A probabilistic power flow calculation method and system based on charging station node fault analysis

Abstract

The present invention discloses a probabilistic power flow calculation method and system based on charging station node fault analysis. The method comprises: first, establishing a road network model and a distribution network model based on graph theory, and considering the coupling relationship between the two to establish a road-to-electricity coupling model; then, constructing an electric vehicle travel chain model based on a Markov chain. On this basis, combining the road-to-electricity coupling model and considering the charging psychology of users, a method for characterizing the real-time dynamic response of electric vehicle load under N-1 faults is proposed. In particular, a probabilistic power flow calculation method based on charging station node fault analysis is used to simulate the electric vehicle load curve after a charging station node fault occurs in the distribution network. Finally, a Monte Carlo method is used to perform probabilistic power flow calculation. The power flow calculation results are used to analyze the secondary impact of N-1 faults, especially the spatiotemporal characteristics of electric vehicles under charging station node faults, on the distribution network power flow. A voltage evaluation index is then constructed to evaluate the importance of charging station nodes.

Description

Technical Field

[0001] The present application belongs to the technical field of probabilistic power flow calculation, and in particular relates to a probabilistic power flow calculation method and system based on charging station node fault analysis. Background Art

[0002] With the rapid development of new energy technologies, the penetration rate of distributed generation (DG) and electric vehicles (EV) in distribution networks is gradually increasing. These uncertainties will affect the stable operation of distribution networks. EVs, as a special type of load in distribution networks, have dual uncertainties in time and space. If a large number of EVs with disorderly charging are connected to the grid, it will inevitably pose a huge challenge to the reliable operation of the distribution network. Furthermore, as the penetration rate of new energy in distribution networks gradually increases, the operating conditions of distribution networks become more complex, the probability of node failure gradually increases, and thus the probability of charging station node failure also gradually increases. However, in the existing analysis and research of distribution network operating conditions, there is almost no existing technology that analyzes the stability and reliability of the entire system under such circumstances by analyzing the failure conditions of charging station nodes.

[0003] For example, the invention patent with publication number CN116485014 A discloses a method for predicting the load of orderly charging of electric vehicles under information coupling. It provides its own real-time status parameters by building a "vehicle-road-network-station" information coupling model to accurately calculate the mileage, time consumed, charging equipment utilization rate and distribution network load fluctuation required for the electric vehicle to go to each charging station when fast charging is needed at the current moment. The car owner goes to the charging station for fast charging through an orderly charging strategy, thereby alleviating the poor charging experience of users, traffic network congestion, low utilization rate of charging station equipment and increased peak-to-valley difference in distribution network load caused by disorderly charging of electric vehicles; the load of electric vehicles on the day of orderly charging is obtained through the load forecasting model, and the predicted load is more realistic and usable. This method is of great significance to the reasonable scheduling and planning of orderly charging of electric vehicles and access to the electric vehicle load distribution network, but it still does not take into account the impact of charging station node failure on the distribution network trend. Summary of the Invention

[0004] Purpose of the invention: To address the problem of insufficient analysis of the impact of EV transfer characteristics on distribution network power flow after a charging station node failure in the current distribution system, the present invention provides a probabilistic power flow calculation method based on charging station node failure analysis. The present invention also provides a probabilistic power flow calculation system based on charging station node failure analysis.

[0005] Technical solution: According to a first aspect of the present invention, a probabilistic power flow calculation method based on charging station node fault analysis is provided, the method comprising:

[0006] Based on graph theory, a road network model and a distribution network model are established. Taking into account the coupling relationship between the two, a road-power coupling model is established to determine the various charging station nodes in the distribution network model.

[0007] Establish a travel demand model and a charging demand model that take into account the temporal and spatial distribution characteristics of electric vehicles, thereby constructing a charging load model that takes into account the temporal and spatial distribution characteristics of electric vehicles;

[0008] Calculating a probabilistic power flow based on the charging load model, the base load model, and the distributed power supply probability model to obtain a charging station node voltage value and a line power value;

[0009] The static voltage stability index is used to measure the impact of charging station node failure on the distribution network flow.

[0010] Further, including:

[0011] The distribution network model is a graphical representation model of the topology and parameters of the power system. The distribution network structure includes a collection of nodes and branches, and the nodes only contain load charging stations and generators. The distribution network structure is represented as follows:

[0012]

[0013] Among them, G g Represents the distribution network topology; V g represents the set of charging station nodes in the distribution network; E g represents the set of distribution network lines; ψ g represents the adjacency matrix of the distribution network; v i g 、v j g represents the charging station node of the distribution network; m represents the number of charging station nodes; i and j represent variables with a value range of 1 to m; d ij g Lines between charging station nodes in the distribution network; i 、x i 、c i 、P i They represent the resistance, reactance, susceptance and line maximum transmission power in the distribution network respectively.

[0014] Further, including:

[0015] The construction of a charging load model for electric vehicles with temporal and spatial distribution characteristics includes:

[0016] The electric vehicle charging load model consists of a travel demand model and a charging behavior model. The travel demand model is the foundational module, which calculates the spatiotemporal distribution of electric vehicle travel paths and charging demand by combining the road network model, electric vehicle travel chains, and temporal and spatial characteristics.

[0017] The core of the travel demand model is to plan the shortest path for electric vehicles based on Dijkstra's theory, and to describe their temporal dynamics and spatial shifts throughout the day by combining the characteristics of their trip chains. In this context, a trip chain refers to the complete process of an electric vehicle starting from its starting point, passing through each destination in a timed sequence according to the itinerary, and finally arriving at the destination. The temporal chain characteristics refer to the temporal dynamics of an electric vehicle's travel throughout the day, including arrival and departure times, driving duration, and parking times at various destinations. The spatial chain characteristics refer to the spatial shifts of an electric vehicle's travel throughout the day, including the purpose of the trip and the distance traveled per trip.

