A self-consistent energy system planning method for highway traffic based on scenario adaptation

Abstract

The present invention discloses a method for planning a self-consistent energy system for highway transportation based on scenario adaptation, and the steps include: first, evaluating the natural endowment characteristics of my country's renewable resources based on statistical data; second, predicting the energy load driven by highway transportation operation and maintenance demand for highway transportation service areas, tunnels, bridges, toll stations and equipment along the road; then, comprehensively dividing the scenarios of the self-consistent energy system for highway transportation dominated by the energy supply mode by integrating the three factors of "source, network and load"; finally, selecting typical scenarios to build a scenario-adapted highway transportation self-consistent energy system architecture, establishing a highway transportation self-consistent microgrid system planning model, and setting corresponding operation control strategies, and using particle swarm solving to obtain the optimal planning scheme. The present invention fully considers the "source-network-load" triple attributes of the highway operation and maintenance scenario, optimizes the highway self-consistent energy system, and provides technical and decision-making support for the planning and design of the future highway transportation self-consistent energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent planning of energy systems, and in particular to a method for planning a self-consistent energy system for highway traffic based on scenario adaptation. Background Art

[0002] With the continuous development of my country's economy, highway transportation, as an important mode of logistics and transportation, has continued to expand its energy consumption. How to build a new highway transportation energy system with clean energy as the main body is very important. The transportation system itself contains sufficient natural endowments, especially in the areas of western my country where highway transportation is accessible. Making full use of these natural resource endowments to form an energy supply system and plan that serves transportation operation and maintenance equipment can not only improve the level of independent guarantee of transportation energy, but also optimize the transportation energy structure. It can also form independent energy supply in areas where transportation is accessible but energy is not, tap new energy supply capabilities from within transportation itself, optimize transportation energy structure, and promote the development of my country's green, low-carbon, and environmentally friendly transportation energy system. Therefore, the energy and transportation systems will gradually evolve into a coordinated development form of mutual integration and integrated connection, and finally form a new type of self-consistent transportation energy system that is clean, low-carbon, integrated and efficient, and realize the self-consistent supply of clean energy, thereby providing a new system solution for the growth of energy demand brought about by the intelligent transportation system.

[0003] CN116596204A proposes a typical scenario integrated energy system planning method and system based on an energy hub, including: obtaining the annual electricity-heat-gas load and wind-solar forecast data of typical scenarios; constructing the energy framework of the integrated energy system of each typical scenario industrial park based on the acquired data, analyzing the energy configuration of each system under different scenarios, matching power supply equipment and coupling equipment for different scenarios, and establishing energy coupling models for each subsystem of the integrated energy system; on the basis of the energy coupling models of each subsystem, combining comprehensive demand response to establish a regional integrated energy system planning model based on the energy hub theory; solving the regional integrated energy system planning model to obtain the commissioning capacity and commissioning years of the planning equipment of the integrated energy system. This invention only uses the typical scenario data of a single system for planning, and is a planning method only for a specific system under a specific scenario, and cannot provide guidance for system planning without a large power grid.

[0004] CN115034567A discloses a method and device for planning a comprehensive energy system solution based on scenario analysis, wherein the method includes: generating several output scenarios of renewable energy output based on renewable energy characteristics; generating several comprehensive energy system planning solutions based on the complementary characteristics of various energy devices; determining the target working parameters of each energy device under each output scenario; screening each energy system solution based on the target output scenario, the target working parameters of each energy device corresponding to the target output scenario, the constraint conditions corresponding to the target output scenario, the comprehensive energy system planning solution strategy and the cost corresponding to each energy system solution and an improved particle swarm-ant colony optimization algorithm, and obtaining the target energy system solution corresponding to the target output scenario. Although the invention generates several uncertain scenarios, it is mainly based on natural conditions, and the scenario division dimensions are relatively small. Since the planning of highway traffic energy system is affected by factors such as large coverage area and wide span, the scenario division needs to be divided from more dimensions such as resources, load and power grid construction. Summary of the invention

[0005] Purpose of the invention: The present invention provides a method for planning a self-consistent energy system for highway transportation based on scenario adaptation when planning and designing a self-consistent energy system for highways under the background of integrated development of highway transportation and energy.

[0006] Technical solution: A scenario-adapted self-consistent energy system planning method for highway transportation, which includes the following steps:

[0007] Step 1: Assessment of the natural endowment characteristics of renewable resources

[0008] Step 1.1: PV resource endowment assessment

[0009] According to the classification standards of the Wind and Solar Energy Assessment Center of the China Meteorological Administration, my country's solar energy resource areas are divided into the following four categories:

[0010] Category I regions (resource-rich zones): The annual radiation is above 6,300 megajoules / square meter, which is equivalent to the heat generated by burning 230 kilograms of standard coal. It mainly includes the Qinghai-Tibet Plateau, northern Gansu, northern Ningxia, southern Xinjiang, northwestern Hebei, northern Shanxi, southern Inner Mongolia, southern Ningxia, central Gansu, eastern Qinghai, southeastern Tibet, etc.

[0011] Category II regions (relatively rich in resources): The annual radiation is 5040-6300 MJ / m2, equivalent to the heat generated by the combustion of 180-230 kg of standard coal. Mainly including Shandong, Henan, southeastern Hebei, southern Shanxi, northern Xinjiang, Jilin, Liaoning, Yunnan, northern Shaanxi, southeastern Gansu, southern Guangdong, southern Fujian, central and northern Jiangsu and northern Anhui.

[0012] Category III regions (general resource zones): annual radiation is 3780-5040 MJ / m2, equivalent to the heat generated by burning 140-180 kg of standard coal. Mainly in the middle and lower reaches of the Yangtze River, Fujian, Zhejiang and part of Guangdong. It is rainy in spring and summer, but the solar energy resources are acceptable in autumn and winter.

[0013] Category IV areas: The annual radiation is below 3780 MJ / m2. Mainly including Sichuan and Guizhou provinces. This area is the area with the least solar energy resources in my country.

[0014] Step 1.2: Wind power potential assessment

[0015] The amount of wind energy resources in a certain area is usually evaluated by two indicators: effective wind energy (wind power) density and annual effective wind hours. When evaluating wind energy reserves, the average wind energy density within a certain area is approximated using the observed discrete values. The commonly used calculation formula is:

[0016]

[0017] Where: ρ is the air density, N is the total observation time: v i is the i-th level value after the wind speed is divided into several levels according to the observation records; N i It is the cumulative time when wind speed level i occurs within a certain period of time.

[0018] The second wind energy resource survey of the China Meteorological Administration conducted a detailed assessment of the country's wind energy resources using theoretical reserves, actual exploitable reserves, and average reserves per unit area as evaluation parameters, and conducted a detailed wind resource endowment analysis on this basis.

[0019] Step 2: Energy load forecasting driven by highway traffic operation and maintenance demand

[0020] Step 2.1: Service area energy consumption analysis

[0021] The energy consumption of service areas is mainly divided into restaurant energy consumption, supermarket energy consumption, guest room energy consumption, office area energy consumption, gas station energy consumption, etc. The types of energy consumption include electricity, natural gas, gasoline, etc. Through the investigation and research of the original service areas, a large amount of data was collected, and the factors affecting the building energy consumption of highway service areas were summarized and studied. The principal component analysis method was used to know that the main component factors affecting the building energy consumption of highway service areas are: passenger flow, service area greening rate, building area, and window-to-wall ratio. The service area data was then processed by the SPSS software system, and a linear relationship between it and the highway service area energy consumption data was obtained, and the multivariate regression analysis method was used for fitting:

[0022] The linear relationship between the energy consumption and passenger flow in the expressway service area can be expressed as:

[0023] E=11.559a+346.77 (2) The linear relationship between the energy consumption of the expressway service area and the building area can be expressed as:

[0024] E=5.697b+4.599 (3) The linear relationship between the energy consumption and greening rate of the expressway service area can be expressed as:

[0025] E=-0.045c+0.8634 (4) The linear relationship between the energy consumption of the high-speed service area and the window-to-wall ratio can be expressed as:

[0026] E=0.093d+3.726 (5)

[0027] Where E is the building energy consumption of the highway service area, tce (tons of standard coal equivalent, 1 ton of standard coal = 8130 kW h); a is the passenger flow of the highway service area, person / year; b is the building area of ​​the highway service area, m 2 ; c is the greening rate of the service area; d is the window-to-wall ratio.

[0028] The determination coefficient of building energy consumption and passenger flow in highway service areas is R2=0.1501, with a good fit; building energy consumption is positively correlated with the area of ​​highway service areas, and the fitting effect is good when the area is below 5000m2; the linear relationship between building energy consumption and window-to-wall ratio is R2=0.1155, with a good effect; building energy consumption is negatively correlated with the greening rate of highway service areas, and the goodness of fit becomes better with the increase of the greening rate.

[0029] Step 2.2: Tunnel energy consumption analysis

[0030] To ensure driving safety, highway tunnels need to be equipped with ventilation, lighting, monitoring, communication and other systems. These systems are operated all year round according to usage requirements. Therefore, tunnels become one of the units that consume the most energy in the entire highway operation and management. Simplifying its energy consumption model, it mainly includes the power consumption of the lighting system Q1, the power consumption of the ventilation system Q2, the power consumption of the monitoring system Q3, and the power consumption of the fire protection system Q4. The total energy consumption of the tunnel is:

[0031] Q S =Q1+Q2+Q3+Q4 (6)

[0032] 1) Lighting system Q1

[0033] According to the "Highway Tunnel Lighting Design Regulations" and the characteristics of tunnel lighting, in order to ensure the driving safety of drivers when passing through the tunnel, it is necessary to lay out lighting for the entrance section, transition section, basic section, exit section and approach road outside the tunnel. The tunnel lighting energy consumption model (two-way and two-way traffic) determined by the requirements for the layout of lighting facilities in the tunnel is as follows:

[0034] Assuming the longitudinal slope is 2%, the lamps are arranged symmetrically on both sides, the road width is 9m, the lamp installation height is 5m, and the brightness outside the tunnel is 4000cd / m2. From the perspective of highway vehicle safety, we set the general speed to 80km / h and the road surface to be a bright road surface (i.e. reinforced concrete). According to the tunnel lighting engineering design case and energy consumption analysis, the tunnel lighting energy consumption is:

[0035] Q1=46L s +209819.2n s (7)

[0036] 2) Ventilation system Q2

[0037] Generally, tunnel ventilation systems are mechanical ventilation. Assuming that 4 groups of 8 fans are set, that is, one fan is set every 320m. The power of a common fan is 11kW, and the annual power consumption is 96360kW h.

