Learning for Feasible Region on Coal Mine Virtual Power Plants with Imperfect Information
Abstract
The feasible region assessment (FRA) in industrial virtual power plants (VPPs) is driven by the need to activate large-scale latent industrial loads for demand response, making it essential to aggregate these flexible resources for peak regulation. However, the large number of devices and the need for privacy preservation in coal mines pose challenges to accurately aggregating these resources into a cohesive coal mine VPP. In this paper, we propose an efficient and reliable data-driven approach for FRA in the coal mine VPP that can manage incomplete information. Our data-driven FRA algorithm approximates equipment and FRA parameters based on historical energy dispatch data, effectively addressing the challenges of imperfect information. Simulation results illustrate that our method approximates the accurate feasible operational boundaries under dynamic and imperfect information conditions.
Keywords
Cite
@article{arxiv.2503.00701,
title = {Learning for Feasible Region on Coal Mine Virtual Power Plants with Imperfect Information},
author = {Hongxu Huang and Ruike Lyu and Cheng Feng and Haiwang Zhong and H. B. Gooi and Bo Li and Rui Liang},
journal= {arXiv preprint arXiv:2503.00701},
year = {2025}
}
Comments
This paper is accepted for 2025 IEEE PES General Meeting