An Innovative Reliability Oriented Approach for Restructured Power System Considering the Impact of Integrating Electric Vehicles and Renewable Energy Resources

Attributing to the irreplaceable quality of nil carbon footprints, the power system considering the integration of electric vehicles and renewable energy resources are appealing growing attention in the recent time, but the reliability of their associated components, is a main matter of concern nowadays. Therefore, in this paper, a novel incentive based fuzzy fault tree analysis (NIBFFTA) approach for restructured power system considering the influence of integrating Electric vehicles and Renewable energy resources-based hybrid wind-solar energy is presented. The approach combines the impacts of different components failure rate and the incentive Gaussian distribution effects under the inducement fuzzy fault tree atmosphere for the grid integrated Renewable energy resources and Electric vehicles configurations. In the basic fault tree analysis, the vague and inaccurate events such as system switches and low power component failures could not be identified competently. Moreover, the probability values of fault occurrences in the complete power system are not considered into account. Additionally, it is quite hard to have a precise assessment of the grid-connected wind energy power systems and EV configuration failure chances or the possibility of occurrence of undesired actions in the complete system because of data deficiency. To overwhelm these demerits, a novel incentive based fuzzy fault tree analysis based on the Gaussian distribution and fuzzy set model is recommended and used for the restructured power system considering the impact of integrating Electric vehicles and Renewable energy resources. Besides, the probability analysis of fault occurrence is also proposed to determine the impact of each basic event of the proposed system on the top event. Furthermore, the prediction analysis of fault occurrence is also done to know the effect of every basic action of the system on the top action. Whereas, prediction analysis factors for the different basic events of the proposed systems are evaluated and these can be used to obtain the real consequence of basic events on the proposed system. It is found that a novel incentive based fuzzy fault tree analysis approach is more significant and efficient than the conventional fault tree method for risk assessment of restructured power system with integrating the electric vehicles and renewable energy resources.


I. INTRODUCTION
Wind and solar power sources are taking up the role of power generation as their level of penetration increases day by day. The combined effect of wind and solar powered generator on entire power system sufficiency is dependent on many factors which includes the probabilistic demonstration of wind and solar power sources, reliability of the systems and capacity of wind and solar power systems [1]. A framework has been proposed in past to know the power system reliability including the impact of various components uncertainties and reliability model is formulated for power converters in the renewable energy system [2]. Whereas the trend of more power accountable travel is causing in increasing more towards the Renewable energy resources [3]. Moreover, with the growing permeation of Electric vehicles, more connections seem between the power system and transportation system, which may give new treats for the propagation of fault occurrence in the limits of various systems, hence risk assessment method is needed for the power system which includes the Electric vehicles and Renewable energy resources [4]. The reliability improvement of power systems including the effect of renewable power resources connected into the grid with Furthermore, a general structure of charging station of electric vehicles has been proposed to know the power electronics based elements reliability in Electric vehicles [25] and hybrid Electric vehicles reliability observation is also important for design, control, planning and management of Electric vehicles [26], [27]. Moreover, energy conversion methods have been introduced for renewable based generation that allow every element of renewable energy sources to operate as suitable point that gives maximum power [28]. Further, Latin hypercube sampling approach has been proposed for assessment of reliability of power systems which includes the wind-solar power sources, with a prominence on the bus loads changes and unpredictable behavior of wind-solar power productions [29] and a novel approach has been proposed to assess scheme generating capability reliability indices including the time reliant loads and power sources [30]. The goal of this article is to evaluate the influence of integrating wind energy with small hydropower plants on the power system's reliability [31] and many stochastic computational methods to fuzzy system have been introduced [32], [33], [34]. Whereas, Due to the continued rise of wind and solar generators, the contribution of renewable energies in power networks is increasing. Traditional reliability evaluation methods are inapplicable because to the intermittency and uncertainty of these resources therefore new approaches have been introduced [35], [36], [37]. Through qualitative or quantitative research, fault trees are commonly used to analyze the unreliability of electrical or mechanical systems [38], [39], [40].
Hence different methodology is utilized to get the chosen operation of wind-solar power sources, Also, a reliability valuation framework has been proposed to know the impact of solar-wind power sources and electric vehicles integration into the power grid, because the data received is inadequate: therefore, the present approach is proposed. Previous methods have not provided the good outcomes because of the insufficient data and computational burden. Hence, in this article, an innovative reliability-oriented approach has been proposed for restructured power systems considering the impact of integrating Electric vehicles and renewable energy sources. This technique is based on novel incentive built fuzzy fault tree analysis to overcome the practical reliability investigation problem of solar-windelectric vehicle connected power system. This approach is applied to obtain the different reliability indices and fault chances of the top event failure with varying the load as well. The obtained outcomes are compared with different previous methods like electrical loss minimization technique, chronological multiple state probability model, system state generating method and probabilistic minimal cut-set-based iterative methodology, but these techniques involve the lengthy process and need extra time to now the reliability of the system as compared with the proposed technique. Besides, prediction analysis factor for the different basic events is calculated and it could be used to get the real consequence of basic events on the proposed power system. As found from the outcomes of the proposed system, it could be seen that the integration of wind and solar power system with electric vehicles into the grid can improve the reliability of the entire system, particularly when conventional thermal power plant is associated with the grid. So, it is important to increase more renewable energy sources to maintain the VOLUME XX, 2017 1 reliability of the system when Electric vehicles are associated to the distribution networks. In this paper, solar-wind power and electric vehicles integrated grid-connected system is presented in Section II. In Section III, a novel incentive based fuzzy fault tree analysis (NIBFFTA) approach is proposed however, NIBFFTA based risk assessment approach for power system including the wind-solar energy systems and Electric vehicles is discussed in Section VI. In Section V, case study with the proposed NIBFFTA based risk assessment approach is presented. Finally, concluding statements are given in Section VI.

II. SOLAR-WIND POWER AND ELECTRIC VEHICLES INTEGRATED GRID-CONNECTED SCHEME
The present belongings of the conventional energy sources would be finished soon reason being the depletion of fossil fuel reserves and major portion of this reserve is being used by transportation sector, therefore, the power system is forced to use the Renewable energy resources. Hence, Electric vehicles electrification with grid connected solar-wind energy system is the best way to abate their reliance on fossil fuels. The use of renewable energy-based power system reduces the carbon footprints also, as their units need not to be consumed by thermal power plant. Therefore, electrification of electric vehicles with grid-connected windsolar energy system plays a noteworthy role in decreasing the greenhouse effect. Though, Electric vehicles conversion increases the load and it affects the power system which would finally affect the system reliability. Therefore, the reliability assessment of restructured power system taking the impact of integrating Electric vehicles and renewable energy sources need to be investigated. Fig. 1 shows the solar-wind power and electric vehicles integrated grid-connected scheme [5]. In the spring and fall, wind power is at its peak. When solar panels are set off at night, wind turbines start in, hence it works both day and night. There is no need of adding the extra storage system in the proposed system, it would increase the overall cost. Moreover, the power from the grid can also be utilized when there is less power from the solarwind energy systems. Fig.1. Solar-wind power integrated grid connected system with electric vehicles Different data of solar energy system, wind energy system and electric vehicles are used to investigate the proposed NIBFFTA approach. The electric vehicle is composed of a motor and as related controller of the motor. The motor is used to drive the vehicles wheel with the rear axle. According to the need of driving system, controller is used to control the direction, speed and torque of the associated motor. Moreover, the electric vehicles charging need may affect the power system, for prize limits, lower number of switches can be used with the drive controller system. For installing the wind-solar energy-based system, firstly good area is chosen so that the system reliability could be enhanced. The area is selected where precise and significant solar irradiation and speed of wind is found. The different methods can be used to predict the solar radiation and wind speed. These can give the information about the sunlight receive and wind speed throughout the year and then the load can be calculated in accordance with that size of wind power system and solar power system. The particular site and size of the plant are important to feed the power to different users. Among the different energy resources available at present the wind and solar energy-based system are best suited reason being the abundant obtainability of wind and solar power and these sources generates less pollution, therefore these sources decrease the carbon footprints and hence, decrease the global warming. Hence, this work presents the risk assessment of the power system including the renewable energy resources and Electric vehicles. The key faults of the solar-wind power and Electric vehicles integrated gridconnected scheme are as follows:

III. NOVEL INCENTIVE BASED FUZZY FAULT TREE ANALYSIS (NIBFFTA) APPROACH
Reliability model is the base and foundation for reliability assessment of the system. Therefore, a novel incentive based fuzzy fault tree analysis method has been proposed to know the reliability of power system taking the impact of windsolar power systems and EVs. The solar-wind and Electric vehicles-based system configuration fault rate could define the discrete distribution firstly. The reliability of the system increases as the wind-solar and electric vehicles insertion increases in the power system. To know the reliability of the system firstly following process is adopted.

Phase 1
Know the real value of N(k) factor of every component of the grid connected wind-solar energy system and Electric vehicles.
Phase 2 Apply the consequences of phase 1 and estimate the various factors.
Where, F and P are the force and prediction factor, u represents the number of units of system and e represents the forced outage value when extra units of different systems are added.
Phase 3 Apply the consequences of phase 1 phase 2 and assess the factors.  The fuzzy fault tree method is added with the above proposed technique after knowing the failure data from the above analysis , these data is used in fuzzy fault tree. Basically, the fuzzy fault tree technique is based on fuzzy sets theory.
The suspicious of rates of failure of the basic event and top event occurance chance, the fuzzy number is taken to know the probabilities of the different events and to lower the complexitity in the probability calculation. Many types of fuzzy number are used for this calculation but for easy algebra operation in this study, the triangular fuzzy number is chosen. The memebership function is used to know the probability of the different fault events. The proposed function is defined as The array (b, , ) is used to define the proposed function, where mean of the number is b, and , are the distribution curve left and right end points respectively.
The event A in fuzzy number is defined as: The event chance occurance for the specfic confidence level § (0 ≤ § ≤ 1), is presented as: Where the confidence level is § .
Additionally, information from the professional could be taken to get the effect of the different faults on the power system. This proposed technique is used for risk evaluation of grid-connected solar-wind energy system and Electric vehicles.
Furthermore, the fault tree digaram is made for the proposed power system which considers the effect of wind-solar based power systems and Electric vehicles that shows the procedure from the top event to bottom event for the entire proposed system.
After considering some records of failure and experts views, basic event failure is calculated. T he proposed approach provides the failure rates of rest events. Hence, this approach is used for risk analysis of the power system taking the effects of solar-wind energy systems and Electric vehicles and it is more adaptive and flexible for the reliability assessment.

IV. NIBFFTA BASED RISK ASSESSMENT APPROACH FOR POWER SYSTEM INCLUDING THE WIND-SOLAR ENERGY SYSTEM AND ELECTRIC VEHICLES
This method provides the probability of all the events from basic to the top, for this purpuse, ten types of systems are considered i.e. grid-connected power system with Renewable energy resources and different configurations of gridconnected system with renewable power resouces and electric vehicles. Fig.2 represents the flow chart of the novel incentive based fuzzy fault tree analysis-based gridconnected solar-wind and Electric vehicles system risk assessment method [5]. The following steps have been taken for the application of proposed NIBFFT based risk assessment for the power system.

