Modeling and Managing Risky Assets in Day Ahead Power Markets

Speaker: Michael Caramanis (Boston University)

Biography:Michael Caramanis is Boston University Professor of Mechanical and Systems Engineering. He served at the Greek National Energy council (1976-79) the MIT Energy Laboratory (1979-82) and was chair of the Greek Regulatory Authority for Energy from February 2005 till recently. He has directed several research projects sponsored by NSF, EPRI, NYSERDA and the Industry, authored many journal articles and co-authored the classic book Spot Pricing of Electricity, Kluwer, 1988. He researches complex stochastic production systems and decision support in real-time electric power markets. He currently focuses on the Cyber-Physical-Energy-System/Smart Grid.

Abstract:Present Day Ahead (DA) power markets make integer unit commitment decisions and schedule the resulting available capacity across energy and reserves to minimize the costs of (i) balancing energy supply and demand and (ii) securing system reliability. Following the closing of the DA market where financial or virtual bids are allowed, ISOs, whose assessment of demand uncertainty differs from that of the demand bidding market participants (financial, load serving entities etc.), perform an out of market Reliability Unit Commitment (RUC) step that may augment the units committed during the DA market clearing process. 

Whereas system reliability has been reasonably assured in the past through securing exogenously determined levels of reserves to guard against by and large uncorrelated contingencies (N-1 or N-1.5 largest unit capacity), ever increasing volatile renewable generation is introducing spatiotemporally correlated risky generation and demand assets (e.g. Transmission connected wind farms and distribution connected PV) rendering future optimal system reliability assurance a harder task. As such, future DA power markets face much more demanding unit commitment, economic dispatch, and pricing requirements that depend crucially on assessing and managing individual asset risks and their impact on the power system.

We propose a new DA market design that internalizes reserve requirements through probabilistic system reliability constraints including both energy and reserve bids and offers by supply as well as demand with risky capacity availabilities. Uncertain weather and associated power forecasts are modeled through an uncertainty set of spatiotemporally jointly distributed trajectories of asset-specific available capacity. The uncertainty set is designed to provide probabilistic system reliability guarantees. Moreover, demand response – sometimes referred to as acceptable quality of supply — is commoditized by allowing demand to plan on and provide reserves of different types. Thus consumption (and generation) available capacity is modeled in terms of a basket of diversified products consisting of energy as well as reserves (primary, secondary, tertiary, storage-like etc.) and priced accordingly. Most notably, DA market clearing prices are dependent not only on the dual variables of energy balance constraints but also on the dual variables of reliability constraints associated with each trajectory in the aforementioned uncertainty set modulated by the impact of each asset-specific product on the left hand side of the linear reliability constraint.

We will present the proposed market design featuring endogenous reserve requirement determination, discuss the acceptability to stakeholders of expected-marginal-cost-based clearing prices, and speculate on other market pricing alternatives.

Authors:strong> P. Andrianesis, D. Bertsimas, M. Caramanis (presenting author)

Power Systems Optimization at ARPA-E

Speaker: Richard O’Neill (ARPA-E)

Biography:Dr. Richard O’Neill currently serves as a Distinguished Senior Fellow at the Advanced Research Projects Agency – Energy (ARPA-E). Prior to his time at ARPA-E, O’Neill served as the Chief Economic Advisor at the Federal Energy Regulatory Commission (FERC) and as the Director of FERC’s Office of Pipeline and Producer Regulation.  During his time at FERC, O’Neill led the Chairman’s teams to develop policy and restructure the natural gas and the electric power markets, develop oil-pipeline rate index by benchmarking to actual industry costs, and increase the efficiency of FERC’s market software. Further, O’Neill led a group that developed transmission switching software that would ultimately become an ARPA-E project and was integral to the initial design of ARPA-E’s Grid Optimization Competition.

O’Neill has served on the computer science and business faculties at Louisiana State University and the University of Maryland, where he holds a B.S. in chemical engineering, an MBA, and a Doctorate in operations research.

