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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.
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.
Many real life optimization problems are multi-objective problems where objectives under consideration usually conflict with each other and they are also stochastic due to inherent uncertainties. The electricity Generation Expansion Planning (GEP) problem is an example of such problems in which the goal is to expand the electric power network with new power plant investments including renewable resources. Decisions are made where and when to build new power plants and which technology to choose for new investments. Objectives can include but are not limited to minimization of the cost and pollutant emissions and maximization of reliability. There are inherent uncertainties in the GEP problem due to climate change, demand increase, fuel prices, technological progress and many other aspects that have to be considered. Some of these uncertainties directly affect the objective functions and some affect the constraint sets in the optimization model. In this study, a new uncertainty metric, the Pareto Uncertainty Index (PUI), is presented. The PUI includes uncertainty as part of the Pareto optimality concept so that the decision or policy maker can observe the uncertainty of Pareto optimal solutions. Using the PUI approach for objective function uncertainties and chance constrained programming or scenarios for constraint set uncertainties, a new multi-objective stochastic genetic algorithm, the Pareto Uncertain Genetic Algorithm (PUGA), is presented in this research, as well. In contrast with the other multi-objective genetic algorithms and classical methods, PUGA can incorporate both the multi-objective and stochastic aspects of problem solving without any transformation. A new post-Pareto pruning approach that reduces the number of Pareto optimal solutions to a smaller practical set is also included in PUGA with the help of the uncertainty information preserved in the PUI. Furthermore, this uncertainty information is used for risk assessments of solutions depending on the risk preferences of decision makers. The PUI and PUGA concepts are demonstrated and tested on several problems including the US Northeast region generation expansion planning (NEGEP) problem.
This book introduces the fundamentals of probability, statistical, and reliability concepts, the classical methods of uncertainty quantification and analytical reliability analysis, and the state-of-the-art approaches of design optimization under uncertainty (e.g., reliability-based design optimization and robust design optimization). The topics include basic concepts of probability and distributions, uncertainty quantification using probabilistic methods, classical reliability analysis methods, time-variant reliability analysis methods, fundamentals of deterministic design optimization, reliability-based design optimization, robust design optimization, other methods of design optimization under uncertainty, and engineering applications of design optimization under uncertainty.
Uncertainties in Modern Power Systems combines several aspects of uncertainty management in power systems at the planning and operation stages within an integrated framework. This book provides the state-of-the-art in electric network planning, including time-scales, reliability, quality, optimal allocation of compensators and distributed generators, mathematical formulation, and search algorithms. The book introduces innovative research outcomes, programs, algorithms, and approaches that consolidate the present status and future opportunities and challenges of power systems. The book also offers a comprehensive description of the overall process in terms of understanding, creating, data gathering, and managing complex electrical engineering applications with uncertainties. This reference is useful for researchers, engineers, and operators in power distribution systems. Includes innovative research outcomes, programs, algorithms, and approaches that consolidate current status and future of modern power systems Discusses how uncertainties will impact on the performance of power systems Offers solutions to significant challenges in power systems planning to achieve the best operational performance of the different electric power sectors
This volume provides resourceful thinking and insightful management solutions to the many challenges that decision makers face in their predictions, preparations, and implementations of the key elements that our societies and industries need to take as they move toward digitalization and smartness. The discussions within the book aim to uncover the sources of large-scale problems in socio-industrial dilemmas, and the theories that can support these challenges. How theories might also transition to real applications is another question that this book aims to uncover. In answer to the viewpoints expressed by several practitioners and academicians, this book aims to provide both a learning platform which spotlights open questions with related case studies. The relationship between Industry 4.0 and Society 5.0 provides the basis for the expert contributions in this book, highlighting the uses of analytical methods such as mathematical optimization, heuristic methods, decomposition methods, stochastic optimization, and more. The book will prove useful to researchers, students, and engineers in different domains who encounter large scale optimization problems and will encourage them to undertake research in this timely and practical field. The book splits into two parts. The first part covers a general perspective and challenges in a smart society and in industry. The second part covers several case studies and solutions from the operations research perspective for large scale challenges specific to various industry and society related phenomena.
This book presents a panoramic look at the transformation of the transmission network in the context of the energy transition. It provides readers with basic definitions as well as details on current challenges and emerging technologies. In-depth chapters cover the integration of renewables, the particularities of planning large-scale systems, efficient reduction and solution methods, the possibilities of HVDC and super grids, distributed generation, smart grids, demand response, and new regulatory schemes. The content is complemented with case studies that highlight the importance of the power transmission network as the backbone of modern energy systems. This book will be a comprehensive reference that will be useful to both academics and practitioners.
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.