Dynamic probabilistic load flow calculation method and system considering vehicle network interaction and wind-solar correlation

Abstract

The invention discloses a dynamic probabilistic load flow calculation method and system considering vehicle network interaction and wind-light correlation, and the method comprises the steps: constructing a wind-light power generation dynamic probability distribution model based on the parameter distribution condition of wind-light unit power generation data, and calculating the wind-light correlation through a first function by the wind-light power generation dynamic probability distribution model; establishing a vehicle charging and discharging load model according to the traffic network of the vehicle, the travel mode and the electrical load, wherein the vehicle charging and discharging load probability model is used for acquiring vehicle charging and discharging load data; and performing dynamic probabilistic load flow calculation through the vehicle charging and discharging load probability model and the wind-solar power generation dynamic probability distribution model to obtain the accuracy of the probability distribution model and the dynamic operation characteristics of the system. According to the method, the accuracy of the probability distribution model is improved by establishing the dynamic probability distribution model of the wind and the light and describing the correlation between the wind and the light. Fine dynamic probabilistic load flow calculation is realized through probability modeling of wind and light power generation and electric vehicle charging and discharging loads.

Description

Technical Field

[0001] The present invention relates to the technical field of probabilistic power flow calculation, and in particular to a dynamic probabilistic power flow calculation method and system taking into account vehicle-grid interaction and wind-solar correlation. Background Art

[0002] At present, when new energy sources such as wind power, photovoltaic power, and electric vehicles are connected to the power grid on a large scale, there is a strong correlation between wind farms and photovoltaic power stations with close geographical locations, and the charging and discharging loads of electric vehicles show strong spatiotemporal distribution characteristics, resulting in insufficient consideration of the spatial characteristics of the power system, so that the current probabilistic power flow calculation method cannot reflect the actual operation status of the system. Therefore, it is necessary to propose a dynamic probabilistic power flow calculation method and power flow calculation system that considers vehicle-grid interaction and wind-solar correlation. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a dynamic probabilistic power flow calculation method and system that takes into account vehicle-grid interaction and wind-solar correlation to solve the problem that existing methods cannot accurately evaluate the power system operation characteristics and uncertainty impact of wind-solar power generation and electric vehicle charging and discharging loads.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation, including: constructing a wind-solar power generation dynamic probability distribution model based on parameter distribution of wind-solar unit power generation data, wherein the wind-solar power generation dynamic probability distribution model calculates wind-solar correlation using a first function;

[0008] Establishing a vehicle charging and discharging load model according to the vehicle's traffic network, travel mode and power load, wherein the vehicle charging and discharging load probability model is used to obtain vehicle charging and discharging load data;

[0009] Dynamic probability power flow calculation is performed through the vehicle charging and discharging load probability model and the wind and solar power generation dynamic probability distribution model to obtain the accuracy of the probability distribution model and the dynamic operation characteristics of the system.

[0010] As a preferred solution of the dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation described in the present invention, wherein:

[0011] As a preferred scheme of the dynamic probabilistic power flow calculation method considering vehicle-grid interaction and wind-solar correlation described in the present invention, the dynamic probability distribution model of wind-solar power generation constructed based on the parameter distribution of wind-solar generator power generation data includes: a wind-solar parameter probability model in which the wind-solar generator power generation data conforms to the parameter model, and a wind-solar non-parametric kernel density estimation probability model in which the wind-solar generator power generation data does not conform to the parameter model.

[0012] As a preferred scheme of the dynamic probabilistic power flow calculation method considering the vehicle-grid interaction and wind-solar correlation described in the present invention, the parameter model includes: a parameter distribution model based on the power output characteristics of the wind-solar unit. If the actually measured wind speed or light intensity data can fit the distribution, it is judged that the data conforms to the parameter model.

[0013] As a preferred solution of the dynamic probabilistic power flow calculation method considering vehicle-grid interaction and wind-solar correlation described in the present invention, wherein: the vehicle charging and discharging load probability model includes a road network-distribution network interaction model established based on the vehicle traffic road network and its influencing factors; the road network-distribution network interaction model includes a node set, a road section set, a travel cost of a road section, and a set of multiple time periods divided into a whole day in the traffic road network; the influencing factors include the road impedance of the vehicle driving subject to the road section impedance and the cross node impedance; and,

[0014] A vehicle information matrix model is built based on vehicle type, single vehicle driving characteristic parameters, and single vehicle charging characteristic parameters.

[0015] As a preferred solution of the dynamic probability flow calculation method considering vehicle-grid interaction and wind-solar correlation described in the present invention, the dynamic probability flow calculation is performed through the vehicle charging and discharging load probability model and the wind-solar power generation dynamic probability distribution model, including: the wind-solar data samples calculated by the first function are sampled independently, and according to the Nataf transformation theory, the spatiotemporal correlation coefficient matrix of the standard normal random variable matrix Y is expressed as:

[0016]

[0017] Where φ(·) represents the joint probability density function of the standard normal distribution, ρ ij represents the components of the spatial correlation coefficient matrix, μ i and σ i They represent the expected and standard deviation of the power generated by the i-th DG field.

[0018] As a preferred solution of the dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation described in the present invention, it also includes: according to the component ρ of the spatial correlation coefficient matrix 0ij , we get the spatial correlation coefficient matrix H of the standard normal distribution matrix Y; according to the Nataf inverse transformation, we transform the independent standard normal distribution matrix S K×T Transformed into a standard normal distribution matrix Y with correlation K×T , according to the equal probability conversion principle, the DG output samples including correlation are calculated as follows:

[0019]

[0020] Among them, x ij represents the power generated by the i-th distributed generation field at the j-th time step, y ij represents the standard normal random variable after Nataf transformation, It represents the inverse cumulative distribution function of the i-th distributed generation electric field at the j-th time step.

