Electric bus route vehicle scheduling method under "light-storage-grid" coordinated power supply

Abstract

The application relates to an electric bus route vehicle scheduling method under a light-storage-network coordinated power supply mode, and relates to an electric bus route vehicle scheduling method.The application aims to solve the problem that existing researches mainly face the electric bus scheduling technology under the power supply mode of a single national power grid, without considering the influence of the introduction of photovoltaic power generation and energy storage system power supply, so that the carbon emission of the bus system is high, and the electric fee expenditure of the bus route is high.The process is as follows:1, collecting basic data;2, defining symbols;3, calculating the charging capacity and charging time of the electric bus during the day and at night;4, calculating the available energy of the energy storage system;5, based on the establishment of an optimization model;6, solving the optimization model, outputting an optimal scheduling scheme, including the charging start time, charging time, charging type, electric bus route energy consumption green electricity ratio and all-day charging cost of each electric bus in the scheduling scheme.The application belongs to the technical field of urban public transport operation management.

Description

Technical Field

[0001] This invention belongs to the field of urban public transportation operation and management technology, specifically a method for dispatching electric bus routes under the coordinated power supply of "photovoltaic-storage-grid". Background Technology

[0002] Prioritizing the development of public transportation is an effective way to alleviate urban traffic congestion and achieve energy conservation and emission reduction in the transportation system. Due to the advantages of electric buses, such as zero emissions, low noise, and simple operation, my country has been actively replacing fuel-powered buses with electric buses in recent years. However, electric buses consume electricity daily, and the electricity supply mainly comes from the national grid, which is dominated by coal-fired power plants. Therefore, the electric bus system shifts carbon emissions to the power generation side, failing to maximize its emission reduction benefits.

[0003] Photovoltaic power generation, as a clean energy supply technology, has significant advantages in reducing carbon emissions from the power system. Urban public transport hubs, with their large land areas, provide convenience for deploying photovoltaic facilities. If photovoltaic power generation can supply electricity to electric buses, it can not only increase the proportion of clean energy in the electric bus system and reduce carbon emissions at the source, but also allow buses to recharge during the day, avoiding the impact of concentrated charging at night on the national grid. However, photovoltaic power generation is intermittent and fluctuates, thus requiring energy storage systems to address its instability. Due to weather factors, photovoltaic power generation alone cannot stably supply electricity to the electric bus system; supplementary power from the national grid is still necessary, forming a coordinated "photovoltaic-storage-grid" power supply model.

[0004] This collaborative power supply approach will significantly change the charging strategies and vehicle dispatching schemes for electric buses. However, existing research mainly focuses on electric bus dispatching technology under a single national grid power supply mode, without considering the impact of introducing photovoltaic power generation and energy storage systems on the vehicle dispatching of electric bus routes. Therefore, there is currently a lack of electric bus route dispatching technology under the synergy of "photovoltaic-storage-grid". Summary of the Invention

[0005] The purpose of this invention is to address the problem that existing research mainly focuses on electric bus dispatching technology under a single national grid power supply mode, without considering the impact of introducing photovoltaic power generation and energy storage systems on the dispatching of electric bus routes, resulting in high carbon emissions and high electricity costs for bus routes. Therefore, this invention proposes a method for dispatching electric bus routes under a "photovoltaic-storage-grid" coordinated power supply.

[0006] The specific process of a vehicle dispatching method for electric bus routes under a "photovoltaic-storage-grid" coordinated power supply is as follows:

[0007] Step 1: Collect basic data;

[0008] Step 2: Define the symbols;

[0009] Step 3: Calculate the daytime and nighttime charging amount and charging time of the electric bus based on Step 1 and Step 2;

[0010] Step 4: Calculate the available energy of the energy storage system based on Steps 1, 2, and 3;

[0011] Step 5: Establish an optimization model based on Steps 1, 2, 3, and 4;

[0012] Step 6: Solve the optimization model established in Step 5 and output the optimal scheduling plan, including the charging start time, charging time, charging type, energy consumption green electricity ratio of electric bus route, and total daily charging cost for each electric bus in the scheduling plan.

[0013] The beneficial effects of this invention are as follows:

[0014] The electric bus dispatching technology under the "photovoltaic-storage-grid" collaborative power supply strategy proposed in this invention can achieve deep integration of urban public transport and clean energy supply technologies, reduce carbon emissions of the public transport system from the source, reduce electricity costs for bus routes, and achieve sustainable development for public transport companies. Attached Figure Description

[0015] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0016] Specific Implementation Method 1: The specific process of the electric bus route vehicle dispatching method under the "photovoltaic-storage-grid" coordinated power supply in this implementation method is as follows:

[0017] Step 1: Collect basic data;

[0018] Step 2: Define the symbols;

[0019] Step 3: Calculate the daytime and nighttime charging amount and charging time of the electric bus based on Step 1 and Step 2;

[0020] Step 4: Calculate the available energy of the energy storage system based on Steps 1, 2, and 3;

[0021] Step 5: Establish an optimization model based on Steps 1, 2, 3, and 4;

[0022] Step 6: Solve the optimization model established in Step 5 and output the optimal scheduling plan, including the charging start time, charging time, charging type, energy consumption green electricity ratio of electric bus route, and total daily charging cost for each electric bus in the scheduling plan.

[0023] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that basic data is collected in step 1; the specific process is as follows:

[0024] Step 1.1: Investigate the number of electric buses K, and number the electric buses as k, k = 1, 2, ..., K;

[0025] The number of buses N during the daytime operating hours is obtained from the bus route's departure timetable. Each bus is then numbered according to its departure order, with n representing the bus number, where n = 1, 2, ..., N (a bus trip consists of an electric bus departing from the starting station, arriving at the terminal station, and returning to the starting station).

