CN114498635A - Power distribution network optimal scheduling method and system considering electric vehicle charging priority - Google Patents

Power distribution network optimal scheduling method and system considering electric vehicle charging priority Download PDF

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CN114498635A
CN114498635A CN202210176433.7A CN202210176433A CN114498635A CN 114498635 A CN114498635 A CN 114498635A CN 202210176433 A CN202210176433 A CN 202210176433A CN 114498635 A CN114498635 A CN 114498635A
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load
electric vehicle
charging
power
distribution network
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CN114498635B (en
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顾杨青
兴胜利
吴博文
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Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Suzhou Power Supply Co of State Grid Jiangsu Electric Power 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
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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]
    • 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
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems
    • 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
    • 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
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/14—Plug-in electric vehicles

Abstract

The application discloses a power distribution network optimal scheduling method and system considering electric vehicle charging priority, wherein the method comprises the following steps: s1, setting optimization scheduling related parameters and dividing peak, valley and average time periods of daily load; s2, setting an electric vehicle charging strategy and corresponding constraints of the working mode of the electric vehicle in peak, valley and ordinary periods; s3, establishing an optimization model with the minimum load change degree of the power distribution network as a target based on the electric vehicle charging strategy and corresponding constraints; s4, a V2G algorithm solving strategy based on the load time interval is formulated, the optimization model is solved based on the optimization scheduling related parameters, and the power distribution network optimization scheduling scheme is obtained. According to the invention, the working mode of the electric vehicle is selected according to the current time period load condition of the power grid, and corresponding charging and discharging strategies are formulated according to different working modes, so that the minimum change degree of the load curve of the power grid is ensured in the process of optimizing dispatching, and the safe and stable operation of the power grid is maintained.

Description

Power distribution network optimal scheduling method and system considering electric vehicle charging priority
Technical Field
The invention belongs to the technical field of V2G, and relates to a power distribution network optimal scheduling method and system considering electric vehicle charging priority.
Background
V2G describes the relationship of the electric vehicle to the grid, when the electric vehicle is not in use, the electric energy of the on-board battery is sold to the system of the grid, and if the on-board battery needs to be charged, the current flows from the grid to the vehicle.
The holding capacity of electric vehicles will continue to increase in a two-carbon context. For the power grid, the electric automobile is an uncertain load, and a series of problems such as overload of power grid equipment, interference of power grid voltage stability and harmonic influence can be caused. Meanwhile, the electric automobile can also discharge battery energy to the power grid through vehicle-mounted application to the power grid, so as to provide support for the power grid.
At present, a plurality of scholars and experts carry out full research on the V2G technology, and great results are obtained. In addition to theoretical achievements, V2G test point work has been carried out in partial cities at home and abroad, and a foundation is laid for theoretical research and actual scheduling problems of electric vehicles in the future. However, the V2G technology involves many parties including power distribution networks and electric vehicle users, and the interest balance is always a relatively complex and non-negligible point.
Disclosure of Invention
In order to solve the defects in the prior art, the power distribution network optimal scheduling method and system considering the charging priority of the electric automobile are provided, the balance points of the gains of the two parties are searched, the optimal scheduling strategy is executed according to the electric quantity of the electric automobile and the load condition of the power distribution network, the safe and stable operation of the power distribution network is realized while the charging requirement of an electric automobile user is met, and the balance of the gains of the two parties is realized.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a power distribution network optimal scheduling method considering electric vehicle charging priority comprises the following steps:
s1, collecting various types of load data and electric vehicle information in the region, setting relevant parameters for optimizing scheduling and dividing peak, valley and average time periods of daily load;
s2, considering the charging priority of the electric vehicle, setting up the charging strategy and corresponding constraints of the electric vehicle in the peak period, the valley period and the flat period, wherein the working modes in the peak period, the valley period and the flat period are respectively the peak regulation mode, the valley filling mode and the low-power vehicle priority charging mode;
s3, establishing an optimization model with the minimum load change degree of the power distribution network as a target based on the electric vehicle charging strategy and corresponding constraints;
s4, a V2G algorithm solving strategy based on the load time interval is formulated, the optimization model is solved based on the optimization scheduling related parameters, and the power distribution network optimization scheduling scheme is obtained.
