RAMS evaluation method and index system for highway transportation self-consistent energy system considering uncertain wind and solar power output

Abstract

The present invention discloses a RAMS evaluation method for a highway transportation self-consistent energy system considering uncertain wind and solar power output, comprising the following steps: constructing a microgrid architecture of the highway transportation self-consistent energy system, and analyzing the "source-grid-load-storage" basic operation mode of the highway transportation self-consistent energy system; proposing a component fault state model, a power generation side model, a storage side model, a load side model and a system control strategy according to the operation mode of the highway transportation self-consistent energy system; utilizing Latin hypercube sampling technology to generate multiple scenarios, adopting synchronous back substitution method to reduce scenarios, and then modeling and analyzing the randomness and volatility of wind power and photovoltaic power; constructing evaluation indicators for four aspects of reliability, availability, maintainability and safety of the highway transportation energy system, and proposing a RAMS comprehensive evaluation model for the highway transportation self-consistent energy system based on TOPSIS method and grey correlation analysis method; based on the established RAMS evaluation model, four key characteristics of the system can be effectively evaluated, providing a key basis for subsequent system performance improvement and system optimization.

Description

Technical Field

[0001] The present invention relates to RAMS evaluation of a highway transportation self-consistent energy system, and in particular to a RAMS evaluation method and index system for a highway transportation self-consistent energy system considering uncertain wind and solar power output. Background Art

[0002] RAMS assessment, as one of the commonly used methods for comprehensive assessment, has certain advantages, and its application to the self-consistent energy system of highway transportation is feasible. By evaluating the reliability, availability, maintainability, and safety of the highway transportation energy system, an assessment result with both breadth and depth, efficiency and accuracy can be achieved. Highway transportation energy systems are extremely widely distributed and the facilities are highly complex. Traditional RAMS assessments cannot meet existing needs. In order to improve the safe and stable operation capabilities of highway transportation energy systems, the development of RAMS assessment methods for highway transportation energy systems is an important link. With the continuous construction of highway transportation energy systems, the requirements for the reliability of rail transportation energy systems are becoming increasingly urgent, and we are facing new challenges:

[0003] (1) The safe and reliable operation of the highway transportation energy system must be ensured. The system must maintain high reliability throughout its 40-year life cycle, ensuring that the system's availability and maintainability meet standards while also ensuring safety protection.

[0004] (2) To achieve the transformation of energy facilities on the transportation side, it is necessary to accelerate the construction of green transportation infrastructure and improve the inherent safety and reliability of the system.

[0005] The above challenges are all related to the RAMS characteristics of highway transportation energy systems. The better the RAMS characteristics, the lower the failure rate, the easier it is to maintain, and the safer it is. Therefore, the importance of RAMS characteristics of highway and rail transportation energy systems is self-evident.

[0006] Patent publication number CN116545028A discloses a multi-mode energy scheduling method for a self-consistent waterway energy system. Based on the energy balance between renewable energy generation, traditional generation, and load demand, the system is innovatively divided into five operating modes. An operating strategy for each operating mode is constructed, and the energy interaction between the system and the main power grid is deeply explored to ensure maximum utilization of the power grid and energy storage system, minimizing system curtailment of wind and solar power and load shedding. An optimization scheduling model suitable for the proposed operating strategy is constructed. Based on the currently commonly used economic objective function, penalty costs for curtailment of wind and solar power and load shedding costs are added to minimize curtailment of wind and solar power and load shedding. The DQN algorithm is used to solve the system optimization scheduling model, avoiding the poor solution stability of currently commonly used swarm intelligence algorithms and improving the stability of the model solution. This patent analyzes the dynamic characteristics of the system's supply and demand balance from the perspective of energy scheduling, but ignores the static properties determined by factors such as the parameters of the system's components and the uncertainty brought about by renewable energy generation. In addition, this patent focuses on evaluating and improving the system's wind and solar power curtailment rate and load shedding power. The evaluation indicators are relatively single and it is impossible to make a comprehensive evaluation of the system from multiple angles. Summary of the Invention

[0007] Purpose of the invention: In view of the increasingly integrated transportation network and energy network, in order to effectively solve the performance evaluation problem in the planning and design of the self-consistent energy system for highway transportation in the central and western regions of my country, a RAMS evaluation method and indicator system for the self-consistent energy system for highway transportation considering the uncertain wind and solar power output is proposed and established, which can effectively evaluate the four key characteristics of the system and provide a key basis for subsequent system performance improvement and system optimization.

[0008] Technical Solution: To achieve the above objectives, the present invention provides a RAMS evaluation method and index system for a highway transportation self-consistent energy system considering uncertain wind and solar power output, comprising the following steps:

[0009] S1: Construct a microgrid architecture for a self-consistent energy system for highway transportation and analyze the basic “source-grid-load-storage” operation mode of the self-consistent microgrid system for highway transportation;

[0010] S2: Based on the operation mode of the highway traffic self-consistent energy system, a component fault state model, a power generation side model, an energy storage side model, a load side model and a system control strategy are proposed;

[0011] S3: Use Latin Hypercube sampling technology to generate multiple scenarios and adopt synchronous back-substitution to reduce scenarios, thereby modeling and analyzing the randomness and volatility of wind power and photovoltaic power;

[0012] S4: Construct evaluation indicators for the reliability, availability, maintainability, and safety of highway transportation energy systems, and propose a comprehensive evaluation model for highway transportation self-consistent energy systems (RAMS) based on the TOPSIS method and grey correlation analysis method.

[0013] S5: Based on the established RAMS assessment model, the four key characteristics of the system can be effectively evaluated.

[0014] Furthermore, the models to be constructed in step S2 are: component fault state model, power generation side model, energy storage side model, load side model and system control strategy. The specific models are:

[0015] S2.1: Component Fault State Model:

[0016] The time-varying nature of the operating states of photovoltaic and wind power generation equipment directly leads to the time-varying nature of the system operating states. For a system with n devices, assuming that each device obeys the normal-fault two-state model, the total number of system states is N = 2 n System status S k The instantaneous probability distribution and system state are

[0017]

[0018] S k =[s1,s2,…,s n ](2)

[0019] Where W is the system state S k The collection of components in normal working state; F is the system state S k The collection of components in a faulty state.

[0020] In this system, the failure rate and repair rate of components can be represented by a uniform distribution between [0,1]. The component status is obtained by randomly generating the probability characteristics of the components. Assuming that each component has only two states: normal operation and failure, and the states of each component are independent of each other, let d PF,i is the invalidity of the i-th element, and a random number U is drawn from a uniform distribution between [0,1]. i , then:

[0021]

[0022] By extracting n random numbers and applying formula (3), the system state S can be determined.

[0023] S2.2: Power generation side model:

[0024] The power output curve of the equipment, the turbine hub height and the wind speed at the hub height are the main determinants of the wind turbine output power, which can be expressed as:

[0025]

[0026] Where: P w is the output power of the wind turbine; S is the actual wind speed at the hub height of the turbine; 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 wr is the rated power of the wind turbine.

[0027] The output power of photovoltaic power generation equipment is mainly affected by factors such as solar radiation intensity and temperature. The output power P of photovoltaic equipment pv It can be expressed as:

[0028]

[0029] Where: P pvr is the rated power of the photovoltaic equipment; For photovoltaic construction areas (latitude ) hourly mean of surface solar radiation intensity at hour h on day d in month m; θ T is the power temperature coefficient of the photovoltaic device; T pv is the operating temperature of the photovoltaic equipment; T c The reference temperature of the photovoltaic device is 25℃.

[0030] Gas turbines are more environmentally friendly than diesel engines. The output power of a gas turbine per unit time is expressed as:

[0031]

[0032] Where: P gt is the output power of the gas turbine per unit time; V is the volume of natural gas consumed by the gas turbine per unit time; Q is the combustion calorific value of natural gas; η gt is the power generation efficiency of the gas turbine.

[0033] S2.3: Energy storage side model:

[0034] The battery model considering self-discharge and charge and discharge power is:

[0035]

[0036] Where: β is the self-discharge power of the battery; t is the sampling point at a certain moment in the planning period, and the minimum unit of the sampling interval is Δt, which is 1h; is the electrical energy stored in the battery at time t, which takes into account the battery's previous stored energy and the current net input and output power and charge and discharge cycle efficiency; is the charge and discharge power of the battery at time; η cbat ,η dbat It is the battery charging and discharging efficiency.

