A power distribution method for electric vehicles based on micro-grid power quality state

Abstract

The application provides a kind of electric vehicle power distribution method based on micro-grid power quality state, including electric vehicle reaches micro-grid after transmitting electric vehicle real-time information, respectively from control center and micro-grid aggregator collect large power grid information and micro-grid information;Optimize electric vehicle charging scheme;According to the change of micro-grid power quality, the change of electric vehicle related information, the active power and reactive power distribution scheme of electric vehicle, distributed renewable energy and energy storage station in ac and dc energy exchange process in micro-grid is optimized.The application combines the influence of electric vehicle charging in micro-grid, distributed renewable energy grid-connected on power quality, optimizes electric vehicle charging scheme and active power and reactive power distribution scheme of multiple subjects in ac and dc energy exchange process in micro-grid according to the change of grid and user real-time information, meets the driving demand of electric vehicle user and distributed renewable energy local consumption demand and supports the normal operation of micro-grid.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a power allocation method for electric vehicles based on the power quality status of a microgrid. Background Technology

[0002] Electric vehicles, as a means of transportation that connects low-carbon power generation and low-carbon energy consumption in the transportation system, have received widespread attention. With the rapid increase in the number of electric vehicles, the large-scale connection of these vehicles to the grid also poses challenges to the stability and security of grid operation. Therefore, the rational and orderly arrangement of electric vehicle charging is of great significance for maintaining the stability and security of grid operation.

[0003] However, due to the regularity and randomness of electric vehicle charging, and the influence of traffic and charging facilities, random concentrated charging behavior in specific areas and time periods may cause power quality problems such as supply and demand imbalance and voltage exceeding limits in the power grid, threatening the safe and stable operation of the power grid and negatively impacting the user experience.

[0004] In recent years, the grid connection of distributed renewable energy and energy storage at all levels have continued to develop. Although there have been significant improvements in the economic efficiency and reliability of electricity use, the grid power quality has been reduced due to the grid connection of a large number of power electronic devices. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for allocating electric vehicle power based on the power quality status of a microgrid. This invention can mitigate the adverse effects of electric vehicle charging power and distributed renewable energy generation power on the microgrid.

[0006] The technical solution of this invention is: a power allocation method for electric vehicles based on the power quality status of a microgrid, comprising:

[0007] S1) Upon receiving information that an electric vehicle has arrived at the microgrid, the system transmits real-time information about the electric vehicle and collects overall grid information and microgrid information from the control center and the microgrid aggregator, respectively.

[0008] S2) Optimize the electric vehicle charging scheme based on electric vehicle charging information;

[0009] S3) Determine whether the grid connection of distributed renewable energy generation or the charging and discharging of electric vehicles in the microgrid causes changes in the power quality of the microgrid; if the power quality changes, optimize the active and reactive power allocation scheme of electric vehicles, distributed renewable energy generation, and energy storage stations in the microgrid during AC and DC energy exchange; if not, control the charging piles according to the optimized electric vehicle charging scheme in step S2).

[0010] S4) Determine whether the relevant information of electric vehicles has changed. If so, re-optimize the electric vehicle charging scheme and the power allocation scheme of electric vehicles, distributed renewable energy generation and energy storage stations in the microgrid during the AC and DC energy exchange process.

[0011] If the electric vehicle information remains unchanged, the optimized electric vehicle charging scheme in step S3) will be maintained until the expected time or the expected battery state of charge.

[0012] Preferably, in step S1), the real-time information of the electric vehicles includes the number of electric vehicles, charging time, charging power, battery rated capacity, initial battery state of charge, and expected battery state of charge.

[0013] Preferably, in step S1), the overall power grid information includes real-time electricity price, predicted electricity price, current power quality of the power grid, and predicted power quality.

[0014] Preferably, in step S1), the microgrid information includes real-time and predicted data of wind power generation, real-time and predicted data of photovoltaic power generation, rated capacity of energy storage, maximum charging and discharging power of energy storage, remaining capacity of energy storage, power load curve of microgrid, current power quality and predicted power quality.

[0015] Preferably, in step S2), it is determined whether the user has entered the electric vehicle charging parameters within the specified time.

[0016] If a user inputs electric vehicle charging parameters within a specified time, the electric vehicle aggregator will optimize the electric vehicle charging solution based on the collected aggregator information and the user input parameters, while also considering the influence of the electric vehicle's location, time, and seasonal characteristics. The aggregator information includes real-time electric vehicle information, main grid information, microgrid information, electric vehicle battery type, battery loss characteristic curve, battery aging degree, weather temperature, humidity, etc.

[0017] If the user fails to input the electric vehicle charging parameters within the specified time, the electric vehicle aggregator will first collect the user's historical data and then derive the electric vehicle charging parameters based on the user's historical data.

[0018] Simultaneously, the impact of user location, time, and season on electric vehicle energy consumption is considered, along with aggregator information and user input parameters to optimize electric vehicle charging solutions.

