BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT DTSTART:20260329T020000 TZOFFSETFROM:+0000 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST END:DAYLIGHT BEGIN:STANDARD DTSTART:20261025T010000 TZOFFSETFROM:+0100 TZOFFSETTO:+0000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260709T105504Z UID:9B9471C4-86FA-4430-AB6A-E8665687C62D DTSTART;TZID=Europe/London:20260714T120000 DTEND;TZID=Europe/London:20260714T130000 DESCRIPTION:[]\n\nEvent style: online\n\nDate and time: 14th July 2026\, 12 -1pm\n\nAgenda:\n\n12:00 – 12:05: Welcome. Intro of IEEE PES WiP + intro of CIGRE UK NGN\n\n12:05 – 12:20: Speaker 1 (15 min)\n\n12:20 – 12:35 : Speaker 2 (15 min)\n\n12:35 – 12:50: Speaker 3 (15 min)\n\n12:50 – 1 3:00 Q&A + discussion\n\nAbstract:\n\nArtificial intelligence is set to pl ay a transformative role in modern energy systems\, offering significant b enefits in efficiency\, reliability\, and the integration of renewable res ources. From enhanced forecasting to intelligent grid management\, AI has the potential to reshape how power systems are planned and operated.\n\nHo wever\, realizing these benefits requires more than just advanced algorith ms. Integrating AI into energy systems demands robust data infrastructure\ , domain expertise\, and alignment with existing operational frameworks. A t the same time\, challenges such as data limitations\, system complexity\ , regulatory constraints\, and trust in AI-driven decisions continue to sh ape its adoption.\n\nThe event on Powering the Future with AI: Energy Syst ems & Smart Grids brings together experts to explore these opportunities\, requirements\, and current constraints\, offering practical insights into the path toward more intelligent and resilient energy systems.\n\n1. Spea ker 1: Dr. Da Huo\n\nShort Bio: Dr. Da Huo is a Senior Lecturer in Electri cal Engineering at Queen Mary University of London. His research innovates on model-based optimisation and machine learning methods to optimise the operations and planning of flexible resources\, power transmission/distrib ution systems\, microgrids and whole systems to enhance network security\, cost-effectiveness\, and reliability. To date\, Huo has been successful w ith 7 research grant applications\, and co-authored over 45 peer-reviewed research papers.\n\nTalk title: AI in Power Systems: an overview and case study\n\nShort abstract: This talk provides an overview of the application of AI in power systems\, highlighting the progression from conventional m odel-based optimisation to learning-based methods. It will also present a case study project focusing on the use of AI for forecasting the energy fl exibility of consumers\, which enables demand flexibility providers to bet ter estimate domestic flexibility and tailor flexibility provision.\n\n2. Speaker 2: Amber Elinsky\n\nShort Bio: Amber is a project manager at IONAT E with a background in general physics and religious studies. Derived from her academic years\, Amber specialises in cross-discipline integration\, delivering projects that develop power electronics and intelligent control algorithms into a single\, hybrid transformer device for the power grid.\ n\nTalk Title: Transforming Electrical Networks with AI-Powered Hybrid Int elligent Transformers\n\nAbstract: As the traditional power grid infrastru cture ages out and new constraints are introduced into the electrical netw orks\, it is clear that a fundamental shift in the way the power grids are supported and maintained is required. A modern society requires modern so lutions.\nThis presentation will introduce a use case where AI is a tool t hat can provide long term visibility of the electrical grid health and beh aviour. Hybrid Intelligent Transformers (HITs) integrate hardware and soft ware to gather real-time network data from which AI models can provide cla rity on load forecasting\, managing constraints\, and unlock additional ne twork capacity. IONATE's HITs provide data in unmatched granularity and de pths. AI is used as a tool to understand this data and improve grid operat ions in areas that have not been considered before.