CN117353355A - Wind power system optimization scheduling method considering regulation potential of electric automobile - Google Patents

Wind power system optimization scheduling method considering regulation potential of electric automobile Download PDF

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Publication number
CN117353355A
CN117353355A CN202311280011.5A CN202311280011A CN117353355A CN 117353355 A CN117353355 A CN 117353355A CN 202311280011 A CN202311280011 A CN 202311280011A CN 117353355 A CN117353355 A CN 117353355A
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electric automobile
scheduled
schedulable
time
electric
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王钦
陈业夫
蔡新雷
董锴
孟子杰
喻振帆
祝锦舟
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Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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    • H—ELECTRICITY
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

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Abstract

The invention discloses a wind power system optimization scheduling method considering the regulation potential of electric vehicles, which comprises the steps of constructing regulation potential evaluation indexes of all electric vehicles according to the schedulable time and schedulable space of each electric vehicle to be scheduled, grouping all electric vehicles to be scheduled according to the regulation potential evaluation indexes of each electric vehicle to be scheduled to obtain a plurality of electric vehicle groups, obtaining an electricity price subsidy strategy according to the regulation potential evaluation indexes, constructing a total regulation potential evaluation index model according to the electricity price subsidy strategy, and solving to obtain a scheduling strategy, so that electric network personnel schedule the electric vehicles to be scheduled according to the scheduling strategy, and scheduling the electric vehicles according to the regulation potential of the electric vehicles and the wind power consumption requirement of a power network, thereby improving the scheduling efficiency and the scheduling accuracy and reducing the running cost of the power network.

Description

Wind power system optimization scheduling method considering regulation potential of electric automobile
Technical Field
The invention relates to the technical field of power grid dispatching, in particular to a wind power system optimization dispatching method considering the regulation potential of an electric automobile.
Background
Along with the promotion of the 'double carbon' strategic target, the power grid of China is accelerated to be converted into a clean and intelligent novel power system. To achieve this goal, collaborative scheduling of source-end-of-load adjustable resources must be advanced. Wind power is used as a source-end adjustable resource with mature technology, large installed capacity and strong schedulability, and is rapidly developed at home and abroad.
As a common charge end adjustable resource, the electric automobile has the characteristics of cleanness and no pollution emission, and the permeability of the electric automobile in the field of civil automobiles is rapidly improved. However, due to the strong random characteristics of wind power and electric vehicles, if the wind power and the electric vehicles are connected in a large scale without regulation, the safety operation of a power system is greatly threatened, so that the electric vehicles and the wind power are cooperatively scheduled by utilizing the self energy storage characteristics of the electric vehicles, and the impact on a power grid is reduced. The single electric automobile has extremely weak adjustable capability, so the common electric automobile virtual power plant technology aggregates a plurality of electric automobiles with small single capacity and numerous electric automobiles into a plurality of clusters, and uniformly schedules the clusters as virtual energy storage equipment so as to provide enough flexibility and scheduling space for a power grid, reduce peak-valley difference of power grid load and realize the absorption of new energy sources such as wind power and the like on the premise of meeting the travel requirements of users.
At present, a wind power system is faced with the problem of wind abandoning, namely the problem that wind power generation cannot be consumed due to insufficient power grid capacity. In order to solve the problem, a learner proposes a method for guiding an electric automobile to participate in wind power absorption. However, the conventional electric vehicle grouping method cannot fully describe the regulation and control characteristics of different types of electric vehicles. Therefore, a new method is needed to solve the problem of wind abandon caused by insufficient capacity of the power grid, and an optimization strategy capable of guiding the electric automobile to participate in wind power absorption is provided.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the invention provides the optimization scheduling method for the wind power-containing system considering the regulation potential of the electric automobile, which schedules the electric automobile by considering the regulation potential of the electric automobile and the wind power consumption requirement of the power grid, improves the scheduling efficiency and the scheduling accuracy, and reduces the running cost of the power grid.
The first aspect of the embodiment of the invention provides a wind power system optimization scheduling method considering the regulation potential of an electric automobile, which comprises the following steps:
constructing regulation potential evaluation indexes of each electric automobile according to the schedulable time and schedulable space of each electric automobile to be scheduled;
Grouping all the electric vehicles to be scheduled according to the regulation potential evaluation indexes of the electric vehicles to be scheduled to obtain a plurality of electric vehicle groups, and obtaining an electricity price subsidy strategy according to the regulation potential evaluation indexes;
and constructing a total regulation potential evaluation index model according to the electricity price subsidy strategy, and solving to obtain a scheduling strategy so that power grid personnel schedule the electric automobile to be scheduled according to the scheduling strategy.
According to the embodiment, the regulation potential evaluation indexes of the electric vehicles are constructed according to the schedulable time and the schedulable space of the electric vehicles to be scheduled, all the electric vehicles to be scheduled are clustered according to the regulation potential evaluation indexes of the electric vehicles to be scheduled, a plurality of electric vehicle groups are obtained, an electricity price subsidy strategy is obtained according to the regulation potential evaluation indexes, a total regulation potential evaluation index model is constructed according to the electricity price subsidy strategy, and the scheduling strategy is obtained, so that electric network personnel schedule the electric vehicles to be scheduled according to the scheduling strategy, and the electric vehicles are scheduled by considering the regulation potential size of the electric vehicles and the wind power consumption requirement of the electric network, so that the scheduling efficiency and the scheduling accuracy are improved, and the running cost of the electric network is reduced.
In one possible implementation manner of the first aspect, the regulatory potential evaluation index of each electric automobile is constructed according to the schedulable time and the schedulable space of each electric automobile to be scheduled, specifically:
constructing a schedulable time evaluation index according to the schedulable time of each electric automobile to be scheduled, wherein the schedulable time evaluation index is as follows:
wherein C is time,i As a time evaluation index of the electric automobile i to be scheduled, T adj,i For the schedulable time of the electric automobile i to be scheduled, T stay,i Indicating the residence time of the electric automobile i to be scheduled;
constructing a schedulable space evaluation index according to the schedulable capacity of each electric automobile to be scheduled, wherein the schedulable space evaluation index is as follows:
wherein C is vol,i Evaluating an index C for a schedulable space of the electric automobile i to be scheduled EV,i C, an average schedulable power influence factor of the electric automobile i to be scheduled equ,l The average equivalent load influence factor of the electric automobile i to be scheduled is;
obtaining a regulatory potential evaluation index of each electric automobile to be scheduled according to the schedulable space evaluation index and the schedulable time evaluation index of each electric automobile to be scheduled, wherein the expression of the regulatory potential evaluation index is as follows:
in the method, in the process of the invention,c, evaluating indexes for regulating and controlling potential of electric automobile to be regulated time,i C is a time evaluation index of the electric automobile i to be scheduled vol,i And evaluating the index for the schedulable space of the electric automobile i to be scheduled.
