Real-time voltage regulation method for new energy automobile

Abstract

The invention relates to the field of voltage regulation and control, in particular to a real-time voltage regulation method for a new energy automobile. Initializing a power distribution network voltage measurement module, and collecting voltage data of each node of the power distribution network; setting a voltage deviation threshold value, and judging whether the voltage is in a normal range or not; collected voltage is compared with a set voltage deviation threshold value, a result obtained after preliminary voltage regulation of an on-load voltage regulation transformer is compared with the set voltage deviation threshold value, and a result obtained after cooperative voltage regulation of an electric vehicle charging pile is compared with the set voltage deviation threshold value. According to a comparison result, tap adjustment of an on-load voltage regulation transformer, cooperative voltage regulation of an electric vehicle charging pile and voltage regulation participated by a distributed photovoltaic system are carried out in sequence; and formulating an optimization strategy based on historical data and results of three times of voltage regulation. By means of cooperative voltage regulation, optimal control, intelligent management and the like, the voltage stability is effectively improved, and the adaptability and flexibility of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of voltage regulation, and in particular to a real-time voltage regulation method for new energy vehicles. Background Art

[0002] With the popularity of electric vehicles and the development of distributed energy, the voltage stability of the distribution network faces new challenges. In the prior art, on-load voltage regulators often work independently when regulating voltage, without fully considering the synergy between electric vehicle charging piles and distributed photovoltaic systems. The change in charging power of electric vehicle charging piles may cause voltage fluctuations. This is because when multiple electric vehicles are charged at the same time or the charging power suddenly increases, it will cause a rapid change in the grid current. According to Ohm's law, the voltage drop on the line impedance will also change accordingly, resulting in voltage fluctuations. For example, during peak hours of electricity consumption, a large number of electric vehicles are charged at the same time, which may cause a sudden drop in the local grid voltage and affect the normal electricity consumption of other users. The output power of the distributed photovoltaic system is affected by factors such as light and temperature and is uncertain. Changes in light intensity will directly affect the output power of photovoltaic cells, while changes in temperature will affect the efficiency of photovoltaic cells. In actual operation, due to the changeable weather conditions, the output power of the distributed photovoltaic system may fluctuate frequently, bringing challenges to the stable operation of the power grid. For example, on sunny days, the output power of the photovoltaic system is high, which may cause the grid voltage to increase; on cloudy days or at night, the output power of the photovoltaic system is reduced, which may cause the grid voltage to drop. In addition, existing technologies lack real-time and accuracy in voltage regulation, making it difficult to meet the increasingly complex needs of power grids.

[0003] To improve these deficiencies, some existing technologies attempt to adopt simple collaborative control strategies, such as controlling the output of electric vehicle charging piles and distributed photovoltaic systems according to a preset schedule or voltage threshold. However, these methods lack flexibility and adaptability and cannot be dynamically adjusted according to the real-time status of the power grid.

[0004] In summary, the existing technology has many problems in terms of the synergy between electric vehicle charging piles, distributed photovoltaic systems and on-load voltage regulators, the real-time and accuracy of voltage regulation, etc., and it is difficult to meet the increasingly complex power grid needs. Summary of the invention

[0005] In view of the above deficiencies in 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 in the prior art that the on-load voltage regulator often works independently when adjusting the voltage, and does not fully consider the synergy between the electric vehicle charging pile and the distributed photovoltaic system, resulting in a lack of real-time and accuracy in voltage regulation, and it is difficult to meet the increasingly complex power grid needs.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0007] A new energy vehicle real-time voltage regulation method comprises the following steps:

[0008] S1. System initialization and real-time data collection: Initialize the distribution network voltage measurement module, collect voltage data of each node in the distribution network in real time, connect to the electric vehicle charging pile management system, and 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. Perform preliminary voltage regulation of the on-load tap-changing transformer, compare the voltage collected in S1 with the voltage deviation threshold set in S2, and adjust the tap of the on-load tap-changing transformer according to the comparison result;

[0011] S4. Electric vehicle charging pile coordinated voltage regulation, using the on-load voltage regulating transformer in S3 to compare the initial voltage regulation result with the set voltage deviation threshold. If the voltage still exceeds the threshold, the electric vehicle charging pile coordinated voltage regulation is performed;

[0012] S5. The distributed photovoltaic system participates in voltage regulation, and the result of coordinated voltage regulation of the electric vehicle charging pile in S4 is compared with the set voltage deviation threshold. If the voltage still exceeds the threshold, the distributed photovoltaic system is started to participate in voltage regulation to adjust the active output and reactive output of the distributed photovoltaic system;

[0013] S6. Model predictive control and optimization: Based on historical data and the results of S1 to S5, a predictive model is established, and the optimization strategy is formulated using the results predicted by the predictive model.

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

[0015] Furthermore, in S2, the calculation formula for setting the voltage deviation threshold is:

[0016]

[0017]

[0018]

[0019]

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

[0021] Furthermore, in S3, a method for adjusting the tap of an on-load tap-changing transformer is performed:

[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, that is, determine:

[0023] Real-time voltage V i (t)Whether it belongs to

[0024] when When the load tap-changing transformer is in operation, adjust the tap of the on-load tap-changing transformer;

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

[0026] According to the voltage regulation characteristics and regulation rules of the transformer, calculate the output voltage of the voltage regulating transformer The calculation formula is:

[0027]

[0028]

[0029] in, is the reference voltage; is the coefficient related to the transformer characteristics; is the tap position at time t; The tap adjustment amount is determined according to the voltage regulation characteristics and regulation rules of the transformer; is the rated voltage of the power grid; ΔV i (t) is the tap adjustment amount determined according to the voltage regulation characteristics and regulation rules of the transformer.

[0030] Furthermore, in S4, a method for coordinated voltage regulation of electric vehicle charging piles is performed:

[0031] If the voltage still exceeds the threshold after the initial voltage regulation by the on-load voltage regulator, The electric vehicle charging pile is coordinated to adjust the voltage, and the adjustment formula of the electric vehicle charging pile power is:

[0032]

[0033]

[0034]

[0035]

[0036] In the formula, represents the power of the electric vehicle charging pile at node i at time t; is the power variation of coordinated voltage regulation; V a (t) is the power of the electric vehicle charging pile at node i at time t; is the reference voltage; is the rated power of the electric vehicle charging pile at node i;

[0037] is the value of the ratio function of the charging power of the electric vehicle charging pile at node i to the rated power at time t-1; k1, k2, k3, k4, k5, k6, k7, k8 are parameters related to the charging pile and battery characteristics; is a coefficient related to the characteristics of the charging pile; f1, f2, f3 are the frequency parameters of the corresponding stages; t0 is the time when charging starts; t max is the estimated time when charging will end;

[0038] It is a function that considers the state of charge and priority; SOC i (t) is the battery charge percentage of the charging pile at node i at time t; SOC max SOC is the maximum charge percentage of the battery; min is the minimum battery charge percentage; k9, k 10 , k 11 , k 12 is the adjustment coefficient; α, β are exponential parameters.

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

[0040] The adjustment formula of active output is expressed as:

[0041]

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

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

[0044]

[0045] is the coefficient related to the characteristics of the photovoltaic system; is the output voltage of the photovoltaic system; is the phase angle of the output current.

[0046] S6. Model predictive control and optimization, based on historical data and the results of S1 to S5, uses the results predicted by the predictive model to formulate an optimization strategy.

[0047] Further, the S6 includes:

[0048] S61. Establish a prediction model, select a prediction model, and use historical data to train the prediction model so that it can learn the trend and law of voltage changes, 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:

[0050]

[0051]

[0052] f(V i (t)) is the voltage prediction 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. Prediction of voltage change trend: the current results of S1 to S5 are used as input to the trained prediction model to predict the voltage change trend in the future, including voltage magnitude, phase and other information;

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

[0055] S64. Optimization solution: Use optimization algorithms to optimize the formulated voltage regulation strategy and find the optimal voltage regulation strategy combination. During the optimization solution process, continuously evaluate the impact 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.

