Active power distribution network optimization method for network access of electric vehicle charging, replacing and storing integrated station

Abstract

The invention discloses an active power distribution network optimization method for network access of an electric vehicle charging, replacing and storing integrated station, and discloses a charging, replacing and storing integrated station optimization scheduling method based on Monte Carlo simulation, a dynamic battery replacing strategy and an echelon utilization strategy. According to the method, an integrated mathematical model of the charging, replacing and storing integrated station is built, and a multi-target optimization model with the purpose of minimizing network loss, voltage deviation and load peak-valley difference is built. By solving the optimization model, optimal dispatching of the power flow of the power distribution network can be achieved, and then accurate control over the grid-side current, voltage and load level is achieved. Compared with direct access to the power distribution network, the method provided by the invention can effectively solve the problems of overlarge load, increased voltage fluctuation and the like of the power distribution network caused by high-power load access. According to the time-of-use electricity price signal, charging can be increased in the power grid load off-peak period (low electricity price), charging can be reduced or discharging can be provided in the load peak period (high electricity price), and therefore peak load shifting is achieved, and the load peak-valley difference is effectively stabilized.

Description

Technical Field

[0001] This invention relates to the field of integrated charging, swapping, and energy storage stations, and in particular to an active distribution network optimization method for the grid connection of integrated charging, swapping, and energy storage stations for electric vehicles. Background Technology

[0002] An integrated electric vehicle charging, battery swapping, and energy storage station is a comprehensive facility that combines charging stations, battery swapping stations, and tiered energy storage systems. The charging station, through an orderly charging management system, can control the charging and discharging behavior of electric vehicles, thereby indirectly participating in power grid dispatching. The battery swapping station, through standardized battery modules, enables rapid battery replacement, reducing vehicle downtime. The energy storage station, utilizing large-capacity batteries or other energy storage technologies, can effectively balance the grid load and improve energy efficiency. This integrated station is characterized by its flexibility and efficiency, capable of adjusting charging, battery swapping, and energy storage modes according to actual needs, reducing operating costs. Its development not only accelerates the widespread adoption of electric vehicles but also promotes the efficient integration and utilization of new energy resources.

[0003] As a special type of grid load, the integration of charging, swapping, and energy storage stations into the distribution network has both positive impacts and challenges. Existing integrated charging, swapping, and energy storage stations have not been properly planned, and their integration may lead to local overload, affecting power quality and grid security. Therefore, an active distribution network optimization method for the integration of electric vehicle charging, swapping, and energy storage stations into the grid is needed to solve the problem of local overload. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the existing technology, it is desirable to provide an active distribution network optimization method for the grid connection of electric vehicle charging, swapping and storage integrated stations.

[0005] This invention provides an active distribution network optimization method for the grid connection of integrated electric vehicle charging, swapping, and energy storage stations, which specifically includes the following steps:

[0006] S100: Through the integrated model of the charging, swapping and energy storage station, obtain the charging load data of the charging station, the swapping data of the swapping station and the charging and discharging data of the energy storage station.

[0007] S200. Calculate and obtain wind power data through wind power generation strategies. The wind power data is the actual output power P of the wind turbine generator. WT ;

[0008] S300. Calculate and obtain photovoltaic data through a photovoltaic power generation strategy. The photovoltaic data is the photovoltaic output power P. PV ;

[0009] S400. Based on the charging load data, battery swapping data, charging and discharging data, wind power data, and photovoltaic data, establish an optimization model for multi-objective coordination of the active distribution network. Construct a multi-objective function for the optimization model, which includes a network loss function, a voltage deviation function, and a load peak-valley difference function.

[0010] S500. Solve the multi-objective function to obtain the minimum values ​​of the network loss function, voltage deviation function, and load peak-valley difference function.

[0011] According to the technical solution provided in the embodiments of this application, step S100 specifically includes the following steps:

[0012] S110. The integrated model of the charging, battery swapping, and energy storage station includes the Monte Carlo simulation method, a dynamic battery swapping strategy, and a tiered utilization strategy. The charging load data of the charging station is obtained through the Monte Carlo simulation method, where the charging load data is the total charging power P of the charging station within time period t. BCS(t) ;

[0013] S120. Obtain battery swapping data of the battery swapping station through a dynamic battery swapping strategy. The battery swapping data is the net charging power P of the battery swapping station within time period t. BSS(t) ;

[0014] S130. Obtain the charging and discharging data of the energy storage station through a tiered utilization strategy. The charging and discharging data is the total power P of the energy storage station within time period t. ESS (t).

