Investment in renewable technologies will need to rapidly increase in order to meet the world's future energy supply while reducing the associated greenhouse gas emissions. The Committee on Climate Change estimates that the installed renewable energy (RE) capacity needs to be quadrupled to achieve net zero. However, renewable energy projects are capital intensive and the costs and returns depend on a large number of factors such as location and renewable resource. A major barrier to successful interconnection of renewable energy (solar/wind/storage) projects is the lack of information on the grid network/conditions and unpredictability of capital and operational costs. This means investors must estimate the connection cost of projects which is risky and inaccurate and leads to major investment uncertainty. To address this challenge, Enian, in collaboration with academics from the University of Edinburgh, have developed a RE Deal Management and Collaboration Platform which helps streamline project qualification and uses proprietary algorithms to predict RE project costs (LCOE, annual energy output, technical, economic metrics). Although proving useful, to meet expressed industry demand and overcome major investment barriers, it is critical that that the technology is advanced. The proposed project will build on this early achievement to develop the capability to enable power grid data to be digitally captured, calculated and visualised to produce cost prediction models for single interconnection points/integrated networks using machine-enhanced automated processes, thus providing the first data-driven RE analytics platform that enables operational costs of grid-connected solar PV, wind and storage to be rapidly and accurately determined, offering a unique scalable solution for improved and de-risked RE planning and investment. Early feasibility has been investigated, this project advancing the concept to TRL5\. Impacts include improved, de-risked, and accelerated decision making leading to increased investments (~30% more RE projects supported); valuable time and cost savings in project due diligence (20 weeks, £500k per year per company); \>1M tonnes CO2e saved over 5 years due to more RE projects gaining investment. Wider applicability to other power (waste-to-energy, hydro), commercial property/land, waste management/recycling, electrified transport (EV charging networks). The project will deliver significant export led growth for lead applicant Enian, a substantial ROI, increased employment and further opportunity for R&D investment. Project partner the University of Edinburgh will gain crucial commercial knowledge to be applied to future R&D.

"To meet future energy supply and Paris Agreement targets, the Renewable Energy share must double by 2030, requiring annual average investment of over $900 billion, considerably more than currently achieved ($286 billion in 2015). Analysis of 2016 IRENA and BNEF reports and extensive market consultation with renewable energy project developers, investors, support networks and leading renewable energy sector experts, has identified that one of the main challenges constraining investment in the RE sector is the ability to accurately calculate the return on investment of projects and thus determine a project's value as an investment proposition using data in a cost effective, time efficient way. The lack of sophisticated, data-driven tools to determine the 'bankability' of projects cost-effectively and time-efficiently, leaves renewable energy market uncertainties unaddressed, resulting in a lack of investment and a loss of opportunity for renewable energy project developers and services providers throughout the value chain. The proposed project seeks to develop a data-driven assessment tool, 'Renewable Energy Performance Score (REPSCORE)' which Enian Limited can use to more accurately, efficiently and cost effectively pre-qualify Renewable Energy projects for users (renewable energy investment and development teams operating globally) of their digital Deal Management and Collaboration Platform (DMCP). REPSCORE uses predictive algorithms to rapidly and accurately determine the economic and technological performance of projects to more efficiently and cost effectively pre-qualify projects on Enian's DMCP, radically enhancing decision-making for investors and accelerating the overall pace of capital deployment into renewable energy projects. Enian in collaboration with mathematicians from the University of Edinburgh (UoE), have developed a basic MVP (TRL3) model. This project will assess the feasibility of transforming the MVP into an automated web-based, data driven application (TRL5) to assess both operational and non-operational projects, and the feasibility of integration with Enian's DMCP. The algorithmic based automated means of renewable energy project qualification will reduce costs of data analysis; accelerate project assessment process, and improve decision making by reducing and quantifying uncertainty as well as reducing human error and bias =\> boosting private investment in renewable energy projects by ~20%. The project will deliver significant export led growth for lead applicant Enian, a substantial ROI, increased employment and further opportunity for R&D investment. Project partner UoE will gain crucial commercial knowledge to be applied to future R&D."