Scheduling method for electric bus vehicles considering source-load bilateral fluctuation

Abstract

The application belongs to the technical field of urban public traffic management, and particularly relates to an electric bus scheduling and charging scheduling method considering source-load bilateral fluctuation. The application aims to solve the problems of intermittent source-side photovoltaic power generation and random bus operation energy consumption, which cause source-load bilateral fluctuation of the bus system, resulting in the problems of mismatch between charging demand and power supply capacity in some periods, inability to complete operation tasks in time, and low energy storage utilization efficiency, and proposes the electric bus scheduling and charging scheduling method considering source-load bilateral fluctuation. The process is as follows: calculating the remaining electric quantity of the bus battery based on the optimized period; calculating the remaining electric quantity of the energy storage battery based on the remaining electric quantity of the bus battery; constructing an opportunity constraint programming model based on the remaining electric quantity of the bus battery and the remaining electric quantity of the energy storage battery; solving the opportunity constraint programming model to obtain an optimal feasible solution, and the optimal feasible solution includes the electric bus scheduling and charging scheduling scheme.

Description

Technical Field

[0001] This invention belongs to the field of urban public transportation management technology, specifically relating to a method for scheduling and charging electric buses. Background Technology

[0002] Urban public transportation systems are gradually transforming towards electrification and intelligentization. Electric buses, with their advantages of zero emissions and low noise during operation, have become an important vehicle for green transportation development. However, the current electric bus system mainly relies on the power grid powered primarily by thermal power generation, and its carbon emission efficiency needs to be further improved. At the same time, the centralized charging mode of buses at night also puts significant load pressure on the power grid.

[0003] Transforming the traditional single-grid power supply model into a hybrid power supply model that integrates photovoltaics, energy storage, and the grid offers a new approach to solving the aforementioned problems. Under this hybrid model, electric buses can prioritize using photovoltaic power generation for daytime recharging, effectively reducing electricity costs and the impact on the grid, while simultaneously promoting the use of renewable energy and deep decarbonization of public transport companies. However, the intermittency of photovoltaic power generation on the source side and the randomness of bus operating energy consumption on the load side result in fluctuations on both the source and load sides of the public transport system. This leads to problems such as a mismatch between charging demand and power supply capacity during certain periods, inability to complete operational tasks in a timely manner, and low energy storage utilization efficiency.

[0004] Existing technologies assume that the energy consumption and photovoltaic power generation of electric buses are stable values, which is clearly unsuitable for the operational needs of electric buses under random fluctuations in energy sources and loads. Therefore, it is urgent to establish a scheduling and charging dispatching method for electric buses that considers fluctuations on both the energy source and load sides, tailored to the current operational characteristics of electric bus systems. Summary of the Invention

[0005] The purpose of this invention is to address the problem that the intermittency of existing source-side photovoltaic power generation and the randomness of energy consumption in bus operation on the load side cause fluctuations in the bus system on both the source and load sides, resulting in a mismatch between charging demand and power supply capacity and low energy storage utilization efficiency during certain periods. Therefore, this invention proposes a scheduling and charging dispatching method for electric buses that takes into account the fluctuations on both the source and load sides.

[0006] The specific process of the electric bus scheduling and charging dispatch method considering the fluctuations on both the source and load sides is as follows:

[0007] Step 1: Calculate the remaining battery power of the bus based on the optimized time period;

[0008] Step 2: Calculate the remaining power of the energy storage battery based on the remaining power of the bus battery;

[0009] Step 3: Based on the remaining power of the bus battery and the remaining power of the energy storage battery, construct an opportunity-constrained programming model;

[0010] Step 4: Solve the opportunity-constrained programming model to obtain the optimal feasible solution, which includes the scheduling and charging dispatch scheme for electric buses.

[0011] The beneficial effects of this invention are as follows:

[0012] This invention considers the fluctuations on both the source and load sides under a hybrid power supply mode of photovoltaic, energy storage, and grid. It calculates the probability distribution of operating energy consumption, calculates the remaining battery capacity of the bus based on this probability distribution, calculates the remaining battery capacity of the energy storage battery based on the same probability distribution and the remaining battery capacity, and constructs a chance-constrained programming model based on both. Solving this model yields the optimal feasible solution, which includes electric bus scheduling and charging dispatching schemes. This invention's method for scheduling and charging electric buses, considering the fluctuations on both the source and load sides, effectively addresses the problem of mismatch between charging demand and power supply capacity, preventing timely completion of operational tasks. This invention improves the flexibility and adaptability of the dispatching scheme, fully utilizes photovoltaic resources and energy storage systems, prioritizes the use of clean energy, reduces system carbon emissions at the source, enhances operational stability and economy in complex environments, and solves the problem of low energy storage utilization efficiency. Attached Figure Description

[0013] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0014] Specific implementation method one: Combining Figure 1 This embodiment describes the specific process of the electric bus scheduling and charging dispatching method that considers fluctuations on both the power source and load sides.

[0015] Step 1: Calculate the remaining battery power of the bus based on the optimized time period;

[0016] Step 2: Calculate the remaining power of the energy storage battery based on the remaining power of the bus battery;

[0017] Step 3: Based on the remaining power of the bus battery and the remaining power of the energy storage battery, construct an opportunity-constrained programming model;

[0018] Step 4: Solve the opportunity-constrained programming model to obtain the optimal feasible solution, which includes the scheduling and charging dispatch scheme for electric buses.

[0019] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that step 1 calculates the remaining battery power of the bus based on the optimized time period; the specific process is as follows:

[0020] Step 11: Optimize the time period to be the last operating time of the bus route on the same day. (i.e., the time when the last electric bus returns to the depot) until the end of the bus route's operation the following day. The time range between;

[0021] The optimization period is divided into nighttime and daytime; the nighttime time span is defined as... The daytime span is defined as ; Indicates the departure time of the first bus during the day; Indicates time;

[0022] Step 12: Calculate the charging amount for the electric bus; the specific process is as follows:

[0023] electric buses At the end of the shift After, the shift will be executed. Charge during the initial idle period, but the battery's SOC at the end of charging should not exceed [a certain value]. ;

[0024] , Number the electric bus The total number of electric buses deployed on bus routes was investigated.

[0025] in This indicates the maximum SOC (State of Charge) of the bus battery, expressed in % (%).

[0026] electric buses Last shift Subsequent charging amount The calculation formula is shown below:

[0027] (1)

[0028]

[0029]

[0030] In the formula, the shift scheduling variable If electric buses During the shift The schedule will continue to be operated. ,but ;otherwise , , For shift numbering; This indicates the charging power of the charging station, measured in kW. The charging efficiency of the charging station is expressed as % (%). Indicates electric bus Last shift After, the shift will be executed. The idle time before, in minutes; Indicates that electric buses will be used exist Battery SOC charging time The required time is in minutes. Indicates electric bus exist Battery SOC at any given time, expressed as % Indicates train number Departure time at the originating station; Indicates train number Arrival time at the originating station; Indicates the rated capacity of the bus vehicle's battery;

[0031] Calculate the start and end times of charging; the specific process is as follows:

[0032] Calculate electric buses Last shift The charging start time afterward and the time when charging ends :

[0033]

[0034]

[0035] Step 13: Calculate the electric bus Continuous operation schedule , , The SOC of the arriving battery and the SOC of the departing battery during the process.

[0036] The other steps and parameters are the same as in Specific Implementation Method 1.

[0037] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that: in step 13, the calculation of the electric bus... Continuous operation schedule , , The arrival battery SOC and departure battery SOC during the process; the specific process is as follows:

[0038] Step 131: Calculate the electric bus The SOC (State of Charge) of the battery upon arrival is calculated using the following formula:

[0039] (2)

[0040] In the formula, Indicates electric bus exist Battery SOC at any given moment; Indicates train number Arrival time at the originating station; Indicates train number Operating energy consumption; This indicates the rated capacity of the battery in the electric bus; Indicates existence , Represents any ;

[0041] Calculate the shift based on historical shift operation energy consumption data. Operating energy consumption mean and variance , Follow the mean variance is The normal distribution, i.e. ;

[0042] probability density function Represented as:

[0043]

[0044] In the formula, For train schedules Standard deviation of operating energy consumption , For shift number, This refers to the total number of shifts executed each day.

[0045] Step 132: Calculate the electric bus based on step 131. Off-station battery SOC (unknown for electric buses) Whether it is full or not, the calculation formula is as follows:

[0046] (3)

[0047] In the formula, Indicates electric bus exist Battery SOC at any given moment; Indicates train number Departure time at the originating station;

[0048] Calculate electric buses Mean and variance of SOC of batteries leaving the station when not fully charged, and electric buses The probability of the battery not being fully charged;

[0049] The specific process is as follows:

[0050] 1) Calculate electric buses The mean and variance of the State of Charge (SOC) of off-site batteries when not fully charged; the specific process is as follows:

[0051] electric buses Off-site battery SOC when not fully charged: ;

[0052] Follows a normal distribution, calculate mean and variance :

[0053]

[0054]

[0055] The above summation formula In The value ranges from 1 to large. The addition formula below The loop is from 0 to The addition formula below That is, from 0 to large ;

[0056] In the formula, This represents the average energy consumption of shift j. This represents the variance of the energy consumption of shift j.

[0057] 2) Calculate electric buses The probability of an incompletely charged state occurring; the specific process is as follows:

[0058]

[0059]

[0060] In the formula, Represents probability;

[0061] express The probability of;

[0062] express The probability of;

[0063] express The probability of;

[0064] express The probability of;

[0065] For random variables The distribution function;

[0066] Energy consumption of shift operation It follows a normal distribution, therefore a random variable It also follows a normal distribution;

[0067] Step 133: Calculate the SOC of the battery of electric bus m after it finishes its shift j and continues its shift k without recharging. The calculation formula is as follows:

[0068] (4)

[0069] In the formula, Indicates that electric bus m is in Battery SOC at any given time, expressed as % Indicates electric bus Off-site battery SOC (State of Charge) when not fully charged, in percentages (%) Indicates train number Arrival time at the originating station;

[0070] The formula for calculating the SOC (State of Charge) of the battery of electric bus m after it leaves the station is as follows: (The formula is not shown in the original text.)

[0071] (5)

[0072] In the formula, Indicates electric bus exist Battery SOC at any given time, expressed as % This indicates the departure time of train number k at the originating station;

[0073] According to formulas (4) and (5), Depend on and The linear combination was calculated, and and They all follow a normal distribution, therefore It also follows a normal distribution;

[0074] calculate probability density function :

[0075]

[0076]

[0077]

[0078] In the formula, This represents the average SOC (State of Charge) of the battery at the start time of shift k for electric bus m. Let S represent the variance of the battery SOC at the start time of shift k for electric bus m. express The mean; express The variance; This represents the average energy consumption of shift j. This represents the variance of the energy consumption of shift j.

[0079] Step 134: Calculate the SOC of electric bus m's battery after it finishes its shift j, recharges, and continues its shift k. (It is unknown whether electric bus m is fully charged.) The calculation formula is as follows:

[0080] (6)

[0081] In the formula, Indicates electric bus Last shift The amount of charge after charging;

[0082] Step 135: Calculate the electric bus The probability density function, mean, and variance of the state of charge (SOC) of the battery leaving the station before it is fully charged after the end of shift j, and the electric bus The probability of the battery not being fully charged after the end of shift j; the specific process is as follows:

[0083] Step 1351: Calculate the electric bus The probability density function, mean, and variance of the state of charge (SOC) of the battery leaving the station when it is not fully charged after the end of shift j; the specific process is as follows:

[0084] electric buses The SOC of the battery leaving the station if it is not fully charged after the end of shift j is , Follows a normal distribution, calculate probability density function :

[0085]

[0086]

[0087]

[0088] In the formula, the shift scheduling variable If electric buses During the shift The schedule will continue to be operated. ,but ;otherwise , , For shift numbering; This represents the idle time of electric bus m after the end of its shift j and before the start of its shift k, in minutes.

[0089] Step 1352: Calculate the electric bus The probability of the battery not being fully charged after the end of shift j is expressed as:

[0090]

[0091] In the formula, For random variables The distribution function; Indicates that electric bus m is in Battery SOC at any given time, expressed as % This indicates that electric buses will be used in... Battery SOC charging time The required time is in minutes. express The probability of;

[0092] express The probability of; express The probability of.

[0093] Other steps and parameters are the same as in specific implementation method one or two.

[0094] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that, in step 2, the remaining power of the energy storage battery is calculated based on the probability distribution of operating energy consumption and the remaining power of the bus battery; the specific process is as follows:

[0095] Step 21: Calculate the cumulative charge received by the energy storage system from photovoltaic charging up to time t. As shown in the following formula:

[0096] (7)

[0097] In the formula, for The actual photovoltaic power generation at any given time; The efficiency of photovoltaic power generation facilities in delivering energy to energy storage systems, expressed as a percentage, is calculated as follows: Follows a normal distribution;

[0098] calculate mean and variance :

[0099]

[0100]

[0101] In the formula, This represents the estimated photovoltaic power generation at time t; This indicates the error term in the photovoltaic power generation estimation. The statistical variance;

[0102] The Actual photovoltaic power generation at any given time The acquisition process is as follows;

[0103] 1) Calculation Estimated photovoltaic power generation at any given time The specific process is as follows:

[0104]

[0105] In the formula, The solar irradiance under standard test conditions is taken as 1000 W / m². 2 ; The ambient temperature under standard test conditions is taken as 25℃; For each moment under different weather scenarios Solar irradiance, in W / m 2 ; for The operating temperature of the photovoltaic array at any given time, in °C; The power temperature coefficient is expressed in % (%). The installed capacity of the photovoltaic power generation system is expressed in kW.

[0106] 2) Estimated photovoltaic power generation at any given time With history True value of photovoltaic power generation at any given time By subtracting historical values, we obtain the photovoltaic power generation estimation error term. ;

[0107] Error Term for Photovoltaic Power Generation Estimation under Different Weather Scenarios get Statistical variance , Follows the pattern with mean 0 and variance of normal distribution ,Right now ;

[0108] Error term in photovoltaic power generation estimation under corresponding weather scenarios The probability density function is shown in the following equation:

[0109]

[0110] In the formula, Error term for photovoltaic power generation estimation The probability density function; Error term for photovoltaic power generation estimation Standard deviation;

[0111] 3) Based on Estimated photovoltaic power generation at any given time And the error term in photovoltaic power generation estimation ,calculate Actual photovoltaic power generation at any given time , ; Indicates actual photovoltaic power generation Follow the mean variance is The normal distribution; the specific process is as follows:

[0112]

[0113] in, This indicates the departure time of train number 1 at the originating station; Indicates the end time of the bus route's operation the following day;

[0114] Step 22: Calculate the cumulative amount of electricity provided by the energy storage system in the total amount of electricity consumed by the electric bus to complete charging up to time t. As shown in the following formula:

[0115] (8)

[0116] In the formula, ,like ,but ,otherwise ; Indicates train number Arrival time at the originating station; Indicates train number Departure time at the originating station; Indicates electric bus exist A way to charge at any time, If energy storage is used for power supply, then ,otherwise ; For indicator functions, when hour Select 1 if the value is 1, otherwise select 0.

[0117] The specific process is as follows:

[0118] according to Mean of a normal distribution and variance ,based on mean and variance ,calculate mean and variance :

[0119]

[0120]

[0121] In the formula, For binary variables, When it follows a normal distribution ,otherwise ;

[0122] Step 23: Based on the cumulative charging amount obtained by the energy storage system from photovoltaic charging up to time t calculated in Step 21. And the accumulated electricity provided by the energy storage system from the electricity consumed by the electric bus to complete charging by time t in step 22. Calculate the remaining power of the energy storage system at time t. .

[0123] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0124] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One to Four in that, in step 23, the cumulative charging amount obtained by the energy storage system from photovoltaic charging up to time t is based on the calculation in step 21. And the accumulated electricity provided by the energy storage system from the electricity consumed by the electric bus to complete charging by time t in step 22. Calculate the remaining power of the energy storage system at time t. As shown in the following formula:

[0125] (9)

[0126] In the formula, Indicates in The remaining power of the energy storage system at any given time;

[0127] based on mean and variance as well as mean and variance ,calculate mean and variance The specific process is as follows:

[0128]

[0129]

[0130] In the formula, Indicates in The remaining power of the energy storage system at any given time.

[0131] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0132] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One to Five in that, in step 3, a chance-constrained programming model is constructed based on the remaining power of the bus battery and the remaining power of the energy storage battery; the specific process is as follows:

[0133] Step 31: Calculate the proportion of photovoltaic power in the energy consumption of the public transportation system. Maximize total charging cost Minimization is the optimization objective, as shown in the following equation:

[0134] (10)

[0135] (11)

[0136] In the formula, The total overnight charging amount for electric buses and energy storage systems is expressed in kWh. The price per unit of photovoltaic power generation is expressed in yuan / kWh; The unit price of electricity supplied by the power grid at time t is expressed in yuan / kWh. The total cost of charging electric buses and energy storage systems from the grid at night, in yuan; Indicates electric bus exist A way to charge at any time, If the mains power supply is used, then ,otherwise If electric buses If charging is not performed at time t, then ;

[0137] Step 32: Set the constraints for the opportunity-constrained programming model.