[0018] The charging behavior model is based on the user's charging psychology and the state of charge of the electric vehicle to obtain the charging demand of a single electric vehicle at a charging station connected to the distribution network. The user's charging psychology is used to reflect the charging decision of each electric vehicle: at the beginning of each trip, the user will judge the final capacity of the trip. The remaining battery capacity at the end of the trip can be described as:

[0019] S(t end )=S(t ini )-Dis·ω car ;

[0020] Among them, S(t end ) and S(t ini ) represent the capacity values ​​of the electric vehicle at the time of arrival at the destination and the time of departure respectively; Dis represents the distance formed; w car Indicates the power consumption per unit distance of an electric vehicle.

[0021] At this time, we use a random number r between 0 and 1 to represent the user's charging psychology. If the random number r is greater than S(t end ) or S(t end ) is less than 20% of the maximum battery capacity, the user chooses to charge; if the random number r is less than S(t end ), and S(t end ) is greater than 20% of the maximum battery capacity, charging is not selected. It can be described as follows:

[0022]

[0023] Wherein, μ represents the user's charging status. If μ = 1, it means charging is selected; if μ = 0, it means not charging; S max Indicates the maximum capacity of an electric vehicle battery.

[0024] Further, including:

[0025] If the user chooses to charge, the travel route will be replanned, that is, through the road-electricity coupling model, Dijkstra's theory will be used to generate a new path through the charging station node. The charging demand of a single electric vehicle at the grid access node b is expressed as:

[0026]

[0027] Where t represents the time; P(t) represents the charging demand of a single electric vehicle at time t, t0 represents the time when charging starts; T c Indicates charging time; P b (t) represents the charging demand of the electric vehicle connected to the charging station node b in the distribution network at time t; b represents the charging station node.

[0028] In the charging vehicle probability load model, the electric vehicle charging load approximately obeys the normal distribution, and the electric vehicle charging load P c The probability density function of the normal distribution is shown in the formula.

[0029]

[0030] Where, p(P c ) represents the probability density function of electric vehicle charging load; P c Indicates the charging load of electric vehicles; μ c and σ c are the expected value and standard deviation of the active power demand of a single electric vehicle at a certain moment; N(·) represents the normal distribution function.

[0031] By integrating the travel demand model and the charging behavior model, an overall framework for charging load prediction is formed.

[0032] Further, including:

[0033] If a charging station node fails, the corresponding network node is not considered when Dijkstra's theorem is used to generate a new path through the charging station node. For example, suppose grid nodes 3' and 5' are both charging station nodes, corresponding to network nodes 2 and 4, respectively. The user's original optimal network charging path is 1→2→6, where 1 is the network node at the departure time and 6 is the trip's destination node. If grid node 3' fails, network node 2 is not considered when Dijkstra's theorem is used to generate a new path through the charging station node, and the new path becomes 1→4→6. If the failed charging station node is repaired, it will be considered.

[0034] Further, including:

[0035] The probabilistic power flow is calculated based on the charging load model, the basic load model and the distributed power supply probability model.

[0036] The basic load refers to the long-term stable load in the distribution network. The corresponding load model is as follows:

[0037]

[0038] Among them, f(P L ) represents the base load active power P L The probability density distribution function of f(Q L ) is the basic load reactive power Q L The probability density distribution function of L and Q L are the active power and reactive power of the base load respectively; μ PL and μ QL They are active power P L and reactive power Q L The mean of PL and σ QL are the standard deviations of active power and reactive power respectively; σ 2 PL and σ 2 QL are the variances of active power and reactive power, respectively; exp[·] represents the exponential function with the natural constant e as the base.

[0039] The distributed power probability model includes a wind power output probability model and a photovoltaic output probability model. The wind power output model in the distributed power model is characterized by Weibull distribution, and the photovoltaic output model is characterized by Beta distribution. The wind power and photovoltaic random variables are respectively characterized by K distribution. wt and K pv Expressed as follows, the probability density functions of the two are as follows:

[0040]

[0041] in, represents the probability density function of wind power output that conforms to the Weibull distribution; υ represents the wind speed; k is the scale parameter and shape parameter of the Weibull distribution; exp[·] represents the exponential function with the natural constant e as the base;

[0042]

[0043] Among them, F pv (s, α, β) represents the probability density function of photovoltaic output that conforms to the Beta distribution; s represents the light intensity; α and β are the shape parameters of the Beta distribution; Γ(·) represents the Gamma function.

[0044] The static voltage stability index is used to measure the impact of a charging station node failure on the distribution network flow, including: using the obtained node voltage value to represent the static voltage stability index, which is expressed as:

[0045]

[0046] Among them, L j It represents the static voltage stability index corresponding to node j, using L j The voltage stability is judged by the distance from the critical point 0. The farther the distance, the better the voltage stability. Z ij Represents the impedance of branch ij, R ij Represents the resistance of branch ij, R ij represents the resistance of branch ij, X ij Represents the reactance of branch ij; S j represents the apparent power of node j, P j represents the active power of node j, Q j represents the reactive power of node j; U i It represents the voltage amplitude of node i. The importance of the electric vehicle charging station node refers to the impact of the failure of the electric vehicle charging station node on the distribution network flow quantified by the evaluation index. The greater the impact, the more important the electric vehicle charging station node.

[0047] Probabilistic power flow calculation involves incorporating the base load values ​​of each node at each moment, the charging load values ​​of the charging station nodes, and the output values ​​of the distributed power generation (DG) output from the probabilistic load model for charging vehicles, the base load model, and the distributed power generation (DG) into the power system power flow calculation to derive the voltage and power values ​​for each node. The present invention uses the Newton-Raphson power flow calculation method in Matpower for probabilistic power flow calculation. Matpower is a toolkit for power flow calculations. The Newton-Raphson power flow calculation method is a mathematical method.

