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  • Data-driven approaches to managing uncertain load control in sustainable power systems (project outputs)

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    Creator:
    Bowen Li, Yiling Zhang, Siqian Shen, and Johanna Mathieu
    Description:
    The project outputs summarize all the publications, talks, and codes we accomplished under this NSF funding. In the project, we develop methodologies to manage uncertainty in future electric power systems and quantify how uncertainty affects power system sustainability. and Talks, papers, and poster in Deep Blue:  http://hdl.handle.net/2027.42/149653
    Keyword:
    chance constraint, distributionally robust optimization, optimal power flow, demand response, and unimodality
    Citation to related publication:
    B. Li and J. L. Mathieu, "Analytical reformulation of chance-constrained optimal power flow with uncertain load control," 2015 IEEE Eindhoven PowerTech, Eindhoven, 2015, pp. 1-6.  https://doi.org/10.1109/PTC.2015.7232803, B. Li, J. L. Mathieu and R. Jiang, "Distributionally Robust Chance Constrained Optimal Power Flow Assuming Log-Concave Distributions," 2018 Power Systems Computation Conference (PSCC), Dublin, 2018, pp. 1-7.  https://doi.org/10.23919/PSCC.2018.8442927, B. Li, M. Vrakopoulou and J. L. Mathieu, "Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part II: Analytical Reformulation," in IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1618-1625, March 2019.  https://doi.org/10.1109/TSG.2017.2773603, B. Li, R. Jiang and J. L. Mathieu, "Distributionally Robust Chance-Constrained Optimal Power Flow Assuming Unimodal Distributions With Misspecified Modes," in IEEE Transactions on Control of Network Systems, vol. 6, no. 3, pp. 1223-1234, Sept. 2019.  https://doi.org/10.1109/TCNS.2019.2930872, B. Li, R. Jiang and J. L. Mathieu, "Distributionally robust risk-constrained optimal power flow using moment and unimodality information," 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, 2016, pp. 2425-2430.  https://doi.org/10.1109/CDC.2016.7798625, B. Li, S. D. Maroukis, Y. Lin and J. L. Mathieu, "Impact of uncertainty from load-based reserves and renewables on dispatch costs and emissions," 2016 North American Power Symposium (NAPS), Denver, CO, 2016, pp. 1-6.  https://doi.org/10.1109/NAPS.2016.7747830, G. Martínez, J. Liu, B. Li, J. L. Mathieu and C. L. Anderson, "Enabling renewable resource integration: The balance between robustness and flexibility," 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, 2015, pp. 195-202.  https://doi.org/10.1109/ALLERTON.2015.7447004, J. Liu, M. G. Martinez, B. Li, J. Mathieu and C. L. Anderson, "A Comparison of Robust and Probabilistic Reliability for Systems with Renewables and Responsive Demand," 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, 2016, pp. 2373-2380.  https://doi.org/10.1109/HICSS.2016.297, Li, B., Jiang, R. & Mathieu, J.L. "Ambiguous risk constraints with moment and unimodality information." Math. Program. 173, 151–192 (2019).  https://doi.org/10.1007/s10107-017-1212-x, M. Vrakopoulou, B. Li and J. L. Mathieu, "Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part I: Formulation and Scenario-Based Analysis," in IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1608-1617, March 2019.  https://doi.org/10.1109/TSG.2017.2773627, Y. Zhang, S. Shen and J. L. Mathieu, "Data-driven optimization approaches for optimal power flow with uncertain reserves from load control," 2015 American Control Conference (ACC), Chicago, IL, 2015, pp. 3013-3018.  https://doi.org/10.1109/ACC.2015.7171795, Y. Zhang, S. Shen and J. L. Mathieu, "Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads," in IEEE Transactions on Power Systems, vol. 32, no. 2, pp. 1378-1388, March , and Y. Zhang, S. Shen, B. Li and J. L. Mathieu, "Two-stage distributionally robust optimal power flow with flexible loads," 2017 IEEE Manchester PowerTech, Manchester, 2017, pp. 1-6.  https://doi.org/10.1109/PTC.2017.7981202
    Discipline:
    Engineering