New Decision Framework Finds Optimal Hybrid Renewable Energy Systems for Remote Regions

Researchers in India have developed an integrated decision-based optimization framework that combines techno-economic optimization with multi-criteria performance evaluation to systematically select optimal hybrid renewable energy system configurations for remote regions. The study, published in Clean Technologies and Environmental Policy, demonstrates the framework through a case study of a remote Indian location, achieving a net present cost of approximately USD 158,000, a cost of energy of USD 0.046/kWh, a renewable fraction exceeding 91%, and an unmet load below 0.5%.

Reliable and sustainable electricity access remains a significant challenge in remote regions due to weak grid infrastructure, geographical constraints, high dependence on fossil fuels, and the intermittent nature of renewable energy resources. Although hybrid renewable energy systems offer a promising solution, the absence of a structured and transparent decision-making approach may lead to suboptimal configuration selection. The new framework addresses this gap by combining HOMER Pro-based techno-economic optimization with multi-criteria decision-making for the systematic selection of optimal hybrid renewable energy configurations.

Candidate systems incorporating solar photovoltaic generation, wind turbines, battery energy storage, and auxiliary grid support were evaluated under realistic resource and load conditions representative of remote regions in India. A representative case study with an average daily load of 280–320 kWh/day, mean solar irradiation of 5.1 kWh/m²/day, and average wind speed of 4.2 m/s was investigated. The optimal hybrid configuration achieves a net present cost of approximately USD 158,000 and a cost of energy of USD 0.046/kWh, with a renewable fraction exceeding 91% and an unmet load below 0.5%.

Furthermore, the proposed system reduces annual carbon dioxide emissions by approximately 88% compared with conventional grid-based electricity supply, corresponding to avoided emissions of nearly 72,000 kg/year. Sensitivity analysis identifies solar resource availability, battery capital cost, and discount rate as the dominant factors affecting system optimality. The results demonstrate that the proposed framework enables transparent, reproducible, and multi-criteria selection of hybrid renewable energy systems beyond conventional single-metric optimization, providing a scalable decision-support methodology for sustainable electrification planning in remote regions.

The framework’s key innovation lies in its integration of two complementary analytical layers. The first layer uses HOMER Pro, a widely adopted software tool for hybrid optimization of multiple energy resources, to perform techno-economic optimization across candidate configurations. The second layer applies multi-criteria performance evaluation to systematically rank the optimized configurations according to multiple performance dimensions rather than a single economic metric. This dual-layer approach ensures that the final selection reflects not only cost minimization but also reliability, environmental performance, and system robustness.

The study’s sensitivity analysis provides particularly valuable insights for planners and policymakers. By identifying solar resource availability, battery capital cost, and discount rate as the dominant factors affecting system optimality, the framework highlights where data quality and cost assumptions matter most in remote electrification planning. This finding has direct implications for how decision-makers prioritize site assessment, technology procurement, and financial structuring when deploying hybrid renewable energy systems in regions with weak grid infrastructure.

The environmental benefits quantified in the case study are substantial. The optimal hybrid configuration reduces annual carbon dioxide emissions by approximately 88% compared with conventional grid-based electricity supply, corresponding to avoided emissions of nearly 72,000 kg/year. This reduction reflects the high renewable fraction exceeding 91% achieved by the optimal configuration, which relies primarily on solar photovoltaic generation supported by battery energy storage and auxiliary grid support rather than fossil fuel-based backup.

The framework’s emphasis on transparency and reproducibility addresses a persistent weakness in hybrid renewable energy system planning. Conventional approaches that rely on single-metric optimization may produce configurations that minimize cost but sacrifice reliability or environmental performance, particularly under the intermittent conditions characteristic of solar and wind resources in remote regions. By systematically evaluating multiple criteria, the proposed framework produces selections that are defensible across economic, technical, and environmental dimensions simultaneously.

The scalability of the decision-support methodology is another notable contribution. While the case study focuses on a remote Indian location with specific resource and load characteristics, the framework itself is designed to be transferable to other remote regions with different resource profiles. The combination of HOMER Pro-based optimization and multi-criteria evaluation can be adapted to candidate systems with varying proportions of solar photovoltaic generation, wind turbines, battery energy storage, and auxiliary grid support, making the methodology relevant for sustainable electrification planning across diverse geographical and institutional contexts.

As global efforts to expand renewable energy access accelerate, frameworks like the one proposed in this study offer a structured pathway for ensuring that hybrid renewable energy systems deployed in remote regions are not only economically viable but also reliable, environmentally beneficial, and robust under uncertainty. The integration of techno-economic optimization with multi-criteria decision-making represents a meaningful advance beyond conventional single-metric approaches, providing planners with a transparent and reproducible tool for selecting optimal hybrid renewable energy configurations in the communities that need them most.

Subject of Research: An integrated decision-based optimization framework for hybrid renewable energy systems in remote regions: a case study of a remote Indian location

Article Title: An integrated decision-based optimization framework for hybrid renewable energy systems in remote regions: a case study of a remote Indian location

Article References: Kumari, D., Sharma, A., & Kaur, G. (2026). An integrated decision-based optimization framework for hybrid renewable energy systems in remote regions: a case study of a remote Indian location. Clean Technologies and Environmental Policy, 28(11), Article 271. https://doi.org/10.1007/s10098-026-03618-1

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03618-1

Keywords: hybrid renewable energy systems, remote electrification, HOMER Pro, multi-criteria decision-making, solar photovoltaic, battery energy storage, India, clean energy policy, integrated, decision-based, optimization, framework