In a possible implementation manner of the first aspect, the schedulable time evaluation index is constructed according to the schedulable time of each electric automobile to be scheduled, and specifically is:
obtaining charging time according to the charging power and the charging electric quantity of each electric automobile to be scheduled, wherein the calculation formula of the charging time is as follows:
in SOC 0,i The initial SOC value and the SOC of the ith electric automobile aim,i Is the target SOC value of the ith electric automobile, E i Battery capacity of ith electric automobile, P cha Charging power eta of electric automobile cha The charging efficiency of the electric automobile is improved;
the residence time of each electric automobile to be scheduled is determined, and the calculation formula of the residence time is as follows:
wherein t is dep,i For the estimated departure time of the ith electric vehicle, t arr,i Is the arrival time of the ith electric automobile, t dep,i <t arr,i The electric automobile is connected in the current day and leaves the next day;
and obtaining the schedulable time of each electric automobile to be scheduled according to the charging time and the residence time, wherein the schedulable time has the formula:
T adj,i ＝T stay,i -T need,i
wherein T is adj,i Representing schedulable time, T, of an electric vehicle to be scheduled stay,i Indicating the residence time of the electric automobile to be scheduled, T need,i Representing the charging time of the electric automobile i to be scheduled;
and obtaining a dispatching time evaluation index of the electric automobile according to the dispatchable time and the residence time.
In a possible implementation manner of the first aspect, the schedulable space evaluation index is constructed according to the schedulable capacity of each electric automobile to be scheduled, and specifically is:
obtaining a schedulable space evaluation index according to the average schedulable power influence factors and the average equivalent load influence factors of the electric vehicles to be scheduled, wherein the average schedulable power influence factors are as follows:
wherein C is EV,i An average schedulable power influence factor of the electric automobile i to be scheduled;
the formula of the average equivalent load influence factor is:
P equ,l ＝P base,l -P ther,l -P wind,l
wherein C is equ,l Representing average schedulable power influence factor, P, of electric automobile i to be scheduled wind,l 、P ther,l 、P base,l Wind power output, thermal power unit output and base load in the first period respectively, N i,l And the total number of the electric vehicles accessed in the power grid is the i-th electric vehicle access period.
In one possible implementation manner of the first aspect, all electric vehicles to be scheduled are grouped according to the regulatory potential evaluation indexes of each electric vehicle to be scheduled, so as to obtain a plurality of electric vehicle groups, which specifically are:
Determining a weight matrix of the schedulable time evaluation index and the schedulable space evaluation index, wherein the weight matrix is as follows:
wherein alpha represents the weight of the schedulable time evaluation index, and beta represents the weight of the schedulable space evaluation index;
obtaining a grouping index of the electric automobile to be scheduled according to the weight and the regulation potential evaluation index, wherein the expression of the grouping index is as follows:
wherein C is div,i For the index of the grouping,is a weight matrix>Evaluating indexes for the regulation potential of the electric automobile to be scheduled;
and grouping all the electric vehicles to be scheduled according to the grouping index to obtain a plurality of electric vehicle groups.
In a possible implementation manner of the first aspect, the electricity price subsidy strategy is obtained according to the regulation potential evaluation index, specifically:
calculating the charging electricity price of each electric automobile group in the first period, wherein the charging electricity price has the following expression:
Z k,l ＝(1-I level,k,l -δ P,l )c l
wherein, c l For the peak-to-valley basic electricity price of the first period, I level,k,l Regulatory potential stimulus for the Kth electric automobile group, delta P,l For wind power absorption excitation factors, l=1, 2,3, …, T,
in the method, in the process of the invention,average regulation potential evaluation total index representing the first period of the Kth electric automobile group, a and b are constant parameters, N k,l The number of electric vehicles managed by the kth electric vehicle cluster in the first period;
and obtaining the electricity price subsidy strategy according to the charging electricity price of each electric automobile group in the first period.
In one possible implementation manner of the first aspect, the total regulatory potential evaluation index model is constructed according to the electricity price subsidy strategy and solved to obtain the scheduling strategy, specifically:
obtaining a first objective function according to the peak-valley difference of the total load of the power grid, and obtaining a second objective function according to the electricity price subsidy strategy;
normalizing the first objective function and the second objective function to obtain an objective function of the total regulation potential evaluation index model, wherein the objective function is as follows:
ω 1 +ω 2 ＝1
wherein F is 1max And F is equal to 2max Respectively a first objective function F 1 With a second objective function F 2 Equivalent maximum value of F 1max For the variance of the grid base load, F 2max For the charging cost of the electric automobile under disordered charging, P dis l For the total charging power omega of the electric automobile in the disordered charging scene in the period l 1 And omega 2 Respectively a first objective function F 1 With a second objective function F 2 Weight coefficient of (2);
determining constraint conditions of a total regulation potential evaluation index model, wherein the constraint conditions are as follows:
c l,min ≤Z k,l ≤c l,max
δ c,k,l δ d,k,l ＝0
-P Cha N k,l η cha δ d,k,l ≤P k,l ≤P Cha N k,l η cha δ c,k,l
wherein, c l,min And c l,max Respectively the lower limit and the upper limit of the peak-valley basic electricity price, P k,l The charging power of the kth electric automobile cluster in the ith period,for the average state of charge (SOC) of the kth electric automobile cluster in the ith period k,i The state of charge (SOC) of the ith electric vehicle belonging to the kth electric vehicle cluster min Is the minimum value of the state of charge and the SOC of a single electric automobile max Is the maximum value of the charge state of a single electric automobile, E k The sum of the charge and discharge electric quantity of the kth electric automobile cluster in all the time periods is N k The total number of all electric vehicles in the kth electric vehicle cluster;
and solving the total regulation potential evaluation index model to obtain a scheduling strategy.
The second aspect of the embodiment of the invention provides a wind power system optimization scheduling system considering the regulation potential of an electric automobile, which comprises the following components:
the evaluation index construction module is used for constructing regulation and control potential evaluation indexes of the electric vehicles according to the schedulable time and the schedulable space of the electric vehicles to be scheduled;
the electric price subsidy strategy is used for grouping all the electric vehicles to be scheduled according to the regulation potential evaluation indexes of the electric vehicles to be scheduled to obtain a plurality of electric vehicle groups, and obtaining the electric price subsidy strategy according to the regulation potential evaluation indexes;
And the dispatching strategy is used for constructing a total regulation potential evaluation index model according to the electricity price subsidy strategy and solving the total regulation potential evaluation index model to obtain the dispatching strategy so that power grid personnel dispatch the electric automobile to be dispatched according to the dispatching strategy.