[0019] Preferably, in step S2), the electric vehicle charging parameters include the desired battery state of charge, the maximum battery charging and discharging power, whether the lowest cost is the optimization objective, and the expected end time of charging.

[0020] Preferably, in step S2), the user historical data includes user charging time, expected battery state of charge, and whether cost is the optimization objective.

[0021] Preferably, in step S2), the calculation formula for the electric vehicle charging scheme optimization process is as follows:

[0022] minC EV ;

[0023]

[0024] SOC add =(L e +T e +S e )×(SOC obj，user -SOC ini );

[0025]

[0026] SOC obj =SOC obj,user +SOC add ;

[0027] SOC min ≤SOC obj ≤SOC max ;

[0028] SOC(t end )≥SOC obj ;

[0029]

[0030] P min ≤P EV (t)≤P max ;

[0031]

[0032] In the formula, C EV Represents the total cost of charging an individual electric vehicle; t0 and t end Q represents the initial charging time and the end charging time of the electric vehicle, respectively; EV Indicates the amount of electricity charged for an electric vehicle; a price Indicates the electricity price for charging electric vehicles; BG EV This represents the cost of battery degradation during the charging process of an electric vehicle; SOC (State of Charge) add This indicates the vehicle's energy consumption due to factors such as location, time, and season of the electric vehicle; L e T e Se These represent the energy consumption coefficients for geographical factors, time factors, and seasonal factors, respectively; SOC obj,user This represents the expected state of charge of the electric vehicle battery, either input by the user or obtained by the aggregator based on the user's historical data; f1, f2, and f3 represent the coefficient functions of geographical factors, time factors, and seasonal factors, respectively. ε1 and ε2 represent the latitude and longitude and terrain type information of the electric vehicle's location, respectively; ε1 and ε2 represent the specific charging time and user time type information of the electric vehicle, respectively; s1 and s2 represent the season and month information, respectively; SOC obj State of Charge (SOC) indicates the actual target state of charge of an electric vehicle battery during charging. max and SOC min These represent the maximum and minimum state of charge values ​​during the charging process of an electric vehicle battery, respectively; P EV and A EV These represent the electric vehicle battery charging power and rated battery capacity, respectively; P max and P min These represent the maximum and minimum charging power of the electric vehicle battery, respectively; SOC ini Q represents the initial state of charge of an electric vehicle battery; EV SOC(t) represents the amount of charge generated by the electric vehicle battery; Δt represents the time interval between charging the electric vehicle battery; SOC(t) represents the total charge generated by the battery. end This indicates the state of charge of the electric vehicle battery at the end of charging.

[0033] Preferably, in step S2), the formula for calculating the electric vehicle charging parameters based on historical data of car users is as follows:

[0034] SOC obj,user =g1(SOC) his );

[0035] t end =g2(t end,his );

[0036] In the formula, g1 and g2 represent the target state of charge (SOC) and charging termination time functions of the electric vehicle battery, respectively, derived from historical user data; his This represents historical data on the target state of charge of electric vehicle batteries, which is similar to the current time, geographical location, season, and location type of the electric vehicle; t end,his This data represents historical data on the end time of electric vehicle battery charging, and is similar to the current time, geographical location, season, and location of the electric vehicle.

[0037] Preferably, in step S3), when the electric vehicle or distributed renewable energy generation is connected to the microgrid, if the power quality parameters fluctuate more than the preset first set value during the charging process of the electric vehicle, the electric vehicle charging scheme and the active and reactive power allocation scheme of the electric vehicle are re-optimized; otherwise, the charging pile, distributed renewable energy generation rectification and inverter facilities are controlled according to the original scheme.

[0038] Preferably, in step S3), when the power quality of the microgrid fluctuates beyond a preset first set value after the electric vehicle or distributed renewable energy generation is connected to the microgrid, the electric vehicle charging scheme and the active and reactive power allocation scheme of the electric vehicle are optimized; otherwise, the power allocation is carried out with the goal of absorbing the distributed renewable energy generation power and electric vehicle charging and discharging power within the microgrid.

[0039] Preferably, in step S3), the formula for determining whether the power quality of the microgrid exceeds a preset first set value is:

[0040]

[0041] B th1 =B max -b th1 ;

[0042] B th2 =B min +b th2 ;

