COVER
PREFACE
CONTRIBUTORS
LIST OF FIGURES
LIST OF TABLES
ABOUT THE EDITORS
CHAPTER 1: INTRODUCTION
1. 1.1 BACKGROUND
2. 1.2 EVOLUTIONARY COMPUTATION: A SUCCESSFUL BRANCH OF CI
3. REFERENCES
CHAPTER 2: OVERVIEW OF APPLICATIONS IN POWER AND ENERGY SYSTEMS
1. 2.1 APPLICATIONS TO POWER SYSTEMS
2. 2.2 SMART GRID APPLICATION COMPETITION SERIES
3. REFERENCES
CHAPTER 3: POWER SYSTEM PLANNING AND OPERATION
1. 3.1 INTRODUCTION
2. 3.2 UNIT COMMITMENT
3. 3.3 ECONOMIC DISPATCH BASED ON GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZATION
4. 3.4 DIFFERENTIAL EVOLUTION IN ACTIVE POWER MULTI‐OBJECTIVE OPTIMAL DISPATCH
5. ACKNOWLEDGEMENT
6. 3.5 HYDROTHERMAL COORDINATION
7. ACKNOWLEDGEMENT
8. 3.6 META‐HEURISTIC METHOD FOR GMS BASED ON GENETIC ALGORITHM
9. ACKNOWLEDGEMENT
10. 3.7 LOAD FLOW
11. ACKNOWLEDGEMENT
12. 3.8 ARTIFICIAL BEE COLONY ALGORITHM FOR SOLVING OPTIMAL POWER FLOW
13. ACKNOWLEDGEMENT
14. 3.9 OPF TEST BED AND PERFORMANCE EVALUATION OF MODERN HEURISTIC OPTIMIZATION
15. ACKNOWLEDGMENT
16. 3.10 TRANSMISSION SYSTEM EXPANSION PLANNING
17. 3.11 CONCLUSION
18. REFERENCES
CHAPTER 4: POWER SYSTEM AND POWER PLANT CONTROL
1. 4.1 INTRODUCTION
2. 4.2 LOAD FREQUENCY CONTROL – OPTIMIZATION AND STABILITY
3. 4.3 CONTROL OF FACTS DEVICES
4. 4.4 HYBRID OF ANALYTICAL AND HEURISTIC TECHNIQUES FOR FACTS DEVICES
5. 4.5 POWER SYSTEM AUTOMATION
6. 4.6 POWER PLANT CONTROL
7. 4.7 PREDICTIVE CONTROL IN LARGE‐SCALE POWER PLANT
8. 4.8 CONCLUSION
9. REFERENCES
CHAPTER 5: DISTRIBUTION SYSTEM
1. 5.1 INTRODUCTION
2. 5.2 ACTIVE DISTRIBUTION NETWORK PLANNING
3. 5.3 OPTIMAL SELECTION OF DISTRIBUTION SYSTEM ARCHITECTURE
4. 5.4 CONSERVATION VOLTAGE REDUCTION PLANNING
5. 5.5 DYNAMIC DISTRIBUTION NETWORK EXPANSION PLANNING WITH DEMAND SIDE MANAGEMENT
6. ACKNOWLEDGEMENT
7. APPENDIX A
8. 5.6 GA‐GUIDED TRUST‐TECH METHODOLOGY FOR CAPACITOR PLACEMENT IN DISTRIBUTION SYSTEMS
9. 5.7 NETWORK RECONFIGURATION
10. 5.8 DISTRIBUTION SYSTEM RESTORATION
11. ACKNOWLEDGEMENT
12. 5.9 GROUP‐BASED PSO FOR SYSTEM RESTORATION
13. 5.10 MVMO FOR PARAMETER IDENTIFICATION OF DYNAMIC EQUIVALENTS FOR ACTIVE DISTRIBUTION NETWORKS
14. 5.11 PARAMETER ESTIMATION OF CIRCUIT MODEL FOR DISTRIBUTION TRANSFORMERS
15. REFERENCES
CHAPTER 6: INTEGRATION OF RENEWABLE ENERGY IN SMART GRID
1. 6.1 INTRODUCTION
2. 6.2 RENEWABLE ENERGY SOURCES
3. 6.3 OPERATION AND CONTROL OF SMART GRID
4. 6.4 COMPLIANCE OF REACTIVE POWER REQUIREMENTS IN WIND POWER PLANTS
5. 6.5 PHOTOVOLTAIC CONTROLLER DESIGN
6. 6.6 DEMAND SIDE MANAGEMENT AND DEMAND RESPONSE
7. 6.7 EPSO‐BASED SOLAR POWER FORECASTING
8. 6.8 LOAD DEMAND AND SOLAR GENERATION FORECAST FOR PV INTEGRATED SMART BUILDINGS
9. 6.9 MULTI‐OBJECTIVE PLANNING OF PUBLIC ELECTRIC VEHICLE CHARGING STATIONS
10. 6.10 DISPATCH MODELING INCORPORATING MANEUVER COMPONENTS, WIND POWER, AND ELECTRIC VEHICLES
11. 6.11 CONCLUSIONS
12. REFERENCES
CHAPTER 7: ELECTRICITY MARKETS
1. 7.1 INTRODUCTION
2. 7.2 BIDDING STRATEGIES
3. 7.3 MARKET ANALYSIS AND CLEARING
4. 7.4 ELECTRICITY MARKET FORECASTING
5. 7.5 SIMULTANEOUS BIDDING OF V2G IN ANCILLARY SERVICE MARKETS USING FUZZY OPTIMIZATION
6. ACKNOWLEDGEMENT
7. 7.6 CONCLUSIONS
8. REFERENCES
INDEX
END USER LICENSE AGREEMENT

