CN110378548B - Electric automobile virtual power plant multi-time scale response capability assessment model construction method - Google Patents
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Abstract
The invention discloses a method for constructing a multi-time scale response capability evaluation model of an electric automobile virtual power plant, which is characterized by extracting four typical scenes under different response modes on the basis of fully considering time sequence states including SOC and charge-discharge states in an EVs evaluation process, analyzing single EV power constraint and available capacity under a continuous time section and providing a single and continuous time section response capability evaluation model of a single electric automobile. And on the basis, an EVPP multi-time scale response capability assessment model containing day-ahead and day-within correction is provided: evaluating the EVPP day-ahead response capability by utilizing EVs state prediction data in the day-ahead; and determining a specific regulation and control result according to an EVPP regulation and control strategy comprehensively considering indexes of response time margin and charge state margin in the day, further correcting a day-ahead response capacity evaluation result, and realizing dynamic update of EVPP response capacity while effectively tracking a system regulation and control target.
Description
Technical Field
The invention relates to the field of power scheduling, in particular to a method for constructing a multi-time scale response capability assessment model of an electric automobile virtual power plant.
Background
With the rapid development of social economy, the demand of China on energy is increasing day by day, but fossil energy is exhausted day by day, the development of nuclear energy is limited, and a series of problems such as resource shortage, climate warming, environmental pollution and the like are faced in the traditional power generation mode taking coal and petroleum as main energy sources. In addition, the increasingly worsening of ecological environment and the higher and higher requirements of users on the quality of electric energy make the development and utilization of renewable energy sources gradually become the necessary way for social sustainable development. In recent years, the large-scale application of intermittent renewable energy power generation technology has brought unprecedented uncertainty to the safe and efficient operation of power systems. The fluctuation of the power output of the source side needs to be balanced by arranging a large-capacity rotary standby unit. The method not only brings high cost to the construction and operation of the power enterprises, but also can not effectively meet the requirement of rapid power balance of the system due to the lag of response speed, and both the economy and the safety of the system are seriously challenged.
The large-scale popularization of Electric Vehicles (EVs) is an important way for realizing low-carbon traffic development, and the Electric vehicles have wide attention worldwide due to the advantages of energy conservation and zero emission. According to the research report of electric Vehicle development strategy issued by the department of industry and informatization, the reserved quantity of electric vehicles in China is estimated to reach 6000 thousands of vehicles in 2030, the peak load charging of the electric vehicles reaches 479GW, and the capacity of the electric vehicles has the potential of V2G (Vehicle-to-Grid) interaction with a power system. The EVs battery has flexible power bidirectional regulation characteristics, so that on one hand, the power fluctuation of renewable energy sources can be stabilized by changing the charge-discharge state of the EVs battery, and auxiliary service is provided for a power system; on the other hand, the attractive force of the incentive electricity price can be directly controlled or weighed to decide whether to participate in the V2G response so as to obtain certain economic benefit. In order to participate in system optimization operation, an Electric Vehicle Virtual Power plant (EVPP) is responsible for aggregating EVs in a certain area, and meanwhile, the charging and discharging processes of the EVs are uniformly managed, so that a way is provided for the EVs which are scattered and have small capacity to participate in Power system optimization scheduling.
There are a lot of literature on the research on the evaluation of EVPP responsiveness, and the research on the evaluation of EVPP responsiveness is yet to be further developed: 1) only a certain time section response power constraint is considered, and the change of the available response capacity of the discontinuity when the EVPP is continuous in the evaluation period is ignored; 2) the influence of the timing State (State of Charge (SOC) and Charge-discharge State) after the EVPP responds to the system scheduling command on the change response capability is ignored, and the response capability is closely related to the scheduling policy of the EVPP.
Disclosure of Invention
The invention aims to provide a multi-time scale response capability evaluation model of an electric automobile virtual power plant, which is characterized in that four typical scenes under different response modes are extracted on the basis of fully considering time sequence states including SOC and charge-discharge states in an EVs evaluation process, the power constraint and available capacity of a single EV under a continuous time section are analyzed, and a single and continuous time section response capability evaluation model of the single electric automobile is provided. And on the basis, an EVPP multi-time scale response capability evaluation model containing day-ahead and day-within correction is provided: evaluating the EVPP day-ahead response capability by utilizing EVs state prediction data in the day-ahead; and determining a specific regulation and control result according to an EVPP regulation and control strategy comprehensively considering indexes of response time margin and charge state margin in the day, further correcting a day-ahead response capacity evaluation result, and realizing dynamic update of EVPP response capacity while effectively tracking a system regulation and control target.
