Nature Inspired Optimization For Electrical Power System
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Nature Inspired Optimization for Electrical Power System
This book presents a wide range of optimization methods and their applications to various electrical power system problems such as economical load dispatch, demand supply management in microgrids, levelized energy pricing, load frequency control and congestion management, and reactive power management in radial distribution systems. Problems related to electrical power systems are often highly complex due to the massive dimensions, nonlinearity, non-convexity and discontinuity associated with objective functions. These systems also have a large number of equality and inequality constraints, which give rise to optimization problems that are difficult to solve using classical numerical methods. In this regard, nature inspired optimization algorithms offer an effective alternative, due to their ease of use, population-based parallel search mechanism, non-dependence on the nature of the problem, and ability to accommodate non-differentiable, non-convex problems. The analytical model of nature inspired techniques mimics the natural behaviors and intelligence of life forms. These techniques are mainly based on evolution, swarm intelligence, ecology, human intelligence and physical science.
Applications of Nature-Inspired Computing in Renewable Energy Systems
Renewable energy is crucial to preserve the environment. This energy involves various systems that must be optimized and assessed to provide better performance; however, the design and development of renewable energy systems remains a challenge. It is crucial to implement the latest innovative research in the field in order to develop and improve renewable energy systems. Applications of Nature-Inspired Computing in Renewable Energy Systems discusses the latest research on nature-inspired computing approaches applied to the design and development of renewable energy systems and provides new solutions to the renewable energy domain. Covering topics such as microgrids, wind power, and artificial neural networks, it is ideal for engineers, industry professionals, researchers, academicians, practitioners, teachers, and students.
Nature-Inspired Optimization Algorithms for Cyber-Physical Systems
Cyber-physical systems (CPS) integrate computation, communication, control, and physical elements to achieve shared goals with minimal human intervention, encompassing smart technologies such as cities, cloud computing, and smart grids. As CPS components expand, generating vast amounts of data, they face challenges in areas like resource management, security, computation offloading, and automation, demanding advanced techniques beyond traditional algorithms. Nature-inspired optimization algorithms, drawing on natural phenomena, offer scalable and adaptable solutions for these complex issues, making them essential for addressing CPS challenges efficiently and enhancing their role in our daily lives. Nature-Inspired Optimization Algorithms for Cyber-Physical Systems provides relevant theoretical frameworks and the latest empirical research findings in the area. It explores the nature-inspired optimization algorithms intended to boost the performance of CPS. Covering topics such as ant colony optimization, data analysis, and smart cities, this book is an excellent resource for teaching staff, researchers, academicians, graduate and postgraduate students, and more.