[0021] In a second aspect, the present invention provides a dynamic probabilistic power flow calculation system considering vehicle-grid interaction and wind-solar correlation, comprising:

[0022] A first model building module is used to build a dynamic probability distribution model of wind-solar power generation based on the parameter distribution of wind-solar power generation data, wherein the dynamic probability distribution model of wind-solar power generation calculates the wind-solar correlation using a first function;

[0023] A second model building module is used to establish a vehicle charging and discharging load model according to the vehicle's traffic network, travel mode and power load, and the vehicle charging and discharging load probability model is used to obtain vehicle charging and discharging load data;

[0024] The calculation module is used to perform dynamic probability power flow calculation through the vehicle charging and discharging load probability model and the wind and solar power generation dynamic probability distribution model to obtain the accuracy of the probability distribution model and the dynamic operation characteristics of the system.

[0025] In a third aspect, the present invention provides a computing device, comprising:

[0026] Memory and processor;

[0027] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the dynamic probabilistic power flow calculation method considering vehicle-grid interaction and wind-solar correlation are implemented.

[0028] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the dynamic probabilistic power flow calculation method that considers vehicle-grid interaction and wind-solar correlation.

[0029] Compared with the prior art, the present invention has the following beneficial effects: the present invention combines parametric and non-parametric probability modeling theories to establish a dynamic probability distribution model for wind and solar power, and uses Copula functions to characterize the correlation between wind and solar power, thereby improving the accuracy of the probability distribution model. By modeling the probability of wind and solar power generation and electric vehicle charging and discharging loads, a more sophisticated dynamic probability flow calculation is achieved, which is highly reliable and can effectively predict the behavior of the power grid in the face of fluctuations in new energy power generation and changes in electric vehicle loads, and further demonstrate the influence of electric vehicle charging demand on power grid voltage, which is of great significance to the stable operation of the power grid in the context of large-scale popularization of electric vehicles in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 labor. Among them:

[0031] Figure 1 A schematic diagram of a method flow of a dynamic probabilistic power flow calculation method and system considering vehicle-grid interaction and wind-solar correlation according to an embodiment of the present invention;

[0032] Figure 2 A flow chart of a method for calculating a dynamic probabilistic power flow and a method for modeling a wind-solar integrated dynamic probabilistic model of a system taking into account vehicle-grid interaction and wind-solar correlation according to an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of a road network trunk road according to a method and system for calculating a dynamic probabilistic power flow that considers vehicle-grid interaction and wind-solar correlation according to an embodiment of the present invention;

[0034] Figure 4 A scatter plot and histogram of the actual active power and simulated active power of a photovoltaic unit in a dynamic probabilistic power flow calculation method and system considering vehicle-grid interaction and wind-solar correlation according to an embodiment of the present invention; wherein Figure 4 (a) is a scatter plot of the actual active power of PV1 and PV2. Figure 4 (b) is a scatter plot of the simulated active power of PV1 and PV2. Figure 4 (c) in the figure is the histogram of the actual active power of PV1. Figure 4 (d) in the figure is the histogram of the actual active power of PV1. Figure 4 (e) in the figure is the histogram of the actual active power of PV2. Figure 4 (f) in the figure is the histogram of PV2 simulated active power;

[0035] Figure 5 A comparison diagram of the cumulative distribution functions of node voltage and branch active power of a dynamic probabilistic power flow calculation method and system considering vehicle-grid interaction and wind-solar correlation according to an embodiment of the present invention;

[0036] Figure 6 A comparison diagram of the expected and standard deviation of node voltage and branch active power of a dynamic probabilistic power flow calculation method and system considering vehicle-grid interaction and wind-solar correlation according to an embodiment of the present invention;

[0037] Figure 7 A dynamic probability density comparison diagram of distributed power generation before and after grid connection according to a dynamic probability power flow calculation method and system considering vehicle-grid interaction and wind-solar correlation according to an embodiment of the present invention; wherein Figure 7 (a) is the voltage dynamic probability density of node 6 before photovoltaic grid connection. Figure 7 (b) is the voltage dynamic probability density of node 6 after photovoltaic grid connection. Figure 7 (c) is the voltage dynamic probability density of node 14 before the wind turbine is connected to the grid. Figure 7 (d) in it is the voltage dynamic probability density of node 14 after the wind turbine is connected to the grid;

[0038] Figure 8 A dynamic probability density comparison diagram of electric vehicles before and after grid connection according to a dynamic probability power flow calculation method and system considering vehicle-grid interaction and wind-solar correlation according to an embodiment of the present invention; wherein Figure 8 (a) is the 24h voltage data of each node before the electric vehicle is connected to the grid. Figure 8 (b) in the figure is the 24h voltage data of each node after the electric vehicle is connected to the grid. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0042] Example 1

[0043] Reference Figure 1-2 , is an embodiment of the present invention, which provides a dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation, including:

[0044] S100: Based on the parameter distribution of wind-solar generator power generation data, a wind-solar power generation dynamic probability distribution model is constructed, and the wind-solar power generation dynamic probability distribution model calculates the wind-solar correlation using a first function;

[0045] It should be noted that before building a wind-solar correlation model, it is necessary to perform parameter fitting of the wind-solar unit power generation data to obtain the probability distribution function of its output. In the embodiment of the present application, the comprehensive probability modeling method of wind-solar power generation power considering the parameter and non-parameter probability combined theory is used. Considering that the wind-solar data may not conform to the parameter distribution, the non-parameter fitting is combined to expand the probability of accurately simulating the wind-solar distribution characteristics. The flow chart of the comprehensive probability modeling method of the wind-solar unit is shown in Figure 2.