[0038]

[0039] 3) Monitoring system Q3

[0040] According to the highway tunnel traffic engineering design specifications, the camera should be 100m-400m outside the tunnel. It is assumed that a camera is set every 150m in the tunnel, and the vehicle detector is set every 500m. Assuming a one-way four-lane, 8 cameras in one place, the camera power is 25w, and the annual power consumption is 219kW h.

[0041]

[0042] 4) Fire fighting system Q4

[0043] The traffic light is set 20m before the tunnel entrance. According to the fire automatic alarm design specification 0 12.1 highway tunnel, an alarm telephone should be set at the tunnel entrance and exit and every 200m in the tunnel, and a flashing red fire sound and light alarm should be set every 50m. An sound and light alarm device indicating a fire in the tunnel should be set within 50m to 250m in front of the tunnel entrance. Assuming that the power of the fire sound and light alarm is 10w and the annual power consumption is 87.6kW h; the power of the alarm telephone is 20w and the annual power consumption is 175.2kW h, the energy consumption calculation model of the fire protection system is as follows

[0044]

[0045] In summary, the calculation method of annual energy consumption of tunnel is as follows:

[0046]

[0047] Q s is the annual energy consumption of the tunnel, in kW·h; Ls is the total length of the tunnel, in meters; n s It is the total number of tunnels in this section of expressway, in units of tunnels.

[0048] Step 2.3: Bridge energy analysis

[0049] There are many kinds of electrical equipment around bridges. To ensure driving safety, highway bridges need to be equipped with lighting, monitoring, communication, emergency power systems and other systems. A bridge with a length of L kilometers usually requires (L-1) monitoring and detection equipment. Its energy consumption model is;

[0050] Q q =P b1 T b1 L B +P b2 T b2 (L B -1) (12)

[0051] Q q is the annual energy consumption of the bridge, in kW h; P b1 is the load of the bridge lighting system, in kW / km, and is taken as 15.98; T b1 is the working time of the lighting system, h; L B is the total length of the bridge, km; P b2 is the rated power of the monitoring and detection equipment, kW; T b2 is the working time of monitoring and detection equipment, h.

[0052] Step 2.4: Energy consumption of toll booths

[0053] The main energy consumption of highway toll stations comes from the toll collection system, monitoring system, lighting system, and daily management and office. The commonly used energy consumption calculation formula is:

[0054]

[0055] E to,m,i,o The total energy consumption of toll collection, monitoring, lighting, and daily management and office work in a year at the toll station (MJ); i is the number of the i-th type of equipment (units); w i is the power of the ith device (W / unit); t i is the operating time of the i-th equipment (hours); i is the type of equipment.

[0056] By summarizing the energy consumption of each part in a typical toll station, the annual energy consumption calculation formula of the toll station is as follows:

[0057] Q t =E to,m,i,o n t , (14)

[0058] Q t is the annual energy consumption of the toll station, kW·h; n t is the number of toll booths on this road section, .

[0059] Step 3: Comprehensively consider the three factors of “source, network and load” to carry out the division of the self-consistent energy system scenario of highway transportation dominated by energy supply mode;

[0060] Step 3.1: Divide according to local conditions

[0061] Considering the local environmental factors of the highway, namely the abundance of solar radiation and wind energy resources, regional division is carried out. Taking into account the distribution of solar energy resources and wind energy resources, it is divided into resource-rich areas, general resource areas and resource-poor areas. Wind power combined with solar energy resource zoning: Taking into account the abundance of wind and solar resources, China is divided into three regions: resource-rich areas, general resource areas and resource-poor areas. Among them, resource-rich areas mainly include Xinjiang, Inner Mongolia, Tibet (except southeastern Tibet), and western regions such as Qinghai and Ningxia; general resource areas mainly include northeastern Heilongjiang, southeastern Tibet, southern Gansu, southern Yunnan and other regions; resource-poor areas include East China, South China, Central China, Chongqing and Guizhou and other regions.

[0062] Step 3.2: Divide according to network conditions

[0063] Considering the density of local power grid coverage and the number of charging piles, the area is divided into strong network area, weak network area and no network area by comprehensively considering the strength of the power grid and the magnitude of charging piles.

[0064] Step 3.3: Divide according to load

[0065] The electricity load in the eastern region, especially in Beijing and Shanghai, where the population density is high, is relatively high, while the electricity load in sparsely populated areas in the west, such as Tibet and Xinjiang, is relatively low. The load of China's road traffic can be divided into large load and small load.

[0066] Step 3.4: Comprehensive scene segmentation

[0067] Taking into account the three factors of "source, network and load", the three modes of "adapting to local conditions", "adapting to the network" and "adapting to load" are superimposed together to form a set of operating scenarios of a self-consistent energy system for road transportation.

[0068] Step 4: Select typical scenarios to build a scenario-adapted highway traffic self-consistent energy system architecture, establish a highway traffic self-consistent microgrid system planning model, set up corresponding operation control strategies, and use particle swarm solving to obtain the optimal planning solution.

[0069] Step 4.1: Build the models of each unit of the system

[0070] 1) Photovoltaic power generation model

[0071] The output power of the photovoltaic array has certain fluctuations and regularity, which is mainly affected by factors such as solar radiation intensity and temperature. The output power P of the photovoltaic array pv It can be expressed as:

[0072]

[0073] Where: P s is the rated power of the PV array; For the photovoltaic construction area (latitude ) is the hourly average of the surface solar radiation intensity at hour h on day d in month m; θ T is the power temperature coefficient of the photovoltaic array, taking -0.35% / C; T is the operating temperature of the photovoltaic array; T ref is the reference temperature of the photovoltaic array, which is 25℃.

[0074] The Beta distribution model is considered to be able to effectively fit the long-term probability distribution of solar radiation intensity, and its probability distribution density function f2(G) is:

[0075]

[0076]

[0077]

[0078] Where: G max is the maximum solar radiation intensity during the simulation period; σ2 and μ2 are the variance and mean of the solar radiation intensity samples respectively; α and β are the shape parameters of the Beta distribution.

[0079] 2) Wind power generation model

[0080] Wind power output can be expressed by the product of installed capacity and output coefficient:

[0081] P w =μ w P w,r (19)

[0082] Where: P w Output power to wind farm; μ w P is the wind power output coefficient, which indicates the proportion of wind power output power to rated power; w,r is the rated output power of wind power.

[0083] The output power of a wind turbine generator set mainly depends on the power output curve of the unit, the hub height of the wind turbine and the wind speed at the hub height, which can be expressed as:

[0084]

[0085] Where: P w is the output power of the wind turbine generator set; S is the actual wind speed of the wind turbine at the wheel height; S i , S c and S R are the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine respectively; P R is the rated power of the wind turbine.

[0086] The distribution characteristics obtained by the probability statistics of wind speed can measure the distribution of wind energy resources in a place. The frequency distribution of wind speed is the main form of reflecting this characteristic. The Weibull distribution curve is often used to fit the probability distribution of wind speed at different height layers. The expression of its probability density function is:

[0087]

[0088]

[0089]

[0090] Where: σ1 and μ1 are the variance and mean of the actual wind speed samples, respectively; k and c are the shape parameter and scale parameter of the Weibull distribution, respectively; Γ(·) is the Gamma function.

[0091] 3) Battery model

[0092] During the energy optimization scheduling process, the remaining energy of the battery is calculated based on the battery power on the DC bus side during the charging and discharging process:

[0093]

[0094] Where: E bat (t) is the battery power at time t; P cbat The power for charging the battery; P dbat is the battery discharge power; σ is the battery self-discharge coefficient, which is 0.0001 / h; Δt is the step length, 1h; η c b at is the battery charging efficiency, take 0.9; ηdb at is the battery discharge efficiency, which is taken as 0.9.

[0095] 4) Hydrogen energy storage model

[0096] The electrolyzer is usually an alkaline electrolyzer, which is the preferred device for large-scale hydrogen production. It electrolyzes water into hydrogen and oxygen. The output power of the electrolyzer is:

[0097] P ec =Pin η ec (25)

[0098] Where: P in is the power input to the electrolyzer; η ec The electrolysis efficiency of the electrolytic cell is generally 60%.

[0099] The hydrogen storage tank has two functions: one is to store the hydrogen produced by the electrolyzer; the other is to supply hydrogen to the fuel cell. The mathematical model of the hydrogen storage tank is:

[0100]

[0101] Where: E st (t) is the energy stored in the hydrogen storage tank at time t; η e-DC and η f-DC are the working efficiencies of the electrolyzer and fuel cell converter, respectively, which are generally 90%, η st It is the working efficiency of the hydrogen storage tank, generally 50%.

[0102] The fuel cell uses a proton membrane exchange fuel cell. This technology uses hydrogen and oxygen as fuel, converts the chemical energy inside the fuel into electrical energy and stores it. Its output power is:

[0103] K0=K d ×K r (27)

[0104] Where P st-f is the power delivered from the hydrogen storage tank to the fuel cell, η f The efficiency of the fuel cell.

[0105] 5) Gas turbine auxiliary energy supply model

[0106] The relationship between the power generation of a gas turbine and the natural gas consumption is:

[0107]

[0108] Where: r gt,gas Indicates the natural gas flow consumed by the gas turbine, in m 3 / t;Q gas The calorific value of natural gas combustion is 33486.8 kJ / m 3 ; α is the conversion coefficient between kilowatt and kilojoule, which is 3600; P gt is the power generation capacity of the gas turbine, in kW; η gt The power generation efficiency of the gas turbine.

[0109] 6) Diesel engine auxiliary energy supply model

[0110] For diesel generators, fuel consumption F (in L, kg or m 3 ) and its output power (in kW) can be expressed as:

[0111] F=F0Y gen +F1P gen (29)

[0112] Where Y gen is the rated power of the diesel generator (in kW); F0 is the intercept coefficient of the diesel generator fuel curve, that is, the no-load fuel consumption of the diesel generator per unit power (in L / (kW-h)); F1 is the slope of the diesel generator fuel curve (in L / (kWh)).

[0113] The diesel generator consumption characteristic function can be expressed by the following quadratic function model:

[0114]

[0115] In the formula, f(P gen ) is the fuel cost of the diesel generator per hour (in yuan / h); a, b, c are the coefficients of the cost function, which can be obtained by fitting the consumption characteristic curve of the diesel generator; P gen is the output of the diesel generator (in kW).

