arXiv subjects

Alfredo Oneto

Publications and source records attributed to Alfredo Oneto.

3 recordsLinked to original sources

arXiv2026

Prosumer Synchronisation Risk: Impacts of Time-Varying Tariffs on Distribution Network Expansion

The rapid deployment of distributed energy resources, including heat pumps, electric vehicles, photovoltaics, and battery storage, is reshaping the operation of low-voltage networks. While distribution system operators often aim to develop time-varying tariffs to incentivise network-friendly behaviour and defer reinforcements, they risk triggering prosumer synchronisation. As prosumers follow price signals, their behaviour may synchronise, avoiding existing load peaks while creating undesirable peaks at other times. This paper quantifies the impact on low-voltage network reinforcement needs when shifting from flat tariffs to two time-varying tariffs: Time-of-Use and Real-Time Pricing. Applying a mixed-integer linear model across 471 low-voltage reference networks in Switzerland, we evaluate expansion requirements under projections of distributed energy resource deployment for 2050. In the model, prosumers operate their distributed energy resources in accordance with their subscribed electricity tariff to minimise costs, and the distribution system operator subsequently optimises reinforcements. Our results demonstrate that while adopting time-varying tariffs can reduce reinforcement investment costs by more than 50%, high adoption rates can reverse this declining trend. Specifically, once prosumer adoption exceeds the optimal thresholds of 50% for Time-of-Use and 40% for Real-Time Pricing, reinforcement needs increase by 39% and 134%, respectively, for an additional 20% of tariff adoption.

eess.SY↗

arXiv2025

Future Deployment and Flexibility of Distributed Energy Resources in the Distribution Grids of Switzerland

The decarbonization goals worldwide drive the energy transition of power distribution grids, which operate under increasingly volatile conditions and closer to their technical limits. In this context, localized operational data with high temporal and spatial resolution is essential for their effective planning and regulation. Nevertheless, information on grid-connected distributed energy resources, such as electric vehicles, photovoltaic systems, and heat pumps, is often fragmented, inconsistent, and unavailable. This work introduces a comprehensive database of distributed energy resources and non-controllable loads allocated in Switzerland's medium- and low-voltage distribution grid models, covering over 2 million points of connection. Remarkably, this data specifies the flexibility capabilities of the controllable devices, with a set of projections aligned with national forecasts for 2030, 2040, and 2050. The database supports studies on flexibility provision of distributed energy resources, distribution grid resilience, and national energy policy, among other topics. Importantly, its modular structure allows users to extract national- and local-scale information across medium- and low-voltage systems, enabling broad applicability across locations.

eess.SY↗

arXiv2025

Wasserstein-based Kernel Principal Component Analysis for Clustering Applications

Many data clustering applications must handle objects that cannot be represented as vectors. In this context, the bag-of-vectors representation describes complex objects through discrete distributions, for which the Wasserstein distance provides a well-conditioned dissimilarity measure. Kernel methods extend this by embedding distance information into feature spaces that facilitate analysis. However, an unsupervised framework that combines kernels with Wasserstein distances for clustering distributional data is still lacking. We address this gap by introducing a computationally tractable framework that integrates Wasserstein metrics with kernel methods for clustering. The framework can accommodate both vectorial and distributional data, enabling applications in various domains. It comprises three components: (i) an efficient approximation of pairwise Wasserstein distances using multiple reference distributions; (ii) shifted positive definite kernel functions based on Wasserstein distances, combined with kernel principal component analysis for feature mapping; and (iii) scalable, distance-agnostic validity indices for clustering evaluation and kernel parameter optimization. Experiments on power distribution graphs and real-world time series demonstrate the effectiveness and efficiency of the proposed framework.

cs.LG↗