Multi-uncertainty electric power traffic coupling system optimization regulation and control method

Abstract

The invention provides a multi-uncertainty optimization regulation and control method for an electric power traffic coupling system, relates to the technical field of traffic, solves the problem of impact on a power grid caused by changes of various driving states of a vehicle and influences of different driving states on a road network in the prior art, and provides a system optimization regulation and control method. Comprising the following steps: acquiring a multi-uncertainty traffic network-road-vehicle modeling result; obtaining a modeling result of the power distribution network with multiple uncertainties; determining a photovoltaic power generation on-grid electric quantity calculation model and determining an electric vehicle disordered charging model; according to the time-space characteristics of the electric vehicle, combining the disordered charging model and the ordered charging model of the electric vehicle, and utilizing a particle swarm optimization algorithm to obtain a multi-system optimization regulation and control method model; and controlling the electric traffic according to the regulation and control method model. According to the scheme, the electric traffic is optimized according to various driving states of the vehicle and the power grid state so as to achieve the purpose of minimum operation cost of the interconnection system.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to an optimization and control method for a multiple-uncertainty electric transportation coupling system. Background Art

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The descriptions herein may include concepts that may be explored, but not necessarily concepts that have been previously thought of or explored. Therefore, unless otherwise noted herein, the content described in this section is not prior art with respect to the specification and claims of this application, and is not admitted to be prior art by inclusion in this section.

[0003] As a special load, electric vehicles are connected to the power system in large quantities. The uncertainty and volatility of their charging behavior will have a great impact on the power quality, network loss, etc. of the power system. On the other hand, as a means of transportation, the travel mode of electric vehicles will be affected by traffic road conditions and traffic management policies.

[0004] Therefore, in this way, electric vehicles become the link between the power system and the transportation system, and the traffic flow and the power grid flow interact with each other through the fast charging stations on the road sections. In the prior art, a two-stage charging scheme for electric vehicle agents is proposed to minimize the charging cost of each electric vehicle user while considering the uncertainty of new energy power generation and the capacity limitation of charging agents. A multi-objective, multi-stage coordinated planning model is proposed for the configuration of charging station facilities. The random flow allocation model of FISK is used to model the traffic flow of the traffic network, and the uncertainty of the charging load of electric vehicles is modeled at the same time, aiming to minimize the operating investment cost of the distribution network and maximize the annual capture flow of the traffic network. However, since the transportation network is not considered, only the electricity price setting and energy scheduling strategies of smart grid retailers in the power market environment are studied.

[0005] Taking into account that electric vehicles have both traffic driving characteristics and charging load characteristics, which will have an interactive impact on the distribution network system and the transportation system, due to the failure to fully consider the coupling degree between traffic conditions and vehicle dynamic driving, the state transfer of vehicles is characterized by establishing a probability transfer model between urban functional areas and a parking probability model, which has strong regional characteristics and randomness.

[0006] The power generation capacity of photovoltaic power generation systems is affected by seasonal weather. The access of large-capacity grid-connected photovoltaic power stations and distributed photovoltaic power sources has brought a series of problems to the planning, operation and management of the power grid. Therefore, it is difficult for photovoltaic power generation to achieve full dispatch of the power system. Integrating large-scale photovoltaic power stations into the power grid may affect the safe and reliable operation of the power grid.

[0007] However, the impact of changes in various driving states of vehicles during actual travel and the impact of different driving states on the road network on the power grid needs to be considered, but the existing technology does not consider this problem. Therefore, how to optimize electric transportation based on various driving states of vehicles and power grid states is a technical problem that needs to be solved. Summary of the invention

[0008] In order to solve the above technical problems, the purpose of the present invention is to provide a method for optimizing and controlling a power-transport coupling system with multiple uncertainties, which is characterized by comprising:

[0009] Obtaining modeling results of a traffic network-road-vehicle with multiple uncertainties, wherein the modeling results of the traffic network-road-vehicle include road capacity modeling, road ambient temperature modeling, automobile travel demand modeling, electric vehicle battery initial SOC modeling, and electric vehicle SOC modeling at the end of charging;

[0010] Obtaining modeling results of a distribution network with multiple uncertainties, wherein the modeling results of the distribution network include: distributed power generation output uncertainty modeling and load uncertainty modeling;

[0011] Determine the calculation model for photovoltaic power generation and the disorderly charging model for electric vehicles;

[0012] According to the spatiotemporal characteristics of the electric vehicle, combined with the disordered charging model and the ordered charging model of the electric vehicle, the minimum mean square error of the daily load of the photovoltaic power generation system of the electric vehicle is set as the objective function, and combined with the constraints of the electric vehicle, the particle swarm optimization algorithm is used to obtain a model of the optimization and control method of the power-transportation coupling system with multiple uncertainties.

[0013] Optionally, in some embodiments, the photovoltaic power generation grid-connected electricity calculation model is expressed by Formula 1 as follows:

[0014]

[0015] Where: H A Indicates the total solar radiation on the horizontal surface, in kWh / m 2 ; E p Indicates the amount of electricity generated on the grid, in kWh; P AZ Indicates the irradiance under standard conditions, the unit is kWp; E S represents the installed capacity of the component, and the constant is 1; K represents the comprehensive efficiency coefficient, including the PV component type correction factor, the inclination angle of the PV array, the azimuth correction factor, the availability of the PV power generation system, the light utilization rate, the inverter efficiency, the collection line loss, the step-up transformer loss, and the PV component surface contamination correction factor.

