A real-time voltage regulation method for new energy vehicles

Abstract

The present application relates to the field of voltage regulation, especially to a real-time voltage regulation method for new energy vehicles. A power distribution network voltage measurement module is initialized to collect voltage data of each node of the power distribution network; a voltage deviation threshold is set to determine whether the voltage is within a normal range; the collected voltage is compared with the set voltage deviation threshold, the result after preliminary voltage regulation of the on-load voltage regulating transformer is compared with the set voltage deviation threshold, and the result after collaborative voltage regulation of the electric vehicle charging pile is compared with the set voltage deviation threshold; based on the comparison results, tap adjustment of the on-load voltage regulating transformer, collaborative voltage regulation of the electric vehicle charging pile, and participation of the distributed photovoltaic system in voltage regulation are sequentially performed; and based on historical data and the results of three times of voltage regulation, an optimization strategy is formulated. The present application effectively improves voltage stability and enhances the adaptability and flexibility of the system through collaborative voltage regulation, optimization control, intelligent management and other means.

Description

Technical Field

[0001] This invention relates to the field of voltage regulation, and more particularly to a method for real-time voltage regulation in new energy vehicles. Background Technology

[0002] With the widespread adoption of electric vehicles and the development of distributed energy, the voltage stability of power distribution networks faces new challenges. In existing technologies, on-load tap changers often operate independently when regulating voltage, without fully considering the synergistic effect of electric vehicle charging stations and distributed photovoltaic (PV) systems. Changes in the charging power of electric vehicle charging stations can lead to voltage fluctuations. This is because when multiple electric vehicles charge simultaneously or the charging power suddenly increases, it causes rapid changes in the grid current. According to Ohm's law, the voltage drop across the line impedance also changes accordingly, resulting in voltage fluctuations. For example, during peak electricity consumption periods, a large number of electric vehicles charging simultaneously may cause a sudden drop in local grid voltage, affecting the normal electricity use of other users. Furthermore, the output power of distributed PV systems is affected by factors such as sunlight and temperature, exhibiting uncertainty. Changes in sunlight intensity directly affect the output power of PV cells, while temperature changes affect their efficiency. In actual operation, due to variable weather conditions, the output power of distributed PV systems may fluctuate frequently, posing a challenge to the stable operation of the power grid. For instance, on sunny days, the higher output power of the PV system may lead to an increase in grid voltage; while on cloudy days or at night, the reduced output power of the PV system may lead to a decrease in grid voltage. Furthermore, existing technologies lack real-time performance and accuracy in voltage regulation, making it difficult to meet the increasingly complex demands of the power grid.

[0003] To address these shortcomings, some existing technologies attempt to employ simple collaborative control strategies, such as controlling the output of electric vehicle charging stations and distributed photovoltaic systems based on preset schedules or voltage thresholds. However, these methods lack flexibility and adaptability, and cannot dynamically adjust according to the real-time state of the power grid.

[0004] In summary, existing technologies have many problems in terms of the synergistic effect of electric vehicle charging piles, distributed photovoltaic systems and on-load tap changers, and the real-time performance and accuracy of voltage regulation, making it difficult to meet the increasingly complex grid demands. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a real-time voltage regulation method for new energy vehicles, so as to solve the problem that in the prior art, the on-load tap changer often works independently when regulating voltage, without fully considering the synergistic effect of electric vehicle charging piles and distributed photovoltaic systems, resulting in a lack of real-time performance and accuracy in voltage regulation, which makes it difficult to meet the increasingly complex grid demands.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for real-time voltage regulation in new energy vehicles includes the following steps:

[0008] S1. System initialization and real-time data acquisition: Initialize the distribution network voltage measurement module, collect voltage data of each node in the distribution network in real time, and connect to the electric vehicle charging pile management system to obtain the status information of the electric vehicle charging pile.

[0009] S2. Real-time monitoring and evaluation of distribution network voltage, setting voltage deviation thresholds, and determining whether the voltage is within the normal range;

[0010] S3. Initial voltage adjustment of the on-load tap-changing transformer: The voltage collected in S1 is compared with the voltage deviation threshold set in S2, and the taps of the on-load tap-changing transformer are adjusted according to the comparison result.

[0011] S4. Coordinated voltage regulation of electric vehicle charging piles: The result of the initial voltage regulation by the on-load tap changer in S3 is compared with the set voltage deviation threshold. If the voltage still exceeds the threshold, coordinated voltage regulation of electric vehicle charging piles is performed.

[0012] S5. Distributed photovoltaic system participates in voltage regulation. The result of voltage regulation in S4 is compared with the set voltage deviation threshold. If the voltage still exceeds the threshold, the distributed photovoltaic system is activated to participate in voltage regulation and the active and reactive power outputs of the distributed photovoltaic system are adjusted.

[0013] S6. Model Predictive Control and Optimization: Based on historical data and the results from S1 to S5, establish a predictive model and use the results predicted by the predictive model to formulate an optimization strategy.