[0046] In an embodiment of the present application, based on the parameter distribution of wind and solar power generation data, a wind and solar power generation dynamic probability distribution model constructed includes: a wind and solar parameter probability model in which the wind and solar power generation data conforms to the parameter model, and a wind and solar non-parametric kernel density estimation probability model in which the wind and solar power generation data does not conform to the parameter model.

[0047] In an embodiment of the present application, the parameter model includes: a parameter distribution model based on the power output characteristics of the wind and solar power generator set. If the actually measured wind speed or light intensity data can fit the distribution, it is judged that the data conforms to the parameter model.

[0048] In an optional embodiment, for wind power generation, its output power depends on wind speed. Since wind speed is affected by meteorological conditions, the output power is highly random. According to previous wind speed probability statistics, it generally follows a two-parameter Weibull distribution. For photovoltaic power generation, its output power depends on light intensity. Since light intensity is also random, its output power is also random, and light intensity can be approximately regarded as a Beta distribution. If the actually measured wind speed or light intensity data can fit these distributions well, it is considered that the data conforms to the parametric model. If the wind and light data do not conform to the above-mentioned parametric distribution, a non-parametric kernel density estimation method is used to fit the data so that it can adapt to a wider range of data distribution characteristics, rather than just being limited to a specific parametric distribution.

[0049] Exemplarily, when the parameter model is a wind power probability model based on Weibull distribution, the probability density function of wind speed v is as follows:

[0050]

[0051] Where c and k are the scale parameter and shape parameter of the Weibull distribution, respectively.

[0052] The functional relationship between wind power output active power and wind speed is:

[0053]

[0054] Among them, P r is the rated power of the wind turbine, v ci is the cut-in wind speed, v r is the rated wind speed, v co To cut out wind speed.

[0055] Exemplarily, when the parameter model is a photovoltaic probability model based on Beta distribution, the probability density function is as follows:

[0056]

[0057] Among them, r and r max are the actual light intensity and the maximum light intensity respectively, Γ is the Gammma function, and α and β are the shape parameters of the Beta distribution.

[0058] The relationship between the output power of photovoltaic power generation and light intensity:

[0059] P PV = r·A·η

[0060] Where A is the total area of ​​the solar cell array and η is the photoelectric conversion efficiency.

[0061] In an optional embodiment, when the power generation data of the wind and solar power generator set does not conform to the parameter model, a non-parametric kernel density estimation data sample method is considered, and its approximate estimated probability density function is as follows:

[0062]

[0063] Among them, n is the number of samples of measured data, h is the bandwidth, K is the kernel function, P i is the i-th sample of the output power P.

[0064] In the embodiment of the present application, the wind-solar power generation dynamic probability distribution model uses the first function to calculate the wind-solar correlation, including:

[0065] The dynamic probability distribution model of wind and solar power generation describes the relationship between random variables by combining the correlation between random variables and their marginal distribution through Copula function.

[0066] It should be noted that in the embodiment of the present application, the first function for calculating the wind-solar correlation is selected as the Copula function. The Copula function can study the correlation between random variables separately from the marginal distribution of the variables. It can not only fully describe the relationship between the input random variables, but also is not restricted by the marginal distribution type of the input random variables and whether they are linearly correlated.

[0067] Specifically, the Copula function is as follows:

[0068] H(x1,x2,···,x n )=C(F1(x1),F2(x2),···,F n (x n ))

[0069] Among them, x i is a random variable, F i (x i ) is the corresponding marginal distribution function, H(·) is the joint distribution function, and C(·) is the Copula connection function.

[0070] In a preferred embodiment, the shortest distance method based on kernel density estimation is adopted, and the kernel density based on the product kernel is As an estimate of the true distribution function, calculate each candidate Copula function and The distance d k , select d k The smallest Copula function is the optimal Copula.

[0071]

[0072] Among them, x i is a random variable, dk For distance.

[0073] In another optional embodiment, the Copula function can also be selected as Archimedean Copula function, including Gumbel Copula function, Clayton Copula function and Frank Copula function, etc. Another type is elliptic Copula function, including normal and t-Copula function, etc.

[0074] It should be noted that, before selecting the optimal Copula function, the embodiment of the present application needs to determine the parameter θ of the Copula function to be selected based on the data sample, and obtain the parameter θ.

[0075] In a preferred embodiment, the present application selects the maximum likelihood estimation method to obtain the parameter θ, and the maximum likelihood estimation method finds the best estimate of the parameter θ by maximizing the likelihood function. The likelihood function is a function that describes the likelihood of the model parameters based on the existing observation data. By solving the parameter θ that maximizes the likelihood function value, an estimate of the model parameters can be obtained. This method can provide unbiased or asymptotically unbiased estimates in many cases, and the accuracy of the estimate will gradually increase as the sample size increases.

[0076] In another optional embodiment, the parameter θ may be obtained by using methods such as maximum likelihood estimation, distribution estimation, semi-parametric estimation, and distribution estimation.

[0077] S200: Establishing a vehicle charging and discharging load model according to the vehicle's traffic network, travel mode and power load, and the vehicle charging and discharging load probability model is used to obtain vehicle charging and discharging load data;

[0078] In an embodiment of the present application, the vehicle charging and discharging load probability model includes a road network-distribution network interaction model established based on the vehicle traffic network and its influencing factors; the road network-distribution network interaction model includes a node set, a road section set, a travel cost of a road section, and a set of multiple time periods divided into the whole day in the traffic network; the influencing factors include the road impedance in which the vehicle is affected by the road section impedance and the intersection node impedance.

