Integrating decision matrix and mind mapping for optimal residential battery storage solutions
Author Identifier (ORCID)
Mehrdad Ghahramani: https://orcid.org/0000-0002-5926-0996
Daryoush Habibi: https://orcid.org/0000-0002-7662-6830
Asma Aziz: https://orcid.org/0000-0003-3538-0536
Abstract
The rapid advancement of residential battery energy storage systems is reshaping consumer energy management and grid interaction. However, selecting an optimal battery solution remains challenging due to financial, operational, and environmental complexities. This paper introduces a comprehensive decision-making framework that integrates mathematical modeling, a decision matrix, mind mapping, and correlation analysis to aid consumers in selecting suitable battery storage options. The framework systematically evaluates critical factors such as capital expenditure (CAPEX), operational costs (OPEX), payback periods, and battery sizing, enabling consumers to assess economic and non-economic drivers. By incorporating tailored guidance for varied consumer profiles and addressing factors like government incentives, tariff structures, and battery degradation, the framework is particularly relevant for markets like Australia. Practical tools and accessible takeaways ensure this methodology is userfriendly for consumers without technical expertise, helping them make more informed decisions. Our proposed framework supports the adoption of broader battery energy storage systems, contributing to energy resilience and sustainability in an evolving energy landscape.
Document Type
Conference Proceeding
Date of Publication
6-12-2025
Publication Title
2024 International Conference on Sustainable Technology and Engineering
Publisher
IEEE
School
School of Engineering
RAS ID
82162
Copyright
subscription content
Comments
Ghahramani, M., Habibi, D., Ghamari, S., & Aziz, A. (2024). Integrating decision matrix and mind mapping for optimal residential battery storage solutions. In 2024 International Conference on Sustainable Technology and Engineering (i-COSTE) (pp. 1-8). IEEE. https://doi.org/10.1109/i-COSTE63786.2024.11024695