A flexible power distribution substation voltage cooperative management method considering electric vehicle charging

Abstract

This invention discloses a flexible distribution network voltage coordination management method considering electric vehicle charging, comprising the following steps: sampling from the probability density functions of the initial daily travel time and parking duration of electric vehicles at each node of the distribution network, establishing and sampling the distribution function of the daily travel distance of electric vehicles, and predicting the corresponding charging time and power consumption based on the sampling results; calculating the attractiveness and impedance of charging stations at each node, and using this to calculate user charging intentions; calculating the electric vehicle charging load of charging stations at each node based on user charging intentions and power consumption to obtain charging demand prediction curves for charging stations at different nodes; and using an optimization model to solve for the charging demand prediction curves for charging stations at different nodes to obtain voltage management schemes. This invention ensures reasonable resource scheduling before the daytime, thereby meeting the intraday voltage regulation capability.

Description

Technical Field

[0001] This invention relates to distribution transformer area voltage management technology, specifically to a flexible distribution transformer area voltage collaborative management method that takes into account electric vehicle charging. Background Technology

[0002] With the widespread adoption of electric vehicles, their charging behavior, as a new type of high-power, random load, poses a severe challenge to the safe and stable operation of low-voltage distribution networks, especially flexible distribution substations. Compared with high-voltage transmission networks and rigid distribution networks, low-voltage flexible distribution substations oriented towards end users typically have the following inherent characteristics: First, their power supply radius is relatively long, and the line cross-section is relatively small, resulting in high line impedance (especially inductive impedance), making the voltage at the end of the line extremely sensitive to load fluctuations; second, the types of user loads within the substation are diverse and dispersed, and residential electricity consumption exhibits significant randomness and intermittency, which inherently makes voltage deviation problems easy to occur; third, traditional voltage regulation methods for low-voltage distribution substations are very limited, mainly relying on the tap switching of on-load tap changers. This mechanical regulation method has a slow response speed and lacks active reactive power compensation equipment such as static var generators, making it difficult to achieve rapid and accurate voltage control.

[0003] Against this backdrop, a large number of electric vehicles are randomly and haphazardly connected to the charging circuit at the user side. Their high charging power and unpredictable start-stop times add a layer of "random, short-duration, high-power" new load to the existing random load in the distribution area, resulting in severe power fluctuations. The impact on voltage is transmitted through the "power loss-voltage drop" link. This transmission fluctuation generates a significant voltage drop across the line impedance, easily leading to voltage exceedances (too low or too high) at local nodes in the distribution area, especially at the end of the line, severely affecting power quality and the safety of electrical equipment. Therefore, how to effectively assess and suppress the negative impact of random electric vehicle charging on the voltage of flexible distribution areas has become a critical technical problem urgently needing to be solved in the field of distribution network operation and control. Summary of the Invention

[0004] The technical problem this invention aims to solve is to provide a flexible distribution station voltage coordination management method that considers electric vehicle charging, addressing the aforementioned problems in existing technologies. This method involves reasonably predicting the current charging power of electric vehicle charging stations and then adjusting the voltage accordingly to achieve voltage management during different time periods, such as weekdays and holidays.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging includes the following steps: Obtain the probability density function of the initial travel time and parking duration of electric vehicles at each node of the distribution network on a typical day, as well as the road topology information. The typical day is a weekday or a holiday. Sampling is performed from the probability density function to obtain the initial daily travel time and parking duration of the selected electric vehicles. The distribution function of the daily driving distance of the electric vehicles is established and sampled to obtain the daily driving distance of different electric vehicles. Based on the initial daily travel time, parking duration, and daily driving distance of the selected electric vehicles, the corresponding charging time and power consumption are predicted. The attractiveness and resistance of each charging station are calculated, and the user's charging intention is calculated based on the attractiveness and resistance of each charging station. The electric vehicle charging load of each charging station is calculated based on the user's charging intention and the power consumption, thereby obtaining the charging demand prediction curve of different charging stations. For the charging demand prediction curves of charging stations at different nodes, an optimization model is used to solve for voltage mitigation solutions by considering the switching costs of on-load tap-changing transformers and switchable capacitors, the power loss cost objective function of the distribution network, and the constraints of the distribution network.