[0138] The other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0139] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One through Six in that, in step 32, constraints are set for the opportunity-constrained programming model; the specific process is as follows:

[0140] The equation constraint for the total nighttime charging amount of the electric bus and energy storage system is shown in the following formula:

[0141] (12)

[0142] In the formula, The last bus route of the day The remaining power of the energy storage system, in kWh; Electric buses at the end of the day's bus route operation Battery SOC, in percentages (%)

[0143] Constraining the total cost of charging electric buses and energy storage systems from the grid at night. As shown in the following formula:

[0144] (13)

[0145] The constraint requires that each shift be operated by an electric bus, as shown in the following formula:

[0146] (14)

[0147] Electric buses are restricted to using only one charging method per charge, as shown in the following formula:

[0148] (15)

[0149] The constraint is that the duration of a single charge for an electric bus must be greater than the minimum charging duration, as shown in the following formula:

[0150] (16)

[0151] In the formula, Minimum charging duration for electric buses, in minutes; For electric buses Last shift The end time of charging after the initial charge; For electric buses Last shift The start time of charging after the initial charge; For any;

[0152] For the objective function respectively and Constructing probabilistic constraints:

[0153]

[0154]

[0155] Restricting electric buses The battery's state of charge (SOC) and the remaining capacity of the energy storage system must be within the specified range:

[0156]

[0157]

[0158]

[0159] In the formula, For a pre-defined confidence level; For the objective function At a confidence level of at least The maximum value that is taken at that time (which is known); For the objective function At a confidence level of at least The minimum value taken at that time (which is known), in yuan; The lower limit of SOC for bus batteries, in percentages (%). The maximum SOC (State of Charge) for bus batteries, expressed as a percentage. The lower limit of the SOC of energy storage batteries, in percentages (%). The upper limit of the SOC of energy storage batteries, in percentages (%). Indicates the schedule of electric buses Battery SOC at the end of the process; Indicates the schedule of electric buses Battery SOC at the start; express The remaining power of the energy storage system at all times; This indicates the rated capacity of the battery in the energy storage system; express The probability of; express The probability of; express The probability of; express The probability of; express The probability of;

[0160] The range of values ​​for the decision variables in a constrained-chance-constrained programming model is shown in the following formula:

[0161] (17)

[0162] (18)

[0163] In the formula, This refers to the rated capacity of the energy storage system's batteries. The lower limit of the SOC of energy storage batteries, in percentages (%). This represents the upper limit of the SOC (State of Charge) of the energy storage battery, in percentages.

[0164] The other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0165] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods One through Seven in that step 4 involves solving the opportunity-constrained programming model to obtain the optimal feasible solution, which includes the electric bus vehicle scheduling and charging dispatch scheme; the specific process is as follows:

[0166] Step 41: Transform the opportunity-constrained programming model; the specific process is as follows:

[0167] Step 411: Perform a deterministic transformation on the chance-constrained programming model; the specific process is as follows:

[0168] Constraints Transform into a definite equivalence class:

[0169]

[0170] Constraints Transform into a definite equivalence class:

[0171]

[0172] In the formula,

[0173] Represents random variables The distribution function;

[0174] for The inverse function;

[0175] Constraints Transform into a definite equivalence class. The specific process is as follows:

[0176] 1) Based on the actual photovoltaic power generation Distribution generation A random value, based on A vector is formed from random values. ;

[0177] Randomly generated The operating energy consumption values ​​are respectively ,based on Constructing vectors ;

[0178] They respectively follow the mean of variance is The normal distribution;

[0179] In the formula, The energy consumption for electric bus route 1; The operating energy consumption of electric bus route 2; Electric bus schedules Operating energy consumption;

[0180] This represents the average energy consumption of electric bus trip 1. This represents the average energy consumption of electric bus trip 2. Electric bus schedules The average operating energy consumption;

[0181] Let Variance be the operating energy consumption of electric bus route 1; Let Variance be the operating energy consumption of electric bus route 2; Electric bus schedules The variance of operating energy consumption;

[0182] 2) Generation indivual Number , ; The value is a positive integer. ;

[0183] generate indivual Number , ;

[0184] Introduce variables for each scenario ,variable Indicates whether the constraint is satisfied. ,if, ;otherwise, ;

[0185] Each scenario includes 1 pair and ;

[0186] Constraints Transform into a definite equivalence class. ; indicates as:

[0187] (19)

[0188]

[0189] (20)

[0190] In the formula, express The remaining power of the energy storage system at all times;

[0191] M is a sufficiently large constant that makes the inequality hold; it is an extreme parameter.

[0192] Variables in formula (19) The value equals 1, so inequality (19) holds; variable Not equal to 1, inequality (19) also holds; formula (20) statistically The case where it equals 1 requires that in all scenarios... The case where the value equals 1 is large enough;

[0193] Step 412: Linearize the opportunity-constrained programming model transformed in Step 411 to obtain the linearized opportunity-constrained programming model; the specific process is as follows:

[0194] Shift scheduling variables With charging scheduling variables , There are product terms and the objective function of formula (10) is... To prevent the chance-constrained programming model from exhibiting nonlinearity due to its fractional nature, an intermediate variable is introduced. , and :

[0195]

[0196]

[0197] Based on intermediate variables , and Transforming equation (11), we obtain the following equation:

[0198] (twenty one)

[0199] in ;

[0200] Based on intermediate variables Transforming equation (12), we obtain the following equation:

[0201] (twenty two)

[0202] Based on intermediate variables , Pair After transformation, we get the following formula:

[0203] (twenty three)

[0204] Based on intermediate variables , Pair After transformation, we get the following formula:

[0205] (twenty four)

[0206] The constraints are as follows:

[0207] (25)

[0208] (26)

[0209] (27)

[0210] (28)

[0211] (29)

[0212] (30)

[0213] (31)

[0214] In the formula, Represents random variables The distribution function; for The inverse function; For a pre-defined confidence level;

[0215] Step 42: Decompose the original scheduling problem into a restricted master problem and a pricing subproblem using the Dantzig-Wolfe decomposition algorithm;

[0216] The original scheduling problem is represented by equations (12), (13), (14), (15), (16), (17), (18), (21), (22), (23), (24), (25), (26), (27), (28), (29), (30), and (31).

[0217] Step 43: Solve the original scheduling problem to obtain the optimal feasible solution, which includes the scheduling scheme for electric buses and the charging schedule.

[0218] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0219] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods One through Eight in that, in step 42, the original scheduling problem is decomposed into a restricted master problem and a pricing subproblem using the Dantzig-Wolfe decomposition algorithm; the specific process is as follows:

[0220] Step 421: Construct the constrained master problem; the specific process is as follows:

[0221] 1) Define the decision variables for the main problem. If and only if the electric bus m selects a vehicle scheduling and charging scheme hour, ,otherwise ;

[0222] 2) Introduce auxiliary variables ,like ,but ,otherwise ; This indicates that if energy storage charging is used during the idle time of shift i and shift j, the binary variable is 1.

