This paper aims to assess the weather risk exposure of Indian power sector from both generation and demand sides. The study considers two representative firms – firstly, Damodar Valley Corporation (DVC), a hydro-generator, to assess its rainfall exposure, and secondly, Calcutta Electric Supply Corporation (CESC), a retail power supplier, to assess the temperature sensitivity of power demand. The study opts for ‘Value at Risk’ approach, which combines both the sensitivity of power variables towards weather variable and the probability of weather change. The sensitivity is measured using regression analysis with autoregressive distributed lag (ARDL). Parametric distributions are fitted to weather data to assess probabilities. Due to the ‘fat-tail’ characteristic of the fitted distribution, a ‘conditional value-at-risk’ model is considered more effective. The study reveals that the hydroelectricity generation is highly exposed to monsoon rainfall fluctuation and hence the hydro-generator may experience substantial loss of revenue due to insufficient monsoon, whereas the revenue of retail power distributor is moderately exposed to fluctuation of daily surface temperature.
A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk
