arXiv subjects

Joseph Nyangon

Publications and source records attributed to Joseph Nyangon.

5 recordsLinked to original sources

arXiv2026

Contested Temporalities in Critical Minerals and Resource Extraction for Electric Vehicles

The global push for electric vehicles (EVs) has sharply increased demand for critical minerals such as cobalt and lithium, creating a tension between rapid industrial growth and long-term sustainability. Extraction is concentrated in a few regions -- notably the Democratic Republic of Congo (DRC), Chile, and Argentina -- where it has produced serious socio-environmental harms, including ecosystem degradation, labour exploitation, and the displacement of Indigenous communities. In the DRC, cobalt mining is frequently linked to child labour and hazardous working conditions; in Chile, lithium extraction intensifies water scarcity and threatens local agriculture and biodiversity. Policy instruments such as the U.S. Inflation Reduction Act (IRA) seek to promote ethical sourcing, but an extraction-driven model continues to deepen global inequalities. This chapter examines the contested temporalities of the transition, in which the short-term economic incentives of extraction conflict with longer-term environmental and social goals. It argues for a place-based framework built on community-centred governance, sustainable mining practices, and circular-economy strategies, including recycling and material substitution, to align resource security with equity and ensure that the shift to EVs does not reproduce the injustices it aims to address.

eess.SY↗

arXiv2026

Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review

The integration of machine learning with domain-specific physics is transforming the design, monitoring, and control of electricity systems, where data scarcity, limited interpretability, and the need to enforce physical laws constrain purely data-driven models. Physics-informed machine learning (PIML) addresses these limitations by embedding governing equations directly into the learning process, yielding accurate, efficient, and scalable solutions for Industry 4.0 applications. This article reviews hybrid PIML architectures for electricity systems, including physics-informed neural networks (PINNs), Deep Operator Networks (DeepONets), Fourier Neural Operators, Extreme Learning Machine-enhanced PINNs, graph-based PINNs (PIGNNs), and domain-decomposition PINNs. Each approach is examined through case studies spanning field analysis, fault detection, digital twins, surrogate modeling, and control optimization. The review shows that embedding Maxwell's equations and other first-principles constraints substantially improves predictive accuracy under sparse and noisy data, reduces simulation time by orders of magnitude relative to finite element methods, and enhances generalization across operating regimes. Hybrid frameworks consistently outperform purely data-driven baselines on parameter sensitivity, dynamic behavior, and robustness, while supporting real-time digital-twin calibration and uncertainty quantification. Persistent challenges include training instability for stiff multi-scale problems, computational cost of high-fidelity models, and the absence of standardized benchmarks. The findings demonstrate that PIML enables a paradigm shift from black-box data-driven methods to transparent, physics-informed strategies, positioning the field for sustained innovation in resilient and intelligent electricity systems.

eess.SY↗

arXiv2026

How Carbon Border Adjustment Mechanism is Energizing the EU Carbon Market and Industrial Transformation

The global carbon market is fragmented and characterized by limited pricing transparency and empirical evidence, creating challenges for investors and policymakers in identifying carbon management opportunities. The European Union is among several regions that have implemented emissions pricing through an Emissions Trading System (EU ETS). While the EU ETS has contributed to emissions reductions, it has also raised concerns related to international competitiveness and carbon leakage, particularly given the strong integration of EU industries into global value chains. To address these challenges, the European Commission proposed the Carbon Border Adjustment Mechanism (CBAM) in 2021. CBAM is designed to operate alongside the EU ETS by applying a carbon price to selected imported goods, thereby aligning carbon costs between domestic and foreign producers. It will gradually replace existing carbon leakage mitigation measures, including the allocation of free allowances under the EU ETS. The initial scope of CBAM covers electricity, cement, fertilizer, aluminium, iron, and steel. As climate policies intensify under the Paris Agreement, CBAM-like mechanisms are expected to play an increasingly important role in managing carbon-related trade risks and supporting the transition to net zero emissions.

eess.SY↗

arXiv2024

Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis

Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volatility management. This study focuses on enhancing electricity price forecasting in California's grid, addressing challenges from complex generation data and heteroskedasticity. Utilizing principal component analysis (PCA), we analyze CAISO's hourly electricity prices and demand from 2016-2021 to improve day-ahead forecasting accuracy. Initially, we apply traditional outlier analysis with the interquartile range method, followed by robust PCA (RPCA) for more effective outlier elimination. This approach improves data symmetry and reduces skewness. We then construct multiple linear regression models using both raw and PCA-transformed features. The model with transformed features, refined through traditional and SAS Sparse Matrix outlier removal methods, shows superior forecasting performance. The SAS Sparse Matrix method, in particular, significantly enhances model accuracy. Our findings demonstrate that PCA-based methods are key in advancing electricity price forecasting, supporting renewable integration and grid management in day-ahead markets. Keywords: Electricity price forecasting, principal component analysis (PCA), power system planning, heteroskedasticity, renewable energy integration.

econ.EM↗

arXiv2024

Strengthening Power System Resilience to Extreme Weather Events Through Grid Enhancing Technologies

Climate change significantly increases risks to power systems, exacerbating issues such as aging infrastructure, evolving regulations, cybersecurity threats, and fluctuating demand. This paper focuses on the utilization of Grid Enhancing Technologies (GETs) to strengthen power system resilience in the face of extreme weather events. GETs are pivotal in optimizing energy distribution, enabling predictive maintenance, ensuring reliable electricity supply, facilitating renewable energy integration, and automating responses to power instabilities and outages. Drawing insights from resilience theory, the paper reviews recent grid resilience literature, highlighting increasing vulnerabilities due to severe weather events. It demonstrates how GETs are crucial in optimizing smart grid operations, thereby not only mitigating climate-related impacts but also promoting industrial transformation. Keywords: Climate change, power systems, grid enhancing technologies (GETs), power system resilience, extreme weather

eess.SY↗