1. Distributionally Robust Transmission Expansion Planning: A Multi-Scale Uncertainty Approach
- Author
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Alexandre Velloso, Alexandre Street, and David Pozo
- Subjects
Mathematical optimization ,Mathematical problem ,Linear programming ,Computer science ,020209 energy ,media_common.quotation_subject ,As is ,Energy Engineering and Power Technology ,Robust optimization ,02 engineering and technology ,Ambiguity ,Optimization and Control (math.OC) ,Robustness (computer science) ,Scalability ,FOS: Mathematics ,0202 electrical engineering, electronic engineering, information engineering ,Probability distribution ,Electrical and Electronic Engineering ,Mathematics - Optimization and Control ,media_common - Abstract
We present a distributionally robust optimization (DRO) approach for the transmission expansion planning problem, considering both long- and short-term uncertainties on the system demand and non-dispatchable renewable generation. On the long-term level, as is customary in industry applications, we address the deep uncertainties arising from social and economic transformations, political and environmental issues, and technology disruptions by using long-term scenarios devised by experts. In this setting, many exogenous long-term scenarios containing partial information about the random parameters, namely, the average and the support set, can be considered. For each long-term scenario, a conditional ambiguity set models the incomplete knowledge about the probability distribution of the uncertain parameters in the short-term operation. Consequently, the mathematical problem is formulated as a DRO model with multiple conditional ambiguity sets. The resulting infinite-dimensional problem is recast as an exact, although very large, finite mixed-integer linear programming problem. To circumvent scalability issues, we propose a new enhanced-column-and-constraint-generation (ECCG) decomposition approach with an additional Dantzig--Wolfe procedure. In comparison to existing methods, ECCG leads to a better representation of the recourse function and, consequently, tighter bounds. Numerical experiments based on the benchmark IEEE 118-bus system are reported to corroborate the effectiveness of the method.
- Published
- 2020
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