Micro-grid optimization scheduling method based on large-scale electric vehicle access

Abstract

The invention discloses a microgrid optimization scheduling method based on large-scale electric vehicle access, and the method comprises the steps: constructing a generator set model and an electric vehicle charging and discharging cost model, carrying out the charging and discharging optimization, building a microgrid income mathematical model, determining a constraint condition, and meeting the stable operation of a microgrid. And finally, obtaining an optimal solution by adopting a particle swarm algorithm. According to the method, by means of the behaviors of discharging in the peak load period and charging in the valley load period, the peak load shifting effect can be well achieved, so that the load fluctuation of the power grid is reduced, meanwhile, for an electric vehicle user, smaller charging cost can be generated, and the charging efficiency is improved. The problems that the consideration of model construction in the micro-grid is not comprehensive and the algorithm is low in efficiency are solved.

Description

Technical Field

[0001] This invention relates to the field of microgrid optimization technology, and more specifically, to a microgrid optimization scheduling method based on large-scale electric vehicle access. Background Technology

[0002] A microgrid is a small-scale power generation system primarily composed of energy storage devices, energy conversion systems, load monitoring, distributed power sources, and a series of protection devices. Distributed power sources include small power generation equipment such as gas turbines, photovoltaic units, and wind turbines. Microgrids are characterized by low pollution, low cost, and ease of management and maintenance. They can perform complex functions such as self-protection, fault detection, and power balance control in islanded mode, and can also interconnect with the main grid in grid-connected mode. The emergence of microgrids provides a new pathway for the consumption of renewable energy.

[0003] The electric vehicle industry is developing rapidly, and the number of electric vehicle users is increasing. People often charge their vehicles randomly according to their personal needs. When electric vehicles are connected to microgrids, it can lead to uneven grid load, increasing the complexity and cost of grid operation. For example, disorderly charging can cause a sharp increase in grid load within a specific period, creating a "peak-on-peak" phenomenon, which seriously affects the stability of the power system. To cope with load spikes caused by disorderly charging, the grid may need to strengthen its infrastructure or introduce energy storage devices, increasing operating costs. Therefore, to address the problems that disorderly charging may cause to microgrids, such as voltage drops and increased peak load curves, efficient scheduling and management strategies are needed to balance charging supply and demand. Discharging during peak load periods and charging during off-peak periods can effectively "shift peaks and fill valleys," thereby reducing grid load fluctuations. At the same time, considering the differences in energy supply, it can reduce charging costs for car users.

[0004] CN114358588A, a method for scheduling electric vehicle charging and discharging in a microgrid considering real-time electricity prices, includes the following steps: obtaining the time when the electric vehicle starts charging, the battery's state of charge, and the departure time and battery level set by the owner; determining the optimal peak-to-peak price from the previous day's simulated scheduling of the microgrid, using it as the peak-to-peak price for the current day, and dividing the real-time electricity price into peak, valley, and flat periods; constraining the charging and discharging power of the electric vehicle in each period based on the real-time electricity price and the charging and discharging characteristics of the electric vehicle; and determining the charging and discharging power of the electric vehicle in each period based on the charging and discharging power constraints of the electric vehicle, with the goal of minimizing overall cost and microgrid output cost, thus completing the electric vehicle charging and discharging scheduling. While this patent effectively reduces microgrid operating costs, minimizes microgrid output fluctuations, and plays a role in peak shaving and valley filling for microgrid load, improving microgrid operational stability, it does not consider the differences in power and cost among various energy sources in the microgrid, requiring further optimization in functional balance and minimizing cost. Furthermore, the method employs the traditional particle swarm optimization algorithm, which suffers from low efficiency in the early global search and later local search phases. Summary of the Invention

[0005] The main technical problem to be solved by this invention is that the existing technology does not take into account the incomplete construction of the model in the microgrid and the algorithm has low efficiency. This invention provides a microgrid optimization scheduling method based on large-scale electric vehicle access.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A microgrid optimization scheduling method based on large-scale electric vehicle access includes the following steps:

[0008] S1. Construct a microgrid system model that includes a generator set model and an electric vehicle charging and discharging cost model;

[0009] The generator set model outputs power for charging electric vehicles. The generator set model includes a wind power generator set model, a photovoltaic generator set model, a gas turbine set model, and an energy storage battery set model.

