BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240329T125210Z UID:449EB72B-8CC5-4253-8D5A-1694117FF950 DTSTART;TZID=America/New_York:20240325T110000 DTEND;TZID=America/New_York:20240325T120000 DESCRIPTION:The stochasticity of power injections from weather-dependent re newable energy resources challenges power system operations and increases the risk-exposure of electricity market participants. Defining safety regi ons for which any operative or market decision must be viable can be an ef fective and tractable tool to overcoming these challenges. Yet\, designing such safety regions from historical data with respect to their impact on the quality of the decision risk is tricky. This seminar discusses two pro posals for data-driven robust decision tools in renewable-dominant electri c power systems that explicitly internalize probabilistic performance metr ics of their solution. First\, we discuss an approach to learn prescriptiv e safety regions for wind power generation. We leverage recent results fro m differentiable programming to create individual safety intervals for eac h wind generator that adapt to current grid situations such that they opti mize an operator-defined risk target. Second\, we discuss a method to inte rnalize adversarial uncertainty sets in electricity market clearing such t hat consumer risk is minimized. We define these sets using a factor stress ing approach inspired from financial engineering and show that consumer ri sk in terms of excess payments can be effectively reduced at a low overall system cost.\n\nSpeaker(s): Robert Mieth\n\nAgenda: \n- Talk by Robert Mi eth at 11:00 am\n- Lunch box after the talk at 12:00 pm\n- You don't have to be an IEEE member to attend this meeting.\n\nRoom: 202\, Bldg: ECE\, Ne w Jersey Institute of Technology\, 141 Warren St\, Newark\, New Jersey\, U nited States\, 07103 LOCATION:Room: 202\, Bldg: ECE\, New Jersey Institute of Technology\, 141 W arren St\, Newark\, New Jersey\, United States\, 07103 ORGANIZER:marcos.netto@njit.edu SEQUENCE:25 SUMMARY:Managing Risk Exposure in Renewable-Dominant Power System Operation s and Electricity Markets URL;VALUE=URI:https://events.vtools.ieee.org/m/411349 X-ALT-DESC:Description: <br /><p>The stochasticity of power injections from weather-dependent renewable energy resources challenges power system oper ations and increases the risk-exposure of electricity market participants. Defining safety regions for which any operative or market decision must b e viable can be an effective and tractable tool to overcoming these challe nges. Yet\, designing such safety regions from historical data with respec t to their impact on the quality of the decision risk is tricky. This semi nar discusses two proposals for data-driven robust decision tools in renew able-dominant electric power systems that explicitly internalize probabili stic performance metrics of their solution. First\, we discuss an approach to learn prescriptive safety regions for wind power generation. We levera ge recent results from differentiable programming to create individual saf ety intervals for each wind generator that adapt to current grid situation s such that they optimize an operator-defined risk target. Second\, we dis cuss a method to internalize adversarial uncertainty sets in electricity m arket clearing such that consumer risk is minimized. We define these sets using a factor stressing approach inspired from financial engineering and show that consumer risk in terms of excess payments can be effectively red uced at a low overall system cost.</p><br /><br />Agenda: <br /><p>- Talk by Robert Mieth at 11:00 am<br>- Lunch box after the talk at 12:00 pm<br>- You don't have to be an IEEE member to attend this meeting.</p> END:VEVENT END:VCALENDAR