[0026] According to the national time-of-use electricity pricing policy, a 24-hour day is divided into P time periods, and the State Grid electricity price for time period p is Q. p p = 1, 2, ..., P, Q p The unit is yuan / kWh; the electricity price for photovoltaic power generation in time period p is z. pv , z pv The unit is yuan / kWh;

[0027] Step 1.2: Investigate the daily energy consumption of trip n over the past 30 days, and take the average daily energy consumption of trip n over the past 30 days as the energy consumption value W of trip n. n W n The unit is kWh;

[0028] Step 1.3: Divide the day into Q equal time periods. The ratio of photovoltaic power generation in time period q to the duration of the time period is taken as the average photovoltaic output. q = 1, 2, ..., Q The unit is kW;

[0029] Step 1.4: Investigate the battery capacity of bus k. The unit is kWh;

[0030] Investigate the capacity of energy storage systems E ess E ess The unit is kWh;

[0031] The study investigated the conversion efficiency μ of photovoltaic power transmission to energy storage systems and the charging power η of charging piles, where μ is in % and η is in kW.

[0032] The other steps and parameters are the same as in Specific Implementation Method 1.

[0033] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that symbols are defined in step 2; the specific process is as follows:

[0034] Step 2.1, Shift execution variables:

[0035] Define a 0-1 variable x k,n This represents the relationship between vehicle k and shift n. If vehicle k operates on shift n, then x k,n =1, otherwise x k,n =0;

[0036] Define a 0-1 variable y k,n,n′ If shift n and shift n′ are adjacent shifts performed by vehicle k, then y k,n,n′ =1, otherwise y k,n,n′ =0, n∈N, n′∈N, n<n′;

[0037] Step 2.2, Daytime charging variables:

[0038] Define a 0-1 variable d k,n This indicates whether vehicle k is charged after shift n during daytime charging; if so, then d... k,n =1, otherwise d k,n =0;

[0039] Using b k,n With c k,n Two 0-1 variables are used together to determine the power supply method for daytime charging:

[0040] When b k,n =1 and c k,n When = 1, vehicle k uses a photovoltaic device to supply power after shift n ends;

[0041] When b k,n =1 and c k,n When the value is 0, an energy storage system is used for power supply;

[0042] When b k,n When c = 0, then regardless of c k,n =0 or 1 indicates that the power supply is from the State Grid.

[0043] In the formula, b k,n Indicates whether photovoltaic resources are used for power supply during daytime charging; c k,n This variable indicates whether photovoltaic devices are used to power daytime charging.

[0044] Calculate variable c according to equations (1)-(4). k,n and b k,n :

[0045]

[0046]

[0047]

[0048]

[0049] In the formula, for The power output of a photovoltaic power generation device at any given time is expressed in kW; among which Let n be the end time of train number n; for The power output of a photovoltaic power generation device at any given time, expressed in kW; for Available energy in a real-time energy storage system, measured in kWh; for Available energy in a time-of-use energy storage system, measured in kWh; SOC min This is the lower limit of SOC, expressed in %. The charging amount of vehicle k after completing n shifts, in kWh; r n for The number of electric buses that are constantly being charged using photovoltaic power generation devices; Let k be the vehicle at the end time of trip n; The start time of charging for vehicle k during the daytime charging phase; R represents the daytime charging time for electric bus k after its nth trip, expressed in minutes. k,n Indicates that vehicle k is in Is the photovoltaic power generation device currently charging? If it is charging, then R k,n =1, otherwise R k,n =0; · indicates multiplication;

[0050] Step 2.3, Nighttime Charging Variables:

[0051] Using 0-1 variables h k Determine the power supply method during overnight charging;

[0052] When h k When = 1, vehicle k is powered by an energy storage system;

[0053] When h k When the value is 0, the power supply is from the State Grid.

[0054] Calculate the variable h according to equation (5). k :

[0055]

[0056] In the formula, SOC max This represents the upper limit of SOC, expressed as a percentage. This represents the remaining energy after the energy storage system supplies power to vehicle k, expressed in kWh.

[0057] Other steps and parameters are the same as in specific implementation method one or two.

[0058] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that step 3 calculates the daytime and nighttime charging amount and charging time of the electric bus based on steps 1 and 2; the specific process is as follows:

[0059] To extend the battery life of electric buses, vehicles are only allowed to be charged once during the day and once at night.

[0060] Step 3.1: Calculate the end time of shift n according to formula (6). The remaining battery power of the corresponding vehicle k:

[0061]

[0062] In the formula, For electric buses k The remaining battery charge at any given time, in kWh; The remaining battery charge of electric bus k at the end of its n-1 shift is expressed in kWh; y k,n-1,n The variable is whether shift n and shift n-1 are adjacent shifts executed by vehicle k; The amount of charge given to vehicle k after shift n-1, in kWh;

[0063] Because the vehicles are fully charged overnight, the electric buses are fully charged before their first shift each day. In the formula, The remaining battery charge of electric bus k at the time of its first daily trip, in kWh;

[0064] Step 3.2: Calculate the daytime charging amount and nighttime charging amount of vehicle k according to equations (7) and (8):

[0065]

[0066]

[0067] In the formula, The amount of overnight charging for electric buses is expressed in kWh. The overnight charging time for electric bus k, in minutes;

[0068] Step 3.3: Calculate the daytime charging time and nighttime charging time of the electric bus k according to equations (9) and (10) respectively:

[0069]

[0070]

[0071] In the formula, The remaining battery charge of electric bus k at the end of shift n-1 is expressed in kWh. This represents the remaining battery power of electric bus k after all daytime trips have been completed, expressed in kWh. The daytime charging time for electric bus k after it performs n shifts, in minutes; The charging time for electric bus k at night is given in minutes.

