Electric bus scheduling and charging scheduling method considering source-load bilateral fluctuation

Abstract

The invention belongs to the technical field of urban public transport management, and particularly relates to an electric public transport scheduling and charging scheduling method considering source-load double-side fluctuation. The objective of the invention is to solve the problems that a public transportation system has source-load double-side fluctuation due to the intermittency of existing source-side photovoltaic power generation and the randomness of load-side public transportation operation energy consumption, so that the charging demand and the power supply capacity in part of time periods are not matched, the operation task cannot be completed in time, and the energy storage utilization efficiency is low. The invention provides an electric bus scheduling and charging scheduling method considering source-load double-side fluctuation. The method comprises the steps of calculating bus battery remaining capacity based on an optimization time period; calculating the remaining capacity of the energy storage battery based on the remaining capacity of the bus battery; based on the bus battery remaining capacity and the energy storage battery remaining capacity, constructing a chance constraint planning model; and solving the opportunity constraint planning model to obtain an optimal feasible solution which comprises an electric bus scheduling and charging scheduling scheme.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban public transport management, and particularly relates to an electric bus scheduling and charging scheduling method. BACKGROUND

[0002] The urban public transport system is gradually transforming towards electrification and intelligentization. Electric buses have become an important carrier for green transportation development due to their zero emission and low noise during operation. However, the current electric bus system mainly relies on power grid power supply based on thermal power generation, and the carbon emission benefit needs to be further improved. At the same time, the centralized charging mode of buses at night also brings a large load pressure to the power grid.

[0003] The traditional single power grid power supply mode is changed to a hybrid power supply mode of photovoltaic, energy storage and power grid cooperation, which brings a new way to solve the above problems. Under the hybrid power supply mode of photovoltaic, energy storage and power grid, electric buses can preferentially use photovoltaic power generation to realize daytime power compensation, effectively reducing the power cost and the impact on the power grid, and promoting the utilization of renewable energy and the deep decarbonization of public transport enterprises. However, the intermittency of source-side photovoltaic power generation and the randomness of load-side bus operation energy consumption cause fluctuations on both source and load sides of the bus system, resulting in problems such as mismatch between charging demand and power supply capacity, inability to complete operation tasks in time, and low utilization efficiency of energy storage.

[0004] The existing related technology assumes that the energy consumption of electric buses and the photovoltaic power generation power are stable values, which obviously cannot adapt to the operation demand of electric buses under the random fluctuations of source and load. Therefore, it is urgent to establish an electric bus scheduling and charging scheduling method considering the fluctuations on both source and load sides in view of the operation characteristics of the current electric bus system. SUMMARY

[0005] The purpose of the present application is to solve the problem of the intermittency of source-side photovoltaic power generation and the randomness of load-side bus operation energy consumption, which causes fluctuations on both source and load sides of the bus system, resulting in problems such as mismatch between charging demand and power supply capacity, and low utilization efficiency of energy storage. Therefore, an electric bus scheduling and charging scheduling method considering fluctuations on both source and load sides is proposed.

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

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

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

[0009] Step 3, constructing an opportunity constraint programming model based on the remaining battery power of the bus and the remaining battery power of the energy storage battery;

[0010] Step 4, solving the chance-constrained programming model to obtain an optimal feasible solution, and the optimal feasible solution comprises the electric bus scheduling and charging scheduling scheme.

[0011] The present application has the following advantages:

[0012] The present application considers the bilateral fluctuation of source and load in the hybrid power supply mode of photovoltaic, energy storage and power grid, calculates the probability distribution of operation energy consumption, calculates the remaining battery capacity of the electric bus based on the probability distribution of operation energy consumption, calculates the remaining battery capacity of the energy storage battery based on the probability distribution of operation energy consumption and the remaining battery capacity of the electric bus, constructs a chance-constrained programming model based on the remaining battery capacity of the electric bus and the remaining battery capacity of the energy storage battery, solves the chance-constrained programming model to obtain an optimal feasible solution, and the optimal feasible solution comprises the electric bus scheduling and charging scheduling scheme; the electric bus scheduling and charging scheduling method considering the bilateral fluctuation of source and load can effectively deal with the problem that the charging demand and the power supply capacity do not match, and the operation task cannot be completed in time; the present application improves the flexibility and adaptability of the scheduling scheme, fully utilizes the photovoltaic resources and the energy storage system, realizes the preferential use of clean energy, reduces the system carbon emission from the source, enhances the operation stability and economy in complex environment, and solves the problem of low energy storage utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0014] Specific implementation one: combining Figure 1 The specific process of the electric bus scheduling and charging scheduling method considering the bilateral fluctuation of source and load in the present embodiment is as follows:

[0015] Step 1, calculating the remaining battery capacity of the electric bus based on the optimization period;

[0016] Step 2, calculating the remaining battery capacity of the energy storage battery based on the remaining battery capacity of the electric bus;

[0017] Step 3, constructing a chance-constrained programming model based on the remaining battery capacity of the electric bus and the remaining battery capacity of the energy storage battery;

[0018] Step 4, solving the chance-constrained programming model to obtain an optimal feasible solution, and the optimal feasible solution comprises the electric bus scheduling and charging scheduling scheme.

[0019] Specific implementation two: the difference between the present embodiment and specific implementation one is that the remaining battery capacity of the electric bus is calculated based on the optimization period in step 1; the specific process is as follows:

[0020] Step 11, the optimization period is: the end time of the bus line operation 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 The amount of charge after 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, execution shift Idle time before, unit: min; Indicates the electric bus In The time required to charge the battery SOC to The time required, unit: min; Indicates the electric bus In The battery SOC at the time, unit: %; Indicates the shift Departure time at the starting station; Indicates the shift Arrival time at the starting station; Indicates the bus battery capacity;

[0031] Calculate the charging start time and end time; the specific process is:

[0032] Calculate the electric bus End of shift After the charging start time And the charging end time :

[0033]

[0034]

[0035] Step 13, calculate the electric bus Continuous operation shift , , The process of arriving at the station battery SOC and leaving the station battery SOC.

[0036] Other steps and parameters are the same as in the first embodiment.

[0037] The third embodiment is different from the first or second embodiment: in step 13, the arriving station battery SOC and the leaving station battery SOC during the continuous operation shift of the electric bus are calculated; the specific process is: , ,

[0038] Step 131, calculate the electric bus Arriving at the station battery SOC, the calculation formula is as follows:

[0039] (2)

[0040] In the formula, Indicates the 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:

[0050] 1). Calculate the electric bus The mean and variance of the off-site battery SOC in the case of not being fully charged; the specific process is:

[0051] Electric bus The off-site battery SOC in the case of not being fully charged is ;

[0052] Subject to normal distribution, calculate the mean and variance :

[0053]

[0054]

[0055] The value of in the above is 1 to a large , the loop of below the summation formula is 0 to , and below the summation formula is also 0 to a large ;

[0056] In the formula, represents the mean of the energy consumption of the jth shift, and represents the variance of the energy consumption of the jth shift;

[0057] 2). Calculate the probability of the electric bus not being fully charged; the specific process is:

[0058]

[0059]

[0060] In the formula, represents the probability;

[0061] represents the probability of ;

[0062] represents the probability of ;

[0063] represents the probability of ;

[0064] represents 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] wherein, denotes the mean value of the battery SOC at the start of the shift k of the electric bus m, denotes the variance of the battery SOC at the start of the shift k of the electric bus m; denotes the mean value of ; denotes the mean value of ; denotes the mean value of the energy consumption of the shift j, denotes the variance of the energy consumption of the shift j;

[0079] Step 134, calculate the battery SOC of the electric bus m at the off-site after charging and continuing to perform the shift k after the end of the shift j of the electric bus m (not knowing whether the electric bus m is fully charged), the calculation formula is as follows:

[0080] (6)

[0081] wherein, denotes the charging amount of the electric bus after the end of the shift ;

[0082] Step 135, calculate the probability density function, mean value, variance of the off-site battery SOC of the electric bus after the end of the shift j without being fully charged, and the occurrence probability of the electric bus after the end of the shift j without being fully charged; the specific process is as follows:

[0083] Step 1351, calculate the probability density function, mean value and variance of the off-site battery SOC of the electric bus after the end of the shift j without being fully charged; the specific process is as follows:

[0084] The off-site battery SOC of the electric bus after the end of the shift j without being fully charged is , subject to normal distribution, calculate the probability density function of :

[0085]

[0086]

[0087]

[0088] wherein, the shift scheduling variable , if the electric bus is performing the shift Continue to perform shift If ; otherwise , , is the shift number; represents the idle time of the electric bus m after finishing shift j and before performing shift k, in min;

[0089] Step 1352, calculate the probability of the electric bus m not being fully charged after finishing shift j; represented as:

[0090]

[0091] In the formula, is the distribution function of the random variable ; represents the battery SOC of the electric bus m at time , in %; represents the time required to charge the battery SOC of the electric bus m at time to , in min; represents the probability of ;

[0092] represents the probability of ; represents the probability of .