[0016] Optionally, in some embodiments, the electric vehicle disorderly charging model is expressed by Formula 2 as follows:

[0017]

[0018] Where: T C Indicates the duration of the electric vehicle charging time, in hours; S indicates the daily mileage, in km; W 100 Indicates the power consumption per 100 kilometers, in kWh / 100km; P c Indicates charging power, in kW; η EV Indicates charging efficiency.

[0019] Optionally, in some embodiments, the minimum mean square error of the daily load of the photovoltaic power generation system of the electric vehicle is set as the objective function, which conforms to Formula 3, as follows:

[0020]

[0021] Where: F represents the mean square error of daily load at the power grid end; P i,ev,t Indicates the charge and discharge capacity of electric vehicles; P ave Indicates the deviation P ori,t The arithmetic mean of the total amount; P ori,t Represents the difference between load and PV output at time t.

[0022] Optionally, in some embodiments, the deviation P ori,t The arithmetic mean of the total amount and the difference between the load and the photovoltaic output at time t are obtained using the following formula 4:

[0023]

[0024] P ori,t =P dt -P pv

[0025] Where: P dt represents the foundation load at time t; P pv represents the photovoltaic power generation at time t.

[0026] Optionally, in some embodiments, the constraints of the electric vehicle include: electric vehicle charging power constraint, electric vehicle battery remaining power constraint, dispatchable load constraint, and electric vehicle power battery SOC constraint.

[0027] Optionally, in some embodiments, the electric vehicle charging power constraint complies with Formula 5, as follows:

[0028]

[0029] Where: ——Charging power of electric vehicles when charging in an unordered manner;

[0030] ——Charging power of electric vehicles when they are charged in an orderly manner.

[0031] Optionally, in some embodiments, the remaining power constraint of the electric vehicle battery complies with Formula 6, as follows:

[0032] P loadmax ≤P iloadmax (6)

[0033] Where: P loadmax represents the peak load before optimization;

[0034] P iloadmax Indicates the optimized load peak.

[0035] Optionally, in some embodiments, the dispatchable load constraint complies with Formula 7, as follows:

[0036]

[0037] Where: ——maximum discharge power;

[0038] ——Maximum charging power.

[0039] Optionally, in some embodiments, the electric vehicle power battery SOC constraint complies with Formula 8, as follows:

[0040] SOC min ≤SOC t ≤SOC max (8)

[0041] Where: SOC min Indicates the minimum state of charge of the power battery; SOC max Indicates the maximum value of the power battery state of charge

[0042] Optionally, in some embodiments, the schedulable time constraint complies with Formula 9, as follows:

[0043] T arivall ≤T≤T leave (9)

[0044] Where: T arivall Indicates the time when the electric vehicle enters the working area; T leave Indicates the time when the electric vehicle leaves the work zone.

[0045] Optionally, in some embodiments, the modeling of the traffic network-road-vehicle sets the traffic network GT (N,A), where N is the node set and A is the road set; let S be the charging station node set, and W is the set of origin-destination (OD) pairs of all cars; and are the path sets between the fuel vehicle and electric vehicle in (OD) pairs ω∈W respectively;

[0046] The road capacity modeling adopts formula 10 as follows:

[0047]

[0048] Among them, road a = (i, j) ∈ A (i, j ∈ N and i and j are the starting point and end point of road a respectively), and its capacity has a random fluctuation ξ C,a It is characterized by normal distribution, denoted as ξ C,a ~N(μ C,a ,σ C,a ), μ C,a and σ C,a They are respectively C,a The mean and standard deviation of

[0049] Optionally, in some embodiments, the road environment temperature modeling adopts formula 11 as follows:

[0050]

[0051] Where: n is the power index; b n For a given coefficient, is the energy consumption rate of electric vehicles, ξ T Ambient temperature; and / or,

[0052] The automobile travel demand modeling is consistent with random fluctuations and Normal distribution of

[0053] Optionally, in some embodiments, the initial SOC modeling of the electric vehicle battery conforms to Formula 12, as follows:

[0054]

[0055] Optionally, in some embodiments, the SOC modeling of the electric vehicle at the end of charging obeys a normal distribution, and the mean of the distribution is related to the expected charging target.

[0056] Optionally, in some embodiments, the distributed power output uncertainty modeling specifically includes: a wind power generation model, a photovoltaic power generation model;

[0057] The wind power generation model conforms to formula 13, as follows:

[0058]

[0059] Where P w,t is the output power, N is the number of wind turbines in the wind farm, P N is the rated power, V w,t is the wind speed, V rated is the rated power wind speed, V in 、V out are the cut-in and cut-out wind speeds of the fan respectively;

[0060] The photovoltaic power generation model conforms to formula 14, as follows:

[0061]

[0062] Where: P pv is the rated capacity of photovoltaic power generation, f pv is the efficiency of the photovoltaic power generation system, S t is the irradiation intensity, S st is the standard light irradiance, α is the temperature coefficient, T t is the working temperature during period t, which can be estimated based on the light intensity and ambient temperature. st The standard operating temperature for photovoltaic cells

[0063] Optionally, in some embodiments, the load uncertainty modeling includes: a conventional user load model and an electric vehicle model;

[0064] The conventional user load model conforms to formula 15, as follows:

[0065]

[0066] P B,i (i∈E N ) is the active power of other loads in the distribution network except the charging load of the charging station, which seems to obey the normal distribution and is denoted by P B,i ~N(μ B,i ,σ B,i ), where μ B,i and σ B,i P B,i , the mean and standard deviation, P B,i is the probability density function; the power factor and active power P of other loads except the charging load of the charging station at node i B,i The reactive power Q can be obtained B,i ;

[0067] The electric vehicle model conforms to Equation 16 as follows:

[0068]

[0069] τ acc , τ lea are the mean time of electric vehicles arriving at and leaving the electric vehicle charging station; σ acc , σ lea are the standard deviations of the time it takes for electric vehicles to arrive at and leave the electric vehicle charging station, respectively.

