CNIC: U.S.-Netherlands Planning Visit for Cooperative Research on Intelligent Methods Under Uncertainty for Renewable Energy Driven Smart Grids

这项新的、催化性的美国-荷兰研究合作致力于可再生能源驱动的智能电网。可再生能源包括定期补充的资源，如阳光、风、雨、潮汐和地热。为了寻求创新方法来管理可再生能源的不确定性，美国首席研究员（PI）和一名研究生将访问荷兰，与欧洲智能能源研究的领导者代尔夫特理工大学（Delft University of Technology）的同行开展合作。在那里，他们打算共同努力，改进目前的智能电网技术，以便在面对不确定的发电情况时更好地预测消费者需求，这在可再生能源系统中经常出现。如果成功，他们的初步结果将有助于改善电网运营商和消费者之间的双向沟通。早期的结果和后续的研究可能会产生更广泛的影响，通过新的方法来塑造管理策略，以模拟消费者的能源使用。通过采用新的人工智能方法（AI），即对电网的智能控制，成功可能意味着更好的长期预测。该团队希望确定可再生能源发电的不确定性所带来的挑战，并开始在两个优先领域研究应对这些挑战的智能方法：(a)规划分散的发电和存储，以及(b)管理由于可再生能源供应和消费者需求之间的异步而导致的电网拥堵。PI将与荷兰在人工智能、电力系统和技术政策方面经验丰富的知名研究人员团队合作。他们将获得来自荷兰中压电网的真实运行和能源使用数据，并打算开始开发可扩展的算法，用于在多代理设置中进行个人决策。此外，预计此次合作将产生更广泛的影响，将智能能源系统引入佐治亚大学的研究和教学中，从而有助于培养美国本科生和研究生在具有工业相关性的创新和快速增长的能源部门。

This new, catalytic U.S.-Netherlands research collaboration addresses renewable energy-driven smart grids. Renewable energy sources include resources that are regularly replenished, such as sunlight, wind, rain, tidal waves, and geothermal heat. To pursue innovative approaches for managing the uncertainty of renewable energy sources, the U.S. principal investigator (PI) and a graduate student will visit the Netherlands to begin a collaboration with counterparts at the Delft University of Technology, a leader in European smart energy research. There they intend to work together to improve current smart grid technology for better prediction of consumer demand in the face of uncertain power generation, as is often the case in renewable energy systems. If successful, their preliminary results should contribute to improving bidirectional communication between grid operators and consumers. Early results and follow-on research may have broader impact by shaping management strategies through new approaches to modeling consumer energy usage. Success could mean better long-term prediction by employing new artificial intelligence approaches (AI), i.e., smart controls for power grids.The team expects to identify the challenges posed by the uncertainty of renewable energy generation and begin investigating intelligent methods for meeting these challenges in two priority areas: (a) planning for decentralized power generation and storage, and (b) managing congestion in grids due to asynchrony between renewable energy supply and consumer demand. The PI will work with an experienced team of eminent Dutch researchers in AI, power systems, and technology policy. They will have real operating and energy-use data from a medium voltage grid in Netherlands and intend to start developing scalable algorithms for individual decision making in multi-agent settings. Further, broader impacts are anticipated from this collaboration with an introduction of smart energy systems into research and teaching at the University of Georgia, thereby contributing to training U.S. undergraduate and graduate students in an innovative and rapidly growing energy sector with industrial relevance.