[0014] Furthermore, the status information of the electric vehicle charging station includes: charging power, battery capacity, charging demand, etc.

[0015] Furthermore, in step S2, the formula for calculating the voltage deviation threshold is:

[0016]

[0017]

[0018]

[0019]

[0020] in, This is the lower limit of the voltage deviation threshold; V is the upper limit of the voltage deviation threshold. n The rated voltage of the power grid; δ min and δ maxThese are the minimum and maximum allowable relative deviations of voltage, determined based on power system standards, equipment requirements, and operating experience. and For additional voltage tolerance; and For voltage deviation compensation related to load changes; k l P is a coefficient related to load characteristics and grid parameters. l，j (t) represents the change in power of the load at node j over time t; Z l The impedance of the line between the two nodes.

[0021] Furthermore, in S3, the method for adjusting the tap changer of the on-load tap-changing transformer is as follows:

[0022] S31. Determine the tap position of the voltage regulating transformer, compare the real-time voltage with the set voltage deviation threshold, and determine whether the voltage is within the normal range.

[0023] Real-time voltage V i Does (t) belong to

[0024] when At that time, the tap changers of the on-load tap-changing transformer should be adjusted.

[0025] S32. Adjust the tap changers of the on-load tap-changing transformer:

[0026] Calculate the output voltage of the voltage regulating transformer based on its voltage regulation characteristics and adjustment rules. The calculation formula is:

[0027]

[0028]

[0029] in, Reference voltage; These are coefficients related to transformer characteristics; The tap position at time t; This refers to the tap adjustment amount determined based on the transformer's voltage regulation characteristics and regulation rules. The rated voltage of the power grid; ΔV i (t) is the tap adjustment amount determined based on the voltage regulation characteristics and regulation rules of the transformer.

[0030] Furthermore, in step S4, the method for coordinated voltage regulation of electric vehicle charging stations is as follows:

[0031] If the voltage still exceeds the threshold after initial voltage regulation by an on-load tap-changing transformer, that is... Then, coordinated voltage regulation of electric vehicle charging stations is implemented. The formula for adjusting the power of electric vehicle charging stations is as follows:

[0032]

[0033]

[0034]

[0035]

[0036] In the formula, This represents the power of the electric vehicle charging station at node i at time t; The power change due to coordinated voltage regulation; V a (t) represents the power of the electric vehicle charging station at node i at time t; Reference voltage; The rated power of the electric vehicle charging station at node i;

[0037] The value of the ratio of the charging power of the electric vehicle charging pile to the rated power at node i at time t-1; k1, k2, k3, k4, k5, k6, k7, k8 are parameters related to the characteristics of the charging pile and the battery. The coefficients are related to the characteristics of the charging pile; f1, f2, and f3 are the frequency parameters for the corresponding stages; t0 is the charging start time; t max This is the estimated time for the charging to finish;

[0038] A function that takes into account the state of charge and priority; SOC i (t) represents the battery charge percentage at time t when the charging station is at node i; SOC max It is the maximum percentage of battery capacity; SOC min It is the minimum percentage of battery charge; k9, k 10 k 11 k 12 α is the adjustment coefficient; β are exponential parameters.

[0039] Furthermore, in step S5, the active power output and reactive power output of the distributed photovoltaic system are adjusted:

[0040] The formula for adjusting active power output is expressed as follows:

[0041]

[0042] f represents the maximum output power of a distributed photovoltaic system under ideal conditions. l (t) is the illumination intensity function; ft (t) is a temperature function; ε l , λ l I is the temperature correlation coefficient; l (t) represents the measured light intensity; T t (t) represents the measured temperature value;

[0043] The adjustment of reactive power output is achieved by controlling the output current of the inverter, as expressed by the formula:

[0044]

[0045] A coefficient related to the characteristics of the photovoltaic system; This refers to the output voltage of the photovoltaic system. The phase angle of the output current.

[0046] S6. Model Predictive Control and Optimization: Based on historical data and the results from S1 to S5, optimization strategies are formulated using the results predicted by the predictive model.

[0047] Further, S6 includes:

[0048] S61. Establish a prediction model, select a prediction model, train the prediction model using historical data so that it can learn the trend and pattern of voltage change, and continuously adjust the parameters of the model to improve the accuracy and generalization ability of the prediction model.

[0049] The prediction model is expressed as follows:

[0050]

[0051]

[0052] f(V i (t) represents the predicted voltage value of node i at time t; t is the number of historical data samples; K(V) i (t),V j (t-τ)) is the kernel function; σ is the parameter of the kernel function; m is the number of historical data samples; ω is the bias term; τ is the time delay;

[0053] S62. Voltage change trend prediction: The current results from S1 to S5 are used as inputs to the trained prediction model to predict the voltage change trend in the future, including information such as voltage magnitude and phase.

[0054] S63. Formulate an optimization strategy and determine the adjustment strategy based on the voltage change trend predicted in S62;

[0055] S64. Optimize the solution: Use optimization algorithms to optimize the established voltage regulation strategy and find the optimal combination of voltage regulation strategies. During the optimization process, continuously evaluate the impact of different combinations of voltage regulation strategies on the objective function to ensure that the optimal solution found can meet the requirements of voltage stability and system loss minimization.

[0056] S65. Implement voltage regulation strategy: Apply the optimal voltage regulation strategy obtained from the optimization solution to the actual power system, and make corresponding adjustments to on-load tap-changing transformers, electric vehicle charging piles and distributed photovoltaic systems.

[0057] Furthermore, in S61, during the prediction model training process, the Lagrange multiplier α j The bias term ω is determined by minimizing the following function:

[0058]

[0059]

[0060] α i and α j These are Lagrange multipliers used to adjust model parameters; y i and y j The corresponding target voltage value is usually the actual voltage value from historical data; Constraints are typically used to ensure the uniqueness of the model solution; 0 ≤ α i ≤C represents α i The value of is between 0 and C, where C is a preset constant used to control the complexity and generalization ability of the model.

[0061] Furthermore, in S63, the method for determining the adjustment strategy is as follows:

[0062] S631. Objective Function Construction: The objective function is to minimize voltage deviation and system losses. It comprehensively considers the voltage regulation capabilities and limitations of on-load tap-changing transformers, electric vehicle charging stations, and distributed photovoltaic systems. The objective function is expressed as follows:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] η and γ are weighting coefficients; P is the reference voltage at node i;loss (t) represents the system loss at time t; ΔV tai (t) represents the output voltage of the voltage regulating transformer at node i at time t; P is a coefficient related to transformer characteristics. fl (t) represents the transformer loss; P ll (t) represents the line loss at time t; P ol For other loss factors; k′ l For line loss correlation coefficient; k o For other loss correlation coefficients;

[0069] This is the lower limit of the voltage deviation threshold; This is the upper limit of the voltage deviation threshold; The power of the electric vehicle charging station at node i at time t; Let be the active power output of the distributed photovoltaic system at node i at time t;

[0070] S632. Setting the bundle conditions,

[0071] For on-load tap-changing transformers, considering the limitations of their tap adjustment range, the following constraints are set:

[0072]

[0073] This is the lower limit of the output voltage of the on-load tap-changing transformer; This represents the upper limit of the output voltage of the on-load tap-changing transformer.

[0074] For electric vehicle charging stations, power adjustment constraints are set based on their rated power and battery characteristics:

[0075]

[0076] The active and reactive power outputs of distributed photovoltaic systems should also be limited by their capacity and operating characteristics, and constraints should be set as follows:

[0077] and

[0078] This represents the lower limit of the active power output of a distributed photovoltaic system. This represents the upper limit of the active power output of a distributed photovoltaic system. This represents the lower limit of reactive power output for a distributed photovoltaic system. This represents the upper limit of reactive power output for a distributed photovoltaic system.

[0079] S633. Formulation of a coordinated voltage regulation strategy: Based on the voltage deviation, determine the voltage regulation sequence and coordination method of on-load tap-changing transformers, electric vehicle charging piles, and distributed photovoltaic systems.

[0080] Furthermore, in S64, the optimization algorithm is as follows:

[0081] S641. Initialize the particle swarm and randomly generate a group of particles. Each particle represents a combination of voltage regulation strategies, including the tap position of the on-load tap changer, the power adjustment amount of the electric vehicle charging pile, and the active and reactive power output adjustment amount of the distributed photovoltaic system.

[0082] S642. Calculate fitness. For each particle (representing a combination of voltage regulation strategies), calculate the particle fitness value according to the objective function in S63. The fitness value is the reciprocal or the opposite of the objective function value.

[0083] S643. Update the individual optimal solution and the global optimal solution. For each particle, compare its current fitness value with the fitness value of the particle's individual optimal solution. If the current fitness value is better, update the individual optimal solution to the current particle's position. At the same time, compare the individual optimal solutions of all particles and find the best individual optimal solution as the global optimal solution.

[0084] S644. Update the particle's velocity and position. Update the particle's velocity according to the following formula:

[0085] in, and and are the velocities of particle n in the k-th and k+1-th iterations, respectively; μ is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; This is the individual optimal solution for particle n in the kth iteration; This is the globally optimal solution; Let n be the position of particle n in the k-th iteration;

[0086] Update the particle's position based on the updated velocity:

[0087]

[0088] Let n be the position of particle n in the (k+1)th iteration;

[0089] S645. Iterative optimization involves repeatedly calculating fitness, updating individual optimal solutions and global optimal solutions, and updating particle velocity and position until the preset convergence condition is met.

[0090] S646. Output the optimal solution. After the algorithm converges, output the voltage regulation strategy combination corresponding to the global optimal solution, which is the optimal voltage regulation strategy combination.

[0091] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are as follows:

[0092] Improving voltage stability through coordinated voltage regulation of on-load tap-changing transformers, electric vehicle charging stations, and distributed photovoltaic systems can more effectively address voltage fluctuations and ensure voltage stability within the normal range. For example, when electric vehicle charging power changes or the output power of the distributed photovoltaic system fluctuates, other systems can adjust promptly to compensate for voltage deviations.

[0093] To reduce system losses, with the objective function of minimizing voltage deviation and system losses, this study comprehensively considers the voltage regulation capabilities and limitations of on-load tap-changing transformers, electric vehicle charging stations, and distributed photovoltaic systems. This approach optimizes voltage regulation strategies and reduces system losses such as transformer and line losses. By rationally adjusting the power output of electric vehicle charging stations and the active power output of distributed photovoltaic systems, unnecessary energy transmission and losses can be reduced, thereby improving energy utilization efficiency.

[0094] To enhance the system's adaptability and flexibility, factors such as variations in charging power of electric vehicle charging stations, uncertainties in the output power of distributed photovoltaic systems, and load changes are considered. The system can formulate corresponding voltage regulation strategies based on actual conditions, thus improving its adaptability to complex power grid environments. Employing model predictive control and optimization, the system can predict voltage change trends and formulate voltage regulation strategies in advance, improving its foresight and flexibility. The optimization algorithm continuously evaluates the impact of different voltage regulation strategy combinations on the objective function, finding the optimal combination to ensure the system maintains good performance under various operating conditions.

[0095] By fully utilizing distributed energy resources, including distributed photovoltaic systems participating in voltage regulation, it is possible to maximize their clean energy output, reduce dependence on traditional energy sources, and also help balance the power distribution of the power grid, thereby improving grid stability. Through coordinated voltage regulation, distributed energy resources and traditional voltage regulation equipment can be better integrated, achieving efficient energy utilization and optimized allocation.

[0096] By enhancing the intelligence level of the power grid and utilizing predictive models and optimization algorithms, intelligent regulation of grid voltage has been achieved, improving the grid's automation and intelligence levels. Based on historical data and real-time monitoring results, the grid can continuously learn and optimize voltage regulation strategies, enabling it to better adapt to ever-changing electricity demands and operating environments. Attached Figure Description

[0097] Figure 1 This is a flowchart of the method of the present invention;

[0098] Figure 2 This is a flowchart of the tap adjustment method for an on-load tap-changing transformer;

[0099] Figure 3 Flowchart of a method for developing optimization strategies using the results of predictive models;

[0100] Figure 4 A flowchart illustrating the method for determining the adjustment strategy based on the voltage change trend predicted by S62;

[0101] Figure 5 This is a flowchart of the optimization algorithm method in S64. Detailed Implementation

[0102] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0103] S1. System initialization and real-time data acquisition: Initialize the distribution network voltage measurement module, collect voltage data of each node in the distribution network in real time, and connect to the electric vehicle charging pile management system to obtain the status information of the electric vehicle charging pile.

[0104] The status information of the electric vehicle charging station includes: charging power, battery capacity, charging demand, etc.

[0105] S2. Real-time monitoring and evaluation of distribution network voltage, setting voltage deviation thresholds to determine whether the voltage is within the normal range. The voltage deviation thresholds are determined comprehensively based on factors such as power system standards and specifications, rated voltage and withstand capability of electrical equipment, load characteristics, and the topology and operating conditions of the power grid.

[0106] In step S2, the formula for calculating the voltage deviation threshold is as follows:

[0107]

[0108]

[0109]

[0110]

[0111] in, This is the lower limit of the voltage deviation threshold; V is the upper limit of the voltage deviation threshold. n The rated voltage of the power grid; δ min and δ max These are the minimum and maximum allowable relative deviations of voltage, determined based on power system standards, equipment requirements, and operating experience. and For additional voltage tolerance, some special cases or uncertainties, such as power grid transients and measurement errors, are taken into account; and For voltage deviation compensation related to load changes; k l P is a coefficient related to load characteristics and grid parameters. l，j (t) represents the change in power of the load at node j over time t; Z l,ij Let be the impedance of the line between node i and node j.

[0112] S3. Initial voltage adjustment of the on-load tap-changing transformer: The voltage collected in S1 is compared with the voltage deviation threshold set in S2, and the taps of the on-load tap-changing transformer are adjusted according to the comparison result.

[0113] Tap adjustment method for on-load tap-changing transformers:

[0114] S31. Determine the tap position of the voltage regulating transformer, compare the real-time voltage with the set voltage deviation threshold, and determine whether the voltage is within the normal range.

[0115] Real-time voltage V i Does (t) belong to

[0116] when At that time, the tap changer of the on-load tap-changing transformer is adjusted.

[0117] S32. Perform tap adjustment on the on-load tap-changing transformer:

[0118] Calculate the output voltage of the voltage regulating transformer based on its voltage regulation characteristics and adjustment rules. The calculation formula is:

[0119]

[0120]

[0121] in, Reference voltage; These are coefficients related to transformer characteristics; The tap position at time t; This refers to the tap adjustment amount determined based on the transformer's voltage regulation characteristics and regulation rules. The rated voltage of the power grid; ΔV i (t) is the tap adjustment amount determined based on the voltage regulation characteristics and regulation rules of the transformer.

[0122] S4. Coordinated voltage regulation of electric vehicle charging piles: The result of the initial voltage regulation by the on-load tap changer in S3 is compared with the set voltage deviation threshold. If the voltage still exceeds the threshold, coordinated voltage regulation of electric vehicle charging piles is performed.

[0123] Methods for coordinated voltage regulation of electric vehicle charging stations:

[0124] If the voltage still exceeds the threshold after initial voltage regulation by an on-load tap-changing transformer, that is... Then, coordinated voltage regulation of electric vehicle charging stations is implemented. The formula for adjusting the power of electric vehicle charging stations is as follows:

[0125]

[0126]

[0127]

[0128]

[0129] In the formula, This represents the power of the electric vehicle charging station at node i at time t; The power change due to coordinated voltage regulation; V a (t) represents the power of the electric vehicle charging station at node i at time t; Reference voltage; The rated power of the electric vehicle charging station at node i;

[0130] The value of the ratio of the charging power to the rated power of the electric vehicle charging pile at node i at time t-1; k1, k2, k3, k4, k5, k6, k7, k8 are parameters related to the characteristics of the charging pile and the battery. The coefficients are related to the characteristics of the charging pile; f1, f2, and f3 are the frequency parameters for the corresponding stages; t0 is the charging start time; t max It is the estimated time to end the charging process; in the early stage of charging, the charging power gradually increases and takes into account a certain periodic fluctuation; in the middle stage of charging, the charging power fluctuates within a certain range; in the later stage of charging, the charging power gradually decreases and also has a certain periodic fluctuation.

[0131] A function that takes into account the state of charge and priority; SOC i (t) represents the battery charge percentage at time t when the charging station is at node i; SOC max It is the maximum percentage of battery capacity; SOC min It is the minimum percentage of battery charge; k9, k 10 k 11 k12 α is the adjustment coefficient; β are exponential parameters; when the battery level is below the threshold and the priority is high, the charging power is high and gradually decreases as the battery level increases; when the battery level is in the middle range and the priority is medium, the charging power gradually decreases as the battery level increases; when the battery level is close to full or the priority is low, the charging power decreases rapidly.

[0132] S5. Distributed photovoltaic system participates in voltage regulation. Based on the results of S4, if the voltage is still unstable after the electric vehicle charging pile coordinates voltage regulation, the distributed photovoltaic system is activated to participate in voltage regulation, and the active and reactive power outputs of the distributed photovoltaic system are adjusted.

[0133] If the voltage is still unstable after the electric vehicle charging station coordinates voltage regulation, that is... Start the distributed photovoltaic system to participate in voltage regulation;

[0134] The adjustment of active power output is based on factors such as light intensity and temperature, and is expressed by the formula:

[0135]

[0136] f represents the maximum output power of a distributed photovoltaic system under ideal conditions. l (t) is the illumination intensity function; f t (t) is a temperature function; ε l , λ l I is the temperature correlation coefficient; l (t) represents the measured light intensity; T t (t) represents the measured temperature value;

[0137] The adjustment of reactive power output is achieved by controlling the output current of the inverter, as expressed by the formula:

[0138]

[0139] A coefficient related to the characteristics of the photovoltaic system; This refers to the output voltage of the photovoltaic system. The phase angle of the output current.

[0140] S6. Model Predictive Control and Optimization: Based on historical data and the results from S1 to S5, establish an optimization model and formulate optimization strategies using the results predicted by the predictive model.

[0141] S6 includes:

[0142] S61. Establish a prediction model, select a prediction model, train the prediction model using historical data so that it can learn the trend and pattern of voltage change, and continuously adjust the parameters of the model to improve the accuracy and generalization ability of the prediction model.

[0143] The prediction model is expressed as follows:

[0144]

[0145]

[0146] f(V i (t) represents the predicted voltage value of node i at time t; t is the number of historical data samples; α j For Lagrange multipliers; K(V) i (t),V j (t-τ)) is the kernel function; σ is the parameter of the kernel function; m is the number of historical data samples; ω is the bias term; τ is the time delay;

[0147] S62. Voltage change trend prediction: The current results from S1 to S5 are used as input to the trained prediction model to predict the voltage change trend in the future (such as the next few minutes or hours), including information such as voltage magnitude and phase.