[0043] In the formula, B, B max and B min These represent the current, maximum, and minimum feasible power quality values ​​of the microgrid, respectively; α1, α2, α3, α4, α5, α6, α7, α8, δ1, δ2, δ3, δ4, δ5, δ6, δ7, and δ8 all represent the current power quality index parameters of the microgrid; β1, β2, β3, β4, β5, β6, β7, β8, ρ1, ρ2, ρ3, ρ4, ρ5, ρ6, ρ7, and ρ8 all represent the maximum power quality index parameters of the microgrid; γ1, γ2, γ3, γ4, γ5, γ6, γ7, γ8, σ1, σ2, σ3, σ4, σ5, σ6, σ7, and σ8 all represent the minimum power quality index parameters of the microgrid; B th1 and B th2 Both represent the first setpoint for microgrid power quality; b th1 and b th2The values ​​represent the maximum and minimum power quality parameters of the microgrid, respectively; h1, h2, and h3 represent the current, maximum, and minimum voltage deviation values ​​of the microgrid, respectively; l1, l2, and l3 represent the current, maximum, and minimum transformer load rates of the microgrid, respectively; m1, m2, and m3 represent the current, maximum, and minimum line load rates of the microgrid, respectively; n1, n2, and n3 represent the current, maximum, and minimum three-phase unbalance of the microgrid, respectively; u1, u2, and u3 represent the current, maximum, and minimum harmonic pollution values ​​of the microgrid, respectively; w1, w2, and w3 represent the current, maximum, and minimum total power load of the microgrid, respectively; v1, v2, and v3 represent the current, maximum, and minimum power load unbalance of the microgrid, respectively; and z1, z2, and z3 represent the current, maximum, and minimum frequency values ​​of the microgrid, respectively.

[0044] Preferably, the active and reactive power within the microgrid is allocated to support its normal operation. The formula for matching the active and reactive power within the microgrid is as follows:

[0045] mina price (t);

[0046]

[0047] P EV (t)=P EV-grid (t)+P EV-ren (t)+P EV-sto (t)

[0048] P EV-grid (t)≤P EV (t)

[0049] P EV-ren (t)≤min(P EV (t),P ren (t))

[0050] P ren (t)=P wind (t)+P solar (t)

[0051] P EV-sto (t)≤min(P EV (t),P sto (t))

[0052] P wind (t)+P solar (t)+P sto (t)+P grid (t)+P EV(t)+P other (t)-P out (t)=0

[0053] R wind (t)+R solar (t)+R sto (t)+R grid (t)+R EV (t)+R other (t)=0

[0054]

[0055] In the formula, a price Indicates the electricity price for charging electric vehicles; a grid P represents the price of electricity from the power grid. EV-grid P EV-ren and P EV-sto These represent the matching power between electric vehicles and the power grid, and between renewable energy generation and energy storage within the microgrid; a ren and a sto P represents the price of renewable electricity generated and the price of electricity generated from energy storage within the microgrid, respectively. EV Power for charging electric vehicle batteries; P wind P solar P sto P grid and P other P represents the active power output from wind power generation, photovoltaic power generation, energy storage, the power grid, and other electrical loads within the microgrid; out R represents the total power curtailed from wind and solar power within the microgrid; EV R wind R solar R sto R grid and R other θ1, θ2, θ3, θ4, θ5 and θ6 represent the reactive power output of electric vehicles, wind power generation, photovoltaic power generation, energy storage, power grid and other power loads in the microgrid, respectively; θ1, θ2, θ3, θ4, θ5 and θ6 represent the adjustable coefficients of active power and reactive power of distributed wind power generation, photovoltaic power generation, energy storage power generation, power grid transmission, electric vehicle charging and other power loads, respectively.

[0056] Preferably, in step S4), when the electric vehicle-related information changes and fluctuates beyond a preset second set value during the electric vehicle charging process, the electric vehicle charging scheme, active and reactive power allocation scheme, and distributed renewable energy power allocation scheme are re-optimized; otherwise, the charging piles, distributed renewable energy power generation rectification, and inverter facilities are controlled according to the original scheme.

[0057] Preferably, in step S4), the electric vehicle-related information refers to information where the actual electric vehicle information, the actual overall power grid information, the actual microgrid information, and the information obtained during the optimization process differ significantly.

[0058] The beneficial effects of this invention are as follows:

[0059] 1. This invention combines the characteristics of the impact of electric vehicle charging and distributed renewable energy grid connection on power quality within a microgrid. Based on real-time changes in grid and user information, it optimizes the electric vehicle charging scheme and the active and reactive power allocation scheme of distributed renewable energy generation within the microgrid to maintain the normal operation of the microgrid while meeting the driving needs of users and the local consumption needs of distributed renewable energy. Attached Figure Description

[0060] Figure 1 This is a flowchart of the method of the present invention;

[0061] Figure 2 This is a system block diagram corresponding to the method of the present invention;

[0062] Figure 3 This is a flowchart of the electric vehicle charging parameter evaluation process of the present invention;

[0063] Figure 4 This is a flowchart of the microgrid power quality assessment process of the present invention;

[0064] Figure 5 This is a flowchart of the distributed renewable energy generation power allocation scheme for the microgrid of the present invention;

[0065] Figure 6 This is a flowchart of the active and reactive power allocation scheme for the electric vehicle of the present invention.