The invention further comprises the following preferred embodiments:
preferably, the S1 specifically includes the following steps:
s1.1, collecting different types of load data in the region to obtain a typical daily load curve in the region;
the different types of load data comprise civil load data and commercial load data;
s1.2, collecting electric vehicle information in an area, including information of electric vehicle charging facilities, charging time and the like, so as to investigate the charging and discharging characteristics of the electric vehicle to obtain the schedulable condition of the electric vehicle, and setting optimized scheduling related parameters according to the schedulable condition of the electric vehicle, wherein the optimized scheduling related parameters include electric vehicle parameters and power distribution network parameters;
and S1.3, dividing the daily load into three time interval ranges of peak-valley average according to a typical daily load curve and distribution network parameters.
Preferably, in S1.2, the electric vehicle parameters include the number of electric vehicles and upper and lower limits SOC of electric quantity of the electric vehiclesmax、SOCminMaximum value of charging and discharging power PChmax、PDchmaxAnd a power priority bound Emin；
The parameters of the power distribution network comprise an optimized duration and a load duration demarcation value limit Pmax、PminAnd the base load P of the power gridload。
Preferably, in S1.3, the range of the peak-to-valley level load is defined as shown in formula (1):
Figure BDA0003519190320000021
in the formula: ploadAnd (t) represents the base load of the power grid at the moment t.
Preferably, in S2, the electric vehicle charging strategy in the valley filling mode is a valley filling strategy: the electric automobile only charges and does not discharge and whole electric automobile can participate in charging, and the electric automobile that the electric quantity is low can be preferentially charged as follows specifically:
Figure BDA0003519190320000022
in the formula, EminIs a power priority bound;
the corresponding constraint is a power distribution network power constraint, namely, a power balance limit is shown as a formula (3):
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (3)
in the formula: pgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PCharging(t) represents a charging load at time t;
Ploss(t) represents the power loss of the network at time t.
Preferably, in S2, the electric vehicle charging strategy of the priority charging mode is a priority charging strategy: the electric automobile can be charged and discharged, and the electric automobile with low electric quantity can be charged preferentially;
the corresponding constraints include:
electric quantity restraint of the electric automobile:
Figure BDA0003519190320000031
in the formula, EminIs a power priority bound;
SOC is the state of charge of the electric vehicle;
the distribution network power constraint, i.e. the power balance limit, is shown as equation (5):
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (5)
in the formula: pgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PCharging(t) represents the total charging load at time t;
Ploss(t) represents the power loss of the network at time t.
Preferably, in S2, the electric vehicle charging strategy in the peak shaving mode is a peak shaving strategy: only the electric quantity being below the electric quantity priority limit EminThe electric automobile can be charged preferentially and is higher than EminThe electric vehicle carries out grid-connected discharge;
the corresponding constraint is a power distribution network power constraint, namely, a power balance limit is shown as a formula (6):
Pgrid(t)+PDischarging(t)＝Pload(t)+Ploss(t) (6)
in the formula: pgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PDischarging(t) represents a discharge load at time t;
Ploss(t) represents the power loss of the network at time t.
Preferably, the S3 establishes an optimization model with the minimum degree of load change of the power distribution network as a target based on the electric vehicle charging strategy and corresponding constraints, and specifically includes the following steps:
s3.1, establishing an optimization model by taking the minimum load change degree of the power distribution network as a target, wherein the target function is as follows:
Figure BDA0003519190320000041
Figure BDA0003519190320000042
in the formula: Δ p (t) is the degree of distribution network load variation;
Pload(t) represents the grid base load at time t;
Ploss(t) represents the power loss of the network at time t;
PEV(t) is the total charge-discharge power of the electric vehicle;
Ptargetmin(t) is the minimum target load for the valley fill mode, and PminThe values of (A) are the same;
Ptargetmax(t) is the maximum target load in peak shaving mode and priority charging mode, and PmaxThe values of (A) are the same;
Pmin、Pmaxrepresenting a load period demarcation value;
s3.2, determining each constraint of an optimization model, wherein the constraint of the optimization model comprises 2 types: distribution network load constraints and electric vehicle constraints.