In one possible implementation manner of the second aspect, the regulatory potential evaluation index of each electric automobile is constructed according to the schedulable time and the schedulable space of each electric automobile to be scheduled, specifically:
constructing a schedulable time evaluation index according to the schedulable time of each electric automobile to be scheduled, wherein the schedulable time evaluation index is as follows:
wherein C is time,i For electricity to be scheduledTime evaluation index of motor car i, T adj,i For the schedulable time of the electric automobile i to be scheduled, T stay,i Indicating the residence time of the electric automobile i to be scheduled;
constructing a schedulable space evaluation index according to the schedulable capacity of each electric automobile to be scheduled, wherein the schedulable space evaluation index is as follows:
wherein C is vol,i Evaluating an index C for a schedulable space of the electric automobile i to be scheduled EV,i C, an average schedulable power influence factor of the electric automobile i to be scheduled equ,l The average equivalent load influence factor of the electric automobile i to be scheduled is;
obtaining a regulatory potential evaluation index of each electric automobile to be scheduled according to the schedulable space evaluation index and the schedulable time evaluation index of each electric automobile to be scheduled, wherein the expression of the regulatory potential evaluation index is as follows:
In the method, in the process of the invention,c, evaluating indexes for regulating and controlling potential of electric automobile to be regulated time,i C is a time evaluation index of the electric automobile i to be scheduled vol,i And evaluating the index for the schedulable space of the electric automobile i to be scheduled.
In one possible implementation manner of the second aspect, the schedulable time evaluation index is constructed according to the schedulable time of each electric automobile to be scheduled, and specifically is:
obtaining charging time according to the charging power and the charging electric quantity of each electric automobile to be scheduled, wherein the calculation formula of the charging time is as follows:
in SOC 0,i The initial SOC value and the SOC of the ith electric automobile aim,i Is the target SOC value of the ith electric automobile, E i Battery capacity of ith electric automobile, P cha Charging power eta of electric automobile cha The charging efficiency of the electric automobile is improved;
the residence time of each electric automobile to be scheduled is determined, and the calculation formula of the residence time is as follows:
wherein t is dep,i For the estimated departure time of the ith electric vehicle, t arr,i Is the arrival time of the ith electric automobile, t dep,i <t arr,i The electric automobile is connected in the current day and leaves the next day;
and obtaining the schedulable time of each electric automobile to be scheduled according to the charging time and the residence time, wherein the schedulable time has the formula:
T adj,i ＝T stay,i -T need,i
Wherein T is adj,i Representing schedulable time, T, of an electric vehicle to be scheduled stay,i Indicating the residence time of the electric automobile to be scheduled, T need,i Representing the charging time of the electric automobile i to be scheduled;
and obtaining a dispatching time evaluation index of the electric automobile according to the dispatchable time and the residence time.
Drawings
Fig. 1: the flow diagram of one embodiment of the wind power system optimization scheduling method considering the regulation potential of the electric automobile is provided by the invention;
fig. 2: the regulation potential evaluation index schematic diagram of the large-scale electric vehicle of one embodiment of the wind power system optimization scheduling method considering the regulation potential of the electric vehicle is provided by the invention;
fig. 3: the invention provides a power grid equivalent load and electric vehicle load curve schematic diagram under different conditions of one embodiment of a wind power system optimization scheduling method considering electric vehicle regulation potential;
fig. 4: the system structure schematic diagram of another embodiment of the wind power system optimization scheduling method considering the regulation potential of the electric automobile is provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a flow chart of an embodiment of a wind power system optimization scheduling method with electric vehicle regulation potential according to the embodiment of the invention includes steps S11 to S13, where the steps are specifically as follows:
s11, constructing regulation potential evaluation indexes of the electric vehicles according to the schedulable time and the schedulable space of the electric vehicles to be scheduled.
In a preferred embodiment, the regulation potential evaluation index of each electric automobile is constructed according to the schedulable time and the schedulable space of each electric automobile to be scheduled, specifically:
constructing a schedulable time evaluation index according to the schedulable time of each electric automobile to be scheduled, wherein the schedulable time evaluation index is as follows:
wherein C is time,i As a time evaluation index of the electric automobile i to be scheduled, T adj,i For the schedulable time of the electric automobile i to be scheduled, T stay,i Representing electricity to be scheduledThe residence time of the motor vehicle i;
constructing a schedulable space evaluation index according to the schedulable capacity of each electric automobile to be scheduled, wherein the schedulable space evaluation index is as follows:
wherein C is vol,i Evaluating an index C for a schedulable space of the electric automobile i to be scheduled EV,i C, an average schedulable power influence factor of the electric automobile i to be scheduled equ,l The average equivalent load influence factor of the electric automobile i to be scheduled is;
obtaining a regulatory potential evaluation index of each electric automobile to be scheduled according to the schedulable space evaluation index and the schedulable time evaluation index of each electric automobile to be scheduled, wherein the expression of the regulatory potential evaluation index is as follows:
in the method, in the process of the invention,c, evaluating indexes for regulating and controlling potential of electric automobile to be regulated time,i C is a time evaluation index of the electric automobile i to be scheduled vol,i And evaluating the index for the schedulable space of the electric automobile i to be scheduled.
In a preferred embodiment, a schedulable time evaluation index is constructed according to the schedulable time of each electric automobile to be scheduled, specifically:
obtaining charging time according to the charging power and the charging electric quantity of each electric automobile to be scheduled, wherein the calculation formula of the charging time is as follows:
in SOC 0,i The initial SOC value and the SOC of the ith electric automobile aim,i Is the target SOC value of the ith electric automobile, E i Battery capacity of ith electric automobile, P cha Charging power eta of electric automobile cha The charging efficiency of the electric automobile is improved;
the residence time of each electric automobile to be scheduled is determined, and the calculation formula of the residence time is as follows:
wherein t is dep,i For the estimated departure time of the ith electric vehicle, t arr,i Is the arrival time of the ith electric automobile, t dep,i <t arr,i The electric automobile is connected in the current day and leaves the next day;
and obtaining the schedulable time of each electric automobile to be scheduled according to the charging time and the residence time, wherein the schedulable time has the formula:
T adj,i ＝T stay,i -T need,i
wherein T is adj,i Representing schedulable time, T, of an electric vehicle to be scheduled stay,i Indicating the residence time of the electric automobile to be scheduled, T need,i Representing the charging time of the electric automobile i to be scheduled;
and obtaining a dispatching time evaluation index of the electric automobile according to the dispatchable time and the residence time.
In a preferred embodiment, a schedulable space evaluation index is constructed according to the schedulable capacity of each electric automobile to be scheduled, specifically:
obtaining a schedulable space evaluation index according to the average schedulable power influence factors and the average equivalent load influence factors of the electric vehicles to be scheduled, wherein the average schedulable power influence factors are as follows:
wherein C is EV,i An average schedulable power influence factor of the electric automobile i to be scheduled;
the formula of the average equivalent load influence factor is:
P equ,l ＝P base,l -P ther,l -P wind,l
wherein C is equ,l Representing average schedulable power influence factor, P, of electric automobile i to be scheduled wind,l 、P ther,l 、P base,l Wind power output, thermal power unit output and base load in the first period respectively, N i,l And the total number of the electric vehicles accessed in the power grid is the i-th electric vehicle access period.
