as follows:
in the formula, PtIs the total charging load of the electric automobile at the time t, GtThe component of the load guidance curve at time t that the main control center is required to follow is given.
11. The method of charging an electric vehicle of claim 10, wherein the secondary control constraints comprise:
in the formula (I), the compound is shown in the specification,
P tthe upper limit and the lower limit of the total charging power of the electric automobile at the moment t; etFor the total energy that the electric vehicle has been charged at time t,
E tthe upper limit and the lower limit of the total charging energy of the electric automobile at the moment t;
wherein, the
P tSatisfies the following conditions:
P t＝0
in the formula, τjIs the chargeable period of the jth electric vehicle, andj＝[τbegin,j,τend,j]wherein, τbegin,jIs the lower bound of the charging interval; tau isend,jIs the upper bound of the charging interval;
the sum of rated charging power of the electric automobile at the time t is contained in all chargeable periods; plimitIs the total power upper limit; the above-mentioned
E tSatisfies the following conditions:
in the formula (I), the compound is shown in the specification,
the energy required by the jth electric automobile.
12. The method for charging the electric vehicle according to claim 11, wherein the step of bringing the battery information and the charging time of the electric vehicle into a pre-constructed optimization model of the secondary control center for calculation based on each secondary control center comprises the following steps:
and solving by adopting a direct solution method, a distributed algorithm or a probability transfer matrix method.
13. The electric vehicle charging method according to claim 1, wherein the secondary control center formulates a charging load following guidance curve for each electric vehicle under the secondary control center based on the load guidance curve of the secondary control center, and the method comprises the following steps: and setting the charging power of each electric automobile at each moment in the time range of the load guidance curve.
14. The method of charging an electric vehicle according to claim 13, wherein the electric vehicle follows a guideline curve for charging according to the charging load, comprising: the electric automobile adjusts the charging power according to the charging time and the charging power set at the charging time.
15. The method for charging an electric vehicle according to any one of claims 1 to 14, wherein the positive electrode of the lithium ion battery comprises a current collector, an active material layer and a polymer conductive layer, which are sequentially provided;
the active material layer includes a first active material layer, a second active material layer, and a third active material layer; the first active material layer is made of lithium cobaltate particles, a binder and a thickening agent, the second active material layer is made of lithium cobaltate particles, lithium nickel cobalt manganese oxide particles, a binder and a thickening agent, and the third active material layer is made of lithium nickel cobalt manganese oxide particles, a binder and a thickening agent;
the polymer conducting layer is made of conducting polymer, inorganic filler and binder, and the conducting polymer is selected from polyaniline, polythiophene or polypyrrole.
16. The method for charging an electric vehicle according to claim 15, wherein the inorganic filler is selected from titanium dioxide, zirconium dioxide or silicon dioxide.
17. The method for charging an electric vehicle according to claim 15, wherein PVDF is used as the binder in each of the first active material layer, the second active material layer, the third active material layer, and the polymer conductive layer.
18. A wide-area distributed large-scale electric vehicle charging system, comprising: the system comprises a secondary control center computing module, a main control center module and an electric vehicle charging computing module;
the secondary control center computing module is configured to: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation, and uploading the calculation result to a control center calculation module; the system is also used for formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center and issuing the charging load following guidance curve to the corresponding electric automobile;
the control center calculation module: substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers, and sending the load guidance curves of the secondary control centers to corresponding secondary control center modules;
electric automobile calculation module that charges: and the other electric vehicle is used for charging according to the charging load following the guide curve.
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| EP3013630B1 (en) * | 2013-08-09 | 2017-02-01 | Qualcomm Incorporated | Systems, methods, and apparatus related to detecting and identifying electric vehicle and charging station |
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