Ordered charging scheduling method for electric vehicles based on improved Kepler algorithm

Abstract

The invention discloses an electric vehicle ordered charging scheduling method based on an improved Kepler algorithm. The electric vehicle ordered charging scheduling method comprises the following steps: step 1, establishing a Prophet model to predict photovoltaic power generation power in a future time period; 2, an objective function is constructed, battery loss cost, scheduling cost of an agent and power grid load variance minimization serve as the objective function, and the scheduling cost of the agent is calculated according to the photovoltaic power generation power in the future period obtained by the step 1; 3, constraint conditions are set, wherein the charging and discharging power, the state of charge, the charging amount and the actual Transform capacity of the electric vehicle are constrained; and Step 4, defining the objective function and constraint conditions on the basis of an improved algorithm, and solving the scheduling problem of ordered charging of the electric vehicle. The peak load shifting effect of the dispatching model on the electrical load is achieved, and the convergence speed and convergence precision of algorithm solving are improved.

Description

Technical Field

[0001] The invention relates to the technical field of power systems, and in particular to an orderly charging scheduling strategy for electric vehicles based on an improved Kepler algorithm. Background Art

[0002] As more and more countries advocate the development of new energy technologies to protect the environment, in the automotive industry, electric vehicles continue to increase in annual sales due to their huge clean and environmental benefits. Currently, there are more than 1.2 billion electric vehicles on the planet. It is expected that by 2035, this number will increase to a staggering 2 billion. The gradual increase in the per capita ownership of electric vehicles means more charging needs. If electric vehicles are charged in an unordered manner, it will have a huge impact on the balance of supply and demand in the power grid, such as reduced power quality, increased peak load, excessive harmonic injection, and power loss.

[0003] The patent with publication number CN117507867A discloses a method for orderly charging of community electric vehicles. The method constructs a two-layer multi-objective optimization model with the community load peak-to-valley difference and user charging cost minimized, and uses the rat swarm optimization algorithm to solve the optimization model to obtain the optimal charging plan; the patent with publication number CN116127828A discloses an electric vehicle charging method, device and related equipment based on time-of-use electricity prices. The patent takes the minimization of the peak-to-valley difference of the total power grid load as the objective function, and solves the electric vehicle orderly charging model through the artificial bee colony algorithm.

[0004] The above inventions do not consider the real-time changes in the battery capacity of electric vehicles, and do not consider the interests of electric vehicles, charging pile agents, and power grids to constrain the interests of the three parties in constructing the objective function. In addition, in the prior art, the solution algorithm also has problems such as slow convergence speed and insufficient convergence accuracy. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problems existing in the existing orderly charging scheduling technology of electric vehicles, such as ignoring the real-time changes of the battery capacity of electric vehicles, slow convergence speed of scheduling model solution, insufficient convergence accuracy, resulting in scheduling efficiency and accuracy unable to meet the development needs of electric vehicles, and to propose an orderly charging scheduling method for electric vehicles based on an improved Kepler algorithm.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] An orderly charging scheduling method for electric vehicles based on an improved Kepler algorithm comprises the following steps:

[0008] Step 1: Establish a Prophet model to predict photovoltaic power generation in the future;

[0009] Step 2: Construct the objective function: the objective function is to minimize the battery loss cost, the dispatching cost of the agent, and the grid load variance; among them, the dispatching cost of the agent is calculated based on the photovoltaic power generation power in the future period obtained by Step 1;

[0010] Step 3: Set constraints: constrain the charging and discharging power, state of charge, charging capacity, and actual capacity of the Transformer.

[0011] Step 4: Optimize and process the above objective function and constraints based on the chaos mapping-lateral crossing-Lévy flight-Kepler algorithm to solve the scheduling problem of orderly charging of electric vehicles.

[0012] In Step 1, the Prophet model consists of the following four functions:

[0013]

[0014] In the formula, represents the photovoltaic power generation power predicted in the future i-th time period, g (i) Represents the overall growth trend of photovoltaic power generation, which is a trend function, s (i) Indicates a periodic trend, for example, a periodic function, h (i) represents the impact of non-fixed photovoltaic power generation failure on the prediction results, which is the influence function, ε (i) It indicates abnormal fluctuations that cannot be predicted by the prediction model and is a random error function.

[0015] The battery loss cost minimization calculation in Step 2 includes:

[0016] First, one hour is a time period, so there are 24 time periods in a day; starting from 0:00 in a day and ending at 24:00, the corresponding time period is recorded as i = 1, 2, ..., 24; within a day, the electric vehicles are numbered as j = 1, 2, ..., m in the order of the time they are connected to the power grid;

[0017] Next, introduce the 0-1 variable n ij , which represents the alternating state of charging and discharging of electric vehicles:

[0018]

[0019] Where P i,j is the charging and discharging power of the jth electric vehicle in the i-th time period; when P i,j >0, it means the jth electric car is in charging state; when P i,j <0, it means the electric vehicle is in a discharging state; when P i,j=0, it means that the electric vehicle is neither charging nor discharging;

[0020] Then, the Gauss-Newton iteration method was used to perform nonlinear fitting on the relationship between the number of charge and discharge alternations and the remaining capacity of various batteries. The fitting curve is g j (n); furthermore, the sum of the battery loss costs of each electric vehicle h1 is minimized as follows:

[0021]

[0022] In the formula, is the cumulative number of charge and discharge alternations when the jth electric vehicle is connected to the grid, c j is the battery cost of the jth electric car, n jmax is the maximum number of charge and discharge cycles that the jth electric vehicle battery can withstand.

[0023] The scheduling cost minimization calculation in Step 2 includes:

[0024] The dispatch cost is composed of the electricity purchase cost paid by the charging pile agent to the grid and the discharge cost paid by the charging pile agent to the electric vehicle;

[0025] 1) Calculation of electricity purchase cost

[0026] Introduce the concept of energy storage capacity to measure the real-time storage energy of the charging pile;

[0027] 0≤C i ≤C max ,i=1,2,…,24

[0028] In the formula, C i is the energy stored in the charging pile in the i-th time period, C max Energy storage capacity for charging piles;

[0029] If the sum of the photovoltaic power generation energy, the energy obtained from the power grid and the energy storage of the charging pile in the previous period is greater than the charging demand of the electric vehicle in this period, the excess energy will be stored in the next period. Otherwise, there will be no energy storage in this period, that is:

[0030]

[0031] In the formula, T is the time of each period, that is, T = 1h, is the photovoltaic power generation power in the i-th time period, and is the power obtained by the charging pile from the power grid in the i-th time period. When , it means that the charging pile obtains power from the power grid; when When , it means that the grid obtains power from the charging pile;

[0032] In the i-th time period, the electricity purchase cost paid by the charging pile agent to the grid is price i ,Right now:

[0033]

[0034] In the formula, is the price of electricity purchased by the charging pile agent from the power grid in the i-th time period, The price of electricity sold by the charging pile agent to the grid in the i-th time period;

[0035] Therefore, the electricity purchase cost paid by the charging pile agent to the power grid in one day is h 2,1 It can be expressed as:

[0036]

[0037] 2) Calculation of discharge cost

[0038] When p i <0, it means that the electric vehicle is in a discharging state, so the discharging cost of each model of electric vehicle within one day is h 2,2 It can be expressed as:

[0039]

[0040] In the formula, p i,j is the charging and discharging power of the j-th electric vehicle in the i-th time period, is the discharge compensation price of electric vehicles in the i-th time period;

[0041] Therefore, the scheduling cost h2 is minimized as:

[0042] minh2=h 2,1 +h 2,2 .

[0043] The grid load variance minimization calculation in Step 2 includes:

[0044] The average value μ of the grid load is expressed as:

[0045]

[0046] In the formula, is the power obtained by the charging pile from the power grid in the i-th period, n is the number of periods in a day, that is, n = 24;

[0047] Then, the power load variance is calculated; the smaller the variance, the smaller the grid load change, the more stable the load, and the higher the grid stability; the minimization of the grid load variance h3 is expressed as:

[0048]

[0049] In Step 3, the charging and discharging power are constrained as follows:

[0050] Whether the electric vehicle is in the charging or discharging state, its power should be less than or equal to the maximum power. When it is not connected to the power grid, the power is 0, that is:

[0051]

[0052] Where P i,j is the charging and discharging power of the jth electric vehicle in the i-th time period, P jmax is the maximum charging and discharging power of the jth electric vehicle, is the time when the jth electric vehicle is connected to the grid, The user of the jth electric vehicle inputs the time when the charging pile leaves the grid;

[0053] In Step 3, the state of charge is constrained as follows:

[0054] To prevent rapid battery degradation, the residual power of an electric vehicle after discharge should not be less than 10% of the rated capacity, and the residual power of an electric vehicle after charging should not exceed 95% of the rated capacity; that is:

[0055]

[0056] In the formula, SOC i,j is the state of charge of the j-th electric vehicle after the i-th time period, is the state of charge of the jth electric vehicle when it is connected to the grid, B j The battery capacity of the jth electric vehicle, η is the charging and discharging efficiency;

[0057] In Step 3, the charge amount is constrained as follows:

[0058] From the time an electric vehicle is connected to the grid until it leaves the grid, the amount of electricity supplied by the charging pile to the electric vehicle should meet the user's established needs. Therefore:

[0059]

[0060] In the formula, The expected state of charge of the charging pile when leaving the grid is input for the jth electric vehicle user, g j (n) is a nonlinear fitting function of the number of charge and discharge alternations of the j-th electric vehicle and the remaining capacity of various batteries;

[0061] In Step 3, the actual capacity of Transformer is constrained as follows:

[0062] The total power of the entire dispatching system in any time period should not exceed the distribution capacity of the area, so:

[0063]

[0064] In the formula, is the basic load of the area where the electric vehicles connected to the grid are located in the i-th time period, S max is the distribution Transformer capacity of the area, is the power factor.

[0065] The specific steps of solving the scheduling model by the chaos mapping-lateral crossing-Lévy flight-Kepler algorithm in Step 4 are as follows:

[0066] 1) Use the fourth-order Chebyshev chaotic map to construct the original solar system with M planets and N dimensions, and initialize the orbital eccentricity, revolution period and position of the planets; position X r,s The initialization formula is as follows:

[0067] chaos(r+1)=cos(4×cos -1 (chaos(r)))、

[0068]

[0069] In the formula, r is the number of planets, one planet represents a set of candidate solutions, and s is the decision variable X in the optimization model. r,s There are 24×m decision variables in this optimization model. i,j , 24 and λ1, λ2, λ3, chaos(r) is the chaos parameter of the rth planet, The decision variables X r,s The upper and lower limits of

[0070] 2) Calculate the fitness function value of each planet, and fit the planet with the minimum fitness value best Planet X as the Sun in Galaxy best , and calculate the mass of all celestial bodies in the galaxy and the gravitational force between the sun and other planets;