[0010] The electric vehicle charging and discharging cost model is constructed based on the allowable charging and discharging time and the electricity price during the charging period;

[0011] S2. In the time-of-use electricity price sequence, select periods with lower electricity prices for charging and discharging; exclude discharging during periods with lower electricity prices. Obtain the charging cost and discharging benefit, expressed as follows:

[0012]

[0013] Wherein, the charging power is P1, and the discharging power is P2;

[0014] S3. Prioritizing the maximization of renewable energy absorption, and taking operational revenue as the optimization object, the microgrid revenue mathematical model is established as follows:

[0015]

[0016] Among them, C income C1, C2, and C3 represent the revenue generated by the generating unit, the fuel cost of the generating unit, the operating and maintenance costs of the generating unit, and the energy exchange cost between the microgrid and the main grid, respectively. 充 For charging costs, C 放 For discharge efficiency;

[0017] S4. In order for the microgrid to operate stably, the optimal solution is obtained by using the microgrid system power balance, the maximum power of the power generation system and the state of charge of the energy storage battery as constraints.

[0018] Furthermore, the charging and discharging times of electric vehicles are expressed as follows:

[0019] Expected charging duration:

[0020]

[0021] Actual discharge duration:

[0022]

[0023] Among them, the remaining driving range of the electric vehicle is S1, the expected driving range is S2, the range is S3, the power consumption per 100 kilometers is L, the charging power is P1, and the discharging power is P2.

[0024] Furthermore, taking a day as the cycle, we use T = [T0, T1, ... T 23 ] represents a time vector of one period, where T K Let k be the time period. The time-of-use electricity price for the 24 time periods is: C = [C0, C1, ... Ck]. 23 ], where C K For [T] k ,T k+1 The electricity price corresponds to the time period k = 0, 1, ..., 23. The charging and discharging sequence is represented by j = [j0, j1, ..., j]. 23 ] indicates that, among them

[0025]

[0026] Strategies for finding the best electricity price for charging include:

[0027] (1) Find the index of the time series corresponding to the highest electricity price during the allowed charging period. Its expression is:

[0028]

[0029] subsc{a i} represents taking vector a = {a1, a2, ..., a...} i ,L,a n} element a in i The subscript i; max{a} represents taking the maximum element of vector a; Represents vector j 充 Multiply by the corresponding elements in C, C = [C0, C1, ... C 23 [This refers to the time-of-use electricity price for 24 different time periods;]

[0030] (2) If t3>t2+1, then during the period with the highest electricity price i, electric vehicles do not need to participate in charging. In this case, let j 充 =0, then j in the charge / discharge sequence 充 Updated to:

[0031]

[0032] Electricity price optimization strategies for discharge include:

[0033] (1) Find the index of the time series corresponding to the lowest electricity price during the allowed discharge period. Its expression is:

[0034]

[0035] Where min{a} represents taking the minimum element of vector a;

[0036] (2) Let j i =0, meaning the period with the lowest electricity price, during which electric vehicles do not participate in discharging, resulting in the discharge time series j. 放 Updated to:

[0037]

[0038] Furthermore, C1, C2, and C3 are represented as follows:

[0039]

[0040] Among them, C fuel The value of fuel (LHV) represents the fuel cost of the gas turbine, η represents the lower heating value of the fuel, and C represents the efficiency of the gas turbine. i Let i = 1, 2, 3, 4 represent the operation and maintenance costs required for the photovoltaic generator, wind turbine, gas turbine, and energy storage battery module, respectively. C in (t) represents the electricity price that the microgrid purchases from the main grid, P in (t) represents the amount of electricity purchased by the microgrid from the main grid, C out(t) represents the electricity price that the microgrid sells to the main grid, P out (t) represents the amount of electricity sold by the microgrid to the main grid.

[0041] Furthermore, the power balance constraints of the microgrid system are expressed as follows:

[0042]

[0043] in The output power of an electric vehicle during charging and discharging at time t, P L (t) represents the output power of the residential load at time t, P PV (t) represents the output power of the wind turbine generator at time t, P WT (t) represents the output power of the photovoltaic generator at time t, P MT (t) Output power of the gas turbine unit at time t, P bat (t) The output power of the energy storage battery pack at time t, P grid (t) represents the output power of the large power grid at time t.