[0223] 3) Calculate the selection of vehicle scheduling and charging schemes for electric buses (m). The amount of charge already completed from the energy storage system at any time t As shown in the following formula:

[0224] (32)

[0225] in, This indicates the amount of charge added to electric bus m after its last trip i. For indicator functions, when hour Select 1 if the value is 1, otherwise select 0.

[0226] 4) Calculate the remaining power of the energy storage system under the selected vehicle scheduling and charging scheme. As shown in the following formula:

[0227] (33)

[0228] in, This indicates that electric bus m has selected a vehicle scheduling and charging scheme. The amount of charge already completed from the energy storage system at any time t; express The remaining power of the energy storage system at all times;

[0229] 5) Calculate the vehicle scheduling and charging plan for electric buses (m). Cost As shown in the following formula:

[0230] (34)

[0231] (35)

[0232] (36)

[0233] In the formula, and These are the green electricity ratio and total charging cost for electric buses (m); the green electricity ratio is the proportion of photovoltaic power in the energy consumption of the public transportation system. Let be the set of objective functions for electric buses, where The objective function is green electricity ratio. This represents the total charging cost as the objective function. express or , and These represent the weights of the green electricity ratio and the total charging cost, respectively. , , ; express or ;

[0234] The constrained master problem model is constructed as follows:

[0235] (37)

[0236] (38)

[0237] (39)

[0238] (40)

[0239] (41)

[0240] (42)

[0241] Equation (37) represents the objective function;

[0242] Formula (38) ensures that all trips during operating hours are served by one and only one electric bus;

[0243] Equation (39) ensures that each electric bus can only choose one feasible option;

[0244] Equation (40) ensures that the energy storage system meets the upper and lower limits of power constraints;

[0245] Constraint (41) means that the frequency of feasible scenarios (the ratio of scenarios with uncertain constraints to the total number of generated scenarios in Monte Carlo simulation) is used to control the overall feasibility under uncertain factors (under random factors (energy consumption fluctuations, photovoltaic power output fluctuations), it is not necessary for all scenarios generated by Monte Carlo simulation to meet the constraints, the frequency only needs to be greater than a certain confidence level), and does not directly participate in the structure of a single column (it does not generate dual variables, does not participate in the solution of subproblems, the solution of subproblems is a column of coefficients of the main problem);

[0246] Step 422: Construct the pricing subproblem; the specific process is as follows:

[0247] Introduction and As dual variables of constraints (38) and (39) in the main problem;

[0248] Introduction and As the two dual variables corresponding to the upper and lower bounds of the bilateral constraint (40);

[0249] Calculate the test number expression for the restricted principal problem (43):

[0250] (43)

[0251] The pricing sub-problem model is constructed as follows:

[0252] (44)

[0253] (45)

[0254] (46)

[0255] (47)

[0256] (48)

[0257] (49)

[0258] (50)

[0259] (51)

[0260] (52)

[0261] (53)

[0262] (54)

[0263] (55)

[0264] The other steps and parameters are the same as those in one of the specific implementation methods one to eight.

[0265] Specific Implementation Method Ten: This implementation method differs from Specific Implementation Methods One through Nine in that step 43 solves the original scheduling problem to obtain the optimal feasible solution, which includes the electric bus vehicle scheduling and charging scheduling scheme; the specific process is as follows:

[0266] Step 431, Remaining power of the instantaneous energy storage system The value range is discrete in 1kWh increments, and the lower limit of the energy storage system's capacity is set as... Remaining power of the instantaneous energy storage system ( );

[0267] Step 432: Formulate an initial electric bus scheduling and charging dispatch plan using empirical methods. Use the initial electric bus scheduling and charging dispatch plan as the initial solution. The initial solution is the root node in the branch and bound algorithm and is also the node to be branched at the moment.

[0268] Let the current optimal feasible solution be The objective function value corresponding to the current optimal feasible solution is ;

[0269] Step 433, let the number of iterations be... The initial lower bound of the branch and bound algorithm Set it to 0, and use the objective function value of the initial feasible solution as the initial upper bound. ;

[0270] Step 434: Take the transformed opportunity-constrained programming model as the original scheduling problem (Equations (12), (13), (14), (15), (16), (17), (18), (21), (22), (23), (24), (25), (26), (27), (28), (29), (30), (31)), and decompose the original scheduling problem into a restricted master problem and a pricing subproblem;

[0271] Step 435: Solve the restricted master problem and obtain the dual variables of constraint (38). The dual variables of constraint (39) Dual variables of bilateral constraint equation (40) and ;

[0272] Dual variables based on constraint (38) The dual variables of constraint (39) Dual variables of bilateral constraint equation (40) and Construct a pricing subproblem;

[0273] Step 436: Solve the pricing subproblem and determine whether the test number of the optimal solution (vehicle scheduling scheme) to the pricing subproblem is negative;

[0274] If the test number of the optimal solution to the pricing subproblem (vehicle scheduling scheme) is negative, it means that there is still a feasible vehicle scheduling scheme with optimization space. Add all vehicle scheduling schemes with negative test numbers in the process of solving the pricing subproblem to the restricted main problem and return to step 435.

[0275] If the test number of the optimal solution to the pricing subproblem (vehicle scheduling scheme) is positive, then the test numbers of all vehicle scheduling schemes generated during the solution of the pricing subproblem are positive, and proceed to step 437.

[0276] Step 437, the first The optimal solution to the restricted principal problem at the node of the next iteration is denoted as... , The corresponding objective function value is denoted as ;

[0277] Step 438, Judgment:

[0278] if ,and If it is not an integer, proceed to step 439;

[0279] if ,and If it is 0 or 1, then update. , Delete the current node and proceed to step 4311;

[0280] if Delete the current node and proceed to step 4311;

[0281] Step 439: Select the value closest to 0.5 and The corresponding electric bus m;

[0282] Then, constraints are added to the pricing subproblem of the current node. To construct the left child node, constraints are added to the pricing subproblem of the current node. To construct the right child node, let the left and right child nodes be defined. ;

[0283] Step 4310: Set the left child node as the new current node, add the right child node to the list of nodes to be processed, and update the iteration count. Return to step 435;

[0284] Step 4311: Determine if the current node list still contains nodes to be processed.

