A Quantitative Approach to Improving Operational Resilience in Distribution Networks through Risk Analysis and Smart Grid Techniques

This paper presents a novel risk-based analysis framework designed to enhance the resilience of power distribution networks by leveraging a failure probability metric for assessing interruption likelihood. To simulate failure scenarios, Monte Carlo simulation is employed in conjunction with a decision tree approach. Additionally, the framework incorporates the calculation of the cost of energy not supplied. The effectiveness of two smart grid techniques - automatic fault location, isolation, and service restoration, along with demand side management - are evaluated within this framework. By implementing these techniques, the operational resilience of the distribution network can be substantially improved. Empirical validation using a modified IEEE 136-bus test system demonstrates the efficacy of the proposed framework in aiding distribution network operators in making informed investment decisions geared towards enhancing system resilience, with a strong emphasis on risk mitigation. Adopting this risk-based analysis framework enables operators to proactively identify vulnerable areas within the network and implement appropriate measures to mitigate potential disruptions. Ultimately, this approach leads to a more resilient and dependable power distribution infrastructure.