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This research is dedicated to the study of electric power system generation expansion planning considering uncertainty of climate change. Policymakers across the world are increasingly concerned about the effects of climate change and its impact on human systems when making decisions. Electric power Generation Expansion Planning (GEP) problems that determine the optimal expansion capacity and technology under particular technical constraints, given cost and policy assumptions are undoubtedly among those decisions. Now and in the future, climate change is and will be affecting new power plant investment decisions and the electricity generation system in more uncertain ways. The power system needs to be more reliable, cost-effective and environmentally friendly when exposed to higher temperature, less precipitation and more intense and frequent extreme events. However, incorporating the climate change effects into a GEP model has rarely been attempted before in the literature. The best approach to comprehensively model those uncertainties into electricity generation, and to optimize the generation planning under uncertainty needs be studied in a more specific way. In this research, a preliminary GEP model is proposed with available input data from various resources. Discrete scenarios and robust optimization are adopted to specifically model uncertainty. Relationships between climate change and GEP parameters are defined and considered in each scenario. The preliminary GEP model is then solved under each scenario to identify the climate change impact on the generation expansion planning decision. Two robust optimization models are presented and solved to find the optimal results under uncertainty: Model 1 is expected total cost minimization and Model 2 is maximum regret minimization. Both models find a compromise solution that is good for all scenarios, which avoids the possible risk associated with a poor decision that is only optimal for one particular scenario. The results suggest recommendations for further power system uncertainty modeling and risk management.
This book presents design principles, performance assessment and robust optimization of different poly-generation systems using renewable energy sources and storage technologies. Uncertainties associated with demands or the intermittent nature of renewables are considered in decision making processes. Economic and environmental benefits of these systems in comparison with traditional fossil fuels based ones are also provided. Case studies, numerical results, discussions, and concluding remarks have been presented for each proposed system/strategy. This book is a useful tool for students, researchers, and engineers trying to design and evaluate different zero-energy and zero-emission stand-alone grids.
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Electric Energy Systems, Second Edition provides an analysis of electric generation and transmission systems that addresses diverse regulatory issues. It includes fundamental background topics, such as load flow, short circuit analysis, and economic dispatch, as well as advanced topics, such as harmonic load flow, state estimation, voltage and frequency control, electromagnetic transients, etc. The new edition features updated material throughout the text and new sections throughout the chapters. It covers current issues in the industry, including renewable generation with associated control and scheduling problems, HVDC transmission, and use of synchrophasors (PMUs). The text explores more sophisticated protections and the new roles of demand, side management, etc. Written by internationally recognized specialists, the text contains a wide range of worked out examples along with numerous exercises and solutions to enhance understanding of the material. Features Integrates technical and economic analyses of electric energy systems. Covers HVDC transmission. Addresses renewable generation and the associated control and scheduling problems. Analyzes electricity markets, electromagnetic transients, and harmonic load flow. Features new sections and updated material throughout the text. Includes examples and solved problems.
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.
Classical and Recent Aspects of Power System Optimization presents conventional and meta-heuristic optimization methods and algorithms for power system studies. The classic aspects of optimization in power systems, such as optimal power flow, economic dispatch, unit commitment and power quality optimization are covered, as are issues relating to distributed generation sizing, allocation problems, scheduling of renewable resources, energy storage, power reserve based problems, efficient use of smart grid capabilities, and protection studies in modern power systems. The book brings together innovative research outcomes, programs, algorithms and approaches that consolidate the present state and future challenges for power. Analyzes and compares several aspects of optimization for power systems which has never been addressed in one reference Details real-life industry application examples for each chapter (e.g. energy storage and power reserve problems) Provides practical training on theoretical developments and application of advanced methods for optimum electrical energy for realistic engineering problems
The last problem concerns a multistage adaptive investment planning problem while considering uncertain future demand at various locations. To solve this multi-level optimization problem, we take advantage of affine policies to transform it to a single-level optimization problem. Our numerical examples show the benefits of using this multistage adaptive robust planning model over both traditional stochastic programming and single-level robust optimization approaches. Based on numerical studies in the three projects, we conclude that our approaches provide effective and efficient modeling and computational tools for advanced power systems expansion planning.
An examination of systematic techniques for the design of sustainable processes and products, this book covers reducing energy consumption, preventing pollution, developing new pathways for biofuels, and producing environmentally friendly and high-quality products. It discusses innovative design approaches and technological pathways that impact ene
This book covers robust optimization theory and applications in the electricity sector. The advantage of robust optimization with respect to other methodologies for decision making under uncertainty are first discussed. Then, the robust optimization theory is covered in a friendly and tutorial manner. Finally, a number of insightful short- and long-term applications pertaining to the electricity sector are considered. Specifically, the book includes: robust set characterization, robust optimization, adaptive robust optimization, hybrid robust-stochastic optimization, applications to short- and medium-term operations problems in the electricity sector, and applications to long-term investment problems in the electricity sector. Each chapter contains end-of-chapter problems, making it suitable for use as a text. The purpose of the book is to provide a self-contained overview of robust optimization techniques for decision making under uncertainty in the electricity sector. The targeted audience includes industrial and power engineering students and practitioners in energy fields. The young field of robust optimization is reaching maturity in many respects. It is also useful for practitioners, as it provides a number of electricity industry applications described up to working algorithms (in JuliaOpt).