[0006] Furthermore, when obtaining the probability density functions of the initial travel time and parking duration of electric vehicles at each node of the distribution network on a typical day, a Gaussian mixture model is specifically used to process the historical data of typical days containing the initial travel time and parking duration to obtain the probability density functions of the initial travel time and parking duration of electric vehicles at each node of the distribution network on a typical day.

[0007] Furthermore, when predicting the corresponding charging time and power consumption based on the extracted daily initial travel time, parking duration, and daily driving distance of electric vehicles, the steps for predicting the charging time include: Calculate the corresponding speed based on the average driving time and daily driving distance of the electric vehicle; The initial charging time is calculated based on the electric vehicle's initial daily travel time, parking duration, daily driving distance, and speed. The mathematical expression is as follows:

[0008] In the formula, It is the initial charging time of the i-th electric vehicle selected; It is the initial departure time of the i-th electric vehicle on that day; It represents the daily driving distance of the i-th extracted electric vehicle; It is the speed of the i-th electric vehicle that was sampled; It is the parking duration of the i-th electric vehicle selected.

[0009] Furthermore, when predicting the corresponding charging time and power consumption based on the extracted daily initial travel time, parking duration, and daily driving distance of electric vehicles, the steps for predicting power consumption include: The mathematical expression for calculating the unit power consumption of an electric vehicle during operation is as follows:

[0010] In the formula, Power consumption per unit; For speed; For congestion parameters; , , These are constant values; The daily power consumption of different electric vehicles is obtained by multiplying their daily driving distance by the corresponding unit power consumption.

[0011] Furthermore, the mathematical expression for the distribution function of the daily driving distance of electric vehicles is as follows:

[0012] In the formula, The daily driving distance of different electric vehicles; This is the expected value; This represents the standard deviation of the daily travel distance.

[0013] Furthermore, before calculating the attractiveness and resistance of each charging station, the following steps are taken: using Dijkstra's algorithm, with the time spent between nodes as weights, to calculate the shortest travel path from the electric vehicle to each charging station, as shown in the following formula:

[0014] In the formula, , which is a selected path; This represents the total number of paths. These are weight values; Let i be the distance along path i; Let be the speed of the electric vehicle on path i; For congestion parameters.

[0015] Furthermore, when calculating the attractive and resistive forces at each charging station node, the mathematical expression for the attractive force is as follows:

[0016] In the formula, The appeal of charging stations; The evaluation index for charging station i; The unit charging cost for charging station i; , These are the influencing parameters; It is a constant value; The mathematical expression for resistance is as follows:

[0017] In the formula, The indirect cost for user j at node; Let J be the shortest distance that a user at node j travels to charging station i. Let be the speed at which the user at node j travels to charging station i. The unit charging cost for charging station i; This refers to the unit electricity consumption of users at node j traveling to charging station i.

[0018] Furthermore, when calculating user charging intentions based on the attractiveness and resistance of each charging station, and calculating the electric vehicle charging load of each charging station based on user charging intentions and the power consumption, the following steps are included: The probability that a user will choose charging station i at node j to charge their electric vehicle is calculated using the following mathematical expression:

[0019] In the formula, The appeal of charging stations; The evaluation index for charging station i; The unit charging cost for charging station i; , These are the influencing parameters; It is a constant value; The indirect cost for user j at node; Let J be the shortest distance that a user at node j travels to charging station i. Let be the speed at which the user at node j travels to charging station i. The unit charging cost for charging station i; The unit electricity consumption for users traveling from node j to charging station i; A collection of electric vehicle charging stations; The mathematical expression for calculating the charging amount of all electric vehicles at node j is as follows:

[0020] In the formula, This represents the amount of charge at node j. Let r be the charging amount of electric vehicle k when the congestion coefficient is r; Let k be the daily driving distance of electric vehicle k; Let k be the unit power consumption of electric vehicle k when the congestion coefficient is r; Let j be the number of electric vehicles at node j; The mathematical expression for calculating the electric vehicle charging load of charging station i is as follows:

[0021] In the formula, Let J be the shortest distance that a user at node j travels to charging station i. J represents the unit electricity consumption of a user traveling from node j to charging station i; J is the set of distribution network nodes. The probability that user j selects charging station i.