[0048] Another aspect of the present invention provides a probabilistic power flow calculation system based on distribution network N-1 fault analysis, the system comprising:

[0049] The distribution network model construction module is used to establish the road network model and the distribution network model based on graph theory, and consider the coupling relationship between the two to establish the road-power coupling model and determine the various charging station nodes in the distribution network model;

[0050] The charging load model construction module is used to establish a travel demand model and a charging demand model that take into account the temporal and spatial distribution characteristics of electric vehicles, thereby constructing a charging load model that takes into account the temporal and spatial distribution characteristics of electric vehicles;

[0051] A probabilistic power flow calculation module is used to calculate the probabilistic power flow based on the charging load model, the basic load model and the distributed power supply probabilistic model, so as to obtain the charging station node voltage value and the line power value;

[0052] The characterization module is used to use the static voltage stability index to measure the impact of the failure of the charging station node on the distribution network flow.

[0053] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0054] This application constructs an electric vehicle travel chain model based on the Markov chain. On this basis, combined with the road-electricity coupling model and considering the user's charging psychology, a real-time dynamic response characterization method of electric vehicle load under charging station node failure is proposed. The proposed method studies the spatiotemporal transfer characteristics of electric vehicles under charging station node failure. In terms of the user's charging psychology choice, if the user chooses to charge, the travel route will be replanned. That is, through the previously constructed road-electricity coupling model, Dijkstra theory is used to generate a new path passing through the charging station node, so that the user can charge the electric vehicle in time.

[0055] The present invention considers the electric vehicle load model, distributed power supply model and basic load model input system of charging station node failure to perform probabilistic power flow calculation, and proposes a graph theory-based probabilistic power flow calculation method for charging station nodes in a distribution network containing large-scale electric vehicles. The power flow calculation results are used to analyze the secondary impact of the spatiotemporal characteristics of electric vehicles on the distribution network power flow under charging station node failure.

[0056] This application constructs a static voltage stability index and compares the voltage stability indexes of different charging station nodes after failure to evaluate the importance of electric vehicle charging station nodes. The importance of electric vehicle charging station nodes refers to the impact of electric vehicle charging station node failure on the distribution network flow quantified by evaluation indicators. The greater the impact, the more important the electric vehicle charging station node. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the conventional technology, the following briefly introduces the drawings required for use in the embodiments or the conventional technology descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 This is a flow chart of power flow calculation according to an embodiment of the present invention;

[0059] Figure 2 The user charging psychological simulation process described in the embodiment of the present invention;

[0060] Figure 3 A road network topology diagram according to an embodiment of the present invention;

[0061] Figure 4 A diagram showing the system structure according to an embodiment of the present invention;

[0062] Figure 5 The electric vehicle load simulation result according to the embodiment of the present invention;

[0063] Figure 6 This is the voltage data of the node after the N-1 fault according to the embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention and not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] First, to facilitate understanding of the present application, a more comprehensive description of the present application will be provided below with reference to the accompanying drawings. The drawings illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The term "and / or" as used herein includes any and all couplings of one or more of the associated listed items.

[0067] It will be understood that the terms "first," "second," etc. used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish a first element from another element.

[0068] The following are some explanations of some terms involved in this application to facilitate understanding of this application:

[0069] Step 1: Based on graph theory, a road network model and a distribution network model are established. Considering the coupling relationship between the two, a road-electricity coupling model is established. An electric vehicle travel chain model is constructed based on a Markov chain. On this basis, combined with the road-electricity coupling model and taking into account user charging psychology, a method for characterizing the real-time dynamic response of electric vehicle load under N-1 faults is proposed to simulate the transfer characteristics of electric vehicles under N-1 faults, thereby deriving the electric vehicle load curve.

[0070] Step 2: Using the Monte Carlo method to perform probabilistic power flow calculations, a graph-theory-based method for calculating the N-1 probabilistic power flow of a distribution network containing large-scale electric vehicles is proposed. The power flow calculation results are used to analyze the secondary impact of the spatiotemporal characteristics of electric vehicles on the distribution network power flow under the N-1 fault.

[0071] Step 3: Construct a static voltage stability index based on the voltage quantity under N-1 fault to evaluate the importance of the EV charging station node.

[0072] The flow calculation process of the present invention is as follows Figure 1 The specific steps include:

[0073] 1. Real-time dynamic response characterization method of electric vehicle load under N-1 fault

[0074] Graph theory, a branch of combinatorics, is often used to analyze pairwise relationships between research objects. The basic principle of graph theory can be described as using points and connecting lines to represent the relationships and structures between research objects. This paper uses graph theory to model both road networks and power grids.

[0075] (1) Road network model

[0076] The road network model refers to the connection state model between each road network node in the traffic road network topology structure. In the road network topology diagram, the line segment represents the road and the point represents the road network node. The road network model is shown in formula (1). Figure 3 shown.

[0077]

[0078] In formula (1), G r Represents the road network topology; where V r Represents a set of road nodes; v i r 、v j r represents the road node; n represents the number of road nodes; E r Represents a set of road segments; d ij r Indicates the distance between road nodes; i and j represent variables, with a value range of 1 to n; D r Represents the adjacency matrix of the road network, e ij r It represents the length of the road section, and its calculation formula is shown in formula (2).

[0079]

[0080] In formula (2), e ij r 、e ji r Indicates the length of the road section; d ij r Indicates the distance between road nodes; v i r 、v j r represents a road node; i and j represent variables, ranging from 1 to n; inf represents infinity. According to formula (1)-formula (2), D r It can be expressed as shown in formula (3).