[0072] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0073] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One to Four in that step 4 calculates the available energy of the energy storage system based on steps 1, 2, and 3; the specific process is as follows:

[0074] Step 4.1: Based on the end time of each shift The daytime available energy of the energy storage system is calculated on a rolling basis based on the photovoltaic output and the power supply status of the photovoltaic device used by the vehicle. The calculation method is shown in Equation (11):

[0075]

[0076] In the formula, Let n be the start time of shift n. Let n be the start time of train number n-1, where n-1 = 0. for The available energy of the instantaneous energy storage system is expressed in kWh; when n = 0 This represents the available energy of the energy storage system before the electric bus performs its first trip.

[0077] Step 4.2: Calculate the daytime photovoltaic resource waste based on the energy storage system capacity, as shown in equation (12):

[0078]

[0079] In the formula, Let $\frac{n}{n-1}$ represent the amount of photovoltaic resources wasted by the energy storage system due to capacity limitations between the start time of shift $n$ and the start time of shift $n-1$, expressed in kWh. To ensure the equation holds true, it is stipulated that...

[0080] Step 4.3: Arrange nighttime charging sequentially according to vehicle number, and calculate the available energy of the energy storage system at the start time of nighttime charging for vehicle k, as shown in equation (13):

[0081]

[0082] In the formula, The available energy in the energy storage system at the moment the nighttime vehicle k begins charging, measured in kWh;

[0083] Clearly, the available energy of the energy storage system at the start of the nighttime charging phase is equal to the available energy at the end of the daytime charging phase.

[0084] In the formula, The remaining energy in the energy storage system before the start of nighttime charging, expressed in kWh; The remaining energy in the energy storage system after shift N for daytime charging, expressed in kWh.

[0085] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0086] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One to Five in that, in step 5, an optimization model is established based on steps 1, 2, 3, and 4; the specific process is as follows:

[0087] An optimization model is established with the goal of maximizing the energy consumption-to-green-electricity ratio of electric bus routes and minimizing the total daily charging cost.

[0088] Step 5.1: Calculate the energy consumption-to-green-energy ratio (i.e., the proportion of clean energy in the energy used in the electric bus route) according to formula (14):

[0089]

[0090] Step 5.2: Calculate the total daily charging cost of the electric bus according to formula (15):

[0091]

[0092] In the formula, The daytime charging cost for vehicle k, in yuan; The cost of charging vehicle k at night, in yuan;

[0093] Step 5.2.1: Calculate the daytime charging cost according to equation (16):

[0094]

[0095] In the formula, This represents the daytime charging amount of vehicle k within time period p after the end of shift n, expressed in kWh.

[0096] Step 5.2.2: Calculate the nighttime charging cost according to equation (17):

[0097]

[0098] In the formula, This represents the amount of electricity charged by vehicle k during the night after shift n ends, within time period p, expressed in kWh.

[0099] Step 5.3: Optimize constraints.

[0100] The other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0101] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One through Six in that the constraints are optimized in step 5.3; the specific process is as follows:

[0102] Step 5.3.1: Ensure that each shift has a vehicle executing the rule and that the rule is executed once.

[0103]

[0104] Step 5.3.2: Ensure that there is no time conflict between shift n and shift n′ performed by vehicle k.

[0105]

[0106] In the formula, Indicates the start time of train number n′; Indicates the end time of train number n;

[0107] Step 5.3.3: Ensure the battery SOC value of the electric bus remains within the specified range during charging. min SOC max ]Inside:

[0108]

[0109]

[0110] Step 5.3.4: Constrain the electric bus to have a charging time greater than the minimum charging time T during both daytime and nighttime. cmin :

[0111]

[0112]

[0113] Step 5.3.5: Constrain the available energy of the energy storage system to not exceed the upper and lower limits of the available energy of the energy storage system.

[0114]

[0115] In the formula, ρ lThe lower limit of the SOC value for energy storage systems, in %; ρ h This represents the upper limit of the State of Charge (SOC) value for energy storage systems, expressed as a percentage.

[0116] Step 5.3.6: Constrain energy storage system waste to not exceed the maximum limit:

[0117]

[0118] In the formula, This represents the average photovoltaic power output within time period q, in kW.

[0119] Step 5.3.7: Constraining the range of values ​​for decision variables in the model:

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] In the formula, M is an infinite positive integer.

[0127] The other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0128] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods One to Seven in that step 6 involves solving the optimization model established in step 5; the specific process is as follows:

[0129] Step 6.1, Input parameters:

[0130] Maximum number of iterations iter max The number of population scheduling schemes is len, and the number of iterations is iter = 0;

[0131] Step 6.2: Generate the initial population:

[0132] Based on the departure time of each shift, each shift is assigned to the electric bus fleet in turn to form a feasible vehicle scheduling scheme. The allocation must meet the time feasibility constraints (18)-(19).

[0133] Calculate the corresponding charging scheme based on the vehicle scheduling scheme (see step 6.5 for details). If the charging scheme is feasible, add the scheduling scheme to the parent population H; if the charging scheme is not feasible, delete it and regenerate a new vehicle scheduling scheme to solve the charging scheme again.

[0134] When the size of the parent population H reaches the number of population scheduling schemes len, proceed to step 6.3;

[0135] Step 6.3: Determine if iter ≤ iter max If the condition is met, proceed to step 6.4; otherwise, proceed to step 6.7.

[0136] Step 6.4, Offspring Population Generation:

[0137] Step 6.4.1: Set the number of offspring individuals ζ = 0;

[0138] Step 6.4.2: Randomly select a scheduling scheme ω from the parent population H, and randomly select a vehicle k from the scheduling scheme ω that uses the State Grid for charging. sg With a vehicle k that uses an energy storage system for charging ess ;

[0139] If no vehicle that meets the requirements can be selected, a new scheduling scheme is selected from the parent population H; if a vehicle that meets the requirements exists, proceed to step 6.4.3.