[0116] Step 4.2: Select typical scenarios and determine system architecture and operation control strategy

[0117] Based on typical scenarios, energy storage systems and auxiliary energy supply systems are selected to build a scenario-adaptive self-consistent energy system architecture for highway traffic, and the system operation control strategy is determined under the current architecture.

[0118] Step 4.3: System optimization configuration model construction

[0119] 1) Objective function

[0120] ①Economical

[0121] The total net current cost C of the self-consistent energy system of highway transportation NPC The minimum is the economic goal, C NPC It is to discount the cost values ​​generated during the system life cycle to the initial year for statistics, including initial investment cost and operation and maintenance cost:

[0122] minC NPC =C initial +C om (31)

[0123] Where: C initial is the annual initial investment cost of each power generation unit and energy storage unit in the system; Com It is the annual operation and maintenance cost of each power generation unit and energy storage unit in the system.

[0124] The total annual installation cost is the sum of the annual installation costs of each micro-source and energy storage:

[0125]

[0126] In the formula They are the annual installation costs of photovoltaic, wind turbine, battery, electrolyzer, hydrogen storage tank and fuel cell, and the calculation of each component is as follows:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] Where: N WT is the number of fans installed; P MT is the rated output power of the fan; U MT L is the investment cost per rated power of the fan; MT is the service life of the fan, r is the capital discount rate, which is 0.04. PV is the number of photovoltaic panels installed; P PV is the rated output power of the photovoltaic panel; U PV L is the investment cost per unit rated power of photovoltaic panels; PV is the service life of the photovoltaic panel. N bat is the number of installed batteries; V bat is the rated voltage of the battery, I bat is the rated current of the battery; U bat L is the investment cost per unit capacity of the battery; bat Is the battery life. :N ele is the number of electrolytic cells installed; P ele is the rated power of the electrolyzer; U ele L is the investment cost per unit rated power of the electrolyzer; ele is the service life of the electrolytic cell. N Qh2 is the number of hydrogen storage tanks installed; P Qh2 is the capacity of the hydrogen storage tank; U Qh2 is the investment cost per unit capacity of hydrogen storage tank; L Qh2 is the service life of the hydrogen storage tank. Nfc is the number of fuel cells installed; P fc is the rated power of the fuel cell; U fc L is the investment cost of fuel cell unit rated power; fc The service life of the fuel cell.

[0134] The annual operation and maintenance cost is the sum of the operation and maintenance costs of each micro-source and energy storage battery, calculated as follows:

[0135]

[0136] Wherein, m=1…6 represents wind turbine, photovoltaic panel, battery, electrolyzer, hydrogen storage tank, and fuel cell respectively; N m represents the number of micro-sources installed; P m Represents the output of each micro-source; M m It is the operation and maintenance cost per unit power of the micro source.

[0137] ②Reliability

[0138] The average power supply reliability of the system refers to the ratio of the power supply obtained by the highway service area in a year to the actual load demand. Assuming that the power supply time required by the service area in a year is 8760h, the calculation formula of the average power supply reliability of the system ASAI is:

[0139]

[0140] Where: Q i is the load demand of the system at time i; Ql i The system is temporarily powered off.

[0141] ③ Utilization rate of renewable energy

[0142]

[0143] Where: U ren is the utilization rate of renewable energy; E ren is the total power generation of the power generation unit; E excess The excess power of the power generation unit.

[0144] 2) Constraints

[0145] ①Micro power output constraints

[0146] Wind power output constraints:

[0147] 0≤P wt (i)≤N wt p wt (i) (42)

[0148] Where N wtis the number of wind turbines configured in the system, p wt (i) is the output of a single wind turbine at time i.

[0149] Photovoltaic output constraints:

[0150] 0≤P pv (i)≤N pv p pv (i) (43)

[0151] Where N pv is the number of photovoltaic panels configured in the system, p pv (i) is the output of a single photovoltaic panel at time i.

[0152] ②Battery constraints

[0153] Battery capacity constraints:

[0154] E batmin ≤E bat (t)≤E batmax (44)

[0155] Where: E bat (t) is the remaining capacity of the battery; E batmin is the minimum capacity of the battery; E batmax The maximum capacity of the battery.

[0156] Battery charging and discharging power limit:

[0157]

[0158] Where: P cbatmax is the maximum charging power of the battery; P dbatmax is the maximum discharge power of the battery.

[0159] ③ Constraints of hydrogen energy storage system

[0160] Hydrogen storage tank capacity constraints:

[0161] H batmin ≤H bat (t)≤H batmax (46)

[0162] Where: H bat (t) is the capacity status of the hydrogen storage tank; H batmin is the minimum value of the hydrogen storage tank capacity state; H batmax It is the maximum value of the hydrogen storage tank capacity status.

[0163] Electrolytic cell power limit:

[0164] 0≤P ele (t)≤P elemax(47)

[0165] Where: P elemax is the maximum power of the electrolytic cell.

[0166] Fuel cell power limitations:

[0167] 0≤P fc (t)≤P fcmax (48)

[0168] Where: P fcmax is the maximum power of the fuel cell.

[0169] ⑤Power balance constraint

[0170] At any time, the output power of each power generation unit and energy storage device must meet the load requirements:

[0171] P wt (i)+P pv (i)+P water (i)+P dbat (i)+P fc (i) = P Load (i)+P cbat (i)+P ele (i) (49)

[0172] Where: P wt Output for the fan; P pv For photovoltaic output; P water (i) small hydropower output; dbat is the battery discharge power; P fc Powering the fuel cell; P Load is the microgrid load; P cbat The power for charging the battery; P ele Power the electrolyzer.

[0173] Step 4.4: Initialize the parameters of each device in the system, and input the typical daily geographic information (wind speed, light intensity and temperature) of the four seasons of the selected road section in each scenario and the load data of the highway infrastructure into the system.

[0174] Step 4.5: To solve the above problems, heuristic optimization algorithms such as particle swarm optimization and genetic algorithm are used to find the optimal solution, obtain the optimal planning scheme of highway traffic self-consistent microgrid system, output the system output and analyze it.

[0175] The present invention provides a computer-readable medium for storing software, wherein the software includes instructions that can be executed by one or more computers, and the instructions enable the one or more computers to perform operations through such execution, and the operations include any one of the processes of the scene-adaptive highway traffic self-consistent energy system planning method mentioned above.

[0176] The present invention also provides a computer system, characterized in that it comprises:

[0177] one or more processors;

[0178] A memory storing operable instructions, wherein when the instructions are executed by the one or more processors, the one or more processors are caused to perform operations, wherein the operations include any one of the above-mentioned process steps of the method for planning a self-consistent energy system for highway traffic based on scenario adaptation.

[0179] So far, the natural resource endowment characteristics are evaluated and the highway traffic load is preliminarily predicted, and the highway traffic self-consistent energy system scenarios are divided based on the three factors of "source, network, and load". By selecting typical scenarios to build a scenario-adaptive highway traffic self-consistent energy system architecture, a highway traffic self-consistent microgrid system planning model is established, and corresponding operation control strategies are set. Using the particle swarm solution model, the present invention obtains a scenario-adaptive highway traffic self-consistent energy system planning scheme.

[0180] The present invention proposes a planning method for a highway traffic self-consistent energy system based on scenario adaptation for actual operation scenarios of highways, including: evaluation of natural endowment conditions of highway areas under highway operation scenarios; evaluation of loads in highway operation scenarios; determining the operation scenarios of the highway traffic self-consistent energy system based on the natural endowment and load evaluation results, determining the system unit composition adapted to the scenarios, and constructing a scenario-adaptive highway traffic self-consistent energy system architecture; building a scenario-adaptive highway traffic self-consistent energy system planning model based on the scenario-adaptive highway traffic self-consistent energy system architecture, setting working parameters of each unit of the system, and using a particle swarm algorithm to solve the highway traffic self-consistent energy system planning model, and finally obtaining the optimal planning scheme for the scenario-adaptive highway traffic self-consistent energy system.

[0181] The scenarios covered by the present invention are wide in dimensions and are more suitable for highway traffic systems. The present invention not only takes into account the distribution of natural resources, fluctuations in traffic load sources, and the strength of power grid construction, but also conducts comprehensive system planning on issues such as power supply security and resource consumption in the absence of a large power grid. It is more suitable for energy system planning under highway operation scenarios, and is more instructive for self-consistent energy system planning for highway traffic under the scenario of integrated development of energy and transportation.

[0182] This invention evaluates and analyzes the characteristics of my country's natural resource endowment based on statistical data, and proposes a method for predicting highway operation and maintenance load. It also divides highway traffic operation scenarios into 12 typical scenarios based on the three factors of "source, network, and load", and proposes a system optimal planning method based on scenario adaptation. The final planning scheme has good economy and reliability, and provides technical and decision-making support for the planning and design of future highway traffic self-consistent energy systems.

[0183] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0184] 1) Based on statistical data, the natural endowment characteristics of my country's renewable resources are evaluated, and an energy load forecasting method driven by highway traffic operation and maintenance needs is designed for highway traffic service areas, tunnels, bridges, toll stations and energy consumption scenarios along the road.

[0185] 2) Considering the three factors of "source, network and load", 12 typical operation and maintenance scenarios are divided for the self-consistent energy system of highway transportation, laying the foundation for system planning in specific application scenarios in the future.

[0186] 3) A scenario-adaptive highway traffic self-consistent energy system architecture was built, a highway traffic self-consistent microgrid system planning model was established, and corresponding operation control strategies were set to achieve system optimization planning and ensure the economy and reliability of the system. Under this planning scheme, the highway traffic self-consistent energy system can operate safely, stably and self-consistently. BRIEF DESCRIPTION OF THE DRAWINGS

[0187] Figure 1 A diagram for the operation scenario division of the highway traffic self-consistent energy system

[0188] Figure 2 Regional distribution table of operation scenarios of highway traffic self-consistent energy system

[0189] Figure 3 System structure diagram for the scenario of abundant resources + small load + weak network

[0190] Figure 4 Flow chart of system operation control strategy in the scenario of abundant resources + small load + weak network

[0191] Figure 5 The original data of wind speed, light intensity and load are shown in the figure.

