Risk-based Project Scheduling Model of Large-scale Renewable Power Plant Constructions through Monte Carlo Simulation

Abstract

The construction projects of large-scale renewable power plants have a large schedule uncertainty due to environmental variability, regulatory approvals, and supply chain dependency. Conventional deterministic methods of scheduling, like the Critical Path Method (CPM), assume that activity times are constant, and thus they tend to underestimate the risk of delay. This paper constructs a risk-based project planning model of the construction of renewable megaprojects with the help of Monte Carlo simulation. The structure proposed incorporates deterministic network logic with probabilistic modeling of the duration of activities and the use of correlated risk structure to produce statistically dominant completion forecasts. The model is validated by the use of a 500 MW utility-scale solar power plant. The 10,000 simulation run results are that the deterministic baseline duration of 24 months is underestimated by 14.2 with the 82 percent likelihood of schedule overrun. Sensitivity and criticality analysis find grid interconnection and civil works as the most significant causes of schedule variance. Moreover, correlation modeling adds to predicted variance by 33 percent, which proves that it is beneficial to address interdependent risks. The results indicate that probabilistic and correlation-integrated scheduling present a more realistic and reliable foundation of contingency planning and high-confidence project delivery in the construction of renewable megaprojects.