In the embodiment, electric automobile regulation potential evaluation indexes are built from two angles of schedulable time and schedulable space, and a potential evaluation model of large-scale electric automobile participation in power grid regulation is built. From two angles of time and space, a schedulable time evaluation index and a schedulable capacity evaluation index of the electric automobile are respectively provided, and the schedulable space of the electric automobile can adopt schedulable capacity measurement. In a general assessment model, the schedulable capacity is generally equivalent to the predicted required charge of the electric vehicle, but the latter is actually part of the schedulable time index calculation formula, affecting the scientificity of the regulatory potential assessment of the electric vehicle. Therefore, the method provides a new electric vehicle schedulable capacity evaluation index. Fig. 2 is a graph of a regulatory potential evaluation index of a large-scale electric vehicle, and the two are combined to provide a regulatory potential evaluation total index, so that the cluster division of the large-scale electric vehicle is performed based on the regulatory potential evaluation total index.
Length T required for charging of ith electric vehicle need,i The ratio of the expected required charge amount to the charging power is defined as follows:
In SOC 0,i The initial SOC value and the SOC of the ith electric automobile aim,i Is the target SOC value of the ith electric automobile, E i Battery capacity of ith electric automobile, P cha Charging power eta of electric automobile cha The charging efficiency of the electric automobile is improved;
the residence time of each electric automobile to be scheduled is determined, and the calculation formula of the residence time is as follows:
wherein t is dep,i For the estimated departure time of the ith electric vehicle, t arr,i Is the arrival time of the ith electric automobile, t dep,i <t arr,i The electric automobile is connected in the current day and leaves the next day;
the i-th electric automobile has the following schedulable time:
T adj,i ＝T stay,i -T need,i
wherein T is adj,i Representing schedulable time, T, of an electric vehicle to be scheduled stay,i Indicating the residence time of the electric automobile to be scheduled, T need,i Representing the charging time of the electric automobile i to be scheduled;
in summary, the expression of the schedulable time evaluation index of the ith electric automobile is as follows:
wherein C is time,i And (3) evaluating the index T for the time of the electric automobile i to be scheduled adj,i For the schedulable time of the electric automobile i to be scheduled, T stay,i Indicating the residence time of the electric automobile i to be scheduled;
definition C EV,i C, an average schedulable power influence factor of the ith electric automobile equ,l Average equivalent load shadow for ith electric automobileThe response factor has the expression:
Wherein C is EV,i An average schedulable power influence factor of the electric automobile i to be scheduled;
the formula of the average equivalent load influence factor is:
P equ,l ＝P base,l -P ther,l -P wind,l
wherein C is equ,l Representing average schedulable power influence factor, P, of electric automobile i to be scheduled wind,l 、P ther,l 、P base,l Wind power output, thermal power unit output and base load in the first period respectively, N i,l And the total number of the electric vehicles accessed in the power grid is the i-th electric vehicle access period.
In summary, the schedulable space evaluation index is:
wherein C is vol,i Evaluating an index C for a schedulable space of the electric automobile i to be scheduled EV,i C, an average schedulable power influence factor of the electric automobile i to be scheduled equ,l And the average equivalent load influence factor of the electric automobile i to be scheduled is obtained.
The total index of the electric automobile regulation potential evaluation measures the comprehensive performance of the electric automobile on the schedulable time index and the schedulable capacity index. The expression of the regulatory potential evaluation total index of the ith electric automobile is as follows:
in the method, in the process of the invention,evaluating the index for the regulation potential of the electric automobile to be scheduled, C time,i C, evaluating the index for the time of the electric automobile i to be scheduled vol,i And evaluating the index for the schedulable space of the electric automobile i to be scheduled.
S12, grouping all the electric vehicles to be scheduled according to the regulation potential evaluation indexes of the electric vehicles to be scheduled to obtain a plurality of electric vehicle groups, and obtaining an electricity price subsidy strategy according to the regulation potential evaluation indexes.
In a preferred embodiment, all electric vehicles to be scheduled are grouped according to the regulatory potential evaluation indexes of each electric vehicle to be scheduled, so as to obtain a plurality of electric vehicle groups, specifically:
determining a weight matrix of the schedulable time evaluation index and the schedulable space evaluation index, wherein the weight matrix is as follows:
wherein alpha represents the weight of the schedulable time evaluation index, and beta represents the weight of the schedulable space evaluation index;
obtaining a grouping index of the electric automobile to be scheduled according to the weight and the regulation potential evaluation index, wherein the expression of the grouping index is as follows:
wherein C is div,i For the index of the grouping,is a weight matrix>For electricity to be scheduledEvaluation indexes of regulation potential of an automobile;
as a preferred scheme, all electric vehicles to be scheduled are clustered according to a clustering index to obtain a plurality of electric vehicle clusters.
The electricity price subsidy strategy is obtained according to the regulation potential evaluation index, and specifically comprises the following steps:
calculating the charging electricity price of each electric automobile group in the first period, wherein the charging electricity price has the following expression:
Z k,l ＝(1-I level,k,l -δ P,l )c l
wherein, c l For the peak-to-valley basic electricity price of the first period, I lvel,k,l Regulatory potential stimulus for the Kth electric automobile group, delta P,l For wind power absorption excitation factors, l=1, 2,3, …, T,
In the method, in the process of the invention,average regulation potential evaluation total index representing the first period of the Kth electric automobile group, a and b are constant parameters, N k,l The number of electric vehicles managed by the kth electric vehicle cluster in the first period;
and obtaining the electricity price subsidy strategy according to the charging electricity price of each electric automobile group in the first period.
In the embodiment, large-scale electric vehicles are clustered based on the size of the regulatory potential, and electric vehicle dispatching is performed by taking the cluster as a unit. In order to avoid the problems of dimension disasters and the like caused by a centralized control mode as much as possible, the method groups the large-scale electric vehicles based on the size of the regulation potential, and schedules the electric vehicles by taking the clusters as units, so that the dimension of the control variable in the optimization model is equal to the number of the electric vehicle clusters, and the solving difficulty is greatly reduced. Meanwhile, the uniformity of cluster scheduling is guaranteed due to the similarity of individual regulation potential of the electric vehicles in the clusters, and the large-scale electric vehicles are convenient to participate in the consumption of new energy sources such as wind power.
Considering that the power grid has different scheduling requirements on the schedulable time and the schedulable capacity of the large-scale electric automobile in different scheduling scenes, the method is definedThe electric automobile grouping weight vector represents the emphasis in the power grid dispatching, and the expression is as follows:
Wherein, alpha represents the weight of the schedulable time evaluation index, and beta represents the weight of the schedulable space evaluation index.
Definition C div,\ The expression of the grouping index of the ith electric automobile is as follows:
wherein C is div,i For the index of the grouping,for the weight matrix, < >>Evaluating indexes for the regulation potential of the electric automobile to be scheduled;
grouping all electric vehicles to be scheduled according to the grouping index to obtain a plurality of electric vehicle groups, wherein the principle of large-scale electric vehicle group division is as follows:
1) All electric vehicles are divided into a non-schedulable large class and a schedulable large class, and the main basis of the division is whether the electric vehicles are willing to accept power grid scheduling or not and have V2G conditions.
2) All electric vehicles belonging to the schedulable major class are further divided into m clusters, and the dividing rule is that the number of electric vehicles governed by the clusters is basically the same.