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

[0094] Embodiment four: the difference between this embodiment and one of embodiments one to three is that the remaining capacity of the energy storage battery is calculated based on the probability distribution of the operating energy consumption and the remaining capacity of the bus battery in step 2; the specific process is:

[0095] Step 21, calculate the cumulative charging capacity of the energy storage system obtained from photovoltaic charging up to time t , as shown in the following formula:

[0096] (7)

[0097] In the formula, is the actual photovoltaic power at time ; is the efficiency of the photovoltaic power generation facility delivering energy to the energy storage system, in %, and obeys 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] the photovoltaic power generation power estimation error term under the corresponding weather scenario the probability density function of the photovoltaic power generation power estimation error term is as shown in the following formula:

[0109]

[0110] In the formula, the photovoltaic power generation power estimation error term the probability density function of the photovoltaic power generation power estimation error term; the standard deviation of the photovoltaic power generation power estimation error term

[0111] 3), based on the estimated value of the photovoltaic power generation power at the moment and the photovoltaic power generation power estimation error term , the actual photovoltaic power generation power at the moment is calculated , ; the actual photovoltaic power generation power obeys a normal distribution with a mean of and a variance of ; the specific process is as follows:

[0112]

[0113] wherein, the departure time of the shift 1 at the starting station is represented; the time when the next day's bus line operation ends is represented;

[0114] Step 22, the cumulative electric quantity provided by the energy storage system in the electric quantity consumed by the electric bus for charging completed by the time t is calculated as shown in the following formula:

[0115] (8)

[0116] In the formula, if , then , otherwise ; the arrival time of the shift at the starting station is represented; the departure time of the shift at the starting station is represented; the charging mode of the electric bus at the moment is represented; if the energy storage is used for power supply, then , otherwise ; is an indicator function, when​ hour Select 1 if the value is 1, otherwise select 0.

[0117] The specific process is as follows:

[0118] according to The mean of a normally distributed system 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 the mean value of and variance , calculate the mean value of and variance , the specific process is:

[0128]

[0129]

[0130] In the formula, represents the remaining energy of the energy storage system at .

[0131] The other steps and parameters are the same as one of the first to fourth embodiments.

[0132] Embodiment six: The difference between this embodiment and the first to fifth embodiments is that in step 3, an opportunity constraint programming model is constructed based on the remaining energy of the bus battery and the remaining energy of the energy storage battery; the specific process is:

[0133] Step 31, the photovoltaic power source proportion in the bus system energy consumption is maximized and the total charging cost is minimized as the optimization objective, as shown in the following formula:

[0134] (10)

[0135] (11)

[0136] In the formula, is the total charging amount of the electric bus and the energy storage system at night, with the unit of kWh; is the photovoltaic power generation unit price, with the unit of yuan / kWh; is the power grid power supply unit price at t time, with the unit of yuan / kWh; is the total cost of the electric bus and the energy storage system charging from the power grid at night, with the unit of yuan; represents the charging mode of the electric bus at time, ; if the power grid power supply is adopted, then , otherwise ; if the electric bus does not charge at t time, then ;

[0137] Step 32, the constraint condition of the opportunity constraint programming model is set.

[0138] The other steps and parameters are the same as one of the first to fifth embodiments.

