Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

  title={Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment},
  author={Chao Duan and Lin Jiang and Wanliang Fang and Jun Liu},
  journal={IEEE Transactions on Power Systems},
This paper proposes a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors. The proposed formulation minimizes expected total operation costs, including the costs of generation, reserve, wind curtailment, and load shedding, while guaranteeing the system security. Without any presumption about the probability distribution of the uncertainties, the proposed method constructs an ambiguity set of… CONTINUE READING


Publications citing this paper.


Publications referenced by this paper.
Showing 1-10 of 40 references

Data-Driven Stochastic Unit Commitment for Integrating Wind Generation

IEEE Transactions on Power Systems • 2016
View 6 Excerpts
Highly Influenced

Evenly sensitive KS-type inference on distributions

M. Goldman, D. M. Kaplan
Tech. Rep. 13–19, Working paper, 2015. [Online]. Available at: • 2015
View 4 Excerpts
Highly Influenced

Fast Identification of Inactive Security Constraints in SCUC Problems

IEEE Transactions on Power Systems • 2010
View 3 Excerpts
Highly Influenced

Distributionally robust chance-constrained voltage-concerned DC-OPF with Wasserstein metric

C. Duan, W. Fang, L. Jiang, L. Yao, J. Liu
arXiv:1706.05538, 2017. • 2017
View 1 Excerpt

Integration of power-tohydrogen in day-ahead security-constrained unit commitment with high wind penetration

M. Ban, J. Yu, M. Shahidehpour, Y. Yao
J. Mod. Power Syst. Clean Energy, vol. 5, no. 3, pp. 337–349, 2017. • 2017
View 1 Excerpt