[0044] Furthermore, the output power of a wind turbine generator set is expressed as:

[0045]

[0046] Where, v, v in v out v e These represent the actual wind speed, the wind turbine cut-in wind speed, the wind turbine cut-out wind speed, and the wind turbine rated wind speed, respectively. ω1, ω2, and ω3 are characteristic parameters of the generator. P e This is the rated power of the wind turbine generator;

[0047] Furthermore, the output power of the photovoltaic generator set is expressed as:

[0048]

[0049] Among them, P PV P represents the theoretical output power of the photovoltaic cell array. STC G represents the maximum output power of the photovoltaic cell array under the baseline measurement, and G is the intensity of sunlight. STC α represents the intensity of light under a reference measurement. PV T is the light temperature coefficient. C T represents the surface temperature of a photovoltaic cell. STC This is the reference temperature for the photovoltaic cell array.

[0050] Furthermore, due to unstable factors such as weather, the actual output power of a photovoltaic (PV) module is less than its theoretical output power. The actual output power can be expressed as:

[0051]

[0052] Where P real,PV f represents the actual output power of the photovoltaic cell array. PV For the output efficiency of the photovoltaic cell array, N PV This refers to the number of cells in a photovoltaic cell module.

[0053] Furthermore, the constraint on the maximum power of the power generation system is expressed as:

[0054] 0≤P MT (t)≤P MTmax .

[0055] Furthermore, excessive charging and discharging can severely damage energy storage batteries and reduce their lifespan. Therefore, the state of charge constraint for energy storage batteries is expressed as follows:

[0056] SOC min ≤SOC≤SOC max

[0057] The state of charge of an energy storage battery is expressed as follows:

[0058]

[0059] Where SOC(t) is the remaining charge of the energy storage battery at time t, δ is the inherent discharge efficiency of the energy storage battery, λ is the charging and discharging efficiency of the energy storage battery, and P in (t), P out (t) represents the charging and discharging power, respectively, P e This refers to the rated capacity of the energy storage battery.

[0060] Furthermore, the particle swarm optimization algorithm includes the following steps:

[0061] S41. Randomly initialize the particle position and velocity, wherein the particle position is:

[0062] X i =(x i1 ,x i2 ,…,x id ) T

[0063] The particle velocity is:

[0064] V i =(v i1 ,v i2 ,…,v id ) T ;

[0065] S42. Update the particle velocity and position, and iterate continuously to find the optimal solution until the optimal solution is found. The update formula for particle velocity and position is:

[0066]

[0067] In the formula, i = 1, 2, 3, ..., m; m is the maximum number of particles; r1 and r2 are random numbers between 0 and 1 that satisfy a uniform distribution. and Let c1 and c2 represent the position and velocity of particle i in the d-th dimension during the k-th iteration, respectively; ω is the inertia weight; and c1 and c2 are both learning factors. and Let them represent the individual optimal solution and the global optimal solution of the i-th particle in the d-th dimension during the k-th iteration, respectively.

[0068] S43. Adjust the particle inertia weight and learning factor;

[0069] The inertial weight is expressed as:

[0070]

[0071] Where k represents the algebra of the current iteration, k max Indicates the total number of iterations;

[0072] The learning factor is represented as:

[0073]

[0074] Compared with existing technologies, the beneficial effects are:

[0075] This invention addresses the dual charging and discharging behavior of electric vehicles (EVs). Based on the charging and discharging intentions of EV users, it connects a large number of EVs to a microgrid. By discharging during peak load periods and charging during off-peak periods, it effectively achieves peak shaving and valley filling, thereby reducing grid load fluctuations. Simultaneously, it results in lower charging costs for EV users.

[0076] The method described in this invention takes community microgrids as the research background, takes the total operating revenue of the microgrid as the optimization object, and constructs an optimization scheduling model containing wind, solar, gas, storage and electric vehicles. By improving the inertia weight, individual learning factor and social learning factor in the particle swarm algorithm, the optimization scheduling model is optimized, and a community microgrid optimization scheduling method for large-scale electric vehicle access is given. Attached Figure Description

[0077] Figure 1 This is a block diagram of the microgrid system of the present invention.

[0078] Figure 2This is a flowchart of the charging sequence optimization process of the present invention.