[0071] 3) Using the horizontal cross strategy, each of the M-1 planets orbiting the sun generates a new satellite to strengthen the competition between celestial bodies. The specific formula is as follows:

[0072]

[0073] In the formula, t is the number of iterations, rand is a random number between 0 and 1, c is a random number between -1 and 1, and X a (t) and X b(t) are two random planet positions, Sate a (t), Sate b (t) are respectively X a (t) and X b (t) satellite (candidate solution); whether the satellite can replace the planet depends on their respective fitness values. Only when the best fitness value is better than the planet can it be retained in the subsequent position update operation. The specific formula is as follows:

[0074]

[0075] 4) Calculate the speed of the planet's revolution around the sun and the normalized Euclidean distance R between the planet and the sun r (t), and update the position of the planet using the following formula:

[0076] When R r (t)<0.5:

[0077]

[0078] When R r (t)≥0.5:

[0079]

[0080] In the formula, is the position of the rth planet, is the position of the sun, and are the positions of two random planets; h is the adaptive factor that controls the distance between the sun and the current planet in the current iteration number t, is a 0-1 operator, η is a ±1 operator, The revolution speed of the rth planet, F gr (t) the gravitational force between the rth planet and the sun, rand3 is a normally distributed random number;

[0081] 5) Using the Levy flight strategy, the planets closer to the sun are searched locally near the sun by small-step jumps, and the planets farther from the sun are searched in unknown areas of space by large-step jumps; the specific formula is as follows:

[0082]

[0083] Where γ is the transmission parameter, χ is the search step, u and v are random operators following the normal distribution, and β∈(0,2) is the parameter that controls the shape of the distribution;

[0084] 6) Determine whether the same result is obtained in consecutive iterations. If yes, exit the iteration loop; if not, update and archive the new non-inferior solution and continue iterating.

[0085] A chaotic mapping-lateral crossing-Levi's flight-Kepler algorithm is used to solve a scheduling model; the method comprises the following steps:

[0086] 1) Use the fourth-order Chebyshev chaotic map to construct the original solar system with M planets and N dimensions, and initialize the orbital eccentricity, revolution period and position X of the planets. r,s ;

[0087] 2) Calculate the fitness function value of each planet, and fit the planet with the minimum fitness value best Planet X as the Sun in Galaxy best , and calculate the mass of all celestial bodies in the galaxy and the gravitational force between the sun and other planets;

[0088] 3) Using the horizontal crossing strategy, each of the M-1 planets orbiting the sun generates a new satellite to strengthen the competition between celestial bodies;

[0089] 4) Calculate the speed of the planet's revolution around the sun and the normalized Euclidean distance R between the planet and the sun r (t), and update the position of the planet;

[0090] 5) Using the Levy flight strategy, the planets closer to the sun are searched locally near the sun by small jumps, while the planets farther from the sun are searched in unknown areas of space by large jumps;

[0091] 6) Determine whether the same result is obtained in consecutive iterations. If yes, exit the iteration loop; if not, update and archive the new non-inferior solution and continue iterating.

[0092] In step 1), position X r,s The initialization formula is as follows:

[0093] chaos(r+1)=cos(4×cos -1 (chaos(r)))、

[0094]

[0095] In the formula, r is the number of planets, one planet represents a set of candidate solutions, s is the number of decision variables in the optimization model, and there are 24×m decision variables in the optimization model. i,j , 24 and λ1, λ2, λ3, chaos(r) is the chaos parameter of the rth planet, The decision variables X r,s The upper and lower limits of

[0096] In step 3), the specific formula used is as follows:

[0097]

[0098] In the formula, t is the number of iterations, rand is a random number between 0 and 1, c is a random number between -1 and 1, and X a (t) and X b (t) are two random planet positions, Sate a (t), Sate b (t) are respectively X a (t) and X b (t) satellite (candidate solution); whether the satellite can replace the planet depends on their respective fitness values. Only when the best fitness value is better than the planet can it be retained in the subsequent position update operation. The specific formula is as follows:

[0099]

[0100] In step 4), the position of the planet is updated using the following formula:

[0101] When R r (t)<0.5:

[0102]

[0103] When R r (t)≥0.5:

[0104]

[0105] In the formula, is the position of the rth planet, is the position of the sun, and are the positions of two random planets; h is the adaptive factor that controls the distance between the sun and the current planet in the current iteration number t, is a 0-1 operator, η is a ±1 operator, The revolution speed of the rth planet, F gr (t) the gravitational force between the rth planet and the sun, rand3 is a normally distributed random number;

[0106] In step 5), the specific formula is as follows:

[0107]

[0108]

[0109] Where γ is the transmission parameter, χ is the search step, u and v are random operators following the normal distribution, and β∈(0,2) is a parameter that controls the shape of the distribution.

