Further information for select events
Frequency regulation through vehicle-to-grid: Robust decision-making and the EU law
Speaker:
Prof. Dr. Daniel Kuhn, EPFL
Date and Time:
Monday, October 30st, 2023 / 11:00 AM
Location:
ETZ E 81
Zoom Link:
https://ethz.zoom.us/j/61731326992
Abstract
Vehicle-to-grid is a concept for mitigating the growing storage demand of electricity grids by using the batteries of parked electric vehicles for providing frequency regulation. Vehicles owners offering frequency regulation promise to charge or discharge their batteries whenever the grid frequency deviates from its nominal value, and they must be able to honor their promises for all frequency deviation trajectories that satisfy certain properties prescribed by EU law. We show that the relevant EU regulations can be encoded exactly in a robust optimization model, and we use this model to demonstrate that the penalties for non-compliance with market rules are currently too low. This suggests that “crime pays” and that the stability of the electricity grid is jeopardized if many frequency providers abuse the system, which could ultimately result in blackouts. The decision problem of a vehicle owner constitutes a non-convex robust optimization problem affected by functional uncertainties. By exploiting the structure of the uncertainty set and exact linear decision rules, however, we can prove that this problem is equivalent to a tractable linear program. Through numerical experiments based on data from France, we quantify the economic value of vehicle-to-grid and elucidate the financial incentives of vehicle owners, aggregators, equipment manufacturers, and regulators. The proposed robust optimization model is relevant for a range of applications involving energy storage.
Biography
Daniel Kuhn is a Professor of Operations Research in the College of Management of Technology at EPFL, where he holds the Chair of Risk Analytics and Optimization. His research interests revolve around stochastic, robust and distributionally robust optimization, and his principal goal is to develop efficient algorithms as well as statistical guarantees for data-driven optimization problems. This work is primarily application-driven, the main application areas being energy systems, machine learning, business analytics and finance. Before joining EPFL, Daniel Kuhn was a faculty member in the Department of Computing at Imperial College London and a postdoctoral researcher in the Department of Management Science and Engineering at Stanford University. He holds a PhD degree in Economics from the University of St. Gallen and an MSc degree in Theoretical Physics from ETH Zurich. He is an INFORMS fellow and the recipient of several research and teaching prizes including the Friedrich Wilhelm Bessel Research Award by the Alexander von Humboldt Foundation and the Frederick W. Lanchester Prize by INFORMS. He is the editor-in-chief of Mathematical Programming and the area editor for continuous optimization of Operations Research.
Integrating generation capacity expansion planning and resource adequacy
Speaker:
Prof. Dr. Daniel Kirschen, University of Washington
Date and Time:
Tuesday, October 3rd, 2023 / 13:30 PM
Location:
ETZ E 81
Zoom Link:
https://ethz.zoom.us/j/61731326992
Abstract
Generation expansion planning aims to determine which generating plants should be built to minimize the sum of the costs of investments and operation over the planning horizon. Ensuring that this set of generating plants meets adequacy criteria is a difficult problem because a complex optimization must be combined with a detailed simulation of the operation of the system. This presentation will discuss how resource adequacy can be more closely integrated within the optimization of generation capacity. As the proportion of variable energy resources such as wind and solar increases, adequacy issues can arise at any time of the year and no longer just during peak load periods. We propose a technique to iteratively identify and integrate critical risk periods in the optimization.
Biography
Daniel Kirschen is the Donald W. and Ruth Mary Close Professor of Electrical and Computer Engineering at the University of Washington. He is the author of the textbook “Fundamentals of Power System Economics”, which has been translated in Chinese, Farsi, Korean, and Greek. He has published over 200 scientific papers. He is a Fellow of the IEEE and a Fellow of the Chinese Society for Electrical Engineering. His research focuses on the integration of renewable energy sources in the grid, power system economics and power system resilience. He received his Electromechanical Engineering degree from the Université Libre de Bruxelles (Belgium) and his MS and PhD degrees from the University of Wisconsin-Madison. Prior to joining the University of Washington, he taught for 16 years at The University of Manchester (UK). Before becoming an academic, he worked for Control Data and Siemens on the development of application software for utility control centers.
Enabling a responsive grid with distributed load control & optimization
Speaker:
Prof. Dr. Mads Almassalkhi, University of Vermont, USA
Date and Time:
Wednesday, August 9th, 2023 / 11:15 AM
Location:
ETZ E 8
Zoom Link:
https://ethz.zoom.us/j/61731326992
Abstract
Public policies are requiring large-scale deployments of clean energy and electrification of entire industries while expecting a reliable supply of electricity. However, as these efforts scale up, new operational grid challenges will arise. To overcome these challenges, distributed control of responsive, residential electric loads (e.g., electric water heaters, HVAC, and electric vehicle charging) and energy storage is expected to play a major role in enabling the transition to a clean energy future. However, at scale, these electric load control schemes must be responsive to 1) system-wide incentive signals, 2) local network conditions, and 3) local quality of service (QoS) requirements from end-users. This talk will present recent results on distributed load control methods that ensure real-time responsiveness, network-awareness, and QoS guarantees, including field demonstrations involving more than 200 loads in people’s homes. We will also explore the role of convex restrictions in enabling network-aware load control schemes and overcoming critical information and control gaps between today’s grid operators and load aggregators. Finally, the talk will highlight upcoming cyber-physical testbeds at the University of Vermont, including a 100kVA field site for testing control and optimization algorithms for hybrid energy systems.
Biography
Mads R. Almassalkhi holds the L. Richard Fisher Chair of Electrical Engineering and is Associate Professor at the University of Vermont. His research interests lie at the intersection of power and energy systems, mathematical optimization, and control systems and focus on developing practical, yet rigorous methods for improving responsiveness and resilience of energy and power systems. He currently serves as Associate Editor at IEEE PES Transactions on Power Systems and Chair of IEEE Control Systems Society (CSS) Technical Committee on Energy Systems. His work has been highlighted by IEEE Spectrum Magazine in 2022 and recognized with a recent NSF CAREER award in 2021 and the Outstanding Junior Faculty award in his college in 2016. He also holds a joint appointment as Chief Scientist in PNNL’s Optimization & Control Group (OCG) and was Otto Mønsted Visiting Professor at DTU Wind & Energy Systems (WES) in Denmark in 2021-22. He has translated his research into multiple commercial products via different startup companies. Most recently, he co-founded Packetized Energy with colleague from UVM, which was recently acquired by EnergyHub, the largest DR provider in the U.S.
Learning in games with applications to electricity markets
Speaker:
Prof. Dr. Maryam Kamgarpour, École Polytechnique Fédérale de Lausanne (EPFL)
Date and Time:
Wednesday, May 10th, 2023 / 11:00 AM
Location:
ETZ G 91
Zoom Link:
https://ethz.zoom.us/j/61731326992
Abstract
A rising challenge in control of large-scale control systems such as the energy and the transportation networks is to address autonomous decision making of interacting agents. Game theory provides a framework to model and analyze this class of problems. In several realistic applications, such as power markets, each player has partial information about the cost functions and actions of other players. Thus, a learning approach is needed to design optimal decisions for each player. I will present our work on designing algorithms for learning in convex and non-convex games. In the convex setting, I will present our zeroth-order gradient descent based algorithm and discuss conditions for its convergence. In the non-convex setting, I will present our no-regret algorithm that leverages probabilistic estimation of a players' cost function. I will discuss the applicability of the approaches to electricity markets.
