Work

Optimization of Power System Flexibility Through AI‐Driven Dynamic Load Management and Renewable Integration

Year: 2025

Type: article

Abstract: ABSTRACT This paper introduces an advanced framework to enhance power system flexibility through AI‐driven dynamic load management and renewable energy integration. Leveraging a transformer‐based predictive model and MATPOWER simulations on the IEEE 14‐bus system, the study achieves significant improvements in system efficiency and stability. Key contributions include a 44% reduction in total power losses, enhanced voltage stability validated through the Fast Voltage Stability Index (FVSI), and optimized renewable energy utilization. Comparative analyses demonstrate the superiority of AI‐based approaches over traditional models such as ARIMA, with the transformer model achieving significantly lower forecasting errors. The proposed methodology highlights the transformative potential of AI in addressing the challenges of modern power grids, paving the way for more resilient, efficient, and sustainable energy systems.

Source: Battery energy

DOI: 10.1002/bte2.20250009

Authors: Saad Hayat, Aamir Nawaz, Aftab Ahmed Almani, Zahid Javid, William Holderbaum

Institutions: Gomal University, Glasgow Caledonian University, Manchester Metropolitan University

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $3,000