[0140] Step 6.4.3: Move vehicle k sg and k ess The scheduled shifts are arranged according to their start times.

[0141] Based on the order of the start times of each shift, determine which shift n is added to vehicle k. sg After determining whether the scheduling scheme satisfies the time feasibility constraints (18)-(19), if it does, then add shift n to vehicle k. sg The scheduling plan is as follows: otherwise, add shift n to vehicle k. ess The scheduling plan;

[0142] Update vehicle k sg and k ess For each shift being executed, a new shift schedule ω′ is generated;

[0143] Step 6.4.4: Calculate the charging plan based on the scheduling plan ω′ (see step 6.5 for details). If a feasible charging plan exists, add the scheduling plan ω′ to the offspring population R; otherwise, return to step 6.4.2.

[0144] Step 6.4.5: Let ζ = ζ + 1, and determine whether the current number of offspring individuals ζ is equal to 1 / 2len: if it is, go to step 6.6; otherwise, go to step 6.4.2.

[0145] Step 6.5: Calculate the charging plan:

[0146] Each scheduling plan ω contains the schedules of K vehicles, and the set of schedule numbers for vehicle k is defined as Φ. k k = 1, 2, ..., K;

[0147] Where Φ k Contains n k There are several train services, denoted by m, where m = 1, 2, ..., n. k ;

[0148] Let f k,m This indicates whether the electric bus k is charged after its shift m ends. If it is charged, then f is used. k,m =1, otherwise f k,m =0;

[0149] Step 6.6 Population screening;

[0150] Step 6.7 Output the optimal scheduling scheme in the current population, including the charging start time, charging time, charging type, energy consumption green electricity ratio of electric bus route, and total daily charging cost for each electric bus in the scheduling scheme.

[0151] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0152] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods One through Eight in that the charging scheme is calculated in step 6.5:

[0153] Each scheduling plan ω contains the schedules of K vehicles, and the set of schedule numbers for vehicle k is defined as Φ. k (k = 1, 2, ..., K), where Φ k Contains n k There are several train services, denoted by m, where m = 1, 2, ..., n. k ;

[0154] Let f k,m This indicates whether the electric bus k is charged after its shift m ends. If it is charged, then f is used. k,m =1, otherwise f k,m =0;

[0155] The specific process is as follows:

[0156] Step 6.5.1: Calculate the estimated remaining battery power of the electric bus k after the completion of shift m during the daytime operation phase according to formula (31):

[0157]

[0158] In the formula, The remaining battery power of vehicle k at the start of shift m is expressed in kWh. d represents the remaining battery power of vehicle k at the end of shift m-1, in kWh; k,m W is a 0-1 variable used to determine whether vehicle k should take shift m; k,m f represents the electricity consumed by vehicle k during shift m, expressed in kWh. k,m The variable is a 0-1 variable used to determine whether vehicle k should be charged after its shift m ends. The amount of charge given to vehicle k after shift m-1, expressed in kWh.

[0159] Step 6.5.2: Calculate set Φ according to equation (32). k The variable c of the middle shift m k,m ;

[0160] If the schedule plan set Φ k There exists a variable c with shift m. k,m =1 and the charging process meets the constraints (19)-(25). Randomly select a shift that meets the constraints and record its charging start time and charging time. Record the power supply method of vehicle k as photovoltaic power generation device power supply and modify the photovoltaic power generation power according to formula (32).

[0161]

[0162] In the formula, Let m be the end time of train m. Let m be the start time of train number m. Let t represent the power generation capacity of the photovoltaic power generation device at time t, in kWh. The modified power output of the photovoltaic power generation device at time t is expressed in kWh.

[0163] If for all shifts m of vehicle k, the variable c k,m If the value of vehicle k is 0, then store vehicle k in set C. pv ;

[0164] Step 6.5.2: Calculate the available energy of the energy storage system according to equation (11);

[0165] Get set C pv The vehicle k and its corresponding set Φ k Calculate set Φ according to equation (2)k The variable b of the middle shift m k,m ;

[0166] If the schedule plan set Φ k There exists a variable b with shift m. k,m =1 and the charging process meets the constraints (19)-(25). Randomly select a shift that meets the constraints and record its charging start time and charging time. Record the daytime power supply mode of vehicle k as the power supply of the energy storage system. Recalculate the energy of the energy storage system according to the formula (11).

[0167] If for all shifts m of vehicle k, the variable b k,m If the value of vehicle k is 0, then store vehicle k in set C. ess ;

[0168] Step 6.5.3: Obtain set C ess The vehicle k and its corresponding set Φ k Assuming daytime charging occurs after shift m, the corresponding daytime charging cost is calculated according to equation (16).

[0169] If the charging process meets constraints (19)-(25), then... Record to set C sg ;

[0170] Otherwise, according to set C sg The number of internal elements is determined as follows:

[0171] If C sg If the set is not empty, record the start time and charging time of the charging process with the lowest daytime charging cost, and record the charging type of vehicle k as State Grid power supply.

[0172] If C sg If the set is empty, exit the generation of this charging scheme and record it as having no feasible charging scheme.

[0173] When set C ess The set C corresponding to vehicle k in the data sg If none of the sets are empty, the charging scheme is feasible; otherwise, the charging scheme is not feasible.

[0174] Step 6.5.4: Calculate the variable h of vehicle k according to equation (5). k The nighttime charging cost of vehicle k is calculated according to formula (17);

[0175] Step 6.5.5: Calculate the energy consumption-to-green-electricity ratio Z of the electric bus route according to equations (14) and (15) for the scheduling scheme ω. 1,ω With all-day charging cost Z 2,ω ;

[0176] Step 6.5.6, Return to charging plan: Daytime and nighttime charging start time, charging time and charging type for each vehicle, energy consumption green electricity ratio and total daily charging cost of electric bus routes according to the scheduling plan.

