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

这个新的，催化剂美国-荷兰研究合作解决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，潮汐波和地热。 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导者德尔夫特理工大学的同行合作。 在那里，他们打算共同努力，改进当前的智能电网技术，以便在发电量不确定的情况下更好地预测消费者需求，这在可再生能源系统中经常发生。 如果成功的话，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。 早期结果和后续研究可能会产生更广泛的影响，通过新的方法来塑造管理战略，以模拟消费者的能源使用。 成功可能意味着通过采用新的人工智能方法（AI），即，电网智能控制。该团队预计将确定可再生能源发电的不确定性所带来的挑战，并开始研究智能方法，以应对两个优先领域的挑战：（a）规划分散式发电和存储，以及（B）管理由于可再生能源供应和消费者需求之间的不确定性而导致的电网拥塞。 PI将与经验丰富的荷兰知名研究人员团队在人工智能，电力系统和技术政策方面合作。 他们将拥有来自荷兰中压电网的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，用于多智能体环境中的个人决策。 此外，更广泛的影响，预计从这种合作与智能能源系统引入研究和教学在格鲁吉亚大学，从而有助于培训美国本科生和研究生在一个创新和快速增长的能源部门与工业相关性。

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.