[0110] Compared with the prior art, the present invention has the following technical effects:

[0111] 1) The present invention develops a scheduling strategy for orderly charging of electric vehicles by energy storage charging piles to reduce the adverse effects of electric vehicles connected to the grid;

[0112] 2) The present invention constructs a model and structure of a dispatching system that is safe, reliable and sustainable from the perspectives of users, charging pile agents and power grid companies;

[0113] 3) Based on the original Kepler algorithm, the present invention combines chaotic mapping, lateral crossing and Levy flight strategy to propose an improved Kepler optimization algorithm to overcome its shortcomings such as imbalance between exploration and development, slow convergence speed and insufficient convergence accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0115] Figure 1 It is a structural diagram of the scheduling system of the present invention;

[0116] Figure 2 The flowchart of the improved Kepler algorithm of the present invention is as follows;

[0117] Figure 3 A comparison diagram of power grid load under the dispatching strategy of the present invention and under the disordered dispatching strategy;

[0118] Figure 4 This is a comparison chart of the optimization iterations of the improved Kepler algorithm of the present invention and the classic Kepler algorithm;

[0119] Figure 5 This is a trend chart of battery loss cost changes in the present invention. DETAILED DESCRIPTION

[0120] Figure 1 As shown, this paper provides an orderly charging scheduling method for electric vehicles based on an improved Kepler algorithm, comprising the following steps:

[0121] Step 1: Establish a Prophet model to predict photovoltaic power generation in the future;

[0122] Step 2: Construct the objective function: the objective function is to minimize the battery loss cost, the dispatching cost of the agent, and the grid load variance; among them, the dispatching cost of the agent is calculated based on the photovoltaic power generation power in the future period obtained by Step 1;

[0123] Step 3: Set constraints: constrain the charging and discharging power, state of charge, charging capacity, and actual capacity of the Transformer.

[0124] Step 4: Optimize and process the above objective function and constraints based on the chaos mapping-lateral crossing-Lévy flight-Kepler algorithm to solve the scheduling problem of orderly charging of electric vehicles.

[0125] The Prophet model in Step 1 is a decomposable time series model, which consists of the following four functions:

[0126]

[0127] In the formula, represents the photovoltaic power generation power predicted in the future i-th time period, g (i) Represents the overall growth trend of photovoltaic power generation, which is a trend function, s (i) Indicates a periodic trend, for example, a periodic function, h (i) represents the impact of non-fixed photovoltaic power generation failure on the prediction results, which is the influence function, ε (i) It indicates abnormal fluctuations that cannot be predicted by the prediction model and is a random error function.

[0128] The battery loss cost minimization calculation in Step 2 includes:

[0129] First, one hour is a time period, so there are 24 time periods in a day. Starting from 0:00 in a day and ending at 24:00, the corresponding time period is recorded as i = 1, 2, ..., 24. Within a day, the electric vehicles are numbered as j = 1, 2, ..., m in the order of the time they are connected to the power grid.

[0130] Next, introduce the 0-1 variable n ij , which indicates the alternating state of charging and discharging of electric vehicles:

[0131]

[0132] Where P i,j is the charging and discharging power of the jth electric vehicle in the i-th time period. i,j >0, it means the jth electric car is in charging state; when P i,j <0, it means the electric vehicle is in a discharging state; when P i,j When =0, it means that the electric vehicle is neither charging nor discharging.

[0133] Then, the Gauss-Newton iteration method was used to perform nonlinear fitting on the relationship between the number of charge and discharge alternations and the remaining capacity of various batteries. The fitting curve is g j(n). Then, the sum of the battery loss costs of each electric vehicle h1 is minimized as:

[0134]

[0135] In the formula, is the cumulative number of charge and discharge alternations when the jth electric vehicle is connected to the grid, c j is the battery cost of the jth electric car, n jmax is the maximum number of charge and discharge cycles that the jth electric vehicle battery can withstand.

[0136] The scheduling cost minimization calculation in Step 2 includes:

[0137] The dispatch cost consists of the electricity purchase cost paid by the charging pile agent to the grid and the discharge cost paid by the charging pile agent to the electric vehicle.

[0138] 1) Calculation of electricity purchase cost

[0139] The concept of energy storage capacity is introduced to measure the real-time storage energy of the charging pile.

[0140] 0≤C i ≤C max ,i=1,2,…,24

[0141] In the formula, C i is the energy stored in the charging pile in the i-th time period, C max It is the energy storage capacity of the charging pile.

[0142] If the sum of the photovoltaic power generation energy, the energy obtained from the power grid and the energy storage of the charging pile in the previous period is greater than the charging demand of the electric vehicle in this period, the excess energy will be stored in the next period. Otherwise, there will be no energy storage in this period, that is:

[0143]

[0144] In the formula, T is the time of each period, that is, T = 1h, is the photovoltaic power generation power in the i-th time period, and is the power obtained by the charging pile from the power grid in the i-th time period. When , it means that the charging pile obtains power from the power grid; when When , it means that the grid obtains power from the charging pile.

[0145] In the i-th time period, the electricity purchase cost paid by the charging pile agent to the grid is price i ,Right now:

[0146]

[0147] In the formula, is the price of electricity purchased by the charging pile agent from the power grid in the i-th time period, The price of electricity sold by the charging pile agent to the grid in the i-th time period.

[0148] Therefore, the electricity purchase cost paid by the charging pile agent to the power grid in one day is h 2,1 It can be expressed as:

[0149]

[0150] 2) Calculation of discharge cost

[0151] When p i <0, it means that the electric vehicle is in a discharging state, so the discharging cost of each model of electric vehicle within one day is h 2,2 It can be expressed as:

[0152]

[0153] In the formula, p i,j is the charging and discharging power of the j-th electric vehicle in the i-th time period, is the discharge compensation price of the electric vehicle in the i-th time period.

[0154] Therefore, the scheduling cost h2 is minimized as:

[0155] minh2=h 2,1 +h 2,2

[0156] The grid load variance minimization calculation in Step 2 includes:

[0157] The average value μ of the grid load is expressed as:

[0158]

[0159] In the formula, is the power obtained by the charging pile from the power grid in the i-th period, and n is the number of periods in a day, that is, n=24.

