Reliability Assessment of Microgrid-Integrated Electrical Distribution System

Due to the transition of traditional power system to smart structure, integration of renewable energy sources is of great importance. It is performed by using distributed generators and Micro grid. Integration of renewable sources is very useful for the reliability enhancement of distribution system as energy can be supplied locally at distributed level. Therefore, this chapter provides an overview of various reliability methods which are utilized for finding the impact of renewable energy generation on distribution system reliability assessment. These renewable energy sources are integrated in the distribution system at distributed and Micro grid level. Various characteristics of renewable energy sources are discussed to model them. A general problem of reliability assessment in terms of reliability indices is also discussed.

Reliability Enhancement of Electric Distribution Network Using Optimal Placement of Distributed Generation

As energy demand is increasing, power systems’ complexities are also increasing. With growing energy demand, new ways and techniques are formulated by researchers to increase the efficiency and reliability of power systems. A distribution system, which is one of the most important entities in a power system, contributes up to 90% of reliability problems. For a sustainable supply of power to customers, the distribution system reliability must be enhanced. Distributed generation (DG) is a new way to improve distribution system reliability by bringing generation nearer to the load centers. Artificial intelligence (AI) is an area in which much innovation and research is going on. Different scientific areas are utilizing AI techniques to enhance system performance and reliability. This work aims to apply DG as a distributed source in a distribution system to evaluate its impacts on reliability. The location of the DG is a design criteria problem that has a relevant effect on the reliability of the distribution system. As the distance of load centers from the feeder increases, outage durations also increase. The reliability was enhanced, as the SAIFI value was reduced by almost 40%, the SAIDI value by 25%, and the EENS value by 25% after injecting DG into the distribution network. The artificial neural network (ANN) technique was utilized to find the optimal location of the DG; the results were validated by installing DG at prescribed localities. The results showed that the injection of DG at proper locations enhances the reliability of a distribution system. The proposed approach was applied to thte Roy Billinton Test System (RBTS). The implementation of the ANN technique is a unique approach to the selection of a location for a DG unit, which confirms that applying this computational technique could decrease human errors that are associated with the hit and trial methods and could also decrease the computational complexities and computational time. This research can assist distribution companies in determining the reliability of an actual distribution system for planning and expansion purposes, as well as in injecting a DG at the most optimal location in order to enhance the distribution system reliability.

Evaluation of Distribution System Reliability Indices Using Fuzzy Reasoning Approach

The most fundamental problems in the distribution system are the quality, the continuity, and the power supply. Political and economic changes were accompanied by changes in the structure of the electric load in the distribution network. Lack of investment and aging of the distribution company assets was accompanied by a decrease in the reliability of the distribution system. Identification and classification of assets from the point of view of their maintenance and replacement was one of the problems that were posed to the engineers. Fuzzy logic can be successfully used to evaluate distribution system reliability indices. In this paper fuzzy logic is used to evaluate the distribution system reliability indices of lines and transformers using six input variables. These variables considered the most important are: Age, Operation, Maintenance, Electrical current loading, Exposure and Weather conditions (Wind or Temperature). The fuzzy inferences knowledge-based IF-THEN rule is developed using Matlab Fuzzy software. The detailed analysis of the fuzzy system surfaces shows that the factors taken in consideration are dynamically and accurately connected to each other. The constructed rules based in engineering experience accurately represent the Reliability Indices.

Reliability and protection in distribution power system considering customer-based indices

A stable and reliable electric power supply system is a pre-requisite for the technological and economic growth of any nation. Nigeria's power supply has been experiencing incessant power interruptions caused by a failure in the distribution system. This paper developed a system planning approach as part of the key mitigation strategies for improved reliability and protection of the distribution network. The developed algorithm is tested using 33kV feeder supplying electricity to Kaura-Namoda, Zamfara State, Nigeria. A customer-based reliability index was used as a tool to evaluate the reliability assessment of the feeder test system. The result showed that alternative 3 gives better results in terms of improvement of the system average interruption duration index (SAIDI), which in turn gives the minimum interrupted energy. Also, it is found that a greater number of sectionalizing switches do not give better results. It is very important to place the sectionalizing switches at a strategic location. If it is located at such points that will facilitate to sectionalize the faulty sections faster and to make the supply available to the unfaulty part of the network. Hence the utility company should apply this mitigation algorithm for system reliability improvement, depending on their needs and requirements. Thus, utilities can optimize network performance and better serve customers by adopting mitigation strategies in addressing trouble-prone areas to achieve a stable and reliable supply Keywords: distribution system; reliability; reliability indices; system performance evaluation; protection system; mitigation algorithms and sectionalizing switches

Distributed Generation Impact on Distribution System Reliability

Reliability is the most important factor of distribution system and this system should be operated economically with low customer loads interruption. This is because that the distribution system gives supply to customers from transmission system. There are some power quality issues due to the failures of components in distribution system. Researchers are going on to assess the reliability of the power system. In the power system, reliability evaluation is an important aspect in complete electric distribution system planning and operation. Due to the extreme scale of problem, it is not possible to conduct reliability on complete power system, it is performed independently. Hence, In this paper, the reliability of distribution system is evaluated by using an analytical method is described and is applied to the IEEE RBTS BUS-6. Development of reliability model of distribution system using Electrical Transient Analyzer Program (ETAP) software is developed. And the Distributed Generation is introduced for the improvement of reliability. Reliability indices are such as System Average Interruption Frequency Indices (SAIFI), System Average Interruption Duration Indices (SAIDI), Customer Average Interruption Frequency Indices (CAIFI), Customer Average Interruption Duration Indices (CAIDI), Energy Not Supplied (ENS), Average Service Availability Indices (ASAI), etc. The performance of reliability of the system is shown by these indices

Information System Design for Calculating the Reliability of Electricity Distribution System in Pekalongan Substation Based on Android OS

Reliability of distribution networks is a factor that greatly affects customers as consumers of electricity. Analyzing and calculating the reliability of distribution networks are determined by the reliability index including SAIFI, SAIDI, CAIDI, and ASAI. On the other hand, smartphone technology is growing rapidly with a variety of applications to help simplify and accelerate human work in several fields of work. This paper delivers the design of an Android-phone-based analytic tool for distribution system reliability index measurement by developing it on Android application software. This application is named KALINDA, stands for Kalkulator Indeks Keandalan (Reliability Index Calculator), and created by using Android Studio IDE. We compare the data result between KALINDA calculations and manual calculations. The results obtained from the KALINDA application are declared to be valid accurate.