Smart Zero-energy Buildings and Communities for Smart Grids E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 345

Veröffentlichungsjahr: 2022

Beschreibung

Smart zero-energy buildings and communities have a major role to play in the evolution of the electric grid towards alignment with carbon neutrality policies. The goal to reduce greenhouse gas emissions in the built environment can be pursued through a holistic approach, including the drastic reduction of buildings' energy consumption. The state-of-the-art in this field relates, on the one hand, to design methodologies and innovative technologies which aim to minimize the energy demand at the building level. On the other hand, the development of information and communication technologies, along with the integration of renewable energy and storage, provide the basis for zero and positive energy buildings and communities that can produce, store, manage and exchange energy at a local level. This book provides a structured and detailed insight of the state-of-the-art in this context based on the analysis of real case studies and applications.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

List of Acronyms

1 The Role of Smart Grids in the Building Sector

1.1. Smart and zero-energy buildings

1.2. Smart and zero-energy communities

1.3. Conclusion and future prospects

2 Integrated Design (ID) Towards Smart Zero-energy Buildings and Smart Grids

2.1. Introduction

2.2. Methodology

2.3. Integrated design in smart and zero-energy buildings

2.4. ID process principles and guidelines

2.5. Scope of services

2.6. Remuneration models

2.7. Application of evaluation tools

2.8. Sustainability certification

2.9. Consultancy and quality assurance

2.10. Measurement of design quality criteria

2.11. Defining a client’s objectives

2.12. Defining the tenant’s objectives

2.13. Best practice sites

3 Data Analysis and Energy Modeling in Smart and Zero-energy Buildings and Communities

3.1. Energy signature for the NTL of Cyprus Institute

3.2. Athalassa Campus and the NTL building

3.3. Linear Fresnel solar collector at the NTL building, Cyprus Institute

3.4. Conclusion

4 On the Comparison of Occupancy in Relation to Energy Consumption and Indoor Environmental Quality: A Case Study

4.1. Introduction

4.2. Methodology

4.3. Description of the case building

4.4. Description of the experimental procedure

4.5. Results

4.6. Discussion and concluding remarks

5 Indoor Environmental Quality and Energy Consumption Assessment and ANN Predictions for an Integrated Internet-based Energy Management System Towards a Zero-energy Building

5.1. Introduction

5.2. Description of the SDE buildings

5.3. The power loads and hourly energy consumption

5.4. Indoor environmental quality

5.5. Cross correlation

5.6. Prediction using artificial neural networks (ANN)

5.7. Specifications for an integrated internet-based energy management system towards a zero-energy building

5.8. Conclusion

6 Objective and Subjective Evaluation of Thermal Comfort in the Loccioni Leaf Lab, Italy

6.1. Introduction

6.2. Background information

6.3. Methodology

6.4. Collection of building background data

6.5. Collection of monitored data

6.6. Right-Now questionnaire survey

6.7. Results

6.8. Conclusion

7 Smart Meters and User Engagement in the Leaf House

7.1. Introduction

7.2. Methodology

7.3. Analysis of user engagement

7.4. Results

7.5. Conclusion

8 Integration of Energy Storage in Smart Communities and Smart Grids

8.1. Energy storage systems in smart grids

8.2. Energy storage and smart grids: case studies

8.3. Conclusion and future prospects

Conclusion and Recommendations

References

List of Authors

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1. Components of the smart grid

Chapter 2

Figure 2.1. The methodological approach of smart technologies

Figure 2.2. The methodology of implemented work

Figure 2.3. Early design phases impact on performance, costs, and disruption (w...

Figure 2.4. Traditional versus Integrated Planning

Figure 2.5. Overview of the ID process

Figure 2.6. Timeframe of ID related tasks (www.integrateddesign.eu)

Figure 2.7. The three-level-model for ID-related remuneration of design works (...

Figure 2.8. Location of Tombazis office building (red) and city center (blue)

Figure 2.9. a) The block of buildings that includes the Tombazis building (left...

Figure 2.10. Artificial pool, sculpture, and entrance to the Tombazis building

Figure 2.11. Interior spaces

Figure 2.12. a) Natural and b) artificial lighting

Figure 2.13. Location of APIVITA building (red) and city center (blue)

Figure 2.14. APIVITA interior space

Figure 2.15. Plantation in the area of APIVITA building

Figure 2.16. Location of the SNFCC (in blue)

Figure 2.17. The Stavros Niarchos Foundation Cultural Center and Park © Yiorgis...

