Electric vehicle V2G scheduling method considering consumption of photovoltaic power and off-peak power

Abstract

The invention relates to an electric vehicle V2G scheduling method considering consumption of photovoltaic power and off-peak power, and the method comprises the steps: building an EV disordered charging model based on a user driving behavior in a power distribution network system containing photovoltaic power generation; establishing peak clipping-oriented price excitation; combining the established EV disordered charging model and price excitation to construct a photovoltaic and EV cooperative power generation optimization scheduling model; solving the photovoltaic and EV cooperative power generation optimal scheduling model by adopting an improved multi-target particle swarm algorithm, and outputting to obtain an EV ordered charging and discharging strategy; and correspondingly controlling the charging and discharging behaviors of the EV according to the EV ordered charging and discharging strategy. Compared with the prior art, the method can reduce the load variance of the power grid, achieves the efficient utilization of photovoltaic and off-peak power, and improves the V2G income of the electric vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle to grid (V2G) optimization scheduling of electric vehicles, and in particular to an electric vehicle V2G scheduling method that takes into account the consumption of photovoltaic and off-peak electricity. Background Art

[0002] With the current increasing installed capacity of distributed photovoltaic power and the growing number of electric vehicles (EVs), the dual nature of EVs as both sources and loads can be leveraged through V2G technology, treating EVs as distributed energy storage systems. By absorbing photovoltaic power and feeding it back to the grid, this can mitigate the impact of distributed photovoltaic power on the distribution network. Furthermore, EVs can fully consume or store green electricity, potentially reducing the need for energy storage facilities for distributed generation and enhancing the green attributes of EVs. This will also further increase the penetration of distributed photovoltaic power in the distribution network. Furthermore, during peak load periods, EVs can feed green electricity back to the grid through V2G technology, achieving peak load regulation and carbon emission reduction.

[0003] Based on the above analysis, as V2G technology matures, EV driving behavior and user discharge willingness will affect the large-scale promotion of V2G technology. Research on the coordinated optimization scheduling of distribution network photovoltaic and V2G under the influence of EV driving behavior and user discharge willingness will help achieve the goal of an environmentally friendly new power system. Most existing studies focus on the economic scheduling of EV users participating in the ancillary service market based on time-of-use electricity prices. It is usually assumed that the charging and discharging electricity prices are the same, and users obtain benefits by taking advantage of the peak-valley difference in electricity prices. However, these studies do not clearly set the discharge electricity price for EVs to participate in peak-shaving ancillary services. At the same time, some studies also ignore the impact of EV users' travel needs on their participation in scheduling decisions. For example, Chinese patent CN111431198A discloses a distribution network energy storage peak-shaving method that takes into account the V2G capability of electric vehicles under peak-valley electricity prices. The method first obtains the basic parameters and operating constraints of a single electric vehicle; based on the basic parameters and operating constraints of the electric vehicle, a V2G capability boundary is formed for the electric vehicle and a fixed-duration V2G capability prediction model is established; based on the distribution network peak-valley electricity prices and the fixed-duration electric vehicle V2G capability prediction model, the total number of electric vehicles in the distribution network is considered, and a fixed-duration V2G strategy for large-scale electric vehicles to respond to peak-valley electricity prices is established; based on the distribution network daily load curve, the energy storage optimization configuration method is determined based on the optimal day-ahead charging and discharging economic efficiency of energy storage under peak-valley electricity prices, and the energy storage charging and discharging power curve is superimposed with the V2G power curve of large-scale electric vehicles responding to peak-valley electricity prices to obtain the distribution network day-ahead dispatching power curve; based on the day-ahead dispatching strategy, with the optimal peak shaving and valley filling effect as the objective function, the electric vehicle and energy storage V2G are optimized in a smaller time scale to obtain the distribution network's intraday dispatching power curve. This patent establishes a large-scale response strategy based on the "total number of electric vehicles in the distribution network", but does not consider the regional scheduling of vehicles (such as the differences between residential and commercial areas) and does not divide the electricity consumption characteristics of different areas, thus underestimating the complexity of scheduling.

[0004] Chinese patent CN114757507A discloses a V2G control method for electric vehicles based on dynamic regional dispatch electricity prices. Based on the results of an electric vehicle travel behavior simulation and prediction model, the method obtains the spatiotemporal distribution characteristics of electric vehicle charging and discharging demand. An electric vehicle charging demand model is constructed. A dynamic update strategy for regional dispatch electricity prices is proposed, taking into account regional grid operating status differences and electric vehicle access. An electric vehicle charging and discharging control model is constructed, taking into account the demands of multiple stakeholders, including distribution network operators, electric vehicle integrators, and electric vehicle users. A cost-benefit analysis scheme is designed for these stakeholders, and an iterative optimization approach is performed based on the economic cost analysis results to achieve optimal control of electric vehicle charging and discharging. This method's dynamic update of regional dispatch electricity prices relies on real-time feedback from the grid status and electric vehicle access. Communication delays or insufficient data processing capacity can lead to delayed price updates, weakening the control effectiveness. Furthermore, this method, based on economic dispatch using time-of-use electricity prices to participate in ancillary service markets, lacks a price incentive mechanism and explicitly sets a discharge price for EVs participating in peak-shaving ancillary services. Furthermore, it ignores the impact of EV users' travel needs on their participation in dispatch decisions.

