BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260116T030144Z UID:0927A33E-DCC6-4062-A478-808A8EC07350 DTSTART;TZID=America/Los_Angeles:20260115T170000 DTEND;TZID=America/Los_Angeles:20260115T181500 DESCRIPTION:[]\n\nLarge Language Models (LLMs) are emerging as transformati ve tools for the next generation of power system engineering and operation s. The Electrical AI Copilot concept envisions an intelligent assistant th at leverages LLMs to support grid engineers and operators in real time—e nhancing decision-making\, situational awareness\, and automation. By inte grating domain-specific data\, operational procedures\, and simulation too ls\, an Electrical AI Copilot can interpret technical queries\, generate c ode for grid analysis\, summarize reports\, and even suggest optimal contr ol or contingency actions. It serves as a bridge between human expertise a nd complex system intelligence\, providing natural language interaction fo r tasks such as outage diagnosis\, renewable dispatch planning\, and prote ction coordination. Ultimately\, this LLM-driven assistant aims to improve reliability\, efficiency\, and safety of power systems while enabling a n ew era of human–AI collaboration in the electric grid ecosystem. Finally \, an ETAP electrical copilot demonstration will be presented.\n\nAbout th e Speakers:\n\nAhmed Saber received his Ph.D. from the University of the R yukyus\, Japan\, in 2007. He is currently the Vice President of Optimizati on and AI at ETAP R&D\, USA\, where he contributions to AI-driven methods\ , products\, and systems for power system prediction\, optimization\, effi ciency\, sustainability\, and operator assistance through large language m odels (LLMs). His pioneering research led to a novel deep learning-based m odel that improved load forecasting accuracy\, CO2 estimation\, efficiency \, and operator support for power system optimization and sustainability. Dr. Saber’s research has received national and international funding\, i ncluding support from the U.S. Department of Energy (DoE). With over 100 t echnical publications and three patents on AI applications for power syste ms\, his expertise spans AI/ML for power systems\, smart grids\, energy st orage\, renewables\, power system forecasting and optimization\, cybersecu rity\, real-time systems\, and operations research.\n\n[A person in a suit \n\nAI-generated content may be incorrect.]\n\nTANUJ KHANDELWAL (Senior Me mber\, IEEE) received the bachelor’s degree in electronics and telecommu nications engineering from the University of Bombay\, in 1999\, and the ma ster’s degree in electrical engineering from California State University Long Beach\, in 2001. Before joining ETAP\, he was an Associate Engineer with PricewaterhouseCoopers. He has been working as an Electrical Engineer with the Engineering Consulting Services Department\, ETAP\, since 2001. His duties involve algorithm design\, testing\, engineering and software s upport\, training\, and application engineering for ETAP family of product s. He is a Group Member of the IEEE Std. 739 (Bronze Book) and IEEE Std. 5 51 (Brown Book) and a member of the IEEE Rail Transit Vehicle Interface St andards Committee.\n\nVirtual: https://events.vtools.ieee.org/m/523538 LOCATION:Virtual: https://events.vtools.ieee.org/m/523538 ORGANIZER:srazanaq@uwo.ca SEQUENCE:11 SUMMARY:IEEE PES Lecture: Empowering Power Engineers using LLM and Electric al AI Copilot URL;VALUE=URI:https://events.vtools.ieee.org/m/523538 X-ALT-DESC:Description: <br /><p><br><img style="display: block\; margin-le ft: auto\; margin-right: auto\;" src="https://events.vtools.ieee.org/vtool s_ui/media/display/06e0bce6-c310-46f1-b63a-22d828ed4fda" alt="" width="756 " height="86"></p>\n<p class="MsoNormal"><span style="font-size: 11pt\; fo nt-family: arial\, helvetica\, sans-serif\;">Large Language Models (LLMs) are emerging as transformative tools for the next generation of power syst em engineering and operations. The&nbsp\;<em>Electrical AI Copilot</em> co ncept envisions an intelligent assistant that leverages LLMs to support gr id engineers and operators in real time&mdash\;enhancing decision-making\, situational awareness\, and automation. By integrating domain-specific da ta\, operational procedures\, and simulation tools\, an Electrical AI Copi lot can interpret technical queries\, generate code for grid analysis\, su mmarize reports\, and even suggest optimal control or contingency actions. It serves as a bridge between human expertise and complex system intellig ence\, providing natural language interaction for tasks such as outage dia gnosis\, renewable dispatch planning\, and protection coordination. Ultima tely\, this LLM-driven assistant aims to improve reliability\, efficiency\ , and safety of power systems while enabling a new era of human&ndash\;AI collaboration in the electric grid ecosystem. Finally\, an ETAP electrical copilot demonstration will be presented. &nbsp\;</span></p>\n<p class="x_ MsoNormal">&nbsp\;</p>\n<p><span style="text-decoration: underline\; font- size: 12pt\;"><span style="color: rgb(0\, 0\, 0)\; text-decoration: underl ine\;"><strong>About the Speakers:</strong></span></span></p>\n<p class="x msonormal" style="margin-bottom: 12.0pt\;"><img src="https://events.vtools .ieee.org/vtools_ui/media/display/181ceb3f-dd4e-4693-9c44-617d03e66a15" wi dth="222" height="198"></p>\n<p class="x_MsoNormal"><span data-olk-copy-so urce="MessageBody">Ahmed Saber received his Ph.D. from the University of t he Ryukyus\, Japan\, in 2007. He is currently the Vice President of Optimi zation and AI at ETAP R&amp\;D\, USA\, where he contributions to AI-driven methods\, products\, and systems for power system prediction\, optimizati on\, efficiency\, sustainability\, and operator assistance through large l anguage models (LLMs). His pioneering research led to a novel deep learnin g-based model that improved load forecasting accuracy\, CO2 estimation\, e fficiency\, and operator support for power system optimization and sustain ability. </span>Dr. Saber&rsquo\;s research has received national and inte rnational funding\, including support from the U.S. Department of Energy ( DoE). With over 100 technical publications and three patents on AI applica tions for power systems\, his expertise spans AI/ML for power systems\, sm art grids\, energy storage\, renewables\, power system forecasting and opt imization\, cybersecurity\, real-time systems\, and operations research.</ p>\n<p class="x_MsoNormal"><img src="https://events.vtools.ieee.org/vtools _ui/media/display/205af9f2-1455-41ae-a1f8-9a3afbad4299" alt="A person in a suit\n\nAI-generated content may be incorrect." width="184" height="227"> </p>\n<p class="x_MsoNormal"><span data-olk-copy-source="MessageBody">TANU J KHANDELWAL (Senior Member\, IEEE) received the bachelor&rsquo\;s degree in electronics and telecommunications engineering from the University of B ombay\, in 1999\, and the master&rsquo\;s degree in electrical engineering from California State University Long Beach\, in 2001. Before joining ETA P\, he was an Associate Engineer with PricewaterhouseCoopers. He has been working as an Electrical Engineer with the Engineering Consulting Services Department\, ETAP\, since 2001. His duties involve algorithm design\, tes ting\, engineering and software support\, training\, and application engin eering for ETAP family of products. He is a Group Member of the IEEE Std. 739 (Bronze Book) and IEEE Std. 551 (Brown Book) and a member of the IEEE Rail Transit Vehicle Interface Standards Committee.</span></p> END:VEVENT END:VCALENDAR