no code implementations • 22 Sep 2023 • Heng Liang, Changhong Zhao
The traditional machine learning models to solve optimal power flow (OPF) are mostly trained for a given power network and lack generalizability to today's power networks with varying topologies and growing plug-and-play distributed energy resources (DERs).
no code implementations • 27 May 2023 • Dongxiang Yan, Tongxin Li, Changhong Zhao, Han Wang, Yue Chen
This necessitates new business models in the power sector to hedge against uncertainties while imposing a strong coupling between the connected power and transportation networks.
no code implementations • 31 Mar 2023 • Jinyan Su, Changhong Zhao, Di Wang
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general $\ell_p^d$ spaces.
no code implementations • 7 Jul 2022 • Zhenyi Yuan, Changhong Zhao, Jorge Cortes
This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees.
no code implementations • 9 Feb 2022 • Wei Lin, Changhong Zhao
Distributed energy resources (DERs) in distribution networks can be aggregated as a virtual power plant (VPP) for transmission-level operations.
no code implementations • 2 Dec 2021 • Wei Lin, Changhong Zhao
To address this challenge, a characterization method is presented in this paper for the intraday operation of a VPP based on the concepts of nonanticipativity and robustness to DERs' uncertainties.
no code implementations • 1 Feb 2021 • Sidun Fang, Chenxu Wang, Yashen Lin, Changhong Zhao
The conventionally independent power, water, and heating networks are becoming more tightly connected, which motivates their joint optimal energy scheduling to improve the overall efficiency of an integrated energy system.
no code implementations • 21 Jan 2021 • Tong Wu, Changhong Zhao, Ying-Jun Angela Zhang
In this way, the dual update of ADMM can be encrypted by PHE.