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.
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Introduction
Electricity demand is increasing as industrial production, electrification, cooling loads and data-centre activity expand. The International Energy Agency projects continued strong global electricity-demand growth through 2027 [1]. Although low-emission generation is expanding rapidly, conventional thermal units remain operationally important in many power systems because they can provide dispatchable generation, reserve capacity and load-following support. Their continuing role places sustained pressure on operators to improve heat rate, reduce fuel consumption and emissions, preserve equipment reliability and maintain acceptable grid response.
A steam-based thermal power plant is a tightly coupled energy-conversion system. Combustion in the boiler determines steam generation, turbine inlet conditions influence shaft work, condenser pressure affects expansion and cycle efficiency, and feedwater pumping closes the Rankine cycle. Fuel flow, excess air, feedwater flow, steam pressure, steam temperature, turbine admission, and cooling-water conditions therefore cannot be treated as independent control variables. A set-point that improves one local indicator may worsen another plant-wide objective. For example, higher fuel flow may increase gross output while raising total fuel burn and carbon emissions; lower-load operation can reduce absolute fuel use while increasing heat rate and cost per unit electricity. The control problem is consequently multi-variable and multi-criteria rather than a sequence of isolated component adjustments [14], [15].
Digital Twin research addresses this integration problem by linking a virtual representation to the physical or operational state of an engineering asset. General Digital Twin literature emphasizes model fidelity, bidirectional information flow and decision support [3], [4], while recent reviews show rapidly growing application across the power-generation sector [2]. In thermal power plants, Digital Twin studies have been used for plant-performance optimization [5], web-based representation and interaction [6], collaborative condition monitoring [7], turbine control-stage monitoring [8] and turbine-system energy-efficiency optimization [9]. These studies demonstrate technical feasibility but also show a recurring fragmentation of scope: some emphasize platform architecture, some concentrate on one subsystem, and others optimize a narrow efficiency metric without integrating technical, environmental and economic consequences.
The research underlying this article addresses that fragmentation through a simulation-based plant-wide framework. Four thermodynamic subsystems—boiler, steam turbine, condenser and feedwater pump—are linked to a time-resolved simulation engine and a decision layer that evaluates power output, thermal efficiency, heat rate, fuel consumption, carbon emissions, revenue, operating cost, availability, reliability and selected grid indicators. Metaheuristic search is represented through Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) modules. Here, the term hybrid refers to the integration of first-principles component modelling, deterministic dynamic simulation, metaheuristic search logic and multi-domain performance criteria. It does not imply a newly derived PSO–GA fusion operator.
The article is deliberately more conservative than the thesis wherever the evidence is incomplete. The underlying work is computational: no live plant sensors were connected, no hardware-in-the-loop testing was conducted, and no field deployment was performed. In addition, the thesis defines the PSO/GA optimization layer but does not report sufficient optimizer hyperparameters, convergence traces, final optimized control vectors or baseline-versus-optimized comparisons to support a numerical claim of optimization gain. The strongest defensible contribution is therefore an integrated simulation and performance-evaluation framework that is optimization-ready and internally validated. This distinction is central to interpreting the results and to defining the next work required for an industrial Digital Twin.
Conclusion
A hybrid multi-criteria simulation model for thermal power plant performance and control was developed from the underlying Master's research. The model integrates first-principles representations of the boiler, steam turbine, condenser and feedwater pump with one-second deterministic simulation, plant-wide KPI calculation, computational validation and an optimization-ready decision layer based on PSO and GA concepts. Over the 24 h case, the simulated plant produced 147.9–217.5 MW with an average thermal efficiency of 41.8% and average heat rate of 8,608 kJ/kWh. Reported daily generation was 4,568.5 MWh, with 378.0 t of fuel consumption and 1,039.5 t of CO₂ emissions. Revenue and gross operating profit were $296,953.27 and $43,315.27, respectively.
The main contribution is not a claimed new optimizer or an industrially deployed Digital Twin. It is the integration of thermodynamic, environmental, economic and operational criteria within a single simulation workflow in which control choices can be evaluated before implementation. Energy-balance error at or below 0.01% per step and the reported benchmark deviation below 1.5% support computational consistency, while the absence of plant-data validation defines the present boundary of confidence. The framework is therefore suitable as a research test bed and decision-support prototype. Its progression to a field-grade Digital Twin requires complete optimization reporting, parameter disclosure, sensor-based calibration, uncertainty analysis and staged closed-loop validation.
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