Real-time exergoeconomic optimization of a steam power plant using a soft computing-fuzzy inference system

Because of the non-linear and non-explicit relationships between system parameters and incomplete and/or complicated mathematical model, iterative approaches that work based on the experts' knowledge to optimize or improve complex energy systems. Main focus on this study was to encode the iterative approach in the form of a fuzzy inference system, FIS, to maximize profit of a power plant through controlling the effective operating parameters, while the load of a power plant is changed. As a benchmark power station, a 250 MW unit in the Shahid-Rajaei steam power was considered for optimization. The profits of the power plant at various loads were estimated using exergoeconomic analysis; then, it was optimized through optimal regulating the flow rates of extracted steam from the high and low pressure turbines in an iterative procedure. The FIS worked based on the magnitude of the cost of exergy destruction of feed-water heaters. The faster computation time of the FIS compared to the GA, make it as a suitable option for the real-time optimization in form of fuzzy controllers. Using the developed FIS, the profit of the power plant at 100, 80, and 60% of the nominal power was increased 153.0, 322.0, and 280.0 $ h−1, respectively.