Abstract:This presentation will discuss the power system optimization programs at ARPA-E.  Future ISO day-ahead and real-time markets will present new challenges to solving these markets. The GO competition is aimed at solving the OPF problems better and faster. We will discuss the first challenges and the results. The HIPPO project goal is a 10x speed up of the day-ahead MISO market.  It is going into a new phase with additional market designs to solve. PERFORM: is just starting is focused on  risk-aware optimization.

Risk Quantification and Trading in Electricity Markets

Speaker: Yury Dvorkin (New York University)

Biography:Yury Dvorkin (Member, IEEE) received the Ph.D. degree from the University of Washington, Seattle, WA, USA, in 2016. He is currently an Assistant Professor and the Goddard Faculty Fellow with the Department of Electrical and Computer Engineering, New York University, New York, NY, USA, with a joint appointment with the New York University’s Center for Urban Science and Progress. His research interests include cyber–physical energy systems, environment, and economics. He was awarded the Scientific Achievement Award by Clean Energy Institute (University of Washington) for his doctoral dissertation in 2016, the NSF CAREER Award in 2019, and the Goddard Junior Faculty Award with New York University in 2019. He is an Associate Editor of the IEEE Transactions on Smart Grid.

Abstract:Relative to financial markets, current electricity market designs lack sophisticated means of systemic and asset-level risk quantification and trading, thus producing market outcomes incapable of incentivizing cost-efficient risk mitigation. This presentation explores parallels between electricity and financial markets and describes a stochastic market design that allows for (i) explicitly quantifying and limiting the risk of market constraint violations and (ii) introducing such risk-trading instruments as Arrow-Debreu Securities. Taken together, explicit risk quantification and trading make it possible to produce uncertainty- and risk-aware energy and reserve allocations and prices that achieve desirable market design properties (e.g. efficiency, revenue adequacy, cost recovery) in a renewable-rich markets. 

Solving Multi-Contingency AC Power Flow Problems with Convex Relaxations

Speaker: Carleton Coffrin (Los Alamos National Laboratory)

Biography:Carleton Coffrin is a staff scientist in Los Alamos National Laboratory’s Advanced Network Science Initiative. His research interests focus on how optimization algorithms can be applied to applications on infrastructure networks. His background spans many forms of optimization including mathematical programing, constraint programming, and local search. Recently Carleton has been exploring online learning via open-online courses and youtube videos as well was novel computing architectures such as, quantum computers, neuromorphic computers and memristors.

Abstract:The task of finding AC feasible power flow solutions in severely damaged power networks has been a long standing open problem. The crux of the challenge is that as a power network’s configuration departs from its established operating point, the task of adjusting the generator dispatch, voltage profile and system loading to ensure AC feasibility becomes “maddeningly difficult” to do by hand or heuristics. Attempts to automate this process with simple power flow approximations, such as the DC Power Flow model, have met with limited success. In this work we highlight the mathematical properties of the AC Power Flow equations that make solving multi-contingency problems particularly challenging. We then propose novel methods based on convex relaxations to find reasonable operating points in damaged power networks. The resulting approach can find AC feasible power flow solutions to severely damaged networks in seconds and, to the best of our knowledge, represents the first reliable approach to solving the long standing multi-contingency AC power flow problem. The proposed approach has been effective in quantifying the vulnerabilities of real-world power networks to extreme events such as hurricanes and storm surge.

A Two-Stage Decomposition Approach for AC Optimal Power Flow

Speaker: Andreas Wächter (Northwestern University)

Biography:Andreas Wächter is a Professor at the Industrial Engineering and Management Sciences Department at Northwestern University. He obtained his master’s degree in Mathematics at the University of Cologne, Germany, in 1997, and this Ph.D. in Chemical Engineering at Carnegie Mellon University in 2002. Before joining Northwestern University in 2011, he was a Research Staff Member in the Department of Mathematical Sciences at IBM Research in Yorktown Heights, NY. His research interests include the design, analysis, implementation and application of numerical algorithms for nonlinear continuous and mixed-integer optimization. He is a recipient of the 2011 Wilkinson Prize for Numerical Software and the 2009 Informs Computing Society Prize for his work on the open-source optimization package Ipopt. He is currently spending a year at Los Alamos National Laboratory as the Ulam Fellow.

