Energy Digest

A new three-phase power flow model called Dist3Flow is proposed to capture the inherent imbalances of unbalanced distribution networks with distributed energy resources (DERs). This model uses nodal voltages and active/reactive power flows as state variables, models lines using nonlinear equations, and loads/DERs via ZIP models and P-Q control. The solution is obtained using a backward/forward sweep algorithm and validated against OpenDSS across various configurations.

Model-free control for grid-connected power converters overcomes limitations of traditional mathematical modeling by using an ad-hoc simple model compensated by high-rate evaluation of dynamics. An intelligent proportional-integral (iPI) controller is synthesized and validated on a 16 kW experimental test bench, demonstrating its benefits in controlling secondary voltage. The approach has been successfully applied to grid-connected power converters, offering advantages in control participation.

Inverter-based resources (IBRs) can cause converter-driven stability issues even under strong grid connections as their number increases, leading to oscillations with frequencies such as 150 Hz in large-scale energy storage systems. The dynamic interactions among power conversion systems of ESSs can be intensified as the system scale extends, reducing damping and causing instability. Careful planning and functional control design are crucial to mitigate these issues and ensure stability in IBR-dominated systems.

Large-scale bidirectional inverter-based stations can induce instability in power systems due to converter-driven stability issues, particularly when connected through AC connections, which is mitigated by switching to DC connections. This method reduces instability risks if the DC line resistance is lower than the AC line reactance, and tuning control parameters improves critical stability under DC connections. The use of DC-IBSs is preferred for high-voltage transmission due to its stability advantages.

A new sensitivity factor called line outage impact factor (LOIF) is proposed to enhance transmission observability, revealing more effective impacts of transmission outages on other lines than existing factors like LODF. LOIF's detection accuracy is found to be higher than LODF in several test systems using machine learning algorithms. The method shows potential for real-world applications, particularly in large-scale power systems.

The FP-AMM mechanism employs a two-stage stochastic clearing rule that treats scarcity allocation as a controlled, stateful process and ensures equitable treatment across market intervals. It converges almost surely to a unique fixed point with a contraction factor of $q\in(0,1)$, and guarantees practical ultimate boundedness of the allocation tracking error. The mechanism achieves fairness convergence on all benchmarks, reducing peak weak-bus fairness error by up to 55%.

McWC (Multiple cyclicity and Wavelet Decomposition with Channel Correlation) is a long-term time series forecasting model that decouples cyclical information from data using multi-layer cyclicity construction module. The model then extracts inter-channel correlations using multi-layer perceptron, models high-frequency and low-frequency information using wavelet decomposition, and aggregates results to obtain output. McWC achieves state-of-the-art performance on six real-world datasets with excellent computational efficiency and historical information extraction capabilities.

Centrally connected energy storage systems (CESSs) exhibit dynamic superimposition characteristics when using single-type control, with grid-forming (GFM) CESSs having improved damping as the number of ESSs increases, but this advantage decreases for grid-following (GFL) CESSs. Hybridizing GFL and GFM controls in CESSs can mitigate instability risks from modal resonance between different control types. Implementing grid-forming control in large-scale integration is preferred due to its damping sensitivity, but with limitations in scenarios with significant power reversal.