Electric vehicle charging station frequency modulation scheduling method and system considering voltage safety reserved capacity

Abstract

The invention discloses an electric vehicle charging station frequency modulation scheduling method and system considering voltage safety reserved capacity, and relates to the field of power system optimization scheduling. Evaluating the maximum voltage regulation safety demand of the power distribution network by utilizing Monte Carlo simulation and load flow calculation based on the operating environment parameters of the power distribution network; based on the historical frequency modulation data, quantifying the relationship among the frequency modulation capacity, the charging and discharging energy and the frequency modulation income; constructing an electric vehicle charging station adjustable capability evaluation model, dividing an electric vehicle adjustable energy interval, and quantifying charging station power and energy upper and lower limits; and with the purpose of minimizing the operation cost, constraints include node voltage safety reservation, influence of frequency modulation on energy and charging and discharging power limitation, and the frequency modulation capacity reported in each time period is obtained. And constructing a charging station real-time regulation and control strategy responding to the power grid frequency modulation and voltage regulation signal, and designing a scheduling sequence according to the adjustable capability interval and the safety scheduling time index of the electric vehicle. The system safety and practicability are considered while the economical efficiency is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power system optimization scheduling, and more specifically to a frequency regulation scheduling method and system for electric vehicle charging stations that considers voltage safety reserve capacity. Background Technology

[0002] In power systems, voltage stability is crucial for ensuring system operational safety and power quality. However, with the large-scale integration of renewable energy and the increasing spatiotemporal heterogeneity of load demand, traditional centralized regulation methods are insufficient to meet the dynamic demands of new power grids due to high investment costs and poor flexibility. Electric vehicle charging stations, as distributed adjustable resources, leverage their bidirectional power regulation capabilities deployed at load centers and their resource reuse characteristics requiring no additional hardware. This not only reduces the investment costs of traditional energy storage but also enables node voltage boosting through discharge at feeder ends. Furthermore, they can absorb excess power during surges in photovoltaic output via V2G, achieving voltage regulation accuracy of ±2% of rated voltage. Simultaneously, charging station clusters can reduce user charging costs through peak-valley arbitrage while maintaining a dynamic voltage buffer zone of 0.93 pu ≤ V ≤ 1.07 pu in the distribution network. During peak periods of solar curtailment, they can shift charging demand to renewable energy output ranges, promoting clean energy consumption. More importantly, the distributed regulation resource pool formed by millions of charging stations has an aggregated capacity exceeding that of traditional small gas turbines and other resources. The main challenge at present is how to systematically couple and model the charging behavior of electric vehicle charging stations with voltage safety constraints in order to build a collaborative optimization framework for electric vehicle charging stations to participate in grid voltage regulation.

[0003] Existing electric vehicle charging station scheduling methods often aim to minimize charging costs or maximize overall benefits after entering the market. This approach suffers from several problems: First, it fails to adequately account for local voltage regulation needs, leading to voltage exceeding limits in order to meet contracted quantities. Second, for the frequency modulation market, frequency signals are often measured in seconds or minutes and involve frequent bidirectional adjustments, while voltage regulation is typically scheduled unidirectionally on an hourly basis. This time-scale mismatch means that current scheduling methods do not consider the directionality of frequency signals or the SOC requirements of charging stations.

[0004] Therefore, how to provide a dispatching strategy that takes into account both the voltage regulation needs of the local distribution network and the peak shaving and frequency regulation needs of the power grid is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a frequency regulation scheduling method and system for electric vehicle charging stations that considers voltage safety reserve capacity. While ensuring scheduling economy, it also takes into account system safety, and has stronger practicality and adaptability.

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

[0007] A frequency regulation scheduling method for electric vehicle charging stations that considers voltage safety reserve capacity includes the following steps:

[0008] Obtain the operating environment parameters of the distribution network, and evaluate the maximum voltage regulation safety requirements of the distribution network based on the operating environment parameters using Monte Carlo simulation and power flow calculation.

[0009] Based on historical frequency modulation service information, the relationship between frequency modulation capacity and charging / discharging energy, as well as the relationship between frequency modulation capacity and frequency modulation revenue, are quantified.

[0010] An adjustable capacity assessment model for electric vehicle charging stations is constructed, dividing the adjustable energy range of electric vehicles and quantifying the upper and lower limits of charging station power and energy. A cost minimization objective function is constructed, considering the safety reserve power of node voltage, the impact of frequency regulation on charging and discharging capacity, and the charging and discharging power constraints of electric vehicles, to obtain the frequency regulation capacity reported to the grid for each time period.

[0011] A real-time control strategy for charging stations that responds to frequency and voltage regulation signals from the power grid is constructed, and the scheduling sequence of electric vehicles is designed based on the adjustable capacity range of each electric vehicle and the safe scheduling time index.

[0012] Optionally, it also includes analyzing the peak shaving and voltage regulation effects of charging stations in various scenarios. The peak shaving and voltage regulation effects include the degree of reduction of the peak-valley difference in the power grid and the maintenance of node voltage, which are used to assess safety.

[0013] Optionally, obtain the operating environment parameters of the distribution network, including the topology of the distribution network to clarify the connection relationship of each node, obtain the node reference voltage as the standard for voltage evaluation, collect power flow data such as the active power, reactive power and admittance parameters between nodes, and obtain the time series curves of photovoltaic output and load to understand the energy supply and demand situation at different times.

[0014] Optionally, the maximum voltage regulation safety requirement of the distribution network can be evaluated based on the operating environment parameters of the distribution network using Monte Carlo simulation and power flow calculation. Specifically, Monte Carlo simulation and optimal power flow method are used to determine the voltage regulation power requirement necessary for the distribution network to maintain voltage safety at different times and in different scenarios, and the maximum voltage regulation power requirement at each time in each scenario is used as the output quantity.

[0015] Optionally, the maximum voltage regulation safety requirement of the distribution network can be evaluated using Monte Carlo simulation and power flow calculation based on the operating environment parameters of the distribution network. The specific calculation process is as follows:

[0016] A scenario set S is generated based on the uncertainties in photovoltaic output and load output.

[0017] The optimal power flow for each scenario is calculated using the following formula:

[0018] minf = λs (P load,t +P EV,base,t -P pv,t );

[0019] In the formula: λ s For real-time electricity prices; P load,t Forecast the power demand of other loads in the distribution network; P pv,t Contribute to photovoltaic forecasting; P EV,base,t Predicted power of charging stations when they do not provide frequency regulation or voltage regulation services to the power grid;

[0020] The constraints are conventional power and voltage constraints for power grids, as follows:

[0021]

[0022] In the formula, k:j→k represents the set of end nodes k starting from node j; P represents the active and reactive power outputs of the load at node j and the distributed generation at time t; jk,t and Q jk,t Let r be the active power and reactive power flowing through branch ij at time t. ij Let I be the resistance of branch ij. ij,t Let I be the square of the branch current ij at time t. ij max V represents the maximum squared allowable current in branch ij. ref Represents the reference voltage; x ij Uij is the reactance of branch ij; U1 and Uj,t are the squares of the voltage amplitudes at the balancing node and node j at time t; ε is the allowable voltage deviation.

[0023] The voltage amplitude V at each node is obtained through power flow calculation. i,calc Afterwards, relative to the reference voltage V i,ref The quantization formula for the offset is as follows:

[0024] ΔV i =|V i,calc -V i,ref |;

[0025] In the formula: V i,calc The actual voltage magnitude of node i is obtained through power flow calculation; V i,ref The reference voltage of node i is used to select the node with the largest voltage offset as the master node. The normalized voltage is defined as follows: The required minimum voltage regulation amplitude is:

[0026]

[0027] To obtain the active power compensation of the charging station to meet the minimum voltage regulation amplitude required by the i-th node, the following control objective is established:

[0028]

[0029] 2)

[0030] Where: ΔP EVS,j This refers to the active power adjustment of the charging station, and its maximum value is... Set according to historical information;

[0031] Solving the above objective function, we derive the condition for minimizing the active power compensation of the charging station under the minimum voltage regulation requirement of the i-th node:

[0032]

[0033] In the formula: For the voltage-power sensitivity of node i and node j, design proportional reactive power compensation and active power compensation related to the sensitivity based on the above formula:

[0034]

[0035] In the formula: It is the standardized active power compensation of the j-th node, denoted as:

[0036] Based on the charging station voltage regulation resource scheduling volume in multiple scenarios, a charging station voltage regulation demand curve is generated, and the calculation formula is as follows:

[0037]

[0038] In the formula: P safe,i (t) represents the voltage regulation demand power of the charging station at node i at time t, which is also the voltage regulation safety capacity that needs to be reserved; ΔP s,i,t Let S represent the voltage regulation power deviation required to maintain voltage safety at node i under scenario s and time t; S represents the set of all possible scenarios; and T represents all times within the time period. This formula indicates that the voltage regulation safety reserve is the demand quantity with the largest absolute value across all scenarios at each time (keeping the original positive or negative sign).

[0039] Optionally, it also includes quantifying the demand for charging station energy storage capacity and corresponding revenue based on historical frequency regulation signals to determine the frequency regulation service capacity; assuming the historical frequency regulation capacity of the power grid dispatch is S. reg,sch The frequency modulation signal sent is P reg,τ , has -S reg,sch ≤P reg,τ ≤S reg,sch The number of frequency modulation commands issued per unit time is T. regIf the execution time of each instruction is ΔT, then the charging station needs to reserve at least an upward adjustment capacity S to meet the grid frequency regulation requirements. reg,max Reduce capacity S reg,min :

[0040]

[0041] At this point, the benefit obtained through frequency modulation capacity is:

[0042]

[0043] In the formula: S reg,sch For frequency modulation capacity, the revenue per megawatt is λ. c Yuan; λ mis Penalty for mismatch in regulation per megawatt-hour during frequency regulation.

[0044] Optionally, based on the travel patterns of electric vehicles, the safe voltage of the power distribution network, and the SOC constraints of electric vehicles' charging and discharging, a scheduling model for charging and discharging of electric vehicles at charging stations is established, specifically including the following steps:

[0045] Based on the probabilistic fitting of electric vehicle charging parameters, the Monte Carlo algorithm is used to fit the charging power of a single electric vehicle as shown in the following formula:

[0046]

[0047] In the formula: P k,t This represents the actual charging power of electric vehicle k at time t. and P represents the start and end times of charging the electric vehicle, respectively. t P represents the total load power of the charging station at time t. car,k The charging power for electric vehicle k;

[0048] Based on the electric vehicle charging power fitting model, the battery percentage of an electric vehicle after charging is complete is shown below:

[0049]

[0050] In the formula, SOC start,k and SOC end,k Let B represent the battery SOC of electric vehicle k at the start of charging and the end of the demand-side response period, respectively. k Let represent the battery capacity of electric vehicle k, and Δt represent the charging time step.

[0051] Statistical analysis of electric vehicle charging data was conducted, and a normal distribution was used to fit the expected SOC of electric vehicles, as shown below:

[0052]

[0053] In the formula, SOC need This represents the percentage of electricity required for future electric vehicle travel, where μ1 and σ1 represent the expected and fluctuating values ​​of the percentage of electricity required for future electric vehicle travel, respectively.

[0054] When the reduction in electric vehicle capacity is less than the difference between the electric vehicle's fitted capacity and the required capacity, reducing the capacity will not affect the user's future travel needs. Therefore, this range is defined as the easily adjustable potential range [0, Q]. k,min When the reduction in electric vehicle power exceeds the difference between the electric vehicle's fitted power and the required power, further reduction will disrupt the user's travel and may even cause economic losses. Therefore, this range is set as the general adjustable potential range for electric vehicles [Q]. k,min Q k,max ], Q k,min With Q k,max The minimum and maximum adjustable amounts for the total time period of an electric vehicle are defined as follows:

[0055] Q k,min =B k (SOC end,k -SOC need,k );

[0056] Q k,max =B k SOC end,k ;

[0057] During the charging and discharging process of an electric vehicle, there are minimum and maximum permissible states of charge (SOC), which are respectively represented by... and This is used to ensure that the battery operates in a safe condition;

[0058] The upper limit of the battery energy of electric vehicle k at time t is obtained from the charging station. Battery energy lower limit E k,t Maximum charging power Maximum discharge power P k,t Aggregate all electric vehicle demand response events k∈J at the charging station to obtain the total energy limit of the charging station. Total Energy

[0059] lower limit E t Maximum total charging power Maximum total discharge power P t .

[0060]

[0061] Optionally, a quantitative model of the frequency regulation capacity of electric vehicle charging stations, taking into account voltage safety reserve capacity, can be established.

[0062] A quantitative model for the frequency regulation capacity of electric vehicle charging stations, considering voltage safety reserve capacity, is established. Each charging station aims to minimize its own next-day operating cost F. Constraints include voltage regulation reserve constraints required by the power grid, upper and lower limits constraints on charging and discharging power, and upper and lower limits constraints on charging and discharging energy. The charging and discharging power P of the charging station in the next 24 hours can be calculated. EV,t and the frequency regulation capacity S provided by the power grid reg,sch,t The specific process is as follows:

[0063] min F=∑((λ t -c ev,t )P EV,t +c dr (P EV,t -P EV,base,t )-R FM,t );

[0064] In the formula: λ t Let P be the electricity purchase cost of the charging station at time t, and let P be a known quantity. EV,t Let c be the charging / discharging power of the electric vehicle charging station at time t, and let c be the quantity to be determined. ev,t The price of charging an electric vehicle is a known quantity; c dr For electric vehicles to participate in demand response compensation, let P be a known quantity. EV,base,t For charging stations that do not provide charging power curves for frequency and voltage regulation, R is a known quantity. FM,t The revenue that a charging station gains from participating in frequency regulation is calculated using the following formula:

[0065]

[0066] In the formula: S reg,sch,t Let be the capacity participating in frequency regulation during time period t, and be the quantity to be determined. The revenue per unit frequency regulation capacity is λ per megawatt. c Element, is a known quantity; λ mis During frequency regulation, resources are subject to a known penalty per megawatt-hour for regulation mismatch; P EV (t) represents the charging and discharging power of the electric vehicle charging station at time t, which is the quantity to be determined; P safe (t) represents the power demand for distribution network voltage regulation safety capacity, which is P safe,i r(t) omits the representation of node positions and is a known quantity; r(t) is the normalized frequency modulated signal and is a known quantity.

[0067] Constraints are set based on voltage safety reserved power settings:

[0068]

[0069] C reg (t)≤| P t -P safe (t)|if P safe (t) < 0;

[0070] C reg (t)=S reg,sch,t ;

[0071] In the formula: C reg (t) represents the maximum frequency regulation power of the charging station during time period t.

[0072] Energy constraints:

[0073]

[0074] In the formula, based on historical data, the number of hours in which the maximum continuous SOC change is in the same direction (positive or negative) is n. This means that continuous SOC changes need to consider the cumulative impact of frequency regulation demand on SOC. The maximum historical SOC change after a single scheduling period is... Minimum value is The cumulative impact needs to be multiplied by the number of hours, n.

[0075] The above process will yield the frequency regulation capacity that the charging station can provide to the power grid in the next 24 hours.

[0076] The real-time scheduling process for voltage and frequency regulation is as follows:

[0077] At time t, evaluate all electric vehicles and calculate the remaining charging time for each electric vehicle.

[0078] When t left,k <=B k (SOC need,k -SOC start,k ) / P car,k If the vehicle is within the normal adjustable capacity range, and there is no special signal from the power grid (such as emergency load shedding), it is excluded from the dispatchable cluster.

[0079] It will be in the easily adjustable potential range [0, Q] k,min The resources will be further screened. As the safe scheduling time for electric vehicles, the electric vehicle cluster J with a positive safe scheduling time. sd It can provide reverse charging capability, and the remaining resources J usd Only charging power reduction is available. All resource charging benchmarks are P. base =B k (SOC need,k -SOCk,t ) / t left .

[0080] The power adjustment signal is a frequency modulation signal (located in the range [-S)). reg,sch,t ,+S reg,sch,t [Internal] and voltage regulation signal P safe,i The superposition of (t) signals has both positive and negative values, with the negative response having a priority of J. sd Power reduction — J usd Power reduction — J sd Power backfeed, J sd The power reduction order is as follows: charging is stopped sequentially from longest to shortest safe scheduling time. J usd The power reduction order is Q k,min The charging is stopped sequentially from largest to smallest. If neither of them can provide power reduction, then J will reduce the charging speed. sd Power is supplied in reverse order according to the safety scheduling time, from longest to shortest, until the signal is satisfied. The priority of the forward response is J. usd Power increase — J sd Power increases, J usd Power increase according to Q k,min The power is increased from small to large, J usd The power is increased sequentially according to the safe scheduling time, from smallest to largest, until the signal is satisfied.

[0081] A frequency regulation scheduling system for electric vehicle charging stations that considers voltage safety reserve capacity includes:

[0082] Distribution network operating environment parameters and voltage regulation safety requirement assessment module: used to obtain distribution network operating environment parameters, and based on the distribution network operating environment parameters, use Monte Carlo simulation and power flow calculation to assess the maximum voltage regulation safety requirement of the distribution network;

[0083] Electric vehicle charging station adjustable boundary construction module: Based on historical data, simulates the charging demand of electric vehicles at charging stations, divides the adjustable energy range of electric vehicles, and quantifies the upper and lower limits of charging station power and energy.

[0084] The module for constructing the scheduling model for electric vehicle charging and discharging: Based on historical frequency regulation service information, it quantifies the relationship between frequency regulation capacity and charging / discharging energy, as well as the relationship between frequency regulation capacity and frequency regulation revenue; it constructs an objective function to minimize costs (including charging station electricity purchase costs, electric vehicle user charging revenue, frequency regulation ancillary service revenue, and electric vehicle participation in scheduling compensation costs), and solves the function under the conditions of considering node voltage safety reserve power, the impact of frequency regulation on charging and discharging energy, and electric vehicle charging and discharging power constraints, to obtain the frequency regulation capacity reported to the grid for each time period;

[0085] Electric vehicle real-time control module: used to construct a real-time control strategy for charging stations in response to grid frequency and voltage regulation signals, and to design the electric vehicle scheduling sequence based on the adjustable capacity range of each electric vehicle and the safe scheduling time index.

[0086] Optionally, a safety assessment module is also included: used to analyze the peak shaving and voltage regulation effects of charging stations in various scenarios. The peak shaving and voltage regulation effects include the degree of reduction of the peak-valley difference in the power grid and the maintenance of node voltage, thereby achieving a safety assessment.

[0087] As can be seen from the above technical solutions, compared with the prior art, this invention discloses a frequency regulation scheduling method and system for electric vehicle charging stations that considers voltage safety reserve capacity. Regarding grid voltage safety and stability, by evaluating node voltage deviation through power flow calculation and combining it with voltage-power sensitivity analysis, the required power injection amount for voltage regulation can be accurately determined, keeping the node voltage within a safe range of 0.95 pu to 1.05 pu, with an adjustment accuracy of ±2% of the rated voltage. Simultaneously, utilizing the distributed regulation characteristics of charging station clusters, dynamic voltage buffering is achieved through V2G discharge or charging power adjustment during sudden increases in photovoltaic output or peak loads, suppressing voltage fluctuations. In terms of improving system economy and resource utilization, a peak-shaving cost quantification model considering real-time electricity prices is established, allowing charging stations to participate in peak-valley arbitrage and deep peak shaving to reduce charging costs. Frequency regulation revenue improves economic efficiency and can also shift charging demand to the renewable energy output range to reduce curtailment of solar power and improve renewable energy utilization. To enhance scheduling robustness and adaptability, the Monte Carlo algorithm is used to fit the charging power and SOC probability distribution of electric vehicles, and adjustable potential ranges are divided to address charging randomness. Multiple objectives are integrated in the objective function to achieve a win-win situation for both the power grid and users. For resource scheduling efficiency optimization, hierarchical optimization reduces model size and improves real-time scheduling efficiency. Power ranges for charging stations are divided based on voltage safety requirements to avoid power competition or oscillations. Attached Figure Description

[0088] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0089] Figure 1 Flowchart for calculating maximum voltage safety requirements;

[0090] Figure 2 This is a diagram of the IEEE 33-node system.

[0091] Figure 3 The charging and discharging power of electric vehicle charging stations in 200 scenarios;

[0092] Figure 4 It can handle the load in 200 scenarios;

[0093] Figure 5 Power reserve to meet voltage safety requirements in all scenarios 24 hours a day;

[0094] Figure 6 This refers to the frequency modulation capacity.

[0095] Figure 7 The voltage regulation requirement for the power distribution network charging station is reserved at a time when it is zero.

[0096] Figure 8 The voltage regulation requirement for the power distribution network charging station is reserved for positive timing;

[0097] Figure 9 The analysis of the voltage involved in frequency regulation for charging stations is based solely on frequency regulation and the safety constraints of the distribution network.