[0015] According to the technical solution provided in the embodiments of this application, step S110 of the Monte Carlo simulation method specifically includes the following steps:

[0016] S111. Based on the log-normal distribution, the daily mileage s of an electric vehicle is obtained using a probability formula. The probability formula is as follows:

[0017]

[0018] In the formula, μ D and σ D These represent the expected value and standard deviation of the daily mileage, respectively.

[0019] S112. Substitute the daily mileage s of the electric vehicle into the first formula to calculate the charging time t. The first formula is as follows:

[0020]

[0021] In the formula, s represents the daily mileage of the electric vehicle, and P C η is the inherent charging power of the charging station's charging piles. evc For charging efficiency, W100 Electricity consumption per 100 kilometers;

[0022] S113. Substitute the charging time t into the second formula to obtain the total charging power of the charging station during the time period t. The second formula is as follows:

[0023]

[0024] In the formula, P BCS(t) P is the total charging power of the charging station during time period t. C (i,t) represents the charging power of the i-th electric vehicle at time t; N C It refers to the number of cars.

[0025] According to the technical solution provided in the embodiments of this application, step S120, the dynamic battery swapping strategy specifically includes the following steps:

[0026] S121. Divide the battery SOC into W equally spaced intervals and establish the battery state transition equation, which is as follows:

[0027]

[0028] Where: N 1,e,t N 2,e,t …N W,e,t These correspond to W SOC intervals [SOC0, SOC1], [SOC1, SOC2], ..., [SOC... W-1 SOC W The number of batteries; N 1,c,t N 2,c,t ,…N W-1,c,t They are respectively with N 1,e,t N 2,e,t …N W-1,e,t The relevant number of rechargeable batteries, which will leave the current range after charging and enter a higher SOC range; N 2,d,t N 3,d,t …N W,d,t N 2,e,t N 3,e,t …N W,e,t With the associated number of discharged batteries, these batteries will leave the current range after discharge and enter a lower SOC range with N k,c,t +N k,d,t =N k,e,t (k = 2, 3, ..., W); N new,t This represents the number of batteries that need to be replaced during time period t.

[0029] S122. Based on the battery state transition equation, obtain the number N of charging points at the battery swapping station during time period t. all,c,t And the number of discharged batteries Nall,d,t ;

[0030] S123, Change the number of charging batteries N all,c,t And the number of discharged batteries N all,d,t Substituting into the third set of formulas, we obtain the charging and discharging power of all batteries in the battery swapping station, as well as the net charging power of the battery swapping station during time period t.

[0031]

[0032] In the formula: P fix,c P fix,d These represent the charging and discharging power of a single battery, respectively; N all,c,t N all,d,t P represents the total number of charging and discharging batteries at the battery swapping station. all,c,t P all,d,t Let P represent the total charging and discharging power of the battery during time period t. BSS(t) This represents the net charging power of the battery swapping station during time period t.

[0033] According to the technical solution provided in the embodiments of this application, in step S130, the cascade utilization strategy specifically includes: establishing a bidirectional inverter operation model to obtain the total power of the energy storage station within time period t. The fourth set of formulas for the bidirectional inverter operation model is as follows:

[0034]

[0035] In the formula: P ESS (t) represents the total power of the energy storage station during time period t; E ESS (t) represents the total energy in time period t; The rated charging and discharging power of the cascade energy storage power station; and These represent the charging and discharging efficiencies of the energy storage station, respectively; ω c (t), ω d (t) is a charging / discharging state indicator variable.

[0036] According to the technical solution provided in the embodiments of this application, in step S400, the optimization model satisfies a set of constraint formulas, which are as follows:

[0037]

[0038]

[0039] In the formula: u(j) represents the set of the first nodes of the branch whose end node is j; v(j) represents the set of the end nodes of the branch whose first node is j; P ij,t Q is the active power of branch (i,j) during time period t. ij,t Reactive power; r ijx ij I ij,t The resistance, reactance, and current of branch (i,j) are represented in sequence; P j,t Q j,t These represent the net active power and reactive power injected into node j during time period t; U i,t Let i be the voltage at node i during time period t; The active load of node j in time period t is the base load, which refers to the background load of the system before it is connected to the integrated station. Similarly, For reactive load, This refers to the active power of the wind turbine. The active power of a photovoltaic system can be obtained through wind turbines and photovoltaic power generation systems; and The reactive power injected into node j during time period t represents the power from the wind turbine, photovoltaic system, CB (converter-emergent circuit), and SVC (supplier-vehicle control circuit).

[0040] According to the technical solution provided in the embodiments of this application, in step S500, the expression of the multi-objective function f is as follows:

[0041] f = w1f1 + w2f2 + w3f3

[0042] In the formula, f1 is the network loss function, f2 is the voltage deviation function, f3 is the load peak-valley difference function, and w1, w2, and w3 are weighting coefficients.

