Electrical Energy Storage for Buildings in Smart Grids

 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 9. Juli 2019
  • |
  • 398 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-05867-0 (ISBN)
 
Current developments in the renewable energy field, and the trend toward self-production and self-consumption of energy, has led to increased interest in the means of storing electrical energy; a key element of sustainable development. This book provides an in-depth view of the environmentally responsible energy solutions currently available for use in the building sector. It highlights the importance of storing electrical energy, demonstrates the many services that the storage of electrical energy can bring, and discusses the important socio-economic factors related to the emergence of smart buildings and smart grids. Finally, it presents the methodological tools needed to build a system of storage-based energy management, illustrated by concrete, pedagogic examples.
1. Auflage
  • Englisch
  • Somerset
  • |
  • USA
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 44,26 MB
978-1-119-05867-0 (9781119058670)
weitere Ausgaben werden ermittelt
Benoit Robyns is Research Director at HEI-Yncrea Lille, and Vice President of Energy and Societal Transition at Lille Catholic University. He is the head of the "Power Systems" team at L2EP

Arnaud Davigny is a lecturer at HEI-Yncrea Lille and researcher at L2EP

Herve Barry is a lecturer at Lille Catholic University, Faculty of Management, Economics and Sciences

Sabine Kazmierczak is a lecturer at Lille Catholic University, Faculty of Management, Economics and Sciences

