Parametric analysis and optimization of 660 MW supercritical power plant.

The newly set up power plant has been committed to fulfilling the power supply demand of the world. Therefore, optimizing operating variables within constraints of varying power demand becomes necessary. The research aims to identify and optimize several parameters influencing the performance and efficiency of a 660 MW supercritical power plant at different operating conditions, such as steam temperature, pressure, feedwater flow rate, and fuel consumption. Ultimately, the research aims to contribute to developing sustainable and environmentally friendly power generation technologies. The present study covers the multi-objective optimization of a 660 MW capacity fossil fuel-fired SUPP. The overall plant efficiency, cost of electricity, and exergetic efficiency are taken as objective functions. The Particle Swarm Optimization (PSO) technique and a semi-empirical model of energy, economic, and exergy analysis of fossil fuel-fired SUPP have been employed. The varying power outputs, coal calorific value, amount of coal consumption, inlet temperature, and pressure conditions of turbines set are decision variables taken for the study. The parametric study was carried out with the variation in plant load and mass of coal consumption concerning the variation of the objective function. The lower temperature at the inlet of the low-pressure turbine is preferred for lowing the cost of electricity. The maximum value of plant efficiency of 41.643% and exergy efficiency of 39.834% with a minimum cost of electricity of 3.1456 INR/Unit have been evaluated using multi-objective PSO. The outcome of the present study is that the optimized value of decision variables will reduce the dependency on high-grade coal from an energy, exergy, and economic point of view. The outcome of the present study will explore the scope for future researchers and engineers. • Parametric and multi-objective optimization of supercritical power plant. • Metaheuristic approach Particle Swarm Optimization is preferred. • The increase in plant and exergy efficiency reduces the cost of electricity. • Integration of semi-imperial thermodynamic model with multi-objective optimization. [ABSTRACT FROM AUTHOR]