[0079] Figure 3 This is a flowchart of the discharge sequence optimization process of the present invention.

[0080] Figure 4 This is a flowchart of the particle swarm algorithm of the present invention.

[0081] Figure 5 This is a comparison diagram of the algorithms of this invention. Detailed Implementation

[0082] The following examples further explain and clarify the invention, but the specific examples do not limit the invention in any way.

[0083] Example 1

[0084] This embodiment provides a microgrid optimization scheduling method based on large-scale electric vehicle access, the steps of which include:

[0085] S1. Construct a microgrid system model;

[0086] S1. Construct a microgrid system model that includes a generator set model and an electric vehicle charging and discharging cost model;

[0087] like Figure 1 The generator set model outputs power for charging electric vehicles. The generator set model includes a wind power generator set model, a photovoltaic generator set model, a gas turbine generator set model, and an energy storage battery set model.

[0088] in:

[0089] The output power of a wind turbine generator model is related to the wind speed. The output power of wind power generation is expressed as:

[0090]

[0091] Where v, v in v out v e These represent the actual wind speed, the wind turbine cut-in wind speed, the wind turbine cut-out wind speed, and the wind turbine rated wind speed, respectively. ω1, ω2, and ω3 are characteristic parameters of the generator, where ω1 is considered zero in actual calculations. P e This is the rated power of the wind turbine generator;

[0092] The output power of a photovoltaic (PV) generator is primarily determined by factors such as sunlight intensity and ambient temperature. Higher sunlight intensity results in a greater maximum output power for the PV cells. Conversely, increased ambient temperature affects the temperature of the internal components of the PV cells, thus reducing their maximum output power. The theoretical output power of a PV cell is expressed as:

[0093]

[0094] Where P PV P represents the theoretical output power of the photovoltaic cell array. STC G represents the maximum output power of the photovoltaic cell array under the baseline measurement, and G is the intensity of sunlight. STC α represents the intensity of light under a reference measurement. PV T is the light temperature coefficient. C T represents the surface temperature of a photovoltaic cell. STC This is the reference temperature for the photovoltaic cell array.

[0095] Due to unstable factors such as weather, the actual output power of a photovoltaic (PV) module is less than its theoretical output power. The actual output power is expressed as:

[0096]

[0097] Where P real,PV f represents the actual output power of the photovoltaic cell array. PV For the output efficiency of the photovoltaic cell array, N PV This refers to the number of cells in a photovoltaic cell module.

[0098] Gas turbines primarily use natural gas and methane as fuel, and their fuel cost model can be expressed as follows:

[0099]

[0100] Where C MT For fuel costs (i.e., the cost of generating electricity), C fu The unit price of the gas is , LHV is the lower calorific value of the natural gas, and P is the lower calorific value of the gas. MT The output power of the gas turbine unit, η represents the efficiency of the gas turbine unit.

[0101] The energy storage battery pack model mainly has two states: charging and discharging. At a certain moment, the state of charge relationship of the energy storage battery is:

[0102]

[0103] Where SOC(t) is the remaining charge of the energy storage battery at time t, δ is the inherent discharge efficiency of the energy storage battery (the power consumption required to maintain its own chemical reaction), λ is the charging and discharging efficiency of the energy storage battery (which is approximately the same), and P in (t), P out (t) represents the charging and discharging power, respectively, P e This refers to the rated capacity of the energy storage battery.

[0104] The electric vehicle charging and discharging cost model is constructed based on the allowable charging and discharging time and the electricity price during the charging period.

[0105] S2. Select the time period with the lower electricity price in the time-of-use electricity price sequence for charging and discharging. Do not participate in discharging during the time period with the lower electricity price, so as to obtain the charging cost and discharging benefit.

[0106] S21. The remaining driving range, expected driving range, and range of an electric vehicle determine its charging and discharging behavior and the time required for charging and discharging. The mathematical model for the charging and discharging participation time is as follows:

[0107] Expected charging duration:

[0108]

[0109] Actual discharge duration:

[0110]

[0111] Among them, the remaining driving range of the electric vehicle is S1, the expected driving range is S2, the range is S3, the power consumption per 100 kilometers is L, the charging power is P1, and the discharging power is P2.

