Rahman Khorramfar - EIMC2 Seminars

This page contains the past and upcoming seminars organized by the Energy & Infrastructure Systems: Modeling, Computing, and Control (EIMC2) group at the Laboratory for Information and Decision Systems (LIDS), MIT.

These seminars provide a platform for researchers and practitioners whose work broadly fits within the scope of EIMC2 to disseminate their ideas, receive feedback, and connect with colleagues. The presentation format is in-person, but a Zoom link is available to join virtually. We record presentations with the speakers' permission and archive them at 'EIMC2 Seminars' channel on YouTube.

Please reach out to the seminar co-organizers, Rahman Khorramfar (khorram@mit.edu) or Audun Botterud (audunb@mit.edu), for questions, comments, or speaker suggestions.

Upcoming Seminars

Past Seminars (reverse chronological order)

32 - Dan Kell: Advancing Grid Development with Modern Grid Technologies: Unlocking Transmission Capacity Through the Right Planning and Study Tools

Date and location: May 12 (Tuesday), 11am at 45-600B

Speaker: Dan Kell, BBA Engineering

Abstract: As power systems transition to support electrification, decarbonisation, and increasing electricity demand, the ability to fully utilise and expand existing transmission infrastructure has become critical. Advanced grid technologies—such as High Voltage Direct Current (HVDC), Flexible AC Transmission Systems (FACTS), and Inverter-Based Resources (IBR)—offer powerful opportunities to unlock additional transmission capacity, improve controllability, and enhance system performance. However, the benefits of these technologies cannot be realised through traditional planning approaches alone. Their successful integration depends on the use of appropriate analysis tools, models, and study methodologies that accurately capture fast dynamics, control interaction, and system-wide impacts. This presentation explores how modern grid technologies can be deployed to advance grid development while highlighting the essential role of advanced planning, stability, and EMT-based studies in enabling confident, timely, and cost-effective decisions.

Speaker Bio: Mr. Kell is a professional engineer with extensive experience in utility and international consultancy, specializing in HVDC/FACTS technologies and renewable energy integration. He has contributed to over 50 GW of HVDC projects, supporting all phases from feasibility studies and technical specifications to tender evaluations, commissioning, and equipment upgrade assessments. He has supported a number of clients' long-term transmission plans and how to integrate new facilities into the grid.

His expertise includes advanced power system simulation using PSCAD and RTDS, enabling detailed electromagnetic transient modeling for system failure analysis, islanded-grid studies, and the integration of renewables, large loads, and FACTS devices. Mr. Kell chairs the NERC Project 2022-04 EMT Modeling initiative, which is shaping how electromagnetic transient studies are incorporated into planning standards. He completed his BSc and MSc at the University of Manitoba.

31 - Qianwen Xu: AI-Driven Stability and Control of Converter-Dominated Energy Systems (Slides)

Date and location: April 30(Thursday), 11am at 45-600B

Speaker: Qianwen Xu, KTH Royal Institute of Technology

Abstract: Modern power systems are rapidly evolving into converter-dominated energy systems driven by distributed energy resources, electrified transport, and emerging large-scale loads such as data centers. This transformation fundamentally reshapes the nature of grid stability and operation: stability is no longer governed by physical inertia, but by fast, complex converter interactions, and system operation must cope with increasing uncertainty and scale.

This talk presents how artificial intelligence can enable reliable and scalable operation of such systems across three layers. First, transfer learning-based approaches are developed for fast, online modeling of converter-interfaced assets, enabling accurate system representation without detailed device-level knowledge. Second, AI-driven stability assessment frameworks are developed to allow real-time evaluation of converter–grid interactions. Third, safe deep reinforcement learning methods are presented for large-scale grid operation, ensuring guaranteed safety while optimizing performance. Together, these advances point toward a new paradigm of autonomous, AI-enabled energy systems, where learning-based methods are tightly integrated with physical constraints to ensure stability, efficiency, and resilience.

Speaker Bio: Qianwen Xu is an Associate Professor at KTH Royal Institute of Technology, Sweden, where she leads the Intelligent Sustainable Grid Lab and co-directs the Dig-It Lab. She received her PhD from Nanyang Technological University, Singapore in 2018. Her research focuses on modeling, stability assessment, and control of power electronics-dominated energy systems, with applications in microgrids, electrified transport, large-scale grids, and data center energy systems. She serves as an Associate Editor for several leading IEEE Transactions. She has received the IEEE J. David Irwin Early Career Award (2025). Her work has also been recognized by the Royal Swedish Academy of Engineering Sciences (IVA 100) for its societal impact.

30 - Ivan Petrić: Stability of Complex Converter-Dominated Power Systems: An Impedance-Based Perspective

Date and location: April 23 (Thursday), 11am at 45-600B

Speaker: Ivan Petrić, Director of Power Electronics R&D at 1st Avenue Power (1AP)

Abstract: Power systems are undergoing a fundamental transformation, driven by the rapid proliferation of inverter-based resources (IBRs) and the new load trends such as GW-scale data centers. This shift introduces stability challenges unlike those encountered in traditional power systems, with critical dynamics emerging at microsecond-to-millisecond timescales. Enabling large-scale converter-to-grid integration requires a holistic perspective that bridges power systems, power electronics, and control theory. Power electronic converters are the physical links between diverse dynamical systems, and, beyond energy conversion, their information processing capabilities are rapidly increasing. As such, they can assume a key role in actively stabilizing the grid via robust, adaptive, and data-driven control algorithms.

This talk presents impedance-based modeling and passivity theory as a unifying framework for both analyzing stability of IBR-dominated grids and designing converter-level control in pursuit of system-level objectives. The presentation starts with converters operating at the “grid-edge”, whose single-port impedance properties are relevant for the interconnection stability. It is shown how digital control delays introduce negative resistance behavior at high frequencies, where classical active damping techniques are ineffective. Oversampled digital modulation is presented as a solution that inherently extends dissipative behavior to very high frequencies, enabling robust stability. The impedance framework is then extended to interlinking converters operating at the “grid center”, with dynamic uncertainties at all ports. Unterminated MIMO impedances are defined, accounting for port-coupling dynamics. Using an HVDC case study, it is demonstrated that single-port analysis can lead to incorrect stability predictions due to port-level unobservability. Finally, the concept of all-port impedance passivity is introduced and used to develop decentralized stability indicators. In an example scenario, these tools are applied to stabilize an interlinking DC-AC converter, without requiring knowledge of grid impedances.

The talk concludes with a discussion of open challenges and future research directions toward robust integration and stability of increasingly complex, converter-dominated power systems.

Speaker Bio: Ivan Petrić was born in Belgrade, Serbia, in 1994. He received the B.Sc. and M.Sc. degrees in electrical engineering from the University of Belgrade, Serbia, in 2017 and 2018, respectively, and the Ph.D. degree in electrical engineering from the University of Padova, Italy, in 2023. In 2022, he was a Visiting Researcher with the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Since 2023, he has been an Affiliate Member of the Department of Electrical, Computer and Energy Engineering at the University of Colorado, Boulder. He is currently the Director of Power Electronics R&D at 1st Avenue Power, a Silicon Valley start-up in clean energy. His research interests include optimization, modeling, and control of power electronic converters, with applications to grid-connected systems, in which he has authored 20 journal publications.