[0043] According to the technical solution provided in the embodiments of this application, the network loss function is as follows:

[0044]

[0045] In the formula, N i A set of network branches; I ij,t Let r be the current flowing through the branch at time t; ij 24 represents the resistance of the (i,j) branch; 24 represents 24 hours in a day.

[0046] The voltage deviation function is as follows:

[0047]

[0048] In the formula, U i U represents the voltage value of the i-th node at time t. i,ref The reference voltage value is represented by 1, N. B Indicates the number of nodes;

[0049] The peak-valley difference function is as follows:

[0050] f3 = min(maxP(t) - minP(t))

[0051] In the formula, P(t) is the load power of the distribution network during a time period t in one day.

[0052] According to the technical solution provided in the embodiments of this application, solving the objective function in step S400 involves solving the network loss function, voltage deviation function, and load peak-valley difference function separately.

[0053] The voltage deviation function solution process specifically includes the following steps:

[0054] S521. Introduce slack variables and reconstruct to obtain new power flow equations:

[0055] The slack variables are as follows:

[0056]

[0057] The new power flow equation is as follows:

[0058]

[0059] In the formula, P ij,t Q is the active power of branch (i,j) during time period t. ij,t Reactive power; r ij x ij I ij,t The resistance, reactance, and current of branch (i,j) are represented in sequence; P j,t Q j,t These represent the net active power and reactive power injected into node j during time period t; U i,t Let i be the voltage at node i during time period t;

[0060] S522. Reconstruct and obtain new current-voltage constraint equations; the new current-voltage constraint equations are as follows:

[0061]

[0062] S523. Introduce additional constraints to linearize the voltage deviation function f2 and obtain a new expression for the voltage deviation function f2.

[0063] The additional constraints are as follows:

[0064]

[0065] In the formula, a i b i As an auxiliary variable;

[0066] The new expression for the voltage deviation function f2 is as follows:

[0067]

[0068] In the formula, a i b i N is an auxiliary variable. B Indicates the number of nodes.

[0069] Compared with the prior art, the beneficial effects of the present invention are:

[0070] This invention constructs an integrated model of a charging, battery swapping, and energy storage station through Monte Carlo simulation, dynamic battery swapping strategies, and tiered utilization strategies. Based on this integrated model, this invention establishes a multi-objective optimization model, whose optimization objectives include minimizing network losses, minimizing voltage deviation, and minimizing load peak-to-valley differences. By solving this optimization model, this invention can achieve optimized scheduling of power flow in the distribution network, thereby controlling the current, voltage, and load levels of the distribution network.

[0071] Compared to direct connection to the distribution network, the method of this invention can effectively solve the problems of excessive distribution network load and increased voltage fluctuation caused by the connection of high-power loads. This invention can increase charging during the off-peak hours (low electricity price) and reduce charging or provide discharging during the peak hours (high electricity price) according to the time-of-use electricity price signal, thereby achieving peak shaving and valley filling and effectively smoothing the load peak-valley difference.

[0072] Furthermore, this invention treats the integrated charging, swapping, and energy storage station as an active and controllable resource, taking into account unstable power sources such as wind power and photovoltaic power. By establishing a set of constraint formulas for the integrated charging, swapping, and energy storage station, wind turbines, photovoltaics, and reactive power compensation devices, the synergistic effect of the integrated charging, swapping, and energy storage station with wind turbines, photovoltaics, and reactive power compensation devices is realized. The method of this invention can actively participate in the optimized operation of the power grid, achieve voltage stability and reduce grid losses, and ultimately achieve global optimization, thereby improving the stability of the entire system.

[0073] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0074] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0075] Figure 1 A flowchart illustrating the steps of an active distribution network optimization method for integrating electric vehicle charging, swapping, and energy storage stations into the grid, as provided in this application embodiment. Detailed Implementation

[0076] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0077] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0078] Please refer to Figure 1 The present invention provides an active distribution network optimization method for the grid connection of electric vehicle charging, swapping and storage integrated stations, which specifically includes the following steps:

[0079] S100: Through the integrated model of the charging, swapping and energy storage station, obtain the charging load data of the charging station, the swapping data of the swapping station and the charging and discharging data of the energy storage station.

[0080] Step S100 specifically includes the following steps:

[0081] S110. The integrated model of the charging, battery swapping, and energy storage station includes the Monte Carlo simulation method, a dynamic battery swapping strategy, and a tiered utilization strategy. The charging load data of the charging station is obtained through the Monte Carlo simulation method, where the charging load data is the total charging power P of the charging station within time period t. BCS(t) In reality, the daily mileage, initial state of charge (SOC), charging demand, and charging power of electric vehicles are highly random and uncertain. Monte Carlo simulation can simulate various possible situations of these uncertain variables through a large number of random samples, thereby more realistically reflecting the actual operation scenario of charging stations.

