Smart Buildings, Smart Communities and Demand Response

1 Cover

2 Title page Engineering, Energy and Architecture Set coordinated by Lazaros E Mavromatidis Volume 8

3 Copyright First published 2020 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 Ltd 27-37 St George’s Road London SW19 4EU UK www.iste.co.uk John Wiley & Sons, Inc. 111 River Street Hoboken, NJ 07030 USA www.wiley.com © ISTE Ltd 2020 The rights of Denia Kolokotsa and Nikos Kampelis to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Control Number: 2020945010 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-78630-426-1

4 Preface

5 Nomenclature

6 1 Demand Response in Smart Zero Energy Buildings and Grids 1.1. Introduction 1.2. Smart and zero energy buildings 1.3. DR and smart grids 1.4. Scientific focus of the book 1.5. Book outline and objectives

7 2 DR in Smart and Near-zero Energy Buildings: 2.1. The Leaf Lab industrial building, AEA Italy 2.2. The Leaf House residential building, AEA Italy

8 3 Performance of Industrial and Residential Near-zero Energy Buildings 3.1. Materials and methods 3.2. Energy performance analysis 3.3. Discussion 3.4. Conclusion

9 4 HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response 4.1. Methodology 4.2. GA optimization model 4.3. Model of energy cost 4.4. Results and discussion 4.5. Conclusion and future steps

10 5 Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions 5.1. Infrastructure and methods 5.2. Day-ahead GA cost of energy/load shifting optimization based on ANN hourly power predictions 5.3. ToU case study 5.4. DA real-time case study 5.5. Limitations of the proposed approach 5.6. Conclusion

11 Conclusions and Recommendations

12 References

13 List of Authors

14 Index

15 End User License Agreement

1 Chapter 1Figure 1.1. Smart grid NIST conceptual modelFigure 1.2. DSM power profile change objectives (Koliou 2016)Figure 1.3. Open ADR 2.0 simple DR deployment scenario (Direct 1&2; OpenADR Alli...Figure 1.4. Open ADR 2.0 facilitator and aggregator DR deployment scenarios (Fac...Figure 1.5. Microgrid conceptual architecture (Zia et al. 2018)

2 Chapter 2Figure 2.1. The Leaf Community map. For a color version of this figure, see www....Figure 2.2. The Leaf LabFigure 2.3. The Leaf House

3 Chapter 3Figure 3.1. The model of the Leaf Lab in Google SketchUp. For a color version of...Figure 3.2. First floor, east office, measured and simulated indoor temperature....Figure 3.3. Ground floor, Leaf Lab reception, measured and simulated indoor temp...Figure 3.4. HVAC system validation based on monthly electrical energy consumptio...Figure 3.5. The Leaf House and its thermal energy model using OpenStudio plugin....Figure 3.6. Leaf House PV system monthly energy production for 2015 (MyLeaf)

4 Chapter 4Figure 4.1. Genetic algorithm (GA)-based heating, ventilation and air conditioni...Figure 4.2. Leaf Community electrical energy consumption and unit cost of energy...Figure 4.3. GAHVAC optimization results for January 25, 2018 (winter). For a col...Figure 4.4. GA HVAC optimization results for March 27, 2018 (spring). For a colo...Figure 4.5. GA HVAC optimization results for August 15, 2018 (summer). For a col...Figure 4.6. GA HVAC optimization results for September 10, 2018 (autumn). For a ...Figure 4.7. GA HVAC optimization results for September 21, 2018 (autumn). For a ...Figure 4.8. GA HVAC optimization results for November 20, 2018 (winter). For a c...Figure 4.9. GA HVAC optimization results for November 22, 2018 (winter). For a c...Figure 4.10. GA HVAC optimization results for November 25, 2018 (winter). For a ...

5 Chapter 5Figure 5.1. Methodological frameworkFigure 5.2. Flowchart of the developed approachFigure 5.3. Prediction of net electrical power consumption of L2, L4 and L5 for ...Figure 5.4. Prediction of net electrical power consumption of L2, L4 and L5 for ...Figure 5.5. Prediction of net electrical power consumption of L2, L4 and L5 for ...Figure 5.6. Prediction of net electrical power consumption for L2, L4 and L5 fro...Figure 5.7. Energy pricing profiles used in the baseline and optimized scenariosFigure 5.8. GA optimization power and cost results for L2, L4 and L5 on 24/7/17....Figure 5.9. GA optimization power and cost results for the Leaf Lab, the Summa a...Figure 5.10. GA optimization power and cost results for total power on 24/7/17 (...Figure 5.11. Mathematical model of a neuronFigure 5.12. Real versus predicted net microgrid electrical power on 20/3/17Figure 5.13. GA obtained load shifting solution for 20/3/17Figure 5.14. Cost of electrical energy based on the DARTP scheme, as obtained by...Figure 5.15. Real versus predicted net microgrid electrical power on 1/8/17Figure 5.16. GA obtained load shifting solution for 1/8/17. For a color version ...Figure 5.17. Cost of electrical energy based on the DARTP scheme, as obtained by...Figure 5.18. Real versus predicted net microgrid electrical power on 14/11/17Figure 5.19. GA obtained load shifting solution for 14/11/17. For a color versio...Figure 5.20. Cost of electrical energy based on the DARTP scheme, as obtained by...Figure 5.21. GA obtained load shifting solution for 14/11/17. For a color versio...Figure 5.22. Cost of electrical energy based on the DARTP scheme, as obtained by...

1 Chapter 3Table 3.1. Validation of the Leaf Lab model based on data from MyLeafTable 3.2. Leaf House energy consumption data for 2015 (MyLeaf)Table 3.3. Normalized primary energy consumption in the design and operational p...

2 Chapter 5Table 5.1. Pilot buildings in the Leaf CommunityTable 5.2. MBE and MAPE for ANN predictionsTable 5.3. Results of the optimization on 24/7/17 during the summer periodTable 5.4. Results of the optimization on 20/11/17 during the winter periodTable 5.5. Summary of ANN predictions (Pearson’s correlation coefficient R) for ...Table 5.6. Summary of ANN predictions (Pearson’s correlation coefficient R) for ...

1 Cover

2 Table of Contents

3 Title page Engineering, Energy and Architecture Set coordinated by Lazaros E Mavromatidis Volume 8

4 Copyright First published 2020 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 Ltd 27-37 St George’s Road London SW19 4EU UK www.iste.co.uk John Wiley & Sons, Inc. 111 River Street Hoboken, NJ 07030 USA www.wiley.com © ISTE Ltd 2020 The rights of Denia Kolokotsa and Nikos Kampelis to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Control Number: 2020945010 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-78630-426-1