[0160] Then, the power load variance is calculated. The smaller the variance, the smaller the grid load change, the more stable the load, and the higher the grid stability. The grid load variance h3 is minimized as:

[0161]

[0162] Since the optimization objectives of the three objective functions are all minimized, this paper introduces a positive number λ before each objective function and then sums them up, transforming the multi-objective function into a single objective function:

[0163] minh=λ1h1+λ2h2+λ3h3

[0164]

[0165] In Step 3, the charging and discharging power are constrained as follows:

[0166] Whether the electric vehicle is in the charging or discharging state, its power should be less than or equal to the maximum power. When it is not connected to the power grid, the power is 0, that is:

[0167]

[0168] Where P i,j is the charging and discharging power of the jth electric vehicle in the i-th time period, P jmax is the maximum charging and discharging power of the jth electric vehicle, is the time when the jth electric vehicle is connected to the grid, The user of the jth electric vehicle inputs the time when the charging pile leaves the grid.

[0169] In Step 3, the state of charge is constrained as follows:

[0170] To prevent rapid battery degradation, the residual power of an electric vehicle after discharge should not be less than 10% of the rated capacity, and the residual power of an electric vehicle after charging should not exceed 95% of the rated capacity. That is:

[0171]

[0172] In the formula, SOC i,j is the state of charge of the j-th electric vehicle after the i-th time period, is the state of charge of the jth electric vehicle when it is connected to the grid, B j The battery capacity of the jth electric car, η is the charging and discharging efficiency.

[0173] In Step 3, the charge amount is constrained as follows:

[0174] From the time an electric vehicle is connected to the grid until it leaves the grid, the amount of electricity supplied by the charging pile to the electric vehicle should meet the user's established needs. Therefore:

[0175]

[0176] In the formula, The expected state of charge of the charging pile when leaving the grid is input for the jth electric vehicle user, g j (n) is a nonlinear fitting function of the number of charge and discharge alternations of the j-th electric vehicle and the remaining capacity of various batteries.

[0177] In Step 3, the actual capacity of Transformer is constrained as follows:

[0178] The total power of the entire dispatching system in any time period should not exceed the distribution capacity of the area, so:

[0179]

[0180] In the formula, is the basic load of the area where the electric vehicles connected to the grid are located in the i-th time period, S max is the distribution Transformer capacity of the area, is the power factor.

[0181] like Figure 2 As shown, the specific steps of solving the scheduling model by the chaos mapping-lateral crossing-Lévy flight-Kepler algorithm in Step 4 are as follows:

[0182] 1) Use the fourth-order Chebyshev chaotic map to construct the original solar system with M planets and N dimensions, and initialize the orbital eccentricity, revolution period and position of the planets; position X r,s The initialization formula is as follows:

[0183] chaos(r+1)=cos(4×cos -1 (chaos(r)))、

[0184]

[0185] In the formula, r is the number of planets, one planet represents a set of candidate solutions, and s is the decision variable X in the optimization model. r,s There are 24×m decision variables in this optimization model. i,j , 24 and λ1, λ2, λ3, chaos(r) is the chaos parameter of the rth planet, The decision variables X r,s The upper and lower limits of

[0186] 2) Calculate the fitness function value of each planet, and fit the planet with the minimum fitness value best Planet X as the Sun in Galaxy best , and calculate the mass of all celestial bodies in the galaxy and the gravitational force between the sun and other planets;

[0187] 3) Using the horizontal cross strategy, each of the M-1 planets orbiting the sun generates a new satellite to strengthen the competition between celestial bodies. The specific formula is as follows:

[0188]

[0189] In the formula, t is the number of iterations, rand is a random number between 0 and 1, c is a random number between -1 and 1, and X a (t) and X b (t) are two random planet positions, Sate a (t), Sate b (t) are respectively X a (t) and X b (t) satellite (candidate solution); whether the satellite can replace the planet depends on their respective fitness values. Only when the best fitness value is better than the planet can it be retained in the subsequent position update operation. The specific formula is as follows:

[0190]

[0191] 4) Calculate the speed of the planet's revolution around the sun and the normalized Euclidean distance R between the planet and the sun r (t), and update the position of the planet using the following formula:

[0192] When R r (t)<0.5:

[0193]

[0194] When R r (t)≥0.5:

[0195]

[0196] In the formula, is the position of the rth planet, is the position of the sun, and are the positions of two random planets; h is the adaptive factor that controls the distance between the sun and the current planet in the current iteration number t, is a 0-1 operator, η is a ±1 operator, The revolution speed of the rth planet, F gr (t) the gravitational force between the rth planet and the sun, rand3 is a normally distributed random number;

[0197] 5) Using the Levy flight strategy, the planets closer to the sun are searched locally near the sun by small-step jumps, and the planets farther from the sun are searched in unknown areas of space by large-step jumps; the specific formula is as follows:

[0198]

[0199] Where γ is the transmission parameter, χ is the search step, u and v are random operators following the normal distribution, and β∈(0,2) is the parameter that controls the shape of the distribution;

[0200] 6) Determine whether the same result is obtained in consecutive iterations. If yes, exit the iteration loop; if not, update and archive the new non-inferior solution and continue iterating.