29 - Navid Azizan: Machine Learning with Guarantees for Infrastructure Systems: From Safe Control to Market Equilibrium

Date and location: April 16 (Thursday), 11am at 45-600B

Speaker: Navid Azizan, MIT

Abstract: Modern energy and infrastructure systems are increasingly data-driven, yet deploying AI in these domains is bottlenecked by the need for strict guarantees—both for physical safety and economic efficiency. In this talk, I will present two recent frameworks from our group that embed rigorous mathematical guarantees into learning-based algorithms to address these complementary challenges. First, I will introduce HardNet, a novel neural network architecture designed to inherently satisfy input-dependent hard inequality constraints. While standard neural networks rely on soft penalties that fail to guarantee safety, HardNet embeds a differentiable enforcement layer that guarantees strict feasibility while provably retaining universal approximation capabilities. I will demonstrate its application in learning reliable, fast optimization solvers and safe control policies for physical systems. Second, focusing on markets, I will address the challenge of equilibrium pricing under incomplete information. Efficient market clearing requires matching supply and demand, yet system operators rarely know the competing suppliers' private cost functions. I will present an online learning framework that dynamically learns equilibrium prices. By sequentially interacting with suppliers, this approach achieves strict sublinear regret bounds for unmet demand, costs, and payments, even when demand varies dynamically. Together, these results illustrate how blending machine learning, optimization, and control theory can enable the safe, reliable, and efficient operation of next-generation infrastructure systems.

Speaker Bio: Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Associate Professor at MIT in LIDS, MechE, and IDSS. His research interests broadly lie in machine learning, systems and control, and mathematical optimization. His research lab focuses on various aspects of reliable intelligent systems, with an emphasis on principled learning and optimization algorithms, with applications to autonomy and sociotechnical systems. He obtained his PhD in Computing and Mathematical Sciences (CMS) from the California Institute of Technology (Caltech) in 2020, his MSc in electrical engineering from the University of Southern California in 2015, and his BSc in electrical engineering with a minor in physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University in 2021. Additionally, he was a research scientist intern at Google DeepMind in 2019 and held the Esther and Harold E. Edgerton (1927) Chair at MIT from 2022 to 2025. His work has been recognized by several awards, including the National Science Foundation CAREER Award, research awards from Google, Amazon, MathWorks, and IBM, and Best Paper awards and nominations at several venues, including ACM Greenmetrics, the Learning for Dynamics & Control (L4DC), and INFORMS JFIG. He was named in the list of Outstanding Academic Leaders in Data by the CDO Magazine for two consecutive years in 2024 and 2023, received the 2020 Information Theory and Applications (ITA) “Sun” (Gold) Graduation Award, and was named an Amazon Fellow in AI in 2017 and a PIMCO Fellow in Data Science in 2018. His mentorship has been recognized with the Frank E. Perkins Award for Excellence in Graduate Advising (MIT Institute Award) in 2025 and the UROP Outstanding Mentor Award in 2023. Early in his academic journey, he was the first-place winner and a gold medalist at the 2008 National Physics Olympiad in Iran. He founded and co-organized the popular “Control Meets Learning” Virtual Seminar Series during the pandemic.

28 - Panagiotis Andrianesis: Scheduling and Pricing Challenges in Low-Carbon Power Grids

Date and location: April 2 (Thursday), 11am at 45-600B

Speaker: Panagiotis Andrianesis, Mines Paris - PSL

Abstract: In this talk, we discuss scheduling and pricing challenges that arise in low-carbon power grids, characterized by stochastic and/or distributed clean energy resources. In the first part of the talk, we consider risk-driven methodologies to achieve higher adoption of stochastic resources, such as wind farms, and a more efficient and reliable system operation. We propose a data-driven method for forming ellipsoidal uncertainty sets tailored to renewable-rich systems and showcase the applicability of our approach on the adaptive robust unit commitment problem using ensemble wind forecasts from a large US ISO territory. If time allows, we will also discuss the theory and application of convex hull pricing in non-convex electricity markets and illustrate how Dantzig-Wolfe decomposition offers an exact computation method that provides intuition on the price formation rationale. In the second part, we consider the day-ahead operational planning problem of radial distribution networks hosting Distributed Energy Resources (DERs) including rooftop solar and storage-like loads, such as electric vehicles. We initiate the discussion with the definition and discovery of Distribution Locational Marginal Costs (DLMCs) that are key to optimal DER scheduling by modeling losses, ampacity/voltage congestion, and, most importantly, dynamic asset degradation. We then propose a data-driven approach for accurate marginal cost estimation taking a first step towards the design of DLMC-based dynamic and adaptive distribution network tariffs.

Speaker Bio: Panagiotis Andrianesis is a Graduate of the Hellenic Army Academy, also holding a B.Sc. degree in economics from the National and Kapodistrian University of Athens, and a Diploma in electrical and computer engineering from the National Technical University of Athens, Greece. He received an M.Sc. degree in production management, and a Ph.D. degree from the University of Thessaly, Greece. He is currently an Associate Professor at Mines Paris, PSL University, France, and has been a Research Associate Professor of Systems Engineering in Boston University (BU). He was an Assistant Professor in the Department of Wind and Energy Systems at the Technical University of Denmark, a Senior Fellow at the Institute of Sustainable Energy, and a Postdoctoral Associate at BU, and has served as a Consultant and Research Associate with ECCO International Inc. His research interests include power system analysis, optimization and economics. He was the recipient of the 2010 IEEE APS Pre-Doctoral Research Award and the 2024 ERC Starting Grant.

27 - Christian Kaps: Moving Money Around: Mobile Energy Storage and the Value of Geospatial Flexibility (Video)

Date and location: March 19 (Thursday), 11am at 45-600B

Speaker: Christian Kaps, Harvard University

Abstract: The growth of renewable energy has intensified the need for solutions that can address temporal and spatial mismatches between electricity supply and demand, arising from the intermittency of renewables. While battery storage has emerged as a key tool to store renewable electricity for future use, storage developers face diminishing and uncertain returns. In this paper, we first demonstrate how local competition and transient transmission capacity congestion lead to low and uncertain returns for Stationary Energy Storage System (SESS) operators. Using six years of price data from the PJM Interconnection and a staggered diff-in-diff approach, we estimate that an additional storage installation reduces existing SESS's annual profit at that location by $7,820 per megawatt on average. We then estimate how Mobile Energy Storage Systems (MESS) -- an innovative business model that allows grid-scale batteries to be relocated -- can increase operators' returns and improve grid-level congestion by providing geospatial flexibility. We introduce a dynamic programming-based forecasting framework for MESS developers' relocation and operating decisions and show that, even when relocation costs are high, MESS's profit consistently outperforms even the best-sited SESS by 11-47%. In part, this profit increase is driven by MESS capturing up to 22.5% more value from congestion prices than SESS, better aligning developer and grid incentives. Our findings quantify how competition and transience of price patterns limit storage profitability and highlight how leveraging mobile storage inventory in the energy transition can help storage developers, grid planners, and policymakers to accelerate storage build-out, renewable integration, and grid flexibility.

Speaker Bio: Christian Kaps is an Assistant Professor in the Technology and Operations Management unit at Harvard Business School. His research focuses on emerging topics in sustainable electricity generation and storage - notably how new technologies, sustainability behavior, and policies shape the energy market of the future. Kaps received his PhD in Operations Management at the Wharton School of Business of the University of Pennsylvania, where he was advised by Serguei Netessine.

26 - Wenqi Cui: Adaptive Voltage Control Under Rapid Net-Load Fluctuations in Distribution Systems (Video, Slides)

Date and location: March 12 (Thursday), 11am at 45-600B

Speaker: Wenqi Cui, New York University

Abstract: Power distribution systems are increasingly exposed to net loads with pronounced time variability, driven by rapidly ramping data-center demand (e.g., AI training workloads) and highly variable distributed energy resources. This talk presents adaptive voltage control strategies for distribution systems operating under significant time-varying net load. I will first introduce an adaptive scheme tailored to AI training workloads, which reduces voltage violations with minimal control effort. I will then show how short-term load forecasting can be leveraged into online voltage control, and how its integration with adaptive control improves robustness to prediction errors. Lastly, I will describe a successive linearization approach for voltage control under nonlinear power-flow models, and demonstrate how it enables adaptation as the operating point shifts.