Christophe Saudemont is a Professor at HEI-Yncrea Lille and researcher at L2EP

Dhaker Abbes is a lecturer at HEI-Yncrea Lille and researcher at L2EP

Bruno Francois is a Professor at Ecole Centrale de Lille and researcher at L2EP
  • Cover
  • Half-Title Page
  • Title Page
  • Copyright Page
  • Contents
  • Foreword
  • Introduction
  • 1. Storing Electrical Energy in Habitat: Toward "Smart Buildings" and "Smart Cities"
  • 1.1. Toward smarter electrical grids
  • 1.1.1. The move to decentralize electrical grids
  • 1.1.2. Smart grids
  • 1.2. Storage requirements in buildings
  • 1.3. Difficulties in storing electrical energy
  • 1.4. Electricity supply in buildings
  • 1.4.1. Building supply and consumption
  • 1.4.2. Self-production and self-consumption
  • 1.4.3. Micro-grids
  • 1.5. Smart buildings
  • 1.6. Smart cities
  • 1.7. Socio-economic questions
  • 1.7.1. Toward new economic models
  • 1.7.2. Social acceptability
  • 1.8. Storage management
  • 1.9. Methodologies used in developing energy management for storage systems
  • 2. Energy Storage in a Commercial Building
  • 2.1. Introduction
  • 2.2. Managing energy storage in a supermarket
  • 2.2.1. Introduction
  • 2.2.2. System characteristics
  • 2.2.3. Electricity billing
  • 2.2.4. Objectives of the energy management strategy
  • 2.2.5. Fuzzy logic supervisor
  • 2.2.6. Simulation
  • 2.2.7. Performance analysis using indicators
  • 2.3. Conclusion
  • 2.4. Acknowledgments
  • 3. Energy Storage in a Tertiary Building, Combining Photovoltaic Panels and LED Lighting
  • 3.1. Introduction
  • 3.2. DC network architecture
  • 3.3. Energy management
  • 3.3.1. Specification
  • 3.3.2. System inputs/outputs
  • 3.3.3. Functional graph
  • 3.3.4. Determination of membership functions
  • 3.3.5. Operational graph
  • 3.3.6. Fuzzy rules
  • 3.4. Simulation results
  • 3.4.1. Case 1: favorable grid access conditions (GAC)
  • 3.4.2. Case 2: unfavorable GACs
  • 3.4.3. Case 3: variable GAC
  • 3.4.4. Comparison of results
  • 3.5. Conclusion
  • 3.6. Acknowledgments
  • 4. Hybrid Storage Associated with Photovoltaic Technology for Buildings in Non-interconnected Zones
  • 4.1. Introduction
  • 4.2. Photovoltaic systems in buildings and integration into the grid
  • 4.2.1. Context and economic issues
  • 4.2.2. Examples of projects
  • 4.3. Importance of storage in photovoltaic systems
  • 4.3.1. Photovoltaic systems for isolated sites
  • 4.3.2. Photovoltaic systems connected to the grid
  • 4.3.3. Hybrid storage
  • 4.3.4. Electronic conversion structures for hybrid storage
  • 4.4. Photovoltaic generator with hybrid storage system
  • 4.4.1. Case study
  • 4.4.2. Principles and standards for frequency support
  • 4.4.3. Calculating battery wear
  • 4.5. Energy management
  • 4.5.1. Methodology
  • 4.5.2. Operating specifications
  • 4.5.3. Supervisor structure and determination of input/output
  • 4.5.4. Functional graphs
  • 4.5.5. Membership functions
  • 4.5.6. Operating graphs
  • 4.5.7. Fuzzy rules
  • 4.5.8. Evaluation indicators
  • 4.6. Simulation results
  • 4.6.1. Supervisor validation
  • 4.6.2. Life expectancy of storage elements
  • 4.6.3. Efficiency
  • 4.6.4. Levelized cost of energy
  • 4.7. Experimental validation of energy management
  • 4.7.1. Definition of tests
  • 4.7.2. Experimental results
  • 4.8. Conclusion
  • 4.9. Acknowledgments
  • 5. Economic and Sociological Implications of Smart Grids
  • 5.1. Introduction
  • 5.2. Actor diversity in smart grids
  • 5.3. Economic and sociological implications of smart grids
  • 5.3.1. Introduction
  • 5.3.2. Implications of smart grids for the value chain
  • 5.3.3. The "downstream" role of smart grids
  • 5.3.4. The "upstream" role of smart grids
  • 5.3.5. Demand management programs
  • 5.4. Social acceptability
  • 5.4.1. Introduction
  • 5.4.2. Conceptual frameworks: points of reference
  • 5.4.3. Studies of social acceptability
  • 5.4.4. Theoretical application of voluntary load reduction within a reference framework
  • 5.4.5. Quality of the load reduction contract
  • 5.5. Conclusion
  • 5.6. Acknowledgments
  • 6. Energy Mutualization for Tertiary Buildings, Residential Buildings and Producers
  • 6.1. Introduction
  • 6.2. Energy mutualization between commercial, tertiary and residential buildings, producers and grid managers
  • 6.2.1. Grid actors
  • 6.2.2. Energy service aggregator
  • 6.2.3. Case study: structure of the micro-grid
  • 6.2.4. Consumption and production profiles of actors in the micro-grid
  • 6.3. Management of energy mutualization for tertiary buildings, residential buildings and energy producers
  • 6.3.1. Objectives and constraints of actors in the micro-grid
  • 6.3.2. Supervisor structure: input and output variables
  • 6.3.3. Functional graphs
  • 6.3.4. Membership functions
  • 6.3.5. Operating graphs
  • 6.3.6. Fuzzy rules
  • 6.3.7. Indicators
  • 6.4. Case study
  • 6.4.1. Characteristics of the micro-grid
  • 6.4.2. Scenarios
  • 6.5. Load reduction
  • 6.5.1. Load reduction principle
  • 6.5.2. Introduction to load reduction and acceptability
  • 6.5.3. Simulation of energy management with load reduction
  • 6.6. Conclusion
  • 6.7. Acknowledgments
  • 6.8. Appendix 1
  • 7. Centralized Management of a Local Energy Community to Maximize Self-consumption of PV Production
  • 7.1. Introduction
  • 7.2. Energy management issues in residential neighborhoods
  • 7.2.1. Electric grid management: basic principles
  • 7.2.2. The move toward smart grids
  • 7.2.3. A few applications of micro-grids for managing local energy communities
  • 7.3. The active PV generator
  • 7.3.1. Current PV production
  • 7.3.2. Limits and necessary developments
  • 7.3.3. Cascade structure
  • 7.3.4. Domestic application
  • 7.3.5. Energy management of the DC bus
  • 7.3.6. Energy management of ultracapacitors
  • 7.4. Micro-grid management
  • 7.4.1. Organization of electrical grid management
  • 7.4.2. Key functions
  • 7.4.3. Characteristics of local controllers for distributed production
  • 7.4.4. Fundamentals of power balancing
  • 7.4.5. Load management
  • 7.5. Application to the context of a residential electrical network
  • 7.5.1. From managing domestic demand to managing domestic production
  • 7.5.2. Residential grids and application of micro-grid concepts
  • 7.5.3. Energy management of a micro-grid
  • 7.6. Prediction techniques and data processing
  • 7.6.1. Predicting PV production
  • 7.6.2. Load prediction
  • 7.6.3. Energy estimation
  • 7.7. Day ahead operational planning and half-hourly power reference calculations
  • 7.7.1. Objectives
  • 7.7.2. Constraints
  • 7.7.3. Determinist algorithm for generator use
  • 7.7.4. Practical application
  • 7.8. Medium-term energy management
  • 7.8.1. Reducing observed deviations
  • 7.8.2. Energy management to minimize the aging of batteries
  • 7.9. Short-term energy management
  • 7.9.1. Primary frequency regulation
  • 7.9.2. Power balancing strategies in the active generator
  • 7.10. Experimental testing using real-time simulation
  • 7.10.1. Benefits of real-time simulation
  • 7.10.2. The Electrical Power Management Lab
  • 7.10.3. Experimental implementation
  • 7.10.4. Analysis of self-consumption in a house
  • 7.10.5. Increasing the proportion of PV use in a residential grid
  • 7.11. Review of scientific contributions and methodological summary
  • 7.12. Concluding thoughts and research perspectives
  • 8. Reversible Charging from Electric Vehicles to Grids and Buildings
  • 8.1. Introduction
  • 8.2. Reversible charging of electric vehicles
  • 8.2.1. Vehicle to Grid
  • 8.2.2. Vehicle to Home and to Building
  • 8.2.3. Vehicle to Station and energy hubs
  • 8.2.4. Energy service aggregator
  • 8.3. Potential services and energy management of reversible EV fleets
  • 8.3.1. Services supplied by V2G
  • 8.3.2. Energy management of a V2G fleet
  • 8.4. Vehicle to Station: V2S
  • 8.4.1. Impact and contribution of EVs in a railway station carpark
  • 8.4.2. V2S: contribution of V2G technology in a station parking lot
  • 8.5. V2H
  • 8.6. Conclusion
  • 8.7. Acknowledgments
  • 8.8. Appendix
  • 8.8.1. Detailed functional graphs for the V2G application
  • References
  • Index
  • Other titles from iSTE in Energy
  • EULA

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