List of Tables

Chapter 1
1. Table 1.2.1 Research Documents by EC Algorithm 2014–Present
Chapter 2
1. Table 2.1.1 Research Documents Produced in 2010–2018 According to SCOPUS...
2. Table 2.2.1 Summary of the GECAD Smart Grid Application Competition Editions
3. Table 2.2.2 Main Results of PES GM 2017 and WCCI 2018 Edition
Chapter 3
1. Table 3.2.1 Evolution of UCP Solution Methodology
2. Table 3.3.1 Cost Coefficient of the Test System
3. Table 3.3.2 Basic Characteristics and Parameters in Each Case
4. Table 3.3.3 Objective Function Values in Each Case
5. Table 3.3.4 Cost Coefficient of Test System with Quadratics Cost Functions
6. Table 3.3.5 Comparison of Simulation Results of Each Method
7. Table 3.3.6 Cost Coefficient of Test System with Valve Point Effects
8. Table 3.3.7 Comparison Simulation Results of Each Method
9. Table 3.3.8 Comparison Simulation Results of Each Method (Demand = 2400 MW)...
10. Table 3.3.9 Comparison Simulation Results of Each Method (Demand = 2500 MW)...
11. Table 3.3.10 Comparison Simulation Results of Each Method (Demand = 2600 MW)...
12. Table 3.3.11 Comparison Simulation Results of Each Method (Demand = 2700 MW)...
13. Table 3.4.1 External Solutions at the Final Generation by Different Algorithms...
14. Table 3.4.2 Average of Metric C at the Final Generation
15. Table 3.4.3 Metric S at the Final Generation by Different Algorithms
16. Table 3.4.4 Optimization Results in Different Scheduling Modes
17. Table 3.5.1 Hydro Plants' Data
18. Table 3.5.2 Simulations for the Decomposition Schemes
19. Table 3.6.1 Production Cost Calculated in Each Case (US$ 10⁵)
20. Table 3.6.2 Optimal at 3 and 4 Period (US$ 10⁵)
21. Table 3.6.3 Optimal at 4 and 5 Period (US$ 10⁵)
22. Table 3.6.4 Aspiration Level and Weighting Factor
23. Table 3.6.5 Input Data of Generators
24. Table 3.6.6 Result
25. Table 3.6.7 Comparison of Results Obtained by Changing Aspiration Level of Supp...
26. Table 3.6.8 The Objective Types for Five Cases in this Case Study
27. Table 3.6.9 Results
28. Table 3.6.10 Energy Production According to the Type of Fuel (MWh)
29. Table 3.7.1 Six‐Bus Simulation Applying the Proposed PSO Methodology: Buses’ Pa...
30. Table 3.7.2 Six‐Bus Simulation Applying the Proposed PSO Methodology: Load Flow...
31. Table 3.7.3 Fourteen‐Bus Simulation Applying the Proposed PSO Methodology: Buse...
32. Table 3.7.4 Fourteen‐Bus Simulation Applying the Proposed PSO Methodology: Load...
33. Table 3.8.1 Major Types of Power System Problems
34. Table 3.8.2 Parameters for ABC Algorithm
35. Table 3.8.3 Comparison of Case 1 Fuel Cost
36. Table 3.8.4 Comparison of Case 2 Fuel Cost
37. Table 3.8.5 Comparison of Case 3 Power Loss
38. Table 3.9.1 Composition of the Benchmark Systems Used in the OPF Test Bed
39. Table 3.9.2 Optimization Variables Associated with Controllable Elements Listed...
40. Table 3.9.3 Optimization Variables Associated with Controllable Elements Listed...
41. Table 3.9.4 Test Cases for the IEEE 57‐Bus System
42. Table 3.9.5 Test Cases for the IEEE 118‐Bus System
43. Table 3.9.6 Test Cases for the IEEE 300‐Bus System
44. Table 3.9.7 Test Cases for the Offshore Wind Power Plant
45. Table 3.9.8 Optimization Variables for Offshore WPP's ORPD Test Case
46. Table 3.9.9 Statistics of Results Obtained with Top 5 Algorithms
47. Table 3.10.1 Fuzzy Rules
Chapter 4
1. Table 4.2.1 System Parameters of a Two‐area Thermal–thermal Power System...
2. Table 4.2.2 Optimized Controller Gains with Different Cost Functions
3. Table 4.2.3 Optimized Controller Gains and R Using ISE Cost Function
4. Table 4.2.4 Optimized Controller Gains and B_i Using ISE Cost Function
5. Table 4.2.5 Optimized Controller Gains, R, and B_i Using ISE Cost Function
6. Table 4.2.6 System Parameters of a Single‐area Hydropower System
7. Table 4.2.7 Variation of Communication Delay as a Function of Secondary Control...
8. Table 4.3.1 Comparison of Advantages of FACTS
9. Table 4.3.2 Sensitivities of 5‐Bus System
10. Table 4.3.3 PI Sensitivities of 5‐Bus System for Different Loading Limits
11. Table 4.3.4 Sensitivities of 5‐Bus System
12. Table 4.3.5 PI Sensitivities of 5‐Bus System for Different Loading Limits
13. Table 4.3.6 Sensitivities () of 5‐Bus System
14. Table 4.3.7 Line Flows (in p.u.) of 5‐Bus System
15. Table 4.3.8 Sensitivities () of 5‐Bus System
16. Table 4.3.9 PI Sensitivities of 5‐Bus System for Different Loading Limits
17. Table 4.3.10 Generator Data
18. Table 4.3.11 Sensitivity Factors and Rating of Lines
19. Table 4.3.12 Optimal Generation Schedule
20. Table 4.4.1 Selected Candidate Sets that Passed the HSV and RHP‐zero Tests in S...
21. Table 4.4.2 The Effects of TCSC on TTC
22. Table 4.5.1 Generating Unit Groups
23. Table 4.5.2 Load Shedding Automatons Thresholds
24. Table 4.5.3 Two‐class Partition of the Operating Points
25. Table 4.5.4 Two‐Class Partition of Pairs “OP‐CCV”
26. Table 4.5.5 Classification of OPs and “OP‐CCV” Pairs
27. Table 4.5.6 Evaluation of Classification Performance
28. Table 4.5.7 Generation Shedding and Fast Valving
29. Table 4.5.8 Two‐class partition of the Operating Points
30. Table 4.5.9 Two‐class Partition of Pairs “OP‐CCV”
31. Table 4.5.10 Classification of Ops and “OP‐CCV” Pairs
32. Table 4.5.11 Evaluation of Classification Performance
33. Table 4.5.12 Comparison of OP Classification by Each Method
34. Table 4.5.13 Comparison of Performance of Each Method
35. Table 4.5.14 Load Shedding Performance for Each Method
36. Table 4.5.15 Load Profile of the OP Under Study
37. Table 4.5.16 Curtailed Load for Each Area
38. Table 4.5.17 RBFNN Regression Performance Evaluation for Testing Set
39. Table 4.5.18 Unit Commitment Patterns for the OP Under Study
40. Table 4.5.19 Dispatch and Operational Cost for the Unit Commitment Patterns und...
41. Table 4.5.20 Attributes used by the GA‐aided DT
42. Table 4.5.21 Proposed Islanding
43. Table 4.5.22 Proposed Islanding of IΕΕΕ 118 Buses Test System – Proposed Island...
44. Table 4.5.23 Proposed Islanding of IΕΕΕ 118 Buses Test System – Proposed Island...
45. Table 4.6.1 Nomenclature
46. Table 4.6.2 The Selected Input and Output Variables of the RST Model
47. Table 4.6.3 The Limitations of the Separated Overfire Air Valve for OP 3,8,12,1...
48. Table 4.6.4 Original NO_x Emission and Boiler Efficiency for OP 3,8,12,17
Chapter 5
1. Table 5.2.1 Technical and Economical Specification of the Available Conductors...
2. Table 5.2.2 GA Parameters
3. Table 5.4.1 Intensity Scale of Importance
4. Table 5.4.2 Results of CVR Simulation Using PSO
5. Table 5.4.3 Results of CVR Simulation Using AHP
6. Table 5.5.1 Population Sizing‐Free Scheme
7. Table 5.5.2 Benchmark Network Size
8. Table 5.5.3 Planning Periods of Networks 1, 2, and 3
9. Table 5.5.4 Solutions of DNEP Considering CAPEX vs. DSM
10. Table 5.5.5 DNEP Solution: Total Cost (CAPEX + DSM) vs. Energy Losses...
11. Table 5.5.6 DNEP Solutions: Total Cost (CAPEX + DSM) vs. CML per Year...
12. Table 5.6.1 System Summary
13. Table 5.6.2 System Parameters
14. Table 5.6.3 Capacitor Cost
15. Table 5.6.4 GA and Trust‐Tech Settings
16. Table 5.6.5 Segments of GA Generations
17. Table 5.6.6 GA Optimal Capacitor Placement (Seg. 4, Up to 82 Generations)
18. Table 5.6.7 GA Optimal Capacitor Placement (Seg. 3, Up to 49 Generations)
19. Table 5.6.8 GA Optimal Capacitor Placement (Seg. 5, Up to 131 Generations)
20. Table 5.6.9 GA Optimal Capacitor Placement (3500 Generations)
21. Table 5.6.10 Trust‐Tech Solutions (Seg. 4, Up to 82 Generations)
22. Table 5.6.11 Trust‐Tech Optimal Capacitor Placement (Seg. 4, Up to 82 Generation...
23. Table 5.6.12 Trust‐Tech Solutions (Seg. 3, Up to 49 Generations)
24. Table 5.6.13 Trust‐Tech Optimal Capacitor Placement (Seg. 3, Up to 49 Generation...
25. Table 5.6.14 Trust‐Tech Solutions (Seg. 5, Up to 131 Generations)
26. Table 5.6.15 Trust‐Tech Optimal Capacitor Placement (Seg. 5, Up to 131 Generati...
27. Table 5.7.1 Features Present in Distributed Systems and Smart‐Grid Devices...
28. Table 5.9.1 123‐Node Network Component List
29. Table 5.9.2 Solutions Obtained from PSO on Fault 160–167 at Light‐Loaded Level...
30. Table 5.9.3 PSO on Fault 160–167 at Heavy‐Loaded Conditions
31. Table 5.9.4 K‐means (Set the Number of Clusters to 4)
32. Table 5.9.5 Improvement in Local Search Stage
33. Table 5.9.6 1101‐Node Network Component List
34. Table 5.9.7 Solutions Obtained in Group‐based PSO on Fault 297–298...
35. Table 5.9.8 Solutions Obtained by Stage III: Local Stage
36. Table 5.10.1 Current Limits According to Pqflag Priority
37. Table 5.10.2 Derivation of Low‐Voltage Tripping Logic
38. Table 5.10.3 MVMO Internal Variable Settings
39. Table 5.10.4 Optimized Parameters for Sub Test Case 1
40. Table 5.10.5 Root Mean Square Values for Active and Reactive Power Curves
Chapter 6
1. Table 6.2.1 Energy Resources Characteristics and Costs
2. Table 6.2.2 Network Characteristics
3. Table 6.2.3 Electric Vehicles by Consumer Type
4. Table 6.2.4 Consumers Characteristics
5. Table 6.2.5 Consumers Characteristics
6. Table 6.4.1 MVMO Parameters
7. Table 6.4.2 Study Cases for the AC‐connected Wind Farm
8. Table 6.6.1 Consumers and DR Programs
9. Table 6.7.1 Parameters of DA‐Clustering
10. Table 6.7.2 Parameters of ANNs
11. Table 6.7.3 Parameters of PSO and EPSO
12. Table 6.7.4 Simulation Results of Each Method
13. Table 6.8.1 Classification of Prediction Techniques Based Forecast Time Horizon
14. Table 6.8.2 Daily Forecast Error Comparison for AEB
15. Table 6.8.3 Seasonal Day Ahead Forecast Error Comparison
16. Table 6.9.1 The EV Charging Demand Coordinates and Their EV Amounts
17. Table 6.9.2 EV Charging Station Candidates' Location Coordinates
18. Table 6.9.3 Typical Solutions in the Pareto Set
19. Table 6.10.1 Composition of IEEE 118‐Bus Test System
20. Table 6.10.2 Wind Turbine Power Curve and Wind Speed Rayleigh Distribution Para...
21. Table 6.10.3 Direct, Underestimated Penalty, and Overestimated Penalty Cost Coe...
22. Table 6.10.4 Expected Value of the Total Generation Cost for 16 Runs in Each Po...
23. Table 6.10.5 Average Running Time for Each Population‐Based Algorithm
Chapter 7
1. Table 7.4.1 MAPE Forecast Error Values (%)
2. Table 7.4.2 Average Execution Times of the SVM Approach
3. Table 7.4.3 Average Execution Times of the ANN Approach
4. Table 7.5.1 Fuzzy Parameters for the Forecasted Quantities
5. Table 7.5.2 Sensitivity of Profits to Fuzzy Income Limits

List of Illustrations

Chapter 1
1. Figure 1.1.1 Publications of CI in power systems and smart grid (2002–2018)....
2. Figure 1.2.1 Number of publications by EC methods in power systems and smart...
3. Figure 1.2.2 Genetic algorithm process flowchart.
4. Figure 1.2.3 Lagged particles and waiting phenomena in (a) PSO and (b) QPSO....
5. Figure 1.2.4 Multi‐objective particle swarm optimization flowchart.
Chapter 2
1. Figure 2.2.1 Overview of the aggregator energy management problem.
Chapter 3
1. Figure 3.2.1 UCP solution paths.
2. Figure 3.2.2 UCP solution using heuristic methods.
3. Figure 3.2.3 Initial generation of population.
4. Figure 3.2.4 ISO activities.
5. Figure 3.2.5 Co‐ordination between ISO and GENCO.
6. Figure 3.3.1 Structure of simple genetic algorithm.
7. Figure 3.3.2 Search mechanism of PSO.
8. Figure 3.3.3 Piecewise quadratic cost function of a generator.
9. Figure 3.3.4 Cost function with five valves.
10. Figure 3.3.5 A unit with prohibited operating zone.
11. Figure 3.3.6 Encoding schemes. (a) Series encoding and (b) embedded encoding...
12. Figure 3.3.7 Pseudocode for the new crossover technique.
13. Figure 3.3.8 Pseudocode for the deterministic crowding GA.
14. Figure 3.3.9 Artificial immune genetic algorithm flowchart.
15. Figure 3.3.10 Flowchart of the genetic algorithm based on the Lagrange metho...
16. Figure 3.3.11 Adjustment strategy for an individual's position within bounda...
17. Figure 3.3.12 Comparison of inertia weights for IWA and CIWA.
18. Figure 3.3.13 Convergence characteristics of the CSPSO.
19. Figure 3.3.14 PSO with penalty for Gworst search mechanism.
20. Figure 3.3.15 Schematic of dynamic space reduction strategy.
21. Figure 3.4.1 Solution distribution by different selection strategies. (a) Cr...
22. Figure 3.4.2 The calculation flowchart of IDEMO algorithm.
23. Figure 3.4.3 Pareto fronts of different algorithms.
24. Figure 3.4.4 Convergence curves of outer solutions for cost.
25. Figure 3.5.1 Simulation‐based optimization for solving hydrothermal coordina...
26. Figure 3.5.2 Hydro generation (MW) – Solution Baseline (gray) versus Groupin...
27. Figure 3.5.3 Monthly total hydro generation for the planning horizon (MW).
28. Figure 3.6.1 Methods for analyzing the generator maintenance scheduling prob...
29. Figure 3.6.2 Configuration of the solutions searched in vertex by convention...
30. Figure 3.6.3 Configuration of the solutions searched by time axis shift meth...
31. Figure 3.6.4 Possible time range of generator maintenance of the example pro...
32. Figure 3.6.5 Initial time range of generator maintenance of the example prob...
33. Figure 3.6.6 Concept of flexibility.
34. Figure 3.6.7 Year load curve (weekly load peaks).
35. Figure 3.6.8 Convergence of the objective function (μ).
36. Figure 3.6.9 Standard deviation of supply reserve rate and LOLP.
37. Figure 3.6.10 Standard deviation of EDNS according to iteration.
38. Figure 3.6.11 Maintenance powers at each week.
39. Figure 3.6.12 A search method using genetic algorithm.
40. Figure 3.6.13 Flowchart of genetic algorithm.
41. Figure 3.6.14 Generator maintenance scheduling system flowchart.
42. Figure 3.6.15 Starting screen of visualization of the GMS.
43. Figure 3.6.16 Preferences of the GMS.
44. Figure 3.6.17 Running process of the GMS by GA.
45. Figure 3.6.18 User‐friendly visualization results of the GMS.
46. Figure 3.6.19 User‐friendly visualization (calendar style) results of the GM...
47. Figure 3.6.20 Total system result.
48. Figure 3.6.21 The share of power production average of all cases.
49. Figure 3.7.1 Three bus power system.
50. Figure 3.7.2 Flowchart of the proposed hybrid PSO algorithm.
51. Figure 3.7.3 Six‐bus power system [28].
52. Figure 3.7.4 Decrease of global score for the 6‐bus power system.
53. Figure 3.7.5 Voltage profile of the system buses of 6‐bus power system.
54. Figure 3.7.6 Fourteen‐bus power system.
55. Figure 3.7.7 Decrease of global score for the 14‐bus power system.
56. Figure 3.7.8 Voltage profile of the system buses of 14‐bus power system.
57. Figure 3.8.1 IEEE 30‐bus system.
58. Figure 3.8.2 Convergence characteristics of ABC method in case 1.
59. Figure 3.8.3 Effect of valve‐point loading on a quadratic cost function.
60. Figure 3.8.4 Convergence characteristics of ABC method in case 2.
61. Figure 3.8.5 Convergence characteristics of ABC method in case 3.
62. Figure 3.8.6 Convergence characteristics of ABC method in case 4.
63. Figure 3.8.7 Voltage profiles for case 1 and case 4.
64. Figure 3.9.1 Offshore WPP with optimization‐based management of reactive pow...
65. Figure 3.9.2 Simple 2D illustration of the Movement Rule in DEEPSO.
66. Figure 3.9.3 Algorithmic procedure of MVMO‐PHM. The fitness evaluation and c...
67. Figure 3.9.4 Layout of the set of solution archive.
68. Figure 3.9.5 Procedure for parent selection in MVMO‐PHM.
69. Figure 3.10.1 Simple divide‐to‐conquer strategy (branching).
Chapter 4
1. Figure 4.2.1 Turbine mechanical power variation following a step change in g...
2. Figure 4.2.2 Speed power response of governor.
3. Figure 4.2.3 Two‐area system.
4. Figure 4.2.4 Two‐area thermal power system.
5. Figure 4.2.5 System performance with different cost functions.
6. Figure 4.2.6 System performance with different parameter optimization.
7. Figure 4.2.7 Single‐area hydro system.
8. Figure 4.2.8 Impact of communication delay on frequency.
9. Figure 4.2.9 Impact of secondary controller gain in the presence of communic...
10. Figure 4.3.1 Representation of different controllers.
11. Figure 4.3.2 Model of transmission line.
12. Figure 4.3.3 Model of TCSC.
13. Figure 4.3.4 Injection model of TCSC.
14. Figure 4.3.5 Equivalent circuit of TCPAR.
15. Figure 4.3.6 Injection model of TCPAR.
16. Figure 4.3.7 Equivalent circuit of UPFC.
17. Figure 4.3.8 Vector diagram of UPFC.
18. Figure 4.3.9 Equivalent circuit injection model of UPFC.
19. Figure 4.3.10 Equivalent circuit of GUPFC.
20. Figure 4.3.11 Injection model of GUPFC.
21. Figure 4.3.12 5‐Bus system.
22. Figure 4.4.1 One‐line diagram of a 5‐area study system.
23. Figure 4.4.2 The critical eigenvector and the corresponding bus number.
24. Figure 4.4.3 Bus voltage magnitude profile when system is heavily stressed....
25. Figure 4.4.4 Voltage profile of the system after placing 145 Mvar at bus 40....
26. Figure 4.4.5 Convergence of GA and PSO on the level of compensation.
27. Figure 4.4.6 Convergence of GA and PSO on the average best‐so‐far.
28. Figure 4.4.7 The critical eigenvector and the corresponding bus number in sy...
29. Figure 4.4.8 Bus voltage magnitude profile when system is heavily stressed i...
30. Figure 4.4.9 The RGA‐number of candidate sets 3, 6, and 9 (pre‐fault).
31. Figure 4.4.10 The RGA‐number of candidate sets 34 and 37 (pre‐fault).
32. Figure 4.4.11 The RGA‐number of candidate sets 31 and 37 (pre‐fault).
33. Figure 4.4.12 The RGA‐number of candidate sets 3, 6, and 9 (post‐fault).
34. Figure 4.4.13 The RGA‐number of candidate sets 34, 37, and 40 (post‐fault)....
35. Figure 4.4.14 The RGA‐number of candidate sets 22 and 37 (post‐fault).
36. Figure 4.4.15 The convergence characteristic of fitness function with PSO to...
37. Figure 4.4.16 The convergence characteristic of fitness function with GCPSO ...
38. Figure 4.4.17 The convergence characteristic of fitness function with GA to ...
39. Figure 4.5.1 Framework of the proposed method.
40. Figure 4.5.2 Simulation of load shedding strategies.
41. Figure 4.5.3 Flowchart of the proposed methodology.
42. Figure 4.5.4 Decision tree for load shedding.
43. Figure 4.5.5 Genetic algorithm‐aided DT.
44. Figure 4.5.6 Mapping of system buses before and after the VLT‐SOM training....
45. Figure 4.5.7 Radial basis function neural network.
46. Figure 4.5.8 A sample decision tree.
47. Figure 4.6.1 The schematic structural view of vertical spindle roller pressu...
48. Figure 4.6.2 Model verification results.
49. Figure 4.6.3 Sketch of the boiler of the investigated power plant.
50. Figure 4.6.4 Structure of the NMPC for reheated steam temperature control.
51. Figure 4.6.5 Reheater damper control search window for loading‐down process....
52. Figure 4.6.6 Damper, bypass, and RST responses due to load change from 100 t...
53. Figure 4.6.7 Process of immunological tolerance test.
54. Figure 4.6.8 Schematic figure for receptor editing.
55. Figure 4.6.9 Working principle of ICSMOA.
56. Figure 4.6.10 Detailed procedure of memory cells processing.
57. Figure 4.6.11 The optimization results for OP 3,8,12, and 17 using the ICSMO...
58. Figure 4.7.1 Simplified NARMA neural network model.
59. Figure 4.7.2 Schematic diagram for supercritical power unit.
60. Figure 4.7.3 Model inputs and outputs for supercritical boiler unit.
61. Figure 4.7.4 NARMA model for load and main steam pressure characteristics.
62. Figure 4.7.5 Model test under wide load‐changing condition
63. Figure 4.7.6 Schematics of two typical coordinated control modes. (a) Boiler...
64. Figure 4.7.7 Intelligent coordinated predictive optimal control scheme.
65. Figure 4.7.8 Test results when load drops from 600 to 540 MW.
66. Figure 4.7.9 Test results when load drops from 540 to 480 MW.
Chapter 5
1. Figure 5.2.1 (a) Six‐bus distribution network, (b) candidate planning soluti...
2. Figure 5.2.2 21‐Bus distribution network.
3. Figure 5.2.3 (a) Yearly load profile and (b) yearly wind generation profile....
4. Figure 5.2.4 Fitness function value evolution in Cases 1–3.
5. Figure 5.2.5 (a) Solution in Case 1, (b) solution in Case 2, and (c) solutio...
6. Figure 5.3.1 (a) Global minima and local minima. (b) Pareto front.
7. Figure 5.3.2 Equivalent circuit of VSC for power flow studies.
8. Figure 5.3.3 Generic distribution system with n asynchronized zones.
9. Figure 5.3.4 IEEE type AC4A excitation system.
10. Figure 5.3.5 Simplified schematic for AC/DC power system.
11. Figure 5.3.6 Flowchart for the sequential power flow approach.
12. Figure 5.3.7 Voltage limits specified by MIL‐Std 1399, section 300.
13. Figure 5.3.8 Safe operation region for VSC.
14. Figure 5.3.9 Frequency envelopes for the 400 Hz systems in marine applicatio...
15. Figure 5.3.10 Pulsed load profile.
16. Figure 5.3.11 Pareto front and selection of efficient Pareto solutions.
17. Figure 5.3.12 Multi‐objective optimization flowchart.
18. Figure 5.4.1 Concept of PSO.
19. Figure 5.4.2 Modification of particle's velocity and position.
20. Figure 5.4.3 PSO flowchart.
21. Figure 5.4.4 A hierarchy model for CVR.
22. Figure 5.4.5 Global best value at each iteration.
23. Figure 5.4.6 Particle's movement for CVR.
24. Figure 5.5.1 MV distribution network with three feeders.
25. Figure 5.5.2 An example linkage tree.
26. Figure 5.5.3 Different clusters approach different parts of the optimal Pare...
27. Figure 5.5.4 Benchmark MV distribution networks with existing assets [130]....
28. Figure 5.28 Network 3: CAPEX vs. DSM.
29. Figure 5.5.6 Pareto fronts of MO optimizations for three networks.
30. Figure 5.5.7 Network 2: total cost (CAPEX + DSM) vs. energy loss.
31. Figure 5.5.8 Fronts of MO optimization (CAPEX + DSM) vs. energy loss for thr...
32. Figure 5.5.9 Network 1: total cost (CAPEX + DSM) vs. CML.
33. Figure 5.5.10 Fronts of MO optimization (CAPEX + DSM) vs. CML for three netw...
34. Figure 5.6.1 Convergence for equilibrium points.
35. Figure 5.6.2 x_exit should be very close to the exact exit point.
36. Figure 5.6.3 Flowchart of the GA‐guided Trust‐Tech.
37. Figure 5.6.4 GA cost function value (Seg. 4, up to 82 generations).
38. Figure 5.6.5 GA cost function value (3500 generations).
39. Figure 5.6.6 Solution comparison.
40. Figure 5.7.1 Smart‐grid in layers.
41. Figure 5.7.2 EDS and its representation by graph. (a) Example of a typical E...
42. Figure 5.7.3 Example of service restoration. (a) Section in fault. (b) New c...
43. Figure 5.7.4 Example of distribution system with three coalitions: coalition...
44. Figure 5.7.5 Example of electrical path between substation 0 and coalition 2...
45. Figure 5.7.6 Substation agent (SAg) actions in a flowchart.
46. Figure 5.7.7 Depth‐first search algorithm for the creation of coalitions.
47. Figure 5.7.8 Modified Dijkstra algorithm for finding the minimal path betwee...
48. Figure 5.7.9 Load agent (LAg) actions in a flowchart.
49. Figure 5.7.10 Flowchart of switching agent (SWAg).
50. Figure 5.7.11 Sequence chart of the multi‐agent system.
51. Figure 5.8.1 Actions and stages to prepare power system restoration plans.
52. Figure 5.8.2 Example of six‐switch system.
53. Figure 5.9.1 An illustration of three steps involved in the traditional PSO ...
54. Figure 5.9.2 The procedure of Stage I.
55. Figure 5.9.3 Procedure of the three‐stage group‐based PSO.
56. Figure 5.9.4 The top three solution points in each group.
57. Figure 5.9.5 The flowchart of Stage 1 for the group‐based PSO method.
58. Figure 5.9.6 Group‐based PSO algorithm flowchart for the service restoration...
59. Figure 5.9.7 The IEEE 123‐node feeder test case.
60. Figure 5.9.8 One‐line diagram of the 394‐bus, 1101‐node test system.
61. Figure 5.10.1 Distribution network configurations. (a) Passive network. (b) ...
62. Figure 5.10.2 Concept of aggregation.
63. Figure 5.10.3 Flowchart for identification of MVMO parameters.
64. Figure 5.10.4 MVMO archive where solutions are stored.
65. Figure 5.10.5 Procedure of mutation of selected genes.
66. Figure 5.10.6 Test case: IEEE 34‐bus feeder system.
67. Figure 5.10.7 PowerFactory representation of PV station.
68. Figure 5.10.8 DE for test case shown.
69. Figure 5.10.9 Basic block diagram of PVD1 model.
70. Figure 5.10.10 Protection logic behind PVD1 model.
71. Figure 5.10.11 Convergence of optimization.
72. Figure 5.10.12 0.30 p.u. voltage level. Solid line: detailed model; dotted l...
73. Figure 5.10.13 0.65 p.u. voltage level. Solid line: detailed model; dotted l...
74. Figure 5.10.14 0.50 p.u. voltage level. Solid line: detailed model; dotted l...
75. Figure 5.10.15 0.75 p.u. voltage level. Solid line: detailed model; dotted l...
76. Figure 5.11.1 Simplified equivalent circuit of a transformer winding.
77. Figure 5.11.2 Equivalent circuit for multiple coupling windings.
78. Figure 5.11.3 Coefficients estimation approach.
79. Figure 5.11.4 DEEPSO flowchart.
80. Figure 5.11.5 Measured and FEM simulated signal in a real transformer.
81. Figure 5.11.6 Correlation coefficient indicator (1 − ρ) for each target...
82. Figure 5.11.7 Relative error indicator (η) for each target function.
83. Figure 5.11.8 Relative factor indicator (r) for each target function.
84. Figure 5.11.9 MIN‐MAX indicator (1 − MM) for each target function.
85. Figure 5.11.10 DABS indicator for each target function.
86. Figure 5.11.11 ASLE indicator for each target function.
87. Figure 5.11.12 Spectrum deviation indicator (σ) for each target functio...
88. Figure 5.11.13 Measured and simulated signals with correlation coefficient....
89. Figure 5.11.14 Measured and simulated signals with relative error.
90. Figure 5.11.15 Measured and simulated signals with relative factor indicator...
91. Figure 5.11.16 Measured and simulated signals with MIN‐MAX indicator.
92. Figure 5.11.17 Measured and simulated signals with DABS indicator.
93. Figure 5.11.18 Measured and simulated signals with ASLE indicator.
94. Figure 5.11.19 Measured and simulated signals with spectrum deviation indica...
Chapter 6
1. Figure 6.2.1 37‐Bus distribution network.
2. Figure 6.2.2 Energy mix in 2050 [29].
3. Figure 6.2.3 Consumers' profiles.
4. Figure 6.2.4 External suppliers' price based on Nord Pool day‐ahead market....
5. Figure 6.2.5 Energy resources scheduling using MINLP.
6. Figure 6.2.6 Scheduled energy by resource in the 24 periods.
7. Figure 6.2.7 Operation costs in the 24 periods.
8. Figure 6.2.8 Electric vehicles and demand response scheduling using MINLP.
9. Figure 6.2.9 Electric vehicles and demand response scheduling using SA.
10. Figure 6.2.10 Electric vehicles and demand response scheduling using SADT.
11. Figure 6.2.11 Electric vehicles and demand response scheduling using ERS2A....
12. Figure 6.2.12 Electric vehicles and demand response scheduling using PSO.
13. Figure 6.2.13 Electric vehicles and demand response scheduling using EPSO.
14. Figure 6.2.14 Electric vehicles and demand response scheduling using MoPSO....
15. Figure 6.2.15 Electric vehicles and demand response scheduling using 2sPSO....
16. Figure 6.3.1 Concept of FRIENDS.
17. Figure 6.3.2 Solution algorithm of planning problem.
18. Figure 6.3.3 Voltage control area.
19. Figure 6.3.4 The interaction diagram of agent operations during normal state...
20. Figure 6.3.5 The interaction diagram of agent operations during emergency st...
21. Figure 6.3.6 Structure of real‐scale smart grid experimental system.
22. Figure 6.3.7 Protection scheme of the system.
23. Figure 6.4.1 Predictive control optimization by MVMO.
24. Figure 6.4.2 Multilayer perceptron.
25. Figure 6.4.3 MVMO‐based procedure for optimal reactive power management.
26. Figure 6.4.4 Solution archive.
27. Figure 6.4.5 Variable mapping.
28. Figure 6.4.6 Borssele wind farm layout with AC cable.
29. Figure 6.4.7 Wind speed variation.
30. Figure 6.4.10 Case 3: (a) Hourly Q set‐points of every wind turbine, (b) hou...
31. Figure 6.4.11 Case 4: (a) Hourly Q set‐points of every wind turbine, (b) red...
32. Figure 6.4.8 Case 1: (a) Hourly Q set‐points of every wind turbine, (b) hour...
33. Figure 6.4.9 Case 2: (a) Hourly Q set‐points of every wind turbine, (b) redu...
34. Figure 6.4.10 Case 3: (a) Hourly Q set‐points of every wind turbine, (b) hou...
35. Figure 6.4.11 Case 4: (a) Hourly Q set‐points of every wind turbine, (b) red...
36. Figure 6.4.12 Case 5: (a) Hourly Q set‐points of every wind turbine, (b) hou...
37. Figure 6.4.13 Case 6: (a) Hourly Q set‐points of every wind turbine, (b) red...
38. Figure 6.5.1 Single diode equivalent of PV module.
39. Figure 6.5.2 (a) I‐V characteristics and (b) P‐V characteristics with irradi...
40. Figure 6.5.3 (a) I‐V characteristics and (b) P‐V characteristics with temper...
41. Figure 6.5.4 Movement of particles during optimization.
42. Figure 6.5.5 PV system with boost converter.
43. Figure 6.5.6 Duty cycle movement during uniform shading in (a) iteration 1, ...
44. Figure 6.5.7 Duty cycle movement during particle shading in (a) iteration 1,...
45. Figure 6.5.8 Flowchart to obtain optimal maximum power point for PV systems ...
46. Figure 6.6.1 Lagged particles and wait among particles in QPSO.
47. Figure 6.6.2 Waiting phenomena compared between (a) PSO and (b) QPSO.
48. Figure 6.6.3 Flowchart of QPSO algorithm.
49. Figure 6.6.4 Generation and consumption diagram.
50. Figure 6.6.5 Scheduling results: (a) all resources; (b) shift DR income, and...
51. Figure 6.7.1 Structure of RBFN.
52. Figure 6.7.2 Comparison between actual value and predicted value by proposed...
53. Figure 6.7.3 Behavior of errors of MLP and the proposed method, (a) case of ...
54. Figure 6.8.1 Global energy consumption and CO₂ emission scenario.
55. Figure 6.8.2 Overview of UQ solar PV network connected with power grid.
56. Figure 6.8.3 AEB PV integrated smart building at UQ St. Lucia Campus.
57. Figure 6.8.4 GCI PV integrated smart building at UQ St. Lucia Campus.
58. Figure 6.8.5 Electrical power flow diagram of GCI building.
59. Figure 6.8.6 Classification of forecast techniques.
60. Figure 6.8.7 Wavelet decomposition and composition process of load and PV si...
61. Figure 6.8.8 Proposed neural network ensemble‐based forecast framework.
62. Figure 6.8.9 Actual and predicted load demand of proposed spring day.
63. Figure 6.8.10 Actual and predicted output of proposed framework for summer d...
64. Figure 6.8.11 Actual and predicted output of proposed framework for summer C...
65. Figure 6.8.12 Actual and predicted output of proposed framework for spring C...
66. Figure 6.9.1 The two‐layer chromosome coding structure.
67. Figure 6.9.2 Flowchart of the crossover operator for improved SPEA2.
68. Figure 6.9.3 Flowchart of the mutation operator for improved SPEA2.
69. Figure 6.9.4 Distribution of EV charging demand points and potential EV char...
70. Figure 6.9.5 Pareto front of the tri‐objective EV charging station layout pl...
71. Figure 6.9.6 The final EV charging station layout planning scheme obtained b...
72. Figure 6.10.1 Heuristic algorithms flowchart.
Chapter 7
1. Figure 7.3.1 Main user interface of the MASCEM electricity market simulator....
2. Figure 7.3.2 MASCEM output for sellers' bid information in the day‐ahead spo...
3. Figure 7.3.3 MASCEM output for buyers' bid information in the day‐ahead spot...
4. Figure 7.3.4 MASCEM output chart of Seller 22 for the day‐ahead spot market....
5. Figure 7.3.5 MASCEM output chart of Seller 7 for the day‐ahead spot market....
6. Figure 7.3.6 MASCEM output chart of Seller 7 for the balancing market.
7. Figure 7.5.1 Fuzzy membership function.
8. Figure 7.5.2 Fuzzy model of EVA income.
9. Figure 7.5.3 Fuzzy model of expected regulation‐up deployments.
10. Figure 7.5.4 Average percentage of EV availability.
11. Figure 7.5.5 Average ancillary service prices for the simulation period with...
12. Figure 7.5.6 Average POP.
13. Figure 7.5.7 Average regulation‐up.
14. Figure 7.5.8 Average regulation‐down.
15. Figure 7.5.9 Average responsive reserve.
16. Figure 7.5.10 Total expected and actual EVA profits for different energy cos...
17. Figure 7.5.11 Final SOC statistics.
18. Figure 7.5.12 Total actual profits for different levels of final desired SOC...

CONTRIBUTORS

Ali T. Al‐Awami, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

David L. Alvarez, Universidad Nacional de Colombia, Bogotá, Colombia

Alexandre P. Alves da Silva, Vale S.A., Rio de Janeiro, Brazil

Kyungsung An, SK Telecom, Seoul, Korea

Eduardo N. Asada, University of São Paulo, São Carlos, Brazil

Wenlei Bai, ABB Enterprises Software Inc. Houston, TX, USA

Jens C. Boemer, Electric Power Research Institute, Seattle, USA

Luiz Eduardo Borges da Silva, Itajuba Federal University, Itajuba, MG, Brazil

Peter A. N. Bosman, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

Leonel Carvalho, INESC TEC, Porto, Portugal

Hsiao‐Dong Chiang, Cornell University, Ithaca, NY, USA

Jaeseok Choi, Gyeongsang National University, Jinju, Korea

Jinda Cui, Lehigh University, Bethlehem, PA, USA

Anna Carolina R.H. da Silva, Eletrobras, Rio de Janeiro, Brazil

Ibrahim Eke, Kirikkale University, Kirikkale, Turkey

Ahmed Elsayed, Florida International University, Miami, FL, USA

István Erlich, University of Duisburg‐Essen, Duisburg, Germany

Pedro Faria, Polytechnic of Porto, Porto, Portugal

Malihe Maghfoori Farsangi, Shahid Bahonar University of Kerman, Kerman, Iran

Pavlos S. Georgilakis, National Technical University of Athens (NTUA), Athens, Greece

Marinus O. W. Grond, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

Digvijay Gusain, TU Delft, Delft, The Netherlands

Nikos D. Hatziargyriou, National Technical University of Athens (NTUA), Athens, Greece

Kyeon Hur, Yeonsei University, Seoul, Korea

Nikolaos C. Koutsoukis, National Technical University of Athens (NTUA), Athens, Greece

Germano Lambert‐Torres, Gnarus Institute, Itajuba, MG, Brazil

Han La Poutré, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

Kwang Y. Lee, Baylor University, Waco, TX, USA

Yeonchan Lee, Gyeongsang National University, Jinju, Korea

João Bosco A. London Jr, University of São Paulo, São Carlos, Brazil

Ngoc Hoang Luong, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

Liangyu Ma, North China Electric Power University, Baoding, China

Vladimiro Miranda, INESC TEC / University of Porto, Porto, Portugal

Sukumar Mishra, Indian Institute of Technology Delhi, New Delhi, India

Osama Mohammed, Florida International University, Miami, FL, USA

H. Morais, INESC‐ID / University of Lisbon, Lisbon, Portugal

Hiroyuki Mori, Meiji University, Nakano‐city, Tokyo, Japan

Mithulananthan Nadarajah, The University of Queensland, Brisbane, Queensland, Australia

Koichi Nara, Ibaraki University, Ibaraki, Japan

Mario Ndreko, TenneT TSO GmbH, Bayreuth, Germany

Hossein Nezamabadi‐pour, Shahid Bahonar University of Kerman, Kerman, Iran

Narayana Prasad Padhy, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

Peter Palensky, TU Delft, Delft, The Netherlands

Jong‐Bae Park, Konkuk University, Seoul, Korea

Tiago Pinto, Polytechnic of Porto, Porto, Portugal

Deepak Pullaguram, National Institute of Technology, Warangal, Telangana, India

Muhammad Qamar Raza, The University of Queensland, Brisbane, Queensland, Australia

Sergio Rivera, Universidad Nacional de Colombia, Bogotá, Colombia

Andrés Romero, Universidad Nacional de San Juan, San Juan, Argentina

Rubén Romero, São Paulo State University, Ilha Solteira, São Paulo, Brazil

Jose Rueda, TU Delft, Delft, The Netherlands

Camila Paes Salomon, Itajuba Federal University, Itajuba, MG, Brazil

Filipe O. Saraiva, Federal University of Pará, Belém, Brazil

Dushyant Sharma, Indian Institute of Technology Jodhpur, Rajasthan, India

Ruifeng Shi, North China Electric Power University, Beijing, China

Sishaj Pulikottil Simon, National Institute of Technology Tiruchirappalli, Tamilnadu, India

S. N. Singh, Madan Mohan Malaviya University of Technology, Gorakhpur, India

João Soares, Polytechnic of Porto, Porto, Portugal

Aldir S. Souza, State University of Piauí, Teresina, Piauí, Brazil

T. Sousa, Technical University of Denmark, Lyngby, Denmark

Wei Sun, University of Central Florida, Orlando, FL, USA

Masato Takahashi, Fuji Electric Co., Hino‐city, Tokyo, Japan

Aimilia‐Myrsini Theologi, Jedlix Smart Charging, Rotterdam, The Netherlands

Zita A. Vale, Polytechnic of Porto, Porto, Portugal

Kumar Venayagamoorthy, Clemson University, Clemson, SC, USA

E.M. Voumvoulakis, National Technical University of Athens (NTUA), Athens, Greece

Shuo Wang, China Electric Power Planning & Engineering Institute, Beijing, China

Xiao Wu, Southeast University, Nanjing, China

Shu Xia, North China Electric Power University, Beijing, China

Tianshi Xu, Tianjin University, Tianjin, PRC

Yong‐Feng Zhang, University of Jinan, Jinan, China

Ming Zhou, North China Electric Power University, Beijing, China

Qun Zhou, University of Central Florida, Orlando, FL, USA

Jón Atli Benediktsson	Xiaoou Li	Jeffrey Reed
Bimal Bose	Peter Lian	Diomidis Spinellis
David Alan Grier	Andreas Molisch	Sarah Spurgeon
Elya B. Joffe	Saeid Nahavandi	Ahmet Murat Tekalp

APPLICATIONS OF MODERN HEURISTIC OPTIMIZATION METHODS IN POWER AND ENERGY SYSTEMS

DISCLAIMER

PREFACE

CONTRIBUTORS