[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] Constrained electric bus Battery SOC and the remaining energy of the energy storage system are within a specified interval:

[0156]

[0157]

[0158]

[0159] wherein, is a pre-specified confidence level; is the objective function at the maximum value (known) taken when the confidence level is at least ; is the objective function at the minimum value (known) taken when the confidence level is at least ; is the lower limit of the bus battery SOC, in %; is the upper limit of the bus battery SOC, in %; is the lower limit of the energy storage battery SOC, in %; is the upper limit of the energy storage battery SOC, in %; denotes the battery SOC at the end of the execution of a shift of the electric bus; denotes the battery SOC at the start of the execution of a shift of the electric bus; denotes the remaining energy of the energy storage system at the time ; denotes the rated capacity of the battery of the energy storage system; denotes the probability of ; denotes the probability of ; denotes the probability of ; denotes the probability of ; denotes the probability of ; denotes the probability of ;

[0160] The decision variables of the constrained chance-constrained programming model are constrained to take values in the following ranges:

[0161] (17)

[0162] (18)

[0163] wherein, is the rated capacity of the energy storage system battery; is the lower limit of the energy storage battery SOC, in %; is the upper limit of the energy storage battery SOC, in %.

[0164] The other steps and parameters are the same as one of the first to sixth embodiments.

[0165] The eighth embodiment is different from one of the first to seventh embodiments in that the step 4 is to solve the chance-constrained programming model to obtain an optimal feasible solution, and the optimal feasible solution includes the electric bus scheduling and charging scheduling scheme; and the specific process is as follows:

[0166] Step 41, transforming the chance-constrained programming model; the specific process is as follows:

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

[0168] transforming the constraint into a deterministic equivalent class:

[0169]

[0170] transforming the constraint into a deterministic equivalent class:

[0171]

[0172] wherein,

[0173] denotes the distribution function of the random variable ;

[0174] is the inverse function of ;

[0175] transforming the constraint into a deterministic equivalent class, ; the specific process is as follows:

[0176] 1) generating a number of random values according to the distribution of the actual photovoltaic power ; based on the number of random values, a vector is formed;

[0177] randomly generating a number of running energy consumption values, respectively ; based on the number of running energy consumption values, a vector ​constituting vectors ;

[0178] respectively subject to normal distribution with mean and variance ;

[0179] wherein, is the operation energy consumption of electric bus service 1; is the operation energy consumption of electric bus service 2; is the operation energy consumption of electric bus service ;

[0180] is the mean of operation energy consumption of electric bus service 1; is the mean of operation energy consumption of electric bus service 2; is the mean of operation energy consumption of electric bus service ;

[0181] is the variance of operation energy consumption of electric bus service 1; is the variance of operation energy consumption of electric bus service 2; is the variance of operation energy consumption of electric bus service ;

[0182] 2) generating , numbered , , ; , taking positive integer value, ;

[0183] generating , numbered , , ;

[0184] introducing variable for each scenario, variable represents whether constraint is satisfied, if yes, ; otherwise, ;

[0185] each scenario includes 1 pair of and ;

[0186] transforming constraint into a certain equivalence class, ; represented as:

[0187] (19)​

[0188]

[0189] (20)

[0190] In the formula, Indicates The remaining power of the energy storage system at the moment;

[0191] M is a constant large enough to make the inequality hold, which is an extreme parameter;

[0192] The variable In formula (19) is equal to 1, and the inequality (19) holds; the variable Is not equal to 1, and the inequality (19) also holds; formula (20) counts The case where the variable Is equal to 1, and requires that the variable In all scenarios is large enough;

[0193] Step 412, linearize the chance-constrained programming model converted in step 411 to obtain a linearized chance-constrained programming model; the specific process is:

[0194] The shift scheduling variable And the charging scheduling variable There is a product item between them, and the objective function Of formula (10) is a fraction, so the chance-constrained programming model still has nonlinearity, so an intermediate variable , And Is introduced:

[0195]

[0196]

[0197] Based on the intermediate variables , And , formula (11) is converted to the following formula:

[0198] (21)

[0199] Wherein ;

[0200] Based on the intermediate variable , formula (12) is converted to the following formula:

[0201] (22)

[0202] Based on the intermediate variable 、 transforming formula (23) into formula (24):

[0203] (23)

[0204] based on the intermediate variable 、 transforming formula (23) into formula (24):

[0205] (24)

[0206] The constraints are as follows:

[0207] (25)

[0208] (26)

[0209] (27)

[0210] (28)

[0211] (29)

[0212] (30)

[0213] (31)

[0214] wherein, denotes the distribution function of the random variable ; is the inverse function of ; is a pre-given confidence level;

[0215] Step 42, decompose the original scheduling problem into a restricted master problem and a pricing sub-problem according to the Dantzig-Wolfe decomposition algorithm;

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

[0217] Step 43, solve the original scheduling problem to obtain an optimal feasible solution, and the optimal feasible solution includes an electric bus vehicle scheduling and charging scheduling scheme.

