CAREER: Cyber Physical Solution for High Penetration Renewables in Smart Grid

职业：智能电网中高渗透可再生能源的网络物理解决方案

• 批准号: 1553494
• 负责人: Arif Sarwat
• 金额: $ 43.7万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553494&HistoricalAwards=false
• 关键词: CAREER; Cyber; Physical; Solution; Penetration

Effective integration of large amounts of renewable energy into the grid is of utmost importance for sustainable future and greener smart cities. Due to the unpredictable variations in weather, over 80% of the available renewable energy from solar and wind sources cannot be harnessed effectively. Large scale and cost-effective integration of photovoltaic energy into the smart grid is challenging due to: (a) unpredictability and intermittency of weather pattern, (b) fast morning ramp up and afternoon ramp down of solar generation that triggers instabilities in the grid, (c) unavailability of solar generation at sun down requiring the need for locational energy storage facilities, and (d) lack of technologies for efficient and intelligent on-demand sharing of solar generation with conventional power generation in the grid. Current technologies of solar integration are based on unreliable weather prediction and ineffective load sharing that make the overall grid performance unreliable and inefficient, thus necessitating the need for a broader outlook of the whole picture. This research brings a holistic vision of the future smart grid as a synergistic integration of its various components with novel computational tools for forecasting and intelligent load sharing with distributed energy storage. The study collects real-time Photovoltaic (PV) data from the plant, conducts high-end modeling, analysis and visualization on various datasets to understand, predict and mitigate the system instabilities and fluctuations triggered by PV intermittencies. This solution can be used in the planning process at the command and control centers for electric utilities.The developed approach, which is an adaptive, resilient, efficient and effective integration of renewables, will be applicable broadly in the energy sector thereby reducing carbon footprint and making the system stable under expected high penetration of renewable sources and unanticipated intermittencies. This solution fills the gap that will help our nation steer closer to the ultimate goal of a sustainable future involving a smart clean power grid. This project will pursue several outreach activities to engage with students from underrepresented groups.

将大量可再生能源有效整合到电网中对于可持续的未来和更绿色的智能城市至关重要。 由于天气的不可预测变化，超过80%的太阳能和风能可再生能源无法有效利用。 将光伏能源大规模且经济高效地整合到智能电网中具有挑战性，原因在于：（a）天气模式的不可预测性和不稳定性，（B）太阳能发电的快速上午斜升和下午斜降，其触发电网中的不稳定性，（c）太阳能发电在日落时不可用，需要位置能量存储设施，以及（d）缺乏在电网中将太阳能发电与传统发电按需高效和智能地共享的技术。目前的太阳能集成技术基于不可靠的天气预测和无效的负载共享，这使得整体电网性能不可靠且效率低下，因此需要对整体情况进行更广泛的展望。 这项研究带来了未来智能电网的整体愿景，将其各种组件与新型计算工具协同集成，用于预测和智能负载共享与分布式储能。该研究从工厂收集实时光伏（PV）数据，对各种数据集进行高端建模，分析和可视化，以了解，预测和减轻光伏发电引发的系统不稳定性和波动。 该解决方案可用于电力公司指挥和控制中心的规划过程中。所开发的方法是一种自适应、弹性、高效和有效的可再生能源集成，将广泛适用于能源行业，从而减少碳足迹，并使系统在可再生能源的预期高渗透率和不可预见的污染下保持稳定。这一解决方案填补了差距，将帮助我们的国家更接近涉及智能清洁电网的可持续未来的最终目标。该项目将开展若干外联活动，与代表性不足群体的学生接触。