Traffic self-consistent energy system architecture design method

Abstract

The invention discloses a traffic self-consistent energy system architecture design method, and relates to the field of traffic self-consistent energy systems, and the method comprises the following steps: S1, determining a traffic self-consistent energy system engineering construction space range, determining traffic power load power data needing to be supplied in the system, and then sequentially dividing space modules, determining the specific feature size of each divided space module; s2, power generation and energy storage equipment capable of being arranged in each space module is determined, and the maximum installation number of the power generation and energy storage equipment in each space module and the single machine cost of the power generation and energy storage equipment are obtained; s3, single-machine output power of the power generation equipment is determined; s4, selecting two of the economy, the technicality and the environmental protection as optimization objectives, determining two optimization objective functions, and then selecting an operation mode; s5, solving the traffic self-consistent energy system architecture design optimization model to obtain a final traffic self-consistent energy system architecture composition; and factors are comprehensively considered, and the applicability is higher.

Description

Technical Field

[0001] The present invention relates to the field of transportation self-consistent energy systems, and in particular to a transportation self-consistent energy system architecture design method. Background Art

[0002] With the steady development of the transportation industry, the level of transportation electrification has gradually increased. At the same time, the penetration rate of green new energy in the transportation industry is increasing. The transportation field has a wide space and rich wind, light and hydropower resources. Building a self-consistent energy system for transportation and using renewable energy in the transportation space to meet the power needs of transportation infrastructure is an effective way to address the "dual carbon" goals and the diversity of the transportation environment. The design and optimization of the architecture of the self-consistent energy system for transportation is the only way to build and use the self-consistent energy system for transportation, which is of great significance to the diversification, greening and convenience of transportation energy use.

[0003] The existing self-consistent energy system technology in the transportation field mainly focuses on a specific transportation facility or environment, and lacks adaptability to different transportation modes, transportation facilities, and engineering environments.

[0004] Different modes of transportation, such as highways, railways, inland waterways, etc., different transportation facilities, such as service areas, toll stations, railway stations, docks, and different transportation facilities are widely present in various engineering environments. Different engineering environments, such as plains under strong network conditions, mountainous areas under weak network conditions, and deserts under no network conditions, face different components and system construction and operation constraints in the planning and design of the transportation self-consistent energy system. In addition, the construction goals of the transportation self-consistent energy system include economic benefits, green transformation of transportation energy use, and maintaining normal operation of traffic loads. Therefore, the existing technical methods of transportation self-consistent energy systems in the transportation field usually consider factors one-sidedly, have poor applicability, and have room for further improvement. Summary of the invention

[0005] The present invention provides a method for designing a traffic self-consistent energy system architecture to solve the problem of poor applicability of the existing traffic self-consistent energy system architecture in the field of transportation.

[0006] A method for designing a traffic self-consistent energy system architecture of the present invention adopts the following technical solution: the method comprises the following steps:

[0007] S1: Determine the spatial scope of the construction of the transportation self-consistent energy system project, determine the traffic power load power data required to be supplied in the system, and then divide the space modules in turn, and clarify the specific characteristic dimensions of each divided space module. Specifically, n is the total number of space modules, j is a certain space module, j = 1, 2, ..., n;

[0008] S2: According to the specific characteristic dimensions of each space module in step S1, determine the power generation and energy storage equipment that can be deployed in each space module, and obtain the maximum installation quantity of the power generation and energy storage equipment in each space module and the unit cost of the power generation and energy storage equipment;

[0009] S3: determining the environmental climate data involved in each space module of the power generation equipment in step S2, and then determining the single-unit output power of the power generation equipment in combination with the environmental climate data and the factory technical parameters of the power generation equipment;

[0010] S4: Select two of the three items of economy, technology and environmental protection as optimization targets, and determine two optimization objective functions. Then, select the operation mode based on the construction requirements of whether the transportation self-consistent energy system can be connected to the grid, the strength of the public power grid, and whether the temporary power outage of part of the load in the system can be accepted;

[0011] S5: According to the operation mode selected in step S4, the corresponding technical indicator constraints are clarified in combination with the constraints, and a transportation self-consistent energy system architecture design optimization model is constructed. The transportation power load power data in step S1, the maximum installation quantity of power generation and energy storage equipment in each space module in step S2 and the unit cost of power generation and energy storage equipment, and the unit output power of the power generation equipment in step S3 are used to solve the transportation self-consistent energy system architecture design optimization model to obtain the final transportation self-consistent energy system architecture composition.

[0012] Preferably, the power generation and energy storage equipment includes power generation equipment and energy storage equipment; the power generation equipment includes photovoltaic panels, wind turbines, and water turbines, and the corresponding environmental climate data are light radiation density data, wind power data, and water flow data, respectively.

