Data-Driven Distributionally Robust Stochastic Control of Energy Storage for Wind Power Ramp Management Using the Wasserstein Metric
Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul 08826, Korea
Energies 2019, 12(23), 4577; https://doi.org/10.3390/en12234577
Received: 7 November 2019
/
Revised: 24 November 2019
/
Accepted: 26 November 2019
/
Published: 1 December 2019
(This article belongs to the Section F: Electrical Engineering)
The integration of wind energy into the power grid is challenging because of its variability, which causes high ramp events that may threaten the reliability and efficiency of power systems. In this paper, we propose a novel distributionally robust solution to wind power ramp management using energy storage. The proposed storage operation strategy minimizes the expected ramp penalty under the worst-case wind power ramp distribution in the Wasserstein ambiguity set, a statistical ball centered at an empirical distribution obtained from historical data. Thus, the resulting distributionally robust control policy presents a robust ramp management performance even when the future wind power ramp distribution deviates from the empirical distribution, unlike the standard stochastic optimal control method. For a tractable numerical solution, a duality-based dynamic programming algorithm is designed with a piecewise linear approximation of the optimal value function. The performance and utility of the proposed method are demonstrated and analyzed through case studies using the wind power data in the Bonneville Power Administration area for the year 2018.
View Full-Text
Keywords:
energy storage operation; wind ramp rate; renewable integration; stochastic control; dynamic programming; distributionally robust optimization; linear programming
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
MDPI and ACS Style
Yang, I. Data-Driven Distributionally Robust Stochastic Control of Energy Storage for Wind Power Ramp Management Using the Wasserstein Metric. Energies 2019, 12, 4577. https://doi.org/10.3390/en12234577
AMA Style
Yang I. Data-Driven Distributionally Robust Stochastic Control of Energy Storage for Wind Power Ramp Management Using the Wasserstein Metric. Energies. 2019; 12(23):4577. https://doi.org/10.3390/en12234577
Chicago/Turabian StyleYang, Insoon. 2019. "Data-Driven Distributionally Robust Stochastic Control of Energy Storage for Wind Power Ramp Management Using the Wasserstein Metric" Energies 12, no. 23: 4577. https://doi.org/10.3390/en12234577
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
