Study on Power Generation Economics Considering Environmental Impact: A Novel Optimization Approach Using Modified Real Coded Genetic Algorithm for Dynamic Load Model

The present work illuminates the real time optimization of economic emission load dispatch problem using modified real coded genetic algorithm. With the increase of power demand generation of power is also increased and expected to be increased too swiftly. Nevertheless its consequences are immense release of nitrogen oxides (NOx), sulphur oxides (SOx) and carbon oxides (COx) from fossil-fuelled based electric power generating stations and it causes damage not only human beings but also other life-forms. Such kind of atmospheric pollution is most accountable for global warming. Keeping in mind about the novel task of reducing pollutants for clean environment it is required to optimize not only fuel cost but emission also. Real time economic emission load dispatch helps to schedule operating generators outcome followed by load demand minimizing fuel cost and emission simultaneously over a certain period of time. Modified real coded genetic algorithm is nothing but a fresh presentation of real coded genetic algorithm with some changes.The computational results reveal that current proposal has excellent convergence characteristics and test results are superior and comparable to other.