Improving Distribution Network Climate Resilience Using Statistical Models for Conventional and Technology Agnostic Solutions

Public Power infrastructure provides multidimensional support systems and facilities across the horizontal dynamics of living i.e., from individual level until the national and global development level indicators.

With increasing severity of climate events such as cyclones, floods, hurricanes over the last decades in select spatial geographies, the climate resilience has been an area of study considering its probabilistic nature of re-occurrences. As such it becomes important at this juncture that a resilient distribution network is planned upfront considering such climate risks taking into cognizance of conventional approach of network upgrades as well as including new evolving technologies such as solar roof top, mini grids, Battery Energy Storage Systems etc.

The power distribution sector has witnessed significant reforms and investments over last couple of decades for providing last mile connectivity to the ultimate consumers, however the coastal regions of India and pacific islands are marred with climatic events and usage of standard electrical distribution system equipment are not capable of sustaining these events leading to significant power outage and delay in restoration of essential services.

It is pertinent to mention that the climate events are probabilistic in nature and hence necessitates developing statistical mathematical modelling tool based on available historical data. In this paper Weibull distribution for wind risk analysis and failure probability assessment followed by Monte Carlo simulation for arriving as energy not served (ENS) values for each probable occurrence of wind risk events have been considered with assessment of Solar PV and BESS in minimizing the impact of such climatic events in power distribution system.

A novel methodology with case-study in southern Asian country- Bangladesh has been proposed in this paper. The approach follows a sequential task analysis that uses Insights drawn from each stage for further analysis.