CN117639039A - Electric automobile allocation excitation method participating in electric quantity allocation of power grid - Google Patents
Electric automobile allocation excitation method participating in electric quantity allocation of power grid Download PDFInfo
- Publication number
- CN117639039A CN117639039A CN202311591798.7A CN202311591798A CN117639039A CN 117639039 A CN117639039 A CN 117639039A CN 202311591798 A CN202311591798 A CN 202311591798A CN 117639039 A CN117639039 A CN 117639039A
- Authority
- CN
- China
- Prior art keywords
- electric automobile
- charging
- electric
- discharge
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005284 excitation Effects 0.000 title claims abstract description 181
- 238000000034 method Methods 0.000 title claims abstract description 52
- 230000007246 mechanism Effects 0.000 claims abstract description 159
- 230000004044 response Effects 0.000 claims abstract description 73
- 238000007599 discharging Methods 0.000 claims abstract description 69
- 230000033228 biological regulation Effects 0.000 claims abstract description 20
- 230000002776 aggregation Effects 0.000 claims abstract description 18
- 238000004220 aggregation Methods 0.000 claims abstract description 18
- 230000003993 interaction Effects 0.000 claims abstract description 7
- 238000005457 optimization Methods 0.000 claims abstract description 6
- 230000005611 electricity Effects 0.000 claims description 67
- 230000008901 benefit Effects 0.000 claims description 63
- 230000001105 regulatory effect Effects 0.000 claims description 29
- 230000008859 change Effects 0.000 claims description 16
- 239000000178 monomer Substances 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 16
- 230000001276 controlling effect Effects 0.000 claims description 14
- 230000006870 function Effects 0.000 claims description 12
- 239000008186 active pharmaceutical agent Substances 0.000 claims description 9
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 7
- 230000003828 downregulation Effects 0.000 claims description 7
- 229910001416 lithium ion Inorganic materials 0.000 claims description 7
- 238000004146 energy storage Methods 0.000 claims description 6
- 238000011160 research Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 230000003827 upregulation Effects 0.000 claims description 6
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 4
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- 101000802640 Homo sapiens Lactosylceramide 4-alpha-galactosyltransferase Proteins 0.000 claims description 3
- 102100035838 Lactosylceramide 4-alpha-galactosyltransferase Human genes 0.000 claims description 3
- 238000012614 Monte-Carlo sampling Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000015556 catabolic process Effects 0.000 claims description 3
- 238000006731 degradation reaction Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 238000011835 investigation Methods 0.000 claims description 3
- 230000001737 promoting effect Effects 0.000 claims description 2
- 230000003213 activating effect Effects 0.000 claims 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 229920000642 polymer Polymers 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000029305 taxis Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention relates to the technical field of electric quantity allocation of an electric power system, in particular to an electric automobile allocation excitation method participating in electric quantity allocation of a power grid. Which comprises the following steps: s1: based on the statistical data of the charging behavior characteristics, establishing a power and energy response boundary of the single electric automobile; s2: a charging and discharging excitation mechanism of the cluster is provided by combining the self preference of a user and the battery loss, and the charging and discharging constraint of each single electric automobile is simulated according to Monte Carlo, so that the power and energy response boundary constraint of the electric automobile cluster is established; s3: based on the time sequence of electric vehicle charging and discharging regulation control and the frequency modulation requirement of the multi-stage market, a multi-variety profit optimization model considering multi-stage time sequence interaction is established, and the profit of the aggregation company in the energy-frequency modulation market is improved.
Description
Technical Field
The invention relates to the technical field of electric quantity allocation of an electric power system, in particular to an electric automobile allocation excitation method participating in electric quantity allocation of a power grid.
Background
With the high-speed development of Electric Vehicles (EV), under the regulation of a polymer, electric vehicles can participate in an energy-frequency modulation market as a distributed energy storage resource. However, the charging requirement of the user has uncertainty, and with the development of the fast charging technology of the automobile, the load impact on the power grid is larger in the peak period of charging, and in the valley period of charging, larger power resource waste exists, so that peak clipping and valley filling are necessary to ensure the stable running of the power grid, wherein the fluctuation of the power grid load can be effectively smoothed by utilizing the charging and discharging requirements of different electric automobiles in different time periods, but the existing research lacks an excitation mechanism for encouraging the electric automobiles to actively participate in the charging and discharging bidirectional interaction frequency modulation, and the comprehensive consideration is not carried out on the multi-stage interaction influence of the charging and discharging of the electric automobiles in the energy-frequency modulation market, so that the income of the participation of an aggregator in the power market cannot be accurately evaluated; it is necessary to design a new solution to meet the actual grid operation requirements.
Disclosure of Invention
The invention provides an electric automobile allocation excitation method for participating in electric quantity allocation of a power grid, which can consider a multi-variety profit optimization model of multi-stage time sequence interaction, and effectively promote the profit of an aggregator in the energy-frequency modulation market on the basis of guaranteeing the charging requirement of a user.
An electric automobile allocation excitation method for participating in electric quantity allocation of a power grid comprises the following steps:
s1: based on the statistical data of the charging behavior characteristics, establishing a power and energy response boundary of the single electric automobile;
s2: a charging and discharging excitation mechanism of the cluster is provided by combining the self preference of a user and the battery loss, and the charging and discharging constraint of each single electric automobile is simulated according to Monte Carlo, so that the power and energy response boundary constraint of the electric automobile cluster is established;
s3: based on the time sequence of electric vehicle charging and discharging regulation control and the frequency modulation requirement of the multi-stage market, a multi-variety profit optimization model considering multi-stage time sequence interaction is established, and the profit of the aggregation company in the energy-frequency modulation market is improved.
The step S1 specifically comprises the following steps:
the method comprises the steps Of analyzing the difference Of the Charge control and the discharge control Of the single electric automobile in response to a polymerizer by considering the vehicle type, the Charge requirement, the initial Charge State (SOC), the self preference and the battery loss Of a single electric automobile user, and establishing the power and energy response boundary Of the single electric automobile based on the statistical data Of the Charge behavior characteristics.
S1.1: firstly, an electric automobile is regulated and controlled by a polymerizer to participate in an energy-frequency modulation market, and the key point of the polymerizer is to determine the charging time, the on-station charging time and the initial charge state of the electric automobile, so as to determine the charging state of each electric automobile. The charging state is obtained according to the charging demand ratio of various electric vehicles at each moment and the charging initial electric quantity state ratio of various electric vehicles, the charging demand in the early morning is relatively low for various vehicle types, the charging initial electric quantity state is concentrated between 30% and 60%, the charging demand in the private vehicle commuting time is relatively high in other time, the charging demand of the taxi is relatively high in the midday period, and the demands of other vehicle types are relatively balanced in the daytime period.
S1.2: after the electric automobile is accessed into the energy market, the response modes of the electric automobile can be divided into 3 types according to the power transmission direction of each electric automobile, and the response modes are defined as follows:
(1) Charging Mode (CM): the electric automobile absorbs electric energy by real-time charging power;
(2) Idle Mode (IM): the electric automobile is transmitted without power;
(3) Discharge Mode (DM): the electric automobile discharges electric energy with real-time discharge power.
The 3 response modes have direct influence on the change of the electric state of the electric automobile, so that the change of the electric state of each electric automobile is shown in the formula (1):
wherein: s is(s) i (t) is the electric quantity state of the ith electric automobile at the moment t;
p c,i (t) and p d,i (t) respectively charging power and discharging power of the ith electric automobile at the moment t;
Δt is the study time interval; Δt (delta t) i For the ith user in delta t timeCharging time period;
η c and eta d The charging efficiency and the discharging efficiency of the electric automobile are respectively;
I ev,i and (t) is the state of the response mode of the ith electric automobile.
Monomer electric automobile is in charging period [ t ] 0 ,t end ]The operation boundary of the residual charge in the battery is shown in a formula (2), and the operation boundary of the power is shown in a formula (3):
wherein:and->The upper limit and the lower limit of the SOC of the ith EV at the moment t are respectively;
wherein:and->The upper and lower limits of the charging power of the ith EV at time t.
The electric automobile polymerizer responds to the frequency modulation of the electric power market by controlling the charge and discharge time period and the charge and discharge power of the electric automobile, and the operation area of the charge residual quantity and the power of the single electric automobile is constructed as follows under the assumption that the charge time period of the single electric automobile is [ t0, tend ];
s 0 an initial electric quantity state for starting charging of the electric automobile;
C is the battery capacity of the electric automobile;
s need is an electric automobileEnding the required electric quantity state of charging;
s min a minimum state of charge to prevent overdischarge;
p c，max is the maximum charging power;
p d，max is the maximum discharge power;
s +/(-) the upper limit and the lower limit of the residual electric quantity of the electric automobile at each moment are respectively set;
p +/(-) the upper limit and the lower limit of the charge and discharge power of the electric automobile at each moment are respectively set;
s 'and p' represent specific examples of the state of charge and the charging power of the electric vehicle during charging, respectively.
The 'a- & gtb- & gtc' in the formula (2) is the upper limit of the operation area, and represents that the electric automobile user arrives at a station and charges until the electric automobile is full, and then is in an IM state until the electric automobile is charged;
in the formula (2), a- & gt, g- & gt, f- & gt, d' is the lower limit of the operation area, and represents that the electric automobile user arrives at a station to discharge until the electric automobile user discharges to a threshold value s min And then the charging request is always from the IM point to the f point, so that the user is ensured to finish the charging request before the charging is finished.