[0013] Preferably, the operation modes in step S4 include the following four:

[0014] Operation mode A-temporary power outage of low-importance loads: self-generation and self-use, surplus power storage. At this time, the system is off-grid. When the real-time power supply of renewable energy such as light, wind, and water, as well as power generation and energy storage equipment is insufficient, the power demand of traffic loads is reduced, and the low-importance traffic power loads are temporarily powered off;

[0015] Operation mode B-Controllable distributed power supply: self-generation and self-use, surplus power storage, controllable distributed power supply as backup power supply to make up the power output difference;

[0016] Operation mode C-power supply from the large power grid: self-generation and self-use, surplus power storage, and the large power grid as a backup power supply to make up for the power output difference. At this time, the system is in a grid-connected state, but the power supply capacity of the power grid is weak, and only temporary one-way power supply can be provided to the system, and there may be regular power outages;

[0017] Operation mode D-full grid connection: self-generation and self-use, surplus power to the grid. At this time, the system is in a full grid-connected state. When the supply of renewable energy power is insufficient, the energy storage equipment will supply power, followed by the large power grid to supplement the power. When there is surplus renewable energy power, the energy storage equipment will be charged first, and the rest will be sent to the grid.

[0018] Preferably, the two optimization objective functions in step S4 are two of the economic measurement index, technical measurement index, and environmental measurement index functions, which are as follows:

[0019] The economic efficiency is measured by the total net present value C NPC , for different operation modes, the total net present value C NPC The calculation is different, as follows:

[0020] Operation Mode A:

[0021] C NPC =C initial +C operate +C replace -C salvage

[0022] Operation Mode B:

[0023] C NPC =C initial +C operate +C replace -C salvage +C fuel +C backup

[0024] Operation mode C:

[0025] C NPC =C initial +C operate +C replace -C salvage +C buy +C backup

[0026] Operation mode D:

[0027] C NPC =C initial +C operate +C replace -C salvage +C buy -C sell +C backup

[0028] In the above formula:

[0029] C initial Refers to the construction cost, C initial =∑1≤j≤n (C initial，j ×N j )

[0030] C operat Refers to operation and maintenance costs,

[0031] C replace Refers to the replacement cost,

[0032] C salvage Refers to the residual value,

[0033] C fuel Refers to fuel costs,

[0034] C buy The cost of purchasing electricity.

[0035] C sell Refers to the revenue from electricity sales,

[0036] C backup Refers to the installed capacity cost of backup power generation and energy storage equipment, C backup =M backup ×max[P backup (t)]

[0037] The technical measurement indicator is the power outage probability LOLP, which is as follows:

[0038]

[0039] The environmental performance index is the self-consistency rate U re , as follows:

[0040]

[0041] In the above formula, C initial，j , C operate，j , C replace，j Respectively represent the unit cost, annual operation and maintenance cost, and replacement cost of the power generation and energy storage facilities in the jth space module; N j is the installed capacity of power generation and energy storage facilities in the jth space module; r is the annual interest rate; T j is the equipment life of the power generation and energy storage facilities in the jth space module; Z j is the number of replacements of the power generation and energy storage facilities in the jth space module during the system life; D is the residual value ratio of the power generation and energy storage facilities; M buy and M sell They are the electricity purchase price and electricity sales price respectively; M fuelis the fuel price of the backup power generation facility, E fuel is the motor power generation coefficient of the corresponding fuel; M backup P is the unit connection cost of the diesel motor or transformer in the grid-connected state; backup (t), P loss (t), P ex (t) are the backup power supply power, insufficient renewable energy power, and surplus renewable energy power in the system at time t; P load (t) is the electric power demand of the traffic load required to be supplied in the system at time t; T represents the simulation calculation time period; Y represents the system design life; Δt represents the unit time scale (1h).

[0042] Preferably, in step S5, the technical indicator constraints corresponding to each operation mode are composed of two parts, one part is a mandatory constraint, and the other part is a free option constraint, and the selection range of the free option constraint corresponding to each operation mode is different;

[0043] The mandatory constraints are composed of the following technical indicator constraints a1-a4:

[0044] a1: Power balance constraints of the traffic self-consistent energy system architecture:

[0045]

[0046] a2: Constraints on the output power of a single power generation and energy storage device:

[0047]

[0048] a3: Constraints on the number of installed capacity of power generation and energy storage equipment:

[0049] 0≤N j ≤M j，max

[0050] a4: Energy storage equipment charge and discharge depth constraints:

[0051] 0≤SOC min ≤SOC(t)≤SOC max ≤1

[0052] The free choice constraints are respectively composed of one or more of the following technical indicator constraints a5-a9:

[0053] a5: Maximum acceptable renewable energy curtailment rate E ex，max constraint:

[0054]

[0055] a6: Maximum acceptable power failure probability LOLP maxconstraint:

[0056]

[0057] a7: The minimum acceptable renewable energy self-consistency rate U re，min constraint:

[0058]

[0059] a8: Weak network power outage time constraints:

[0060] [TD start , TD end )

[0061] a9: Maximum acceptable probability of temporary power outage of low-importance loads R cut，max constraint:

[0062]

[0063] Where P ex (t) represents the remaining output power of renewable energy at time t, which is the grid-connected power in operation mode D; P j (t) represents the actual output power of the jth type of equipment at time t; P j，min , P j，max They represent the rated minimum and maximum single-unit output power of the j-th power supply facility respectively; SOC min , SOC max are the minimum and maximum allowable discharge depths of the energy storage facility, respectively; SOC(t) is the actual discharge depth of the energy storage facility at time t; P renew (t) represents the total output power of renewable energy sources such as light, wind and water at time t; TD start and TD end Indicates the start and end time of regular daily power outages under weak network conditions. If the grid cannot be connected or there is no power outage in the grid, the default value is TD start =TD end =0; R cut represents the probability of temporary power outage of low-importance loads; T cut It indicates the total duration of temporary power outage of low-importance loads during the entire life cycle of the system under operation mode A; b，max Both represent A b The maximum value of the form A b，min Both represent A b The minimum value of