Preferably, S3.2, the power constraint of the power distribution network is shown in formulas (3), (5) and (6), and the power distribution network is used for protecting the stability of the power grid by coordinating supply and demand balance;
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (3)
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (5)
Pgrid(t)+PDischarging(t)＝Pload(t)+Ploss(t) (6)
in the formula: pgrid(t) represents the total load of the power grid at time t;
PCharging(t) represents a charging load at time t;
PDischarging(t) represents a discharge load at time t;
the electric vehicle constraint comprises electric quantity constraint and charge-discharge power constraint;
the electric quantity constraint is as shown in formula (9):
SOCmin≤SOC≤SOCmax (9)
in the formula, SOCmax、SOCminThe electric quantity of the electric automobile has upper and lower limits;
the power constraint is as shown in equation (10):
Figure BDA0003519190320000051
in the formula: pChmaxRepresents a maximum value of the charging power;
PDchmaxindicating the maximum value of the discharge power.
Preferably, the S4 specifically includes the following steps:
s4.1, making a V2G algorithm solving strategy based on a load time interval;
1) initializing the SOC and the base load of the battery of the electric automobile;
2) selecting a valley filling, peak shaving or priority charging mode according to the load time period condition of the power grid;
3) executing strategies and constraints corresponding to the selected mode, solving a target in the current time period according to the target function, and updating data and time dimensions of the electric vehicle;
and S4.2, solving the optimization model according to the solving strategy of the S4.1, and determining the execution state of the electric automobile.
The invention also provides a power distribution network optimal scheduling system considering the charging priority of the electric automobile, which comprises:
the data acquisition module is used for acquiring various load data and electric vehicle information in an area, setting relevant parameters for optimizing scheduling and dividing peak, valley and average time periods of daily load;
the charging strategy making module is used for considering the charging priority of the electric automobile, making the charging strategy of the electric automobile and corresponding constraints of the working mode of the electric automobile in peak, valley and flat periods, wherein the working modes of the peak, valley and flat periods are respectively peak regulation, valley filling and low-power automobile priority charging modes;
the optimization model building module is used for building an optimization model by taking the minimum load change degree of the power distribution network as a target based on the electric vehicle charging strategy and corresponding constraints;
and the optimization scheduling module is used for making a V2G algorithm solving strategy based on the load time interval, solving the optimization model based on the optimization scheduling related parameters and obtaining an optimization scheduling scheme of the power distribution network.
The beneficial effect that this application reached:
according to the invention, the working mode of the electric vehicle is selected according to the current time period load condition of the power grid, and corresponding charging and discharging strategies are formulated according to different working modes, so that the minimum change degree of the load curve of the power grid can be ensured in the process of optimizing dispatching, and the safe and stable operation of the power grid can be maintained. In addition, the low-power-quantity user is guaranteed to obtain the priority charging right in the optimization process, and the benefit of the user is guaranteed.
1. According to the invention, three different working modes can be selected according to the load condition of the distribution network, so that the safety and stability of the distribution network are ensured;
2. according to the invention, the electric automobile executes corresponding charging and discharging strategies according to different working modes, so that the preferential charging of the low-power electric automobile can be realized, and the user benefit is ensured;
3. the V2G optimization result of the invention can effectively reduce the load change degree of the power distribution network, so that the energy curve obtained from the superior power grid is stable, and the stable operation of the superior power grid is facilitated.