Biography
Maryam Kamgarpour holds a Doctor of Philosophy in Engineering from the University of California, Berkeley and a Bachelor of Applied Science from University of Waterloo, Canada. Her research is on safe decision-making and control under uncertainty, game theory and mechanism design, mixed integer and stochastic optimization and control. Her theoretical research is motivated by control challenges arising in intelligent transportation networks, robotics, power grid systems and healthcare. She is the recipient of NASA High Potential Individual Award, NASA Excellence in Publication Award, and the European Union (ERC) Starting Grant.
Discrete nonlinear optimization: Modeling and solutions via novel hardware and decomposition algorithms
Speaker:
Dr. David E. Bernal Neira, Associate Scientist, NASA
Date and Time:
Tuesday, February 21st, 2023 / 14:00 PM
Location:
ETZ E 6
Zoom Link:
https://ethz.zoom.us/j/61731326992
Abstract
Optimization problems arise in different areas of Logistics, Manufacturing, Process Systems Engineering (PSE), and Energy Systems Engineering, and solving these problems efficiently is essential for addressing important industrial applications. Quantum computers have the potential to efficiently solve challenging nonlinear and combinatorial problems. However, available quantum computers cannot efficiently address practical problems; they are limited to small sizes and do not handle constraints well. In this talk and tutorial, we present the modeling strategy of problems as Mixed-Integer Nonlinear Programs (MINLP), explain some of the approaches that quantum computers use to solve Quadratic Unconstrained Binary Optimization (QUBO) problems, and propose hybrid classical-quantum algorithms to solve a class of MINLP, mixed-binary quadratically constrained programs (MIQCP) and apply decomposition strategies to break them down into QUBO subproblems that can be solved by quantum computers. This approach is based on a copositve optimization reformulation of the optimization problems, integrated within a cutting plane algorithm. The overall algorithm provides optimality convergence guarantees, yet it is robust to suboptimal solutions of the QUBO problems, which are usually provided by the hardware-based approaches (e.g., Quantum Annealing) to QUBO (arXiv:2207.13630). We will also cover different approaches to formulating and solving Quadratic Unconstrained Binary Optimization (QUBO) problems through unconventional computation methods, including but not limited to Quantum algorithms, and discuss how these approaches lead to algorithms able to outperform classical solution approaches.
Biography
David E. Bernal Neira has a Ph.D. in Chemical Engineering from Carnegie Mellon University. He worked in theory, algorithms, and software for nonlinear discrete optimization, also known as Mixed-Integer Nonlinear Programming (MINLP) and Generalized Disjunctive Programming (GDP), and their applications to Process Systems Engineering. He also complemented that algorithm development by studying the usage of quantum algorithms got nonlinear combinatorial optimization. During his Ph.D., he developed and co-taught the course on Quantum Integer Programming and Machine Learning, which has already been taught for three years at CMU and replicated in several institutes worldwide. David is currently an associate scientist in quantum computing at the Quantum Artificial Intelligence Laboratory at NASA and the Research Institute of Advanced Computer Science (RIACS) of the Universities Space Research Association (USRA), developing and benchmarking quantum and Physics-inspired methods for optimization and chemistry. After this position, David will start a tenure-track position at the Davidson School of Chemical Engineering at Purdue University.
Renewables in electricity markets and distributionally robust Bernoulli newsvendor problems
Speaker:
Prof. Dr. Pierre Pinson, Imperial College London
Date and Time:
Monday, January 23, 11.00 am
Location:
ETZ E 8
Zoom Link:
https://ethz.zoom.us/j/61731326992
Abstract
Renewable energy generation is to be offered through electricity markets, quite some time in advance. This then leads to a problem of decision-making under uncertainty, which may be seen as a newsvendor problem. Contrarily to the conventional case for which underage and overage penalties are known, such penalties in the case of electricity markets are unknown, and difficult to estimate. In addition, one is actually only penalized for either overage or underage, not both. Consequently, we look at a slightly different form of a newsvendor problem, for a price-taker participant offering in electricity markets, which we refer to as Bernoulli newsvendor problem. After showing that its solution is consistent with that for the classical newsvendor problem, we then introduce distributionally robust versions, with ambiguity possibly about both the probabilistic forecasts for power generation and the chance of success of the Bernoulli variable. All these distributionally robust Bernoulli newsvendor problems admit closed-form solutions. We finally use simulation studies, as well as a real-world case-study application, to illustrate the workings and benefits from the approach.
Biography
Pierre Pinson is the Chair of Data-centric Design Engineering at Imperial College London, Dyson School of Design Engineering (U.K.), a Chief Scientist at the data, analytics and AI company Halfspace (Copenhagen, Denmark) and the Editor-in-Chief of the International Journal of Forecasting. Before joining Imperial College London, he was a Professor at the Technical University of Denmark, successively in the Department of Electrical Engineering and in the Department of Technology, Management and Economics. He has been a guest at a number of renowned institutions, including University of Oxford, Isaac Newton Institute for Mathematical Sciences (Cambridge), University of Washington etc. Pierre is internationally recognized as a leading academic in forecasting, (stochastic) optimization and game theory for energy systems and markets, thanks to his multidisciplinary expertise in Operations Research and Management Science, Statistics, Economics, Meteorology and Energy/Electrical Engineering. For his achievements, he has received a number of awards, including IEEE Fellow, ISI/Clarivate Highly-cited Researcher, etc.
Mobilizing Demand Flexibility in Wholesale Electricity Markets with VPP Supply Functions
Speaker:
Prof. Dr. Shmuel S. Oren, Professor of the Graduate School, University of California at Berkeley
(Joint work with Hung Po Chao, ETA and Alex Papalexopoulos, ZOME)
Date and Time: Friday, January 20, 15.00
Location:
ETZ E 6
Abstract
FERC Order 2222 requires ISOs to develop market mechanisms that will enable aggregators of distributed resources to participate in the electricity wholesale market. In this work we describe the construction of a supply function for an aggregator’s virtual power plant (VPP) based on a portfolio of curtailable devices, categorized into priority tranches, that are controlled by the aggregator through edge technology behind the meter. Only the nameplate capacity of the curtailed devices are known to the aggregator while the energy yield from curtailment is uncertain. However, the supply function of the VPP offered by the aggregator into the wholesale market must specify deliverable energy quantity as function of wholesale price, like any other generator. We employ a revenue management methodology to construct a supply function with controlled delivery risk, based on priority tranches of the curtailed devices and offline estimates of the energy yield probability distributions. This work is part of an ARPA E project aimed at implementing a VPP based on aggregated demand curtailments at PJM.
Biography
Dr. Shmuel S. Oren is Professor of the Graduate School in the Department of Industrial Engineering and Operations Research at UC Berkeley and is a co-founder and the Berkeley site director, of PSerc. He has been a member of the California ISO Market Surveillance Committee and a consultant to many private and public entities in the US and abroad. He holds a Ph.D in Engineering Economic Systems from Stanford University. He is a recipient of the INFORMS Hotelling Medal for life time achievement in Energy Natural Resources and Environment and the IEEE Outstanding Power Systems Educator Award. He is a Member of the US National Academy of Engineering, a Life Fellow of the IEEE and Fellow of INFORMS.
Analysis and representation of non-stationary signals in inertia-reduced power grids
Speaker:
Prof. Dr. Mario Paolone, École Polytechnique Fédérale de Lausanne (EPFL)
Date and Time:
Monday, October 3, 11.00 am
Location:
CHN E46
Zoom Link:
https://ethz.zoom.us/j/61731326992
Abstract
Power systems are rapidly evolving towards low-inertia networks and system operators are confronted to new challenges to operate these systems safely. The significant increase in renewable energy sources and inverter-connected devices that, as such, do not provide any inertia, results in the loss of mechanical rotational inertia inherent in synchronous generators. This change increases the likelihood of large and frequent variations in the AC voltage and current signals. Consequently, processing and modelling techniques of power grid signals relying on the well-known phasor model, and the assumption of quasi-stationarity, may no longer be valid. Indeed, severe electromechanical transients produce signal dynamics that may exhibit continuous broadband frequency spectra whose characteristics are insufficiently captured by the narrowband and discrete phasor representation. To address this concern, the seminar discusses cases where phasor-based analysis has been proven to be inadequate, justifying the search for alternative methods. Several approaches that allow for more rigorous representations of signal dynamics are discussed, including dynamic phasor representation techniques and a functional basis analysis method that integrates the Hilbert Transform and the analytic signal model. The performance of these methods is discussed by comparing them to conventional phasor-based analysis for the case of non-signal stationary conditions.
Biography
Mario Paolone received the M.Sc. (Hons.) and Ph.D. degrees in electrical engineering from the University of Bologna, Italy, in 1998 and 2002. From 2005 to 2011, he was an Assistant Professor in power systems with the University of Bologna. Since 2011, he has been with the Swiss Federal Institute of Technology, Lausanne, Switzerland, where he is Full Professor and the Chair of the Distributed Electrical Systems Laboratory. His research interests focus on power systems with particular reference to real-time monitoring and operational aspects, power system protections, dynamics and transients. Dr. Paolone’s most significant contributions are in the field of PMU-based situational awareness of active distribution networks (ADNs) and in the field of exact, convex and computationally-efficient methods for the optimal planning and operation of ADNs. Dr. Paolone is Fellow of the IEEE and was the founder Editor-in-Chief of the Elsevier journal Sustainable Energy, Grids and Networks.
Batch Reinforcement Learning for Network-Safe Demand Response
Speaker
Prof. Dr. Antoine Lesage-Landry, Polytechnique Montréal, QC, Canada
Date:
Tuesday, March 1, 2022 / 02:00 PM
Remote:
Join Zoom Meeting
https://ethz.zoom.us/j/69151722619
Meeting ID: 691 5172 2619
Abstract
The increasing demand response capacity in electric power systems means that significant amounts of power adjustment can be made throughout distribution grids. This creates risks of distribution network constraint violations due to, e.g., a large number of air conditioner units being turned on simultaneously.
In this talk, we present a batch reinforcement learning-based demand response approach which also prevents distribution network constraint violations in unknown grids. We use the fitted Q-iteration (FQI) to compute a network-safe policy from historical measurements for thermostatically controlled load aggregations providing frequency regulation. We test our approach in a numerical case study based on real load profiles from Austin, TX, USA. Our numerical case study shows that our approach leads to a 95% reduction, on average, in the total number of rounds with at least a constraint violation when compared to a grid-agnostic approach and while providing acceptable setpoint tracking performance.
Then, with the objective of scaling efficiently our approach to a larger number of demand response aggregations, we formulate an efficient approximation for multi-agent batch reinforcement learning, the approximate multi-agent fitted Q iteration (AMAFQI). We propose an iterative policy search and show that it yields a greedy policy with respect to multiple approximations of the centralized, learned Q-function. In each iteration and policy evaluation, AMAFQI requires a number of computations that scales linearly with the number of agents whereas the analogous number of computations increase exponentially for the FQI. Numerical examples illustrate the significant computation time reduction when using AMAFQI instead of FQI in multi-agent problems and corroborate the similar decision-making performance of both approaches.
Biography
Antoine Lesage-Landry is an Assistant Professor in the Department of Electrical Engineering at Polytechnique Montréal, QC, Canada. He received the B.Eng. degree in Engineering Physics from Polytechnique Montréal, QC, Canada, in 2015, and the Ph.D. degree in Electrical Engineering from the University of Toronto, ON, Canada, in 2019. From 2019 to 2020, he was a Postdoctoral Scholar in the Energy & Resources Group at the University of California, Berkeley, CA, USA. His research interests include optimization, online learning and their application to power systems with renewable generation.
Cyber-physical risk modeling with imperfect cyber-attackers
Speaker
Dr. Efthymios Karangelos
Date:
Wednesday, December 8, 2021 / 11.00 am
Remote:
Join Zoom Meeting
https://ethz.zoom.us/j/65640401888
Meeting ID: 656 4040 1888
Abstract
We model the risk posed by a malicious cyber-attacker seeking to induce grid insecurity by means of a load redistribution attack, while explicitly acknowledging that such an actor would plausibly base its decision strategy on imperfect information. More specifically, we introduce a novel formulation for the cyber-attacker's decision-making problem and analyze the distribution of decisions taken with randomly inaccurate data on the grid branch admittances or capacities, and the distribution of their respective impact. Our findings indicate that inaccurate admittance values most often lead to suboptimal cyber-attacks that still compromise the grid security, while inaccurate capacity values result in notably less effective attacks. We also find common attacked cyber-assets and common affected physical-assets between all (random) imperfect cyber-attacks, which could be exploited in a preventive and/or corrective sense for effective cyber-physical risk management.
Biography
Dr. Efthymios Karangelos received the Diploma degree in mechanical engineering from the National Technical University of Athens, Athens, Greece, in 2005, and the M.Sc. degree in power systems engineering and economics and the Ph.D. degree in electrical engineering from the University of Manchester, Manchester, U.K., in 2007 and 2012, respectively. In 2012, he joined the Department of Electrical Engineering and Computer Science, University of Liege, as a Post-Doctoral Researcher. His current research interests include power system modeling, reliability and risk management and stochastic optimization.
Decomposition Algorithms for Optimal Power Systems Infrastructure Planning
Speaker: Prof. Ignacio E. Grossmann, Carnegie Mellon University, Pittsburgh, USA
Date: Friday, 14th June 2019, 16:00
Place: ETH Zurich, Room ETZ E7, Gloriastrasse 35, 8092 Zurich
Abstract
In this talk, we addresses the long-term planning of electric power infrastructures considering high renew- able penetration. We propose a deterministic Mixed-Integer Linear Programming (MILP) formulation for long-term planning of electric power infrastructure by simultaneously considering annual investment decisions and hourly operational decisions. The modeling framework takes the viewpoint of a central planning entity whose goal is to identify the source (nuclear, coal, natural gas, wind and solar), generation technology (e.g., steam, combustion and wind turbines, photo-voltaic and concentrated solar panels), location (regions), and capacity of future power generation technologies that can meet the projected electricity demand, while minimizing the amortized capital investment of all new generating units, the operating costs of both new and existing units, and corresponding environmental costs (e.g. carbon tax and renewable generation quota). The major challenge lies in the multi-scale integration of detailed operation decisions at the hourly (or sub-hourly) level with investment planning decisions over a few decades. We adopt judicious approximations and aggregations to improve its tractability and, to overcome computational challenges, we propose a decomposition algorithm based on Nested Benders Decomposition for multi-period MILP problems. Our decomposition adapts previous nested Benders methods by handling integer and continuous state variables. We apply the proposed modeling framework to a case study in the Electric Reliability Council of Texas (ERCOT) region, and demonstrate large computational savings from our decomposition. We then extend the proposed deterministic model to Multistage Stochastic Mixed-integer Programming in order to handle uncertainties such as fuel prices, load demand, renewable generation, disruptive technologies, and future policies. To be able to solve such a large-scale model, we decompose the problem using Stochastic Dual Dynamic Integer Programming (SDDiP), and take advantage of parallel processing to solve it more efficiently. The proposed framework is also applied to a case study in the ERCOT region, and we show that the parallelized SDDiP algorithm allows the solution of instances with quadrillions of variables and constraints.
