Globally-optimal Solution Algorithm for LCQP-based Security Constrained AC Optimal Power Flow in Electric Power Grids
Speaker: Masoud Barati (University of Pittsburgh)
Abstract:
The primary objective of this project is to develop high-performance optimization techniques that can effectively address the nonconvexity in a broad range of nonlinear energy problems, particularly the Optimal Power Flow (OPF) problem. While there is currently an urgent need for developing smart and robust OPF solvers, the conventional options currently available for DC-OPF are quite limited. This research will fundamentally address the Security Constrained Optimal Power Flow (SCOPF) with active and reactive quadratic constraints, forming a quadratic programming optimization problem that arises in many power grid planning and operation applications. Besides being non-convex, these problems are identified to be NP-hard. We propose a Globally-optimal Algorithm for SCOPF, a joint suite of Alternate Direction Method and Penalized Semidefinite Relaxations. In order to enforce a low-rank optimal solution, a penalized-SDP is proposed where the objective function is a nonconvex quadratic function with multiple negative eigenvalues, subject to linear constraints (LCQP). The proposed solution methodology in response to the resulting LCQP is centered on a joint suite of basic and powerful optimization techniques in convex optimizations, e.g., linearized approximation techniques, linear and global search procedures, bi-linear and convex relaxation, as well as the alternate direction methods. We also furnish our approach with new schemes and theories to establish the reasonable convergence of the algorithm and guarantee the global optimality of the solutions. We devised a new successive linear optimization based branch and bound approach founded on the principles of the classical linear approximation, improved convex relaxation, and the branch-and-bound technique to find the global optimal solution of the SCOPF problem. With the fast and robust linear programming and convex solvers, the envisioned our algorithm will push the frontiers of advanced power grid optimizations, specifically tailored to the SCOPF. We will also pursue examining the performance of the proposed algorithm and analyze its efficiency on the existing test cases including the large-scale synthetic data sets, under both pre- and post-contingency events addressing transmission lines and generating units’ outage scenarios.
Biography:
Masoud Barati is assistant professor of electrical and computer engineering at the University of Pittsburgh. He received his Ph.D. in electrical engineering from Illinois Institute of Technology in 2013. He was visiting professor at Booths Business school at University of Chicago. His research interests include developing optimization and mathematical model and algorithms for smart grid, wide area monitoring, and power system resiliency and recovery.
Contingency Screening for Power Grids Using Worst-case Analyses and Hierarchical Programming
Speaker: Hatim Djelassi (RWTH Aachen University)
Abstract:
We consider the problem of categorizing contingencies in power grids under uncertainty in order to support the provision of (n-1)-reliability. Following previous work (Fliscounakis et al. in IEEE Transactions on Power Systems 28(4):4909-4917, 2013), our aim is to categorize contingencies according to the preventive control actions that are required to guarantee nominal operation of a power grid under uncertainty and optimal corrective control actions. This problem formulation results in an existence-constrained semi-infinite program (ESIP), which belongs to the family of hierarchical programs. As a power grid model, we employ the DC power flow approximation together with disjunctive models for load distribution, bus merging and splitting, and phase shifting transformers (Djelassi et al. in Power Systems Computation Conference, 2018).
Due to the lack of rigorous algorithms for the solution of ESIPs, prior considerations of the categorization problem were limited to solving a feasibility problem. Now, our recently proposed algorithm for the global solution of ESIPs absent convexity assumptions (Djelassi and Mitsos in Journal of Optimization Theory and Applications, submitted 2019) enables the solution of the categorization problem. We present the results of a screening of contingencies on a large-scale power grid instance, where we solve the categorization problem for each member of a set of contingencies. We discuss the information that can be gained from categorization of contingencies as well as the tractability of the proposed approach for large-scale contingency screenings.
Biography:
Hatim Djelassi is a research associate at Professor Alexander Mitsos’ lab (AVT Process Systems Engineering) at RWTH Aachen University. He holds a bachelor’s degree in mechanical engineering with a specialization in energy engineering and a master’s degree in mechanical engineering with specializations in simulation and fluid technology from RWTH Aachen University. He is currently pursuing a doctorate degree under the supervision of Professor Alexander Mitsos. His research interests are optimization algorithms for hierarchical programs and their application in process and energy systems engineering.