Abstract:The alternating current optimal power flow (AC-OPF) problem is critical to power system operations and planning, but it is generally hard to solve due to its nonconvex and large-scale nature. This paper proposes a scalable decomposition approach in which the power network is decomposed into a master network and a number of subnetworks, where each network has its own AC-OPF subproblem. This formulates a two-stage optimization problem and requires only a small amount of communication between the master and subnetworks. The key contribution is a smoothing technique that renders the response of a subnetwork differentiable with respect to the input from the master problem, utilizing properties of the barrier problem formulation that naturally arises when subproblems are solved by a primal-dual interior-point algorithm. Consequently, existing efficient nonlinear programming solvers can be used for both the master problem and the subproblems. The advantage of this framework is that speedup can be obtained by processing the subnetworks in parallel, and it has convergence guarantees under reasonable assumptions. The formulation is readily extended to instances with stochastic subnetwork loads. Numerical results show favorable performance and illustrate the scalability of the algorithm which is able to solve instances with more than 11 million buses.

On solving ACOPF and SCACOPF problems using nonlinear programming and high-performance computing

Speaker: Cosmin Petra (Lawrence Livermore National Laboratory)

Biography:Cosmin Petra is a computer scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. Cosmin’s work focuses on high-performance computing algorithms and C/C++ solvers for mathematical optimization with emphasis on applications in complex energy systems. Prior to joining the Center for Applied Scientific Computing, Cosmin was with Argonne National Laboratory as a computational mathematician and, prior to that, hold software engineer/numerical analyst positions in IT industry. Cosmin obtained an M.S. and a Ph.D. in Applied Mathematics from the University of Maryland, Baltimore County, in 2006 and 2009, respectively. Cosmin’s team won the first place in the recent ARPA-E GO Competition Challenge 1.

Abstract:The security-constrained AC optimal power flow problem (SCACOPF) is a critical piece in the daily operations of power systems. We will present a solution methodology for SCACOPF problems that uses nonlinear programming, interior-point algorithms, and parallel computing to solve realistic SCACOPF instances in realtime. We will outline our primal decomposition approach and discuss its mathematical advantages and challenges as well as its current distributed-memory asynchronous (lock free) parallel implementation. In the second part of the talk, we will discuss opportunities for considerable improvements of realtime ACOPF optimization capabilities that can be obtained from the computing power increase expected from the emerging GPU processors. To this end, we will present challenges in utilizing effectively such computing hardware for the solution of large-scale ACOPF problems.

QA – Recent Advances in Solving Large-Scale AC OPF

Electricity Demand Side Management via Time-and-Level-of-Use Pricing

Speaker: Miguel Anjos (University of Edinburgh)

Biography:Miguel Anjos is a Professor and Chair of Operational Research in the School of Mathematics, University of Edinburgh.  In addition, he is a fellow of EUROPT, the European Working Group on Continuous Optimization, and the Schöller Senior Fellow for 2020. Miguel holds an Inria International Chair and is a fellow of the Canadian Academy of Engineering. Miguel is also a licensed professional engineer in Ontario and a senior member of IEEE. Miguel’s research is concerned with using mathematical optimization to provide guaranteed optimal or near-optimal solutions for important classes of large-scale discrete nonlinear optimization problems arising in engineering applications. In particular, mathematical optimization can help to improve the overall performance of the electric power system. Miguel has been working to support the development of smart grids.