[0070] The above technical solution of the present invention has at least the following beneficial technical effects:

[0071] The technical solution of the present invention takes into account the modeling results of the traffic network-road-vehicle with multiple uncertainties. Specifically, the road capacity modeling, road ambient temperature modeling, automobile travel demand modeling, electric vehicle battery initial SOC modeling, and electric vehicle SOC modeling at the end of charging are taken into account, and the modeling results of the distribution network with multiple uncertainties are obtained, including distributed power source output uncertainty modeling and load uncertainty modeling. At the same time, the changes in various driving states of vehicles in actual travel and the impact of different driving states on the road network on the power grid are taken into account. According to the various driving states of vehicles and the power grid state, the power transportation is optimized to achieve the minimum operating cost of the interconnected system. On the basis of considering the disordered charging model of electric vehicles, the optimal distribution network is considered, and an optimization and control model of the power transportation coupling system with multiple uncertainties is established. The travel decision and charging behavior of electric vehicles are coordinated and controlled through electricity prices, the optimal output of the distribution network units is optimized, and the road flow distribution of the traffic network is optimized to minimize the total operating cost of the interconnected system. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the conventional technology, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0073] Figure 1 It is a process flow chart of a method for optimizing and controlling a power-transportation coupling system with multiple uncertainties provided by an embodiment of the present invention.

[0074] Figure 2 It is a flow chart for solving an optimization and control model of an electric-transportation coupling system with multiple uncertainties provided by an embodiment of the present invention.

[0075] Figure 3 The present invention provides a flow chart of a particle swarm algorithm. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] It should be noted that the sequence numbers mentioned in this application do not necessarily mean that the steps must be strictly followed in the actual implementation process. The sequence numbers are used to distinguish each step for ease of explanation and to avoid confusion.

[0078] The coordinated operation of urban distribution networks and transportation networks spans the two major fields of electricity and transportation. From a scientific point of view, it is an emerging interdisciplinary research direction. In reality, it is necessary to break through the information barriers of the two independent systems. Therefore, it is necessary to design a reasonable coordinated operation architecture of distribution networks and transportation networks, propose a mathematical description of the equilibrium state of urban distribution-transportation networks, and study the properties of network equilibrium solutions, so as to lay a theoretical foundation for the promotion of the coordinated operation of urban distribution networks and transportation networks.

[0079] Traffic flow prediction not only requires the prediction of traffic volume, but also requires the prediction of parameters such as road section travel time and average speed. Traffic flow changes randomly over time and is easily disturbed by external factors. It is a real-time, non-stationary, nonlinear random process. Its changes are not only related to the traffic flow conditions in the previous few periods of the current monitoring point, but also affected by the traffic conditions in the previous few moments of the upstream and downstream monitoring points and the current moment. At the same time, real-time traffic flow will also be affected by weather changes such as rain and snow, various sudden traffic accidents and changes in the traffic environment. Therefore, it is necessary to predict traffic flow and take into account the impact of vehicle constraints.

[0080] Through theoretical analysis and case studies, it is found that there is a mutual connection between the power grid and the transportation network: the flow distribution of the transportation network will affect the load distribution of the power grid, and the electricity price of the charging station will affect the flow distribution of the transportation network.

[0081] Therefore, an embodiment of the present invention provides a method for optimizing and controlling a power-transport coupling system with multiple uncertainties, such as Figure 1 As shown, including:

[0082] S101: Obtaining modeling results of a traffic network-road-vehicle with multiple uncertainties, wherein the modeling results of the traffic network-road-vehicle include road capacity modeling, road ambient temperature modeling, automobile travel demand modeling, electric vehicle battery initial SOC modeling, and electric vehicle battery SOC modeling at the end of charging.

[0083] S102: Obtain modeling results of a distribution network with multiple uncertainties, wherein the modeling results of the distribution network include: distributed power generation output uncertainty modeling and load uncertainty modeling.

[0084] S103: Determine a photovoltaic power generation grid-connected electricity calculation model and determine an electric vehicle disorderly charging model;

[0085] S104: According to the spatiotemporal characteristics of the electric vehicle, combined with the disordered charging model and the ordered charging model of the electric vehicle, the minimum mean square error of the daily load of the photovoltaic power generation system of the electric vehicle is set as the objective function, and combined with the constraints of the electric vehicle, a particle swarm optimization algorithm is used to obtain a model of the optimization and control method of the power-transportation coupling system with multiple uncertainties.

[0086] S105: Controlling the electric traffic according to the optimization control method model of the electric traffic coupling system with multiple uncertainties.

[0087] Optionally, as one embodiment, the photovoltaic power generation grid-connected electricity calculation model is expressed by Formula 1 as follows:

[0088]

[0089] Where: H A Indicates the total solar radiation on the horizontal surface, in kWh / m 2 ; E p Indicates the amount of electricity generated on the grid, in kWh; P AZ Indicates the irradiance under standard conditions, the unit is kWp; E S represents the installed capacity of the component, and the constant is 1; K represents the comprehensive efficiency coefficient, including the PV component type correction factor, the inclination angle of the PV array, the azimuth correction factor, the availability of the PV power generation system, the light utilization rate, the inverter efficiency, the collection line loss, the step-up transformer loss, and the PV component surface contamination correction factor.