[0037] The battery state of charge model is as follows:

[0038]

[0039] Where: SOC t is the state of charge of the battery at time t; C bat is the rated capacity of the battery.

[0040] Hydrogen energy storage models include:

[0041] (1) Electrolyzer model:

[0042] Large-scale hydrogen production often uses alkaline electrolyzers to electrolyze water into hydrogen and oxygen. The output power of the electrolyzer is:

[0043] P eleo =η ele P elei (9)

[0044] Where: P elei is the input power of the electrolytic cell; η ele is the electrolyzer efficiency.

[0045] (2) Hydrogen storage tank model:

[0046] The hydrogen storage tank has two main 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:

[0047]

[0048] Where: The energy stored in the hydrogen tank at time t; is the output power of the fuel cell at time t; η fc The efficiency of the fuel cell.

[0049] (3) Hydrogen fuel cell model

[0050] Fuel cells usually use solid oxide as fuel. The output power of the fuel cell is:

[0051] P fc =η fc P h-fc (11)

[0052] Where: P h-fc is the input power from the hydrogen storage tank to the fuel cell.

[0053] S2.4: Load side model:

[0054] This paper analyzes the annual energy demand of highway infrastructure. The energy consumption calculation model of infrastructure is:

[0055] Q=Q f +Q s +Q q +Q t +Q y (12)

[0056] Where: Q is the total energy consumption of infrastructure on the highway; Q f is the energy consumption of the service area; Q s is the tunnel energy consumption; Q q is the energy consumption of the bridge; Q t is the energy consumption of the toll station; Q y Energy consumption of equipment along the line.

[0057] (1) Service area energy consumption model:

[0058] The energy consumption of service areas mainly comes from restaurants, supermarkets, gas stations, etc. By analogy, the hourly energy consumption data of the service areas is accumulated and calculated. The specific formula is as follows:

[0059]

[0060] Where: T is the time period, measured in hours, and is 8760 for the whole year; is the energy consumption of the ith service area on the highway for one hour; is the energy consumption of the jth parking lot on the highway for one hour; k and l are the number of service areas and parking lots on the highway, respectively.

[0061] (2) Tunnel energy consumption model:

[0062] To ensure driving safety in highway tunnels, ventilation, lighting and other systems are required. The energy consumption model is as follows:

[0063] Q s =351L s +210782.8n s (14)

[0064] Where: L s is the total length of the tunnel; n s is the total number of tunnels on the expressway.

[0065] (3) Bridge energy consumption model:

[0066] To ensure driving safety, highway bridges need to be equipped with lighting, monitoring, communication, emergency power and other systems. The energy consumption model is as follows:

[0067] Q q =140.438L q -438(15)

[0068] Where, L q is the length of the bridge.

[0069] (4) Toll station energy consumption model:

[0070] The energy consumption of highway toll stations mainly comes from toll collection, monitoring, lighting systems and daily management and office work. Its energy consumption model is as follows:

[0071] Q t =328n t (16)

[0072] Where n t is the number of toll booths.

[0073] (5) Energy consumption model of equipment along the line:

[0074] The main energy-consuming devices along highways are vehicle detectors and emergency telephones. Based on the monitoring and communication requirements of highway trunk lines and practical experience, a highway with a length of L kilometers requires (L-1) vehicle detectors and 2 (L-1) emergency telephones. The energy consumption model is:

[0075] Q r =T(L t -1)(P c +2P d )(17)

[0076] Where, L t is the total length of the highway, P c is the power of the vehicle monitoring equipment, P d It is the power of emergency phone.

[0077] In step S3, Latin hypercube sampling technology is used to generate multiple scenarios, and synchronous back-substitution method is used to reduce the scenarios, and then the randomness and volatility of wind power and photovoltaic power are modeled and analyzed. The specific process is as follows:

[0078] S3.1: Scenario generation technology based on Latin hypercube sampling: When wind power forecasts follow a Weibull distribution, photovoltaic forecasts follow a normal distribution, and load forecasts follow a normal distribution, a multi-scenario set of wind power, photovoltaic output, and load demand can be predicted. The main steps are:

[0079] 1) Divide the 24-hour probability distribution into n probability intervals to represent typical time periods of each day.

[0080] 2) Use random sampling within each probability interval, and make the sampling results of each interval independent and of equal probability.

[0081] The probability of each interval can be expressed as: p in =p(x in ∈S in )(18)

[0082] Among them, p in is the sampling probability of variable i in the nth interval, where ∑p in =1;x in is the sample of variable i in the nth interval, S in is the threshold of variable i in the nth interval.

[0083] 3) Inverse transform the probability distribution function to obtain the sampling value of the sampling point. The sample value corresponding to each subinterval is:

[0084]

[0085] Among them, x i is the sample value corresponding to each subinterval; It is the inverse of the probability distribution function f(·).

[0086] S3.2: Wind and solar scene reduction technology based on synchronous back-substitution method: Since the generation of a large number of scenes will increase the burden of solution calculation, this paper adopts synchronous back-substitution method to reduce the scenes. The reduced typical scene set can better reflect the probability distribution of the original scene set. Its main steps are:

[0087] 1) Compare the distance between any scene and any other scene, and take the scene with the closest distance;

[0088]

[0089] Where D i For scene x i Probabilistic distance to any other scene; λ i For scene x i The probability of occurrence; d(x i ,x j ) for two scenes x i with x j Euclidean distance; n so The number of scenes generated after Latin hypercube sampling.

[0090] 2) Merge the scenes with the closest distance;

[0091] Select the scene x that is closest to you i, as shown in the following formula (21); delete the scene with the closest distance above, and add the probability of this scene appearing to the scene with the closest distance x j Delete the scene closest to x i Post x j The probability is shown in formula (22):

[0092]

[0093] λ′ j =λ j +λ i (twenty two)

[0094] Where D min For any scene and scene x i The nearest probability distance.

[0095] 3) Repeat the above steps until the number of remaining scenes reaches a predetermined value.

[0096] After the generation and reduction of scenarios, the final number of wind power scenarios, photovoltaic scenarios and load scenarios are n respectively. WT 、n PV 、n Load Finally, the typical output scenarios are combined into n s , and its probability and typical scenario probability are:

[0097] n sup,s =n WT n PV (twenty three)

[0098]

[0099] Where: WT ,λ PV ,λ load are the probabilities corresponding to wind power, photovoltaic and load scenarios respectively.

[0100] The step S4 is specifically as follows:

[0101] S4.1: Normal RAMS evaluation index system

[0102] S4.1.1: Reliability evaluation indicators: This paper proposes the following indicators to evaluate system reliability:

[0103] 1) Probability of insufficient power supply P ie,t

[0104] The probability of insufficient power supply reflects the probability that the power generation on the source side cannot meet the demand on the load side at a certain moment or in a certain period of time. The probability of insufficient power supply of the system at this moment is obtained by taking the weighted average of the probabilities corresponding to the scenarios at different moments as weights.

[0105]

[0106] Where, P ie,t is the probability of insufficient power supply in 24 hours; pi t,s is the system state index, which represents the supply and demand relationship of sample s in the generated uncertainty-based supply and demand curve; sup,t,s ,λ load,t,s are the probabilities of the power generation and power consumption of sample s at time t; P sup,t,s 、P load,t,s are the power generation and power consumption of sample s at time t; P ie,ave is the average probability of power shortage; H is the time step, and the probability of power shortage in different time periods can be evaluated based on different H.

[0107] 2) Loss of Energy Expected (LOEE)

[0108] The power shortage expectation represents the total amount of power that the system lacks at a certain moment or over a period of time. Similarly, the average of multiple scenarios is calculated using probability as weight to reflect the system power shortage expectation.

[0109]

[0110] Where λ sup,s ,λ load,s are the occurrence probabilities of the output scenario and load scenario respectively; P drump,s is the load shedding amount in scenario S; LOEET is the expected power shortage per hour, and LOEE is the total power shortage in the entire time period.

[0111] 3) Effective power supply expectation (EPSE)

[0112] Indicates the total amount of electricity provided to the system load side and stored on the energy storage side.

[0113]

[0114] Where, P load,s,t is the load demand power at time t under scenario s; P bc,s,t is the charging power of the energy storage unit at the time s, t in the scenario.