Abstract:Time-and-Level-of-Use (TLOU) is a recently proposed pricing policy for electric energy, extending Time-of-Use with the addition of a capacity that users can book for a given time frame, reducing their expected energy cost if they respect this self-determined capacity limit. In this presentation we will present the principles of TLOU and provide an overview of the work done on mathematical optimization approaches for various aspects of TLOU and its potential impact on power systems operations.

Restriction: Please note that I am hosting an ENRE Online Event on Thu 10 Dec for 1000-1130 Eastern time, and that I am on a PhD defense on Fri 11 Dec for 0900-1300 Eastern time. Please schedule my talk to avoid those conflicts. 

Optimization formulations for storage devices

Speaker: Ross Baldick (University of Texas, Austin)

Biography:Ross Baldick is a Professor Emeritus in the Department of Electrical & Computer Engineering at The University of Texas at Austin. Dr. Baldick has published over one hundred refereed journal articles and has research interests in a number of areas in electric power. He is a former editor of IEEE Transactions on Power Systems and former chairman of the System Economics Sub-Committee of the IEEE Power Engineering Society Power Systems Analysis, Computation, and Economics Committee.  Dr. Baldick is a Fellow of the IEEE and the recipient of the 2015 IEEE PES Outstanding Power Engineering Educator Award.

His current research involves optimization, economic theory, and statistical analysis applied to electric power system operations, the public policy and technical issues associated with electric transmission under electricity market restructuring, the robustness of the electricity system to terrorist interdiction, electrification of the transportation industry, and the economic implications of integration of renewables.

Abstract:We consider a storage device, such as a pumped storage hydro generator, that has a state-of-charge together with mutually exclusive charging and generating modes and which is purchasing or selling electricity. We develop valid inequalities for a simplified storage model that uses binary variables to represent the charging and generating modes and also consider an objective that evaluates the net benefit of traded electricity and stored energy. We present conditions for the optimum of the continuous relaxation of the formulation to have binary values for the charging and generation mode variables and demonstrate the result numerically with a small example system. We also numerically verify that the valid inequalities can significantly improve the computation time for large-scale systems compared to existing models in the literature. This is joint work with Yonghong Chen and Bing Huang.

Optimization methods for coordination of power and water distribution networks

Speaker: Niko Gatsis (University of Texas, San Antonio)

Biography:Nikolaos Gatsis (M’13) received the Diploma (Hons.) degree in electrical and computer engineering from the University of Patras, Patras, Greece, in 2005, and the M.Sc. degree in electrical engineering and the Ph.D. degree in electrical engineering (with a minor in mathematics) from the University of Minnesota, Minneapolis, MN, USA, in 2010 and 2012, respectively. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA. His research focuses on optimal and secure operation of smart power grids and other critical infrastructures, including water distribution networks and the global positioning system. He was a recipient of the NSF CAREER Award. He has co-organized symposia in the area of smart grids in the IEEE global conference on signal and information processing (GlobalSIP) in 2015 and IEEE GlobalSIP in 2016. He has also served as a Co-Guest Editor for a special issue of the IEEE Journal of Selected Topics in Signal Processing on critical infrastructures.

Abstract:Although electricity distribution grids and water distribution systems are traditionally operated independently, these systems are interconnected and interdependent. The interdependency is chiefly manifested through the electricity consumption of electric-driven water pumps. The coordination of the two networks can bring about benefits to both systems in terms of economic and sustainable operation. This talk presents optimization methods for managing resources across power and water distribution networks while simultaneously accounting for the coupling between the two infrastructures. The principal challenge arises from the nonconvex water network hydraulics. Novel successive linearization methods provide an efficient solution to the problem. Case studies illustrate the benefits with respect to decoupled designs and are verified by the benchmark software EPANET.