[0082] In step S110, the Monte Carlo simulation method specifically includes the following steps:

[0083] S111. Based on the log-normal distribution, the daily mileage s of an electric vehicle is obtained using a probability formula. The probability formula is as follows:

[0084]

[0085] In the formula, μ D and σ D These represent the expected value and standard deviation of the daily mileage, respectively.

[0086] S112. Substitute the daily mileage s of the electric vehicle into the first formula to calculate the charging time t. The first formula is as follows:

[0087]

[0088] In the formula, s represents the daily mileage of the electric vehicle, and P C η is the inherent charging power of the charging station's charging piles. evc For charging efficiency, W 100 Electricity consumption per 100 kilometers;

[0089] S113. Substitute the charging time t into the second formula to obtain the total charging power of the charging station during the time period t. The second formula is as follows:

[0090]

[0091] In the formula, P BCS(t) P is the total charging power of the charging station during time period t. C (i,t) represents the charging power of the i-th electric vehicle at time t; N C It refers to the number of cars.

[0092] Based on the probability formula for the daily mileage of electric vehicles using the log-normal distribution, the daily mileage *s* of a random electric vehicle can be obtained. Then, using the first formula, based on the inherent charging power, charging efficiency, and energy consumption per 100 kilometers, the charging time *t* required for the corresponding daily mileage *s* can be obtained. Here, *t* represents the charging time for a single electric vehicle's daily mileage *s*. Finally, the second formula can be used to obtain the total charging power *P* of all electric vehicles within time *t*. BCS(t) .

[0093] S120. Obtain battery swapping data of the battery swapping station through a dynamic battery swapping strategy. The battery swapping data is the net charging power P of the battery swapping station within time period t. BSS(t) With the number of charging and discharging batteries as the core control variable, the system employs refined management based on SOC segmentation, combined with complex optimizations such as dynamic battery flow balancing, charging and discharging priority scheduling, and multiple operational constraints. This control method can more realistically reflect the operation strategy of the battery swapping station and achieve its goal of "efficient operation".

[0094] In step S120, the dynamic battery swapping strategy specifically includes the following steps:

[0095] S121. Divide the battery SOC into W equally spaced intervals and establish the battery state transition equation, which is as follows:

[0096]

[0097] Where: N 1,e,t N 2,e,t …N W,e,t These correspond to W SOC intervals [SOC0, SOC1], [SOC1, SOC2], ..., [SOC... W-1 SOC W The number of batteries; N1,c,t N 2,c,t ,…N W-1,c,t They are respectively with N 1,e,t N 2,e,t …N W-1,e,t The relevant number of rechargeable batteries, which will leave the current range after charging and enter a higher SOC range; N 2,d,t N 3,d,t …N W,d,t N 2,e,t N 3,e,t …N W,e,t With the associated number of discharged batteries, these batteries will leave the current range after discharge and enter a lower SOC range with N k,c,t +N k,d,t =N k,e,t (k = 2, 3, ..., W); N new,t This represents the number of batteries that need to be replaced during time period t.

[0098] The first line of the formula indicates the batteries in the lowest SOC range, which come from existing stock + newly replaced batteries + batteries discharged from the previous stage; the middle lines of the formula indicate that batteries in the middle range may be charged from low SOC, discharged from high SOC, and may also be charged to higher SOC or discharged to lower SOC, so there are four flow directions; the last line of the formula indicates that the batteries in the highest SOC range mainly come from batteries fully charged at the second highest SOC, and the decrease is due to discharge or replacement.

[0099] S122. Based on the battery state transition equation, obtain the number N of charging points at the battery swapping station during time period t. all,c,t And the number of discharged batteries N all,d,t ;

[0100] S123, Change the number of charging batteries N all,c,t And the number of discharged batteries N all,d,t Substituting into the third set of formulas, we obtain the charging and discharging power of all batteries in the battery swapping station, as well as the net charging power of the battery swapping station during time period t.

[0101]

[0102] In the formula: P fix,c P fix,d These represent the charging and discharging power of a single battery, respectively; N all,c,t N all,d,t P represents the total number of charging and discharging batteries at the battery swapping station. all,c,t P all,d,t Let P represent the total charging and discharging power of the battery during time period t. BSS(t) This represents the net charging power of the battery swapping station during time period t.