Biography
Prof. Ignacio E. Grossmann is the Rudolph R. and Florence Dean University Professor of Chemical Engineering, and former Department Head at Carnegie Mellon University. He obtained his B.S. degree in Chemical Engineering at the Universidad Iberoamericana, Mexico City, in 1974, and his M.S. and Ph.D. in Chemical Engineering at Imperial College in 1975 and 1977, respectively. After working as an R&D engineer at the Instituto Mexicano del Petróleo in 1978, he joined Carnegie Mellon in 1979. He was Director of the Synthesis Laboratory from the Engineering Design Research Center in 1988-93. He is director of the "Center for Advanced Process Decision-making" which comprises a total of 20 petroleum, chemical and engineering companies. Ignacio Grossmann is a member of the National Academy of Engineering , Mexican Academy of Engineering, and associate editor of AIChE Journal and member of editorial board of Computers and Chemical Engineering, Journal of Global Optimization, Optimization and Engineering, Latin American Applied Research, and Process Systems Engineering Series. He was Chair of the Computers and Systems Technology Division of AIChE, and co-chair of the 1989 Foundations of Computer-Aided Process Design Conference and 2003 Foundations of Computer-Aided Process Operations Conference. He is a member of the American Institute of Chemical Engineers, Institute for Operations Research and Management Science, Mathematical Optimization Society, and American Chemical Society.
Cloud Energy Storage: Concept, Business Model and Key Technologies
Speaker: Prof. Dr. Ning Zhang, Tsinghua University, Beijing, China
Date: Monday, 15th April 2019, 15:15
Place: ETH Zurich, Room ETZ J 91, Gloriastrasse 35, 8092 Zurich
Abstract
Energy storage is extensively recognized as a significant potential resource for balancing generation and load in future power systems. The sharing economy and smart grid enable a new type of energy storage—cloud energy storage—that is capable of using large number of energy storage devices to provide energy storage services for multiple users and purposes. This grid based storage service business model enables ubiquitous and on-demand access to a shared pool of grid-scale energy storage resources. It provides users the ability to store and withdraw electrical energy to and from centralized or distributed batteries. Such sharing in both the energy and power capacity of energy storage make possible a substantially lower energy storage service cost compared with placing specialized storage devices for each of the consumer and purposes. The talk will describe the concept of cloud energy storage and the research carried out in our group. The key technologies required for the planning, operation, control and communication of cloud energy storage will also be highlighted.
Biography
Ning Zhang is an associate professor in the Department of Electrical Engineering, Tsinghua University. He got his B.Sc. degree from Tsinghua University, Beijing, China in 2007. He got his Ph.D in electrical engineering with Excellent Doctoral Thesis Award and Excellent Graduate Student Award from Tsinghua University in 2012. He was a research associate in The University of Manchester from Oct. 2010 to Jul. 2011 and a research assistant in Harvard University from Dec. 2013 to Mar 2014. He is a IEEE Senior Member, Cigre Member, Secretary of Cigre C1.39, Member of Cigre C6/C1.33 and C6/C2.34. He has more than 100 publications with a H-index of 22 in Google Scholar. He was awarded The World Federation of Engineering Organizations (WFEO) Young Engineers for UN Sustainable Development Goals in 2018, and Young Elite Scientists Sponsorship Program by Chinese Association of Science and Technology in 2016. He serves as the editor of IEEE Transactions on Power Systems (TPWRS), International Transactions on Electrical Energy Systems (ITEES), CSEE Journal of Power and Energy Systems (CSEE JPES), Journal of Modern Power Systems and Clear Energy (MPCE) and the Protection and Control of Modern Power Systems (PCMP).
His research interests include multiple energy system, power system planning and operation with renewable energy (wind power photovoltaic, concentrated solar power).
Data and models, their role in the design and operation of future electricity grids
Speaker: Prof. Dr. Mark O'Malley, University College Dublin / US National Renewable Energy Laboratory
Date: Wednesday, 3rd April 2019, 16:15
Place: ETH Zurich, Room ETZ E6, Gloriastrasse 35, 8092 Zurich
Abstract
The levels of variable renewable energy in our electricity grids is increasing rapidly with some systems recording extremely high penetrations (e.g. up to 100 %). At these high penetration levels, the fundamental characteristics of the electricity grid are changed and there is a need to rethink how we design and operate electricity grids. This transition will need to be managed carefully in order to avoid reliability impacts and excessive costs. The role of quality data, derived from measurement and experimentation, and models of the grid, including networks, generators and demand, will be central to success. I will present my group’s work on power system dynamics, analysis and optimization, grounded in data and models,that have allowed real grids to increase their penetration levels of variable renewable energy. Future challenges at very high penetrations will be described and the need for better data and models will be highlighted.
Biography
Mark O’Malley is Chief Scientist, Energy Systems Integration at the National Renewable Energy Laboratory, USA. He is on sabbatical from University College Dublin where he is the Professor of Electrical Engineering. He is a Foreign Member of the US National Academy of Engineering, a member of the Royal Irish Academy and a Fellow of the Institute of Electrical and Electronic Engineers and has received two Fulbright Fellowships. He is recognized as a world authority on Energy Systems Integration and in particular grid integration of renewable energy. He works closely and collaboratively with researchers in other disciplines, including economists, social scientists and geologists, and is on the advisory board of the European Platform for Energy Research in the Socio-Economic Nexus. Most recently, he was the James M. Flaherty Visiting Professor in Electrical Engineering at McGill University where he worked on strategies to decarbonize the combined Eastern Canada and North Eastern US electricity grids. He has very strong industry collaborations and is the Chair of the Research and Education working group of the Energy Systems Integration Group, a global organization that brings together industry, regulators, policy makers and the research community to further our collective knowledge and understanding in Energy Systems Integration.
Dispatching Stochastic Heterogeneous Resources Accounting for Grid and Battery Losses
Speaker: Dr. Eleni Stai, EPFL Lausanne
Date: Wednesday, 27th March 2019, 11:10
Place: ETH Zurich, Room ETZ E6, Gloriastrasse 35, 8092 Zurich
Abstract
The increasing deployment of distributed energy resources, if adopted passively, can lead to raising the investment and operation costs of power distribution systems. Alternatively, distributed resources can be aggregated into a single entity to trade electrical energy or to provide system support services such as dispatchability or reserve. We compute an optimal day-ahead dispatch plan for distribution networks with stochastic resources and batteries, while accounting for grid and battery losses. We formulate and solve a scenario-based AC Optimal Power Flow (OPF), which is by construction non-convex. We propose a novel iterative scheme, Corrected DistFlow (CoDistFlow), to solve the scenario-based AC OPF problem in radial networks. It uses a modified branch flow model for radial networks with angle relaxation that accounts for line shunt capacitances. At each step, it solves a convex problem based on a modified DistFlow OPF with correction terms for line losses and node voltages. Then, it updates the correction terms using the results of a full load flow. We prove that under a mild condition, a fixed point of CoDistFlow provides an exact solution to the full AC power flow equations. We propose treating battery losses similarly to grid losses by using a single-port electrical equivalent instead of battery efficiencies. We evaluate the performance of the proposed scheme in a real electrical network. Finally, we propose an intra-day re-dispatch scheme, which applies Receding Horizon Control over the CoDistFlow algorithm, and we evaluate it in a real Swiss grid using real data.
Biography
Eleni Stai received the B.Sc./M.Sc. in Electrical and Computer Engineering (ECE) from the National Technical University of Athens (NTUA), Greece, in 2009, the B.Sc. in Mathematics from the National and Kapodistrian University of Athens, Greece, in 2013, the M.Sc. in Applied Mathematical Sciences from NTUA in 2014 and the Ph.D. in Electrical Engineering from NTUA in 2015. Currently, she is a Postdoctoral researcher at the Laboratory for Communications & Applications 2 at École Polytechnique Fédérale de Lausanne (EPFL). She has received the Chorafas Foundation Best Ph.D. Thesis award, the Thomaidis Foundation Best M.Sc. Thesis award and the Best Paper Award in ICT 2016. Her main research interests include energy management and control of electrical grids, smart grids, network design and optimization, data analytics on complex networks. She has co-authored the book “Evolutionary Dynamics of Complex Communications Networks” (Taylor and Francis Group, CRC Press).
Flexibility in European electricity systems: Horizon 2020 insights on markets, regulation, technology and emerging business models
Speaker: Ben Bowler, EMAX Consultancy
Date: Thursday, 20th December 2018, 11:00
Place: ETH Zurich, Room ETF C 109, Sternwartstrasse 7, 8092 Zurich
Abstract
Our electricity network requirements are changing as we decarbonise generation and electrify demand. We need a system that can respond to much greater fluctuations in demand while relying on intermittent, variable renewable energy sources. Technology, regulation and wholesale markets must also adapt, and network operators face new challenges as they manage and invest in their networks. As a result, new commercial opportunities are emerging for those generators, consumers and intermediaries that can manage their flexibility.
During his presentation, Ben Bowler will discuss emerging business models for flexibility in the electricity network with special focus on demand response, energy storage and aggregation. He will relate his experience in the 20M Euro Horizon 2020 ‘Flexitranstore’ project, which focuses on transmission level flexibility, wholesale markets, and energy storage. He will describe emerging opportunities in wholesale markets across Europe, changing regulatory requirements, and the impact on network owners and operators. Using examples from Europe, he will explore what lessons we can apply to Switzerland as it implements the 2050 energy strategy.
Biography
Ben Bowler (Energy Markets and Innovation Expert, EMAX Consultancy – Brussels) is work package lead for future business models, market design and regulation in the 20M Flexitranstore project. The project has 28 partners from across Europe and includes multiple demonstration projects focused on energy storage integration with wind power, gas generation, and market simulations for electricity networks.
Understanding the value and applicability of linearized Optimal Power Flow formulations with regards to grid and loading characteristics
Speaker: Dr. Panayiotis (Panos) Moutis, DEPsys and CMU
Date: Monday, 26th November 2018, 15:30
Place: ETH Zurich, Room ETZ E 6, Gloriastrasse 35, 8092 Zurich
Abstract
There have been considerable advances in solving the AC OPF in a relaxed form, to exploit either a semi-definitive or second order programming solving setting. Under certain conditions, the solutions of these forms prove to be globally optimum solutions of the non-linear non-convex original problem. However, even for the simplest cases of practical applications, employing such approaches is computationally tedious (e.g. day-ahead security-constrained OPF). The described challenge becomes greater and more complicated when seeking to determine any type of dispatching control actions for the multiple, largely diverse and widely dispersed distribution systems. Furthermore, the necessity to tackle this challenge is current and of high value, due to the deregulation of the electricity markets, the push for and subsequent considerable penetration of renewables, and the most recent opportunities offered by energy storage and demand response programs. In this talk the two classic (DC and Decoupled), the most recent and two novel proposals for the linearization of the AC OPF formulation are presented, discussed and assessed. Several typical metrics are exhaustively employed, the feasibility gap is introduced and statistical analysis is conducted on a wide range of scenarios. Particularly interesting remarks and observations are made regarding the behavior of the linearized formulations concerning the parameters of each case/scenario of the problem.
Biography
Panayiotis (Panos) Moutis, PhD, joined DEPsys as a Research Fellow on September 2018 and the Scott Institute for Energy Innovation at Carnegie Mellon University in August 2018, following his appointment as a postdoctoral research associate at the School of Electrical and Computer Engineering (Feb. 2016) at the same University. In 2014 he was awarded a fellowship by Arup UK (through the University of Greenwich), on the “Research Challenge of Balancing Urban Microgrids in Future Planned Communities”, whereas in 2013 he won the “IEEE Sustainability 360o Contest” on the topic of Power. Between 2007 and 2015, as part of the SmartRUE research group he contributed in over a dozen R&D projects funded by the European Commission. Panos received both his diploma (2007) and his PhD (2015) degrees in Electrical & Computer Engineering at the National Technical University of Athens, Greece, and has published more than 20 papers. He has accumulated over 10 years of experience as a technical consultant on projects of Renewable Energy Sources and Energy Efficiency, currently serving as CTO of Proterima (energy consultants), Greece. He is a senior member of multiple IEEE societies, the Chair of the IEEE Smart Grid Publications Committee, the Editor-in-Chief of the “IEEE Smart Grid Newsletter”, and has served as the Editor-in-Chief of the “IEEE Smart Grid Compendium of Journal Publications, vol. 1”.
Data-driven Security-Constrained Optimal Power Flow
Speaker: Prof. Dr. Spyros Chatzivasileiadis, Technical University of Denmark
Date: 12th November 2018, 16:15
Place: ETH Zurich, Room ETZ E 8, Gloriastrasse 35, 8092 Zurich
Abstract
Current electricity market clearing tools use linear approximations to represent the secure operating region of the power system. These approximations are either too conservative or inaccurate, leading to additional costs of millions of euros every year. In this talk, we introduce a framework to unify security considerations with electricity market operations. Using data-driven techniques, we extract an accurate representation of the non-convex security region, derive linear decision rules, and incorporate them as conditional constraints in a tractable optimization problem. We propose two types of formulations to be used by both market and system operators. We accurately capture both steady-state and dynamic stability constraints, while being less conservative than current approaches. Our approach is scalable to large systems and can eliminate redispatching costs, leading to savings of millions of euros per year. In a case study, we show how it can substantially increase the considered feasible space, and identify the global optimum, while drastically reducing the computation time.