[0285] If the list of nodes to be processed is not empty, select the nodes from the list. The node with the smallest value is selected as the new current node, and the iteration count is updated. Return to step 435;

[0286] If the list of nodes to be processed is empty, proceed to step 4312;

[0287] Step 4312: Record the optimal feasible solution. and the corresponding objective function value ;

[0288] Step 4313, Update Remaining power of the instantaneous energy storage system ;

[0289] if Proceed to step 432;

[0290] if Proceed to step 4314;

[0291] Step 4314, Statistics Remaining power of the instantaneous energy storage system The optimal feasible solution for all possible values and the corresponding objective function value ;

[0292] All objective function values The minimum value in is denoted as objective function value The corresponding optimal feasible solution is denoted as And record the objective function value. corresponding The value of the remaining power of the energy storage system at any time;

[0293] Optimal feasible solution This includes scheduling and charging plans for electric buses.

[0294] The other steps and parameters are the same as those in any of the specific implementation methods one to nine.

[0295] This invention may have other embodiments. Without departing from the spirit and essence of this invention, those skilled in the art can make various corresponding changes and modifications according to this invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A method for scheduling and charging of electric buses considering fluctuations on both the source and load sides, characterized in that: The specific process of the method is as follows: Step 1: Calculate the remaining battery power of the bus based on the optimized time period; Step 2: Calculate the remaining power of the energy storage battery based on the remaining power of the bus battery; Step 3: Based on the remaining power of the bus battery and the remaining power of the energy storage battery, construct an opportunity-constrained programming model; Step 4: Solve the opportunity-constrained programming model to obtain the optimal feasible solution, which includes the electric bus scheduling and charging dispatch scheme. In step 1, the remaining battery power of the bus is calculated based on the optimized time period; the specific process is as follows: Step 11: Optimize the time period to be the last operating time of the bus route on the same day. Until the last bus route operation time t the next day end The time range between; the optimization period is divided into night and day; the night time span is defined as Daytime duration is defined as This indicates the departure time of the first bus during the day; t represents the time. Step 12: Calculate the charging amount for the electric bus; the specific process is as follows: Electric bus m charges during the idle time between the end of its shift i and the start of its shift j, but the battery's state of charge (SOC) at the end of charging cannot exceed a certain value. m is the electric bus number, and M is the total number of electric buses equipped on the surveyed bus routes. Indicates the maximum SOC of the bus battery; The amount of electricity charged after electric bus m finishes its trip i The calculation formula is shown below: In the formula, the shift scheduling variable x m,i,j If electric bus m continues to run shift j after running shift i, then x ∈ {0, 1}. m,i,j =1; otherwise x m,i,j =0, i<j≤I, j is the shift number; P CP θ represents the charging power of the charging pile; θ represents the charging efficiency of the charging pile. This indicates the idle time of electric bus m after the end of shift i and before the start of shift j. This indicates that electric buses will be used in... Battery SOC charging time Time required; Step 13: Calculate the SOC of the battery at the destination and the SOC of the battery at the departure during the continuous operation of electric bus m for trips i, j, and k.

2. The method for scheduling and charging of electric buses considering both source and load fluctuations according to claim 1, characterized in that: In step 13, the SOC of the battery upon arrival and the SOC of the battery upon departure are calculated during the continuous operation of electric buses m for trips i, j, and k. The specific process is as follows: Step 131: Calculate the SOC of the battery when the electric bus arrives at the station m, as shown in the following formula: In the formula, Indicates that electric bus m is in Battery SOC at any given moment; This indicates the arrival time of train i at its originating station; E represents the energy consumption of shift j. EB This indicates the rated capacity of the battery in the electric bus; Calculate the operating energy consumption of shift j based on historical shift operation energy consumption data. mean μ j and variance Follows the mean μ j variance is The normal distribution, i.e. Step 132: Calculate the SOC of the electric bus m leaving the station based on step 131, as shown in the following formula: In the formula, Indicates that electric bus m is in Battery SOC at any given moment; This indicates the departure time of train number j at the originating station; Step 133: Calculate the SOC of the battery of electric bus m after it finishes its shift j and continues its shift k without recharging. The calculation formula is as follows: In the formula, Indicates that electric bus m is in Battery SOC at any given moment; The state of charge (SOC) of the battery when the electric bus m leaves the station indicates that it is not fully charged. This indicates the arrival time of train j at its originating station; The formula for calculating the SOC (State of Charge) of the battery of electric bus m after it leaves the station is as follows: (The formula is not shown in the original text.) In the formula, Indicates that electric bus m is in Battery SOC at any given moment; This indicates the departure time of train number k at the originating station; Step 134: Calculate the SOC of the electric bus m after it finishes its shift j, recharges, and continues its shift k. The calculation formula is shown below: In the formula, This indicates the amount of charge added to electric bus m after its last trip j. Step 135: Calculate the probability density function, mean, and variance of the battery SOC when electric bus m is not fully charged after the end of its journey j, and the probability of electric bus m being not fully charged after the end of its journey j.