[0056] S65. Implement the voltage regulation strategy, implement the optimal voltage regulation strategy obtained by optimization into the actual power system, and make corresponding adjustments to the on-load voltage regulating transformer, electric vehicle charging pile and distributed photovoltaic system.

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

[0058]

[0059]

[0060] α i and α j is the Lagrange multiplier, used to adjust the model parameters; y i and j The corresponding voltage target value is usually the actual voltage value in the historical data; Constraints are usually used to ensure the uniqueness of the model solution; 0≤α i ≤C means α i The value range 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:

[0062] S631. Objective function construction, with minimization of voltage deviation and system loss as the objective function, the objective function comprehensively considers the voltage regulation capabilities and limitations of on-load tap-changing transformers, electric vehicle charging piles and distributed photovoltaic systems, and the objective function is expressed as:

[0063]

[0064]

[0065]

[0066]

[0067]

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

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

[0070] S632. Setting of constraint conditions,

[0071] For the on-load tap-changing transformer, considering the limitation of its tap adjustment range, the constraint conditions are set as follows:

[0072]

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

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

[0075]

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

[0077] and

[0078] It is the lower limit of the active output of the distributed photovoltaic system; It is the upper limit of the active output of the distributed photovoltaic system; It is the lower limit of reactive power output of distributed photovoltaic system; It is the upper limit of reactive power output of distributed photovoltaic system;

[0079] S633. Formulate a coordinated voltage regulation strategy, and determine the voltage regulation sequence and coordination method of the on-load voltage-regulating transformer, electric vehicle charging pile and distributed photovoltaic system according to the voltage deviation.

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

[0081] S641. Initialize the particle swarm and randomly generate a group of particles, each particle representing 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, the active and reactive output adjustment amount of the distributed photovoltaic system, etc.;

[0082] S642. Calculate the fitness. For each particle (representing a voltage regulation strategy combination), calculate the particle fitness value according to the objective function in S63. The fitness value is the reciprocal or 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 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. At the same time, compare the individual optimal solutions of all particles to 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 at the kth and k+1th iterations respectively; μ is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; is the individual optimal solution of particle n at the kth iteration; is the global optimal solution; is the position of particle n at the kth iteration;

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

[0087]

[0088] is the position of particle n at the k+1th iteration;

[0089] S645. Iterative optimization, repeatedly calculating fitness, updating individual optimal solutions and global optimal solutions, and updating particle speeds and positions until the preset convergence conditions are met;

[0090] 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.

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

[0092] Improve voltage stability. Through the coordinated voltage regulation of on-load voltage-regulating transformers, electric vehicle charging piles and distributed photovoltaic systems, voltage fluctuations can be more effectively dealt with and voltage stability can be ensured to be within the normal range. For example, when the charging power of electric vehicles changes or the output power of distributed photovoltaic systems fluctuates, other systems can make timely adjustments to compensate for voltage deviations.

[0093] Reducing system losses, with minimizing voltage deviation and system losses as the objective function, comprehensively considering the voltage regulation capabilities and limitations of on-load voltage-regulating transformers, electric vehicle charging piles and distributed photovoltaic systems, can optimize the voltage regulation strategy and reduce system losses such as transformer losses and line losses. By reasonably adjusting the power of electric vehicle charging piles and the active output of distributed photovoltaic systems, unnecessary energy transmission and losses can be reduced, and energy utilization efficiency can be improved.

