{"id":3499,"date":"2023-03-10T13:34:58","date_gmt":"2023-03-10T13:34:58","guid":{"rendered":"https:\/\/ieeecscai.wpengine.com\/2023\/?page_id=3499"},"modified":"2023-05-18T07:03:37","modified_gmt":"2023-05-18T07:03:37","slug":"chilukuri-mohan","status":"publish","type":"page","link":"https:\/\/cai.ieee.org\/2023\/chilukuri-mohan\/","title":{"rendered":"Chilukuri Mohan"},"content":{"rendered":"\t\t
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Chilukuri Mohan
\nProfessor, Dept of Electrical Engineering & Computer Science
\nSyracuse University\n<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\"\"<\/p>

June 6, 11:00am
Location: Santa Clara II<\/h5>

Panel Moderator: Power Grid Operations and Planning Under Uncertainty: How Can AI Help Address Existing Challenges?<\/a><\/span><\/h4>

Power grid uncertainties have increased dramatically in recent years. The rapid integration of intermittent energy resources such as wind and solar energy systems is expected to increase the grid uncertainties further. These uncertainties challenge the everyday grid operation and planning if not correctly modeled and quantified. In recent years, there has been significant research and development in data-driven modeling of distributed renewable energy resources. Powered by recent advancements in artificial intelligence and machine learning, these data-driven models are especially robust to handle the dynamic nature of intermittent energy resources. In addition to generation uncertainties, extreme weather patterns have led to an increase in failure uncertainties. Hence, modeling and predicting failures in near real-time is more critical than ever in preventing widespread blackouts. However, traditional statistical techniques fail to predict future events under these interdependent uncertainties. As a result, there has been significant interest in recent years to develop more accurate data-driven failure models that are also efficient and fast enough for near real-time decision-making. This panel will discuss various AI-centric research and development efforts to address the existing challenges of power grid uncertainties. These efforts include generation forecast and modeling, cascading failure prediction, and power grid operations and planning under uncertainty. The panelists will discuss how AI could complement traditional power grid analysis techniques to address the existing problems and where AI techniques are limited in addressing those challenges. The panelists will also discuss their outreach and industry collaboration efforts.<\/p>

\"\"<\/a>Dr. Chilukuri Mohan<\/strong> has a PhD in Computer Science from SUNY Stony Brook, and is a Professor at Syracuse University, where he has previously served as Electrical Engineering and Computer Science Department Chair and Interim Dean of the College of Engineering.\u00a0 He has published extensively in AI, including three books (on Neural Networks, Expert Systems, and Anomaly Detection), and his current research areas include evolutionary algorithms, machine learning, bioinformatics, and network science.\u00a0\u00a0 He had led a Smart Grid work-force development initiative, leading to revitalized power systems curricula in six academic institutions in central and western New York. He serves on three journal editorial boards, and he received the 2019 IEEE Region 1 Technological Innovation (Academic) Award for the development of novel algorithms in computational intelligence.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Chilukuri Mohan Professor, Dept of Electrical Engineering & Computer Science Syracuse University June 6, 11:00amLocation: Santa Clara II Panel Moderator: Power Grid Operations and Planning Under Uncertainty: How Can AI Help Address Existing Challenges? Power grid uncertainties have increased dramatically in recent years. The rapid integration of intermittent energy resources such as wind and solar […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"class_list":["post-3499","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/3499","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/comments?post=3499"}],"version-history":[{"count":0,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/3499\/revisions"}],"wp:attachment":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/media?parent=3499"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}