[0066] Figure 7 This is a flowchart for determining changes in electric vehicle-related information according to the present invention. Detailed Implementation

[0067] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0068] like Figure 1 and Figure 2 As shown, this embodiment provides a power allocation method for electric vehicles based on the power quality status of a microgrid, including:

[0069] S1) Upon receiving information that an electric vehicle has arrived at the microgrid, real-time information about the electric vehicle is transmitted, and overall grid information and microgrid information are collected from the control center and the microgrid aggregator, respectively. The real-time information about the electric vehicle includes the number of electric vehicles, charging time, charging power, battery rated capacity, initial battery state of charge, and expected battery state of charge. The overall grid information includes real-time electricity price, predicted electricity price, current power quality, and predicted power quality. The microgrid information includes real-time and predicted data for wind power generation and photovoltaic power generation within the microgrid, rated energy storage capacity, maximum charge / discharge power of energy storage, remaining energy storage capacity, microgrid power load curve, current power quality, and predicted power quality.

[0070] S2) Optimize the electric vehicle charging scheme; specifically, determine whether the user inputs the electric vehicle charging parameters within the specified time; in this embodiment, the electric vehicle charging parameters include the expected battery state of charge, the maximum battery charging and discharging power, whether the lowest cost is taken as the optimization goal, and the expected end time of charging.

[0071] If a user inputs electric vehicle charging parameters within a specified time, the electric vehicle aggregator will optimize the electric vehicle charging solution based on the collected aggregator information and the user input parameters, while also considering the influence of the electric vehicle's location, time, and seasonal characteristics. The aggregator information includes real-time electric vehicle information, main grid information, microgrid information, electric vehicle battery type, battery loss characteristic curve, battery aging degree, weather temperature, humidity, etc.

[0072] If the user fails to input the electric vehicle charging parameters within the specified time, the electric vehicle aggregator first collects the user's historical data and derives the electric vehicle charging parameters based on the user's historical data. At the same time, by considering the impact of the user's location, time, and seasonal characteristics on the energy consumption of the electric vehicle, the aggregator information and the user's input parameters are combined to optimize the electric vehicle charging scheme. In this embodiment, the user's historical data includes the user's charging time, expected battery state of charge, and whether cost is used as the optimization target.

[0073] S3) Determine whether the grid connection of distributed renewable energy generation or the charging and discharging of electric vehicles in the microgrid causes changes in the power quality of the microgrid; if the power quality changes, optimize the active and reactive power allocation scheme of electric vehicles, distributed renewable energy generation, and energy storage stations in the microgrid during AC and DC energy exchange; if not, control the charging piles according to the optimized electric vehicle charging scheme in step S2).

[0074] S4) Determine whether the relevant information of electric vehicles has changed. If so, re-optimize the electric vehicle charging scheme and the power allocation scheme of electric vehicles, distributed renewable energy generation and energy storage stations in the microgrid during the AC and DC energy exchange process.

[0075] If the relevant information about electric vehicles remains unchanged, the original optimized electric vehicle charging scheme will be maintained until the expected time or the expected state of battery charge.

[0076] In a preferred embodiment, the model used in step S2) during the optimization of the electric vehicle charging scheme is:

[0077] minC EV ;

[0078]

[0079] SOC add =(L e +T e +S e )×(SOC obj,user -SOC ini );

[0080]

[0081] SOC obj =SOC obj,user +SOC add ;

[0082] SOC min ≤SOC obj ≤SOC max ;

[0083] SOC(t end )≥SOC obj ;

[0084]

[0085] P min ≤P EV (t)≤P max

[0086]

[0087] In the formula, C EV Represents the total cost of charging an individual electric vehicle; t0 and t end Q represents the initial charging time and the end charging time of the electric vehicle, respectively; EV Indicates the amount of electricity charged for an electric vehicle; a price Indicates the electricity price for charging electric vehicles; BG EVThis represents the cost of battery degradation during the charging process of an electric vehicle; SOC (State of Charge) add This indicates the vehicle's energy consumption due to factors such as location, time, and season of the electric vehicle; L e T e S e These represent the energy consumption coefficients for geographical factors, time factors, and seasonal factors, respectively; SOC obj,user This represents the expected state of charge of the electric vehicle battery, either input by the user or obtained by the aggregator based on the user's historical data; f1, f2, and f3 represent the coefficient functions of geographical factors, time factors, and seasonal factors, respectively. ε1 and ε2 represent the latitude and longitude and terrain type information of the electric vehicle's location, respectively; ε1 and ε2 represent the specific charging time and user time type information of the electric vehicle, respectively; s1 and s2 represent the season and month information, respectively; SOC obj State of Charge (SOC) indicates the actual target state of charge of an electric vehicle battery during charging. max and SOC min These represent the maximum and minimum state of charge values ​​during the charging process of an electric vehicle battery, respectively; P EV and A EV These represent the electric vehicle battery charging power and rated battery capacity, respectively; P max and P min These represent the maximum and minimum charging power of the electric vehicle battery, respectively; SOC ini Q represents the initial state of charge of an electric vehicle battery; EV SOC(t) represents the amount of charge generated by the electric vehicle battery; Δt represents the time interval between charging the electric vehicle battery; SOC(t) represents the total charge generated by the battery. end This indicates the state of charge of the electric vehicle battery at the end of charging.