[0090] Optionally, as one embodiment, the electric vehicle disorderly charging model is expressed by Formula 2 as follows:

[0091]

[0092] Where: T C Indicates the duration of the electric vehicle charging time, in hours; S indicates the daily mileage, in km; W 100 Indicates the power consumption per 100 kilometers, in kWh / 100km; P c Indicates charging power, in kW; η EV Indicates charging efficiency.

[0093] Optionally, as one embodiment, the minimum mean square error of the daily load of the photovoltaic power generation system of the electric vehicle is set as the objective function, which conforms to Formula 3, as follows:

[0094]

[0095] Where: F represents the mean square error of daily load at the power grid end; P i,ev,t Indicates the charge and discharge capacity of electric vehicles; P ave Indicates the deviation P ori,t The arithmetic mean of the total amount; P ori,t Represents the difference between load and PV output at time t.

[0096] Optionally, as one embodiment, the deviation P ori,t The arithmetic mean of the total amount and the difference between the load and the photovoltaic output at time t are obtained using the following formula 4:

[0097]

[0098] P ori,t =P dt -P pv

[0099] Where: P dt represents the foundation load at time t; P pv represents the photovoltaic power generation at time t.

[0100] Optionally, as one embodiment, the constraints of the electric vehicle include: electric vehicle charging power constraint, electric vehicle battery remaining power constraint, dispatchable load constraint, and electric vehicle power battery SOC constraint.

[0101] Optionally, as one embodiment, the electric vehicle charging power constraint complies with Formula 5, as follows:

[0102]

[0103] Where: ——Charging power of electric vehicles when they are charged in an unordered manner;

[0104] ——Charging power of electric vehicles when they are charged in an orderly manner.

[0105] Optionally, as one embodiment, the remaining power constraint of the electric vehicle battery complies with Formula 6, as follows:

[0106] P loadmax ≤P iloadmax (6)

[0107] Where: P loadmaxrepresents the peak load before optimization;

[0108] P iloadmax Indicates the optimized load peak.

[0109] Optionally, as one embodiment, the dispatchable load constraint complies with Formula 7, as follows:

[0110]

[0111] Where: ——maximum discharge power;

[0112] ——Maximum charging power.

[0113] Optionally, as one embodiment, the electric vehicle power battery SOC constraint complies with Formula 8, as follows:

[0114] SOC min ≤SOC t ≤SOC max (8)

[0115] Where: SOC min Indicates the minimum state of charge of the power battery; SOC max Indicates the maximum value of the power battery state of charge

[0116] Optionally, as one embodiment, the schedulable time constraint complies with Formula 9, as follows:

[0117] T arivall ≤T≤T leave (9)

[0118] Where: T arivall Indicates the time when the electric vehicle enters the working area; T leave Indicates the time when the electric vehicle leaves the work zone.

[0119] The following is an explanation.

[0120] The power generation capacity of photovoltaic power generation systems is affected by seasonal weather. The access of large-capacity grid-connected photovoltaic power stations and distributed photovoltaic power sources has brought a series of problems to the planning, operation and management of the power grid. Therefore, it is difficult for photovoltaic power generation to achieve full dispatch of the power system. Integrating large-scale photovoltaic power stations into the power grid may affect the safe and reliable operation of the power grid.

[0121] According to relevant regulations, the calculation model for photovoltaic power generation is shown in Formula 1:

[0122]

[0123] Where: HA ——Total solar radiation on the horizontal surface, unit: kWh / m 2 ;

[0124] E p ——On-grid electricity generation, in kWh;

[0125] P AZ ——irradiance under standard conditions, in kWp;

[0126] E S ——Component installation capacity, the constant is 1;

[0127] K——comprehensive efficiency coefficient, including the photovoltaic module type correction factor, the inclination angle of the photovoltaic array, the azimuth correction factor, the availability of the photovoltaic power generation system, the light utilization rate, the inverter efficiency, the collection line loss, the step-up transformer loss, the photovoltaic module surface contamination correction factor, etc.

[0128] The application of V2G technology for electric vehicles is mainly reflected in the orderly charging process. The charging behavior of electric vehicles involves many factors, and the charging behavior is random in time and space. Therefore, different considerations will form different electric vehicle charging prediction models and prediction results. The time distribution characteristics of electric vehicle charging load are crucial to the operation of the power grid and the system emission reduction process. If the peak of the user's charging load coincides with the peak of the basic load of the power grid, it may cause problems such as insufficient power supply capacity. The spatial distribution of electric vehicle charging load mainly considers the daily mileage of electric vehicle users, which involves factors such as road distribution and traffic planning, and has an important impact on the operation of the power grid and charging load prediction.

[0129] In the absence of relevant policy guidance and control, the random charging method of users is called disorderly charging.

[0130] At present, most cities in my country lack orderly charging facilities and technologies, so disorderly charging still dominates the mainstream. If a large number of users perform disorderly charging during peak hours, the peak-to-valley difference in the load curve of the entire network will be widened, increasing the operating pressure of the power generation and distribution system. Disorderly charging and discharging will also have an adverse effect on the local distribution network, such as causing overloads of transformers, lines and other equipment, reducing power supply reliability, and deteriorating power factor. Therefore, establishing a reasonable and effective disorderly charging and discharging model is of great significance for evaluating its carbon reduction effect on the system and optimizing operating costs.

[0131] The disorderly charging model of electric vehicles is shown in Formula 2:

[0132]

[0133] Where: T C ——The duration of the electric vehicle charging time, in hours;

[0134] S——Daily mileage, unit: km;

[0135] W 100 ——Power consumption per 100 kilometers, unit: kWh / 100km;

[0136] P c ——Charging power, in kW;

[0137] η EV ——Charging efficiency.

