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E-raamat: Smart Buildings, Smart Communities and Demand Response

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  • Ilmumisaeg: 31-Dec-2020
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119804352
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 31-Dec-2020
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119804352

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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.

Preface ix
Nikos Kampelis
Nomenclature xv
Nikos Kampelis
Chapter 1 Demand Response in Smart Zero Energy Buildings and Grids
1(36)
Nikos Kampelis
1.1 Introduction
1(1)
1.2 Smart and zero energy buildings
2(7)
1.3 DR and smart grids
9(23)
1.3.1 DR and congestion management
18(1)
1.3.2 DR and AS
19(2)
1.3.3 DR programs
21(1)
1.3.4 Building level DR
22(4)
1.3.5 District level DR and microgrids
26(5)
1.3.6 ANN-based short-term power forecasting
31(1)
1.4 Scientific focus of the book
32(2)
1.5 Book outline and objectives
34(3)
Chapter 2 DR in Smart and Near-zero Energy Buildings: The Leaf Community
37(6)
Nikos Kampelis
Konstantinos Gobakis
Vagias Vagias
Denia Kolokotsa
Laura Standardi
Daniela Isidori
Cristina Cristalli
Fabio Maria Montagnino
Filippo Paredes
Pietro Muratore
Luca Venezia
Marina Kyprianou Dracou
Alaric Montenon
Andri Pyrgou
Theoni Karlessi
Mattheos Santamouris
2.1 The Leaf Lab industrial building, AEA Italy
39(2)
2.2 The Leaf House residential building, AEA Italy
41(2)
Chapter 3 Performance of Industrial and Residential Near-zero Energy Buildings
43(22)
Nikos Kampelis
Konstantinos Gobakis
Vagias Vagias
Denia Kolokotsa
Laura Standardi
Daniela Isidori
Cristina Cristalli
Fabio Maria Montagnino
Filippo Paredes
Pietro Muratore
Luca Venezia
Marina Kyprianou Dracou
Alaric Montenon
Andri Pyrgou
Theoni Karlessi
Mattheos Santamouris
3.1 Materials and methods
44(7)
3.1.1 Energy simulation model
45(6)
3.2 Energy performance analysis
51(10)
3.2.1 The Leaf Lab
51(6)
3.2.2 The Leaf House
57(4)
3.3 Discussion
61(2)
3.4 Conclusion
63(2)
Chapter 4 HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response
65(30)
Nikos Kampelis
Nikolaos Sifakis
Denia Kolokotsa
Konstantinos Gobakis
Konstantinos Kalattzakis
Daniela Isidori
Cristina Cristalli
4.1 Methodology
66(4)
4.2 GA optimization model
70(2)
4.3 Model of energy cost
72(1)
4.4 Results and discussion
73(19)
4.4.1 Scenario 1: January 25, 2018 (winter)
74(2)
4.4.2 Scenario 2: March 27, 2018 (spring)
76(1)
4.4.3 Scenario 3: August 15, 2018 (summer)
77(4)
4.4.4 Scenario 4: September 10, 2018 (autumn)
81(3)
4.4.5 Scenario 5: September 21, 2018 (autumn)
84(1)
4.4.6 Scenario 6: November 20, 2018 (winter)
84(4)
4.4.7 Scenario 7: November 22, 2018 (winter)
88(1)
4.4.8 Scenario 8: November 25, 2018 (winter)
88(4)
4.5 Conclusion and future steps
92(3)
Chapter 5 Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions
95(48)
Nikos Kampelis
Elisavet Tsekeri
Denia Kolokotsa
Konstantinos Kalattzakis
Daniela Isidori
Cristina Cristalli
5.1 Infrastructure and methods
100(4)
5.2 Day-ahead GA cost of energy/load shifting optimization based on ANN hourly power predictions
104(2)
5.3 ToU case study
106(15)
5.3.1 ANN-based predictions
106(6)
5.3.2 GA optimization results
112(9)
5.4 DA real-time case study
121(18)
5.4.1 ANN-based predictions
121(5)
5.4.2 Combined ANN prediction/GA optimization results
126(13)
5.5 Limitations of the proposed approach
139(1)
5.6 Conclusion
139(4)
Conclusions and Recommendations 143(4)
Nikos Kampelis
References 147(16)
List of Authors 163(4)
Index 167
Denia Kolokotsa is Associate Professor at the School of Environmental Engineering of the Technical University of Crete in Greece, President of the European Cool Roofs Council, Editor of the Elsevier Energy and Buildings Journal and former Editor-in-Chief of the Advances in Building Energy Research Journal.

Nikos Kampelis works as a Research Associate at the Energy Management in the Built Environment Research (EMBER) laboratory of the Technical University of Crete. He completed his PhD on demand response for the optimal integration of loads and renewable energy in microgrids.