[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 of as shown in the following formula:

[0230] (34)

[0231] (35)

[0232] (36)

[0233] wherein, and respectively represent the green electricity ratio and the total charging cost of the electric bus m; the green electricity ratio is the ratio of the photovoltaic electricity energy in the energy consumption of the bus system; is a set of objective functions of the electric bus, wherein represents the objective function green electricity ratio, and represents represents the objective function total charging cost; represents or , and respectively represent the weight of the green electricity ratio and the total charging cost, wherein , , ; represents or ;

[0234] The main problem model is constructed as shown in the following formula:

[0235] (37)

[0236] (38)

[0237] (39)

[0238] (40)

[0239] (41)

[0240] (42)

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

[0242] Formula (38) ensures that all classes are served by one and only one electric bus within the operation time;

[0243] Formula (39) ensures that each electric bus can only select one feasible solution;

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

[0245] Constraint (41) indicates that the frequency of feasible scenarios (the ratio of scenarios that meet the constraints to the total number of scenarios generated in Monte Carlo simulation) is used to control the overall feasibility under uncertainty (not all scenarios generated in Monte Carlo simulation need to meet the constraints under random factors (energy consumption fluctuations, photovoltaic output fluctuations), and the frequency greater than a certain confidence level is sufficient) without directly participating in the structure of a single column (without generating dual variables and participating in the solution of the subproblem, the solution of the subproblem is a column coefficient of the main problem);

[0246] Step 422, constructing a pricing subproblem; the specific process is:

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

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

[0249] Calculate the test number expression (43) of the limiting main problem:

[0250] (43)

[0251] Construct the pricing subproblem model as shown in the following equation:

[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 (vehicle scheduling scheme) of 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.

[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] The present application can have other various embodiments, and those skilled in the art can make various corresponding changes and modifications according to the present application without departing from the spirit and essence of the present application, and these corresponding changes and modifications shall all belong to the protection scope of the claims of the present application.

Claims

1. A method for scheduling and charging of electric buses considering bilateral fluctuations of source load, characterized in that: The method specifically comprises the following steps: Step 1, calculating the remaining battery capacity of the electric bus based on an optimized time period; Step 2, calculating the remaining battery capacity of the energy storage battery based on the remaining battery capacity of the electric bus; Step 3, constructing an opportunity constraint programming model based on the remaining battery capacity of the electric bus and the remaining battery capacity of the energy storage battery; Step 4, solving the opportunity constraint programming model to obtain an optimal feasible solution, and the optimal feasible solution comprises an electric bus scheduling and charging scheduling scheme. 2.The method of claim 1, wherein the method further comprises: In the step 1, the remaining battery capacity of the electric bus is calculated based on an optimized time period, and 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 service of the following day The time range between; the optimization period is divided into night and day; the night time span is defined as The daytime span is defined as ; Indicates the departure time of the first bus during the day; Indicates time; Step 12, calculating the charging capacity of the electric bus; the specific process is as follows: Electric bus At the end of the shift , the idle time before the start of the shift , but the battery SOC at the end of charging cannot be greater than ; , Number of electric buses, Total number of electric buses equipped on the bus line under investigation; Upper limit of bus battery SOC; Electric bus End shift Charging amount after The calculation formula is as follows: (1) In the formula, the shift scheduling variable , if the electric bus executes the shift after executing the shift , then ; otherwise , , is the shift number; represents the charging power of the charging pile; represents the charging efficiency of the charging pile; represents the idle time of the electric bus after ending the shift before executing the shift ; represents the time required to charge the battery SOC of the electric bus at the time to . Step 13, calculating electric bus Continuous operating shift , , Station battery SOC and off-station battery SOC during the process. 3.The method of claim 2, wherein the method further comprises: The step 13 calculates the electric bus Continuous operation shift , , The station battery SOC and the off-station battery SOC in the process; the specific process is: Step 131, calculating the electric bus The battery SOC at the station, as shown in the following formula: (2) In the formula, representing an electric bus At the battery SOC at the time; representing a shift the arrival time at the starting station; representing a shift the operating energy consumption; representing the rated capacity of the electric bus battery; Calculate the historical trip energy consumption data Running energy consumption The mean And variance , Subject to normal distribution with mean Variance , that is ; Step 132, calculating the electric bus based on step 131 Off-site battery SOC, as shown in the following formula: (3) In the formula, representing an electric bus At the battery SOC at the time; representing a shift departure time at the starting station; Step 133, calculating the battery SOC of the electric bus m at a station after the electric bus m ends the jth shift and continues to perform the kth shift without charging, and the calculation formula is as shown in the following formula: (4) In the formula, the battery SOC of the electric bus m at the time point t; the battery SOC of the electric bus m at the time point t; the battery SOC of the electric bus m at the time point t; the time point t at which the electric bus m returns to the starting station; the time point t at which the electric bus m returns to the starting station;​ Step 133, calculating the battery SOC of the electric bus m at a station after the electric bus m ends the jth shift and continues to perform the kth shift without charging, and the calculation formula is as shown in the following formula: (5) In the formula, representing an electric bus At the battery SOC at the time; representing the departure time of shift k at the starting station; Step 134, calculating the battery SOC of the electric bus m at a station after the electric bus m ends the jth shift and continues to perform the kth shift after charging, and the calculation formula is as shown in the following formula: (6) In the formula, representing an electric bus end of shift after the charging amount; Step 135, Calculate the electric bus Probability density function, mean, variance of the off-station battery SOC when ending shift j with not fully charged, and the electric bus Probability of occurrence when ending shift j with not fully charged.

4. The electric bus scheduling and charging scheduling method considering source-load bilateral fluctuation according to claim 3, characterized in that: In the step 2, the remaining battery capacity of the energy storage battery is calculated based on the remaining battery capacity of the electric bus, and the specific process is as follows: Step 21, calculate the cumulative charge obtained by the energy storage system from the photovoltaic charging up to time t As shown in the following formula: (7) In the formula, is the actual photovoltaic power at the moment; is the efficiency of the photovoltaic power generation facility to deliver energy to the energy storage system; The Actual photovoltaic power at the moment The acquisition process is: 1) Calculation Estimated photovoltaic power generation at any given time ;2) will Estimated photovoltaic power generation at any given time With history True value of photovoltaic power generation at any given time By subtracting, we obtain the error term for photovoltaic power generation estimation. 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 ;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; Step 22, calculate the accumulated electric quantity provided by the energy storage system in the electric quantity consumed by the electric bus for completing charging at time t As shown in the following formula: (8) In the formula, , if , then , otherwise ; denotes a shift of the bus returning to the starting station at the arrival time; denotes a shift of the bus at the departure time from the starting station; denotes the charging mode of the electric bus at the time , and ; If energy storage is used, then , else ; is an indicator function that takes the value 1 when and 0 otherwise . Step 23, calculating the accumulated charge amount obtained by the energy storage system from the photovoltaic charging station based on the calculation of step 21 and the accumulated electric amount provided by the energy storage system in the electric bus charging station consumed by the electric bus completed charging up to time t of step 22 , calculating the residual electric amount of the energy storage system at time t .

5. The method of claim 4, wherein the method further comprises: 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: (9) In the formula, indicates the remaining energy of the energy storage system at time.

6. The method of claim 5, wherein the method further comprises: In the step 3, the opportunity constraint programming model is constructed based on the remaining battery capacity of the electric bus and the remaining battery capacity of the energy storage battery, and the specific process is as follows: Step 31, proportion of photovoltaic power in bus system energy consumption Maximizing total charging cost Minimizing as an optimization objective, as shown in the following formula: (10) (11) In the formula, is the total charging amount of the electric bus and the energy storage system at night; is the unit price of photovoltaic power generation, in yuan / kWh; is the unit price of power supply at time t; is the total cost of charging the electric bus and the energy storage system from the power grid at night; represents the electric bus at time t, ; If the power grid is used for power supply, then , otherwise ; if the electric bus is not charged at time t, then ; Step 32, setting the constraint conditions of the opportunity constraint programming model.

