Computer Applications and Software, Volume. 42, Issue 4, 271(2025)
CVAR-BASED WASSERSTEIN DISTRIBUTIONALLY ROBUST SELF-SCHEDULING UNDER PRICE UNCERTAINTY
[1] [1] Yamin H, Al-Agtash S, Shahidehpour M. Security-constrained optimal generation scheduling for GENCOs[J]. IEEE Transactions on Power Systems, 2004, 19(3): 1365-1372.
[2] [2] Scarf H. A min-max solution of an inventory problem[M]. Santa Monica: Rand Corporation, 1957.
[3] [3] Rahimian H, Mehrotra S. Distributionally robust optimization: A review[EB]. https://ui.adsabs.harvard.edu/abs/2019arXiv190805659R.
[4] [4] Zheng X, Chen H, Xu Y, et al. A mixed-integer SDP solution approach to distributionally robust unit commitment with second order moment constraints[J]. CSEE Journal of Power and Energy Systems, 2020, 6(2): 374-383.
[5] [5] Delage E, Ye Y. Distributionally robust optimization under moment uncertainty with application to data-driven problems[J]. Operations Research, 2010, 58(3): 595-612.
[6] [6] Bayraksan G, Love D K. Data-driven stochastic programming using Phi-divergences[M]//The Operations Research Revolution: Tutorials in Operations Research. Catonsville: INFORMS, 2015.
[7] [7] Chen Y, Guo Q, Sun H, et al. A distributionally robust optimization model for unit commitment based on Kullback-Leibler divergence[J]. IEEE Transactions on Power Systems, 2018, 33(5): 5147-5160.
[8] [8] Duan C, Fang W, Jiang L, et al. Distributionally robust chance-constrained approximate AC-OPF with Wasserstein metric[J]. IEEE Transactions on Power Systems, 2018, 33 (5): 4924-4936.
[9] [9] Wang Y, Yang Y, Tang L, et al. A Wasserstein based twostage distributionally robust optimization model for optimal operation of CCHP micro-grid under uncertainties[J]. International Journal of Electrical Power & Energy Systems, 2020, 119(3): 105941,
[13] [13] Zhu R, Wei H, Bai X. Wasserstein metric based distributionally robust approximate framework for unit commitment[J]. IEEE Transactions on Power Systems, 2019, 34(4): 2991-3001.
[14] [14] Fournier N, Guillin A. On the rate of convergence in Wasserstein distance of the empirical measure[J]. Probability Theory & Related Fields, 2015, 162(3/4): 707-738.
[15] [15] Rockafellar R T, Uryasev S. Optimization of conditional valueat-risk[J]. Journal of Risk, 1999, 2(3): 21-42.
[16] [16] Shapiro A. On duality theory of conic linear problems[M]//Semi-infinite programming. Boston MA: Springer, 2001.
[17] [17] Rockafellar R T. Convex analysis[M]. Princeton: Princeton University Press, 1970.
[18] [18] Zhao C, Guan Y. Data-driven risk-averse stochastic optimization with Wasserstein metric[J]. Operations Research Letters, 2018, 46(2): 262-267.
[19] [19] Boyd S, Parikh N, E C hu, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations & Trends in Machine Learning, 2010, 3(1): 1-122.
[20] [20] Yang L, Zhang T, Chen G, et al. Fully distributed economic dispatching methods based on alternating direction multiplier method[J]. Journal of Electrical Engineering and Technology, 2018, 13(5): 1778-1790.
[21] [21] Yang L, Yang Y, Chen G, et al. Distributionally robust framework and its approximations based on vector and region split for self-scheduling of generation companies[J]. IEEE Transactions on Industrial Informatics, 2022, 18(8): 5231-5241.
[22] [22] Yang L, Luo J, Xu Y, et al. A distributed dual consensus ADMM based on partition for DC-DOPF with carbon emission trading[J]. IEEE Transactions on Industrial Informatics, 2019, 16(3): 1858-1872.
Get Citation
Copy Citation Text
Yang Linfeng, Guo Hongwu, Yang Ying, Li Jie, Pan Shanshan. CVAR-BASED WASSERSTEIN DISTRIBUTIONALLY ROBUST SELF-SCHEDULING UNDER PRICE UNCERTAINTY[J]. Computer Applications and Software, 2025, 42(4): 271
Category:
Received: Feb. 20, 2022
Accepted: Aug. 25, 2025
Published Online: Aug. 25, 2025
The Author Email:
