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About this book

Fuzzy logic has vast applications in power and electrical engineering. This collection is the first book to cover research advancements in the application of fuzzy logic in the planning and operation of smart grids. A global group of researchers and scholars present innovative approaches to fuzzy-based smart grid planning and operation, cover theoretical concepts and experimental results of the application of fuzzy-based techniques, and define and apply these techniques to deal with smart grid issues. Applications of Fuzzy Logic in Planning and Operation of Smart Grids is an ideal resource for researchers on the theory and application of fuzzy logic, practicing engineers working in electrical power engineering and power system planning, and post-graduates and students in advanced graduate-level courses.

Table of Contents


Chapter 1. Fuzzy-Based Optimal Integration of Multiple Distributed Generations

This chapter introduces an important multiobjective optimization strategy based on the algorithm of whale optimization (MOWOA) and fuzzy decision-making for efficient integration of several distributed generations (DGs) into radial distribution networks (RDNs). The optimum allocation of DGs to RDNs is applied to minimize power losses and voltage deviation (VD) and to optimize the voltage stability index (VSI) at the same time. The compromise solution of the optimum size and location of DGs is reached based on a fuzzy decision-making process. The MOWOA algorithm is approved using the IEEE radial distribution: 33- and 69-buses. The performance of the MOWOA is assessed by a detailed analysis with other competitive optimization techniques. The results indicate that the MOWOA with the fuzzy decision-making is successful in assigning a minimum power loss and convergence rates into the DGs allocation problem.
Ali Selim, Salah Kamel, Francisco Jurado

Chapter 2. Fuzzy Logic-Based Planning and Operation of a Resilient Microgrid

The uncertain nature of natural, man-made, and complex phenomena poses a challenge to microgrid (MG) functioning. In the face of such unexpected events, the power supply to the customer degrades and it becomes necessary to manage the performance of MG components. Therefore, the resilience of MG should be a priority. Resilience prepares the system to handle the operational loss and recover quickly to its pre-disturbance state. It is the ability to adapt to changing conditions, withstand it, and rapidly recover from uncertain natural disasters, man-made interruptions, and complex events termed as high-impact low-probability (HILP) events. MG planning and operation strategy for such HILP events enhances resilience. To analyze this strategy, the uncertain nature of MGs needs to be addressed. However, resilience study can be extended throughout the power system but is more suitable for MGs. It is due to the location of MGs at different terrains that makes it more vulnerable to HILP events. Crisp value of resilience parameter fails to capture the wide range of variations in MG behavior. To incorporate these significant variations, fuzzy-based resilience is required. The fuzzy-based resilience planning and operation is flexible and allows variabilities associated with changing environment. This chapter provides a comprehensive analysis of fuzzy-based resilience assessment for MG planning and operation against windstorm. Weibull wind assessment estimates the maximum likelihood of wind speed distribution in a particular region. Distribution lines are the exposed component during windstorm, so the probability of impacting the MG connectivity is very high. Therefore, this chapter focuses on distribution line fragility. The fragility curve of distribution lines depicts the wind speed-dependent failure probability. The region-specific wind profile of windstorms is mapped to the fragility curve of lines to obtain the time and hazard-dependent operational status. The Monte-Carlo probabilistic assessment measures this disruption status of lines by comparing the failure of lines as a function of weather parameter. To evaluate the influence of uncertain parameters on the operation and planning of MG, fuzzy-based system average interruption frequency index (FSAIFI), fuzzy-based system average interruption duration index (FSAIDI), and fuzzy-based average service availability index (FASAI) are calculated. For MG resilience planning, it is essential to assess the time-varying nature of these indices. The characteristics of these indices are thus assessed using the resilience triangle. It describes the resilience level of a system during each specific phase of the windstorm, which are pre-disturbance, degraded, and restorative stages. This analysis is tested on IEEE 33-bus system. Also, a comparative assessment of the resilience triangle and trapezoid approach for the IEEE 33-bus system is provided. This graphical representation of fuzzy-based performance parameters provides an insight into the impact of uncertainties on the MG under HILP events.
Sonal, Debomita Ghosh

Chapter 3. Evaluation and Assessment of Smart Grid Reliability Using Fuzzy Multi-criteria Decision-Making