The step S2 specifically comprises the following steps:
and a charging and discharging excitation mechanism of the cluster is provided in combination with the self preference of a user and the battery loss, and the charging and discharging constraint of each single electric automobile is simulated according to Monte Carlo, so that the power and energy response boundary constraint of the electric automobile cluster is established, and the charging excitation electricity price and the discharging subsidy electricity price are determined according to the charging and discharging excitation mechanism, so that the active regulation participation degree of the electric automobile user is effectively evaluated.
S2.1: in the day-ahead energy market, electric automobile aggregators know day-ahead electricity prices at all times, and make charging plan power of the next day under the guidance of the day-ahead electricity prices. In the real-time energy market, the aggregators formulate charging electricity prices at each moment according to the income margin of the aggregators. The method takes the difference between the electricity price issued by the energy market and the electricity price issued by the aggregator as the incentive discount offer, and refers to the discount offer as charging incentiveExcitation price. When the electric automobile has a charging requirement, the automobile owner can know the charging incentive electricity price delta r issued by the current aggregator CS (t) to decide whether to respond to the charging stimulus mechanism (Charging Incentive Mechanism, CIM). If the electric vehicle responds to the charging excitation mechanism, the electric vehicle can enjoy the charging electricity price and the intelligent charging service issued by the aggregator, and the aggregator can arrange the charging process of the electric vehicle during the charging of the electric vehicle, and provide the frequency modulation and energy service to obtain benefits by controlling the charging of the electric vehicle. If the electric automobile does not respond to the charging excitation mechanism, the electric automobile is insensitive to the charging excitation electricity price issued by the aggregation, the electric automobile is not regulated and controlled by the aggregation to be charged, and the electric automobile is automatically connected into an energy market for charging until the electric quantity state reaches the electric quantity required by a user.
S2.2: electric vehicle users typically have a lower preference for discharge excitation mechanisms (Discharge incentive mechanism, DIM) than for response to charge excitation mechanisms, including additional battery loss due to discharge. To encourage the electric vehicle to participate in the discharge, the aggregator needs to conduct market research on electric vehicle discharge preference and formulate a discharge incentive price to attract electric vehicle users to participate in the discharge. In addition, to compensate for the loss of battery life due to the discharge of an electric vehicle, the polymerizer needs to provide additional discharge compensation.
And S2.3, taking a charging excitation mechanism and a discharging excitation mechanism into consideration, and restricting the power operation of the single electric automobile.
And S2.4, sampling the response boundary of each single electric automobile according to Monte Carlo simulation, and obtaining the upper and lower boundaries of the energy and power of the electric automobile cluster.
Specifically, in step S2.1;
for the electric automobile cluster, the proportion of the electric automobiles responding to the charging excitation mechanism is shown as the formula (4):
ρ CIM ＝f CIM (Δr CS (t)) (4)
wherein: function f CIM The method comprises the steps that (-) is used for calculating the proportion of the electric vehicles in the cluster responding to a charging excitation mechanism; Δr CS (t) is electric steamThe charge incentive price issued by the vehicle aggregator at time t serves as an incentive price for incentive the electric vehicle to respond to the charge incentive mechanism.
Assume that the charging period of the ith monomer electric automobile is [ t ] i,0 ,t i,end ]Considering the response charging excitation mechanism of the electric vehicle, the regulation period of the electric vehicle aggregator during the charging of the electric vehicle is shown in formula (5):
wherein: [ t ] i,0 ,t i,1 ]The method comprises the steps of responding to an adjustable time period of a charging excitation mechanism for an ith monomer electric automobile;responding to a charging excitation mechanism for the ith electric automobile, otherwise +.>Is 0.
Specifically, in step S2.2;
the battery losses include ambient temperature, charge-discharge rate, depth of discharge, and number of cycles. If the electric automobile provided with the lithium battery works in a normal temperature environment, the average charge and discharge power is not high, and the influence of the ambient temperature and the charge and discharge rate is negligible. Therefore, the scheme considers the influence of the discharge depth and the cycle number of the lithium ion battery on the loss of the lithium ion battery. The relationship between the depth of discharge and the cycle number of the lithium ion battery under normal conditions is shown in the formula (6), so that the total charge and discharge energy under the depth of discharge D can be estimated by the formula (7) in the service life of the battery.
f N (D)＝2151·D -2.301 ,D∈[0,0.9] (6)
Wherein: d is the depth of discharge; function f N (. Cndot.) is used to calculate the number of battery cycles at a certain depth of discharge.
G(D)＝2·C·D·f N (D) (7)
Wherein: g (D) is the total charge-discharge energy of the electric vehicle at depth of discharge D.
In practical applications, the electric vehicle battery is not always charged or discharged at the same depth of discharge. To evaluate the degradation cost of battery discharge, the average value of the total charge-discharge energy at different D values was calculated according to formula (8). The calculation of the additional battery loss cost per unit discharge energy is shown in formula (9).
Wherein:is the average value of the total charge and discharge energy under different discharge depths; d (D) j For a particular depth of discharge value; n (N) D The number of the depth of discharge values is taken.
Wherein: r is (r) 0 Battery loss cost per unit discharge energy; r is (r) C And purchasing costs for the battery.
And (3) calculating the discharge patch price given by the electric vehicle aggregator according to the formula (10), wherein an electric vehicle user can select whether to respond to the discharge excitation mechanism according to own preference. If the electric automobile responds to the discharging excitation mechanism, the electric automobile can enjoy intelligent discharging service of an aggregator and obtain discharging patches, the aggregator can arrange a discharging process of the electric automobile during charging of the electric automobile, and the electric automobile is controlled to provide frequency modulation and energy service to obtain benefits. If the electric vehicle does not respond to the discharge excitation mechanism, the electric vehicle is insensitive to the price of the discharge patch, and the electric vehicle is not regulated by a polymerizer to discharge. Considering that the willingness of the electric automobile users to respond to the discharge excitation mechanism is different, the proportion of the electric automobile responding to the discharge excitation mechanism is calculated by utilizing the formula (11) based on investigation data.
r DS ＝r 1 +∑(r 0 ) (10)
Wherein: r is (r) DS The price of the discharge patch is compensated; sigma (r) 0 ) Compensating for discharge battery loss of the electric automobile during the whole charging period; r is (r) 1 The discharge incentive price determined by the electric automobile aggregator in the day before is used as the incentive price for attracting the electric automobile to respond to the DIM.
ρ DIM ＝f DIM (r DS ) (11)
Wherein: function f CIM And the (-) is used for calculating the proportion of the electric vehicles in the cluster responding to the discharge excitation mechanism.
The charge incentive mechanism may help the electric vehicle user save charge costs, while the discharge incentive mechanism may help the electric vehicle user achieve additional benefits. The purpose of electric vehicle users responding to the charge incentive mechanisms is to achieve economic benefits, so they are also willing to participate in the discharge incentive mechanism. Thus, if ρ CIM ≥ρ DIM Then an electric vehicle responsive to the discharge excitation mechanism may be randomly selected among electric vehicles responsive to the charge excitation mechanism; if ρ CIM ＜ρ DIM All electric vehicles responding to the charging excitation mechanism can be considered to be responding to the discharging excitation mechanism, and the rest electric vehicles can be randomly extracted from the electric vehicles responding to the charging excitation. Considering the electric vehicle response discharge excitation mechanism, the regulation period of the electric vehicle aggregator during the electric vehicle charging is shown in formula (12):
Wherein: [ t ] i,0 ,t i,2 ]The method comprises the steps of responding to an adjustable time period of a discharge excitation mechanism for an ith monomer electric automobile; τ i =1 is the i-th electric vehicle response discharge excitation mechanism, otherwise τ i Is 0; upsilon (v) i Is thatAnd τ i Maximum value of (v) i =1 is the ith electric car responseA charge excitation mechanism or a discharge excitation mechanism is adopted, otherwise v i Is 0.
S2.3, taking a charging excitation mechanism and a discharging excitation mechanism into consideration, wherein the power operation constraint of the monomer electric automobile is shown as a formula (13):
wherein: zeta type toy i (t) is a binary variable for controlling that the ith vehicle cannot meet the charge and discharge requirements at the same time, if ζ i (t) =1 is that the ith vehicle is charged at time t, otherwise is discharged.
The energy operation constraint of the monomer electric automobile is shown as a formula (14):
the specific steps of S2.4 are as follows:
s2.4.1 dividing the time of day into different study periods, and calculating the proportion rho of the response charging excitation mechanism of the electric automobile according to the formula (4) and the formula (11) CIM And a ratio ρ of the response discharge excitation mechanism DIM ；
S2.4.2 generating a model, an on-station charging time length and a charging start SOC of the single electric automobile in a Monte Carlo sampling mode;
s2.4.3 to construct a random number by combining the random number with ρ CIM And ρ DIM Determining whether the electric vehicle responds to the charging excitation mechanism and the discharging excitation mechanism by comparing to determine the binary variable τ i And v i ；
S2.4.4 obtaining the upper and lower boundaries of the energy and power operation of the single electric automobile according to the formulas (13) and (14);
s2.4.5 repeatedly sampling the rows S2.4.2 to S2.4.4, and calculating the upper and lower boundaries of the electric automobile cluster energy and power operation according to the formula (15).
Wherein: e (E) + (t) and E - (t) respectively representing the upper and lower energy boundaries of the EV cluster at the time t; n (N) i The quantity of the electric vehicles which can be regulated and controlled by the electric vehicle polymerizer; p (P) + (t) and P - (t) represents the upper and lower power boundaries of the EV cluster at time t, respectively.
