Abstract:This paper addresses the generation expansion planning (GEP) problem, formulated as a mixed-integer linear programming model with intertemporal storage constraints. Being generally NP-hard, the problem's computational complexity grows sharply with the planning horizon and the number of binary variables. While previous research has tackled this challenge using heuristic time series aggregation (TSA) methods, we propose a theoretically grounded marginal-cost-based TSA, designed to construct an aggregated model that preserves the active constraints of its full-scale counterpart, thereby explicitly targeting exact temporal aggregation. This TSA method is embedded within solution algorithms that iteratively refine theoretically validated bounds on the maximum error introduced by the temporal aggregation relative to conventional full-scale optimization, thus offering a formal performance guarantee to the decision-maker. Numerical results highlight the computational advantages of the proposed algorithms, which notably recover tractability whereas full-scale optimization proves intractable.
| Subjects: | Optimization and Control (math.OC) |
| Cite as: | arXiv:2605.23589 [math.OC] |
| (or arXiv:2605.23589v1 [math.OC] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23589 arXiv-issued DOI via DataCite (pending registration) |
From: Luca Santosuosso [view email]
[v1]
Fri, 22 May 2026 12:59:07 UTC (1,778 KB)
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