[0079] Specifically, the mathematical model of the road network topology is described as follows:

[0080]

[0081] Among them, G is the traffic road network, V is the set of road section nodes in the road network, E is the set of road sections in the road network, W is the road travel cost of each road section, that is, the set of weights such as road section length, travel speed, travel time and travel cost is called road resistance, and K is the set of divided time periods, that is, the whole day is divided into m time periods.

[0082] In an optional embodiment, the influencing factors of the vehicle charging and discharging load probability model may be urban inner roads, urban suburban roads, and rural roads.

[0083] In a preferred embodiment, the embodiment of the present application is aimed at urban internal roads. In the urban road network, traffic lights are often set at intersections for control. Vehicle driving is affected by the road section impedance and time delays occur at the intersections. Therefore, the road resistance of urban roads can be expressed as:

[0084]

[0085] in, represents the node impedance model, Represents the road link impedance model.

[0086] According to the urban traffic conditions classification standard, the saturation S evaluation index is: smooth (0<S≤0.6), slow (0.6<S≤0.8), crowded (0.8<S≤1.0) and severe congestion (1.0<S≤1.2). The road intersections and sections have different traffic capacities, and the section impedance and node impedance models corresponding to different saturations can be obtained.

[0087] Specifically, the road section impedance model is expressed as:

[0088]

[0089] Among them, saturation S = Q / C, Q is the traffic flow of the road section, C is the traffic capacity, t0 is the zero flow travel time, and α and β are impedance influencing factors respectively.

[0090] Specifically, the node impedance model is expressed as:

[0091]

[0092] Among them, c is the signal period, λ is the green-to-signal ratio, and q is the vehicle arrival rate of the road section.

[0093] Specifically, the road impedance model is expressed as:

[0094]

[0095] In an embodiment of the present application, a vehicle information matrix model is constructed based on vehicle type, single vehicle driving characteristic parameters, and single vehicle charging characteristic parameters.

[0096] Specifically, vehicles are divided into three categories, namely private cars, taxis, and other public vehicles. Commuter private cars mainly travel between residence and workplace, with a long parking time and fixed parking location, and the main charging method is slow charging at the destination; the travel time and starting and ending points of taxis are relatively random, the driving routes are not fixed, and the number of trips is high, so the charging method is fast charging at the charging station; other public vehicles (official private cars, commercial vehicles, and functional vehicles, etc.) mainly travel between workplaces and workplaces, with a high number of trips, long charging time, and irregular charging locations. Depending on the charging needs, they can choose to slow charge at the destination or fast charge at the charging station during the trip.

[0097] Specifically, the bicycle driving characteristic parameters include the starting node, the destination node, the starting time, and the return time.

[0098] It should be noted that for each type of electric vehicle, in order to simulate its travel characteristics, the OD start-end matrix method is introduced for analysis, that is, the start-end matrix is ​​inferred through the changes in traffic flow in each period of the road section, and the Monte Carlo sampling method is used to assign the travel start and end point locations, initial and return times to each EV. The OD start-end probability matrix is ​​expressed as:

[0099]

[0100] Among them, the matrix elements represents the number of electric vehicles with node i as the starting point and node j as the destination (i.e., from node i to node j) during this period. represents the probability of an electric vehicle with node j as the destination among electric vehicles with node i as the initial node during the period from T to T+1, It represents the probability that the electric vehicle stays at the same place and does not travel during this period.

[0101] Specifically, the charging characteristic parameters of a single vehicle include battery capacity, initial capacity, remaining capacity, and power consumption per unit mileage.

[0102] In an optional embodiment, in order to prevent the impact of overcharging on battery safety, it is assumed that the state of charge (SOC) at the initial moment is the battery level of the electric vehicle just after charging, and the SOC after charging is set to 0.8-0.9 of the battery capacity.

[0103]

[0104] in, is the capacity parameter of each type of battery, α i With β i represents the gamma distribution parameter, and i is the type of electric vehicle.

[0105] According to the electric vehicle battery data fitting results, the commuter private car parameters are set as α1=10.08,β1=0.8;for taxi α2=4.5,β2=6.3; other public vehicles are α3=8.7, β3=3.2.

[0106] In an optional embodiment, in an urban traffic system, the vehicle speed is mainly affected by the road capacity and the traffic volume, while the power consumption per unit mileage is mainly affected by the ambient temperature and the vehicle speed. Therefore, the present application introduces a speed-flow utility model, considers the road capacity and the traffic volume, and can calculate the vehicle speed v ij At the same time, a unit mileage power consumption calculation model is also used to consider the actual congestion level and vehicle status of different levels of roads, so as to obtain the unit mileage power consumption E m On this basis, a real-time power consumption model considering ambient temperature and vehicle speed is constructed as follows:

[0107]

[0108] E sum =E T +E m

[0109]

[0110] Among them, v ij-m represents the zero flow velocity of section ij, C ij is the traffic capacity of road section ij, which is related to the road grade, q ij is the section flow of section ij, E T When the ambient temperature is T, the vehicle moves at a speed v ij The amount of electricity consumed by the air conditioner when driving S kilometers, T min is the upper limit of air conditioning heating temperature, T max is the lower limit of air conditioning refrigeration temperature, W L and W R is the cooling and heating power of the air conditioner, which are 1.2kW and 1.5kW respectively, E sum Indicates that when the ambient temperature is T, the vehicle speed v ij Real-time power consumption per mile while driving.

[0111] In an optional embodiment, if the process of vehicles arriving at each charging station follows a Poisson distribution, the number of charging demands for services at the charging station per hour is used as the parameter λ, then the average waiting time W of the system is q for:

[0112]

[0113] Among them, c is the number of chargers in the charging station, μ is the number of vehicles served by each charger per unit time, and ρ is the service intensity of the charger.