The step S3 specifically comprises the following steps:
s3.1: the charging process of the electric automobile is regulated and controlled by the aggregation businessman to participate in multi-stage and multi-variety markets, so that operation benefits are obtained, and the operation benefits comprise the following aspects:
(1) Charging fees purchased in the energy market before date;
(2) The cost of the frequency modulation power purchased in real time;
(3) Capacity and mileage benefits of participating in frequency modulation;
(4) And regulating and controlling the charging income and subsidy expense of the electric automobile cluster.
S3.2: the electric automobile polymerizer can use the large-scale electric automobile battery energy storage system as an adjustable load to participate in the energy-frequency modulation market, so that multiple benefits are obtained, and the aim of participating in the energy-frequency modulation market by the polymerizer is the maximum.
S3.1.1: when an electric vehicle aggregator participates in the day-ahead energy market, a charging plan of the electric vehicle needs to be formulated according to the market electricity price so as to charge the electric vehicle on the next day. After determining the charging schedule, the aggregator needs to purchase the corresponding electricity amount according to the charging schedule's demand, and thus its charging cost in the market in the day before can be represented by formula (16):
wherein: f (F) 1 Charging cost for the electric automobile polymerizer in the energy market before the day; r is (r) Chr (k) Electricity prices in k time periods for the day-ahead energy market; p (P) Chr (k) Charging plan power formulated for the k period for the aggregator; k is the whole research period; Δk is the interval of the period.
The electric automobile polymerizer participates in real-time frequency modulation to cause the change of charging power, so that the obtained frequency modulation power cost is shown as a formula (17):
wherein: f (F) 2 The power cost of the real-time response frequency modulation is used for the electric automobile polymerizer; r is (r) RT (k) Electricity prices in k time periods for the real-time energy market; p (P) UP (k) And P DN (k) The power of the aggregate is tuned up and down for k periods, respectively.
S3.1.2: in the frequency modulation market, electric automobile aggregators respond to changes in frequency modulation signals by up-or down-modulating the frequency, thereby obtaining compensation benefits from the market for frequency modulation. Wherein the compensation benefit includes a capacity benefit and a mileage benefit. In the PJM market, the up-regulated capacity and the down-regulated capacity are symmetrically equal, and the capacity gain of the aggregate participating in the frequency modulation market is shown in formula (18):
Wherein: f (F) 3 Frequency modulation capacity benefits for electric automobile aggregators; r is (r) RC (k) The electricity price is the frequency modulation capacity in k time periods; p (P) RC (k) Frequency modulation capacity of the aggregator in k period; lambda is the performance score.
The electric automobile aggregator responds to the change of the frequency modulation signal by adjusting the charge and discharge power of the electric automobile, and can calculate the frequency modulation output mileage of the electric automobile in the process of participating in the frequency modulation auxiliary service according to the formula (19), wherein the mileage benefit of the aggregator participating in the frequency modulation market is shown as the formula (20).
Wherein: m is m UP (k) And m DN (k) The upward and downward frequency modulation output mileage of the electric automobile cluster in the k period are respectively; n (N) m Number of time intervals for the signal in the k period; a (j, t) is a frequency modulation indication signal issued by a frequency modulation market at the time t, A (j, t) E < -1,1]，t∈k，j∈N m 。
Wherein: f (F) 4 Frequency modulation mileage benefits for the aggregator; r is (r) M (k) And the electricity price is the frequency modulation mileage electricity price in the k period.
The electric automobile cluster provides frequency modulation and energy service in response to the charging excitation mechanism and the discharging excitation mechanism, the aggregator obtains charging benefits given by the electric automobile cluster and feeds the charging benefits and the subsidy cost given by the aggregator to the discharging subsidy of the electric automobile cluster, and the charging benefits and the subsidy cost given by the aggregator to the electric automobile cluster are shown as formula (21):
wherein: f (F) 5 Charge benefits and subsidy fees for the aggregator; p (k) is the net power of the flexible electric automobile in response to excitation; p (P) 0 (k) The net power of the inflexible electric automobile; p (P) EVA (k) Net power for the electric vehicle aggregator response; r is (r) CS (k) Charging electricity prices issued by electric automobile aggregators in the k period; Δr CS (k) And (5) exciting the charging excitation electricity price of the user for the electric automobile aggregator in the k period.
At F 5 The first term gives the charge compensation fee to the electric vehicle participating in the charge excitation to the electric vehicle, the second term gives the charge excitation fee to the electric vehicle participating in the charge excitation to the electric vehicle, and the third term gives the charge fee to the electric vehicle cluster to the electric vehicle.
S3.2: the electric automobile polymerizer can use a large-scale electric automobile battery energy storage system as an adjustable load to participate in the energy-frequency modulation market, so that multiple benefits are obtained, the maximum acquisition of the electric automobile polymerizer to participate in the energy-frequency modulation market is targeted, and an objective function is shown as a formula (22):
max F＝-F 1 -F 2 +F 3 +F 4 +F 5 (22)
the charging plan power and the net energy after the response signal, which are determined by the electric automobile aggregator in each period, must not be lower than the lower energy boundary and must not be higher than the upper energy boundary:
wherein: e (E) +/(-) (k) The energy upper and lower boundaries of the electric automobile cluster in the period t are defined.
The electric car aggregator responds to the change of the frequency modulation signal by adjusting the power, so that the net power responded by the aggregator must not be lower than the highest discharge power of the period and must not be higher than the highest charge power of the period in each period:
P - (k)≤P(k)≤P + (k) (24)
wherein: p (P) +/(-) (k) And the power upper and lower boundaries of the electric automobile cluster in the k period are defined.
The up-regulation capacity and the down-regulation capacity are positive numbers:
3. the beneficial effects are that:
(1) The invention considers the difference of the electric automobile response to the charge control and the discharge control of the aggregation provider, combines the self preference of the user and the battery loss, and establishes a charge-discharge excitation mechanism so as to effectively evaluate the excitation cost of the electric automobile user for participating in frequency modulation and the active regulation participation degree of the electric automobile user.
(2) According to the invention, on the aspect of the obtained active regulation participation of the electric automobile users, the time sequence of electric automobile charging and discharging regulation control and the frequency modulation requirement of a multi-stage market are considered, a multi-stage optimization operation model of an electric automobile aggregator of an energy-frequency modulation market is established, the economic benefits in a multi-variety electric power market are optimized, and the aggregator is helped to determine the planned charging power and the reported frequency modulation capacity of each stage.
(3) According to the invention, the income results are compared, so that the electric automobile polymerizer can participate in the frequency modulation market to obtain the maximum operation income on the premise of not only guaranteeing the charging requirement of the electric automobile.
Drawings
FIG. 1 is a graph showing the charge demand ratio at each moment of various electric vehicles according to the present invention;
FIG. 2 is a graph showing initial charge status ratios for various types of electric vehicle charging according to the present invention;
FIG. 3 is an operating region of a single electric vehicle charge;
FIG. 4 is response survey data for an electric vehicle user;
FIG. 5 is an electric vehicle aggregator revenue at discharge incentive price;
FIG. 6 is a net energy distribution in two scenarios;
fig. 7 is a comparison of frequency modulation capacity in two scenarios.
Detailed Description
The present invention will be described in detail with reference to fig. 1 to 7.
An electric automobile allocation excitation method for participating in electric quantity allocation of a power grid comprises the following steps:
s1: based on the statistical data of the charging behavior characteristics, establishing a power and energy response boundary of the single electric automobile;
s2: a charging and discharging excitation mechanism of the cluster is provided by combining the self preference of a user and the battery loss, and the charging and discharging constraint of each single electric automobile is simulated according to Monte Carlo, so that the power and energy response boundary constraint of the electric automobile cluster is established;
s3: based on the time sequence of electric vehicle charging and discharging regulation control and the frequency modulation requirement of the multi-stage market, a multi-variety profit optimization model considering multi-stage time sequence interaction is established, and the profit of the aggregation company in the energy-frequency modulation market is improved.
Further, the step S1 specifically includes the following steps:
the method comprises the steps Of analyzing the difference Of the Charge control and the discharge control Of the single electric automobile in response to a polymerizer by considering the vehicle type, the Charge requirement, the initial Charge State (SOC), the self preference and the battery loss Of a single electric automobile user, and establishing the power and energy response boundary Of the single electric automobile based on the statistical data Of the Charge behavior characteristics.
S1.1: firstly, an electric automobile is regulated and controlled by a polymerizer to participate in an energy-frequency modulation market, and the key point of the polymerizer is to determine the charging time, the on-station charging time and the initial charge state of the electric automobile, so as to determine the charging state of each electric automobile. The charging state is obtained according to the charging demand ratio of various electric vehicles at each moment and the charging initial electric quantity state ratio of various electric vehicles, the charging demand in the early morning is relatively low for various vehicle types, the charging initial electric quantity state is concentrated between 30% and 60%, the charging demand in the private vehicle commuting time is relatively high in other time, the charging demand of the taxi is relatively high in the midday period, and the demands of other vehicle types are relatively balanced in the daytime period.
S1.2: after the electric automobile is accessed into the energy market, the response modes of the electric automobile can be divided into 3 types according to the power transmission direction of each electric automobile, and the response modes are defined as follows:
(1) Charging Mode (CM): the electric automobile absorbs electric energy by real-time charging power;
(2) Idle Mode (IM): the electric automobile is transmitted without power;
(3) Discharge Mode (DM): the electric automobile discharges electric energy with real-time discharge power.
