Uncertainty handling techniques in power systems: A critical review

Abstract

Integration of renewable generations with electrical power systems has gained considerable attention in recent years due to environmental and economic benefits. However, this integration introduces additional uncertainties into the existing system and requires appropriate uncertainty modeling for power systems. Typically the uncertainties in power systems are modeled using probabilistic or possibilistic approaches. A combined probabilistic-possibilistic approach is necessary when some uncertain variables are probabilistic and others are possibilistic. This paper presents a complete review of uncertainty categorization and several techniques to address the uncertainty in power systems, along with the merits and weaknesses of each technique. The challenges have been highlighted for future research directions. Analytical and approximate methods are reviewed in this paper when wind power generations are integrated into the existing power grid. Considering the uncertainties of wind power generation and system load demands, the basic probabilistic methods such as Monte-Carlo simulation, cumulant, and 2n+1 point estimation methods are implemented. To explore the capability and shortcoming of these basic methods, a 72-bus equivalent system of Indian southern region power grid is taken into consideration. The results obtained using Monte-Carlo simulation method are treated as a benchmark to analyze the performance of the cumulant and 2n+1 point estimation methods.