[0098] Figure 10 The analysis of the voltage involved in frequency regulation for charging stations is based solely on frequency regulation and the safety constraints of the distribution network.

[0099] Figure 11 The hourly frequency regulation benefit is not considered when voltage regulation is applied;

[0100] Figure 12 This is the frequency modulation and voltage regulation output curve when the voltage is increased;

[0101] Figure 13 This is the output curve of frequency modulation and voltage regulation when the voltage is reduced. Detailed Implementation

[0102] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0103] This invention discloses a frequency regulation scheduling method for electric vehicle charging stations that considers voltage safety reserve capacity, comprising the following steps:

[0104] Obtain the operating environment parameters of the distribution network, and evaluate the maximum voltage regulation safety requirements of the distribution network based on the operating environment parameters using Monte Carlo simulation and power flow calculation.

[0105] Based on historical frequency regulation service information, the relationship between frequency regulation capacity and charging / discharging energy, as well as the relationship between frequency regulation capacity and frequency regulation revenue, are quantified. An adjustable capacity assessment model for electric vehicle charging stations is constructed, dividing the adjustable energy range of electric vehicles and quantifying the upper and lower limits of charging station power and energy. A cost minimization objective function is constructed, considering the safety reserve power of node voltage, the impact of frequency regulation on charging / discharging energy, and the charging / discharging power constraints of electric vehicles, to obtain the frequency regulation capacity reported to the grid for each time period. A real-time control strategy for charging stations in response to grid frequency and voltage regulation signals is constructed, and the electric vehicle scheduling sequence is designed based on the adjustable capacity range of each electric vehicle and the safe scheduling time index.

[0106] For details, see Figure 1 Step 1: Obtain the operating environment parameters of the distribution network, including topology, node reference voltage, power flow data, photovoltaic and load time series curves, etc., and assess the maximum voltage regulation safety requirements of the distribution network based on Monte Carlo simulation and power flow calculation; based on historical frequency regulation service information, quantify the relationship between frequency regulation capacity and charging and discharging energy, as well as the relationship between frequency regulation capacity and frequency regulation revenue.

[0107] Step 2: Construct an adjustable capacity assessment model for electric vehicle charging stations, divide the adjustable energy range of electric vehicles, and quantify the upper and lower limits of charging station power and energy.

[0108] Step 3: Construct an objective function to minimize costs (including electricity purchase costs for charging stations, charging revenue for electric vehicle users, frequency regulation ancillary service revenue, and compensation costs for electric vehicles participating in dispatching), and solve the function under the conditions of considering the reserved power for node voltage safety, the impact of frequency regulation on charging and discharging capacity, and the charging and discharging power constraints of electric vehicles, to obtain the frequency regulation capacity reported to the grid for each time period.

[0109] Step 4: Based on the adjustable energy range of electric vehicles in Step 2 and the cost minimization objective function in Step 3, construct a real-time control strategy for charging stations that responds to grid frequency and voltage regulation signals, and design the electric vehicle scheduling sequence according to the adjustable capacity range of each electric vehicle and the safe scheduling time index.

[0110] Step 5: Based on the above steps, analyze the peak shaving and voltage regulation effects of charging stations in various scenarios, including the degree of reduction of the peak-valley difference in the power grid and the maintenance of node voltage, and evaluate its safety.

[0111] Step one involves acquiring the operating environment parameters of the distribution network, including the network topology to clarify the connection relationships between nodes, obtaining the node reference voltage as a standard for voltage assessment, collecting power flow data such as the active power, reactive power, and admittance parameters between nodes, and simultaneously acquiring the time-series curves of photovoltaic output and load to understand the energy supply and demand situation at different times. Based on this, Monte Carlo simulation and optimal power flow methods are used to determine the voltage regulation power requirements necessary for maintaining voltage safety in the distribution network at these different times and scenarios, and the maximum voltage regulation power requirements at each time and scenario are used as the output. Figure 1 The specific calculation process for the above steps is as follows.

[0112] A scenario set S is generated based on the uncertainties in photovoltaic output (beta distribution) and load output (normal distribution).

[0113] Solve for the optimal power flow in each scenario:

[0114] minf = λ s (P load,t +P EV,base,t -P pv,t );

[0115] In the formula: λ s For real-time electricity prices; P load,t Forecast the power demand of other loads in the distribution network; P pv,t Contribute to photovoltaic forecasting; P EV,base,t Predicted power of charging stations when they do not provide frequency regulation or voltage regulation services to the power grid;

[0116] The constraints are conventional power and voltage constraints for power grids, as follows:

[0117]

[0118] In the formula, k:j→k represents the set of end nodes k starting from node j. Let P be the active and reactive power outputs of the load at node j and the distributed power source at time t. jk,t and Q jk,t Let r be the active power and reactive power flowing through branch ij at time t. ij Let I be the resistance of branch ij. ij,t Let I be the square of the branch current ij at time t. ij max V represents the maximum squared current allowed in branch ij. ref Represents the reference voltage. x ij Let Uij be the reactance of branch j. U1 and Uj,t are the squares of the voltage amplitudes at the balancing node and node j at time t. ε is the allowable voltage deviation, typically taken as 0.05 pu, but can be relaxed to 0.07 pu in distribution networks.

[0119] The voltage amplitude V at each node is obtained through power flow calculation. i,calc Then, its relative to the reference voltage V i,ref The offset can be quantized using the following formula:

[0120] ΔV i =|V i,calc -V i,ref |;

[0121] In the formula: V i,calc The actual voltage magnitude of node i is obtained through power flow calculation; V i,ref This is the reference voltage of node i. The node with the largest voltage offset (let's assume it's node i) is selected as the master node, and the normalized voltage is defined as... The required minimum voltage regulation amplitude (VAR) is

[0122]

[0123] Since the voltage of the master node depends on the injected power of related nodes, and the mutual sensitivity of related nodes is non-zero, we can control their power injection to mitigate voltage offset. To obtain the active power compensation of the charging station to meet the minimum voltage regulation amplitude required by the i-th node, the following control objective is established:

[0124]

[0125] 2)

[0126] Where: ΔP EVS,j This refers to the active power adjustment of the charging station, and its maximum value is... Settings based on historical information.