[0114] In an optional embodiment, since private cars mainly travel between residence and work and have a long parking time, the discharge demand can be met and private cars are set as the discharge object. First, when electric car j enters the charging station, it will have an initial charge state SOC j,0 , and the user can set the expected charging end time t j,end And the expected state of charge SOC j,exp At the same time, considering the user's temporary car use behavior and battery capacity limitations, electric vehicles often have a lower limit value of the state of charge SOC j,min and upper limit SOC j,max Based on this, electric vehicles can be divided into three types according to the status information of electric vehicles and user needs, and by comparing various constraints: mandatory charging, chargeable and dischargeable, and non-chargeable. At the same time, V j =1, V j =0 and V j =-1 represents the charging, idling and discharging states of the electric vehicle respectively.

[0115] Condition 1: In order to meet the charging needs of users, electric vehicles must ensure that the state of charge is within t j,end Reach SOC before the end j,exp If the electric vehicle’s station stay time is not enough to ensure that it can be charged to SOC j,exp , the electric vehicle will remain in the forced charging state, that is, V j =1, until the charge state of the electric vehicle meets the user's needs.

[0116] Condition 2: Considering the user's temporary car demand (SOC j,min To ensure the charge required by users for temporary use of the vehicle), if the real-time SOC is less than SOC j,min , the electric vehicle will remain in a forced charging state and cannot discharge, that is, V j = 1. If the real-time SOC of the electric vehicle is greater than SOC j,min , the electric vehicle will end the mandatory charging state. This indicator can usually be set to 50% to 60%, that is, the electric vehicle will continue to be mandatory charged after entering the station until the charge state reaches 50% to 60%, after which it can be regulated and meet the user charging needs in condition 1 before leaving the station.

[0117] Condition 3: Considering the limitation of battery capacity: When the state of charge of the electric vehicle reaches SOC j,maxIt no longer performs charging behavior, that is, V j = -1 or V j = 0. Otherwise, the electric vehicle can perform a series of actions such as charging, discharging and idling, that is, V j =-1, 1 or 0.

[0118] Based on this, when the electric vehicle's state of charge does not meet conditions 1 and 2 (i.e., it does not meet the user's charging needs or the user's temporary car use needs), it will be classified as type 1 and remain in a forced charging state. If conditions 1 and 2 are met, but condition 3 has not been met, that is, the electric vehicle's charging state has not reached SOC j,max , it is classified as Type 2, which is an electric vehicle capable of charging, discharging and idling.

[0119] The process steps of the established electric vehicle charging and discharging demand prediction model are as follows:

[0120] 1) First, establish a certain scale of urban traffic network model and distribution network model, the traffic network node and distribution network node number correspondence as shown in Table 1, introduce various types of electric vehicles into the established urban regional traffic nodes;

[0121] Table 1 Correspondence table between distribution network nodes and transportation network nodes

[0122]

[0123]

[0124] 2) Secondly, the driving characteristics and charging characteristics parameters are generated for each EV introduced through Monte Carlo random sampling;

[0125] 3) The Floyd algorithm is used to search for the shortest path between OD and EV for each EV. The vehicle travels according to the planned path and updates the driving parameters and charging parameters in real time.

[0126] 4) Determine the charging demand model and perform corresponding slow charging at the destination or fast charging at the charging station when the charging demand is triggered.

[0127] 5) The charging and discharging load of private cars is updated by judging the discharge power model conditions, and then the charging and discharging load of electric vehicles at each node is superimposed with the basic load to obtain the total load of the node.

[0128] S300: Perform dynamic probability power flow calculation through the vehicle charging and discharging load probability model and the wind and solar power generation dynamic probability distribution model to obtain the accuracy of the probability distribution model and the dynamic operation characteristics of the system;

[0129] In the embodiment of the present application, the dynamic probability flow calculation is performed by the vehicle charging and discharging load probability model and the wind and solar power generation dynamic probability distribution model, including: the wind and solar data samples calculated by the first function are sampled independently, and according to the Nataf transformation theory, the spatiotemporal correlation coefficient matrix of the standard normal random variable matrix Y is expressed as:

[0130]

[0131] Where φ(·) represents the joint probability density function of the standard normal distribution, ρ ij represents the components of the spatial correlation coefficient matrix, μ i and σ i They represent the expected and standard deviation of the power generated by the i-th DG field.

[0132] It should be noted that the present application uses Nataf transformation to separate the correlated wind and solar data samples. Nataf transformation only needs to input the marginal distribution function of the variable and the correlation coefficient between the variables, and is not limited by the variable distribution type.

[0133] In the embodiment of the present application, it also includes: according to the component ρ of the spatial correlation coefficient matrix 0ij , we get the spatial correlation coefficient matrix H of the standard normal distribution matrix Y; according to the Nataf inverse transformation, we transform the independent standard normal distribution matrix S K×T Transformed into a standard normal distribution matrix Y with correlation K×T , according to the equal probability conversion principle, the DG output samples including correlation are calculated as follows:

[0134]

[0135] Among them, x ij represents the power generated by the i-th distributed generation field at the j-th time step, y ij represents the standard normal random variable after Nataf transformation, It represents the inverse cumulative distribution function of the i-th distributed generation electric field at the j-th time step.

[0136] It should be noted that the spatial correlation coefficient matrix H calculated by this application may not be positive definite or full rank. In view of this situation, this application performs singular value decomposition on H:

[0137] H=BB T

[0138] According to the Nataf inverse transformation, the independent standard normal distribution matrix S K×T Transformed into a standard normal distribution matrix Y with correlation K×T The process is as follows:

[0139] Y=BS

[0140] This application generates input random variable data samples with correlation by Copula function, generates input random independent variable samples that meet the correlation conditions through correlation processing, obtains the semi-invariant of the output variable through power flow calculation, and finally obtains the probability distribution of the output variable through the Gram-Charlier series expansion method.

