Smart Buildings, Smart Communities and Demand Response -

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Preface ix Nikos KAMPELIS Nomenclature xv Chapter 1. Demand Response in Smart Zero Energy Buildings and Grids 1 1.1. Introduction 1 1.2. Smart and zero energy buildings 2 1.3. DR and smart grids 9 1.3.1. DR and congestion management 18 1.3.2. DR and AS 19 1.3.3. DR programs 21 1.3.4. Building level DR 22 1.3.5. District level DR and microgrids 26 1.3.6. ANN-based short-term power forecasting 31 1.4. Scientific focus of the book 32 1.5. Book outline and objectives 34 Chapter 2. DR in Smart and Near-zero Energy Buildings: The Leaf Community 37 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. The Leaf House residential building, AEA Italy 41 Chapter 3. Performance of Industrial and Residential Near-zero Energy Buildings 43 3.1. Materials and methods 44 3.1.1. Energy simulation model 45 3.2. Energy performance analysis 51 3.2.1. The Leaf Lab 51 3.2.2. The Leaf House 57 3.3. Discussion 61 3.4. Conclusion 63 Chapter 4. HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response 65 Nikos KAMPELIS, Nikolaos SIFAKIS, Denia KOLOKOTSA, Konstantinos GOBAKIS, Konstantinos KALAITZAKIS, Daniela ISIDORI, Cristina CRISTALLI 4.1. Methodology 66 4.2. GA optimization model 70 4.3. Model of energy cost 72 4.4. Results and discussion 73 4.4.1. Scenario 1: January 25, 2018 (winter) 74 4.4.2. Scenario 2: March 27, 2018 (spring) 76 4.4.3. Scenario 3: August 15, 2018 (summer) 77 4.4.4. Scenario 4: September 10, 2018 (autumn) 81 4.4.5. Scenario 5: September 21, 2018 (autumn) 84 4.4.6. Scenario 6: November 20, 2018 (winter) 84 4.4.7. Scenario 7: November 22, 2018 (winter) 88 4.4.8. Scenario 8: November 25, 2018 (winter) 88 4.5. Conclusion and future steps 92 Chapter 5. Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions 95 Nikos KAMPELIS, Elisavet TSEKERI, Denia KOLOKOTSA, Konstantinos KALAITZAKIS, Daniela ISIDORI, Cristina CRISTALLI 5.1. Infrastructure and methods 100 5.2. Day-ahead GA cost of energy/load shifting optimization based on ANN hourly power predictions 104 5.3. ToU case study 106 5.3.1. ANN-based predictions 106 5.3.2. GA optimization results 112 5.4. DA real-time case study 121 5.4.1. ANN-based predictions 121 5.4.2. Combined ANN prediction/GA optimization results 126 5.5. Limitations of the proposed approach 139 5.6. Conclusion 139 Conclusions and Recommendations 143 References 147 List of Authors 163 Index 167