[0177] The other steps and parameters are the same as those in one of the specific implementation methods one to eight.

[0178] Specific Implementation Method Ten: This implementation method differs from Specific Implementation Methods One to Nine in that the population screening in step 6.6 is performed in this way; the specific process is as follows:

[0179] Step 6.6.1 Merge individuals from the offspring population R and the parent population H to form a new population O. Calculate the non-dominated sequence θ of the scheduling scheme ω for population O using the non-dominated sorting algorithm. ω The specific process is as follows:

[0180] For each scheduling scheme, ω has a parameter Π. ω and Λ ω , among which Π ω Λ represents the number of dominant individuals (ω) in the population. ω This represents the set of scheduling schemes dominated by ω;

[0181] For each scheduling scheme ω, the energy consumption-to-green-electricity ratio Z′ of the electric bus route is calculated according to equation (14). 1,ω The total daily charging cost Z′ is calculated according to equation (15). 2,ω ;

[0182] Search all scheduling schemes within the population, compare the dominance relationships one by one, and determine the following method:

[0183] If scheduling scheme 1 is compared with scheduling scheme 2, Z′ 11 >Z′ 12 And Z′ 21 >Z′ 22 If the scheduling scheme 1 controls the scheduling scheme 2, and the scheduling scheme 2 is controlled by the scheduling scheme 1, then the other situations do not constitute a control relationship.

[0184] In the formula, Z′ 11 For the energy consumption and green electricity ratio of the electric bus route in scheduling scheme 1, Z′ 12 For the energy consumption and green electricity ratio of the electric bus route in scheduling scheme 2, Z′ 21 Z′ represents the total daily charging cost for scheduling scheme 1. 22 The total daily charging cost for scheduling option 2;

[0185] Based on steps (i)-(v), the number of shifts ω that are allocated is obtained. ω And the set of scheduling schemes Λ controlled by scheduling scheme ω ωAnd calculate the nondominated sequence θ ω :

[0186] (i) Store all scheduling schemes within the population into the DOM set;

[0187] (ii) Find all Π in the DOM collection ω The scheduling scheme with a value of 0 is stored in the set LEV. σ (In the first iteration, σ = 1), let LEV be... σ The number of scheduling schemes in the system is NUM σ ;

[0188] (iii) For set LEV σ Each scheduling scheme ω(ω∈[1,NUM) in the table σ ]), denoted as Π ω The number of scheduling schemes in NUMS is ω Update Λ ω (ω∈[1,NUMS ω The scheduling parameters Π in ]) ω , ling Π ω =Π ω -1;

[0189] (iv) Define set LEV σ Let σ be the unsupported set of the σ-th layer, and let θ be the non-dominated sequence of individuals in the unsupported set. ω Individuals in the same set of levels have the same non-dominated sequence, i.e., θ ω =σ; Update the layer number σ, let σ = σ + 1;

[0190] (v) Repeat (i)-(iv) until all individuals are assigned to the non-dominated sequence, and the non-dominated sorting ends;

[0191] Step 6.6.2 Calculate the crowding degree μ of the scheduling scheme according to formula (33). ω :

[0192] μ ω =(Z 1,ω+1 -Z 1,ω-1 )+(Z 2,ω+1 -Z 2,ω-1 (33)

[0193] Among them, Z 1,ω+1 Z 1,ω-1 Z represents the energy consumption and green electricity ratio of electric bus routes for the ω+1 and ω-1th scheduling schemes; 2,ω+1 Z 2,ω-1 The total daily charging cost for the ω+1 and ω-1th shift scheduling schemes;

[0194] Step 6.6.3 compares the scheduling schemes within population O, and the optimality judgment method is as follows:

[0195] For any two scheduling schemes ω1 and ω2 within population O, scheduling scheme ω1 is considered superior to scheduling scheme ω2 if and only if the non-dominated sequence of scheduling scheme ω1 is less than or equal to the non-dominated sequence of scheduling scheme ω2, and the crowding degree of scheduling scheme ω1 is greater than or equal to the crowding degree of scheduling scheme ω2.

[0196] The scheduling schemes within population O are sorted according to the optimality method, and the top len scheduling schemes of population O from high to low constitute the next generation parent population H.

[0197] Step 6.6.4 Set the current algorithm iteration number iter = iter + 1, and go to step 6.3.

[0198] The other steps and parameters are the same as those in any of the specific implementation methods one to nine.

