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This book focuses on near-zero energy buildings (NZEBs), smart communities and microgrids. In this context, demand response (DR) is associated with significant environmental and economic benefits when looking at how electricity grids, communities and buildings can operate optimally. In DR, the consumer becomes a prosumer with an important active role in the exchange of energy on an hourly basis. DR is gradually gaining ground with respect to the reduction of peak loads, grid balancing and dealing with the volatility of renewable energy sources (RES). This transition calls for high environmental awareness and new tools or services that will improve the dynamic as well as secure multidirectional exchange of energy and data. Overall, DR is identified as an important field for technological and market innovations aligned with climate change mitigation policies and the transition to sustainable smart grids in the foreseeable future. Smart Buildings, Smart Communities and Demand Response provides an insight into various intrinsic aspects of DR potential, at the building and the community level.

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Table of Contents

Cover

Title page

Copyright

Preface

Nomenclature

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

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

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

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

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

Conclusions and Recommendations

References

List of Authors

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1. Smart grid NIST conceptual model

Figure 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)

Chapter 2

Figure 2.1. The Leaf Community map. For a color version of this figure, see www....

Figure 2.2. The Leaf Lab

Figure 2.3. The Leaf House

Chapter 3

Figure 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)

Chapter 4

Figure 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 ...

Chapter 5

Figure 5.1. Methodological framework

Figure 5.2. Flowchart of the developed approach

Figure 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 scenarios

Figure 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 neuron

Figure 5.12. Real versus predicted net microgrid electrical power on 20/3/17

Figure 5.13. GA obtained load shifting solution for 20/3/17

Figure 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 1/8/17

Figure 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 14/11/17

Figure 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...

List of Tables

Chapter 3

Table 3.1. Validation of the Leaf Lab model based on data from MyLeaf

Table 3.2. Leaf House energy consumption data for 2015 (MyLeaf)

Table 3.3. Normalized primary energy consumption in the design and operational p...

Chapter 5

Table 5.1. Pilot buildings in the Leaf Community

Table 5.2. MBE and MAPE for ANN predictions

Table 5.3. Results of the optimization on 24/7/17 during the summer period

Table 5.4. Results of the optimization on 20/11/17 during the winter period

Table 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 ...

Guide

Cover

Table of Contents

Title page

Copyright

Preface

Nomenclature

Begin Reading

Appendix

References

Index

End User License Agreement

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Engineering, Energy and Architecture Set

coordinated by Lazaros E Mavromatidis

Volume 8

Smart Buildings, Smart Communities and Demand Response

Edited by

Denia Kolokotsa Nikos Kampelis

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

Preface

Background

Demand response (DR) is associated with significant environmental and economic benefits when looking at how the electricity grid can operate optimally. Adding flexibility in power consumption provides a sound basis for improving the grid’s environmental performance and efficiency. For example, reducing peak loads at grid level could lead to a lower level of operation for generation plants with a high running cost, low efficiency and low environmental performance. Furthermore, as the storage of electricity is bound to technical and economic constraints, the absorption of excess electricity from renewable energy sources is feasible through a demand following generation concept.

DR is gradually gaining ground with respect to (1) the reduction of peak loads; (2) grid balancing; and (3) dealing with the volatility of renewable energy sources (RES). In this context, demand side management techniques such as peak clipping, valley filling, load shifting and flexible load shape are already being employed. Also, various DR programs are being designed and implemented, including critical peak pricing, capacity bidding, thermostat/direct load control and fast DR dispatch/ancillary services.

In DR, the consumer becomes a prosumer with an important active role in the exchange of energy on an hourly basis. This transition calls for high environmental awareness and new tools and services which will improve the dynamic, as well as secure multidirectional exchange of energy and data. Overall, DR is identified as an important field for technological and market innovations aligned with climate change mitigation policies and the transition to sustainable smart grids in the foreseeable future.

Why this book?

This book provides an insight into various intrinsic aspects related to the assessment of DR potential, at the building and the community level. Issues pertaining to the use of building energy models, compared to actual performance, and smart monitoring are addressed. Furthermore, temperature set-point adjustment, which is a standard practice in controlling heating, ventilation and air conditioning (HVAC) systems, is assessed with the aid of simulation, to investigate the optimality in multidynamic systems’ operation. On the other hand, the book focuses on load shifting optimization at the community level on the basis of time of use (ToU) and real-time pricing (RTP). The rationale behind this is that energy markets should be operated in a transparent manner inducing higher efficiency of power grids through the promotion of renewable energy. In this context, it is foreseen that a high penetration of RES, given their minimal operational expenses and environmental advantages, should be reflected in the time slots of low costs and consumer prices. In this way, all consumers will be provided with a clear roadmap and the necessary motivational factors in order to adjust our consumption when possible and take advantage of electric energy availability from clean resources.

Who is this book for?

This book focuses on near-zero energy buildings (NZEBs), smart communities and microgrids. Therefore, on one hand, it would be valuable for experts, professionals and postgraduates with an interest in (1) highly efficient buildings and communities; (2) smart monitoring systems; and (3) building energy modeling. On the other hand, the book would be beneficial for professionals with an interest in building or community level power predictions and optimization, as well as about how such tools and techniques can be utilized to evaluate DR at the building and/or district level.

Structure

Firstly, a comprehensive approach for evaluating the performance of industrial and residential smart energy buildings/NZEBs is presented. A detailed audit of construction characteristics, installed systems and controls is conducted and presented. Subsequently, holistic data from advanced metering and sensor equipment are explored to verify energy consumption and actual building energy performance. Dynamic energy models are developed, validated and tested to explore key aspects of the operational behavior of buildings and systems, and draw essential knowledge about their performance. Consumption data based on real measurements is compared, on one hand, with dynamic building model simulation results and on the other hand, with the initial annual energy consumption, obtained via the building’s energy efficiency certification scheme prior to construction. Findings are explored to address the actual performance gap, reflect on the limitations of each approach and highlight important conclusions.

Secondly, the book focuses on how DR can be applied at the building level. A novel evaluation and optimization methodology, in the context of the building level DR, is presented. To this end, DR is assessed with the aid of an RTP scheme based on the actual energy market data. In this context, HVAC system performance is evaluated according to the energy consumption, the corresponding energy costs and the indoor thermal comfort.

Thirdly, the book describes how DR can be applied at the community level by exploiting predictions of day-ahead consumption and/or production and load shifting. The benefits of this approach are evaluated in terms of the economic savings based on a flat versus ToU tariff and an RTP scheme. The reliable prediction of power consumption and/or production 24 hours ahead is performed using artificial neural network modeling, whereas load shifting optimization is conducted using a genetic algorithm dual-objective optimization algorithm.

In Chapter 2, the smart and zero energy building facilities used as case studies for evaluating DR at the building and the community levels are presented.

Chapter 3 provides a thorough analysis of the performance of residential and industrial buildings with the aid of measurements and how they can be utilized for building energy modeling and validation purposes.

Chapter 4 presents a newly developed approach for optimizing the operation of HVAC systems from a DR perspective.

Chapter 5 presents a novel approach for the community level prediction and optimization in a DR setting.

Finally, the overall conclusions and recommendations arising from the findings of this research are presented.

Acknowledgments

The editors express their deepest appreciation to all the authors for their contribution and to the European Commission, for allocating the funds in order for the Smart GEMS project to be implemented. Special thanks are owed to Dr. Cristina Cristalli, Head of Research for Innovation in the Loccioni Group and to the Loccioni Group for providing access and support for research activities in the framework of Smart GEMS project to be conducted in their industrial high-end facilities.

Nikos KAMPELIS

September 2020

Nomenclature

Acronyms

AC

Alternating Current

AMI

Advanced Metering Infrastructure

ANN

Artificial Neural Network

ARC

Aggregators or Retail Customers

AS

Ancillary Services

BEMS

Building Energy Management System

biPV

Building-Integrated PhotoVoltaic

CHP

Cogeneration of Heat and Power

CO

2-eq

Carbon Dioxide Equivalent Emissions

COP

Coefficient Of Performance

CPP

Critical Peak Pricing

CSP

Curtailment Service Provider

Cv

Coefficient of Variance

DA

Day Ahead

DARTP

Day-Ahead Real-Time Pricing

DC

Direct Current

DEMS

District Energy Management Systems

DER

Distributed Energy Resources

DG

Diesel Generator

DHW

Domestic Hot Water

DR

Demand Response

DRP

Demand Response Providers

DSM

Demand Side Management

DSO

Distribution System Operator

EED

Energy Efficiency Directive

EER

Energy Efficiency Ratio

EMS

Energy Management System

ESCO

Energy Service COmpany

FC

Fuel Cell

GA

Genetic Algorithm

HRES

Hybrid Renewable Energy System

HVAC

Heating, Ventilation and Air Conditioning

ID

Integrated Design

IoT

Internet of Things

IPMVP

International Performance Measurement and Verification Protocol

MAPE

Mean Absolute Percentage Error

MBE

Mean Bias Error

MILP

Mixed Integer Linear Programming

MINLP

Mixed Integer NonLinear Programming

MIP

Mixed Integer Programming

MPPT

Maximum Power Point Tracking

MT

Micro-Turbine

NARX

Nonlinear AutoRegressive ANN with eXogenous input

NIST

National Institute of Standards and Technology

NZEB

Near-Zero Energy Building

OpenADR

Open Automated Demand Response

PMV

Predicted Mean Vote

PPD

Percentage of People Dissatisfied

PSO

Particle Swarm Optimization

PV

PhotoVoltaic

RES

Renewable Energy Sources

RH

Relative Humidity

RMSE

Root Mean Squared Error

RTO

Regional Transmission Operator

RTP

Real-Time Pricing

SaaS

Software as a Service

SDG

Sustainable Development Goal

ToU

Time of Use

VEN

Virtual End Node

VTN

Virtual Transfer Node

WT

Wind Turbine

ZEB

Zero Energy Building

Symbols

C

i

Day-ahead price per hour for hours 1–24

C

_(

E

,

T

)

Total energy plus taxes (€)

Day-ahead hourly unit cost of energy in each building (€/kWh)

C

T

Total tax charges (€)

C

S

Energy procurement cost (€)

C

N

Network services cost (€)

C

S

,

F

Energy procurement fixed cost component (€/kWh)

C

E

D

D

Daily excise duty on electricity and taxes (€)

C

v

,

u

Various costs normalized per kWh (€/Wh)

C

F

Fixed cost component (€)

C

p

max

Maximum power cost component (€/kW)

C

A

T

Active energy cost component (€/kWh)

C

A

UC

Fixed cost for up to 4 GWh per month (€/kWh

C

E

D

H

Excise duty per kWh (€/kWh)

C

F

A

A

Parameter to account for F, AT, and A-UC components (€/kWh)

C

p

max

,

F

Maximum power fixed cost component (€/kW)

Icl

Clothing insulation (m

2

K/W)

IVA

Value added tax (€)

Load

S

h

i

ft

Daily load shift (kWh)

GA optimized hourly electrical energy (kWh) at building or building group level

M

Metabolic rate (W/m

2

)

P

i

Hourly average power consumption of the HVAC in kW (equivalent to kWh)

Hourly temperature set points of the HVAC system the next day

C

ost

E

Daily energy operating costs (€)

C

ost

E

_

Lap

Daily energy operating costs of Leaf Lab (L4) building (€)

C

ost

E

_

S

u

mma

Daily energy operating costs of Summa (L2) building (€)

C

ost

E

__

k

ite

Daily energy operating costs of Kite (L5) building (€)

DA

h

Day-ahead market prices (€/kWh)

DA

N

,

h

DA price flexible factor per hour ℎ (€/kWh)

R

Pearson’s coefficient

RH

Relative humidity (%)

T

air

Air temperature (T

air

) (°C)

Tr

Mean radiant temperature (°C)

V

air

Relative air velocity (m/s)

W

Effective mechanical power (W/m

2

)

W

c

Weighting coefficient for the daily operational cost of energy for the HVAC

w

pm

v

Weighting coefficient for the daily thermal comfort

Hourly value of total energy consumption in each building (kWh)

Baseline hourly electrical energy (kWh) based on day-ahead neural network predictions

Chapter written by Nikos KAMPELIS.

1Demand Response in Smart Zero Energy Buildings and Grids

1.1. Introduction

In broad terms, demand response (DR) refers to retail customers participating in electricity markets by responding to varying prices over time (U.S. Department of Energy 2006). DR is otherwise defined as “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” (Losi et al. 2015). However, DR is inextricably linked to smart grids since an optimum response to real-time signals or any kind of dynamic information requires interoperability, embedded intelligence and advanced controls working harmonically in the same direction.