[0138] On the basis of considering the spatiotemporal characteristics of electric vehicles, combined with the disordered charging model and the orderly charging model of electric vehicles, the minimum mean square error of daily load of the photovoltaic power generation system taking into account electric vehicles is set as the objective function, and the optimal control strategy of the power-transportation coupling system considering multiple uncertainties is solved to obtain the corresponding control model.

[0139] The specific model solving process is as follows: Figure 2 shown.

[0140] The objective function is determined as shown in Formula 3:

[0141]

[0142] P ori,t =P dt -P pv

[0143] Where: P ori,t ——The difference between load and photovoltaic output at time t;

[0144] P dt ——base load at time t;

[0145] P pv ——PV power generation at time t;

[0146] P ave ——Deviation P ori,t The arithmetic mean of the total.

[0147] The formula for the mean square error of daily load at the power grid end is shown in Formula 4:

[0148]

[0149] Where: F——daily load mean square error at the power grid end;

[0150] P i,ev,t ——The charging and discharging amount of electric vehicles.

[0151] Considering that electric vehicles are subject to substantial constraints, the constraints of electric vehicles can be determined as follows:

[0152] 1) The electric vehicle charging power constraint is shown in Formula 5:

[0153]

[0154] Where: ——Charging power of electric vehicles when charging in an unordered manner;

[0155] ——Charging power of electric vehicles when they are charged in an orderly manner.

[0156] 2) The remaining power constraint of electric vehicle battery is shown in formula 6

[0157] P loadmax ≤P iloadmax (6)

[0158] Where: P loadmax ——Load peak value before optimization;

[0159] P iloadmax ——Optimized load peak.

[0160] 3) The dispatchable load constraint is shown in Formula 7:

[0161]

[0162] Where: ——maximum discharge power;

[0163] ——Maximum charging power.

[0164] 4) The SOC constraint of electric vehicle power battery is shown in formula 8:

[0165] SOC min ≤SOC t ≤SOC max (8)

[0166] Where: SOC min ——Minimum value of power battery state of charge;

[0167] SOC max ——The maximum value of the power battery state of charge.

[0168] 5) The schedulable time constraint is shown in Formula 9:

[0169] T arivall ≤T≤T leave

[0170] Where: T arivall - the time when the electric vehicle enters the work area;

[0171] T leave ——The time when the electric vehicle leaves the work area.

[0172] In recent years, bioinformatics has been successfully integrated into evolutionary algorithms, resulting in more breakthrough research results for metaheuristic algorithms. In the field of combinatorial optimization and numerical optimization, multi-objective evolutionary algorithms have become an important research direction, including multi-objective particle swarm optimization, multi-objective bee colony optimization, multi-objective ant colony optimization, multi-objective immune algorithm, multi-objective differential algorithm, etc. As an evolutionary algorithm inspired by the foraging behavior of bird groups in nature, particle swarm optimization has the advantages of simple implementation, efficient search, and fast convergence. It has been widely used in various complex experimental tests and practical engineering applications.

[0173] The specific particle swarm solution algorithm process can be found in the attached Figure 3 As shown in the figure, the optimization control method model of the power-transportation coupling system with multiple uncertainties can be obtained.

[0174] The uncertainty modeling is described below.

[0175] For the uncertainty modeling of the "road-vehicle" of the transportation network, the present invention uses a probability distribution function to characterize the random characteristics of the uncertainty factors of the "road" and "vehicle" of the transportation network. For the "road", the random variables include road capacity and road ambient temperature; for the "vehicle", the random variables include vehicle travel demand and battery state of charge (SOC) at different travel stages. Given a transportation network G T (N, A), where N is a node set (the traffic network nodes in this invention include traffic intersections and charging stations when vehicles are charging), and A is a road set. S is set as the charging station node set, and W is the set of origin-destination (OD) pairs of all cars; and are the path sets between fuel vehicles and electric vehicles in (OD) pairs ω∈W respectively.

[0176] Therefore, the uncertainty factors of "road" and "vehicle" in the transportation network can be modeled as follows:

[0177] Road capacity modeling: Road capacity refers to the maximum traffic flow under normal traffic conditions. In the operation of the traffic network, for road a = (i, j) ∈ A (i, j ∈ N and i and j are the starting point and end point of road a respectively), the random fluctuation of its capacity ξ C,a It can be described by normal distribution, denoted as ξ C ,a~N(μ C ,a,σ C ,a), where μ C,a and σ C,a They are respectivelyC,a The mean and standard deviation of . Therefore, ξ C,a The probability density function of can be expressed as formula 10:

[0178]

[0179] Road environment temperature modeling: The ambient temperature in the traffic network itself has a large uncertainty, and the random characteristics of the ambient temperature can be described by normal distribution. Since the road environment temperature will directly affect the various properties of electric vehicle batteries (charging and discharging efficiency, etc.), and is also related to the user's decision-making behavior such as whether to use air conditioning, this paper further constructs the electric vehicle energy consumption rate based on the user travel record data collected by FleetCarma in North America. and ambient temperature T The functional relationship between them is:

[0180]

[0181] Where: n is the power index; b n is a given coefficient.

[0182] Automobile travel demand modeling: Automobile travel demand refers to the mobility demand between OD pairs, and the random fluctuation of travel demand between fuel vehicles and electric vehicles between OD pairs ω and It can be described by normal distribution.