[0115] 4) Load fluctuation f load

[0116]

[0117] Where, P loade,ave represents the average system load under scenario S; P storage,s,t Indicates the value when the energy storage system is regarded as an equivalent load; P c,s,t 、P f,s,t are the charging and discharging amounts of the energy storage system at time t under scenario S respectively.

[0118] 5) LOET (Loss of Energy Time)

[0119]

[0120] S4.1.2: Usability evaluation metrics

[0121] 1) Self-consistency rate R s

[0122] The self-consistency rate represents the ability of a self-consistent energy system to be self-sufficient in electricity over a full time period. It is calculated by dividing the system's expected effective power supply by the system's total load demand.

[0123]

[0124] 2) New energy utilization rate R u

[0125] The ratio of a system's effective wind and solar output to its total wind and solar output measures the system's utilization of renewable resources. This is calculated by dividing the expected effective power supply by the system's total source-side power generation, as shown in the following formula.

[0126]

[0127] 3) Availability A

[0128] Availability represents the percentage of time a self-consistent energy system is operational within a given time period. This is calculated by dividing the available time by the cycle duration, as shown in the following formula.

[0129]

[0130] S4.1.3: Maintainability evaluation indicators

[0131] 1) Maintenance Time MTTM (Maintain Time)

[0132] The time taken to go from the initial normal state to the fault state and then to the next normal state can be calculated using the component fault state model obtained in S2.

[0133]

[0134] Where, T(S n ) is the normal state moment; T(S n+1 ) is the moment of the next normal state; N is the number of times the repair is completed.

[0135] 2) Repair and restoration degree R cover

[0136] Regarding the degree of system recovery after repair, this paper defines the degree of repair recovery and calculates the minimum value of power generation P in the fault interval. f,s,min The ratio of the difference between the maximum and minimum power generation during the time period.

[0137]

[0138] P range,s =P sup,s,max -P sup,s,min (40)

[0139] Where, P f,s,min is the minimum power generation in the fault interval of scenario S; P range,s is the difference between the maximum and minimum values within the S scenario time period; P sup,s,max is the maximum power generation in the S scenario; P sup,s,min is the minimum power generation in scenario S.

[0140] 3) Maintenance costs C

[0141] Maintenance costs refer to the costs required to repair the system.

[0142] C=C w *MTTM+C r *M (41)

[0143] Where C w is the hourly wage of maintenance workers; M is the number of maintenance equipment.

[0144] S4.1.4: Safety Assessment Indicators

[0145] 1) Load failure severity S a,t

[0146] Five levels, 0, 1, 2, 3, and 4, are defined based on the number and severity of traffic accidents caused by the system's load loss at this moment to describe the severity of the danger to the system at this moment.

[0147]

[0148] Table 1 Load failure severity

[0149]

[0150] 2) Risk duration T risk

[0151]

[0152] System loss state, in S a,t When it is greater than 20%, the system is considered to be in a risky state and the parameter is set to 1, and the total risk duration is recorded. On the contrary, when S a,t When it is less than 20%, the parameter is set to 0 and is not recorded in the risk duration.

[0153] 3) Risk Index R isk

[0154] R isk =1-e λ (45)

[0155]

[0156] Where N is the total number of power generation and power consumption equipment.

[0157] S4.2: Emergency RAMS evaluation index system

[0158] The emergency state refers to the system state that is activated when the self-consistent energy system is severely damaged after encountering extreme weather disasters or major accidents. At this time, the system will give priority to ensuring the supply of primary and secondary loads.

[0159] S4.2.1: Definition of system emergency state

[0160] Define the system emergency state impact factor (hereinafter referred to as emergency factor) ξ, at this time the system power generation P sup As shown in the following formula.

[0161]

[0162] P sup,ξ =P pv ξ p +P wind ξ w (49)

[0163] Where O p (s), O w (s) are the equipment failure rates under different weather conditions or accidents as shown in the following table; N p 、N w are the number of photovoltaic panels and wind turbines respectively.

[0164] Table 2 Photovoltaic panel failure rate

[0165]

[0166] Table 3 Fan failure rate table

[0167]

[0168] S4.2.2: Load Classification

[0169] As a new type of integrated transportation system, the highway self-consistent energy system differs from conventional highways in terms of energy demand, service area functionality, and green energy applications. Currently, a unified and clear classification of various load levels within the new highway self-consistent energy system is implemented to avoid issues such as unclear power supply load classification caused by unclear load levels. Based on the relevant provisions of current national standards and the degree of impact of power outages on the system, the highway self-consistent energy system is classified as follows based on power supply reliability requirements and the impact of power outages on personal safety and economic losses.

[0170] 1) Level 1 load

[0171] Primary loads are essential equipment for the operation of a transportation system. In tunnels, these include lighting systems, fire protection systems, ventilation systems, and monitoring facilities; in service areas, these include fire protection, security monitoring, and information communications; in bridges, these include lighting systems; and in toll booths, these include toll collection systems and monitoring systems.

[0172] 2) Secondary load

[0173] Secondary loads are equipment that can optimize the operation of the transportation system. In service areas, they include gas stations and charging stations; in toll stations, they include daily management offices.

[0174] S4.2.3: Reliability Assessment Indicators

[0175] 1) Probability of insufficient power supply

[0176]

[0177] Where, P ie,t,1 is the probability of insufficient power supply for the first-level load; P ie,t,2 is the probability of insufficient power supply to the secondary load;

[0178] 2) Expectations of insufficient power

[0179]

[0180] 3) Effective power supply expectation (EPSE)

[0181]

[0182] 4) Load fluctuation

[0183] 1. First-level load fluctuation f load1

[0184]

[0185] 2. Secondary load fluctuation f load2

[0186]

[0187] 5) LOET (Loss of Energy Time)

[0188]

[0189] S4.2.4: Availability indicators

[0190] 1) Self-consistency rate R s

[0191] The self-consistency rate represents the ability of a self-consistent energy system to be self-sufficient in electricity over a full time period. It is calculated by dividing the system's expected effective power supply by the system's total load demand.

[0192]

[0193] 2) New energy utilization rate R u

[0194] The ratio of a system's effective wind and solar output to its total wind and solar output measures the system's utilization of renewable resources. This is calculated by dividing the expected effective power supply by the system's total source-side power generation, as shown in the following formula.

[0195]

[0196] 3) Availability A

[0197] Availability represents the percentage of time a self-consistent energy system is operational within a given time period. This is calculated by dividing the available time by the cycle duration, as shown in the following formula.

[0198]

[0199] S4.2.5: Maintainability indicators

[0200] In an emergency, the system is repaired from its initial damaged state, with a repair rate μ, which is related to the system components, the skill level and number of repair workers, and the extent of the damage. To simplify the maintainability calculation, the possibility of further system failures is not considered.

[0201] 1) Maintenance Time MTTM (Maintain Time)

[0202] The time taken to go from the initial normal state to the fault state and then to the next normal state can be calculated using the component fault state model obtained in S2.

[0203]

[0204] Where, T(S n ) is the normal state moment; T(S n+1 ) is the next normal state moment.

[0205] 2) Repair and restoration degree

[0206] For the degree of system recovery after maintenance, this paper defines the maintenance recovery curve R cover,t and the degree of repair and restoration P recover , calculate the ratio of the difference between the power generation at normal time and the power generation at the initial fault time in the fault interval to the difference between the maximum and minimum power generation in the time period.

[0207]

[0208] Where N normal,t,s N is the normal number of components at time t in scenario S; all,t,s is the total quantity; P s (t normal ) is the power generation during normal maintenance hours; P s (t0) is the power generation at the time of initial fault; P s,max 、P s,min are the maximum and minimum power generation in the power generation range respectively.

[0209] 3) Maintenance costs C

[0210] Maintenance costs refer to the costs required to repair the system.

[0211] C=C w ×MTTM+C r ×M (69)

[0212] Where C w is the hourly wage of maintenance workers; M is the number of maintenance equipment.

[0213] S4.2.6: Safety indicators

[0214] 1) Load loss severity

[0215]

[0216] 2) Risk Index

[0217] R isk =1-e λ (72)

[0218]

[0219] P loade,s,t =P load,s,t +P bdc,s,t (74)

[0220] 3) Duration of risk

[0221]

[0222] Furthermore, the step S5 is specifically as follows:

[0223] S5.1: Standardize the calculated RAMS indicators:

[0224] For benefit-based indicators, the standardized formula is:

[0225]

[0226] For cost-based indicators, the standardized formula is:

[0227]

[0228] Where: x ij represents the jth index value of the i-th evaluation object, x′ ij Indicates the normalized index value, max x ij is the maximum value of the jth index, min x ij is the minimum value of the j-th indicator.

