Hybrid Multi-Criteria Simulation Model for Power Plant Optimal Performance and Control

Abstract

Thermal power plant performance depends strongly on coupled control variables whose effects extend simultaneously to power output, fuel use, heat rate, efficiency, emissions, and operating economics. This study develops a hybrid multi-criteria simulation model by reorganizing a simulation-based Digital Twin into an integrated engineering decision-support framework. The model combines first-principles representations of the boiler, steam turbine, condenser and feedwater pump with deterministic one-second time-step simulation, fourth-order Runge-Kutta integration, metaheuristic optimization modules based on Particle Swarm Optimization and Genetic Algorithm concepts, and technical, environmental, economic and grid-related performance criteria. A 24 h operating cycle generated 86,400 time-stamped simulation states. Reported output ranged from 147.9 to 217.5 MW, with an average of 190.4 MW. Mean thermal efficiency and heat rate were 41.8% and 8,608 kJ/kWh, respectively, while total generated energy was 4,568.5 MWh. The simulation consumed 378.0 t of fuel and emitted 1,039.5 t of CO₂, corresponding to approximately 0.228 kg CO₂/kWh. Total revenue and gross operating profit were $296,953.27 and $43,315.27 over the simulated day. Computational validation maintained per-step energy-balance error at or below 0.01%, and the thesis reports benchmark deviation below 1.5% over the stated load range. The principal contribution is the integration of plant-wide thermodynamic simulation with multi-domain performance assessment and an optimization-ready control layer. Because the source study does not report complete optimizer hyperparameters, convergence histories or final optimized decision vectors, no quantified PSO-versus-GA improvement is claimed. The framework is therefore positioned as a validated computational test bed for plant performance and control studies rather than a field-deployed Digital Twin.