Drawings
FIG. 1 is a flow chart of a power distribution network optimal scheduling method taking into account charging priority of electric vehicles according to the present invention;
FIG. 2 is a flow chart of an embodiment of the present invention;
FIG. 3 is a flow chart of an embodiment of the valley filling mode of the present invention;
FIG. 4 is a flow chart of an implementation of the peak shaving mode of the present invention;
FIG. 5 is a flow chart illustrating a preferred charging mode according to the present invention;
FIG. 6 is a raw load curve in an embodiment of the present invention;
fig. 7 is the optimized load in the embodiment of the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, embodiment 1 of the present invention provides a power distribution network optimal scheduling method taking into account charging priorities of electric vehicles, where the method includes the following steps:
s1, collecting various types of load data and electric vehicle information in the region, setting relevant parameters for optimizing scheduling and dividing peak, valley and average time periods of daily load;
in a preferred but non-limiting embodiment of the invention, said S1 comprises in particular the following steps:
s1.1, collecting different types of load data in the region to obtain a typical daily load curve in the region;
the different types of load data comprise civil load data and commercial load data;
s1.2, collecting electric vehicle information in an area, wherein the electric vehicle information comprises information such as electric vehicle charging facilities and charging time, obtaining schedulable conditions of the electric vehicles by investigating and researching the charging and discharging characteristics of the electric vehicles, and setting relevant optimized scheduling parameters according to the schedulable conditions of the electric vehicles, wherein the relevant optimized scheduling parameters comprise electric vehicle parameters and power distribution network parameters;
the electric vehicle parameters comprise the quantity of the electric vehicles and the electric quantity upper and lower limits SOC of the electric vehiclesmax、SOCminMaximum value of charging and discharging power PChmax、PDchmaxAnd a power priority bound Emin；
The parameters of the power distribution network comprise an optimized duration and a load duration demarcation value limit Pmax、PminAnd the base load P of the power gridload。
S1.3, dividing the daily load into three time interval ranges of peak-valley average according to a typical daily load curve and distribution network parameters:
further preferably, the peak-to-valley level loading is defined by the formula (1):
Figure BDA0003519190320000071
in formula (1): ploadAnd (t) represents the base load of the power grid at the moment t.
S2, considering the charging priority of the electric vehicle, setting up the charging strategy and corresponding constraints of the electric vehicle in the peak period, the valley period and the flat period, wherein the working modes of the peak period, the valley period and the flat period are respectively the peak regulation mode, the valley filling mode and the low-power vehicle priority charging mode;
1) executing a valley filling strategy during the valley period;
in the valley fill strategy, the electric vehicles are charged and not discharged, all vehicles can participate in charging, all electric vehicles can participate in charging, and the electric vehicles with low electric quantity can be charged preferentially, specifically as follows:
Figure BDA0003519190320000072
in the formula, EminIs a power priority bound;
the corresponding constraint is a power distribution network power constraint, and the power balance is as shown in formula (3):
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (3)
in the formula: pgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PCharging(t) represents a charging load at time t;
Ploss(t) represents the power loss of the network at time t.
2) In the ordinary period, a priority charging strategy is executed;
in the priority charging strategy, the electric automobile can be charged and discharged, and the electric automobile with low electric quantity can be charged preferentially;
the corresponding constraints include:
electric quantity restraint of the electric automobile:
executing a corresponding strategy according to the SOC condition of the electric automobile, wherein the behavior process of the electric automobile is shown as the formula (4):
Figure BDA0003519190320000073
in the formula, EminIs a power priority bound;
power constraint of the power distribution network: the power balance is as shown in equation (5):
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (5)
in the formula: p isgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PCharging(t) represents the total charging load at time t;
Ploss(t) represents the power loss of the network at time t.
3) Executing a peak regulation strategy in a peak period;
in the peak shaving strategy, only the electric quantity is lower than EminThe electric automobile can be charged preferentially and is higher than EminThe electric vehicle carries out grid-connected discharge;
the corresponding constraint is a power distribution network power constraint: the power balance of the electric quantity is shown as the formula (6):
Pgrid(t)+PDischarging(t)＝Pload(t)+Ploss(t) (6)
in the formula: pgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PDischarging(t) represents a discharge load at time t;
Ploss(t) represents the power loss of the network at time t.