[0064] The selection range of the free option constraints corresponding to each operating mode is as follows:

[0065] The selection range of the free option constraints corresponding to operation mode A: renewable energy curtailment rate, power shortage probability, and temporary power outage time probability constraints of low-importance loads;

[0066] The selection range of the free option constraints corresponding to operation mode B: renewable energy curtailment rate and self-consistency rate;

[0067] The selection range of the free option constraints corresponding to operation mode C: renewable energy curtailment rate, power shortage probability, self-consistency rate and weak network power outage time constraints;

[0068] The selection range of the free option constraints corresponding to operation mode D: renewable energy grid sales rate and self-consistency rate.

[0069] Preferably, the specific method of constructing the traffic self-consistent energy system architecture design optimization model in step S5 is to constrain the optimization objectives and operation modes selected in step S4 and the technical indicators selected in step S5 to form an optimization model.

[0070] Preferably, the process of solving the optimization model of the traffic self-consistent energy system architecture design in step S5 is as follows:

[0071] S51: Population initialization generation, individuals represent a set of one-dimensional vector solutions within the maximum installation quantity range of power generation and energy storage facilities in each space module determined in step S2, the number of individuals is determined to be 200, the initial population is generated using the Logistic mapping model, and the actual installation quantity x of power generation and energy storage equipment in the jth space module is obtained using the following formula i，j :

[0072] β i，j =μβ i，j (1-β i，j ), β 0，j ∈(0,1);μ∈[3.57,4]

[0073] x i，j =x j，min +(x j，max -x j，min )β i，j

[0074] In the above formula, i is an individual, i = 1, 2, ..., 200; β i，j is the jth variable x in the ith individual i，j The corresponding chaotic vector, where β 0，j and μ are random numbers within the range of values, x i，j represents the actual number of installed power generation and energy storage equipment allowed to be installed in the jth space module;

[0075] S52: According to the actual installed number of power generation and energy storage equipment in step S51, a time series simulation operation is performed to obtain power data during the operation and energy storage capacity change data of the energy storage equipment.

[0076] S53: According to the power data during operation generated in step S52 and the unit cost of the power generation and energy storage equipment in step S2, the function value of the selected constraint condition is calculated, and the two optimization objective function values ​​are converted into a negative optimization function G(x) whose function value is smaller and better. The penalty function P(x) is composed of the selected constraint condition, and then the fitness value F(x) of the two optimization objectives selected in step S4 is calculated respectively by the following formula:

[0077]

[0078]

[0079]

[0080] Where: f(x) is the objective function corresponding to the selected optimization index, where the optimization index can be the total net present value, power shortage probability or self-consistency rate; x is the optimization variable, which refers to the number of installed power generation and energy storage equipment in this case;

[0081] Step S54: According to the fitness value F(x) in step S5, the individuals in the population are quickly sorted and the crowding degree is calculated, and then the inheritance and mutation operations are performed;

[0082] Step S55: repeating steps S52, S53 and S54 until the maximum number of iterative evolutions reaches 300, then ending the operation, outputting the final offspring population, and calculating and outputting the optimization objective function, constraint condition function and fitness function value of the final offspring population according to steps S52 and S53, wherein the elite retention strategy is executed starting from the second iteration;

[0083] Step S56: The two optimization target fitness values ​​of the individuals in the population output by the final iteration in step S55 are normalized according to the following formula, and then sorted according to the average value of the normalized values ​​of the two optimization targets, and the individual with the largest score value is selected as the final design solution:

[0084]

[0085] In the above formula, y i，j is the relative fitness value of individual i, x i，j is the kth optimization goal of individual i, k = 1, 2; X j is the kth optimization target fitness value of all individuals in the population.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] 1. The present invention can be used to design self-consistent energy system architecture solutions at different transportation facilities such as service areas, toll stations, stations, ports or docks under different transportation modes such as roads, railways or water transportation, and flexibly select the actual operation mode of the transportation self-consistent energy system architecture according to the conditions of the large power grid (no grid, weak grid, strong grid), taking into account comprehensive factors and having stronger applicability.

[0088] 2. The present invention can directly obtain the equipment installation quantity and spatial location layout of the traffic self-consistent energy system architecture plan by clearly dividing the available space modules, thereby avoiding conflicts between the traffic self-consistent energy system architecture plan and the subsequent site engineering construction layout design plan.

