Integrating Machine Learning for Risk Assessment in Renewable Energy Investments in Developing Economies

Published: April 17, 2025

Renewable energy investments are critical for addressing poverty and driving sustainable development in developing economies. However, these face significant challenges, including economic volatility, political instability, environmental complexities, which hinder their successful implementation. This study examines the role of machine learning (ML) models as innovative tools assessing mitigating risks associated with renewable investments. The study's objectives include analysing multifaceted investments, evaluating effectiveness ML techniques, such predictive analytics, classification models, neural networks, risk assessment, proposing strategies to facilitate integration into decision-making processes. A qualitative research methodology was adopted, utilizing a comprehensive desk review existing literature. findings reveal that enhance accuracy efficiency assessments by providing advanced capabilities, improving decision-making, complexities economic, political, uncertainties. Despite potential, challenges data quality issues, technological barriers, limited expertise economies remain hurdles. recommends policy reforms support adoption, targeted capacity-building initiatives improve science, strategic collaborations among governments, private sectors, international organizations.

Language: Английский

Estimation of Quantitative Inertia Requirement Based on Effective Inertia Using Historical Operation Data of South Korea Power System

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10555 - 10555

Published: Dec. 2, 2024

In low-inertia systems with a high penetration of renewable energy, the rotational kinetic energy and inertia constant are significant factors in determining frequency stability. The released owing to decrease during contingency represents portion that synchronous machine possesses normal state. However, when securing or planning additional resources secure stability, state is analyzed as standard rather than amount fault. Therefore, this paper, we define actual emitted from Effective inertia. order evaluate various operating conditions, conducted comprehensive review on approximately 24,627 cases years 2019, 2020, 2021. As result, low both low- high-frequency nadirs were observed, indicating uncertainty. presented consistent trend regarding release aligned minimum frequency. For instance, required satisfy was 23 GWs, while 858 MWs. We emphasize based includes imaginary does not contribute frequency, resulting an requirement greater needed for stability future system, will be utilized market FFR (Fast Frequency Response) resource design.

Language: Английский

Catalytic Biolubricant Production from Canola Oil Through Double Transesterification with Methanol and Neopentyl Glycol

Catalysts, Journal Year: 2024, Volume and Issue: 14(11), P. 748 - 748

Published: Oct. 23, 2024

In the current environmental scenario, proposal of alternatives for petroleum-based products has considerably increased, with aim looking bioproducts interesting properties such as biodegradability, sustainability and efficiency, among others. this sense, role biolubricants is promising, offering a wide range possibilities through different methods operating conditions. Specifically, double transesterification could be suitable process in biorefinery context. The work was to produce biolubricant methanol neopentyl glycol (NPG) under reaction conditions by using homogeneous catalysis (sodium methoxide). Different catalyst concentrations, other changes (temperature ranging between 100 140 °C NPG/FAME ratios 0.5 2), were used, obtaining high conversion values (96%) final product viscosity (20.7 cSt), which allows its use engine oil (SAE 5W). conclusion, biodiesel production feasible catalysis, proving feasibility at laboratory scale. Further studies, including heterogeneous catalysts, well implementation semi-industrial scale, are recommended.

Language: Английский