Work

Deep learning-enabled spatiotemporal sustainable energy strategy for rural multiple microgrids

Year: 2025

Type: article

Abstract: Sustainable economic development entails effective energy management. Yet there is a dearth of schemes that jointly capture spatial and temporal energy dynamics, while ensuring fairness in underserved regions for geographically distant microgrids through virtual energy sharing. This work proposes a spatiotemporal framework, for microgrid energy management, stimulating exchange of excess renewable energy. The recommended scheme facilitates intelligent utilization of renewable energy, with moderated costs of generation and distribution. A simulated case study of two rural microgrid locations in India, compliant with India's National Grid, validated the propriety of the proffered approach. Findings showed a 30.7% reduction in dependency on the main grid, with drops in annual energy imports from (19.1 - 13.2) MWh. Besides, multi-microgrid coordination delivered cost savings exceeding INR 15,000, validated during monsoon and winter seasons. This study corroborates the potential of coordinated microgrids to enhance resilience, inclusivity and clean energy, in support of UN Sustainable Development Goals 7, 9, 11, 12, and 13.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.107569

Authors: Vipina Valsan, A.M. Abhishek Sai, Anu G Kumar, Aryadevi Remanidevi Devidas, Maneesha Vinodini Ramesh, Kanakasabapathy P

Institution: Amrita Vishwa Vidyapeetham

Language: English

Topic: Microgrid Control and Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

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

Open Access status: gold

Estimated APC paid: $1,940