[0005] Patent CN118651117A discloses a dynamic regulation system and method for electric vehicles participating in the power grid based on V2G technology. The dynamic regulation system includes a data acquisition module for collecting battery data and battery charging and discharging process data; a battery management module for monitoring and managing the electric vehicle battery status based on the data from the data acquisition module to optimize the battery charging and discharging process; and a dynamic response unit for dynamically adjusting the electric vehicle's charging and discharging strategy based on grid demand and electricity price changes. However, this method also ignores the impact of EV users' travel needs on their participation in scheduling decisions.

[0006] In summary, the disorderly charging of large-scale electric vehicles connected to the power grid will lead to excessive load variance in the distribution network system. Existing research cannot fully absorb photovoltaic and off-peak electricity, making it difficult to ensure the V2G benefits of electric vehicles. Summary of the Invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide an electric vehicle V2G scheduling method that takes into account the consumption of photovoltaic and off-peak power, which can reduce the grid load variance, achieve efficient utilization of photovoltaic and off-peak power, and improve the V2G benefits of electric vehicles.

[0008] The purpose of the present invention can be achieved by the following technical solution: a V2G dispatching method for electric vehicles considering the consumption of photovoltaic and off-peak electricity, comprising:

[0009] S1. In a distribution network system including photovoltaic power generation, establish an EV disorderly charging model based on user driving behavior;

[0010] Establish price incentives for peak shaving;

[0011] S2. Combining the established EV disorderly charging model and price incentives, a photovoltaic and EV collaborative power generation optimization scheduling model is constructed;

[0012] S3. Using an improved multi-objective particle swarm algorithm, we solve the optimal scheduling model for PV and EV coordinated power generation and output an EV orderly charging and discharging strategy.

[0013] S4. According to the EV orderly charging and discharging strategy, the charging and discharging behavior of the EV is controlled accordingly.

[0014] Furthermore, the EV disorderly charging model established in step S1 includes the probability density of the EV charging start time, the probability density of the EV daily mileage, the EV charging time, and the charging power of the EV cluster.

[0015] Furthermore, the charging start time t of the EV satisfies the normal distribution, and its probability density f(t) is:

[0016]

[0017] Among them, σ t is the variance of the charging start time t, μ t is the mean value at the start time t of charging;

[0018] The EV's daily mileage S approximately satisfies the log-normal distribution, and its probability density function f(S) is:

[0019]

[0020] Among them, σ s is the variance of mileage S, μ s is the mean value of mileage S;

[0021] The charging time required for an EV with a range of S is T c for:

[0022]

[0023] Among them, T c is the charging time, P is the EV charging and discharging power, η is the charging and discharging efficiency, and E is the power consumption per kilometer;

[0024] Divide a day into 24 time periods, and add the charging power of M EVs in the i-th period to obtain the charging power P of the EV cluster in the i-th period. sum,i :

[0025]

[0026] The charging power P of the EV cluster in the work area sum,i Defined as Charging power P of EV cluster in residential area sum,i Defined as

[0027] Furthermore, the process of establishing price incentives in step S1 is as follows:

[0028] Set the compensation price for V2G services and introduce the compensation coefficient C p , formulate corresponding compensation coefficient rules, and combine time-of-use electricity prices to define the charging and discharging service price of EVs participating in peak shaving and valley filling auxiliary services. The compensation coefficient is determined by the peak shaving amount and the EV user participation coefficient:

[0029] C p =C q C j

[0030]

[0031] Among them, C p is the compensation coefficient, C q is the peak-shaving and valley-filling demand coefficient, specifically the load P that needs to be reduced according to the system n To determine its size, C j is the compensation coefficient of EV user participation, ξ is the price compensation coefficient of peak demand, P l (t) is the grid load, P mean is the average daily load of the power grid, c j is the compensation coefficient of EV user participation, c jb is the actual participation of EV users, is the number of EV users participating in V2G in period t, and N is the total number of EVs;

[0032] The charging and discharging service price of the EV participating in the peak shaving and valley filling auxiliary service is R p (t):

[0033] R p (t)=R(t)×C p

[0034] Among them, R(t) is the time-of-use electricity price.

[0035] Furthermore, step S2 includes the following steps:

[0036] S21. Establish an orderly charging and discharging model for electric vehicles based on the electricity consumption characteristics of work areas and residential areas;

[0037] S22. On the power generation side, the goal is to minimize the daily load variance of the distribution network;

[0038] On the user side, the goal is to minimize the user's charging costs;

[0039] Combining electric vehicle charging and discharging constraints, charging and discharging time constraints, EV battery SOC (State of Charge) constraints and photovoltaic charging constraints, an optimal scheduling model for photovoltaic and EV collaborative power generation is constructed.

[0040] Furthermore, the specific process of step S21 is as follows:

[0041] Determine the total power consumption and power supply in the work area, and the total power consumption and power supply in the residential area;

[0042] Combined with the maximum EV driving distance, the starting SOC of the EV when it arrives at the work area is obtained through Monte Carlo, and the charging and discharging time of the EV in the work area are calculated;

[0043] Combined with the maximum EV driving distance and the SOC in the working area, the SOC of the EV when it reaches the residential area is calculated, and the charging and discharging times of the EV in the residential area are also calculated.

