There has been a growing incidence of adverse weather events leading to substantial power black outs in recent years. Proper hardening of the distribution system significantly improves its resilience to extreme climatic conditions. In this paper, we propose a set of four resilience indices to evaluate the resilience of the distribution system from various perspectives and combine them into a single index to get a holistic measure of distribution feeder resilience. This resilience framework has the capability to analyze realistic performance curves (PCs) of the distribution system with multiple periods of performance degradation and recovery. Additionally, a greedy resilience hardening strategy is proposed which uses the resilience framework and historical storm outage data for determining the set of lines to be hardened to maximally improve the resilience of the distribution feeder. The proposed resilience framework and greedy line hardening strategy are implemented on a real-world distribution feeder (Feeder 91) to demonstrate their efficacy.

Short-term forecasting of non-conforming net load (STFNL) plays a vital role for operating a power system in secure and efficient manner. However, power system load consumption is affected by a variety of external factors and thus includes high levels of volatilities. These volatilities cause STFNL to be a challenging task and inaccurate as more distributed energy resources (DERs) continue to integrate into the power grid. Estimating the average hourly locational distribution of system loads becomes a constant daily challenge to transmission system operators as more non-visible DERs are connected to the distribution system. This paper proposes two commonly used machine-learning and deep learning methods used for load forecasting, i.e., the ensemble bagged and the long short-term memory neural network method. The advantages, features and applications of these methods are used to propose a fusion forecasting model that improves the forecasting accuracy. Additionally, data engineering and preprocessing options are used to increase the accuracy of the proposed model. A comparative study based on real-world transmission grid net load data is performed to verify that the proposed methodology is capable of reaching a relatively higher forecasting accuracy with lower error indices.