S3, establishing an optimization model with the minimum load change degree of the power distribution network as a target based on the electric vehicle charging strategy and corresponding constraints;
the method specifically comprises the following steps:
s3.1, establishing an optimization model by taking the minimum load change degree of the power distribution network as a target, wherein the target function is as follows:
Figure BDA0003519190320000081
Figure BDA0003519190320000082
in the formula: Δ p (t) is the degree of distribution network load variation;
Pload(t) represents the grid base load at time t;
Ploss(t) represents the power loss of the network at time t;
PEV(t) is the total charge-discharge power of the electric vehicle;
Ptargetmin(t) is the minimum target load for the valley fill mode, and PminThe values of (A) are the same;
Ptargetmax(t) is the maximum target load in peak shaving mode and priority charging mode, and PmaxThe values of (A) are the same;
Pmin、Pmaxrepresenting a load period demarcation value;
s3.2, determining each constraint of an optimization model, wherein the constraint of the optimization model comprises 2 types: power distribution network load constraint and electric vehicle constraint;
the power constraint of the power distribution network is shown in formulas (3), (5) and (6), and the stability of the power grid is protected by coordinating supply and demand balance;
the electric vehicle constraint mainly comprises electric quantity constraint and charge-discharge power constraint;
the electric quantity constraint is as shown in formula (9):
SOCmin≤SOC≤SOCmax (9)
in the formula, SOCmax、SOCminThe electric quantity of the electric automobile has upper and lower limits;
the power constraint is as shown in equation (10):
Figure BDA0003519190320000091
in the formula: pChmaxRepresents a maximum value of the charging power;
PDchmaxindicating the maximum value of the discharge power.
S4, a V2G algorithm solving strategy based on the load time interval is formulated, the optimization model is solved based on the optimization scheduling related parameters, the power distribution network optimization scheduling scheme is obtained, and the optimized peak-valley difference result and the priority-considered electric vehicle charging and discharging plan are obtained.
The method specifically comprises the following steps:
s4.1, making a V2G algorithm solving strategy based on a load time interval;
the solving strategy of the V2G algorithm is shown in fig. 2, and the solving process includes the following parts:
1) at the beginning stage of the algorithm, scalar quantities such as the SOC and the base load of the battery of the electric automobile are initialized;
2) and selecting a valley filling, peak shaving or priority charging mode, namely valley filling, peak shaving or priority charging according to the load time period condition of the power grid.
3) Executing strategies and constraints corresponding to the selected modes, wherein the strategies of different modes are respectively shown in fig. 3-5 (the charging power limit in fig. 3-5 is formula (9), and the power balance limit in fig. 3-5 is formula (3) (5) (6)), solving the target in the current time period according to the objective function, and updating the data and the time dimension of the electric vehicle;
and S4.2, solving the optimization model according to the solving strategy of the S4.1, determining the execution state of the electric automobile, and obtaining the optimized peak-valley difference result and the electric automobile charging and discharging plan considering the priority.
The invention relates to a power distribution network optimal scheduling system considering electric vehicle charging priority, which comprises:
the data acquisition module is used for acquiring various load data and electric vehicle information in an area, setting relevant parameters for optimizing scheduling and dividing peak, valley and average time periods of daily load;
the charging strategy making module is used for considering the charging priority of the electric automobile, making the charging strategy of the electric automobile and corresponding constraints of the working mode of the electric automobile in peak, valley and flat periods, wherein the working modes of the peak, valley and flat periods are respectively peak regulation, valley filling and low-power automobile priority charging modes;
the optimization model building module is used for building an optimization model by taking the minimum load change degree of the power distribution network as a target based on the electric vehicle charging strategy and corresponding constraints;
and the optimization scheduling module is used for making a V2G algorithm solving strategy based on the load time interval, solving the optimization model based on the optimization scheduling related parameters and obtaining an optimization scheduling scheme of the power distribution network.