[0094] Enhance the adaptability and flexibility of the system, taking into account factors such as the charging power change of electric vehicle charging piles, the output power uncertainty of distributed photovoltaic systems, and load changes, and be able to formulate corresponding voltage regulation strategies according to actual conditions, thereby enhancing the system's adaptability to complex power grid environments. Model predictive control and optimization can be used to predict voltage change trends and formulate voltage regulation strategies in advance, thereby improving the system's foresight and flexibility. The optimization algorithm can continuously evaluate the impact of different voltage regulation strategy combinations on the objective function, find the optimal voltage regulation strategy combination, and ensure that the system can maintain good performance under different operating conditions.

[0095] Fully utilizing distributed energy and the participation of distributed photovoltaic systems in voltage regulation can fully utilize their clean energy output and reduce dependence on traditional energy. It also helps to balance the power distribution of the power grid and improve the stability of the power grid. Through coordinated voltage regulation, distributed energy and traditional voltage regulation equipment can be better integrated to achieve efficient utilization and optimal configuration of energy.

[0096] Improve the intelligence level of the power grid, use prediction models and optimization algorithms to achieve intelligent control of the power grid voltage, and improve the automation and intelligence level of the power grid. It can continuously learn and optimize the voltage regulation strategy based on historical data and real-time monitoring results, so that the power grid can better adapt to the changing power demand and operating environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 is a flow chart of the method of the present invention;

[0098] Figure 2 A flow chart of a tap adjustment method for an on-load tap-changing transformer;

[0099] Figure 3 A flow chart of the method for developing an optimization strategy for using the results predicted by the prediction model;

[0100] Figure 4 A flow chart of a method for determining an adjustment strategy according to a voltage variation trend predicted by S62;

[0101] Figure 5 Flow chart of the optimization algorithm method in S64. DETAILED DESCRIPTION

[0102] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

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

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

[0105] S2. Real-time monitoring and evaluation of distribution network voltage, setting voltage deviation threshold, judging whether the voltage is within the normal range. The voltage deviation threshold is determined based on the standards and specifications of the power system, the rated voltage and tolerance of electrical equipment, load characteristics, and the topological structure and operating conditions of the power grid;

[0106] In S2, the calculation formula for setting the voltage deviation threshold is:

[0107]

[0108]

[0109]

[0110]

[0111] in, is the lower limit of the voltage deviation threshold; is the upper limit of the voltage deviation threshold, V n is the rated voltage of the power grid; min and δ max They are the minimum and maximum relative deviations allowed for voltage determined according to power system standards, equipment requirements and operating experience; and For additional voltage tolerance, some special cases or uncertainties are considered, such as grid transient processes and measurement errors; and k is the voltage deviation compensation term related to load change; l is a coefficient related to load characteristics and grid parameters; P l，j (t) is the change of load power at node j at time t; Z l,ij is the impedance of the line between node i and node j.

[0112] S3. Perform preliminary voltage regulation of the on-load tap-changing transformer, compare the voltage collected in S1 with the voltage deviation threshold set in S2, and adjust the tap of the on-load tap-changing transformer according to the comparison result;

[0113] Tap adjustment method of on-load tap-changing transformer:

[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, that is, determine:

[0115] Real-time voltage V i (t)Whether it belongs to

[0116] when When the load voltage regulating transformer is in operation, the tap voltage of the on-load voltage regulating transformer is adjusted;

[0117] S32. Perform tap voltage regulation of on-load tap-changing transformer:

[0118] According to the voltage regulation characteristics and regulation rules of the transformer, calculate the output voltage of the voltage regulating transformer The calculation formula is:

[0119]

[0120]

[0121] in, is the reference voltage; is the coefficient related to the transformer characteristics; is the tap position at time t; The tap adjustment amount is determined according to the voltage regulation characteristics and regulation rules of the transformer; is the rated voltage of the power grid; ΔV i (t) is the tap adjustment amount determined according to the voltage regulation characteristics and regulation rules of the transformer.

[0122] S4. Electric vehicle charging pile coordinated voltage regulation, using the on-load voltage regulating transformer in S3 to compare the initial voltage regulation result with the set voltage deviation threshold. If the voltage still exceeds the threshold, the electric vehicle charging pile coordinated voltage regulation is performed;

[0123] Method for coordinated voltage regulation of electric vehicle charging piles:

[0124] If the voltage still exceeds the threshold after the initial voltage regulation by the on-load voltage regulator, The electric vehicle charging pile is coordinated to adjust the voltage, and the adjustment formula of the electric vehicle charging pile power is:

[0125]

[0126]

[0127]

[0128]

[0129] In the formula, represents the power of the electric vehicle charging pile at node i at time t; is the power variation of coordinated voltage regulation; V a (t) is the power of the electric vehicle charging pile at node i at time t; is the reference voltage; is the rated power of the electric vehicle charging pile at node i;

[0130] is the value of the ratio function of the charging power of the electric vehicle charging pile at node i to the rated power at time t-1; k1, k2, k3, k4, k5, k6, k7, k8 are parameters related to the charging pile and battery characteristics; is a coefficient related to the characteristics of the charging pile; f1, f2, f3 are the frequency parameters of the corresponding stages; t0 is the time when charging starts; t max It is the estimated time when charging will end. In the early stage of charging, the charging power increases gradually, and a certain periodic fluctuation is taken into account. In the middle stage of charging, the charging power fluctuates within a certain range. In the late stage of charging, the charging power decreases gradually, and there is also a certain periodic fluctuation.

[0131] It is a function that considers the state of charge and priority; SOC i (t) is the battery charge percentage of the charging pile at node i at time t; SOC max SOC is the maximum charge percentage of the battery; min is the minimum battery charge percentage; k9, k 10 , k 11 , k12 is the adjustment coefficient; α, β are exponential parameters; when the battery power is below the threshold and the priority is high, the charging power is high and gradually decreases with the increase of the battery power; when the battery power is in the middle range and the priority is medium, the charging power gradually decreases according to the increase of the battery power; when the battery power is close to full or the priority is low, the charging power decreases rapidly.

[0132] S5. The distributed photovoltaic system participates in voltage regulation. According to the result of S4, if the voltage is still not stable after the coordinated voltage regulation of the electric vehicle charging piles, the distributed photovoltaic system is started to participate in voltage regulation to adjust the active output and reactive output of the distributed photovoltaic system;

[0133] If the voltage of the electric vehicle charging pile is still not stable after coordinated voltage regulation, Start distributed photovoltaic systems to participate in voltage regulation;

[0134] The active output is adjusted according to factors such as light intensity and temperature. The formula is:

[0135]

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

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

[0138]

[0139] is the coefficient related to the characteristics of the photovoltaic system; is the output voltage of the photovoltaic system; is the phase angle of the output current.

[0140] S6. Model predictive control and optimization: Based on historical data and the results of S1 to S5, an optimization model is established, and the optimization strategy is formulated using the results predicted by the predictive model.

[0141] The S6 includes:

[0142] S61. Establish a prediction model, select a prediction model, and use historical data to train the prediction model so that it can learn the trend and law of voltage changes, 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:

[0144]

[0145]

[0146] f(V i (t)) is the voltage prediction value of node i at time t; t is the number of historical data samples; α j is the Lagrange multiplier; 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 of 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 voltage magnitude, phase and other information;

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

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

[0150] S631. Objective function construction, with minimization of voltage deviation and system loss as the objective function, the objective function comprehensively considers the voltage regulation capabilities and limitations of on-load tap-changing transformers, electric vehicle charging piles and distributed photovoltaic systems, and the objective function is expressed as:

[0151]

[0152]

[0153]

[0154]

[0155]

[0156] η and γ are weight coefficients; is the reference voltage of node i; P loss (t) is the system loss at time t, which can take into account transformer loss, line loss, 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 output of the distributed photovoltaic system; ΔV tai (t) is the output voltage of the voltage regulating transformer at node i at time t; is a coefficient related to transformer characteristics; P fl (t) is transformer loss; P ll (t) is the line loss; P ol Other loss factors;