[0141] The embodiment of the present application establishes a dynamic probability distribution model of wind and solar power by combining parametric and non-parametric probability modeling theories, and uses Copula functions to characterize the correlation between wind and solar power, thereby improving the accuracy of the probability distribution model. By modeling the probability of wind and solar power generation and electric vehicle charging and discharging loads, a more sophisticated dynamic probability flow calculation is achieved with strong reliability, which can effectively predict the behavior of the power grid in the face of fluctuations in new energy power generation and changes in electric vehicle loads, and further demonstrate the influence of electric vehicle charging demand on the power grid voltage, which is of great significance to the stable operation of the power grid in the context of large-scale popularization of electric vehicles in the future.

[0142] This embodiment also provides a dynamic probability power flow calculation system that takes into account vehicle-grid interaction and wind-solar correlation. It should be noted that the technical solution of this dynamic probability power flow calculation system that takes into account vehicle-grid interaction and wind-solar correlation and the technical solution of the dynamic probability power flow calculation method that takes into account vehicle-grid interaction and wind-solar correlation belong to the same concept. For details that are not described in detail in the technical solution of the dynamic probability power flow calculation system that takes into account vehicle-grid interaction and wind-solar correlation in this embodiment, please refer to the description of the technical solution of the dynamic probability power flow calculation method that takes into account vehicle-grid interaction and wind-solar correlation.

[0143] In this embodiment, a dynamic probability power flow calculation system considering vehicle-grid interaction and wind-solar correlation includes:

[0144] The first model building module is used to build a dynamic probability distribution model of wind-solar power generation based on the parameter distribution of wind-solar power generation data, and the dynamic probability distribution model of wind-solar power generation calculates the wind-solar correlation with the first function;

[0145] The second model building module is used to establish a vehicle charging and discharging load model according to the vehicle's traffic network, travel mode and power load. The vehicle charging and discharging load probability model is used to obtain vehicle charging and discharging load data;

[0146] The calculation module is used to perform dynamic probability flow calculations through the vehicle charging and discharging load probability model and the wind and solar power generation dynamic probability distribution model to obtain the accuracy of the probability distribution model and the dynamic operation characteristics of the system.

[0147] In an optional embodiment, the calculation module further includes:

[0148] Input wind and solar measured data, EV related parameters, and distribution network load initial data;

[0149] Given the measured wind and solar data, select the optimal Copula function to generate data samples containing correlations;

[0150] According to EV-related parameters, an electric vehicle information matrix model is built to simulate the travel needs of various types of EVs;

[0151] Given a sample of wind and solar power output data, the power distribution function of each unit is calculated using a comprehensive probability modeling method for wind and solar power units.

[0152] The 24h charging time and space distribution of EV is obtained according to vehicle conditions and charging requirements;

[0153] The correlated wind and solar outputs are made independent through Nataf transformation combined with singular value decomposition, and the expected values ​​of photovoltaic output and wind power output at time t are calculated and connected to the corresponding grid nodes.

[0154] Superimpose EV charging and discharging loads with the original loads;

[0155] According to the expected value of the power injected into the base load at time t, a deterministic power flow calculation is performed to obtain X, Z and matrices S and T at time t;

[0156] According to the expected values ​​of photovoltaic output and wind power output at time t, the origin moment is calculated, and then the semi-invariants of wind and solar power are calculated. The semi-invariants of the node voltage and branch power flow at time t are finally obtained by adding them to the semi-invariants of the current injected power.

[0157] The probability distribution characteristics of node state variables and branch power flow variables are obtained using the Gram-Charlier series expansion.

[0158] This embodiment further provides a computing device, which is applicable to the case of a dynamic probability power flow computing method considering vehicle-grid interaction and wind-solar correlation, including:

[0159] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the dynamic probabilistic power flow calculation method that takes into account vehicle-grid interaction and wind-solar correlation as proposed in the above embodiment.

[0160] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a dynamic probabilistic power flow calculation method considering vehicle-grid interaction and wind-solar correlation as proposed in the above embodiment is implemented.

[0161] The storage medium proposed in this embodiment and the dynamic probabilistic power flow calculation method proposed in the above embodiment that takes into account vehicle-grid interaction and wind-solar correlation belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0162] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0163] Example 2

[0164] Reference Figure 3-4 , is an embodiment of the present invention, which provides a specific implementation example of a dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation, and verifies the accuracy of the probability distribution model.

[0165] The example of this embodiment selects a 39-node test system in Matpower. The example stipulates that the load fluctuation load is normally distributed, the given power parameter is used as the expected value, and the standard deviation is 5% of the expected value. Nodes 3 and 4 are connected to photovoltaic power generation equipment with rated powers of 2MW and 5MW respectively, and the photovoltaic power generation power is taken from the monitoring data of Brussels and Flemish regions in Belgium; Node 6 is connected to photovoltaic power generation equipment with a rated power of 400kW, and its shape parameters are 0.69 and 2.13 respectively. Nodes 26 and 27 are connected to wind turbines with a rated power of 2MW, and the wind power generation power is taken from the monitoring data of Flanders and Wallonia in Belgium. Node 14 is connected to a wind turbine with a rated power of 0.3MW, a rated wind speed of 15m / s, a cut-in wind speed of 4m / s, and a cut-out wind speed of 25m / s. Since the selected monitoring data comes from the real power grid, the monitoring data needs to be scaled proportionally to achieve adaptation to the needs of the case in this embodiment.

