Data-Driven Energy and Reserve Management of Prosumers Under Multi-Uncertainties

In this paper, we propose a data-driven approach for prosumers to manage energy and reserves under multi-faceted uncertainties in electricity markets. We account for the uncertainties associated with renewable power generation, market prices, and the deployment ratio of regulation reserves. These uncertainties are rarely addressed simultaneously in prior studies, despite the significant impact they can have. Notably, the uncertainty surrounding the deployment ratio of regulation reserves, which represents the call-up ratio of reserve capacity provided by prosumers, has long been overlooked. In this work, we address these interconnected uncertainties within a unified framework, employing a Wasserstein distance-based distributionally robust optimization (WDRO) approach to hedge against them. The proposed model grapples with a substantial computational burden as it tackles multiple uncertainties concurrently. To enhance computational efficiency, we utilize novel approximation techniques to transform the WDRO model into a more tractable form. Furthermore, we analyze the optimality gaps in the WDRO objective function of the approximation approach. Simulation results demonstrate the efficacy of the proposed model and solution methods.