QA – Demand Side Management, Storage, and Coordination of Power and Water Networks

Data-Driven Sample Average Approximation with Covariate Information

Speaker: Guzin Bayraksan (Ohio State University)

Biography:Guzin Bayraksan is an Associate Professor in the Department of Integrated Systems Engineering at the Ohio State University, and an affiliated faculty member of the Sustainability Institute. She received her Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin. She is the recipient of 2012 NSF CAREER award, 2012 Five Star Faculty Award (UA), and the 2008 INFORMS best case study award. Her research interests are in stochastic optimization, particularly Monte Carlo simulation-based and data-driven methods for stochastic programming with applications to water resources management and energy systems.

Abstract:We study optimization for data-driven decision-making when we have historical observations of the uncertain parameters (e.g., demand) within the optimization model together with concurrent observations of covariates (e.g., seasonality, weather forecast).  Given a new covariate observation, the goal is to choose a decision (e.g., generator schedules) that minimizes the expected cost conditioned on this observation.  We investigate three data-driven frameworks that integrate a machine learning prediction model within a stochastic programming sample average approximation (SAA) for approximating the solution to this problem.  The frameworks we investigate are flexible and accommodate parametric, nonparametric, and semiparametric regression techniques.  We derive conditions on the data generation process, the prediction model, and the stochastic program under which solutions of these data-driven SAAs are consistent and asymptotically optimal, and also derive convergence rates and finite sample guarantees.  Computational experiments validate our theoretical results and demonstrate the potential advantages of our data-driven formulations over existing approaches.   Potential applications in energy and extensions will be briefly discussed.

Capacity Planning with JuDGE

Speaker: Andy Philpott (University of Auckland)

Biography:Dr. Andy Philpott is a Professor at University of Auckland, and he is the director of the Electric Power Optimization Centre. He received his PhD and MPhil from University of Cambridge, and his BSc and BA degrees from Victoria University of Wellington. His research interests span most of mathematical programming, in particular linear, non-linear and stochastic programming and their application to operations research problems, in particular optimal planning under uncertainty, capacity expansion in telecommunications and power networks, optimal power generation hydro-electric power systems, stochastic optimization in supply chains, and optimal yacht routing under uncertainty. Much of his recent research has been conducted as part of the Electric Power Optimization Centre, which develops optimization and equilibrium models of electricity markets.

Abstract:JuDGE is an open-source Julia package that solves multi-stage stochastic programming models for capacity expansion. It applies a form of Dantzig-Wolfe decomposition to decouple a scenario tree into nodes representing different states of the world. The first part of the talk will briefly cover the theory underlying JuDGE and describe some recent applications of JuDGE to problems in electricity transmission expansion planning and long-term planning for 100% renewable electricity systems. The second part of the talk will be more tutorial in nature, demonstrating the features of the package, and how to download and apply it to capacity planning problems.

Using Optimization to remove barriers for Machine Learning Applications in Power Systems

Speaker: Spyros Chatzivasileiadis (Technical University of Denmark)

Biography:Spyros Chatzivasileiadis received the Diploma degree in electrical and computer engineering from the National Technical University of Athens, Athens, Greece, in 2007, and the Ph.D. degree from ETH Zurich, Zurich, Switzerland, in 2013. He is currently an Associate Professor with the Technical University of Denmark (DTU), Kongens Lyngby, Denmark. Before that, he was a Postdoctoral Researcher with the Massachusetts Institute of Technology, Cambridge, MA, USA and with Lawrence Berkeley National Laboratory, Berkeley, CA, USA. On March 2016, he joined the Center of Electric Power and Energy, DTU. He is currently working on power system optimization and control of AC and HVDC grids, including semidefinite relaxations, distributed optimization, and data-driven stability assessment.

Abstract:In this talk, we introduce methods that remove the barrier for applying neural networks in real-life power systems, and unlock a series of new applications. More specifically, we introduce a framework for (i) verifying neural network behavior in power systems and (ii) obtain provable worst-case guarantees of their performance. Up to this moment, neural networks have been applied in power systems as a black-box; this has presented a major barrier for their adoption in practice. Using a rigorous framework based on mixed integer linear programming, our methods can determine the range of inputs that neural networks classify as safe or unsafe; and, when it comes to regression neural networks, our methods allow to obtain provable worst-case guarantees of the neural network performance. Such methods have the potential to build the missing trust of power system operators on neural networks, and unlock a series of new applications in power systems and other safety-critical systems.