The 3 response modes have direct influence on the change of the electric state of the electric automobile, so that the change of the electric state of each electric automobile is shown in the formula (1):
wherein: s is(s) i (t) is the electric quantity state of the ith electric automobile at the moment t; p is p c,i (t) and p d,i (t) respectively charging power and discharging power of the ith electric automobile at the moment t; Δt is the study time interval; Δt (delta t) i Charging time length of the ith user in delta t time; η (eta) c And eta d The charging efficiency and the discharging efficiency of the electric automobile are respectively; i ev,i And (t) is the state of the response mode of the ith electric automobile.
Referring to fig. 3, the single electric vehicle is charged for a charging period t 0 ,t end ]The operation boundary of the residual charge in the battery is shown in a formula (2), and the operation boundary of the power is shown in a formula (3):
Wherein:and->The upper limit and the lower limit of the SOC of the ith EV at the moment t are respectively;
wherein:and->The upper and lower limits of the charging power of the ith EV at time t.
Electric automobile polymerizers respond to frequency modulation of the electric power market by controlling charge and discharge time periods and charge and discharge power of electric automobiles, assuming that the charge time period of a single electric automobile is [ t0, tend ]]Its charge remaining power and powerIs constructed as follows, s 0 An initial electric quantity state for starting charging of the electric automobile; c is the battery capacity of the electric automobile; s is(s) need The method comprises the steps of (1) ending a required electric quantity state of charge of an electric automobile; s is(s) min A minimum state of charge to prevent overdischarge; p is p c，max Is the maximum charging power; p is p d，max Is the maximum discharge power; s is(s) +/(-) The upper limit and the lower limit of the residual electric quantity of the electric automobile at each moment are respectively set; p is p +/(-) The upper limit and the lower limit of the charge and discharge power of the electric automobile at each moment are respectively set; s 'and p' represent specific examples of the state of charge and the charging power of the electric vehicle during charging, respectively. 'a- & gt, b- & gt, c' is the upper limit of the operation area, and represents that the electric automobile user arrives at a station and charges until the electric automobile is full, and then is in an IM state until the electric automobile is charged; 'a- & gt g- & gt f- & gt d' is the lower limit of the operation area and represents that the electric automobile user arrives at a station to discharge until the electric automobile user discharges to a threshold value s min And then the charging request is always from the IM point to the f point, so that the user is ensured to finish the charging request before the charging is finished.
Further, the step S2 specifically includes the following steps:
and a charging and discharging excitation mechanism of the cluster is provided in combination with the self preference of a user and the battery loss, and the charging and discharging constraint of each single electric automobile is simulated according to Monte Carlo, so that the power and energy response boundary constraint of the electric automobile cluster is established, and the charging excitation electricity price and the discharging subsidy electricity price are determined according to the charging and discharging excitation mechanism, so that the active regulation participation degree of the electric automobile user is effectively evaluated.
S2.1: in the day-ahead energy market, electric automobile aggregators know day-ahead electricity prices at all times, and make charging plan power of the next day under the guidance of the day-ahead electricity prices. In the real-time energy market, the aggregators formulate charging electricity prices at each moment according to the income margin of the aggregators. The incentive discount offer is an effective measure for promoting the electric automobile to participate in regulation, and the scheme takes the difference between the electricity price issued by the energy market and the electricity price issued by the aggregation provider as the incentive discount offer, and the discount offer is called as the charging incentive electricity price. When the electric automobile has a charging requirement, the automobile owner can know the charging incentive electricity price delta r issued by the current aggregator CS (t)To determine whether to respond to the charging activation mechanism (Charging Incentive Mechanism, CIM). If the electric vehicle responds to the charging excitation mechanism, the electric vehicle can enjoy the charging electricity price and the intelligent charging service issued by the aggregator, and the aggregator can arrange the charging process of the electric vehicle during the charging of the electric vehicle, and provide the frequency modulation and energy service to obtain benefits by controlling the charging of the electric vehicle. If the electric automobile does not respond to the charging excitation mechanism, the electric automobile is insensitive to the charging excitation electricity price issued by the aggregation, the electric automobile is not regulated and controlled by the aggregation to be charged, and the electric automobile is automatically connected into an energy market for charging until the electric quantity state reaches the electric quantity required by a user. For the electric automobile cluster, the proportion of the electric automobiles responding to the charging excitation mechanism is shown as the formula (4):
ρ CIM ＝f CIM (Δr CS (t)) (4)
wherein: function f CIM The method comprises the steps that (-) is used for calculating the proportion of the electric vehicles in the cluster responding to a charging excitation mechanism; Δr CS And (t) the charge incentive electricity price issued by the electric automobile aggregator at the time t is used as an incentive price for incentive of the electric automobile to respond to the charge incentive mechanism.
Assume that the charging period of the ith monomer electric automobile is [ t ] i,0 ,t i,end ]Considering the response charging excitation mechanism of the electric vehicle, the regulation period of the electric vehicle aggregator during the charging of the electric vehicle is shown in formula (5):
Wherein: [ t ] i,0 ,t i,1 ]The method comprises the steps of responding to an adjustable time period of a charging excitation mechanism for an ith monomer electric automobile;responding to a charging excitation mechanism for the ith electric automobile, otherwise +.>Is 0.
S2.2: electric vehicle users typically have a lower preference for discharge excitation mechanisms (Discharge incentive mechanism, DIM) than for response to charge excitation mechanisms, including additional battery loss due to discharge. To encourage the electric vehicle to participate in the discharge, the aggregator needs to conduct market research on electric vehicle discharge preference and formulate a discharge incentive price to attract electric vehicle users to participate in the discharge. In addition, to compensate for the loss of battery life due to the discharge of an electric vehicle, the polymerizer needs to provide additional discharge compensation.
The battery losses include ambient temperature, charge-discharge rate, depth of discharge, and number of cycles. If the electric automobile provided with the lithium battery works in a normal temperature environment, the average charge and discharge power is not high, and the influence of the ambient temperature and the charge and discharge rate is negligible. Therefore, the scheme considers the influence of the discharge depth and the cycle number of the lithium ion battery on the loss of the lithium ion battery. The relationship between the depth of discharge and the cycle number of the lithium ion battery under normal conditions is shown in the formula (6), so that the total charge and discharge energy under the depth of discharge D can be estimated by the formula (7) in the service life of the battery.
f N (D)＝2151·D -2.301 ,D∈[0,0.9] (6)
Wherein: d is the depth of discharge; function f N (. Cndot.) is used to calculate the number of battery cycles at a certain depth of discharge.
G(D)＝2·C·D·f N (D) (7)
Wherein: g (D) is the total charge-discharge energy of the electric vehicle at depth of discharge D.
In practical applications, the electric vehicle battery is not always charged or discharged at the same depth of discharge. To evaluate the degradation cost of battery discharge, the average value of the total charge-discharge energy at different D values was calculated according to formula (8). The calculation of the additional battery loss cost per unit discharge energy is shown in formula (9).
Wherein:is the average value of the total charge and discharge energy under different discharge depths; d (D) j For a particular depth of discharge value; n (N) D The number of the depth of discharge values is taken.
Wherein: r is (r) 0 Battery loss cost per unit discharge energy; r is (r) C And purchasing costs for the battery.
And (3) calculating the discharge patch price given by the electric vehicle aggregator according to the formula (10), wherein an electric vehicle user can select whether to respond to the discharge excitation mechanism according to own preference. If the electric automobile responds to the discharging excitation mechanism, the electric automobile can enjoy intelligent discharging service of an aggregator and obtain discharging patches, the aggregator can arrange a discharging process of the electric automobile during charging of the electric automobile, and the electric automobile is controlled to provide frequency modulation and energy service to obtain benefits. If the electric vehicle does not respond to the discharge excitation mechanism, the electric vehicle is insensitive to the price of the discharge patch, and the electric vehicle is not regulated by a polymerizer to discharge. Considering that the willingness of the electric automobile users to respond to the discharge excitation mechanism is different, the proportion of the electric automobile responding to the discharge excitation mechanism is calculated by utilizing the formula (11) based on investigation data.
r DS ＝r 1 +∑(r 0 ) (10)
Wherein: r is (r) DS The price of the discharge patch is compensated; sigma (r) 0 ) Compensating for discharge battery loss of the electric automobile during the whole charging period; r is (r) 1 The discharge incentive price determined by the electric automobile aggregator in the day before is used as the incentive price for attracting the electric automobile to respond to the DIM.
ρ DIM ＝f DIM (r DS ) (11)
Wherein: function f CIM And the (-) is used for calculating the proportion of the electric vehicles in the cluster responding to the discharge excitation mechanism.
The charge incentive mechanism may help the electric vehicle user save charge costs, while the discharge incentive mechanism may help the electric vehicle user achieve additional benefits. The purpose of electric vehicle users responding to the charge incentive mechanisms is to achieve economic benefits, so they are also willing to participate in the discharge incentive mechanism. Thus, if ρ CIM ≥ρ DIM Then an electric vehicle responsive to the discharge excitation mechanism may be randomly selected among electric vehicles responsive to the charge excitation mechanism; if ρ CIM ＜ρ DIM All electric vehicles responding to the charging excitation mechanism can be considered to be responding to the discharging excitation mechanism, and the rest electric vehicles can be randomly extracted from the electric vehicles responding to the charging excitation. Considering the electric vehicle response discharge excitation mechanism, the regulation period of the electric vehicle aggregator during the electric vehicle charging is shown in formula (12):
Wherein: [ t ] i,0 ,t i,2 ]The method comprises the steps of responding to an adjustable time period of a discharge excitation mechanism for an ith monomer electric automobile; τ i =1 is the i-th electric vehicle response discharge excitation mechanism, otherwise τ i Is 0; upsilon (v) i Is thatAnd τ i Maximum value of (v) i =1 responds to the charge excitation mechanism or the discharge excitation mechanism for the ith electric vehicle, otherwise, v i Is 0.