[0044] Furthermore, the total power consumption of the working area is:

[0045]

[0046] The power supply of the working area is:

[0047]

[0048] in, is the power consumption of the working area, is the basic load power consumption of the working area, Charging power for EVs in the work area, P Grid,1 is the mains power supply power, P pv is the photovoltaic power generation power, Feeding power to EV, Power supply to the work area;

[0049] During the day, only photovoltaic power is used to charge EV in the work area, and the basic load power consumption of the work area is The power supplied by the mains is P Grid,1 and EV feed power supply:

[0050]

[0051] The total power consumption of the residential area is:

[0052]

[0053] The power supply to the residential area is:

[0054]

[0055] in, For residential area electricity consumption, is the basic load power consumption in residential areas, Charging power for EVs in residential areas, p Grid，2 The mains power supply power, Feeding power to EV, Power supply for residential areas.

[0056] Furthermore, the starting SOC of the EV when it reaches the working area is:

[0057]

[0058] S max =Q ev / E

[0059] Among them, S max is the maximum driving distance of EV, S OC,start1 is the starting SOC when the EV arrives at the working area, Q ev is the rated capacity of the EV battery, and S is the actual EV mileage;

[0060] When the initial SOC of the EV is no more than 0.5 when it arrives at the working area, the charging time of the EV in the working area is T ch1 for:

[0061] T ch1 =(0.9-S OC )Q ev / Pη

[0062] When the initial SOC of the EV is greater than 0.5 when it arrives at the working area, the discharge time of the EV in the working area is T dis1 for:

[0063] T dis1 =(S OC -0.5)Q ev / Pη

[0064] The SOC of the EV when it arrives at the residential area is:

[0065]

[0066] When the EV's initial SOC is no more than 0.3 in a residential area, the charging time of the EV in the residential area is T ch2 for:

[0067] Tch2 =(0.9-S OC )Q ev / Pη

[0068] When the EV's initial SOC is greater than 0.3 in a residential area, the EV's discharge time in the residential area is T dis2 for:

[0069] T dis2 =(S OC -0.3)Q ev / Pη.

[0070] Furthermore, the objective function of the photovoltaic and EV collaborative power generation optimization scheduling model in step S22 includes the grid daily load variance objective function min F1 and the charging cost objective function min F2:

[0071]

[0072] Among them, P l (t) is the basic load value of the power grid at time t, P pv (t) is the photovoltaic power generation power at time t, P ev (t) is the charging and discharging power of the electric vehicle at time t, P avg (t) is the average daily load of the power grid, R(t) is the time-of-use electricity price, R c (t) and R d (t) are the charging and discharging electricity prices of EV at time t, respectively, and Δt is the charging and discharging time;

[0073] The electric vehicle charging and discharging constraints are:

[0074] P·η·T c =(0.9-S OC )·Q ev

[0075] The charge and discharge time constraints are:

[0076]

[0077] Among them, t start The time when electric vehicles start charging, t end The charging end time;

[0078] The EV battery SOC constraint is:

[0079]

[0080] The photovoltaic charging constraint is: considering the maximum absorption of photovoltaic power, photovoltaic power is used only as the power source for EV charging in the working area. Therefore, the charging power of EV in the working area must not be greater than the photovoltaic power generation power, that is,

[0081] Furthermore, the improved multi-objective particle swarm algorithm in step S3 specifically introduces the Manhattan distance in the Niche niche technology to calculate the distance between individuals. The set of the current dominant solution k has p non-dominated solutions, which is expressed as x=(x1, x2,…, x p ), we need to calculate the Manhattan distance from each solution to other solutions, and the calculation formula is:

[0082]

[0083] Among them, M ij Represents the solution x i Settlement j The Manhattan distance between t (x i ) and F t (x j ) are the function values ​​of the i-th and j-th solutions at the t-th objective respectively;

[0084] u i and u j is the weight of the i-th and j-th solutions, which is used to adjust the weight between each target so that the importance of each target can be accurately reflected when calculating the Manhattan distance;

[0085] N(k) is the Manhattan distance between all non-dominated solutions in the Niche, which is used to reflect the degree of aggregation of solutions in the Niche. In the optimization process of multi-objective particle swarm, the Manhattan distance metric is used to maintain the diversity and distribution of solutions.

[0086] The process of solving the photovoltaic and EV coordinated power generation optimization scheduling model in step S3 is as follows:

[0087] Input the electricity load, photovoltaic power generation and time-of-use electricity price of a certain area;

[0088] Initialize the SOC of the EV cluster, randomly initialize the inertia weight and particle velocity V i , calculate the fitness of the particle swarm and select P for the particle best , update the particle's velocity V i and position X i , the non-dominated sorting of the niche technology sorts the objective function and updates the particle P best , where a particle represents a solution, which includes the minimum load variance and the minimum charging cost. The particle's speed is the direction and step size of the particle's exploration in space, and the particle's position corresponds to the parameter vector of the current solution;

[0089] Store the first particle in the external archive, and start from the second particle to determine whether particle i dominates the particles in the external archive. If it dominates, mark and delete all dominated particles, and add particles to the external archive; if it is dominated, do not add particles to the external archive; if it is not dominated, delete the individual with the worst density, add particles to the external archive, update the external archive, and select G for each particle. best ;

[0090] If J>J max Then end, if J≤J max Then continue to update the particle's velocity Vi and position Xi until J>J max Then it ends, where J is the current number of iterations, J max is the maximum number of iterations.