[0166] The road network of this embodiment includes 29 nodes and 49 roads. The road network is divided into a work area, a residential area 1, a residential area 2, and a commercial area. The schematic diagram of the road network is as follows: Figure 3As shown in the figure, the electric vehicle charging demand model is adopted to put the electric vehicle charging load into the grid nodes corresponding to each road network node. Assuming that the grid nodes corresponding to each road network have fast charging services, there are 12,000 commuter private cars, 4,000 taxis and 4,000 other public vehicles in the road network area.

[0167] Given the monitoring data of two photovoltaic units, the Copula function is used to generate 1000 simulated active output samples of the photovoltaic units that meet the correlation, and then compare them with the actual monitored active output samples. Figure 4 (a) and (b) show the scatter plots of the actual active output and simulated active output of the two PV units, respectively. Figure 4 (c) and (e) are histograms of the actual active output of the two PV units. Figure 4 (d) and (f) are histograms of the simulated active output of two PV units. The accuracy of the simulated active output samples of the correlated PV units is quantitatively evaluated. To this end, two indicators are selected to quantitatively analyze the accuracy. The root mean square index ξ is used to characterize the calculation accuracy of the model from the perspective of probability distribution characteristics, and the relative error index ε is used to characterize the calculation accuracy of the model from the perspective of probability distribution characteristics. s To evaluate the accuracy of the constructed model in terms of numerical features.

[0168]

[0169] Among them, P fi and P ri are the values ​​of the simulated value and the actual value at the i-th point on the cumulative distribution curve, respectively, and N is the number of points on the cumulative distribution curve.

[0170]

[0171] Among them, s represents the feature type, including expected value, standard deviation, skewness coefficient and kurtosis coefficient; P fs and P rs They are photovoltaic active output P PV The simulated and actual values ​​of

[0172] P PV The root mean square index ξ is 1.57%. PV The relative error index ε s The expected value is 0.13, the standard deviation is 0.11, the skewness coefficient is 0.58, and the kurtosis coefficient is 0.54. The quantitative analysis of the above results shows that the relevant wind and solar data samples generated by the Copula function can be used as reference data for the actual operation of the system.

[0173] Example 3

[0174] Reference Figures 5 to 8, is an embodiment of the present invention, which provides a specific implementation example of a dynamic probabilistic power flow calculation method considering vehicle-grid interaction and wind-solar correlation, and analyzes and verifies the dynamic operation characteristics of the system.

[0175] This embodiment uses the output variables obtained by the Monte Carlo simulation method considering the correlation between wind and solar power as reference values ​​to evaluate the accuracy of the static performance of the proposed method. The results are shown in Table 2:

[0176] Table 2 Statistics of average relative error and maximum error of output variables

[0177]

[0178] It can be seen from Table 2 that the output variable results of the present application and the Monte Carlo method are basically consistent. From the probability distribution characteristics of the output variables, the root mean square index ξ is selected to calculate the calculation error of the proposed method and the Monte Carlo simulation method. The output variable ARMA statistical results are shown in Table 3:

[0179] Table 3 Output variable ARMA statistical results

[0180]

[0181] It can be seen from Table 3 that the ARMA statistical result value is small, indicating that the simulation data of the present application method can accurately reflect the probability distribution characteristics of each output variable.

[0182] In this embodiment, the cumulative distribution curves of nodes and branches near and far from the photovoltaic power plant are selected for comparison, such as Figure 5 As shown, (a) and (b) are node 5 and branches 5-8 that are closer to the photovoltaic access point, respectively, and (c) and (d) are node 24 and branches 16-24 that are farther from the access point, respectively. The cumulative distribution function (CDF) of the node voltage and branch active power have the same trend in comparison, proving that the method of the present application has high accuracy in static performance.

[0183] In order to verify the impact of wind-solar correlation on tidal current, this application selects three methods for comparison: case 1 uses the traditional semi-invariant method that does not consider wind-solar correlation; case 2 is the semi-invariant method based on Nataf transformation; case 3 is the method of this application, that is, Copula is used to generate correlated wind-solar samples, and then the Nataf transformation method is used to generate independent wind-solar samples with correlation for semi-invariant calculation.

[0184] from Figure 6In general, it can be seen that after wind power and photovoltaic power are connected to the grid, the voltage of each node and the active power of each branch will change greatly, indicating that the randomness of wind power and photovoltaic power has a greater impact on the system. Figure 6 It can be seen from (a) and (b) that the expected value of the voltage amplitude of each node and the expected value of the active power of each branch have the same change trend, indicating that the influence of wind-solar correlation on the expected value is small. Figure 6 It can be seen from (c) and (d) that the standard deviations of the voltage amplitudes of each node and the active power of each branch are somewhat different. For this reason, the average value of the standard deviation is used as a reference to obtain the impact of correlation on the operation status of the power grid. Compared with case 1, the mean standard deviations of the voltage amplitudes of case 2 and case 3 increased by 1.58% and 5.12% respectively, and the mean standard deviations of the active power of the branches increased by 6.09% and 9.20% respectively. It can be seen that case 3 has a greater impact on the fluctuation degree of the power grid than case 1 and case 2, and can better reflect the wind and solar output situation considering correlation.

[0185] Figure 7 (a) and (b) show the dynamic probability density comparison of the voltage amplitude of node 6 before and after photovoltaic grid connection. It can be seen from the figure that the regular changes in photovoltaic power generation have an impact on the power flow distribution in the power grid. During the day, the probability of the node voltage connected to photovoltaic power generation being at a slightly higher position is higher, while in the morning and evening, due to the influence of light factors, the probability distribution of the node voltage is the same as the node voltage not connected to photovoltaic power generation.

[0186] Figure 7 (c) and (d) show the dynamic probability density comparison of the voltage amplitude of node 14 before and after the wind power is connected to the grid. As can be seen from the figure, due to the randomness and correlation of wind power output, the dynamic probability distribution of its node voltage after grid connection has a peak-shaving and valley-filling phenomenon.