The invention particularly relates to a method for constructing a multi-time scale response capability assessment model of an electric automobile virtual power plant, which comprises the following steps:
(1) considering the electric automobile time sequence state including the charge state and the charge-discharge state, and constructing a single and continuous time section single electric automobile response capability model;
(2) constructing a multi-time scale response capability assessment model of the electric automobile virtual power plant containing day-ahead and day-within correction: evaluating the day-ahead response capability of the electric automobile virtual power plant by using the electric automobile state prediction data in the day-ahead; and correcting the day-ahead response capability evaluation result according to a specific regulation and control result in a day, and realizing dynamic update of the response capability of the electric automobile virtual power plant while effectively tracking a system regulation and control target.
Further, the construction process of the single electric vehicle response capability model is as follows:
for the ith (i ═ 1,2 … N) vehicle EV in the cluster of N electric vehicles i In other words, a given time EV without any participation in a response i State of charge and discharge σ (t) and state of charge S at time t i (t) and the relationship between:
in the formula: the value of sigma (t) is 0, and 1 is that the electric automobile is in an idle state and a charging state respectively; s. the i max As EV i An upper SOC limit of (1); s. the i d Is EV i The minimum value of the electric quantity demand of the battery for traveling; t is t i s 、t i d Are each EV i Network access and network leaving moments;
the response capability when participating in different response modes is expressed as:
in the formula: p i m Rated power for the electric vehicle; I. II and III respectively indicate three response modes of the electric automobile, namely switching from an idle state to a discharge state, switching from a charge state to the idle state and switching from the idle state to the charge state, P i u,I (t)、P i u,II (t)、P i u,III (t) the response capacities of the modes I, II and III at the moment t are respectively in kW; idle → discharge and charge → idle response capability is positive, which represents the up-regulation of the output of the single electric vehicle; idle → Charge response capability is negative, indicating Single electric steamThe vehicle output is reduced.
Further, under the condition that the specific regulation and control strategy of the virtual power plant of the electric automobile in the day ahead is unknown, the day-ahead response capability of the virtual power plant of the electric automobile is evaluated by utilizing a single time section response capability model under the condition that the model does not participate in any response:
the upper and lower boundaries of the day-ahead response capability of the virtual power plant of the electric automobile are shown as follows:
in the formula: n is the number of electric vehicles regulated and controlled by the electric vehicle virtual power plant, and the unit is a vehicle;
S i (t) implementing a rolling update of the electric vehicle state by:
in the formula:
is EV i SOC at the moment of network access; eta c Efficiency of charging the battery; eta d The cell discharge efficiency; q i Is the battery capacity; p is i uc Satisfies the formula P for the charging power when not participating in the response i uc (t)＝P i m σ (t); I. II, III and IV respectively refer to four response modes of the electric automobile, namely switching from an idle state to a discharge state, switching from a charge state to the idle state, switching from the idle state to the charge state and switching from the discharge state to the idle state, P i I ，P i II ，P i III ，P i IV EV respectively determined for regulatory strategy i The response power of participation modes I, II, III and IV is kW and is shown as the following formula:
further, the steps of evaluating the day-to-day correction response capability of the electric automobile virtual power plant are as follows:
1) the time axis is divided into a plurality of uniform evaluation periods T, and the current moment nT is the starting point of the (n +1) th scheduling period;
2) supposing that the maximum response capacity of the electric vehicle virtual power plant at the moment nT obtained according to the day-ahead response capacity evaluation model is P (nT), namely the state of the electric vehicle virtual power plant at the moment nT is consistent with a day-ahead predicted value; then, the response capacity of [ nT, (n +1) T ] time period is obtained by utilizing the intra-day correction model, and the response capacity is reported to a power grid dispatching center;
3) determining the response action of the electric automobile virtual power plant in the [ nT, (n +1) T ] time period according to a power response curve issued by a power grid dispatching center;
4) considering the influence of the regulation and control strategy of the electric vehicle virtual power plant in the [ nT, (n +1) T ] period, updating the cluster state of the electric vehicles in a rolling mode at the (n +1) T moment, and assuming that the maximum response capacity of the electric vehicle virtual power plant at the (n +1) T moment becomes P ((n +1) T), iterating along with the progress of the evaluation period, and further determining the response capacity of the electric vehicle virtual power plant in each evaluation period.
