Abstract
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.
Highlights
To decarbonize the electric power grid, there have been growing efforts to utilize clean, renewable energy sources
Our simulation studies indicate that the proposed method reduces the ramp penalty by 4.82% on average compared to the standard stochastic optimal control method
This is because the training set distribution is different from the test set distribution, i.e., the training set does not offer useful information about the behavior of wind power ramping in the near future
Summary
To decarbonize the electric power grid, there have been growing efforts to utilize clean, renewable energy sources. The associated stochastic optimal control problems are solved by dynamic programming or its approximate version, which often allows important structural properties of optimal strategies This method requires knowledge about the probability distribution of all the uncertainties such as future wind power generation. To account for these limitations, we seek an efficient storage operation strategy for wind power ramp management when only an inaccurate probability distribution of wind power generation is available This method is based on distributionally robust stochastic control, which minimizes the expected value of a given cost function in the face of the worst-case distribution drawn from a known set, called the ambiguity set [13,14,15,16,17,18,19].
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