Calibration of a simplified thermodynamic model for VVER-1200-based nuclear power plants using evolutionary algorithms

A thermal power plant's efficiency and output power are very sensitive to its surrounding weather conditions. Since a nuclear power plant (NPP) usually runs at lower thermodynamic efficiency compared to other thermal power plants, an additional decrease in output power may challenge the economic viability of the project. Thus, it is very important to establish a sufficiently accurate model than can depict the correlation between NPP output power and condenser pressure. This work attempts to calibrate a simplified thermodynamic model using two evolutionary algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). For GA, the initial population is varied in the range of 10-1000, while the mutation and crossover rates are taken as 0.01 and 0.50, respectively. For PSO, the swarm size is varied within the range of 100-1000. Results reveal that the calibrated model has more accurate predictions compared to the original model. The model calibrated with GA is found to be slightly better performing than the one calibrated with PSO. Additionally, the calibration process is observed to be insensitive to the reference condenser pressure. Finally, it is estimated that the efficiency of the plant can go down to 33.56% at 15kPa condenser pressure compared to 37.30% at 4kPa.