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E-raamat: From Smart Grids to Smart Cities: New Challenges i n Optimizing Energy Grids: New Challenges in Optimizing Energy Grids [Wiley Online]

Edited by (Polytechnic of Bari), Edited by , Edited by (Polytechnic of Bari), Edited by (Polytechnic of Bari), Edited by (University of Bologna)
  • Formaat: 368 pages
  • Ilmumisaeg: 13-Jan-2017
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119116082
  • ISBN-13: 9781119116080
Teised raamatud teemal:
  • Wiley Online
  • Hind: 174,45 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
From Smart Grids to Smart Cities: New Challenges i n Optimizing Energy Grids: New Challenges in Optimizing Energy GridsSuurem pilt
  • Formaat: 368 pages
  • Ilmumisaeg: 13-Jan-2017
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119116082
  • ISBN-13: 9781119116080
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119116080
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 hasnt 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 .
Preface xi
Introduction xvii
Massimo La Scala
Sergio Bruno
Chapter 1 Unbalanced Three-Phase Optimal Power Flow for the Optimization of MV and LV Distribution Grids
1(42)
Sergio Bruno
Massimo La Scala
1.1 Advanced distribution management system for smart distribution grids
1(4)
1.2 Secondary distribution monitoring and control
5(3)
1.2.1 Monitoring and representation of LV distribution grids
6(1)
1.2.2 LV control resources and control architecture
7(1)
1.3 Three-phase distribution optimal power flow for smart distribution grids
8(3)
1.4 Problem formulation and solving algorithm
11(9)
1.4.1 Main problem formulation
11(1)
1.4.2 Application of the penalty method
12(2)
1.4.3 Definition of an unconstrained problem
14(1)
1.4.4 Application of a quasi-Newton method
15(3)
1.4.5 Solving algorithm
18(2)
1.5 Application of the proposed methodology to the optimization of a MV network
20(11)
1.5.1 Case A: optimal load curtailment
23(3)
1.5.2 Case B: conservative voltage regulation
26(2)
1.5.3 Case C: voltage rise effects
28(2)
1.5.4 Algorithm performance
30(1)
1.6 Application of the proposed methodology to the optimization of a MV/LV network
31(7)
1.6.1 Case D: LV network congestions
33(3)
1.6.2 Case E: minimization of losses and reactive control
36(1)
1.6.3 Algorithm performance
37(1)
1.7 Conclusions
38(1)
1.8 Acknowledgments
38(1)
1.9 Bibliography
39(4)
Chapter 2 Mixed Integer Linear Programming Models for Network Reconfiguration and Resource Optimization in Power Distribution Networks
43(46)
Alberto Borghetti
2.1 Introduction
43(1)
2.2 Model for determining the optimal configuration of a radial distribution network
44(10)
2.2.1 Objective function and constraints of the branch currents
46(2)
2.2.2 Bus voltage constraints
48(2)
2.2.3 Bus equations
50(2)
2.2.4 Line equations
52(1)
2.2.5 Radiality constraints
53(1)
2.3 Test results of minimum loss configuration obtained by the MILP model
54(11)
2.3.1 Illustrative example
54(3)
2.3.2 Tests results for networks with several nodes and branches
57(5)
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(3)
2.4 MILP model of the WO problem
65(9)
2.4.1 Objective function
66(1)
2.4.2 Branch equations
67(2)
2.4.3 Bus equations
69(3)
2.4.4 Branch and node constraints
72(2)
2.5 Test results obtained by the WO MILP model
74(11)
2.5.1 TS1
74(3)
2.5.2 TS2
77(1)
2.5.3 TS3
78(7)
2.6 Conclusions
85(1)
2.7 Acknowledgments
85(1)
2.8 Bibliography
86(3)
Chapter 3 The Role of Nature-inspired Metaheuristic Algorithms for Optimal Voltage Regulation in Urban Smart Grids
89(40)
Giovanni Acampora
Davide Caruso
Alfredo Vaccaro
Autilia Vitiello
Ahmed F. Zobaa
3.1 Introduction
89(3)
3.2 Emerging needs in urban power systems
92(1)
3.3 Toward smarter grids
93(4)
3.4 Smart grids optimization
97(2)
3.5 Metaheuristic algorithms for smart grids optimization
99(16)
3.5.1 Genetic algorithm
99(2)
3.5.2 Random Hill Climbing algorithm
101(1)
3.5.3 Particle Swarm Optimization algorithm
101(2)
3.5.4 Evolution strategy
103(3)
3.5.5 Differential evolution
106(2)
3.5.6 Biogeography-based optimization
108(1)
3.5.7 Evolutionary programming
109(1)
3.5.8 Ant Colony Optimization algorithm
110(3)
3.5.9 Group Search Optimization algorithm
113(2)
3.6 Numerical results
115(12)
3.6.1 Power system test
116(8)
3.6.2 Real urban smart grid
124(3)
3.7 Conclusions
127(1)
3.8 Bibliography
127(2)
Chapter 4 Urban Energy Hubs and Microgrids: Smart Energy Planning for Cities
129(48)
Eleonora Riva Sanseverino
Vincenzo Domenico Genco
Gianluca Scaccianoce
Valentina Vaccaro
Raffaella Riva Sanseverino
Gaetano Zizzo
Maria Luisa Di Silvestre
Diego Arnone
Giuseppe Paterno
4.1 Introduction
129(5)
4.1.1 Microgrids versus urban energy hubs
131(3)
4.2 Approaches and tools for urban energy hubs
134(9)
4.2.1 Policy
134(1)
4.2.2 Analysis
135(4)
4.2.3 Optimal design and operation tools
139(4)
4.3 Methodology
143(9)
4.3.1 Building type and urban energy parameter specification
143(4)
4.3.2 Mobility simulator
147(4)
4.3.3 Energy simulation and electrical load estimation for buildings
151(1)
4.3.4 Optimization and simulation software for district
151(1)
4.4 Application
152(18)
4.4.1 Analysis
152(8)
4.4.2 Simulations and optimization
160(8)
4.4.3 Mobility and effects of policies and smart charging on peaking power
168(2)
4.5 Conclusions
170(1)
4.6 Bibliography
171(6)
Chapter 5 Optimization of Multi-energy Carrier Systems in Urban Areas
177(54)
Sergio Bruno
Silvia Lamonaca
Massimo La Scala
5.1 Introduction
177(3)
5.2 Optimal control strategy for a small-scale multi-carrier energy system
180(18)
5.2.1 The proposed architecture
180(3)
5.2.2 Mathematical formulation
183(7)
5.2.3 Test results
190(8)
5.3 Optimal design of an urban energy district
198(29)
5.3.1 Energy district for urban regeneration: the San Paolo Power Park
199(2)
5.3.2 Optimal design of the energy district
201(4)
5.3.3 Integer variables and design choices
205(1)
5.3.4 Mathematical formulation of the optimal control problem
206(8)
5.3.5 Test results
214(13)
5.4 Conclusions
227(1)
5.5 Acknowledgments
228(1)
5.6 Bibliography
228(3)
Chapter 6 Optimal Gas Flow Algorithm for Natural Gas Distribution Systems in Urban Environment
231(42)
Ugo Stecchi
Gaetano Abbatantuono
Massimo La Scala
6.1 Introduction
231(5)
6.2 Natural gas network evolution
236(3)
6.3 Implementing the monitoring and control system in the "Gas Smart Grids" pilot project
239(7)
6.3.1 SCADA system
240(4)
6.3.2 Controlling FRUs' setpoints
244(2)
6.4 Basic equations under steady-state conditions
246(7)
6.5 Gas load flow formulation
253(3)
6.6 Gas optimal flow method
256(2)
6.7 Optimizing turbo-expander operations
258(4)
6.8 Optimizing pressure profiles on the low pressure distribution grids
262(8)
6.9 Conclusions
270(1)
6.10 Acknowledgements
270(1)
6.11 Bibliography
270(3)
Chapter 7 Multicarrier Energy System Optimal Power Flow
273(36)
Soheil Derafshi Beigvand
Hamdi Abdi
Massimo La Scala
7.1 Introduction
273(3)
7.2 Basic concepts and assumptions
276(7)
7.2.1 MEC and energy hub
276(3)
7.2.2 CHP units
279(3)
7.2.3 General assumptions
282(1)
7.3 Problem formulation
283(4)
7.3.1 Electrical power balance equations
283(1)
7.3.2 Gas energy flow equation
283(2)
7.3.3 Modeling of energy hubs
285(1)
7.3.4 MECOPF problem
286(1)
7.4 Time varying acceleration coefficient gravitational search algorithm
287(5)
7.4.1 A brief comparison between the main structures of TVAC-GSA and PSO
291(1)
7.5 TVAC-GSA-based MECOPF problem
292(2)
7.6 Case study simulations and results
294(6)
7.7 Conclusions
300(1)
7.8 Appendix 1
301(2)
7.9 Appendix 2
303(2)
7.10 Bibliography
305(4)
List of Authors 309(2)
Index 311
Massimo La Scala, Professor of Electrical Systems for Energy, DEI, Polytechnic of Bari. Sergio Bruno, Politechnic of Bari, Electrical Engineering.

Carlo Alberto Nucci is full professor of Electrical Power Systems at the University of Bologna and the Editor in Chief of the Electric Power System Research Journal.

Ugo Stecchi, Polytechnic of Bari.
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