La Scala
John Wiley & Sons
e druk, 2017
9781848217492
From Smart Grids to Smart Cities: New Challenges i n Optimizing Energy Grids
New Challenges in Optimizing Energy Grids
Specificaties
Gebonden, 368 blz.
|
Engels
John Wiley & Sons |
e druk, 2017
ISBN13: 9781848217492
Rubricering
Levertijd ongeveer 8 werkdagen
Samenvatting
This book addresses different algorithms and applications based on the theory of multiobjective goal attainment optimization. In detail the authors show as the optimal asset of the energy hubs network which (i) meets the loads, (ii) minimizes the energy costs and (iii) assures a robust and reliable operation of the multicarrier energy network can be formalized by a nonlinear constrained multiobjective optimization problem. Since these design objectives conflict with each other, the solution of such the optimal energy flow problem hasn t got a unique solution and a suitable trade off between the objectives should be identified. A further contribution of the book consists in presenting real–world applications and results of the proposed methodologies developed by the authors in three research projects recently completed and characterized by actual implementation under an overall budget of about 23 million .
Specificaties
Inhoudsopgave
<p>Preface xi</p>
<p>Introduction xvii<br />Massimo LA SCALA and Sergio BRUNO</p>
<p>Chapter 1. Unbalanced Three–Phase Optimal Power Flow for the Optimization of MV and LV Distribution Grids 1<br />Sergio BRUNO and Massimo LA SCALA</p>
<p>1.1. Advanced distribution management system for smart distribution grids 1</p>
<p>1.2. Secondary distribution monitoring and control 5</p>
<p>1.2.1. Monitoring and representation of LV distribution grids 6</p>
<p>1.2.2. LV control resources and control architecture 7</p>
<p>1.3. Three–phase distribution optimal power flow for smart distribution grids 8</p>
<p>1.4. Problem formulation and solving algorithm 11</p>
<p>1.4.1. Main problem formulation 11</p>
<p>1.4.2. Application of the penalty method 12</p>
<p>1.4.3. Definition of an unconstrained problem 14</p>
<p>1.4.4. Application of a quasi–Newton method 15</p>
<p>1.4.5. Solving algorithm 18</p>
<p>1.5. Application of the proposed methodology to the optimization of a MV network 20</p>
<p>1.5.1. Case A: optimal load curtailment 23</p>
<p>1.5.2. Case B: conservative voltage regulation 26</p>
<p>1.5.3. Case C: voltage rise effects 28</p>
<p>1.5.4. Algorithm performance 30</p>
<p>1.6. Application of the proposed methodology to the optimization of a MV/LV network 31</p>
<p>1.6.1. Case D: LV network congestions 33</p>
<p>1.6.2. Case E: minimization of losses and reactive control 36</p>
<p>1.6.3. Algorithm performance 37</p>
<p>1.7. Conclusions 38</p>
<p>1.8. Acknowledgments 38</p>
<p>1.9. Bibliography 39</p>
<p>Chapter 2. Mixed Integer Linear Programming Models for Network Reconfiguration and Resource Optimization in Power Distribution Networks 43<br />Alberto BORGHETTI</p>
<p>2.1. Introduction 43</p>
<p>2.2. Model for determining the optimal configuration of a radial distribution network 44</p>
<p>2.2.1. Objective function and constraints of the branch currents 46</p>
<p>2.2.2. Bus voltage constraints 48</p>
<p>2.2.3. Bus equations 50</p>
<p>2.2.4. Line equations 52</p>
<p>2.2.5. Radiality constraints 53</p>
<p>2.3. Test results of minimum loss configuration obtained by the MILP model 54</p>
<p>2.3.1. Illustrative example 54</p>
<p>2.3.2. Tests results for networks with several nodes and branches 57</p>
<p>2.3.3. Comparison between the MILP solutions for the test networks with the corresponding PF calculation results relevant to the obtained optimal network configurations 62</p>
<p>2.4. MILP model of the VVO problem 65</p>
<p>2.4.1. Objective function 66</p>
<p>2.4.2. Branch equations 67</p>
<p>2.4.3. Bus equations 69</p>
<p>2.4.4. Branch and node constraints 72</p>
<p>2.5. Test results obtained by the VVO MILP model 74</p>
<p>2.5.1. TS1 74</p>
<p>2.5.2. TS2 77</p>
<p>2.5.3. TS3 78</p>
<p>2.6. Conclusions 85</p>
<p>2.7. Acknowledgments 85</p>
<p>2.8. Bibliography 86</p>
<p>Chapter 3. The Role of Nature–inspired Metaheuristic Algorithms for Optimal Voltage Regulation in Urban Smart Grids 89<br />Giovanni ACAMPORA, Davide CARUSO, Alfredo VACCARO, Autilia VITIELLO and Ahmed F. ZOBAA</p>
<p>3.1. Introduction 89</p>
<p>3.2. Emerging needs in urban power systems 92</p>
<p>3.3. Toward smarter grids 93</p>
<p>3.4. Smart grids optimization 97</p>
<p>3.5. Metaheuristic algorithms for smart grids optimization 99</p>
<p>3.5.1. Genetic algorithm 99</p>
<p>3.5.2. Random Hill Climbing algorithm 101</p>
<p>3.5.3. Particle Swarm Optimization algorithm 101</p>
<p>3.5.4. Evolution strategy 103</p>
<p>3.5.5. Differential evolution 106</p>
<p>3.5.6. Biogeography–based optimization 108</p>
<p>3.5.7. Evolutionary programming 109</p>
<p>3.5.8. Ant Colony Optimization algorithm 110</p>
<p>3.5.9. Group Search Optimization algorithm 113</p>
<p>3.6. Numerical results 115</p>
<p>3.6.1. Power system test 116</p>
<p>3.6.2. Real urban smart grid 124</p>
<p>3.7. Conclusions 127</p>
<p>3.8. Bibliography 127</p>
<p>Chapter 4. Urban Energy Hubs and Microgrids: Smart Energy Planning for Cities 129<br />Eleonora RIVA SANSEVERINO, Vincenzo Domenico GENCO, Gianluca SCACCIANOCE, Valentina VACCARO, Raffaella RIVA SANSEVERINO, Gaetano ZIZZO, Maria Luisa DI SILVESTRE, Diego ARNONE and Giuseppe PATERNÒ</p>
<p>4.1. Introduction 129</p>
<p>4.1.1. Microgrids versus urban energy hubs 131</p>
<p>4.2. Approaches and tools for urban energy hubs 134</p>
<p>4.2.1. Policy 134</p>
<p>4.2.2. Analysis 135</p>
<p>4.2.3. Optimal design and operation tools 139</p>
<p>4.3. Methodology 143</p>
<p>4.3.1. Building type and urban energy parameter specification 143</p>
<p>4.3.2. Mobility simulator 147</p>
<p>4.3.3. Energy simulation and electrical load estimation for buildings 151</p>
<p>4.3.4. Optimization and simulation software for district 151</p>
<p>4.4. Application 152</p>
<p>4.4.1. Analysis 152</p>
<p>4.4.2. Simulations and optimization 160</p>
<p>4.4.3. Mobility and effects of policies and smart charging on peaking power 168</p>
<p>4.5. Conclusions 170</p>
<p>4.6 Bibliography 171</p>
<p>Chapter 5. Optimization of Multi–energy Carrier Systems in Urban Areas 177<br />Sergio BRUNO, Silvia LAMONACA and Massimo LA SCALA</p>
<p>5.1. Introduction 177</p>
<p>5.2. Optimal control strategy for a small–scale multi–carrier energy system 180</p>
<p>5.2.1. The proposed architecture 180</p>
<p>5.2.2. Mathematical formulation 183</p>
<p>5.2.3. Test results 190</p>
<p>5.3. Optimal design of an urban energy district 198</p>
<p>5.3.1. Energy district for urban regeneration: the San Paolo Power Park 199</p>
<p>5.3.2. Optimal design of the energy district 201</p>
<p>5.3.3. Integer variables and design choices 205</p>
<p>5.3.4. Mathematical formulation of the optimal control problem 206</p>
<p>5.3.5. Test results 214</p>
<p>5.4. Conclusions 227</p>
<p>5.5. Acknowledgments 228</p>
<p>5.6. Bibliography 228</p>
<p>Chapter 6. Optimal Gas Flow Algorithm for Natural Gas Distribution Systems in Urban Environment 231<br />Ugo STECCHI, Gaetano ABBATANTUONO and Massimo LA SCALA</p>
<p>6.1. Introduction 231</p>
<p>6.2. Natural gas network evolution 236</p>
<p>6.3. Implementing the monitoring and control system in the Gas Smart Grids pilot project 239</p>
<p>6.3.1. SCADA system 240</p>
<p>6.3.2. Controlling FRUs setpoints 244</p>
<p>6.4. Basic equations under steady–state conditions 246</p>
<p>6.5. Gas load flow formulation 253</p>
<p>6.6. Gas optimal flow method 256</p>
<p>6.7. Optimizing turbo–expander operations 258</p>
<p>6.8. Optimizing pressure profiles on the low pressure distribution grids 262</p>
<p>6.9. Conclusions 270</p>
<p>6.10. Acknowledgements 270</p>
<p>6.11. Bibliography 270</p>
<p>Chapter 7. Multicarrier Energy System Optimal Power Flow 273<br />Soheil DERAFSHI BEIGVAND, Hamdi ABDI and Massimo LA SCALA</p>
<p>7.1. Introduction 273</p>
<p>7.2. Basic concepts and assumptions 276</p>
<p>7.2.1. MEC and energy hub 276</p>
<p>7.2.2. CHP units 279</p>
<p>7.2.3. General assumptions 282</p>
<p>7.3. Problem formulation 283</p>
<p>7.3.1. Electrical power balance equations 283</p>
<p>7.3.2. Gas energy flow equation 283</p>
<p>7.3.3. Modeling of energy hubs 285</p>
<p>7.3.4. MECOPF problem 286</p>
<p>7.4. Time varying acceleration coefficient gravitational search algorithm 287</p>
<p>7.4.1. A brief comparison between the main structures of TVAC–GSA and PSO 291</p>
<p>7.5. TVAC–GSA–based MECOPF problem 292</p>
<p>7.6. Case study simulations and results 294</p>
<p>7.7. Conclusions 300</p>
<p>7.8. Appendix 1 301</p>
<p>7.9. Appendix 2 303</p>
<p>7.10. Bibliography 305</p>
<p>List of Authors 309</p>
<p>Index 311</p>
<p>Introduction xvii<br />Massimo LA SCALA and Sergio BRUNO</p>
<p>Chapter 1. Unbalanced Three–Phase Optimal Power Flow for the Optimization of MV and LV Distribution Grids 1<br />Sergio BRUNO and Massimo LA SCALA</p>
<p>1.1. Advanced distribution management system for smart distribution grids 1</p>
<p>1.2. Secondary distribution monitoring and control 5</p>
<p>1.2.1. Monitoring and representation of LV distribution grids 6</p>
<p>1.2.2. LV control resources and control architecture 7</p>
<p>1.3. Three–phase distribution optimal power flow for smart distribution grids 8</p>
<p>1.4. Problem formulation and solving algorithm 11</p>
<p>1.4.1. Main problem formulation 11</p>
<p>1.4.2. Application of the penalty method 12</p>
<p>1.4.3. Definition of an unconstrained problem 14</p>
<p>1.4.4. Application of a quasi–Newton method 15</p>
<p>1.4.5. Solving algorithm 18</p>
<p>1.5. Application of the proposed methodology to the optimization of a MV network 20</p>
<p>1.5.1. Case A: optimal load curtailment 23</p>
<p>1.5.2. Case B: conservative voltage regulation 26</p>
<p>1.5.3. Case C: voltage rise effects 28</p>
<p>1.5.4. Algorithm performance 30</p>
<p>1.6. Application of the proposed methodology to the optimization of a MV/LV network 31</p>
<p>1.6.1. Case D: LV network congestions 33</p>
<p>1.6.2. Case E: minimization of losses and reactive control 36</p>
<p>1.6.3. Algorithm performance 37</p>
<p>1.7. Conclusions 38</p>
<p>1.8. Acknowledgments 38</p>
<p>1.9. Bibliography 39</p>
<p>Chapter 2. Mixed Integer Linear Programming Models for Network Reconfiguration and Resource Optimization in Power Distribution Networks 43<br />Alberto BORGHETTI</p>
<p>2.1. Introduction 43</p>
<p>2.2. Model for determining the optimal configuration of a radial distribution network 44</p>
<p>2.2.1. Objective function and constraints of the branch currents 46</p>
<p>2.2.2. Bus voltage constraints 48</p>
<p>2.2.3. Bus equations 50</p>
<p>2.2.4. Line equations 52</p>
<p>2.2.5. Radiality constraints 53</p>
<p>2.3. Test results of minimum loss configuration obtained by the MILP model 54</p>
<p>2.3.1. Illustrative example 54</p>
<p>2.3.2. Tests results for networks with several nodes and branches 57</p>
<p>2.3.3. Comparison between the MILP solutions for the test networks with the corresponding PF calculation results relevant to the obtained optimal network configurations 62</p>
<p>2.4. MILP model of the VVO problem 65</p>
<p>2.4.1. Objective function 66</p>
<p>2.4.2. Branch equations 67</p>
<p>2.4.3. Bus equations 69</p>
<p>2.4.4. Branch and node constraints 72</p>
<p>2.5. Test results obtained by the VVO MILP model 74</p>
<p>2.5.1. TS1 74</p>
<p>2.5.2. TS2 77</p>
<p>2.5.3. TS3 78</p>
<p>2.6. Conclusions 85</p>
<p>2.7. Acknowledgments 85</p>
<p>2.8. Bibliography 86</p>
<p>Chapter 3. The Role of Nature–inspired Metaheuristic Algorithms for Optimal Voltage Regulation in Urban Smart Grids 89<br />Giovanni ACAMPORA, Davide CARUSO, Alfredo VACCARO, Autilia VITIELLO and Ahmed F. ZOBAA</p>
<p>3.1. Introduction 89</p>
<p>3.2. Emerging needs in urban power systems 92</p>
<p>3.3. Toward smarter grids 93</p>
<p>3.4. Smart grids optimization 97</p>
<p>3.5. Metaheuristic algorithms for smart grids optimization 99</p>
<p>3.5.1. Genetic algorithm 99</p>
<p>3.5.2. Random Hill Climbing algorithm 101</p>
<p>3.5.3. Particle Swarm Optimization algorithm 101</p>
<p>3.5.4. Evolution strategy 103</p>
<p>3.5.5. Differential evolution 106</p>
<p>3.5.6. Biogeography–based optimization 108</p>
<p>3.5.7. Evolutionary programming 109</p>
<p>3.5.8. Ant Colony Optimization algorithm 110</p>
<p>3.5.9. Group Search Optimization algorithm 113</p>
<p>3.6. Numerical results 115</p>
<p>3.6.1. Power system test 116</p>
<p>3.6.2. Real urban smart grid 124</p>
<p>3.7. Conclusions 127</p>
<p>3.8. Bibliography 127</p>
<p>Chapter 4. Urban Energy Hubs and Microgrids: Smart Energy Planning for Cities 129<br />Eleonora RIVA SANSEVERINO, Vincenzo Domenico GENCO, Gianluca SCACCIANOCE, Valentina VACCARO, Raffaella RIVA SANSEVERINO, Gaetano ZIZZO, Maria Luisa DI SILVESTRE, Diego ARNONE and Giuseppe PATERNÒ</p>
<p>4.1. Introduction 129</p>
<p>4.1.1. Microgrids versus urban energy hubs 131</p>
<p>4.2. Approaches and tools for urban energy hubs 134</p>
<p>4.2.1. Policy 134</p>
<p>4.2.2. Analysis 135</p>
<p>4.2.3. Optimal design and operation tools 139</p>
<p>4.3. Methodology 143</p>
<p>4.3.1. Building type and urban energy parameter specification 143</p>
<p>4.3.2. Mobility simulator 147</p>
<p>4.3.3. Energy simulation and electrical load estimation for buildings 151</p>
<p>4.3.4. Optimization and simulation software for district 151</p>
<p>4.4. Application 152</p>
<p>4.4.1. Analysis 152</p>
<p>4.4.2. Simulations and optimization 160</p>
<p>4.4.3. Mobility and effects of policies and smart charging on peaking power 168</p>
<p>4.5. Conclusions 170</p>
<p>4.6 Bibliography 171</p>
<p>Chapter 5. Optimization of Multi–energy Carrier Systems in Urban Areas 177<br />Sergio BRUNO, Silvia LAMONACA and Massimo LA SCALA</p>
<p>5.1. Introduction 177</p>
<p>5.2. Optimal control strategy for a small–scale multi–carrier energy system 180</p>
<p>5.2.1. The proposed architecture 180</p>
<p>5.2.2. Mathematical formulation 183</p>
<p>5.2.3. Test results 190</p>
<p>5.3. Optimal design of an urban energy district 198</p>
<p>5.3.1. Energy district for urban regeneration: the San Paolo Power Park 199</p>
<p>5.3.2. Optimal design of the energy district 201</p>
<p>5.3.3. Integer variables and design choices 205</p>
<p>5.3.4. Mathematical formulation of the optimal control problem 206</p>
<p>5.3.5. Test results 214</p>
<p>5.4. Conclusions 227</p>
<p>5.5. Acknowledgments 228</p>
<p>5.6. Bibliography 228</p>
<p>Chapter 6. Optimal Gas Flow Algorithm for Natural Gas Distribution Systems in Urban Environment 231<br />Ugo STECCHI, Gaetano ABBATANTUONO and Massimo LA SCALA</p>
<p>6.1. Introduction 231</p>
<p>6.2. Natural gas network evolution 236</p>
<p>6.3. Implementing the monitoring and control system in the Gas Smart Grids pilot project 239</p>
<p>6.3.1. SCADA system 240</p>
<p>6.3.2. Controlling FRUs setpoints 244</p>
<p>6.4. Basic equations under steady–state conditions 246</p>
<p>6.5. Gas load flow formulation 253</p>
<p>6.6. Gas optimal flow method 256</p>
<p>6.7. Optimizing turbo–expander operations 258</p>
<p>6.8. Optimizing pressure profiles on the low pressure distribution grids 262</p>
<p>6.9. Conclusions 270</p>
<p>6.10. Acknowledgements 270</p>
<p>6.11. Bibliography 270</p>
<p>Chapter 7. Multicarrier Energy System Optimal Power Flow 273<br />Soheil DERAFSHI BEIGVAND, Hamdi ABDI and Massimo LA SCALA</p>
<p>7.1. Introduction 273</p>
<p>7.2. Basic concepts and assumptions 276</p>
<p>7.2.1. MEC and energy hub 276</p>
<p>7.2.2. CHP units 279</p>
<p>7.2.3. General assumptions 282</p>
<p>7.3. Problem formulation 283</p>
<p>7.3.1. Electrical power balance equations 283</p>
<p>7.3.2. Gas energy flow equation 283</p>
<p>7.3.3. Modeling of energy hubs 285</p>
<p>7.3.4. MECOPF problem 286</p>
<p>7.4. Time varying acceleration coefficient gravitational search algorithm 287</p>
<p>7.4.1. A brief comparison between the main structures of TVAC–GSA and PSO 291</p>
<p>7.5. TVAC–GSA–based MECOPF problem 292</p>
<p>7.6. Case study simulations and results 294</p>
<p>7.7. Conclusions 300</p>
<p>7.8. Appendix 1 301</p>
<p>7.9. Appendix 2 303</p>
<p>7.10. Bibliography 305</p>
<p>List of Authors 309</p>
<p>Index 311</p>