Optimal Planning Of Smart Grid With Renewable Energy Resources

Understanding the recent developments in renewable energy is crucial for a range of fields in today’s society. As environmental awareness and the need for a more sustainable future continues to grow, the uses of renewable energy, particularly in areas such as smart grid, must be considered and studied thoroughly to be implemented successfully and move society toward a more sustainable future. Optimal Planning of Smart Grid With Renewable Energy Resources offers a detailed guide to the new problems and opportunities for sustainable growth in engineering by focusing on modeling diverse problems occurring in science and engineering as well as novel effective theoretical methods and robust optimization theories, which can be used to analyze and solve multiple types of problems. Covering topics such as electric drives and energy systems, this publication is ideal for researchers, academicians, industry professionals, engineers, scholars, instructors, and students.

The book deals with integrated distributed energy resources in existing power systems optimally to mitigate power quality issues in power systems. The book is designed for research using modern optimization techniques and a thorough analysis of renewable energy. The book provides an in-depth study of recent trends of research scope around the globe and also includes modern heuristic approaches, hands-on data, and case studies of all important dimensions of distributed energy resources. It addresses key issues such as the integration of DERs and electric vehicles, optimization algorithms, management of DERs with electric vehicles, energy pool management mechanisms, protection, and reliability in the restructured power system. This book will be useful for students, research scholars, practitioners, and academicians.

This book discusses the recent developments in robust optimization (RO) and information gap design theory (IGDT) methods and their application for the optimal planning and operation of electric energy systems. Chapters cover both theoretical background and applications to address common uncertainty factors such as load variation, power market price, and power generation of renewable energy sources. Case studies with real-world applications are included to help undergraduate and graduate students, researchers and engineers solve robust power and energy optimization problems and provide effective and promising solutions for the robust planning and operation of electric energy systems.

This book analyzes the economic and technical effects of demand response programs in smart grids. A variety of operational and financial benefits are offered by demand response programs (DRPs) for load-serving entities, grid operators, and electricity consumers. The most notable advantages of DRPs are presented in this book, including decreased electricity prices, risk management, market power mitigation, and flexibility of market operations. In-depth chapters discuss the integration of demand response programs for the planning and operation of smart grids and explore the uncertainties of market prices, renewable resources and intermittent load management, making this a useful reference for a variety of different organizations and players in the electricity market, such as reliability organizations, distribution companies, transmission companies, and electric end-users.

The surge in renewable and distributed energy sources has posed significant challenges for smart power distribution network (SPDN). These challenges fall into two main categories: the unpredictability of renewable energy sources and the complexities introduced by numerous electrical devices and their interdependencies, affecting forecasting and operational performance. As the emphasis on SPDN's economic and environmental aspects grows, this book focuses on the vital themes of sustainability and cost-efficiency in SPDN forecasting, planning, and operation. It is structured into three key parts: 1. SPDN Situation Awareness: This section assesses prior research methods, analyzes their shortcomings while dissecting SPDN's unique situation awareness characteristics. Then, some forecast and virtual collection methods are presented. 2. Boosting SPDN Planning: Addressing optimal planning challenges in SPDN, this part introduces advanced modelling and algorithm solving techniques, tailored to mitigate SPDN's inherent uncertainty. 3. Enhancing SPDN Operation: Considering a variety of equipment types and controllable loads, this section explores strategies to boost SPDN operational performance. It covers control methodologies for electric vehicles, flexible loads, energy storage, and related equipments, etc. Tailored for university researchers, engineers, and graduate students in electrical engineering and computer science, this book is a valuable resource for comprehending SPDN's situation awareness, planning, and operation intricacies in the context of sustainability and economic efficiency.

Distributed Energy Resources in Local Integrated Energy Systems: Optimal Operation and Planning reviews research and policy developments surrounding the optimal operation and planning of DER in the context of local integrated energy systems in the presence of multiple energy carriers, vectors and multi-objective requirements. This assessment is carried out by analyzing impacts and benefits at local levels, and in distribution networks and larger systems. These frameworks represent valid tools to provide support in the decision-making process for DER operation and planning. Uncertainties of RES generation and loads in optimal DER scheduling are addressed, along with energy trading and blockchain technologies. Interactions among various energy carriers in local energy systems are investigated in scalable and flexible optimization models for adaptation to a number of real contexts thanks to the wide variety of generation, conversion and storage technologies considered, the exploitation of demand side flexibility, emerging technologies, and through the general mathematical formulations established. Integrates multi-energy DER, including electrical and thermal distributed generation, demand response, electric vehicles, storage and RES in the context of local integrated energy systems Fosters the integration of DER in the electricity markets through the concepts of DER aggregation Addresses the challenges of emerging paradigms as energy communities and energy blockchain applications in the current and future energy landscape Proposes operation optimization models and methods through multi-objective approaches for fostering short- and long-run sustainability of local energy systems Assesses and models the uncertainties of renewable resources and intermittent loads in the short-term decision-making process for smart decentralized energy systems

This book gathers selected high-quality research papers presented at International Conference on Renewable Technologies in Engineering (ICRTE 2022) organized by Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India, during October 7–8, 2022. The book includes conference papers on the theme ‘Computational Techniques for Renewable Energy Optimization,’ which aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of renewable energy integration, planning, control, and optimization. It also provides a premier interdisciplinary platform for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of renewable energy and resources.

This brief focuses on stochastic energy optimization for distributed energy resources in smart grids. Along with a review of drivers and recent developments towards distributed energy resources, this brief presents research challenges of integrating millions of distributed energy resources into the grid. The brief then proposes a novel three-level hierarchical architecture for effectively integrating distributed energy resources into smart grids. Under the proposed hierarchical architecture, distributed energy resource management algorithms at the three levels (i.e., smart home, smart neighborhood, and smart microgrid) are developed in this brief based on stochastic optimization that can handle the involved uncertainties in the system.