[0229] S5.2: Determine indicator weights:

[0230] 1) Use the AHP method to calculate the subjective weight: Use the 1-9 scale and its reciprocal scale method to compare the elements of each layer, construct a comparison judgment matrix, and obtain the weight c (a = 1, 2, ..., 4) of the a-th criterion layer relative to the target layer, the weight b of the k-th indicator under the a-th criterion layer relative to the a-th criterion layer k (k=1,2,…,), then the weight of the kth indicator under the ath criterion layer relative to the overall goal is:

[0231] d k =c×bk (81)

[0232] When performing consistency test on judgment matrix, it is necessary to calculate consistency index When the random consistency ratio , the matrix consistency test passes, otherwise it is necessary to reconstruct the judgment matrix to calculate the indicator weights.

[0233] 2) Use entropy method to find objective weights: The method of using the concept of entropy to determine indicator weights is called entropy method. Its essence is to use the amount of information provided by the entropy value of each indicator to determine the indicator weight. This method is simple to calculate and easy to understand. The specific calculation formula is as follows:

[0234]

[0235] In the formula: m is the evaluation object, n is the evaluation index, w i is the weight value of the i-th indicator, H i is the entropy of the i-th indicator, A ij is the standardized evaluation data matrix.

[0236] 3) Calculate the combination weight:

[0237] The combined weight w of the jth indicator of the i-th object j Defined as:

[0238]

[0239] Where: α j is the subjective weight, β j is the objective weight.

[0240] S5.3: Establish a weighted normalization matrix with the following formula:

[0241]

[0242] S5.4: Determine the positive and negative ideal solutions using the following formula:

[0243] Positive ideal solution:

[0244] Negative ideal solution:

[0245] Among them, the positive ideal solution is the set of optimal values of each indicator, and the negative ideal solution is the set of worst values of each indicator.

[0246] S5.5: Calculate the Euclidean distance d + and d - , the formula is as follows:

[0247]

[0248] Where: Represents each solution to the positive ideal solution U + The Euclidean distance of Represents each solution to the negative ideal solution U - The Euclidean distance of .

[0249] S5.6: Calculate the grey correlation coefficient matrix. The calculation formula is as follows:

[0250]

[0251] Where R + Represents each solution and the positive ideal solution U + Grey correlation coefficient matrix, R - Represents each solution and the negative ideal solution U - The grey correlation coefficient matrix, ρ is called the resolution coefficient. The larger ρ is, the smaller the resolution is. ρ∈[0,1], generally ρ=0.5.

[0252] S5.7: Normalize the distance and association using the following formula:

[0253]

[0254]

[0255] Where: is the Euclidean distance calculated in step 5, is the correlation calculated in step 6.

[0256] S5.8: Calculate relative closeness: Calculate the relative closeness of each solution and rank the RAMS performance of the evaluation object according to the size of the grey relational relative closeness. The greater the closeness, the better the solution, and the smaller the closeness, the worse the solution. The calculation formula is as follows:

[0257]

[0258] Where: and It indicates how close and far away the plan is from the ideal plan in terms of location and shape. μ and 1-μ reflect the decision maker's preference for location and shape, and μ = [0,1]. Here, μ = 0.5 is taken.

[0259] The proposed RAMS assessment method for a highway transportation self-consistent energy system that considers uncertain wind and solar power output combines component fault state models, generation-side models, energy storage-side models, load-side models, and system control strategies. It analyzes the system's static and dynamic characteristics from the four dimensions of "source-grid-load-storage," taking into account the randomness and volatility of wind and photovoltaic power. Furthermore, this patent constructs 28 evaluation indicators for highway transportation energy systems in four aspects: reliability, availability, maintainability, and safety, under both normal and emergency conditions. This allows for a comprehensive assessment of the system that balances both breadth and depth.

[0260] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0261] 1. The uncertain wind and solar power output is fully considered, and Latin hypercube sampling technology is used to generate multiple scenarios. A multi-scenario set of wind power, photovoltaic output and load demand is predicted. The synchronous back substitution method is then used to reduce the scenarios. The reduced typical scenario set can better reflect the probability distribution of the original scenario set, and a probability weighted calculation method is adopted to quantify the system's wind, solar and load uncertainties.

[0262] 2. Based on the concept of highway traffic self-consistent energy system, the evaluation indicators of highway traffic energy system reliability, availability, maintainability and safety were constructed, and a RAMS comprehensive evaluation model of highway traffic self-consistent energy system based on TOPSIS method and grey correlation analysis method was proposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0263] Figure 1 is an evaluation flow chart of the method of the present invention;

[0264] Figure 2 The temperature, wind speed, and light intensity data for a typical day on the selected road section;

[0265] Figure 3 The load data for a typical day of the selected road section;

[0266] Figure 4 The generated wind and solar power output and load scenario set;

[0267] Figure 5 This is the set of wind and solar power output and load scenarios after reduction;

[0268] Figure 6 To generate the probability of each typical scenario;

[0269] Figure 7 is the probability of insufficient power supply under three different operating modes under normal conditions;

[0270] Figure 8 Under normal conditions, the power supply is insufficient under three different operating modes;

[0271] Figure 9 Load fluctuations under three different operating modes under normal conditions;

[0272] Figure 10 is the load loss severity under three different operating modes in normal state;

[0273] Figure 11 is the risk index under three different operating modes in normal state;

[0274] Figure 12 It is the first-level load and second-level load data of emergency state;

[0275] Figure 13 In the emergency operation mode, the probability of insufficient power supply to the primary and secondary loads;

[0276] Figure 14 is the probability of insufficient power supply to the primary and secondary loads under the emergency operation model 2;

[0277] Figure 15 The probability of insufficient power supply to the primary and secondary loads under emergency operation mode 3;

[0278] Figure 16 In the emergency operation mode, the power supply to the primary and secondary loads is insufficient;

[0279] Figure 17 In the second emergency operation mode, the power supply to the primary and secondary loads is insufficient;

[0280] Figure 18 In the third emergency operation mode, the power supply to the primary and secondary loads is insufficient;

[0281] Figure 19 In the emergency operation mode, the load fluctuations of the primary load and the secondary load;

[0282] Figure 20 It is the load fluctuation of primary load and secondary load in emergency operation mode 2;

[0283] Figure 21 This is the load fluctuation of the primary load and the secondary load under the emergency operation mode 3;

[0284] Figure 22 These are the maintenance recovery curves under three different operating modes in emergency situations;

[0285] Figure 23 It is the risk index under three different operation modes in emergency state. DETAILED DESCRIPTION

[0286] The present invention is further illustrated below with reference to 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, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0287] In this example, a section of the Beijing-Tibet Expressway G6 in Tibet was used to establish a self-consistent energy system for highway traffic under three different operating modes, and the RAMS performance of the system was evaluated under the three modes. The wind speed, radiation intensity, temperature and load conditions of the section were used as system inputs, such as Figure 2 and 3 The same device parameter settings in each scenario are shown in Table 4.

[0288] Table 4 Equipment parameter settings

[0289]

[0290]

[0291] Tibet, my country, has high annual solar radiation but virtually no backbone transmission grid. A viable approach to addressing self-sustaining highway energy consumption in these areas is to establish off-grid microgrid systems. This hybrid "battery + hydrogen" energy storage system, equipped with stable and inexpensive batteries and environmentally friendly, green, and clean hydrogen energy storage, can regulate power consumption. Finally, a backup power source can be added as a backup to mitigate the intermittent and volatile output of renewable energy. The following three operating modes are categorized based on the different devices involved in the system, as shown in Table 5.

[0292] Table 5 Three operating modes

[0293]

[0294] With the above data, the initial number of scenes is 500, and Latin hypercube sampling can be used to obtain Figure 4 is a set of wind, solar and load output scenarios; it can be obtained by calculation.