[0199] 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 dispatching electric bus routes under a "photovoltaic-storage-grid" coordinated power supply, characterized in that: The specific process of the method is as follows: Step 1 involves collecting basic data; the specific process is as follows: Step 1.1: Investigate the number of electric buses The electric buses were numbered as ; Obtain the number N of daytime operating hours; Time period State Grid electricity price is Time period The electricity price of photovoltaic power generation is ; Step 1.2: Take the average daily travel energy consumption of trip n over the past 30 days as the travel energy consumption value of trip n. ; Step 1.3: Divide the day into Q equal time periods. The ratio of photovoltaic power generation in time period q to the duration of the time period is taken as the average photovoltaic output. , ; Step 1.4: Investigate the battery capacity of bus k. ; Energy storage system capacity ; Conversion efficiency of photovoltaic power transmission to energy storage system Charging power of charging pile ; Step 2 defines the symbols; the specific process is as follows: Step 2.1, Shift execution variables: Define 0-1 variables Indicates vehicle k and shift number The relationship between vehicle k and its shift ,but ,otherwise ; Define 0-1 variables If the schedule With train schedule For the adjacent shifts performed by vehicle k, then ,otherwise , ; Step 2.2, Daytime charging variables: Define 0-1 variables This indicates that vehicle k is in the shift during daytime charging. Does the device need to be charged after use? If so, then... ,otherwise ; use and Two 0-1 variables are used together to determine the power supply method for daytime charging: when and When =1, vehicle k is on the shift Power will be supplied by photovoltaic devices after the event ends; when and When the value is 0, an energy storage system is used for power supply; when At this time, regardless of Both 0 and 1 are powered by the State Grid. In the formula, Indicates whether photovoltaic resources are used for power supply during daytime charging; This variable indicates whether photovoltaic devices are used to power daytime charging. Step 2.3, Nighttime Charging Variables: Using 0-1 variables Determine the power supply method during overnight charging; when At that time, vehicle k is powered by an energy storage system; when At that time, the State Grid will supply power. Step 3 calculates the daytime and nighttime charging amounts and charging times for the electric bus based on steps 1-2; the specific process is as follows: Vehicles are only permitted to be charged once during the day and once at night. Step 3.1: Calculate the shifts according to formula (6). End time The remaining battery power of the corresponding vehicle k: （6） In the formula, For electric buses k The remaining battery power at any given time; The remaining battery power of electric bus k at the end of shift n-1; The variable is whether shift n and shift n-1 are adjacent shifts executed by vehicle k; The amount of charge given to vehicle k after shift n-1 ends; Step 3.2: Calculate the daytime charging amount and nighttime charging amount of vehicle k according to equations (7) and (8): （7） （8） In the formula, The amount of electricity charged overnight for electric buses; Nighttime charging time for electric bus K; Step 3.3: Calculate the daytime charging time and nighttime charging time of the electric bus k according to equations (9) and (10) respectively: （9） （10） In the formula, The remaining battery power of electric bus k at the end of shift n-1; This indicates the remaining battery power of electric bus k after all daytime trips have been completed. The daytime charging time for electric bus k after it performs shift n; Nighttime charging time for electric bus K; Step 4 calculates the available energy of the energy storage system based on steps 1-3; the specific process is as follows: Step 4.1: Based on the end time of each shift The daytime available energy of the energy storage system is calculated on a rolling basis based on the photovoltaic output and the power supply status of the vehicle using the photovoltaic device. The calculation method is shown in Equation (11): （11） In the formula, Let n be the start time of shift n. Let n be the start time of train number n-1, where =0 ; for Available energy of the energy storage system at any time; when n=0 This represents the available energy of the energy storage system before the electric bus performs its first trip. Step 4.2: Calculate the daytime photovoltaic resource waste based on the energy storage system capacity, as shown in equation (12): （12） In the formula, Let $\frac{n}{n-1}$ represent the amount of photovoltaic resources wasted due to capacity limitations in the energy storage system between the start time of shift $n$ and the start time of shift $n-1$. To ensure the equation holds true, it is stipulated that... ; Step 4.3: Arrange nighttime charging according to vehicle number, and calculate the available energy of the energy storage system at the start time of nighttime charging for vehicle k, as shown in equation (13): （13） In the formula, The energy available in the energy storage system at the start of charging vehicle k at night; Step 5 establishes an optimization model based on steps 1-4; the specific process is as follows: Step 5.1: Calculate the energy consumption-to-green-electricity ratio of the electric bus route; Step 5.2: Calculate the total daily charging cost for the electric bus; Step 5.3 involves optimizing constraints; the specific process is as follows: Step 5.3.1: Ensure that each shift has a vehicle executing the rule and that the rule is executed once. Step 5.3.2: Constrain the shifts performed by vehicle k. With train schedule There is no time conflict between them: Step 5.3.3: Ensure that the battery SOC value of the electric bus remains within the specified range during charging. Inside; Step 5.3.4: Ensure that the charging time for electric buses during both daytime and nighttime exceeds the minimum charging time. ; Step 5.3.5: Constrain the available energy of the energy storage system to not exceed the upper and lower limits of available energy; Step 5.3.6: Constrain energy storage system waste to not exceed the maximum limit; Step 6: Solve the optimization model established in Step 5 and output the optimal scheduling plan, including the charging start time, charging time, charging type, energy consumption green electricity ratio of electric bus route, and total daily charging cost for each electric bus in the scheduling plan.

2. The method for dispatching electric bus routes under a "photovoltaic-storage-grid" coordinated power supply according to claim 1, characterized in that: Whether daytime charging uses photovoltaic power generation is a variable. Whether photovoltaic devices are used for power supply during daytime charging is a variable. Calculate according to equations (1)-(4): （1） （2） （3） （4） In the formula, for The power output of a photovoltaic power generation device at any given time is expressed in kW; among which For train schedules The end time; for The power output of a photovoltaic power generation device at any given time, expressed in kW; for Available energy in a real-time energy storage system, measured in kWh; for Available energy in a real-time energy storage system, measured in kWh; This is the lower limit of SOC, expressed in % %. For vehicle k to perform shifts The charging capacity after charging is expressed in kWh. for The number of electric buses that are constantly being charged using photovoltaic power generation devices; For vehicle k on the shift The end time; The start time of charging for vehicle k during the daytime charging phase; The daytime charging time for electric bus k after it performs n shifts, in minutes; Indicates that vehicle k is in Is the photovoltaic power generation device currently charging? If it is charging, then... ,otherwise .

3. The method for dispatching electric bus routes under "photovoltaic-storage-grid" coordinated power supply according to claim 2, characterized in that: The variable for: （5） In the formula, This represents the upper limit of SOC, expressed in % %. This represents the remaining energy after the energy storage system supplies power to vehicle k, expressed in kWh. Because the vehicles are fully charged overnight, the electric buses are fully charged before their first shift each day. ; In the formula, The remaining battery charge of electric bus k at the time of its first daily trip, in kWh; The available energy of the energy storage system at the start of the nighttime charging phase is equal to the available energy at the end of the daytime charging phase, i.e. ; In the formula, The remaining energy in the energy storage system before the start of nighttime charging, expressed in kWh; The remaining energy in the energy storage system after shift N for daytime charging, expressed in kWh.