[0192] Figure 6 Figure 2 shows the system's wind and solar power abandonment and power outage situation

[0193] Figure 7 The overall output diagram of the system

[0194] Figure 8 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0195] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0196] like Figure 8 As shown in the figure, the planning method of self-consistent energy system for highway traffic based on scenario adaptation is as follows:

[0197] Step 1: Assessment of the natural endowment characteristics of renewable resources

[0198] Step 1.1: PV resource endowment assessment

[0199] According to the classification standards of the Wind and Solar Energy Assessment Center of the China Meteorological Administration, my country's solar energy resource areas are divided into the following four categories. The total solar radiation level and regional distribution table are detailed in Appendix 1.

[0200] Category I regions (resource-rich zones): The annual radiation is above 6,300 megajoules / square meter, which is equivalent to the heat generated by burning 230 kilograms of standard coal. It mainly includes the Qinghai-Tibet Plateau, northern Gansu, northern Ningxia, southern Xinjiang, northwestern Hebei, northern Shanxi, southern Inner Mongolia, southern Ningxia, central Gansu, eastern Qinghai, southeastern Tibet, etc.

[0201] Category II regions (relatively rich in resources): The annual radiation is 5040-6300 MJ / m2, equivalent to the heat generated by the combustion of 180-230 kg of standard coal. Mainly including Shandong, Henan, southeastern Hebei, southern Shanxi, northern Xinjiang, Jilin, Liaoning, Yunnan, northern Shaanxi, southeastern Gansu, southern Guangdong, southern Fujian, central and northern Jiangsu and northern Anhui.

[0202] Category III regions (general resource zones): annual radiation is 3780-5040 MJ / m2, equivalent to the heat generated by burning 140-180 kg of standard coal. Mainly in the middle and lower reaches of the Yangtze River, Fujian, Zhejiang and part of Guangdong. It is rainy in spring and summer, but the solar energy resources are acceptable in autumn and winter.

[0203] Category IV areas: The annual radiation is below 3780 MJ / m2. Mainly including Sichuan and Guizhou provinces. This area is the area with the least solar energy resources in my country.

[0204] Appendix 1 Total solar radiation levels and regional distribution

[0205]

[0206]

[0207] Step 1.2: Wind power potential assessment

[0208] The amount of wind energy resources in a certain area is usually evaluated by two indicators: effective wind energy (wind power) density and annual effective wind hours. When evaluating wind energy reserves, the average wind energy density within a certain area is approximated using the observed discrete values. The commonly used calculation formula is:

[0209]

[0210] Where: ρ is the air density, N is the total observation time: v i is the i-th level value after the wind speed is divided into several levels according to the observation records; N i It is the cumulative time when wind speed level i occurs within a certain period of time.

[0211] The second wind energy resource survey of the China Meteorological Administration conducted a detailed assessment of the national wind energy resources using theoretical reserves, actual exploitable reserves, and average reserves per unit area as evaluation parameters, and conducted a detailed wind resource endowment analysis on this basis. The theoretical reserves of wind resources were evaluated under different annual average wind power densities, as shown in Appendix 2.

[0212] Appendix 2 Wind Energy Resource Census Wind Energy Resource Reserves of Each Province (Region, City) (10,000 kW)

[0213]

[0214]

[0215] Analyzing from a regional perspective, my country's wind energy resource-rich areas are mainly distributed in Northwest, North China, Northeast China, and East China, namely the currently recognized "Three Norths" and the southeast coastal wind energy resource-rich belt. These regions are the priority and key development areas for my country's wind energy resources. The theoretical reserves of wind energy resources in the southwest region are particularly rich, which seems inconsistent with the distribution of my country's recognized wind energy resource-rich belts. There are two main reasons for this phenomenon: First, the hinterland of the Qinghai-Tibet Plateau in the region is also a region rich in wind energy resources in my country. The wind energy resources in Tibet and Yunnan alone have reached 772.8 million kW, accounting for 75.9% of the entire southwest region. In fact, except for the Qinghai-Tibet Plateau, the rest of the region has the smallest wind energy resources in the country; second, the geographical area is wide and the terrain is quite complex. Wind energy resources are mainly concentrated at an annual average wind power density of less than 150W / m2, and the current price range does not have the conditions for development; in terms of total theoretical reserves, the Northwest region has the largest, followed by North China, Southwest China, Northeast China and East China, which are 1486.85 million kW, 1030.25 million kW, 1018.47 million kW, 399.18 million kW and 210.74 million kW, respectively, accounting for 34.4%, 23.8%, 23.5%, 9% and 4.9% of the total wind energy resources in the country; Central China and South China are very small in terms of theoretical reserves, technically exploitable reserves and potential technically exploitable reserves.

[0216] Step 2: Energy load forecasting driven by highway traffic operation and maintenance demand

[0217] Step 2.1: Service area energy consumption analysis

[0218] The energy consumption of service areas is mainly divided into restaurant energy consumption, supermarket energy consumption, guest room energy consumption, office area energy consumption, gas station energy consumption, etc. The types of energy consumption include electricity, natural gas, gasoline, etc. Through the investigation and research of the original service areas, a large amount of data was collected, and the factors affecting the building energy consumption of highway service areas were summarized and studied. The principal component analysis method was used to know that the main component factors affecting the building energy consumption of highway service areas are: passenger flow, service area greening rate, building area, and window-to-wall ratio. The service area data was then processed by the SPSS software system, and a linear relationship between it and the highway service area energy consumption data was obtained, and the multivariate regression analysis method was used for fitting:

[0219] The linear relationship between the energy consumption and passenger flow in the expressway service area can be expressed as:

[0220] E=11.559a+346.77 (2) The linear relationship between the energy consumption of the expressway service area and the building area can be expressed as:

[0221] E=5.697b+4.599 (3) The linear relationship between the energy consumption and greening rate of the expressway service area can be expressed as:

[0222] E=-0.045c+0.8634 (4) The linear relationship between the energy consumption of the high-speed service area and the window-to-wall ratio can be expressed as:

[0223] E=0.093d+3.726 (5)

[0224] Where E is the building energy consumption of the highway service area, tce (tons of standard coal equivalent, 1 ton of standard coal = 8130 kW h); a is the passenger flow of the highway service area, person / year; b is the building area of ​​the highway service area, m 2 ; c is the greening rate of the service area; d is the window-to-wall ratio.

[0225] The determination coefficient of building energy consumption and passenger flow in highway service areas is R2=0.1501, with a good fit; building energy consumption is positively correlated with the area of ​​highway service areas, and the fitting effect is good when the area is below 5000m2; the linear relationship between building energy consumption and window-to-wall ratio is R2=0.1155, with a good effect; building energy consumption is negatively correlated with the greening rate of highway service areas, and the goodness of fit becomes better with the increase of the greening rate.

[0226] Step 2.2: Tunnel energy consumption analysis

[0227] To ensure driving safety, highway tunnels need to be equipped with ventilation, lighting, monitoring, communication and other systems. These systems are operated all year round according to usage requirements. Therefore, tunnels become one of the units that consume the most energy in the entire highway operation and management. Simplifying its energy consumption model, it mainly includes the power consumption of the lighting system Q1, the power consumption of the ventilation system Q2, the power consumption of the monitoring system Q3, and the power consumption of the fire protection system Q4. The total energy consumption of the tunnel is:

[0228] Q S =Q1+Q2+Q3+Q4 (6)

[0229] 1) Lighting system Q1

[0230] According to the "Highway Tunnel Lighting Design Regulations" and the characteristics of tunnel lighting, in order to ensure the driving safety of drivers when passing through the tunnel, it is necessary to lay out lighting for the entrance section, transition section, basic section, exit section and approach road outside the tunnel. The tunnel lighting energy consumption model (two-way and two-way traffic) determined by the requirements for the layout of lighting facilities in the tunnel is as follows:

[0231] The energy consumption model of two-way double-traffic tunnel lighting determined by the requirements for the layout of lighting facilities in the tunnel is as follows, where d1 is the annual power consumption of tunnel lighting per unit length, unit: KWh; D1 is the annual power consumption of a single tunnel lighting, unit: KWh;:

[0232] Q1=d1L s +D1n s (7)

[0233] Specifically, assuming that the longitudinal slope is 2%, the lamps are arranged symmetrically on both sides, the road width is 9m, the lamp installation height is 5m, and the brightness outside the tunnel is 4000cd / m2. From the perspective of highway vehicle safety, we set the general vehicle speed to 80km / h and the road surface to be a bright road surface (i.e. reinforced concrete). According to the tunnel lighting engineering design case and energy consumption analysis, the tunnel lighting energy consumption is:

[0234] Q1=46L s +209819.2n s

[0235] 2) Ventilation system Q2

[0236] The ventilation system is mechanical ventilation, with an annual power consumption of D2, unit: KWh; the distance between typhoon fans is d2, unit: m;

[0237]

[0238] Specifically, the general tunnel ventilation system is mechanical ventilation. Assuming that 4 groups of 8 fans are set, that is, one fan is set every 320m. The power of the common fan is 11kW, and the annual power consumption is 96360kW h.

[0239]

[0240] 3) Monitoring system Q3

[0241] The camera should be located 100m-400m outside the tunnel. It is assumed that a camera is set every d3 in the tunnel, and the vehicle detector is set every 500m. Assuming a one-way four-lane road, there are 8 cameras in one place, and the annual power consumption is D3, unit: kW h,

[0242]

[0243] Specifically, according to the highway tunnel traffic engineering design specifications, the camera should be 100m-400m outside the tunnel. It is assumed that a camera is set every 150m in the tunnel, and the vehicle detector is set every 500m. Assuming a one-way four-lane, 8 cameras in one place, the camera power is 25w, and the annual power consumption is 219kW h.

