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With the ongoing investment in data collection and communication technology in power systems, data-driven optimization has been established as a powerful tool for system operators to handle stochastic system states caused by weather- and behavior-dependent resources.
R. T. Rockafellar et al. , “Optimization of conditional value-at-risk,” Journal of Risk , vol. 2, pp. 21–42, 2000
2000
Earlier work this paper cites.
A. Molina-Markham et al. , “Private memoirs of a smart meter,” in Proc. 2nd ACM workshop on embedded sensing systems for energy-efficiency in building , 2010, pp. 61–66
2010
Earlier work this paper cites.
L. Wasserman and S. Zhou, “A statistical framework for differential privacy,” Journal of the American Statistical Association , vol. 105, no. 489, pp. 375–389, 2010
2010
Earlier work this paper cites.
K. Kursawe et al. , “Privacy-friendly aggregation for the smart-grid,” in Proc. International Symposium on Privacy Enhancing Technologies Symposium . Springer, 2011, pp. 175–191
2011
Earlier work this paper cites.
P. C. Alvarez-Esteban et al. , “Similarity of samples and trimming,” 2012
2012
Earlier work this paper cites.
C. Dwork et al. , “The algorithmic foundations of differential privacy,” Foundations and Trends in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
Earlier work this paper cites.
MATPOWER. (2014) CASE5 Power flow data. [Online]. Available: https://matpower.org/docs/ref/matpower5.0/case5.html
2014
Earlier work this paper cites.
Y. Dvorkin et al. , “Uncertainty sets for wind power generation,” IEEE Trans. Power Syst. , vol. 31, no. 4, pp. 3326–3327, 2015
2015
Earlier work this paper cites.
M. Brown et al. , “Characterizing and quantifying noise in PMU data,” in Proc. IEEE Power and Energy Society General Meeting (PESGM) . IEEE, 2016, pp. 1–5
2016
Earlier work this paper cites.
E. Dall’Anese et al. , “Chance-constrained ac optimal power flow for distribution systems with renewables,” IEEE Trans. Power Syst. , vol. 32, no. 5, pp. 3427–3438, 2017
2017
Earlier work this paper cites.
P. Li et al. , “A distributed online pricing strategy for demand response programs,” IEEE Trans. Smart Grid , vol. 10, no. 1, pp. 350–360, 2017
2017
Earlier work this paper cites.
R. Mieth and Y. Dvorkin, “Data-driven distributionally robust optimal power flow for distribution systems,” IEEE Control Systems Letters , vol. 2, no. 3, pp. 363–368, 2018
2018
Earlier work this paper cites.
Y. Guo et al. , “Data-based distributionally robust stochastic optimal power flow—Part I: Methodologies,” IEEE Trans. Power Syst. , vol. 34, no. 2, pp. 1483–1492, 2018
2018
Earlier work this paper cites.
X. Ren et al. , “Datum: Managing data purchasing and data placement in a geo-distributed data market,” IEEE/ACM Trans. Networking , vol. 26, no. 2, pp. 893–905, 2018
2018
Earlier work this paper cites.
R. J. Bessa et al. , “Data economy for prosumers in a smart grid ecosystem,” in Proc. Ninth International Conference on Future Energy Systems , 2018, pp. 622–630
2018
Earlier work this paper cites.
P. M. Esfahani and D. Kuhn, “Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations,” Math. Programming , vol. 171, no. 1-2, 2018
2018
Earlier work this paper cites.
C. Duan et al. , “Distributionally robust chance-constrained approximate ac-opf with wasserstein metric,” IEEE Transactions on Power Systems , vol. 33, no. 5, pp. 4924–4936, 2018
2018
Earlier work this paper cites.
J. Kazempour et al. , “A stochastic market design with revenue adequacy and cost recovery by scenario,” IEEE Trans. Power Syst. , vol. 33, no. 4, pp. 3531–3545, 2018
2018
Earlier work this paper cites.
X. Kuang et al. , “Pricing chance constraints in electricity markets,” IEEE Trans. Power. Syst. , vol. 33, no. 4, pp. 4634–4636, 2018
2018
Earlier work this paper cites.
Siemens, “The grid edge revolution: Innovative drivers towards net-zero energy,” Tech. Rep., 2019. [Online]. Available: new.siemens.com/global/en/company/topic-areas/smart-infrastructure/grid-edge/white-paper-grid-edge-net-zero-energy-drivers.html
2019
Earlier work this paper cites.
A. Agarwal et al. , “A marketplace for data: An algorithmic solution,” in Proc. ACM Conf. on Econ. and Comp. , 2019
2019
Cited alongside, same era.
R. Mieth and Y. Dvorkin, “Online learning for network constrained demand response pricing in distribution systems,” IEEE Trans. Smart Grid , vol. 11, no. 3, pp. 2563–2575, 2019
2019
Cited alongside, same era.
D. Kuhn et al. , “Wasserstein distributionally robust optimization: Theory and applications in machine learning,” in Operations Research & Management Science in the Age of Analytics . INFORMS, 2019, pp. 130–166
2019
Cited alongside, same era.
V. M. Panaretos and Y. Zemel, “Statistical aspects of wasserstein distances,” Annual Review of Statistics and its Application , vol. 6, 2019
2019
Cited alongside, same era.
X. Fang et al. , “Introducing uncertainty components in locational marginal prices for pricing wind power and load uncertainties,” IEEE Trans. Power Syst. , vol. 34, no. 3, pp. 2013–2024, 2019
L. Han et al. , “Monetizing customer load data for an energy retailer: A cooperative game approach,” in Proc. IEEE PowerTech . IEEE, 2021
2021
Later among the works it cites.
A. Gopstein et al. , NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 4.0 . Department of Commerce. National Institute of Standards and Technology, 2021
2021
Later among the works it cites.
A. Hassan et al. , “Privacy-aware load ensemble control: A linearly-solvable mdp approach,” IEEE Trans. Smart Grid , vol. 13, no. 1, 2021
2021
Later among the works it cites.
B. P. Van Parys et al. , “From data to decisions: Distributionally robust optimization is optimal,” Management Science , vol. 67, no. 6, 2021
2021
Later among the works it cites.
C. Ordoudis et al. , “Energy and reserve dispatch with distributionally robust joint chance constraints,” Operations Research Letters , vol. 49, no. 3, pp. 291–299, 2021
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2019
Cited alongside, same era.
R. Mieth and Y. Dvorkin, “Distribution electricity pricing under uncertainty,” IEEE Trans. Power Syst. , 2019
2019
Cited alongside, same era.
PJM. (2020) Phasor Measurement Unit (PMU) Placement Plan in RTEP Planning Process. [Online]. Available: https://pjm.com/-/media/committees-groups/committees/pc/2020/20200707/20200707-item-05a-pmu-rtep.ashx
2020
Cited alongside, same era.
A. Hassan et al. , “Data-driven learning and load ensemble control,” Electric Power Systems Research , vol. 189, p. 106780, 2020
2020
Cited alongside, same era.
N. Tucker et al. , “Constrained Thompson sampling for real-time electricity pricing with grid reliability constraints,” IEEE Trans. Smart Grid , vol. 11, no. 6, pp. 4971–4983, 2020
2020
Cited alongside, same era.
M. Munoz et al. , “Feature-driven improvement of renewable energy forecasting and trading,” IEEE Trans. Power Syst. , vol. 35, no. 5, pp. 3753–3763, 2020
2020
Cited alongside, same era.
C. Goncalves et al. , “Towards data markets in renewable energy forecasting,” IEEE Trans. Sust. Energy , vol. 12, no. 1, pp. 533–542, 2020
2020
Cited alongside, same era.
G. Le Ray and P. Pinson, “The ethical smart grid: Enabling a fruitful and long-lasting relationship between utilities and customers,” Energy Policy , vol. 140, p. 111258, 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
S. Acharya et al. , “False data injection attacks on data markets for electric vehicle charging stations,” Adv. Appl. Energy , vol. 7, 2022
2022
Later among the works it cites.
G. Peng et al. , “Markovian decentralized ensemble control for demand response,” IEEE Control Systems Letters , vol. 6, pp. 3050–3055, 2022
2022
Later among the works it cites.
A. Arrigo et al. , “Wasserstein distributionally robust chance-constrained optimization for energy and reserve dispatch: An exact and physically-bounded formulation,” European Journal of Operational Research , vol. 296, no. 1, pp. 304–322, 2022
2022
Later among the works it cites.
C. Crozier et al. , “Data-driven contingency selection for fast security constrained optimal power flow,” in Proc. 17th Int. Conference on Probabilistic Methods Applied to Power Systems (PMAPS) . IEEE, 2022
2022
Later among the works it cites.
L. Han et al. , “Trading data for wind power forecasting: A regression market with lasso regularization,” Electric Power Systems Research , vol. 212, p. 108442, 2022
2022
Later among the works it cites.
P. Pinson et al. , “Regression markets and application to energy forecasting,” TOP , vol. 30, no. 3, pp. 533–573, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
P. Awasthi et al. , “Distributionally robust data join,” arXiv:2202.05797 , 2022
2022
Later among the works it cites.
A. Arrigo et al. , “Embedding dependencies between wind farms in dist. robust optimal power flow,” IEEE Trans. Power Syst. , 2022
2022
Later among the works it cites.
L. A. Roald et al. , “Power systems optimization under uncertainty: A review of methods and applications,” Electric Power Systems Research , vol. 214, p. 108725, 2023
2023
Closest in time.
J. M. Morales et al. , “Prescribing net demand for two-stage electricity generation scheduling,” Operations Research Perspectives , vol. 10, p. 100268, 2023
2023
Closest in time.
A. A. Raja et al. , “A market for trading forecasts: A wagering mechanism,” International Journal of Forecasting , 2023
2023
Closest in time.
A. Esteban-Pérez and J. M. Morales, “Distributionally robust optimal power flow with contextual information,” European Journal of Operational Research , vol. 306, no. 3, pp. 1047–1058, 2023
2023
Closest in time.
M. Lubin et al. , “Jump 1.0: Recent improvements to a modeling language for mathematical optimization,” Mathematical Programming Computation , 2023, in press
2023
Closest in time.
R. Mieth. (2023) Multi-source DRO OPF Code Supplement. [Online]. Available: https://github.com/mieth-robert/msdro_opf_public
2023
Closest in time.