[0022] Furthermore, the mathematical expression of the objective function is as follows:

[0023]

[0024]

[0025] In the formula, Total cost; This is the voltage over-limit index; This is the penalty factor for voltage exceeding the limit; For power loss in the distribution network; This is the power loss cost coefficient; Cost of switching on-load tap-changing transformers; Cost of switching on / off switchable capacitors; Node voltage; The number of nodes; This is the lower limit of the node voltage. This is the upper limit of the node voltage; Let be the electrical conductance between lines ij; Let be the phase angle difference between lines ij at time t, where:

[0026]

[0027] In the formula: Cost of switching on-load tap changers in on-load tap-changing transformers; Cost of switching on / off switchable capacitors; Let t be the tap position of the nth on-load tap-changing transformer at time t; Let be the number of capacitors switched by the z-th switchable capacitor at time t; This refers to the number of on-load tap-changing transformers; This represents the number of switchable capacitors.

[0028] Furthermore, the power distribution network constraints include: Power flow constraints, mathematically expressed as:

[0029] In the formula: , Let t represent the active and reactive power transmitted from the upstream power grid at node i at time t. , Let be the active and reactive power of the load at node i at time t; Let i be the charging power of the electric vehicle at node i at time t. Let i be the reactive power compensation power at node i at time t; Let be the voltage at node i at time t; , The conductivity and reactance between lines ij; Let be the phase angle difference between lines ij at time t; The transmission power constraint, mathematically expressed as:

[0030] In the formula: , These represent the minimum and maximum active power transmitted from the upstream node at node i; , These represent the minimum and maximum values ​​of the reactive power transmitted from the upstream node at node i. The node voltage constraint, mathematically expressed as:

[0031] In the formula: , Let i be the minimum and maximum voltage values ​​at node i. The voltage constraint at the slack node is mathematically expressed as follows:

[0032] In the formula: To balance the node voltage; Reference voltage value; This refers to the voltage regulation step size of an on-load tap-changing transformer. For a balanced set of nodes; The mathematical expressions for the operating constraints of on-load tap-changing transformers and switchable capacitors are as follows:

[0033]

[0034]

[0035]

[0036] In the formula: , Let these be the minimum and maximum positions of the switching taps of the nth on-load tap-changing transformer. This represents the maximum daily operating time of the nth on-load tap-changing transformer. This represents the maximum number of switchable capacitors that can be switched at the z-th switchable capacitor. This represents the number of switchable capacitors.

[0037] Compared with the prior art, the advantages of the present invention are as follows: This invention addresses the problems of voltage exceeding limits and increased network losses caused by the fluctuations in node power resulting from the connection of a large number of variable and disordered electric vehicle charging loads to the distribution network. It fully considers the dynamic evolution characteristics of charging vehicles, uses a Gaussian mixture model to fit the initial charging time of electric vehicles, and uses a Huff model to determine the user's choice of charging node. It predicts the initial charging time and charging duration of electric vehicles in the region in advance, identifies the most likely charging time and charging node for disordered charging patterns, and thus determines the power of charging station nodes in the region. This allows sufficient voltage regulation resources to be reserved in advance, thereby reducing the difficulty of voltage regulation management. Attached Figure Description

[0038] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0039] Figure 2 This is a flowchart of the electric vehicle charging load demand forecasting stage according to an embodiment of the present invention. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0041] Electric vehicle charging exhibits a strong degree of randomness, with a large load potentially connecting to a charging station at any given time, leading to significant uncertainty. The trend of electric vehicles randomly connecting to the power distribution network varies across different time periods, with the load on the distribution network fluctuating between weekdays and holidays. During peak holiday travel periods, the randomness of electric vehicle connections is even stronger. At such times, the distribution network needs to maintain a greater voltage regulation margin to mitigate issues such as voltage exceeding limits. Therefore, predicting the charging situation of electric vehicles is of paramount importance, ensuring the rational allocation of resources before the day and thus meeting the intraday voltage regulation requirements.

[0042] To address this issue, this embodiment proposes a flexible distribution network voltage collaborative management method that considers electric vehicle charging. A Gaussian mixture model (GMM) is used to fit the dataset of electric vehicle start-up times and charging durations at each node of the distribution network into a linear combination with multiple Gaussian distributions. Then, the driving distance and speed of the electric vehicles are determined based on the distribution network node topology data. Finally, the start-up charging time of the electric vehicles is determined. Subsequently, a Huff model is used to determine the probability of a user selecting a charging station, thus determining the selection status of each charging station. Finally, resources are allocated for collaborative voltage management to ensure that the voltage operates within the normal range.