[0091] Compared with the prior art, the present invention has the following advantages:

[0092] This paper first establishes a model for disordered EV charging based on user driving behavior within a photovoltaic power distribution network. It then proposes a dynamic peak-shaving compensation pricing incentive mechanism. Finally, with the goal of minimizing regional distribution network load variance and user charging costs, it employs an improved multi-objective particle swarm optimization algorithm to develop an optimized strategy for orderly EV charging and discharging. This strategy leverages the dual source-load nature of EVs and fully considers the influence of EV driving behavior and user discharge willingness. This effectively reduces grid load variance, achieves efficient utilization of photovoltaic and off-peak power, and increases EV V2G benefits.

[0093] This paper proposes a new price incentive mechanism for peak shaving. By setting a compensation price for V2G services and introducing a compensation coefficient, it can reflect the differences in EVs participating in V2G services at different time periods. In addition, the charging and discharging service prices for EVs participating in peak shaving and valley filling ancillary services are defined in combination with time-of-use electricity prices. Under the same dispatch capacity, the price incentive mechanism can be used to reduce user costs, effectively enhancing the profit space and enthusiasm of EV users to participate in peak shaving services.

[0094] The present invention models the electricity consumption characteristics of the work area and the residential area separately, that is, determines the total electricity consumption and supply power of the work area, and the total electricity consumption and supply power of the residential area respectively, so as to consider the regional scheduling problem of vehicles. By dividing the electricity consumption characteristics of different areas, it ensures that subsequent scheduling can be more in line with reality.

[0095] The present invention aims to minimize the load variance of the regional distribution network and user charging costs, and uses the Niche niche technology in the genetic algorithm to improve the multi-objective particle swarm algorithm to solve the optimization strategy for the orderly charging and discharging of EVs. On the one hand, during the optimization process of the multi-objective particle swarm, the Manhattan distance metric is used to maintain the diversity and distribution of solutions. On the other hand, it can better solve the load variance and user charging costs, ensure the stability of the power grid operation, and achieve the optimal coordination between the power grid and electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 Schematic diagram of the method flow of the present invention;

[0097] Figure 2 is the probability density distribution of the start time of EV disorderly charging in the embodiment;

[0098] Figure 3 is the probability density distribution of EV daily mileage in the embodiment;

[0099] Figure 4 It is a flowchart of the MOPSO (Multi-objective particle swarm) algorithm improved based on Niche technology in the present invention. DETAILED DESCRIPTION

[0100] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0101] Example

[0102] like Figure 1 As shown in FIG, a V2G dispatching method for electric vehicles that considers the consumption of photovoltaic power and off-peak power includes:

[0103] S1. In a distribution network system including photovoltaic power generation, establish an EV disorderly charging model based on user driving behavior;

[0104] Establish price incentives for peak shaving;

[0105] S2. Combining the established EV disorderly charging model and price incentives, a photovoltaic and EV collaborative power generation optimization scheduling model is constructed;

[0106] S3. Using an improved multi-objective particle swarm algorithm, we solve the optimal scheduling model for PV and EV coordinated power generation and output an EV orderly charging and discharging strategy.

[0107] S4. According to the EV orderly charging and discharging strategy, the charging and discharging behavior of the EV is controlled accordingly.

[0108] This example applies the aforementioned approach. First, a Monte Carlo method is used to establish a model for disorderly EV charging based on user driving behavior within a distribution network system with photovoltaic power generation. A dynamic peak-shaving compensation pricing incentive mechanism is then designed. Next, with the goal of minimizing regional distribution network load variance and user charging costs, a multi-objective particle swarm optimization algorithm is improved using the niche technique within the genetic algorithm to propose an optimization strategy for orderly EV charging and discharging. Finally, a simulation of photovoltaic and EV coordinated power generation is conducted with the load of a distribution network region containing photovoltaic power generation.

[0109] The specific process is:

[0110] (1) Electric vehicle disorderly charging model

[0111] The charging start time t of EV satisfies the normal distribution, and its probability density f(t) is expressed as formula (1).

[0112]

[0113] In order to make the travel pattern more consistent with the vehicle travel behavior, this embodiment modifies the parameters in formula (1). The modified variance σ t =3.5, mean μ t =18.8, the probability density distribution of EV charging start time is as follows Figure 2 shown.

[0114] The daily mileage S of an EV approximately satisfies the log-normal distribution, and its probability density function f(S) is calculated as shown in formula (2).

[0115]

[0116] In this embodiment, the variance is σ s =0.88, mean μ s =3.2, the probability distribution of EV daily mileage is as follows Figure 3 shown.

[0117] The charging time required for an EV with a range of S is T c Calculated by formula (3).

