Affine Arithmetic-Based Methods for Uncertain Power System Analysis presents the unique properties and representative applications of Affine Arithmetic in power systems analysis, particularly as they are deployed for reliability optimization. The work provides a comprehensive foundation in Affine Arithmetic necessary to understand the central computing paradigms that can be adopted for uncertain power flow and optimal power flow analyses. These paradigms are adapted and applied to case studies, which integrate benchmark test systems and full step-by-step procedure for implementation so that readers are able to replicate and modify. The work is presented with illustrative numerical examples and MATLAB computations.
- Provides a uniquely comprehensive review of affine arithmetic in both its core theoretical underpinnings and their developed applications to power system analysis
- Details the exemplary benefits derived by the deployment of affine arithmetic methods for uncertainty handling in decision-making processes
- Clarifies arithmetical complexity and eases the understanding of illustrative methodologies for researchers in both power system and decision-making fields
| Preface |
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| Acknowledgments |
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xiii | |
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1 Uncertainty management in power systems |
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1 | (8) |
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2 | (1) |
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3 | (1) |
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3 | (1) |
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1.4 Non-probabilistic methods |
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4 | (5) |
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6 | (3) |
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2 Elements of reliable computing |
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9 | (14) |
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9 | (1) |
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10 | (4) |
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2.3 Solving uncertain equations by AA |
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14 | (2) |
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2.4 Reliable solution of non-linear equations |
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16 | (2) |
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2.5 Reliable solutions of constrained optimization problems |
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18 | (5) |
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22 | (1) |
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3 Uncertain power flow analysis |
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23 | (26) |
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24 | (1) |
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3.2 Affine arithmetic based solution of the power flow equations |
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25 | (4) |
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29 | (10) |
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3.4 Robust formulation of the power flow equations |
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39 | (3) |
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42 | (7) |
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47 | (2) |
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4 Uncertain optimal power flow analysis |
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49 | (16) |
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4.1 Mathematical background |
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49 | (6) |
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55 | (3) |
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4.3 Robust formulation of optimal power flow problems |
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58 | (4) |
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62 | (1) |
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63 | (2) |
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63 | (2) |
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5 Unified AA-based solution otoncertain PF and OPF problems |
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65 | (16) |
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5.1 Theoretical framework |
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65 | (5) |
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70 | (2) |
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72 | (7) |
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5.4 Computational requirements |
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79 | (1) |
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79 | (2) |
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79 | (2) |
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6 Uncertain power system reliability analysis |
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81 | (12) |
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82 | (3) |
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6.2 Uncertain Markov Chains analysis by AA |
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85 | (3) |
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88 | (5) |
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91 | (2) |
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7 Uncertain analysis of multi-energy systems |
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93 | (12) |
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7.1 Optimal scheduling of an energy hub |
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94 | (4) |
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98 | (7) |
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104 | (1) |
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8 Enabling methodologies for reducing the computational burden in AA-based computing |
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105 | (18) |
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105 | (1) |
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106 | (1) |
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106 | (2) |
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108 | (14) |
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122 | (1) |
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122 | (1) |
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9 Uncertain voltage stability analysis by affine arithmetic |
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123 | (12) |
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9.1 AA-based calculation of PV curves |
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128 | (1) |
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129 | (4) |
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133 | (2) |
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133 | (2) |
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10 Reliable microgrids scheduling in the presence of data uncertainties |
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135 | (10) |
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10.1 Deterministic optimization |
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136 | (2) |
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138 | (1) |
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10.3 Affine arithmetic-based optimization |
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138 | (2) |
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10.4 Numerical results and discussion |
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140 | (3) |
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143 | (2) |
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143 | (2) |
| Index |
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145 | |
Alfredo Vaccaro received his MSc degree cum laude and commendation in Electronic Engineering from the University of Salerno, Italy, and his PhD in Electrical and Computer Engineering from University of Waterloo, Ontario, Canada. He was formerly research fellow at the Power System Group of the Department of Electrical and Information Engineering (DIIIE) of University of Salerno. He then joined the Electric Power Systems at the Department of Engineering, Faculty of Engineering of University of Sannio, where he is now Associate Professor. He has also been chair of the Research & Development Committee of the Opera21 Group SpA in the field of Advanced Information and Communications Technologies for Smart Grids, Task Leader of the strategic scientific initiatives of the Research Consortium on Agent Systems in the field of Smart Energy Networks, and Scientific Director of the bureau of the Research Centre on Pure and Applied Mathematics at the Department of Engineering, University of Sannio. Antonio Pepiciello received his B.S., M.S. and PhD in energy engineering from University of Sannio, Benevento, Italy, where he is currently a postdoctoral scholar. His research interests include integration of renewable energy sources in power systems, power system dynamics, decision making under uncertainty and time synchronization of sensor networks.
Lisainfo e-raamatute kohta