Accurate voltage estimation in distribution networks is critical for real-time monitoring and increasing the reliability of the grid. As DER penetration and distribution level voltage variability increase, robust distribution system state estimation (DSSE) has become more essential to maintain safe and efficient operations. Traditional DSSE techniques, however, struggle with sparse measurements and the scale of modern feeders, limiting their scalability to large networks. This paper presents a hierarchical graph neural network for substation-level voltage estimation that exploits both electrical topology and physical features, while remaining robust to the low observability levels common to real-world distribution networks. Leveraging the public SMART-DS datasets, the model is trained and evaluated on thousands of buses across multiple substations and DER penetration scenarios. Comprehensive experiments demonstrate that the proposed method achieves up to 2 times lower RMSE than alternative data-driven models, and maintains high accuracy with as little as 1% measurement coverage. The results highlight the potential of GNNs to enable scalable, reproducible, and data-driven voltage monitoring for distribution systems.
Power systems are being reshaped by decarbonization, digitalization, and high shares of renewables. At the same time, increasingly severe extreme conditions expose the limits of traditional reliability frameworks, calling for risk-aware, resilience-oriented approaches to address high-impact, low-probability (HILP) events. In this context, this paper presents a comprehensive overview of the foundations of power system resilience. It revisits the transition from reliability to resilience, formalizes key concepts and metrics, and introduces advanced approaches for resilience assessment, including fragility-based modeling, cascading failure analysis, and tail-risk indicators. The paper further examines resilience-oriented investment planning, operational strategies across all event phases, and the role of distributed energy resources, microgrids, and cybersecurity. The analysis highlights that resilience extends reliability by focusing on extreme conditions, fundamentally reshaping decision-making and requiring coordinated strategies across infrastructure, operation, and governance.
This paper introduces a new paradigm for frequency control in power systems based on optimization through Model Predictive Control (MPC). The approach unifies primary and secondary frequency regulation in a multi-horizon structure that coordinates distinct time scales, bridging conventional synchronous generation and converter-interfaced generation (CIG), while also enabling inertial response. This approach enhances system stability while providing a cost-effective solution that respects market inputs and operational constraints. To demonstrate the performance of the proposed paradigm, a two-area system is studied, comprising synchronous generators with prime movers as well as CIG, including battery energy storage systems operating at zero net energy over the long term.
Aggregators of consumer energy resources (CERs) like rooftop solar and battery energy storage (BES) face challenges due to their inherent uncertainties. A sensible approach is to use stochastic optimization to handle such uncertainties, which can lead to infeasible problems or loss in revenues if not chosen appropriately. This paper presents three stochastic optimization methods: risk-neutral, robust, and chance-constrained, to address the impact of CER uncertainties for aggregators who participate in energy and regulation services markets in the Australian National Electricity Market. Furthermore, these methods utilize the flexibility of BES, considering precise state-of-charge dynamics and complementarity constraints, aiming for scalable performance while managing uncertainty. The problems are formed as two-stage stochastic mixed-integer linear programs, with relaxations adopted for large scenario sets. The solution approach employs scenario-based methodologies and affine recourse policies to obtain tractable reformulations. These methods are evaluated in terms of profit and constraint violation risk across use cases reflecting diverse operational and market settings, uncertainty characteristics, and decision-making preferences, offering aggregators insight into the selection of appropriate methods. Numerical results indicate that, while stochastic methods outperform traditional deterministic methods in terms of profit and risk, the risk-neutral method performs best when uncertainty is correctly captured, whereas robust and chance-constrained methods are more effective when uncertainty is misspecified.
Cascading failures pose significant frequency and voltage stability challenges in Renewable Energy Source (RES)-rich grids, especially when the system unintentionally splits, leading to widespread blackouts. This paper presents a novel method for identifying strategic buses for the deployment of decentralized battery energy storage systems (BESS), enabling islanded-mode operation to support split areas and thereby enhance grid resilience against cascading failures. The method performs risk-aware, budget-constrained optimal BESS allocation that accounts for load/RES uncertainty and worst credible contingencies, while integrating dynamic cascading-failure modelling and analysis to evaluate system-wide performance under BESS-provided grid support. It explicitly considers cascade-triggering mechanisms in frequency, voltage, and line loading—such as RoCoF, frequency nadir, voltage bounds, and thermal limits—together with a budget limit, by incorporating them into the optimization constraints. This enables optimal BESS allocation to mitigate cascading risks and enhance decentralized resilience by providing grid services such as voltage and frequency regulation, while reducing reliance on conventional load shedding, particularly during islanded operation through a virtual synchronous machine (VSM) control scheme. Implemented in the Cypriot power grid, the method demonstrates effectiveness in improving system resilience and reducing unsupplied load by 43.1%.