[0112] S22. Optimization of charge / discharge sequence;

[0113] Using a period of one day, let T = [T0, T1, ... T 23 ] represents a time vector of one period, where T K Let k be the time period. The time-of-use electricity price for the 24 time periods is: C = [C0, C1, ... Ck]. 23 ], where C K For [T] k, T k+1 The electricity price corresponds to the time period k = 0, 1, ..., 23. The charging and discharging sequence is represented by j = [j0, j1, ..., j]. 23 ] indicates that, among them

[0114]

[0115] The expected charging end time is t2 = t 2-CX +t1, where t3 represents the actual end time of charging, t1 represents the actual start time of charging, t5 represents the expected end time of discharging, and t4 represents the expected start time of discharging.

[0116] S221. Charging sequence optimization;

[0117] Based on the user's permitted charging time, the system selects the time period with the lower electricity price from the time-of-use pricing sequence for charging, thereby reducing charging costs. Figure 2 Find the index of the time series corresponding to the highest electricity price during the permitted charging period. Its expression is:

[0118]

[0119] subsc{a i} represents taking vector a = {a1, a2, ..., a...} i ,L,a n} element a in i The subscript i; max{a} represents taking the maximum element of vector a; Represents vector j 充 Multiply by the corresponding element in C.

[0120] If t3 > t2 + 1, then during the period with the highest electricity price i, electric vehicles do not need to participate in charging. In this case, let j... 充 =0, then the charge / discharge sequence j 充 The elements to be filled have been updated to:

[0121]

[0122] S222. Discharge sequence optimization;

[0123] Based on the user's permitted discharge time, time periods with lower discharge prices are selected from the time-of-use pricing sequence and will not participate in discharge, such as... Figure 3 Find the index of the time series corresponding to the lowest electricity price within the allowed discharge period, expressed as:

[0124]

[0125] Where min{a} represents taking the minimum element of vector a.

[0126] Let j i =0, meaning the period with the lowest electricity price, during which electric vehicles do not participate in discharging, as determined by the discharging sequence j. 充 The element update formula derivation yields:

[0127]

[0128] Update the discharge time series, then j 放 =(j k ).

[0129] In summary, the charging cost and discharging benefit can be expressed as follows:

[0130]

[0131] S3. Construct a microgrid revenue model;

[0132] Microgrid optimal dispatch prioritizes maximizing the absorption of renewable energy, and its operational benefits are the object to be optimized. The operational benefits of a microgrid can be expressed as follows:

[0133]

[0134] Among them, C income C1, C2, and C2 represent the revenue generated by the generating unit, the fuel cost of the generating unit, the operating and maintenance costs of the generating unit, and the energy exchange cost between the microgrid and the main grid, respectively.

[0135]

[0136] C fuel The value of fuel (LHV) represents the fuel cost of the gas turbine, η represents the lower heating value of the fuel, and C represents the efficiency of the gas turbine. i Let i = 1, 2, 3, 4 represent the operation and maintenance costs required for the photovoltaic generator, wind turbine, gas turbine, and energy storage battery module, respectively. in (t) represents the electricity price that the microgrid purchases from the main grid, P in (t) represents the amount of electricity the microgrid purchases from the main grid. C out (t) represents the electricity price that the microgrid sells to the main grid, P out (t) represents the amount of electricity sold by the microgrid to the main grid.

[0137] S5. Determine the constraints to ensure the stable operation of the microgrid;

[0138] The power balance constraints of a microgrid system are:

[0139]

[0140] in P L (t),P PV (t),P WT (t),P MT (t),P bat (t),P grid (t) represents the output power of electric vehicle charging and discharging, residential load, wind turbine, photovoltaic unit, gas turbine unit, battery bank and large power grid at time t;

[0141] The maximum power constraint of the power generation system is:

[0142] 0≤P MT (t)≤P MTmax ;

[0143] Excessive charging and discharging can severely damage energy storage batteries and reduce their lifespan; therefore, it is necessary to constrain the battery's state of charge.

[0144] SOC min ≤SOC≤SOC max .

[0145] Typically SOC min The value is 0.3, and the SOC is...max The value is 0.95.

[0146] S6. The optimal solution is obtained by using the particle swarm optimization algorithm.