Distributed Energy Resource (DER) Analytics
Speaker: Santiago Grijalva (Georgia Institute of Technology)
Abstract:
For many decades, electrical distribution grids were “wires-only.” They were designed and operated to function as energy pass-through infrastructure connecting bulk transmission system networks to the customer. Distribution systems never had to deal with generating resources. Today, distributed energy resources — such as solar PV, wind, energy storage, flexible loads, fuel cells, distributed generation, and electric vehicle charging infrastructure — are being rapidly deployed in distribution grids, which drastically changes distribution system operation and planning.
This talk will present key applications of machine learning and optimization to tackle emerging problems in distribution system DER integration, including detection and estimation of PV installations through machine learning, energy storage grid services scheduling, and PV fast hosting capacity determination using linearized manifolds.
Biography:
Santiago Grijalva is the Southern Company Distinguished Professor of Electrical and Computer Engineering and director of the Advanced Computational Electricity Systems (ACES) Laboratory at Georgia Tech. His research interests are in decentralized power system control, power system analytics and economics, and future sustainable energy systems. He has been principal investigator for research under DOE, ARPA-E, EPRI, PSERC, NSF, and numerous other sponsors. From 2002 to 2009 he was with PowerWorld Corporation. From 2013 to 2014 he was with the National Renewable Energy Laboratory (NREL) as founding director of the Power System Engineering Center (PSEC). Grijalva is a member of the NIST Federal Smart Grid Advisory Committee. He holds a Ph.D. in electrical and computer engineering from the University of Illinois at Urbana-Champaign.
Wholesale markets: Recent Successes and Emerging Needs
Speaker: Jessica Harrison (Midcontinent Independent System Operator)
Abstract:
The development of ISO/RTOs was a unique innovation in the power sector, leading to new methods of power system optimization and generating continuous evolution in market design.
In recent years, the industry has begun to witness another wave of changes with the advent of digitalization, de-marginalization and decentralization. This has prompted both the opportunity and need for further innovation and adaptation at the wholesale level. This presentation will discuss recent advancements in computational performance and market design as well as emerging opportunities and challenges such as enhanced transmission and distribution coordination, uncertainty adaptation and use of data analytics. Specific examples will be provided for each.
Biography:
Jessica Harrison is the Senior Director of Research and Development in the Market and Grid Strategy division of MISO. In this role, she leads MISO’s research initiatives and coordinates research policies and objectives. Her focus includes pursuing partnerships, researching emerging industry trends and technologies and supporting the integration of future grid resources. Jessica has a diverse background in energy, including experience with engineering analysis, public policy and economic analysis. Prior to joining MISO, Jessica lead consulting teams developing approaches to integrate resources such as distributed energy resources, energy storage and demand response with the electricity grid and the electricity wholesale markets. Ms. Harrison holds dual Master of Science degrees from the Massachusetts Institute of Technology in Technology and Policy and in Civil and Environmental Engineering, and a Bachelor of Science degree in Physics from the University of Michigan.
Lending an Industry Vantage Point
Speakers: R. Craig Higgins, Jeff Baker, and Elise Ferrer (Southern Company)
Abstract:
R. Craig Higgins, Jeff Baker, and Elise Ferrer will discuss the market’s appetite for renewable energy, both from a power and a financial perspective. They will evaluate how the demand for renewables materializes in their contracts by examining their current and future Power Purchase Agreements. A Power Purchase Agreement, or PPA, is a long term contract between two parties for the sale and purchase of electricity. They will also define the role of data & analytics and machine learning in their organization.
Biography:
R. Craig Higgins, Manager, Business Development – Craig Higgins serves as Manager of Business Development for Southern Power, a subsidiary of Southern Company. Prior to his current role within Southern Power, Mr. Higgins had held various positions across the company in Generation Resource Planning and Retail Generation Development. Mr. Higgins was also the Manager of Power Resources at North Carolina Electric Membership Corporation. Mr. Higgins holds a bachelor’s degree in industrial engineering from the Georgia Institute of Technology and a master’s of business administration from Samford University.
Biography:
Jeff Baker, Modeling and Analytics Manager – Jeff manages a team of analysts that support the Commercial Optimization and Trading functions of Southern Power. Jeff has a Ph.D. in Applied Mathematics and has led optimization and analytics efforts in various roles in his career at Southern Company. Prior to his role at Southern Power Jeff worked in Southern Company’s Fleet Optimization and Trading organization which optimized unit commitment and dispatch of the Southern Retail Fleet.