Figure 2.18. The design of the opera

Figure 2.19. The design of the library building

Figure 2.20. The canopy roof

Figure 2.21. a) Location of Karelas Office Park; b) green rooftop of the buildi...

Figure 2.22. Façade movable shading devices

Figure 2.23. Building atrium for natural day-lighting (left), atrium façade bam...

Figure 2.24. Roof garden

Figure 2.25. Central solar thermal system

Chapter 3

Figure 3.1. Energy signature, conceptual schema

Figure 3.2. Athalassa campus and energy grid scheme

Figure 3.3. Fresnel and thermal loop TRNSYS model

Figure 3.4. Simulation model versus data driven model of the NTL

Figure 3.5. Flow chart

Figure 3.6. NTL power demand

Figure 3.7. ETL procedure

Figure 3.8. Re-sampling algorithm

Figure 3.9. Selected dataset

Figure 3.10. Training and test dataset

Figure 3.11. Selected dataset after the ETL procedure

Figure 3.12. Linear regression model with a scatter plot of training data

Figure 3.13. Two variables linear regression model with scatter plot of trainin...

Figure 3.14. Three variables linear regression model with scatter plot of train...

Figure 3.15. Ridge second order degree regression model with scatter plot of tr...

Figure 3.16. Ridge second order degree regression model with a scatter plot of ...

Figure 3.17. Second order ridge regression model with scatter plot of training ...

Figure 3.18. NTL building

Figure 3.19. Linear Fresnel solar collector

Figure 3.20. Different wall structures and materials applied in the NTL buildin...

Figure 3.21. Layout of the SHC (solar heating and cooling) system and integrati...

Figure 3.22. Categorized electrical energy consumption [kWh, %] of the NTL in 2...

Figure 3.23. Histogram of monthly thermal energy gained by the LFR system and m...

Figure 3.24. Histogram of monthly thermal energy gained by the LFR system and m...

Figure 3.25. NTL monthly thermal demand covered by the SHC system in Scenario 1

Figure 3.26. NTL monthly thermal demand covered by the SHC system in Scenario 2

Chapter 4

Figure 4.1. Total energy (excluding lighting) for the three rooms

Figure 4.2. Total illuminance for the three rooms

Figure 4.3. Box plots for a) temperature and b) relative humidity of the three ...

Figure 4.4. CO2 concentrations in the three rooms

Figure 4.5. Lecture theater on a day with high occupancy

Figure 4.6. Energy consumption levels in lecture theater with respect to occupa...

Figure 4.7. Illuminance levels in lecture theater with respect to occupancy

Figure 4.8. Scatter plot of CO2 levels vs energy consumption for a specific day...

Chapter 5

Figure 5.1. The SDE 3 building

Figure 5.2. The entrance and front elevation of SDE 3

Figure 5.3. The position of sensors on the 1st floor of SDE 3

Figure 5.4. The position of sensors on the 2nd floor of SDE 3

Figure 5.5. The position of sensors on the 3rd floor of SDE 3

Figure 5.6. The position of sensors on the 4th floor of SDE 3

Figure 5.7. The energy consumption of the SDE 3 building during April and May ...

Figure 5.8. The power loads histogram (excluding zero values)

Figure 5.9. The energy loads per month for April (top) and May (bottom) 2016

Figure 5.10. The air temperature of 1st floor time series

Figure 5.11. The relative humidity time series

Figure 5.12. The time series of CO2 concentration measurements

Figure 5.13. The time series of illuminance measurements (lux)

Figure 5.14. The discomfort index for the 1st floor

Figure 5.15. The discomfort index for all floors

Figure 5.16. The PMV and PPD index of the ID_18 room on April 11, 2016

Figure 5.17. The PMV and PPD index of the ID_45 room on April 11, 2016

Figure 5.18. The PMV and PPD index of the ID_42 room on April 11, 2016

Figure 5.19. The PMV and PPD index of the ID_43 room on April 11, 2016

Figure 5.20. Cross correlation between indoor air temperature and power loads f...

Figure 5.21. Cross correlation between relative humidity and power loads for th...

Figure 5.22. Cross correlation between relative humidity and temperature for th...