[0148] S63. Formulate an optimization strategy and determine the adjustment strategy based on the voltage change trend predicted in S62;

[0149] In S63, the method for determining the adjustment strategy is as follows:

[0150] S631. Objective Function Construction: The objective function is to minimize voltage deviation and system losses. It comprehensively considers the voltage regulation capabilities and limitations of on-load tap-changing transformers, electric vehicle charging stations, and distributed photovoltaic systems. The objective function is expressed as follows:

[0151]

[0152]

[0153]

[0154]

[0155]

[0156] η and γ are weighting coefficients; P is the reference voltage at node i; loss (t) represents the system loss at time t, which can take into account transformer losses, line losses, etc., and is related to the set voltage deviation threshold, the output voltage of the voltage regulating transformer, the power adjustment of the electric vehicle charging pile, and the active power output of the distributed photovoltaic system; ΔV tai (t) represents the output voltage of the voltage regulating transformer at node i at time t; P is a coefficient related to transformer characteristics. fl (t) represents the transformer loss; P ll (t) represents the line loss; P ol Other loss factors;

[0157] This is the lower limit of the voltage deviation threshold; This is the upper limit of the voltage deviation threshold; The power of the electric vehicle charging station at node i at time t; Let be the active power output of the distributed photovoltaic system at node i at time t;

[0158] S632. Setting the bundle conditions,

[0159] For on-load tap-changing transformers, considering the limitations of their tap adjustment range, the following constraints are set:

[0160]

[0161] This is the lower limit of the output voltage of the on-load tap-changing transformer; This represents the upper limit of the output voltage of the on-load tap-changing transformer.

[0162] For electric vehicle charging stations, power adjustment constraints are set based on their rated power and battery characteristics:

[0163]

[0164] The active and reactive power outputs of distributed photovoltaic systems should also be limited by their capacity and operating characteristics, and constraints should be set as follows:

[0165]

[0166] This represents the lower limit of the active power output of a distributed photovoltaic system. This represents the upper limit of the active power output of a distributed photovoltaic system. This represents the lower limit of reactive power output for a distributed photovoltaic system. This represents the upper limit of reactive power output for a distributed photovoltaic system.

[0167] S633. The formulation of a coordinated voltage regulation strategy involves determining the voltage regulation sequence and coordination method of on-load tap-changing transformers, electric vehicle charging stations, and distributed photovoltaic systems based on the voltage deviation. For example, when the voltage deviation is small, the power of electric vehicle charging stations is adjusted first to reduce frequent operation of the on-load tap-changing transformer; when the voltage deviation is large, the distributed photovoltaic system is activated to participate in voltage regulation to quickly stabilize the voltage.

[0168] S64. Optimize the solution: Use optimization algorithms to optimize the established voltage regulation strategy and find the optimal combination of voltage regulation strategies. During the optimization process, continuously evaluate the impact of different combinations of voltage regulation strategies on the objective function to ensure that the optimal solution found can meet the requirements of voltage stability and system loss minimization.

[0169] S65. Implement voltage regulation strategy: Apply the optimal voltage regulation strategy obtained from the optimization solution to the actual power system, and make corresponding adjustments to on-load tap-changing transformers, electric vehicle charging piles and distributed photovoltaic systems.

[0170] In S61, during the prediction model training process, α j And ω are determined by minimizing the following function:

[0171]

[0172]

[0173] α i and α j These are Lagrange multipliers used to adjust model parameters; y i and y j The corresponding target voltage value is usually the actual voltage value from historical data; Constraints are typically used to ensure the uniqueness of the solution to a model; 0 ≤ α i ≤C represents α i The value of is between 0 and C, where C is a preset constant used to control the complexity and generalization ability of the model.

[0174] In S64, the optimization algorithm is as follows:

[0175] S641. Initialize the particle swarm and randomly generate a group of particles. Each particle represents a combination of voltage regulation strategies, including the tap position of the on-load tap changer, the power adjustment amount of the electric vehicle charging pile, and the active and reactive power output adjustment amount of the distributed photovoltaic system.

[0176] S642. Calculate the fitness. For each particle (representing a combination of voltage regulation strategies), calculate the particle fitness value according to the objective function in S63. The fitness value is the reciprocal or the opposite of the objective function value. The smaller the objective function value, the larger the fitness value, indicating that the voltage regulation strategy combination represented by the particle is better in meeting the requirements of voltage stability and minimizing system losses. The fitness value reflects the degree of excellence of the voltage regulation strategy combination represented by the particle in meeting the requirements of voltage stability and minimizing system losses.

[0177] S643. Update the individual optimal solution and the global optimal solution. For each particle, compare its current fitness value with the fitness value of the particle's individual optimal solution. If the current fitness value is better, update the individual optimal solution to the current particle's position. At the same time, compare the individual optimal solutions of all particles and find the best individual optimal solution as the global optimal solution.

[0178] S644. Update the particle's velocity and position. Update the particle's velocity according to the following formula:

[0179] in, and and are the velocities of particle n in the k-th and k+1-th iterations, respectively; μ is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; This is the individual optimal solution for particle n in the kth iteration; This is the globally optimal solution; Let n be the position of particle n in the k-th iteration;

[0180] Update the particle's position based on the updated velocity:

[0181]

[0182] Let n be the position of particle n in the (k+1)th iteration;

[0183] S645. Iterative optimization involves repeatedly calculating fitness, updating individual and global optimal solutions, and updating particle velocity and position until a preset convergence condition is met, such as reaching the maximum number of iterations or the change in fitness value being less than a certain threshold.