QA – Uncertainty and machine learning in energy systems operations and planning

Data-driven control in autonomous energy systems

Speaker: Florian Dörfler (ETH Zurich)

Biography:Florian Dörfler is an Associate Professor at the Automatic Control Laboratory at ETH Zürich. He received his Ph.D. degree in Mechanical Engineering from the University of California at Santa Barbara in 2013, and a Diplom degree in Engineering Cybernetics from the University of Stuttgart in 2008. From 2013 to 2014 he was an Assistant Professor at the University of California Los Angeles. His primary research interests are centered around control, optimization, and system theory with applications in network systems such as electric power grids, robotic coordination, and social networks. He is a recipient of the distinguished young research awards by IFAC (Manfred Thoma Medal 2020) and EUCA (European Control Award 2020). His students were winners or finalists for Best Student Paper awards at the European Control Conference (2013, 2019), the American Control Conference (2016), the Conference on Decision and Control (2020), the PES General Meeting (2020), and the PES PowerTech Conference (2017). He is furthermore a recipient of the 2010 ACC Student Best Paper Award, the 2011 O. Hugo Schuck Best Paper Award, the 2012-2014 Automatica Best Paper Award, the 2016 IEEE Circuits and Systems Guillemin-Cauer Best Paper Award, and the 2015 UCSB ME Best PhD award.

Abstract:Inspired by recent end-to-end data-driven approaches for power systems operation, power electronics control, and building automation, we consider the problem of optimal and constrained control for unknown systems. A novel data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe control policies using real-time feedback driving the unknown system along a desired trajectory while satisfying system constraints. Using a finite number of data samples from the unknown system, our proposed algorithm uses a behavioral systems theory approach to learn a non-parametric system model used to predict future trajectories. We show that, in the case of deterministic linear time-invariant systems, the DeePC algorithm is equivalent to the widely adopted Model Predictive Control (MPC), but it generally outperforms subsequent system identification and model-based control. To cope with nonlinear and stochastic systems, we propose salient regularizations to the DeePC algorithm, which are founded on recent advances in distributionally robust optimization. We illustrate our results with experimental and simulation case studies from autonomous energy systems.

Generalized Benders Decomposition for Resilient Electricity Networks

Speaker: Saurabh Amin (Massachusetts Institute of Technology)

Biography:Saurabh Amin received the Ph.D. degree in systems engineering from the University of California, Berkeley, CA, USA, in 2011. He is the Robert N. Noyce Career Development Associate Professor with the Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. His research focuses on the design and implementation of high confidence network control algorithms for infrastructure systems. Dr. Amin was the recipient of NSF CAREER award, and Google Faculty Research award.

Abstract:Bilevel mixed-integer programs arise naturally in problems concerning power systems resilience. The Benders Decomposition method attempts to solve such problems by decomposing into a master problem and inner sub-problem(s), and solving them iteratively. In each iteration, a stronger relaxation of the master problem is solved by adding a Benders cut which involves a product-sum of variables in the master problem and values of dual variables in the inner problem. However, the classical method performs poorly when the inner problem consists of binary variables and non-linear constraints. In this work, we suggest a new heuristic to address this limitation by modifying the so-called Generalized Benders Decomposition. Our approach involves modifying the right-hand side of the Benders cut to be a value determined by the sum of values of a subset of inner dual variables. We show how the size of this subset can be selected to achieve a reasonable tradeoff between solution accuracy and computational effort. We also discuss how to strengthen the modified Benders cut by using the reduced cost of inner problem’s dual linear relaxation and leveraging the properties of power system restoration.

