The optimal power flow (OPF) problem, characterized by its inherent complexity and strict
constraints, has traditionally been approached using analytical techniques. OPF enhances …

This paper proposes a model-free self-supervised learning (SSL) method for dispatching
distributed energy resources (DERs). The proposed method first establishes a data-driven …

The serious uncertainties from the extensive integration of renewable energy generations
put forward a higher real-time requirement for power system dispatching. To provide …

Scheduling flexible sources to promote the integration of renewable generation is one
fundamental problem for operating active distribution networks (ADNs). However, existing …

Power systems are transitioning toward renewable sources and electrification, introducing
significant uncertainties in generation and demand that optimal power flow (OPF) methods …

This paper focuses on distribution system featuring renewable energy sources (RESs), and
proposes a credibility theory-based fuzzy chance-constrained AC optimal power flow (FCC …

Transition towards carbon-neutral power systems has necessitated optimization of power
dispatch in active distribution networks (ADNs) to facilitate integration of distributed …

The probabilistic prediction of energy generation by a wind farm quantifies the volatility of
wind power. Thus, accurate probabilistic predictions can provide valuable information for …

The variability of renewables and power demands poses significant challenges for the
dispatch of distributed energy resources (DERs) in distribution networks, as they often …

Due to long optimization time and limited dispatching cycle, multi-area combined
economic/emission dispatch (MACEED) problems are converted into computationally …