Analytics And Optimization For Renewable Energy Integration
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Analytics and Optimization for Renewable Energy Integration
The scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation. It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets.
Data Analytics for Renewable Energy Integration. Technologies, Systems and Society
This book constitutes the revised selected papers from the 6th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2018, held in Dublin, Ireland, in September 2018. The 9 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response, and many others.
Optimization in Sustainable Energy
Author: Prasenjit Chatterjee
language: en
Publisher: John Wiley & Sons
Release Date: 2026-07-14
This state-of-the-art book offers cutting-edge optimization techniques and practical decision-making frameworks essential for enhancing the efficiency and reliability of sustainable energy systems, making it an invaluable resource for researchers, policymakers, and energy professionals. Optimization in Sustainable Energy: Methods and Applications brings together valuable knowledge, methods, and practical examples to help scholars, researchers, professionals, and policymakers address the growing challenges of optimizing sustainable energy. This volume covers a range of topics, including mathematical models, heuristic algorithms, renewable resource management, and energy storage optimization. Each chapter explores a different aspect of sustainable energy, providing both theoretical understanding and practical guidance. The volume explores challenges and opportunities surrounding the integration of multi-criteria decision-making techniques in energy planning, highlighting insights on environmental, economic, and social factors influencing the strategic allocation of resources. The use of evolutionary algorithms, machine learning, and metaheuristics to optimize energy storage, distribution, and optimization are also discussed. The transition towards sustainable energy is at the forefront of global priorities, driven by the urgent need to mitigate climate change, reduce carbon emissions, and enhance energy security. As countries and industries increasingly prioritize renewable sources like wind, solar, and hydroelectric power, the complexity of optimizing these systems becomes a critical challenge. Optimization in Sustainable Energy: Methods and Applications, is a comprehensive exploration of cutting-edge methodologies used to enhance the efficiency, reliability, and performance of sustainable energy systems. Audience Research scholars, academics, students, policymakers, and industry experts in mechanical engineering, electrical engineering, and energy science.