Energy Digest

The paper introduces a computationally efficient method for calculating quantile-resolved hosting capacity distributions for low-voltage grids using linearized power flow models and multivariate normal distribution representations. This approach reduces computational time by orders of magnitude, achieving comparable accuracy to Monte Carlo-based methods with a mean deviation of around 3%. The method's scalability is suitable for real-time grid management tasks such as recalculation in 15-minute cycles.

Researchers developed a tool called SALT to perform high-fidelity steady-state grid analysis using full-physics electromagnetic transient (EMT) models, achieving accuracy while delivering a significant speedup over existing power flow tools. SALT identifies security threats in contingency scenarios with precision, reducing reported line, Q-limit, and voltage violations by 75%, 18%, and 7% compared to traditional power flow methods. The tool's approach exploits the structural properties of power device models and the balanced nature of transmission systems.

Researchers analyzed power system dynamics using autocorrelation of frequency and voltage measurements, identifying local and global properties with different resolution levels. The study was supported by simulations on a stochastic differential algebraic equation model of the IEEE benchmark system. Data came from the Irish All-Island Power System (AIPS) at various measurement resolutions.

Grid-connected solar distributed energy resources (DERs) pose significant risks to power grids due to their widespread Internet exposure, with over 66,000 vulnerable systems discovered through Internet scanning data. These exposed DERs can compromise critical infrastructure, causing voltage and line flow violations that may lead to degraded power quality, damaged components, or even power outages. A study evaluating the risk of compromised DERs on a power grid network found that at least 10,000 systems have known vulnerabilities, such as unauthenticated monitoring and control endpoints.

Distributed droop-free control is proposed for coordinating inverters in AC microgrids to achieve proportional active and reactive power sharing, frequency regulation, and voltage regulation within prescribed bounds. The approach incorporates dynamic models of network lines and loads, enabling accurate representation of transient behavior while capturing the coupled nature of power flow interactions. Real-time simulation studies validate the method's performance improvement over existing quasi-steady state (QSS) based approaches in weak grid conditions.

A novel generative framework is proposed to efficiently solve stochastic model predictive control problems under nonlinear AC power-flow constraints, enabling the recovery of feasible solutions through candidate selection. This framework employs a conditional stochastic neural generator and a feasibility-aware self-supervised distribution-shaping scheme to promote constraint satisfaction and operating economy. The approach demonstrates $100\%$ feasibility and optimality gaps below $2\%$, with computational efficiency suitable for intraday dispatch.

Decentralised stability analysis for AC grids extends to unstable subsystems through a hybrid representation combining power and impedance models at low and high frequencies. Stability conditions are formulated using a stable hybrid model, allowing for plug-and-play compatibility. The analysis is validated on the Kundur two-area system with a decentralised grid code covering frequency ranges relevant to electromagnetic interactions.

A decentralised stability framework for AC grids is presented, allowing for local subsystem stability conditions to collectively imply the stability of the entire grid, with added flexibility and compatibility. The framework generalises previous results and accommodates detailed device and line models in the frequency domain using impedance, admittance, and power-flow models. It has been validated on a modified 9-bus case study demonstrating reduced conservatism compared to traditional methods.

An intelligent fault and lightning detection algorithm is proposed for VSC-MTDC grids using a Residual Network with hybrid attention mechanism (RWHAM). The algorithm converts DC line current traveling waves into time-frequency matrices and uses these matrices to detect internal and external faults and lightning interference. The algorithm achieves high accuracy and rapid response across different types of faults and lightning interference, with excellent generalization ability even when the VSC-MTDC grid parameters change.

SMR operating costs can offset significant investment savings, making them uneconomical without government subsidies or price spikes beyond historic norms. The flexibility of SMRs to generate revenue from both capacity and wholesale energy markets is crucial to their economic viability. In a rapidly changing power grid, reduced marginal costs may be more beneficial than investment cost reductions for the commercial success of SMRs.