Regularized Benders Decomposition for High Performance Capacity Expansion Models

Abstract

We consider electricity capacity expansion models, which optimize investment and retirement decisions by minimizing both investment and operation costs. In order to provide credible support for planning and policy decisions, these models need to include detailed operations and time-coupling constraints, consider multiple possible realizations of weather-related parameters and demand data, and allow modeling of discrete investment and retirement decisions. Such requirements result in large-scale mixed-integer optimization problems that are intractable with off-the-shelf solvers. Hence, practical solution approaches often rely on carefully designed abstraction techniques to find the best compromise between reduced computational burden and model accuracy. Benders decomposition offers scalable approaches to leverage distributed computing resources and enable models with both high resolution and computational performance. In this study, we implement a tailored Benders decomposition method for large-scale capacity expansion models with multiple planning periods, stochastic operational scenarios, time-coupling policy constraints, and multi-day energy storage and reservoir hydro resources. Using multiple case studies, we also evaluate several level-set regularization schemes to accelerate convergence. We find that a regularization scheme that selects planning decisions in the interior of the feasible set shows superior performance compared to previously published methods, enabling high-resolution, mixed-integer planning problems with unprecedented computational performance.