S2.3, taking a charging excitation mechanism and a discharging excitation mechanism into consideration, wherein the power operation constraint of the monomer electric automobile is shown as a formula (13):
wherein: zeta type toy i (t) is a binary variable for controlling that the ith vehicle cannot meet the charge and discharge requirements at the same time, if ζ i (t) =1 is that the ith vehicle is charged at time t, otherwise isAnd (5) discharging.
The energy operation constraint of the monomer electric automobile is shown as a formula (14):
s2.4, sampling the response boundary of each single electric automobile according to Monte Carlo simulation, and obtaining the upper and lower boundaries of the energy and the power of the electric automobile cluster, wherein the specific steps are as follows:
s2.4.1 dividing the time of day into different study periods, and calculating the proportion rho of the response charging excitation mechanism of the electric automobile according to the formula (4) and the formula (11) CIM And a ratio ρ of the response discharge excitation mechanism DIM ；
S2.4.2 generating a model, an on-station charging time length and a charging start SOC of the single electric automobile in a Monte Carlo sampling mode;
S2.4.3 to construct a random number by combining the random number with ρ CIM And ρ DIM Determining whether the electric vehicle responds to the charging excitation mechanism and the discharging excitation mechanism by comparing to determine the binary variableτ i And v i ；
S2.4.4 obtaining the upper and lower boundaries of the energy and power operation of the single electric automobile according to the formulas (13) and (14);
s2.4.5 repeatedly sampling steps S2.4.2 to S2.4.4, and calculating the upper and lower boundaries of the electric automobile cluster energy and power operation according to the formula (15).
Wherein: e (E) + (t) and E - (t) respectively representing the upper and lower energy boundaries of the EV cluster at the time t; n (N) i The quantity of the electric vehicles which can be regulated and controlled by the electric vehicle polymerizer; p (P) + (t) and P - (t) represents the power of EV clusters at time t, respectivelyLower boundary.
Further, the step S3 specifically includes the following steps:
s3.1: the charging process of the electric automobile is regulated and controlled by the aggregation businessman to participate in multi-stage and multi-variety markets, so that operation benefits are obtained, and the operation benefits comprise the following aspects:
(1) Charging fees purchased in the energy market before date;
(2) The cost of the frequency modulation power purchased in real time;
(3) Capacity and mileage benefits of participating in frequency modulation;
(4) And regulating and controlling the charging income and subsidy expense of the electric automobile cluster.
S3.1.1: when an electric vehicle aggregator participates in the day-ahead energy market, a charging plan of the electric vehicle needs to be formulated according to the market electricity price so as to charge the electric vehicle on the next day. After determining the charging schedule, the aggregator needs to purchase the corresponding electricity amount according to the charging schedule's demand, and thus its charging cost in the market in the day before can be represented by formula (16):
Wherein: f (F) 1 Charging cost for the electric automobile polymerizer in the energy market before the day; r is (r) Chr (k) Electricity prices in k time periods for the day-ahead energy market; p (P) Chr (k) Charging plan power formulated for the k period for the aggregator; k is the whole research period; Δk is the interval of the period.
The electric automobile polymerizer participates in real-time frequency modulation to cause the change of charging power, so that the obtained frequency modulation power cost is shown as a formula (17):
wherein: f (F) 2 The power cost of the real-time response frequency modulation is used for the electric automobile polymerizer; r is (r) RT (k) Electricity prices in k time periods for the real-time energy market; p (P) UP (k) And P DN (k) Respectively are provided withThe power is tuned up and down for the k period for the aggregator.
S3.1.2: in the frequency modulation market, electric automobile aggregators respond to changes in frequency modulation signals by up-or down-modulating the frequency, thereby obtaining compensation benefits from the market for frequency modulation. Wherein the compensation benefit includes a capacity benefit and a mileage benefit. In the PJM market, the up-regulated capacity and the down-regulated capacity are symmetrically equal, and the capacity gain of the aggregate participating in the frequency modulation market is shown in formula (18):
wherein: f (F) 3 Frequency modulation capacity benefits for electric automobile aggregators; r is (r) RC (k) The electricity price is the frequency modulation capacity in k time periods; p (P) RC (k) Frequency modulation capacity of the aggregator in k period; lambda is the performance score.
The electric automobile aggregator responds to the change of the frequency modulation signal by adjusting the charge and discharge power of the electric automobile, and can calculate the frequency modulation output mileage of the electric automobile in the process of participating in the frequency modulation auxiliary service according to the formula (19), wherein the mileage benefit of the aggregator participating in the frequency modulation market is shown as the formula (20).
Wherein: m is m UP (k) And m DN (k) The upward and downward frequency modulation output mileage of the electric automobile cluster in the k period are respectively; n (N) m Number of time intervals for the signal in the k period; a (j, t) is a frequency modulation indication signal issued by a frequency modulation market at the time t, A (j, t) E < -1,1]，t∈k，j∈N m 。
Wherein: f (F) 4 Frequency modulation mileage benefits for the aggregator; r is (r) M (k) And the electricity price is the frequency modulation mileage electricity price in the k period.
The electric automobile cluster provides frequency modulation and energy service in response to the charging excitation mechanism and the discharging excitation mechanism, the aggregator obtains charging benefits given by the electric automobile cluster and feeds the charging benefits and the subsidy cost given by the aggregator to the discharging subsidy of the electric automobile cluster, and the charging benefits and the subsidy cost given by the aggregator to the electric automobile cluster are shown as formula (21):
wherein: f (F) 5 Charge benefits and subsidy fees for the aggregator; p (k) is the net power of the flexible electric automobile in response to excitation; p (P) 0 (k) The net power of the inflexible electric automobile; p (P) EVA (k) Net power for the electric vehicle aggregator response; r is (r) CS (k) Charging electricity prices issued by electric automobile aggregators in the k period; Δr CS (k) And (5) exciting the charging excitation electricity price of the user for the electric automobile aggregator in the k period.
At F 5 The first term gives the charge compensation fee to the electric vehicle participating in the charge excitation to the electric vehicle, the second term gives the charge excitation fee to the electric vehicle participating in the charge excitation to the electric vehicle, and the third term gives the charge fee to the electric vehicle cluster to the electric vehicle.
S3.2: the electric automobile polymerizer can use a large-scale electric automobile battery energy storage system as an adjustable load to participate in the energy-frequency modulation market, so that multiple benefits are obtained, the maximum acquisition of the electric automobile polymerizer to participate in the energy-frequency modulation market is targeted, and an objective function is shown as a formula (22):
max F＝-F 1 -F 2 +F 3 +F 4 +F 5 (22)
the charging plan power and the net energy after the response signal, which are determined by the electric automobile aggregator in each period, must not be lower than the lower energy boundary and must not be higher than the upper energy boundary:
wherein: e (E) +/(-) (k) The energy upper and lower boundaries of the electric automobile cluster in the period t are defined.
The electric car aggregator responds to the change of the frequency modulation signal by adjusting the power, so that the net power responded by the aggregator must not be lower than the highest discharge power of the period and must not be higher than the highest charge power of the period in each period:
P - (k)≤P(k)≤P + (k) (24)
Wherein: p (P) +/(-) (k) And the power upper and lower boundaries of the electric automobile cluster in the k period are defined.
The up-regulation capacity and the down-regulation capacity are positive numbers:
examples
As shown in fig. 1 and fig. 2, the key point of the method is to determine the charging time, the charging time at station and the state of charge of the electric vehicles, so as to determine the charging behavior of each electric vehicle. The charge demand of each type of electric vehicle at each moment is shown in fig. 1, and the charge start SOC of each type of electric vehicle is shown in fig. 2. For various vehicle types, the charging requirements in the early morning are low, the initial charge state is concentrated between 30% and 60%, the charging requirements of private vehicles in the time of going to work are high in other times, the charging requirements of taxis in the midday period are high, and the requirements of other types of vehicles in the daytime period are balanced.
As shown in fig. 3, the electric car aggregator responds to the frequency modulation of the electric market by controlling the charge-discharge period and charge-discharge power of the electric car, assuming that the charge period of the single electric car is [ t ] 0 ,t end ]The operating region of its charge state and power is shown in fig. 3. S in the figure 0 The initial electric quantity state for starting charging of the electric automobile; c is the battery capacity of the electric automobile; s is(s) need The method comprises the steps of (1) ending a required electric quantity state of charge of an electric automobile; s is(s) min Minimum state of charge to prevent overdischarge；p c，max Is the maximum charging power; p is p d，max Is the maximum discharge power; s is(s) +/(-) The upper limit and the lower limit of the electric quantity state of the electric automobile at each moment are respectively; p is p +/(-) The upper limit and the lower limit of the charge and discharge power of the electric automobile at each moment are respectively set; s 'and p' represent specific examples of the state of charge and the charging power of the electric vehicle during charging, respectively. 'a- & gt, b- & gt, c' is the upper limit of the operation area, and represents that the electric automobile user arrives at a station and charges until the electric automobile is full, and then is in an IM state until the electric automobile is charged; 'a- & gt g- & gt f- & gt d' is the lower limit of the operation area and represents that the electric automobile user arrives at a station to discharge until the electric automobile user discharges to a threshold value s min And then the charging request is always from the IM point to the f point, so that the user is ensured to finish the charging request before the charging is finished.
Specific examples:
the response excitation ratios of different cost savings to the electric vehicle clusters are shown in fig. 4, for example. For the electric automobile responding to the charging excitation mechanism, the electric automobile aggregator gives the charging excitation electricity price to estimate the cost saved by the electric automobile responding to the charging excitation mechanism, and calculates the participation proportion of the charging excitation mechanism by adopting a conversion mode; for the electric automobile responding to the discharge excitation mechanism, the demand response participation degree is influenced by peak-valley difference, so that the aggregation manufacturer formulates different discharge excitation prices in the range of time-sharing electricity price peak-valley difference, and the participation proportion of the electric automobile responding to the charge excitation mechanism under different discharge patch electricity prices can be reduced. The electric vehicle ratio in response to the charge excitation mechanism and the discharge excitation mechanism can be estimated based on fig. 4.
As shown in fig. 5, the monte carlo method was used to simulate 1000 electric vehicles, and the day was divided into 24 time periods at 1 hour as a study time interval. In order to fully consider the influence of a charge-discharge excitation mechanism on the participation of an electric automobile aggregator in energy-frequency modulation market benefits, an Optimize model is called by using a Python language to solve and analyze the aggregate benefits of the following 2 scenes:
scene 1: considering the influence of a charging excitation mechanism, responding to the regulation and control of an electric vehicle aggregate by the electric vehicle of the charging excitation mechanism, and participating in the frequency modulation of the electric power market;
scene 2: considering the influences of a charging excitation mechanism and a discharging excitation mechanism, and enabling an electric vehicle responding to the charging excitation mechanism and an electric vehicle responding to the discharging excitation mechanism to be regulated and controlled by an electric vehicle aggregator so as to participate in frequency modulation of an electric market;
scene 3: and the influence of an excitation mechanism is considered, the electric automobile cluster is independently regulated and controlled by an electric automobile aggregator, and the electric automobile cluster participates in the frequency modulation of the electric power market.
In scenario 2, the electric vehicle aggregator's revenue is shown in FIG. 5 at different discharge incentive prices. When the discharge excitation price is less than 13 dollars/megawatt, the benefit is less than the maximum benefit because the proportion of the discharge excitation price to the response discharge excitation mechanism of the electric vehicle is smaller; and when the discharge excitation price is more than $ 13/megawatt, the proportion of the discharge excitation price which attracts the electric vehicle to respond to the discharge excitation mechanism is increased, but the acquired benefit is reduced due to the increase of the discharge excitation price. Thus, when the incentive compensation electricity price is $ 13/megawatt, the highest benefit that the aggregator can obtain is $ 1257.94.
The simulation results show that the electric automobile cluster energy in the scene 1 and the scene 2 is shown in fig. 6, wherein the scene 2 is the situation that the electric automobile aggregator has the biggest profit. The energy regulating capacity is high in the midday and at night due to the fact that a large amount of charging requirements exist in the midday and at night; in the morning, the charging requirement is lower, fewer electric vehicles participate in the charging and discharging process, and the upper and lower boundaries of energy and the energy regulating capability are lower. Scenario 1 and scenario 2 consider the implementation of a charging excitation mechanism and a discharging excitation mechanism, wherein the net energy of the electric vehicle cluster meets the minimum energy requirement, fluctuating between the upper and lower boundaries of energy. In scenario 2, considering the effects of a responsive charge excitation mechanism and a responsive discharge excitation mechanism, an electric vehicle aggregator may perform discharge modulation by controlling an electric vehicle responsive to the discharge excitation mechanism when the fm down-regulation power demand is high. When the aggregator selects to enable partial electric vehicles to respond to the frequency modulation down-regulation requirement, the charging process of the emergency electric vehicles does not need to be started and stopped frequently, so that the net energy fluctuation curve is gentle, but the upper limit of the energy requirement under the scene 2 is higher than that of the scene 1.
Fig. 7 shows the fm capacity in scenario 1 and scenario 2, which reported a larger fm capacity than scenario 1, since both the charge pump mechanism and the discharge pump mechanism are considered in scenario 2. Because scene 1 only considers the influence of the charge excitation mechanism on the electric automobile cluster response, the electric automobile cannot feed power to the power grid in the process of participating in frequency modulation, so scene 1 is mainly provided with the up-regulation capacity, and scene 2 comprehensively considers the influence of the charge excitation mechanism and the discharge excitation mechanism on the electric automobile cluster response, and considers both the up-regulation capacity and the down-regulation capacity. The result graph of the frequency modulation capacity shows that the charging requirement and the electricity price have great influence on the frequency modulation capacity, the charging requirement is lower in the early morning period of 2:00-5:00, and the frequency modulation capacities of the scene 1 and the scene 2 are also lower; the electric vehicle is regulated by the polymer to increase charging power to provide up-regulated power when the electricity price is changed from $27.31 to $5.44 per megawatt during midday hours 9:00-10:00, and to increase discharging power to provide down-regulated power when the electricity price is changed from $15.60 per megawatt to $ 29.92 per megawatt during night hours 21:00-22:00.
While the invention has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention, and it is intended that the scope of the invention shall be limited only by the claims appended hereto.
Claims (10)
1. An electric automobile allocation excitation method for participating in electric quantity allocation of a power grid is characterized by comprising the following steps:
s1: based on the statistical data of the charging behavior characteristics, establishing a power and energy response boundary of the single electric automobile;
s2: a charging and discharging excitation mechanism of the cluster is provided by combining the self preference of a user and the battery loss, and the charging and discharging constraint of each single electric automobile is simulated according to Monte Carlo, so that the power and energy response boundary constraint of the electric automobile cluster is established;
s3: based on the time sequence of electric vehicle charging and discharging regulation control and the frequency modulation requirement of the multi-stage market, a multi-variety profit optimization model considering multi-stage time sequence interaction is established, and the profit of the aggregation company in the energy-frequency modulation market is improved.
2. The electric automobile allocation excitation method for participating in electric power allocation of a power grid according to claim 1, wherein the method is characterized by comprising the following steps of: the step S1 specifically comprises the following steps:
The method comprises the steps Of analyzing the difference Of the single electric vehicle in response to the Charge control and the discharge control Of a polymerizer by considering the vehicle type, the Charge demand, the Charge initial State Of Charge (SOC), the self preference and the battery loss Of a single electric vehicle user, and establishing the power and energy response boundary Of the single electric vehicle based on the statistical data Of the Charge behavior characteristics;
s1.1: firstly, an electric automobile is regulated and controlled by a polymerizer to participate in an energy-frequency modulation market, wherein the key point of the polymerizer is to determine the charging time, the on-station charging time and the initial charge state of the electric automobile, so as to determine the charging state of each electric automobile; the charging state is obtained according to the charging demand ratio of various electric vehicles at each moment and the charging initial electric quantity state ratio of various electric vehicles;
s1.2: after the electric automobile is accessed into the energy market, the response modes of the electric automobile can be divided into 3 types according to the power transmission direction of each electric automobile, and the response modes are defined as follows:
(1) Charging Mode (CM): the electric automobile absorbs electric energy by real-time charging power;
(2) Idle Mode (IM): the electric automobile is transmitted without power;
(3) Discharge Mode (DM): the electric automobile discharges electric energy with real-time discharge power;
The 3 response modes have direct influence on the change of the electric state of the electric automobile, so that the change of the electric state of each electric automobile is shown in the formula (1):
wherein: s is(s) i (t) is the electric quantity state of the ith electric automobile at the moment t;
p c,i (t) and p d,i (t) respectively charging power and discharging power of the ith electric automobile at the moment t;
Δt is the study time interval; Δt (delta t) i Charging time length of the ith user in delta t time;
η c and eta d The charging efficiency and the discharging efficiency of the electric automobile are respectively;
I ev,i (t) is the state of the response mode of the ith electric automobile;
monomer electric automobile is in charging period [ t ] 0 ,t end ]The operation boundary of the residual charge in the battery is shown in a formula (2), and the operation boundary of the power is shown in a formula (3):
wherein:and->The upper limit and the lower limit of the SOC of the ith EV at the moment t are respectively;
wherein:and->The upper limit and the lower limit of the charging power of the ith EV at the time t are set;
the electric automobile polymerizer responds to the frequency modulation of the electric power market by controlling the charge and discharge time period and the charge and discharge power of the electric automobile, and the operation area of the charge residual quantity and the power of the single electric automobile is constructed as follows under the assumption that the charge time period of the single electric automobile is [ t0, tend ];
s 0 an initial electric quantity state for starting charging of the electric automobile;
C is the battery capacity of the electric automobile;
s need the method comprises the steps of (1) ending a required electric quantity state of charge of an electric automobile;
s min a minimum state of charge to prevent overdischarge;
p c，max is the maximum charging power;
p d，max is the maximum discharge power;
s +/(-) the upper limit and the lower limit of the residual electric quantity of the electric automobile at each moment are respectively set;
p +/(-) the upper limit and the lower limit of the charge and discharge power of the electric automobile at each moment are respectively set;
s 'and p' respectively represent specific examples of the electric quantity state and the charging power of the electric automobile in the charging process;
the 'a- & gtb- & gtc' in the formula (2) is the upper limit of the operation area, and represents that the electric automobile user arrives at a station and charges until the electric automobile is full, and then is in an IM state until the electric automobile is charged;
in the formula (2), a- & gt, g- & gt, f- & gt, d' is the lower limit of the operation area, and represents that the electric automobile user arrives at a station to discharge until the electric automobile user discharges to a threshold value s min And then the charging request is always from the IM point to the f point, so that the user is ensured to finish the charging request before the charging is finished.
3. The method for allocating and exciting the electric automobile to participate in the allocation of the electric quantity of the power grid according to claim 1, wherein the step S2 specifically comprises the following steps:
according to the method, a charging and discharging excitation mechanism of a cluster is provided in combination with the self preference of a user and the battery loss, and the charging and discharging constraint of each single electric automobile is simulated according to Monte Carlo, so that the power and energy response boundary constraint of the electric automobile cluster is established, and the charging excitation electricity price and the discharging subsidy electricity price are determined according to the charging and discharging excitation mechanism, so that the active regulation participation degree of the electric automobile user is effectively evaluated;
S2.1: in the day-ahead energy market, electric automobile aggregators know day-ahead electricity prices at all times, and make charging plan power of the next day under the guidance of the day-ahead electricity prices. In the real-time energy market, an aggregator formulates charging electricity prices at each moment according to the income margin of the aggregator; the incentive discount offer is an effective measure for promoting the electric automobile to participate in regulation, and the scheme takes the difference between the electricity price issued by the energy market and the electricity price issued by the aggregation provider as the incentive discount offer, and the discount offer is called as the charging incentive electricity price; when the electric automobile has a charging requirement, the automobile owner can know the charging incentive electricity price delta r issued by the current aggregator CS (t) deciding whether to respond to a charging excitation mechanism (Charging Incentive Mechanism, CIM); if the electric automobile responds to the charging excitation mechanism, the electric automobile can enjoy charging electricity price and intelligent charging service issued by an aggregator, and the aggregator can arrange the charging process of the electric automobile during the charging of the electric automobile, and provide frequency modulation and energy service to obtain benefits by controlling the charging of the electric automobile; if the electric automobile does not respond to the charging excitation mechanism, the electric automobile is insensitive to the charging excitation electricity price issued by the aggregation, the electric automobile is not regulated and controlled by the aggregation to be charged, and the electric automobile is automatically connected into an energy market for charging until the electric quantity state reaches the electric quantity required by a user;
S2.2: electric vehicle users typically have a lower preference for discharge incentive mechanisms (Discharge incentive mechanism, DIM) than for response to charge incentive mechanisms, and the aggregator needs to formulate a discharge incentive price; in addition, to compensate for the loss of battery life due to electric vehicle discharge, the polymerizer also needs to provide additional discharge compensation;
and S2.3, taking a charging excitation mechanism and a discharging excitation mechanism into consideration, and restricting the power operation of the single electric automobile.
And S2.4, sampling the response boundary of each single electric automobile according to Monte Carlo simulation, and obtaining the upper and lower boundaries of the energy and power of the electric automobile cluster.
4. The method for activating electric vehicles to participate in electric power distribution according to claim 3, wherein in step S2.1;
for the electric automobile cluster, the proportion of the electric automobiles responding to the charging excitation mechanism is shown as the formula (4):
ρ CIM ＝f CIM (Δr CS (t)) (4)
wherein: function f CIM The method comprises the steps of (-) calculating the proportion of the electric vehicles in the cluster responding to a charging excitation mechanism; Δr CS (t) is the charge incentive electricity price issued by the electric automobile aggregator at the time t, which is used as the incentive price for the electric automobile to respond to the charge incentive mechanism;
assume that the charging period of the ith monomer electric automobile is [ t ] i,0 ,t i,end ]Considering the response charging excitation mechanism of the electric vehicle, the regulation period of the electric vehicle aggregator during the charging of the electric vehicle is shown in formula (5):
wherein: [ t ] i,0 ,t i,1 ]The method comprises the steps of responding to an adjustable time period of a charging excitation mechanism for an ith monomer electric automobile;responding to a charging excitation mechanism for the ith electric automobile, otherwise +.>Is 0.
5. The method for activating electric vehicles to participate in electric power distribution according to claim 3, wherein in step S2.2;
the loss of the battery includes depth of discharge and number of cycles; the relation between the depth of discharge and the cycle number of the lithium ion battery under normal conditions is shown as a formula (6), so that the total charge and discharge energy under the depth of discharge D can be estimated by a formula (7) in the service life of the battery;
f N (D)＝2151·D -2.301 ,D∈[0,0.9] (6)
wherein: d is the depth of discharge; function f N (. Cndot.) is used to calculate the number of battery cycles at a certain depth of discharge;
G(D)＝2·C·D·f N (D) (7)
wherein: g (D) is the total charge and discharge energy of the electric automobile at the depth of discharge D;
in practical application, the electric automobile battery is not always charged or discharged with the same discharging depth, and in order to evaluate the degradation cost of battery discharging, the average value of the total charging and discharging energy under different D values is calculated according to a formula (8), and the calculation of the additional battery loss cost per unit discharging energy is shown as a formula (9);
Wherein:is the average value of the total charge and discharge energy under different discharge depths; d (D) j For a particular depth of discharge value; n (N) D The number of the discharge depth values is the number of the discharge depth values;
wherein: r is (r) 0 Battery loss cost per unit discharge energy; r is (r) C Purchase cost for the battery;
calculating the price of the discharge patch given by the electric automobile aggregator according to the formula (10), wherein an electric automobile user can select whether to respond to a discharge excitation mechanism according to own preference; if the electric automobile responds to the discharge excitation mechanism, the electric automobile can enjoy intelligent discharge service of an aggregator and obtain discharge patches, the aggregator can arrange a discharge process of the electric automobile in a charging period of the electric automobile, and the electric automobile is controlled to provide frequency modulation and energy service to obtain benefits; if the electric automobile does not respond to the discharge excitation mechanism, the electric automobile is insensitive to the price of the discharge patch, and the electric automobile is not regulated by a polymerizer to discharge; considering that the willingness of the electric automobile users to respond to the discharge excitation mechanism is different, calculating the proportion of the electric automobile responding to the discharge excitation mechanism by utilizing the formula (11) based on investigation data;
r DS ＝r 1 +Σ(r 0 ) (10)
wherein: r is (r) DS The price of the discharge patch is compensated; sigma (r) 0 ) Compensating for discharge battery loss of the electric automobile during the whole charging period; r is (r) 1 A discharge incentive price determined by an electric automobile aggregator in the day before, which is used as an incentive price for attracting the electric automobile to respond to the DIM;
ρ DIM ＝f DIM (r DS ) (11)
wherein: function f CIM The (-) is used for calculating the proportion of the electric automobile in the cluster responding to the discharge excitation mechanism;
if ρ CIM ≥ρ DIM Then an electric vehicle responsive to the discharge excitation mechanism may be randomly selected among electric vehicles responsive to the charge excitation mechanism; if ρ CIM ＜ρ DIM All electric vehicles responding to the charging excitation mechanism can be considered to respond to the discharging excitation mechanism, and the rest electric vehicles can be randomly extracted from the electric vehicles responding to the charging excitation; considering the electric vehicle response discharge excitation mechanism, the regulation period of the electric vehicle aggregator during the electric vehicle charging is shown in formula (12):
wherein: [ t ] i,0 ,t i,2 ]The method comprises the steps of responding to an adjustable time period of a discharge excitation mechanism for an ith monomer electric automobile; τ i =1 is the i-th electric vehicle response discharge excitation mechanism, otherwise τ i Is 0; upsilon (v) i Is thatAnd τ i Maximum value of (v) i =1 responds to the charge excitation mechanism or the discharge excitation mechanism for the ith electric vehicle, otherwise, v i Is 0.
6. The electric automobile allocation excitation method for participating in electric network electric quantity allocation according to claim 3, wherein S2.3 is that the power operation constraint of the single electric automobile is shown as a formula (13) by taking a charging excitation mechanism and a discharging excitation mechanism into consideration:
Wherein: zeta type toy i (t) is a binary variable for controlling that the ith vehicle cannot meet the charge and discharge requirements at the same time, if ζ i (t) =1 is that the ith vehicle is charged at time t, otherwise is discharged;
the energy operation constraint of the monomer electric automobile is shown as a formula (14):
7. the method for allocating and exciting the electric automobile participating in the allocation of the electric quantity of the power grid according to claim 3, wherein the specific steps of S2.4 are as follows:
s2.4.1 dividing the time of day into different study periods, and calculating the proportion rho of the response charging excitation mechanism of the electric automobile according to the formula (4) and the formula (11) CIM And a ratio ρ of the response discharge excitation mechanism DIM ；
S2.4.2 generating a model, an on-station charging time length and a charging start SOC of the single electric automobile in a Monte Carlo sampling mode;
s2.4.3 to construct a random number by combining the random number with ρ CIM And ρ DIM Determining whether the electric vehicle responds to the charging excitation mechanism and the discharging excitation mechanism by comparing to determine the binary variableτ i And v i ；
S2.4.4 obtaining the upper and lower boundaries of the energy and power operation of the single electric automobile according to the formulas (13) and (14);
s2.4.5 repeatedly sampling the rows S2.4.2 to S2.4.4, and calculating the upper and lower boundaries of the electric automobile cluster energy and power operation according to the formula (15);
Wherein: e (E) + (t) and E - (t) respectively representing the upper and lower energy boundaries of the EV cluster at the time t; n (N) i The quantity of the electric vehicles which can be regulated and controlled by the electric vehicle polymerizer; p (P) + (t) and P - (t) represents the upper and lower power boundaries of the EV cluster at time t, respectively.