[0201] The present invention also includes a chaos mapping-lateral crossing-Levi's flight-Kepler algorithm, which is used to solve the scheduling model; it includes the following steps:

[0202] 1) Use the fourth-order Chebyshev chaotic map to construct the original solar system with M planets and N dimensions, and initialize the orbital eccentricity, revolution period and position X of the planets. r,s ;

[0203] 2) Calculate the fitness function value of each planet, and fit the planet with the minimum fitness value best Planet X as the Sun in Galaxy best , and calculate the mass of all celestial bodies in the galaxy and the gravitational force between the sun and other planets;

[0204] 3) Using the horizontal crossing strategy, each of the M-1 planets orbiting the sun generates a new satellite to strengthen the competition between celestial bodies;

[0205] 4) Calculate the speed of the planet's revolution around the sun and the normalized Euclidean distance R between the planet and the sun r (t), and update the position of the planet;

[0206] 5) Using the Levy flight strategy, the planets closer to the sun are searched locally near the sun by small jumps, while the planets farther from the sun are searched in unknown areas of space by large jumps;

[0207] 6) Determine whether the same result is obtained in consecutive iterations. If yes, exit the iteration loop; if not, update and archive the new non-inferior solution and continue iterating.

[0208] In step 1), position X r,s The initialization formula is as follows:

[0209] chaos(r+1)=cos(4×cos -1 (chaos(r)))、

[0210]

[0211] In the formula, r is the number of planets, one planet represents a set of candidate solutions, s is the number of decision variables in the optimization model, and there are 24×m decision variables in the optimization model. i,j , 24 and λ1, λ2, λ3, chaos(r) is the chaos parameter of the rth planet, The decision variables X r,s The upper and lower limits of

[0212] In step 3), the specific formula used is as follows:

[0213]

[0214] In the formula, t is the number of iterations, rand is a random number between 0 and 1, c is a random number between -1 and 1, and X a (t) and X b (t) are two random planet positions, Sate a (t), Sate b (t) are respectively X a (t) and X b (t) satellite (candidate solution); whether the satellite can replace the planet depends on their respective fitness values. Only when the best fitness value is better than the planet can it be retained in the subsequent position update operation. The specific formula is as follows:

[0215]

[0216] In step 4), the position of the planet is updated using the following formula:

[0217] When R r (t)<0.5:

[0218]

[0219] When R r (t)≥0.5:

[0220]

[0221] In the formula, is the position of the rth planet, is the position of the sun, and are the positions of two random planets; h is the adaptive factor that controls the distance between the sun and the current planet in the current iteration number t, is a 0-1 operator, η is a ±1 operator, The revolution speed of the rth planet, F gr (t) the gravitational force between the rth planet and the sun, rand3 is a normally distributed random number;

[0222] In step 5), the specific formula is as follows:

[0223]

[0224] Where γ is the transmission parameter, χ is the search step, u and v are random operators following the normal distribution, and β∈(0,2) is a parameter that controls the shape of the distribution.

[0225] Example:

[0226] So far, the task of calculating the orderly charging scheduling strategy for electric vehicles has been successfully completed. The following example is used to illustrate:

[0227] This article collects data from a charging pile in a power distribution area in Jinan City, Shandong Province on July 19, 2024. A total of 36 electric vehicles participated in charging and discharging. It is assumed that these 36 electric vehicles are of the same model and the users agree to the dispatch agreement. The key parameters of the power distribution area, charging pile, and electric vehicle are as follows:

[0228]

[0229] The latest time-of-use electricity prices for 2024 announced by State Grid Shandong Electric Power Company are as follows:

[0230]

[0231]

[0232] The grid load conditions under the present invention and disorderly charging modes are compared. Figure 3 shown.

[0233] It can be clearly seen that in the disordered charging mode, the grid load fluctuates greatly, with the highest peak at around 7:00 pm, which is 230.06kW. In the mode of the present invention, although the maximum peak also occurs between 7:00 and 8:00 pm, it is significantly smaller than the peak in the wireless charging mode. This proves that the model has achieved the goal of "peak shaving" well. In addition, 9:00 a.m. to 5:00 p.m. is a trough of the grid load in the disordered charging mode, while the grid load in the orderly charging mode during the same period shows a smooth downward trend. Therefore, the model still has a good effect on "filling the valley".

[0234] The improved Kepler algorithm of the present invention is compared with the optimization iteration of the classic Kepler algorithm. Figure 4 shown.

[0235] As can be seen from the figure, the objective function value of the improved Kepler algorithm is lower in the convergence stage, which makes the convergence speed faster and the convergence accuracy higher when solving the optimization problem.

[0236] During the scheduling process of the present invention, the battery loss cost change trend is as follows: Figure 5 .

[0237] As can be seen from the figure, during the period of low electricity prices, the battery loss cost shows a slow upward trend. This trend becomes steeper from the normal electricity price period to the peak period, and then to the peak period. The higher the electricity price in the period, the higher the grid load. The faster the battery loss cost rises, the higher the frequency of regulation. This result shows that regulation actions often occur during the period of high grid load, which plays a successful role in "peak shaving and valley filling".

Claims

1. An orderly charging scheduling method for electric vehicles based on an improved Kepler algorithm, characterized in that: The following steps are involved: Step 1: Establish a Prophet model to predict photovoltaic power generation in the future; Step 2: Construct the objective function: the objective function is to minimize the battery loss cost, the dispatching cost of the agent, and the grid load variance; among them, the dispatching cost of the agent is calculated based on the photovoltaic power generation power in the future period obtained by Step 1; Step 3: Set constraints: constrain the charging and discharging power, state of charge, charging capacity, and actual capacity of the Transformer. Step 4: Optimize and process the above objective function and constraints based on the chaos mapping-lateral crossing-Lévy flight-Kepler algorithm to solve the scheduling problem of orderly charging of electric vehicles.

2. The method according to claim 1, characterized in that In Step 1, the Prophet model consists of the following four functions: In the formula, represents the photovoltaic power generation power predicted in the future i-th time period, g (i) Represents the overall growth trend of photovoltaic power generation, which is a trend function, s (i) Indicates a periodic trend, for example, a periodic function, h (i) represents the impact of non-fixed photovoltaic power generation failure on the prediction results, which is the influence function, ε (i) It indicates abnormal fluctuations that cannot be predicted by the prediction model and is a random error function.

3. The method according to claim 1, characterized in that: The battery loss cost minimization calculation in Step 2 includes: First, one hour is a time period, so there are 24 time periods in a day; starting from 0:00 in a day and ending at 24:00, the corresponding time period is recorded as i = 1, 2, ..., 24; within a day, the electric vehicles are numbered as j = 1, 2, ..., m in the order of the time of access to the power grid; Next, introduce the 0-1 variable n ij , which represents the alternating state of charging and discharging of electric vehicles: Where P i,j is the charging and discharging power of the jth electric vehicle in the i-th time period; when P i,j >0, it means the jth electric car is in charging state; when P i,j <0, it means the electric vehicle is in a discharging state; when P i,j =0, it means that the electric vehicle is neither charging nor discharging; Then, the Gauss-Newton iteration method was used to perform nonlinear fitting on the relationship between the number of charge and discharge alternations and the remaining capacity of various batteries. The fitting curve is g j (n); furthermore, the sum of the battery loss costs of each electric vehicle h1 is minimized as follows: In the formula, is the cumulative number of charge and discharge alternations when the jth electric vehicle is connected to the grid, c j is the battery cost of the j-th electric car, is the maximum number of charge and discharge cycles that the jth electric vehicle battery can withstand.

4. The method according to claim 1, characterized in that The scheduling cost minimization calculation in Step 2 includes: The dispatch cost is composed of the electricity purchase cost paid by the charging pile agent to the grid and the discharge cost paid by the charging pile agent to the electric vehicle; 1) Calculation of electricity purchase cost Introduce the concept of energy storage capacity to measure the real-time storage energy of the charging pile; 0≤C i ≤C max ,i=1,2,…,24 In the formula, C i is the energy stored in the charging pile in the i-th time period, C max Energy storage capacity for charging piles; If the sum of the photovoltaic power generation energy, the energy obtained from the power grid and the energy storage of the charging pile in the previous period is greater than the charging demand of the electric vehicle in this period, the excess energy will be stored in the next period. Otherwise, there will be no energy storage in this period, that is: In the formula, T is the time of each period, that is, T = 1h, is the photovoltaic power generation power in the i-th time period, and is the power obtained by the charging pile from the power grid in the i-th time period. When , it means that the charging pile obtains power from the power grid; when When , it means that the grid obtains power from the charging pile; In the i-th time period, the electricity purchase cost paid by the charging pile agent to the grid is price i ,Right now: In the formula, is the price of electricity purchased by the charging pile agent from the power grid in the i-th time period, The price of electricity sold by the charging pile agent to the grid in the i-th time period; Therefore, the electricity purchase cost paid by the charging pile agent to the power grid in one day is h 2,1 It can be expressed as: 2) Calculation of discharge cost When p i <0, it means that the electric vehicle is in a discharging state, so the discharging cost of each model of electric vehicle within one day is h 2,2 It can be expressed as: In the formula, p i,j is the charging and discharging power of the j-th electric vehicle in the i-th time period, is the discharge compensation price of electric vehicles in the i-th time period; Therefore, the scheduling cost h2 is minimized as: minh2=h 2,1 +h 2,2 。 5. The method according to claim 1, characterized in that The grid load variance minimization calculation in Step 2 includes: The average value μ of the grid load is expressed as: In the formula, is the power obtained by the charging pile from the power grid in the i-th period, n is the number of periods in a day, that is, n = 24; Then, the power load variance is calculated; the smaller the variance, the smaller the grid load change, the more stable the load, and the higher the grid stability; the minimization of the grid load variance h3 is expressed as:

6. The method according to any one of claims 1 to 5, characterized in that: In Step 3, the charging and discharging power are constrained as follows: Whether the electric vehicle is in the charging or discharging state, its power should be less than or equal to the maximum power. When it is not connected to the power grid, the power is 0, that is: Where P i,j is the charging and discharging power of the j-th electric vehicle in the i-th time period, is the maximum charging and discharging power of the jth electric vehicle, is the time when the jth electric vehicle is connected to the grid, The user of the jth electric vehicle inputs the time when the charging pile leaves the grid; In Step 3, the state of charge is constrained as follows: To prevent rapid battery degradation, the residual power of an electric vehicle after discharge should not be less than 10% of the rated capacity, and the residual power of an electric vehicle after charging should not exceed 95% of the rated capacity; that is: In the formula, SOC i,j is the state of charge of the j-th electric vehicle after the i-th time period, is the state of charge of the jth electric vehicle when it is connected to the grid, B j The battery capacity of the jth electric vehicle, η is the charging and discharging efficiency; In Step 3, the charge amount is constrained as follows: From the time an electric vehicle is connected to the grid until it leaves the grid, the amount of electricity supplied by the charging pile to the electric vehicle should meet the user's established needs. Therefore: In the formula, The expected state of charge of the charging pile when leaving the grid is input for the jth electric vehicle user, g j (n) is a nonlinear fitting function of the number of charge and discharge alternations of the j-th electric vehicle and the remaining capacity of various batteries; In Step 3, the actual capacity of Transformer is constrained as follows: The total power of the entire dispatching system in any time period should not exceed the distribution capacity of the area, so: In the formula, is the basic load of the area where the electric vehicles connected to the grid are located in the i-th time period, S max is the distribution Transformer capacity of the area, is the power factor.