[0118]

[0119] Where, T c is the charging time; P is the EV charging and discharging power; η is the charging and discharging efficiency; E is the power consumption per kilometer.

[0120] In this embodiment, a day is divided into 24 time periods. Then, the charging power of 1000 EVs in the i-th time period is superimposed according to formula (4), and the charging power P of the EV cluster in the i-th time period is obtained. sum,i .

[0121]

[0122] Among them, the charging power P of the EV cluster in the work area sum,i Defined as Charging power P of EV cluster in residential area sum,i Defined as

[0123] (2) Establishing price incentives for peak shaving

[0124] In order to reflect the differences in EV users' participation in services at different times, this embodiment sets the compensation price for V2G services and introduces the compensation coefficient C p , formulate corresponding compensation coefficient rules. The compensation coefficient is determined by the peak shaving amount and the EV user participation coefficient, and is specifically expressed as formula (5).

[0125] C p =C q C j

[0126]

[0127] In formula (5), C p is the compensation coefficient, C q The peak-shaving demand coefficient is the load reduction P according to the system needs. n To determine its size; C j is the compensation coefficient of EV user participation; ξ is the price compensation coefficient of peak demand; P l (t) is the grid load; P mean is the average daily load of the power grid; c j is the compensation coefficient of EV user participation, c jb It indicates the actual participation of EV users; is the number of EV users participating in V2G in time period t, and N represents the total number of EVs.

[0128] In this embodiment, based on the time-of-use electricity price R(t) in a certain area, the charging and discharging service price of EV participating in the peak shaving and valley filling auxiliary service is defined as R p (t), as shown in formula (7).

[0129] R p (t)=R(t)×C p (7)

[0130] (3) Optimal scheduling model for PV and EV collaborative power generation

[0131] a. Modeling of electricity consumption characteristics in work areas and residential areas

[0132] is the total power consumption of the working area, is the basic load power consumption of the working area, is the EV charging power in the working area, as shown in formula (8).

[0133]

[0134] The mains power supply is P Grid,1 , photovoltaic power generation power is P pv and EV feed power Power supply of working area is the sum of the power supplied by the three power sources, as shown in formula (9).

[0135]

[0136] Considering that only photovoltaic power is used to charge EV in the work area during the day, the base load power consumption of the work area is The power supplied by the mains is P Grid,1 and EV feed power Provided as formula (10).

[0137]

[0138] In the working area, the charging power of EV must not be greater than the photovoltaic power generation power, as shown in formula (11).

[0139]

[0140] Combining equations (8)-(11), we can obtain equation (12).

[0141]

[0142] Electricity consumption in residential areas is the basic load power consumption of residential areas, is the EV charging power in the residential area, and the power consumption in the residential area is the basic load power consumption and EV charging power The sum of , as shown in formula (13).

[0143]

[0144] If there is no photovoltaic system in the residential area, the power supply to the residential area is from the mains and V2G feed. The mains power supply power is P Grid，2 , EV feeding power The power supply to residential areas is the sum of the mains power supply and the EV feed power, as shown in formula (14).

[0145]

[0146] b. Electric vehicle orderly charging and discharging model

[0147] EV maximum driving distance S max Calculated by formula (15), S when arriving at the working area OC,start1 Calculated by formula (16).

[0148] S max =Q ev / E (15)

[0149]

[0150] Where Q ev is the rated capacity of the EV battery, and S is the actual mileage of the EV. The time when the electric vehicle arrives at the work area is t start It still satisfies formula (1), but the modified σ s =0.66, μ s =7.97, and it can be considered that the work area ends at the scheduling time t end If equation (1) is satisfied, then the schedulable period of the work area is (t start ~t end ) interval. When the EV's initial SOC in the working area is not greater than 0.5, the EV's charging time Tch1 in the working area satisfies Equation (17). When the EV's initial SOC in the working area is greater than 0.5, the EV's discharging time Tdis1 in the working area satisfies Equation (18).

[0151] T ch1 =(0.9-S OC )Q ev / Pη (17)

[0152] T dis1 =(S OC -0.5)Q ev / Pη (18)

[0153] The starting SOC upon reaching the residential area is calculated according to Equation (19), while the charging and discharging times of the electric vehicle in the residential area are calculated by Equations (20) and (21), respectively.

[0154]

[0155] When the EV's initial SOC is no more than 0.3 in a residential area, the charging time of the EV in the residential area is T ch2 Satisfy formula (20); when the initial SOC is greater than 0.3, the discharge time of EV in residential area is T dis2 Satisfies formula (21).

[0156] T ch2 =(0.9-S OC)Q ev / Pη (20)

[0157] T dis2 =(S OC -0.3)Q ev / Pη (21)

[0158] c. Objective function

[0159] On the power generation side, the goal is to minimize the daily load variance of the distribution network; on the user side, the goal is to minimize the user's charging costs and establish an optimization model for orderly charging and discharging of EVs.

[0160] The objective function of the daily load variance of the power grid is established as minF1, as shown in formula (22).

[0161]

[0162] In formula (22), P l (t) is the basic load value of the power grid at time t, P pv (t) is the photovoltaic power generation power at time t, P ev (t) is the charging and discharging power of the electric vehicle at time t, P avg (t) is the average daily load of the power grid.