[0103] This step involves dividing the batteries in the battery swapping station into W intervals (e.g., 0-20% charge, 20-40% charge, ... 80-100% charge). Newly swapped batteries (empty batteries) enter the bottom layer (0-20% interval). During charging, batteries flow from the lower layer to the upper layer (e.g., 20% → 40% interval); during discharging, batteries flow from the upper layer to the lower layer (e.g., 80% → 60% interval). Batteries that are swapped out (fully charged batteries) leave from the top layer. Finally, the number of batteries charging / discharging in the current time period t is calculated. Then, based on the charging / discharging power of a single battery and the corresponding number of batteries charging / discharging, the total discharge power and total charging power are obtained. The net charging power P is obtained by the difference between the two. BSS(t) By tracking the battery state flow in SOC intervals, the charging and discharging behavior of battery swapping stations is quantified, ultimately serving the power grid and energy dispatch decisions.

[0104] S130. Obtain the charging and discharging data of the energy storage station through a tiered utilization strategy. The charging and discharging data is the total power P of the energy storage station within time period t. ESS (t); Reusing retired electric vehicle batteries in energy storage systems can extend battery life and improve resource utilization. Although these retired batteries are no longer suitable for the high-performance requirements of electric vehicles, they can still play a role in energy storage applications, such as peak shaving and valley filling, and smoothing out fluctuations in renewable energy output, thereby achieving a win-win situation for both economic and environmental benefits.

[0105] In step S130, the tiered utilization strategy specifically includes: establishing a bidirectional inverter operation model to obtain the total power of the energy storage station within time period t. The fourth set of formulas for the bidirectional inverter operation model is as follows:

[0106]

[0107] In the formula: P ESS (t) represents the total power of the energy storage station during time period t; E ESS (t) represents the total energy in time period t; The rated charging and discharging power of the cascade energy storage power station; and These represent the charging and discharging efficiencies of the energy storage station, respectively; ω c (t), ω d (t) is a charging / discharging state indicator variable.

[0108] In the fourth set of equations, the first equation represents the difference between the charging power consumption and the discharging power of the energy storage station. The second equation represents that charging cannot exceed its rated power, the third equation represents that discharging cannot exceed its rated power, and the fourth equation represents that charging and discharging cannot be carried out simultaneously. Based on the net power of the energy storage station, safety rules are set (it cannot exceed its rated power and cannot be carried out simultaneously). The fifth equation represents that the stored energy of the energy storage station at the current moment is equal to the stored energy at the previous moment plus the net charging and discharging energy of the current period. The sixth equation constrains the stored energy of the energy storage system at any time, ultimately achieving efficient grid interaction.

[0109] In some embodiments, step S200 specifically includes the following steps:

[0110] S210. Obtain the wind speed parameters using the Gaussian distribution formula, which is as follows:

[0111]

[0112] In the formula, σ represents the standard deviation, μ represents the expected value, v represents the wind speed, and t represents the time.

[0113] S220. The output power of the wind turbine and the wind speed satisfy the wind power formula, which is as follows:

[0114]

[0115] In the formula, P WT P r These are the actual output power and rated power of the wind turbine, respectively; ν aw ν r ν ci and ν co The wind speeds, in order, are the actual wind speed, rated wind speed, cut-in wind speed, and cut-out wind speed of the unit.

[0116] Steps S210 and S220 are common formula steps for calculating the actual output power of a wind turbine, and will not be elaborated here.

[0117] In some embodiments, in step S300, the photovoltaic power generation strategy refers to the photovoltaic output power and solar radiation intensity satisfying the photoelectric formula, which is as follows:

[0118] P PV =LSλ

[0119] In the formula, P PV L and L represent photovoltaic output power and solar radiation intensity, respectively; S and λ represent the photosensitive area and photoelectric conversion efficiency of the solar cell, respectively.

[0120] Wherein, the solar radiation intensity L satisfies the Beta distribution formula, which is as follows:

[0121]

[0122] In the formula, L is the radiation intensity; L max α represents the maximum solar radiation intensity; α and β represent the photovoltaic shape parameter and photovoltaic size parameter, respectively.

[0123] Step S300 is a common formula for calculating the photovoltaic output power of photovoltaic power generation, and will not be elaborated here.

[0124] In step S400, the optimization model satisfies a set of constraint formulas, which are as follows:

[0125]

[0126] In the formula: u(j) represents the set of the first nodes of the branch whose end node is j; v(j) represents the set of the end nodes of the branch whose first node is j; P ij,t Q is the active power of branch (i,j) during time period t. ij,t Reactive power; r ij x ij I ij,t The resistance, reactance, and current of branch (i,j) are represented in sequence; P j,t Q j,t These represent the net active power and reactive power injected into node j during time period t; U i,t Let i be the voltage at node i during time period t; The active load of node j in time period t is the base load, which refers to the background load of the system before it is connected to the integrated station. Similarly, For reactive load, This refers to the active power of the wind turbine. The active power of a photovoltaic system can be obtained through wind turbines and photovoltaic power generation systems; and The reactive power injected into node j during time period t represents the power from the wind turbine, photovoltaic system, CB (converter-emergent circuit), and SVC (supplier-vehicle control circuit).

