The widespread deployment of advanced metering infrastructure has led to an increased
interest in data-driven power flow linearization (DPFL) methods. To date, substantial studies …

The multiple energy flow (MEF) model is inherently nonlinear, which challenges the fast
steady-state analysis and the efficient optimization of the integrated energy system (IES) …

With dramatic breakthroughs in recent years, machine learning is showing great potential to
upgrade the toolbox for power system optimization. Understanding the strength and …

The high-precision parameters in distribution networks are difficult to obtain, which brings
difficulties to the model-based methods and analysis. With the widespread deployment of …

This two-part tutorial dives into the field of data-driven power flow linearization (DPFL), a
domain gaining increased attention. DPFL stands out for its higher approximation accuracy …

Traditionally, the electric distribution system operates with uniform energy prices across all
system nodes. However, as the adoption of distributed energy resources (DERs) propels a …

The increasing penetration of distributed energy resources (DERs) may cause security and
economic risks in the distributed network. In this paper, the optimal allocation of battery …

Linearization of power flow is an important topic in power system analysis. The
computational burden can be greatly reduced under the linear power flow (LPF) model while …

With the increasing penetration of renewable energy sources (RES), the value of the
demand response (DR) draws wide attention. In order to realize the coordinated dispatch of …

Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks'
operation and planning. Therefore, there is a need for scalable algorithms that can provide …