[0187] Figure 8 (a) and (b) show the comparison of the expected 24-hour voltage before and after the large-scale electric vehicle charging and discharging load demand is put into the power grid. It can be seen that at the nodes where electric vehicles are put into use, the voltage drops at some times, which shows that electric vehicles have an impact on the expected value of the voltage in the power flow distribution.

[0188] The present invention combines parametric and non-parametric probability modeling theories to establish a dynamic probability distribution model for wind and solar power, and uses Copula functions to characterize the correlation between wind and solar power, thereby improving the accuracy of the probability distribution model. By modeling the probability of wind and solar power generation and electric vehicle charging and discharging loads, a more sophisticated dynamic probability flow calculation is achieved with strong reliability, which can effectively predict the behavior of the power grid in the face of fluctuations in new energy power generation and changes in electric vehicle loads, and further demonstrate the influence of electric vehicle charging demand on power grid voltage, which is of great significance to the stable operation of the power grid in the context of large-scale popularization of electric vehicles in the future.

[0189] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A dynamic probabilistic power flow calculation method considering vehicle-grid interaction and wind-solar correlation, characterized in that: include: Based on the parameter distribution of wind-solar generator power generation data, a wind-solar power generation dynamic probability distribution model is constructed, wherein the wind-solar power generation dynamic probability distribution model calculates the wind-solar correlation using a first function; Establishing a vehicle charging and discharging load model according to the vehicle's traffic network, travel mode and power load, wherein the vehicle charging and discharging load probability model is used to obtain vehicle charging and discharging load data; Dynamic probability power flow calculation is performed through the vehicle charging and discharging load probability model and the wind and solar power generation dynamic probability distribution model to obtain the accuracy of the probability distribution model and the dynamic operation characteristics of the system.

2. The dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation as claimed in claim 1 is characterized in that: The wind-solar power generation dynamic probability distribution model constructed based on the parameter distribution of wind-solar power generation data includes: a wind-solar parameter probability model when the wind-solar power generation data conforms to the parameter model, and a wind-solar non-parametric kernel density estimation probability model when the wind-solar power generation data does not conform to the parameter model.

3. The dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation as claimed in claim 2 is characterized in that: The parameter model includes: a parameter distribution model based on the power output characteristics of the wind and solar power unit. If the actually measured wind speed or light intensity data can fit the distribution, it is judged that the data conforms to the parameter model.

4. The dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation as claimed in claim 3 is characterized in that: The vehicle charging and discharging load probability model includes a road network-distribution network interaction model established based on the vehicle traffic road network and its influencing factors; the road network-distribution network interaction model includes a node set, a road section set, a travel cost of a road section, and a set of multiple time periods divided into a whole day in the traffic road network; the influencing factors include the road impedance of the road section impedance and the cross node impedance that the vehicle is subject to; as well as, A vehicle information matrix model is built based on vehicle type, single vehicle driving characteristic parameters, and single vehicle charging characteristic parameters.

5. The dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation as claimed in claim 4 is characterized in that: The dynamic probability power flow calculation is performed by using the vehicle charging and discharging load probability model and the wind and solar power generation dynamic probability distribution model, including: the wind and solar data samples obtained by the first function calculation are sampled independently, and according to the Nataf transformation theory, the spatiotemporal correlation coefficient matrix of the standard normal random variable matrix Y is expressed as: Where φ(·) represents the joint probability density function of the standard normal distribution, ρ ij represents the components of the spatial correlation coefficient matrix, μ i and σ i They represent the expected and standard deviation of the power generated by the i-th DG field.

6. The dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation as claimed in claim 5 is characterized in that: Also includes: According to the component ρ of the spatial correlation coefficient matrix 0ij , we get the spatial correlation coefficient matrix H of the standard normal distribution matrix Y; according to the Nataf inverse transformation, we transform the independent standard normal distribution matrix S K×T Transformed into a standard normal distribution matrix Y with correlation K×T , according to the equal probability conversion principle, the DG output samples including correlation are calculated as follows: Among them, x ij represents the power generation of the i-th distributed generation field at the j-th time step, y ij represents the standard normal random variable after Nataf transformation, It represents the inverse cumulative distribution function of the i-th distributed generation electric field at the j-th time step.

7. The dynamic probability power flow calculation method considering vehicle-grid interaction and wind-solar correlation as claimed in claim 1 is characterized in that: The dynamic probability distribution model of wind and solar power generation uses the first function to calculate the wind and solar correlation, including: The dynamic probability distribution model of wind and solar power generation describes the relationship between random variables by combining the correlation between random variables and their marginal distribution through Copula function.

8. A dynamic probabilistic power flow calculation system considering vehicle-grid interaction and wind-solar correlation, characterized in that: include: A first model building module is used to build a dynamic probability distribution model of wind-solar power generation based on the parameter distribution of wind-solar power generation data, wherein the dynamic probability distribution model of wind-solar power generation calculates the wind-solar correlation using a first function; A second model building module is used to establish a vehicle charging and discharging load model according to the vehicle's traffic network, travel mode and power load, and the vehicle charging and discharging load probability model is used to obtain vehicle charging and discharging load data; The calculation module is used to perform dynamic probability power flow calculation through the vehicle charging and discharging load probability model and the wind and solar power generation dynamic probability distribution model to obtain the accuracy of the probability distribution model and the dynamic operation characteristics of the system.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the dynamic probabilistic power flow calculation method considering vehicle-grid interaction and wind-solar correlation as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the dynamic probabilistic power flow calculation method considering vehicle-grid interaction and wind-solar correlation as described in any one of claims 1 to 7.

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