[0088] In a preferred embodiment, in step S2), as follows: Figure 3 As shown, the specific steps for collecting user historical data are as follows:

[0089] S21) After collecting historical user data, filter the historical data for regions, times, and seasons that are similar to the current location, time, and season of the electric vehicle;

[0090] S22) Filter historical data for user ratings that are either satisfactory or unrated;

[0091] S23) Obtain current electric vehicle charging parameters through system analysis;

[0092] S24): Based on the charging parameters obtained by the system, determine whether the user has given a rating; if so, save the data to the historical database; if not, mark the data as unrated data and then store it in the historical database.

[0093] In a preferred embodiment, the model for deriving electric vehicle charging parameters based on user historical data in step S2) is as follows:

[0094] SOC obj,user =g1(SOC) his );

[0095] t end =g2(t end,his );

[0096] In the formula, g1 and g2 represent the target state of charge (SOC) and charging termination time functions of the electric vehicle battery, respectively, derived from historical user data; his This represents historical data on the target state of charge of electric vehicle batteries, which is similar to the current time, geographical location, season, and location type of the electric vehicle; t end,his This data represents historical data on the end time of electric vehicle battery charging, and is similar to the current time, geographical location, season, and location of the electric vehicle.

[0097] In a preferred embodiment, in step S3), as follows: Figure 4 As shown, when electric vehicles or distributed renewable energy generation are connected to the microgrid, the system detects whether there are changes in the power source, power load, infrastructure, and power exchange with the main grid within the microgrid. If so, the detection frequency is increased. When the power quality parameters fluctuate beyond the preset first set value during the charging process of electric vehicles, the active and reactive power allocation scheme of electric vehicles is re-optimized. If not, the charging piles, distributed renewable energy generation rectifiers, and inverters are controlled according to the original scheme.

[0098] The power quality parameters mentioned include: microgrid voltage deviation, transformer load rate, line load rate, three-phase imbalance, harmonic pollution value, total power load value, power load imbalance, and frequency.

[0099] In a preferred embodiment, in step S3), when the power quality of the microgrid fluctuates beyond a first set value after the electric vehicle and distributed renewable energy generation are connected to the microgrid, the active and reactive power allocation scheme of the electric vehicle is optimized; otherwise, the power allocation is carried out with the goal of absorbing the renewable power generation and electric vehicle charging and discharging power in the microgrid.

[0100] In a preferred embodiment, in step S3), determining whether the microgrid's power quality exceeds a first set value is achieved by calculating the microgrid's power quality index value using microgrid power index parameters. The calculation formula is as follows:

[0101]

[0102] B th1 =Bmax -b th1

[0103] B th2 =B min +b th2

[0104] In the formula, B, B max and B min These represent the current, maximum, and minimum feasible power quality values ​​of the microgrid, respectively; α1, α2, α3, α4, α5, α6, α7, α8, δ1, δ2, δ3, δ4, δ5, δ6, δ7, and δ8 all represent the current power quality index parameters of the microgrid; β1, β2, β3, β4, β5, β6, β7, β8, ρ1, ρ2, ρ3, ρ4, ρ5, ρ6, ρ7, and ρ8 all represent the maximum power quality index parameters of the microgrid; γ1, γ2, γ3, γ4, γ5, γ6, γ7, γ8, σ1, σ2, σ3, σ4, σ5, σ6, σ7, and σ8 all represent the minimum power quality index parameters of the microgrid; B th1 and B th2 Both represent the first setpoint for microgrid power quality; b th1 and b th2 The values ​​represent the maximum and minimum power quality parameters of the microgrid, respectively; h1, h2, and h3 represent the current, maximum, and minimum voltage deviation values ​​of the microgrid, respectively; l1, l2, and l3 represent the current, maximum, and minimum transformer load rates of the microgrid, respectively; m1, m2, and m3 represent the current, maximum, and minimum line load rates of the microgrid, respectively; n1, n2, and n3 represent the current, maximum, and minimum three-phase unbalance of the microgrid, respectively; u1, u2, and u3 represent the current, maximum, and minimum harmonic pollution values ​​of the microgrid, respectively; w1, w2, and w3 represent the current, maximum, and minimum total power load of the microgrid, respectively; v1, v2, and v3 represent the current, maximum, and minimum power load unbalance of the microgrid, respectively; and z1, z2, and z3 represent the current, maximum, and minimum frequency values ​​of the microgrid, respectively.

