Marija Ilić

Qiao Li, Tao Cui, Rohit Negi et al.

The concept of plug-in electric vehicles (PEV) are gaining increasing popularity in recent years, due to the growing societal awareness of reducing greenhouse gas (GHG) emissions, and gaining independence on foreign oil or petroleum. Large-scale deployment of PEVs currently faces many challenges. One particular concern is that the PEV charging can potentially cause significant impacts on the existing power distribution system, due to the increase in peak load. As such, this work tries to mitigate the impacts of PEV charging by proposing a decentralized smart PEV charging algorithm to minimize the distribution system load variance, so that a `flat' total load profile can be obtained. The charging algorithm is myopic, in that it controls the PEV charging processes in each time slot based entirely on the current power system states, without knowledge about future system dynamics. We provide theoretical guarantees on the asymptotic optimality of the proposed charging algorithm. Thus, compared to other forecast based smart charging approaches in the literature, the charging algorithm not only achieves optimality asymptotically in an on-line, and decentralized manner, but also is robust against various uncertainties in the power system, such as random PEV driving patterns and distributed generation (DG) with highly intermittent renewable energy sources.

Xia Miao, Xiaofan Wu, Ulrich Munz et al.

In this paper, we propose a method for estimating radial distribution grid admittance matrix using a limited number of measurement devices. Neither synchronized three-phase measurements nor phasor measurements are required. After making several practical assumptions, the method estimates even impedances of lines which have no local measurement devices installed. The computational complexity of the proposed method is low, and this makes it possible to use for on-line applications. The effectiveness of the proposed method is tested using data from a real-world distribution grid in Vienna, Austria.

Xia Miao, Qixing Liu, Marija Ilic

This paper is motivated by the need to enhance today's Automatic Generation Control (AGC) for ensuring high quality frequency response in the changing electric power systems. Renewable energy sources, if not controlled carefully, create persistent fast and often large oscillations in their electric power outputs. A sufficiently detailed dynamical model of the interconnected system which captures the effects of fast nonlinear disturbances created by the renewable energy resources is derived for the first time. Consequently, the real power flow interarea oscillations and the resulting frequency deviations are modeled. The modeling is multi-layered, and the dynamics of each layer (component level (generator); control area (control balancing authority), and the interconnected system) is expressed in terms of internal states and the interaction variables (IntV) between the layers and within the layers. E-AGC is then derived using this model to show how these interarea oscillations can be canceled. Simulation studies are carried out on a 5-bus system.

Hiya Gada, Rupamathi Jaddivada, Marija Ilic

The widespread deployment of power electronic technologies is transforming modern power systems into fast, nonlinear, and heterogeneous networks. Conventional modeling and control approaches, rooted in quasi-static analysis and centralized architectures, are inadequate for these converter-dominated systems operating on fast timescales with diverse and proprietary component models. This paper adopts and extends a previously introduced energy space modeling framework grounded in energy conservation principles to address these challenges. We generalize the notion of a port interaction variable, which encodes energy exchange between interconnected components in a unified manner. A multilayered distributed control architecture is proposed in which dynamics of each component are lifted to a linear energy space through well-defined mappings. Distributed control with provable convergence guarantees is derived in energy space using only local states and minimal neighbor information communicated through port interactions. The framework is validated using two examples: voltage regulation in an inverter-controlled RLC circuit and frequency regulation of a synchronous generator. The energy-based controllers show improved transient and steady-state performance with reduced control effort compared to conventional methods.

Haoran Li, Muhao Guo, Marija Ilic et al.

Accurate residential load forecasting is critical for power system reliability with rising renewable integration and demand-side flexibility. However, most statistical and machine learning models treat external factors, such as weather, calendar effects, and pricing, as extra input, ignoring their heterogeneity, and thus limiting the extraction of useful external information. We propose a paradigm shift: external data should serve as meta-knowledge to dynamically adapt the forecasting model itself. Based on this idea, we design a meta-representation framework using hypernetworks that modulate selected parameters of a base Deep Learning (DL) model in response to external conditions. This provides both expressivity and adaptability. We further integrate a Mixture-of-Experts (MoE) mechanism to enhance efficiency through selective expert activation, while improving robustness by filtering redundant external inputs. The resulting model, dubbed as a Meta Mixture of Experts for External data (M2oE2), achieves substantial improvements in accuracy and robustness with limited additional overhead, outperforming existing state-of-the-art methods in diverse load datasets. The dataset and source code are publicly available at https://github.com/haorandd/M2oE2\_load\_forecast.git.