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E-raamat: Metaheuristic Optimization in Power Engineering

(University of Pritina, Faculty of Technical Sciences, Serbia)
  • Formaat: EPUB+DRM
  • Sari: Energy Engineering
  • Ilmumisaeg: 29-May-2018
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785615474
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Metaheuristic Optimization in Power EngineeringSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Energy Engineering
  • Ilmumisaeg: 29-May-2018
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785615474
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This book describes the principles of solving various problems in power engineering via the application of selected metaheuristic optimization methods including genetic algorithms, particle swarm optimization, and the gravitational search algorithm.



A metaheuristic is a consistent set of ideas, concepts, and operators to design a heuristic optimization algorithm, that can provide a sufficiently good solution to an optimization problem with incomplete or imperfect information. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation, are an important application for such methods.

This book describes the principles of solving various problems in power engineering via the application of selected metaheuristic optimization methods including genetic algorithms, particle swarm optimization, and the gravitational search algorithm. Applications covered include power flow calculation; optimal power flow in transmission networks; optimal reactive power dispatch in transmission networks; combined economic and emission dispatch; optimal power flow in distribution networks; optimal volt/var control in distribution networks; optimal placement and sizing of distributed generation in distribution networks; optimal energy and operation management of microgrids; optimal coordination of directional overcurrent relays; and steady-state analysis of self-excited induction generators.

Preface xiii
Acknowledgements xv
Supplementary files xvi
1 Overview of metaheuristic optimization 1(38)
1.1 Introduction
1(1)
1.2 Description of metaheuristics
2(2)
1.3 Principle of population-based metaheuristics
4(27)
1.3.1 Genetic algorithm
6(1)
1.3.2 Differential evolution
7(1)
1.3.3 Evolutionary programing
7(1)
1.3.4 Backtracking search optimization algorithm
8(1)
1.3.5 Particle swarm optimization
9(1)
1.3.6 Ant colony optimization
9(1)
1.3.7 Artificial bee colony
9(1)
1.3.8 Gravitational search algorithm
10(1)
1.3.9 Wind-driven optimization
11(1)
1.3.10 Colliding bodies optimization
12(1)
1.3.11 Black hole algorithm
13(1)
1.3.12 Gray wolf optimizer
14(1)
1.3.13 Firefly algorithm
14(1)
1.3.14 Cuckoo search algorithm
15(1)
1.3.15 Moth swarm algorithm
15(1)
1.3.16 Krill herd algorithm
16(1)
1.3.17 Shuffled frog-leaping algorithm
17(1)
1.3.18 Bacterial colony foraging optimization
17(1)
1.3.19 Biogeography-based optimization
18(1)
1.3.20 Teaching-learning-based optimization
19(1)
1.3.21 League championship algorithm
19(1)
1.3.22 Mine blast algorithm
20(1)
1.3.23 Sine cosine algorithm
21(1)
1.3.24 Harmony search
22(1)
1.3.25 Imperialist competitive algorithm
23(1)
1.3.26 Differential search algorithm
24(1)
1.3.27 Glowworm swarm optimization
25(1)
1.3.28 Spiral optimization algorithm
26(1)
1.3.29 The Jaya algorithm
27(1)
1.3.30 Creating a "new" algorithm
28(3)
1.4 Criticism of metaheuristics
31(2)
1.5 Educational software-metahopt
33(1)
1.6 Conclusion
34(1)
References
35(4)
2 Overview of genetic algorithms 39(36)
2.1 Introduction
39(1)
2.2 Basic structure of the GA
40(1)
2.3 Representation of individuals (encoding)
41(3)
2.3.1 Binary encoding
42(1)
2.3.2 Gray coding
43(1)
2.3.3 Real-value encoding
43(1)
2.4 Population size and initial population
44(1)
2.5 Fitness function
44(2)
2.5.1 Relative fitness
45(1)
2.5.2 Linear scaling
46(1)
2.6 Selection
46(4)
2.6.1 Simple selection
48(1)
2.6.2 Stochastic universal sampling
48(1)
2.6.3 Linear ranking selection
49(1)
2.6.4 Elitist selection
49(1)
2.6.5 k-Tournament selection schemes
50(1)
2.6.6 Simple tournament selection
50(1)
2.7 Crossover
50(3)
2.7.1 One-point crossover
51(1)
2.7.2 Multipoint crossover
51(1)
2.7.3 Uniform crossover
51(1)
2.7.4 Shuffle crossover
52(1)
2.7.5 Arithmetic crossover
52(1)
2.7.6 Heuristic crossover
53(1)
2.8 Mutation
53(1)
2.9 GA control parameters
54(1)
2.10 Multiobjective optimization using GA
55(2)
2.11 Applications of GA to power system problems-literature overview
57(9)
2.11.1 Optimal power flow
57(3)
2.11.2 Optimal reactive power dispatch
60(1)
2.11.3 Combined economic and emission dispatch
60(1)
2.11.4 Optimal power flow in distribution networks
61(2)
2.11.5 Optimal placement and sizing of distributed generation in distribution networks
63(1)
2.11.6 Optimal energy and operation management of microgrids
64(1)
2.11.7 Optimal coordination of directional overcurrent relays
65(1)
2.11.8 Steady-state analysis of self-excited induction generator
66(1)
2.12 Conclusion
66(1)
References
67(8)
3 Overview of particle swarm optimization 75(38)
3.1 Introduction
75(1)
3.2 Description of PSO
76(12)
3.2.1 Parameters of PSO
79(3)
3.2.2 General remarks about PSO
82(1)
3.2.3 MATLAB® code of PSO
83(2)
3.2.4 Example usage of PSO
85(3)
3.3 PSO modifications
88(6)
3.3.1 Population topology
88(1)
3.3.2 Discrete binary PSO
89(1)
3.3.3 Hybrid PSO
90(1)
3.3.4 Adaptive PSO
90(4)
3.4 Applications of PSO to power system problems-literature overview
94(8)
3.4.1 Optimal power flow
94(3)
3.4.2 Optimal reactive power dispatch
97(1)
3.4.3 Economic dispatch
98(1)
3.4.4 Optimal power flow in distribution networks
99(1)
3.4.5 Optimal placement and sizing of distributed generation in distribution networks
100(1)
3.4.6 Optimal energy and operation management of MGs
101(1)
3.4.7 Optimal coordination of directional overcurrent relays
101(1)
3.5 Conclusion
102(1)
References
102(11)
4 Overview of gravitational search algorithm 113(42)
4.1 Introduction
113(2)
4.2 Description of original GSA
115(10)
4.2.1 Parameters of GSA
117(1)
4.2.2 General remarks about GSA
118(2)
4.2.3 MATLAB® code of GSA
120(3)
4.2.4 Example usage of GSA
123(2)
4.3 Binary gravitational search algorithm
125(1)
4.4 Modified GSA
126(2)
4.5 Opposition-based GSA
128(3)
4.5.1 Current optimum opposition-based GSA
129(2)
4.6 Adaptive gbest-guided GSA
131(2)
4.6.1 Slow exploitation of GSA
131(1)
4.6.2 Improving the exploitation of GSA
131(2)
4.7 Self-adaptive GSA
133(2)
4.8 Nondominated sorting GSA
135(4)
4.8.1 Updating the external archive
136(1)
4.8.2 Updating the list of moving agents
137(1)
4.8.3 Updating the mass of moving agents
137(1)
4.8.4 Updating the acceleration of agents
137(1)
4.8.5 The use of mutation operator
138(1)
4.8.6 Update and mutate the position of agents
138(1)
4.9 Clustered-gravitational search algorithm
139(1)
4.10 Hybrid PSO and GSA algorithm
140(5)
4.11 Applications of GSA to power system problems-literature overview
145(4)
4.11.1 Optimal power flow
145(1)
4.11.2 Optimal reactive power dispatch
146(1)
4.11.3 Economic dispatch using GSA
146(1)
4.11.4 Optimal power flow in distribution networks
147(1)
4.11.5 Optimal DG placement and sizing in distribution networks
147(1)
4.11.6 Optimal energy and operation management of microgrids
148(1)
4.11.7 Optimal coordination of overcurrent relays
149(1)
4.12 Conclusion
149(1)
References
150(5)
5 Power-flow calculation 155(22)
5.1 Introduction
155(1)
5.2 Power-flow calculation in transmission networks
156(9)
5.2.1 Power-flow equations
157(1)
5.2.2 Bus classification
158(1)
5.2.3 Solution methods
158(6)
5.2.4 Power-flow software-pfgui
164(1)
5.3 Power-flow calculation in distribution networks
165(11)
5.3.1 Backward/forward sweep power-flow algorithm
170(4)
5.3.2 Power-flow software-pfdngui
174(2)
5.4 Conclusion
176(1)
References
176(1)
6 Optimal power flow in transmission networks 177(58)
6.1 Introduction
177(1)
6.2 Literature overview
178(7)
6.3 Formulation of the OPF problem
185(9)
6.3.1 Equality constraints
186(1)
6.3.2 Inequality constraints
186(2)
6.3.3 Objective function
188(3)
6.3.4 Multiobjective function
191(1)
6.3.5 Transient-stability-constrained OPF
192(2)
6.4 Solution methodology for OPF problem
194(6)
6.4.1 Overview of PSO
194(1)
6.4.2 Application of PSO to the OPF problem
195(1)
6.4.3 Overview of GSA
196(2)
6.4.4 Application of GSA to the OPF problem
198(1)
6.4.5 Overview of hybrid PSOGSA
199(1)
6.4.6 Application of PSOGSA to the OPF problem
199(1)
6.5 Simulation results
200(13)
6.5.1 IEEE 30-bus test system
200(11)
6.5.2 IEEE 118-bus test system
211(2)
6.6 Solution software-opfgui
213(10)
6.7 Conclusion
223(1)
References
224(11)
7 Optimal reactive power dispatch in transmission networks 235(32)
7.1 Introduction
235(1)
7.2 Problem formulation
236(2)
7.3 ORPD using hybrid PSOGSA
238(17)
7.3.1 Overview of PSOGSA
238(2)
7.3.2 Application of PSOGSA to the ORPD problem
240(1)
7.3.3 Simulation results of PSOGSA
241(14)
7.4 ORPD using hybrid GSA-SQP algorithm
255(2)
7.4.1 Application of hybrid GSA-SQP to the ORPD problem
256(1)
7.4.2 Simulation results of hybrid GSA-SQP
257(1)
7.5 Educational program package ORPD
257(6)
7.6 Conclusion
263(1)
References
263(4)
8 Combined economic and emission dispatch 267(30)
8.1 Introduction
267(2)
8.2 Problem formulation
269(4)
8.2.1 Fuel cost function
270(1)
8.2.2 Emission function
270(1)
8.2.3 Constraints
271(1)
8.2.4 Slack generator calculation
272(1)
8.3 Solution method
273(4)
8.3.1 Overview of PSOGSA
273(2)
8.3.2 PSOGSA implementation to the CEED problem
275(2)
8.4 Simulation results
277(9)
8.4.1 Test system 1
277(4)
8.4.2 Test system 2
281(4)
8.4.3 Test system 3
285(1)
8.5 Educational software-ceedgui
286(6)
8.6 Conclusion
292(1)
References
293(4)
9 Optimal power flow in distribution networks 297(40)
9.1 Introduction
297(2)
9.2 Deterministic optimal power flow
299(3)
9.2.1 Objective function
300(1)
9.2.2 Constraints
301(1)
9.3 DG units modeling for OPF
302(5)
9.3.1 Diesel generator
303(1)
9.3.2 Fuel cell
304(1)
9.3.3 Microturbine
304(1)
9.3.4 Wind turbine
304(1)
9.3.5 Photovoltaic
305(1)
9.3.6 Mini hydropower plants
306(1)
9.3.7 Electric grid
306(1)
9.4 Solution methods
307(4)
9.4.1 Genetic algorithm
307(2)
9.4.2 Gravitational search algorithm
309(2)
9.5 Probabilistic optimal power flow
311(5)
9.5.1 Statistical characterization of the input random variables
311(2)
9.5.2 Statistical evaluation of the output variables
313(1)
9.5.3 Procedure for solving probabilistic OPF
314(2)
9.6 Simulation results
316(8)
9.6.1 Deterministic OPF analysis
318(3)
9.6.2 Probabilistic OPF analysis
321(3)
9.7 Solution software-opfdngui
324(3)
9.8 Conclusion
327(5)
References
332(5)
10 Optimal Volt/Var control in distribution networks 337(26)
10.1 Introduction
337(2)
10.2 Decomposition of the voltage-control problem
339(11)
10.2.1 Seasonal control of voltage
340(10)
10.3 Optimal Volt/Var control using metaheuristic optimization
350(9)
10.3.1 Problem formulation
350(2)
10.3.2 Solution method
352(3)
10.3.3 Simulation results
355(4)
10.4 Conclusion
359(1)
References
360(3)
11 Optimal placement and sizing of distributed generation in distribution networks 363(44)
11.1 Introduction
363(6)
11.2 Preliminary locations of DG
369(3)
11.3 Partial search of variants
372(5)
11.3.1 Optimal DG placement by using partial search of variants
373(2)
11.3.2 Optimal DG sizing by using partial search of variants
375(2)
11.4 Genetic algorithm
377(4)
11.4.1 Optimal DG placement and sizing by using GA
378(3)
11.5 Simulation results
381(18)
11.5.1 IEEE 31-bus system
381(10)
11.5.2 Distribution network Zajecar
391(8)
11.6 Educational program package opsdg
399(2)
11.7 Conclusion
401(1)
References
401(6)
12 Optimal energy and operation management of microgrids 407(42)
12.1 Introduction
407(4)
12.2 Problem formulation of EOM
411(5)
12.2.1 Objective function
412(1)
12.2.2 Constraints
412(2)
12.2.3 Distributed generation bids calculation
414(2)
12.3 Solution method
416(3)
12.3.1 Overview of PSO
417(1)
12.3.2 Application of PSO to EOM
418(1)
12.4 Probabilistic EOM of MG
419(5)
12.4.1 Statistical characterization of the input random variables
420(1)
12.4.2 Statistical evaluation of the output variables
421(1)
12.4.3 Procedure for solving probabilistic EOM
422(2)
12.5 Simulation results
424(20)
12.5.1 Microgrid MG1
424(6)
12.5.2 Microgrid MG2
430(7)
12.5.3 MATLAB program eom used for deterministic EOM
437(7)
12.6 Conclusion
444(1)
References
444(5)
13 Optimal coordination of directional overcurrent relays 449(26)
13.1 Introduction
449(1)
13.2 Problem formulation
450(5)
13.2.1 Objective function
451(1)
13.2.2 Limits of the settings
452(1)
13.2.3 Limits of relay operation time
453(1)
13.2.4 Coordination criteria
453(1)
13.2.5 Modification of objective function for minimization of CTI
454(1)
13.3 Solution method
455(4)
13.3.1 Overview of GSA
455(2)
13.3.2 Overview of SQP
457(1)
13.3.3 Hybrid GSA-SQP algorithm
457(1)
13.3.4 Implementation of hybrid GSA-SQP algorithm
458(1)
13.4 Simulation results
459(9)
13.4.1 Test system 1
459(2)
13.4.2 Test system 2
461(2)
13.4.3 Test system 3
463(3)
13.4.4 Statistical evaluation of the results
466(2)
13.5 Educational program package ocdocr
468(3)
13.6 Conclusion
471(1)
References
471(4)
14 Steady-state analysis of self-excited induction generators 475(30)
14.1 Introduction
475(2)
14.2 System configuration
477(1)
14.3 Induction generator model
478(1)
14.4 Steady-state equations of SEIG
479(2)
14.5 Steady-state equations of parallel operated SEIGs
481(3)
14.6 Solution method
484(6)
14.6.1 Overview of genetic algorithm
485(1)
14.6.2 Application of GA to SEIG
486(2)
14.6.3 Application of GA to parallel operated SEIGs
488(2)
14.7 Simulation results
490(11)
14.7.1 Steady-state analysis of SEIG
490(4)
14.7.2 Steady-state analysis of parallel operated SEIGs
494(7)
14.8 Conclusion
501(1)
References
502(3)
Index 505
Jordan Radosavljevi is a Professor of Power Engineering at the Faculty of Technical Sciences of the University of Pritina in Kosovska Mitrovica, Serbia. His research interests lie at the application of new metaheuristic optimization algorithms in solving various problems in power systems. He has authored or co-authored two books and several book chapters and papers in these topics.
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