[0081]

[0082] In formula (3), D r Represents the adjacency matrix of the road network; inf is infinity; d 12 and d 21are the distances between road nodes 1 and 2 in the road network; d 14 and d 41 are the distances between road nodes 1 and 4 in the road network; d 23 and d 32 are the distances between road nodes 2 and 3 in the road network; d 25 and d 52 are the distances between road nodes 2 and 5 in the road network; d 35 and d 53 are the distances between road nodes 3 and 5 in the road network; d 45 and d 54 Both are the distances between road node 4 and road node 5 in the road network.

[0083] (2) Distribution network model

[0084] The distribution network model is a graphical representation of the topology and parameters of the power system. This invention does not include switches when modeling the distribution network. Instead, it simplifies the distribution network structure into a collection of nodes and branches, with nodes containing only loads and generators. The distribution network structure is shown in Equation (4).

[0085]

[0086] In formula (4), G g Represents the distribution network topology; V g represents the set of distribution network nodes; E g represents the set of distribution network lines; ψ g represents the adjacency matrix of the distribution network; v i g 、v j g represents the distribution network node; m represents the number of grid nodes; i and j represent variables, with a value range of 1 to m; d ij g Lines between distribution network nodes; r i 、x i 、c i 、P i They represent the resistance, reactance, susceptance and line maximum transmission power in the distribution network respectively.

[0087] (3) Circuit-electricity coupling model

[0088] A coupling relationship refers to the interaction and mutual influence between two entities. The road-power coupling model represents the interaction between the road network and the distribution network. The road-power coupling model primarily consists of three parts: the road network model, the distribution network model, and the coupling relationship between the two topological structures, as shown in Equation (5).

[0089]

[0090] In formula (5), G represents the circuit-electric coupling topology; G r Represents the road network topology; G g represents the distribution network topology; E r-g represents the network coupling edge set; d ij represents the network coupling edge; v i r represents a road node; v j g Grid node; V r Represents a set of road nodes; V g Represents a collection of distribution network nodes.

[0091] Markov theory describes the random process of an EV transferring from one destination to the next. According to Markov theory, the transfer process of a random event has no aftereffects, and the transfer probability is determined solely by the state at the previous moment. The expression of the Markov chain is shown in Equation (6).

[0092] P(E i →E j )=P(E i |E j )=P ij (6)

[0093] In formula (6), E i and E j Represent the state of random events at the current moment and the next moment respectively; P(E i →E j )、P(E i |E j ) and P ij All are random events from E i Transfer to E j probability.

[0094] The electric vehicle charging load model consists of two parts: the first part is the EV travel demand model; the second part is the EV charging demand model.

[0095] The spatiotemporal characteristics of EVs are described using a trip chain. Based on graph theory, a travel demand model is established that takes into account the spatiotemporal distribution characteristics of EVs. A trip chain refers to the complete process of an electric vehicle starting from a starting point, passing through each travel destination in a time sequence according to the itinerary, and finally arriving at the destination. The trip chain contains several characteristic quantities, which can be divided into two categories: time chain characteristic quantities and space chain characteristic quantities. Both can jointly describe the spatiotemporal distribution characteristics of EVs. Time chain characteristic quantities refer to the temporal dynamic changes of EV travel within a day, mainly composed of the time of arrival and departure at the destination, the driving time, and the parking time at various destinations; space chain characteristic quantities refer to the spatial transfer of EV travel within a day, mainly composed of the travel purpose and the mileage of the EV's single trip.

[0096] Assume that EVs have five travel destinations: residential (H), business office (W), social and entertainment (SR), commercial shopping and dining (SE), and areas with mixed uses (O). Markov theory is used to construct a travel chain. Based on graph theory, Dijsktra's theory is used to plan the shortest route, taking into account road network constraints and factors such as road congestion. Statistical data can be used to obtain some known quantities, and the relationship between spatial and temporal chain characteristics can be used to determine the unknown quantities. Ultimately, all the characteristics of the travel chain can be calculated.

[0097] The degree of road congestion is described by the number of cars traveling on the path between two adjacent road network nodes in the road network, which is inversely proportional to the speed of cars on this road. The specific expression is shown in formula (7):

[0098]

[0099] In formula (7), V i,j (t) represents the formal speed of the i-th EV on the j-th path at time t; L path,j represents the length of the jth path; N car,j (t) represents the number of EVs on the jth path at time t.

[0100] The Dijsktra algorithm is a commonly used algorithm for calculating the shortest path between nodes in a graph. It determines the distance between nodes on a road. The algorithm incorporates graph theory, as detailed below.

[0101] In a weighted graph, each vertex is assigned a unique identifier. The temporary identifier, T, represents the upper limit of the currently estimated shortest path length from the starting vertex to that vertex. The fixed identifier, P, represents the confirmed shortest path length from the starting vertex to that vertex, a finalized value.

[0102] (1) Vertex v1 is labeled P, label d(v1) = 0, vertex vj (j=2,3,…,n) is marked as T, and the label d(v j )=l 1,j ;

[0103] (2) Find the minimum value among all the numbers T, that is: if d(v j0 )=l 1,j0 , v j0 The label of the node is changed from T to P, and then the T labels of other nodes with the label T are recalculated; select vertex v j T label d(v j0 )+l j0,j The smaller of the two is taken as v j The new T label.

[0104] (3) Repeat the above process until the label of the target vertex becomes P.

[0105] Figure 2 The user's charging psychology simulation process diagram is shown. The charging behavior model is based on the user's charging psychology and the electric vehicle's state of charge to obtain the charging demand of a single electric vehicle at a charging station connected to the distribution network. The user's charging psychology is used to reflect the charging decision of each electric vehicle: at the beginning of each trip, the user will judge the final capacity of the trip. The remaining battery capacity at the end of the trip can be described as:

[0106] S(t end )=S(t ini )-Dis·ω car (8)

[0107] Among them, S(t end ) and S(t ini ) represent the capacity values ​​of the electric vehicle at the time of arrival at the destination and the time of departure respectively; Dis represents the distance formed; w car Indicates the power consumption per unit distance of an electric vehicle.

