From Smart Grids to Smart Cities E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 515

Veröffentlichungsjahr: 2017

Beschreibung

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 €.

Leseprobe

Table of Contents

Cover

Dedication

Title

Copyright

Preface

Introduction: From Smart Grids to the Smart Cities: New Paradigms for Future Networks

I.1. The birth of smart grids

I.2. Definition of a smart grid

I.3. Drivers for smart grids

I.4. From smart grids to smart cities

I.5. The smart city: home of advanced energy solutions

I.6. The smart city: a people-oriented environment

I.7. Active energy users and prosumers

I.8. Horizontal integration through energy hubs and energy districts

I.9. Final remarks

I.10. Bibliography

1 Unbalanced Three-Phase Optimal Power Flow for the Optimization of MV and LV Distribution Grids

1.1. Advanced distribution management system for smart distribution grids

1.2. Secondary distribution monitoring and control

1.3. Three-phase distribution optimal power flow for smart distribution grids

1.4. Problem formulation and solving algorithm

1.5. Application of the proposed methodology to the optimization of a MV network

1.6. Application of the proposed methodology to the optimization of a MV/LV network

1.7. Conclusions

1.8. Acknowledgments

1.9. Bibliography

2 Mixed Integer Linear Programming Models for Network Reconfiguration and Resource Optimization in Power Distribution Networks

2.1. Introduction

2.2. Model for determining the optimal configuration of a radial distribution network

2.3. Test results of minimum loss configuration obtained by the MILP model

2.4. MILP model of the VVO problem

2.5. Test results obtained by the VVO MILP model

2.6. Conclusions

2.7. Acknowledgments

2.8. Bibliography

3 The Role of Nature-inspired Metaheuristic Algorithms for Optimal Voltage Regulation in Urban Smart Grids

3.1. Introduction

3.2. Emerging needs in urban power systems

3.3. Toward smarter grids

3.4. Smart grids optimization

3.5. Metaheuristic algorithms for smart grids optimization

3.6. Numerical results

3.7. Conclusions

3.8. Bibliography

4 Urban Energy Hubs and Microgrids: Smart Energy Planning for Cities

4.1. Introduction

4.2. Approaches and tools for urban energy hubs

4.3. Methodology

4.4. Application

4.5. Conclusions

4.6. Bibliography

5 Optimization of Multi-energy Carrier Systems in Urban Areas

5.1. Introduction

5.2. Optimal control strategy for a small-scale multi-carrier energy system

5.3. Optimal design of an urban energy district

5.4. Conclusions

5.5. Acknowledgments

5.6. Bibliography

6 Optimal Gas Flow Algorithm for Natural Gas Distribution Systems in Urban Environment

6.1. Introduction

6.2. Natural gas network evolution

6.3. Implementing the monitoring and control system in the “Gas Smart Grids” pilot project

6.4. Basic equations under steady-state conditions

6.5. Gas load flow formulation

6.6. Gas optimal flow method

6.7. Optimizing turbo-expander operations

6.8. Optimizing pressure profiles on the low pressure distribution grids

6.9. Conclusions

6.10. Acknowledgements

6.11. Bibliography

7 Multicarrier Energy System Optimal Power Flow

7.1. Introduction

7.2. Basic concepts and assumptions

7.3. Problem formulation

7.4. Time varying acceleration coefficient gravitational search algorithm

7.5. TVAC-GSA-based MECOPF problem

7.6. Case study simulations and results

7.7. Conclusions

7.8. Appendix 1: Performance evaluation of TVAC-GSA for five benchmark functions

7.9. Appendix 2: System data of the test case

7.10. Bibliography

List of Authors

Index

End User License Agreement

List of Illustrations

Introduction

Figure I.1. Electricity flows expressed in TWh among market zones and with neighboring countries in 2005 (left) and 2015 (right). Orange encircled numbers represent local demand. Source: Terna

Figure I.2. Smart decision-making with human-in-the-loop

Figure I.3. Basic architecture of a SCADA system

1 Unbalanced Three-Phase Optimal Power Flow for the Optimization of MV and LV Distribution Grids

Figure 1.1. Possible scheme of an ADMS

Figure 1.2. Flow-chart of the proposed algorithm

Figure 1.3. Simplified scheme of the AMET primary substation

Figure 1.4. Planimetry of the AMET urban distribution network. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 1.5. Case A: overall power demand at each feeder (before and after control)

Figure 1.6. Case B: voltage profiles before and after control

Figure 1.7. Case C: voltage profiles before and after control

Figure 1.8. Schematic representation of the 20 kV distribution grid

Figure 1.9. Schematic representation of the 230 V/400 V circuits supplied by the F1M1 substation

Figure 1.10. Case D1: Active power supplied to curtailable loads under the secondary substation #F1M1, before and after control

2 Mixed Integer Linear Programming Models for Network Reconfiguration and Resource Optimization in Power Distribution Networks

Figure 2.1. Illustrative example

Figure 2.2. Optimal configuration of the 15-bus system

Figure 2.3. Optimal configuration of the 32-bus system

Figure 2.4. Optimal configuration of the 69-bus system

Figure 2.5. Optimal configuration of the 83-bus system

Figure 2.6. Optimal configuration of the 135-bus system

Figure 2.7. Test system TS1

Figure 2.8. Test system TS3

3 The Role of Nature-inspired Metaheuristic Algorithms for Optimal Voltage Regulation in Urban Smart Grids

Figure 3.1. Pseudo-code of the Genetic Algorithm [SIM 13]