Biography
Spyros Chatzivasileiadis is an Associate Professor at the Technical University of Denmark. Prior to that he was a postdoc at MIT, and at Lawrence Berkeley National Laboratory (LBNL), USA. Spyros holds a PhD from ETH Zurich, Switzerland (2013) and a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece (2007).
www.chatziva.com
Achieving Robust Power System Operations using Convex Relaxations of the Power Flow Equations
Speaker: Dr. Daniel Molzahn, Argonne National Laboratory
Date: 12th November 2018, 17:00
Place: ETH Zurich, Room ETZ E 8, Gloriastrasse 35, 8092 Zurich
Abstract
Increasing penetrations of uncertain renewable generation challenge the ability of power system operators to ensure secure operations, thus motivating the development of new computational tools that better consider uncertainty. Leveraging recently developed convex relaxations of the power flow equations, this seminar presents two algorithms which can help system operators ensure that fluctuations from renewable generation will not result in violations of engineering constraints. The first algorithm computes robust solutions to optimal power flow (OPF) problems, which seek minimum-cost operating points for power systems. This algorithm is based on the observation that considering uncertainty leads to a tightening of the original, deterministic OPF constraints in order to safely accommodate fluctuations due to uncertain generation. Convex relaxation techniques are used to obtain conservative estimates of the required tightening. The second algorithm uses convex relaxation techniques to certify that no engineering constraint violations can occur for any uncertainty realization within a given range of power injection variations. With the ability to guarantee constraint satisfaction throughout the network given a small number of controlled quantities, this algorithm is particularly valuable for distribution systems with limited measurement and control capabilities.
This is joint work with Professor Line Roald (University of Wisconsin–Madison).
Biography
Daniel Molzahn is a computational engineer at Argonne National Laboratory in the Center for Energy, Environmental, and Economic Systems Analysis (CEEESA). In January 2019, Daniel will be an assistant professor in the Electrical and Computer Engineering Department at the Georgia Institute of Technology. Daniel was a Dow Sustainability Fellow at the University of Michigan. He received the B.S., M.S. and Ph.D. degrees in Electrical Engineering and the Masters of Public Affairs degree from the University of Wisconsin–Madison, where he was a National Science Foundation Graduate Research Fellow. His research interests are in the development of optimization and control techniques for electric power systems.
DownloadSlides (PDF, 4.7 MB)
High Renewable Energy Penetrations within Isolated and Remote Area Power Systems
Speaker: Professor Michael Negnevitsky, University of Tasmania, Australia
Date: Tuesday, 17th July 2018, 15:00
Place: ETH Zurich, ETZ E 7, Gloriastrasse 35, 8092 Zurich
Abstract
Globally, the vast majority of generation within off-grid communities is supplied via diesel generation. The extent to which renewable energy source (RES) technologies can be effectively integrated into these systems depends, to a large degree, on the configuration and control of such existing infrastructure. Utilization and optimization of existing diesel generation is accordingly a key consideration for any successful RES proposal. This paper explores both modern and legacy diesel technology and control, as available to maximize RES penetration within a hybrid diesel islanded network. Diesel generators are relatively inexpensive to purchase, offering a proven, reliable and stable generation source. Diesel generation is also supported via the ease and availability of both supplier engagement and technical expertise, services readily at hand to consumers. Their downside has proven to be the diesel fuel itself, given both volatile commodity pricing and damaging environmental emissions. These issues have created opportunity for alternative generation sources, and as we will see throughout the proceeding chapters, the advent of both available and cost competitive RES technologies has given remote communities genuine generation alternatives. RES technologies will become increasingly important to island countries as they seek to reduce their emissions and operational costs. How readily RES technologies are adopted, will depend on how effectively these technologies can be integrated into existing networks, with this paper advocating hybrid diesel architecture as one solution to quickly and effectively deliver high RES penetrations. How do islanded countries embrace the challenges and opportunities of emerging RES technologies? Will diesel generators become obsolete within these future power systems structures? This paper considers these queries, presenting existing generation as part of the recommended transition. In discussing the role of conventional generation, the audience is asked to recognize the residual value within legacy assets, identifying a cost optimized pathway for improved RES integration.
Biography
Professor Michael Negnevitsky is Chair in Power Engineering and Computational Intelligence and Director of the Centre for Renewable Energy and Power Systems, University of Tasmania, Australia. The primary focus of his research is smart grids, power system security, demand response, and isolated and remote area power systems with high renewable energy penetration. Professor Negnevitsky authorised more than 400 research publications including 91 journal papers, more than 300 conference papers, 12 chapters in books, 2 books, 9 edited conference proceedings and received 4 patents for inventions. He is Fellow of Engineers Australia, and Member of the National ITEE College Board. Professor Negnevitsky is Chair of the IEEE PES Working Group on High Renewable Energy Penetration in Remote and Isolated Power Systems, Vice Chair of the IEEE PES Working Group on Asian and Australasian Infrastructure – Smart Grids with Large Penetration of Renewable Energy, Member of CIGRE AP C4 (System Technical Performance) and CIGRE AP C6 (Distribution Systems and Dispersed Generation), Australian Technical Committee, Member of CIGRE Working Group JWG C1/C2/C6.18 (Coping with Limits for Very High Penetrations of Renewable Energy), International Technical Committee, and Member of CIGRE Working Group C6.30 (The Impact of Battery Energy Storage Systems on Distribution Networks), International Technical Committee.
Statistical learning for optimization and control:
An active set approach
Speaker: Dr. Line Roald, Los Alamos National Laboratory, USA
Date: 6th June 2018, 16:00
Place: ETH Zurich, Room ETZ E 8, Gloriastrasse 35, 8092 Zurich
Abstract
Many engineering applications such as power system optimization and model predictive control (MPC) involve solving similar optimization problems over and over and over again, with only slightly varying input parameters. In this talk, we consider the problem of using information available through this repeated solution process to directly learn the optimal solution as a function of the input parameters, thus reducing the need of for computation in real time.
To overcome limitations of traditional machine learning methods, which typically struggle to enforce feasibility constraints or leverage the knowledge available in the mathematical model, we propose a learning framework based on identifying the relevant sets of active constraints (the so-called active sets). Using the active sets as features enables efficient recovery of the optimal solution, inherently accounts for relevant safety constraints and provides more interpretable results. Further, while the number of possible active sets is combinatorial in the system size, the number of practically relevant active sets is frequently small, which make them simpler objects to learn. To identify those relevant active sets, we propose a streaming algorithm with rigorous probabilistic performance guarantees. To demonstrate the algorithm, we use the optimal power flow (OPF) with renewable energy production as an example. We establish that the number of active sets is typically small, and discuss both the theoretical implications and practical interpretations for power system operation. Finally, the talk will discuss connections to multiparametric programming and explicit MPC.
Biography
Dr. Line Roald received her BSc and MSc in Mechanical Engineering (2012) and PhD in Electrical Engineering (2016) from ETH Zurich in Zurich, Switzerland. She is currently a post-doctoral researcher in the Advanced Network Science Initiative at Los Alamos National Laboratory in Los Alamos, New Mexico, USA. She will join University of Wisconsin - Madison as an assistant professor from August 2018. Her research focuses on modelling and optimization of energy systems under uncertainty.
Decentralization in Energy Systems: Absorbing Solar and
Storage into Grids
Speaker: Prof. Dr. Duncan Callaway, Lawrence Berkeley National Laboratory
Date: 16th May 2018, 10:30
Place: ETH Zurich, Room ETZ E 6, Gloriastrasse 35, 8092 Zurich
Abstract
As prices for solar photovoltaics and battery energy storage plummet, grids around the globe are undergoing tremendous changes. How should we design and operate grids in the future in the presence of these technologies? This talk will cover some of my group’s recent efforts to answer this question. First, I will focus on a new approach to decentralized network optimization – a variant of the primal-dual subgradient method – that can be used to enable grid-integration of distributed energy resources such as solar photovoltaics, batteries and electric vehicles. I will then discuss how grids should be built in the future when distributed energy resource costs are so low. Using a simple concept called an iso-reliability curve, I will explain a method to identify cost-optimal fully decentralized systems – i.e. standalone solar home systems. After applying this method to a large solar resource dataset, I will present results indicating that in many unelectrified parts of the world, future decentralized systems will be able to deliver electricity at costs and reliabilities better than existing centralized grids.
