RAMS assessment approach of self-consistent energy system in highway service areas

Self-consistent energy system (SCES) that integrates volatile renewable energy, challenges power system operation of highway service areas. How to evaluate its resilience and energy efficiency is a key issue for the entire SCES. In this paper, we investigate the assessment approach of SCES. Based on the electricity consumption model consisting of different functional sections of SCES in service areas, by deriving the corresponding three-level indicators from the different functional sections, we propose a new SCES assessment approach from four aspects: reliability, availability, maintainability, and safety (RAMS). Simulation results show that the proposed approach can comprehensively and accurately assess the RAMS of SCES, provides data support for improving the resilience and energy efficiency of SCES, and gives a guide for the development of SCES.


Introduction
In 2020, China officially put forward the strategic goal of "carbon peaking and carbon neutrality", which puts forward higher requirements for renewable energy power generation.With the growing proportion of wind, solar power, and other green energy power generation, the problems of the low utilization rate of natural resources, low energy utilization efficiency, and high energy safety risk in the power system have become increasingly prominent [1,2].The limited capacity of the existing power supply facilities in the highway service areas cannot meet the charging demand of the rising number of renewable energy vehicles.Therefore, SCES should be established as a supplement to the existing power grid.However, solar power, wind power, and other renewable energy generations have the characteristics of volatility and intermittency, which brings a high degree of uncertainty to the SCES in the service area, so the assessment approach of certainty can no longer adapt to the application requirements of the system [3].Therefore, it is necessary to propose an assessment system of SCES and provide decision-making opinions to improve the resilience and energy efficiency of the system.
At present, there are few pieces of research on the systematic assessment of SCES.In [4], Bie et al, proposed a fuzzy dynamic assessment approach to analyze the restrictive factors of renewable energy consumption.In [5], by using dynamic optimal energy flow (OEF), a reliability IES assessment framework is provided, which can reduce the degree of the unreliability of IES.In [6], Kang et al, proposed a standardized approach to multidimensional safety risk indicators by analyzing the characteristics of cluster failures in natural disasters.In [7], Mahadevan et al, proposed a reliability and risk assessment framework based on the uncertainties arising from the system when a high percentage of distributed power sources are connected to the grid and used Stochastic Optimization Algorithms (SOA) to assess the adequacy and flexibility issues related to the generating sets and economic scheduling.However, the assessment targets and assessment frameworks proposed in [5][6][7] can only meet the comprehensive assessment of a certain aspect of the SCES, and cannot fully reflect the characteristics of the system operation.
In this paper, a RAMS assessment approach is proposed to comprehensively assess the resilience and energy efficiency of the SCES [8]. the structure of this paper is subdivided into four parts, the first of which is dedicated to explaining the necessity for a new SCES.The second part will build a model of highway service area electricity consumption.In the third part, we will introduce the new RAMS assessment system of SCES.The last part presents the simulation results and the conclusion.

Electricity consumption model of the highway service area
The SCES model of highway service area mainly consists power side, load side, and energy storage side.The power side includes the power grid, wind power generation, solar power generation, diesel generator power generation, etc.The energy storage side is composed of batteries.The power load side includes the business zone, the car charging station, the management center, and the residential area.The electricity consumption model of the highway service area is shown in Figure 1.

RAMS assessment indicators of SCES in the highway service area
In this paper, a three-level RAMS indicator system of the SCES is proposed to reflect the operating conditions of each section during the system operating cycle, as shown in Figure 2. The first-level assessment indicator reflects the overall macro performance of the SCES in the operation process, indicating the overall operation of the whole SCES in the operation cycle or a certain period, including system resilience and energy efficiency.Based on the system attributes of the self-consistent energy system, the second-level assessment indicator explains the causes of the system resilience and energy efficiency indicator from the four indicators of reliability, availability, maintainability, and safety.
Corresponding to the attributes of the second-level, the third-level indices are introduced, showing the specific features of each section.

Reliability indicators
The reliability of the system can be assessed from the MTBR of each section and the SOH estimation of storage batteries.For new energy power generation with high uncertainty and load with high flexibility, the MTBF is carried out, as shown by Equation (1).The reliability indicators of the energy storage batteries are mainly the SOH estimation of the batteries.The reliability of the energy storage batteries is determined by calculating the battery capacity attenuation, as shown by Equation (2).
where N is the number of failures within the system cycle.T i is the time interval between the and i+1 failures.Q o is the real-time capacity of the battery, and Q N is the rated capacity of the battery.