[0127] Static var compensators (SVCs) and switchable capacitor banks (CBs) are two important types of reactive power compensation devices in distribution networks. An SVC is a continuously adjustable reactive power source, whose output reactive power is limited by its rated capacity; while a CB provides discrete reactive power through tiered switching, and its operation is limited by the number of operations and the upper and lower limits of its reactive power capacity. The reactive power data for both devices can be directly read from the existing system. In the optimization model, these two devices work synergistically to address voltage fluctuations caused by a high proportion of renewable energy and fluctuating loads, achieving voltage stability and reactive power balance in the distribution network.

[0128] The first three terms of the constraint formula set are power flow constraint equations, the fourth term is a power constraint equation, and the fifth term is a current and voltage constraint equation. These equations are combined to form the constraint formula set.

[0129] In some embodiments, in step S500, the expression of the multi-objective function f is as follows:

[0130] f = w1f1 + w2f2 + w3f3

[0131] In the formula, f1 is the network loss function, f2 is the voltage deviation function, f3 is the load peak-valley difference function, and w1, w2, and w3 are weighting coefficients.

[0132] In some embodiments, the network loss function is as follows:

[0133]

[0134] In the formula, N i A set of network branches; I ij,t Let r be the current flowing through the branch at time t; ij Let be the resistance of branch (i,j); 24 represents 24 hours in a day; this function aims to calculate and minimize the total active power loss of the entire distribution network within 24 hours. Network losses are mainly caused by the heat generated by current flowing through the line resistance. Therefore, the higher the current, the higher the resistance, and the more severe the loss. This invention optimizes the charging and discharging behavior of the integrated charging, swapping, and storage station to adjust the power flow distribution of the distribution network, thereby obtaining a current suitable for high-loss lines, and ultimately minimizing the total network loss while satisfying various constraints.

[0135] In some embodiments, the voltage deviation function is as follows:

[0136]

[0137] In the formula, U i This represents the voltage value of the i-th node at time t. U i,ref The reference voltage value is represented by 1, N. B This represents the number of nodes; the function aims to minimize the average deviation of the voltage at all power nodes from their rated standard value over 24 hours. Voltage is a key indicator of power quality; excessively high voltage can damage electrical equipment, while excessively low voltage can affect the normal startup or operational efficiency of equipment. Therefore, by solving this objective function to obtain its minimum value, voltage fluctuations at each node can be effectively mitigated, ensuring that the voltage remains within a safe operating range and guaranteeing the normal and safe operation of electrical equipment.

[0138] In some embodiments, the peak-valley difference function is as follows:

[0139] f3 = min(maxP(t) - minP(t))

[0140] In the formula, P(t) represents the load power of the distribution network during a time period t within a day. This function aims to minimize the difference between the maximum (peak) and minimum (valley) values ​​of the grid load during a day. The greater the fluctuation in grid load, the more reserve capacity the grid needs to maintain to cope with peak loads, which significantly increases operating costs. By solving for the minimum value of this objective function, the grid load fluctuations can be smoothed out, i.e., "peak shaving and valley filling," thereby reducing the grid's reserve capacity requirements and improving its operating efficiency and economy.

[0141] In some embodiments, solving the objective function in step S400 involves solving the network loss function, voltage deviation function, and load peak-valley difference function separately.

[0142] The voltage deviation function solution process specifically includes the following steps:

[0143] S521. Introduce slack variables and reconstruct to obtain new power flow equations:

[0144] The slack variables are as follows:

[0145]

[0146] The new power flow equation is as follows:

[0147]

[0148] In the formula, P ij,t Q is the active power of branch (i,j) during time period t. ij,t Reactive power; r ij x ij I ij,t The resistance, reactance, and current of branch (i,j) are represented in sequence; P j,t Q j,t These represent the net active power and reactive power injected into node j during time period t; U i,t Let i be the voltage at node i during time period t;

[0149] S522. Reconstruct and obtain new current-voltage constraint equations; the new current-voltage constraint equations are as follows:

[0150]

[0151] S523. Introduce additional constraints to linearize the voltage deviation function f2 and obtain a new expression for the voltage deviation function f2.

[0152] The additional constraints are as follows:

[0153]

[0154] In the formula, a i b i As an auxiliary variable;

[0155] The new expression for the voltage deviation function f2 is as follows:

[0156]

[0157] In the formula, a i b i N is an auxiliary variable. B Indicates the number of nodes.

