E-Book, Englisch, 341 Seiten, eBook
Hatanaka / Wasa / Uchida Economically Enabled Energy Management
1. Auflage 2020
ISBN: 978-981-15-3576-5
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Interplay Between Control Engineering and Economics
E-Book, Englisch, 341 Seiten, eBook
ISBN: 978-981-15-3576-5
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
The first three chapters present comprehensive overviews of respective social contexts, underscore the pressing need for economically efficient energy management systems and academic work on this emerging research topic, and identify fundamental differences between approaches in control engineering and economics. In turn, the next three chapters (Chapters 4–6) provide economics-oriented approaches to the subject. The following five chapters (Chapters 7–11) address optimal energy market design, integrating both physical and economic models. The book’s last three chapters (Chapters 12–14) mainly focus on the engineering aspects of next-generation energy management, though economic factors are also shown to play important roles.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
- Introduction
- Balancing Power Market for Power System with Renewable Energy Sources
- A Gap between Problem Formulations of Electricity Pricing by Control Engineers and Economists: A viewpoint from budget constraints of consumers
- Policy Issues and Future Perspectives of The Electricity Industry: Microeconomic Approach
- Nudge and Energy Conservation: Field Experimental Evidence from HEMS and BEMS
- The Welfare Effects of Environmental Taxation and Subsidization on Renewable Energy Sources in an Oligopolistic Electricity Market
- Economic Impact and Market Power of Strategic Aggregators in Energy Demand Network
- Incentive-based Economic and Physical Integration for Dynamic Power Networks
- Dynamic Mechanism Design Theory for Faster Operations of Power Market
- Distributed Optimal Power Management in Real-time Electricity Market
- Real-Time Pricing for Electric Power Systems by Nonlinear Model Predictive Control
- Distributed Multi-Agent Optimization Protocol over Energy Management Networks
- Passivity-Based Cyber-Physical HVAC Energy Management for Multiple Connected Buildings




