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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1 Demand Response in Smart Zero Energy Buildings and Grids
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
4 HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response
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
Conclusions and Recommendations
List of Authors
End User License Agreement
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)
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
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)
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 ...
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...
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...
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 ...
Table of Contents
End User License Agreement
Engineering, Energy and Architecture Set
coordinated by Lazaros E Mavromatidis
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:
27-37 St George’s Road
London SW19 4EU
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
© 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
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.
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.
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.
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.
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.
Advanced Metering Infrastructure
Artificial Neural Network
Aggregators or Retail Customers
Building Energy Management System
Cogeneration of Heat and Power
Carbon Dioxide Equivalent Emissions
Coefficient Of Performance
Critical Peak Pricing
Curtailment Service Provider
Coefficient of Variance
Day-Ahead Real-Time Pricing
District Energy Management Systems
Distributed Energy Resources
Domestic Hot Water
Demand Response Providers
Demand Side Management
Distribution System Operator
Energy Efficiency Directive
Energy Efficiency Ratio
Energy Management System
Energy Service COmpany
Hybrid Renewable Energy System
Heating, Ventilation and Air Conditioning
Internet of Things
International Performance Measurement and Verification Protocol
Mean Absolute Percentage Error
Mean Bias Error
Mixed Integer Linear Programming
Mixed Integer NonLinear Programming
Mixed Integer Programming
Maximum Power Point Tracking
Nonlinear AutoRegressive ANN with eXogenous input
National Institute of Standards and Technology
Near-Zero Energy Building
Open Automated Demand Response
Predicted Mean Vote
Percentage of People Dissatisfied
Particle Swarm Optimization
Renewable Energy Sources
Root Mean Squared Error
Regional Transmission Operator
Software as a Service
Sustainable Development Goal
Time of Use
Virtual End Node
Virtual Transfer Node
Zero Energy Building
Day-ahead price per hour for hours 1–24
Total energy plus taxes (€)
Day-ahead hourly unit cost of energy in each building (€/kWh)
Total tax charges (€)
Energy procurement cost (€)
Network services cost (€)
Energy procurement fixed cost component (€/kWh)
Daily excise duty on electricity and taxes (€)
Various costs normalized per kWh (€/Wh)
Fixed cost component (€)
Maximum power cost component (€/kW)
Active energy cost component (€/kWh)
Fixed cost for up to 4 GWh per month (€/kWh
Excise duty per kWh (€/kWh)
Parameter to account for F, AT, and A-UC components (€/kWh)
Maximum power fixed cost component (€/kW)
Clothing insulation (m
Value added tax (€)
Daily load shift (kWh)
GA optimized hourly electrical energy (kWh) at building or building group level
Metabolic rate (W/m
Hourly average power consumption of the HVAC in kW (equivalent to kWh)
Hourly temperature set points of the HVAC system the next day
Daily energy operating costs (€)
Daily energy operating costs of Leaf Lab (L4) building (€)
Daily energy operating costs of Summa (L2) building (€)
Daily energy operating costs of Kite (L5) building (€)
Day-ahead market prices (€/kWh)
DA price flexible factor per hour ℎ (€/kWh)
Relative humidity (%)
Air temperature (T
Mean radiant temperature (°C)
Relative air velocity (m/s)
Effective mechanical power (W/m
Weighting coefficient for the daily operational cost of energy for the HVAC
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.
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.
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