This work is joint with Devendra Shelar and Ian Hiskens.

Online State Estimation for Systems with Asynchronous Sensors

Speaker: Andrey Bernstein (National Renewable Energy Laboratory)

Biography:Andrey Bernstein received the Ph.D. degree in electrical engineering from the Technion – Israel Institute of Technology, Haifa, Israel, in 2013. Between 2010 and 2011, he was a Visiting Researcher with Columbia University, New York, NY, USA. During 2011–2012, he was a Visiting Assistant Professor with the Stony Brook University, Stony Brook, NY, USA. From 2013 to 2016, he was a Postdoctoral Researcher with Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. Since 2016, he has been a Senior Scientist with National Renewable Energy Laboratory, Golden, CO, USA. His research interests are in the optimization and control problems of complex systems, with application to intelligent power and energy systems.

Abstract:We investigate the problem of estimating the state of a network from heterogeneous sensors with different sampling and reporting rates. In lieu of a batch linear least-squares (LS) approach – well-suited for static networks, where a sufficient number of measurements could be collected to obtain a full-rank design matrix – we propose an online algorithm to estimate the possibly time-varying network state by processing measurements as and when available. The design of the algorithm hinges on a generalized LS cost augmented with a proximal-point-type regularization. With the solution of the regularized LS problem available in closed-form, the online algorithm is written as a linear dynamical system where the state is updated based on the previous estimate and based on the new available measurements. Conditions under which the algorithmic steps are in fact a contractive mapping are shown, and bounds on the estimation error are derived for different noise models. Numerical simulations, including a power system case, are provided to corroborate the analytical findings.

Competitive control of energy systems via online optimization

Speaker: Adam Wierman (California Institute of Technology)

Biography:Adam Wierman is a Professor with the Department of Computing and Mathematical Sciences, California Institute of Technology. He is also the Director of the Information Science and Technology Initiative, Caltech. He is the Founding Director of the Rigorous Systems Research Group and the Co-Director of the Social and Information Sciences Laboratory. His research interests center around resource allocation and scheduling decisions in computer systems and services. He was a recipient of the 2011 ACM SIGMETRICS Rising Star Award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been co-author on papers that received the Best Paper Awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance (twice), IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS.

Abstract:Online optimization is a powerful framework in machine learning that has seen numerous applications to problems in distributed systems, robotics, autonomous planning, and sustainability. At Caltech, we began by applying online optimization to design sustainable data centers a decade ago; and now we have used tools from online optimization to develop algorithms for demand response, energy storage management, electric vehicle charging, and beyond. In this talk, I will highlight both the applications of online optimization and the theoretical progress that has been driven by these applications. Over the past decade, the community has moved from designing algorithms for one-dimensional problems with restrictive assumptions on costs to general results for high-dimensional non-convex problems that highlight the role of constraints, predictions, delay, and more. In particular, I will present a new connection between online optimization and adversarial control has emerged, and I will highlight how advances in online optimization can lead to advances in the control of time-varying linear dynamical systems.

The Economic Value of a Centralized Approach to Distributed Resource Investment and Operation

Speaker: Ben Leibowicz (University of Texas, Austin)

Biography:Dr. Benjamin D. Leibowicz is an Assistant Professor in the Graduate Program in Operations Research and Industrial Engineering at The University of Texas at Austin. The program is part of the Walker Department of Mechanical Engineering. Dr. Leibowicz holds a courtesy appointment in the Lyndon B. Johnson School of Public Affairs, and also supervises student research in the Energy and Earth Resources Graduate Program.

Dr. Leibowicz’s primary research interests are energy systems, energy and climate policy analysis, integrated assessment modeling, sustainable cities, technological change, and innovation. He approaches these topics by developing interdisciplinary models that incorporate diverse methodologies, including optimization, economics, game theory, stochastic control, and general equilibrium.