[0244]

[0245] 4) Fire fighting system Q4

[0246] Traffic lights are set 20m before the tunnel entrance. According to the design specifications for automatic fire alarms, highway tunnels, tunnel entrances and exits, and alarm telephones should be set every d'4m in the tunnel. The annual power generation is D'4, unit: kW h. A flashing red fire sound and light alarm should be set every d'4'm. The annual power generation is D'4', unit: kW h. The energy consumption calculation model of the fire protection system is as follows

[0247]

[0248] In summary, the calculation method of annual energy consumption of tunnel is as follows:

[0249]

[0250] Q s is the annual energy consumption of the tunnel, in kW·h; L s is the total length of the tunnel, in meters; n s is the total number of tunnels in this section of expressway, in units of tunnels;

[0251] Specifically, the traffic light is set 20m before the tunnel entrance. According to the Fire Automatic Alarm Design Specification 0 12.1 Highway Tunnels, alarm telephones should be set at the tunnel entrance and exit and every 200m in the tunnel, and flashing red fire sound and light alarms should be set every 50m. Sound and light alarms indicating fire in the tunnel should be set within 50m to 250m in front of the tunnel entrance. Assuming that the power of the fire sound and light alarm is 10w and the annual power consumption is 87.6kW h; the power of the alarm telephone is 20w and the annual power consumption is 175.2kW h, the energy consumption calculation model of the fire protection system is as follows

[0252]

[0253] In summary, the calculation method of annual energy consumption of tunnel is as follows:

[0254]

[0255] Step 2.3: Bridge energy analysis

[0256] There are many kinds of electrical equipment around bridges. To ensure driving safety, highway bridges need to be equipped with lighting, monitoring, communication, emergency power systems and other systems. A bridge with a length of L kilometers usually requires (L-1) monitoring and detection equipment. Its energy consumption model is;

[0257] Q q =P b1 T b1 L B +P b2 T b2 (L B -1) (12)

[0258] Q q is the annual energy consumption of the bridge, in kW h; P b1 is the load of the bridge lighting system, in kW / km, and is taken as 15.98; T b1 is the working time of the lighting system, h; L B is the total length of the bridge, km; P b2 is the rated power of the monitoring and detection equipment, kW; T b2 is the working time of monitoring and detection equipment, h.

[0259] Step 2.4: Energy consumption of toll booths

[0260] The main energy consumption of highway toll stations comes from the toll collection system, monitoring system, lighting system, and daily management and office. The commonly used energy consumption calculation formula is:

[0261]

[0262] E to,m,i,o The total energy consumption of toll collection, monitoring, lighting, and daily management and office work in a year at the toll station (MJ); i is the number of the i-th type of equipment (units); w i is the power of the ith device (W / unit); t i is the operating time of the i-th equipment (hours); i is the type of equipment.

[0263] By summarizing the energy consumption of each part in a typical toll station, as shown in Appendix 3, the annual energy consumption calculation formula of the toll station is as follows:

[0264] Q t =E to,m,i,o n t (14)

[0265] Appendix 3 Energy consumption calculation table for toll stations

[0266]

[0267] It turns out that:

[0268] Q t =328n t

[0269] Qt is the annual energy consumption of the toll station, kW·h; n t is the number of toll booths on this road section, .

[0270] Step 3: Comprehensively consider the three factors of “source, network, and load” to carry out the division of the self-consistent energy system scenario of highway transportation dominated by energy supply mode

[0271] Step 3.1: Divide according to local conditions

[0272] Considering the local environmental factors of the highway, namely the abundance of solar radiation and wind energy resources, regional division is carried out. Taking into account the distribution of solar energy resources and wind energy resources, it is divided into resource-rich areas, general resource areas and resource-poor areas. Wind power combined with solar energy resource zoning: Taking into account the abundance of wind and solar resources, China is divided into three regions: resource-rich areas, general resource areas and resource-poor areas. Among them, resource-rich areas mainly include Xinjiang, Inner Mongolia, Tibet (except southeastern Tibet), and western regions such as Qinghai and Ningxia; general resource areas mainly include northeastern Heilongjiang, southeastern Tibet, southern Gansu, southern Yunnan and other regions; resource-poor areas include East China, South China, Central China, Chongqing and Guizhou and other regions.

[0273] Step 3.2: Divide according to network conditions

[0274] Considering the density of local power grid coverage and the number of charging piles, the area is divided into strong network area, weak network area and no network area by comprehensively considering the strength of the power grid and the magnitude of charging piles.

[0275] The overall distribution of China's power grid is that the backbone power grid in the eastern region is relatively strong, while the western region, especially Tibet and the northwest region, has poor power grid coverage and is at the end of the network. There is basically no backbone transmission network in areas such as western Sichuan, Haixi, southern Xinjiang and Tibet. Specifically, the power grids in Northeast China, North China, Central China and East China are relatively dense, among which East China, South China, Central China, East and Central North China, Beijing-Tianjin-Hebei region, Qinghai, Ningxia, Gansu, Yunnan, Chongqing, Guizhou and other regions are all strong network areas; while the power grids in Northwest China and Tibet are relatively sparse, such as Xinjiang, Tarim Basin, Jilin Province, Liaoning Province, Hainan Province, Shaanxi-Sichuan border, etc. are all weak network areas, and western Sichuan, Tibet, northeastern Inner Mongolia, and northeastern Heilongjiang are all no network areas.

[0276] Step 3.3: Divide according to load

[0277] The eastern region, especially Beijing and Shanghai, has a high population density, and the electricity load is relatively large. The electricity load is relatively small in the sparsely populated western regions such as Tibet and Xinjiang. Among them, the electricity load is the largest in Beijing-Tianjin-Hebei, North China, and South China, and the smallest in Tibet and Xinjiang. The load of China's road traffic is divided into large load and small load. The large load areas mainly include East China, South China, East and Central North China, Beijing-Tianjin-Hebei, and other regions. Areas not within the large load area are all small load areas.

[0278] Step 3.4: Comprehensive scene segmentation

[0279] Taking into account the three factors of "source, network and load", the three modes of "adapting to local conditions", "adapting to the network" and "adapting to load" are superimposed together to form a set of operation scenarios of the self-consistent energy system for highway transportation, which includes 12 scenarios in total, namely, strong network + large load + rich resources scenario, strong network + small load + rich resources scenario, strong network + small load + general resources scenario, strong network + small load + scarce resources scenario, weak network + small load + general resources scenario, weak network + small load + rich resources scenario, strong network + large load + general resources scenario, weak network + small load + rich resources scenario, strong network + large load + scarce resources scenario, no network + small load + scarce resources scenario, weak network + small load + scarce resources scenario, weak network + small load + general resources scenario, as shown in the attached figure. Figure 1 Shown and attached Figure 2 shown.

[0280] Step 4: Select typical scenarios to build a scenario-adapted highway traffic self-consistent energy system architecture, establish a highway traffic self-consistent microgrid system planning model, set up corresponding operation control strategies, and use particle swarm solving to obtain the optimal planning solution.

[0281] Step 4.1: Build the models of each unit of the system

[0282] 1) Photovoltaic power generation model

[0283] The output power of the photovoltaic array has certain fluctuations and regularity, which is mainly affected by factors such as solar radiation intensity and temperature. The output power P of the photovoltaic array pv It can be expressed as:

[0284]

[0285] Where: P s is the rated power of the PV array; For the photovoltaic construction area (latitude ) is the hourly average of the surface solar radiation intensity at hour h on day d in month m; θ T is the power temperature coefficient of the photovoltaic array, taking -0.35% / C; T is the operating temperature of the photovoltaic array; T ref is the reference temperature of the photovoltaic array, which is 25℃.

[0286] The Beta distribution model is considered to be able to effectively fit the long-term probability distribution of solar radiation intensity, and its probability distribution density function f2(G) is:

[0287]

[0288]

[0289]

[0290] Where: G max is the maximum solar radiation intensity during the simulation period; σ2 and μ2 are the variance and mean of the solar radiation intensity samples respectively; α and β are the shape parameters of the Beta distribution.

[0291] 2) Wind power generation model

[0292] Wind power output can be expressed by the product of installed capacity and output coefficient:

[0293] P w =μ w P w,r (19)

[0294] Where: P w Output power to wind farm; μ w P is the wind power output coefficient, which indicates the proportion of wind power output power to rated power; w,r is the rated output power of wind power.

[0295] The output power of a wind turbine generator set mainly depends on the power output curve of the unit, the hub height of the wind turbine and the wind speed at the hub height, which can be expressed as:

[0296]

[0297] Where: P w is the output power of the wind turbine generator set; S is the actual wind speed of the wind turbine at the wheel height; S i , S c and S R are the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine respectively; P R is the rated power of the wind turbine.

[0298] The distribution characteristics obtained by the probability statistics of wind speed can measure the distribution of wind energy resources in a place. The frequency distribution of wind speed is the main form of reflecting this characteristic. The Weibull distribution curve is often used to fit the probability distribution of wind speed at different height layers. The expression of its probability density function is:

[0299]

[0300]

[0301]

[0302] Where: σ1 and μ1 are the variance and mean of the actual wind speed samples, respectively; k and c are the shape parameter and scale parameter of the Weibull distribution, respectively; Γ(·) is the Gamma function.

[0303] 3) Battery model

[0304] During the energy optimization scheduling process, the remaining energy of the battery is calculated based on the battery power on the DC bus side during the charging and discharging process:

[0305]

[0306] Where: E bat (t) is the battery power at time t; P cbat The power for charging the battery; P dbat is the battery discharge power; σ is the battery self-discharge coefficient, which is 0.0001 / h; Δt is the step length, 1h; η cbat is the battery charging efficiency, take 0.9; η dbat is the battery discharge efficiency, which is taken as 0.9.

[0307] 4) Hydrogen energy storage model

[0308] The electrolyzer is usually an alkaline electrolyzer, which is the preferred device for large-scale hydrogen production. It electrolyzes water into hydrogen and oxygen. The output power of the electrolyzer is:

[0309] P ec =P in η ec (25)

[0310] Where: P in is the power input to the electrolyzer; η ec The electrolysis efficiency of the electrolytic cell is generally 60%.

[0311] The hydrogen storage tank has two functions: one is to store the hydrogen produced by the electrolyzer; the other is to supply hydrogen to the fuel cell. The mathematical model of the hydrogen storage tank is:

[0312]

[0313] Where: E st (t) is the energy stored in the hydrogen storage tank at time t; η e-DC and η f-DC are the working efficiencies of the electrolyzer and fuel cell converter, respectively, which are generally 90%, η st It is the working efficiency of the hydrogen storage tank, generally 50%.

[0314] The fuel cell uses a proton membrane exchange fuel cell. This technology uses hydrogen and oxygen as fuel, converts the chemical energy inside the fuel into electrical energy and stores it. Its output power is:

[0315] K0=K d ×K r (27)

[0316] Where P st-fis the power delivered from the hydrogen storage tank to the fuel cell, η f The efficiency of the fuel cell.

[0317] 5) Gas turbine auxiliary energy supply model

[0318] The relationship between the power generation of a gas turbine and the natural gas consumption is:

[0319]

[0320] Where: r gt,gas Indicates the natural gas flow consumed by the gas turbine, in m 3 / t;Q gas The calorific value of natural gas combustion is 33486.8 kJ / m 3 ; α is the conversion coefficient between kilowatt and kilojoule, which is 3600; P gt is the power generation capacity of the gas turbine, in kW; η gt The power generation efficiency of the gas turbine.