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

[0158] S632. Setting of constraint conditions,

[0159] For the on-load tap-changing transformer, considering the limitation of its tap adjustment range, the constraint conditions are set as follows:

[0160]

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

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

[0163]

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

[0165]

[0166] It is the lower limit of the active output of the distributed photovoltaic system; It is the upper limit of the active output of the distributed photovoltaic system; It is the lower limit of reactive power output of distributed photovoltaic system; It is the upper limit of reactive power output of distributed photovoltaic system;

[0167] S633. Formulate a coordinated voltage regulation strategy, and determine the voltage regulation sequence and coordination mode of the on-load voltage regulating transformer, electric vehicle charging pile and distributed photovoltaic system according to the voltage deviation. For example, when the voltage deviation is small, the power of the electric vehicle charging pile is adjusted first to reduce the frequent operation of the on-load voltage regulating transformer; when the voltage deviation is large, the distributed photovoltaic system is started to participate in the voltage regulation to quickly stabilize the voltage.

[0168] S64. Optimization solution: Use optimization algorithms to optimize the formulated voltage regulation strategy and find the optimal voltage regulation strategy combination. During the optimization solution process, continuously evaluate the impact 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.

[0169] S65. Implement the voltage regulation strategy, implement the optimal voltage regulation strategy obtained by optimization into the actual power system, and make corresponding adjustments to the on-load voltage regulating transformer, electric vehicle charging pile and distributed photovoltaic system.

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

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

[0175] S641. Initialize the particle swarm and randomly generate a group of particles, each particle representing 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, the active and reactive output adjustment amount of the distributed photovoltaic system, etc.;

[0176] S642. Calculate the fitness. For each particle (representing a voltage regulation strategy combination), calculate the particle fitness value according to the objective function in S63. The fitness value is the reciprocal or 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 system loss minimization. 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 system loss minimization.

[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 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. At the same time, compare the individual optimal solutions of all particles to 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 at the kth and k+1th iterations respectively; μ is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; is the individual optimal solution of particle n at the kth iteration; is the global optimal solution; is the position of particle n at the kth iteration;

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

[0181]

[0182] is the position of particle n at the k+1th iteration;

[0183] S645. Iterative optimization, which repeatedly calculates fitness, updates individual optimal solutions and global optimal solutions, and updates particle speeds and positions until a preset convergence condition is met, such as reaching a maximum number of iterations or a change in fitness value that is less than a certain threshold;

[0184] 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.

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

Claims

1. A real-time voltage regulation method for new energy vehicles, characterized in that: include: S1. System initialization and real-time data collection: Initialize the distribution network voltage measurement module, collect voltage data of each node in the distribution network in real time, and obtain the status information of the electric vehicle charging pile; S2. Real-time monitoring and evaluation of distribution network voltage, setting voltage deviation thresholds, and determining whether the voltage is within the normal range; S3. Perform preliminary voltage regulation of the on-load tap-changing transformer, compare the voltage collected in S1 with the voltage deviation threshold set in S2, and adjust the tap of the on-load tap-changing transformer according to the comparison result; S4. Electric vehicle charging pile coordinated voltage regulation, using the on-load voltage regulating transformer in S3 to compare the initial voltage regulation result with the set voltage deviation threshold. If the voltage still exceeds the threshold, the electric vehicle charging pile coordinated voltage regulation is performed; S5. The distributed photovoltaic system participates in voltage regulation, and the result of the coordinated voltage regulation of the electric vehicle charging pile in S4 is compared with the set voltage deviation threshold. If the voltage still exceeds the threshold, the distributed photovoltaic system is started to participate in voltage regulation; S6. Model predictive control and optimization: Based on historical data and the results of S1 to S5, a predictive model is established, and the optimization strategy is formulated using the results predicted by the predictive model.

