Abstract
As more renewables are integrated into the power system, capacity expansion planners need more advanced longterm decision-making tools to properly model short-term stochastic production uncertainty and to explore its effects on expansion decisions. We develop a distributionally robust generation expansion planning model, accounting for a family of potential probability distributions of wind forecast error uncertainty. Aiming to include more realistic distributions, we construct more informed moment-based ambiguity sets by adding structural information of unimodality. We include operational-stage unit commitment constraints and model the risk of operational limit violations in two distinct forms: chance and conditional value-at-risk (CVaR) constraints. In both forms, the resulting expansion planning model is a mixed-integer second-order cone program. Using a thorough out-of-sample numerical analysis, we conclude: (i) the distributionally robust chance-constrained generation expansion planning model exhibits a better out-ofsample performance only if sufficiently accurate information about the first- and the second-order moments as well as the mode location of potential distributions is available; (ii) conversely, if such accurate information is unavailable, the distributionally robust CVaR-constrained generation expansion planning model outperforms; (iii) these two models have a similar performance when unimodality information is excluded.
Original language | English |
---|---|
Journal | IEEE Transactions on Power Systems |
Volume | 36 |
Issue number | 5 |
Pages (from-to) | 4281-4295 |
ISSN | 0885-8950 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Distributionally robust optimization
- Chance constraints
- CVaR constraints
- Generation expansion planning
- Unimodality information