3) All electric vehicles belonging to the non-schedulable major class are classified into the same cluster, and the grouping index C of the electric vehicles j in the cluster is defined div,j The method comprises the following steps:
C div,j ＝0
in order to improve the enthusiasm of electric automobile users in response to power grid dispatching, the invention establishes a group-division time-of-use electricity price scheme in a charging scene for the electric automobile users in a wind power system under the condition of fully considering wind power output uncertainty. According to the scheme, the regulation potential of each cluster and the wind power consumption requirement of the power grid are comprehensively considered, differentiated charging electricity price subsidy is carried out on each cluster, and waste of electricity price subsidy is avoided while the dispatching responsiveness of each cluster to the power grid is improved.
And establishing a grouping time-of-use electricity price model which gives consideration to the difference between wind power consumption and electric automobile clusters to conduct differentiated charging electricity price subsidy for each cluster. At present, the time-of-use electricity price is widely used for guiding users to participate in power grid dispatching activities such as charge and discharge power regulation and control by virtue of the advantage that the load characteristic can be improved. The setting of the time-of-use electricity price can have great influence on the effects of the time-of-use electricity price of the components in the aspects of guiding users to participate in power grid dispatching, enhancing power grid operation safety and the like. The electric price strategy proposed by the prior study is directly used for all electric vehicles, the difference among the electric vehicles is ignored, the electric vehicles with lower control potential waste the high-electricity price subsidy, and the electric vehicles with higher control potential do not have high enthusiasm for the low-electricity price subsidy, so that the electric vehicles are not beneficial to being guided to participate in wind power absorption.
According to the method, the power price subsidy is conducted on each electric automobile cluster according to the size of the cluster regulation potential by formulating a group time-of-use power price strategy, so that the charging cost reduced by the higher regulation potential is more, the enthusiasm of the electric automobile cluster with the higher regulation potential for responding to power grid dispatching is improved, and meanwhile, the waste caused by excessive subsidy on the electric automobile cluster with the lower regulation potential is avoided.
Let Z be k,l The charging electricity price of the kth electric automobile cluster in the ith period is represented by the following mathematical expression:
Z k,l ＝(1-I level,k,l -δ P,l )c l
wherein, c l For the peak-to-valley basic electricity price of the first period, I level,k,l Regulatory potential stimulus for the Kth electric automobile group, delta P,l For wind power absorption excitation factors, l=1, 2,3, …, T,
is provided withEvaluating the total indicator for the average regulatory potential of the kth cluster in the first period, then +.>I level,k,l And delta P,l The following conditions are satisfied:
in the method, in the process of the invention,average regulation potential evaluation total index representing the first period of the Kth electric automobile group, and a and b are constant parameters，N k,l The number of electric vehicles managed by the kth electric vehicle cluster in the ith period is the number of electric vehicles managed by the kth electric vehicle cluster in the kth period.
And S13, constructing a total regulation potential evaluation index model according to the electricity price subsidy strategy, and solving to obtain a dispatching strategy so that power grid personnel dispatch the electric automobile to be dispatched according to the dispatching strategy.
In a preferred embodiment, a total regulatory potential evaluation index model is constructed according to an electricity price subsidy strategy and solved to obtain a scheduling strategy, specifically:
obtaining a first objective function according to the peak-valley difference of the total load of the power grid, and obtaining a second objective function according to the electricity price subsidy strategy;
normalizing the first objective function and the second objective function to obtain an objective function of the total regulation potential evaluation index model, wherein the objective function is as follows:
ω 1 +ω 2 ＝1
Wherein F is 1max And F is equal to 2max Respectively a first objective function F 1 With a second objective function F 2 Equivalent maximum value of F 1max For the variance of the grid base load, F 2max For the charging cost of the electric automobile under disordered charging, P dis l For the total charging power omega of the electric automobile in the disordered charging scene in the period l 1 And omega 2 Respectively a first objective function F 1 With a second objective function F 2 Weight coefficient of (2);
determining constraint conditions of a total regulation potential evaluation index model, wherein the constraint conditions are as follows:
c l,min ≤Z k,l ≤c l,max
δ c,k,l δ d,k,l ＝0
-P Cha N k,l η cha δ d,k,l ≤P k,l ≤P Cha N k,l η cha δ c,k,l
wherein, c l,min And c l,max Respectively the lower limit and the upper limit of the peak-valley basic electricity price, P k,l The charging power of the kth electric automobile cluster in the ith period,for the average state of charge (SOC) of the kth electric automobile cluster in the ith period k,i The state of charge (SOC) of the ith electric vehicle belonging to the kth electric vehicle cluster min Is the minimum value of the state of charge and the SOC of a single electric automobile max Is the maximum value of the charge state of a single electric automobile, E k The sum of the charge and discharge electric quantity of the kth electric automobile cluster in all the time periods is N k The total number of all electric vehicles in the kth electric vehicle cluster;
and solving the total regulation potential evaluation index model to obtain a scheduling strategy.
In this embodiment, the power grid side and the user side respectively use the lowest peak-valley difference of the total load and the lowest charging cost of the electric vehicle user as targets, and use the charging and discharging states of the electric vehicle clusters as decision variables to establish and solve an optimization model comprehensively considering benefits of the power grid and the electric vehicle user.
Under the condition that the electric automobile participates in power grid dispatching, the safe operation of the wind power system is guaranteed, and the charging cost of the electric automobile user is considered. Therefore, the method establishes an objective function considering the minimum peak-valley difference of the power grid and the minimum charging cost of the user on the basis of uncertainty of wind power output and charging load constraint of the electric automobile, and guides the electric automobile cluster to carry out ordered charging and discharging.
The total load of the power grid is defined as a thermal power generating unitThe superposition of the output, the wind power output, the charging and discharging load of the electric automobile and the basic load is often used as an important basis for measuring the effectiveness of the peak shaving measures of the power grid. Therefore, the peak-valley difference of the total load of the power grid is selected as an objective function F 1 The expression is:
F 1 ＝min(P max -P min )
wherein P is max And P min Respectively the maximum value and the minimum value of the total load of the power grid.
The user satisfaction of the electric automobile can be measured through the charge cost of the user. In the method, the charging cost of the electric automobile is charged according to the group time electricity price scheme. Selecting the sum charging cost of the electric automobile users as an objective function F 2 The expression is:
wherein P is dis Is the discharge power of the electric automobile, p d,k,l The discharge electricity price delta of the kth electric automobile cluster in the ith period c,k,l And delta d,k,l Respectively the charging state and the discharging state of the kth electric automobile cluster in the ith period, N k,l The number of electric vehicles managed by the kth electric vehicle cluster in the ith period is the number of electric vehicles managed by the kth electric vehicle cluster in the kth period.