[0321] 6) Diesel engine auxiliary energy supply model

[0322] For diesel generators, fuel consumption F (in L, kg or m 3 ) and its output power (in kW) can be expressed as:

[0323] F=F0Y gen +F1P gen (29)

[0324] Where Y gen is the rated power of the diesel generator (in kW); F0 is the intercept coefficient of the diesel generator fuel curve, that is, the no-load fuel consumption per unit power of the diesel generator (in L / (kW-h)); F1 is the slope of the diesel generator fuel curve (in L / (kWh)); P gen is the output of the diesel generator, in kW;.

[0325] The diesel generator consumption characteristic function can be expressed by the following quadratic function model:

[0326]

[0327] In the formula, f(P gen ) is the fuel cost of the diesel generator per hour (in yuan / h); a, b, c are the coefficients of the cost function, which can be obtained by fitting the consumption characteristic curve of the diesel generator; P gen is the output of the diesel generator (in kW).

[0328] Step 4.2: Select typical scenarios and determine system architecture and operation control strategy

[0329] Based on typical scenarios, energy storage systems and auxiliary energy supply systems are selected to build a scenario-adaptive self-consistent energy system architecture for highway traffic, and the system operation control strategy is determined under the current architecture.

[0330] This example selects the scenario of rich resources + small load + weak network. In this scenario, on the source side, due to the rich wind and solar resources, distributed photovoltaic power generation and distributed wind power generation are the main energy supply methods; on the grid side, since this scenario is located in the northwest of my country, the grid coverage is poor and there is basically no backbone transmission network, so an off-grid operation mode is adopted; on the load side, the infrastructure loads such as highway service area operation and maintenance and lighting are mainly considered, but because it is located in the western region of my country, the traffic load is relatively small; on the storage side, the battery is mainly considered for power regulation and storage of excess energy. Hydrogen energy storage has the characteristics of a wide power operation range and a fast power change rate. Coupled with renewable energy, it can effectively reduce the impact of renewable energy on the power grid and promote the consumption of renewable energy. The system structure diagram under this scenario is shown in the attached figure. Figure 3 shown.

[0331] Under the current system architecture, in order to maximize the consumption of renewable energy output and ensure the reliability of power supply for the transportation system, when the output power of renewable energy does not match the load demand, the battery is first used to smooth the power fluctuations of load and renewable energy. When the battery cannot fully adjust the unbalanced power, the hydrogen energy storage system starts to adjust the remaining unbalanced power. The system operation control strategy process is shown in the attached figure. Figure 4 shown.

[0332] The detailed steps are as follows:

[0333] (1) Start the energy flow of the highway traffic self-consistent energy system at a certain moment. First, calculate the unbalanced power of the system. If the wind and solar power output is not enough to meet the load demand, enter operation mode 1, otherwise enter operation mode 3;

[0334] (2) Operation mode 1: If the remaining battery power can meet the remaining load, and the remaining load is within the battery's maximum discharge power limit, the battery output will meet the remaining load demand; if the remaining battery power can meet the remaining load, but the remaining load exceeds the battery's maximum discharge power limit, the battery will discharge at its maximum power; if the remaining battery power does not meet the remaining load, the battery discharge power will be the minimum of the remaining battery power and the battery's maximum discharge power; calculate the system unbalanced power at this time, and enter operation mode 2;

[0335] (3) Operation mode 2: If the remaining hydrogen storage in the hydrogen storage tank can generate electricity to meet the remaining load, and the remaining load is within the maximum output limit of the hydrogen fuel cell, the hydrogen energy storage system meets the remaining load demand; if the remaining hydrogen storage in the hydrogen storage tank can generate electricity to meet the remaining load, but the remaining load exceeds the maximum output limit of the hydrogen fuel cell, the hydrogen fuel cell outputs at its maximum power; if the remaining hydrogen storage in the hydrogen storage tank cannot generate electricity to meet the remaining load, the output power of the hydrogen fuel cell is the minimum value between the maximum output of the remaining hydrogen storage in the hydrogen storage tank consumed by the hydrogen fuel cell and the maximum output power of the hydrogen fuel cell;

[0336] (4) Operation mode 3: If the maximum chargeable capacity of the battery can absorb the excess wind and solar power output, and the excess wind and solar power output is within the maximum charging power limit of the battery, the battery is charged to absorb the excess wind and solar power output; if the maximum chargeable capacity of the battery can absorb the excess wind and solar power output, but the excess wind and solar power output exceeds the maximum charging power limit of the battery, the battery is charged at the maximum power; if the maximum chargeable capacity of the battery cannot absorb the excess wind and solar power output, the battery charging power takes the minimum value of the maximum chargeable capacity and the maximum charging power of the battery; the system unbalanced power is calculated at this time, and the operation mode 4 is entered;

[0337] (5) Operation mode 4: If the hydrogen energy storage system can absorb excess wind and solar power, and the remaining wind and solar power output is within the maximum output power limit of the electrolyzer, the hydrogen energy storage system will absorb the excess wind and solar power; if the hydrogen energy storage system can meet the remaining load, but the remaining wind and solar power output exceeds the output power limit of the electrolyzer, the electrolyzer will output at its maximum power; if the hydrogen energy storage system cannot absorb excess wind and solar power, the electrolyzer output power will be the minimum of the electrolyzer power consumed by the maximum air intake of the hydrogen storage tank and the maximum output power of the electrolyzer.

[0338] (6) End the system energy flow operation at this moment and enter the next moment.

[0339] Step 4.3: System optimization configuration model construction

[0340] 1) Objective function

[0341] ①Economical

[0342] The total net current cost C of the self-consistent energy system of highway transportation NPC The minimum is the economic goal, C NPC It is to discount the cost values ​​generated during the system life cycle to the initial year for statistics, including initial investment cost and operation and maintenance cost:

[0343] minC NPC =C initial +C om (31)

[0344] Where: C initial is the annual initial investment cost of each power generation unit and energy storage unit in the system; Com It is the annual operation and maintenance cost of each power generation unit and energy storage unit in the system.

[0345] The total annual installation cost is the sum of the annual installation costs of each micro-source and energy storage:

[0346]

[0347] In the formula They are the annual installation costs of photovoltaic, wind turbine, battery, electrolyzer, hydrogen storage tank and fuel cell, and the calculation of each component is as follows:

[0348]

[0349]

[0350]

[0351]

[0352]

[0353]

[0354] Where: N WT is the number of fans installed; P MT is the rated output power of the fan; U MT L is the investment cost per rated power of the fan; MT is the service life of the fan, r is the capital discount rate, which is 0.04. PV is the number of photovoltaic panels installed; P PV is the rated output power of the photovoltaic panel; U PV L is the investment cost per unit rated power of photovoltaic panels; PV is the service life of the photovoltaic panel. N bat is the number of installed batteries; V bat is the rated voltage of the battery, I bat is the rated current of the battery; U bat L is the investment cost per unit capacity of the battery; bat Is the battery life. :N ele is the number of electrolytic cells installed; P ele is the rated power of the electrolyzer; U ele L is the investment cost per unit rated power of the electrolyzer; ele is the service life of the electrolytic cell. N Qh2 is the number of hydrogen storage tanks installed; P Qh2 is the capacity of the hydrogen storage tank; U Qh2 is the investment cost per unit capacity of hydrogen storage tank; L Qh2 is the service life of the hydrogen storage tank. Nfc is the number of fuel cells installed; P fc is the rated power of the fuel cell; U fc L is the investment cost of fuel cell unit rated power; fc The service life of the fuel cell.

[0355] The annual operation and maintenance cost is the sum of the operation and maintenance costs of each micro-source and energy storage battery, calculated as follows:

[0356]

[0357] Wherein, m=1…6 represents wind turbine, photovoltaic panel, battery, electrolyzer, hydrogen storage tank, and fuel cell respectively; N m represents the number of micro-sources installed; P m Represents the output of each micro-source; M m It is the operation and maintenance cost per unit power of the micro source.

[0358] ②Reliability

[0359] The average power supply reliability of the system refers to the ratio of the power supply obtained by the highway service area in a year to the actual load demand. Assuming that the power supply time required by the service area in a year is 8760h, the calculation formula of the average power supply reliability of the system ASAI is:

[0360]

[0361] Where: Q i is the load demand of the system at time i; Ql i The system is temporarily powered off.

[0362] ③ Utilization rate of renewable energy

[0363]

[0364] Where: U ren is the utilization rate of renewable energy; E ren is the total power generation of the power generation unit; E excess The excess power of the power generation unit.

[0365] 2) Constraints

[0366] ①Micro power output constraints

[0367] Wind power output constraints:

[0368] 0≤P wt (i)≤N wt p wt (i) (42) Where Nwt is the number of wind turbines configured in the system, P wt (i) is the total wind power output at time i;

[0369] Photovoltaic output constraints:

[0370] 0≤P pv (i)≤N pv p pv (i) (43)

[0371] Where Npv is the number of photovoltaic panels configured in the system, P pv (i) is the total output of the photovoltaic power generation system at time i;

[0372] ②Battery constraints

[0373] Battery capacity constraints:

[0374] E batmin ≤E bat (t)≤E batmax (44)

[0375] Where: E bat (t) is the remaining capacity of the battery; E batmin is the minimum capacity of the battery; E batmax The maximum capacity of the battery.

[0376] Battery charging and discharging power limit:

[0377]

[0378] Where: P cbatmax is the maximum charging power of the battery; P dbatmax is the maximum discharge power of the battery.

[0379] ③ Constraints of hydrogen energy storage system

[0380] Hydrogen storage tank capacity constraints:

[0381] H batmin ≤H bat (t)≤H batmax (46)

[0382] Where: H bat (t) is the capacity status of the hydrogen storage tank; H batmin is the minimum value of the hydrogen storage tank capacity state; H batmax It is the maximum value of the hydrogen storage tank capacity status.

[0383] Electrolytic cell power limit:

[0384] 0≤P ele (t)≤P elemax (47)

[0385] Where: P elemax is the maximum power of the electrolytic cell.

[0386] Fuel cell power limitations:

[0387] 0≤P fc (t)≤P fcmax (48)

[0388] Where: P fcmax is the maximum power of the fuel cell.

[0389] ⑤Power balance constraint

[0390] At any time, the output power of each power generation unit and energy storage device must meet the load requirements:

[0391] P wt (i)+P pv (i)+P water (i)+P dbat (i)+P fc (i) = P Load (i)+P cbat (i)+P ele (i) (49)

[0392] Where: P wt Output for the fan; P pv For photovoltaic output; P water (i ) Contribute to small hydropower; dbat is the battery discharge power; P fc Powering the fuel cell; P Load is the microgrid load; P cbat The power for charging the battery; P ele Power the electrolyzer.

