Exploring the Potential of Residential Energy Sharing Communities: A Simulation Study on Load Profile Suitability and Profitability

The potential of residential energy sharing communities—especially those with photovoltaic and battery storage systems—to facilitate the transition to decentralised, sustainable renewable energy is explored in this study. The study simulates the profitability of 1,000 communities and looks at the relationship between household load profiles and whether or not they are suitable for energy sharing using actual load profile data from USA. To concisely define the high-dimensional load profiles, a comprehensive collection of electricity parameters was built from both a thorough analysis of the literature and new creations. The inability of Machine Learning (ML) models to predict community revenues effectively, even with efforts in hyperparameteroptimisation and different training dataset configurations, led to recommendations for future research topics. Therefore, a thorough examination of the load profiles of both low- and high-performing communities, theories about the favourable traits of residential load profiles for energy sharing were developed. These theories highlighted the intricate relationship between household energy habits and community energy dynamics.

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Published
2024-08-29
DOI
https://doi.org/10.1109/iceect61758.2024.10739007
Primary Topic
Energy and Environmental Systems
Type
article
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article

Exploring the Potential of Residential Energy Sharing Communities: A Simulation Study on Load Profile Suitability and Profitability

Rajesh Mahadeva, Dheerendra Panwar, Suresh Dodda
Energy and Environmental Systems
article

Exploring the Potential of Residential Energy Sharing Communities: A Simulation Study on Load Profile Suitability and Profitability

Rajesh Mahadeva, Dheerendra Panwar, Suresh Dodda
article en

Abstract

The potential of residential energy sharing communities—especially those with photovoltaic and battery storage systems—to facilitate the transition to decentralised, sustainable renewable energy is explored in this study. The study simulates the profitability of 1,000 communities and looks at the relationship between household load profiles and whether or not they are suitable for energy sharing using actual load profile data from USA. To concisely define the high-dimensional load profiles, a comprehensive collection of electricity parameters was built from both a thorough analysis of the literature and new creations. The inability of Machine Learning (ML) models to predict community revenues effectively, even with efforts in hyperparameteroptimisation and different training dataset configurations, led to recommendations for future research topics. Therefore, a thorough examination of the load profiles of both low- and high-performing communities, theories about the favourable traits of residential load profiles for energy sharing were developed. These theories highlighted the intricate relationship between household energy habits and community energy dynamics.

Indian Institute of Technology Roorkee (IN), Film Independent (US)
Openalex Percentile: Top 83%
Energy and Environmental Systems
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