7. The method according to claim 1, characterized in that The specific steps of solving the scheduling model by the chaos mapping-lateral crossing-Lévy flight-Kepler algorithm in Step 4 are as follows: 1) Use the fourth-order Chebyshev chaotic map to construct the original solar system with M planets and N dimensions, and initialize the orbital eccentricity, revolution period and position of the planets; position X r,s The initialization formula is as follows: chaos(r+1)=cos(4×cos -1 (chaos(r)))、 In the formula, r is the number of planets, one planet represents a set of candidate solutions, and s is the decision variable X in the optimization model. r,s There are 24×m decision variables in this optimization model. i,j , 24 and λ1, λ2, λ3, chaos(r) is the chaos parameter of the rth planet, The decision variables X r,s The upper and lower limits of 2) Calculate the fitness function value of each planet, and fit the planet with the minimum fitness value best Planet X as the Sun in Galaxy best , and calculate the mass of all celestial bodies in the galaxy and the gravitational force between the sun and other planets; 3) Using the horizontal cross strategy, each of the M-1 planets orbiting the sun generates a new satellite to strengthen the competition between celestial bodies. The specific formula is as follows: In the formula, t is the number of iterations, rand is a random number between 0 and 1, c is a random number between -1 and 1, and X a (t) and X b (t) are two random planet positions, Sate a (t), Sate b (t) are respectively X a (t) and X b (t) satellite; whether a satellite can replace a planet depends on their respective fitness values. Only when the best fitness value is better than the planet can it be retained in the subsequent position update operation. The specific formula is as follows: 4) Calculate the speed of the planet's revolution around the sun and the normalized Euclidean distance R between the planet and the sun r (t), and update the position of the planet using the following formula: When R r (t)<0.5: When R r (t)≥0.5: In the formula, is the position of the rth planet, is the position of the sun, and are the positions of two random planets; h is the adaptive factor that controls the distance between the sun and the current planet in the current iteration number t, is a 0-1 operator, η is a ±1 operator, The revolution speed of the rth planet, F gr (t) the gravitational force between the rth planet and the sun, rand3 is a normally distributed random number; 5) Using the Levy flight strategy, the planets closer to the sun are searched locally near the sun by small-step jumps, and the planets farther from the sun are searched in unknown areas of space by large-step jumps; the specific formula is as follows: Where γ is the transmission parameter, χ is the search step, u and v are random operators following the normal distribution, and β∈(0,2) is the parameter that controls the shape of the distribution; 6) Determine whether the same result is obtained in consecutive iterations. If yes, exit the iteration loop; if not, update and archive the new non-inferior solution and continue iterating.

8. A chaotic mapping-lateral crossing-Lévy flight-Kepler algorithm, characterized in that: The chaotic mapping-lateral crossing-Lévy flight-Kepler algorithm is used to solve the scheduling model; it includes the following steps: 1) Use the fourth-order Chebyshev chaotic map to construct the original solar system with M planets and N dimensions, and initialize the orbital eccentricity, revolution period and position X of the planets. r,s ; 2) Calculate the fitness function value of each planet, and fit the planet with the minimum fitness value best Planet X as the Sun in Galaxy best , and calculate the mass of all celestial bodies in the galaxy and the gravitational force between the sun and other planets; 3) Using the horizontal crossing strategy, each of the M-1 planets orbiting the sun generates a new satellite to strengthen the competition between celestial bodies; 4) Calculate the speed of the planet's revolution around the sun and the normalized Euclidean distance R between the planet and the sun r (t), and update the position of the planet; 5) Using the Levy flight strategy, the planets closer to the sun are searched locally near the sun by small jumps, while the planets farther from the sun are searched in unknown areas of space by large jumps; 6) Determine whether the same result is obtained in consecutive iterations. If yes, exit the iteration loop; if not, update and archive the new non-inferior solution and continue iterating.

9. The algorithm according to claim 8, characterized in that In step 1), position X r,s The initialization formula is as follows: chaos(r+1)=cos(4×cos -1 (chaos(r)))、 In the formula, r is the number of planets, one planet represents a set of candidate solutions, s is the number of decision variables in the optimization model, and there are 24×m decision variables in the optimization model. i,j , 24 and λ1, λ2, λ3, chaos(r) is the chaos parameter of the rth planet, The decision variables X r,s The upper and lower limits of In step 3), the specific formula used is as follows: In the formula, t is the number of iterations, rand is a random number between 0 and 1, c is a random number between -1 and 1, and X a (t) and X b (t) are two random planet positions, Sate a (t), Sate b (t) are respectively X a (t) and X b (t) satellite; whether a satellite can replace a planet depends on their respective fitness values. Only when the best fitness value is better than the planet can it be retained in the subsequent position update operation. The specific formula is as follows: In step 4), the position of the planet is updated using the following formula: When R r (t)<0.5: When R r (t)≥0.5: In the formula, is the position of the rth planet, is the position of the sun, and are the positions of two random planets; h is the adaptive factor that controls the distance between the sun and the current planet in the current iteration number t, is a 0-1 operator, η is a ±1 operator, The revolution speed of the rth planet, F gr (t) the gravitational force between the rth planet and the sun, rand3 is a normally distributed random number; In step 5), the specific formula is as follows: Where γ is the transmission parameter, χ is the search step, u and v are random operators following the normal distribution, and β∈(0,2) is a parameter that controls the shape of the distribution.

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