Further, the construction process of the multi-time scale response capability assessment model is as follows:
1) inputting model parameters: the method comprises the steps of carrying out statistical analysis on traffic characteristics such as electric automobile application, battery capacity and energy consumption and travel rules of a user, establishing a probability distribution model of traffic characteristic parameters, obtaining electric automobile network access time, battery charge state upper and lower limits and user travel demand day-ahead prediction data, and obtaining single electric automobile response capacity model charge state and charge and discharge state input parameters;
2) evaluating the day-ahead response capability of the electric automobile virtual power plant under the condition of not participating in response by using day-ahead prediction data;
3) combined with [ (n-1) T, nT]Updating the cluster state of the electric vehicles by using a regulation and control strategy of the virtual power plant of the electric vehicles in the time period, and calculating the corrected response capability in the current evaluation time period day; [ nT, (n +1) T]Can be made withRated power P i m The cluster formed by the electric automobiles participating in the response does not need power correction;
4) combining the electric vehicles EV in the cluster obtained in step 3 in turn k Relevant information, updating other cluster power correction schemes according to whether the power is out of limit;
5) repeating the step 4 until k is not less than the sum of the number of the cluster electric vehicles or enough available capacity cannot be provided, and ending the circulation;
6) and returning to the step 3 to evaluate the correction response capability in the next evaluation period day.
Drawings
FIG. 1 is a flow chart of a method for constructing a multi-time scale response capability assessment model according to the present invention;
FIG. 2 is an exploded view of an electric vehicle response process;
FIG. 3 is a graph of four different scenario analyses for mode I;
FIG. 4 is a diagram of two different scenario analyses in mode II;
FIG. 5 is a graph of a response capability boundary versus analysis.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A multi-time scale response capability assessment model of an electric automobile virtual power plant is shown in FIG. 1, and comprises the following specific steps:
1. building single electric automobile response capability evaluation model
I (i-1, 2 … N) th vehicle EV in a cluster of N electric vehicles i For example, a given time EV without any participation in a response i State of charge and discharge σ (t) and state of charge S at time t i The relationship between (t) is as follows:
in the formula: the value of sigma (t) is 0, and 1 is that the electric automobile is in an idle state and a charging state respectively; s i max As EV i An upper SOC limit of (1); s. the i d As EV i The minimum value of the electric quantity demand of the battery for traveling; t is t i s 、t i d Are each EV i And the network access and the network exit moments.
The response capability when participating in different response modes is as follows:
in the formula: I. II and III respectively refer to three response modes of switching the electric automobile from an idle state to a discharging state, switching from a charging state to the idle state and switching from the idle state to the charging state, as shown in figure 2. P i u,I (t),P i u,II (t),P i u,III (t) is the response capability of time t modes I, II and III respectively, and the unit is as follows: kW. Idle → discharge and charge → idle response capability is positive, which represents the up-regulation of the output of the single electric vehicle; discharging → idle and idle → charging response capability is negative, which indicates the output down-regulation of the single electric vehicle.
2. And constructing a continuous time section single electric automobile response capability evaluation model.
For mode I, the following four typical scenarios are included, where a, B, C, D correspond to four different operating point positions, and their concentrated occurrence will result in drastic changes in the electric vehicle virtual plant response capability, as shown in fig. 3.
1) When nT<t i s <At (n +1) T, EV i There is a period [ nT, t ] of not-connected grid i s ]The corresponding response power is shown in the graph P-t in FIG. 3 (a);
2) when evaluating the time period [ nT, (n +1) T]The internal discharge response is such that S i (t)≤S i min The corresponding response power is shown as the solid line of the P-t diagram in FIG. 3 (b);
EV i reaching the lower bound time t of the controllable region i l This can be obtained by the following equation.
In the formula: eta d The cell discharge efficiency; q i Is the battery capacity; s. the i min Is EV i The lower SOC limit of (c).
3) When the discharge response within the evaluation period [ nT, (n +1) T ] causes the operating point to touch the forced charging boundary ef, the corresponding response power is shown as the solid line of the P-T graph in fig. 3 (c);
EV i time t for achieving forced charging power i c This can be obtained by the following equation.