[0158] Among them, the original expression of the voltage deviation function f2 has the characteristics of multi-objective non-convex nonlinearity, which can be simplified by mixed integer second-order cone programming. The main method is to introduce relaxation variables and linearize the objective function to obtain a new expression that is easy to solve.

[0159] Optionally, the network loss function, voltage deviation function, and load peak-valley difference function can be solved separately using the CPLEX solver. CPLEX (IBMILOG CPLEX Optimizer) is an industrial-grade mathematical optimization solver that can directly solve the above three functions to obtain the minimized function value.

[0160] In the description of this specification, the terms "connection," "installation," and "fixing," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0161] In the description of this specification, the terms "one embodiment," "some embodiments," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0162] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for optimizing an active distribution network for the integration of electric vehicle charging, swapping, and energy storage stations into the grid, characterized in that, Specifically, the steps include the following: S100: Through the integrated model of the charging, swapping and energy storage station, obtain the charging load data of the charging station, the swapping data of the swapping station and the charging and discharging data of the energy storage station. S200. Calculate and obtain wind power data through wind power generation strategies. The wind power data is the actual output power P of the wind turbine generator. WT ; S300. Calculate and obtain photovoltaic data through a photovoltaic power generation strategy. The photovoltaic data is the photovoltaic output power P. PV ; S400. Based on the charging load data, battery swapping data, charging and discharging data, wind power data, and photovoltaic data, establish an optimization model for multi-objective coordination of the active distribution network. Construct a multi-objective function for the optimization model, which includes a network loss function, a voltage deviation function, and a load peak-valley difference function. S500. Solve the multi-objective function to obtain the minimum values ​​of the network loss function, voltage deviation function, and load peak-valley difference function.

2. The active distribution network optimization method for the integrated charging, swapping, and energy storage stations for electric vehicles according to claim 1, characterized in that, Step S100 specifically includes the following steps: S110. The integrated model of the charging, battery swapping, and energy storage station includes the Monte Carlo simulation method, a dynamic battery swapping strategy, and a tiered utilization strategy. The charging load data of the charging station is obtained through the Monte Carlo simulation method, where the charging load data is the total charging power P of the charging station within time period t. BCS(t) ; S120. Obtain battery swapping data of the battery swapping station through a dynamic battery swapping strategy. The battery swapping data is the net charging power P of the battery swapping station within time period t. BSS(t) ; S130. Obtain the charging and discharging data of the energy storage station through a tiered utilization strategy. The charging and discharging data is the total power P of the energy storage station within time period t. ESS (t).

3. The active distribution network optimization method for the integrated charging, swapping, and energy storage stations for electric vehicles according to claim 2, characterized in that, In step S110, the Monte Carlo simulation method specifically includes the following steps: S111. Based on the log-normal distribution, the daily mileage s of an electric vehicle is obtained using a probability formula. The probability formula is as follows: In the formula, μ D and σ D These represent the expected value and standard deviation of the daily mileage, respectively. S112. Substitute the daily mileage s of the electric vehicle into the first formula to calculate the charging time t. The first formula is as follows: In the formula, s represents the daily mileage of the electric vehicle, and P C η is the inherent charging power of the charging station's charging piles. evc For charging efficiency, W 100 Electricity consumption per 100 kilometers; S113. Substitute the charging time t into the second formula to obtain the total charging power of the charging station during the time period t. The second formula is as follows: In the formula, P BCS(t) P is the total charging power of the charging station during time period t. C (i,t) represents the charging power of the i-th electric vehicle at time t; N C It refers to the number of cars.

4. The active distribution network optimization method for the integrated charging, swapping, and energy storage stations for electric vehicles according to claim 2, characterized in that, In step S120, the dynamic battery swapping strategy specifically includes the following steps: S121. Divide the battery SOC into W equally spaced intervals and establish the battery state transition equation, which is as follows: Where: N 1,e,t N 2,e,t …N W,e,t These correspond to W SOC intervals [SOC0, SOC1], [SOC1, SOC2], ..., [SOC... W-1 SOC W The number of batteries; N 1,c,t N 2,c,t ,…N W-1,c,t They are respectively with N 1,e,t N 2,e,t …N W-1,e,t The relevant number of rechargeable batteries, which will leave the current range after charging and enter a higher SOC range; N 2,d,t N 3,d,t …N W,d,t N 2,e,t N 3,e,t …N W,e,t With the associated number of discharged batteries, these batteries will leave the current range after discharge and enter a lower SOC range with N k,c,t +N k,d,t =N k,e,t (k = 2, 3, ..., W); N new,t This represents the number of batteries that need to be replaced during time period t. S122. Based on the battery state transition equation, obtain the number N of charging points at the battery swapping station during time period t. all,c,t And the number of discharged batteries N all,d,t ; S123, Change the number of charging batteries N all,c,t And the number of discharged batteries N all,d,t Substituting into the third set of formulas, we obtain the charging and discharging power of all batteries in the battery swapping station, as well as the net charging power of all batteries in the battery swapping station during time period t. In the formula: P fix,c P fix,d These represent the charging and discharging power of a single battery, respectively; N all,c,t N all,d,t P represents the total number of charging and discharging batteries at the battery swapping station. all,c,t P all,d,t Let P represent the total charging and discharging power of the battery during time period t. BSS(t) This represents the net charging power of the battery swapping station during time period t.