Biography
Duncan Callaway is an Associate Professor of Energy and Resources with an affiliate appointment in Electrical Engineering and Computer Science, and a Faculty Scientist at Lawrence Berkeley National Laboratory. After receiving his PhD from Cornell University he worked in the energy industry, first at Davis Energy Group and later at PowerLight Corporation. He was a member of the research faculty of the Center for Sustainable Systems at the University of Michigan before joining UC Berkeley, where he has been since 2009. Dr. Callaway’s teaching covers energy systems with a focus on the electrical grid and energy efficiency. His research group focuses on developing and applying optimization and control tools to grid challenges, as well as data- and simulation-driven investigations of grid performance.
Dear Colleagues
the Swiss Chapter of the IEEE Power and Energy Society is delighted to invite you the workshop
Energy Strategy Approved – What now?
Last year the Swiss approved the Energy Strategy 2050. With this vote, the Swiss said yes to the support of more renewable generation and no to nuclear energy. While the objectives are clear and policies have been formulated, it is not entirely clear at this point what the consequences will be on the operation of the grid, the reliability of the system and the interactions with the neighboring countries. This workshop is devoted to discussing these questions from the policy, the economic and the technical perspectives.
We pleased to invite to you an interesting workshop on one of the most fundamental questions facing society today.
Date: April 10, 2018
Time: 9:00 to 16:00
Location: ETH Zürich, Alumni Pavillon (MM C78.1)
Confirmed Speakers
Matthias Galus -- BFE
Daniel Clauss -- EKS
Stefan Hirschberg -- PSI
Anton Gunzinger -- SCS
Andreas Ebner -- BKW
The external pagefull program and the external pageregistration link can now be found on the webpage of the chapter: external pagehttps://www.ieee.ch/chapters/pes.
Sincerely,
Henrik Nordborg
Henrik Nordborg
Dr. sc. nat. ETHZ
Studiengangleiter external pageErneuerbare Energien und Umwelttechnik
Partner des external pageInstituts für Energietechnik und des external pageDigitalLab@HSR
HSR Hochschule für Technik Rapperswil
IET Institut für Energietechnik
Oberseestrasse 10
8640 Rapperswil
E-mail:
Tel. +41-55 222 4370
Büro: 3.105
About: external pagewww.nordborg.ch/about
Integrating renewables into Texas grid
Speaker: Dr. Julia Matevosyan, Electric Reliability Council of Texas (ERCOT)
Date: 20th November 2017, 16:15
Place: ETH Zurich, Room ETZ E 81, Gloriastrasse 35, 8092 Zurich
Abstract
ERCOT serves as the independent system operator (ISO) for about 90% of the electric load in the state of Texas. The Texas Interconnection is not synchronously connected with any other grids, so ERCOT is solely responsible for maintaining reliability at the interconnection level. Similarly to many other systems around the world, ERCOT is currently undergoing extensive growth in renewable generation. In 2016, wind generation capacity reached 20% of the ERCOT’s total installed generation capacity and produced 15% of the total system energy for the year. The summer peak load in ERCOT is around 71 GW, while the minimum system load can be as low as 24 GW. On March 23, 2017 wind generation was serving 50 % of system load (28.8 GW) at one point in time. There is over 5 GW of additional wind generation and 1.3 GW of additional solar generation that is likely to come online between the end of 2017 and 2020. There is a number of challenges associated with managing high amount of non-synchronous, variable generation, especially given other specifics of ERCOT grid (remote location of renewable generation, limited HVDC interconnections with other reagions, large variation between minimum and peak load etc.). The presentation will provide high level background information about ERCOT system and market structure and then will focus on Ancillary Services, frequency control challenges with high levels of renewable generation, and interconnection requirements for renewable resources.
Biography
Julia Matevosyan has over 15 years of experience in system planning and wind power integration into power systems. Her PhD and postdoctoral research at the Royal Institute of Technology (KTH), Sweden, was on large-scale integration of wind generation in power systems and optimal coordination of wind and hydro power for efficient use of available transmission capacity. She has broad experience with grid interconnection requirements (Grid Codes) for wind generation resources in Europe and the US. In her previous role as Senior Electrical Engineer at the consulting firm Sinclair Knight Merz (currently Jacobs) she was involved in a number of grid interconnection and grid code compliance studies for wind power plants in the UK. Julia has authored and co-authored over 30 publications, including journals, conference proceedings and book contributions.
She is currently Lead Planning Engineer at Electric Reliability Council of Texas (ERCOT), Resource Adequacy Group, primarily working on operating reserve adequacy for future generation scenarios with large amounts of solar and wind generation. Her main interest are adequacy of system inertial response, system flexibility and frequency performance issues related to high penetration levels of renewable generation as well as integration of distributed generation, demand response and storage. She is involved in improving Ancillary Services for the ERCOT system and is Frequency Response subgroup lead of NERC Essential Reliability Services Working Group.
Microgrids for Grid Resilience
Speaker: Prof. Yin Xu, Beijing Jiaotong University, Beijing, China
Date: Thursday, 5th October 2017, 17:00
Place: ETH Zurich, Room ETZ E6, Gloriastrasse 35, 8092 Zurich
Abstract
Resilience against major disasters, such as major hurricanes or earthquakes, is considered as an essential characteristic of the future smart distribution systems. This seminar will introduce a resilience-oriented method using microgrids as emergency sources to serve critical load when the utility power is unavailable. Technical issues, including dynamic response of distributed generators, scarcity of generation resources, and uncertainties introduced by renewables, will be addressed. Future work on achieving a resilient distribution system will be discussed.
Biography
Yin Xu received his B.E. and Ph.D. degrees in electrical engineering from Tsinghua University, Beijing, China, in 2008 and 2013, respectively.
He is currently an Associate Professor at the School of Electrical Engineering, Beijing Jiaotong University, Beijing, China. During 2013–2016, he was an Assistant Research Professor at the School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA. His research interests include power system resilience, microgrid, distribution system service restoration, and power system electromagnetic transient simulation.
Dr. Xu is currently serving as the secretary of the Distribution Test Feeder Working Group under the IEEE Distribution System Analysis Subcommittee.
Coordination of electricity and natural gas markets with stochastic power producers
Speaker: Christos Ordoudis, Technical University of Denmark
Date: Monday, 3rd July 2017, 16:00
Place: ETH Zurich, Room ETZ E7, Gloriastrasse 35, 8092 Zurich
Abstract
In energy systems with more and more fluctuating renewables, gas-fired power plants (GFPPs) can serve as a back-up technology to ensure security of supply and provide short-term flexibility. Therefore, a tighter coordination between electricity and natural gas networks is foreseen, which in turn calls for revised operational practices and market designs. In this talk, we examine different levels of coordination in terms of system integration and time coupling of trading floors. First, an ideal model is formulated as a two-stage stochastic program in which day-ahead and real-time dispatch of both energy systems is optimized aiming to minimize total expected system cost. In order to evaluate different degrees of system integration and temporal coupling, two deterministic models, one of an integrated energy system and one that treats the two systems independently, are presented. These three models cover a spectrum of potential setups, ranging from an idealized case to current decoupled approaches. This is achieved by a novel formulation for a dynamic natural gas system that uses an outer approximation to linearize the natural gas flow equation, as well as models linepack flexibility and compressors. Aiming to reduce the efficiency gap of the current market from the stochastic ideal model, we formulate two coordination mechanisms based on the natural gas price and the quantity of natural gas that is available to GFPPs, to exploit flexibility in a market environment and cope with forecast errors of stochastic production. Our analysis demonstrates that the proposed models reduce the expected system cost and facilitate the integration of renewable energy sources.