In the formula: eta c The battery is charged efficiently.
4) When nT<t i d <(n +1) T, the corresponding response power is shown as P-T in FIG. 3 (d);
for electric vehicles in scenarios A or D, EV i Responsive time period R i As shown in the following formula:
EV i nonresponsive period U i Can be represented as R i Relative to the full set SComplement, the expression is as follows:
and a responsive period R i Corresponding EV i The response power is shown as follows:
due to EV i Response power vacancy Δ P within evaluation period caused by off-grid i (t) is represented by the following formula:
the corresponding capacity vacancy is shown as the following formula:
3. power constraints and capacity constraints.
EV (electric vehicle) connected to power grid for electric vehicle in scene B or C i The power constraint is shown as follows:
in the formula: p i re (t) is EV i Corrected timing response power, unit: kW.
Available capacity Q for discharge to lower State of Charge scenarios after response i a As shown in the following formula:
for discharging after response toThe situation that the travel demand can be met only by forced charging is required, and the available capacity Q i a As shown in the following formula:
EV i the capacity constraint that should be met is shown as follows:
for mode II: charge → idle, scenario a and scenario D are exactly the same as in mode I. In contrast, two conditions need to be considered: in the charging state and capable of controllably switching to the idle state, corresponding to scenarios B and C as shown in fig. 4.
Charging electric automobile at t i h The controllable area is no longer in a charging state t after being touched to the upper bound of the controllable area at any moment i h Can be obtained from the following formula
Time t to reach forced charging boundary i c This can be obtained by the following equation.
For mode III: idle → charging, requiring to be in idle state and being able to controllably switch to charging state, the four different scenario analyses and their calculation formulas are the same as those of mode II; for mode IV: discharge → idle, requiring to be in the discharge state and being able to controllably switch to the idle state, four different situational analyses and their calculation formulas are the same as in mode I.
4. And evaluating the day-ahead response capability of the electric automobile virtual power plant by using a single time section response capability model under the condition of not participating in any response.
The upper and lower boundaries of the day-ahead response capability of the virtual power plant of the electric automobile are shown as follows:
in the formula: n is the quantity of electric automobile that electric automobile virtual power plant regulated and control, unit: and (4) vehicles.
S i (t) the rolling update of the electric vehicle state can be achieved by the following equation.
In the formula:
as EV i SOC at the time of network access; p i uc The charging power when the charging power does not participate in the response satisfies the following formula;
P i uc (t)＝P i m σ(t)
P i I ，P i II ，P i III ，P i IV EV respectively determined for regulatory strategy i Response power of participation modes I, II, III and IV, unit: kW, the expression is as follows:
5. the method for evaluating the day-to-day correction response capability of the electric automobile virtual power plant comprises the following steps:
the time axis is divided into several uniform evaluation periods T, the current time nT being the start of the (n +1) th scheduling period, as shown in fig. 5.
Firstly, the maximum response capacity of the electric vehicle virtual power plant at the moment nT obtained according to the day-ahead response capacity evaluation model is assumed to be P (nT), namely the state of the electric vehicle virtual power plant at the moment nT is consistent with a day-ahead predicted value. Then, the response capacity of [ nT, (n +1) T ] time period is obtained by utilizing the intra-day correction model, and the response capacity is reported to a power grid dispatching center; and then determining the response action of the virtual electric vehicle power plant in the period of [ nT, (n +1) T ] according to the issued power response curve.
Considering the influence of the regulation and control strategy of the electric vehicle virtual power plant in the [ nT, (n +1) T ] period, updating the electric vehicle cluster state in a rolling mode at the (n +1) T moment, and assuming that the maximum response capacity of the electric vehicle virtual power plant at the (n +1) T moment becomes P ((n +1) T), and determining the response capacity of the electric vehicle virtual power plant in each evaluation period as the evaluation periods progress and iteration is carried out.
6. Establishing model for evaluating day-to-day correction response capability of electric automobile virtual power plant
The sum of the number of the electric vehicles in the clusters B and C is N re Obtaining the power constraint corresponding to the cluster as shown in the following formula
The capacity constraints are respectively shown as follows:
in the formula: p is re (t) and Q a The corrected time sequence power and the usable capacity of the cluster are respectively represented by the following units: kW and kW.h.