[0043] like Figure 1 As shown, the method includes the following steps: S1: Electric vehicle charging load demand forecasting stage.

[0044] Obtain the probability density function of the initial travel time and parking duration of electric vehicles at each node of the distribution network on a typical day, as well as the road topology information. The typical day is a weekday or a holiday. Sampling is performed from the probability density function to obtain the initial daily travel time and parking duration of the selected electric vehicles. The distribution function of the daily driving distance of the electric vehicles is established and sampled to obtain the daily driving distance of different electric vehicles. Based on the initial daily travel time, parking duration, and daily driving distance of the selected electric vehicles, the corresponding charging time and power consumption are predicted. The attractiveness and resistance of each charging station are calculated, and the user's charging intention is calculated based on the attractiveness and resistance of each charging station. The electric vehicle charging load of each charging station is calculated based on the user's charging intention and the power consumption, thereby obtaining the charging demand prediction curve of different charging stations.

[0045] S2: Flexible distribution network voltage collaborative management stage.

[0046] For the charging demand prediction curves of charging stations at different nodes, an optimization model is used to solve for voltage mitigation solutions by considering the switching costs of on-load tap-changing transformers and switchable capacitors, the power loss cost objective function of the distribution network, and the constraints of the distribution network.

[0047] The details of each step are explained below.

[0048] In the electric vehicle charging load demand forecasting stage of step S1, the disorderly nature of electric vehicle charging is the main characteristic. Therefore, the key to load forecasting lies in the timing of charging at charging stations and the charging power at those stations. For different typical daily scenarios, such as weekdays and holidays, the charging patterns of electric vehicles are inconsistent; the number of electric vehicles increases on holidays, leading to greater charging demand. Therefore, this embodiment utilizes historical data from different times and then forecasts different typical daily scenarios based on demand. Specifically, it combines Monte Carlo sampling to predict the possible charging demand of charging stations at different nodes on the day of the current day. The specific steps are as follows: Figure 2 As shown, it includes: S11: Based on different typical days, make predictions. Based on the probability density function fitted by the GMM of historical samples, sample the initial travel time, stay duration and road network information of each charging car user within a 24-hour period of a day. The first vehicle is assigned the number i=1, and the total number of electric vehicles is N.

[0049] In this embodiment, the probability density function is specifically obtained by using a Gaussian mixture model (GMM) to process typical daily historical data containing the initial travel time and parking duration of electric vehicles at each node of the distribution network on a typical day.

[0050] The Gaussian Mixture Model (GMM) is a powerful estimation tool with exceptional capabilities in handling complex data and nonlinear relationships. It can fit datasets with highly varied state distributions into a combination of Gaussian distributions. In this embodiment, based on historical data such as the initial travel time and parking time of electric vehicles at each node within the GMM fitting area, the parameters of the GMM (including the mean, covariance matrix, and mixing coefficients of each Gaussian distribution) are iteratively estimated using the expectation maximization (EM) algorithm. This allows the historical dataset with highly varied state distributions (daily initial travel time and parking duration) to be fitted into a probability density function, yielding the probability density functions of the daily initial travel time and parking duration of electric vehicles at each node of the distribution network. This provides a distributional basis for subsequent Monte Carlo sampling.

[0051] S12: Determine the driving distance. Based on the daily driving distance distribution function of electric vehicles, the driving distance of electric vehicles is determined using Monte Carlo sampling. In this embodiment, information on the road network architecture within the statistical area is used to analyze and calculate the daily driving distance and speed of electric vehicles. Each user's daily driving distance is different, and their willingness to charge and the amount of charge will also vary. This embodiment utilizes existing research results to analyze the daily driving distance of users' electric vehicles based on road topology information. Its distribution function is as follows:

[0052] In the formula, The daily driving distance of different electric vehicles; This is the expected value; This represents the standard deviation of the daily travel distance.

[0053] S13: Determine the charging load power, the specific process is as follows: S131: Predicted charging time.

[0054] In this embodiment, the initial charging time of the electric vehicle is calculated by studying the relationship between various variables. This allows us to determine the charging power of each charging node in the region at each moment, thus providing the corresponding node power for subsequent voltage regulation.