7. The method of claim 6, wherein the method further comprises: In the step 32, the constraint conditions of the opportunity constraint programming model are set, and the specific process is as follows: The equality constraint of the total charging capacity of the electric bus and the energy storage system at night is as shown in the following formula: (12) In the formula, The last bus route of the day The remaining power of the energy storage system; Electric buses at the end of the day's bus route operation Battery SOC; Total cost of constraining electric buses and energy storage systems to charge from the grid at night as shown in the following formula: (13) The constraint that each shift is performed by an electric bus is as shown in the following formula: (14) The constraint that the electric bus can only have one charging mode each time is as shown in the following formula: (15) The constraint that the duration of single charging of the electric bus is greater than the minimum charging duration is as shown in the following formula: (16) wherein is the minimum charging duration for the electric bus; is the electric bus end shift charging end time of the post-charging; is the electric bus end shift charging start time of the post-charging; The constraint that the value range of the decision variable of the opportunity constraint programming model is as shown in the following formula: (17) (18) wherein C is the energy storage system battery rated capacity; Cmin is the energy storage battery SOC lower limit; Cmax is the energy storage battery SOC upper limit. 8.The method of claim 7, wherein the method further comprises: In the step 4, the opportunity constraint programming model is solved to obtain an optimal feasible solution, and the optimal feasible solution comprises an electric bus scheduling and charging scheduling scheme; the specific process is as follows: Step 41, transforming the opportunity constraint programming model; the specific process is as follows: Step 411, performing deterministic transformation on the opportunity constraint programming model; the specific process is as follows: imposing constraints transforming into a determined equivalence class, ; the specific process is: 1) generating a random value based on the distribution of the actual photovoltaic power ;​​​ randomly generated a respective operating energy consumption value , based on a respective vector ; is the operating energy consumption of the electric bus service 1 ; is the operating energy consumption of the electric bus service 2 ; is the operating energy consumption of the electric bus service ; 2), generating one , numbered , ; generating one , numbered , ; Introduce a variable for each scenario , the variable represents whether the constraint is satisfied , if so, ; Otherwise, ; each scene includes 1 pair of and ; imposing constraints transforming into a determined equivalence class, ; is represented as: (19) (20) In the formula, represents the residual power of the energy storage system at the moment; M is a constant; Step 412, linearizing the opportunity constraint programming model after the transformation in the step 411 to obtain a linearized opportunity constraint programming model; the specific process is as follows: Based on intermediate variables , and Converting formula (11) gives the following formula: (21) wherein ; Based on intermediate variables Transforming equation (12) gives the following equation: (22) Based on the intermediate variable , The following equation is obtained: (23) Based on the intermediate variable , The following equation is obtained: (24) The constraints are as follows: (25) (26) (27) (28) (29) (30) (31) wherein denotes the distribution function of the random variable ; is the inverse function of ; is a pre-given confidence level; Step 42, decomposing the original scheduling problem into a limited main problem and a pricing sub-problem according to the Dantzig-Wolfe decomposition algorithm; The original scheduling problem is formula (12), formula (13), formula (14), formula (15), formula (16), formula (17), formula (18), formula (21), formula (22), formula (23), formula (24), formula (25), formula (26), formula (27), formula (28), formula (29), formula (30), formula (31); Step 43, solving the original scheduling problem to obtain an optimal feasible solution, and the optimal feasible solution comprises an electric bus scheduling and charging scheduling scheme. 9.The method of claim 8, wherein the method further comprises: The step 42 decomposes the original scheduling problem into a restricted master problem and a pricing sub-problem according to the Dantzig-Wolfe decomposition algorithm; the specific process is as follows: The step 421 constructs the restricted master problem; the specific process is as follows: 1) Define the master problem decision variables if and only if electric bus m chooses the vehicle scheduling and charging scheme , , otherwise ; 2) Introduce auxiliary variables if then else ; 3) Calculate the electric bus m selection vehicle scheduling and charging scheme the amount of charging completed from the energy storage system at any time t as shown in the following formula: (32) wherein represents the amount of charging after the electric bus m ends the shift i; is an indicator function, which takes the value when 1, otherwise 0. 