Availability indicators
The availability of the system can be assessed from the ratio of available work (A 1 ), WECR (A 2 ), SECR (A 3 ), and URRE (A 4 ).A 1 reflects the resilience of the system.A 2 A 3 and A 4 reflect the energy efficiency of the system.The equations of A 1 , A 2 , A 3 , and A 4 are as follows. 1 where MUT is the normal working time within the working period.MDT is the abnormal working time within the working period.R WEC and R SEC are wind energy curtailment and solar energy curtailment.R WE and R SE are rated solar power generation and rated wind power generation in a given time.Q re , Q g , and Q e are respectively diesel generators, renewable energy generation, and power grid input energy to the system.

Maintainability indicators
The maintainability of the system is mainly assessed from MTTR and MCTR, which is related to the energy efficiency indicator of the system.MTTR and MCTR satisfy where t i is the i time of failure maintenance within the project life cycle, MRC is the cost of fault maintenance, and MEC is additional costs incurred during a breakdown.

Safety indicator
The average number of accidents is a subset of the average number of failures.Therefore, the average failure rate can be calculated by the average failure rate formula, and the average failure rate can be measured by the average observation time and the average number of failures.

The Second-level indicators of RAMS assessment of SCES
Reliability represents the ability of each section of the system to meet the usage requirements, can be expressed as where R is the availability of the system.MTBF i is the meantime without failure of the i-th item of the system.S SOH is the health status of the battery.i α and β are the weight of each item's indicators.
By assuming that all external resources are configured, availability can be calculated under the required function at a specified time interval or instant as follow where A is the availability of the system.A 1j is the ratio of available work of each project in the system.A 2 , A 3 , and A 4 are the system's utilization rate of renewable energy(URRE), wind energy curtailment rate(WECR), and solar energy curtailment rate(SECR), k A and k δ are the weight of each item's indicator.
Maintainability refers to the probability that a product is used in a given situation or in a specified period, which is defined as where M is the maintainability of the system.MTTR n is the mean time to repair the corresponding project.MCTR n . is the mean cost to repair the corresponding project.n e is the weight of the corresponding project.
Safety represents the possibility of a system operating safely within a given period of time under specified conditions, which can be written as where S is the safety degree of the system and H(t) is the average accident rate of the system.

The first-level indicators of RAMS assessment of SCES
In this paper, energy efficiency and resilience, which are opposite to each other, are selected as the macro indicators of the system operation state.Resilience represents the fault-bearing capacity of the system, including the redundancy and balance among the power side, energy storage side, and load side, which are reflected by reliability, availability, and safety.Energy efficiency represents the economy of the system, including energy economy and maintenance economy, which is reflected by average maintenance time, average maintenance cost, and renewable energy utilization rate.

Example analysis
In this paper, the RAMS assessment of the SCES is based on the data of the last 10 years of the highway service area in China.The data of the last 3 years are tested.The average value of the last 3 years, as the output, verifies the validity of the model proposed in this paper and the feasibility of the assessment indicator.

Reliability
The reliability of each section in the system is shown in Figure 3, which shows the relationship between the reliability of each section.3, except for the generators and energy storage batteries, the reliability of other sections has a clearly decreasing trend during this period.The reliability of the business zone, residential area, and management center has high consistency.Wind power and solar power, as the main way of renewable energy generation, their reliability curves have obvious time fluctuations.The reliability of energy storage batteries is mainly indicated by SOH estimation of batteries, so the reliability of energy storage batteries is strongly correlated with service time.

Availability
From Figure 4-6, except for solar power and wind power, the availability of the other sections is equal to the ratio of available work.WECR, SECR, and URRE are only anchored to wind power and solar power, and their availability has a greater variation.

Safety
The safety of the system is measured by the average incident rate, and the results are shown in Figure 10.Since the average accident is a subset of the average failure, there is some convergence in the safety and reliability corresponding to each section.Among them, wind power has a lower safety in summer, which is due to the influence of lightning strikes as well as rainfall in summer.Frequent sandy and dusty weather in spring and autumn significantly reduces the efficiency of solar power generation and affects the life span of solar power modules.The safety of energy storage batteries and generators is closely related to the ambient temperature, and their safety decreases significantly in summer.The safety curves of the business zone, car charging station, management center, and residential area as the power consumption side are close.

Comparison
Table 2 demonstrates the difference between the evaluation method proposed in this paper and other methods.The comparison clearly shows the superiority of the proposed method and indices in this paper.

Figure 9 .
Figure 9. Maintainability statistics of SCES Figure 10.Safety statistics of SCES

Table 1 .
WECR and SECR of SCES.MaintainabilityThe Maintainability data of each section is shown in Figures7-9.Wind power and solar power maintainability vary more with time.Energy storage battery maintenance time is stable.Generator maintenance time has no strong correlation with time.The business zone, car charging station, management center, and residential area as the living district have high similarity in their maintainability curves.