\n\n3. Speaker 3: Anna Carolina Tortora\n\n[]\nShort Bio: Head of Innovation\, Digitalisation & A I at NESO\n\nTitle title: Using AI to keep the lights on today and designi ng tomorrow’s energy system\n\nAbstract: As GB's energy system becomes m ore decentralised\, weather dependent and complex\, balancing supply and d emand is becoming one of the sector’s greatest challenges. In this talk\ , Carolina will explore how NESO is using AI to help maximise the use of r enewable energy\, support a smarter and more resilient grid\, and reduce G B’s reliance on imported energy. She will also reflect on the risks of u sing AI in such a critical system\, and how NESO is approaching them respo nsibly in a fast-changing environment\, as well as the importance of havin g access to reliable\, usable and trustworthy data.\n\nCo-sponsored by: CI GRE UK Next Generation Network\n\nVirtual: https://events.vtools.ieee.org/ m/565277 LOCATION:Virtual: https://events.vtools.ieee.org/m/565277 ORGANIZER:zixuanjia548@gmail.com SEQUENCE:22 SUMMARY:Powering the Future with AI: Energy Systems & Smart Grids URL;VALUE=URI:https://events.vtools.ieee.org/m/565277 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-left: 18.0 pt\; text-indent: -18.0pt\; mso-list: l0 level1 lfo1\; tab-stops: list 18. 0pt\;">&nbsp\;</p>\n<p class="MsoNormal" style="margin-left: 18.0pt\; text -indent: -18.0pt\; mso-list: l0 level1 lfo1\; tab-stops: list 18.0pt\;"><s trong><img src="https://events.vtools.ieee.org/vtools_ui/media/display/f4f 38ace-8974-441b-9ea2-e003eb2e262b" alt="" width="900" height="506"><br></s trong></p>\n<p class="MsoNormal" style="margin-left: 18.0pt\; text-indent: -18.0pt\; mso-list: l0 level1 lfo1\; tab-stops: list 18.0pt\;"><strong>Ev ent style</strong>: online&nbsp\;</p>\n<p class="MsoNormal" style="margin- left: 18.0pt\; text-indent: -18.0pt\; mso-list: l0 level1 lfo1\; tab-stops : list 18.0pt\;"><!-- [if !supportLists]--><strong>Date and time</strong>: 14th July 2026\, 12-1pm</p>\n<p class="MsoNormal" style="margin-left: 18. 0pt\; text-indent: -18.0pt\; mso-list: l0 level1 lfo1\; tab-stops: list 36 .0pt\;"><!-- [if !supportLists]--><strong>Agenda</strong>:</p>\n<p class=" MsoNormal">12:00 &ndash\; 12:05: Welcome. &nbsp\;Intro&nbsp\;of IEEE PES W iP &nbsp\;+ intro of CIGRE UK NGN&nbsp\;</p>\n<p class="MsoNormal">12:05 & ndash\; 12:20: Speaker 1 (15 min)</p>\n<p class="MsoNormal">12:20 &ndash\; 12:35: Speaker 2 (15 min)</p>\n<p class="MsoNormal">12:35 &ndash\; 12:50: Speaker 3 (15 min)</p>\n<p class="MsoNormal">12:50 &ndash\; 13:00 Q&amp\; A + discussion&nbsp\;</p>\n<p class="MsoNormal" style="margin-left: 18.0pt \; text-indent: -18.0pt\; mso-list: l1 level1 lfo2\; tab-stops: list 18.0p t\;"><!-- [if !supportLists]--><strong>Abstract</strong>:</p>\n<p class="M soNormal" style="text-align: justify\;">Artificial intelligence is set to play a transformative role in modern energy systems\, offering significant benefits in efficiency\, reliability\, and the integration of renewable r esources. From enhanced forecasting to intelligent grid management\,&nbsp\ ;AI&nbsp\;has the potential to reshape how power systems are planned and o perated.</p>\n<p class="MsoNormal" style="text-align: justify\;">However\, realizing these benefits requires more than just advanced algorithms. Int egrating&nbsp\;AI&nbsp\;into energy systems demands robust data infrastruc ture\, domain expertise\, and alignment with existing operational framewor ks. At the same time\, challenges such as data limitations\, system comple xity\, regulatory constraints\, and trust in&nbsp\;AI-driven decisions con tinue to shape its adoption.</p>\n<p class="MsoNormal" style="text-align: justify\;">The event on Powering the Future with AI: Energy Systems &amp\; Smart Grids brings together experts to explore these opportunities\, requ irements\, and current constraints\, offering practical insights into the path toward more intelligent and resilient energy systems.</p>\n<p>&nbsp\; </p>\n<p class="MsoListParagraph" style="margin-left: 18.0pt\; mso-add-spa ce: auto\; text-indent: -18.0pt\; mso-list: l0 level1 lfo1\;"><!-- [if !su pportLists]--><strong><span style="mso-bidi-font-family: Aptos\; mso-bidi- theme-font: minor-latin\;"><span style="mso-list: Ignore\;">1.<span style= "font: 7.0pt 'Times New Roman'\;">&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; </sp an></span></span></strong><!