[0393] Step 4.4: Initialize the parameters of each device in the system, and input the typical daily geographic information (wind speed, light intensity and temperature) of the four seasons of the selected road section in each scenario and the load data of the highway infrastructure into the system.

[0394] This example selects the radiation intensity, wind speed, and load data of a certain place in this scenario as the system input, as shown in the attached figure. Figure 5 As shown, the daily average radiation intensity is 174.8W / m2, the daily average wind speed is 6.6m / s, and the daily average load is 178kW.

[0395] The cost of the main equipment used is shown in Appendix 4

[0396] Appendix 4 Equipment cost parameters for different equipment

[0397] equipment Specifications Investment cost Operation and maintenance costs Lifespan / year Wind turbines 100KW 8000 yuan / KW 0.0187 yuan / KW 20 Photovoltaic panels 2KW 2000 yuan / KW 0.0079 yuan / KW 20 Battery 12V / 85A·h 880 yuan / KWh 0.008 yuan / KW 5 Electrolyzer 1kW 5000 yuan / KW 0.03 yuan / KW 10 Hydrogen storage tank 1kW·h 1800 yuan / KW 0 20 Fuel Cells 1kW 10000 yuan / KW 0.5 yuan / KW 10

[0398] Step 4.5: Through iterative optimization of the particle swarm algorithm, the optimal planning scheme of the highway traffic self-consistent microgrid system is obtained, and the system output is output and analyzed.

[0399] The particle swarm algorithm is used to solve the planning model established above, and the optimal planning scheme is obtained as shown in Appendix 5.

[0400] Appendix 5 Optimization Planning Scheme

[0401]

[0402] The system's wind and solar power abandonment and power outage conditions are as follows Figure 6 As shown, the system power shortage rate is 0.8265%, less than 1%, and the wind and solar power abandonment rate is 0. Under this scheme, the system power supply reliability is guaranteed while reducing the wind and solar power abandonment.

[0403] The overall output status of the system is as follows: Figure 7 As shown in the figure, in this scenario, the system wind power output is mainly concentrated at night, and photovoltaic power only outputs during the day. Wind and solar power have good complementarity. During the night time periods of 0-5 and 22-24, the system wind power output is greater than the load. At this time, the batteries and electrolyzers in the hybrid energy storage system successively output to absorb the excess wind power, and the battery charge state and the remaining capacity of the hydrogen storage tank also increase accordingly; during the daytime period of 6-9, the wind power output decreases, and when the photovoltaic output is sufficient, the battery discharges and the fuel cell output meets the excess load demand; during the period of 7-14, the photovoltaic output reaches a higher level, and the hybrid energy storage system is charged again; in the evening and night time periods of 14-21, the wind power output remains at a low level, the photovoltaic output decreases, the wind and solar output cannot meet the load demand, and the hybrid energy storage system discharges to make up for the insufficient power. The system only experienced a small power outage at 21:00, and there was no wind or solar power abandonment in the entire typical scenario. This shows that under the hybrid energy storage system planning scheme, the system can achieve 100% renewable energy absorption and 100% zero carbon emissions, while ensuring economy, it has a very high new energy absorption rate and power supply reliability.

[0404] So far, the natural resource endowment characteristics are evaluated and the highway traffic load is preliminarily predicted, and the highway traffic self-consistent energy system scenarios are divided based on the three factors of "source, network, and load". By selecting typical scenarios to build a scenario-adaptive highway traffic self-consistent energy system architecture, a highway traffic self-consistent microgrid system planning model is established, and corresponding operation control strategies are set. Using the particle swarm solution model, the present invention obtains a scenario-adaptive highway traffic self-consistent energy system planning scheme.

[0405] The specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. A method for planning a self-consistent energy system for highway traffic based on scenario adaptation, characterized in that: The method comprises the following steps: S1, Assessment of natural endowment characteristics of renewable resources; S2, energy load forecast driven by highway transportation operation and maintenance demand; S3, comprehensively consider the three factors of "source, network and load" to carry out the scene division of highway transportation self-consistent energy system dominated by energy supply mode; S4, select typical scenarios to build a scenario-adapted highway traffic self-consistent energy system architecture, establish a highway traffic self-consistent microgrid system planning model, set up corresponding operation control strategies, and use particle swarm solving to obtain the optimal planning solution. The specific steps of S4 are as follows: S 4.1: Construction of each unit model of the system 1) Photovoltaic power generation model The output power of the photovoltaic array P pv It can be expressed as: Where: P s is the rated power of the PV array; It is a photovoltaic area to be built, with a latitude of The hourly mean of the surface solar radiation intensity at hour h on day d in month m; θ T is the power temperature coefficient of the photovoltaic array; T is the operating temperature of the photovoltaic array; T ref is the reference temperature of the photovoltaic array, which is 25°C; The Beta distribution model is considered to be able to effectively fit the long-term probability distribution of solar radiation intensity, and its probability distribution density function f2(G) is: Where: G max is the maximum solar radiation intensity during the simulation period; σ2 and μ2 are the variance and mean of the solar radiation intensity samples, respectively; α and β are the shape parameters of the Beta distribution, and Γ(·) is the Gamma function; 2) Wind power generation model Wind power output can be expressed by the product of installed capacity and output coefficient: P w =μ w P w,r (19) Where: P w Output power to wind farm; μ w P is the wind power output coefficient, which indicates the proportion of wind power output power to rated power; w,r is the rated output power of wind power; The output power of a wind turbine generator set mainly depends on the power output curve of the unit, the hub height of the wind turbine and the wind speed at the hub height, which can be expressed as: Where: P w is the output power of the wind turbine generator set; S is the actual wind speed of the wind turbine at the hub height; S ci , S co and S R are the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine respectively; P R is the rated power of the wind turbine generator set; The Weibull distribution curve is used to fit the probability distribution of wind speed at different altitudes, and the expression of its probability density function is: Where: σ1 and μ 1 are the variance and mean of the actual wind speed samples, respectively; k and c are the shape parameter and scale parameter of the Weibull distribution, respectively; Γ(·) is the Gamma function; 3) Battery model During the energy optimization scheduling process, the remaining energy of the battery is calculated based on the battery power on the DC bus side during the charging and discharging process: Where: E bat (t) is the battery power at time t; P cbat The power for charging the battery; P dbat is the battery discharge power; σ is the battery self-discharge coefficient; Δt is the step size; η cbat is the battery charging efficiency; η dbat is the battery discharge efficiency; 4) Hydrogen energy storage model The electrolyzer is usually an alkaline electrolyzer, which is the preferred device for large-scale hydrogen production. It electrolyzes water into hydrogen and oxygen. The output power of the electrolyzer is: P ec =P in η ec (25) Where: P in is the power input to the electrolyzer; η ec is the electrolysis efficiency of the electrolytic cell; The mathematical model of the hydrogen storage tank is: Where: E st (t) is the energy stored in the hydrogen storage tank at time t; η e-DC and η f-DC are the operating efficiencies of the electrolyzer and fuel cell converter, η st is the working efficiency of the hydrogen storage tank; The output power of the fuel cell is: K0=K d ×K r (27) 5) Gas turbine auxiliary energy supply model The relationship between the power generation of a gas turbine and the natural gas consumption is: Where: r gt,gas Indicates the natural gas flow consumed by the gas turbine, in m 3 / t;Q gas The calorific value of natural gas combustion is 33486.8 kJ / m 3 ; α is the conversion coefficient between kilowatt and kilojoule, which is 3600; P gt is the power generation capacity of the gas turbine, in kW; η gt is the power generation efficiency of the gas turbine; 6) Diesel engine auxiliary energy supply model For a diesel generator, the relationship between fuel consumption F and its output power is expressed as: F=F0Y gen +F1P gen (29) Where Y gen is the rated power of the diesel generator, in kW; F0 is the intercept coefficient of the diesel generator fuel curve, that is, the no-load fuel consumption per unit power of the diesel generator, in L / (kW-h); F1 is the slope of the diesel generator fuel curve, in L / (kWh); P gen is the output of the diesel generator in kW; the fuel consumption F is in L, kg or m 3 ; The diesel generator consumption characteristic function can be expressed by the following quadratic function model: In the formula, f(P gen ) is the fuel cost of the diesel generator per hour, in yuan / h; a, b, c are the coefficients of the cost function, which can be obtained by fitting the consumption characteristic curve of the diesel generator; P gen is the output of the diesel generator, in kW; S 4.2: Select typical scenarios and determine system architecture and operation control strategy Select energy storage systems and auxiliary energy supply systems based on typical scenarios to build a scenario-adaptive self-consistent energy system architecture for highway transportation, and determine the system operation control strategy under the current architecture; S 4.3: System optimization configuration model construction 1) Objective function ①Economical The total net current cost C of the self-consistent energy system of highway transportation NPC The minimum is the economic goal, C NPC It is to discount the cost values ​​generated during the system life cycle to the initial year for statistics, including initial investment cost and operation and maintenance cost: minC NPC =C initial +C om (31) Where: C initial is the annual initial investment cost of each power generation unit and energy storage unit in the system; C om The annual operation and maintenance cost of each power generation unit and energy storage unit in the system; The total annual installation cost is the sum of the annual installation costs of each micro-source and energy storage: In the formula They are the annual installation costs of photovoltaic, wind turbine, battery, electrolyzer, hydrogen storage tank and fuel cell, and the calculation of each component is as follows: Where: N WT is the number of fans installed; P MT is the rated output power of the fan; U MT L is the investment cost per rated power of the fan; MT is the service life of the fan, r is the capital discount rate, N PV is the number of photovoltaic panels installed; P PV is the rated output power of the photovoltaic panel; U PV L is the investment cost per unit rated power of photovoltaic panels; PV is the service life of photovoltaic panels; N bat is the number of installed batteries; V bat is the rated voltage of the battery, I bat is the rated current of the battery; U bat L is the investment cost per unit capacity of the battery; bat Battery life: N ele is the number of electrolytic cells installed; P ele is the rated power of the electrolyzer; U ele L is the investment cost per unit rated power of the electrolyzer; ele is the service life of the electrolytic cell; N Qh2 is the number of hydrogen storage tanks installed; P Qh2 is the capacity of the hydrogen storage tank; U Qh2 is the investment cost per unit capacity of hydrogen storage tank; L Qh2 is the service life of the hydrogen storage tank; N fc is the number of fuel cells installed; P fc is the rated power of the fuel cell; U fc L is the investment cost of fuel cell unit rated power; fc The service life of the fuel cell; The annual operation and maintenance cost is the sum of the operation and maintenance costs of each micro-source and energy storage battery, calculated as follows: Wherein, m=1…6 represents wind turbine, photovoltaic panel, battery, electrolyzer, hydrogen storage tank, and fuel cell respectively; N m represents the number of micro-sources installed; P m Represents the output of each micro-source; M m It is the operation and maintenance cost of micro-source per unit power; ②Reliability The system average power supply reliability refers to the ratio of the power supply obtained by the highway service area in a year to the actual load demand. Assuming that the power supply time required by the service area in a year is 8760h, the calculation formula of the system average power supply reliability ASAI is: Where: Q i is the load demand of the system at time i; Ql i Cut off power to the system at time i; ③ Utilization rate of renewable energy Where: U ren is the utilization rate of renewable energy; E ren is the total power generation of the power generation unit; E excess The excess electricity of the power generation unit; 2) Constraints ①Micro power output constraints Wind power output constraints: 0≤P wt (i)≤N wt p wt (i)(42) In the formula, N wt is the number of wind turbines configured in the system, P wt (i) is the total wind power output at time i; Photovoltaic output constraints: 0≤P pv (i)≤N pv p pv (i)(43) In the formula, N pv P is the number of photovoltaic panels configured in the system, pv (i) is the total output of the photovoltaic power generation system at time i; ②Battery constraints Battery capacity constraints: E batmin ≤E bat (t)≤E batmax (44) Where: E bat (t) is the remaining capacity of the battery; E batmin is the minimum capacity of the battery; E batmax is the maximum capacity of the battery; Battery charging and discharging power limit: Where: P cbatmax is the maximum charging power of the battery; P dbatmax is the maximum discharge power of the battery; ③ Constraints of hydrogen energy storage system Hydrogen storage tank capacity constraints: H batmin ≤H bat (t)≤H batmax (46) Where: H bat (t) is the capacity status of the hydrogen storage tank; H batmin is the minimum value of the hydrogen storage tank capacity state; H batmax It is the maximum value of the hydrogen storage tank capacity state; Electrolytic cell power limit: 0≤P ele (t)≤P elemax (47) Where: P elemax is the maximum power of the electrolytic cell; Fuel cell power limitations: 0≤P fc (t)≤P fcmax (48) Where: P fcmax is the maximum power of the fuel cell; ⑤Power balance constraint At any time, the output power of each power generation unit and energy storage device must meet the load requirements: P wt (i)+P pv (i)+P water (i)+P dbat (i)+P fc (i) = P Load (i)+P cbat (i)+P ele (i) (49) Where: P wt Output for the fan; P pv For photovoltaic output; P water (i) small hydropower output; dbat is the battery discharge power; P fc Powering the fuel cell; P Load is the microgrid load; P cbat The power for charging the battery; P ele Provide power for the electrolyzer; S 4.4: Initialize the parameters of each device in the system, and input the typical daily geographic information of the four seasons and the load data of the highway infrastructure of the selected road section in each scenario into the system; S 4.5: Use particle swarm optimization and genetic algorithm heuristic optimization algorithms to seek the optimal solution, obtain the optimal planning scheme of the highway traffic self-consistent microgrid system, output the system output and analyze it.