[0183] Modeling of the initial SOC of electric vehicle batteries: Since the initial SOC of electric vehicle batteries is different when starting from the starting point, if a probability distribution function is uniformly used to describe the random characteristics of the initial SOC of different electric vehicles, it will lead to large errors. In order to improve the accuracy of modeling, the present invention divides the electric vehicles at the starting point between OD and ω into T w The battery initial SOC of all electric vehicles in each category is approximately replaced by a fixed value. The proportion of electric vehicles in each category is ξ S,w,t ,t=1,2,…,T w , the sum of all categories is 1, that is, formula 12:

[0184]

[0185] SOC modeling at the end of electric vehicle charging: The time that electric vehicle users stay at charging stations has a certain degree of randomness, and the electric vehicle may not be able to accurately complete the expected charging target at the end of charging. This paper assumes that the SOC of the electric vehicle at the end of charging (denoted as ξ F ) approximately follows a normal distribution [5-18], and the mean of the distribution is related to the expected charging target.

[0186] As for the "source-load" multiple uncertainty modeling of the distribution network, there are a large number of uncertainties and correlation factors in the distribution network, which will affect the safe and reliable operation of the distribution network. The development of smart distribution networks is accompanied by the grid connection of large-scale distributed power sources and electric vehicle charging facilities. The intermittent and volatile output of distributed power sources such as wind power generation and photovoltaic power generation, and the randomness of new loads such as electric vehicle charging loads have led to an increase in unstable factors in the operation of distribution networks. The development of smart distribution networks is accompanied by the grid connection of large-scale distributed power sources and electric vehicle charging facilities. The intermittent and volatile output of distributed power sources such as wind power generation and photovoltaic power generation, and the randomness of new loads such as electric vehicle charging loads have led to an increase in unstable factors in the operation of distribution networks. At the same time, the changes in these variables are not completely random, but show a certain regularity due to the influence of factors such as user behavior patterns, external environment, and geographical location, resulting in a certain correlation between some variables.

[0187] For distribution networks with distributed renewable energy, intermittent renewable energy generation (wind power, photovoltaic power generation) and fluctuating loads are the main uncertainties affecting their operation. D (E N ,E L ), where E N is the node set, E L is a branch set. The connection node between the distribution network and the upper power grid is a balancing node, E G is the node set where the power source is located (including the balance node), and The present invention equates distributed renewable energy to loads with opposite power injection directions, and the nodes where the distributed renewable energy is located are not included in the power source node set E G Furthermore, the modeling of the uncertainty factors of the distributed power output of the distribution network using the probability distribution function can include the modeling of the uncertainty of the distributed power output and the modeling of the uncertainty of the load, as follows:

[0188] For the modeling of uncertainty in the output of distributed power sources, wind speed is affected by multiple factors such as region, season, and weather, and has obvious random fluctuations. At present, the methods for modeling the probability distribution of wind speed are mainly divided into parameter estimation and non-parametric estimation. The parameter estimation method selects the existing probability distribution based on experience, and uses the measured data to estimate the parameters of the probability distribution. This method has a simple model, simple calculation, and high accuracy. Therefore, it is widely used in wind power output modeling for reliable evaluation of power systems; the non-parametric estimation method does not require any assumptions in advance, but directly models the wind speed based on the sample data, avoiding the problem of empirical distribution selection, but how to choose a suitable bandwidth for the non-parametric kernel density estimation model is a difficult problem.

[0189] Distributed power output uncertainty modeling can include wind power generation model and photovoltaic power generation model

[0190] In the wind power generation model, the most important factor affecting wind power output is the wind speed. The wind speed data released by the Meteorological Bureau is usually obtained from observations at a 9-meter observation station. However, the wind speed value will reflect the difference in speed due to different heights. The height of the wind turbine is relatively high, and it needs to be calculated using the following formula before it can be used.

[0191]

[0192] Where: V and V0 are the wind speeds at heights H and H0, respectively, in m / s; index n is the correction index of the reduction formula, the value of which is related to the changes in weather and terrain, and its value is generally between 0.125 and 0.5. When the wind turbine is located in an open area with normal stability, its value is generally

[0193] In order to improve the reliability of wind power generation, the wind speed follows the probability distribution function (PDF) based on the corresponding historical data, which is used to fit the wind speed distribution and will not have any impact on the proposed model. The research on the Weibull distribution function is relatively mature, and its description effect is closer to the actual wind speed. Therefore, the Weibull two-parameter distribution function is used to describe the probability density function of wind power:

[0194]

[0195] Where: c and k are the scale parameter and shape parameter of the Weibull distribution function, respectively. c has the same dimension as the speed, while k is a dimensionless quantity. The average wind speed and maximum wind speed are used to estimate the parameters of the Weibull function. The average wind speed V- and the ten-minute average maximum wind speed V observed at time T are obtained from the meteorological data. max , calculate c and k using the following formula.

[0196]

[0197] The wind speed after treatment is

[0198] V w,t =∫PDF(V)×VdV

[0199] The power output of a wind turbine during operation is affected by actual external conditions, such as uncontrollable factors such as the environment and location. Wind power generation has a certain degree of uncontrollability. Currently, wind power generation usually uses the linear expression relationship between wind power and wind speed to estimate wind power output.

[0200] The minimum speed at which the fan blades rotate is the fan's cut-in wind speed. At this time, the fan can be put into normal operation. When the wind speed exceeds its designed maximum wind speed, the fan blades need to be forced to stop to protect the fan from overload. The speed of the fan blades during normal operation is between the cut-in and cut-out wind speeds.

[0201] The output power of the fan can be expressed as formula 13:

[0202]

[0203] Where P w,t is the output power, N is the number of wind turbines in the wind farm, P N is the rated power, V w,t is the wind speed, V rated is the rated power wind speed, V in 、V out are the cut-in and cut-out wind speeds of the fan respectively.

[0204] In the photovoltaic power generation model, a large number of studies have shown that due to the influence of weather factors, the solar radiation intensity received by the photovoltaic power generation array is affected by multiple factors such as geographical location, season, time, and cloud cover, and is highly intermittent and random. Including clouds and shadows above the photovoltaic panels, the solar radiation intensity can be regarded as a Beta distribution, and the Beta distribution function is used to deal with the uncertainty of PV. The Beta distribution function is:

[0205]

[0206] Where a and b are calculated using mean μ and standard deviation σ.

[0207]

[0208] According to the Beta probability distribution function, the radiation absorbed by the solar panel is:

[0209] S t =∫PDF(S)×S ex,t dS

[0210] Where S ex,t is the calculated astronomical radiation value.

[0211] In order to improve the accuracy of the photovoltaic cell power generation model, the power output of the photovoltaic solar panel must take into account the light intensity and temperature. Therefore, the processing expression of the photovoltaic output power is formula 14:

[0212]

[0213] Where: P pv is the rated capacity of photovoltaic power generation, fpv is the efficiency of the photovoltaic power generation system, S t is the irradiation intensity, S st is the standard light irradiation intensity, α is the temperature coefficient, and the present invention takes α=0.00485 / ℃, T t is the working temperature during period t, which can be estimated based on the light intensity and ambient temperature. st The standard temperature at which photovoltaic cells operate.

[0214] Load uncertainty modeling can include conventional user load models, as well as electric vehicle models.

[0215] In the conventional user load model, the user load is affected by many uncertain factors such as weather, time, and user behavior habits, and has a certain degree of randomness. Conventional user load can generally be described by normal distribution. The active power P of other loads in the distribution network except the charging station charging load is B,i (i∈E N ) can be considered to be approximately subject to normal distribution, denoted by P B,i ~N(μ B,i ,σ B,i ), where μ B,i and σ B,i P B,i , the mean and standard deviation. B,i The probability density function of can be expressed as formula 15:

[0216]

[0217] The power factor and active power P of other loads except the charging load of the charging station at node i B,i The reactive power Q can be obtained B,i .

[0218] In the electric vehicle model, the electric vehicle charging load is determined by factors such as the number of electric vehicles, charging method, charging start time, daily mileage, and charging power. These factors have varying degrees of uncertainty and unpredictability, resulting in a certain degree of randomness in the charging load of electric vehicles in both time and space. The modeling of electric vehicle charging load is relatively complex. Generally, it is necessary to obtain the time series of charging load through time series simulation, which is difficult to directly express with a probability distribution expression. However, the literature

[75] points out that when the number of electric vehicles reaches a certain level, the overall charging load level in each time period obeys the normal distribution. Therefore, the probability density function of the electric vehicle charging load can be expressed as follows: Unlike traditional vehicles, new energy electric vehicles do not rely on fuel to provide driving force. Their main power comes from the power system. Studies have shown that the power consumption characteristics of electric vehicles obey the normal distribution in time. The probability density function of the time when EVs arrive at and leave the charging station is as shown in Formula 16:

[0219]

[0220] Where τ acc , τ lea are the mean time of electric vehicles arriving at and leaving the electric vehicle charging station; σ acc , σ lea are the standard deviations of the time it takes for electric vehicles to arrive at and leave the electric vehicle charging station, respectively.

[0221] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above embodiments are implemented.

[0222] An embodiment of the present invention further provides a computer program product, including a computer program, wherein the computer program is stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, so that the electronic device performs the steps of the method described in any one of the above embodiments.

[0223] The technical solution of the present invention takes into account the impact of changes in various driving states of vehicles during actual travel and the impact of different driving states on the road network on the power grid. According to the various driving states of vehicles and the power grid state, the power transportation is optimized to achieve the goal of minimizing the operating cost of the interconnected system. On the basis of considering the disorderly charging model of electric vehicles, the optimal distribution network is considered, and an optimization and control model of the power transportation coupling system with multiple uncertainties is established. The travel decisions and charging behaviors of electric vehicles are coordinated and controlled through electricity prices, the optimal output of the distribution network units is optimized, and the road flow distribution of the transportation network is optimized, so that the total operating cost of the interconnected system is minimized.

[0224] Obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0225] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A method for optimizing and controlling a power-transportation coupling system with multiple uncertainties, characterized in that: include: Obtaining modeling results of a traffic network-road-vehicle with multiple uncertainties, wherein the modeling results of the traffic network-road-vehicle include road capacity modeling, road ambient temperature modeling, automobile travel demand modeling, electric vehicle battery initial SOC modeling, and electric vehicle SOC modeling at the end of charging; Obtaining modeling results of a distribution network with multiple uncertainties, wherein the modeling results of the distribution network include: distributed power generation output uncertainty modeling and load uncertainty modeling; Determine the calculation model for photovoltaic power generation and the disorderly charging model for electric vehicles; According to the spatiotemporal characteristics of the electric vehicle, combined with the disordered charging model and the ordered charging model of the electric vehicle, the minimum mean square error of the daily load of the photovoltaic power generation system of the electric vehicle is set as the objective function, and combined with the constraint conditions of the electric vehicle, the particle swarm optimization algorithm is used to obtain the optimization control method model of the power-transportation coupling system with multiple uncertainties; The electric traffic is controlled according to the multiple uncertainties electric traffic coupling system optimization control method model.

2. The method according to claim 1, characterized in that The photovoltaic power generation grid-connected electricity calculation model is expressed by Formula 1 as follows: Where: H A Indicates the total solar radiation on the horizontal surface, in kWh / m 2 ; E p Indicates the amount of electricity generated on the grid, in kWh; P AZ Indicates the irradiance under standard conditions, the unit is kWp; E S represents the installed capacity of the component, and the constant is 1; K represents the comprehensive efficiency coefficient, including the PV component type correction factor, the inclination angle of the PV array, the azimuth correction factor, the availability of the PV power generation system, the light utilization rate, the inverter efficiency, the collection line loss, the step-up transformer loss, and the PV component surface contamination correction factor.