[0105] In a preferred embodiment, in step S3), as follows: Figure 5 As shown, the distributed renewable energy generation power allocation scheme includes the following steps:

[0106] S311) Obtain distributed renewable energy generation data and predict distributed renewable energy generation data in the future;

[0107] S312) Determine if the electricity price is at its peak; if yes, prioritize allocation to the electricity load and electric vehicles within the microgrid; if no, prioritize allocation to energy storage and electric vehicles within the microgrid; then determine if the distributed renewable energy generation power has been completely absorbed; if yes, return to step S311); if no, allocate to energy storage until the energy storage device reaches 100% charge.

[0108] S313) Determine whether the distributed renewable energy generation power has been completely absorbed; if yes, return to execute S311); if no, allocate it to the grid.

[0109] In a preferred embodiment, in step S3), as follows: Figure 6 As shown, the active and reactive power allocation scheme for electric vehicles includes the following steps:

[0110] S321) Obtain an optimized electric vehicle charging solution;

[0111] S322) Optimize the active power allocation of electric vehicles based on the optimized charging scheme;

[0112] S323) Adjusting reactive power allocation based on active power allocation of electric vehicles;

[0113] S324) Determine whether the power quality of the microgrid exceeds the preset first set value; if yes, execute step S325 first, and then return to execute S321); if no, execute S326 directly.

[0114] S325) Set the optimization scheme as the infeasible region of the optimization decision variable;

[0115] (S326) The plan information is transmitted to the control center and charging facilities.

[0116] In this preferred embodiment, the normal operation of the microgrid is supported by allocating active and reactive power within the microgrid. The formula for the active and reactive power matching model within the microgrid is as follows:

[0117] mina price (t);

[0118]

[0119] P EV (t)=P EV-grid (t)+P EV-ren (t)+P EV-sto (t)

[0120] P EV-grid (t)≤P EV (t)

[0121] PEV-ren (t)≤min(P EV (t),P ren (t))

[0122] P ren (t)=P wind (t)+P solar (t)

[0123] P EV-sto (t)≤min(P EV (t), P sto (t))

[0124] P wind (t)+P solar (t)+P sto (t)+P grid (t)+P EV (t)+P other (t)-P out (t)=0

[0125] R wind (t)+R solar (t)+R sto (t)+R grid (t)+R EV (t)+R other (t)=0

[0126]

[0127] In the formula, a price Indicates the electricity price for charging electric vehicles; a grid P represents the price of electricity from the power grid. EV-grid P EV-ren and P EV-sto These represent the matching power between electric vehicles and the power grid, and between renewable energy generation and energy storage within the microgrid; a ren and a sto P represents the price of renewable electricity generated and the price of electricity generated from energy storage within the microgrid, respectively. EV Power for charging electric vehicle batteries; P wind P solar P sto P grid and P other P represents the active power output from wind power generation, photovoltaic power generation, energy storage, the power grid, and other electrical loads within the microgrid; out R represents the total power curtailed from wind and solar power within the microgrid; EV R wind R solar R sto R grid and Rother θ1, θ2, θ3, θ4, θ5 and θ6 represent the reactive power output of electric vehicles, wind power generation, photovoltaic power generation, energy storage, power grid and other power loads in the microgrid, respectively; θ1, θ2, θ3, θ4, θ5 and θ6 represent the adjustable coefficients of active power and reactive power of distributed wind power generation, photovoltaic power generation, energy storage power generation, power grid transmission, electric vehicle charging and other power loads, respectively.

[0128] In a preferred embodiment, in step S4), as follows: Figure 7 As shown, first collect relevant information about electric vehicles, and then determine whether the recently collected relevant information about electric vehicles has changed from the initial value; if so, determine whether the change in the relevant information about electric vehicles exceeds the second set value; if not, continue to collect relevant information about electric vehicles.

[0129] When the relevant information of electric vehicles changes and fluctuates beyond the second set value during the charging process, the electric vehicle charging scheme, active and reactive power allocation scheme, and distributed renewable energy power allocation scheme will be re-optimized; otherwise, the charging piles, distributed renewable energy power generation rectification, and inverter facilities will be controlled according to the original scheme.

[0130] In a preferred embodiment, in step S4), the electric vehicle-related information refers to information where the actual electric vehicle information, the actual overall power grid information, the actual microgrid information, and the information obtained during the optimization process differ significantly.

