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Advanced optimization methods for integrated power and hydrogen energy systems
Integrating electricity grids with hydrogen production, storage, and utilization technologies demands rigorous multicriteria decision-making and AI-driven optimization. Planning and Operation of Integrated Power and Hydrogen Systems unites engineering principles, economic analysis, machine learning techniques, and policy considerations into a single reference authored by an international team of researchers.
Each chapter combines theoretical depth with practical insights, complemented by selected real-world case studies, showing how discussed methodologies translate into practice. Coverage spans hydrogen production and storage technologies, multicriteria decision-making frameworks for energy system planning and operation, and strategies balancing economic efficiency against carbon footprint reduction. AI and machine learning applications are examined for enhancing system reliability and resilience across diverse operational scenarios.
The book also covers:
Pitched at an intermediate to advanced level, this reference serves energy engineers, policymakers, and industry practitioners working on decarbonization and grid modernization. Researchers and graduate students in energy engineering and environmental science will find the methodologies and case studies directly applicable to their work.
Advanced optimization methods for integrated power and hydrogen energy systems
Integrating electricity grids with hydrogen production, storage, and utilization technologies demands rigorous multicriteria decision-making and AI-driven optimization. Planning and Operation of Integrated Power and Hydrogen Systems unites engineering principles, economic analysis, machine learning techniques, and policy considerations into a single reference authored by an international team of researchers.
Each chapter combines theoretical depth with practical insights, complemented by selected real-world case studies, showing how discussed methodologies translate into practice. Coverage spans hydrogen production and storage technologies, multicriteria decision-making frameworks for energy system planning and operation, and strategies balancing economic efficiency against carbon footprint reduction. AI and machine learning applications are examined for enhancing system reliability and resilience across diverse operational scenarios.
The book also covers:
Pitched at an intermediate to advanced level, this reference serves energy engineers, policymakers, and industry practitioners working on decarbonization and grid modernization. Researchers and graduate students in energy engineering and environmental science will find the methodologies and case studies directly applicable to their work.
Atsiliepimai