[0108] At this time, a random number r between 0 and 1 is used to represent the user's charging psychology. If the random number r is greater than S(t end ) or S(t end ) is less than 20% of the maximum battery capacity, the user chooses to charge; if the random number r is less than S(t end ), and S(t end ) is greater than 20% of the maximum battery capacity, charging is not selected. It can be described as follows:

[0109]

[0110] Wherein, μ represents the user's charging status. If μ = 1, it means charging is selected; if μ = 0, it means not charging; S maxIndicates the maximum capacity of an electric vehicle battery.

[0111] The time when the electric vehicle arrives at the destination is t end The calculation formula is as follows:

[0112]

[0113] In formula (10), t end and t ini They represent the time when the electric vehicle arrives at the destination and the time when it departs; J represents the set of paths that the charging vehicle i has taken; L path,j represents the length of path j; V i,j represents the driving speed of charging car i on path j.

[0114] Furthermore, if a charging station node fails, the corresponding network node is not considered when Dijkstra's theorem is used to generate a new path through the charging station node. For example, suppose grid nodes 3' and 5' are both charging station nodes, corresponding to network nodes 2 and 4, respectively. The user's original optimal network charging path is 1→2→6, where 1 is the network node at the departure time and 6 is the trip's destination node. If grid node 3' fails, network node 2 is not considered when Dijkstra's theorem is used to generate a new path through the charging station node, and the new path becomes 1→4→6. If the failed charging station node is repaired, it will be considered.

[0115] An N-1 failure refers to a component failure in a power system operating normally, ensuring that other equipment is not affected and fails, and that the system as a whole maintains stable operation and normal power supply. In this case, the N-1 failure refers to a failure at a node in the distribution network. In this case, the N-1 failure is defined as a faulty disconnection at a charging station node.

[0116] After completing the above simulation, the charging demand of a single EV at the grid access node b can be obtained as shown in formula (11).

[0117]

[0118] In formula (11), t represents the time; P c (t) represents the charging demand of a single EV at time t, t0 represents the charging start time; T c Indicates charging time; P b (t) represents the charging demand of EV connected to grid node b at time t; b represents the grid node.

[0119] After completing the simulation of EV travel demand and charging demand, a charging load model that considers the temporal and spatial distribution characteristics of electric vehicles can be constructed.

[0120] In the charging vehicle probability load model, the electric vehicle charging load approximately obeys the normal distribution, and the electric vehicle charging load P c The probability density function of the normal distribution is shown in formula (12).

[0121]

[0122] In formula (12), p(P c ) represents the probability density function of electric vehicle charging load; P c Indicates the charging load of electric vehicles; μ c and σ c are the expected value and standard deviation of the active power demand of a single electric vehicle at a certain moment; N(·) represents the normal distribution function.

[0123] 2. Graph Theory-Based N-1 Probabilistic Power Flow Calculation Method for Distribution Networks with Large-Scale Electric Vehicles

[0124] Probabilistic power flow calculation generally refers to inputting random variables such as wind power output, photovoltaic output, and random load into the power system and then performing deterministic power flow calculation as shown in Equation (13). Finally, the probability distribution characteristics of output variables such as node voltage and line power are calculated.

[0125] K(U,δ,…)=G(P,Q) (13)

[0126] In formula (13), K(·) represents the output variable matrix; G(·) represents the power flow equation; U represents the node voltage amplitude; δ represents the node voltage phase angle; P represents active power; and Q represents reactive power.

[0127] The Monte Carlo method, a numerical solution method based on mathematical statistics and probability theory, is one of the basic methods for probabilistic power flow calculations. The Monte Carlo method is widely used in probabilistic power flow calculations due to its easy-to-understand principles, simple programming, and high computational accuracy.

[0128] The probabilistic power flow calculation model consists of a probabilistic load model for charging vehicles, a basic load model, and a distributed power source. The probabilistic load model for charging vehicles is characterized by a normal distribution as shown in formula (12). Probabilistic power flow calculation refers to bringing the basic load values ​​of each node at each moment output by the probabilistic load model for charging vehicles, the basic load model, and the probabilistic model for distributed power sources, the charging load values ​​of the charging station nodes, and the output values ​​of the distributed power sources into the power system power flow calculation to obtain the voltage and power values ​​of each node. In the present invention, the Newton-Raphson power flow calculation method in Matpower is used to perform probabilistic power flow calculation. Matpower is a toolkit for calculating power flow. The Newton-Raphson power flow calculation method is a mathematical method.

[0129] The basic load refers to the long-term stable load in the distribution network, and is characterized by the normal distribution shown in formula (14).

[0130]

[0131] In formula (14), f(P L ) represents the base load active power P L The probability density distribution function of f(Q L ) is the basic load reactive power Q L The probability density distribution function of L and Q L are the active power and reactive power of the base load respectively; μ PL and μ QL They are active power P L and reactive power Q L The mean of PL and σ QL are the standard deviations of active power and reactive power respectively; σ 2 PL and σ 2 QL are the variances of active power and reactive power, respectively; exp[·] represents the exponential function with the natural constant e as the base.

[0132] The distributed power generation probability model includes the wind power output probability model and the photovoltaic output probability model. The wind power output model in the distributed power generation model is characterized by Weibull distribution, and the photovoltaic output model is characterized by Beta distribution. The wind power and photovoltaic random variables are characterized by K distribution. wt and K pv The probability density functions of the two are expressed as shown in formula (15) and formula (16) respectively.