[0163] The charging cost objective function is established as minF2, as shown in Equations (23) and (24).

[0164]

[0165] In formulas (23) and (24), R(t) is the time-of-use electricity price, R c (t) and R d (t) are the charging and discharging electricity prices of EV at time t, and Δt is the charging and discharging time.

[0166] d. The constraints that need to be met by the V2G multi-objective coordinated optimization scheduling model taking into account PV consumption include:

[0167] The charging and discharging constraints of electric vehicles are as follows:

[0168] P·η·T c =(0.9-S OC )·Q ev (25)

[0169] The charge and discharge time constraints are respectively given by equation (26).

[0170]

[0171] Where, t start The time when electric vehicles start charging, t endThis is the end time of charging.

[0172] The EV battery SOC constraint is formula (27).

[0173]

[0174] Photovoltaic charging constraint: Considering the maximum absorption of photovoltaic power, photovoltaic power is only used as the power source for EV charging in the working area. Therefore, the EV charging power must satisfy formula (11).

[0175] (4) Improved multi-objective particle swarm optimization algorithm

[0176] This solution introduces Manhattan distance in Niche technology to calculate the distance between individuals. The set of the current dominant solution k has p non-dominated solutions, which can be expressed as x = (x1, x2, ..., x p ), we need to calculate the Manhattan distance from each solution to other solutions. The calculation formulas are shown in Equations (28) to (30).

[0177]

[0178] In formula (28), M ij Represents the solution x i Settlement j The Manhattan distance between t (x i ) and F t (x j ) are the function values ​​of the i-th and j-th solutions at the t-th target. In formula (29), u i and u j Is the weight of the i-th and j-th solutions, which is used to adjust the weight between each target so that the importance of each target can be accurately reflected when calculating the Manhattan distance in formula (30). In formula (30), N(k) calculates the Manhattan distance between all non-dominated solutions in the Niche, reflecting the degree of aggregation of solutions in the Niche. In the optimization process of the multi-objective particle swarm MOPSO, the Manhattan distance metric is used to maintain the diversity and distribution of solutions. The improved MOPSO algorithm process based on Niche technology is as follows: Figure 4 As shown, Figure 4 Where J is the current iteration number, J max To maximize the number of iterations, we first input the electricity load, photovoltaic power generation, and time-of-use electricity price of a certain region;

[0179] Then initialize the SOC of the electric vehicle cluster, randomly initialize the inertia weight and particle velocity V i , calculate the fitness of the particle swarm and select P for the particle best , update the particle's velocity V i and position X i, the non-dominated sorting of the niche technology sorts the objective function and updates the particle P best , store the first particle in the external archive, and start from the second particle to determine whether particle i dominates the particles in the external archive. If it dominates, mark and delete all dominated particles and add the particle to the external archive; if it is dominated, do not add the particle to the external archive; if it is not dominated, delete the individual with the worst density and add the particle to the external archive. Update the external archive and select G for each particle best If J>J max Then end, if J≤J max Then continue to update the particle's velocity Vi and position Xi until J>J max Then it ends.

[0180] To verify the effectiveness of this solution, this embodiment uses the electricity load in a certain area in summer as basic data, and uses one day's electricity load and photovoltaic power generation load as examples for simulation verification.

[0181] Assume that there are 1,000 identical EVs in the area, with the EV charging and discharging parameters shown in Table 1. The multi-objective particle swarm algorithm population size is set to 100, the number of iterations is 200, the initial learning factors c1 and c2 are 1 and 2, respectively, and the maximum inertia weight is 0.9 and the minimum is 0.4. Table 2 shows that the electricity price structure in this area is time-of-use. The photovoltaic charging price in the work area is 0.25 yuan / kW·h.

[0182] Table 1 EV charging and discharging parameters

[0183] index Numerical Battery rated capacity 40kW·h Power consumption per kilometer 0.15kW·h / km Charge / discharge efficiency 0.9 Charge / discharge power 5kW

[0184] Table 2 Time-of-use electricity price list in a certain area

[0185] Time Electricity price in different working areas (yuan / kWh) Residential area time-of-use electricity price (yuan / kWh) 00:00～6:00 0.348 0.307 6:00～8:00 0.757 0.617 8:00～12:00 1.301 0.617 12:00～14:00 1.607 0.617 14:00～15:00 1.301 0.617 15:00～18:00 0.757 0.617 18:00～21:00 1.301 0.617 21:00～22:00 0.757 0.617 22:00～24:00 0.348 0.307

[0186] Table 3 shows the impact of disordered EV charging at different charging participation levels on grid load and user charging costs. This shows that as EV charging participation increases, disordered EV charging leads to an increase in grid load variance, but has little impact on user charging costs.

[0187] Table 3

[0188] Charging participation rate (%) 0 20 50 80 100 <![CDATA[Peak load (10 4 kW)]]> 1.18 1.21 1.26 1.31 1.34 <![CDATA[Load variance (10 6 kW 2 )]]> 2.39 2.69 3.19 3.75 4.20 User charging cost (yuan / (vehicle / day)) 0 9.76 9.77 9.79 9.83

[0189] This embodiment uses the peak-valley time-of-use electricity price mechanism and the price incentive mechanism in Table 2 to guide orderly V2G charging and discharging. The SOC value of the EV arriving at the work area is calculated using formulas (15) and (16) to determine the number of EVs that can participate in V2G discharge.