3. The method for scheduling and charging of electric buses considering both source and load fluctuations according to claim 2, characterized in that: In step 2, the remaining power of the energy storage battery is calculated based on the remaining power of the bus battery; the specific process is as follows: Step 21: Calculate the cumulative charge received by the energy storage system from photovoltaic charging up to time t. As shown in the following formula: In the formula, P t PV η represents the actual photovoltaic power generation at time t; η is the efficiency of the photovoltaic power generation facility in delivering energy to the energy storage system. The actual photovoltaic power generation P at time t t PV The acquisition process is as follows: 1) Calculate the estimated value of photovoltaic power generation at time t. 2) Estimate the photovoltaic power generation at time t. The true value P of photovoltaic power generation at historical time t t PV By subtracting, we obtain the photovoltaic power generation estimation error term ΔP. PV ; Estimation error term ΔP for photovoltaic power generation under different weather scenarios PV ΔP PV Statistical variance σ 2 αP PV It follows a pattern with a mean of 0 and a variance of σ. 2 The normal distribution N(0,σ) 2 ), that is, ΔP PV ~N(0,σ 2 3) Estimated photovoltaic power generation at time t And the photovoltaic power generation estimation error term ΔP PV Calculate the actual photovoltaic power generation at time t. P represents the actual photovoltaic power generation. t PV Follow the mean The variance is σ 2 The normal distribution; Step 22: Calculate the cumulative amount of electricity provided by the energy storage system in the total amount of electricity consumed by the electric bus to complete charging up to time t. As shown in the following formula: In the formula, like but otherwise This indicates the arrival time of train i at its originating station; This indicates the departure time of train number j at the originating station; This indicates the charging method of electric bus i at time t. If energy storage is used for power supply, then otherwise δ(·) is an indicator function, when hour Select 1 if the value is 1, otherwise select 0. Step 23: Based on the cumulative charging amount obtained by the energy storage system from photovoltaic charging up to time t calculated in Step 21. And the accumulated electricity provided by the energy storage system from the electricity consumed by the electric bus to complete charging by time t in step 22. Calculate the remaining power of the energy storage system at time t.

4. The method for scheduling and charging of electric buses considering both source and load fluctuations according to claim 3, characterized in that: In step 23, the cumulative charge obtained by the energy storage system from photovoltaic charging up to time t is calculated based on step 21. And the accumulated electricity provided by the energy storage system from the electricity consumed by the electric bus to complete charging by time t in step 22. Calculate the remaining power of the energy storage system at time t. As shown in the following formula: In the formula, Indicates in The remaining power of the energy storage system at any given time.

5. The method for scheduling and charging of electric buses considering both source and load fluctuations according to claim 4, characterized in that: In step 3, a chance-constrained programming model is constructed based on the remaining power of the bus battery and the remaining power of the energy storage battery; the specific process is as follows: Step 31: The optimization objectives are to maximize the proportion of photovoltaic power in the energy consumption of the public transportation system (Z1) and minimize the total charging cost (Z2), as shown in the following formula: In the formula, Total overnight charging amount for electric buses and energy storage systems; c PV The price per unit of photovoltaic power generation is expressed in yuan / kWh; Let be the unit price of electricity supplied by the power grid at time t; The total cost of charging electric buses and energy storage systems from the grid at night; This indicates the charging method of electric bus i at time t. If the mains power supply is used, then otherwise If the electric bus m does not charge at time t, then Step 32: Set the constraints for the opportunity-constrained programming model.

6. The method for scheduling and charging of electric buses considering both source and load fluctuations according to claim 5, characterized in that: Step 32 involves setting constraints for the opportunity-constrained programming model; the specific process is as follows: The equation constraint for the total nighttime charging amount of the electric bus and energy storage system is shown in the following formula: In the formula, The last bus route of the day The remaining power of the energy storage system; The SOC of the electric bus m at the end of the day's bus route operation; Constraining the total cost of charging electric buses and energy storage systems from the grid at night. As shown in the following formula: The constraint requires that each shift be operated by an electric bus, as shown in the following formula: Electric buses are restricted to using only one charging method per charge, as shown in the following formula: The constraint is that the duration of a single charge for an electric bus must be greater than the minimum charging duration, as shown in the following formula: In the formula, T min Minimum charging duration for electric buses; The charging end time for electric bus m after its last trip i; The charging start time for electric bus m after its last trip i; The range of values ​​for the decision variables in a constrained-chance-constrained programming model is shown in the following formula: In the formula, E ES This refers to the rated capacity of the energy storage system's batteries. This is the lower limit of the SOC (State of Charge) of energy storage batteries; This represents the upper limit of the SOC (State of Charge) of energy storage batteries.

7. The method for scheduling and charging of electric buses considering both source and load fluctuations according to claim 6, characterized in that: In step 4, the opportunity-constrained programming model is solved to obtain the optimal feasible solution, which includes the electric bus scheduling and charging dispatch scheme; the specific process is as follows: Step 41: Transform the opportunity-constrained programming model; the specific process is as follows: Step 411: Perform a deterministic transformation on the chance-constrained programming model; the specific process is as follows: Constraints Transform into a definite equivalence class. The specific process is as follows: 1) Based on the actual photovoltaic power generation P t PV Distribution generation A random value, based on The vector P is composed of random values. PV ; Randomly generate I operating energy consumption values, respectively based on Forming vector E-; The energy consumption for electric bus route 1; The operating energy consumption of electric bus route 2; The operating energy consumption of electric bus route I; 2) Generate Ω P PV Number Generate Ω E - Number Introduce variable a for each scenario t,ω , variable a t,ω Indicates whether the constraint is satisfied. If so, a t,ω =1; otherwise, a t,ω =0; Each scene includes 1 pair and Constraints Transform into a definite equivalence class. Represented as: In the formula, The remaining charge of the energy storage system at time t represents the energy level at time t; M is a constant. Step 412: Linearize the opportunity-constrained programming model transformed in Step 411 to obtain the linearized opportunity-constrained programming model; the specific process is as follows: Based on intermediate variables Transforming equation (11) with Z1′, we obtain the following equation: Where Z1′=1 / Z1; Based on intermediate variables Transforming equation (12), we obtain the following equation: Based on intermediate variables We get the following formula: Based on intermediate variables We get the following formula: The constraints are as follows: In the formula, Represents random variables The distribution function; for The inverse function; α3 and α4 are pre-given confidence levels; Step 42: Decompose the original scheduling problem into a restricted master problem and a pricing subproblem using the Dantzig-Wolfe decomposition algorithm; The original scheduling problem is represented by equations (12), (13), (14), (15), (16), (17), (18), (21), (22), (23), (24), (25), (26), (27), (28), (29), (30), and (31). Step 43: Solve the original scheduling problem to obtain the optimal feasible solution, which includes the scheduling scheme for electric buses and the charging schedule.