Speaker Bio: Wenqi Cui is the Claire Booth Luce Assistant Professor in the Department of Electrical and Computer Engineering at New York University. Before joining NYU, she was a postdoctoral fellow in the Department of Computing + Mathematical Sciences at California Institute of Technology. She completed her PhD in Electrical and Computer Engineering at the University of Washington in 2024. Her research interests are in the power and energy systems, from the perspective of control, machine learning, and optimization. She was a recipient of the Pioneer Postdoctoral Fellowship, PIMCO Postdoctoral Fellowship, Rushmer Innovator Fellowship, Sarala Vadari Award, among others. She has participated in the Rising Stars in EECS Workshop in 2022 and the Rising Stars in Cyber-Physical Systems Workshop in 2023.

25 - Pablo Duenas: The Iberian Blackout - Causes and Unkonwns (Slides)

Date and Location: February 5, 2026, 11am at MIT 45-600B

Speaker: Pablo Duenas, MIT Energy Initiative

Abstract: On April 28, 2025, Spain and Portugal experienced a sudden, cascading blackout lasting over 12 hours. While early speculation pointed to cyberattacks, sabotage, and geopolitical intrigue, independent investigations revealed something more complex—and more telling about our energy future. This talk deconstructs what actually happened that day, explores the technical and operational vulnerabilities that were exposed, and raises critical questions: What role did the grid's changing composition play? Why did conventional safeguards fail? And what do these unknowns tell us about building resilience in a decarbonizing world? Drawing on system data and technical analyses, we examine the gap between what we think we know about grid stability and what recent events suggest we still need to understand.

Speaker Bio: Pablo Duenas-Martinez is a research scientist at the MIT Energy Initiative and assistant professor at Universidad Pontificia Comillas in Madrid, Spain. His research focuses on economic and regulatory modeling of energy systems, with expertise in market liberalization, decarbonization, distributed energy resources, security of energy supply, and universal energy access. He has advised the energy industry, government agencies, and regulatory bodies on energy system operations and investments worldwide. He holds a BS in industrial engineering and MS in electric power systems from Universidad Pontificia Comillas, a PhD in electrical engineering from the same institution, and a BS in economics from the National Distance Education University in Madrid, Spain.

24 - Age van der Mei: Unlocking Renewable Energy from Overseas using Hydrogen helps Stabilize the Climate

Date and Location: February 5, 2026, 11am at MIT 45-600B

Speaker: Age van der Mei, MIT LIDS Visiting Scholar (2024 - 2025)

Abstract: Hydrogen production from electricity is widely regarded as inefficient and costly, even for climate targets. Nevertheless, given technological progress and the large productivity difference for solar and wind resources, renewable hydrogen can play an unexpected role. Unlocking remote renewable energy using hydrogen remains untested as climate modelling assumes no or little trade between regions/continents. We model and map solar, wind and renewable hydrogen supply cost curves and apply these to model the impact to stabilizing the climate. By applying 10 years of weather data, technology learning curves as well as country-specific labour/capital cost, we find regional hydrogen cost differences become large enough to make pipeline, and later sea transport, economical. Overall, our modeling reveals that renewable hydrogen unlocks remote/overseas solar- and wind resources thereby ~10% of future renewable energy demand, reduces the need for 1-2 Gigaton CO2 by direct-air capture and reduces climate policy costs by ~6%.

Speaker Bio: Age van der Mei is a former visiting scholar at MIT LIDS. His research areas include renewable energy and energy infrastructure. He designs national and global energy infrastructure plans and co-led the design for hydrogen infrastructure in the Netherlands and Northern Germany. Beginning of this year, he won second prize in MIT's Introduction to Deep Learning competition.

23 - Ahmed Al-Ahmed: Watts and Drops: Joint Scheduling of Power and Water in Desalination Plants (Video, Slides)

Date and Location: December 1, 2025, 11am at MIT 45-600B

Speaker: Ahmed S. Alahmed, King Fahd University of Petroleum and Minerals

Abstract: This talk presents an analytical framework for the joint optimization of electricity and water schedules in water desalination and treatment plants. The plant integrates thermal and membrane-based technologies co-located with renewable generation, strategically participating in both water and electricity markets. I show that the optimal policy admits a closed-form, threshold-based structure in renewable availability: membrane-based production increases monotonically, thermal production decreases monotonically, and both respond according to analytically derived thresholds determined by technology parameters and tariff levels. These structural results yield simple, interpretable operational rules and provide immense insights on how water and electricity tariff parameters impact the plant’s optimal operation. Simulation results using realistic tariff and solar profiles quantify the gains from joint scheduling—higher renewable utilization, reduced grid dependence, and significant profit improvements relative to current benchmark algorithms.

Speaker Bio: Ahmed S. Alahmed is an Assistant Professor in the Electrical Engineering (EE) Department at King Fahd University of Petroleum and Minerals (KFUPM) and a Research Scholar at the Smart Mobility and Logistics center. He received his Ph.D. in Electrical and Computer Engineering from Cornell University in 2024. After his Ph.D., he joined LIDS MIT as a postdoctoral scholar working with Audun Botterud and Saurabh Amin. Ahmed’s research focuses on energy and water systems, where he employs tools from optimization and control, mechanism design, and game theory to address challenges in complex societal-scale systems. During his Ph.D., Ahmed interned at NREL, EPRI, and the Brattle Group. His work has been recognized with awards including Best Paper at the 2024 IEEE PES General Meeting and the 2018 IEEE Saudi Arabia Smart Grid Conference. He was also selected as a MiSK Fellow in 2022, a Cornell NextGen Professors Fellow in 2023, and an Ibn Rushd Postdoctoral Fellow in 2024.

22 - Kaarthik Sundar: One Framework to Model Them All—Unifying Energy Systems Modeling via the Lens of Potential-Driven Network Flows

Date and Location: November 20, 2025, 11am at MIT 45-600B

Speaker: Kaarthik Sundar, Los Alamos National Laboratory

Abstract: Modern energy infrastructure, ranging from electric grids and gas pipelines to water and district heating networks, are increasingly interconnected, forming large-scale, multi-physics systems whose operation depends on complex flow interactions. This talk presents a unified mathematical framework for modeling such systems through the lens of potential-driven network flows. We will show how a wide class of energy transport process can be captured using this framework, where each network edge represents a nonlinear relation between potentials (e.g., voltage, pressure, temperature) and flows (e.g., current, mass, heat). Theoretical results on the existence and uniqueness of solutions to these nonlinear systems will be discussed, providing rigorous foundation for coupled energy system analysis. This talk will also outline simple, scalable algorithms for solving and extending these models to cases where each edge is governed by its own nonlinear dynamics, such as nonlinear ODE-based flow laws. Overall, the framework demonstrates how diverse energy carriers can be represented, analyzed and integrated within a single unifying mathematical structure—one framework to model them all.

Speaker Bio: Kaarthik Sundar is a staff scientist at Los Alamos National Laboratory. He earned his Ph.D. in Mechanical Engineering (2016) and M.S. in Electrical Engineering (2012) from Texas A&M University. Prior to that, he earned his bachelor’s degree in Electrical and Electronics Engineering from College of Engineering Guindy, Chennai, India. His research spans the application domains of interdependent energy infrastructure systems, transportation and autonomous systems. He primarily designs and develops simulation, control and optimization algorithms that arise in these applications.