5. The active distribution network optimization method for the integrated charging, swapping, and energy storage stations for electric vehicles according to claim 2, characterized in that, In step S130, the tiered utilization strategy specifically includes: establishing a bidirectional inverter operation model to obtain the total power of the energy storage station within time period t. The fourth set of formulas for the bidirectional inverter operation model is as follows: In the formula: P ESS (t) represents the total power of the energy storage station during time period t; E ESS (t) represents the total energy in time period t; The rated charging and discharging power of the cascade energy storage power station; and These represent the charging and discharging efficiencies of the energy storage station, respectively; ω c (t), ω d (t) is a charging / discharging state indicator variable.

6. The active distribution network optimization method for the integrated charging, swapping, and energy storage stations for electric vehicles according to claim 2, characterized in that, In step S400, the optimization model satisfies a set of constraint formulas, which are as follows: In the formula: u(j) represents the set of the first nodes of the branch whose end node is j; v(j) represents the set of the end nodes of the branch whose first node is j; P ij,t Q is the active power of branch (i,j) during time period t. ij,t Reactive power; r ij x ij I ij,t The resistance, reactance, and current of branch (i,j) are represented in sequence; P j,t Q j,t These represent the net active power and reactive power injected into node j during time period t; U i,t Let i be the voltage at node i during time period t; The active load of node j in time period t is the base load, which refers to the background load of the system before it is connected to the integrated station. Similarly, For reactive load, This refers to the active power of the wind turbine. The active power of a photovoltaic system can be obtained through wind turbines and photovoltaic power generation systems; and The reactive power injected into node j during time period t represents the power from the wind turbine, photovoltaic system, CB (converter-emergent circuit), and SVC (supplier-vehicle control circuit).

7. The active distribution network optimization method for the integrated charging, swapping, and storage stations for electric vehicles according to claim 6, characterized in that, In step S500, the expression for the multi-objective function f is as follows: f = w1f1 + w2f2 + w3f3 In the formula, f1 is the network loss function, f2 is the voltage deviation function, f3 is the load peak-valley difference function, and w1, w2, and w3 are weighting coefficients.

8. The active distribution network optimization method for the integrated charging, swapping, and storage stations for electric vehicles according to claim 7, characterized in that, The network loss function is as follows: In the formula, N i A set of network branches; I ij,t Let r be the current flowing through the branch at time t; ij 24 represents the resistance of the (i,j) branch; 24 represents 24 hours in a day. The voltage deviation function is as follows: In the formula, U i U represents the voltage value of the i-th node at time t. i,ref The reference voltage value is represented by 1, N. B Indicates the number of nodes; The peak-valley difference function is as follows: f3 = min(maxP(t) - minP(t)) In the formula, P(t) is the load power of the distribution network during a time period t in one day.

9. The active distribution network optimization method for the integrated charging, swapping, and energy storage stations for electric vehicles according to claim 8, characterized in that, In step S400, solving the objective function involves solving the network loss function, voltage deviation function, and load peak-valley difference function separately. The voltage deviation function solution process specifically includes the following steps: S521. Introduce slack variables and reconstruct to obtain new power flow equations: The slack variables are as follows: The new power flow equation is as follows: In the formula, P ij,t Q is the active power of branch (i,j) during time period t. ij,t Reactive power; r ij x ij I ij,t The resistance, reactance, and current of branch (i,j) are represented in sequence; P j,t Q j,t These represent the net active power and reactive power injected into node j during time period t; U i,t Let i be the voltage at node i during time period t; S522. Reconstruct and obtain new current-voltage constraint equations; the new current-voltage constraint equations are as follows: S523. Introduce additional constraints to linearize the voltage deviation function f2 and obtain a new expression for the voltage deviation function f2. The additional constraints are as follows: In the formula, a i b i As an auxiliary variable; The new expression for the voltage deviation function f2 is as follows: In the formula, a i b i N is an auxiliary variable. B Indicates the number of nodes.

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