[0089] 3. The present invention divides the operation modes into four architectures, and even under complex and diverse transportation construction and operation conditions, it is possible to obtain the optimal transportation self-consistent energy system architecture solution that meets the current traffic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0091] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0092] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0093] An embodiment of a method for designing a traffic self-consistent energy system architecture of the present invention is as follows: Figure 1 As shown, the method comprises the following steps:

[0094] S1: Determine the spatial scope of the construction of the transportation self-consistent energy system project, determine the traffic power load power data required to be supplied in the system, and then divide the space modules in turn, and clarify the specific characteristic dimensions of each divided space module. Specifically, n is the total number of space modules, j is a certain space module, j = 1, 2, ..., n;

[0095] S2: According to the specific characteristic dimensions of each space module in step S1, determine the power generation and energy storage equipment that can be deployed in each space module, and obtain the maximum installation quantity of the power generation and energy storage equipment in each space module and the unit cost of the power generation and energy storage equipment;

[0096] S3: determining the environmental climate data involved in each space module of the power generation equipment in step S2, and then determining the single-unit output power of the power generation equipment in combination with the environmental climate data and the factory technical parameters of the power generation equipment;

[0097] S4: Select two of the three items of economy, technology and environmental protection as optimization targets, and determine two optimization objective functions. Then, select the operation mode based on the construction requirements of whether the transportation self-consistent energy system can be connected to the grid, the strength of the public power grid, and whether the temporary power outage of part of the load in the system can be accepted;

[0098] S5: According to the operation mode selected in step S4, the corresponding technical indicator constraints are clarified in combination with the constraints, and a transportation self-consistent energy system architecture design optimization model is constructed. The transportation power load power data in step S1, the maximum installation quantity of power generation and energy storage equipment in each space module in step S2 and the unit cost of power generation and energy storage equipment, and the unit output power of the power generation equipment in step S3 are used to solve the transportation self-consistent energy system architecture design optimization model to obtain the final transportation self-consistent energy system architecture composition.

[0099] In this embodiment, it should be noted that the division of the space modules in step S1 is based on specific environmental characteristics. For example, the space modules can be roofs, parking sheds, open spaces, hillsides, road edges, protected areas, etc. The specific characteristic dimensions include length, width, height, inclination angle, etc. See Table 1 for details:

[0100] Table 1 Characteristic dimensions of traffic space and equipment that can be deployed

[0101]

[0102] In this embodiment, it should be noted that in step S2, by comparing the area of ​​each space module with the unit floor area of ​​the deployed power generation and energy storage equipment, the maximum number of installed power generation and energy storage equipment can be obtained; each of the various power generation and energy storage equipment that can be installed has its own factory parameters, and the unit cost of various power generation and energy storage equipment includes the unit purchase fee, the unit installation fee and the unit land cost, etc.

[0103] In this embodiment, it should be noted that in step S3, the power generation equipment includes photovoltaic panels, wind turbines, and water turbines, and the specific calculation method for determining the output power of each power generation and energy storage device is as follows:

[0104] The output power of a photovoltaic panel is P pv for:

[0105] In the above formula, f pv is the actual output power derating factor of the photovoltaic power generation system, P pv , r is the rated power of the photovoltaic system under standard test conditions, R is the actual irradiance on the current photovoltaic panel surface, R s is the irradiance under standard test conditions, α p is the power temperature coefficient, T e is the ambient temperature;

[0106] The single unit output power of the fan P wt for:

[0107] In the above formula, P wt，r Indicates the factory rated power of the fan; v, v r 、v min 、v max Respectively represent real-time wind speed, rated wind speed, cut-in wind speed and cut-out wind speed;

[0108] The single unit output power of the turbine P hyd for:

[0109] In the above formula, Q hyd is the effective flow rate, η hyd is the turbine efficiency, ρ is the water density, g is the gravitational acceleration, f hyd is the head reduction factor, h w is the water head at the water surface drop position, v hyd is the flow rate, representing the flow head, Q stt , Q min , Q max They are actual flow, minimum flow, and maximum flow respectively.

[0110] In this embodiment, it should be noted that in step S4, the role of the transportation self-consistent energy system can be divided into three main aspects: first, reducing the amount of electricity purchased and grid-connected electricity sold by transportation infrastructure, thereby obtaining economic benefits; second, ensuring the normal operation of important transportation infrastructure in areas with no grid or weak grid; third, using the green electricity generated in the system to replace the use of traditional thermal power by transportation infrastructure, thereby achieving low-carbon, environmentally friendly and zero-carbon transportation. The above three aspects can be summarized as economic, technical and environmental protection construction optimization goals. The confirmation of the two optimization goals is independently selected according to the construction needs of the construction entity.

[0111] In this embodiment, the power generation and energy storage equipment includes power generation equipment and energy storage equipment; the power generation equipment includes photovoltaic panels, wind turbines, and water turbines, and the corresponding environmental climate data are light radiation density data, wind power data, and water flow data.

[0112] In this embodiment, the operation modes in step S4 include the following four:

[0113] Operation mode A-temporary power outage of low-importance loads: self-generation and self-use, surplus power storage. At this time, the system is off-grid. When the real-time power supply of renewable energy such as light, wind, and water, as well as power generation and energy storage equipment is insufficient, the power demand of traffic loads is reduced, and the low-importance traffic power loads are temporarily powered off;

[0114] Operation mode B-Controllable distributed power supply: self-generation and self-use, surplus power storage, controllable distributed power supply as backup power supply to make up the power output difference;

[0115] Operation mode C-power supply from the large power grid: self-generation and self-use, surplus power storage, and the large power grid as a backup power supply to make up for the power output difference. At this time, the system is in a grid-connected state, but the power supply capacity of the power grid is weak, and only temporary one-way power supply can be provided to the system, and there may be regular power outages;

[0116] Operation mode D-full grid connection: self-generation and self-use, surplus power to the grid. At this time, the system is in a full grid-connected state. When the supply of renewable energy power is insufficient, the energy storage equipment will supply power, followed by the large power grid to supplement the power. When there is surplus renewable energy power, the energy storage equipment will be charged first, and the rest will be sent to the grid.