3. The method according to claim 1, characterized in that The electric vehicle disorderly charging model is expressed by Formula 2 as follows: Where: T C Indicates the duration of the electric vehicle charging time, in hours; S indicates the daily mileage, in km; W 100 Indicates the power consumption per 100 kilometers, in kWh / 100km; P c Indicates charging power, in kW; η EV Indicates charging efficiency.

4. The method according to claim 1, characterized in that The minimum mean square error of the daily load of the photovoltaic power generation system of electric vehicles is set as the objective function, which conforms to Formula 3, as follows: Where: F represents the mean square error of daily load at the power grid end; P i,ev,t Indicates the charge and discharge capacity of electric vehicles; P ave Indicates the deviation P ori,t The arithmetic mean of the total amount; P ori,t Represents the difference between load and PV output at time t.

5. The method according to claim 4, characterized in that The deviation P ori,t The arithmetic mean of the total amount and the difference between the load and the photovoltaic output at time t are obtained using the following formula 4: P ori,t =P dt -P pv Where: P dt represents the foundation load at time t; P pv represents the photovoltaic power generation at time t.

6. The method according to claim 1, characterized in that The constraints of the electric vehicle include: electric vehicle charging power constraint, electric vehicle battery remaining power constraint, dispatchable load constraint, and electric vehicle power battery SOC constraint.

7. The method according to claim 6, characterized in that The electric vehicle charging power constraint conforms to Formula 5, as follows: Where: ——Charging power of electric vehicles when charging in an unordered manner; ——Charging power of electric vehicles when charging in an orderly manner; and / or, The remaining power constraint of the electric vehicle battery complies with Formula 6, as follows: P loadmax ≤P iloadmax (6) Where: P loadmax represents the peak load before optimization; P iloadmax represents the optimized load peak; and / or, The dispatchable load constraint, in accordance with Formula 7, is as follows: Where: ——maximum discharge power; - maximum charging power; and / or, The electric vehicle power battery SOC constraint complies with Formula 8, as follows: SOC min ≤SOC t ≤SOC max (8) Where: SOC min Indicates the minimum state of charge of the power battery; SOC max Indicates the maximum value of the power battery state of charge; and / or, The schedulable time constraint conforms to Formula 9, as follows: T arivall ≤T≤T leave (9) Where: T arivall Indicates the time when the electric vehicle enters the working area; T leave Indicates the time when the electric vehicle leaves the work zone.

8. The method according to claim 1, characterized in that The modeling of the traffic network-road-vehicle, setting the traffic network G T (N,A), where N is the node set and A is the road set; let S be the charging station node set, and W is the set of origin-destination (OD) pairs of all cars; and are the path sets between the fuel vehicle and electric vehicle in (OD) pairs ω∈W respectively; The road capacity modeling adopts formula 10 as follows: Among them, road a = (i, j) ∈ A (i, j ∈ N and i and j are the starting point and end point of road a respectively), and its capacity has a random fluctuation ξ C,a It is characterized by normal distribution, denoted as ξ C,a ~N(μ C,a ,σ C,a ), μ C,a and σ C,a They are respectively C,a The mean and standard deviation of ; and / or, The road environment temperature modeling adopts formula 11 as follows: Where: n is the power index; b n For a given coefficient, is the energy consumption rate of electric vehicles, ξ T Ambient temperature; and / or, The automobile travel demand modeling is consistent with random fluctuations and Normal distribution; and / or, The initial SOC modeling of the electric vehicle battery conforms to formula 12 as follows: and / or, The SOC modeling of the electric vehicle at the end of charging obeys a normal distribution, and the mean of the distribution is related to the expected charging target.

9. The method according to claim 1, characterized in that: The distributed power output uncertainty modeling specifically includes: a wind power generation model and a photovoltaic power generation model; The wind power generation model conforms to formula 13, as follows: Where P w,t is the output power, N is the number of wind turbines in the wind farm, P N is the rated power, V w,t is the wind speed, V rated is the rated power wind speed, V in 、V out are the cut-in and cut-out wind speeds of the fan respectively; The photovoltaic power generation model conforms to formula 14, as follows: Where: P pv is the rated capacity of photovoltaic power generation, f pv is the efficiency of the photovoltaic power generation system, S t is the irradiation intensity, S st is the standard light irradiance, α is the temperature coefficient, T t is the working temperature during period t, which can be estimated based on the light intensity and ambient temperature. st The standard temperature at which photovoltaic cells operate.

10. The method according to claim 1, characterized in that The load uncertainty modeling includes: a conventional user load model and an electric vehicle model; The conventional user load model conforms to formula 15, as follows: P B,i (i∈E N ) is the active power of other loads in the distribution network except the charging load of the charging station, which seems to obey the normal distribution and is denoted by P B,i ~N(μ B,i ,σ B,i ), where μ B,i and σ B,i P B,i , the mean and standard deviation, P B,i is the probability density function; the power factor and active power P of other loads except the charging load of the charging station at node i B,i The reactive power Q can be obtained B,i ; The electric vehicle model conforms to Equation 16 as follows: τ acc , τ lea are the mean time of electric vehicles arriving at and leaving the electric vehicle charging station; σ acc , σ lea are the standard deviations of the time it takes for electric vehicles to arrive at and leave the electric vehicle charging station, respectively.

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