21 - Samuel Chevalier: Identifying the Smallest Adversarial Load Perturbation that Renders DC-OPF Infeasible (Video, Slides)

Date and Location: November 13, 2025, 11am at MIT 45-600B

Speaker: Samuel Chevalier, University of Vermont

Abstract: What is the globally smallest load perturbation that renders DC-OPF infeasible? Reliably identifying such "adversarial attack" perturbations has useful applications in a variety of emerging grid-related contexts, including machine learning performance verification, cybersecurity, and operational robustness of power systems dominated by stochastic renewable energy resources. In this talk, we formulate the inherently nonconvex adversarial attack problem by applying a parameterized version of Farkas' lemma to a perturbed set of DC-OPF equations. Since the resulting formulation is very hard to globally optimize, we also propose a parameterized generation control policy which, when applied to the primal DC-OPF problem, provides solvability guarantees. Together, these nonconvex problems provide guaranteed upper and lower bounds on adversarial attack size; by combining them into a single optimization problem, we can efficiently "squeeze" these bounds towards a common global solution. We apply these methods on a range of small- to medium-sized test cases from PGLib, benchmarking our results against the best adversarial attack lower bounds provided by Gurobi 12.0's spatial Branch and Bound solver.

Speaker Bio: Samuel Chevalier is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont. His research interests include designing industry-relevant optimization and control strategies for renewable-based power grids, building trustworthy machine learning tools for safety-critical engineering applications, and developing advanced data-driven modeling techniques for the power and energy sectors. Prior to joining UVM, Sam was a Marie Skłodowska-Curie Postdoctoral Fellow at the Technical University of Denmark, where his research focused on developing computational techniques for verifying the performance of Neural Network models of electrical power systems. Sam received his PhD from the Mechanical Engineering department at the Massachusetts Institute of Technology (MIT) in 2021, and he received his BS/MS degrees from the EE department at UVM in 2015/2016.

20 - Audun Botterud: Towards Zero-Carbon: Planning future electricity systems and markets (Video, Slides)

Date and Location: November 6, 2025, 11am at MIT 45-600B

Speaker: Audun Botterud, MIT

Abstract: The electric power system stands at the center of global decarbonization efforts. We discuss the role of capacity expansion modeling in guiding investment decisions in generation and transmission capacity, decarbonization policies, and future electricity market design. We demonstrate the use of large-scale least-cost optimization to identify viable decarbonization pathways under different climate policies, to assess the importance of transmission in zero-carbon systems dominated by wind and solar energy, and to evaluate cost and reliability effects of different transmission policies. We also illustrate how analytical formulations of the capacity expansion problem can provide insights into price formation and revenue sufficiency for wind, solar, and energy storage under long-run equilibrium conditions. We conclude by identifying research directions to better inform planning, market design, and policy in future low-carbon power systems.

Speaker Bio: Audun Botterud is a Principal Research Scientist in the Laboratory for Information and Decision Systems (LIDS) at MIT, where he leads the Energy Analytics Group. He has a co-appointment in the Energy Systems and Infrastructure Assessment Division at Argonne National Laboratory. His research group applies decision science and operations research to study power systems and electricity markets, with a particular focus on the roles of renewable energy and energy storage. Audun holds a M.Sc. (Industrial Engineering) and a Ph.D. (Electrical Power Engineering), both from the Norwegian University of Science and Technology. He was previously with SINTEF Energy Research in Trondheim, Norway, and was recently a Visiting Research Fellow in the Energy Policy Research Group (EPRG) at the University of Cambridge.

19 - Lina Reichenberg: The cannibalization effect for renewable energy - why and when? (Video, Slides)

Date and Location: October 23, 2025, 11am at MIT 45-600B

Speaker: Lina Reichenberg, Chalmers University

Abstract: The cannibalization effect refers to the increased wind and solar generation depressing wholesale electricity prices during periods of peak output, reducing the revenue for those same technologies. But is it always the case that increased wind and solar generation means decreasing revenues? And why does it happen? My talk will be based mainly on this paper Decreasing market value of variable renewables can be avoided by policy action, Energy Economics (2021), in which we show from theory and with simulation examples how market incentives interact with prices, revenue, and costs for renewable electricity systems. The decline in average revenue seen in some recent literature is due to an implicit policy assumption that technologies are forced into the system, whether it be with subsidies or quotas. This decline is mathematically guaranteed regardless of whether the subsidized technology is variable or not. If instead the driving policy is a carbon dioxide cap or tax, wind and solar shares can rise without cannibalizing their own market revenue, even at penetrations of wind and solar above 80%. The strong dependence of market value on the policy regime means that market value needs to be used with caution as a measure of market integration. Declining market value is not necessarily a sign of integration problems, but rather a result of policy choices.

Speaker Bio: Lina Reichenberg, an Associate Professor at Chalmers University of Technology, researches how to design reliable, low-carbon power systems that can integrate very high shares of wind and solar energy. Her work examines how political decisions and technological developments—such as those related to nuclear power, biomass resources, and hydropower—affect future energy costs.

18 - Sadiq Ahmad: Beyond the Limit: Using Continuation Power Flow to Predict and Prevent Grid Collapse (Video, Slides)

Date and Location: October 9, 2025, 11am at MIT 45-600B

Speaker: Sadiq Ahmad, Federal University of Technology, Minna Nigeria; MIT Center for International Studies

Abstract: To ensure sustainable power delivery amid rapid demand growth, modern power grids are characterised by advanced solutions, such as Flexible AC Transmission Systems (FACTS) and Distributed Generation (DG). For instance, FACTS deployment is required to manage congestion and meet technical and multilateral power supply transactions. Thermal, voltage, and stability limitations, however, constrain these power commitments. On the other hand, the drive to decarbonise the power supply framework is evident in the increased penetration of Distributed Generation (DG) within distribution networks. Coupled with increased load demand, the modern power grid becomes stressed and operated closer to its limits. Thus, the Continuation Power Flow (CPF) routine offers a promising tool for tracing the path of power flow solutions closer to their limits as a system parameter (e.g., load, generation) is consecutively increased. This presentation explores the capabilities of MATPOWER’s CPF in modern power grid scenarios, dominated by FACTS and DG planning. The CPF is primarily deployed to coordinate the optimal planning of FACTS and DG in an integrated Transmission and Distribution Network targeted at improving transfer capability, power losses and voltage profile.

Speaker Bio: Dr. Abubakar Sadiq Ahmad is a visiting Research Scholar at MIT’s Centre for International Studies. Sadiq is also auditing courses at the EECS. He is a faculty member at the School of Electrical Engineering and Technology, Federal University of Technology, Minna, Nigeria. Sadiq’s research interests span power systems planning, operations, and optimisation, utilising simulation and analysis tools including MATPOWER, PSAT, and MATLAB. He has contributed to international projects, including the Artificial Intelligence for Clean Energy (AI4CE - 2019) Development funded by the Royal Academy of Engineering (UK). He is currently working on the Homegrown Clean Energy (HCE 2024 – 2027) Solution under the EU Intra-Africa Mobility Programme. Sadiq is the recipient of the prestigious MIT–ETT Fellowship 2025 and the 2019 IEEE Industry Applications Society (IAS) Scholarship. He is passionate about bridging cutting-edge research with real-world impact.