i. Proposed modeling and organization
The ten grid-connected solar-wind and electric vehicles system alternatives are chosen for the analysis purpose as shown in Table 1. IEEE 69 Node Test Feeder is used for this study and the optimal locations are found by algorithm of optimal placement of different energy sources and Electric vehicles, the solar power system is connected to bus 27, wind power system is connected to bus 29, and electric vehicles is connected to bus 40. The large-scale electric vehicles insertion in the distribution network increases the load in the system hence power quality and reliability are affected.
Therefore, the various factors related to the electric vehicles power demands are considered in this work. Different 10 alternatives have been taken in this work, P1 is original system with 15 MW solar power system and 15 MW wind power system, alternative P2 is the 15 MW solar power systems, 15 MW wind power system and 2 MW Electric vehicles configurations. Similarly in the next alternative, 2 MW Electric vehicles are considered with increasing the capacity of solar and wind energy systems gradually.
Hence, firstly, problems identification, data collection and problem solution are investigated. After that different parameters and failure rates are calculated. The failure rate sequences, top and basic events are identified with the proposed approach. After that, fault tree is constructed based VOLUME XX, 2017 1 on the above analysis and risk analysis is done with increasing the solar and wind energy systems capacity. Finally, risk assessment is done based on the proposed NIBFFT method, reviews the results and made decisions.  Data collection are done by gathering information related to solar energy system, wind energy system and Electric vehicles by brush up old records, noticing the operation of the grid connected system with Renewable energy resources and electric vehicles, conducting the survey and taking the views of experts. The outcomes show that the damage in the solar panel, wind generation, electric vehicles motor is frequently happening failure in the system.

ii. Fault tree construction based on the proposed method and identification of risk factors
The impacts of inserting the renewable energy resources and electric vehicles on the reliability of power system are discussed in the proposed work. It includes some advantages and disadvantages when these sources and Electric vehicles are inserted conventional electric power system. Though, the advantages are predominant because of unlimited and costeffective solution. Reliability is the main factor for the researches in this field therefore after getting the previous failure data and expert's opinions, top and basic event failures are determined. The major events failures in the gridconnected solar-wind and Electric vehicles system are as follows: • Solar power system faults (SPSF) • Wind power system faults (WPSF) • Electric vehicles faults (EVF) Therefore, fault tree diagrams are constructed for the proposed power systems that show the top to basic events. Figure represents the fault tree diagram of the grid-linked power system with wind-solar energy resources without the Electric vehicles according the structure and different fault happened in the solar-wind power system. The top event is the solar-wind power system fault and basic faults in the subelements in the system are divided into the basic events.
In the proposed work the task period is chosen as 2 years relaibilty assesement of the entire system.  The key issue in calculating the chances of failure of the gridconnected wind-solar energy resources and Electric vehicles is the insufficient data of different element failures within the entire system. Therefore, the proposed technique is used to overcome the issue. The experts provide the failure probability of all evenets. There are known failure rates of 20 basic events from the research papers and database. The rest events failure rates are to be determined by the proposed method. Fig 3 shows the fault tree model of grid connected renewable energy system with the Electric vehicles according the structure and different fault happened in the solar-wind power system and Electric vehicles.

iii. Basic event failure incident valuation
The probability of occurrence of all the basic events is done and consequences are investigated to know the probability of fault occurrence. The basic events failure rate is considered as fuzzy numbers and the outcomes are changed into the probability scores.  The ranking of fault occurrence can be calculated from the consequence. Therefore, this technique could be used for choosing the proper value of wind-solar and electric vehicles system. The proposed method is reliable and robust especially when most of data are unidentified. The failure risks and corresponding fuzzy numbers are determined and expert views are taken for knowing the rest failure rates.  Table 2 displays fault tree analysis of the solar-wind-Electric vehicles systems. Whereas Table 3 shows failure risks of basic events by the opinion of skilled persons. It can be seen from the table that the expert's opinion is different of all basic events therefore aggregation calculation is carried out using the opinions of experts. VOLUME XX, 2017 1 iv.