[0117] In this embodiment, the two optimization objective functions in step S4 are two of the economic measurement index, technical measurement index and environmental measurement index functions, which are as follows:

[0118] The economic efficiency is measured by the total net present value C NPC , for different operation modes, the total net present value C NPC The calculation is different, as follows:

[0119] Operation Mode A:

[0120] C NPC =C initial +C operate +C replace -C salvage

[0121] Operation Mode B:

[0122] C NPC =C initial +C operate +C replace -C salvage +C fuel +C backup

[0123] Operation mode C:

[0124] C NPC =C initial +C operate +C replace -C salvage +C buy +C backup

[0125] Operation mode D:

[0126] C NPC =C initial +C operate +C replace -C salvage +C buy -C sell +C backup

[0127] In the above formula:

[0128] C initial Refers to the construction cost, C initial =∑ 1≤j≤n (C initial，j ×N j )

[0129] C operat Refers to operation and maintenance costs,

[0130] C replace Refers to the replacement cost,

[0131] C salvage Refers to the residual value,

[0132] C fuel Refers to fuel costs,

[0133] C buy The cost of purchasing electricity.

[0134] C sell Refers to the revenue from electricity sales,

[0135] C backup Refers to the installed capacity cost of backup power generation and energy storage equipment, C backup =M backup ×max[P backup (t)]

[0136] The technical measurement indicator is the power outage probability LOLP, which is as follows:

[0137]

[0138] The environmental performance index is the self-consistency rate U re , as follows:

[0139]

[0140] In the above formula, C initial，j , C operate，j , C replace，j Respectively represent the unit cost, annual operation and maintenance cost, and replacement cost of the power generation and energy storage facilities in the jth space module; N j is the installed capacity of power generation and energy storage facilities in the jth space module; r is the annual interest rate; T j is the equipment life of the power generation and energy storage facilities in the jth space module; Z j is the number of replacements of the power generation and energy storage facilities in the jth space module during the system life; D is the residual value ratio of the power generation and energy storage facilities; M buy and M sell M are the electricity purchase price and electricity sales price respectively; fuel is the fuel price of the backup power generation facility, E fuel is the motor power generation coefficient of the corresponding fuel; M backup P is the unit connection cost of the diesel motor or transformer in the grid-connected state; backup (t), P loss (t), P ex (t) are the backup power supply power, insufficient renewable energy power, and surplus renewable energy power in the system at time t; P load (t) is the electric power demand of the traffic load required to be supplied in the system at time t; T represents the simulation calculation time period; Y represents the system design life; Δt represents the unit time scale (1h).

[0141] In this embodiment, it should be noted that the controllable distributed power source includes a fuel cell and a diesel motor.

[0142] In this embodiment, in step S5, the technical indicator constraints corresponding to each operation mode are composed of two parts, one part is a mandatory constraint, and the other part is a free option constraint, and the selection range of the free option constraint corresponding to each operation mode is different;

[0143] The mandatory constraints are composed of the following technical indicator constraints a1-a4:

[0144] a1: Power balance constraints of the traffic self-consistent energy system architecture:

[0145]

[0146] a2: Constraints on the output power of a single power generation and energy storage device:

[0147]

[0148] a3: Constraints on the number of installed capacity of power generation and energy storage equipment:

[0149] 0≤N j ≤N j，max

[0150] a4: Energy storage equipment charge and discharge depth constraints:

[0151] 0≤SOC min ≤SOC(t)≤SOC max ≤1

[0152] The free choice constraints are respectively composed of one or more of the following technical indicator constraints a5-a9:

[0153] a5: Maximum acceptable renewable energy curtailment rate E ex，max constraint:

[0154]

[0155] a6: Maximum acceptable power failure probability LOLP max constraint:

[0156]

[0157] a7: The minimum acceptable renewable energy self-consistency rate U re，min constraint:

[0158]

[0159] a8: Weak network power outage time constraints:

[0160] [TD start , TD end )

[0161] a9: Maximum acceptable probability of temporary power outage of low-importance loads R cut，max constraint:

[0162]

[0163] Where P ex (t) represents the remaining output power of renewable energy at time t, which is the grid-connected power in operation mode D; P j (t) represents the actual output power of the jth type of equipment at time t; P j，min , P j，max They represent the rated minimum and maximum single-unit output power of the j-th power supply facility respectively; SOC min , SOC maxare the minimum and maximum allowable discharge depths of the energy storage facility, respectively; SOC(t) is the actual discharge depth of the energy storage facility at time t; P renew (t) represents the total output power of renewable energy sources such as light, wind and water at time t; TD start and TD end Indicates the start and end time of regular daily power outages under weak network conditions. If the grid cannot be connected or there is no power outage in the grid, the default value is TD start =TD end =0; R cut represents the probability of temporary power outage of low-importance loads; T cut It indicates the total duration of temporary power outage of low-importance loads during the entire life cycle of the system under operation mode A; b，max Both represent A b The maximum value of the form A b，min Both represent A b The minimum value of

[0164] The selection range of the free option constraints corresponding to each operating mode is as follows:

[0165] The selection range of the free option constraints corresponding to operation mode A: renewable energy curtailment rate, power shortage probability, and temporary power outage time probability constraints of low-importance loads;

[0166] The selection range of the free option constraints corresponding to operation mode B: renewable energy curtailment rate and self-consistency rate;

[0167] The selection range of the free option constraints corresponding to operation mode C: renewable energy curtailment rate, power shortage probability, self-consistency rate and weak network power outage time constraints;

[0168] The selection range of the free option constraints corresponding to operation mode D: renewable energy grid sales rate and self-consistency rate.