[0184] S646. Output the optimal solution. After the algorithm converges, output the voltage regulation strategy combination corresponding to the global optimal solution, which is the optimal voltage regulation strategy combination.

[0185] The above description is a preferred embodiment of the invention and is not intended to limit the scope of the invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A new energy vehicle real-time voltage regulation method, characterized in that, Comprise: S1. System initialization and real-time data acquisition, initialize the power distribution network voltage measurement module, real-time acquisition of voltage data of each node of the power distribution network and acquisition of the state information of the electric vehicle charging pile; S2. Real-time monitoring and evaluation of power distribution network voltage, set the voltage deviation threshold, judge whether the voltage is within the normal range; S3. Preliminary voltage regulation of on-load voltage regulating transformer, compare the voltage collected in S1 with the voltage deviation threshold set in S2, and adjust the tap position of the on-load voltage regulating transformer according to the comparison result; S4. Electric vehicle charging pile cooperative voltage regulation, compare the result of the preliminary voltage regulation of the on-load voltage regulating transformer in S3 with the set voltage deviation threshold, and if the voltage still exceeds the threshold, perform electric vehicle charging pile cooperative voltage regulation; S5. Distributed photovoltaic system participates in voltage regulation, compare the result of the electric vehicle charging pile cooperative voltage regulation in S4 with the set voltage deviation threshold, and if the voltage still exceeds the threshold, start the distributed photovoltaic system to participate in voltage regulation; S6. Model predictive control and optimization, based on historical data and the results of S1 to S5, establish a prediction model, and use the prediction result of the prediction model to develop an optimization strategy; S6 comprises: S61. Establish a prediction model, select a prediction model, train the prediction model with historical data to enable it to learn the trend and regularity of voltage changes, and continuously adjust the parameters of the model to improve the accuracy and generalization ability of the prediction model; The prediction model is represented as: ； ； for the node at time a voltage prediction value; is a number of historical data samples; is a Lagrange multiplier; is a kernel function; is a parameter of the kernel function; is a number of historical data samples; is a bias term; is a time delay; S62. Voltage change trend prediction, input the current results of S1 to S5 into the trained prediction model to predict the voltage change trend in the future period of time, including the size and phase information of the voltage; S63. Develop an optimization strategy, determine the adjustment strategy according to the voltage change trend predicted in S62; S64. Optimization solution, use an optimization algorithm to optimize the developed voltage regulation strategy and find the optimal voltage regulation strategy combination; In the optimization solution process, continuously evaluate the influence of different voltage regulation strategy combinations on the objective function to ensure that the optimal solution found can meet the requirements of voltage stability and system loss minimization; S65. Implement the voltage regulation strategy, implement the optimal voltage regulation strategy obtained by optimization solution to the actual power system, and make corresponding adjustments to the on-load voltage regulating transformer, electric vehicle charging pile and distributed photovoltaic system; In S64, the optimization algorithm: S641. Initialize the particle swarm, randomly generate a group of particles, each particle represents a voltage regulation strategy combination, including the tap position of the on-load voltage regulating transformer, the power adjustment amount of the electric vehicle charging pile, and the active and reactive output adjustment amount of the distributed photovoltaic system; S642. Calculate the fitness, for each particle, calculate the particle fitness value according to the objective function in S63, and the fitness value takes the reciprocal or inverse of the objective function value; S643. Update the individual optimal solution and the global optimal solution. For each particle, compare its current fitness value with the fitness value of the individual optimal solution of the particle. If the current fitness value is better, update the individual optimal solution to the position of the current particle. Meanwhile, compare the individual optimal solutions of all particles to find the optimal individual optimal solution as the global optimal solution. S644. Update the speed and position of the particle according to the following formula: ； in, and and are particles respectively In the Second and third Speed ​​during the next iteration; Inertial weights; and For learning factors; and It is a random number; For particles In the The individual optimal solution at the next iteration; This is the globally optimal solution; For particles In the The position at the next iteration; According to the updated speed, update the position of the particle: ； for particles at the first position at the second iteration; S645. Iterative optimization. Repeat the process of calculating fitness, updating individual optimal solution and global optimal solution, updating particle speed and position until the preset convergence condition is met. S646. Output the optimal solution. When the algorithm converges, output the voltage regulation strategy combination corresponding to the global optimal solution, which is the optimal voltage regulation strategy combination.

2. The real-time voltage regulation method for a new energy vehicle according to claim 1, characterized in that, In S2, the calculation formula of the set voltage deviation threshold is: ； ； ； ； wherein is a lower limit of the voltage deviation threshold; is an upper limit of the voltage deviation threshold, is a rated voltage of the power grid; and are minimum and maximum relative deviations of the voltage allowed according to power system standards, equipment requirements and operational experience, respectively; and are additional voltage tolerances; and are voltage deviation compensation terms related to load changes; are coefficients related to load characteristics and grid parameters; is a power change of the load at the node at time ; is an impedance of a line between nodes.