Risk assessment based on novel incentive based fuzzy fault tree analysis method and management of risk issues
Qualitative proposed fault tree method of different types of systems with probability of top event and basic fuzzy tree analysis is displayed in fig 3. Basic events fuzzy probability scores are calculated for the different basic events of the proposed systems (P1 to P10) and they are presented in Table  5 and Table 6. Defuzzification values based on NIBFFTA are also calculated and are presented in Table 7. After that proposed risk analysis technique is used to know the effect of different basic events on the entire system, it can damage the whole system. Hence, with the proposed NIBFFTA technique, top event probability can be known so the idea of entire system condition can be judged easily.
The probability analysis of fault occurrence is also done to know the effect of every basic action of the system on the top action. The prediction analysis factors for the different basic events of the proposed systems are calculated and it can be used to know the actual effect of basic events on the entire system. The Prediction analysis factors for the different basic events of the proposed systems are presented in Table 2 and  -04 for the alternatives, P2, P3, P4, P5, P6, P7, P8, P9, and P10 respectively and it belongs to the overcharge issue in battery of electric vehicles. This factor could lower the risk limit by knowing the critical issue. These presented values permit the manufacturer to make the suitable component of solar-wind energy system and Electric vehicles to lower the uncertainties and different risk issues. The proposed approach helps in getting the range of failures and to describe the failure rate. With this analysis, the most suitable and reliable solar-wind-Electric vehicles components could be chosen and hence the rates of failure of components could be lowered.  Therefore, from the reliability assessment consequences, top event probability for different kinds of proposed alternative systems is presented in Table 7. Fig.4 shows the top event probability along with basic FTA of proposed different alternatives systems [46]. The proposed work involves the solar power system with rating from 15MW to 95 MW, wind power system with rating from 15MW to 95 MW and 2 MW Electric vehicles. The alternative 1 is original system without adding the extra solar and wind power systems and Electric vehicles. Reliability indices are calculated for the different alternative as large Electric vehicles connection in the distribution network raises the load, therefore, reliability is affected. Hence, solar and wind power system are connected to different buses to enhance the reliability of the power system. Gradually, solar and wind power systems are inserted into the buses and reliability indices are monitored, it can be seen that by inserting the renewable power sources in the power system with Electric vehicles enhances the reliability of the system. The large-scale Electric vehicles insertion in the distribution network increases the load in the system hence power quality and reliability are affected. Therefore, the various factors related to the Electric vehicles power demands are considered in this work.   Table 8. Fig 5 and fig 6 show the reliability indices with proposed NIBFFTA technique for the different alternatives. It could be seen from the Table 8 that LOLP is 0.1728 % with original arrangement i.e., 15 MW wind power system is connected to bus 29 and 15 MW solar power system is connected to bus 2 and LOLP is 0.1732% with 15 MW wind power system is linked to bus 29 and 15 MW solar power system is linked to bus 27 and 2 MW Electric vehicles is linked to bus 40. Therefore LOLP is increased with electric vehicle is linked to the system it affects the reliability but in alterative 3 when solar and wind power insertion into the power system increase with the same value of Electric vehicles i.e. 25 MW wind power system are used and 25 MW solar power system are taken with original 2MW Electric vehicles, the LOLP is 0.1712%. Hence, when renewable energy sources insertion increases in the power system, the LOLP is decreased. Similarly, for the reliability indices LOLE, for alternative 1 when 15 MW solar and 15 MW wind energy systems are used with the grid, it is 52.17 but for alternative 2 when same value of renewable energy sources are used as in alternative 1 but 2 MW Electric vehicles are added, the LOLP is increased i.e., 54.62. However, for alternative 3, when wind and solar power systems values are increased, the LOLP is 52.31, therefore by insertion the more power from the renewable power resources, the realibility is increased. The MTTF is 4.6 for the alternative 1 i.e., without Electric vehicles, but it is 4.3 when extra Electric vehicles are added. Hence, Electric vehicles connection with the grid affects the reliability but for alternative 3, the MTTF is 5.7, therefore by inserting the more wind-solar energy systems the reliability of the system increases. Moreover, the rate of failure for alternative 1, 2, and 3 are 27, 28.1, 26.6 respectively. It can be seen from the values of rate of failure for alternatives 1, 2 and 3, firstly rate of failure increases when Electric vehicles are added in the system, but by giving more power from the renewable sources into the grid with the same Electric vehicles, the rate of failure is decreased. For better reliability of power system, the mean time failure should be lower and rate of failure should be higher.
As seen from the results of different alternative in Table 8, the consequences shows that the integration of wind and solar power system into the grid with Electric vehicles can enhance the reliability of the system, exclusively when conventional thermal power plant is connected with the grid.        Table 11 represents the reliability indices MTTF (yr.) and rate of failure with proposed NIBFFTA technique for the different components of proposed system. These are unidentified events, the mean to failure easily calculated by the proposed techniques. The rate of failure also changes with the proposed system. If the aggregated value of mean time to failure and failure rate are taken as shown in Table 8, then with increasing the value of solar and wind energy system, the value of MTTF increases and rate of failure is decreased. Hence, the different capacities of solar, wind and Electric vehicles systems are determined and integrated to the system to make the different alternatives. Hence, it can be concluded form the values of reliability indices LOLE, LOLP, MTTF, rate of failure, and EENS, that the renewable energy sources insertion into the power system can improve the reliability, especially when Electric vehicles are connected in the system. Therefore, it is important to add VOLUME XX, 2017 1 more renewable energy sources to maintain the reliability of system when Electric vehicles are connected to the distribution networks. The comparison of realibility indices is shown in Table 12, the EENS is basically related to the energy over a provided zone and during a provided time period which is probably low due to lack of resources to meet the required demand. It shows a matrix which can be used as security measure of power supply and it is also used to set the standard of reliability in the power market. There are various features about the EENS cost, about the measurement of economics losses and in dealer money losses because of not providing the expected energy. Therefore, mainly the expanding the population density in terms of consumers caused the collective growth of power consumption, significance of the calculation of EENS becomes more essential. Here, in this analysis, the ENNS is taken as a reliability index for comparison and it could be low for better system reliability. Whereas, Fig. 10