[0131] The embodiments and descriptions above are merely illustrative of the principles and preferred embodiments of the present invention. Various changes and modifications may be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A method for power distribution of an electric vehicle based on the state of power quality of a microgrid, characterized by, The method comprises the following steps: S1), when receiving that the electric vehicle reaches the micro-grid, transmitting the real-time information of the electric vehicle, and collecting the overall power grid information and the micro-grid information from the control center and the micro-grid aggregator respectively; S2), optimizing the electric vehicle charging scheme according to the electric vehicle charging information; S3), determining whether the grid-connection of distributed renewable energy power generation or the charging and discharging of the electric vehicle causes the change of the power quality of the micro-grid; If the power quality changes, the active and reactive power distribution scheme in the AC and DC energy exchange process of the electric vehicle, the distributed renewable energy power generation and the energy storage station in the micro-grid is optimized; If not, the charging pile is controlled according to the optimized electric vehicle charging scheme in step S2); S4), determining whether the electric vehicle related information changes; If yes, the electric vehicle charging scheme and the power distribution scheme in the AC and DC energy exchange process of the electric vehicle, the distributed renewable energy power generation and the energy storage station in the micro-grid are re-optimized; If the electric vehicle related information does not change, the optimized electric vehicle charging scheme in step S3) is maintained until the expected time or the expected battery state of charge; In step S2), it is determined whether the user inputs the electric vehicle charging parameters within the specified time; If the user inputs the electric vehicle charging parameters within the specified time, the electric vehicle aggregator optimizes the electric vehicle charging scheme according to the collected aggregator information and the user input parameters, while considering the influence of the region, time and seasonal characteristics of the electric vehicle; If the user does not input the electric vehicle charging parameters within the specified time, the electric vehicle aggregator first collects the historical data of the vehicle user, and obtains the electric vehicle charging parameters according to the historical data of the vehicle user; Meanwhile, considering the influence of the region, time and seasonal characteristics of the user on the energy consumption of the electric vehicle, the electric vehicle charging scheme is optimized together with the aggregator information and the user input parameters; In step S2), the calculation formula of the electric vehicle charging scheme optimization process is: ； ； ； ； ； ； ； ； ； ； ； wherein C EV represents the total cost of charging an individual electric vehicle; t0and t end represent the initial and final time of charging an electric vehicle, respectively; Q EV represents the charging amount of an electric vehicle; a price represents the charging price of an electric vehicle; BG EV represents the converted cost of battery loss caused by the charging process of an electric vehicle; SOC add represents the energy consumption of an electric vehicle caused by considering the influence of location, time, and seasonal factors; L e , T e , S e represent the energy consumption coefficients of geographical factors, time factors, and seasonal factors, respectively; SOC obj,user represents the expected state of charge of an electric vehicle battery input by a user or obtained by an aggregator based on historical data of the user; f1, f2, and f3 represent the coefficient functions of geographical factors, time factors, and seasonal factors, respectively; , represent the longitude and latitude of the location of an electric vehicle and the terrain type information, respectively; ε1and ε2represent the specific time of charging an electric vehicle and the time type information of a user, respectively; s1and s2represent the season and month information, respectively; SOC obj represents the actual charging target state of charge of an electric vehicle battery; SOC max and SOC min represent the maximum and minimum state of charge values during the charging process of an electric vehicle battery, respectively; P EV and A EV represent the charging power of an electric vehicle battery and the rated capacity of the battery, respectively; P max and P min represent the maximum and minimum charging power of the electric vehicle battery, respectively; SOC ini represents the initial state of charge of the electric vehicle battery; Q EV represents the amount of charge of the electric vehicle battery; represents the time interval of charging of the electric vehicle battery; represents the state of charge of the electric vehicle battery at the end of charging.

2. The method of claim 1, wherein the method is based on the power quality state of the microgrid. In step S1), the real-time information of the electric vehicle includes the number of electric vehicles, charging time, charging power, battery rated capacity, initial battery state of charge and expected battery state of charge; The overall power grid information includes real-time power price, predicted power price, current power quality of the power grid and predicted power quality; The micro-grid information includes real-time data and predicted data of wind power generation, real-time data and predicted data of photovoltaic power generation, energy storage rated capacity, maximum charging and discharging power of energy storage, remaining capacity of energy storage, power load curve of the micro-grid, current power quality and predicted power quality.

3. The method of claim 1, wherein the method is based on the power quality state of the microgrid. In step S2), the electric vehicle charging parameters include the expected battery state of charge, the maximum charging and discharging power of the battery, whether to take the lowest cost as the optimization target, and the expected end charging time; The historical data of the user includes the charging time of the user, the expected battery state of charge, and whether to take the cost as the optimization target.

4. The method of claim 1, wherein the method is based on the power quality state of the microgrid. In step S2), the calculation formula for obtaining the electric vehicle charging parameters according to the historical data of the vehicle user is: ； ； wherein g1 and g2 represent functions of the target state of charge and the charging termination time of the electric vehicle battery, respectively, based on historical data of the user; SOC his represents historical data of the target state of charge of the electric vehicle battery, which is similar to the time, geographic location, season, and type of place in which the electric vehicle is currently located; t end,his represents historical data of the charging termination time of the electric vehicle battery, which is similar to the time, geographic location, season, and type of place in which the electric vehicle is currently located.

