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Optimization in Renewable Energy Systems: Recent Perspectives covers all major areas where optimization techniques have been applied to reduce uncertainty or improve results in renewable energy systems (RES). Production of power with RES is highly variable and unpredictable, leading to the need for optimization-based planning and operation in order to maximize economies while sustaining performance. This self-contained book begins with an introduction to optimization, then covers a wide range of applications in both large and small scale operations, including optimum operation of electric power systems with large penetration of RES, power forecasting, transmission system planning, and DG sizing and siting for distribution and end-user premises. This book is an excellent choice for energy engineers, researchers, system operators, system regulators, and graduate students. - Provides chapters written by experts in the field - Goes beyond forecasting to apply optimization techniques to a wide variety of renewable energy system issues, from large scale to relatively small scale systems - Provides accompanying computer code for related chapters
An original look from a microeconomic perspective for power system optimization and its application to electricity markets Presents a new and systematic viewpoint for power system optimization inspired by microeconomics and game theory A timely and important advanced reference with the fast growth of smart grids Professor Chen is a pioneer of applying experimental economics to the electricity market trading mechanism, and this work brings together the latest research A companion website is available Edit
In light of anthropogenic climate change and the importance of energy to ensure high living standards, energy system optimization is used to explore different energy system layouts. A recent focus has been on determining cost-effective ways to mitigate greenhouse gas emissions. This work investigates how future uncertainties regarding technology costs influence optimization results. This is achieved through energy system optimization aimed at reducing system cost using stochastic optimization with probability distributions to capture expected future costs and uncertainties. Theoretical considerations and a minimal example energy system show that Jensen's inequality leads to an overestimation of necessary system costs when scenario optimization considers only the expected technology cost means. Stochastic optimization is applied to a model of the German energy system, including the electricity, heating, and transport sectors. Results from stochastic optimization are compared to scenario results based on mean cost distributions. The use of a factor effect-based meta-model and fewer optimizations in stochastic analysis are investigated to reduce computational effort. The results confirm the overestimation of necessary costs by scenario optimization, showing a 3.5% overestimation with an 80% emission reduction target and 0.4% for a completely renewable system. Stochastic optimization also provides the interquartile range to characterize uncertainty, with a 13.2 Euro MWh-1 interquartile range (27.3% of the mean) for a completely renewable system. Using 30 to 60 optimizations in the stochastic case yields results similar to 500 optimizations, the benchmark. The proposed meta-models offer limited advantages except for predicting extreme results, which are not evident with fewer optimizations. In some cases, especially for non-renewable systems, the expected values from stochastic optimization differ significantly from scenario optimization results. For instance, at a 20% emission limit of 1990 levels, scenario optimization yields 18% of the CO2 emissions compared to the mean of stochastic optimization. Similar differences are seen in other parameters, though most are well-represented by scenario results. Clustering helps manage the diverse results from stochastic optimization by identifying underlying system layouts. Stochastic optimization with probability distributions is robust, with small changes to distributions having minimal impact on outcomes.
The energy landscape is shifting toward renewable energy sources to mitigate climate change and reduce dependence on fossil fuels. The integration of renewable energy sources into the power grid presents various challenges, including uncertainty and variability of renewable energy sources, grid stability, and management of energy storage. Power system operation and optimization play a crucial role in managing the energy supply-demand balance, reducing operational costs, and improving the reliability of the power system. This call for papers aims to bring together the latest research and practical applications related to power system operation and optimization in the context of high penetration of renewable energy sources. We welcome contributions from researchers and practitioners from a broad range of disciplines to shed light on the challenges and opportunities associated with renewable energy integration in power systems. The objective of this Research Topic is to explore the latest advances in power system operation and optimization with a focus on the high penetration of renewable energy sources. We invite potential authors to submit articles for publication on the Research Topic of Frontiers in Energy Research on Power System Operation and Optimization Considering the High Penetration of Renewable Energy.
The papers presented in this volume address diverse challenges in energy systems, ranging from operational to investment planning problems, from market economics to technical and environmental considerations, from distribution grids to transmission grids and from theoretical considerations to data provision concerns and applied case studies. The International Symposium on Energy System Optimization (ISESO) was held on November 9th and 10th 2015 at the Heidelberg Institute for Theoretical Studies (HITS) and was organized by HITS, Heidelberg University and Karlsruhe Institute of Technology.
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 book analyses the technical and social systems that satisfy these needs and asks how methods can be put into practice to achieve this.
Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy life cycle, considering source, grid, demand, storage, and usage. Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants' behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportation energy systems. A smart and flexible energy system is essential for reaching Net Zero whilst keeping energy bills affordable. This title provides critical information to students, researchers and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity. - Introduces spatiotemporal energy sharing with new energy vehicles and human-machine interactions - Discusses the potential for electrification and hydrogenation in integrated building-transportation systems for sustainable development - Highlights key topics related to traditional energy consumers, including peer-to-peer energy trading and cost-benefit business models