2. A new energy vehicle real-time voltage regulation method according to claim 1, characterized in that: In S2, the calculation formula for setting the voltage deviation threshold is: in, is the lower limit of the voltage deviation threshold; is the upper limit of the voltage deviation threshold, V n is the rated voltage of the power grid; min and δ max They are the minimum and maximum relative deviations allowed for voltage determined according to power system standards, equipment requirements and operating experience; and For additional voltage tolerance; and k is the voltage deviation compensation term related to load change; l is a coefficient related to load characteristics and grid parameters; P l，j (t) is the change of load power at node j at time t; Z l is the impedance of the line between the nodes.

3. A new energy vehicle real-time voltage regulation method according to claim 1, characterized in that: In S3, a method for adjusting the tap of an on-load tap-changing transformer is performed: 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, that is, determine: Real-time voltage V i (t)Whether it belongs to when When the load tap-changing transformer is in operation, adjust the tap of the on-load tap-changing transformer; S32. Adjust the tap of the on-load voltage-regulating transformer and calculate the output voltage of the voltage-regulating transformer according to the voltage-regulating characteristics and regulation rules of the transformer. The calculation formula is: in, is the reference voltage; is the coefficient related to the transformer characteristics; is the tap position at time t; The tap adjustment amount is determined according to the voltage regulation characteristics and regulation rules of the transformer; is the rated voltage of the power grid; is the tap position at time t-1; V i (t) is the output voltage of the transformer when there is no voltage regulation; ΔV i (t) is the tap adjustment amount determined according to the voltage regulation characteristics and regulation rules of the transformer.

4. A new energy vehicle real-time voltage regulation method according to claim 1, characterized in that: In S4, a method for coordinated voltage regulation of electric vehicle charging piles is performed: If the voltage still exceeds the threshold after the initial adjustment by the on-load tap-changing transformer, The electric vehicle charging pile is coordinated to adjust the voltage, 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 i at time t; is the power variation of coordinated voltage regulation; V a (t) is the power of the electric vehicle charging pile at node i at time t; is the reference voltage; is the rated power of the electric vehicle charging pile at node i; is the value of the ratio function of the charging power of the electric vehicle charging pile at node i to the rated power at time t-1; k1, k2, k3, k4, k5, k6, k7, k8 are parameters related to the charging pile and battery characteristics; is a coefficient related to the characteristics of the charging pile; f1, f2, f3 are the frequency parameters of the corresponding stages; t0 is the time when charging starts; t max is the estimated time when charging will end; It is a function that considers the state of charge and priority; SOC a (t) is the battery charge percentage of the electric vehicle charging pile at node i at time t; SOC max SOC is the maximum charge percentage of the battery; min is the minimum battery charge percentage; k9, k 10 , k 11 and k 12 is the adjustment coefficient; α, β are exponential parameters.

5. A new energy vehicle real-time voltage regulation method 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 active output is expressed as: is the active output power of the distributed photovoltaic system; is the maximum output power of the distributed photovoltaic system under ideal conditions; f l (t) is the light intensity function; f t (t) is the temperature function; ε l , l is the temperature correlation coefficient; I l (t) is the measured value of light intensity; T t (t) is the temperature measurement value; The adjustment of reactive power output is achieved by controlling the output current of the inverter, and the formula is expressed as: is the reactive output power of the distributed photovoltaic system; is the coefficient related to the characteristics of the photovoltaic system; is the output voltage of the photovoltaic system; is the phase angle of the output current.

6. A new energy vehicle real-time voltage regulation method according to claim 1, characterized in that: The S6 includes: S61. Establish a prediction model, select a prediction model, and use historical data to train the prediction model so that it can learn the trend and law 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 expressed as: f(V i (t)) is the voltage prediction value of node i at time t; N is the number of historical data samples; χ j is the Lagrange multiplier; 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; S62. Prediction of voltage change trend: the current results of S1 to S5 are used as input to the trained prediction model to predict the voltage change trend in the future, including the voltage magnitude and phase information; S63. Formulate an optimization strategy and determine an adjustment strategy based on the voltage change trend predicted by S62; S64. Optimization solution: Use optimization algorithms to optimize the formulated voltage regulation strategy and find the optimal voltage regulation strategy combination. During the optimization solution process, continuously evaluate the impact 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 into the actual power system, and make corresponding adjustments to the on-load voltage regulating transformer, electric vehicle charging pile and distributed photovoltaic system.