[0169] In this embodiment, it should be noted that the maximum or minimum value of the constraint conditions in a1-a4 directly depends on the factory technical parameters of the device; a8 is the actual power supply condition of the current public power grid; the technical indicator constraints in a5-a7 and a9 are ex , LOLP and R cut The smaller the better, U re The larger the value, the better. The value range is between 0 and 1. The default acceptable worst boundary value is E. ex，max =1,LOLP max =1, R cut，max =1,U re，min = 0. If there are more stringent requirements, the acceptable worst boundary constraint value E can be reset independently. ex，max ,LOLP max , Rcut，max , U re，min , which is used to judge whether the technical performance of the obtained solution can meet the requirements of technical constraints.

[0170] In this embodiment, the specific method of constructing the traffic self-consistent energy system architecture design optimization model in step S5 is to constrain the optimization objectives and operation modes selected in step S4 and the technical indicators selected in step S5 to form an optimization model.

[0171] In this embodiment, the process of solving the traffic self-consistent energy system architecture design optimization model in step S5 is as follows:

[0172] S51: Population initialization generation, individuals represent a set of one-dimensional vector solutions within the maximum installation quantity range of power generation and energy storage facilities in each space module determined in step S2, the number of individuals is determined to be 200, the initial population is generated using the Logistic mapping model, and the actual installation quantity x of power generation and energy storage equipment in the jth space module is obtained using the following formula i，j :

[0173] β i，j =μβ i，j (1-β i，j ), β 0，j ∈(0,1);μ∈[3.57,4]

[0174] x i，j =x j，min +(x j，max -x j，min )β i，j

[0175] In the above formula, i is an individual, i = 1, 2, ..., 200; β i，j is the jth variable x in the ith individual i，j The corresponding chaotic vector, where β 0，j and μ are random numbers within the range of values, x i，j represents the actual number of installed power generation and energy storage equipment allowed to be installed in the jth space module;

[0176] S52: According to the actual installed number of power generation and energy storage equipment in step S51, a time series simulation operation is performed to obtain power data during the operation and energy storage capacity change data of the energy storage equipment.

[0177] S53: According to the power data during operation generated in step S52 and the unit cost of the power generation and energy storage equipment in step S2, the function value of the selected constraint condition is calculated, and the two optimization objective function values ​​are converted into a negative optimization function G(x) whose function value is smaller and better. The penalty function P(x) is composed of the selected constraint condition, and then the fitness value F(x) of the two optimization objectives selected in step S4 is calculated respectively by the following formula:

[0178]

[0179]

[0180]

[0181] Where: f(x) is the objective function corresponding to the selected optimization index, where the optimization index can be the total net present value, power shortage probability or self-consistency rate; x is the optimization variable, which refers to the number of installed power generation and energy storage equipment in this case;

[0182] Step S54: Perform fast non-dominated sorting and crowding calculation on the individuals in the population according to the fitness value F(x) in step S5, and then perform heredity and mutation operations;

[0183] Step S55: repeating steps S52, S53 and S54 until the maximum number of iterative evolutions reaches 300, then ending the operation, outputting the final offspring population, and calculating and outputting the optimization objective function, constraint condition function and fitness function value of the final offspring population according to steps S52 and S53, wherein the elite retention strategy is executed starting from the second iteration;

[0184] Step S56: The two optimization target fitness values ​​of the individuals in the population output by the final iteration in step S55 are normalized according to the following formula, and then sorted according to the average value of the normalized values ​​of the two optimization targets, and the individual with the largest score value is selected as the final design solution:

[0185]

[0186] In the above formula, y i，j is the relative fitness value of individual i, x i，j is the kth optimization goal of individual i, k = 1, 2; X j is the kth optimization target fitness value of all individuals in the population.

[0187] In this embodiment, it should be noted that the above formula is to take the fitness value range of all the schemes obtained, and then take the distance between the fitness value of each scheme and the minimum value, and compare the two to obtain the relative value of the fitness value of each scheme, while removing the influence of different optimization objective function value dimensions.

