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

这一新的、具有催化作用的美国-荷兰研究合作针对的是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。为了寻求管理可再生能源不确定性的创新方法，美国首席研究员(PI)和一名研究生将访问荷兰，开始与欧洲智能能源研究领先者代尔夫特科技大学的同行合作。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时更好地预测消费者需求，就像可再生能源系统中经常出现的情况一样。如果成功，他们的初步成果应该有助于改善电网运营商和用户之间的双向沟通。早期结果和后续研究可能会产生更广泛的影响，通过对消费者能源使用进行建模的新方法来形成管理战略。通过采用新的人工智能方法(AI)，即电网的智能控制，成功可能意味着更好的长期预测。该团队预计将确定可再生能源发电的不确定性带来的挑战，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不同步而导致的电网拥堵。PI将与人工智能、电力系统和技术政策方面的著名荷兰研究人员组成的经验丰富的团队合作。他们将从荷兰的一个中压电网获得真实的运行和能源使用数据，并打算开始开发可扩展的算法，用于多主体环境下的个人决策。此外，随着将智能能源系统引入佐治亚大学的研究和教学，预计这一合作将产生更广泛的影响，从而有助于在与工业相关的创新和快速增长的能源行业培训美国本科生和研究生。

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.