8. The method for scheduling and charging of electric buses considering both source and load fluctuations according to claim 7, characterized in that: In step 42, the original scheduling problem is decomposed into a restricted master problem and a pricing subproblem using the Dantzig-Wolfe decomposition algorithm; the specific process is as follows: Step 421: Construct the constrained master problem; the specific process is as follows: 1) Define the decision variables S for the main problem. δ(m) ∈{0,1}, S ∈{0,1}, if and only if the electric bus m selects the vehicle scheduling and charging scheme δ(m), δ(m) =1, otherwise S δ(m) =0; 2) Introduce auxiliary variables like but otherwise 3) Calculate the amount of charge E already completed from the energy storage system at any time t when selecting the vehicle scheduling and charging scheme δ(m) for the electric bus m. m,δ(m),t As shown in the following formula: in, This represents the charging amount of electric bus m after its last trip i; δ(·) is an indicator function, when... hour Select 1 if the value is 1, otherwise select 0. 4) Calculate the remaining power of the energy storage system under the selected vehicle scheduling and charging scheme. As shown in the following formula: Among them, E m,δ(m),t This represents the amount of charge that the electric bus m has completed from the energy storage system at any time t when the vehicle scheduling and charging scheme δ(m) is selected. express The remaining power of the energy storage system at all times; 5) Calculate the cost C of implementing the vehicle scheduling and charging plan δ(m) for electric buses m. δ(m) As shown in the following formula: In the formula, Z 1,m With Z 2,m These are the green electricity ratio and total charging cost for electric buses (m); the green electricity ratio is the proportion of photovoltaic power in the energy consumption of the public transportation system. Let be the set of objective functions for electric buses, where v = 1 represents the green energy ratio of the objective function, v = 2 represents the total charging cost of the objective function; λ v Let λ1 or λ2 represent the weights of the green electricity ratio and the total charging cost, respectively, where λ1 + λ2 = 1, 0 < λ1 < 1, and 0 < λ2 < 1; Z v,m Z represents 1,m or Z 2,m ; The constrained master problem model is constructed as follows: Step 422: Construct the pricing subproblem; the specific process is as follows: Introducing π i and π m As dual variables to constrain constraints (38) and (39) in the main problem; introduced and As the two dual variables corresponding to the upper and lower bounds of the bilateral constraint (40); Calculate the test number expression for the restricted principal problem (43): The pricing sub-problem model is constructed as follows:

9. The method for scheduling and charging of electric buses considering both source and load fluctuations according to claim 8, characterized in that: In step 43, the original scheduling problem is solved to obtain the optimal feasible solution, which includes the electric bus scheduling and charging dispatch scheme; the specific process is as follows: Step 431, Remaining power of the instantaneous energy storage system The value range is discrete in 1kWh increments, and the lower limit of the energy storage system's capacity is set as... Remaining power of the instantaneous energy storage system Step 432: Develop an initial electric bus scheduling and charging dispatch plan using empirical methods. This initial plan serves as the initial solution, which is also the root node in the branch and bound algorithm and the node to be branched. Let the current optimal feasible solution be X. * The objective function value corresponding to the current optimal feasible solution is F. * ; Step 433: Set the iteration number gen = 0, set the initial lower bound LB of the branch and bound algorithm to 0, and use the objective function value of the initial feasible solution as the initial upper bound UB; Step 434: Using the transformed opportunity-constrained programming model as the original scheduling problem, decompose the original scheduling problem into a restricted master problem and a pricing subproblem; Step 435: Solve the restricted master problem to obtain the dual variable π of constraint (38). i The dual variable π of constraint (39) m Dual variables of bilateral constraint equation (40) and The dual variable π based on constraint (38) i The dual variable π of constraint (39) m Dual variables of bilateral constraint equation (40) and Construct a pricing subproblem; Step 436: Solve the pricing subproblem and determine whether the test number of the optimal solution to the pricing subproblem is negative; If the test number of the optimal solution to the pricing subproblem is negative, it means that there is still a feasible vehicle scheduling scheme with optimization space. All vehicle scheduling schemes with negative test numbers in the process of solving the pricing subproblem are added to the restricted main problem, and the process returns to step 435. If the test number of the optimal solution to the pricing subproblem is positive, then the test numbers of all vehicle scheduling schemes generated during the solution of the pricing subproblem are positive, and proceed to step 437. Step 437: Denote the optimal solution to the restricted principal problem at the node containing the gen-th iteration as X. gen X gen The corresponding objective function value is denoted as Z. gen ; Step 438, if Z gen <UB, and S δ(m) is not an integer, proceed to step 439; If Z gen <UB, and S δ(m) If it is 0 or 1, then update X. * =X gen UB=Z * =Z gen Delete the current node and proceed to step 4311; If Z gen ≥UB, delete the current node and proceed to step 4311; Step 439: Select the S value closest to 0.5 δ(m) and S δ(m) The corresponding electric bus m; then, by adding constraints S to the pricing subproblem of the current node. δ(m) =1 to construct the left child node, by adding constraint S to the pricing subproblem of the current node. δ(m) =0 to construct the right child node, and let LB = Z for both the left and right child nodes. gen ; Step 4310: Set the left child node as the new current node, add the right child node to the list of nodes to be processed, update the iteration count gen = gen + 1, and return to step 435; Step 4311: Determine if the current node list still contains nodes to be processed. If the list of nodes to be processed is not empty, select the node with the smallest LB value in the list as the new current node, update the iteration count gen = gen + 1, and return to step 435. If the list of nodes to be processed is empty, proceed to step 4312; Step 4312: Record the optimal feasible solution X * and the corresponding objective function value Z * ; Step 4313, Update Remaining power of the instantaneous energy storage system if Proceed to step 432; if Proceed to step 4314; Step 4314, Statistics Remaining power of the instantaneous energy storage system The optimal feasible solution X for all possible values * and the corresponding objective function value Z * ; All objective function values ​​Z * The minimum value in is denoted as objective function value The corresponding optimal feasible solution is denoted as And record the objective function value corresponding The value of the remaining power of the energy storage system at any time; Optimal feasible solution This includes scheduling and charging plans for electric buses.

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