4. The method for dispatching electric bus routes under "photovoltaic-storage-grid" coordinated power supply according to claim 3, characterized in that: Step 5.1: Calculate the energy consumption-to-green electricity ratio of the electric bus route. （14） Step 5.2: Calculate the total daily charging cost for the electric bus: （15） In the formula, The daytime charging cost for vehicle k, in yuan; The cost of charging vehicle k at night, in yuan; Step 5.2.1: Calculate the daytime charging cost according to formula (16): （16） In the formula, This represents the daytime charging amount of vehicle k within time period p after the end of shift n, expressed in kWh. ; Step 5.2.2: Calculate the nighttime charging cost according to equation (17): （17） In the formula, This represents the amount of electricity charged by vehicle k during the nighttime period p after the end of shift n, expressed in kWh. .

5. The method for dispatching electric bus routes under "photovoltaic-storage-grid" coordinated power supply according to claim 4, characterized in that: The optimization constraints in step 5.3 are as follows: Step 5.3.1: Ensure that each shift has a vehicle executing the rule and that the rule is executed once. （18） Step 5.3.2: Constrain the shifts performed by vehicle k. With train schedule There is no time conflict between them: （19） In the formula, Indicates train number The beginning moment; Indicates train number The end time; Step 5.3.3: Ensure that the battery SOC value of the electric bus remains within the specified range during charging. Inside: （20） （21） Step 5.3.4: Ensure that the charging time for electric buses during both daytime and nighttime exceeds the minimum charging time. : （22） （23） Step 5.3.5: Constrain the available energy of the energy storage system to not exceed the upper and lower limits of available energy: （24） In the formula, This represents the lower limit of the State of Charge (SOC) value for energy storage systems, expressed in % (%). This represents the upper limit of the State of Charge (SOC) value for energy storage systems, expressed in % (%). Step 5.3.6: Constrain energy storage system waste to not exceed the maximum limit: （25） In the formula, This represents the average photovoltaic power output within time period q. Step 5.3.7: Constraining the range of values ​​for decision variables in the model: （26） （27） （28） （29） （30） In the formula, M is an infinite positive integer.

6. The method for dispatching electric bus routes under "photovoltaic-storage-grid" coordinated power supply according to claim 5, characterized in that: Step 6 involves solving the optimization model established in step 5 and outputting the optimal scheduling plan, including the charging start time, charging time, charging type, energy consumption green electricity ratio of the electric bus route, and total daily charging cost for each electric bus within the scheduling plan. The specific process is as follows: Step 6.1, Input parameters: Maximum number of iterations Number of population scheduling schemes Number of iterations ; Step 6.2: Generate the initial population: Based on the departure time of each shift, each shift is assigned to the electric bus fleet in turn to form a feasible vehicle scheduling scheme. The allocation must meet the time feasibility constraints (18)-(19). Calculate the corresponding charging plan based on the vehicle scheduling plan. If the charging plan is feasible, add the scheduling plan to the parent population. ; If the charging scheme is not feasible, delete it and regenerate a new vehicle scheduling scheme to solve the charging scheme again. When the parent population The scale reaches the number of population scheduling schemes Then proceed to step 6.3; Step 6.3, Judgment If the condition is met, proceed to step 6.4; otherwise, proceed to step 6.

7. Step 6.4, Offspring Population Generation: Step 6.4.1: Determine the number of offspring individuals. ; Step 6.4.2: Randomly select from the parent population Choose one scheduling scheme In the scheduling plan Choose one vehicle that uses the State Grid for charging. With a vehicle that uses an energy storage system for charging ; If a vehicle that meets the requirements cannot be selected, then the selection process will start again from the parent population. Select a new scheduling plan; if there are vehicles that meet the requirements, proceed to step 6.4.3; Step 6.4.3, move the vehicle and The scheduled shifts are arranged according to their start times. Based on the order of the start times of each shift, determine which shift n is added to the vehicle fleet. After determining whether the scheduling scheme satisfies the time feasibility constraints (18)-(19), if the constraints are satisfied, then add shift n to the vehicle schedule. The scheduling plan is as follows; otherwise, add shift n to the vehicle schedule. The scheduling plan; Update vehicles and The new shift schedule is generated based on the shifts performed. ; Step 6.4.4: According to the shift schedule Calculate charging solutions; if a feasible charging solution exists, then incorporate the scheduling plan. Add the offspring population R; otherwise, return to step 6.4.

2. Step 6.4.5, let Determine the current number of offspring individuals. Is it equal to If the condition is met, proceed to step 6.6; otherwise, proceed to step 6.4.