--[endif]--><strong>Speaker 1: Dr. Da Huo</str ong></p>\n<p class="MsoListParagraph" style="margin-left: 18.0pt\; mso-add -space: auto\; text-indent: -18.0pt\; mso-list: l0 level1 lfo1\;"><strong> <img src="https://events.vtools.ieee.org/vtools_ui/media/display/ca643097- f090-4fcb-b2b2-18a88b51d5ba" width="199" height="134"></strong></p>\n<p st yle="text-align: justify\;"><strong>Short Bio</strong>: Dr. Da Huo is a Se nior Lecturer in Electrical Engineering at Queen Mary University of London . His research innovates on model-based optimisation and machine learning methods to optimise the operations and planning of flexible resources\, po wer transmission/distribution systems\, microgrids and whole systems to en hance network security\, cost-effectiveness\, and reliability. To date\, H uo has been successful with 7 research grant applications\, and co-authore d over 45 peer-reviewed research papers.</p>\n<p style="text-align: justif y\;"><strong>Talk title</strong>: AI in Power Systems: an overview and cas e study</p>\n<p style="text-align: justify\;"><strong>Short abstract</stro ng>: This talk provides an overview of the application of AI in power syst ems\, highlighting the progression from conventional model-based optimisat ion to learning-based methods. It will also present a case study project f ocusing on the use of AI for forecasting the energy flexibility of consume rs\, which enables demand flexibility providers to better estimate domesti c flexibility and tailor flexibility provision.</p>\n<p style="text-align: justify\;"><strong>2. &nbsp\; &nbsp\;Speaker 2: Amber Elinsky</strong><br >&nbsp\;<img src="https://events.vtools.ieee.org/vtools_ui/media/display/c 678f605-659f-4ce4-ac66-0a39970f77db" width="199" height="112"></p>\n<p cla ss="MsoNormal"><strong>Short Bio: </strong>Amber is a project manager at I ONATE with a background in general physics and religious studies. Derived from her academic years\, Amber specialises in cross-discipline integratio n\, delivering projects that develop power electronics and intelligent con trol algorithms into a single\, hybrid transformer device for the power gr id.<strong> </strong></p>\n<p style="text-align: justify\;"><strong>Talk T itle: </strong>Transforming Electrical Networks with AI-Powered Hybrid Int elligent Transformers</p>\n<p style="text-align: justify\;"><strong>Abstra ct:&nbsp\;</strong>As the traditional power grid infrastructure ages out a nd new constraints are introduced into the electrical networks\, it is cle ar that a fundamental shift in the way the power grids are supported and m aintained is required. A modern society requires modern solutions.<br>This presentation will introduce a use case where AI is a tool that can provid e long term visibility of the electrical grid health and behaviour. Hybrid Intelligent Transformers (HITs) integrate hardware and software to gather real-time network data from which AI models can provide clarity on load f orecasting\, managing constraints\, and unlock additional network capacity . IONATE's HITs provide data in unmatched granularity and depths. AI is us ed as a tool to understand this data and improve grid operations in areas that have not been considered before.&nbsp\;</p>\n<p style="text-align: ju stify\;"><strong>3. &nbsp\; &nbsp\;Speaker 3: Anna Carolina Tortora</stron g></p>\n<p style="text-align: justify\;"><img src="https://events.vtools.i eee.org/vtools_ui/media/display/97738cf3-9728-4e72-b78e-d9aea4e8cd4d" alt= "" width="200" height="197"><br><strong>Short Bio: </strong>Head of Innova tion\, Digitalisation &amp\; AI at NESO</p>\n<p class="MsoNormal" style="l ine-height: normal\;"><strong>Title title</strong>: Using AI to keep the l ights on today and designing tomorrow&rsquo\;s energy system</p>\n<p class ="MsoNormal" style="line-height: normal\;"><strong>Abstract:&nbsp\;</stron g>As GB's energy system becomes more decentralised\, weather dependent and complex\, balancing supply and demand is becoming one of the sector&rsquo \;s greatest challenges. In this talk\, Carolina will explore how NESO is using AI to help maximise the use of renewable energy\, support a smarter and more resilient grid\, and reduce GB&rsquo\;s reliance on imported ener gy. She will also reflect on the risks of using AI in such a critical syst em\, and how NESO is approaching them responsibly in a fast-changing envir onment\, as well as the importance of having access to reliable\, usable a nd trustworthy data.</p> END:VEVENT END:VCALENDAR