[0127] Solving the above objective function, we derive the condition for minimizing the active power compensation of the charging station under the minimum voltage regulation requirement of the i-th node:

[0128]

[0129] In the formula: Let the voltage-power sensitivity be defined for nodes i and j. Using the above formula, a proportional reactive power compensation / active power compensation related to the sensitivity is designed:

[0130]

[0131] In the formula: It is the standardized active power compensation of the j-th node, denoted as:

[0132] Based on the charging station voltage regulation resource scheduling volume in multiple scenarios, a charging station voltage regulation demand curve is generated, and the calculation formula is as follows:

[0133]

[0134] In the formula: P safe,i (t) is the reserved voltage regulation safety capacity at node i at time t; ΔP s,i,t Let S represent the voltage regulation power demand required to maintain grid voltage security at time t for scenario s and node i; S represents the set of all possible scenarios; and T represents all times within the time period. This formula indicates that the voltage regulation security reserve is the demand with the largest absolute value across all scenarios at each time (keeping the original positive or negative sign). Step two involves quantifying the demand for charging station energy storage capacity and corresponding revenue based on historical frequency regulation signals. Assume that the historical grid dispatch frequency regulation capacity is S. reg,sch The frequency modulation signal sent is P reg,τ , has -S reg,sch ≤P reg,τ ≤S reg,sch The number of frequency modulation commands issued per unit time (usually 1 hour) is T. reg Each instruction takes ΔT to execute. Therefore, to meet the grid frequency regulation requirements, the charging station needs to reserve at least a certain upward regulation capacity S. reg,max Reduce capacity S reg,min .

[0135]

[0136]

[0137] At this point, the benefit obtained through frequency modulation capacity is:

[0138]

[0139] In the formula: S reg,sch For frequency modulation capacity, the revenue per megawatt is λ. c Yuan; λ mis Penalty for mismatch in regulation per megawatt-hour during frequency regulation.

[0140] Step three involves establishing a scheduling model for charging and discharging of electric vehicles based on the travel patterns of electric vehicles and the SOC constraints of electric vehicles.

[0141] Based on the probabilistic fitting of electric vehicle charging parameters, the Monte Carlo algorithm is used to fit the charging power of a single electric vehicle as shown in the following formula.

[0142]

[0143] In the formula: Pk,t This represents the actual charging power of electric vehicle k at time t. and P represents the start and end times of charging the electric vehicle, respectively. t P represents the total load power of the charging station at time t. car,k Let k be the charging power of the electric vehicle. Based on the electric vehicle charging power fitting model, the battery percentage of the electric vehicle after charging is complete is shown below:

[0144]

[0145] In the formula, SOC start,k and SOC end,k Let B represent the battery SOC of electric vehicle k at the start of charging and the end of the demand-side response period, respectively. k Let Δt represent the battery capacity of electric vehicle k, and Δt represent the charging time step.

[0146] By performing statistical analysis on electric vehicle charging data, the expected SOC of electric vehicles was fitted using a normal distribution, as shown below.

[0147]

[0148] In the formula, SOC need σ1 represents the percentage of electricity required for future electric vehicle trips, while μ1 and σ1 represent the expected and fluctuating values ​​of the percentage of electricity required for future electric vehicle trips, respectively.

[0149] When the reduction in electric vehicle capacity is less than the difference between the electric vehicle's fitted capacity and the required capacity, reducing the capacity will not affect the user's future travel needs. Therefore, this range is defined as the easily adjustable potential range [0, Q]. k,min When the reduction in battery capacity of an electric vehicle exceeds the difference between the vehicle's fitted battery capacity and the required battery capacity, further reduction will disrupt the user's travel and may even cause economic losses. Therefore, this range is set as the general adjustable potential range for electric vehicles [Q]. k,min Q k,max ], Q k,min With Q k,max The minimum and maximum adjustable amounts for the total time period of an electric vehicle are represented as follows.

[0150] Q k,min =B k (SOC end,k -SOC need,k );

[0151] Q k,max =B k SOCend,k ;

[0152] During the charging and discharging process of an electric vehicle, there are minimum and maximum permissible states of charge (SOC), which are respectively represented by... and This indicates that the battery is operating in a safe condition.

[0153] The upper limit of the battery energy of electric vehicle k at time t is obtained from the charging station. Battery energy lower limit E k,t Maximum charging power Maximum discharge power P k,t Aggregate all electric vehicle demand response events k∈J at the charging station to obtain the total energy limit of the charging station. Total Energy Lower Limit E t Maximum total charging power Maximum total discharge power P t .

[0154]

[0155]

[0156] Step four: Establish a quantitative model of the frequency regulation capacity of electric vehicle charging stations, considering voltage safety reserve capacity. With the goal of minimizing charging station operating costs, and taking into account grid requirements for voltage regulation reserve, upper and lower limits of charging and discharging power, and upper and lower limits of charging and discharging energy, calculate the frequency regulation capacity that the charging station will provide to the grid for the next 24 hours. The specific process is as follows:

[0157] min∑((λ t -c ev,t )P EV,t +c dr (P EV,t -P EV,base,t )-R FM,t )

[0158] In the formula: λ t P represents the electricity purchase cost at time t for the charging station. EV,t Let c be the charging / discharging power of the electric vehicle charging station at time t; ev,t The price of charging electric vehicles; c dr For electric vehicles to participate in demand response compensation, P EV,base,t The charging station does not provide charging power curves for frequency and voltage regulation; R FM,t The revenue obtained by charging stations from participating in frequency regulation.

[0159]

[0160] In the formula: S reg,sch,t Let t be the capacity participating in frequency regulation, and let λ be the revenue per megawatt of frequency regulation capacity. c Yuan; λ mis During frequency regulation, resources are penalized per megawatt-hour for regulation mismatch; P EV (t) represents the charging and discharging power value of the electric vehicle charging station at time t; P safe (t) represents the power demand for safe voltage regulation in the distribution network; r(t) represents the normalized frequency modulation signal.

[0161] Power allocation based on voltage safety:

[0162]

[0163] C reg (t)≤| P t -P safe (t)|if P safe (t)<0

[0164] C reg (t)=S reg,sch,t

[0165] In the formula: C reg (t) represents the maximum frequency modulation power of the charging station during time period t. Since the actual frequency modulation signal is unknown, in order to ensure that the charging station can provide the promised frequency modulation power in sufficient quantities, the frequency modulation power (charging and discharging) values ​​of the charging station are all set to the frequency modulation capacity values.

[0166] Energy constraints:

[0167]

[0168] In the formula, based on historical data, the number of hours in which the maximum continuous SOC change is in the same direction (positive or negative) is n. This means that continuous SOC changes need to consider the cumulative impact of frequency regulation demand on SOC. The maximum historical SOC change after a single scheduling period is... Minimum value is The cumulative impact needs to be multiplied by the number of hours, n.

[0169] The above process will yield the frequency regulation capacity that the charging station can provide to the power grid in the next 24 hours.

[0170] The real-time scheduling process for voltage and frequency regulation is as follows:

[0171] At time t, evaluate all electric vehicles and calculate the remaining charging time for each electric vehicle.

[0172] When t left,k <=B k (SOCneed,k -SOC start,k ) / P car,k If the vehicle is within the normal adjustable capacity range, and there is no special signal from the power grid (such as emergency load shedding), it is excluded from the dispatchable cluster.

[0173] It will be in the easily adjustable potential range [0, Q] k,min The resources will be further screened. As the safe scheduling time for electric vehicles, the electric vehicle cluster J with a positive safe scheduling time. sd It can provide reverse charging capability, and the remaining resources J usd Only charging power reduction is available. All resource charging benchmarks are P. base =B k (SOC need,k -SOC k,t ) / t left .