[0190] Table 4 shows the number of EVs participating in discharge in work areas and residential areas under different dispatch capacities when the EV charging participation rate is 100%.

[0191] Table 4

[0192]

[0193] Table 5 shows the impact of V2G dispatching capacity in the work area on grid power load and user charging costs when the charging participation rate is 100%.

[0194] Table 5

[0195]

[0196] The data in Table 5 show that there is an optimal solution between the V2G dispatching capacity and the photovoltaic absorption rate or the grid load variance. The application of V2G can effectively reduce the user's charging costs and even generate profits from it.

[0197] Under the condition of the same dispatching capacity, the load variance and load peak after Niche optimization are reduced, and the user charging cost is also reduced or the benefit is increased, indicating that the multi-objective particle swarm algorithm improved by Niche can better solve the load variance and user charging cost.

[0198] As the dispatch capacity increases from 0% to 100%, the PV consumption rate decreases from 41.20% to 6.33%, and the load peak changes slightly with the increase of dispatch capacity. As the EV dispatch capacity increases from 0% to 30%, the load variance gradually decreases and reaches a minimum of 1.29×10 6 kW 2 ,As the dispatch capacity increases from 30% to 100%, the load variance gradually increases.,Reasonable V2G calling is beneficial to the stability and,economy of the grid operation.

Claims

1. A V2G dispatching method for electric vehicles considering the consumption of photovoltaic and off-peak electricity, characterized in that: include: S1. In a distribution network system including photovoltaic power generation, establish an EV disorderly charging model based on user driving behavior; Establish price incentives for peak shaving; S2. Combining the established EV disorderly charging model and price incentives, a photovoltaic and EV collaborative power generation optimization scheduling model is constructed; S3. Using an improved multi-objective particle swarm algorithm, we solve the optimal scheduling model for PV and EV coordinated power generation and output an EV orderly charging and discharging strategy. S4. According to the EV orderly charging and discharging strategy, the charging and discharging behavior of the EV is controlled accordingly.

2. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 1 is characterized in that: The EV disorderly charging model established in step S1 includes the probability density of the EV charging start time, the probability density of the EV daily mileage, the EV charging time, and the charging power of the EV cluster; The charging start time t of the EV satisfies the normal distribution, and its probability density f(t) is: Among them, σ t is the variance of the charging start time t, μ t is the mean value at the charging start time t; The EV's daily mileage S approximately satisfies the log-normal distribution, and its probability density function f(S) is: Among them, σ s is the variance of mileage S, μ s is the mean value of mileage S; The charging time required for an EV with a range of S is T c for: Among them, T c is the charging time, P is the EV charging and discharging power, η is the charging and discharging efficiency, and E is the power consumption per kilometer; Divide a day into 24 time periods, and add the charging power of M EVs in the i-th period to obtain the charging power P of the EV cluster in the i-th period. sum,i : The charging power P of the EV cluster in the work area sum,i Defined as Charging power P of EV cluster in residential area sum,i Defined as 3. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 1 is characterized in that: The process of establishing price incentives in step S1 is as follows: Set the compensation price for V2G services and introduce the compensation coefficient C p , formulate corresponding compensation coefficient rules, and combine time-of-use electricity prices to define the charging and discharging service price of EVs participating in peak shaving and valley filling auxiliary services. The compensation coefficient is determined by the peak shaving amount and the EV user participation coefficient: C p =C q C j Among them, C p is the compensation coefficient, C q is the peak-shaving and valley-filling demand coefficient, specifically the load P that needs to be reduced according to the system n To determine its size, C j is the compensation coefficient of EV user participation, ξ is the price compensation coefficient of peak demand, P l (t) is the grid load, P mean is the average daily load of the power grid, c j is the compensation coefficient of EV user participation, c jb is the actual participation of EV users, is the number of EV users participating in V2G in period t, and N is the total number of EVs; The charging and discharging service price of the EV participating in the peak shaving and valley filling auxiliary service is R p (t): R p (t)=R(t)×C p Among them, R(t) is the time-of-use electricity price.

4. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 3 is characterized in that: The step S2 comprises the following steps: S21. Establish an orderly charging and discharging model for electric vehicles based on the electricity consumption characteristics of work areas and residential areas; S22. On the power generation side, the goal is to minimize the daily load variance of the distribution network; On the user side, the goal is to minimize the user's charging costs; Combining electric vehicle charging and discharging constraints, charging and discharging time constraints, EV battery SOC constraints and photovoltaic charging constraints, an optimal scheduling model for photovoltaic and EV collaborative power generation is constructed.

5. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 4 is characterized in that: The specific process of step S21 is as follows: Determine the total power consumption and power supply in the work area, and the total power consumption and power supply in the residential area; Combined with the maximum EV driving distance, the starting SOC of the EV when it arrives at the work area is obtained through Monte Carlo, and the charging and discharging time of the EV in the work area are calculated; Combined with the maximum EV driving distance and the SOC in the working area, the SOC of the EV when it reaches the residential area is calculated, and the charging and discharging times of the EV in the residential area are also calculated.

6. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 5 is characterized in that: The total power consumption of the working area is: The power supply of the working area is: in, is the electrical power consumed in the work area, is the basic load power consumption of the working area, Charging power for EVs in the work area, P Grid,1 is the mains power supply power, P pv is the photovoltaic power generation power, Feeding power to EV, Power supply to the work area; During the day, only photovoltaic power is used to charge EV in the work area, and the basic load power consumption of the work area is The power supplied by the mains is P Grid,1 and EV feed power supply: The total power consumption of the residential area is: The power supply to the residential area is: in, For residential area electricity consumption, is the basic load power consumption of residential areas, Charging power for EVs in residential areas, P Grid，2 The mains power supply power, Feeding power to EV, Power supply for residential areas.

7. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 6 is characterized in that: The starting SOC of the EV when it reaches the working area is: S max =Q ev / E Among them, S max is the maximum driving distance of EV, S OC,start1 is the starting SOC when the EV arrives at the working area, Q ev is the rated capacity of the EV battery, and S is the actual EV mileage; When the initial SOC of the EV is no more than 0.5 when it arrives at the working area, the charging time of the EV in the working area is T ch1 for: T ch1 =(0.9-S OC )Q ev / Pη When the initial SOC of the EV is greater than 0.5 when it arrives at the working area, the discharge time of the EV in the working area is T dis1 for: T dis1 =(S OC -0.5)Q ev / Pη The SOC of the EV when it arrives at the residential area is: When the EV's initial SOC is no more than 0.3 in a residential area, the charging time of the EV in the residential area is T ch2 for: T ch2 =(0.9-S OC )Q ev / Pη When the EV's initial SOC is greater than 0.3 in a residential area, the EV's discharge time in the residential area is T dis2 for: T dis2 =(S OC -0.3)Q ev / Pη。 8. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 7 is characterized in that: The objective functions of the photovoltaic and EV collaborative power generation optimization scheduling model in step S22 include the grid daily load variance objective function min F1 and the charging cost objective function min F2: Among them, P l (t) is the basic load value of the power grid at time t, P pv (t) is the photovoltaic power generation power at time t, P ev (t) is the charging and discharging power of the electric vehicle at time t, P avg (t) is the average daily load of the power grid, R(t) is the time-of-use electricity price, R c (t) and R d (t) are the charging and discharging electricity prices of EV at time t, respectively, and Δt is the charging and discharging time; The electric vehicle charging and discharging constraints are: P·η·T c =(0.9-·Q OC )·Q ev The charge and discharge time constraints are: Among them, t start The time when electric vehicles start charging, t end The charging end time; The EV battery SOC constraint is: The photovoltaic charging constraint is: considering the maximum absorption of photovoltaic power, photovoltaic power is used only as the power source for EV charging in the working area. Therefore, the charging power of EV in the working area must not be greater than the photovoltaic power generation power, that is, 9. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 8 is characterized in that: The improved multi-objective particle swarm algorithm in step S3 specifically introduces the Manhattan distance in the Niche niche technology to calculate the distance between individuals. The set of the current dominant solution k has p non-dominated solutions, which is expressed as x=(x1, x2, ..., x p ), we need to calculate the Manhattan distance from each solution to other solutions, and the calculation formula is: Among them, M ij Represents the solution x i Reconciliation j The Manhattan distance between t (x i ) and F t (x j ) are the function values ​​of the i-th and j-th solutions at the t-th objective respectively; u i and u j is the weight of the i-th and j-th solutions, which is used to adjust the weight between each target so that the importance of each target can be accurately reflected when calculating the Manhattan distance; N(k) is the Manhattan distance between all non-dominated solutions in the Niche, which is used to reflect the degree of aggregation of solutions in the Niche. In the optimization process of multi-objective particle swarm, the Manhattan distance metric is used to maintain the diversity and distribution of solutions.

10. The electric vehicle V2G scheduling method considering the consumption of photovoltaic and off-peak electricity according to claim 9 is characterized in that: The process of solving the photovoltaic and EV coordinated power generation optimization scheduling model in step S3 is as follows: Input the electricity load, photovoltaic power generation and time-of-use electricity price of a certain area; Initialize the SOC of the EV cluster, randomly initialize the inertia weight and particle velocity V i , calculate the fitness of the particle swarm and select P for the particle best , update the particle's velocity V i and position X i , the non-dominated sorting of the niche technology sorts the objective function and updates the particle P best , where a particle represents a solution, which includes the minimum load variance and the minimum charging cost. The particle's speed is the direction and step size of the particle's exploration in space, and the particle's position corresponds to the parameter vector of the current solution; Store the first particle in the external archive, and start from the second particle to determine whether particle i dominates the particles in the external archive. If it dominates, mark and delete all dominated particles, and add particles to the external archive; if it is dominated, do not add particles to the external archive; if it is not dominated, delete the individual with the worst density, add particles to the external archive, update the external archive, and select G for each particle. best ; If J>J max Then end, if J≤J max Then continue to update the particle's velocity Vi and position Xi until J>J max Then it ends, where J is the current number of iterations, J max is the maximum number of iterations.

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