A Monte Carlo-Based Risk Assessment Method for Distribution Network Operation in Uncertain Environments

The rapid and massive access of renewable distributed generation (DG) and electric vehicle (EV) has intensified intermittent power fluctuations caused by natural variability and user behavior, complicating distribution network operations. To improve grid robustness, reliability, and cost-efficiency, this study proposes a risk assessment methodology incorporating source-load uncertainty. Firstly, Monte Carlo (MC) simulation is employed to conduct sampling analysis on the uncertainty distributions of renewable DG and load demand. Subsequently, system operational risk indices are formulated based on power flow calculation results, including voltage violation, line overload, load loss, and comprehensive risk. Then, a risk mitigation model incorporating flexible load regulation is established through genetic algorithm (GA) optimization to achieve risk reduction and elimination. Finally, case studies and scenario analysis on a modified IEEE 33-bus system validate the effectiveness of the proposed methodology.