[0188] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for designing a self-consistent energy system architecture for transportation, characterized in that: The method comprises the following steps: S1: Determine the spatial scope of the construction of the transportation self-consistent energy system project, determine the traffic power load power data required to be supplied in the system, and then divide the space modules in turn, and clarify the specific characteristic dimensions of each divided space module. Specifically, n is the total number of space modules, j is a certain space module, j = 1, 2, ..., n; S2: According to the specific characteristic dimensions of each space module in step S1, determine the power generation and energy storage equipment that can be deployed in each space module, and obtain the maximum installation quantity of the power generation and energy storage equipment in each space module and the unit cost of the power generation and energy storage equipment; S3: determining the environmental climate data involved in each space module of the power generation equipment in step S2, and then determining the single-unit output power of the power generation equipment in combination with the environmental climate data and the factory technical parameters of the power generation equipment; S4: Select two of the three items of economy, technology and environmental protection as optimization targets, and determine two optimization objective functions. Then, select the operation mode based on the construction requirements of whether the transportation self-consistent energy system can be connected to the grid, the strength of the public power grid, and whether the temporary power outage of part of the load in the system can be accepted; S5: According to the operation mode selected in step S4, the corresponding technical indicator constraints are clarified in combination with the constraints, and a transportation self-consistent energy system architecture design optimization model is constructed. The transportation power load power data in step S1, the maximum installation quantity of power generation and energy storage equipment in each space module in step S2 and the unit cost of power generation and energy storage equipment, and the unit output power of the power generation equipment in step S3 are used to solve the transportation self-consistent energy system architecture design optimization model to obtain the final transportation self-consistent energy system architecture composition.

2. A method for designing a traffic self-consistent energy system architecture according to claim 1, characterized in that: The power generation and energy storage equipment includes power generation equipment and energy storage equipment; the power generation equipment includes photovoltaic panels, wind turbines, and water turbines, and the corresponding environmental climate data are light radiation density data, wind power data, and water flow data.

3. A method for designing a traffic self-consistent energy system architecture according to claim 1, characterized in that: The operation modes in step S4 include the following four: Operation mode A-temporary power outage of low-importance loads: self-generation and self-use, surplus power storage. At this time, the system is off-grid. When the real-time power supply of renewable energy such as light, wind, and water, as well as power generation and energy storage equipment is insufficient, the power demand of traffic loads is reduced, and the low-importance traffic power loads are temporarily powered off; Operation mode B-Controllable distributed power supply: self-generation and self-use, surplus power storage, controllable distributed power supply as backup power supply to make up the power output difference; Operation mode C-power supply from the large power grid: self-generation and self-use, surplus power storage, and the large power grid as a backup power supply to make up for the power output difference. At this time, the system is in a grid-connected state, but the power supply capacity of the power grid is weak, and only temporary one-way power supply can be provided to the system, and there may be regular power outages; Operation mode D-full grid connection: self-generation and self-use, surplus power to the grid. At this time, the system is in a full grid-connected state. When the supply of renewable energy power is insufficient, the energy storage equipment will supply power, followed by the large power grid to supplement the power. When there is surplus renewable energy power, the energy storage equipment will be charged first, and the rest will be sent to the grid.

4. A method for designing a traffic self-consistent energy system architecture according to claim 3, characterized in that: The two optimization objective functions in step S4 are two of the economic measurement index, technical measurement index, and environmental measurement index functions, which are as follows: The economic efficiency is measured by the total net present value C NPC , for different operation modes, the total net present value C NPC The calculation is different, as follows: Operation Mode A: C NPC =C initial +C operate +C replace -C salvage Operation Mode B: C NPC =C initial +C operate +C replace -C salvage +C fuel +C backup Operation mode C: C NPC =C initial +C operate +C replace -C salvage +C buy +C backup Operation mode D: C NPC =C initial +C operate +C replace -C salvage +C buy -C sell +C backup In the above formula: C initial Refers to the construction cost, C initial =∑ 1≤j≤n (C initial,j ×N j ) C operat Refers to operation and maintenance costs, C replace Refers to the replacement cost, C salvage Refers to the residual value, C fuel Refers to fuel costs, C buy The cost of purchasing electricity. C sell Refers to the revenue from electricity sales, C nackup Refers to the installed cost of backup power generation and energy storage equipment, C backup =M backup ×max[P backup (t)] The technical measurement indicator is the power outage probability LOLP, which is as follows: The environmental performance index is the self-consistency rate U re , as follows: In the above formula, C initial,j , C operate,j , C replace,j Respectively represent the unit cost, annual operation and maintenance cost, and replacement cost of the power generation and energy storage facilities in the jth space module; N j is the installed capacity of power generation and energy storage facilities in the jth space module; r is the annual interest rate; T j is the equipment life of the power generation and energy storage facilities in the jth space module; Z j is the number of replacements of the power generation and energy storage facilities in the jth space module during the system life; D is the residual value ratio of the power generation and energy storage facilities; M buy and M sell M are the electricity purchase price and electricity sales price respectively; fuel is the fuel price of the backup power generation facility, E fuel is the motor power generation coefficient of the corresponding fuel; M backup P is the unit connection cost of the diesel motor or transformer in the grid-connected state; backup (t), P loss (t), P ex (t) are the backup power supply power, insufficient renewable energy power, and surplus renewable energy power in the system at time t; P load (t) is the electric power demand of the traffic load required to be supplied in the system at time t; T represents the simulation calculation time period; Y represents the system design life; Δt represents the unit time scale (1h).