The steps S1-S4 are combined to verify the effectiveness of the method by actual data, and the embodiment is as follows:
s1, setting relevant parameters of optimized dispatching according to the investigation condition, wherein the parameters of the electric automobile comprise the number of the electric automobiles, the upper limit of charging and discharging power and EminThe power distribution network parameters comprise optimization duration and load duration division value Pmax、PminAnd the base load P of the power gridloadThe specific parameter settings and raw load curves are shown in table 1 and fig. 6.
TABLE 1
Number of electric vehicles 1000
Optimizing duration 24h
Pmax/kW 6000
Pmin/kW 5000
Emin 55％
SOC
min 20％
SOCmax 100％
Pchmax/kW 120
Pdchmax/kW 120
S2, determining the current load mode of the power grid according to the formula (1), wherein if a peak time interval adopts a peak regulation strategy, a normal time interval adopts a priority charging strategy, and a valley filling strategy is adopted in a valley time interval;
executing a valley filling strategy in a peak time period, wherein all electric vehicles can be charged at the time as shown in formula (2), and the residual electric quantity is greater than EminHysteresis charge, less than EminPreferentially charging, wherein the power balance meets the power constraint of the formula (3);
the priority strategy is executed in the load balancing time period, as shown in a formula (4), and the power balance is as shown in a formula (5);
only supplement is put in the peak load period, and the power balance is shown as a formula (6);
and S3-S4, taking the expressions (7) and (8) as targets, executing corresponding charging strategies in each load time interval, satisfying the power constraints of the expressions (3), (5) and (6) and the charge-discharge constraints and the electric quantity constraints of the electric vehicle of the expressions (9) and (10), executing optimization targets within 24h, and outputting the optimized peak-valley difference result and the electric vehicle charge-discharge plan considering the priority. The optimization data in this example is shown in table 2 and fig. 7.
TABLE 2
Before optimization After optimization Optimized ratio
Peak load 7300 6200 15.1％
Load at low valley 2300 4000 73.9％
Difference between peak and valley 5000 1800 64％
According to the invention, the working mode of the electric vehicle is selected according to the current time period load condition of the power grid, and corresponding charging and discharging strategies are formulated according to different working modes, so that the minimum change degree of the load curve of the power grid can be ensured in the process of optimizing dispatching, and the safe and stable operation of the power grid can be maintained. In addition, the low-power-consumption user is guaranteed to obtain the priority charging right in the optimization process, and the benefit of the user is guaranteed.
1. According to the invention, three different working modes can be selected according to the load condition of the distribution network, so that the safety and stability of the distribution network are ensured;
2. according to the invention, the electric automobile executes corresponding charging and discharging strategies according to different working modes, so that the preferential charging of the low-power electric automobile can be realized, and the user benefit is ensured;
3. the V2G optimization result of the invention can effectively reduce the load change degree of the power distribution network, so that the energy curve obtained from the superior power grid is stable, and the stable operation of the superior power grid is facilitated.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims (11)

1. A power distribution network optimal scheduling method considering electric vehicle charging priority is characterized by comprising the following steps:
the method comprises the following steps:
s1, collecting various types of load data and electric vehicle information in the region, setting relevant parameters for optimizing scheduling and dividing peak, valley and average time periods of daily load;
s2, considering the charging priority of the electric vehicle, setting up the charging strategy and corresponding constraints of the electric vehicle in the peak period, the valley period and the flat period, wherein the working modes in the peak period, the valley period and the flat period are respectively the peak regulation mode, the valley filling mode and the low-power vehicle priority charging mode;
s3, establishing an optimization model with the minimum load change degree of the power distribution network as a target based on the electric vehicle charging strategy and corresponding constraints;
s4, a V2G algorithm solving strategy based on the load time interval is formulated, the optimization model is solved based on the optimization scheduling related parameters, and the power distribution network optimization scheduling scheme is obtained.
