墨尔本大学

The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid dynamics model. To address these challenges, we propose a model-free, nonlinear, and dissipativity-based controller which, when applied to grid-connected virtual synchronous generators (VSGs), enhances power system transient stability. Using input-state data, we train neural networks to learn dissipativity-characterizing matrices that yield stabilizing controllers. Furthermore, we incorporate cost function shaping to improve the performance with respect to the user-specified objectives. Numerical results on a modified, all-VSG Kundur two-area power system validate the effectiveness of the proposed approach.

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.

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.

This study investigates the integration of crowdshipping (CS) into e-commerce reverse logistics, specifically applied to the parcel transport stage between return points and transhipment facilities. Several barriers have limited CS adoption in forward logistics, such as trust, privacy, and security concerns due to direct customer–crowdshipper interaction, and the inability to offer bundled CS tasks that would enable attractive compensation schemes, but these constraints are mitigated in this transport-stage-focused reverse logistics setting. The proposed CS-integrated system operates in a business-to-business (B2B) context, eliminating customer contact and allowing bundled CS task assignment, thereby providing a more secure and operationally efficient setting for CS deployment. In this study, operational and external costs (i.e., per-kilometre social costs) for both conventional and CS-integrated reverse logistics systems are modelled and simulated across diverse spatial and demographic contexts, system scales, configurations, and levels of CS supply and behavioural dynamics. These simulations generate a large database that identifies the conditions under which the CS-integrated system can achieve operational and/or environmental advantages compared with the conventional system. Using a comprehensive simulation experiment based on systematically generated parameter combinations (45,000 simulation runs) and applying decision tree analysis, we find that the CS-integrated system reduces operational costs in nearly all cases, with savings reaching up to 70 %, particularly under conditions with sufficient CS supply and high participation willingness (i.e., low sensitivity to detour and compensation). However, environmental benefits are more context-dependent, emerging mainly in low-density, large-area settings where detours remain minimal. The study underscores the potential of CS to enhance the cost efficiency of reverse logistics while highlighting the importance of policy interventions that limit detours and induced travel demand in the CS-integrated system to ensure that sustainability gains are fully realised.

Diffusion models have demonstrated significant potential in medical image segmentation, yet they remain challenged in capturing complex fine-grained structures. Existing spatial-domain backbones exhibit bias towards low-frequency components, while the denoising process often leads to over-smoothing of high-frequency details, compromising boundary precision and cross-domain generalization. To overcome these limitations, we propose a Spatial-Frequency Aware Diffusion Network (SFA-DiffNet) that jointly models spatial and frequency representations, augmented with conditional guidance from foundation models. This design improves detail preservation and boundary accuracy in our experiments. Specifically, we introduce a Spatial-Frequency Aware Encoder (SFAE) that decomposes features into complementary spatial and frequency representations. It consists of a Spatial Aware Block (SAB) for contextual information learning, a Frequency Aware Block (FAB) for spectral cues exploration, and an Adaptive Fusion Block (AFB) for complementary fusion. Moreover, we develop a foundation model guidance mechanism that dynamically injects semantic priors via an Adaptive Feature Projector (AFP) and Semantic Alignment Fusion Block (SAFB), ensuring alignment between conditional and denoising features. Extensive experiments on multi-modal medical imaging datasets show that SFA-DiffNet achieves competitive performance against existing methods and shows potential for cross-domain generalization. The source code is available at GitHub.