5. A method for designing a traffic self-consistent energy system architecture according to claim 3, characterized in that: In step S5, the technical indicator constraints corresponding to each operation mode are composed of two parts, one part is a mandatory constraint, and the other part is a free option constraint, and the selection range of the free option constraint corresponding to each operation mode is different; The mandatory constraints are composed of the following technical indicator constraints a1-a4: a1: Power balance constraints of the traffic self-consistent energy system architecture: a2: Constraints on the output power of a single power generation and energy storage device: a3: Constraints on the number of installed capacity of power generation and energy storage equipment: 0≤N j ≤N j,max a4: Energy storage equipment charge and discharge depth constraints: 0≤SOC min ≤SOC(t)≤SOC max ≤1 The free choice constraints are respectively composed of one or more of the following technical indicator constraints a5-a9: a5: Maximum acceptable renewable energy curtailment rate E ex,max constraint: a6: Maximum acceptable power failure probability LOLP max constraint: a7: The minimum acceptable renewable energy self-consistency rate U re,min constraint: a8: Weak network power outage time constraints: [TD start ,TD end ) a9: Maximum acceptable probability of temporary power outage of low-importance loads R cut,max constraint: Where P ex (t) represents the remaining output power of renewable energy at time t, which is the grid-connected power in operation mode D; P j (t) represents the actual output power of the jth type of equipment at time t; P j,min , P j,max They represent the rated minimum and maximum single-unit output power of the j-th power supply facility respectively; SOC min , SOC max are the minimum and maximum allowable discharge depths of the energy storage facility, respectively; SOC(t) is the actual discharge depth of the energy storage facility at time t; P renew (t) represents the total output power of renewable energy sources such as light, wind and water at time t; TD start and TD end Indicates the start and end time of regular daily power outages under weak network conditions. If it is impossible to connect to the grid or there is no power outage in the grid, the default value is TD start =TD end =0; R cut Indicates the probability of temporary power outage of low-importance loads; T cut It indicates the total duration of temporary power outage of low-importance loads during the entire life cycle of the system under operation mode A; b,max Both represent A b The maximum value of the form A b,min Both represent A b The minimum value of The selection range of the free option constraints corresponding to each operating mode is as follows: The selection range of the free option constraints corresponding to operation mode A: renewable energy curtailment rate, power shortage probability, and temporary power outage time probability constraints of low-importance loads; The selection range of the free option constraints corresponding to operation mode B: renewable energy curtailment rate and self-consistency rate; The selection range of the free option constraints corresponding to operation mode C: renewable energy curtailment rate, power shortage probability, self-consistency rate and weak network power outage time constraints; The selection range of the free option constraints corresponding to operation mode D: renewable energy grid sales rate and self-consistency rate.

6. A method for designing a traffic self-consistent energy system architecture according to claim 5, characterized in that: The specific method of constructing the optimization model of the traffic self-consistent energy system architecture design in step S5 is to constrain the optimization objectives and operation modes selected in step S4 and the technical indicators selected in step S5 to form an optimization model.

7. A method for designing a traffic self-consistent energy system architecture according to claim 5, characterized in that: The process of solving the optimization model of the traffic self-consistent energy system architecture design in step S5 is as follows: S51: Population initialization generation, individuals represent a set of one-dimensional vector solutions within the maximum installation quantity range of power generation and energy storage facilities in each space module determined in step S2, the number of individuals is determined to be 200, the initial population is generated using the Logistic mapping model, and the actual installation quantity x of power generation and energy storage equipment in the jth space module is obtained using the following formula i,j : b i,j =mv i,j (1-b) i,j ),b 0,j ∈(0,1); μ∈[3.57,4] x i,j =x j,min +(x j,max -x j,min )β i,j In the above formula, i is an individual, i = 1, 2, ..., 200; β i,j is the jth variable x in the ith individual i,j The corresponding chaotic vector, where β 0,j and μ are random numbers within the range of values, x i,j represents the actual number of installed power generation and energy storage equipment allowed to be installed in the jth space module; S52: According to the actual installed number of power generation and energy storage equipment in step S51, a time series simulation operation is performed to obtain power data during the operation and energy storage capacity change data of the energy storage equipment. S53: According to the power data during operation generated in step S52 and the unit cost of the power generation and energy storage equipment in step S2, the function value of the selected constraint condition is calculated, and the two optimization objective function values ​​are converted into a negative optimization function G(x) whose function value is smaller and better. The penalty function P(x) is composed of the selected constraint condition, and then the fitness value F(x) of the two optimization objectives selected in step S4 is calculated respectively by the following formula: Where: f(x) is the objective function corresponding to the selected optimization index, where the optimization index can be the total net present value, power shortage probability or self-consistency rate; x is the optimization variable, which refers to the number of installed power generation and energy storage equipment in this case; Step S54: According to the fitness value F(x) in step S5, the individuals in the population are quickly sorted and the crowding degree is calculated, and then the inheritance and mutation operations are performed; Step S55: repeating steps S52, S53 and S54 until the maximum number of iterative evolutions reaches 300, then ending the operation, outputting the final offspring population, and calculating and outputting the optimization objective function, constraint condition function and fitness function value of the final offspring population according to steps S52 and S53, wherein the elite retention strategy is executed starting from the second iteration; Step S56: The two optimization target fitness values ​​of the individuals in the population output by the final iteration in step S55 are normalized according to the following formula, and then sorted according to the average value of the normalized values ​​of the two optimization targets, and the individual with the largest score value is selected as the final design solution: In the above formula, y i,j is the relative fitness value of individual i, x i,j is the kth optimization target of individual i, k = 1, 2; X j is the kth optimization target fitness value of all individuals in the population.

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