Reliability assessment and energy storage solution for renewable energy integration

Abstract

Integration of large-scale renewable energy systems brings new challenges to power system operation due to their high intermittency. This thesis aims to address two main problems involving renewable energy integration, namely, how to efficiently assess system reliability, and how to optimally utilize energy storage systems. Several efficient simulation methods for reliability assessment are proposed. Their main advantage over conventional methods is that the computational efficiency is greatly improved without affecting solution accuracy. As renewable energy sources are non-dispatchable, energy storage systems (ESS) are commonly deployed to manage their variability. Optimization algorithms based on stochastic programming and stochastic dynamic programming frameworks are developed to determine the optimal size and optimal operation policy for ESS in a large-scale grid-connected renewable power plant. The proposed algorithms allow the ESS operation to be highly adaptive to uncertainties of renewable production and can serve as a guideline for real-time renewable energy management with ESS.