CN111211564B - Demand response method considering electric vehicle charging load space-time distribution - Google Patents

Demand response method considering electric vehicle charging load space-time distribution Download PDF

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CN111211564B
CN111211564B CN202010052832.3A CN202010052832A CN111211564B CN 111211564 B CN111211564 B CN 111211564B CN 202010052832 A CN202010052832 A CN 202010052832A CN 111211564 B CN111211564 B CN 111211564B
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demand response
power
load
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CN111211564A (en
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汤奕
袁泉
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Nanjing Dongbo Intelligent Energy Research Institute Co ltd
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Nanjing Dongbo Intelligent Energy Research Institute Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
    • Y04S10/126—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation the energy generation units being or involving electric vehicles [EV] or hybrid vehicles [HEV], i.e. power aggregation of EV or HEV, vehicle to grid arrangements [V2G]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

The invention discloses a demand response method considering space-time distribution of charging load of an electric automobile, which comprises the following steps: establishing a charge load space-time distribution prediction model of the electric automobile; adding the charging load result of the electric automobile into the original load of each node to perform tidal current operation, and obtaining the voltage deviation of each node and the blocking condition of the transmission line; based on the charging load result of the electric automobile, the potential of the electric automobile participating in demand response is evaluated to obtain the upper limit value of the demand response power at each moment; and calculating the demand response power value participating in scheduling at each moment based on the demand response potential of each node, and substituting the load after demand response into the power grid load flow operation again. The optimal calculation method is based on the time-space distribution characteristics of the charging load of the electric automobile and considers the power flow distribution of the power grid to perform optimal calculation on the demand response power, is beneficial to obtaining the optimal running state of the regional power grid after dispatching, and improves the rationality of the demand response dispatching of the power grid.

Description

Demand response method considering electric vehicle charging load space-time distribution
Technical Field
The invention belongs to the technical field of demand response of power systems, and particularly relates to a demand response method considering space-time distribution of charging loads of an electric automobile.
Background
While electric vehicles are used as vehicles, the charging load of the electric vehicles is also becoming an important component of the load of the power grid. When a large-scale electric vehicle charging load is connected to a power grid, large power impact is brought to the power grid, and the problems of overlarge voltage deviation, reduced power quality and the like can be caused in serious cases. Therefore, the problem of disordered charging of large-scale electric vehicles is urgently solved.
The electric automobile can reasonably change the charging time under the condition of not influencing the normal use of a user, and has strong power grid regulation and control accepting capability, so the electric automobile is one of important elastic regulation and control resources of a power grid. If the capability of the charging load of the electric automobile to participate in the demand response can be accurately evaluated, and a proper demand response strategy is provided, the method can help solve the problem of large-scale disordered charging.
At present, most of researches on the participation of electric vehicle loads in demand response scheduling are only limited to load peak reduction, the consideration on the overall power flow distribution condition of a regional power grid is lacked, the problems of voltage drop, transmission line blockage and the like in the regional power grid cannot be solved, and the consideration on the overall power flow distribution of the regional power grid puts higher requirements on the prediction accuracy of the space-time distribution of the power grid loads.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide the demand response method considering the space-time distribution of the charging load of the electric vehicle, which can more accurately evaluate the dispatching demand of each power grid node, is beneficial to obtaining the optimal running state of a regional power grid after dispatching and improves the rationality of demand response dispatching of the power grid.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention relates to a demand response method considering space-time distribution of charging load of an electric automobile, which comprises the following steps:
(1) establishing a space-time distribution prediction model of the charging load of the electric automobile, wherein the space-time distribution prediction model is used for predicting the charging load of each node in a regional power grid within 24 hours a day;
(2) adding the electric vehicle charging load result obtained by prediction in the step (1) into the original load of each node to perform load flow operation, and obtaining the voltage deviation of each node and the blocking condition of the transmission line, wherein the voltage deviation and the blocking condition of the transmission line are used for verifying the influence of over-low voltage and line blocking on a power grid caused by the access of the large-scale electric vehicle disordered charging load;
(3) evaluating the potential of the electric vehicle participating in the demand response based on the charging load result of the electric vehicle obtained by prediction in the step (1) to obtain the upper limit value of the demand response power at each moment;
(4) and (4) calculating a demand response power value participating in scheduling at each moment based on the demand response potential of each node obtained in the step (3), and substituting the load after demand response into the power grid load flow operation again.
The specific establishment method of the electric vehicle charging load space-time distribution prediction model is as follows:
(a) the Monte Carlo simulation single vehicle travel determining method comprises the following steps: monte Carlo sampling is carried out based on historical traffic data of cities, and the starting position of the nth vehicle is determined
Figure GDA0002929396540000021
Arrival position
Figure GDA0002929396540000022
Departure time
Figure GDA0002929396540000023
And time of arrival
Figure GDA0002929396540000024
Wherein n is a vehicle number;
judging whether the electric vehicle needs to be charged or not according to the residual electric quantity of the electric vehicle when the electric vehicle reaches the end point of each trip, if the residual electric quantity is less than 20%, judging that the electric vehicle needs to be charged, recording the current position and time, and respectively recording the position and the time as schargeAnd tcharge(ii) a If the electric quantity is sufficient and charging is not needed after one trip is finished, switching to the next Monte Carlo sampling;
(b) and establishing an aggregate charging load prediction model.
The method for establishing the aggregation charging load prediction model comprises the following steps:
after Monte Carlo sampling is carried out on all electric automobiles to obtain the travel and electric quantity states of the electric automobiles, an aggregation charging matrix C is established; c is an S multiplied by T matrix, S is the total number of position numbers, T is the total number of time segments in a day, and the initial C matrix value is 0; for each charged electric vehicle, there are:
C(scharge,tcharge)＝C(scharge,tcharge)+pn
wherein s ischargeTo the place of the vehicle at the beginning of the charging action, tchargeTo the charge start time, pnCharging power when charging the nth vehicle;
then an aggregate charging matrix C is obtained:
Figure GDA0002929396540000025
the value of each element in the matrix represents the total charging power at time t for position s; the day-by-day aggregated charging power of each node is as follows:
Figure GDA0002929396540000031
evaluating the potential of the electric vehicle to participate in demand response, wherein the following formula is required to be satisfied:
Figure GDA0002929396540000032
in the formula: SOCx,e(t) is the charge capacity of the electric vehicle of the power grid node x at the time t,
Figure GDA0002929396540000033
the minimum charge, P, required to satisfy the next day trip plan for node x, station e electric vehiclex,eFor node x the rated charging power of the e-th electric vehicle,
Figure GDA0002929396540000034
the planned departure time for node x the next day for the e-th electric vehicle. The response time of each electric automobile needs to meet the following requirements:
Figure GDA0002929396540000035
Figure GDA0002929396540000036
in the formula: epsilon is a step function of the unit,
Figure GDA0002929396540000037
the shortest participation time of the node x the e-th electric vehicle in the demand response every time,
Figure GDA0002929396540000038
for the node x, the shortest time between two times of participation of the No. e electric automobile in demand responseAnd (4) separating.
The calculation steps of the demand response power are as follows:
(1-1) establishing an optimization target
The method is characterized by taking the minimum average voltage deviation of each node in the regional power grid as a target:
Figure GDA0002929396540000039
in the formula: DR (digital radiography)x(t) demand response Power, Δ U, for each node to use at time txAveraging the voltage offsets for each node; u shapex[DRx(t)]Is the voltage at node x at time t, as a function of DRx(t) a complex function of magnitude change;
Figure GDA00029293965400000310
is the nominal voltage of node x;
(1-2) establishing constraints
DRx(t) the following conditional constraints are satisfied, and the demand response power released by each node at each moment cannot exceed the aggregate demand response potential provided by the intelligent load;
DRx(t)≤DRPx(t)
Figure GDA0002929396540000041
in the formula: ld (t) load power at time t, GRlimitLimiting the climbing rate of the generator set; the average slope of the load curve after demand response needs to be between 0 and the generator set ramp rate limit;
DRx(t)＝0
if
Figure GDA0002929396540000042
in the formula:
Figure GDA0002929396540000043
to enable connection of node xThe voltage that is applied is shifted by the lowest value,
Figure GDA0002929396540000044
the maximum voltage offset that node x can accept; pxx′[DRx(t)]For the active power on the link between nodes x and x' at time t, is the following DRx(t) a complex function of the magnitude change, obtained by load flow calculation;
Figure GDA0002929396540000045
is the rated active power on the tie-line between nodes x and x'; when the average slope of the load curve in the set time is within the limit of the climbing rate of the generator set and no voltage out-of-limit exists and the active power flow of the tie line is overloaded, the required response power of the node is 0;
Figure GDA0002929396540000046
Figure GDA0002929396540000047
in the formula: px(t) is the active power of node x at time t, Δ Pxx′(t) is the real power loss at time t, SIG P, on the interconnection between nodes x and xG(t) is the active power output of the generator at time t; in the same way, Qx(t) is the reactive power at node x at time t, Δ Qxx′(t) is the reactive loss, Σ Q, at time t on the connection between nodes x and xG(t) is the reactive power output of the generator at the moment t; and after the demand response, the active and reactive power balance relation of the power grid flow is required to be met.
Compared with the prior art, the invention has the following advantages:
1. the space-time distribution of large-scale unordered charging loads of the electric automobile is considered, so that the influence of load access on power flow distribution of a power grid is accurately evaluated, and the dispatching requirement of each power grid node is more accurately evaluated.
2. The optimal calculation of the demand response power is carried out based on the time-space distribution characteristics of the charging load of the electric automobile and considering the power flow distribution of the power grid, the optimal operation state of the regional power grid after dispatching is facilitated, and the rationality of the power grid demand response dispatching is improved.
Drawings
FIG. 1 is a flowchart of the operation of a demand response method of the present invention in consideration of the spatiotemporal distribution of the charging load of an electric vehicle;
FIG. 2 shows the voltages of nodes of a power grid after a certain electric vehicle is connected with an unordered charging load;
fig. 3 shows the voltages of the nodes of the grid before and after demand response scheduling.
Detailed Description
The invention is described in further detail below with reference to embodiments and with reference to the drawings.
The invention discloses a demand response method considering electric vehicle charging load space-time distribution, provides a prediction model of electric vehicle charging load space-time distribution, predicts the charging load condition of each node in a regional power grid in a day, provides a demand response power calculation method by combining a prediction result and the power grid load flow condition, reasonably schedules large-scale electric vehicle disordered charging loads, and improves the voltage deviation and line blocking conditions in the regional power grid.
The demand response method considering the electric vehicle charging load space-time distribution provides a prediction model of the electric vehicle charging load space-time distribution, predicts the charging load condition of each node in the regional power grid in a day, provides a demand response power calculation method by combining the prediction result and the power grid load flow condition, reasonably schedules the large-scale electric vehicle disordered charging load, and improves the voltage deviation and line blocking condition in the regional power grid.
Referring to fig. 1, a demand response method includes the steps of:
and establishing a space-time distribution prediction model of the charging load of the electric automobile.
And providing a method for evaluating the participation demand response potential of the electric automobile.
And providing a demand response power calculation method in consideration of power grid flow constraint.
In this embodiment, the model for predicting the temporal-spatial distribution of the charging load of the electric vehicle needs to simulate the travel and the charging behavior of each vehicle by a monte carlo method, and records the travel and the charging behavior by using a two-dimensional matrix, where a row represents position space information and a column represents time information. And performing aggregation calculation on the charging load space-time distribution of each vehicle to obtain the large-scale aggregated charging load of the electric automobile. The specific implementation process is as follows:
monte Carlo sampling is carried out according to the historical traffic flow data to determine the starting position of the nth vehicle
Figure GDA0002929396540000051
Arrival position
Figure GDA0002929396540000052
Departure time
Figure GDA0002929396540000053
And time of arrival
Figure GDA0002929396540000054
Where n is the vehicle number.
Judging whether the electric automobile needs to be charged according to the residual electric quantity at the time when the electric automobile reaches the end point of each trip, if the residual electric quantity is less than 20%, judging that the electric automobile needs to be charged, recording the current position and time, and respectively recording the position and the time as schargeAnd tcharge(ii) a And if the electric quantity is sufficient and charging is not needed after one trip, switching to the next Monte Carlo sampling.
After sampling of all electric automobiles is finished, an aggregation charging matrix C is set to be an S multiplied by T matrix, S is the total number of position numbers, T is the total number of time segments in a day, and the initial C matrix value is 0. For each charged electric vehicle, there are:
C(scharge,tcharge)＝C(scharge,tcharge)+pn
wherein s ischargeTo the place of the vehicle at the beginning of the charging action, tchargeTo the charge start time, pnCharging power when charging the nth vehicle.
Then an aggregate charging matrix C is obtained:
Figure GDA0002929396540000061
the value of each element in the matrix represents the total charging power at time t for that location s. Therefore, the aggregated charging power per node day is:
Figure GDA0002929396540000062
in this embodiment, the method for evaluating the demand response potential of the electric vehicle has the demand response scheduling potential if the electric quantity of the electric vehicle satisfies the user trip plan on the next day, and the condition that the charging process cannot be frequently switched on and off needs to be satisfied to ensure the service life of the battery, that is, the following formula needs to be satisfied:
Figure GDA0002929396540000063
in the formula: SOCx,e(t) is the charge capacity of the electric vehicle of the power grid node x at the time t,
Figure GDA0002929396540000064
the minimum charge, P, required to satisfy the next day trip plan for node x, station e electric vehiclex,eFor node x the rated charging power of the e-th electric vehicle,
Figure GDA0002929396540000065
the planned departure time for node x the next day for the e-th electric vehicle.
In order to avoid the damage of the battery caused by frequent disconnection of the charging process, the response time of each electric automobile needs to be satisfied:
Figure GDA0002929396540000066
Figure GDA0002929396540000071
in the formula: epsilon is a step function of the unit,
Figure GDA0002929396540000072
the shortest participation time of the node x the e-th electric vehicle in the demand response every time,
Figure GDA0002929396540000073
the minimum time interval between two times of participation of the No. e electric automobile at the node x is shown.
In this embodiment, the demand response power calculation method considering the power flow constraint of the power grid aims at minimizing the average voltage deviation of each node:
Figure GDA0002929396540000074
in the formula: DR (digital radiography)x(t) demand response Power, Δ U, for each node to use at time txThe voltage offset is averaged for each node. U shapex[DRx(t)]Is the voltage at node x at time t, as a function of DRx(t) a complex function of the magnitude change.
Figure GDA0002929396540000075
The nominal voltage of node x.
At the same time, DRx(t) the constraint that the demand response power released by each node at each time must not exceed the aggregate demand response potential provided by the smart load should also be met.
DRx(t)≤DRPx(t)
Figure GDA0002929396540000076
In the formula: ld (t) load power at time t, GRlimitThe climbing rate of the generator set is limited. The average slope of the load curve after demand response needs to be between 0 and the generator set ramp rate limitIf the value is less than 0, the released demand response potential is too much, unnecessary scheduling cost loss is caused, and if the value is more than the limit of the generator set ramp rate, the power imbalance of the power grid is easily caused.
DRx(t)＝0
if
Figure GDA0002929396540000077
In the formula:
Figure GDA0002929396540000078
the voltage that node x can accept is offset by the lowest value,
Figure GDA0002929396540000079
the voltage that node x can accept is offset by the highest value. Pxx′[DRx(t)]For the active power on the link between nodes x and x' at time t, is the following DRx(t) a complex function of the magnitude change is calculated from the load flow.
Figure GDA00029293965400000710
Is the nominal active power on the link between nodes x and x'. When the average slope of the load curve is within the limit of the climbing rate of the generator set within a certain time and no voltage out-of-limit exists and the active power flow of the connecting line is overloaded, the required response power of the node is 0.
Figure GDA0002929396540000081
Figure GDA0002929396540000082
In the formula: px(t) is the active power of node x at time t, Δ Pxx′(t) is the real power loss at time t, SIG P, on the interconnection between nodes x and xGAnd (t) is the active power output of the generator at the moment t. In the same way, Qx(t) is the reactive power at node x at time t, Δ Qxx′(t) is the reactive loss, Σ Q, at time t on the connection between nodes x and xGAnd (t) is the reactive power output of the generator at the moment t. The active and reactive power balance relation of the power grid current can be still met after the demand response.
It should be noted that, in the given embodiment, a regional power grid system with 43 nodes is taken as a research object, but the present invention is not limited to the given embodiment, and setting different power grid topologies only has an influence on the space-time distribution of the charging load of the electric vehicle, and the principle is the same. Any work done in accordance with the load-space-time prediction idea and the demand response method presented herein is within the scope of protection.
In the embodiment, an actual power grid of a certain city is selected as a research object, the actual power grid comprises 43 power grid nodes, and the space-time distribution of the charging load is predicted according to 130000 current electric vehicles in the city. And substituting the prediction result into the power grid load flow operation to obtain a voltage change curve of each node in each day, as shown in fig. 2.
And then, evaluating the demand response potential according to the proposed demand response strategy, and obtaining the demand response power of each node x at each moment after performing mixed integer nonlinear programming operation. Substituting the loads of the nodes after the response into the power grid load flow calculation again to obtain the minimum voltage before and after the demand response of the nodes, as shown in fig. 3.
It can be seen that the required response can alleviate the voltage offset to a certain extent, and has a good alleviating effect on the node with larger voltage offset.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (3)

1. A demand response method considering space-time distribution of charging loads of an electric automobile is characterized by comprising the following steps:
(1) establishing a space-time distribution prediction model of the charging load of the electric automobile, wherein the space-time distribution prediction model is used for predicting the charging load of each node in a regional power grid within 24 hours a day;
(2) adding the electric vehicle charging load result obtained by prediction in the step (1) into the original load of each node to perform load flow operation, and obtaining the voltage deviation of each node and the blocking condition of the transmission line, wherein the voltage deviation and the blocking condition of the transmission line are used for verifying the influence of over-low voltage and line blocking on a power grid caused by the access of the large-scale electric vehicle disordered charging load;
(3) evaluating the potential of the electric vehicle participating in the demand response based on the charging load result of the electric vehicle obtained by prediction in the step (1) to obtain the upper limit value of the demand response power at each moment;
(4) calculating a demand response power value participating in scheduling at each moment based on the demand response potential of each node obtained in the step (3), and substituting the load after demand response into the power grid load flow operation again;
evaluating the potential of the electric vehicle to participate in demand response, wherein the following formula is required to be satisfied:
Figure FDA0002929396530000011
in the formula: SOCx,e(t) is the charge capacity of the electric vehicle of the power grid node x at the time t,
Figure FDA0002929396530000012
the minimum charge, P, required to satisfy the next day trip plan for node x, station e electric vehiclex,eFor node x the rated charging power of the e-th electric vehicle,
Figure FDA0002929396530000013
planned departure time for node x the next day of the e-th electric vehicle; the response time of each electric automobile needs to meet the following requirements:
Figure FDA0002929396530000014
Figure FDA0002929396530000015
in the formula:
Figure FDA0002929396530000016
the shortest participation time of the node x the e-th electric vehicle in the demand response every time,
Figure FDA0002929396530000017
the minimum time interval between two times of participation of the electric automobile in the No. e electric automobile at the node x is represented by epsilon which is a unit step function; the specific calculation method of the demand response power is as follows:
(1-1) establishing an optimization target
The method is characterized by taking the minimum average voltage deviation of each node in the regional power grid as a target:
Figure FDA0002929396530000021
in the formula: DR (digital radiography)x(t) demand response Power, Δ U, for each node to use at time txAveraging the voltage offsets for each node; u shapex[DRx(t)]Is the voltage at node x at time t, as a function of DRx(t) a complex function of magnitude change;
Figure FDA0002929396530000022
is the nominal voltage of node x;
(1-2) establishing constraints
DRx(t) the following conditional constraints are satisfied, and the demand response power released by each node at each moment cannot exceed the aggregate demand response potential provided by the intelligent load;
DRx(t)≤DRPx(t)
Figure FDA0002929396530000023
in the formula: ld (t) load power at time t, GRlimitLimiting the climbing rate of the generator set; the average slope of the load curve after demand response needs to be between 0 and the generator set ramp rate limit;
DRx(t)＝0
Figure FDA0002929396530000024
in the formula:
Figure FDA0002929396530000025
the voltage that node x can accept is offset by the lowest value,
Figure FDA0002929396530000026
the maximum voltage offset that node x can accept; pxx′[DRx(t)]For the active power on the link between nodes x and x' at time t, is the following DRx(t) a complex function of the magnitude change, obtained by load flow calculation;
Figure FDA0002929396530000027
is the rated active power on the tie-line between nodes x and x'; when the average slope of the load curve in the set time is within the limit of the climbing rate of the generator set and no voltage out-of-limit exists and the active power flow of the tie line is overloaded, the required response power of the node is 0;
Figure FDA0002929396530000028
Figure FDA0002929396530000029
in the formula: px(t) is the active power of node x at time t, Δ Pxx′(t) is the real power loss at time t, SIG P, on the interconnection between nodes x and xG(t) is the active power output of the generator at time t; in the same way, Qx(t) is the reactive power at node x at time t, Δ Qxx′(t) is the reactive loss, Σ Q, at time t on the connection between nodes x and xG(t) is the reactive power output of the generator at the moment t; and after the demand response, the active and reactive power balance relation of the power grid flow is required to be met.
2. The demand response method considering the space-time distribution of the charging load of the electric vehicle as claimed in claim 1, wherein the specific establishment method of the prediction model of the space-time distribution of the charging load of the electric vehicle is as follows:
(a) the Monte Carlo simulation single vehicle travel determining method comprises the following steps:
monte Carlo sampling is carried out based on historical traffic data of cities, and the starting position of the nth vehicle is determined
Figure FDA0002929396530000031
Arrival position
Figure FDA0002929396530000032
Departure time
Figure FDA0002929396530000033
And time of arrival
Figure FDA0002929396530000034
Wherein n is a vehicle number;
judging whether the electric vehicle needs to be charged or not according to the residual electric quantity of the electric vehicle when the electric vehicle reaches the end point of each trip, if the residual electric quantity is less than 20%, judging that the electric vehicle needs to be charged, recording the current position and time, and respectively recording the position and the time as schargeAnd tcharge(ii) a If the electric quantity is sufficient and charging is not needed after one stroke is finished, turning toUntil the next Monte Carlo sample;
(b) and establishing an aggregate charging load prediction model.
3. The demand response method considering the space-time distribution of the charging load of the electric vehicle as set forth in claim 2, wherein the aggregate charging load prediction model is established as follows:
after Monte Carlo sampling is carried out on all electric automobiles to obtain the travel and electric quantity states of the electric automobiles, an aggregation charging matrix C is established; c is an S multiplied by T matrix, S is the total number of position numbers, T is the total number of time segments in a day, and the initial C matrix value is 0; for each charged electric vehicle, there are:
C(scharge,tcharge)＝C(scharge,tcharge)+pn
wherein s ischargeTo the place of the vehicle at the beginning of the charging action, tchargeTo the charge start time, pnCharging power when charging the nth vehicle;
then an aggregate charging matrix C is obtained:
Figure FDA0002929396530000035
the value of each element in the matrix represents the total charging power at time t for position s; the day-by-day aggregated charging power of each node is as follows:
Figure FDA0002929396530000036
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Families Citing this family (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111211564B (en) * 2020-01-17 2021-06-04 南京东博智慧能源研究院有限公司 Demand response method considering electric vehicle charging load space-time distribution
CN111784027A (en) * 2020-06-04 2020-10-16 国网上海市电力公司 Urban range electric vehicle charging demand prediction method considering geographic information
CN112926818A (en) * 2020-12-11 2021-06-08 天津大学 Electric vehicle demand response capability assessment method based on user demand relaxation degree
CN112928766A (en) * 2021-03-02 2021-06-08 上海电力大学 Power distribution network electric vehicle accepting capability evaluation method based on multiple influence factors
CN113705991B (en) * 2021-08-17 2023-08-29 国网四川省电力公司技能培训中心 Establishment and low-carbon scheduling method for multi-energy park
CN114069604B (en) * 2021-08-25 2023-06-23 广西大学 Multi-period capacity assessment method considering coupling effect of electric vehicle charging station
CN113890075B (en) * 2021-09-28 2023-10-20 国网安徽省电力有限公司经济技术研究院 Method for using large-scale electric automobile as flexible climbing resource
CN114006367A (en) * 2021-10-25 2022-02-01 国网山东省电力公司青岛供电公司 Distributed power supply access capability prediction method and system
CN113954680B (en) * 2021-12-07 2023-08-25 国网浙江杭州市萧山区供电有限公司 Electric automobile charging control method based on charging period optimization
CN114444802A (en) * 2022-01-29 2022-05-06 福州大学 Electric vehicle charging guide optimization method based on graph neural network reinforcement learning
CN114626206B (en) * 2022-02-22 2023-02-10 南京理工大学 Alternating current-direct current power distribution network-oriented electric vehicle space-time scheduling modeling method
CN114580194B (en) * 2022-03-16 2024-03-29 国网江苏省电力有限公司苏州供电分公司 Method and system for accessing load boundary of large-scale electric automobile
CN114757422A (en) * 2022-04-19 2022-07-15 浙江大学 Charging pile cost adjustment and evaluation method and device for supporting voltage toughness of urban power grid
CN114781061B (en) * 2022-04-20 2024-01-23 国网江苏省电力有限公司电力科学研究院 Electric automobile cluster response capability assessment method and device
CN114552672B (en) * 2022-04-26 2022-08-12 阿里巴巴（中国）有限公司 Data processing method and storage medium for power system
CN115018379B (en) * 2022-07-18 2023-04-07 东南大学溧阳研究院 Electric vehicle in-day response capability assessment method and system and computer storage medium
CN115051384B (en) * 2022-08-17 2022-11-15 国网浙江省电力有限公司湖州供电公司 Frequency modulation performance evaluation method, device, equipment and medium
CN115848196B (en) * 2022-12-07 2024-01-05 南通国轩新能源科技有限公司 Ordered charging guiding method for electric automobile based on dynamic demand and new energy consumption
CN115797131B (en) * 2023-02-09 2023-05-30 国网浙江电动汽车服务有限公司 Carbon emission monitoring method, device, equipment and readable storage medium
CN115833267A (en) * 2023-02-15 2023-03-21 江西惜能照明有限公司 Charging control method and system based on intelligent lamp pole and block chain
CN116029468B (en) * 2023-03-30 2023-06-13 国网江苏省电力有限公司苏州供电分公司 Power grid risk assessment and advanced scheduling method and system considering electric automobile access
CN116702978B (en) * 2023-06-07 2024-02-13 西安理工大学 Electric vehicle charging load prediction method and device considering emergency characteristics
CN116885698A (en) * 2023-06-16 2023-10-13 西安理工大学 Electric vehicle charging station group load prediction method and device based on multi-graph fusion
CN116911696B (en) * 2023-09-12 2023-12-08 湖北华中电力科技开发有限责任公司 Evaluation method for interaction correspondence capacity of electric automobile participating in power grid
CN117674168B (en) * 2024-01-31 2024-04-16 国网湖北省电力有限公司经济技术研究院 Regional power low-carbon adjustment method and system considering power demand response

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107248010A (en) * 2017-06-06 2017-10-13 重庆大学 The Optimization Scheduling of meter and Load aggregation business and electric automobile response reliability
CN108510128A (en) * 2018-04-11 2018-09-07 华南理工大学广州学院 A kind of region electric vehicle charging load spatial and temporal distributions prediction technique
CN109034648A (en) * 2018-08-13 2018-12-18 华南理工大学广州学院 A kind of electric car cluster demand response potential evaluation method
CN110363332A (en) * 2019-06-21 2019-10-22 国网天津市电力公司电力科学研究院 A kind of electric car charging load spatial and temporal distributions prediction technique based on individual behavior characteristic

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8680812B2 (en) * 2011-03-09 2014-03-25 General Electric Company Methods and systems for charging an electric vehicle
US10164433B2 (en) * 2016-01-19 2018-12-25 Ford Global Technologies, Llc Adjusting electrified vehicle operation to balance electrical grid
CN106384175B (en) * 2016-11-04 2019-10-11 浙江工业大学 A kind of electric car real-time control method based on schedulable ability
CN106877339B (en) * 2017-04-05 2019-07-02 长沙理工大学 A kind of consideration electric car accesses the analysis method of Random-fuzzy trend after power distribution network
CN108470233B (en) * 2018-02-01 2020-05-15 华北电力大学 Demand response capability assessment method and computing device for smart power grid
CN108446796A (en) * 2018-03-01 2018-08-24 国网福建省电力有限公司 Consider net-source-lotus coordinated planning method of electric automobile load demand response
CN110065410B (en) * 2019-05-07 2020-09-18 华南理工大学广州学院 Electric automobile charge and discharge rate control method based on fuzzy control
CN111211564B (en) * 2020-01-17 2021-06-04 南京东博智慧能源研究院有限公司 Demand response method considering electric vehicle charging load space-time distribution

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107248010A (en) * 2017-06-06 2017-10-13 重庆大学 The Optimization Scheduling of meter and Load aggregation business and electric automobile response reliability
CN108510128A (en) * 2018-04-11 2018-09-07 华南理工大学广州学院 A kind of region electric vehicle charging load spatial and temporal distributions prediction technique
CN109034648A (en) * 2018-08-13 2018-12-18 华南理工大学广州学院 A kind of electric car cluster demand response potential evaluation method
CN110363332A (en) * 2019-06-21 2019-10-22 国网天津市电力公司电力科学研究院 A kind of electric car charging load spatial and temporal distributions prediction technique based on individual behavior characteristic

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