Figure 3.2. Pseudo-code of the Random Hill Climbing Algorithm [SIM 13]

Figure 3.3. Pseudo-code of the Particle Swarm Optimization Algorithm [SIM 13]

Figure 3.4. Pseudo-code of the (1+1) Evolution Strategy Algorithm [SIM 13]

Figure 3.5. Pseudo-code of the (µ+1) Evolution Strategy Algorithm [SIM 13]

Figure 3.6. Pseudo-code of the (µ + λ)-ES and (µ, λ)-ES [SIM 13]

Figure 3.7. Application of Differential Evolution Algorithm on a two-dimensional optimization problem [SIM 13]

Figure 3.8. Pseudo-code of the Differential Evolution Algorithm [SIM 13]

Figure 3.9. Pseudo-code of the Biogeography-Based Optimization Algorithm [SIM 13]

Figure 3.10. Pseudo-code of the basic Evolutionary Programming Algorithm [SIM 13]

Figure 3.11. Pseudo-code of Ant Colony Optimization Algorithm to solve the travelling salesman problem [SIM 13]

Figure 3.12. Pseudo-code of the Ant Colony Optimization Algorithm to solve a continuous-domain optimization problem [SIM 13]

Figure 3.13. Pseudo-code of the Group Search Optimization Algorithm [SIM 13]

Figure 3.14. Best values of the control variables of all the algorithms for an IEEE 30-BUS test power system: a) generation voltages; b) transformer tap-ratios; c) reactive power generated by the shunt capacitors. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 3.15. Best values of the control variables of all the algorithms for an IEEE 57-BUS test power system: a) generation voltages; b) transformer tap-ratios; c) reactive power generated by the shunt capacitors. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 3.16. Best-fitness values of biogeography-based optimization (BBO), evolutionary strategy (ES), group search optimizer (GSO), ant colony optimization (ACO) for a real urban grid

Figure 3.17. Best values of the control variables of biogeography-based optimization (BBO), evolutionary strategy (ES), group search optimizer (GSO), ant colony optimization (ACO) for a real urban grid

4 Urban Energy Hubs and Microgrids: Smart Energy Planning for Cities

Figure 4.1. Example of urban energy hub

Figure 4.2. Features of the urban energy hub

Figure 4.3. Methodological approach

Figure 4.4. Google maps view of the Mussomeli municipality (in white) and of the analyzed districts (in red). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 4.5. Identified building typologies for the LD and HD districts. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 4.6. Monthly thermal energy demand for the LD district, DHW is domestic hot water and H is heating

Figure 4.7. Monthly thermal energy demand for the HD district, DHW is domestic hot water and H is heating

Figure 4.8. Low-density district with 100% PV installations

Figure 4.9. High-density district with 100% PV installations

Figure 4.10. Comparison of NPC between scenarios 0 and 1 in the two districts

Figure 4.11. Comparison of emissions between scenarios 0 and 1 in the two districts

Figure 4.12. Yearly emissions per person as a function of the Tdix value: scenario 1 versus scenario 2

Figure 4.13. Net present cost per person as a function of the of the Tdix value: scenario 1 versus scenario 2

Figure 4.14. Yearly emissions per person as a function of the Tdix value: scenario 1 versus scenario 3

Figure 4.15. Net present cost per person as a function of the of the Tdix value: scenario 1 versus scenario 3

Figure 4.16. Yearly emissions per person as a function of the of the Tdix value: scenario 1 versus scenario 4

Figure 4.17. Net present cost per person as a function of the of the Tdix value: scenario 1 versus scenario 4

Figure 4.18. Coverage of electrical demand as a function of PV penetration

Figure 4.19. Emission reduction as a function of PV penetration

Figure 4.20. Yearly emissions per person due to mobility as a function of the Tdix value: all scenarios

Figure 4.21. Effect of travel demand policies on electrical peaking power demand at the HD district parking lot

5 Optimization of Multi-energy Carrier Systems in Urban Areas

Figure 5.1. Schematic representation of the multi-carrier system under study

Figure 5.2. Main scheme of the control architecture

Figure 5.3. Electric demand forecast

Figure 5.4. Heating energy demand forecast

Figure 5.5. Photovoltaics generation forecast

Figure 5.6. Case A, generated electric power). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.7. Case A, electric power demand). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.8. Case A, generated thermal power). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.9. Case B, generated electric power). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.10. Case B, electric power demand). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.11. Case B, storage state of charge

Figure 5.12. Case B, generated thermal power). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.13. Case C, generated electric power). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.14. Case C, electric power demand). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.15. Case C, storage state of charge

Figure 5.16. Case C, generated thermal power). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 5.17. Map of the main end users in the San Paolo district (Bari, Italy)

Figure 5.18. Simplified scheme of the San Paolo Power Park

Figure 5.19. Decomposition of the overall problem into a two-stage optimization

Figure 5.20. Schematic representation of the San Paolo Power Park multi-carrier system

Figure 5.21. Thermal load duration curves: a) heating loads, b) cooling loads

Figure 5.22. Daily chronological curves in summer: a) heating loads, b) cooling loads

Figure 5.23. Daily chronological curves in winter: a) heating loads, b) cooling loads

Figure 5.24. Case D, convergence of the simulated annealing problem: a) candidate solutions; b) best solution across iterations

Figure 5.25. Case D, summer day: a) thermal cooling generation; b) thermal cooling consumption

Figure 5.26. Case D, summer day: a) thermal heating generation; b) thermal heating consumption

Figure 5.27. Case D, winter day: a) thermal cooling generation; b) thermal cooling consumption

Figure 5.28. Case D, winter day: a) thermal heating generation; b) thermal heating consumption

Figure 5.29. Case E1, convergence of the simulated annealing problem: a) candidate solutions; b) best solution across iterations

Figure 5.30. Case E2, convergence of the simulated annealing problem: a) candidate solutions; b) best solution across iterations

Figure 5.31. Case E2, summer day: a) thermal cooling generation; b) thermal cooling consumption

Figure 5.32. Case E2, summer day: a) thermal heating generation; b) thermal heating consumption

Figure 5.33. Case E2, winter day: a) thermal cooling generation; b) thermal cooling consumption

Figure 5.34. Case E2, winter day: a) thermal heating generation; b) thermal heating consumption

6 Optimal Gas Flow Algorithm for Natural Gas Distribution Systems in Urban Environment

Figure 6.1. Picture showing a Regulation and Measurement station at a city-gate gas network

Figure 6.2. Final Reduction Unit in an urban distribution grid during a maintenance intervention

Figure 6.3. Urban Medium Pressure Distribution Grid of the town of Bari (Italy) with a zoomed-in image of the detail of the low pressure grid (courtesy of Amgas Bari SpA). For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 6.4. Remote monitoring system architecture

Figure 6.5. Placement of sensors inside a FRU (left) and on an upstream pipe (right)

Figure 6.6. Picture of different RTU prototypes: a stand-alone RTU powered by a PV (right); switchboard details of a grid-connected RTU ((left)

Figure 6.7. Screenshot of the real SCADA desktop interface: main overview of an urban MP grid. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 6.8. Screenshot of the real SCADA desktop interface and a real-time monitoring interface of a selected FRU. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 6.9. Schematic representation of a two-stage pressure regulator. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 6.10. Schematic showing gas control volume

Figure 6.11. Graph showing flow behavior due to a pressure variation at a selected FRU

Figure 6.12. Schematic showing the gas flow balance at node i

Figure 6.13. Flow chart of “Optimal Gas Flow” solvers

Figure 6.14. Graph showing the turbo-expander relationship between gas flow and power generation

Figure 6.15. Graph showing pressures values upstream each FRU.

Figure 6.16. Map of the test network: medium pressure pipelines in green eliminate and low pressure ones in red. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

Figure 6.17. Pressure and gas flows in normal operating conditions, FRU #1

Figure 6.18. Graph showing pressure and gas flows under normal operating conditions, FRU #2

Figure 6.19. A comparative diagram of pressures at FRU#1 before and after optimization

Figure 6.20. A comparative diagram of pressures at FRU#2 before and after optimization

Figure 6.21. Graph showing estimated hourly pressure deviations at each node after optimization. For a color version of the figure, see www.iste.co.uk/lascala/smart.zip

7 Multicarrier Energy System Optimal Power Flow

Figure 7.1. A schematic layout of a multicarrier energy system

Figure 7.2. Schematic showing the energy hub concept: a) a general representation; b) a special case of energy hub

Figure 7.3. A town as a system of interconnected hubs

Figure 7.4. A comparison of conventional electricity and heat production to CHP units

Figure 7.5. Model of a pipeline equipped with compressor (C) [DER 16a]

Figure 7.6. The concept of gravitational forces between particles [DER 16a]

Figure 7.7. TVAC-GSA-based MECOPF flowchart [DER 16a]

Figure 7.8. A 13-electrical bus, 9-gas node, and 6-energy hub system. Note: results of separated OPFs are depicted

Figure 7.9. Results of the MECOPF problem

Figure 7.10. A comparison between different costs

Figure 7.11. Demand sharing among generation units for the MECOPF problem

Figure 7.12. Graph showing node pressures

Figure 7.13. Graph showing bus voltages

Landmarks

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To my parents Maria and Armando

Advanced SmartGrids Set

coordinated byJean-Claude Sabonnadière and Nouredine Hadjsaïd

From Smart Grids to Smart Cities

New Challenges in Optimizing Energy Grids

Edited by

Massimo La Scala

First published 2017 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

ISTE Ltd

27-37 St George’s Road

London SW19 4EU

UK

www.iste.co.uk

John Wiley & Sons, Inc.

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USA

www.wiley.com

© ISTE Ltd 2017

The rights of Massimo La Scala to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2016956032

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-84821-749-2

Preface

New emerging technologies and regulations are among the forces which are driving the changes in energy systems. This favorable circumstance paves the way to the design of more advanced and effective energy architectures, the solution of old problems with new means, and the solution of issues unresolved because of the lack of tools and methodologies.

Renewable Energy Sources (RES), such as wind and solar power, are rapidly becoming the backbone of the future electric power system since they are environmental-friendly although they create great distress in power grids which should accommodate larger and larger amounts of intermittent power generation. With the increase in efficiency of energy conversion and power electronics, storage systems have become more reliable, less expensive and cleaner, making viable the option of storing significant amount of power, under any form of chemical, thermal, mechanical or electric energy. The potential impact of electric vehicles in energy systems is also huge. The diffusion of such vehicles might move, with regard to the overall energy consumption balance, a significant amount of power from conventional transport fuels to electricity, requiring a complete redesign of most distribution power grids. They will introduce new randomness in the electric system operations due to “moving around” loads.

At residential and urban levels, the increasing penetration of electric vehicles and distributed generation will rapidly transform consumers into “prosumers” that will be able to operate their own devices in order to generate, store and use energy. Managing these micro energy systems will require the achievement of a higher efficiency, which can be reached only through a radical integration of all energy services at urban/residential level (electric supply, natural gas supply, heating, cooling, water, transportation, etc.).

A major example of a key enabling technology (KET) which can drive the transformation of energy infrastructures is the smart grid concept. Smart grids combine a number of technologies with end-user solutions and address new paradigms in the dispersed generation, storage and utilization of the electrical energy, which can find an effective application by a new regulation environment.

In this multifaceted scenario, the energy hub constitutes another key paradigm. It can be conceived as a unit where multiple energy carriers can be converted and conditioned by using a wide spectrum of technologies, such as combined heat and power technology, power-electronic devices and heat exchangers. Consequently, energy hubs could be considered as the trait d’union between different energy infrastructures (i.e. electrical networks, natural gas distribution systems, heat distribution systems) and/or energy users (i.e. producers, consumers) allowing more market and energy efficiency, increasing reliability and facilitating the penetration of intermittent generation. This model can be applied on different scales including industrial plants, larger buildings, urban districts and isolated energy systems.

Starting from the experience in the power sector, in this book, it is shown how some concepts and methodologies developed in this field can be effectively utilized in other realms. Different energy infrastructures share the same needs for more automation, optimization in operations, tools for planning and integration between multiple energy carriers to achieve better performances and efficiency. These issues require new methods and application software whose main core, generally, resides in optimization tools.

The focus of this book is on distribution energy systems and urban energy infrastructures since they show the potential to improve their efficiency and flexibility through the implementation of smart monitoring, new control functions and the integration with other energy carriers. This assumption has been made with the firm belief that, in these areas, smart grids will provide more profound changes in response to challenging problems such as: a wide dispersed generation mostly due to intermittent RES, integrated production, utilization and storage of both thermal and electrical energy for enhancing energy efficiency, more advanced home distribution systems, demand response, etc.

The dramatic changes modern towns are facing during these years require smarter operation of grids according to overall framework of the “smart city” paradigm. A new urbanization is giving rise to the so-called “mega-towns” which require more advanced and secure energy infrastructures. The entrance of new technologies such as photo voltaics widely utilized for residential and tertiary buildings, electrical vehicles, combined heat cooling and power (CHCP), heat pumps for demand response and energy districts changes the usual way energy grids have been operated in cities so far. Other issues are related to a different attitude of customers, which are willing to participate more actively to the energy market and choose among new energy services, this aim being nurtured by a forward-thinking regulation of the sector.

The scope of the book is to provide an integrated vision of problems to researcher, engineers, practitioners, defining the contour of new subjects in energy system optimization. The authors involved in this book were encouraged by a common motivation: to bring together issues that, although in continuity with their previous experience, sketch a new scenario in the energy systems.

The book begins with applications of smart grids in the power sector and concludes with applications to urban distribution systems involving other energy carriers such natural gas, heat/cool district heating, hydrogen. This cultural contamination and novelty is found in the theory as well as in real-world applications. It stems from power systems, which is doubtless the most complex and technologically advanced energy infrastructure, the first one to make a pervasive use of automation and Information and Communication Technologies (ICT) and to experience a drastic market re-regulation and dramatic technological advancements worldwide.

Particular attention is devoted to the actual implementation of the methods proposed here. As a matter of fact, most of the chapters refer to applications developed in research activities which have now finalized to give way to the actual implementation of pilot projects. These projects are briefly described and funding resources are acknowledged in throughout the book. Some of the pilot projects addressed here pushed the equipment and material needs of the research activity and nurtured the support of some companies giving rise to a new laboratory called LabZERO located at the Politecnico di Bari and at the ENEA Research Center in Brindisi, Italy. It was set up carrying out the activities of the “Project ZERO”, concerning the development of research and experimentation activities in the field of green smart technologies and the use of simulation tools and equipment for fast prototyping to reduce the risks of applied research and support product innovation in the path “from concept to market”. Lab ZERO was conceived as a living lab, a user-centered, open-innovation ecosystem combining research, development and innovation processes within a public-private-people partnership. This experience is worth mentioning to underline the link of the book with real applications and to show how pilot projects area good instrument to draw the attention of public institutions and companies to engineering research issues.

The above-mentioned ideas inspired the book whose topics are summarized here.

In the Introduction, terminology, definitions, economical and technical drivers for smart grids are introduced. Smart grids are defined in a broad sense including all energy grids and the integration of advanced distribution grids. Potentials for operation and environmental issues enhancement, safe and secure operations and energy efficiency are addressed with a special insight to the urban environment and its evolution toward smart cities.

In Chapter 1, the features of Advanced Distribution Management Systems are summarized and the optimal power flow (OPF) is presented as basic function, which can be applied effectively for controlling power distribution grids at both medium voltage (MV) and low voltage (LV) level. Specialized formulations, based on nonlinear programming algorithms and a three-phase unbalanced representation of the power grid, are developed for controlling active and reactive resources in distribution systems. Particular attention is devoted to the LV distribution system due to the lack of automation and tools accompanied by the profound changes these grids are experiencing nowadays.

In Chapter 2, mixed integer linear programming (MILP)algorithms are presented for the solution of two optimization problems, which characterize distribution power network operations, namely: the minimum-loss configuration of the network the so-called voltage/var optimization (VVO) problem. The quality of the results and the effects of the proposed linearization are assessed on MV test systems by performing a comparison with nonlinear calculations for optimal configurations.

In Chapter 3, metaheuristic-based optimization algorithms have been addressed for solving complex problems in the smart grid domain. A review of the most advanced metaheuristic algorithms in the task of solving a complex smart grid optimization problems and a comprehensive analysis of the expected performances of the optimization algorithms in terms of convergence, robustness and accuracy are presented in this chapter. The benefits and the limitations of the different solution techniques are highlighted through simulation results obtained on realistic power networks and an actual urban power grid.

In Chapter 4, a review of approaches to urban energy systems study is presented. Urban energy systems are proposed as networks of multi-source hybrid energy hubs, where different energy flows are collected at the same bus and can be stored, delivered or transformed as needed. Since resources and infrastructures interact with each other, definition and boundaries of such energy systems at urban level and the possibility to generate new operational models based on existing critical urban infrastructures is a challenging problem. Thermal, electrical and mobility infrastructures operation are considered as qualifying features of the hub. An optimized design of the energy system serving two different districts is considered as a function of these urban features. The analysis, reported in the chapter, shows how there is a link between energy planning and urban features at district level paving the way to an energy-based territorial planning for urban contexts.

Planning integrated energy systems in towns implies a complex design, which should take into account also a different operation of underlying grids. Interdependencies among different systems should be carefully represented and special solvers are required for optimization. In Chapter 5, an optimization approach was formulated and tested to be applied in operations in presence of multiple energy sources and storage systems according to two strategies aimed to fully take advantage of storage facilities: a greedy algorithm and optimal control. In addition, a design methodology was proposed to maximize the return of the investment in planning new multi-source hybrid energy systems considering optimized operations during the lifespan of the infrastructure. The approach is tested on a real case of an urban regeneration project, aimed to the development of energy facilities to provide discounted energy services in degraded suburban areas to attract new investments. The project includes the installation of a trigeneration plant, district heating and cooling and an on-site steam methane reformer to supply hydrogen to a fleet of public transport vehicles.

In Chapter 6, it is shown how urban gas distribution grids are experiencing changes similar to electric distribution grids due to the deployment of gas smart meters and the more and more pervasive use of ICT tools and automation which allows more effective, safe and secure operations. In this chapter, selected results of a pilot project for the implementation of a gas smart grid in the middle-sized town of Bari in Italy are presented. A SCADA (supervisory control and data acquisition) prototype and a gas flow optimization algorithm (gas optimal flow algorithm) for pressure control across the natural gas grid are described in their actual implementation. This kind of real-time control shows the potential of increasing the power generated by turbo expanders at gas city gates, reducing metering and billing errors due to excessive pressure deviations, ensuring a safe distribution of odorants and providing load relief and peak shaving during emergency conditions. What reported in this chapter is an interesting example of a ‘transposition’ and shifting of experiences coming from two different realms: the power smart grid area and the urban natural gas distribution.

Once presented issues relevant to the integration of different energy substrates in future cities and essential changes in the planning and optimization process, in Chapter 7, the focus is on the concurrent optimization of the distribution grids of two main energy carriers: power and natural gas. The complexity of both networks in terms of their structure, a possible future energy-hub-like architecture, energy flow equations, and different related equality and inequality constraints make the optimization problem highly nonlinear, non-convex and high dimensional. An optimization heuristic method, namely the time varying acceleration coefficient gravitational search algorithm (TVAC-GSA), is proposed to solve OPF problems in multi-carrier energy systems focusing on the interactions between power grid and gas network. The proposed algorithm is based on the Newtonian laws of gravitation and motion. The effectiveness of the approach is tested on a multi-carrier energy architecture characterized by the assumed presence of multiple energy hubs. The concurrent solution of the two grids provide better results than the ones associated to the solution of the two separated systems. Consequently, the concurrent optimization of multiple grids seems to be a good candidate for smart distribution systems, gaining efficiency in the overall system.

After this effort, the authors share the feeling that many results are still on the shelf and many others are still coming out from pilot projects and, in general, what reported here is not exhaustive of the topic. Somehow, this book can appear linked to the Italian experience and regulation. This is due mainly to the territorial basis of the pilot projects and the affiliation of most the authors. It is believed that this not by itself detrimental since the Italian experience in the development of smart grids and smart cities presents some peculiarities such as the early large deployment of smart metering technologies and the setting up of an advanced regulatory framework.

The book covers a wide ground of topics and applications and could not be written without benefitting from the published efforts of other researchers and Institutions reported in the references at the end of each chapter. The authors gratefully acknowledge the financial support from the Italian Ministry of Economic Development and Regione Puglia Government as well as the technical support from the Municipality of Bari for providing technical assistance and support in the implementation of some pilot projects. The authors also acknowledge the contribution of the many people who, in various ways, contributed to the realization of the projects mentioned in the book.

This work would not have been possible without the patience of our families and the encouraging assistance of the publishing editors, to which I express, even on behalf of all contributors, our gratitude.

Finally, I wish to express my sincere thanks to all the authors who contributed to the publication of this book. After all, the book reports the story of the cooperative efforts of a group of enthusiastic researchers working on the same challenging issue of transposing theoretical results on actual demonstrators useful for the everyday life.

Massimo La ScalaBari, ItalyOctober 2016

IntroductionFrom Smart Grids to the Smart Cities: New Paradigms for Future Networks

In this chapter, terminology, definitions, and economic and technical drivers for smart grids and smart cities are introduced. Smart grids, defined according to a wider significance, which include all energy grids and the integration of advanced distribution grids, show significant potential for operation enhancement, safe and secure operations, and energy efficiency. Furthermore, towns are conceived as a natural place for relationships and social productive interactions. A natural convergence of social and technological networks is expected since towns represent a natural arena where these technologies can cooperate to enhance the quality of life of the citizens. The integration of energy grids can play a fundamental role for enhancing efficiency, environmental issues, operation and introducing new business models for innovative services. The implementation of suitable platforms gathering data from advanced distribution grids can provide fruitful information to policy makers and stakeholders, and create consensus in the citizenship.

I.1. The birth of smart grids

The restructuring years, between the late 1980s and the early 2000s, were carriers of profound modifications in the electric power industry with the creation of energy markets, the unbundling of energy services and the affirmation of the “third party access” (TPA) principle. Despite the radical changes that had to be accommodated, in those years, investments in electrical infrastructures followed the business-as-usual scenario, whereas the general concern had been mostly focused on the generation side and on energy markets and services. Soon enough, power systems proved to be inadequate to bear the weight of restructuring.

National bulk power systems were built with the aim to transport the energy generated in large power stations, located where fuel or hydro resources were more abundant and cost-efficient, towards end-users. High-voltage transmission grids were designed to transport electrical power over considerable distances from generation to load centers (cities or large industrial plants), where energy is supplied after a series of voltage transformation. Being traditionally planned and operated by vertical integrated utilities, the electrical power system was not flexible and resilient enough to withstand the operative conditions set by unpredictable market laws rather than centralized scheduling routines.

The Californian energy crisis in 2000–2001 was the first example of how the lack of centralized long-term generation resource planning and the presence of power system physical bottlenecks could trigger strategic speculative behavior of independent producers at the expense of customers and security of supply. In the following years (2003–2004), an uncommon long series of large blackouts were experienced across the world, leading the power system community to interrogate itself if the restructuring process and the energy markets had been the causes of such events. Power system restructuring had not been directly responsible for such events, but it was soon clear that power systems were in need of new strategic investments and that new operative schemes with regard to power system reliability had to be enforced.

Since those years, it has been recognized, first by the scientific community and then by energy market players and governmental actors who agreed that power system flexibility, resilience and efficiency had to be improved. These results would have to be accomplished through the deployment of more sensors and more control resources, the adoption of new technologies (for example, flexible AC transmission systems (FACTS)) and the development of advanced wide-area monitoring and control architectures based on fast computation and communication systems. The idea that power grids must enhance their abilities and evolve towards “supergrids” or “smart grids” became generally accepted.

These same requirements (more automation, more sensors and control capabilities, more flexibility and efficiency) were soon to be applied to distribution grids as well, leading to a broader definition of the “smart grid” paradigm.

Smart or, better, smarter distribution grids are in fact necessary to withstand other significant transformations of electrical power grids.

Since the 1990s, a steady increase in distributed generation (DG) has been observed worldwide.

Although there is no clear consensus about the definition, DG, basically, includes power generation facilities sufficiently smaller than central plants, usually 10 MW or less [INS 03, LAB 07], not centrally planned nor centrally dispatched [CON 03], usually located near the point of use and connected to the distribution networks [LAB 07, INT 02]. The newest definitions of DG tend to consider only those plants that are directly connected to MV and LV distribution systems as in the Italian regulation [AUT 15].

Development of DG was due to different drivers. When a consensus about the dangers of climate change and greenhouse emissions was reached, it became clear that electrical energy had to be produced in an environmentally friendly way mitigating, or even better eliminating, CO2 emissions.

According to this vision, the future energy industry will rely mostly on production from renewable energy sources (RES), combined with fossil-fueled plants equipped with carbon capture and storage technologies and, perhaps, new-generation nuclear power plants.

The growing environmental concern led to the diffusion of incentive policies in the 2000s for the exploitation of renewable sources and to an ever-growing penetration of RES generating units in power systems. Thanks to the enforcement of TPA, independent producers adopting RES technologies (mostly wind or photovoltaics, but also biomasses and biogas) were soon injecting massive amounts of energy into power systems, giving rise to power system reliability concerns mostly due to the intermittent nature of certain energy sources and the fast filling up of transfer capacity in power corridors. Power congestions and RES overproduction are responsible for market inefficiencies (zero or negative energy prices are experienced with growing frequency, for example, Italy experienced for the first time a 2-hours zero price on 16 June 2013, whereas negative prices had already been often cleared in the German market) and for new concerns about power system security (dangerous power system static and dynamic conditions have been often experienced in northern Europe due to intermittent production from off-shore wind farms).

The increasing penetration of RES generation affects not only transmission systems, but also distribution. Owing to renewable portfolio standards and government RES incentive programs worldwide, the number of small generation units connected directly to medium-voltage (MW) and low-voltage (LV) circuits has increased conspicuously in the late 2000s, following the general trend of proliferation of DG.

Another driver for the diffusion of DG was due to the reduction in installation costs for fuel-fired generation technologies, which spread the use of small/medium-sized power stations based on both internal and external combustion cycles. In addition, due to the birth of energy markets for energy efficiency and incentive schemes, combined heat and power (CHP) generation units have been spreading out on power systems (usually using fuel or biomass for combustion or a mix of both). Incentives and reduced capital costs have made DG a reality, placing a great portion of the gross power generation near to the end-users. In this scenario, generation has been partly removed from the bulk power system and is now directly injected in MV and LV distribution systems. For example, according to the Italian Regulation Authority for Electricity Gas and Water (AEEGSI), approximately 16% of the overall gross Italian electricity generation is produced by DG (this number is conservative since it does not include those plants whose capacity is lower than 10 MVA and are not connected to distribution systems) [AUT 15]. This figure is shared by other countries such as the USA, where a capacity exceeding 200 GW was assessed in 2007 over a total nameplate capacity exceeding 1,100 GW [USD 07].

The diffusion of DG in distribution systems created a new category of energy customers that are not only consuming energy but can also produce it. End-users that can alternatively act as users and providers are often called “prosumers” (from the merge of the words producers and consumers). They can be found today in any sector: industrial, tertiary and commerce, and residential. The diffusion of prosumers consistently modifies the scope of distribution systems that have been designed and built with unidirectional schemes in order to convoy energy from the highest voltage level (HV) towards end-users at MV and LV levels. Distribution systems are therefore very large and passive networks with few automation (usually found at primary substations and in primary MV distribution feeders only), very little communication and limited local controls such as voltage regulation.

The increase in DG production may create major security and operative problems at distribution level, worsened also by the fact that the greatest portion of DG is produced by intermittent RES. Typical consequences of DGs are congestions, violations of scheduled power exchanges, overvoltages due to reverse power flows on transformers, possible failures of relays and deterioration of power quality. Such concerns made distribution companies (DisCOs) aware that their systems had to evolve and gain “smartness”, enhancing monitoring and control functions available at control centers.

The evolution of distribution grids towards “smart grids” or (probably better) “smart distribution grids” can be considered to have begun thanks to the diffusion of advanced digital meters, distribution automation, building automation, low-cost cabled and wireless communication systems and the setting up of specific plans for the modernization of distribution systems [BRO 08]. In Europe, for example, the European Commission has promulgated several directives for the development of smart grids and smart metering systems [EUR 09, EUR 06a], that have been already put in action by many EU Member States.

The road towards the “smartification” of distribution grids is clearly a hard path because of the huge extension of such grids, the presence of a large variety of legacy systems, the heterogeneity of DisCos and their related networks. Moreover, as remarked previously, distribution systems were built to be passive networks, with a minimal ability of monitoring and controlling power flows. At the MV distribution level, the topology, the status of circuit breakers and major state variables and flows are generally known through SCADA systems; however, this is not true for LV distribution systems where, also, the population of active end-users and prosumers is in continuous growth. In this case, deploying advanced smart meters with a sufficiently fast time resolution is a fundamental action, as it can provide capillary information about loads and DG. The power system state is known only if most of its parts are monitored and smart control functions can be enabled only once the system state becomes observable [EKA 12]. The issue of monitoring and control MV and LV smart distribution grids is addressed in Chapter 1.

I.2. Definition of a smart grid

In the last 10 years, many attributes and definitions have been given to the notion of “smart grid”. The “smart grid” concept combines a number of technologies with end-user solutions and addresses a number of policy and regulatory drivers. Many possible definitions have been proposed, but a univocal clear one does not exist. Definitions are often overlapping but some discrepancies can be found.

What is clear today is that the “smart grid” just represents the vision that we have of the power grids of the future. It is not an incidental circumstance that the European Technology Platform for Smart Grids, founded in 2005 in order to “formulate and promote a vision for the development of European electricity networks”, was initially called European Technology Platform for Electricity Networks of the Future.

A good synthesis of what is a “smart grid” can be found in a recent document of the U.S. Department of Energy (DOE) that summarizes “the smart grid involves the application of advanced communications and control technologies and practices to improve reliability, efficiency, and security which are key ingredients in the ongoing modernization of the electricity delivery infrastructure” [USD 14]. This general definition nearly embraces any response that can be given to the question “what is a smart grid?”. However, the answers to the question “what can a smart grid do?” do not always coincide.

This terminology arose right after the first reckless years of deregulation and the blackout season in 2003. The first needs, which smart grids were asked to respond to, were mostly based on the problem of preserving power system security and integrity in uncertain scenarios of ever-expanding electricity markets and international political crisis [AMI 04]. The first issue is related to the necessity to upgrade the power grids to allocate variable market transactions, increase capacity and operate the system in a completely different way. In addition, it should be remembered that the early 2000s were also years very close to the terrorist attacks of September 11 2001, and it should not surprise that one of the first prerogatives of smart grids was to develop self-healing mechanism and resilient emergency schemes in order to ensure survival when faced with to cyber or physical terrorist attacks [AMI 04]. In the USA, a very comprehensive early design of smart grids was developed by EPRI in 2004 with the IntelliGrid Architecture. This architecture includes most of the relevant power system functions contained in the modern definitions of smart grids.

In a later vision given by the DOE in 2009, the smart grid:

1) enables informed participation by customers so that consumers can become an integral part of the electric power system, modifying the way they use and purchase electricity, participating in load balancing and helping to ensure reliability;

2) accommodates all generation and storage options, including all distributed energy resources (DER), even being diverse and widespread and in the form of renewables, DG and energy storage;

3) enables new products, services and markets, managing independent grid variables such as energy, capacity, location, time, rate of change, and quality;

4) provides the power quality for the range of needs of different end-users, proving varying grades of power quality with variable prices;

5) optimizes asset utilization and operating efficiency, increasing the efficiency of maintenance procedures, decreasing losses and controlling congestions;

6) operates resiliently to disturbances, attacks and natural disasters, reacting to such events isolating the faulted elements and keeping normal operation in the rest of the system [USD 09].

Concurrently to the USA and all other countries, the European Union (EU) developed its own power network strategic awareness. This vision is mostly founded on its own strategies and policies for 2020, concerning the meeting of the Kyoto protocol objectives, the low-carbonization of energy industry, the increase in energy efficiency and conservation, and the development of a green and sustainable economy [EUR 06b]. Smart grids are considered part of the interventions necessary to meet the EU challenges and opportunities of the 21st Century and fulfill the expectations of society. The main goals to be achieved are more or less similar to the DOE strategy: develop a user-centric approach, innovate and renew an ageing infrastructure, increase the security of supply and expand liberalized markets with new products and services. However, a much greater emphasis is put on the environmental issues, on the necessity to integrate RES generation units, increase the social responsibility and sustainability, optimize visual impacts and land-use, increase interoperability among the physical interconnected European infrastructures, and simplify and reduce permission times for new infrastructures.

As remarked in [USD 09], due to the variety and high number of stakeholders, the definition of a smart grid might change according to the specific needs expressed by each participant (back again to the question “what should a smart grid do?”). The technology options at hand and the number of functionalities to be enabled are so many that a variety of broad definitions can be given at transmission, distribution and utilization level [GEL 09].

Most definitions put emphasis on the role of Information and Communication Technologies (ICTs), which improve system operation, billing and maintenance, and will be able to create a suitable communication platform for the development of the new energy services. However, in our opinion, the same emphasis should be devoted to the new actuators and devices (mostly derived from power electronics technologies) and control technologies, which will be able to “close the loop” at the operation level. Similar attention should be devoted to the regulatory framework, which is very important in eliminating barriers that can set technology free to fulfill the market needs and change the structure of the energy business. The creation of a real competitive market and consumer awareness can contribute to create new business models and opportunities by the use of emerging technologies, such as micro-grids, RES, electric vehicles and electrical storage.

I.3. Drivers for smart grids

The interest in smart grids has been increasing since this terminology was first generally accepted. Today, many national governments are encouraging smart grid initiatives, as they provide a cost-effective way to modernize the power system infrastructure, enabling, at the same time, the integration of low-carbon energy resources in power system operation and the fulfillment of security and power quality requirements [EKA 12]. Therefore, the accomplishment of smart grids is envisioned as an important economic and commercial opportunity to develop new products and services and promote the green economy.

The social and economic impacts of smart grid technologies are potentially huge. In 2012, according to Bloomberg New Energy Finance analysts, smart grid technologies registered an increase of 7% in investments worldwide, with a global turnover estimated to be approximately $14 billion and an expected future yearly increase of 10% [BLO 13]. As reported in [USD 14], the electricity industry spent an estimated total $18 billion for smart grid technology deployed in the United States during the 4-year period of 2010–2013.

Investments in Europe in 2012 were significantly smaller [BLO 13] than those in the rest of the world, mostly because of the inhomogeneous implementation stage of smart meters deployment campaigns. In Italy, approximately 35 million smart meters were installed starting from the year 2000, with a €2 billion investment by the major distribution utilities. Nowadays, a new generation of more advanced so-called “2.0 smart meters” is going to be deployed replacing the previous ones, placing Italy among vanguard experiences in this field with great development opportunities for the entire production chain.

Expected figures for the European markets can be considered close to the expectations from the US power industry, where EPRI calculated that the investment needed to realize the envisioned power delivery system of the future is between $338 and $476 billion (with a total net benefit expected in the range between $1,294 and $2,028 billion) [EPR 11].

The expected benefits result from the growth of renewable power generation and storage, from the increased use of electric vehicles, and from the avoided expenses due to system inefficiencies, bottlenecks and ageing of power systems.

In most fully industrialized countries, power systems started to take their modern form in the 1950s under the economic and industrial growth that followed the Second World War. Since the late 1990s, most transmission and distribution system components that were installed in the years of power system expansion had been arriving at the end of their life span. The capital costs of extensive large replacement campaigns are very high, but it is still an opportunity to install new devices that can provide a wider variety of functions with small incremental costs.

The need to refurbish transmission and distribution grids is an obvious chance to innovate them with new designs and operating practices. A classic example is given by protection relays installed on transmission lines. The newest digital relays are intelligent electronic devices (IEDs) that have at their own disposal local computation capabilities and are able to establish a two-way communication channel with theoretically any other objects in the power system (substation controllers, SCADA server, other IEDs, etc.). The substitution of old protection relays with the newest digital ones is a process that has been going on in the last 20 years for replacing old components and achieving substation automation. However, these same components, with marginal extra costs, can be equipped with signal processing