2. The power distribution network optimal scheduling method considering electric vehicle charging priority according to claim 1, wherein the method comprises the following steps:
the S1 specifically includes the following steps:
s1.1, collecting different types of load data in the region to obtain a typical daily load curve in the region;
the different types of load data comprise civil load data and commercial load data;
s1.2, collecting electric vehicle information in an area, including information of electric vehicle charging facilities, charging time and the like, so as to investigate the charging and discharging characteristics of the electric vehicle to obtain the schedulable condition of the electric vehicle, and setting optimized scheduling related parameters according to the schedulable condition of the electric vehicle, wherein the optimized scheduling related parameters include electric vehicle parameters and power distribution network parameters;
and S1.3, dividing the daily load into three time interval ranges of peak-valley average according to a typical daily load curve and distribution network parameters.
3. The power distribution network optimal scheduling method considering electric vehicle charging priority according to claim 1, wherein the method comprises the following steps:
in S1.2, the electric vehicle parameters comprise the number of the electric vehicles and the upper and lower limits of electric quantity SOC of the electric vehiclesmax、SOCminMaximum value of charging and discharging power PChmax、PDchmaxAnd a power priority bound Emin；
The parameters of the power distribution network comprise an optimized duration and a load duration demarcation value limit Pmax、PminAnd the base load P of the power gridload。
4. The power distribution network optimal scheduling method considering electric vehicle charging priority according to claim 3, wherein the method comprises the following steps:
in S1.3, the range of the peak-valley normal-period load is defined as shown in formula (1):
Figure FDA0003519190310000021
in the formula: ploadAnd (t) represents the base load of the power grid at the moment t.
5. The power distribution network optimal scheduling method considering electric vehicle charging priority according to claim 1, wherein the method comprises the following steps:
in S2, the electric vehicle charging strategy in the valley filling mode is a valley filling strategy: the electric automobile only charges and does not discharge and whole electric automobile can participate in charging, and the electric automobile that the electric quantity is low can be preferentially charged as follows specifically:
Figure FDA0003519190310000022
in the formula, EminIs a power priority bound;
the corresponding constraint is a power distribution network power constraint, namely, a power balance limit is shown as a formula (3):
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (3)
in the formula: pgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PCharging(t) represents a charging load at time t;
Ploss(t) represents the power loss of the network at time t.
6. The power distribution network optimal scheduling method considering electric vehicle charging priority according to claim 1, wherein the method comprises the following steps:
in S2, the electric vehicle charging policy of the priority charging mode is a priority charging policy: the electric automobile can be charged and discharged, and the electric automobile with low electric quantity can be charged preferentially;
the corresponding constraints include:
electric quantity restraint of the electric automobile:
Figure FDA0003519190310000023
in the formula, EminIs a power priority bound;
SOC is the state of charge of the electric vehicle;
the distribution network power constraint, i.e. the power balance limit, is shown as equation (5):
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (5)
in the formula: pgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PCharging(t) represents the total charging load at time t;
Ploss(t) represents the power loss of the network at time t.
7. The power distribution network optimal scheduling method considering electric vehicle charging priority according to claim 1, wherein the method comprises the following steps:
in S2, the electric vehicle charging strategy in the peak shaving mode is a peak shaving strategy: only the electric quantity being below the electric quantity priority limit EminCan be charged preferentially and is higher than EminThe electric vehicle carries out grid-connected discharge;
the corresponding constraint is a power distribution network power constraint, namely, a power balance limit is shown as a formula (6):
Pgrid(t)+PDischarging(t)＝Pload(t)+Ploss(t) (6)
in the formula: pgrid(t) represents the total load of the power grid at time t;
Pload(t) represents the grid base load at time t;
PDischarging(t) represents a discharge load at time t;
Ploss(t) Representing the power loss of the network at time t.
8. The power distribution network optimal scheduling method considering electric vehicle charging priority according to claim 1, wherein the method comprises the following steps:
based on the electric vehicle charging strategy and corresponding constraints, the S3 establishes an optimization model with the minimum load change degree of the power distribution network as a target, and specifically comprises the following steps:
s3.1, establishing an optimization model by taking the minimum load change degree of the power distribution network as a target, wherein the target function is as follows:
Figure FDA0003519190310000031
Figure FDA0003519190310000032
in the formula: Δ p (t) is the degree of distribution network load variation;
Pload(t) represents the grid base load at time t;
Ploss(t) represents the power loss of the network at time t;
PEV(t) is the total charge-discharge power of the electric vehicle;
Ptargetmin(t) is the minimum target load for the valley fill mode, and PminThe values of (A) and (B) are the same;
Ptargetmax(t) is the maximum target load in peak shaving mode and priority charging mode, and PmaxThe values of (A) are the same;
Pmin、Pmaxrepresenting a load period demarcation value;
s3.2, determining each constraint of an optimization model, wherein the constraint of the optimization model comprises 2 types: distribution network load constraints and electric vehicle constraints.
9. The power distribution network optimal scheduling method considering electric vehicle charging priority according to claim 8, wherein the method comprises the following steps:
s3.2, the power constraint of the power distribution network is shown in formulas (3), (5) and (6), and the stability of the power distribution network is protected by coordinating supply and demand balance;
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (3)
Pgrid(t)＝Pload(t)+PCharging(t)+Ploss(t) (5)
Pgrid(t)+PDischarging(t)＝Pload(t)+Ploss(t) (6)
in the formula: pgrid(t) represents the total load of the power grid at time t;
PCharging(t) represents a charging load at time t;
PDischarging(t) represents a discharge load at time t;
the electric vehicle constraint comprises electric quantity constraint and charge-discharge power constraint;
the electric quantity constraint is as shown in formula (9):
SOCmin≤SOC≤SOCmax (9)
in the formula, SOCmax、SOCminThe electric quantity of the electric automobile has upper and lower limits;
the power constraint is as shown in equation (10):
Figure FDA0003519190310000041
in the formula: pChmaxRepresents a maximum value of the charging power;
PDchmaxindicating the maximum value of the discharge power.
10. The optimal scheduling method for the power distribution network considering the charging priority of the electric vehicle as claimed in claim 1, wherein:
the S4 specifically includes the following steps:
s4.1, making a V2G algorithm solving strategy based on a load time interval;
1) initializing the SOC and the base load of the battery of the electric automobile;
2) selecting a valley filling, peak shaving or priority charging mode according to the load time period condition of the power grid;
3) executing strategies and constraints corresponding to the selected mode, solving a target in the current time period according to the target function, and updating data and time dimensions of the electric vehicle;
and S4.2, solving the optimization model according to the solving strategy of the S4.1, and determining the execution state of the electric automobile.
11. An electric vehicle charging priority-based power distribution network optimal scheduling system for operating the electric vehicle charging priority-based power distribution network optimal scheduling method according to any one of claims 1 to 9, wherein the electric vehicle charging priority-based power distribution network optimal scheduling system comprises:
the system comprises:
the data acquisition module is used for acquiring various load data and electric vehicle information in an area, setting relevant parameters for optimizing scheduling and dividing peak, valley and average time periods of daily load;
the charging strategy making module is used for considering the charging priority of the electric automobile, making the charging strategy of the electric automobile and corresponding constraints of the working mode of the electric automobile in peak, valley and flat periods, wherein the working modes of the peak, valley and flat periods are respectively peak regulation, valley filling and low-power automobile priority charging modes;
the optimization model building module is used for building an optimization model by taking the minimum load change degree of the power distribution network as a target based on the electric vehicle charging strategy and corresponding constraints;
and the optimization scheduling module is used for making a V2G algorithm solving strategy based on the load time interval, solving the optimization model based on the optimization scheduling related parameters and obtaining an optimization scheduling scheme of the power distribution network.
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