{"id":3568,"date":"2023-03-14T13:26:26","date_gmt":"2023-03-14T13:26:26","guid":{"rendered":"https:\/\/ieeecscai.wpengine.com\/2023\/?page_id=3568"},"modified":"2023-05-18T07:03:43","modified_gmt":"2023-05-18T07:03:43","slug":"nancy-min","status":"publish","type":"page","link":"https:\/\/cai.ieee.org\/2023\/nancy-min\/","title":{"rendered":"Nancy Min"},"content":{"rendered":"\t\t
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Nancy Min
\nCEO, ecoLong\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>

Panelist: 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>Nancy Min<\/strong> is the Founder and CEO of ecoLong, an energy technology company that is committed to building interconnected and resilient communities.\u00a0 She leads the research and development of multiple U.S. DOE funded projects including a blockchain based peer to peer transactive energy platform. She has experience driving technology innovation in start-ups, technology strategy and implementing large-scale enterprise systems in the federal and state government, and corporate finance in large companies. She has testified before the U.S. House Committee on Small Business Subcommittee on Underserved, Agriculture, and Rural Business Development on \u201cPrioritizing Small Underserved and Rural Businesses in the SBIR\/STTR Programs\u201d on ways to foster diversity and inclusion among innovative companies. She is also passionate about giving back to the community and is involved in multiple open-source software technology communities.<\/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":"

Nancy Min CEO, ecoLong June 6, 11:00amLocation: Santa Clara II Panelist: 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 energy systems is expected to increase the grid uncertainties […]<\/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-3568","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/3568","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=3568"}],"version-history":[{"count":0,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/3568\/revisions"}],"wp:attachment":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/media?parent=3568"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}