[0055] First, based on the average driving time and daily driving distance d of the electric vehicle i Calculate the corresponding average velocity v i Then, based on the electric vehicle's initial daily travel time, parking duration, daily driving distance, and speed, the initial charging time is calculated using the following mathematical expression:

[0056] In the formula, It is the initial charging time of the i-th electric vehicle selected; It is the initial departure time of the i-th electric vehicle on that day; It represents the daily driving distance of the i-th extracted electric vehicle; It is the speed of the i-th electric vehicle that was sampled; It is the parking duration of the i-th electric vehicle selected.

[0057] S132: Predicted power consumption.

[0058] The GMM (Gross Model) can be used to fit the driving conditions of an electric vehicle, including the driving distance and average speed. This allows for a detailed analysis of the unit power consumption during the driving process. Different speeds result in different levels of energy loss for the electric vehicle. The mathematical expression for calculating the unit power consumption of an electric vehicle during driving is as follows:

[0059] In the formula, Power consumption per unit; For speed; For congestion parameters; , , These are constant values.

[0060] Based on the combined analysis of unit power consumption and daily driving distance, the power consumption of electric vehicles can be obtained. Specifically, the daily power consumption is calculated by multiplying the daily driving distance of different electric vehicles by their corresponding unit power consumption. The mathematical expression is as follows:

[0061] Specifically, the driving distance d of an electric vehicle was obtained through GMM fitting. i Driving speed v i Then the daily power consumption can be calculated:

[0062] If a user needs to charge their electric vehicle and is located at node j, they will choose the charging station node that best serves their interests. The most important influencing factor is the distance to the charging station, so they will generally choose the nearest charging station, i.e., select the shortest charging path.

[0063] S133: Calculation of the shortest charging path and the probability of selecting a charging station.

[0064] To find the shortest charging path, a comprehensive analysis of road conditions is needed. This embodiment uses Dijkstra's algorithm, with the time spent between nodes as weights. The optimized solution formula for the shortest distance problem is as follows:

[0065] In the formula, , is a path selected from node j where the user is located to node i where the charging station is located; This represents the total number of paths. These are weight values; Let i be the distance along path i; Let be the speed of the electric vehicle on path i; For congestion parameters.

[0066] For the probability of choosing a charging station, this embodiment uses the Huff model to analyze user charging intention. The output of the Huff model is the probability of a user choosing a certain event. Applied to electric vehicle charging, this means the probability of a user going to a certain charging station. It can statistically analyze the increasing trend and number of electric vehicle loads at a certain node under certain conditions. Therefore, the probability of an electric vehicle user at node j in the flexible distribution transformer area choosing a certain charging station i is:

[0067] In the formula: The probability of user j choosing charging station i; The appeal of charging stations; Select the impedance of charging station i for user node j; A collection of electric vehicle charging stations; The attractiveness value of the charging station; The resistance value for a user traveling to a charging station.

[0068] The formula above is rather general and cannot specifically analyze the trend of users choosing charging stations in different scenarios. Therefore, we introduce the specific number of charging piles at the node charging station, the charging power, and the unit charging cost to analyze the attractiveness of charging station i to users:

[0069] In the formula, The appeal of charging stations; The evaluation index for charging station i; The unit charging cost for charging station i; , These are the influencing parameters; It is a constant value.

[0070] Simultaneously considering the direct electricity loss costs that users will incur when selecting a charging station, as well as the indirect losses due to necessary travel time, a more detailed description of the resistance of charging station i to user at node j is provided. ;

[0071] In the formula, The indirect cost for user j at node; Let J be the shortest distance that a user at node j travels to charging station i. Let be the speed at which the user at node j travels to charging station i. The unit charging cost for charging station i; This refers to the unit electricity consumption of users at node j traveling to charging station i.

[0072] Therefore, according to Dijkstra's algorithm, the shortest charging path is selected, and the shortest path is L. i,j Now that all influencing factor values ​​are known, using Huff to calculate charging node selection intention, the probability that a user will choose charging station i at node j to charge their electric vehicle is predicted as follows:

[0073] S134: Node charging power analysis.

[0074] Monte Carlo sampling based on the distribution function can determine the daily driving distance of different electric vehicles. Analyzing the charging amount of electric vehicles under different levels of road congestion, the charging amount at node j can be expressed as...