[0133]

[0134] In formula (15), represents the probability density function of wind power output that conforms to the Weibull distribution; υ represents the wind speed; k is the scale parameter and shape parameter of the Weibull distribution; exp[·] represents the exponential function with the natural constant e as the base.

[0135]

[0136] In formula (16), F pv (s, α, β) represents the probability density function of photovoltaic output that conforms to the Beta distribution; s represents the light intensity; α and β are the shape parameters of the Beta distribution; Γ(·) represents the Gamma function.

[0137] The probabilistic power flow calculation method based on charging station node fault analysis of the present invention is actually an N-1 probabilistic power flow calculation method for distribution networks containing large-scale electric vehicles based on graph theory. It combines graph theory, probability theory and power flow calculation methods, and aims to evaluate the power flow distribution and uncertainty of distribution networks containing large-scale electric vehicles under N-1 fault conditions.

[0138] The power flow calculation results include node voltage values ​​and line power values.

[0139] The secondary impact of the spatiotemporal characteristics of electric vehicles on the distribution network current under N-1 faults refers to the premise that the spatiotemporal uncertainty of electric vehicle loads has an impact on the distribution network current. After the N-1 fault occurs in the system, the electric vehicles are again temporally and spatially transferred due to the fault, which in turn causes a secondary impact on the distribution network current.

[0140] 3. Determine the importance of charging station nodes

[0141] The static voltage stability index is used to measure the degree of voltage collapse and characterize the severity of the system instability. The calculation formula of the static voltage stability index is shown in formula (15).

[0142]

[0143] In formula (15), L j It represents the static voltage stability index corresponding to node j, using L j The voltage stability is judged by the distance from the critical point 0. The farther the distance, the better the voltage stability. Z ij Represents the impedance of branch ij, R ij Represents the resistance of branch ij, R ij represents the resistance of branch ij, X ij Represents the reactance of branch ij; S j represents the apparent power of node j, P j represents the active power of node j, Q j represents the reactive power of node j; U i Represents the voltage amplitude of node i.

[0144] The importance of an electric vehicle charging station node refers to the impact of a failure of an electric vehicle charging station node on the distribution network flow, which is quantified through evaluation indicators. The greater the impact, the more important the electric vehicle charging station node is.

[0145] The technical effects of the present invention will be further illustrated by examples below:

[0146] The present invention uses the IEEE-33 node distribution network as the basis for example analysis. Its structure is as follows: Figure 4As shown in Figure 1. Based on the different uses of the load, the road area covered by the power grid is divided into five categories: residential area (H), business office area (W), social and entertainment area (SR), commercial shopping and dining area (SE), and area with other uses (O). A charging station is set in each area. The corresponding relationship between the charging station and the grid node is as follows: Figure 4 shown.

[0147] The number of electric vehicles is 2500. In this paper, the grid node corresponding to the charging station is named the charging station node. Assuming that the charging station node in area H fails during the daytime from 10:30 to 11:00, the transfer characteristics of the electric vehicle load are simulated, and the following is obtained: Figure 5 The EV load curve shown is given by Figure 5 It can be seen that during the fault period, EV load fluctuations increased significantly, and at the moment the node failed, the overall load showed a downward trend, indicating that the load was instantly lost at that point. After the fault was repaired, the EV load showed an upward trend, showing the characteristics of load transfer.

[0148] The Monte Carlo method is used to calculate the probability flow, and the voltage data of each node at each time after N-1 fault is obtained as follows: Figure 6 As shown in Table 1, the voltage data after a node fault is compared with the power flow calculation results obtained under normal system operation. The power flow calculation results with and without faults are used to analyze the secondary impact of the spatiotemporal characteristics of electric vehicles on the distribution network power flow under the N-1 fault. Then, assuming that the charging station loads in areas W, SR, SE, and O fail in sequence, the static voltage stability index values ​​of typical system nodes after the fault are calculated, as shown in Table 1.

[0149] Table 1 Static voltage stability indicators of some nodes 10:45

[0150]

[0151] Analysis of Table 1 shows that when the charging station node in area W, that is, node 13, fails, the voltage stability index of the overall system node is closer to 0, indicating that the system node is more unstable in this case. Therefore, the charging station node in area W is relatively important, and the voltage weak point under the current working conditions is obtained through the voltage stability index.

[0152] A second aspect of the present invention provides a probabilistic power flow calculation system based on charging station node fault analysis, the system comprising:

[0153] The distribution network model construction module is used to establish the road network model and the distribution network model based on graph theory, and consider the coupling relationship between the two to establish the road-power coupling model and determine the various charging station nodes in the distribution network model;

[0154] The charging load model construction module is used to establish a travel demand model and a charging demand model that take into account the temporal and spatial distribution characteristics of electric vehicles, thereby constructing a charging load model that takes into account the temporal and spatial distribution characteristics of electric vehicles;

[0155] A probabilistic power flow calculation module is used to calculate the probabilistic power flow based on the charging load model, the basic load model and the distributed power supply probabilistic model, so as to obtain the charging station node voltage value and the line power value;

[0156] The characterization module is used to use the static voltage stability index to measure the impact of the failure of the charging station node on the distribution network flow.

[0157] A third aspect of the present application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the aforementioned probabilistic power flow calculation method based on charging station node fault analysis.

[0158] A fourth aspect of the present application provides a computer program, which, when executed by a processor, implements the steps of the aforementioned probabilistic power flow calculation method based on charging station node fault analysis.

[0159] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0160] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0161] The scope of protection of the present disclosure is not limited to the above-described embodiments. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the scope and spirit of the present disclosure. If such modifications and variations fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include such modifications and variations.