[0174] The power adjustment signal is a frequency modulation signal (located in the range [-S)). reg,sch,t ,+S reg,sch,t [Internal] and voltage regulation signal P safe,i The superposition of (t) signals has both positive and negative values, with the negative response having a priority of J. sd Power reduction — J usd Power reduction — J sd Power backfeed, J sd The power reduction order is as follows: charging is stopped sequentially from longest to shortest safe scheduling time. J usd The power reduction order is Q k,min The charging is stopped sequentially from largest to smallest. If neither of them can provide power reduction, then J will reduce the charging speed. sd Power is supplied in reverse order according to the safety scheduling time, from longest to shortest, until the signal is satisfied. The priority of the forward response is J. usd Power increase — J sd Power increases, J usd Power increase according to Q k,min The power is increased from small to large, J usd The power is increased sequentially according to the safe scheduling time, from smallest to largest, until the signal is satisfied.

[0175] This embodiment also discloses a frequency regulation scheduling system for electric vehicle charging stations that considers voltage safety reserve capacity, including:

[0176] Distribution network operating environment parameters and voltage regulation safety requirement assessment module: used to obtain distribution network operating environment parameters, and based on the distribution network operating environment parameters, use Monte Carlo simulation and power flow calculation to assess the maximum voltage regulation safety requirement of the distribution network;

[0177] Electric vehicle charging station adjustable boundary construction module: Based on historical data, simulate the charging demand of electric vehicles at the charging station, divide the adjustable energy range of electric vehicles, and quantify the upper and lower limits of power and energy of the charging station.

[0178] Electric vehicle charging and discharging scheduling model construction module: Based on historical frequency regulation service information, quantify the relationship between frequency regulation capacity and charging and discharging energy, as well as the relationship between frequency regulation capacity and frequency regulation revenue; construct an objective function to minimize cost, and solve it under the conditions of considering the node voltage safety reserve power, the impact of frequency regulation on charging and discharging energy, and the charging and discharging power constraints of electric vehicles, to obtain the frequency regulation capacity reported to the grid for each time period;

[0179] Electric vehicle real-time control module: used to construct a real-time control strategy for charging stations in response to grid frequency and voltage regulation signals, and to design the electric vehicle scheduling sequence based on the adjustable capacity range of each electric vehicle and the safe scheduling time index.

[0180] Optionally, a safety assessment module is also included: used to analyze the peak shaving and voltage regulation effects of charging stations in various scenarios. The peak shaving and voltage regulation effects include the degree of reduction of the peak-valley difference in the power grid and the maintenance of node voltage, thereby achieving a safety assessment.

[0181] The following content verifies the proposed method.

[0182] like Figure 2 As shown, this invention's example focuses on an IEEE 33-node distribution network, analyzing the optimal power demand of electric vehicle charging stations under the premise of considering voltage safety constraints. By changing different load and renewable energy output scenarios, the optimal power demand of electric vehicles under different conditions is calculated, and the impact of voltage safety constraints on charging station operation planning is considered, providing a reference for the planning and actual operation of charging stations.

[0183] In order to comprehensively analyze the power demand of charging stations, this invention studies the generation of multiple simulation scenarios on the power distribution network for different electric vehicle charging and discharging load levels. Figure 3 The data showcased sample data on the charging and discharging power of electric vehicle charging stations across 200 scenarios. Figure 4 This reflects changes in users' electricity consumption behavior at different times. Such fluctuations can be caused by a variety of factors, including changes in daily life and production activities, seasonal demand fluctuations, and load surges caused by sudden events.

[0184] The present invention is based on Figure 3 and Figure 4 Data processing is performed to evaluate the operation of charging stations under different scenarios. Among these scenarios, this invention selects representative scenarios, such as... Figure 5As shown, this is to more clearly illustrate the power demand characteristics of electric vehicle charging stations over a 24-hour period. To ensure voltage safety, this invention further analyzes the charging and discharging power of electric vehicle charging stations under various scenarios, such as... Figure 6 As shown in the figure, this analysis demonstrates the charging and discharging capabilities that charging stations need to possess while ensuring the stable operation of the power grid.

[0185] This invention selects the frequency regulation signal from the PJM market and, considering the characteristics of the IEEE 33-bus system, uses Matlab programming to demonstrate the revenue potential of charging stations participating in the frequency regulation market. By analyzing power demand and the regulation signal, the revenue from frequency regulation is quantified, and its economic viability in actual operation is calculated. In this process, this invention establishes a power-down frequency regulation application model based on voltage safety to maximize economic benefits while ensuring a stable power supply. The frequency regulation price setting is shown in Table 1.

[0186] Table 1. Frequency modulation prices in the ancillary services market

[0187]

[0188]

[0189] pass Figure 7 It can be observed that the frequency regulation capacity of charging stations varies across different time periods based on electricity demand, market signals, and system operating conditions. Based on this, the hourly revenue of charging stations participating in frequency regulation can be calculated as follows: Figure 8 As shown.

[0190] Based on the charging station's participation in frequency regulation, and according to step two, under the safety constraints of the charging station's participation in distribution network voltage regulation, this invention selects as follows: Figure 2 The IEEE 33-node distribution network shown is used as a case study, with new energy sources and load output scenarios as follows: Figure 3-4 As shown. The maximum power demand for voltage regulation in the distribution network is calculated for each time period, resulting in the required voltage regulation power that the charging station needs to provide to ensure voltage safety, as shown below. Figure 5 As shown.

[0191] Calculate the frequency regulation capacity of the charging station considering voltage regulation safety and the frequency regulation capacity not considering voltage regulation requirements, as follows: Figure 6 As shown.

[0192] To further demonstrate the importance of considering the voltage safety requirements of the power distribution network when determining the capacity of charging stations, this invention selects times 4, 8, and 20 for voltage comparison analysis. At time 4, the charging station's reserved voltage regulation requirement is zero; at time 8, the charging station's reserved voltage regulation resource is positive; and at time 20, the charging station's reserved voltage regulation resource is negative. This results in a voltage comparison analysis at these three times, considering only frequency regulation and frequency regulation after considering grid voltage safety constraints. Figure 7 , Figure 8 , Figure 9 As shown.

[0193] It can be observed that the voltage of the charging station participating in market frequency regulation is within the obtained frequency regulation capacity after considering the safety constraints of the distribution network voltage. The distribution network voltage is consistently within a safe range. Ultimately, the hourly frequency regulation revenue of the charging station considering the distribution network voltage regulation demand can be obtained as follows: Figure 10 As shown, the frequency regulation benefit is significantly reduced after considering voltage regulation requirements (compared to...). Figure 11 Frequency regulation capacity gains without considering voltage regulation requirements.

[0194] To further analyze the peak-shaving behavior of charging stations at different operating times, the active power and SOC variation curves at selected typical times are shown as follows: Figure 12 and Figure 13 As shown, the power operation of the charging station under voltage safety considerations and the changes in the SOC curve can be observed at different time periods.

[0195] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0196] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity, characterized in that, Includes the following steps: Obtain the operating environment parameters of the distribution network, and evaluate the maximum voltage regulation safety requirements of the distribution network based on the operating environment parameters using Monte Carlo simulation and power flow calculation. Based on historical frequency modulation service information, the relationship between frequency modulation capacity and charging / discharging energy, as well as the relationship between frequency modulation capacity and frequency modulation revenue, are quantified. Construct an adjustable capacity assessment model for electric vehicle charging stations, divide the adjustable energy range of electric vehicles, and quantify the upper and lower limits of power and energy of charging stations. Construct an objective function to minimize operating costs, and solve it under the conditions of considering the safety reserve power of node voltage, the impact of frequency regulation on charging and discharging capacity, and the charging and discharging power constraints of electric vehicles, to obtain the frequency regulation capacity reported to the grid for each time period; A real-time control strategy for charging stations that responds to frequency and voltage regulation signals from the power grid is constructed, and the scheduling sequence of electric vehicles is designed based on the adjustable capacity range of each electric vehicle and the safe scheduling time index.

2. The frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity according to claim 1, characterized in that, It also includes analyzing the peak shaving and voltage regulation effects of charging stations in various scenarios. The peak shaving and voltage regulation effects include the degree of reduction of the peak-valley difference in the power grid and the maintenance of node voltage, which are used to assess safety.

3. The frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity according to claim 1, characterized in that, Obtain operating environment parameters of the distribution network, including the topology of the distribution network to clarify the connection relationship of each node, obtain the node reference voltage as the standard for voltage assessment, collect power flow data such as the active power, reactive power and admittance parameters between nodes, and obtain the time series curves of photovoltaic output and load to understand the energy supply and demand situation at different times.

4. The frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity according to claim 1, characterized in that, Based on the operating environment parameters of the distribution network, Monte Carlo simulation and power flow calculation are used to evaluate the maximum voltage regulation safety requirement of the distribution network. Specifically, Monte Carlo simulation and optimal power flow method are used to determine the voltage regulation power requirement necessary for the distribution network to maintain voltage safety at different times and in different scenarios, and the maximum voltage regulation power requirement at each time and in each scenario is used as the output quantity.

5. The frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity according to claim 1, characterized in that, Based on the operating environment parameters of the distribution network, Monte Carlo simulation and power flow calculation are used to assess the maximum voltage regulation security requirement of the distribution network. The specific calculation process is as follows: A scenario set S is generated based on the uncertainties in photovoltaic output and load output. The optimal power flow for each scenario is calculated using the following formula: min f=λ s (P load,t +P EV,base,t -P pv,t ); In the formula: λ s For real-time electricity prices; P load,t Forecast the power demand of other loads in the distribution network; P pv,t Contribute to photovoltaic forecasting; P EV,base,t Predicted power of charging stations when they do not provide frequency regulation and voltage regulation services to the power grid; The constraints are conventional power and voltage constraints for power grids, as follows: In the formula, k:j→k represents the set of end nodes k starting from node j; P represents the active and reactive power outputs of the load at node j and the distributed generation at time t; jk,t and Q jk,t Let r be the active power and reactive power flowing through branch ij at time t. ij Let I be the resistance of branch ij. ij,t Let I be the square of the branch current ij at time t. ij max V represents the maximum squared allowable current in branch ij. ref Represents the reference voltage; x ij Uij is the reactance of branch ij; U1 and Uj,t are the squares of the voltage amplitudes at the equilibrium node and node j at time t; ε represents the allowable voltage deviation; The voltage amplitude V at each node is obtained through power flow calculation. i,calc Afterwards, relative to the reference voltage V i,ref The quantization formula for the offset is as follows: ΔV i =|V i,calc -V i,ref |; In the formula: V i,calc The actual voltage magnitude of node i is obtained through power flow calculation; V i,ref The reference voltage of node i is used to select the node with the largest voltage offset as the master node. The normalized voltage is defined as follows: The required minimum voltage regulation amplitude is: To obtain the active power compensation of the charging station to meet the minimum voltage regulation amplitude required by the i-th node, the following control objective is established: Where: ΔP EVS,j This refers to the active power adjustment of the charging station, and its maximum value is... Set according to historical information; Solving the above objective function, we derive the condition for minimizing the active power compensation of the charging station under the minimum voltage regulation requirement of the i-th node: In the formula: For the voltage-power sensitivity of node i and node j, design proportional reactive power compensation and active power compensation related to the sensitivity based on the above formula: In the formula: It is the standardized active power compensation of the j-th node, denoted as: Based on the charging station voltage regulation resource scheduling volume in multiple scenarios, a charging station voltage regulation demand curve is generated, and the calculation formula is as follows: In the formula: P safe,i (t) is the reserved voltage regulation safety capacity at node i at time t; ΔP s,i,t S represents the voltage regulation power required to maintain grid voltage security at time t for scenario s and node i; S represents the set of all scenarios; and T represents all times within the time period.

6. The frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity according to claim 1, characterized in that, This also includes the demand for charging station energy storage capacity and corresponding revenue based on the quantification of frequency regulation service capacity using historical frequency regulation signals; assuming the historical frequency regulation capacity of the power grid dispatch is S. reg,sch The frequency modulation signal sent is P reg,τ , has -S reg,sch ≤P reg,τ ≤S reg,sch The number of frequency modulation commands issued per unit time is T. reg If the execution time of each instruction is ΔT, then the charging station needs to reserve at least an upward adjustment capacity S to meet the grid frequency regulation requirements. reg,max Reduce capacity S reg,min : At this point, the benefit obtained through frequency modulation capacity is: In the formula: S reg,sch For frequency modulation capacity, the revenue per megawatt is λ. c Yuan; λ mis Penalty for mismatch in regulation per megawatt-hour during frequency regulation.