Biography:
Elise Ferrer, Wholesale Marketing Analyst – Elise works as a data analyst supporting a range of internal customers on projects and performing several roles within the knowledge transformation process. Prior to her role at Southern Power, Elise worked in EY’s San Francisco office as a risk consultant. She holds undergraduate degrees from the University of Alabama in Management Information Systems and Accounting. She is currently pursuing her Masters in Analytics from Georgia Tech.
From the ACOPF to the ARPA-E Go Competition
Speaker: Hassan Hijazi (Los Alamos National Laboratory)
Abstract:
In this talk, we will present our latest contributions in terms of algorithm design and software development for solving the ACOPF problem with global optimality guarantees. We will then discuss Challenge 1 of the ARPA-E Go Competition and introduce the approach adopted to tackle large-scale security-constrained ACOPF problems.
Biography:
Hassan Lionel Hijazi received a Ph.D. in computer science from Aix-Marseille University in France. During his early career, Hijazi was part of multiple research labs including the Computer Science Laboratory of the Ecole Polytechnique in France, National ICT Australia, the Australian National University, and CSIRO.
Hijazi is currently a scientist at Los Alamos National Laboratory. His main field of expertise is mixed-integer nonlinear optimization with applications in energy systems.
He was the laureate of the 2015 “Rising Star” award by the Australian Society for Operations Research.
New Optimization Approaches in Power Flow Analysis and Robust State Estimation
Speaker: Xiaoming Huo (Georgia Institute of Technology)
Abstract:
To recover the power flow state (PFS) matrix, it is known that solving the corresponding system of nonlinear power flow equations can be formulated as a semi-definite programming (SDP) problem with a rank-1 constraint. The associated numerical problem is computationally challenging and intrinsically NP-hard. To obtain a rank-1 solution in the semi-definite programming problem, convex regularization is often used.
We study two approaches to improve the SDP approach. In the first one, we consider a new semi-definite programming algorithm to solve both power flow analysis and robust state estimation problem. Rather than convex relaxation, we consider a sequence of optimization problems that solves two types of convex problems alternatively. Convergence analysis provides the conditions under which the equivalency holds between the original problem and the newly proposed sequential optimization problem.
In the second approach, we explore a novel non-convex weighted nuclear norm regularization approach. Comparing to existing regularization approaches, the proposed weighted nuclear norm approach can converge faster to a rank-1 solution. We propose an algorithm with a convergence guarantee.
Numerical results on the 4-bus, 9-bus, and the 118-bus power flow systems shows that our proposed methods can better recover the rank-1 solution in comparison with the existing methods, as our method improves both the robustness and the accuracy. We also test the proposed method for a 2383-bus system and obtain the state-of-the-art result.
Based on joint work with Chuanping Yu, Zhen Zhong, Li-Hsiang Lin, and Bei Gou.
Biography:
Xiaoming Huo is an A. Russell Chandler III Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.
Huo’s research interests include statistical theory, statistical computing, and issues related to data analytics. He has made numerous contributions on topics such as sparse representation, wavelets, and statistical problems in detectability. His papers appeared in top journals, and some of them are highly cited. He is a senior member of IEEE since May 2004. He was a Fellow of IPAM in September 2004. He won the Georgia Tech Sigma Xi Young Faculty Award in 2005. His work has led to an interview by Emerging Research Fronts in June 2006 in the field of mathematics — every two months, one paper is selected.
Huo received a B.S. in mathematics from the University of Science and Technology, China, in 1993, and an M.S. in electrical engineering and Ph.D. in statistics from Stanford University in 1997 and 1999, respectively. Since August 1999, he has been an assistant/associate/full professor with the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He represented China in the 30th International Mathematical Olympiad (IMO), which was held in Braunschweig, Germany, in 1989, and received a golden prize.
Moment/sum-of-squares for optimal power flow and transient stability
Speaker: Cedric Josz (Columbia University)
Abstract:
Convex relaxations have proven to be an effective tool for providing lower bounds to challenging instances of the optimal power flow. We show how convex relaxations constructed with the moment/sum-of-squares approach yield tight lower bounds for a large class of instances, as well as the globally optimal voltages. In addition, this approach can be applied in the dynamic setting, in particular for transient stability analysis. This enables the computation of certified inner and outer approximations for the region of attraction of a nominal operating point. In order to determine whether a post-disturbance point requires corrective actions to ensure stability, one then simply needs to check the sign of a polynomial evaluated at that point. Thus, computationally expensive dynamical simulations are only required for post-disturbance points in the region between the inner and outer approximations.