Prior to joining UT Austin, Dr. Leibowicz received both PhD and MS degrees in Management Science and Engineering from Stanford University, and earned a BA in Physics from Harvard University. While working toward his PhD, he was a research fellow in the Energy and Transitions to New Technologies groups at the International Institute for Applied Systems Analysis.

Abstract:Distributed energy resources have been almost exclusively deployed and operated under a decentralized decision-making process. In this paper, we assess the evolution of a power system with centrally planned utility-scale generation, transmission, distribution, and distributed resources. We adapt a capacity expansion model to represent both centralized and decentralized decision-making paradigms under various electricity rate structures. This paper shows that a centralized planning approach could save 7% to 37% of total system costs over a 15-year time horizon using a Western United States utility as a case study. We show that centralized decision-making deploys substantially more utility-scale solar and distributed storage compared to a decentralized decision-making paradigm. We demonstrate how a utility could largely overcome the complications of decentralized distributed resource decision-making by incentivizing regulators to develop electricity rates that more closely reflect time- and location-specific, long-run marginal costs. The results from this analysis yield insights that are useful for long-term utility planning and electric utility rate design.

Distributional and environmental justice accounting for the U.S. electricity sector

Speaker: Inês Azevedo (Stanford University)

Biography:Professor Azevedo is Associate Professor in Energy Resources Engineering at Stanford University. Inez received her PhD in Engineering and Public Policy from Carnegie Mellon University in 2009. Inez is passionate about solving problems that include environmental, technical, economic, and policy issues, where traditional engineering approaches play an important role but cannot provide a complete answer. In particular, she is interested in assessing how energy systems are likely to evolve, which requires comprehensive knowledge of the technologies that can address future energy needs and the decision-making process followed by various agents in the economy. She has been awarded “Young Scientists Under 40” by World Economic Forum (WEF) in 2014, Philip L. Dowd Fellowship Award from CMU in 2017, and C3E Women in Clean Energy Research Award in 2017.

Guiding innovation: using optimization methods to evaluate the design space for novel low-carbon energy technologies

Speaker: Jesse Jenkins (Princeton University)

Biography:Jesse D. Jenkins is an assistant professor at Princeton University with a joint appointment in the Department of Mechanical and Aerospace Engineering and the Andlinger Center for Energy and Environment. He is also an affiliated faculty with the Center for Policy Research in Energy and Environment at the Princeton School of Public and International Affairs and an associated faculty at the High Meadows Environment Institute.  

Jesse is a macro-scale energy systems engineer with a focus on the rapidly evolving electricity sector, including the transition to zero-carbon resources, the proliferation of distributed energy resources, and the role of electricity in economy-wide decarbonization. 

Jesse completed a PhD in Engineering Systems (’18) and MS in Technology and Policy (’14) at the Massachusetts Institute of Technology and a BS in Computer and Information Science (’06) at the University of Oregon. He worked previously as a postdoctoral Environmental Fellow at the Harvard Kennedy School and the Harvard University Center for the Environment, a researcher at the MIT Energy Initiative, a research fellow at Argonne National Laboratory, the Director of Energy and Climate Policy at the Breakthrough Institute, and a Policy and Research Associate at Renewable Northwest. 

Abstract:Development & commercialization of novel energy technologies takes time. New technologies will enter evolving future electricity systems and compete with a range of extant resources, introducing considerable uncertainty for developers, researchers, and funders. Given limited resources, which avenues for cost or performance improvement are most important for future commercial success and societal impact? This presentation will discuss how optimization models of long-term electricity system capacity expansion and wide-scale parametric uncertainty analysis can be employed to evaluate the “technology design space” for novel low-carbon technologies and provide insights to guide R&D efforts and funding and policy decisions. Results from a recently completed evaluation of long duration energy storage will be presented. Extensions of this general approach to evaluate design choices for flexible carbon capture and geothermal energy systems will also be described.

QA – Economics, Environmental Justice, and the Future of Energy System Transition