7. The frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity according to claim 1, characterized in that, Based on the travel patterns of electric vehicles and the SOC constraints of electric vehicle charging and discharging, a scheduling model for charging and discharging of electric vehicles at charging stations is established, which includes the following steps: Based on the probabilistic fitting of electric vehicle charging parameters, the Monte Carlo algorithm is used to fit the charging power of a single electric vehicle as shown in the following formula: In the formula: P k,t This represents the actual charging power of electric vehicle k at time t. and P represents the start and end times of charging the electric vehicle, respectively. t P represents the total load power of the charging station at time t. car,k The charging power for electric vehicle k; Based on the electric vehicle charging power fitting model, the battery percentage of an electric vehicle after charging is complete is shown below: In the formula, SOC start,k and SOC end,k Let B represent the battery SOC of electric vehicle k at the start of charging and the end of the demand-side response period, respectively. k Let represent the battery capacity of electric vehicle k, and Δt represent the charging time step. Statistical analysis of electric vehicle charging data was conducted, and a normal distribution was used to fit the expected SOC of electric vehicles, as shown below: In the formula, SOC need This represents the percentage of electricity required for future electric vehicle travel, where μ1 and σ1 represent the expected and fluctuating values ​​of the percentage of electricity required for future electric vehicle travel, respectively. When the reduction in electric vehicle capacity is less than the difference between the electric vehicle's fitted capacity and the required capacity, reducing the capacity will not affect the user's future travel needs. Therefore, this range is defined as the easily adjustable potential range [0, Q]. k,min When the reduction in electric vehicle power exceeds the difference between the electric vehicle's fitted power and the required power, further reduction will disrupt the user's travel and may even cause economic losses. Therefore, this range is set as the general adjustable potential range for electric vehicles [Q]. k,min Q k,max ], Q k,min With Q k,max The minimum and maximum adjustable amounts for the total time period of an electric vehicle are defined as follows: Q k,min =B k (SOC end,k -SOC need,k ); Q k,max =B k SOC end,k ; During the charging and discharging process of an electric vehicle, there are minimum and maximum permissible states of charge (SOC), which are respectively represented by... and This is used to ensure the battery operates in a safe state; it yields the upper limit of the battery energy of the electric vehicle at time t at the charging station. Battery energy lower limit E k,t Maximum charging power Maximum discharge power P k,t Aggregate all electric vehicle demand response events k∈J at the charging station to obtain the total energy limit of the charging station. Total Energy Lower Limit E t Maximum total charging power Maximum total discharge power P t ; 8. The frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity according to claim 1, characterized in that, A quantitative model for the frequency regulation capacity of electric vehicle charging stations, considering voltage safety reserve capacity, is established. Each charging station aims to minimize its own next-day operating cost F. Constraints include voltage regulation reserve constraints required by the power grid, upper and lower limits constraints on charging and discharging power, and upper and lower limits constraints on charging and discharging energy. The charging and discharging power P of the charging station in the next 24 hours is then calculated. EV,t and the frequency regulation capacity S provided by the power grid reg,sch,t The specific process is as follows: min F=∑((λ t -c ev,t )P EV,t +c dr (P EV,t -P EV,base,t )-R FM,t ); In the formula: λ t Let P be the electricity purchase cost of the charging station at time t, and let P be a known quantity. EV,t Let c be the charging / discharging power of the electric vehicle charging station at time t, and let c be the quantity to be determined. ev,t The price of charging an electric vehicle is a known quantity; c dr For electric vehicles to participate in demand response compensation, let P be a known quantity. EV,base,t For charging stations that do not provide charging power curves for frequency and voltage regulation, R is a known quantity. FM,t The revenue that a charging station gains from participating in frequency regulation is calculated using the following formula: In the formula: S reg,sch,t Let be the capacity participating in frequency regulation during time period t, and be the quantity to be determined. The revenue per unit frequency regulation capacity is λ per megawatt. c Element, is a known quantity; λ mis During frequency regulation, resources are subject to a known penalty per megawatt-hour for regulation mismatch; P EV (t) represents the charging and discharging power of the electric vehicle charging station at time t, which is the quantity to be determined; P safe (t) represents the power demand for distribution network voltage regulation safety capacity, which is P safe,i (t) omits the representation of node positions and is a known quantity; r(t) is the normalized frequency modulated signal and is a known quantity; Constraints are set based on voltage safety reserved power settings: C reg (t)≤| P t -P safe (t)| if P safe (t)<0; C reg (t)=S reg,sch,t ; In the formula: C reg (t) represents the maximum frequency regulation power of the charging station during time period t; Energy constraints: In the formula, based on historical data, the maximum number of hours in which the maximum continuous SOC change occurs in the same direction is n. This means that continuous SOC changes need to consider the cumulative impact of frequency regulation demand on SOC. The maximum historical SOC change after a single scheduling period is... Minimum value is The cumulative impact needs to be multiplied by the number of hours, n. The above process will yield the frequency regulation capacity S that the charging station will provide to the power grid in the next 24 hours. reg,sch,t and frequency modulation capacity S reg,sch,t Submitted to the power grid.

9. A frequency regulation scheduling method for electric vehicle charging stations considering voltage safety reserve capacity according to claim 1, characterized in that, Based on real-time voltage and frequency regulation scheduling signals, the order in which electric vehicles participate in real-time frequency and voltage regulation scheduling is determined. The specific process is as follows: At time t, evaluate all electric vehicles and calculate the remaining charging time for each electric vehicle. When t left,k <=B k (SOC need,k -SOC start,k ) / P car,k The vehicles are within the normal adjustable capacity range and are excluded from the schedulable cluster; It will be in the easily adjustable potential range [0, Q] k,min The resources will be further screened; As the safe scheduling time for electric vehicles, the electric vehicle cluster J with a positive safe scheduling time. sd Provides reverse charging capability, remaining resources J usd Only charging power reduction is provided; all resource charging benchmarks are P. base =B k (SOC need,k -SOC k,t ) / t left ; The power adjustment signal is a frequency modulation signal (located in the range [-S)). reg,sch,t ,+S reg,sch,t [Internal] and voltage regulation signal P safe,i The superposition of (t) signals has both positive and negative values, with the negative response having a priority of J. sd Power reduction — J usd Power reduction — J sd Power backfeed, J sd The power reduction order is as follows: charging is stopped sequentially from longest to shortest safe scheduling time. J usd The power reduction order is Q k,min The charging is stopped sequentially from largest to smallest. If neither of them can provide any more power, then J will reduce the charging speed. sd Power is supplied in reverse order according to the safety scheduling time, from longest to shortest, until the signal is satisfied; the priority of the forward response is J. usd Power increase — J sd Power increases, J usd Power increase according to Q k,min The power is increased from small to large, J usd The power is increased sequentially according to the safe scheduling time, from smallest to largest, until the signal is satisfied.

10. A frequency regulation scheduling system for electric vehicle charging stations that considers voltage safety reserve capacity, characterized in that, include: Distribution network operating environment parameters and voltage regulation safety requirement assessment module: used to obtain distribution network operating environment parameters, and based on the distribution network operating environment parameters, use Monte Carlo simulation and power flow calculation to assess the maximum voltage regulation safety requirement of the distribution network; Electric vehicle charging station adjustable boundary construction module: Based on historical data, simulate the charging demand of electric vehicles at the charging station, divide the adjustable energy range of electric vehicles, and quantify the upper and lower limits of power and energy of the charging station. Electric vehicle charging and discharging scheduling model construction module: Based on historical frequency regulation service information, quantify the relationship between frequency regulation capacity and charging and discharging energy, as well as the relationship between frequency regulation capacity and frequency regulation revenue; construct an objective function to minimize cost, and solve it under the conditions of considering the node voltage safety reserve power, the impact of frequency regulation on charging and discharging energy, and the charging and discharging power constraints of electric vehicles, to obtain the frequency regulation capacity reported to the grid for each time period; Electric vehicle real-time control module: used to construct a real-time control strategy for charging stations in response to grid frequency and voltage regulation signals, and to design the electric vehicle scheduling sequence based on the adjustable capacity range of each electric vehicle and the safe scheduling time index; Safety assessment module: used to analyze the peak shaving and voltage regulation effect of charging stations in various scenarios. The peak shaving and voltage regulation effect includes the degree of reduction of the peak-valley difference of the power grid and the maintenance of node voltage, so as to realize the safety assessment.

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