Massimo La Scala is Full Professor of Electrical Energy Systems at Politecnico di Bari in Italy and IEEE Fellow. He has been the Principal Investigator of numerous research projects in smart grids and smart cities and scientific consultant for the Italian Ministry of the Economic Development and AEEGSI, the Italian Regulatory Authority of Electricity, Gas and Water. He is the director of the "Laboratory for the development of renewables and energy efficiency: Lab ZERO" at Politecnico di Bari.

• Preface xiIntroduction xviiMassimo LA SCALA and Sergio BRUNOChapter 1 Unbalanced Three-Phase Optimal Power Flow for the Optimization of MV and LV Distribution Grids 1Sergio BRUNO and Massimo LA SCALA1.1 Advanced distribution management system for smart distribution grids 11.2 Secondary distribution monitoring and control 51.2.1 Monitoring and representation of LV distribution grids 61.2.2 LV control resources and control architecture 71.3 Three-phase distribution optimal power flow for smart distribution grids 81.4 Problem formulation and solving algorithm 111.4.1 Main problem formulation 111.4.2 Application of the penalty method 121.4.3 Definition of an unconstrained problem 141.4.4 Application of a quasi-Newton method 151.4.5 Solving algorithm 181.5 Application of the proposed methodology to the optimization of a MV network 201.5.1 Case A: optimal load curtailment 231.5.2 Case B: conservative voltage regulation 261.5.3 Case C: voltage rise effects 281.5.4 Algorithm performance 301.6 Application of the proposed methodology to the optimization of a MV/LV network 311.6.1 Case D: LV network congestions 331.6.2 Case E: minimization of losses and reactive control 361.6.3 Algorithm performance 371.7 Conclusions 381.8 Acknowledgments 381.9 Bibliography 39Chapter 2 Mixed Integer Linear Programming Models for Network Reconfiguration and Resource Optimization in Power Distribution Networks 43Alberto BORGHETTI2.1 Introduction 432.2 Model for determining the optimal configuration of a radial distribution network 442.2.1 Objective function and constraints of the branch currents 462.2.2 Bus voltage constraints 482.2.3 Bus equations 502.2.4 Line equations 522.2.5 Radiality constraints 532.3 Test results of minimum loss configuration obtained by the MILP model 542.3.1 Illustrative example 542.3.2 Tests results for networks with several nodes and branches 572.3.3 Comparison between the MILP solutions for the test networks with the corresponding PF calculation results relevant to the obtained optimal network configurations 622.4 MILP model of the VVO problem 652.4.1 Objective function 662.4.2 Branch equations 672.4.3 Bus equations 692.4.4 Branch and node constraints 722.5 Test results obtained by the VVO MILP model 742.5.1 TS1 742.5.2 TS2 772.5.3 TS3 782.6 Conclusions 852.7 Acknowledgments 852.8 Bibliography 86Chapter 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 ZOBAA3.1 Introduction 893.2 Emerging needs in urban power systems 923.3 Toward smarter grids 933.4 Smart grids optimization 973.5 Metaheuristic algorithms for smart grids optimization 993.5.1 Genetic algorithm 993.5.2 Random Hill Climbing algorithm 1013.5.3 Particle Swarm Optimization algorithm 1013.5.4 Evolution strategy 1033.5.5 Differential evolution 1063.5.6 Biogeography-based optimization 1083.5.7 Evolutionary programming 1093.5.8 Ant Colony Optimization algorithm 1103.5.9 Group Search Optimization algorithm 1133.6 Numerical results 1153.6.1 Power system test 1163.6.2 Real urban smart grid 1243.7 Conclusions 1273.8 Bibliography 127Chapter 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 PATERNÒ4.1 Introduction 1294.1.1 Microgrids versus urban energy hubs 1314.2 Approaches and tools for urban energy hubs 1344.2.1 Policy 1344.2.2 Analysis 1354.2.3 Optimal design and operation tools 1394.3 Methodology 1434.3.1 Building type and urban energy parameter specification 1434.3.2 Mobility simulator 1474.3.3 Energy simulation and electrical load estimation for buildings 1514.3.4 Optimization and simulation software for district 1514.4 Application 1524.4.1 Analysis 1524.4.2 Simulations and optimization 1604.4.3 Mobility and effects of policies and smart charging on peaking power 1684.5 Conclusions 1704.6 Bibliography 171Chapter 5 Optimization of Multi-energy Carrier Systems in Urban Areas 177Sergio BRUNO, Silvia LAMONACA and Massimo LA SCALA5.1 Introduction 1775.2 Optimal control strategy for a small-scale multi-carrier energy system 1805.2.1 The proposed architecture 1805.2.2 Mathematical formulation 1835.2.3 Test results 1905.3 Optimal design of an urban energy district 1985.3.1 Energy district for urban regeneration: the San Paolo Power Park 1995.3.2 Optimal design of the energy district 2015.3.3 Integer variables and design choices 2055.3.4 Mathematical formulation of the optimal control problem 2065.3.5 Test results 2145.4 Conclusions 2275.5 Acknowledgments 2285.6 Bibliography 228Chapter 6 Optimal Gas Flow Algorithm for Natural Gas Distribution Systems in Urban Environment 231Ugo STECCHI, Gaetano ABBATANTUONO and Massimo LA SCALA6.1 Introduction 2316.2 Natural gas network evolution 2366.3 Implementing the monitoring and control system in the “Gas Smart Grids” pilot project 2396.3.1 SCADA system 2406.3.2 Controlling FRUs’ setpoints 2446.4 Basic equations under steady-state conditions 2466.5 Gas load flow formulation 2536.6 Gas optimal flow method 2566.7 Optimizing turbo-expander operations 2586.8 Optimizing pressure profiles on the low pressure distribution grids 2626.9 Conclusions 2706.10 Acknowledgements 2706.11 Bibliography 270Chapter 7 Multicarrier Energy System Optimal Power Flow 273Soheil DERAFSHI BEIGVAND, Hamdi ABDI and Massimo LA SCALA7.1 Introduction 2737.2 Basic concepts and assumptions 2767.2.1 MEC and energy hub 2767.2.2 CHP units 2797.2.3 General assumptions 2827.3 Problem formulation 2837.3.1 Electrical power balance equations 2837.3.2 Gas energy flow equation 2837.3.3 Modeling of energy hubs 2857.3.4 MECOPF problem 2867.4 Time varying acceleration coefficient gravitational search algorithm 2877.4.1 A brief comparison between the main structures of TVAC-GSA and PSO 2917.5 TVAC-GSA-based MECOPF problem 2927.6 Case study simulations and results 2947.7 Conclusions 3007.8 Appendix 1 3017.9 Appendix 2 3037.10 Bibliography 305List of Authors 309Index 311