[0295] The RAMS evaluation method and index system of the highway transportation self-consistent energy system considering the uncertain wind and solar power output provided by the present invention are used to perform system evaluation, such as Figure 1 As shown, the specific steps are as follows:

[0296] S1: Construct a microgrid architecture for a self-consistent energy system for highway transportation and analyze the basic “source-grid-load-storage” operation mode of the self-consistent microgrid system for highway transportation;

[0297] S2: Based on the operation mode of the highway traffic self-consistent energy system, a component fault state model, a power generation side model, an energy storage side model, a load side model and a system control strategy are proposed;

[0298] S2.1: Component Fault State Model:

[0299] The time-varying nature of the device operating state directly leads to the time-varying nature of the system operating state. For a system with n devices, assuming that each device obeys the normal-fault two-state model, the total number of system states is N = 2 n System status S k The instantaneous probability distribution and system state are

[0300]

[0301] S k =[s1,s2,…,s n ](2)

[0302] Where W is the system state S k The collection of components in normal working state; F is the system state S k The collection of components in a faulty state.

[0303] In this system, the failure rate and repair rate of components can be represented by a uniform distribution between [0,1]. The component status is obtained by randomly generating the probability characteristics of the components. Assuming that each component has only two states: normal operation and failure, and the states of each component are independent of each other, let d PF,i is the invalidity of the i-th element, and a random number U is drawn from a uniform distribution between [0,1]. i , then:

[0304]

[0305] By extracting n random numbers and applying formula (3), the system state S can be determined.

[0306] S2.2: Power generation side model:

[0307] The power output curve of the equipment, the turbine hub height and the wind speed at the hub height are the main determinants of the wind turbine output power, which can be expressed as:

[0308]

[0309] Where: P w is the output power of the wind turbine; S is the actual wind speed at the hub height of the turbine; S ci 、S co and S rare the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine respectively; P wr is the rated power of the wind turbine.

[0310] The output power of photovoltaic power generation equipment is mainly affected by factors such as solar radiation intensity and temperature. The output power P of photovoltaic equipment pv It can be expressed as:

[0311]

[0312] Where: P pvr is the rated power of the photovoltaic equipment; For photovoltaic construction areas (latitude ) hourly mean of surface solar radiation intensity at hour h on day d in month m; θ T is the power temperature coefficient of the photovoltaic device; T pv is the operating temperature of the photovoltaic equipment; T c The reference temperature of the photovoltaic device is 25℃.

[0313] Gas turbines are more environmentally friendly than diesel engines. The output power of a gas turbine per unit time is expressed as:

[0314]

[0315] Where: P gt is the output power of the gas turbine per unit time; V is the volume of natural gas consumed by the gas turbine per unit time; Q is the combustion calorific value of natural gas; η gt is the power generation efficiency of the gas turbine.

[0316] S2.3: Energy storage side model:

[0317] The battery model considering self-discharge and charge and discharge power is:

[0318]

[0319] Where: β is the self-discharge power of the battery; t is the sampling point at a certain moment in the planning period, and the minimum unit of the sampling interval is Δt, which is 1h; is the electrical energy stored in the battery at time t, which takes into account the battery's previous stored energy and the current net input and output power and charge and discharge cycle efficiency; is the charge and discharge power of the battery at time; η cbat ,η dbat It is the battery charging and discharging efficiency.

[0320] The battery state of charge model is as follows:

[0321]

[0322] Where: SOC t is the state of charge of the battery at time t; C bat is the rated capacity of the battery.

[0323] Hydrogen energy storage models include:

[0324] 1) Electrolyzer model:

[0325] Large-scale hydrogen production often uses alkaline electrolyzers to electrolyze water into hydrogen and oxygen. The output power of the electrolyzer is:

[0326] P eleo =η ele P elei (9)

[0327] Where: P elei is the input power of the electrolytic cell; η ele is the electrolyzer efficiency.

[0328] 2) Hydrogen storage tank model:

[0329] The hydrogen storage tank has two main 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:

[0330]

[0331] Where: The energy stored in the hydrogen tank at time t; is the output power of the fuel cell at time t; η fc The efficiency of the fuel cell.

[0332] 3) Hydrogen fuel cell model

[0333] Fuel cells usually use solid oxide as fuel. The output power of the fuel cell is:

[0334] P fc =η fc P h-fc (11)

[0335] Where: P h-fc is the input power from the hydrogen storage tank to the fuel cell.

[0336] S2.4: Load side model:

[0337] This paper analyzes the annual energy demand of highway infrastructure. The energy consumption calculation model of infrastructure is:

[0338] Q=Q f +Q s +Q q +Qt +Q y (12)

[0339] Where: Q is the total energy consumption of infrastructure on the highway; Q f is the energy consumption of the service area; Q s is the tunnel energy consumption; Q q is the energy consumption of the bridge; Q t is the energy consumption of the toll station; Q y Energy consumption of equipment along the line.

[0340] 1) Service area energy consumption model:

[0341] The energy consumption of service areas mainly comes from restaurants, supermarkets, gas stations, etc. By analogy, the hourly energy consumption data of the service areas is accumulated and calculated. The specific formula is as follows:

[0342]

[0343] Where: T is the time period, measured in hours, and is 8760 for the whole year; is the energy consumption of the ith service area on the highway for one hour; is the energy consumption of the jth parking lot on the highway for one hour; k and l are the number of service areas and parking lots on the highway, respectively.

[0344] 2) Tunnel energy consumption model:

[0345] To ensure driving safety in highway tunnels, ventilation, lighting and other systems are required. The energy consumption model is as follows:

[0346] Q s =351L s +210782.8n s (14)

[0347] Where: L s is the total length of the tunnel; n s is the total number of tunnels on the expressway.

[0348] 3) Bridge energy consumption model:

[0349] To ensure driving safety, highway bridges need to be equipped with lighting, monitoring, communication, emergency power and other systems. The energy consumption model is as follows:

[0350] Q q =140.438L q -438(15)

[0351] Where, L q is the length of the bridge.

[0352] 4) Toll station energy consumption model:

[0353] The energy consumption of highway toll stations mainly comes from toll collection, monitoring, lighting systems and daily management and office work. Its energy consumption model is as follows:

[0354] Q t =328n t (16)

[0355] Where n t is the number of toll booths.

[0356] 5) Energy consumption model of equipment along the line:

[0357] The main energy-consuming devices along highways are vehicle detectors and emergency telephones. Based on the monitoring and communication requirements of highway trunk lines and practical experience, a highway with a length of L kilometers requires (L-1) vehicle detectors and 2 (L-1) emergency telephones. The energy consumption model is:

[0358] Q r =T(L t -1)(P c +2P d )(17)

[0359] Where, L t is the total length of the highway, P c is the power of the vehicle monitoring equipment, P d It is the power of emergency phone.

[0360] In step S3, Latin hypercube sampling technology is used to generate multiple scenarios, and synchronous back-substitution method is used to reduce the scenarios, and then the randomness and volatility of wind power and photovoltaic power are modeled and analyzed. The specific process is as follows:

[0361] S3.1: Scene Generation Technology Based on Latin Hypercube Sampling

[0362] In this example, the wind power forecast follows the Weibull distribution, the photovoltaic forecast follows the normal distribution, and the load forecast follows the normal distribution. This allows us to predict multiple scenarios of wind power, photovoltaic output, and load demand. Figure 4 shown.

[0363] S3.2: Wind-solar scene reduction technology based on synchronous back-substitution method.

[0364] Since the generation of a large number of scenarios will increase the burden of solving the problem, this paper adopts the synchronous back-substitution method to reduce the scenarios. The reduced typical scenario set can better reflect the probability distribution of the original scenario set. The reduced scenarios and the scenario probabilities are as follows: Figure 5 、 Figure 6 shown.

[0365] S4. A RAMS evaluation method and index system for a highway transportation self-consistent energy system considering uncertain wind and solar power output according to claim 1, characterized in that: said step S4 specifically comprises:

[0366] S4.1: Normal RAMS evaluation index system

[0367] S4.1.1: Reliability evaluation indicators: This paper proposes the following indicators to evaluate system reliability:

[0368] 1) Probability of insufficient power supply P ie,t

[0369] The probability of insufficient power supply reflects the probability that the power generation on the source side cannot meet the demand on the load side at a certain moment or in a certain period of time. The probability corresponding to the scenarios at different moments is used as the weight, and the weighted average is calculated to obtain the probability of insufficient power supply of the system at this moment. The calculation results are as follows: Figure 7 As shown, it can be seen that the probability of insufficient 24h power supply of the system is the lowest in the third operating mode; the second operating mode is second; and the first operating mode is the worst.