5. The method of claim 1, wherein the method is based on the power quality state of the microgrid. In step S3, when the electric vehicle or the distributed renewable energy generation is connected to the micro-grid, if the micro-grid power quality changes and exceeds the preset first setting value during the electric vehicle charging process, the electric vehicle charging scheme and the active and reactive power distribution scheme of the electric vehicle, the distributed renewable energy generation and the energy storage station in the AC and DC energy exchange process are re-optimized; otherwise, the charging pile, the distributed renewable energy generation rectification and inversion facility are controlled according to the original scheme. Or when the electric vehicle or the distributed renewable energy generation is connected to the micro-grid, if the micro-grid power quality changes and exceeds the preset first setting value, the electric vehicle charging scheme and the active and reactive power distribution scheme of the electric vehicle are optimized; otherwise, the power distribution is performed by taking the distributed renewable energy generation power and the electric vehicle charging and discharging power in the micro-grid as the target.

6. The method of claim 5, wherein the method further comprises: In step S3, the formula for judging whether the micro-grid power quality exceeds the preset first setting value is: ； ； In the formula, B, B max and B min respectively represent the current value, the maximum value and the minimum value of the feasible range of the micro-grid power quality; α1, α2, α3, α4, α5, α6, α7, α8, δ1, δ2, δ3, δ4, δ5, δ6, δ7, δ8 all represent the current power quality index parameters of the micro-grid; β1, β2, β3, β4, β5, β6, β7, β8, ρ1, ρ2, ρ3, ρ4, ρ5, ρ6, ρ7, ρ8 all represent the maximum power quality index parameters of the micro-grid; γ1, γ2, γ3, γ4, γ5, γ6, γ7, γ8, σ1, σ2, σ3, σ4, σ5, σ6, σ7, σ8 all represent the minimum power quality index parameters of the micro-grid; B th1 and B th2 all represent the first set value of the micro-grid power quality; b th1 and b th2 respectively represent the maximum and minimum power quality index parameters of the micro-grid; h1, h2 and h3 respectively represent the current value, the maximum deviation value and the minimum deviation value of the micro-grid voltage deviation within the allowed range; 、 and respectively represent the current value, the maximum value and the minimum value of the micro-grid transformer load rate within the allowed range; m1, m2 and m3 respectively represent the current value, the maximum value and the minimum value of the micro-grid line load rate within the allowed range; n1, n2 and n3 respectively represent the current value, the maximum value and the minimum value of the micro-grid three-phase unbalance degree within the allowed range; u1, u2 and u3 respectively represent the current value, the maximum value and the minimum value of the micro-grid harmonic pollution within the allowed range; w1, w2 and w3 respectively represent the current value, the maximum value and the minimum value of the total power load of the micro-grid within the allowed range; v1, v2 and v3 respectively represent the current value, the maximum value and the minimum value of the power load unbalance degree of the micro-grid within the allowed range; z1, z2 and z3 respectively represent the current value, the maximum value and the minimum value of the micro-grid frequency within the allowed range.

7. The method of claim 1, wherein the method is based on the state of power quality of the microgrid. The active and reactive power in the micro-grid is supported by distributing the active and reactive power in the AC and DC energy exchange process of the multiple subjects in the micro-grid, and the active and reactive power matching formula in the micro-grid is: ； where a price represents the electric vehicle charging power price; a grid represents the grid power price; P EV-grid , P EV-ren and P EV-sto represent the matching power of the electric vehicle, the renewable energy generation and the energy storage generation in the microgrid, respectively; a ren and a sto represent the renewable energy generation power price and the energy storage generation power price in the microgrid, respectively; is the electric vehicle battery charging power; P wind , P solar , P sto , P grid and P other represent the active power output of the wind power generation, the photovoltaic power generation, the energy storage, the grid and other power loads in the microgrid, respectively; P out represents the total wind and light abandoned power in the microgrid; R EV , R wind , R solar , R sto , R grid and R other represent the reactive power output of the electric vehicle, the wind power generation, the photovoltaic power generation, the energy storage, the grid and other power loads in the microgrid, respectively; θ1, θ2, θ3, θ4, θ5 and θ6 represent the adjustable coefficients of the active power and the reactive power of the distributed wind power generation, the photovoltaic power generation, the energy storage generation, the grid transmission, the electric vehicle charging and other power loads, respectively.

8. The method of claim 1, wherein the method is based on the state of power quality of the microgrid. In step S4, when the electric vehicle related information changes and exceeds the preset second setting value during the electric vehicle charging process, the electric vehicle charging scheme and the active and reactive power distribution scheme of the electric vehicle, the distributed renewable energy generation and the energy storage station in the AC and DC energy exchange process are re-optimized; otherwise, the charging pile, the distributed renewable energy generation rectification and inversion facility are controlled according to the original scheme. The electric vehicle related information refers to the actual electric vehicle information, the actual overall power grid information and the actual micro-grid information.

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