• Krishnanand
• Tushar Agrawal

ELD is a tool that schedules genset output to operate the power system most economically based on specified load requirements, or in other words, we can say that the main goal of economic load scheduling is to optimize all system constraints. At the same time, various generator sets are distributed at the lowest possible operating cost. The input/output characteristics of modern units are inherently highly non-linear (with valve point effects, rate limiting, etc.) and have multiple local minimum points in the cost function. In this regard, random search algorithms such as (GA), (ES), (EP), (PSO) can prove that (SA) is very effective in solving highly nonlinear ELD problems, and the shape is not limited. Cost curve. (GA) is a soft computing technique used to find exact or approximate solutions to optimization and search problems. Genetic algorithms are classified as global search heuristics. An algorithm to get the best solution to the optimization problem. The individual's performance is evaluated by the fitness function (ie, the objective function) and the problem is minimized, in which case particles with lower values have higher performance. The best experience for each particle in the iteration is stored in its memory, called Personal Best (Pbest). The best value of Pbest (minimum) in the iteration determines the global best value (Gbest).

Economic Load Dispatch (ELD), Genetic algorithms (GA), Evolutionary strategies (ES), Evolutionary programming (EP), Particle swarm optimization (PSO)

"Multi objective Economic and Emission Dispatch in Multi Machine System Using Modified Particle Swarm Optimization ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.593-598, June 2019, Available :http://www.jetir.org/papers/JETIR1907387.pdf

"Multi objective Economic and Emission Dispatch in Multi Machine System Using Modified Particle Swarm Optimization ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp593-598, June 2019, Available at : http://www.jetir.org/papers/JETIR1907387.pdf