[0075] In the formula: Let r be the charging amount of electric vehicle k when the congestion coefficient is r; Let k be the daily driving distance of electric vehicle k; Let k be the unit power consumption of electric vehicle k when the congestion coefficient is r; Let j be the number of electric vehicles at node j.

[0076] In summary, the electric vehicle charging load Pi at charging station i is:

[0077] In the formula: Let J be the shortest distance that a user at node j travels to charging station i. The unit electricity consumption for users traveling from node j to charging station i; The probability of user j choosing charging station i; J It is a set of distribution network nodes.

[0078] S14: The total number of electric vehicles is N. In each hour, a different number of electric vehicles travel. Repeat step S13, each time based on the average travel time and distance d of the electric vehicles. i The average velocity v can be obtained i Then according to Calculate the initial charging time; then according to First, calculate the energy consumption during driving; then, use Dijkstra's algorithm to calculate the probability that electric vehicle i will choose charging station i at node j; finally, t can be obtained. i The charging load P of charging node i at time moment i It can predict the initial charging time and charging amount of N electric vehicles in the region at charging node i within a 24-hour period of a day.

[0079] In the flexible distribution transformer area voltage coordination management stage of step S2 in this embodiment, the main controllable resources of the transformer area are on-load tap changers (OLTC) and switchable capacitor banks (SCB). The charging power and charging time of electric vehicles have already been predicted. During the day-ahead control stage, the voltage can be controlled to prevent it from exceeding limits by adjusting the taps of the OLTC and SCB, thereby improving the voltage quality when disorderly electric vehicles connect to the grid.

[0080] In this embodiment, for the objective function of the optimization model, the primary objective of the day-ahead scheduling phase is to prevent voltage overshoot and maintain voltage stability and security. Secondly, it aims to minimize economic costs and enhance economic efficiency while minimizing voltage fluctuations. This primarily considers the switching costs of OLTC and SCB and the power loss costs of the distribution network. The mathematical expression of the objective function is as follows:

[0081]

[0082]

[0083] In the formula: Total cost; This is the voltage over-limit index; This is the penalty factor for voltage exceeding the limit; For power loss in the distribution network; This is the power loss cost coefficient; For OLTC switching costs; SCB switching cost; Node voltage; The number of nodes; This is the lower limit of the node voltage. This is the upper limit of the node voltage; Let be the electrical conductance between lines ij; Let be the phase angle difference between lines ij at time t.

[0084] OLTC and SCB are discrete mechanical structures, and their lifespan gradually decreases with the number of switching operations. Therefore, when considering them in the overall voltage management cost, it is necessary to minimize the number of switching operations to improve the economic efficiency of the reactive power voltage management scheme.

[0085]

[0086] In the formula: Cost of tapping and cutting OLTC; Cost of SCB switching; Let t be the tap position of the nth OLTC; Let be the number of capacitors switched on by the z-th SCB at time t; The number of OLTCs; This represents the number of SCBs.

[0087] In this embodiment, the distribution network constraints of the optimization model mainly include power flow constraints, power transmission constraints of upstream substations, node voltage constraints, and OLTC and SCB operation constraints, etc., and their formulas are as follows: Current constraints:

[0088] In the formula: , Let t represent the active and reactive power transmitted from the upstream power grid at node i at time t. , Let be the active and reactive power of the load at node i at time t; Let i be the charging power of the electric vehicle at node i at time t. Let i be the reactive power compensation power at node i at time t; Let be the voltage at node i at time t; , The conductivity and reactance between lines ij; Let be the phase angle difference between lines ij at time t.

[0089] Transmission power constraints:

[0090] In the formula: , These represent the minimum and maximum active power transmitted from the upstream node at node i; , These represent the minimum and maximum reactive power transmitted from the upstream node at node i.

[0091] Node voltage constraints:

[0092] In the formula: , Let i be the minimum and maximum voltage values ​​at node i.

[0093] Balance node voltage constraints:

[0094] In the formula: To balance the node voltage; Reference voltage value; This refers to the voltage regulation step size of an on-load tap-changing transformer. This is a set of balanced nodes.