The corrected power obtained by making up the power vacancy of the front k electric vehicles in the clusters A and D is assumed to be P re (t) remaining available capacity is Q 0 The total power correction after the first k corrections is Δ P (k) (t), and Δ P (0) And (t) is constant zero, and the three satisfy the following relation.
In the formula: the charge-discharge efficiency at time η (t) is represented as:
the sum of the number of the electric vehicles in the clusters A and D is N unre To EV k For example, there are two defining schemes depending on whether the power is out-of-limit:
when the power is not over-limit, the total power correction amount Δ P (k) (t) is represented by the following formula:
when the power is out of limit, note
To exceed the set of power constraint periods without an out-of-limit condition, as shown in the following equation.
the specific definition scheme is shown as the following formula. If the power out-of-limit condition still exists, the calculation is carried out circularly according to the expression.
In the formula:
represents the unit of power out-of-limit duration: h.
7. constructing a multi-time scale response capability evaluation model:
1) inputting model parameters. The method comprises the steps of carrying out statistical analysis on traffic characteristics such as electric automobile application, battery capacity and energy consumption of a user, travel rules and the like, establishing a probability distribution model of traffic characteristic parameters, obtaining electric automobile network access time, battery charge state upper and lower limits and user travel demand day-ahead prediction data by utilizing a Monte Carlo algorithm, and obtaining single electric automobile response capability model charge state and charge and discharge state input parameters;
2) evaluating the day-ahead response capability of the electric automobile virtual power plant under the condition of not participating in response by using day-ahead prediction data;
3) and updating the cluster state of the electric vehicles by combining with the regulation and control strategy of the electric vehicle virtual power plant in the [ (n-1) T, nT ] period, and calculating the corrected response capability in the current evaluation period day.
[nT,(n+1)T]Can be at rated power P i m The cluster of responding electric vehicles does not need to be power corrected. For cluster AD and cluster BC, each electric automobile is sequentially judged to be [ nT, (n +1) T]The type of the response scene where the user is located, and relevant information is recorded. If the scene A or D is located, a corresponding controllable time set R is calculated i Uncontrollable time set U i And its corresponding power Δ P i (t) and capacity Δ Q i (ii) a If in scenario B or C, calculating power constraint boundary and available capacity Q i a . Obtaining the [ nT, (n +1) T of the electric automobile virtual power plant after the superposition of different cluster response capacities]And correcting the response capacity within the period of days.
4) Successively combining the EVs in the clusters AD obtained in step 3 k And related information, updating the BC power correction scheme of the cluster according to whether the power is out of limit.
5) Repeating the step 4 until k is not less than N unre Or fail to provide sufficient available capacity, i.e., Q 0 <ΔQ k When so, the cycle ends.
6) And returning to the step 3 to evaluate the correction response capability in the next evaluation period day.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A method for constructing a multi-time scale response capability assessment model of an electric automobile virtual power plant is characterized by comprising the following steps:
(1) considering the electric vehicle time sequence state including the charge state and the charge-discharge state, and constructing a single and continuous time section single electric vehicle response capability model;
(2) constructing a multi-time scale response capability assessment model of the electric automobile virtual power plant containing day-ahead and day-within correction: evaluating the day-ahead response capability of the electric automobile virtual power plant by using the electric automobile state prediction data in the day-ahead; modifying the response capability evaluation result in the day ahead according to a specific regulation and control result in the day, and realizing dynamic update of the response capability of the electric automobile virtual power plant while effectively tracking a system regulation and control target;
the construction process of the single electric vehicle response capability model is as follows:
in the cluster formed by N electric vehicles, the ith, i is 1,2, …, N, vehicle EV i In other words, a given time EV without any participation in a response i State of charge and discharge σ (t) and state of charge S at time t i (t) and the relationship between:
in the formula: the value of sigma (t) is 0, and 1 is that the electric automobile is in an idle state and a charging state respectively; s. the i max As EV i An upper SOC limit of (1); s. the i d As EV i The minimum value of the electric quantity demand of the battery for traveling; t is t i s 、t i d Are each EV i Network access and network leaving moments;
the response capability when participating in different response modes is expressed as:
in the formula: p i m Rated power for the electric vehicle; I. II and III respectively indicate three response modes of the electric automobile, namely switching from an idle state to a discharge state, switching from a charge state to the idle state and switching from the idle state to the charge state, P i u,I (t)、P i u,II (t)、P i u,III (t) response capabilities of the modes I, II and III at the moment t are respectively, and the unit is kW; idle → discharge and charge → idle response capability is positive, which represents the up-regulation of the output of the single electric vehicle; idle → charging response capacity is a negative value, which indicates that the output of the single electric vehicle is reduced;
under the condition that the specific regulation and control strategy of the virtual power plant of the electric automobile in the day ahead is unknown, the day-ahead response capability of the virtual power plant of the electric automobile is evaluated by utilizing a single time section response capability model under the condition that the model does not participate in any response:
the upper and lower boundaries of the day-ahead response capability of the electric automobile virtual power plant are shown as follows:
in the formula: n is the number of electric vehicles regulated and controlled by the electric vehicle virtual power plant, and the unit is a vehicle;
S i (t) implementing a rolling update of the electric vehicle state by:
in the formula:
as EV i SOC at the moment of network access; eta c Efficiency of charging the battery; eta d The efficiency of discharge for the cell; q i Is the battery capacity; p i uc Satisfies the formula P for the charging power when not participating in the response i uc (t)＝P i m σ (t); I. II, III and IV respectively refer to four response modes of the electric automobile, namely switching from an idle state to a discharge state, switching from a charge state to the idle state, switching from the idle state to the charge state and switching from the discharge state to the idle state, P i I ，P i II ，P i III ，P i IV EVs determined separately for regulatory strategies i The response power of participation modes I, II, III and IV is kW and is shown as the following formula:
2. the method for constructing the multi-time scale response capability assessment model of the electric vehicle virtual power plant according to claim 1, wherein the step of modifying the response capability assessment in the day of the electric vehicle virtual power plant is as follows:
1) the time axis is divided into a plurality of uniform evaluation periods T, and the current moment nT is the starting point of the (n +1) th scheduling period;
2) supposing that the maximum response capacity of the electric vehicle virtual power plant at the moment nT obtained according to the day-ahead response capacity evaluation model is P (nT), namely the state of the electric vehicle virtual power plant at the moment nT is consistent with a day-ahead predicted value; then, the response capacity of [ nT, (n +1) T ] time period is obtained by utilizing the intra-day correction model, and the response capacity is reported to a power grid dispatching center;
3) determining the response action of the electric automobile virtual power plant in the [ nT, (n +1) T ] time period according to a power response curve issued by a power grid dispatching center;
4) considering the influence of the regulation strategy of the electric automobile virtual power plant in the [ nT, (n +1) T ] time period, updating the cluster state of the electric automobiles in a rolling mode at the (n +1) T time period, and assuming that the maximum response capacity of the electric automobile virtual power plant at the (n +1) T time period is changed into P ((n +1) T), iterating along with the progression of the evaluation time period, and further determining the response capacity of the electric automobile virtual power plant at each evaluation time period.
3. The method for constructing the multi-time scale response capability assessment model of the electric automobile virtual power plant according to claim 2, wherein the multi-time scale response capability assessment model is constructed by the following steps:
1) inputting model parameters: the method comprises the steps of carrying out statistical analysis on the usage of an electric vehicle of a user, the capacity and energy consumption of a battery and traffic characteristics of a trip rule, establishing a probability distribution model of traffic characteristic parameters, obtaining network access time of the electric vehicle, upper and lower limits of the charge state of the battery and day-ahead prediction data of the trip demand of the user, and obtaining the charge state and charge-discharge state input parameters of a response capability model of the single electric vehicle;
2) evaluating the day-ahead response capability of the electric automobile virtual power plant under the condition of not participating in response by using day-ahead prediction data;
3) combined with [ (n-1) T, nT]Updating the cluster state of the electric vehicles by using a regulation and control strategy of the electric vehicle virtual power plant in a time period, and calculating the corrected response capacity in the current evaluation time period day; [ nT, (n +1) T]Can be at rated power P i m The cluster formed by the electric automobiles participating in the response does not need power correction;
4) combining the electric vehicles EV in the cluster obtained in step 3 in turn k Relevant information, updating other cluster power correction schemes according to whether the power is out of limit;
5) repeating the step 4 until k is not less than the sum of the number of the cluster electric vehicles or enough available capacity cannot be provided, and ending the circulation;
6) and returning to the step 3 to evaluate the correction response capability in the next evaluation period day.
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