17 - Bolun Xu: Energy Storage in Electricity Market, Equilibrium Models & Learning-Enabled Participation (Video, Slides)

Date and Location: October 2, 2025, 11am at MIT 45-600B

Speaker: Bolun Xu, Columbia University

Abstract: Energy storage resources—particularly batteries—are rapidly becoming key participants in electricity markets. By arbitraging price differences, they generate profit and help reduce peak demand and mitigate fluctuations from renewable resources. However, their effective market participation is complicated by the need to account for future price opportunities and associated uncertainties. Consequently, energy storage operators are allowed to withhold capacity for legitimate economic reasons, rather than strictly basing market offers on physical cost factors. These market dynamics pose unique challenges for storage operators seeking to optimize returns and for system regulators tasked with preventing market power abuse and facilitating social welfare convergence. This talk introduces the challenges and opportunities in scaling up energy storage deployments in power systems. I will highlight the need for advanced tools to optimize and monitor storage operations. I will also present a theoretical framework that clarifies the complexities of energy storage market-offer design and interpretation, and propose solutions to facilitate efficient storage operations in electricity markets.

Speaker Bio: Dr. Bolun Xu is an Assistant Professor in Earth and Environmental Engineering at Columbia University, with an affiliation in Electrical Engineering. He received his Ph.D. from the University of Washington, M.S. from ETH Zurich, and B.S. from Shanghai Jiao Tong University, all in electrical engineering. Before joining Columbia, he was a postdoctoral fellow at the MIT Energy Initiative. His research focuses on designing and optimizing sustainable energy and power systems and integrating emerging technologies. Dr. Xu is a recipient of the NSF CAREER Award, the Outstanding Young Investigator Award from the IISE Energy Systems Division, and the Early Career Award from the INFORMS Energy, Natural Resources, and Environment (ENRE) Section.

16 - Philipp Staudt: The Digitalization Triangle of the Energy Transition: The Interplay of Technology, Behavior, and Regulation (Video)

Date and Location: July 17, 2025, 11am at MIT 45-600B

Speaker: Philipp Staudt, University of Oldenburg

Abstract: The ongoing digitalization of the energy system offers significant potential for efficiency improvements, flexibility options, and new business models. While digitalization in the German low-voltage grid is still progressing relatively slowly, international examples demonstrate how the combination of time-variable renewable electricity generation and digitally controlled consumption can be practically implemented. Central to a profound understanding of the digital energy transition is the interplay of three dimensions that lend themselves to structuring the necessary research: technology, behavior, and regulation. Socio-technical interactions arise between these dimensions, forming the core focus of research in Information Systems. In my presentation, I explore these interactions and illustrate through various studies: 1. how the digital visualization of electricity consumption can influence individual energy literacy and investment decisions in efficient technologies, 2. how regulatory frameworks for storage usage, amplified by the widespread introduction of digital metering infrastructure, can change the behavior of numerous households and thereby transform the overall market, and 3. how digital technology is employed within the regulatory framework of §14a of the German Energy Industry Act (EnWG) to integrate different interests. These experimental, simulation-based, and qualitative studies emphasize the importance of a socio-technical understanding of the interactions between the described dimensions to design future-proof energy markets and systems. This underscores the pivotal role of Information Systems research in this context.

Speaker Bio: Philipp Staudt is a professor for Information Systems with a focus on digital sustainability and digital technology for sustainability. He has extensively researched the energy transition and the integration of renewable resources in the energy market using digital technology. He also works on understanding the impact of information systems on organizational sustainability and the impact of digital sustainability on government and society. Philipp holds a Bachelor's degree in Industrial Engineering and a Master's in Mathematics and Economics from KIT, where he also did his PhD on transmission congestion management. Before going to Oldenburg, he was a Postdoc at the MIT Energy Initiative.

15 - Pablo Ducru: Techno-economics of the energy transition -- a macro perspective

Date and Location: May 15, 2025, 11am at MIT 45-600B

Speaker: Pablo Ducru, MIT

Abstract: This lecture seeks to bring clarity to the climate change problem. We cover the key factors at play – from physics, to technology, to economics – showing how population, growth, the economy-energy coupling, and the carbon-intensity of the energy system, contribute to greenhouse gas emissions and rising temperatures. This will make clear the levers we have – from zero-carbon energy, to efficiency gains, to carbon pricing – so you can focus your attention on the most impactful solutions. This will clarify how you can contribute to solving climate change: modeling and monitoring the climate; deploying existing technologies (solar, wind, hydro, nuclear, to grid and energy infrastructures — supply, demand, transmission, efficiency, and storage), building the regulatory frameworks and leveraging finance and markets to create economic incentives with system-wide impact (and for different income-level countries), or developing the new technologies needed (energy, materials, industry, adaptation, etc.).

Speaker Bio: Pablo Ducru is a Franco-Mexican scientist, and entrepreneur. Pablo is a lecturer at MIT. Prior to this, Pablo co-founded Raive, an artificial intelligence (AI) company building vision-language foundation models to power the world’s content, enabling large-scale training, hosting, and servicing of custom models, so that everyone can have and exchange their own personalized AI models – an infrastructure upon which a market of AIs with IP attribution and AI royalties can emerge. Pablo is a world-expert in nuclear data and developing algorithms for high-performance computations. He received his PhD in computational nuclear science and engineering, worked at Los Alamos and Oak Ridge National Labs, developing new nuclear physics and statistical learning models for high-performance diffusion models. Pablo is also a Schwarzman Scholar from Tsinghua University. He conducted quantitative finance research for energy markets, and was advisor to the Minister of Economy of Mexico. In France, Pablo graduated from École Polytechnique (X-2011), where he served as an Officer in the Navy.

14 - Conleigh Byers: Electricity Market Design and Risk Trading with Heterogeneous Demand

Date and Location: May 8, 2025, 11am at MIT 45-600B

Speaker: Conleigh Byers, Harvard University

Abstract: The rise of variable renewable energy, storage, and load growth due to data centers with increasing extreme weather events has called into question whether current electricity market designs are sufficient for a decarbonized future. Many systems today have turned to some form of centrally administered resource adequacy mechanism beyond short-run markets to manage risk. Energy consumers are heterogeneous – differentiated by demand profiles, flexibility, reliability requirements, and risk appetite. The primary contributions of this work are (1) formulation of a non-algorithmic method for incomplete markets with risk-trading (2) incorporation of a multitude of swap and option contract designs (3) incorporation of endogenous demand in market participation and risk trading. From this framework, we can explore which contracts designs are appropriate for which types of heterogeneous demand. These conclusions should in turn influence the ongoing debate on organized long-run markets for electricity.

Speaker Bio: Conleigh Byers is an Environmental Fellow at the Harvard University Center for the Environment and the Harvard Kennedy School of Government. Her research uses tools from operations research, electrical engineering, and economics to design decarbonized energy systems, with a focus on electricity market design and power systems operations and planning. She received a doctorate in Electrical Engineering from ETH Zürich, a dual masters from MIT in Electrical Engineering & Computer Science and Technology & Policy, and an undergraduate degree from Princeton University.

13- Lingling Fan: Connect Control Theory with the Practical World: Replication of power grid dynamic events and control design for inverter-based resources (Video, Slides)

Date and Location: May 1, 2025, 11am at MIT 45-600B

Speaker: Lingling Fan, University of South Florida

Abstract: In this talk, the speaker will share her experiences on connecting control theory for real-world problem solving, including replication of power grid dynamic events and control design for inverter-based resources. While control theory is well-developed, implementing it for real-world problem-solving requires integrating it with domain knowledge. Creating abstractions, formulating linear time-invariant (LTI) systems, and designing feedback systems all require domain knowledge. The first example examines subsynchronous resonance events that led to rotor shaft damage in synchronous generators. The second example explores control design for inverters to achieve key control functions while ensuring tight current regulation. In both cases, LTI systems are constructed for modeling and analysis. Numerical demonstrations will highlight the effectiveness of classic control theory.

Speaker Bio: Dr. Lingling Fan is Professor at the Department of Electrical Engineering at the University of South Florida. Prior to her academic career, she was with Midwest ISO as a transmission planning engineer. Dr. Fan is research active in control, system identification, and stability analysis of power systems, power electronic converters, and electric machines. Dr. Fan is the founding co-chair of IEEE Power and Energy Society’s Wind SSO/IBR SSO task force. She has authored/co-authored three books on dynamic modeling of power grids, synchronous machines, and inverter-based resources. Dr. Fan served as the Editor-in-Chief of IEEE Electrification Magazine from 2020 to 2024 and Consulting Editor of IEEE transactions on Sustainable Energy from 2018 to 2020. Currently, she serves in the steering committee of IEEE Electrification Magazine and the editorial board of IEEE transactions on Energy Conversion. Dr. Fan was elevated to IEEE Fellow class 2022 for her contributions to stability analysis and control of inverter-based resources. She received Energy Systems Integration Group (ESIG) Excellent Award for her contributions to power system dynamics and system oscillations in IBR penetrated power grids in March 2024.

12- Mehdi Jafari: Grid Planning for Reliability: Rethinking ELCC

Date and Location: April 10, 2025, 11am at MIT 45-600B

Speaker: Mehdi Jafari, Staff Software Engineer, Form Energy

Abstract: Modern electricity systems demand both cost-effectiveness and high reliability, especially with increasing renewable energy integration. Systems planners must navigate complex conditions such as decarbonization goals, variable weather patterns, and increasing demands to ensure continuous reliable supply. A key metric for reliability is the Effective Load Carrying Capability (ELCC) which has inherent complexities: it's highly sensitive to interdependent factors, such as system configuration, installed capacity, resource diversity, and ordering effect. Traditional methods use generic ELCC values from a third party that can result in suboptimal investment decisions. To address these limitations, this talk presents a novel multi-scenario co-optimization model. This framework integrates capacity expansion, production cost model, and reliability constraints, enabling the endogenous calculation of capacity accreditations and eliminating the need for predefined ELCC values. By directly co-optimizing economic and reliability subproblems, this approach overcomes dimensionality challenges and the ordering effect, delivering improved accuracy and speed, and ultimately facilitating the development of robust and least-cost electricity portfolios.

Speaker Bio: Mehdi Jafari is a Staff Software Engineer – Modeling & Optimization – at Form Energy with over ten years of research and work experience in power and energy systems. He holds a Ph.D. in Electrical Engineering and was a Postdoctoral Associate at LIDS (2018-2022). Dr. Jafari's work focuses on developing advanced energy storage and renewable energy solutions for modern energy systems, employing optimization algorithms, probabilistic analysis, and techno-economic decision-making. At Form Energy, he is developing high-fidelity energy storage system models, granular electricity system models for reliability and integrated resource planning studies, and models for time-domain reduction and spatiotemporal flexibility. He has authored over 40 publications in peer-reviewed journals and conferences.

11- Michael Davidson: Optimizing Land and Grid Impacts of Renewables Dominant Power Systems (Video)

Date and Location: March 26, 2025, 11am at MIT 45-600B

Speaker: Michael Davidson, UC San Diego, UCSD

Abstract: Widespread use of renewable electricity sources is necessary to address climate change, but their intermittency, geospatial variability, and large land footprints create unique challenges for optimizing deployment of continental systems. In this talk, I will describe the key computational trade-offs and optimization methods to plan large-scale power systems and present a modeling framework to consider spatially granular land and grid impacts. Applying this to China, a plausible scenario leads to 2,000 and 3,900 GW of wind and solar capacity, respectively, to reach carbon neutrality in 2060. Competition for alternative uses of land, notably agriculture, lead to key trade-offs between technology choice and location. Temporal scale-up and institutional limitations create additional barriers to building renewable dominant systems by mid-century. New areas of model development that combine engineering and political economy insights will be discussed.

Speaker Bio: Michael Davidson is an assistant professor joint with the Mechanical and Aerospace Engineering Department and School of Global Policy and Strategy at the University of California, San Diego, where he leads the Power Transformation Lab. Dr. Davidson’s teaching and research focus on the engineering implications and institutional conflicts inherent in deploying low-carbon energy at scale to mitigate environmental harms. Michael holds a Ph.D. in engineering systems and an S.M. in technology and policy from MIT, and a B.S. in mathematics and physics and a B.A. in Japanese studies from Case Western Reserve University. He has held fellowships at the Harvard Kennedy School and Tsinghua University, and worked on U.S.-China climate policy for the Natural Resources Defense Council.

10- Pablo Ruiz: Efficient and Reliable Congestion Mitigation with Topology Optimization: Experience in the Midcontinent ISO Footprint (Video, Slides)

Date and Location: March 13, 2025, 11am at MIT 45-600B

Speaker: Pablo Ruiz, NewGrid, The Brattle Group and Boston University

Abstract: Timely and effective management of transmission system limitations has become critical to support the ongoing energy transition as well as economic development. Additional transmission capability is needed quickly to support increased deployment of variable energy resources, which often are located away from demand centers, the electrification of traditionally fossil energy uses, and other load growth, such as data centers. Transmission congestion can become acute in many regions, and has imposed over $20 billion of added costs on US transmission customers in 2022. It also impacts grid reliability and resilience throughout the country. To cost-effectively manage transmission constraints will require improved congestion management practices and technologies to better utilize the existing grid, especially as new transmission facilities are planned and constructed. Transmission topology optimization identifies grid reconfiguration options to quickly re-route power flow around bottlenecks, increasing transfer capability across congested areas. These reconfiguration solutions reduce the market redispatch and generation curtailments otherwise necessary to manage congestion and, by doing so, provide operational, efficiency, reliability, and resilience benefits. To facilitate the use of reconfigurations and provide another congestion management tool to transmission customers, the Midcontinent ISO (MISO) has implemented a process for market participants to submit reconfiguration proposals that show promise after initial analysis. The Southwest Power Pool (SPP) and the Electric Reliability Council of Texas (ERCOT) have similar process initiatives under discussion. This presentation will demonstrate the reliability, cost-saving, environmental and curtailment-mitigation impacts of reconfigurations identified on behalf of market participants in MISO, as well as illustrate additional benefits that could be obtained if topology optimization opportunities were used proactively and consistently across RTO footprints.

Speaker Bio: Pablo A. Ruiz is Co-founder and CEO of NewGrid, a grid optimization company, where he leads the teams that develop topology optimization software technology and provide congestion monitoring and mitigation services to utilities and market participants. He is also a senior consultant at The Brattle Group and Associate Research Professor of Mechanical Engineering at Boston University. He was the Principal Investigator for the U.S. DOE ARPA-E Transmission Topology Control Algorithms project, which led to the formation of NewGrid. Pablo holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign.

9- Line Roald: Carbon-Sensitive Electric Loads and their Impacts on Emissions (Video, Slides)

Date and Location: March 6, 2025, 11am at MIT 45-600B

Speaker: Line Roald, University of Wisconsin-Madison

Abstract: An increasing number of electric loads can be characterized as "carbon-sensitive", meaning that they are willing to adapt their real-time electricity use in response to the current carbon intensity of the grid. Examples of carbon-sensitive consumers include hyperscale computing companies, who want to publicly demonstrate their commitment to clean energy, and hydrogen producers, who are incentivized by tax credits or valuable "green" classifications. Individual consumers, often on flat electricity tariffs, simply want to contribute to a cleaner planet. While there is a lot of interest in "doing the right thing" to reduce the carbon footprint of our electricity use, there is concerningly little understanding of what the "right" action for end consumers actually is. We will discuss our analysis of common carbon emission metrics and demonstrate their sometimes counterintuitive and counterproductive impacts on grid emissions. We will then discuss some more holistic approaches to allow loads to expose carbon preferences to the grid.

Speaker Bio: Line Roald is an Associate Professor in the Department of Electrical and Computer Engineering at University of Wisconsin—Madison. She received her Ph.D. degree in Electrical Engineering (2016) from ETH Zurich, Switzerland, and was a postdoctoral research fellow at Los Alamos National Laboratory. She is the recipient of an NSF CAREER award, the Vilas Early Career Investigator Award and several best paper awards. Her research interests center around modeling and optimization of energy systems, with a particular focus on managing uncertainty and risk from extreme weather and renewable energy variability.

8- Emiliano Dall'Anese: Constrained Online Feedback Optimization: Recent Advances and Applications to Power Systems (Video)

Date and Location: Feb 27, 2025, 11am at MIT 45-600B

Speaker: Emiliano Dall'Anese, Boston University

Abstract: Online feedback optimization (OFO) is a framework to design feedback controllers that regulate a system – either dynamical or modeled by an algebraic map - toward equilibria defined as optimal solutions of an optimization problem. By leveraging the interplay of OFO and control barrier functions (CBFs), this talk presents new strategies for the design of feedback controllers that drive the system to solutions of optimization problems with steady-state constraints on both inputs and states. In this talk, we first present a new state feedback controller based on CBF tools and a continuous approximation of the projected gradient flow; we show that the equilibria of the interconnection between the plant and the proposed controller correspond to critical points of the constrained (possibly nonconvex) optimization problem. Sufficient conditions to ensure that, for the closed-loop system, isolated locally optimal solutions of the optimization problem are locally exponentially stable are presented, and forward invariance of the set of feasible inputs is established. Then, we present an application of the proposed method in the context of power distribution systems; in this application, the OFO method is utilized to solve an AC optimal power flow (OPF) problem based only on voltage measurements (and without requiring measurements of the power consumption of non-controllable assets). We show that the proposed method leads to significantly less voltage violations in a regime with high renewable generation relative to competing alternatives.

Speaker Bio: Emiliano Dall'Anese is an Associate Professor in the Department of Electrical and Computer Engineering at Boston University, where he is also an appointed faculty with the Division of Systems Engineering. He is an affiliate faculty with the Center for Information & Systems Engineering and the Institute for Global Sustainability. He received the Ph.D. in Information Engineering from the Department of Information Engineering, University of Padova, Italy, in 2011. He was with the University of Minnesota as a postdoc (2011-2014), the National Renewable Energy Laboratory as a senior researcher (2014-2018), and the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder as a faculty (2018-2024). His research interests span the areas of optimization, control, and learning; current applications include power systems and autonomous systems. He received the National Science Foundation CAREER Award in 2020, the IEEE PES Prize Paper Award in 2021, the IEEE Transactions on Control of Network Systems Best Paper Award in 2023, and the IEEE PES ISGT Europe best paper award in 2024.

7- Rahman Khorramfar: Robust Power-Gas Infrastructure Planning under Supply and Demand Uncertainties (Slides)

Date and Location: Nov 26, 2024, 4pm at MIT 45-500A

Speaker: Rahman Khorramfar, MIT

Abstract: Achieving economy-wide decarbonization via variable renewable energy (VRE) expansion and electrification of end-uses requires new approaches for energy infrastructure planning that consider, among other factors, weather-driven uncertainty in demand and VRE supply. A planning model that fails to account for these uncertainties can hinder the intended transition efforts and increase the risk of disruptions in energy supply, especially during extreme weather conditions. Here, we propose a distributional robust optimization (DRO) approach for the generation and transmission expansion problem (GTEP) of joint power-gas infrastructure and operations planning under the uncertainty of both demand and renewable supply. Aiming to construct a more realistic approach while avoiding significant computational burden, we embed the spatial correlation in both electric power and gas networks as part of the ambiguity set design. Furthermore, we consider the risk-awareness of the energy planners in the modeling framework via the conditional value-at-risk (CVaR) metric.

Speaker Bio: Rahman Khorramfar is currently a postdoctoral associate at MIT Energy Initiative and LIDS, developing advanced optimization and machine learning tools for design, planning, and operations of climate-resilient energy systems. He received PhD in Industrial and Systems Engineering from NC State University where he primarily worked on hierarchical decision-making and optimization under uncertainty and their applications in production planning, supply chain, and capacity expansion problems.

6- Deepjyoti Deka: Fast Data-Driven Optimization and Risk Estimation Using Limited Data in Power Grids (Video, Slides)

Date and Location: Nov 12, 2024, 4pm at MIT 45-500A

Speaker: Deep Deka, MIT

Abstract: Fast optimization of generator dispatch and associated risk estimation are crucial to ensuring reliable and economical delivery of electricity in power grids. AC Optimal Power Flow (OPF), with non-linear AC power flow equations and constraints, is the primary optimization framework used for power grid operational and planning problems. In this talk, we present results on two Bayesian data-driven proxies to learn AC power flow equations and AC -OPF surrogates using limited data, with a focus on probabilistic predictions.

The first data-driven model uses graph-structured Gaussian process (GP) model for risk assessment of critical voltage constraints. The proposed GP is based on a novel kernel, named the vertex-degree kernel (VDK), that decomposes the voltage-load relationship based on the network graph, and is estimated using Active Learning. Simulations demonstrate that the proposed VDK-GP achieves a reduction in computational time by 15 times over Monte- Carlo simulations in estimating system risk.

The second model termed BayesOPF is a Bayesian Neural Networks (BNN) based proxy for AC-OPF using limited OPF training time and samples. BayesOPF is trained in a sandwiched manner that alternates between a supervised learning step for minimizing cost, and an unsupervised learning step for enforcing feasibility. We show that BayesOPF outperforms conventional neural network architectures on OPF problems, without requiring any correction or projection step.

Speaker Bio: Deepjyoti Deka is a research scientist at MIT Energy Initiative. His research interests lie at the intersection of machine learning and optimization for tractable sensing and secure operation in renewable rich power grids. From 2018-2024, he was a staff scientist in the Theoretical Division at Los Alamos National Laboratory and served as a PI/co-PI for DOE and internal projects on machine learning in power systems and optimization of interdependent networks. He received the M.S. and Ph.D. degrees in Electrical and Computer Engineering (ECE) from the University of Texas, Austin, TX, USA, in 2011 and 2015, respectively. He completed his undergraduate degree in Electronics and Communication Engineering (ECE) from IIT Guwahati, India with an institute silver medal as the best outgoing student of the department in 2009. Dr. Deka is a senior member of IEEE, and has served as an editor on IEEE Transactions on Smart Grid.

5- Nicolas Stevens: On Some Advantages of Convex Hull Pricing for the European Electricity Auction (Video, Slides)

Date and Location: Oct 29, 2024, 4pm at MIT 45-500A

Speaker: Nicolas Stevens, UCLouvain, Belgium & Harvard

Abstract: Since the liberalization of the power sector and the creation of wholesale electricity markets, the question of how to price the non-convexities that are present in the market has attracted the interest of both academics and practitioners. Over the years, US markets have adopted different and evolving pricing rules which are still vividly debated. Since the “Trilateral Market Coupling” (2006), the European day-ahead market has opted for a notably different pricing rule, and there is currently research undertaken by the EU stakeholders to reform it. Our work aims at contributing to the debate. We analyze six different pricing methods. We establish several mathematical properties for enabling their comparison. Our findings are illustrated on stylized examples and numerical simulations that are performed on realistic auction datasets. Both theoretical and numerical evidences that are gathered in our paper point towards the advantages of the so-called “convex hull pricing” approach.

Speaker Bio: Nicolas Stevens is a PhD candidate at the Center for Operations Research and Econometrics (CORE), UCLouvain, Belgium. He is also currently a visiting researcher at the Harvard Kennedy School. His PhD dissertation, Price Formation with Non-Convexities : Theory and Applications for the Electricity Market, examines how the pricing rules in electricity auctions may accommodate the non-convexities that are present in the market orders. His research interests include energy economics, energy policy, market design, auctions and operations research. He also holds a Master of Engineering in Applied Mathematics from UCLouvain. In the past, Nicolas worked in the industry as a consultant in the energy sector on various topics including the design of the European day-ahead market in collaboration with the European Power Exchanges.

4- Yuanyuan Shi: Learning for Power Grid and Building Control (Video, Slides)

Date and Location: Oct 28, 2024, 11am at MIT 45-500A

Speaker: Yuanyuan Shi, UC San Diego

Abstract: In this talk, I will share our recent progress on developing learning algorithms for real-world energy system control, with stability and computational tractability guarantees.

The first application is power grid voltage control. I will introduce a novel neural network architecture – monotone neural network (MNN) that ensure the network output is a monotone function of the input. MNN is achieved by first designing neural networks that are convex (with universal approximation guarantee) and using gradients of convex functions to ensure monotonicity. We show that MNN is a powerful structure for voltage control – with stability guarantee and superior performance compared to standard neural networks.

The second application is building control. There is an emergent need to model indoor air quality to improve occupant health and building energy efficiency. A fundamental challenge is that building airflow dynamics are governed by nonlinear partial differential equations (PDEs) with unknown parameters, which are computationally prohibitive from a real‑time control perspective. I will introduce our work on PDE‑constrained optimization for building model identification and designing neural operator learning for efficient PDE system control.

Speaker Bio: Yuanyuan Shi is an Assistant Professor at the Department of Electrical and Computer Engineering at the University of California San Diego. She received her Ph.D. in Electrical and Computer Engineering (ECE) from the University of Washington in 2020. From 2020 to 2021, she was a Postdoctoral Scholar at Caltech. Her research focuses on machine learning, dynamical systems and control, with applications to sustainable energy systems. She is a recipient of the Hellman Fellowship in 2023, the UW Clean Energy Institution Scientific Achievement Award in 2020, and best paper finalist from ACM e-Energy 2022.

3- Hannele Holttinen & Julia Matevosyan: Recommended Practices for Wind and Solar Integration Studies (Video, Slides)

Date and Location: Oct 25, 2024, 11am at MIT 45-322

Speakers: Hannele Holttinen (Aalto University) & Julia Matevosyan (Energy Systems Integration Group, ESIG

Abstract: The International Energy Agency (IEA) WIND Task 25 ”Design and Operation of Energy Systems with Large Amounts of Variable Generation” has compiled Recommended Practices for power system impact studies, commonly known as wind and solar integration studies. It provides research institutes, consultants, and system operators with the best available information on how to perform an integration study, considering resource adequacy, operational, and stability challenges. It is also useful in benchmarking any integration studies: the recommendations check list can be used to identify what has and has not been taken into account. The latest update, to Edition 3, includes recommendations for very high wind and solar shares– wind and solar dominated power systems, with sector coupling and energy system integration.

Speaker Bio:

Hannele Holttinen is currently Professor of Practice at Aalto University, Finland, as well as RDI Lead, Energy systems at CLIC Innovation Oy. Through her own consultancy Recognis Oy she continues coordinating international research on grid integration at IEA WIND (Task 25), and at G-PST (Global Power System Transformation Consortium). She worked previously at VTT Technical Research Centre of Finland in different fields of wind energy and energy system integration research.

Julia Matevosyan is a Chief Engineer at Energy Systems Integration Group (ESIG) and has more than 20 years of experience in the power industry. Prior to joining ESIG, Julia was the Lead Planning Engineer of the Electric Reliability Council of Texas (ERCOT). In her time with ERCOT, she worked on adequacy of system inertial response, system flexibility, frequency control and performance issues related to high penetration levels of inverter-based generation and ancillary services market design. Julia received her BSc from Riga Technical University in Latvia, and her MSc and PhD from the Royal Institute of Technology (KTH) in Sweden

2- Mark O’Malley: Inertia in Power Systems and in Power Systems Research (Video)

Date and Location: Oct 18, 2024, 4pm at MIT 45-332

Speaker: Mark O’Malley, Imperial College London

Abstract: Connection to the power system via synchronous machines (SMs) was the norm for fossil fuels, nuclear, hydro etc. and they inherently have inertial response. Variable renewable energy (VRE) sources (wind & solar photovoltaic) are connected to the grid with power electronic (inverters/converters). These inverter-based resources (IBRs) do not behave like SMs and e.g. have no inertial response. With increasing penetrations of VRE inertia in power systems is now a very popular research topic. The impact on power systems of this trend and its impacts on power systems and the urgent need for research will be explored. When coupled with “inertia” in the power systems research community some interesting observations can be made.

Speaker Bio: Mark O’Malley is the Leverhulme Professor of Power Systems at Imperial College London. He is a co-founder of the Global Power System Transformation Consortium, Global Director for the international research centre, Electric Power for a Carbon free Society and a Hagler Fellow at Texas A&M. He is a Foreign Member of the US National Academy of Engineering, a member of the Royal Irish Academy, a Fellow of the Institute of Electrical and Electronic Engineers and a foreign fellow of the Chinese Society for Electrical Engineering and has received two Fulbright Fellowships.

1- Hongbo Sun: Phase Management of Power Distribution Systems

Date and Location: Oct 1, 2024, 4pm at MIT 45-500A

Speaker: Hongbo Sun,Mitsubishi Electric Research Laboratories

Abstract: The power grid is designed as a three-phase alternating current (AC) system. To operate and control the grid effectively, it is crucial to know precisely how power sources and consumers are connected to its phases. Additionally, the three phases of the grid must be balanced as much as possible; otherwise, system stability, power quality, and equipment safety will be significantly compromised. This talk will cover two important topics for phase management in distribution systems: phase identification for loads without phase labels, and load phase reconfiguration for phase imbalance mitigation. First, we will present a data-driven method for phase identification, inspired by concepts from information theory. This method analyzes event signatures from customer locations and the feeder head, using data from smart meters. By examining the cross-entropy of their mutual information, it accurately identifies the correct phases for both wye and delta-connected loads. Next, we will introduce a data-driven approach for scheduling phase rebalancing over a finite horizon. Phase scheduling is formulated as a Markov Decision Process (MDP), and an Imitation Learning (IL) framework, implemented through Ensemble Random Forests, is used to streamline the process of sequential load phase swapping. The previously costly multi-objective optimization is now limited to steps where phase swapping is needed.

Speaker Bio: Dr. Hongbo Sun is currently a Senior Principal Research Scientist at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, Massachusetts, USA. He received his Ph.D. degree in Electrical Engineering, specializing in Power Systems, from Chongqing University, China. His research areas include power transmission and distribution, power system operation and control, microgrids, hybrid AC/DC systems, and AI/ML applications.

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