4) Calculate the remaining energy of the energy storage system under the selected vehicle scheduling and charging scheme As shown in the following formula: (33) wherein, represents the electric bus m selects the vehicle scheduling and charging scheme the amount of charge completed from the energy storage system at any time t; represents the amount of charge remaining in the energy storage system at time t; 5) Calculate the cost of the electric bus m performing the vehicle scheduling and charging scheme As shown in the following equation:​ (34) (35) (36) In the formula, and respectively represent the green electricity ratio and the total charging cost of the electric bus m; the green electricity ratio is the proportion of photovoltaic electricity in the energy consumption of the bus system; is a set of objective functions of the electric bus, wherein represents the objective function of the green electricity ratio, and represents represents the objective function of the total charging cost; represents or , and respectively represent the weight of the green electricity ratio and the total charging cost, wherein , , ; represents or ; The restricted master problem model is constructed as shown in the following formula: (37) (38) (39) (40) (41) (42) The step 422 constructs the pricing sub-problem; the specific process is as follows: Introduce and as dual variables of the constraints (38) and (39) in the master problem; introduce and as two dual variables corresponding to the lower and upper bounds of the bilateral constraint (40); The checking number expression (43) of the restricted master problem is calculated: (43) The pricing sub-problem model is constructed as shown in the following formula: (44) (45) (46) (47) (48) (49) (50) (51) (52) (53) (54) (55)。 10. The method of claim 9, wherein the method further comprises: The step 43 solves the original scheduling problem to obtain an optimal feasible solution, and the optimal feasible solution includes an electric bus scheduling and charging scheduling scheme; the specific process is as follows: Step 431, obtaining the value interval of the remaining energy of the energy storage system at the time point is discretized at intervals of 1 kWh, and the lower limit value of the energy of the energy storage system is set to the value interval of the remaining energy of the energy storage system at the time point ; Step 432, formulating an initial electric bus scheduling and charging scheduling scheme by an empirical method, taking the initial electric bus scheduling and charging scheduling scheme as an initial solution, the initial solution being a root node in a branch and bound algorithm part and also a node to be branched at present; setting a current optimal feasible solution as , and a target function value corresponding to the current optimal feasible solution as ; Step 433, let the iteration number the initial lower bound of the branch and bound algorithm is set to 0 and the objective function value of the initial feasible solution is taken as the initial upper bound is set to 0 and the objective function value of the initial feasible solution is taken as the initial upper bound ; The step 434 takes the transformed chance-constrained programming model as the original scheduling problem, and decomposes the original scheduling problem into a restricted master problem and a pricing sub-problem; Step 435, solve the restricted master problem to obtain the dual variables of constraint (38) , the dual variables of constraint (39) , the dual variables of bilateral constraint (40) and ; dual variables of constraint (38) dual variables of constraint (39) dual variables of bilateral constraint (40) and constructing the pricing subproblem; The step 436 solves the pricing sub-problem, and judges whether the checking number of the optimal solution of the pricing sub-problem is negative; If the checking number of the optimal solution of the pricing sub-problem is negative, it indicates that there still exists a vehicle scheduling scheme which is feasible and has optimization space, and all vehicle scheduling schemes with negative checking numbers in the pricing sub-problem solving process are added to the restricted master problem, and the step 435 is returned to; If the checking number of the optimal solution of the pricing sub-problem is positive, all vehicle scheduling schemes generated in the pricing sub-problem solving process have positive checking numbers, and the step 437 is entered; Step 437, the optimal solution of the restrictive master problem of the node at the second iteration is recorded as , , the corresponding objective function value is recorded as ; Step 438, if , and is not an integer, go to step 439; If , and is 0 or 1, then update , , delete the current node and go to step 4311; If , delete the current node and go to step 4311; Step 439, select the closest to 0.5 and corresponding to the electric bus m; then construct the left child node by adding a constraint condition to the pricing sub-problem of the current node , construct the right child node by adding a constraint condition to the pricing sub-problem of the current node , set the left child node and the right child node ; Step 4310, set the left child node as the new current node, and add the right child node to the list of nodes to be processed, update the iteration times , return to step 435; The step 4311 judges whether the current node list still includes a to-be-processed node: 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; If the to-be-processed node list is empty, the step 4312 is entered; Step 4312, record optimal feasible solution and the corresponding objective function value ; Step 4313, updating Time instant energy storage system remaining energy ; If , go to step 432; if , go to step 4314; Step 4314, statistics Residual energy of energy storage system at time Optimal feasible solution in all value cases And the corresponding objective function value ; 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; optimal feasible solution comprise electric bus vehicle scheduling and charging scheduling schemes.

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