  Energy systems planning models identify least-cost strategies for expansion
and operation of energy systems and provide decision support for investment,
planning, regulation, and policy. Most are formulated as linear programming
(LP) or mixed integer linear programming (MILP) problems. Despite the relative
efficiency and maturity of LP and MILP solvers, large scale problems are often
intractable without abstractions that impact quality of results and
generalizability of findings. We consider a macro-energy systems planning
problem with detailed operations and policy constraints and formulate a
computationally efficient Benders decomposition separating investments from
operations and decoupling operational timesteps using budgeting variables in
the master model. This novel approach enables parallelization of operational
subproblems and permits modeling of relevant constraints coupling decisions
across time periods (e.g. policy constraints) within a decomposed framework.
Runtime scales linearly with temporal resolution; tests demonstrate substantial
runtime improvement for all MILP formulations and for some LP formulations
depending on problem size relative to analagous monolithic models solved with
state-of-the-art commercial solvers. Our algorithm is applicable to planning
problems in other domains (e.g. water, transportation networks, production
processes) and can solve large-scale problems otherwise intractable. We show
that the increased resolution enabled by this algorithm mitigates structural
uncertainty, improving recommendation accuracy.
