Filippo Pecci

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 largescale 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.

GenX is a highly-configurable, open source electricity resource capacity expansion model that incorporates several state-of-the-art practices in electricity system planning to offer improved decision support for a changing electricity landscape. GenX is a constrained linear or mixed integer linear optimization model that determines the portfolio of electricity generation, storage, transmission, and demand-side resource investments and operational decisions to meet electricity demand in one or more future planning years at lowest cost, while subject to a variety of power system operational constraints, resource availability limits, and other imposed environmental, market design, and policy constraints. GenX features a modular and transparent code structure developed in Julia + JuMP. The model is designed to be highly flexible and configurable for use in a variety of applications from academic research and technology evaluation to public policy and regulatory analysis and resource planning.

The paper addresses the inefficiencies in large-scale energy systems planning models, traditionally based on linear programming (LP) or mixed integer linear programming (MILP). These models often struggle with tractability due to necessary abstractions that compromise result quality. To overcome this, the authors introduce a novel Benders decomposition approach. This method separates investment from operational decisions and decouples operational time steps using budgeting variables, enabling parallel processing of subproblems and accommodating policy constraints over time. This new approach significantly improves runtime, scaling linearly with temporal resolution, and shows marked runtime reductions for all MILP and some LP formulations, varying with problem size. Beyond energy, this algorithm is applicable to planning in domains like water, transportation, and production processes. Notably, it can tackle large-scale problems otherwise intractable. The enhanced resolution achieved through this method reduces structural uncertainty, thereby improving the accuracy of planning and investment recommendations.