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

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.

Towards Sustainable Chemical Processes describes a comprehensive framework for sustainability assessment, design and the processes optimization of chemical engineering. Beginning with the analysis and assessment in the early stage of chemical products’ initiating, this book focuses on the combination of science sustainability and process system engineering, involving mathematical models, industrial ecology, circular economy, energy planning, process integration and sustainability engineering. All chapters throughout answered two fundamental questions in depth: (1) what tools and models are available to be used to assess and design sustainable chemical processes, (2) what the core theories and concepts are to get into the sustainable chemical process fields. Therefore, Towards Sustainable Chemical Processes is an indispensable guide for chemical engineers, researchers, students, practitioners and consultants in sustainability related area. Provides innovative, novel and comprehensive methods and models for sustainability assessment, design and optimization, and synthesis and integration of chemical engineering processes Combines sustainability science with process system engineering Integrates mathematical models, industrial ecology, circular economy, energy planning, process integration and sustainability engineering Includes new case studies related to renewable energy, resource management, process synthesis and process integration

This study develops a novel neutrosophic optimization model to address these uncertainties. It involves treating product demands, waste targets, and economic benefits as interval-valued neutrosophic numbers. Three characteristic functions under the neutrosophic environment are considered: membership, non-membership, and indeterminacy. Two case studies are used to illustrate the model: one involves a polygeneration plant, and another involves an integrated biorefinery. Sensitivity analyses were performed for each case, adjusting the levels of risk tolerance in the neutrosophic environment.

The control of power systems and power plants is a subject of growing interest which continues to sustain a high level of research, development and application in many diverse yet complementary areas, such as maintaining a high quality but economical service and coping with environmental constraints. The papers included within this volume provide the most up to date developments in this field of research.

This book successfully illustrates the modeling of electricity prices with the help of stochastic processes. The relatively new phenomenon of negative prices is also integrated into the models. The integration of feed-in from wind power plants in energy models is also very innovative. This approach helps to simulate electricity prices in order to take into account the "merit-order effect of renewable energy". Finally, the models are used for the techno-economic evaluation of energy storages.

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.