Claims

1. A probabilistic power flow calculation method based on charging station node failure analysis, characterized in that: The method comprises: Based on graph theory, a road network model and a distribution network model are established. Taking into account the coupling relationship between the two, a road-electricity coupling model is established, and each charging station node in the distribution network model is determined; the road-electricity coupling model is a model that represents the interaction relationship between the road network model and the distribution network model; Establish a travel demand model and charging behavior model that consider the temporal and spatial distribution characteristics of electric vehicles, thereby constructing a charging load model for electric vehicles that takes into account the temporal and spatial distribution characteristics. The travel demand model plans the shortest path for electric vehicles based on Dijkstra's theory and combines the travel chain characteristics of electric vehicles to describe their temporal dynamic changes and spatial transfers within a day. The charging behavior model derives the charging demand of a single electric vehicle at a charging station node connected to the distribution network based on user charging psychology and the charge state of the electric vehicle. Calculating a probabilistic power flow based on the charging load model, the base load model, and the distributed power generation probabilistic model to obtain a charging station node voltage value and a line power value; the base load model refers to a long-term stable load in the distribution network; The static voltage stability index is used to measure the impact of charging station node failure on the distribution network flow; The distributed power probability model includes a wind power output probability model and a photovoltaic output probability model. The wind power output model in the distributed power model is characterized by Weibull distribution, and the photovoltaic output model is characterized by Beta distribution. The wind power and photovoltaic random variables are respectively characterized by K distribution. wt and K pv Expressed as follows, the probability density functions of the two are as follows: in, represents the probability density function of wind power output that conforms to the Weibull distribution; υ represents the wind speed; k is the scale parameter and shape parameter of the Weibull distribution; exp[·] represents the exponential function with the natural constant e as the base; Among them, F pv (s, α, β) represents the probability density function of photovoltaic output that conforms to the Beta distribution; s represents the light intensity; α and β are the shape parameters of the Beta distribution; Γ(·) represents the Gamma function; The use of a static voltage stability index to measure the impact of a charging station node failure on the distribution network flow includes: using the obtained node voltage value to represent the static voltage stability index, which is expressed as: In formula (12), L j It represents the static voltage stability index corresponding to node j, using L j The voltage stability is judged by the distance from the critical point 0. The farther the distance, the better the voltage stability. Z ij Represents the impedance of branch ij, R ij represents the resistance of branch ij, X ij Represents the reactance of branch ij; S j represents the apparent power of node j, P j represents the active power of node j, Q j represents the reactive power of node j; U i It represents the voltage amplitude of node i. The importance of the electric vehicle charging station node refers to the impact of the failure of the electric vehicle charging station node on the distribution network flow quantified by the evaluation index. The greater the impact, the more important the electric vehicle charging station node.

2. The probabilistic power flow calculation method based on charging station node failure analysis according to claim 1 is characterized in that: The distribution network model is a graphical representation model of the topology and parameters of the power system. The distribution network structure includes a collection of nodes and branches, and the nodes only contain load charging stations and generators. The distribution network structure is represented as follows: Among them, G g Represents the distribution network topology; V g represents the set of charging station nodes in the distribution network; E g represents the set of distribution network lines; ψ g represents the adjacency matrix of the distribution network; v i g 、v j g represents the charging station node of the distribution network; m represents the number of charging station nodes; i and j represent variables with a value range of 1 to m; d ij g Lines between charging station nodes in the distribution network; i 、x i 、c i 、P i They represent the resistance, reactance, susceptance and line transmission limit power in the distribution network respectively; The road network model refers to the connection state model between each road network node in the traffic road network topology structure. In the road network topology diagram, the line segment represents the road and the point represents the road network node. The road network model is shown in formula (2): Among them, G r Represents the road network topology; where V r Represents a set of road nodes; v i r 、v j r represents the road node; n represents the number of road nodes; E r Represents a set of road segments; d ij r Indicates the distance between road nodes; i and j represent variables, with a value range of 1 to n; D r Represents the adjacency matrix of the road network, e ij r It represents the length of the road section, and its calculation formula is shown in formula (3): Among them, e ij r 、e ji r Indicates the length of the road section; d ij r Indicates the distance between road nodes; v i r 、v j r Indicates a road node; i and j represent variables, with a value range of 1 to n; inf represents infinity; The road-electricity coupling model includes: a coupling relationship refers to an interaction and mutual influence between two things. The road-electricity coupling model is a model that represents the interaction between the road network model and the distribution network model. The corresponding coupling relationship between topological structures is expressed as follows: Where G represents the circuit-electricity coupling topology; G r Represents the road network topology; G g represents the distribution network topology; E r-g represents the network coupling edge set; d ij represents the network coupling edge; v i r Represents a road node; v j g Grid node; V r Represents a road node set; V g Represents a collection of distribution network nodes.

3. The probabilistic power flow calculation method based on charging station node failure analysis according to claim 1 is characterized in that: The construction of a charging load model for electric vehicles with temporal and spatial distribution characteristics includes: The electric vehicle charging load model includes a travel demand model and a charging behavior model, wherein the travel demand model is used to calculate the spatiotemporal distribution of the electric vehicle's driving path and charging demand by combining the road network model, the electric vehicle's travel chain, the time chain characteristic quantity, and the space chain characteristic quantity; The travel chain in the travel demand model refers to the complete process of an electric vehicle starting from a starting point, passing through each travel destination one by one in a time sequence according to the itinerary, and finally arriving at the trip destination; the time chain characteristic quantity refers to the temporal dynamic changes of the electric vehicle's travel within a day, including the time of arrival and departure from the destination, the driving time, and the parking time at various destinations; the spatial chain characteristic quantity refers to the spatial transfer of the electric vehicle's travel within a day, including the travel purpose and the mileage of the electric vehicle's single trip; The user's charging psychology is used to reflect the charging decision of each electric vehicle: at the beginning of each trip, the user will judge the final capacity of the trip, and the remaining battery capacity at the end of the trip is described as: S(t end )=S(t ini )-Dis·ω car ; (5) Among them, S(t end ) and S(t ini ) represent the capacity values ​​of the electric vehicle at the time of arrival at the destination and the time of departure respectively; Dis represents the distance formed; w car Indicates the power consumption per unit distance of electric vehicles; At this time, a random number r between 0 and 1 is used to represent the user's charging psychology. If the random number r is greater than S(t end ) or S(t end ) is less than 20% of the maximum battery capacity, the user chooses to charge; If the random number r is less than S(t end ), and S(t end ) is greater than 20% of the maximum battery capacity, charging is not selected; Specifically it can be described as: Wherein, μ represents the user's charging status. If μ = 1, it means charging is selected; if μ = 0, it means not charging; S max Indicates the maximum capacity of an electric vehicle battery.