Smart grid is a new paradigm that integrates traditional electricity grid and communication networks. Reliability is a critical challenge associated with smart grid and needs to be addressed. Based on comprehensive literature review and experts’ judgments, we develop a model to identify the most important criteria that have an impact on smart grid reliability from the perspective of users. The model takes into account three main criteria: “Big Data Management,” “Communication System,” and “System Functionality.” The fuzzy analytic hierarchy process is applied to analyze and prioritize these criteria based on the triangular fuzzy numbers and triangular membership function.
The results show that the “Big Data Management” main criterion has a significant impact on smart grid reliability, followed by “Communication System.” Furthermore, “Data Analytics” and “Data Visualization” were ranked as the most influential sub-criteria that influence smart grid reliability. Four various sensitivity analysis strategies have been applied to investigate the stability and robustness of results. This chapter provides meaningful implications and future research directions that are useful for many practitioners, engineers, academicians, and electricity policy makers to focus their efforts on smart grid reliability.
Ibrahim Mashal

Chapter 4. Fuzzy Realizations of Adaptive Autonomy in Smart Grid

Smartness is a prerequisite of autonomy; thus, the smart grid inherits a certain degree of autonomy due to its centralized, local, or distributed autonomous decision and control functionalities. Nevertheless, neither the smart grid itself nor the humans in/out/above the loop are ready for pressing the full autonomy red button. The main reason is the tacit nature of human knowledge necessary for systems operation, considering the capability and maturity levels of the present state of decision and control abilities of smart grid. Therefore, various levels of autonomy/automation (LOA) should be conditionally granted to the smart grid in a spectrum from non-automation to full-automation for different functions and situations, which is known as adaptive autonomy in human-automation systems literature.
This chapter is dedicated to the realization of the adaptive autonomy concept as an expert system, using the fuzzy set concept. Two adaptive autonomy expert systems are designed for distribution automation best performance and smart grid cybersecurity management implementations. Fuzzy systems are more suitable to grasp human expertise, especially due to their ability in representing real-world situations. Furthermore, intelligent systems generally, fuzzy system specifically, are capable of finding the right solutions for the new situations. Here, the fuzzy rule base and fuzzy inference engines are developed for both applications; subsequently, the performances of the resulting fuzzy expert systems are evaluated. Moreover, the gradient descent algorithm is expressed as an efficient method to optimize the adaptive autonomy fuzzy expert systems.
Morteza Khosravi, Alireza Fereidunian

Chapter 5. Application of Fuzzy Logic in the Operation of a V2G System in the Smart Grid

The rise in environmental concerns worldwide has led governments and organizations to look for sustainable technologies. The transportation sector is a major contributor to environmental pollution. Hence, electric vehicles for transportation are considered a technology that can reduce harmful emissions to the environment. EVs’ contribution is not just limited to reducing environmental degradation. EVs provide various other services such as distributed generation, voltage and frequency regulation, and many more, as reported in the literature. V2G technology can help to reap the full benefits of EVs. The book chapter will describe the V2G system and its integration with the smart grid using the fuzzy logic-based controller and the supporting entities. The controller of the V2G system is required to be robust and intelligent. The fuzzy logic controller fits the criteria of being used in the V2G system and has been implemented and tested successfully in various reported works in the literature. The design of a fuzzy logic controller and considerations for selecting the type and number of membership functions will be discussed in this chapter. The chapter will present the hybridization of the controller using artificial intelligence techniques and supporting algorithms. Further, the challenges in the real-time implementation of fuzzy logic-based controllers complying with the smart grid challenges will also be discussed. A detailed example of designing a V2G controller using fuzzy logic is presented, which will help the readers understand its design and deployment challenges. The designed fuzzy logic controller demonstrates its effectiveness in the robust and smart operation of a V2G system. Each section of the chapter is planned to give readers a detailed insight into developing a V2G system with a fuzzy logic controller and its entities.
Bikash Sah, Praveen Kumar, D. P. Kothari

Chapter 6. Fuzzy Optimization for Uncertain Power Market Operations

Handling uncertainties while partaking in electricity markets is a major concern for participating operators. Also, the abundance of prosumers in future distribution systems necessitates the need for a reliable optimization methodology that can handle possible uncertainties, such as those associated with renewable resources or electric vehicles, while participating in the electricity market. Fuzzy optimization offers a traceable and scalable solution that can alleviate this problem. In this chapter, fuzzy linear programming is introduced and applied to the case of a parking garage operator participating in the energy arbitrage market while satisfying the uncertain needs of the owners of electric vehicles (EVs). The uncertainties associated with the EV mobility, such as the EV type mix using the parking lot, their initial and final states of charge, and their departure time, are considered. In addition, another application is presented where a fuzzy optimization problem is formulated for the case of a virtual power plant (VPP), operating a mix or resources and loads. The purpose of the problem formulation is to provide an optimal bidding strategy for a VPP participating in the wholesale markets while considering the uncertainties associated with wind and solar generations. The main objective is to introduce a framework that maximizes the VPP’s profits.
Ali T. Al-Awami, Samy Faddel, Ammar Muqbel


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