Artificial Intelligence in Power System Optimization
CRC Press – 2013 – 520 pages
With the considerable increase of AI applications, AI is being increasingly used to solve optimization problems in engineering. In the past two decades, the applications of artificial intelligence in power systems have attracted much research. This book covers the current level of applications of artificial intelligence to the optimization problems in power systems. This book serves as a textbook for graduate students in electric power system management and is also useful for those who are interested in using artificial intelligence in power system optimization.
Introduction: Importance of Power System Optimization. Artificial Intelligence as a New Trend for Optimization Problems. Artificial Intelligence Applications in Power Systems. Overview of the Book. References.
Economic Dispatch: Introduction. Generator Incremental Cost Curve. Economic Dispatch Problem Formulation without Loss. Economic Dispatch Considering Transmission Loss. Economic Dispatch with Ramp Rate Constraint. Fuel Constrained Economic Dispatch. Economic Dispatch Considering Emission. Economic Dispatch with Transmission Constraint. Economic Dispatch with Non-smooth Cost Functions. Combined Heat and Power Economic Dispatch. Hydrothermal Economic Dispatch. Optimal Power Dispatch in Competitive Electricity Supply Industry. Summary. Problems. References.
Unit Commitment: Introduction. Unit Commitment Problem Formulation. Unit Commitment Solution Methods. Constrained Unit Commitment. Security Constrained Unit Commitment. Priced-Based Unit Commitment. Summary. Problems. References.
Hydrothermal Scheduling: Introduction. Hydroelectric Plants Model. Hydrothermal Scheduling Formulation. Hydro Thermal Scheduling Solution Methods. Hydro Units in Series. Pumped Storage Hydro Plants. Problem Formulation for Hydrothermal Scheduling for both Hydro Pumped Storage Hydro Plants. Solution Methods for Hydrothermal Scheduling including Pumped Storage Hydro Plants. Summary. Problems. References.
Optimal Power Flow: Introduction. Optimal Power Flow Problem Formulation. Optimal Real Power Dispatch with Network Limit Constraints. Neural Network Application to Optimal Power Flow. Particle Swarm Optimization for Optimal Power Flow. Summary. Problems. References.
Reactive Optimal Power Dispatch: Introduction. Reactive Power Essence in Power Systems. Conventional Optimal Reactive Power Dispatch. Optimal Reactive Power Dispatch under Deregulated Electricity Market. TVAC-PSO Based Optimal Reactive Power Dispatch under Deregulated Electricity Market. Summary. Problems. References.
Available Transfer Capability: Introduction. Transmission Transfer Capability Concepts. Available Transfer Capability Principles. Available Transfer. Capability Definition and Determination. Methodologies to Calculate ATC. Available Transfer Capability Calculation. Calculation of Total Transfer Capability by Evolutionary Programming. Total Transfer Capability Enhancement using Hybrid. Evolutionary Algorithm. Optimal Placement of Multi-Type FACTS Devices for ATC Enhancement Using HEA. Summary. Problems. References.
Appendix A: Mathematical Model Derivations
Appendix B: Data of Example Systems
Appendix C: Results of Examples
Appendix D: Tips for Programming In Matlab