[0147] Example 2

[0148] This embodiment is based on the particle swarm optimization algorithm provided in Embodiment 1, such as Figure 4 The particle swarm optimization algorithm is an intelligent algorithm based on evolutionary computation technology. It simulates the foraging behavior of bird flocks by observing their behavior in nature and using massless particles. Its core idea is to obtain the optimal solution through information sharing and collaborative cooperation among individuals. The steps include:

[0149] S61. Random initialization;

[0150] The particle's position is:

[0151] X i =(x i1 ,x i2 ,…,x id ) T

[0152] The particle velocity is:

[0153] V i =(v i1 ,v i2 ,…,v id ) T .

[0154] S62. The particle updates its velocity and position information, and iterates continuously to find the optimal solution until it finds the best solution. The update formula is:

[0155]

[0156] In the formula, i = 1, 2, 3, ..., m; m is the maximum number of particles; r1 and r2 are random numbers between 0 and 1 that satisfy a uniform distribution. and Let c1 and c2 represent the position and velocity of particle i in the d-th dimension during the k-th iteration, respectively; ω is the inertia weight; and c1 and c2 are both learning factors. and Let represent the individual optimal solution and the global optimal solution of the i-th particle in the d-th dimension during the k-th iteration, respectively.

[0157] S63. Adjust particle inertia weights;

[0158] To satisfy the algorithm's requirement of performing a global search in the early stages and a local search in the later stages, and considering that a larger inertia weight ω is beneficial for global search and a smaller one is beneficial for local search, a decreasing inertia weight is adopted:

[0159]

[0160] Where k represents the algebra of the current iteration, k max ω0 represents the total number of iterations, and ω0 is the initial inertia weight.

[0161] S64. Adjust the learning factor of the particles;

[0162] A larger c1 indicates stronger global search capability, while a larger c2 indicates stronger local search capability. During iteration, a global search is performed first, followed by a local search, and the learning factor is improved to...

[0163]

[0164] Where k represents the algebra of the current iteration, k max This represents the total number of iterations. Since the values ​​of c1 and c2 are not fixed, after using several proposed numerical ranges, it was found that the optimization results are relatively better within the range of 1 to 2. Therefore, the value of c is chosen. 11 =2,c 12 =1,c 21 =1,c 22 =1.

[0165] Example 3

[0166] The traditional particle swarm optimization (PSO) algorithm and its improved version were used to solve the problem, with a particle swarm size of 100 and a maximum number of iterations of 500. Both methods were run 100 times. Table 1 compares the results of the traditional and improved PSO algorithms.

[0167] Table 1

[0168] Traditional particle swarm optimization Improved Particle Swarm Optimization Number of runs 100 100 Runtime / s 389 366

[0169] As can be seen from Table 4, the improved particle swarm optimization algorithm outperforms the traditional particle swarm optimization algorithm in terms of running time. Similarly, from... Figure 5 The algorithm comparison shows that, under the same operating cost, the improved particle swarm optimization yields greater operational benefits.

[0170] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A microgrid optimization scheduling method based on large-scale electric vehicle access, characterized by the following steps: include: S1. Construct a microgrid system model that includes a generator set model and an electric vehicle charging and discharging cost model; The generator set model outputs power for charging electric vehicles. The generator set model includes a wind power generator set model, a photovoltaic generator set model, a gas turbine set model, and an energy storage battery set model. The electric vehicle charging and discharging cost model is constructed based on the allowable charging and discharging time and the electricity price during the charging period; S2. In the time-of-use electricity price sequence, select periods with lower electricity prices for charging and discharging; exclude discharging during periods with lower electricity prices. Obtain the charging cost and discharging benefit, expressed as follows: Among them, the charging power is The discharge power is ; S3. Prioritizing the maximization of renewable energy absorption, and taking operational revenue as the optimization object, the microgrid revenue mathematical model is established as follows: in, , , C3 represents the revenue generated by the generating unit, the fuel cost of the generating unit, the operating and maintenance costs of the generating unit, and the energy exchange cost between the microgrid and the main grid, respectively. For charging costs, For discharge efficiency; S4. In order for the microgrid to operate stably, the optimal solution is obtained by using the microgrid system power balance, the maximum power of the power generation system and the state of charge of the energy storage battery as constraints.

2. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 1, characterized in that, The charging and discharging times of an electric vehicle are expressed as follows: Expected charging duration: Actual discharge duration: The remaining driving range of the electric vehicle is Expected mileage The driving range is Electricity consumption per 100 kilometers Charging power is The discharge power is .

3. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 1, characterized in that, Strategies for finding the best electricity price for charging include: (1) Find the index of the time series corresponding to the highest electricity price during the allowed charging period. Its expression is: Indicates taking a vector Middle elements subscript ; Indicates taking a vector The largest element; Representing vectors and Multiply corresponding elements in the middle. Time-of-use electricity pricing is provided for 24 different time periods. (2) If The highest electricity price period Electric vehicles can operate without charging, at which point... In the charge / discharge sequence Updated to: 。 4. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 1, characterized in that, Electricity price optimization strategies for discharge include: (1) Find the index of the time series corresponding to the lowest electricity price during the allowed discharge period. Its expression is: in Indicates taking a vector The smallest element; (2) Let During the period of lowest electricity price, electric vehicles do not participate in discharging, resulting in a discharge time series. Updated to: 。 5. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 1, characterized in that, , , They are represented as follows: in, This indicates the fuel cost of a gas turbine. This indicates the lower heating value of the fuel. This indicates the efficiency of the gas turbine. , =1, 2, 3, 4 represent the operation and maintenance costs required for photovoltaic generators, wind turbines, gas turbines, and energy storage battery modules, respectively. This indicates the electricity price that the microgrid purchases from the main grid. For microgrids to purchase electricity from the main grid, This represents the price at which a microgrid sells electricity to the main grid. This refers to the amount of electricity sold by the microgrid to the main grid.

6. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 1, characterized in that, The power balance constraints of a microgrid system are expressed as follows: in , Charging and discharging electric vehicles Output power at any moment For the residents' load Output power at any moment For wind power generator sets Output power at any moment For photovoltaic generator sets in Output power at any moment Gas turbine units Output power at any moment Energy storage battery packs in Output power at any moment For large power grids Output power at any given moment.

7. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 6, characterized in that, The output power of a wind turbine generator is expressed as: in, , , , These are the actual wind speed, the wind speed at which the fan cuts in, the wind speed at which the fan cuts out, and the rated wind speed of the fan, respectively. , , These are the characteristic parameters of the generator. This is the rated power of the wind turbine.

8. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 6, characterized in that, The output power of a photovoltaic generator set is expressed as: in, This represents the actual output power of the photovoltaic cell module. For the output efficiency of photovoltaic cell arrays, The number of cells in a photovoltaic cell module. The theoretical output power of the photovoltaic cell array is expressed as: This represents the maximum output power of the photovoltaic cell array under the baseline measurement. The intensity of light, This indicates the intensity of light under a reference measurement. The photothermal coefficient, This indicates the surface temperature of the photovoltaic cell array. This is the reference temperature for the photovoltaic cell array.

9. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 1, characterized in that, The constraint on the maximum power of the power generation system is expressed as: 。 10. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 1, characterized in that, The state-of-charge constraint of an energy storage battery is expressed as: The state of charge of an energy storage battery is expressed as follows: in, Let be the remaining charge of the energy storage battery at time t. This refers to the inherent discharge efficiency of energy storage batteries. For the charging and discharging efficiency of energy storage batteries, , These are the charging and discharging powers, respectively. This refers to the rated capacity of the energy storage battery.

11. The microgrid optimization scheduling method based on large-scale electric vehicle access according to claim 1, characterized in that, The steps of the particle swarm optimization algorithm include: S41. Randomly initialize the particle position and velocity, wherein the particle position is: The particle velocity is: ； S42. Update the particle velocity and position, and iterate continuously to find the optimal solution until the optimal solution is found. The update formula for particle velocity and position is: ； In the formula, ; The maximum number of particles; and A random number between 0 and 1 that follows a uniform distribution; and Let represent the position and velocity of particle i in the d-th dimension during the k-th iteration, respectively; Inertial weights; and All are learning factors; and Let them represent the individual optimal solution and the global optimal solution of the i-th particle in the d-th dimension during the k-th iteration, respectively. S43. Adjust the particle inertia weight and learning factor; The inertial weight is expressed as: in Let be the algebra of the current iteration. Indicates the total number of iterations; The learning factor is represented as: 。

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