VI. CONCLUSION
In order to get a more reliable prediction of the overall power system considering the impact of integrating electric vehicles and renewable energy systems, a detailed investigation of the reliability problems in the proposed system is presented in this paper by using the novel incentive based fuzzy fault tree analysis (NIBFFTA) approach. The basic events fuzzy probability scores and prediction analysis factor for the different basic events of the proposed systems are calculated, furthermore, the probability analysis of fault occurrence is also proposed to determine the effect of each basic event of the system on the top event. The prediction analysis factor for the different basic events of the proposed systems is calculated and it can be used to get the real consequence of basic events on the proposed system. Moreover, grid-integrated solar-wind energy systems and electric vehicles with the various components are considered with the proposed NIBFFTA method to know the reliability of system while changing the capacity of solar, wind and Electric vehicles system. Hence, total 10 alternatives are considered. Further, reliability indices such as LOLE, LOLP, EENS, MTTF, failure rate and top event failure probability with for the proposed different alternatives are calculated. Besides, the reliability index EENS with proposed NIBFFTA technique for the different alternatives with changing the load are also calculated. Furthermore, reliability indices MTTF (yr.) and rate of failure with proposed NIBFFTA technique for the different components of proposed grid connected solar-wind Electric vehicles system are also determined. As found from the consequences of different alternative, it could be seen that the integration of wind and solar power system into the grid with electric vehicles can enhance the system reliability, particularly when conventional thermal power plant is linked with the grid. Consequently, it is important to add more renewable power sources to maintain the system reliability when electric vehicles are connected to the distribution networks. The proposed method is likely to be scalable to big grid-connected solar and wind power systems with Electric vehicles, changeable to different types of solar and wind power systems with other renewable energy resources where the reliability is the vital factor in the design of the system.