4. The probabilistic power flow calculation method based on charging station node failure analysis according to claim 3 is characterized in that: If the user chooses to charge, the travel route will be replanned. That is, through the road-electricity coupling model, Dijkstra's theory is used to generate a new path through the charging station node. The charging demand of a single electric vehicle at the grid access node b is expressed as: Where, t represents the time; P c (t) represents the charging demand of a single electric vehicle at time t, t0 represents the charging start time; T c Indicates charging time; P b (t) represents the charging demand of the electric vehicle connected to the charging station node b in the distribution network at time t; b represents the charging station node; By integrating the travel demand model and the charging behavior model, an overall framework for charging load prediction is formed; In the charging vehicle probability load model, the electric vehicle charging load approximately obeys the normal distribution, and the electric vehicle charging load P c The probability density function of the normal distribution is expressed as follows: Among them, p(P c ) represents the probability density function of electric vehicle charging load; P c Indicates the charging load of electric vehicles; μ c and σ c are the expected value and standard deviation of the active power demand of a single electric vehicle at a certain moment; N(·) represents the normal distribution function.

5. The probabilistic power flow calculation method based on charging station node failure analysis according to claim 3 is characterized in that: If a charging station node fails, the road network node corresponding to the faulty charging station node in the road network model is not considered when generating a new path passing through the charging station node using Dijkstra theory.

6. The probabilistic power flow calculation method based on charging station node failure analysis according to claim 5 is characterized in that: The probability power flow is calculated based on the charging load model, the basic load model and the distributed power supply probability model; the load model corresponding to the basic load is expressed as follows: Among them, f(P L ) represents the base load active power P L The probability density distribution function of f(Q L ) is the basic load reactive power Q L The probability density distribution function of L and Q L are the active power and reactive power of the base load respectively; μ PL and μ QL They are active power P L and reactive power Q L The mean of PL and σ QL are the standard deviations of active power and reactive power respectively; σ 2 PL and σ 2 QL are the variances of active power and reactive power, respectively; exp[·] represents the exponential function with the natural constant e as the base.

7. The probabilistic power flow calculation method based on charging station node failure analysis according to claim 6 is characterized in that: Probabilistic power flow calculation refers to bringing the basic load values ​​of each node at each moment, the charging load values ​​of the charging station nodes, and the output values ​​of the distributed power sources output by the probabilistic load model of the charging vehicle, the basic load model, and the probabilistic model of the distributed power sources into the power system power flow calculation to obtain the voltage and power values ​​of each node; specifically, the Newton-Raphson power flow calculation method in Matpower is used for probabilistic power flow calculation.

8. A probabilistic power flow calculation system based on charging station node fault analysis, characterized in that: The system includes: The distribution network model construction module is used to establish the road network model and the distribution network model based on graph theory, and consider the coupling relationship between the two to establish the road-power coupling model and determine the various charging station nodes in the distribution network model; The charging load model construction module is used to establish a travel demand model and a charging demand model that take into account the temporal and spatial distribution characteristics of electric vehicles, thereby constructing a charging load model that takes into account the temporal and spatial distribution characteristics of electric vehicles; A probabilistic power flow calculation module is used to calculate the probabilistic power flow based on the charging load model, the basic load model and the distributed power supply probabilistic model, so as to obtain the charging station node voltage value and the line power value; A characterization module is used to measure the impact of charging station node failures on distribution network power flow using a static voltage stability index; The distributed power probability model includes a wind power output probability model and a photovoltaic output probability model. The wind power output model in the distributed power model is characterized by Weibull distribution, and the photovoltaic output model is characterized by Beta distribution. The wind power and photovoltaic random variables are respectively characterized by K distribution. wt and K pv Expressed as follows, the probability density functions of the two are as follows: in, represents the probability density function of wind power output that conforms to the Weibull distribution; υ represents the wind speed; k is the scale parameter and shape parameter of the Weibull distribution; exp[·] represents the exponential function with the natural constant e as the base; Among them, F pv (s, α, β) represents the probability density function of photovoltaic output that conforms to the Beta distribution; s represents the light intensity; α and β are the shape parameters of the Beta distribution; Γ(·) represents the Gamma function; The use of a static voltage stability index to measure the impact of a charging station node failure on the distribution network flow includes: using the obtained node voltage value to represent the static voltage stability index, which is expressed as: In formula (12), L j It represents the static voltage stability index corresponding to node j, using L j The voltage stability is judged by the distance from the critical point 0. The farther the distance, the better the voltage stability. Z ij The impedance R of branch ij ij represents the resistance of branch ij, X ij Represents the reactance of branch ij; S j represents the apparent power of node j, P j represents the active power of node j, Q j represents the reactive power of node j; U i It represents the voltage amplitude of node i. The importance of the electric vehicle charging station node refers to the impact of the failure of the electric vehicle charging station node on the distribution network flow quantified by the evaluation index. The greater the impact, the more important the electric vehicle charging station node.

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