Figure 5.23. The prediction of outdoor temperature 24 hours ahead using NARX

Figure 5.24. The prediction of outdoor relative humidity 24 hours ahead

Figure 5.25. The prediction of power loads 24 hours ahead for case 1

Figure 5.26. The prediction of power loads for case 2

Figure 5.27. The phases of integrated energy management system implementation

Figure 5.28. The HVAC system of TUC

Figure 5.29. The CS NET WEB

Figure 5.30. a) The conventional architecture of the energy management, b) the ...

Figure 5.31. The communication protocols among the various components

Figure 5.32. The installation of the multi-purpose controller

Chapter 6

Figure 6.1. Outline of the study methodology

Figure 6.2. Location of the Loccioni Leaf Lab

Figure 6.3. Office spaces considered for the study and location of monitoring e...

Figure 6.4. Screenshot of MyLeaf platform

Figure 6.5. Testing of the Right-Now survey (general statistics)

Figure 6.6. Testing of the Right-Now survey (individual responses)

Figure 6.7. Internal and external temperature diurnal variation between 22/07/2...

Figure 6.8. Location of the MyLeaf sensors in the studied spaces

Figure 6.9. Difference between maximum and minimum sensor reading for matched r...

Figure 6.10. Average room temperature in different office spaces and different ...

Figure 6.11. Frequency distribution diagrams for the Comfort Meter measured par...

Figure 6.12. MyLeaf and Comfort Meter measured air temperatures for matched res...

Figure 6.13. Thermal sensation votes (total sample)

Figure 6.14. Thermal sensation in different office spaces and different times o...

Figure 6.15. Acceptability of the thermal sensation (total sample)

Figure 6.16. Acceptability of thermal conditions in different office spaces and...

Figure 6.17. Preference for change in thermal sensation (total sample)

Figure 6.18. Thermal sensation preference in different office spaces and differ...

Figure 6.19. Thermal comfort per gender (total sample)

Figure 6.20. Thermal comfort per time seated (total sample)

Figure 6.21. Thermal comfort per desk location (total sample)

Figure 6.22. Mean thermal sensation and average internal temperature for differ...

Figure 6.23. Mean thermal sensation and average internal temperature for differ...

Figure 6.24. Mean thermal sensation and average internal temperature for differ...

Figure 6.25. Mean thermal sensation and average internal temperature for differ...

Figure 6.26. Objective and subjective concurrent measurements of thermal sensat...

Figure 6.27. Boxplots with average internal temperature vs. a) acceptability (l...

Chapter 7

Figure 7.1. Objectives of this study

Figure 7.2. The Leaf House

Figure 7.3. Location of the Leaf House (Google maps)

Figure 7.4. The Leaf House

Figure 7.5. The control room in the Leaf House

Figure 7.6. Summary of the main topics discussed at the focus group

Figure 7.7. The topics covered in the questionnaire

Figure 7.8. Demographics, socioeconomic background

Figure 7.9. Physiological, social and behavioral aspects

Figure 7.10. Information level

Figure 7.11. Health and comfort

Figure 7.12. Living situation

Chapter 8

Figure 8.1. The energy storage systems categories

Figure 8.2. Thermal properties of sensible heat materials (Li 2016; Kalogirou ...

Figure 8.3. The Leaf Community smart grid

Figure 8.4. System architecture

Figure 8.5. Thermal storage – ground source heat pumps (GSHP) connection schem...

Figure 8.6. The batteries storage system in the Leaf Community

Figure 8.7. TES charging during weekends and temperature of thermal energy stor...

Figure 8.8. Thermal power exchange with the leaf microgrid

Figure 8.9. Peak shaving of microgrid using BES

Figure 8.10. Peak shaving strategy

Figure 8.11. Performance of self-consumption

Figure 8.12. The poly-generative system outline

Figure 8.13. The layout of the installation site at IDEA srl. (Long 38.10o Lat ...

Figure 8.14. Schematic illustrating principle of operation of the thermocline t...

Figure 8.15. Distribution of temperatures profile in a charged tank installed i...

Figure 8.16. The organic Rankine cycle (ORC) generator

Figure 8.17. Organic Rankine cycle (ORC) section of the pilot plant in Palermo

Figure 8.18. Steady-state calculation of daily solar thermal energy gain and li...

Figure 8.19. Thermal energy in the solar field with a safety overheating temper...

