Economic planning and operation in electric power system using meta heuristics on cuckoo search algorithm

Abstract

Acknowledgements

List of Figures

List of Tables

Abbreviations

1 Introduction

2 Literature Review

2.1 Heuristics and meta-heuristics:
- 2.1.1 Heuristics:
- 2.1.2 Meta-heuristics:
2.2 Particle Swarm Optimization
2.3 Differential Evolution
2.4 Harmony Search Algorithm
2.5 Teaching-learning-based optimization
2.6 Moth-Flame Optimization
2.7 Discussion
- 2.7.1 Apply a meta-heuristic for solving a problem
- 2.7.2 Effectiveness of meta-heuristics

3 Self-Learning Cuckoo search algorithm

3.1 Cuckoo search Algorithm
- 3.1.1 Cuckooâs breeding behavior
- 3.1.2 Lévy flight
- 3.1.3 Conventional Cuckoo search algorithm
3.2 Proposed Self-learning Cuckoo Search Algorithm
3.3 Evaluation on tested benchmarks
3.4 Applications on engineering problems

4 Multi-Area Economic dispatch problem

4.1 Introduction
- 4.1.1 Economic dispatch
- 4.1.2 Multi-area economic dispatch:
4.2 Problem formulation
- 4.2.1 Objective function:
- 4.2.2 Operating constraints:
  - 4.2.2.1 Real balanced-power constraint:
  - 4.2.2.2 Limitation of output power:
  - 4.2.2.3 Limitation of transmission lines:
  - 4.2.2.4 Prohibited operating zone constraint:
4.3 Previous works on Multi-area economic dispatch problem
4.4 Implementation for Multi-area economic dispatch problem
- 4.4.1 Determining output power of slack generator in each area
- 4.4.2 Solution vector:
- 4.4.3 Fitness function:
- 4.4.4 Overall procedure of the proposed method for MAED:
4.5 Numerical results
- 4.5.1 Case study 1:
4.6 Conclusions

5 Optimal power flow problem

5.1 Introduction
5.2 Problem formulation
- 5.2.1 Objective function
- 5.2.2 Operational constraints
  - 5.2.2.1 Power balance constraint
  - 5.2.2.2 Limited constraints of generators
  - 5.2.2.3 Shunt-VAR compensators capacity
  - 5.2.2.4 Limitation of tap changers of transformers
  - 5.2.2.5 Limitation of load bus voltages
  - 5.2.2.6 Capacity of transmission lines
5.3 Previous works on optimal power flow studies
5.4 Implementation of Self-learning Cuckoo Search for OPF
- 5.4.1 Controllable and dependent variables:
- 5.4.2 Fitness function
- 5.4.3 Overall procedure:
- 5.4.4 Example of Optimal power flow problem
5.5 Simulation results
- 5.5.1 Case study 1: IEEE 30-bus system
  - 5.5.2.1 Continuous variables of capacitors
  - 5.5.2.2 Binary capacitors
5.6 Conclusion

6 Optimal Reactive Power Dispatch

6.1 Previous works on optimal reactive power dispatch
6.2 Problem Formulation
- 6.2.1 Objective function
  - 6.2.2.1 Power balance constraint:
  - 6.2.2.2 Limitation constrains of generators
  - 6.2.2.3 Limitation of shunt-VAR compensators
  - 6.2.2.4 Limitation of transformer load changers
  - 6.2.2.5 Limitation of load bus voltages
  - 6.2.2.6 Limitation of transmission lines
6.3 Implementation of Self-Learning Cuckoo Search for ORPD
- 6.3.1 Constraint handling
- 6.3.2 Overall procedure
6.4 Numerical results
6.5 Conclusions

7 Optimal sizing and placement of shunt VAR compensators

7.1 Previous works on optimal reactive power dispatch
7.2 Objectives and operational constraints
- 7.2.1 Objectives
  - 7.2.1.1 The active power losses
  - 7.2.1.2 The voltage deviation
  - 7.2.1.3 The investment cost
  - 7.2.2.1 Power balance constraint
  - 7.2.2.2 Limitation of SVC devices
  - 7.2.2.3 Limitation of bus voltages
7.3 Implementation and the fitness function
- 7.3.1 Solution vector
- 7.3.2 Fitness function
- 7.3.3 Limitation of solution vector and initialization
- 7.3.4 Overall procedure
7.4 Simulation results
7.5 Conclusions

8 Conclusion

A Data of Multi-Area Economic Dispatch

B Data of the IEEE 30-bus system

C Data of the IEEE 57-bus system

D Data of the IEEE 118-bus system

E Data of the IEEE 300-bus system

F Matlab code of Self-Learning Cuckoo search algorithm for Example 4.1

Bibliography

List of Publications