A decision support system to assess the feasibility of onshore renewable energy infrastructure

Beriro, Darren; Nathanail, Judith; Salazar, Juan; Kingdon, Andrew; Marchant, Andrew; Richardson, Steve; Gillet, Andy; Rautenberg, Svea; Hammond, Ellis; Beardmore, John; Moore, Terry; Angus, Phil; Waldron, Julie; Rodrigues, Lucelia; Nathanail, Paul

Abstract

This article introduces a new web-based decision support system created for early-stage feasibility assessments of renewable energy technologies in England, UK. The article includes a review of energy policy and regulation in England and a critical evaluation of literature on similar decision support systems. Overall, it shows a novel solution for a repeatable, scalable digital evidence base for the policy compliant deployment of renewable energy technologies. Data4Sustain is a spatial decision support system developed to quickly identify the feasibility of seven renewable energy technologies across large areas including wind, solar, hydro, shallow and geothermal. A multi-actor approach was used to identify the key factors that influence the technical feasibility of these technologies to generate electricity or heat for local consumption or regional distribution. The research demonstrates opportunities to improve the links between policy and regulation with deployment of renewable energy technologies using novel approaches to digital planning. Deployed, resilient, cost-effective and societally accepted renewable energy generation infrastructure has a role to play in ensuring universal access to affordable, reliableand modern energy supply. This is central to supporting a concerted transition to a low-carbon future in order to address climate change. The selection and siting of renewable energy technology is driven by natural resource availability and physical and regulatory constraints. These factors inform early-stage feasibility of renewables, helping to focus investment of time and money. Understanding their relative importance and identifying the most suitable technologies is a highly complex task due to the disparate and often unconnected sources of data and information needed. Data4Sustain help to overcome these challenges.

Citation

Beriro, D., Nathanail, J., Salazar, J., Kingdon, A., Marchant, A., Richardson, S., Gillet, A., Rautenberg, S., Hammond, E., Beardmore, J., Moore, T., Angus, P., Waldron, J., Rodrigues, L., & Nathanail, P. (2022). A decision support system to assess the feasibility of onshore renewable energy infrastructure. Renewable and Sustainable Energy Reviews, 168, Article 112771. https://doi.org/10.1016/j.rser.2022.112771

• Journal Article Type: Article
• Acceptance Date: Jul 4, 2022
• Online Publication Date: Jul 16, 2022
• Publication Date: 2022-10
• Deposit Date: Aug 25, 2022
• Publicly Available Date: Aug 25, 2022
• Journal: Renewable and Sustainable Energy Reviews
• Print ISSN: 1364-0321
• Electronic ISSN: 1879-0690
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 168
• Article Number: 112771
• DOI: https://doi.org/10.1016/j.rser.2022.112771
• Keywords: Renewable Energy, Sustainability and the Environment
• Public URL: https://nottingham-repository.worktribe.com/output/9410765
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1364032122006554?via%3Dihub