Disruptive platform to accelerate renewable energy with machine learning and grid data analytics

需要迅速增加对可再生技术的投资，以满足世界未来的能源供应，同时减少相关的温室气体排放。气候变化委员会估计，要实现净零排放，可再生能源装机容量需要翻两番。然而，可再生能源项目是资本密集型项目，成本和回报取决于地点和可再生资源等大量因素。可再生能源(太阳能/风能/储存)项目成功互联的一个主要障碍是缺乏有关电网/条件的信息，以及资本和运营成本的不可预测性。这意味着投资者必须估计项目的连接成本，这是有风险和不准确的，并导致重大投资不确定性。为了应对这一挑战，埃尼安与爱丁堡大学的学者合作开发了一个可再生能源交易管理和协作平台，该平台有助于简化项目资格，并使用专有算法来预测可再生能源项目成本(LCOE、年度能源产量、技术、经济指标)。尽管事实证明是有用的，但为了满足表达的行业需求和克服主要投资障碍，这项技术的先进性至关重要。拟议的项目将以这一早期成果为基础，开发能够以数字方式捕获、计算和显示电网数据的能力，以利用机器增强的自动化流程为单个互联点/综合网络生成成本预测模型，从而提供首个数据驱动的可再生能源分析平台，使并网太阳能光伏、风能和存储的运营成本能够快速准确地确定，并为改善和降低可再生能源规划和投资提供独特的可扩展解决方案。已对早期的可行性进行了调查，该项目将这一概念提出到TRL5中。影响包括改进、降低风险和加快决策，从而增加投资(支持的可再生能源项目增加约30%)；在项目尽职调查中节省宝贵的时间和成本(20周，每个公司每年50万GB)；由于更多可再生能源项目获得投资，在5年内节省了100万吨二氧化碳。更广泛地适用于其他电力(废物转化为能源、水电)、商业地产/土地、废物管理/回收、电气化交通(电动汽车充电网络)。该项目将为主要申请者埃尼安带来显著的出口拉动型增长，大幅提高投资回报率，增加就业机会，并提供更多研发投资机会。项目合作伙伴爱丁堡大学将获得关键的商业知识，用于未来的研发。

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.