2. The method for planning a self-consistent energy system for highway traffic based on scenario adaptation according to claim 1 is characterized in that: S1 includes photovoltaic resource endowment assessment and wind resource endowment assessment, and the specific steps are: S1.1: Photovoltaic resource endowment assessment According to the classification standards of the Wind and Solar Energy Assessment Center of the China Meteorological Administration, solar energy resource regions are divided into the following four categories: Category I areas: annual radiation is above 6300 MJ / m2; Category II areas: annual radiation is 5040-6300 MJ / m2; Category III areas: annual radiation is 3780-5040 MJ / m2; Category IV areas: annual radiation is below 3780 MJ / m2; S1.2: Wind resource assessment Wind energy resources are evaluated by two indicators: effective wind energy density and annual effective wind hours. When evaluating wind energy resource reserves, the observed discrete values ​​are used to approximate the average wind energy density within a certain geographical area. The calculation formula is: Where: ρ is the air density, N is the total observation time: v i is the i-th level value after the wind speed is divided into several levels according to the observation records; N i It is the cumulative time when wind speed level i occurs within a certain period of time.

3. The method for planning a self-consistent energy system for highway traffic based on scenario adaptation according to claim 1 is characterized in that: The S2 specifically includes: S2.1: Energy consumption analysis of highway service areas The linear relationship between the energy consumption and passenger flow in the expressway service area can be expressed as: E=11.559a+346.77 (2) The linear relationship between the energy consumption and building area of ​​the highway service area can be expressed as: E=5.697b+4.599 (3) The linear relationship between the energy consumption and greening rate of the highway service area can be expressed as: E=-0.045c+0.8634 (4) The linear relationship between the energy consumption of the highway service area and the window-to-wall ratio can be expressed as: E=0.093d+3.726 (5) Where E is the building energy consumption of the highway service area, unit: tce; a is the passenger flow of the highway service area, unit: person / year; b is the building area of ​​the highway service area, unit: m 2 ; c is the greening rate of the service area; d is the window-to-wall ratio; S2.2: Tunnel energy consumption analysis The energy consumption model includes the power consumption of the lighting system Q1, the power consumption of the ventilation system Q2, the power consumption of the monitoring system Q3, and the power consumption of the fire protection system Q4. The total energy consumption of the tunnel is: <h2 style=";text-align:left;direction:ltr">Q<h2 style=";text-align:left;direction:ltr"> S <h2 style=";text-align:left;direction:ltr"> =Q1+Q2+Q3+Q4 (6) 1) Lighting system Q1 The energy consumption model of two-way and two-way traffic tunnel lighting determined by the requirements for the layout of lighting facilities in the tunnel is as follows: where d1 is the annual electricity consumption of tunnel lighting per unit length, unit: KWh; D1 is the annual electricity consumption of a single tunnel lighting, unit: KWh; Q1=d1L s +D1n s (7) 2) Ventilation system Q2 The ventilation system is mechanical ventilation, with an annual power consumption of D2, unit: KWh; the distance between typhoon fans is d2, unit: m; 3) Monitoring system Q3 According to the highway tunnel traffic engineering design specifications, the camera should be 100m-400m outside the tunnel. It is assumed that a camera is set every d3 in the tunnel, and the vehicle detector is set at a distance of 500m. Assuming a one-way four-lane, there are 8 cameras in one place, and the annual power consumption is D3, unit: kW h, 4) Fire fighting system Q4 Traffic lights are set 20m before the tunnel entrance. According to the design specifications for automatic fire alarms, highway tunnels, tunnel entrances and exits, and alarm telephones should be set every d'4m in the tunnel. The annual power generation is D'4, unit: kW h. A flashing red fire sound and light alarm should be set every d″4m. The annual power generation is D″4, unit: kW h. The energy consumption calculation model of the fire protection system is as follows In summary, the calculation method of annual energy consumption of tunnel is as follows: Q s is the annual energy consumption of the tunnel, in kW·h; L s is the total length of the tunnel, in meters; n s To measure the total number of highway tunnels on a road section, the unit is tunnel; S2.3: Bridge energy consumption calculation method There are many kinds of electrical equipment around bridges. To ensure driving safety, highway bridges need to be equipped with lighting, monitoring, communication, emergency power systems and other systems. A bridge with a length of L kilometers needs L-1 monitoring and detection equipment. Its energy consumption model is; Q q =P b1 T b1 L B +P b2 T b2 (L B -1)(12) Q q is the annual energy consumption of the bridge, in kW h; P b1 is the load of the bridge lighting system, in kW / km, and is taken as 15.98; T b1 is the working time of the lighting system, h; L B is the total length of the bridge, km; P b2 is the rated power of the monitoring and detection equipment, kW; T b2 is the working time of monitoring and detection equipment, h; S2.4 Calculation method of energy consumption at toll stations The main energy consumption of highway toll stations comes from the toll collection system, monitoring system, lighting system, and daily management office. The energy consumption calculation formula is: E to,m,i,o The total energy consumption for toll collection, monitoring, lighting, and daily management and office work in a year at the toll station is in MJ; q i is the number of the i-th type of equipment: the unit is unit; w i is the power of the ith device: the unit is W / unit; t i is the operating time of the i-th equipment: the unit is hours; i is the equipment type; By summarizing the energy consumption of each part in a typical toll station, the annual energy consumption calculation formula of the toll station is as follows: Q t =E to,m,i,o n t (14) Q t is the annual energy consumption of the toll station, unit: kW·h; n t To calculate the number of toll stations on a road section, unit: number.

4. The method for planning a self-consistent energy system for highway traffic based on scenario adaptation according to claim 1 is characterized in that: The specific steps of S3 are: S 3.1: Division according to local conditions According to the abundance of local solar radiation and wind energy resources, the regions are divided into resource-rich areas, resource-average areas and resource-poor areas by comprehensively considering the distribution of solar energy resources and wind energy resources. Wind power combined with solar energy resource zoning: Taking into account the abundance of wind and solar energy resources, China is divided into three regions: resource-rich areas, resource-average areas and resource-poor areas. S 3.2: Division according to network conditions Considering the density of local power grid coverage and the number of charging piles, the area is divided into strong network area, weak network area and no network area by comprehensively considering the strength of the power grid and the magnitude of charging piles; S 3.3: Classification according to load According to the size of electricity load, China's road traffic load is divided into large load and small load; S 3.4: Comprehensive scene division Taking the three factors of "source, network and load" into consideration, the three modes of "adapting to local conditions", "adapting to the network" and "adapting to load" are superimposed together to form a set of operating scenarios for a self-consistent energy system for road transportation.

5. A computer-readable medium storing software, characterized in that: The software includes instructions that can be executed by one or more computers, and the instructions enable the one or more computers to perform operations through such execution, and the operations include the process of the scene-adaptive highway traffic self-consistent energy system planning method as claimed in any one of claims 1-4.

6. A computer system, characterized in that: include: one or more processors; A memory storing operable instructions, wherein when the instructions are executed by the one or more processors, the one or more processors perform operations, wherein the operations include the process of a method for planning a self-consistent energy system for road traffic based on scenario adaptation as claimed in any one of claims 1 to 4.

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