Biography:
Cédric Josz has been an assistant professor at Columbia University since 2019. Prior to that, he did a postdoc at the University of California, Berkeley and LAAS CNRS in Toulouse, France. He earned his Ph.D. from the University of Paris VI in applied mathematics in 2016, in collaboration with the French transmission system operator and the French Institute for Research in Computer Science and Automation.
Leveraging machine learning to make probabilistic SCOPF more tractable/scalable/interpretable.
Speakers: Efthymios Karangelos and Louis Wehenkel (University of Liège)
Abstract:
The first part of the talk introduces the probabilistic SCOPF formulation, and discusses its increased complexity in terms of computational tractability and scalability, as well as in terms of interpreting its results. The second part of the talk discusses ongoing research aiming at using ideas from machine learning in order to circumvent some of these difficulties.
Biography:
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, Liege, Belgium, as a Post-Doctoral Researcher. His current research interests include power system modeling, reliability and risk management and stochastic optimization.
Biography:
Louis Wehenkel graduated in Electrical Engineering (Electronics) in 1986 and received the Ph.D. degree in 1990, both from the University of Liège (Belgium), where he is full professor of electrical engineering and computer science. His research interests lie in the fields of stochastic methods for modeling, optimization, machine learning and data mining, with applications in complex systems, in particular large scale power systems planning, operation and control, industrial process control, bioinformatics and computer vision. Recently, he has been the Scientific Advisor of the GARPUR European FP7 project.
The Value of Including Unimodality Information in Distributionally Robust Optimal Power Flow
Speaker: Johanna Mathieu (University of Michigan)
Abstract:
To manage renewable generation and load consumption uncertainty, chance-constrained DC optimal power flow (DC-OPF) formulations and various solution methodologies have been proposed. However, conventional solution approaches often rely on accurate estimates of uncertainty distributions, which may not exist. When the distributions are not known but can be limited to a set of plausible distributions, termed an ambiguity set, distributionally robust (DR) optimization can be used to ensure that chance constraints hold for all distributions in that set. However, DR DC-OPF yields conservative solutions if the ambiguity set is too large. Renewable generation and load consumption forecast error distributions are generally unimodal and so including only unimodal distributions within the ambiguity set reduces the conservatism of the DR DC-OPF. We have developed exact reformulations, approximations, and efficient solving techniques to include unimodality information into DR DC-OPF problems. This talk will describe these results and the value of including unimodality information in DR DC-OPF problems assessed through case studies on the IEEE 118-bus and 300-bus systems modified to include high penetrations of renewable generation.
Co-authors: Bowen Li, Ruiwei Jiang
Biography:
Quasi-Stochastic Electricity Markets
Speaker: Jacob Mays (Cornell University)
Abstract:
Operators of wholesale electricity markets clear sequential, deterministic auctions that attempt to represent an underlying dynamic, stochastic reality, with the additional complicating factor of non-convex production costs. In order to help resolve price formation issues that arise in this context, market designers have proposed operating reserve demand curves that inject an element of stochasticity into deterministic market clearing formulations, altering the procurement of reserves and therefore the pricing of both reserves and energy. The construction of these curves relies on contentious administrative parameters that lack strong theoretical justification. This paper proposes instead to construct curves based on reserve valuations implicit in non-market reliability processes performed by system operators. The proposed strategy promotes greater consistency between commitment decisions and eventual prices, reducing the need for out-of-market uplift payments or enhanced pricing schemes to address non-convexity.
Biography:
Jacob Mays is a postdoctoral associate and incoming assistant professor in the School of Civil and Environmental Engineering at Cornell University. His research focuses on applications of stochastic optimization and statistical learning in energy systems. He holds an A.B. in chemistry and physics from Harvard University, an MEng in energy systems from the University of Wisconsin–Madison, and a Ph.D. in industrial engineering and management sciences from Northwestern University.