Figure 8.20. Thermal energy produced in the solar field and its modification du...

Figure 8.21. Thermocline effect measured by specific thermal sensors (PT100) di...

Figure 8.22. Electric power produced by the rank organic Rankine cycle (ORC) by...

List of Tables

Chapter 2

Table 2.1. Overview of the measurability criteria of ID

Chapter 3

Table 3.1. Dataset filters

Table 3.2. Data exploration for a heating dataset

Table 3.3. Data exploration for a cooling dataset

Table 3.4. Correlation matrix for a heating dataset. For a color version of thi...

Table 3.5. Correlation matrix for a cooling dataset. For a color version of thi...

Table 3.6. Metrics results

Table 3.7. Metrics results

Table 3.8. Main features of the installed LFR system

Table 3.9. NTL electric energy consumption in 2014 and 2015 due to a HVAC syste...

Table 3.10. Main monthly thermal energy features of LFR production in Scenario ...

Table 3.11. Main monthly thermal energy features of LFR production in Scenario ...

Table 3.12. Main monthly results of the simulations in reference to 2015, in te...

Table 3.13. Normalized primary energy consumption in the design and operational...

Chapter 4

Table 4.1. Technical specifications of the sensors

Table 4.2. Monthly values for all measured parameters within the three rooms

Chapter 5

Table 5.1. Building envelope and systems of SDE 3

Table 5.2. The monitoring equipment of the SDE 3 building

Table 5.3. The air temperature statistical analysis

Table 5.4. The statistical analysis for CO2 concentration

Table 5.5. The statistical analysis for illuminance levels on 1st floor

Table 5.6. An indicative list of devices for the integrated energy management. ...

Chapter 6

Table 6.1. Guidelines for the positioning of the Comfort Meter

Table 6.2. Study variables and source of information

Table 6.3. Characteristics of the Loccioni Leaf Lab office spaces

Table 6.4. Characteristics of study participants

Table 6.5. Minimum, maximum, mean values and standard deviations for internal a...

Table 6.6. Minimum, maximum, mean values and standard deviations of matched sen...

Table 6.7. Minimum, maximum, mean values and standard deviations of matched sen...

Table 6.8. Thermal sensation votes (per office space)

Table 6.9. Mean thermal sensation for the monitoring period (per office space)

Table 6.10. Acceptability of the thermal sensation (per office space)

Table 6.11. Mean acceptability of the thermal sensation (per office space)

Table 6.12. Preference for change in thermal sensation (per office space)

Table 6.13. Mean preference for change in thermal sensation (per office space)

Table 6.14. Correlation matrix for studied variables (MyLeaf measurements and R...

Table 6.15. Correlation matrix for studied variables (Comfort Meter measurement...

Table 6.16. Acceptable and unacceptable temperatures

Table 6.17. Preferred temperatures

Chapter 8

Table 8.1. Electric vehicles case studies

Table 8.2. Case studies of mechanical energy storage

Table 8.3. Advantages and disadvantages of concentrated solar power (CSP) techn...

Table 8.4. Leaf Lab heating, ventilation, and air conditioning system (HVAC) he...

Table 8.5. Fresnel parameters used for the efficiency calculation

Table 8.6. Monthly solar thermal energy, efficiency, and thermal power of IDEA'...

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

List of Acronyms

Begin Reading

Conclusion and Recommendations

References

List of Authors

Index

End User License Agreement

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Engineering, Energy and Architecture Set

coordinated by

Lazaros E Mavromatidis

Volume 9

Smart Zero-energy Buildings and Communities for Smart Grids

Edited by

Nikos Kampelis

Denia Kolokotsa

First published 2022 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUKwww.iste.co.uk

John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USAwww.wiley.com

© ISTE Ltd 2022

The rights of Nikos Kampelis and Denia Kolokotsa to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.

Library of Congress Control Number: 2021948475

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-78630-684-5

Preface

Smart and zero-energy buildings and communities have a major role to play in the evolution of the building sector and of the electric grid (i.e. the smart grid) toward the necessary transition in line with current carbon neutrality policies, climate change mitigation and adaptation. In this sense, the goal for the reduction of greenhouse emissions in the built environment can be pursued through a holistic approach whereas the consumption of energy in buildings is drastically reduced. State of the art in this field relates, on the one hand, to the implementation of energy efficient design methodologies and innovative technologies which guarantee the maximum energy saving potential in buildings. On the other hand, the advancement of ICT technologies, along with the integration of renewables and storage at building and at district level, provide the means for zero or positive net energy buildings and districts by producing, storing, managing and exchanging energy at the local level. In this direction, the challenges related with the volatility of renewable energy sources at grid level can become more manageable. New and emerging roles and services linking the building sector with smart grids in the future should be transparent and promote sustainability. This requires inspiring, fair, effective and innovative policies providing the roadmap for this transition and major research, innovation and training initiatives that will (1) support the market in providing solutions supporting mass scale deployment of environmentally friendly, energy efficient technologies and (2) educate the society about the importance of this transition and the role each and every one of us has to play.

Why this book?

This book is a collaborative work between research and industrial partners in the framework of the Smart GEMS Marie Curie project (2015–2020). Research and training activities in Smart GEMS were implemented through the physical mobility (secondments) of staff between academic and industrial partners during a five-year period. It provided academic staff with the opportunity to expand their views by working with leading companies in the fields of advanced building/district energy management, renewable technologies and storage integration, as well as in the production of smart metering systems. Likewise, industrial personnel had the opportunity to work in different academic environments across Europe and establish a more coherent understanding about the broader scientific and technological capabilities outside their field of specialization, thus complementing their field of expertise and in various cases diving deeper into co-developing and exploring state-of-the-art techniques and methodologies. This book provides a thorough reading of many of the concepts dealt with in Smart GEMS through the close collaboration of industrial and academic partners. Adding to the first publication, entitled Smart Buildings, Smart Communities and Demand Response, published by ISTE Ltd and John Wiley & Sons, this book provides complementary material and an insight into the fields of smart grids, smart and zero-energy buildings, integrated design, data analysis and energy modeling, indoor environmental quality, user engagement and energy storage in smart communities and smart grids.

Who is this book for?

This book focuses on smart nearly zero-energy buildings (NZEB), smart communities and smart grids. Therefore, it is mainly valuable for experts, professionals and researchers with an interest in (1) energy efficient buildings and communities; (2) smart building systems and innovative applications; and (3) integration of renewable energy technologies and storage at the building and district level.

Structure

First, smart grids are defined and their role in integrating more renewable energy sources, smart buildings and distributed generators, thereby revolutionizing the electric power network, is presented. Concepts such as smart and zero-energy buildings and communities along with smart metering, demand response and distributed systems are outlined to provide the state of the art, opportunities and the challenges for minimizing buildings' carbon footprint.

Second, the main principles of integrated design and decision making for smart zero-energy buildings and grids are documented and explored with the aid of best practice examples. Benefits, barriers and methodologies for addressing the potential and evaluating the impact of integrated design are explained.

In Chapter 3, with the aid of case studies, we present data analysis and energy modeling of smart and zero-energy buildings and communities for the evaluation and management of energy operations in buildings integrating innovative renewable energy technologies.

In Chapter 4, the impact of human presence to the energy consumption and indoor air quality within an educational building of the National University of Singapore (NUS) is investigated. An experimental campaign took place and data was analyzed with respect to the energy consumption and air quality of three rooms, each one with different usage and occupancy. The impact of different occupancy patterns on the energy demand, the illuminance of the building, as well as the internal levels of temperature, relative humidity and CO2, are examined.

In Chapter 5, the energy consumption and indoor environmental quality of one of the three buildings of the Design and Environment School (SDE3) of the National University of Singapore is evaluated and cross-correlated based on a different perspective. Prediction algorithms based on artificial neural network models are tested.

In Chapter 6, objective and subjective evaluation of thermal comfort is analyzed in the context of a unique smart zero-energy industrial facility in Italy.

In Chapter 7, the user engagement of residents in a smart zero-energy building in the same area of Italy is investigated in order to provide the framework for analyzing individual preferences, identifying consumption patterns and assessing the utilisability of information provided to users as well as how effective this is in supporting behavioral change.

Chapter 8 deals with the integration of energy storage in smart communities and smart grids. The various energy storage technologies are presented including electrical, mechanical, chemical and thermal. Energy storage and optimization of its utilization in smart grids integrating renewable energy technologies is explored through state-of-the-art case studies.

Finally, the conclusion outlines the main and overall conclusions and recommendations stemming from the findings of the presented research.

Acknowledgments

The editors express their deepest appreciation and gratitude to all partners, personnel, and researchers for their unique contributions, time and efforts which altogether resulted in making this publication happen. We are also very thankful to the European Commission and the EU taxpayer for devoting the necessary financial resources for the implementation of the Smart GEMS project.

Nikos KAMPELISNovember 2021

List of Acronyms

AMI

Advanced Metering Infrastructure

ANN

Artificial Neural Network

AP

Accredited Professional

ATES

Aquifer Thermal Energy Storage

BAS

Building Automation System

BCVTB

Building Controls Virtual Test Bed

BEMS

Building Energy Management System

BIM

Building Information Modeling

biPV

building-integrated PhotoVoltaics

BMS

Building Management System

CAES

Compressed Air Energy Storage

CDD

Cooling Degree Days

CHP

Cogeneration of Heat and Power

COP

Coefficient Of Performance

CPC

Compound Parabolic Collector

CSP

Curtailment Service Provider

Cv

Coefficient of variance

DA

Day-Ahead

DC

Direct Current

DER

Distributed Energy Resources

DG

Diesel Generator

DHW

Domestic Hot Water

DNI

Direct Normal Irradiance

DR

Demand Response

DSG

Direct Steam Generation

DSM

Demand Side Management

EED

Energy Efficiency Directive

EER

Energy Efficiency Ratio

EES

Electrical Energy Storage

EMS

Energy Management System

EPBD

Energy Performance Buildings Directive

ES

Energy Signature

ESEER

European Seasonal Energy Efficiency Ratio

ETL

Extract, Transform, Load

EV

Electric Vehicles

FC

Fuel Cell

FCU

Fan Coil Units

FMU

Functional Mock-up Units

G2V

Grid-to-Vehicle

GA

Genetic Algorithm

GSHP

Ground Source Heat Pumps

HDD

Heating Degree Days

HESS

Hybrid Energy Storage Systems

HMI

Human Machine Interface

HPS

Hydro-Pumped Systems

HRU

Heat Recovery Units

HTF

Heat Transfer Fluid

HVAC

Heating, Ventilation, Air Conditioning

HVDC

High Voltage Direct Current

IAM

Incident Angle Modifier

ICT

Information and Computer Technology

ID

Integrated Design

IED

Integrated Energy Design

IoT

Internet of Things

k-NN

k-Nearest Neighbor

KPI

Key Performance Indicator

LCA

LifeCycle Analysis

LCC

LifeCycle Cost

LCCA

LifeCycle Cost Assessment

LFC

Linear Fresnel Collectors

LFR

Linear Fresnel Reflector

LOLP

Loss Of Load Probability

MAPE

Mean Average Percentage Error

MS

Member States

MS

Molten Salt

MS-TES

Molten Salt Thermal Energy Storage

NARX

Nonlinear AutoRegressive network with eXogenous input

NUS

National University of Singapore

NZEB

Nearly Zero-Energy Building

ORC

Organic Rankine Cycle

PCM

Phase Change Material

PEV

Plug-in Electric Vehicles

PLC

Programmable Logic Controller

PMP

Performance Measurement Protocols

PMV

Predicted Mean Vote

POD

Point Of Delivery

PPD

Percentage of People Dissatisfied

PSO

Particle Swarm Optimization

PTC

Parabolic Trough Collectors

PV

PhotoVoltaic

R

Pearson's coefficient

RES

Renewable Energy Sources

RforI

Research for Innovation

RH

Relative Humidity

RMSE

Root Mean Squared Error

SCTF

Single Coil Twin Fan

SDE

School of Design and Environment

SEER

Seasonal Energy Efficiency Ratio

SHC

Solar Heating and Cooling

SME

Small and Medium Enterprises

SMERC

SMart grid Energy Research Center

SMES

Superconducting Magnetic Energy Storage

SPSS

Statistical Package for Social Sciences

TES

Thermal Energy Storage

V2B

Vehicle-to-Building

V2G

Vehicle-to-Grid

VRFB

Vanadium Redox Flow Batteries

WT

Wind Turbine

ZEB

Zero-Energy Buildings

Chapter written by Nikos KAMPELIS.

1The Role of Smart Grids in the Building Sector

A smart grid is a dynamically interactive real-time infrastructure concept that encompasses the many visions of the stakeholders of diverse energy systems (El-Hawary 2014). Smart grids are electrical power grids that are more efficient and more resilient, and therefore “smarter”, than existing conventional power grids. The smartness is focused not only on the elimination of blackouts, but also on making the grid greener, more efficient, adaptable to customers’ needs, and therefore, less costly (El-Hawary 2014; Giordano et al. 2013). Smart grids incorporate innovative IT technology that allows for two-way communication between the utility and its customers/users. As a result, the sensing along the transmission lines and the sensing from the customer’s side is what makes the grid “smart”.

Like the Internet, the smart grid consists of controls, computers, automation, new technologies, smart buildings and equipment working together, but in this case these technologies will work with the electrical grid to respond digitally to the quickly changing energy demands of the users. Therefore smart grids create an exceptional opportunity for the support of the development of smart zero-energy buildings and communities, and they offer a step towards the Internet of Things (IoT) for the Energy and Building Industry (Chen et al. 2013; Zhen et al. 2012).

Smart grids open the door to new applications with far-reaching interdisciplinary impacts: providing the capacity to safely integrate more renewable energy sources (RES), smart buildings and distributed generators into the network; delivering power more efficiently and reliably through demand response and comprehensive control and monitoring capabilities; using automatic grid reconfiguration to prevent or restore outages (self-healing capabilities); enabling consumers to have greater control over their electricity consumption and to actively participate in the electricity market.

Smart grids can create a revolution in the building sector. The accumulated experience of the last few decades has shown that the hierarchical, centrally controlled grid of the 20th century is ill-suited to the needs of the 21st century. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability. The role of buildings in this framework is crucial. This chapter addresses critical issues related to smart grid technologies and the integration of buildings in this new power grid framework (Güngör et al. 2011). The main objective of this chapter is to provide a contemporary view of the current state of the art in the potential of buildings and communities to be integrated in smart grids, as well as to discuss the still-open research issues in this field. Since the vast majority of smart grids’ potential customers are buildings (residential, commercial, retail and industrial) and communities, the chapter addresses the challenges posed by smart grids on the building and community level.

1.1. Smart and zero-energy buildings

The energy consumption for buildings accounts for 40% of the energy used worldwide. It has become a widely-accepted fact that measures and changes in the building modus operandi can yield substantial energy savings, minimizing the buildings’ carbon footprint (Santamouris and Kolokotsa 2013; Deng et al. 2014). Moreover, buildings in the near future should be able to produce the amount of energy they consume, that is, become zero or nearly zero-energy buildings (ZEBs) (Kolokotsa et al. 2011; Pyloudi et al. 2015). This is a mandatory requirement based on the fact that by December 31, 2020, all new buildings were nearly zero-energy consumption buildings. New buildings occupied and owned by public authorities needed to comply with the same criteria by December 31, 2018 (Kapsalaki and Leal 2011; Kolokotsa et al. 2011).

ZEBs are buildings that work in synergy with the grid, avoiding putting additional stress on the power infrastructure (Li et al. 2013). Achieving a ZEB includes, apart from minimizing the required energy through efficient measures and covering the minimized energy needs by adopting renewable sources, a series of optimized and well-balanced operations between consumption and production, coupled with successful grid integration (Carlisle et al. 2009).

Information and computer enabled technologies (ICT) and smart grids implementation are the keys to achieve the aforementioned zero energy goals (Privat 2013). ICT for energy management in buildings has evolved considerably in the last decades, leading to a better understanding and usage of the term “smart buildings” (Nikolaou et al. 2012). Advances in the design, operation optimization and control of energy-influencing building elements (e.g. HVAC, solar, fuel cells (FC), CHP, shading, natural ventilation, etc.) unleashed the potential for the realization of significant energy savings and efficiencies in the operation of both new and existing dwellings worldwide. Smart buildings ready to be interconnected with smart grids should comply with the following requirements:

a) incorporation of smart metering;

b) demand response capabilities;

c) distributed architecture;

d) interoperability.

1.1.1. Smart metering

Smart metering is a prerequisite and starting point for the effective implementation of smart grids and zero-energy buildings. In Finland, the usage of smart metering encouraged consumers to increase energy efficiency by 7%. In order for electricity providers to deliver intelligent services for customers, bidirectional metering interfaces should be used to obtain customers’ energy demand information (Bae 2014). Moreover, through the advances of smart metering, sensors-based approaches can be exploited to provide power load forecasting (Jain et al. 2014). Data collected from smart meters, building management systems and weather stations can be used by advanced artificial intelligent techniques and machine learning algorithms to infer the complex relationships between energy consumption and various variables such as temperature, solar radiation, time of day and occupancy (Mellit and Pavan 2010; Gobakis et al. 2011; Zhao and Magoulès 2012; Jain et al. 2014; Jetcheva et al. 2014; Papantoniou et al. 2016). Due to the fast development and application of low cost options for energy metering in recent years, power load prediction is becoming increasingly relevant and cost effective (Fan 2014; Jain et al. 2014).

Smart metering with sensor-based approaches was exploited in the framework of the Green@Hospital project (www.greenhospital-project.eu/). In this project, the outdoor temperatures and hospitals’ energy demand were predicted for 4, 8, 12 and 24 hours ahead (Papantoniou et al. 2015). This prediction is then used for optimal control of the hospitals’ air handling units, leading to an almost 20% reduction of the energy used. Other researchers exploit neural networks’ capabilities for 24 h-ahead building-level electricity load forecasting, using data collected from various operational commercial and industrial building sites (Jetcheva 2014). Data mining-based approaches which collate models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy, are also developed. This approach was adopted to analyze the large energy consumption data of the tallest building in Hong Kong (Fan 2014) with very satisfactory results. These ensemble models can be valuable tools for developing strategies of fault detection and diagnosis, operation optimization and interactions between buildings and smart grids. Moreover, data processing and interpretations extracted by the smart meter can provide useful information for the buildings’ energy behavior. Advanced techniques such as cluster analysis are used by various researchers (Nikolaou et al. 2012; Panapakidis 2014), leading to the determination of optimum clustering procedures as well as building benchmarking.

1.1.2. Demand response (DR)

DR (Bartusch and Alvehag 2014; Li and Hong 2014) offers the ability to apply changes in the electricity usage by the consumers from their normal consumption patterns in response to changes in electricity pricing over time (Bradley et al. 2013). This leads to lower energy demand during peak hours or during periods that the electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be a more cost-effective alternative than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. DR is expected to increase energy market efficiency and security of supply, which will ultimately benefit customers by way of providing options for managing their electricity costs, which leads to a reduced environmental impact.

The already available DR programs are generally categorized into incentive and price-based programs. Incentive-based programs provide economic incentives for customers to reduce demand at times of capacity shortage or exceptionally high electricity prices, whereas price-based demand response programs involve dynamic tariff rates that promote general changes in patterns of electricity use. Time-of-use tariffs, which are one of the major price-based DR programs in use involve different unit prices within different blocks of time, and reflect the average cost of utilities during these periods (Bartusch and Alvehag 2014).

There are some efforts at the country level that show the benefits of DR in electricity supply. The policy discussions in the UK on the economic case for DR are analyzed by Bradley et al. (2013). A cost/benefit analysis is performed in a quantitative manner showing that the benefits on a country level are clearly very significant, that is, there was a 2.8% reduction in overall electricity use and a 1.3% shift in peak demand. Moreover, the economic viability of the DR mainly depends on ensuring participation by the end users, that is, the building sector. An increase in participation can be ensured by lowering the participant costs and sharing of benefits. Finally, it is revealed that the actual costs of the infrastructure are also affected by customer engagement and trust. An empirical study for Sweden is performed by Bartusch and Alvehag (2014) in order to estimate the end users’ response to a demand-based time-of-use electricity distribution tariff among Swedish single-family residential houses. The study showed that in the long term, the residential households still respond to the price signals of the tariff by cutting demand in peak hours and shifting electricity consumption from peak to off-peak hours.

Energy efficient smart buildings are possible by integrating a smart meter, smart sockets, domestic renewable energy generation and energy storage systems for integrated energy management, and this integrated system supports demand side load management, distributed generation and distributed storage provisions of future smart grids (Kayo et al. 2014; Keles et al. 2015). Consequently, the effective integration of buildings in smart grids requires appropriate levels of digital technology and interoperability (Oliveira-Lima et al. 2014). The successful implement-station of DR, as mentioned in the previous paragraphs, requires near-real-time power management (Hong et al. 2014) as well as advanced building automation and communication protocols. More information on the various communication protocols that can support the ZEB perspective can be found in Kolokotsa et al