8. The method for allocating and exciting the electric automobile to participate in the allocation of the electric quantity of the power grid according to claim 1, wherein the step S3 specifically comprises the following steps:
s3.1: the charging process of the electric automobile is regulated and controlled by the aggregation businessman to participate in multi-stage and multi-variety markets, so that operation benefits are obtained, and the operation benefits comprise the following aspects:
(1) Charging fees purchased in the energy market before date;
(2) The cost of the frequency modulation power purchased in real time;
(3) Capacity and mileage benefits of participating in frequency modulation;
(4) Regulating and controlling charging income and subsidy expense of the electric automobile cluster;
s3.2: the electric automobile polymerizer can use the large-scale electric automobile battery energy storage system as an adjustable load to participate in the energy-frequency modulation market, so that multiple benefits are obtained, and the aim of participating in the energy-frequency modulation market by the polymerizer is the maximum.
9. The electric vehicle deployment excitation method involved in power grid power deployment of claim 8, wherein S3.1.1: when an electric vehicle aggregator participates in the day-ahead energy market, a charging plan of the electric vehicle needs to be formulated according to the market electricity price so as to charge the electric vehicle on the next day. After determining the charging schedule, the aggregator needs to purchase the corresponding electricity amount according to the charging schedule's demand, and thus its charging cost in the market in the day before can be represented by formula (16):
Wherein: f (F) 1 Charging cost for the electric automobile polymerizer in the energy market before the day; r is (r) Chr (k) Electricity prices in k time periods for the day-ahead energy market; p (P) Chr (k) Charging plan power formulated for the k period for the aggregator; k is the whole research period; Δk is the interval of time;
the electric automobile polymerizer participates in real-time frequency modulation to cause the change of charging power, so that the obtained frequency modulation power cost is shown as a formula (17):
wherein: f (F) 2 The power cost of the real-time response frequency modulation is used for the electric automobile polymerizer; r is (r) RT (k) Electricity prices in k time periods for the real-time energy market; p (P) UP (k) And P DN (k) Respectively, the power of the up and down frequency modulation of the k period of the aggregator;
s3.1.2: in the frequency modulation market, the electric automobile polymerizer responds to the change of the frequency modulation signal through upward or downward frequency modulation, so that compensation income of the market for frequency modulation is obtained; wherein the compensation benefits comprise capacity benefits and mileage benefits, the up-regulation capacity and the down-regulation capacity are symmetrically equal, and the capacity benefits of the aggregation quotient participating in the frequency modulation market are shown as formula (18):
wherein: f (F) 3 Frequency modulation capacity benefits for electric automobile aggregators; r is (r) RC (k) The electricity price is the frequency modulation capacity in k time periods; p (P) RC (k) Frequency modulation capacity of the aggregator in k period; lambda is the performance score;
The electric automobile aggregator responds to the change of the frequency modulation signal by adjusting the charge and discharge power of the electric automobile, and can calculate the frequency modulation output mileage of the electric automobile in the process of participating in the frequency modulation auxiliary service according to a formula (19), wherein the mileage gain of the aggregator participating in the frequency modulation market is shown as a formula (20);
wherein: m is m UP (k) And m DN (k) The upward and downward frequency modulation output mileage of the electric automobile cluster in the k period are respectively; n (N) m Number of time intervals for the signal in the k period; a (j, t) is a frequency modulation indication signal issued by a frequency modulation market at the time t, A (j, t) E < -1,1]，t∈k，j∈N m ；
Wherein: f (F) 4 Frequency modulation mileage benefits for the aggregator; r is (r) M (k) The electricity price of the frequency modulation mileage in the k period;
the electric automobile cluster provides frequency modulation and energy service in response to the charging excitation mechanism and the discharging excitation mechanism, the aggregator obtains charging benefits given by the electric automobile cluster and feeds the charging benefits and the subsidy cost given by the aggregator to the discharging subsidy of the electric automobile cluster, and the charging benefits and the subsidy cost given by the aggregator to the electric automobile cluster are shown as formula (21):
wherein: f (F) 5 Charge benefits and subsidy fees for the aggregator; p (k) is the net power of the flexible electric automobile in response to excitation; p (P) 0 (k) The net power of the inflexible electric automobile; p (P) EVA (k) Net power for the electric vehicle aggregator response; r is (r) CS (k) Charging electricity prices issued by electric automobile aggregators in the k period; Δr CS (k) Exciting a charging excitation electricity price of a user for an electric automobile aggregator in a k period;
at F 5 The first term gives the charge compensation fee to the electric vehicle participating in the charge excitation to the electric vehicle, the second term gives the charge excitation fee to the electric vehicle participating in the charge excitation to the electric vehicle, and the third term gives the charge fee to the electric vehicle cluster to the electric vehicle.
10. The electric automobile allocation excitation method for participating in electric power allocation of the power grid according to claim 1, wherein S3.2: the electric automobile polymerizer can use a large-scale electric automobile battery energy storage system as an adjustable load to participate in the energy-frequency modulation market, so that multiple benefits are obtained, the maximum acquisition of the electric automobile polymerizer to participate in the energy-frequency modulation market is targeted, and an objective function is shown as a formula (22):
max F＝-F 1 -F 2 +F 3 +F 4 +F 5 (22)
the charging plan power and the net energy after the response signal, which are determined by the electric automobile aggregator in each period, must not be lower than the lower energy boundary and must not be higher than the upper energy boundary:
wherein: e (E) +/(-) (k) The energy upper boundary and the energy lower boundary of the electric automobile cluster in the period t are provided;
the electric car aggregator responds to the change of the frequency modulation signal by adjusting the power, so that the net power responded by the aggregator must not be lower than the highest discharge power of the period and must not be higher than the highest charge power of the period in each period:
P - (k)≤P(k)≤P + (k) (24)
Wherein: p (P) +/(-) (k) The power upper and lower boundaries of the electric automobile cluster in the k period;
the up-regulation capacity and the down-regulation capacity are positive numbers:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311591798.7A CN117639039A (en) | 2023-11-27 | 2023-11-27 | Electric automobile allocation excitation method participating in electric quantity allocation of power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311591798.7A CN117639039A (en) | 2023-11-27 | 2023-11-27 | Electric automobile allocation excitation method participating in electric quantity allocation of power grid |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117639039A true CN117639039A (en) | 2024-03-01 |
Family
ID=90021030
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311591798.7A Pending CN117639039A (en) | 2023-11-27 | 2023-11-27 | Electric automobile allocation excitation method participating in electric quantity allocation of power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117639039A (en) |
Cited By (1)
| 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 |
-
2023
- 2023-11-27 CN CN202311591798.7A patent/CN117639039A/en active Pending
Cited By (2)
| 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 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103683424A (en) | Electric vehicle charging station sequential charging control method based on dynamic time-of-use electricity price | |
| CN103679299A (en) | Electric automobile optimal peak-valley time-of-use pricing method giving consideration to owner satisfaction degree | |
| CN117639039A (en) | Electric automobile allocation excitation method participating in electric quantity allocation of power grid | |
| CN109193718A (en) | A kind of selection electric car networking regulation method being adapted to V2G | |
| CN107618392A (en) | The charging electric vehicle load Stochastic accessing control system and method for charging pile self-decision | |
| CN112590601B (en) | V2G charging station system based on edge computing platform | |
| CN110852494A (en) | Electric vehicle probability load prediction method considering demand response | |
| CN111832940B (en) | Electric automobile charging scheduling method, device and storage medium | |
| CN115630796A (en) | Electric vehicle multi-objective optimization charging scheduling method under hybrid demand response | |
| CN113054669A (en) | Block chain technology-based distribution network peak-shifting valley-leveling self-adaptive self-balancing method | |
| CN110979083B (en) | Bidirectional charge and discharge control system and method for electric automobile | |
| CN115439138A (en) | Electric vehicle charging and discharging power optimal distribution method and system | |
| CN109515244B (en) | Charging method, device and system | |
| CN110826801A (en) | Distributed electric energy management method for electric vehicle charging station | |
| CN112332433B (en) | Transferable load capacity analysis method for electric vehicle participated in valley filling auxiliary service | |
| CN112109580B (en) | Micro-grid electric automobile charge and discharge control system with electric quantity self-distribution function | |
| CN116215297A (en) | Ordered charging control method considering charging continuity of electric automobile | |
| CN113949090B (en) | Real-time demand response interaction method based on electric automobile cluster | |
| CN115675162B (en) | Multi-objective optimization charging control method and device for electric vehicle and storage medium | |
| Singhal et al. | Transactive electric vehicle agent: design and performance evaluation | |
| CN117895510B (en) | Electric automobile cluster participation power grid peak shaving method and system based on aggregation business mode | |
| Zhang et al. | Multi-time Scale Optimal Bidding Strategy for an EV Aggregator in Energy and Regulation Markets | |
| CN115378009B (en) | Day-ahead regulation and control plan generation method and system based on electric automobile grouping | |
| CN114781061B (en) | Electric automobile cluster response capability assessment method and device | |
| CN117374954A (en) | Optimized scheduling method and system for participation of electric automobile aggregators in demand response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |