Computational intelligence based technique for solving economic dispatch problem / Nur Azzammudin Rahmat - Rahmat, Nur Azzammudin (Institute of Graduate Studies, UiTM, 2016)
Electrical industry is no longer seen as a service to fulfill electrical requirement. Nowadays, the industry is more on business, and the utilities seek profits through efficient energy generation and supply. In order to maximize profits, the energy providers or utilities conduct economic load dispatch (ELD) process, where energy is feasibly generated and delivered to satisfy consumers’ needs. However, the method is restricted by several constraints that cause challenges in providing satisfying energy dispatch. Therefore, this research proposes a new computational technique termed as Differential Evolution Immunized Ant Colony Optimization (DEIANT) as an approach to solve the complex economic dispatch process. DEIANT is developed through hybridizing Ant Colony Optimization (ACO), Differential Evolution (DE) and Artificial Immune System (AIS) together. The coding were written in MATLAB (Matrix Laboratory) software. The development of DEIANT technique is consequently utilized to solve ELD, which is performed on IEEE 30, 57 and 118 Reliable Test Systems (RTS). These test systems are also used to perform the study for the whole research. New economic emission dispatch (EED) solving techniques termed as pollutant-based and fuelbased EED have been consequently developed. This is to address the emission release during energy producing process. For this study, three fossil-fuels namely petroleum, coal, and natural gas are highlighted. These fossil-fuels combustion produces different pollutants including carbon oxides (COᵪ), sulphur oxides (SOᵪ), and nitrogen oxides (NOᵪ)…