期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 35, 期 5, 页码 3353-3365出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.2979118
关键词
Uncertainty; Robustness; Biological system modeling; Planning; Investment; Optimization; Economics; Ambiguity aversion; deep uncertainty; distributionally robust optimization; multi-scale uncertainty; renewable generation; transmission expansion planning
资金
- Financiadora deEstudos e Projetos/Programa de Incentivo a Pos-Graduacao (FINEP/PIPG)
- Skoltech NGP Program (Skoltech-MIT joint project)
- CNPq - Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
- FAPERJ - Fundacao de Amparo a Pesquisa do Estado doRio de Janeiro
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.
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