[0370] 2) Loss of Energy Expected (LOEE)

[0371] The power shortage expectation represents the total amount of power that the system lacks at a certain moment or within a period of time. Similarly, the average value under multiple scenarios is calculated using probability as weight to reflect the power shortage expectation of the system. The calculation results are as follows: Figure 8 As shown in Table 8, the expected values of power shortage in the second and third operating modes are not much different. In the first operating mode, the expected value of power shortage is higher.

[0372] 3) Effective power supply expectation (EPSE)

[0373] It represents the total amount of electricity provided to the system load side and stored on the energy storage side. The calculation results are shown in Table 8.

[0374] 4) Load fluctuation f load

[0375] Load fluctuation refers to the load fluctuation at a certain moment, and this indicator is defined according to the calculation formula of variance. The calculation results are as follows: Figure 9 As shown in the figure, it can be seen that under the first operating mode, the system load fluctuates greatly, while the load fluctuations under the second and third operating modes are relatively stable.

[0376] S4.1.2: Usability evaluation metrics

[0377] 1) Self-consistency rate Rs

[0378] The self-consistency rate represents the ability of a self-consistent energy system to be self-sufficient in electricity over a full time period. It is calculated by dividing the system's expected effective power supply by the system's total load demand.

[0379] 2) New energy utilization rate R u

[0380] The ratio of a system's effective wind and solar output to its total wind and solar output is used to measure the system's utilization of renewable resources. This is calculated by dividing the expected effective power supply by the system's total source-side power generation.

[0381] 3) Availability A

[0382] Availability represents the percentage of time a self-consistent energy system is operational within a certain time period. This is calculated by dividing the available time by the cycle length.

[0383] The calculation results of the above indicators are shown in Table 8.

[0384] S4.1.3: Maintainability evaluation indicators

[0385] 1) Maintenance Time MTTM (Maintain Time)

[0386] The time taken to go from the initial normal state to the fault state and then to the next normal state can be calculated using the system fault state model obtained in Section 2.1.

[0387] 2) Repair and restoration degree R cover

[0388] Regarding the degree of system recovery after repair, this paper defines the degree of repair recovery and calculates the minimum value of power generation P in the fault interval. f,s,min The ratio of the difference between the maximum and minimum power generation during the time period.

[0389] 3) Maintenance costs C

[0390] Maintenance costs refer to the costs required to repair the system.

[0391] The calculation results of maintenance time, maintenance recovery degree and maintenance cost are shown in Table 8.

[0392] S4.1.4: Safety Assessment Indicators

[0393] 1) Load failure severity S a,t

[0394] According to the number and severity of traffic accidents caused by the loss of load at this moment, five levels of 0, 1, 2, 3, and 4 are defined to describe the severity of the danger of the system at this moment. The calculation results are as follows Figure 10 shown.

[0395] 2) Risk duration T risk

[0396] The calculation results are shown in Table 8.

[0397] 3) Risk Index R isk

[0398] The risk index is defined to reflect the risk level of the system at different times. The calculation results are as follows Figure 11 As shown in the figure, it can be seen that in the first operating mode, the system operation risk index is higher, in the second mode there is a certain risk, and in the third mode there is no risk.

[0399] S4.2: Emergency RAMS evaluation index system

[0400] The emergency state refers to the system state that is activated when the self-consistent energy system is severely damaged after encountering extreme weather disasters or major accidents. At this time, the system will give priority to ensuring the supply of primary and secondary loads.

[0401] S4.2.1: Definition of system emergency state

[0402] Define the system emergency state impact factor (hereinafter referred to as emergency factor) ξ, at this time the system power generation P sup As shown in the following formula.

[0403]

[0404] P sup, ξ=P pv ξ p +P wind ξ w (46)

[0405] Where O p (s), O w (s) are the equipment failure rates under different weather conditions or accidents as shown in the following table; N p 、N w are the number of photovoltaic panels and wind turbines respectively.

[0406] Table 6 Photovoltaic panel failure rate table

[0407]

[0408] Table 7 Fan failure rate table

[0409]

[0410] In this example, O p (s), O w(s) is 50% to simplify the calculation. In the specific implementation process, you can refer to the above table for calculation.

[0411] S4.2.2: Load Classification

[0412] As a new type of integrated transportation system, the highway self-consistent energy system differs from conventional highways in terms of energy demand, service area functionality, and green energy applications. Currently, a unified and clear classification of various load levels within the new highway self-consistent energy system is implemented to avoid issues such as unclear power supply load classification caused by unclear load levels. Based on the relevant provisions of current national standards and the degree of impact of power outages on the system, the highway self-consistent energy system is classified as follows based on power supply reliability requirements and the impact of power outages on personal safety and economic losses.

[0413] 1) Level 1 load

[0414] Primary loads are essential equipment for the operation of a transportation system. In tunnels, these include lighting systems, fire protection systems, ventilation systems, and monitoring facilities; in service areas, these include fire protection, security monitoring, and information communications; in bridges, these include lighting systems; and in toll booths, these include toll collection systems and monitoring systems.

[0415] 2) Secondary load

[0416] Secondary loads are equipment that can optimize the operation of the transportation system. In service areas, they include gas stations and charging stations; in toll stations, they include daily management offices.

[0417] In this example, the values of the primary and secondary loads are defined as follows: Figure 12 shown.

[0418] S4.2.3: Reliability Assessment Indicators

[0419] 1) Probability of insufficient power supply

[0420] In the first operating mode, the calculation results are as follows: Figure 13 As shown in the figure, the probability of insufficient power supply for both primary and secondary loads is high. In the second operation mode, the calculation results are as follows: Figure 14 As shown in the figure, the probability of insufficient power supply for the primary load of the system is low, but the probability of insufficient power supply for the secondary load is high. In the third operation mode, the calculation results are as follows: Figure 15 As shown in the figure, at this time, there is no power shortage for the primary load of the system, but there is still a certain power shortage for the secondary load.

[0421] 2) Low Energy Expectation (LOEE)

[0422] In the first operating mode, the calculation results are as follows: Figure 16 As shown, the expected power shortage of the first-level load is small, and the expected power shortage of the second-level load is large. In the second operating mode, the calculation results are as follows Figure 17 As shown, there is no power shortage for the first-level load at this time, and the power shortage for the second-level load is relatively large from 5 to 8 o'clock, and there is a certain power shortage at 18:00 and 22:00. In the third operating mode, the calculation results are as follows Figure 18 As shown in Table 11, the power shortage of the same primary load is expected to be 0, and there is a small amount of power shortage only at 5:00 and 6:00. The total amount of power shortage expected in one day is shown in Table 11.

[0423] 3) Power supply expectation EPSE

[0424] The calculation results are shown in Table 11.

[0425] 4) Duration of power shortage.

[0426] The calculation results are shown in Table 11

[0427] 5) Load fluctuation

[0428] In the first operating mode, the calculation results are as follows: Figure 19 As shown in the figure, it can be concluded that in this operating mode, both the primary and secondary loads have large fluctuations, with the main fluctuations occurring between 5 and 8 o'clock and 22 o'clock. In the second operating mode, the calculation results are as follows: Figure 20 At this time, only the secondary load has a large fluctuation at 6 o'clock, and the primary and secondary loads are relatively stable at other times. In the third operating mode, the calculation results are as follows Figure 21 There are also large load fluctuations at 5:00 and 6:00, but the overall degree of fluctuation is somewhat lower than that of the second operation mode.

[0429] S4.2.4: Availability indicators

[0430] 1) Self-consistency rate R s

[0431] 2) New energy utilization rate R u

[0432] 3) Availability A

[0433] The calculation results of the above three indicators are shown in Table 11.

[0434] S4.2.5: Maintainability indicators

[0435] In an emergency, the system is repaired from its initial damaged state, with a repair rate μ, which is related to the system components, the skill level and number of repair workers, and the extent of the damage. To simplify the maintainability calculation, the possibility of further system failures is not considered.

[0436] 1) Maintenance Time MTTM (Maintain Time)

[0437] The calculation results are shown in Table 11

[0438] 2) Repair and restoration degree

[0439] Figure 22 As shown in the figure, starting from the initial system state 0.5, the three operating modes reach the fully repaired state after 6, 12, and 15 hours of maintenance respectively.