7. A new energy vehicle real-time voltage regulation method according to claim 6, characterized in that: In S61, during the prediction model training process, α j and ω are determined by the following functions: 0≤α i ≤C,i=1,2,3…m; α i and α j is the Lagrange multiplier, used to adjust the model parameters; y i and j The corresponding voltage target value is usually the actual voltage value in the historical data; Constraints are used to ensure the uniqueness of the model solution; 0≤α i ≤C means α i The value range is between 0 and C, where C is a preset constant used to control the complexity and generalization ability of the model.

8. A new energy vehicle real-time voltage regulation method according to claim 6, characterized in that: In S63, the method for determining the adjustment strategy is: S631. Objective function construction, with minimization of voltage deviation and system loss as the objective function, the objective function comprehensively considers the voltage regulation capabilities and limitations of on-load tap-changing transformers, electric vehicle charging piles and distributed photovoltaic systems, and the objective function is expressed as: η and γ are weight coefficients; is the reference voltage of node i; P loss (t) is the system loss at time t; ΔV tai (t) is the output voltage of the voltage regulating transformer at node i at time t; is a coefficient related to transformer characteristics; P fl (t) is transformer loss; P ll (t) is the line loss at time t; P ol (t) is other loss factors; is the lower limit of the voltage deviation threshold; is the upper limit of the voltage deviation threshold; is the power of the electric vehicle charging pile at node i at time t; is the active output of the distributed photovoltaic system at node i at time t; S632. Setting of constraint conditions, For the on-load tap-changing transformer, considering the limitation of its tap adjustment range, the constraint conditions are set as follows: It is the lower limit of the output voltage of the on-load tap-changing transformer; It is the upper limit of the output voltage of the on-load tap-changing transformer; For electric vehicle charging piles, set power adjustment constraints based on their rated power and battery characteristics: The active and reactive output of distributed photovoltaic systems should also be limited by their capacity and operating characteristics. The constraints are set as follows: and It is the lower limit of the active output of the distributed photovoltaic system; It is the upper limit of the active output of the distributed photovoltaic system; It is the lower limit of reactive power output of distributed photovoltaic system; It is the upper limit of reactive power output of distributed photovoltaic system; S633. Formulate a coordinated voltage regulation strategy, and determine the voltage regulation sequence and coordination method of the on-load voltage-regulating transformer, electric vehicle charging pile and distributed photovoltaic system according to the voltage deviation.

9. A new energy vehicle real-time voltage regulation method according to claim 6, characterized in that: In the S64, the optimization algorithm is: S641. Initialize the particle swarm and randomly generate a group of particles, each particle representing 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, the active and reactive output adjustment amount of the distributed photovoltaic system, etc.; S642. Calculate the fitness. For each particle, calculate the particle fitness value according to the objective function in S63. The fitness value is the reciprocal or opposite 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. At the same time, compare the individual optimal solutions of all particles to find the best individual optimal solution as the global optimal solution. S644. Update the particle's velocity and position. Update the particle's velocity according to the following formula: in, and and are the velocities of particle n at the kth and k+1th iterations respectively; μ is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; is the individual optimal solution of particle n at the kth iteration; is the global optimal solution; is the position of particle n at the kth iteration; Update the particle's position based on the updated velocity: is the position of particle n at the k+1th iteration; S645. Iterative optimization, repeatedly calculating fitness, updating individual optimal solutions and global optimal solutions, and updating particle speeds and positions until the preset convergence conditions are 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.

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