The field of power systems is moving towards innovative solutions to address the challenges of stability, optimization, and control in the face of increasing decentralization and renewable energy integration. Researchers are exploring new approaches to mitigate active power oscillations, optimize voltage regulation, and improve power flow solvers. Notably, the use of machine learning and physics-informed models is becoming more prevalent, enabling more accurate and efficient solutions.

Some noteworthy papers in this area include: Mitigation of Active Power Oscillation in Multi-VSG Grids, which proposes a physically intuitive RLC equivalent circuit model to explain the root causes of APOs and two mode-specific mitigation strategies. NEO-Grid, a neural approximation framework for optimization and control in distribution grids, which leverages neural network surrogates for power flow and deep equilibrium models for closed-loop control. Physics-informed GNN for medium-high voltage AC power flow, which combines an edge-aware attention mechanism with a backtracking line-search-based globalized correction operator to improve accuracy and speed. Coordinated vs. Sequential Transmission Planning, which describes a multistage, multi-locational planning model that co-optimizes generation, storage, and transmission investments, leading to lower transmission upgrade needs and total system costs. Data-Driven Optimal Power Flow, which proposes a novel data-driven representation of nonlinear power flow equations for radial grids based on Willems' Fundamental Lemma, allowing for direct integration of input/output data into power flow optimization.