In order to eliminate dimension influence, a linear weighting method is adopted to convert a multi-objective optimization problem into a single-objective optimization problem and normalize the single-objective optimization problem, and the objective function after conversion is as follows:
wherein F is 1max And F is equal to 2max Respectively a first objective function F 1 With a second objective function F 2 Equivalent maximum value of F 1max For the variance of the grid base load, F 2max For the charging cost of the electric automobile under disordered charging, P dis l For the total charging power omega of the electric automobile in the disordered charging scene in the period l 1 And omega 2 Respectively a first objective function F 1 With a second objective function F 2 Weight coefficient of (2);
constraint conditions:
1) Charging electricity price constraint of electric automobile
The charging electricity price of any electric automobile cluster in any period of time l is limited in the charging basic electricity price range, namely:
c l,min ≤Z k,l ≤c l,max
wherein, c l,min And c l,max The lower limit and the upper limit of the peak-valley basic electricity price are respectively defined.
2) Electric automobile cluster charge-discharge uniqueness constraint
For convenience in management, the same electric automobile cluster performs charge and discharge scheduling uniformly, and one cluster cannot be in a charge or discharge state at the same time in the same period, namely:
δ c,k,l δ d,k,l ＝0
3) Electric automobile cluster charge-discharge power constraint
The charge and discharge power of each period of the electric automobile cluster is smaller than the total charge and discharge power of electric automobiles managed by the period cluster, namely:
-P Cha N k,l η cha δ d,k,l ≤P k,l ≤P Cha N k,l η cha δ c,k,l
wherein p is k,l And the charging power of the kth electric automobile cluster in the ith period.
4) Electric automobile cluster state of charge constraint
The average SOC of any electric automobile cluster in any period should be limited to a certain range, namely:
in the method, in the process of the invention,for the average state of charge (SOC) of the kth electric automobile cluster in the ith period k,i The state of charge (SOC) of the ith electric vehicle belonging to the kth electric vehicle cluster min Is the minimum value of the state of charge and the SOC of a single electric automobile max Is the maximum value of the charge state of a single electric automobile.
5) Electric automobile cluster charge capacity constraint
The sum of the charge and discharge electric quantity of the cluster k may not exceed the total charge requirement, namely:
wherein E is k The sum of the charge and discharge electric quantity of the kth electric automobile cluster in all the time periods is N k And the total number of the electric vehicles is the total number of all the electric vehicles in the kth electric vehicle cluster.
The CPLEX solver is a high-performance solver with the characteristics of strong robustness, high optimization efficiency, no sinking into a local optimal solution and the like, and is suitable for solving an optimization model provided by the method. According to the method, CPLEX is called by a YALMIP toolbox to respectively solve the proposed multi-objective optimization model in 96 scheduling periods, so that an optimal scheduling plan suitable for the electric automobile with the anti-peak shaving characteristic wind power output is obtained, and the effectiveness of the proposed optimization scheduling model is verified.
The equivalent load curve of the power grid and the load curve of the electric automobile are shown in fig. 3. The method provided by the invention obviously reduces the charging cost of the user and the air discarding quantity of the power grid, and greatly reduces the equivalent load peak-valley difference of the power grid under the condition that the quantity of the electric vehicles participating in dispatching is limited. It follows that the method provided herein is effective in improving the operational safety reliability, wind power consumption capability and user engagement of wind power systems.
Example two
Correspondingly, referring to fig. 4, fig. 4 is a wind power system optimization scheduling system with electric automobile regulation potential, provided by the invention, as shown in the figure, the wind power system optimization scheduling system with electric automobile regulation potential comprises:
the evaluation index construction module 401 is configured to construct a regulatory potential evaluation index of each electric vehicle according to the schedulable time and the schedulable space of each electric vehicle to be scheduled;
the electricity price subsidy strategy 402 is configured to group all electric vehicles to be scheduled according to the regulatory potential evaluation indexes of each electric vehicle to be scheduled to obtain a plurality of electric vehicle groups, and obtain the electricity price subsidy strategy according to the regulatory potential evaluation indexes;
and the dispatching strategy 403 is used for constructing a total regulation potential evaluation index model according to the electricity price subsidy strategy and solving the total regulation potential evaluation index model to obtain the dispatching strategy so that the electric power grid personnel dispatch the electric vehicle to be dispatched according to the dispatching strategy.
In a preferred embodiment, the regulation potential evaluation index of each electric automobile is constructed according to the schedulable time and the schedulable space of each electric automobile to be scheduled, specifically:
constructing a schedulable time evaluation index according to the schedulable time of each electric automobile to be scheduled, wherein the schedulable time evaluation index is as follows:
wherein C is time,i As a time evaluation index of the electric automobile i to be scheduled, T adj,i For the schedulable time of the electric automobile i to be scheduled, T stay,i Indicating the residence time of the electric automobile i to be scheduled;
constructing a schedulable space evaluation index according to the schedulable capacity of each electric automobile to be scheduled, wherein the schedulable space evaluation index is as follows:
/>
wherein C is vol,i Evaluating an index C for a schedulable space of the electric automobile i to be scheduled EV,i Average schedulable power shadow for electric automobile i to be scheduledResponse factor, C equ,l The average equivalent load influence factor of the electric automobile i to be scheduled is;
obtaining a regulatory potential evaluation index of each electric automobile to be scheduled according to the schedulable space evaluation index and the schedulable time evaluation index of each electric automobile to be scheduled, wherein the expression of the regulatory potential evaluation index is as follows:
in the method, in the process of the invention,c, evaluating indexes for regulating and controlling potential of electric automobile to be regulated time,i C is a time evaluation index of the electric automobile i to be scheduled vol,i And evaluating the index for the schedulable space of the electric automobile i to be scheduled.
In a preferred embodiment, a schedulable time evaluation index is constructed according to the schedulable time of each electric automobile to be scheduled, specifically:
obtaining charging time according to the charging power and the charging electric quantity of each electric automobile to be scheduled, wherein the calculation formula of the charging time is as follows:
in SOC 0,i The initial SOC value and the SOC of the ith electric automobile aim,i Is the target SOC value of the ith electric automobile, E i Battery capacity of ith electric automobile, P cha Charging power eta of electric automobile cha The charging efficiency of the electric automobile is improved;
the residence time of each electric automobile to be scheduled is determined, and the calculation formula of the residence time is as follows:
wherein t is dep,i For the estimated departure time of the ith electric vehicle, t arr,i Is the arrival time of the ith electric automobile, t dep,i <t arr,i The electric automobile is connected in the current day and leaves the next day;
and obtaining the schedulable time of each electric automobile to be scheduled according to the charging time and the residence time, wherein the schedulable time has the formula:
T adj,i ＝T stay,i -T need,i
wherein T is adj,i Representing schedulable time, T, of an electric vehicle to be scheduled stay,i Indicating the residence time of the electric automobile to be scheduled, T need,i Representing the charging time of the electric automobile i to be scheduled;
and obtaining a dispatching time evaluation index of the electric automobile according to the dispatchable time and the residence time.
The wind power system optimization scheduling system with the electric automobile regulation potential can implement the wind power system optimization scheduling method with the electric automobile regulation potential. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the method embodiments described above, and in this embodiment, no further description is given.
The more detailed working principle and the step flow of this embodiment can be, but not limited to, those described in the related embodiment one.
In summary, the embodiment of the invention has the following beneficial effects:
according to the electric vehicle to be scheduled, the control potential evaluation indexes of the electric vehicles are constructed according to the schedulable time and the schedulable space of the electric vehicles to be scheduled, all the electric vehicles to be scheduled are clustered according to the control potential evaluation indexes of the electric vehicles to be scheduled, a plurality of electric vehicle groups are obtained, an electricity price subsidy strategy is obtained according to the control potential evaluation indexes, a total control potential evaluation index model is constructed according to the electricity price subsidy strategy, and solving is carried out to obtain a scheduling strategy, so that electric network personnel schedule the electric vehicles to be scheduled according to the scheduling strategy, and the electric vehicles are scheduled by considering the electric vehicle control potential and the wind power consumption requirement of the electric network, so that the scheduling efficiency and the scheduling accuracy are improved, and the running cost of the electric network is reduced.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.

Claims (10)

1. The wind power system optimization scheduling method considering the regulation potential of the electric automobile is characterized by comprising the following steps of:
constructing regulation potential evaluation indexes of each electric automobile according to the schedulable time and schedulable space of each electric automobile to be scheduled;
grouping all the electric vehicles to be scheduled according to the regulation potential evaluation indexes of the electric vehicles to be scheduled to obtain a plurality of electric vehicle groups, and obtaining an electricity price subsidy strategy according to the regulation potential evaluation indexes;
and constructing a total regulation potential evaluation index model according to the electricity price subsidy strategy, and solving to obtain a scheduling strategy so that power grid personnel schedule the electric automobile to be scheduled according to the scheduling strategy.
2. The optimization scheduling method for the wind power-containing system considering the control potential of the electric automobile according to claim 1, wherein the construction of the control potential evaluation index of each electric automobile according to the schedulable time and the schedulable space of each electric automobile to be scheduled is specifically as follows:
constructing a schedulable time evaluation index according to the schedulable time of each electric automobile to be scheduled, wherein the schedulable time evaluation index is as follows:
wherein C is time，i And (3) evaluating the index T for the time of the electric automobile i to be scheduled adj，i For the schedulable time of the electric automobile i to be scheduled, T stay，i Indicating the residence time of the electric automobile i to be scheduled;
constructing a schedulable space evaluation index according to the schedulable capacity of each electric automobile to be scheduled, wherein the schedulable space evaluation index is:
wherein C is vol，i Evaluating an index C for the schedulable space of the electric automobile i to be scheduled EV，i C, an average schedulable power influence factor of the electric automobile i to be scheduled is C equ，l The average equivalent load influence factor of the electric automobile i to be scheduled is given;
obtaining a regulatory potential evaluation index of each electric automobile to be scheduled according to the schedulable space evaluation index and the schedulable time evaluation index of each electric automobile to be scheduled, wherein the expression of the regulatory potential evaluation index is:
In the method, in the process of the invention,evaluating the index for the regulation potential of the electric automobile to be scheduled, C time，i C, evaluating the index for the time of the electric automobile i to be scheduled vol，i And evaluating the index for the schedulable space of the electric automobile i to be scheduled.
3. The optimization scheduling method for the wind power-containing system considering the electric automobile regulation potential according to claim 2, wherein the constructing a schedulable time evaluation index according to the schedulable time of each electric automobile to be scheduled specifically comprises:
obtaining charging time according to the charging power and the charging electric quantity of each electric automobile to be scheduled, wherein the calculation formula of the charging time is as follows:
in SOC 0，i The initial SOC value and the SOC of the ith electric automobile aim，i Is the target SOC value of the ith electric automobile, E i Battery capacity of ith electric automobile, P cha Charging power eta of electric automobile cha The charging efficiency of the electric automobile is improved;
determining the residence time of each electric automobile to be scheduled, wherein the calculation formula of the residence time is as follows:
wherein t is dep，i For the estimated departure time of the ith electric vehicle, t arr，i Is the arrival time of the ith electric automobile, t dep，i ＜t arr，i The electric automobile is connected in the current day and leaves the next day;
obtaining schedulable time of each electric automobile to be scheduled according to the charging time and the residence time, wherein the schedulable time is expressed as follows:
T adj，i ＝T stay，i -T need，i
Wherein T is adj，i Representing schedulable time, T, of an electric vehicle to be scheduled stay，i Indicating the residence time of the electric automobile to be scheduled, T need，i Charging indicating electric automobile i to be scheduledAn electrical time;
and obtaining a dispatching time evaluation index of the electric automobile according to the dispatching time and the residence time.
4. The optimization scheduling method for the wind power-containing system considering the electric automobile regulation potential according to claim 2, wherein the constructing a schedulable space evaluation index according to the schedulable capacity of each electric automobile to be scheduled specifically comprises:
obtaining a schedulable space evaluation index according to the average schedulable power influence factors and the average equivalent load influence factors of the electric vehicles to be scheduled, wherein the average schedulable power influence factors are as follows:
wherein C is EV，i An average schedulable power influence factor of the electric automobile i to be scheduled;
the formula of the average equivalent load influence factor is as follows:
P equ，l ＝P base，l -P ther，l -P wind，l
wherein C is equ，l Representing the average schedulable power influence factor, P, of the electric automobile i to be scheduled wind，l 、P ther，l 、P base，l Wind power output, thermal power unit output and base load in the I time period respectively, N i，l And the total number of the electric vehicles accessed in the power grid is the i-th electric vehicle access period.
5. The optimization scheduling method for the wind power-containing system considering the control potential of the electric vehicles according to claim 1, wherein the grouping of all the electric vehicles to be scheduled according to the control potential evaluation index of each electric vehicle to be scheduled is performed to obtain a plurality of electric vehicle groups, specifically:
determining a weight matrix of a schedulable time evaluation index and a schedulable space evaluation index, wherein the weight matrix is as follows:
wherein alpha represents the weight of the schedulable time evaluation index, and beta represents the weight of the schedulable space evaluation index;
obtaining a grouping index of the electric automobile to be scheduled according to the weight and the regulation potential evaluation index, wherein the expression of the grouping index is as follows:
wherein C is div，i For the index of the grouping,for the weight matrix, < >>Evaluating indexes for the regulation potential of the electric automobile to be scheduled;
and grouping all the electric vehicles to be scheduled according to the grouping index to obtain a plurality of electric vehicle groups.
6. The optimization scheduling method for the wind power-containing system considering the regulation potential of the electric automobile according to claim 1, wherein the electricity price subsidy strategy is obtained according to the regulation potential evaluation index, specifically:
Calculating the charging electricity price of each electric automobile group in the I period, wherein the charging electricity price has the following expression:
Z k，l ＝(1-I level，k，l -δ P，l )c l
wherein, c l For the peak-to-valley basic electricity price of the I-th period, I level，k，l Regulatory potential stimulus for the Kth electric automobile group, delta P，l For wind power to dissipate excitation factors, l=1, 2,3,..,
in the method, in the process of the invention,average regulation potential evaluation total index representing the I period of the Kth electric automobile group, a and b are constant parameters, N k，l The number of electric vehicles managed by the kth electric vehicle cluster in the ith period;
and obtaining an electricity price subsidy strategy according to the charging electricity price of each electric automobile group in the I period.
7. The optimization scheduling method for the wind power-containing system considering the regulation potential of the electric automobile according to claim 1, wherein the method is characterized in that a total regulation potential evaluation index model is built according to the electricity price subsidy strategy and solved to obtain a scheduling strategy, and specifically comprises the following steps:
obtaining a first objective function according to the peak-valley difference of the total load of the power grid, and obtaining a second objective function according to the electricity price subsidy strategy;
normalizing the first objective function and the second objective function to obtain an objective function of the total regulatory potential evaluation index model, wherein the objective function is:
ω 1 +ω 2 ＝1
Wherein F is 1max And F is equal to 2max Respectively a first objective function F 1 With a second objective function F 2 Equivalent maximum value of F 1max For the variance of the grid base load, F 2max For the charging cost of the electric automobile under disordered charging, P disI In order to realize the total charging power omega of the electric automobile in a disordered charging scene in the period I 1 And omega 2 Respectively a first objective function F 1 With a second objective function F 2 Weight coefficient of (2);
determining constraint conditions of the total regulatory potential evaluation index model, wherein the constraint conditions are as follows:
c l，min ≤Z k，l ≤c l，max
δ c，k，l δ d，k，l ＝0
-P Cha N k，l η cha δ d，k，l ≤P k，l ≤P Cha N k，l η cha δ c，k，l
wherein, c l，min And c l，max Respectively the lower limit and the upper limit of the peak-valley basic electricity price, P k，l The charging power of the kth electric automobile cluster in the ith period,for the average state of charge, SOC, of the kth electric automobile cluster in the ith period k，i The state of charge (SOC) of the ith electric vehicle belonging to the kth electric vehicle cluster min Is the minimum value of the state of charge and the SOC of a single electric automobile max Is the maximum value of the charge state of a single electric automobile, E k The sum of the charge and discharge electric quantity of the kth electric automobile cluster in all the time periods is N k The total number of all electric vehicles in the kth electric vehicle cluster;
and solving the total regulation potential evaluation index model to obtain a scheduling strategy.
8. An optimization scheduling system of a wind-containing power system for considering the regulation potential of an electric automobile, which is characterized by comprising:
The evaluation index construction module is used for constructing regulation and control potential evaluation indexes of the electric vehicles according to the schedulable time and the schedulable space of the electric vehicles to be scheduled;
the electric price subsidy strategy is used for grouping all the electric vehicles to be scheduled according to the regulation potential evaluation indexes of the electric vehicles to be scheduled to obtain a plurality of electric vehicle groups, and obtaining the electric price subsidy strategy according to the regulation potential evaluation indexes;
and the scheduling strategy is used for constructing a total regulation potential evaluation index model according to the electricity price subsidy strategy and solving the total regulation potential evaluation index model to obtain the scheduling strategy so that power grid personnel schedule the electric automobile to be scheduled according to the scheduling strategy.
9. The optimal scheduling system for the wind power-containing system for considering the control potential of the electric automobile according to claim 8, wherein the control potential evaluation index of each electric automobile is constructed according to the schedulable time and the schedulable space of each electric automobile to be scheduled, specifically:
constructing a schedulable time evaluation index according to the schedulable time of each electric automobile to be scheduled, wherein the schedulable time evaluation index is as follows:
wherein C is time，i And (3) evaluating the index T for the time of the electric automobile i to be scheduled adj，i For the schedulable time of the electric automobile i to be scheduled, T stay，i Indicating the residence time of the electric automobile i to be scheduled;
constructing a schedulable space evaluation index according to the schedulable capacity of each electric automobile to be scheduled, wherein the schedulable space evaluation index is:
wherein C is vol，i Evaluating an index C for the schedulable space of the electric automobile i to be scheduled EV，i C, an average schedulable power influence factor of the electric automobile i to be scheduled is C equ，l The average equivalent load influence factor of the electric automobile i to be scheduled is given;
obtaining a regulatory potential evaluation index of each electric automobile to be scheduled according to the schedulable space evaluation index and the schedulable time evaluation index of each electric automobile to be scheduled, wherein the expression of the regulatory potential evaluation index is:
in the method, in the process of the invention,to be the instituteC, evaluating an index of regulation potential of the electric automobile to be scheduled time，i C, evaluating the index for the time of the electric automobile i to be scheduled vol，i And evaluating the index for the schedulable space of the electric automobile i to be scheduled.
10. The optimal scheduling system for the wind power-containing system for accounting for the regulatory potential of electric vehicles according to claim 8, wherein the constructing a schedulable time evaluation index according to the schedulable time of each electric vehicle to be scheduled is specifically as follows:
Obtaining charging time according to the charging power and the charging electric quantity of each electric automobile to be scheduled, wherein the calculation formula of the charging time is as follows:
in SOC 0，i The initial SOC value and the SOC of the ith electric automobile aim，i Is the target SOC value of the ith electric automobile, E i Battery capacity of ith electric automobile, P cha Charging power eta of electric automobile cha The charging efficiency of the electric automobile is improved;
determining the residence time of each electric automobile to be scheduled, wherein the calculation formula of the residence time is as follows:
wherein t is dep，i For the estimated departure time of the ith electric vehicle, t arr，i Is the arrival time of the ith electric automobile, t dep，i ＜t arr，i The electric automobile is connected in the current day and leaves the next day;
obtaining schedulable time of each electric automobile to be scheduled according to the charging time and the residence time, wherein the schedulable time is expressed as follows:
T adj，i ＝T stay，i -T need，i
wherein T is adj，i Representing schedulable time, T, of an electric vehicle to be scheduled stay，i Indicating the residence time of the electric automobile to be scheduled, T need，i Representing the charging time of the electric automobile i to be scheduled;
and obtaining a dispatching time evaluation index of the electric automobile according to the dispatching time and the residence time.
CN202311280011.5A 2023-09-28 2023-09-28 Wind power system optimization scheduling method considering regulation potential of electric automobile Pending CN117353355A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117895510A (en) * 2024-03-14 2024-04-16 山东建筑大学 Electric automobile cluster participation power grid peak shaving method and system based on aggregation business mode
CN118100180A (en) * 2024-04-29 2024-05-28 南京邮电大学 Electric vehicle charging and discharging low-carbon scheduling method considering regulation potential evaluation

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117895510A (en) * 2024-03-14 2024-04-16 山东建筑大学 Electric automobile cluster participation power grid peak shaving method and system based on aggregation business mode
CN117895510B (en) * 2024-03-14 2024-05-28 山东建筑大学 Electric automobile cluster participation power grid peak shaving method and system based on aggregation business mode
CN118100180A (en) * 2024-04-29 2024-05-28 南京邮电大学 Electric vehicle charging and discharging low-carbon scheduling method considering regulation potential evaluation
CN118100180B (en) * 2024-04-29 2024-06-21 南京邮电大学 Electric vehicle charging and discharging low-carbon scheduling method considering regulation potential evaluation

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