3. The real-time voltage regulation method for a new energy vehicle according to claim 1, characterized in that, In S3, the tap adjustment method of the on-load voltage regulating transformer is performed: S31. Determine the tap position of the voltage regulating transformer. Compare the real-time voltage with the set voltage deviation threshold to determine whether the voltage is within the normal range, that is, to determine whether: Real-time voltage Belongs to ; When the tap of the on-load tap-changing transformer is adjusted; S32. Tap adjustment of the on-load regulating transformer is performed, and the output voltage of the regulating transformer is calculated according to the regulating characteristics and the regulating rules of the transformer The calculation formula is: ； ； wherein, is a reference voltage; is a coefficient related to the transformer characteristics; is the tap position at time is the tap position at time is a tap adjustment amount determined according to the voltage regulation characteristics and the regulation rule of the transformer; is a rated voltage of the power grid; is the tap position at time is the tap position at time is an output voltage of the transformer when operating without voltage regulation; is a tap adjustment amount determined according to the voltage regulation characteristics and the regulation rule of the transformer.

4. The real-time voltage regulation method for new energy vehicles according to claim 1, characterized in that, In S4, the electric vehicle charging pile cooperative voltage regulation method is performed: If the voltage still exceeds the threshold value after the preliminary adjustment of the on-load voltage regulating transformer, i.e. The electric vehicle charging pile cooperative voltage regulation is performed, and the adjustment formula of the electric vehicle charging pile power is: ； ； ； ； In the formula, represents the power of the electric vehicle charging pile at node at time ; is the power change amount of cooperative voltage regulation; represents the voltage of the electric vehicle charging pile at node at time ; is the reference voltage; represents the rated power of the electric vehicle charging pile at node ; the proportion of the charging power of the electric vehicle charging pile at the node is a function of the rated power at time ; , , , , , , , is a parameter related to the charging pile and battery characteristics; is a coefficient related to the charging pile characteristics; , , is the frequency parameter of the corresponding stage; is the time when charging starts; is the time when the charging is expected to end; is a function of state of charge and priority; is the battery percentage of the electric vehicle charging station at the node at time ; is the maximum battery percentage; is the minimum battery percentage; , , and are tuning coefficients; , is an exponential parameter.

5. The real-time voltage regulation method for new energy vehicles according to claim 1, characterized in that, In S5, the active output and reactive output of the distributed photovoltaic system are adjusted: The adjustment formula of the active output is represented as: ； is the active output power of the distributed photovoltaic system; is the maximum output power of the distributed photovoltaic system under ideal conditions; is the irradiance function; is the temperature function; , is the temperature correlation coefficient; is the irradiance measurement value; is the temperature measurement value; The adjustment of the reactive output is realized by controlling the output current of the inverter, and the formula is represented as: ； reactive output power for a distributed photovoltaic system; a coefficient related to the photovoltaic system characteristics; an output voltage for the photovoltaic system; a phase angle of the output current.

6. The real-time voltage regulation method for a new energy vehicle according to claim 1, characterized in that, In the S61, in the prediction model training process, and is determined by the following function: ； ； and is the Lagrange multiplier, which is used to adjust the model parameters; and is the corresponding voltage target value, which is usually the actual voltage value in the historical data; The constraint condition is used to ensure the uniqueness of the model solution; represents The value range of to , is a preset constant, which is used to control the complexity and generalization ability of the model.

7. The real-time voltage regulation method for new energy vehicles according to claim 1, characterized in that, In S63, the method for determining the adjustment strategy is: S631. Objective function construction. The objective function is to minimize the voltage deviation and system loss. The objective function considers the voltage regulation capability and limitation of the on-load voltage regulating transformer, the electric vehicle charging pile and the distributed photovoltaic system. The objective function is represented as: ； ； ； ； ； and is a weight factor; is a node reference voltage; is a system loss at time ; is a voltage output of the regulating transformer at node at time ; is a coefficient related to the transformer characteristics; is a transformer loss; is a line loss at time ; is other loss factors; is a lower limit for the voltage deviation threshold; is an upper limit for the voltage deviation threshold. the power at the node at time t; the active output of the distributed photovoltaic system at the node at time t; S632. Setting of constraint conditions, For the on-load voltage regulating transformer, considering the limitation of its tap adjustment range, the constraint condition is set as: ； lower limit value of the output voltage of the on-load tap changer transformer; upper limit value of the output voltage of the on-load tap changer transformer; For the electric vehicle charging pile, according to its rated power and battery characteristics, the constraint condition of power adjustment is set: ； The active output and reactive output of the distributed photovoltaic system should also be limited by its capacity and operating characteristics, and the constraint condition is set as: and ; a lower limit value for the active output of the distributed photovoltaic system; an upper limit value for the active output of the distributed photovoltaic system; a lower limit value for the reactive output of the distributed photovoltaic system; an upper limit value for the reactive output of the distributed photovoltaic system; S633. Cooperative voltage regulation strategy formulation. According to the voltage deviation, determine the voltage regulation sequence and cooperative mode of the on-load voltage regulating transformer, the electric vehicle charging pile and the distributed photovoltaic system.

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