Biography
Christos Ordoudis is currently pursuing the Ph.D. degree at the Department of Electrical Engineering of the Technical University of Denmark. There he belongs to the Energy Analytics and Markets group. His research interests include energy markets modeling, power and gas systems economics and operations, optimization theory and decision-making under uncertainty. He received the Dipl.-Eng. Degree from the Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece, in 2012 and the M.Sc. degree in sustainable energy from the Technical University of Denmark in 2014.
Prosumer-centric electricity markets: energy collectives and peer-to-peer exchanges
Speaker: Prof. Pierre Pinson, DTU, Copenhagen
Date: Friday, 7 April 2017, 11:15
Place: ETH Zürich, Room HG E 21 (Main building), Rämistrasse 101, 8092
Abstract
The way society perceives energy production and consumption is evolving rapidly with the deployment of dispersed renewable generation capacities, storage, electric cars, etc. While system organization has rapidly evolved from an integrated hierarchical structure to a more decentralized model, electricity markets are still not up to date with the ongoing transformation of our economies towards more consumer- centric structures. Consumer-centric markets may be organized as fully distributed peer-to-peer structures, but also most likely based on virtual communities of actors of the energy system with common interests (so-called energy collectives), most likely financial, but not necessarily. In this talk we will review recent proposals for design and operations of consumer-centric electricity markets under these alternative paradigms. In practice this translates to using consensus-based optimization approaches for peer-to-peer trading (allowing for product differentiation) or ADMM-like techniques for energy collectives (ADMM: Alternating Direction Method of Multipliers). Perspectives related to the actual deployment and demonstration of such markets will be discussed, as well as a number of mathematical/ICT challenges in such high-dimensional distributed/decentralized market structures.
Biography
Pierre Pinson is a Professor at the Centre for Electric Power and Energy (CEE) of the Technical university of Denmark (DTU, Dept. of Electrical Engineering), also heading a group focusing on Energy Analytics & Markets. He holds a M.Sc. In Applied Mathematics from INSA Toulouse and a Ph.D. In Energy Engineering from Ecole de Mines de Paris (France). He acts (or has acted) as an Editor for the IEEE Transactions on Power Systems, the International Journal of Forecasting and Wind Energy. His main research interests are centered around the proposal and application of mathematical methods for electricity markets and power systems operations, including forecasting. He has published extensively in some of the leading journals in Meteorology, Power Systems Engineering, Statistics and Operations Research. He has been a visiting researcher at the University of Oxford (Mathematical Institute) and the University of Washington in Seattle (Dept. of Statistics), as well as a scientist at the European Center for Medium-range Weather Forecasts (ECMWF, UK) and a visiting professor at Ecole Normale Superieure (Rennes, France). He is leading a number of initiatives aiming to profoundly rethink electricity markets for future renewable-based power systems and with a more proactive role of consumers. This focus on consumer-centric and community-driven electricity markets translates into proposals for peer-to-peer energy exchange, from mathematical framework to actual demonstration in Denmark.
Speaker: Dr. Audun Botterud, Argonne National Laboratory
Date: Friday, 27 January 2017, 10:15
Place: ETH Zürich, Raum ETZ E8, Gloriastrasse 35, 8092 Zurich
Abstract
This presentation discusses the status of renewable energy and electricity markets in the United States and Europe, with focus on similarities and differences in renewable energy resources, incentive schemes, electricity market design, as well as current and future solution to provide more flexibility in the power system. I will give an overview of related research on improved electricity market operations with renewable energy, including probabilistic forecasting, stochastic unit commitment, dynamic operating reserves, and flexible ramping products. Moreover, I will discuss recent work on generation expansion planning and electricity market design to ensure capacity adequacy and revenue sufficiency in electricity markets with increasing shares of renewable energy.
Biography
Audun Botterud has 15 years of experience with modeling and analysis of electricity markets and renewable energy in the United States and Europe. He is a Principal Energy Systems Engineer in the Center for Energy, Environmental, and Economic Systems Analysis at Argonne National Laboratory and a Principal Research Scientist in Laboratory for Information and Decision Systems at Massachusetts Institute of Technology. He received a M.Sc. in Industrial Engineering (1997) and a Ph.D. in Electrical Power Engineering (2004), both from the Norwegian University of Science and Technology. He was previously with SINTEF Energy Research in Trondheim, Norway. His research interests include power systems planning and economics, electricity markets, grid integration of renewable energy, energy storage, and stochastic optimization. He is the co-chair for the IEEE Task Force on Bulk Power System Operations with Variable Generation.
Speaker: Prof. Dr. Taha Selim Ustun, Carnegie-Mellon University, Pittsburgh, PA, USA
Date: Friday, 20 January 2017, 10:00
Place: ETH Zürich, Raum ETZ E7, Gloriastrasse 35, 8092 Zurich
Abstract
For decades, power networks have kept their orthodox operation principles and little has changed. However, recent technological developments and the increasing popularity of Renewable Energy due climate change concerns tilted this equilibrium. Conventional bulk-generation approach is replaced with distributed generation; passive distribution networks started taking active roles in grid management while new devices such as smart meters and electric vehicles introduced to the grid for the first time. These developments have impacted power networks to the point that power engineers need to revise operation and planning strategies.
In this talk, I will give an overview of different research projects currently ongoing in Carnegie Mellon University’s SmartGrid Research Group (SRG). These projects span a large interdisciplinary area where Electric Vehicle mobility, Cognitive Radio Communication, Power Protection Schemes and Graph Theory in Computer Science stand side-by-side. In particular this presentation will showcase research tackling paradigm changes in following areas: New Protection Challenges and Solutions, Standard Communication in SmartGrids, Electric Vehicles & Demand Side Management and SmartGrids with Distributed Generation for Rural Electrification.
Biography
Taha Selim Ustun received his Ph.D. degree in electrical engineering from Victoria University, Melbourne, VIC, Australia. He is currently an Assistant Professor of Electrical Engineering with the School of Electrical and Computer Engineering, Carnegie-Mellon University, Pittsburgh, PA, USA. He has taught courses such as Fundamentals in Power Systems, Renewable Energy, Microgrids and New Age Power Networks, Power Electronics and Embedded Systems. His research interests include power systems protection, communication in power networks, distributed generation, microgrids, and smartgrids. He has over 40 publications that appeared in international peer-reviewed journals and conferences. He is a reviewer in reputable journals and has taken active roles in organizing international conferences and chairing sessions. He delivered talks for World Energy Council, Waterloo Global Science Initiative, European Union Energy Initiative (EUEI) Qatar Foundation and Advanced Industrial Science and Technology, Japan. He has also been invited to run short courses in Africa, India and China.