From smart grids to smart cities : new challenges in optimizing energy grids

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