[0440] 3) Maintenance costs C

[0441] The calculation results are shown in Table 11

[0442] S4.2.6: Safety indicators

[0443] 1) Load loss severity

[0444] The calculation results are shown in Table 11

[0445] 2) Risk Index

[0446] The calculation results are as follows Figure 23 As shown in the figure, under emergency conditions, the risk indexes of the three different operating modes decrease in a step-by-step manner. When only the first and second level loads are met, the risk indexes are also higher than those in normal conditions.

[0447] 3) Duration of risk

[0448] The calculation results are shown in Table 11.

[0449] After the calculation in step 5, the subjective and objective weights, as well as the TOPSIS evaluation results, can be obtained as shown in Tables 9, 10, 12, and 13. It can be seen that under normal conditions, operating mode 3 has the highest score, but the gap with operating mode 2 is not large; under emergency conditions, operating mode 2 has the highest score, and there is a large gap with operating mode 3. Therefore, it is finally concluded that operating mode 2 is the best, operating mode 3 is the second, and operating mode 1 has the worst system performance.

[0450] Table 8 Normal RAMS index evaluation results

[0451]

[0452]

[0453] Table 9 Weights of normal RAMS index system

[0454]

[0455] Table 10 Evaluation scores

[0456]

[0457] Table 11 Emergency RAMS assessment results

[0458]

[0459] Table 12 Emergency RAMS evaluation index weights

[0460]

[0461]

[0462] Table 13 Evaluation results

[0463]

Claims

1. A RAMS assessment method for a highway transportation self-consistent energy system considering uncertain wind and solar power output, characterized by: The steps include: S1: Construct a microgrid architecture for a self-consistent energy system for highway transportation and analyze the basic "source-grid-load-storage" operation mode of the self-consistent microgrid system for highway transportation; S2: Based on the operation mode of the highway traffic self-consistent energy system, the models to be constructed are proposed: component fault state model, power generation side model, energy storage side model, load side model and system control strategy; S3: Use Latin Hypercube sampling technology to generate multiple scenarios and adopt synchronous back-substitution to reduce scenarios, thereby modeling and analyzing the randomness and volatility of wind power and photovoltaic power; S4: Construct evaluation indicators for the reliability, availability, maintainability, and safety of the highway transportation energy system, and propose a comprehensive evaluation model for the highway transportation self-consistent energy system RAMS based on the TOPSIS method and the grey correlation analysis method. The specific steps of step S4 are: S4.1: Normal RAMS evaluation index system S4.1.1: Reliability evaluation indicators: The following indicators are proposed to evaluate the reliability of the system: 1) Probability of insufficient power supply P ie,t The probability of insufficient power supply reflects the probability that the power generation on the source side cannot meet the demand on the load side at a certain moment or in a certain period of time; Where, P ie,t is the probability of insufficient power supply in 24 hours; pi t,s is the system state index, which represents the supply and demand relationship of sample s in the generated uncertainty-based supply and demand curve; sup,t,s ,λ load,t,s are the probabilities of the power generation and power consumption of sample s at time t; P sup,t,s 、P load,t,s are the power generation and power consumption of sample s at time t; P ie,ave is the average probability of power shortage; H is the time step, and the probability of power shortage in different time periods can be evaluated according to different H; 2) Low Energy Expectation (LOEE) The power shortage expectation represents the total amount of power that the system will lack at a certain moment or over a period of time. Similarly, the average of multiple scenarios is calculated using probability as weight to reflect the system power shortage expectation. 3) Expected effective power supply Indicates the total amount of electricity provided to the system load side and stored on the energy storage side; 4) Load fluctuation 5) Duration of power shortage S4.1.2: Availability evaluation indicators include self-consistency rate, new energy utilization rate, and availability; S4.1.3: Maintainability evaluation indicators include repair time, repair recovery level, and repair cost; S4.1.4: Safety assessment indicators include load failure severity, risk duration, and risk index; S4.2: Emergency RAMS evaluation index system The emergency state refers to the system state activated when the self-consistent energy system encounters extreme weather disasters or major accidents and the system is severely damaged. At this time, the system will give priority to ensuring the supply of primary and secondary loads; S4.2.2: Load Classification 1) Level 1 load The first-level load is the equipment that must be operated when the transportation system is in operation; 2) Secondary load Secondary loads are equipment that can optimize system operation when the transportation system is in operation; S4.2.3: Reliability assessment indicators include power supply shortage probability, power shortage expectation, power supply expectation, load fluctuation, and power supply shortage duration; The probability of insufficient power supply Where, P ie,t,1 is the probability of insufficient power supply for the first-level load; P ie,t,2 The probability of insufficient power supply to the secondary load S4.2.4: Availability indicators include self-consistency rate, new energy utilization rate, and availability; S4.2.5: Maintainability indicators include repair time, repair recovery level, and repair cost; S4.2.6: Safety indicators include load loss severity, risk index, and risk duration; S5: Based on the established RAMS assessment model, evaluate the four key characteristics of the system.

2. The RAMS evaluation method for a highway transportation self-consistent energy system considering uncertain wind and solar power output according to claim 1 is characterized by: The models to be constructed in step S2 are: component fault state model, power generation side model, energy storage side model, load side model and system control strategy. The specific models are: S2.1: Component Fault State Model: The time-varying nature of the device operating state directly leads to the time-varying nature of the system operating state. For a system with n devices, assuming that each device obeys the normal-fault two-state model, the total number of system states is N = 2 n , system status S k The instantaneous probability distribution and system state are S k =[s1,s2,…,s n ](2) Where W is the system state S k The set of components in normal working state; F is the system state S k The collection of components in a faulty state, The failure rate and repair rate of a component can be represented by a uniform distribution between [0,1]. The component state is obtained by randomly generating the probability characteristics of the component. Assuming that each component has only two states: normal operation and failure, and the states of each component are independent of each other, let d PF,i is the invalidity of the i-th element, and a random number U is drawn from a uniform distribution between [0,1]. i , then: Draw n random numbers and use formula (3) to determine the system state S; S2.2: Power generation side model: The power output curve of the equipment, the turbine hub height and the wind speed at the hub height are the determining factors of the wind turbine output power, which can be expressed as: Where: P w is the output power of the wind turbine; S is the actual wind speed at the hub height of the turbine; 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 wr is the rated power of the wind turbine; The output power of photovoltaic power generation equipment is affected by solar radiation intensity and temperature factors. The output power P of photovoltaic equipment is pv It can be expressed as: Where: P pvr is the rated power of the photovoltaic equipment; For photovoltaic construction areas (latitude ) hourly mean of surface solar radiation intensity at hour h on day d in month m; θ T is the power temperature coefficient of the photovoltaic device; T pv is the operating temperature of the photovoltaic equipment; T c is the reference temperature of the photovoltaic equipment; The output power of the gas turbine per unit time is expressed as: Where: P gt is the output power of the gas turbine per unit time; V is the volume of natural gas consumed by the gas turbine per unit time; Q is the combustion calorific value of natural gas; η gt is the power generation efficiency of the gas turbine; S2.3: Energy storage side model: The battery model considering self-discharge and charge and discharge power is: Where: β is the battery self-discharge power; t is the sampling point at a certain moment in the planning period; Δt is the minimum unit of the sampling interval; is the electrical energy stored in the battery at time t; is the charge and discharge power of the battery at time t; η cbat ,η dbat It is the battery charging and discharging efficiency; The battery state of charge model is as follows: Where: SOC t is the state of charge of the battery at time t; C bat is the rated capacity of the battery; Hydrogen energy storage models include: (1) Electrolyzer model: Large-scale hydrogen production uses an alkaline electrolyzer to electrolyze water into hydrogen and oxygen. The output power of the electrolyzer is: P eleo =η ele P elei (9) Where: P elei is the input power of the electrolytic cell; η ele is the electrolyzer efficiency; (2) Hydrogen storage tank model: The mathematical model of the hydrogen storage tank is: Where: The energy stored in the hydrogen tank at time t; is the output power of the fuel cell at time t; η fc For the efficiency of fuel cells; (3) Hydrogen fuel cell model The output power of the fuel cell is: P fc =η fc P h-fc (11) Where: P h-fc is the input power from the hydrogen storage tank to the fuel cell; S2.4: Load side model: The annual energy demand of highways and the energy consumption calculation model of infrastructure are as follows: Q=Q f +Q s +Q q +Q t +Q y (12) Where: Q is the total energy consumption of infrastructure on the highway; Q f is the energy consumption of the service area; Q s is the tunnel energy consumption; Q q is the energy consumption of the bridge; Q t is the energy consumption of the toll station; Q y Energy consumption of equipment along the line; (1) Service area energy consumption model: By analogy, the hourly energy consumption data of the service area is accumulated and calculated. The specific formula is as follows: Where: T is the time period, measured in hours, and is 8760 for the whole year; is the energy consumption of the ith service area on the highway for one hour; is the energy consumption of the jth parking lot on the highway for one hour; k and l are the number of service areas and parking lots on the highway respectively; (2) Tunnel energy consumption model: The tunnel should be equipped with ventilation and lighting systems, and its energy consumption model is: Q s =351L s +210782.8n s (14) Where: L s is the total length of the tunnel; n s is the total number of tunnels on the expressway; (3) Bridge energy consumption model: Highway bridges need to be equipped with lighting, monitoring, communication, and emergency power systems. The energy consumption model is as follows: Q q =140.438L q -438 (15) Where L q is the length of the bridge; (4) Toll station energy consumption model: The energy consumption of highway toll stations comes from toll collection, monitoring, lighting systems, and daily management and office work. Its energy consumption model is as follows: Q t =328n t (16) In the formula, n t is the number of toll booths; (5) Energy consumption model of equipment along the line: Energy-consuming devices along highways are vehicle detectors and emergency telephones. Based on the monitoring and communication requirements of highway trunk lines, a highway with a length of L kilometers requires (L-1) vehicle detectors and 2 (L-1) emergency telephones. The energy consumption model is: Q r =T(L t -1)(P c +2P d ) (17) Where, L t is the total length of the highway, P c is the power of the vehicle monitoring equipment, P d It is the power of emergency phone.

3. The RAMS evaluation method for a highway transportation self-consistent energy system considering uncertain wind and solar power output according to claim 1 is characterized by: In step S3, Latin hypercube sampling technology is used to generate multiple scenarios, and synchronous back-substitution method is used to reduce scenarios, thereby modeling and analyzing the randomness and volatility of wind power and photovoltaic power. The specific process is as follows: S3.1: Scenario Generation Technology Based on Latin Hypercube Sampling: When wind power forecasts follow a Weibull distribution, PV forecasts follow a normal distribution, and load forecasts follow a normal distribution, a multi-scenario set of wind power, PV output, and load demand can be predicted. The main steps are: 1) Divide the 24-hour probability distribution into n probability intervals to represent typical time periods of each day; 2) Random sampling is used within each probability interval, and the sampling results of each interval are made independent of each other. The probability of each interval can be expressed as: p in =p(x in ∈S in ) (18) Among them, p in is the sampling probability of variable i in the nth interval, where ∑p in =1;x in is the sample of variable i in the nth interval, S in is the threshold of variable i in the nth interval; 3) Inverse transform the probability distribution function to obtain the sampling value of the sampling point; the sample value corresponding to each subinterval is: Among them, x i is the sample value corresponding to each subinterval; is the inverse of the probability distribution function f(·); S3.2: Scenery reduction technology based on synchronous back-substitution: Since the generation of a large number of scenes will increase the computational burden of the solution, the synchronous back-substitution method is used to reduce the scenes. The reduced set of typical scenes can reflect the probability distribution of the original scene set. The specific steps are as follows: 1) Compare the distance between any scene and any other scene, and select the scene with the closest distance; Where D i For scene x i Probabilistic distance to any other scene; λ i For scene x i The probability of occurrence; d(x i ,x j ) for two scenes x i with x j Euclidean distance; n so is the number of scenes generated after Latin hypercube sampling; 2) Merge the scenes with the closest distance; Select the scene x that is closest to you i , as shown in formula (21); delete the scene with the closest distance above, and add the probability of this scene appearing to the scene with the closest distance x j On, delete the scene closest to x i Post x j The probability is shown in formula (22): λ′ j =λ j +λ i (22) Where D min For any scene and scene x i The nearest probability distance; 3) Repeat the above steps until the number of remaining scenes reaches a predetermined value; After the generation and reduction of scenarios, the final number of wind power scenarios, photovoltaic scenarios and load scenarios are n respectively. WT 、n PV 、n Load Finally, the typical output scenarios are combined into n s , and its probability and typical scenario probability are: n sup,s =n WT n PV (23) Where: WT ,λ PV ,λ load are the probabilities corresponding to wind power, photovoltaic and load scenarios respectively.

4. The RAMS evaluation method for a highway transportation self-consistent energy system considering uncertain wind and solar power output according to claim 1 is characterized by: The step S5 is specifically as follows: S5.1: Standardize the calculated RAMS indicators: For benefit-based indicators, the standardized formula is: For cost-based indicators, the standardized formula is: Where: x ij represents the jth index value of the i-th evaluation object, x′ ij Indicates the normalized index value, maxx ij is the maximum value of the j-th index, minx ij is the minimum value of the jth indicator; S5.2: Determine indicator weights: 1) Use the AHP method to calculate the subjective weight: Use the 1-9 scale and its reciprocal scale method to compare the elements of each layer, construct a comparison judgment matrix, and obtain the weight c (a = 1, 2, ..., 4) of the a-th criterion layer relative to the target layer, the weight b of the k-th indicator under the a-th criterion layer relative to the a-th criterion layer k (k=1,2,…,), then the weight of the kth indicator under the ath criterion layer relative to the overall goal is: d k =c×b k (81) When performing consistency test on judgment matrix, it is necessary to calculate consistency index When the random consistency ratio When , the matrix consistency test passes, otherwise it is necessary to reconstruct the judgment matrix to calculate the indicator weights; 2) Using the entropy method to determine objective weights: The method of using the concept of entropy to determine indicator weights is called the entropy method. Its essence is to use the amount of information provided by the entropy value of each indicator to determine the indicator weight. The specific calculation formula is as follows: In the formula: m is the evaluation object, n is the evaluation index, w i is the weight value of the i-th indicator, H j is the entropy of the jth index, A ij It is a standardized evaluation data matrix; 3) Calculate the combination weight: The combined weight w of the jth indicator of the i-th object j Defined as: Where: α j is the subjective weight, β j is the objective weight, S5.3: Establish a weighted normalization matrix with the following formula: S5.4: Determine the positive and negative ideal solutions using the following formula: Positive ideal solution: Negative ideal solution: Among them, the positive ideal solution is the set of optimal values of each indicator, and the negative ideal solution is the set of worst values of each indicator; U is the weighted normalization matrix, uij is the normalized value of the jth indicator of the i-th object; wj is the combined weight of the jth indicator; x′ ij is the value after normalization; S5.5: Calculate the Euclidean distance d + and d - , the formula is as follows: Where: Represents each solution to the positive ideal solution U + The Euclidean distance of Represents each solution to the negative ideal solution U - The Euclidean distance of ; M is the set of evaluation objects; S5.6: Calculate the grey correlation coefficient matrix. The calculation formula is as follows: Where R + Represents each solution and the positive ideal solution U + Grey correlation coefficient matrix, R - Represents each solution and the negative ideal solution U - The grey correlation coefficient matrix, ρ is called the resolution coefficient. The larger ρ is, the smaller the resolution is, ρ∈[0,1]; S5.7: Normalize the distance and association using the following formula: Where: is the Euclidean distance calculated in step S5.5, r i + 、r i - is the correlation calculated in S5.6; S5.8: Calculate relative closeness: Calculate the relative closeness of each solution and rank the RAMS performance of the evaluation object according to the size of the grey relational relative closeness. The larger the closeness, the better the solution, and the smaller the closeness, the worse the solution. The calculation formula is as follows: Where: F i + and F i - It indicates how close and far away the scheme is from the ideal scheme in terms of location and shape. μ and 1-μ reflect the decision maker’s preference for location and shape, and μ∈[0,1].

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 RAMS assessment method of a road transportation self-consistent energy system considering uncertain wind and solar power output as claimed in any one of claims 1 to 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 are caused to perform operations, wherein the operations include the process of a RAMS evaluation method for a self-consistent energy system for highway transportation considering uncertain wind and solar power output as claimed in any one of claims 1 to 4.

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