[0095] OLTC and SCB operational constraints:

[0096]

[0097]

[0098]

[0099] In the formula: , Let these be the minimum and maximum positions of the switching taps of the nth on-load tap-changing transformer. This represents the maximum daily operating time of the nth on-load tap-changing transformer. This represents the maximum number of switchable capacitors that can be switched at the z-th switchable capacitor. This represents the number of switchable capacitors.

[0100] In summary, this invention proposes a method for coordinated voltage management of flexible distribution transformer areas considering electric vehicle charging. When coordinating voltage in flexible distribution transformer areas using this method, the charging status of electric vehicles is first predicted, with different predictions made for different time periods, such as weekdays and holidays, to achieve precise control of day-ahead scheduling and provide a load basis for the rational allocation of resources within the area. Then, a day-ahead scheduling model is established to optimize voltage stability, reduce voltage fluctuations, and minimize the number of discrete actions of the OLTC and SCB, extending equipment lifespan while also considering economic factors. Finally, the model is solved to determine the resource allocation situation within the area.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging, characterized in that, Includes the following steps: Obtain the probability density function of the initial travel time and parking duration of electric vehicles at each node of the distribution network on a typical day, as well as the road topology information. The typical day is a weekday or a holiday. Sampling is performed from the probability density function to obtain the initial daily travel time and parking duration of the selected electric vehicles. The distribution function of the daily driving distance of the electric vehicles is established and sampled to obtain the daily driving distance of different electric vehicles. Based on the initial daily travel time, parking duration, and daily driving distance of the selected electric vehicles, the corresponding charging time and power consumption are predicted. The attractiveness and resistance of each charging station are calculated, and the user's charging intention is calculated based on the attractiveness and resistance of each charging station. The electric vehicle charging load of each charging station is calculated based on the user's charging intention and the power consumption, thereby obtaining the charging demand prediction curve of different charging stations. For the charging demand prediction curves of charging stations at different nodes, an optimization model is used to solve for voltage mitigation solutions by considering the switching costs of on-load tap-changing transformers and switchable capacitors, the power loss cost objective function of the distribution network, and the constraints of the distribution network.

2. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging according to claim 1, characterized in that, When obtaining the probability density functions of the initial travel time and parking duration of electric vehicles at each node of the distribution network on a typical day, a Gaussian mixture model is used to process the historical data of the typical day containing the initial travel time and parking duration to obtain the probability density functions of the initial travel time and parking duration of electric vehicles at each node of the distribution network on a typical day.

3. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging as described in claim 1, characterized in that, When predicting charging time and energy consumption based on the extracted initial daily travel time, parking duration, and daily driving distance of electric vehicles, the steps for predicting charging time include: Calculate the corresponding speed based on the average driving time and daily driving distance of the electric vehicle; The initial charging time is calculated based on the electric vehicle's initial daily travel time, parking duration, daily driving distance, and speed. The mathematical expression is as follows: In the formula, It is the initial charging time of the i-th electric vehicle selected; It is the initial departure time of the i-th electric vehicle on that day; It represents the daily driving distance of the i-th extracted electric vehicle; It is the speed of the i-th electric vehicle that was sampled; It is the parking duration of the i-th electric vehicle selected.

4. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging according to claim 3, characterized in that, When predicting charging time and power consumption based on the extracted initial daily travel time, parking duration, and daily driving distance of electric vehicles, the steps for predicting power consumption include: The mathematical expression for calculating the unit power consumption of an electric vehicle during operation is as follows: In the formula, Power consumption per unit; For speed; For congestion parameters; , , These are constant values; The daily power consumption of different electric vehicles is obtained by multiplying their daily driving distance by the corresponding unit power consumption.

5. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging according to claim 1, characterized in that, The mathematical expression for the distribution function of the daily driving distance of electric vehicles is as follows: In the formula, The daily driving distance of different electric vehicles; This is the expected value; This represents the standard deviation of the daily travel distance.

6. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging according to claim 4, characterized in that, Before calculating the attractiveness and resistance of each charging station, the following steps are taken: Using Dijkstra's algorithm, with the time spent between nodes as weights, the shortest travel path from the electric vehicle to each charging station is calculated, as shown in the following formula: In the formula, , which is a selected path; This represents the total number of paths. These are weight values; Let i be the distance along path i; Let be the speed of the electric vehicle on path i; For congestion parameters.

7. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging according to claim 6, characterized in that, When calculating the attractive and resistive forces at each charging station node, the mathematical expression for the attractive force is as follows: In the formula, The appeal of charging stations; The evaluation index for charging station i; The unit charging cost for charging station i; , These are the influencing parameters; It is a constant value; The mathematical expression for resistance is as follows: In the formula, The indirect cost for user j at node; Let J be the shortest distance that a user at node j travels to charging station i. Let be the speed at which the user at node j travels to charging station i. The unit charging cost for charging station i; This refers to the unit electricity consumption of users at node j traveling to charging station i.

8. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging according to claim 7, characterized in that, When calculating user charging intentions based on the attractiveness and resistance of each charging station, and calculating the electric vehicle charging load of each charging station based on user charging intentions and the power consumption, the calculation includes: The probability that a user will choose charging station i at node j to charge their electric vehicle is calculated using the following mathematical expression: In the formula, The appeal of charging stations; The evaluation index for charging station i; The unit charging cost for charging station i; , These are the influencing parameters; It is a constant value; The indirect cost for user j at node; Let J be the shortest distance that a user at node j travels to charging station i. Let be the speed at which the user at node j travels to charging station i. The unit charging cost for charging station i; The unit electricity consumption for users traveling from node j to charging station i; A collection of electric vehicle charging stations; The mathematical expression for calculating the charging amount of all electric vehicles at node j is as follows: In the formula, This represents the amount of charge at node j. Let r be the charging amount of electric vehicle k when the congestion coefficient is r; Let k be the daily driving distance of electric vehicle k; Let k be the unit power consumption of electric vehicle k when the congestion coefficient is r; Let j be the number of electric vehicles at node j; The mathematical expression for calculating the electric vehicle charging load of charging station i is as follows: In the formula, Let J be the shortest distance that a user at node j travels to charging station i. J represents the unit electricity consumption of a user traveling from node j to charging station i; J is the set of distribution network nodes. The probability that user j selects charging station i.

9. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging according to claim 1, characterized in that, The mathematical expression of the objective function is as follows: In the formula, Total cost; This is the voltage over-limit index; This is the penalty factor for voltage exceeding the limit; For power loss in the distribution network; This is the power loss cost coefficient; Cost of switching on-load tap-changing transformers; Cost of switching on / off switchable capacitors; Node voltage; The number of nodes; This is the lower limit of the node voltage. This is the upper limit of the node voltage; Let be the electrical conductance between lines ij; Let be the phase angle difference between lines ij at time t, where: In the formula: Cost of switching on-load tap changers in on-load tap-changing transformers; Cost of switching on / off switchable capacitors; Let t be the tap position of the nth on-load tap-changing transformer at time t; Let be the number of capacitors switched by the z-th switchable capacitor at time t; This refers to the number of on-load tap-changing transformers; This represents the number of switchable capacitors.

10. The method for coordinated voltage management of flexible distribution network areas considering electric vehicle charging according to claim 9, characterized in that, The power distribution network constraints include: Power flow constraints, mathematically expressed as: In the formula: , Let t represent the active and reactive power transmitted from the upstream power grid at node i at time t. , Let be the active and reactive power of the load at node i at time t; Let i be the charging power of the electric vehicle at node i at time t. Let i be the reactive power compensation power at node i at time t; Let be the voltage at node i at time t; , The conductivity and reactance between lines ij; Let be the phase angle difference between lines ij at time t; The transmission power constraint, mathematically expressed as: In the formula: , These represent the minimum and maximum active power transmitted from the upstream node at node i; , These represent the minimum and maximum values ​​of the reactive power transmitted from the upstream node at node i. The node voltage constraint, mathematically expressed as: In the formula: , Let i be the minimum and maximum voltage values ​​at node i. The voltage constraint at the slack node is mathematically expressed as follows: In the formula: To balance the node voltage; Reference voltage value; This refers to the voltage regulation step size of an on-load tap-changing transformer. For a balanced set of nodes; The mathematical expressions for the operating constraints of on-load tap-changing transformers and switchable capacitors are as follows: In the formula: , Let these be the minimum and maximum positions of the switching taps of the nth on-load tap-changing transformer. This represents the maximum daily operating time of the nth on-load tap-changing transformer. This represents the maximum number of switchable capacitors that can be switched at the z-th switchable capacitor. This represents the number of switchable capacitors.

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