2. Step 6.5: Calculate the charging plan: Each scheduling plan Total of The schedule plan for vehicle k is defined as follows: The set of schedule plan numbers for vehicle k is defined as... ; in Includes There are several shifts, with m representing the shift number. ; make This indicates whether the electric bus k is charged after its shift m ends. If it is charged, then... ,otherwise ; Step 6.6 Population screening; Step 6.7 Output the optimal scheduling scheme in the current population, including the charging start time, charging time, charging type, energy consumption green electricity ratio of electric bus route and total daily charging cost for each electric bus in the scheduling scheme. The charging scheme is calculated in step 6.5: Each scheduling plan Total of The schedule plan for vehicle k is defined as follows: The set of schedule plan numbers for vehicle k is defined as... ,in Includes There are several shifts, with m representing the shift number. ; make This indicates whether the electric bus k is charged after its shift m ends. If it is charged, then... ,otherwise ; The specific process is as follows: Step 6.5.1: Calculate the estimated remaining battery power of the electric bus k after the completion of shift m during the daytime operation phase according to formula (31): （31） In the formula, The remaining battery power of vehicle k at the start of shift m is expressed in kWh. The remaining battery power of vehicle k at the end of shift m-1 is expressed in kWh. The variable is a 0-1 variable used to determine whether vehicle k should take shift m. The electricity consumed by vehicle k for each shift m, expressed in kWh. The variable is a 0-1 variable used to determine whether vehicle k should be charged after its shift m ends. The amount of charge given to vehicle k after shift m-1, expressed in kWh. Step 6.5.2: Calculate the set according to equation (32). The variable of the number of middle shifts m ; If the schedule is set There is a variable m for the shift. The charging process meets the constraints (19)-(25). Randomly select a shift that meets the constraints and record its charging start time and charging time. Record the power supply method of vehicle k as photovoltaic power generation device and modify the photovoltaic power generation power according to formula (32). （32） In the formula, Let m be the end time of train m. Let m be the start time of train number m. for The power generation capacity of a photovoltaic power generation device at any given time, expressed in kWh; For the revised The power generation capacity of a photovoltaic power generation device at any given time, expressed in kWh; If for all shifts m of vehicle k, the variable holds... Then store vehicle k in set ; Step 6.5.2: Calculate the available energy of the energy storage system according to equation (11); Get collection Vehicle k and its corresponding set Calculate the set according to equation (2) The variable of the number of middle shifts m ; If the schedule is set There is a variable m for the shift. And the charging process meets the constraints (19)-(25). Randomly select a shift that meets the constraints and record its charging start time and charging time. Record the daytime power supply mode of vehicle k as the power supply of the energy storage system. And recalculate the energy of the energy storage system according to the formula (11). If for all shifts m of vehicle k, the variable holds... Then store vehicle k in set ; Step 6.5.3: Obtain the set Vehicle k and its corresponding set Assuming daytime charging occurs after shift m, the corresponding daytime charging cost is calculated according to equation (16). ; If the charging process meets constraints (19)-(25), then... Record to collection ; Otherwise, according to the set The number of internal elements is determined as follows: like If the set is not empty, record the start time and charging time of the charging process with the lowest daytime charging cost, and record the charging type of vehicle k as State Grid power supply. like If the set is empty, exit the generation of this charging scheme and record it as having no feasible charging scheme. When set The set corresponding to vehicle k in If none of the sets are empty, the current charging scheme is a feasible scheme; otherwise, the current charging scheme is an infeasible scheme. Step 6.5.4: Calculate the variable of vehicle k according to equation (5). The nighttime charging cost of vehicle k is calculated according to formula (17); Step 6.5.5: Calculate the shift scheduling scheme according to equations (14) and (15). The energy consumption ratio of electric bus routes to green electricity With the cost of charging all day ; Step 6.5.6, Return to charging plan: daytime and nighttime charging start time, charging time and charging type for each vehicle, energy consumption green electricity ratio and total daily charging cost of electric bus routes according to the scheduling plan; The population screening in step 6.6 is as follows: Step 6.6.1 Merge individuals from the offspring population R and the parent population H to form a new population. Calculate the population using the non-dominated sorting algorithm. Scheduling plan nondominated sequences ; The specific process is as follows: For each scheduling plan All have parameters and ,in Indicates in the population The number of people controlled Then it means A set of scheduling schemes under control; For each scheduling plan The energy consumption-to-green electricity ratio of electric bus routes is calculated according to formula (14). Calculate the total daily charging cost according to formula (15). ; Search all scheduling schemes within the population, compare the dominance relationships one by one, and determine the following method: If scheduling plan 1 is compared with scheduling plan 2 and If the scheduling scheme 1 controls the scheduling scheme 2, and the scheduling scheme 2 is controlled by the scheduling scheme 1, then the other situations do not constitute a control relationship. in, The energy consumption and green electricity ratio of the electric bus route under scheduling scheme 1. The energy consumption and green electricity ratio of the electric bus route in scheduling scheme 2. The total daily charging cost for scheduling option 1. The total daily charging cost for scheduling option 2; Based on steps (i)-(v), the scheduling scheme is obtained. The number of people controlled and scheduling plan A collection of controllable scheduling schemes And calculate the non-dominated sequence. : (i) Store all scheduling schemes in the population into a set. ; (ii) Find the set All The scheduling plan is generated and stored in a collection. ,remember The number of scheduling schemes in the middle is ; (iii) For sets Each scheduling scheme ,remember The number of scheduling schemes in the middle is ;renew Parameters of the scheduling scheme ,make ; (iv) Define sets For the first Layered non-supported fit sets, marking the non-dominated sequences of individuals within the non-supported fit sets. Individuals in the same set of levels have the same non-dominated sequence, that is Update the number of layers ,make ; (v) Repeat (i)-(iv) until all individuals are assigned to the non-dominated sequence, and the non-dominated sorting ends; Step 6.6.2 Calculate the crowding degree of the scheduling scheme according to formula (33). : (1) In the formula, , For the first , Energy consumption and green electricity ratio of electric bus routes under a scheduling scheme; , For the first , The total daily charging cost for each scheduling plan; Step 6.6.3 Compare populations The optimality judgment method for the internal scheduling scheme is as follows: For population Any two scheduling schemes and If and only if the scheduling scheme The non-dominated sequence is less than or equal to the scheduling scheme. The non-dominated sequence, and the scheduling scheme The crowding level is greater than or equal to the scheduling scheme. When considering the level of congestion, the scheduling scheme is considered... It is superior to the scheduling scheme. of; Based on the optimality method, the population The internal scheduling schemes are sorted from high to low to obtain the population. The former Each scheduling scheme constitutes the next generation parent population H; Step 6.6.4 Set the current algorithm iteration count. Proceed to step 6.3.

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