arXiv subjects

Aidan O'Sullivan

Publications and source records attributed to Aidan O'Sullivan.

4 recordsLinked to original sources

arXiv2026

Decarbonising price formation: unit-level evidence on battery storage and the imbalance price in the GB Balancing Mechanism

Renewables now dominate Great Britain's generation mix but rarely occupy the marginal price-setting position, which raises the question of which flexible technologies translate a renewable-rich system into real-time price formation. This study reconstructs the price-ranked edge of the eligible bid or offer stack in the GB Balancing Mechanism across 50,684 half-hourly Settlement Periods from 2023 to 2025 and attributes it to individual Balancing Mechanism Units, separating long-system bid-active from short-system offer-active conditions and retaining co-marginal ties. Batteries rose from 0.8% to 36.2% of bid-active and from 3.2% to 26.9% of offer-active marginality, displacing combined-cycle gas on the bid side and pumped storage on the offer side. Capacity-normalised marginal capture rose on both sides, and unit-quarter fixed effects show that an additional 100 MW of Balancing Mechanism active capacity was associated with a 0.55 and 0.48 percentage-point increase in quarterly bid- and offer-side marginal share. By 2025, battery actions were around £9 to 10/MWh more favourable than volume-matched non-battery alternatives drawn from the same ladder, yet batteries were under-represented by 12.9 percentage points in the highest-priced 5% of short-system periods. Individual batteries remain consistent with price-taking, while the fleet has become endogenous to routine balancing price formation.

stat.AP↗

arXiv2026

Analysing drivers and interdependencies in European electricity markets using XAI

Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions. While deep neural networks (DNNs) have demonstrated strong predictive capabilities for electricity prices, their lack of interpretability limits their usefulness for understanding the underlying drivers of price formation. This paper addresses this gap by combining DNN models with explainable artificial intelligence (XAI) techniques to analyse the determinants of electricity prices across 39 European bidding zones. We employ SHAP (SHapley Additive exPlanations) to quantify feature contributions and apply and extend SSHAP, an aggregation framework to improve interpretability in high-dimensional settings. The analysis identifies that renewable energy sources, particularly solar, play a disproportionately important role in price formation despite their lower share in total power generation. Gas prices remain a dominant and consistent driver across electricity markets, while interconnections significantly shape price dynamics, highlighting the strong interdependence of European electricity systems. In addition, a synthetic EU-wide electricity market is constructed to explore the counterfactual scenario of a fully integrated market with a single price.

cs.AI↗

arXiv2020

Reinforcement Learning for Electricity Network Operation

This paper presents the background material required for the Learning to Run Power Networks Challenge. The challenge is focused on using Reinforcement Learning to train an agent to manage the real-time operations of a power grid, balancing power flows and making interventions to maintain stability. We present an introduction to power systems targeted at the machine learning community and an introduction to reinforcement learning targeted at the power systems community. This is to enable and encourage broader participation in the challenge and collaboration between these two communities.

eess.SP↗

arXiv2019

Don't go chasing artificial waterfalls: Simulating cascading failures in the power grid and the impact of artificial line-limit methods on results

Research into cascading failures in power-transmission networks requires detailed data on the capacity of individual transmission lines. However, these data are often unavailable to researchers. As a result, line limits are often modelled by assuming they are proportional to some average load. Little research exists, however, to support this assumption as being realistic. In this paper, we analyse the proportional-loading (PL) approach and compare it to two linear models that use voltage and initial power flow as variables. In conducting this modelling, we test the ability of artificial line limits to model true line limits, the damage done during an attack and the order in which edges are lost. we also test how accurately these methods rank the relative performance of different attack strategies. We find that the linear models are the top-performing method or close to the top in all tests. In comparison, the tolerance value that produces the best PL limits changes depending on the test. The PL approach was a particularly poor fit when the line tolerance was less than two, which is the most commonly used value range in cascading-failure research. We also find indications that the accuracy of modelling line limits does not indicate how well a model will represent grid collapse. In addition, we find evidence that the network's topology can be used to estimate the system's true mean loading. The findings of this paper provide an understanding of the weaknesses of the PL approach and offer an alternative method of line-limit modelling.

eess.SY↗