Real Time Optimal Control of A Universal Power Flow Controller
Speaker: Sakis Meliopoulos (Georgia Institute of Technology)
Abstract:
Real-time optimization of extra high voltage systems has been an elusive goal. As FACTS devices are becoming faster, real time optimization of systems with FACTS devices is a desirable goal. We present an autonomous system for real time optimization and its planned application on a universal power flow controller at the 345 kV system of the New York Power Authority. The system consists of a dynamic state estimator that provides the feedback in terms of the real time model and operating condition with a latency of less than 1.5 cycles, an autonomous formulation of an optimal power flow, the real time solution of the optimal power flow and subsequently controlling the universal power flow controller. The objective of the optimal power flow is to levelize the power flow in the circuits as well as the voltage magnitude.
The overall system will be demonstrated on the New York Power Authority system at Marcy substation. The universal power flow controller is installed on the 345 kV system and it is capable of controlling MVAr injection as well as MW flow via series transformers capable of inserting up to 100 kV in series each and shunt reactors capable of injecting up to 300 MVArs each. Both are controlled via power electronic systems allowing fast control.
The work is supported by ARPA-E.
Biography:
A. P. Sakis Meliopoulos (M ’76, SM ’83, F ’93) was born in Katerini, Greece. He received the M.E. and E.E. diploma from the National Technical University of Athens, Greece, in 1972; the M.S.E.E. and Ph.D. degrees from the Georgia Institute of Technology in 1974 and 1976, respectively. In 1971, he worked for Western Electric in Atlanta, Georgia. In 1976, he joined the Faculty of Electrical Engineering, Georgia Institute of Technology, where he is presently the Georgia Power Distinguished Professor, the Georgia Tech Site Director of PSERC, and associate director for cyber-physical security of the Georgia Tech Institute for Information Security and Privacy (IISP). He is active in teaching and research in the general areas of modeling, analysis, and control of power systems. Dr. Meliopoulos has pioneered several new analysis and design techniques for bulk power reliability analysis, safety, protection and electromagnetic compatibility of electric power systems. Most well-known is the EPRI transmission reliability program TRELLS (now renamed TransCARE), the GPS-synchronized harmonic state measurement system for transmission systems (first (1993) wide area measurement system on NYPA), the distributed dynamic state estimation method, the setting-less relay, and the CYMSA software (Cyber-Physical Modeling and Simulation for Situational Awareness). He is the author of the books: (a) Power Systems Grounding and Transients, Marcel Dekker, 1988, (b) Lightning and Overvoltage Protection, Section 27, Standard Handbook for Electrical Engineers, McGraw Hill, 1993, and (c) Application of Time-Synchronized Measurements in Power System Transmission Networks, Springer, 2014. He holds three patents and he has published over 400 technical papers. In 2005 he received the IEEE Richard Kaufman Award and in 2010 received the George Montefiori Award, Sigma Xi Young Faculty award (1981), the outstanding Continuing Education Award, Georgia Institute of Technology (twice 2002 and 2014), the 2017 D. Scott Wills, ECE Distinguished Mentor Award, and several of his papers have received the best paper award. Dr. Meliopoulos is the Chairman of the Georgia Tech Protective Relaying Conference, a Fellow of the IEEE and a member of Sigma Xi.
Convex Relaxations of the Power Flow Equations: Recent Advances and Emerging Applications
Speaker: Daniel Molzahn (Georgia Institute of Technology)
Abstract:
The power flow equations model the relationship between voltages phasors and power flows and are therefore at the heart of many optimization and control problems relevant to electric power systems. The nonlinearity of the power flow equations results in a variety of algorithmic and theoretical challenges. Accordingly, many convex relaxation techniques have been developed to simplify the power flow equations. After overviewing a variety of previously proposed convex relaxation techniques, this presentation will describe recent advances in a particular formulation known as the QC relaxation. This presentation then provides examples showing how convex relaxation techniques help improve the tractability of emerging applications in power system optimization, including enabling data-driven algorithms for security assessment.
Biography:
Daniel Molzahn is an assistant professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. Daniel also holds an appointment as a computational engineer in the Energy Systems Division at Argonne National Laboratory. Daniel was a Dow postdoctoral fellow at the University of Michigan. He completed 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 application of optimization techniques to electric power systems.
Mobilizing Grid Flexibility for Renewables Integration through Topology Control and Dynamic Thermal Ratings
Speaker: Shmuel Oren (University of California, Berkeley)
Abstract:
The rapid penetration of renewable resources into the electricity supply mix poses challenges for the efficient dispatch of resources due to the inherent uncertainty and variability of such resources. Hence, in order to accommodate large amounts of renewables, it is necessary to account for their output uncertainty and mobilize the flexibility of the system embedded in conventional generation, demand side resources, and the transmission grid. In this talk we formulate a stochastic unit commitment optimization in which we expand the traditional recourse actions that are available to mitigate the adverse effect of renewables variability. In particular, we include in these recourse action, topology control through transmission switching and dynamic line ratings that account for the heating and cooling of transmission lines. We will demonstrate the potential gains from such recourse actions through test cases and discuss heuristic approaches for alleviating the computational burden resulting from such a formulation.
Joint work with Jiaying Shi.
Biography:
Shmuel S. Oren is the Earl J. Isaac Chair Professor in the Science and Analysis of Decision Making in the Industrial Engineering and Operations Research Department at the University of California, Berkeley and is Co-PI at the Tsinghua-Berkeley Shenzhen Institute (TBSI). He is a co-founder and the Berkeley site director of PSERC. His academic research focuses on planning and scheduling of power systems and on electricity market design and regulation. Oren was a member of the California ISO Market Surveillance Committee and a consultant to various private and government organizations in the U.S. and abroad, including the Public Utility Commission of Texas (PUCT) and the California Public Utility Commission (CPUC). He holds a Ph.D. in engineering economic systems from Stanford University and is a Life Fellow of IEEE, Fellow of the Institute for Operations Research and Management Science (INFORMS), and is a member of the U.S. National Academy of Engineering.
Models and Algorithms for Energy Storage
Speaker: Warren Powell (Princeton University)
Abstract:
Energy storage is emerging as a critical technology for handling the variability of renewables. As a problem class, energy storage is astonishingly rich, as it involves planning over multiple time scales, different generation and demand patterns, rolling forecasts, transmission constraints, and pricing dynamics, along with the complex physics that arise with different materials and technologies. While energy storage might be viewed as a form of inventory system, the dimensions of an energy storage problem are much more complex. In this talk, Powell will review the different dimensions of an energy storage problem and describe proper methods for modeling the dynamics, different types of uncertainty, and in particular, the design of control policies. He will review the four classes of policies that are fundamental to any sequential decision problem and demonstrate how all four play a role in the solution of energy storage problem.
Biography:
Warren Powell is a faculty member in the Department of Operations Research and Financial Engineering at Princeton University where he has taught since 1981. In 1990, he founded CASTLE Laboratory which spans research in computational stochastic optimization with applications initially in transportation and logistics. In 2011, he founded the Princeton laboratory for Energy Systems Analysis (PENSA) to tackle the rich array of problems in energy systems analysis. In 2013, this morphed into “CASTLE Labs,” focusing on computational stochastic optimization and learning.
Decomposable Formulation of Security Constraints for Power System Optimization and Optimal Energy Exchange
Speaker: Feng Qiu (Argonne National Lab)
Abstract:
When solving large-scale power systems optimization problems, such as the Day-Ahead Security-Constrained Unit Commitment Problem (DA SCUC), one of the most complicating factors is modeling transmission and security constraints. The most common formulation used in the industry, based on Injection Shift Factors (ISF), yields very dense and unstructured constraints, which not only may cause performance issues, but also makes it unsuitable for decentralized studies, such as optimal energy exchange pricing. In this work, we present a novel DC power flow formulation which has a decomposable block-diagonal structure, scales well for large systems, and can efficiently handle N-1 security requirements. The formulation also provides interpretable shadow prices, which can be used to determine optimal energy exchange prices between neighboring zones. Benchmarks on multi-zonal security-constrained unit commitment problems show that the proposed formulation can reliably and efficiently solve instances with up to 6,515 buses, with no convergence or numerical issues.
Biography:
Feng Qiu is a principal computational scientist and the manager of the Advanced Grid Modeling, Optimization, and Analytics group in the Energy Systems Division at Argonne National Laboratory. He received his Ph.D. from the School of Industrial and Systems Engineering at Georgia Tech in 2013. His current research interests include optimization and simulation methods, machine learning, and data analytics for power system operations and planning, grid resilience, cloud computing, and energy sector cyber security.
Convex Restrictions for Optimal Power Flow
Speaker: Line Roald (University of Wisconsin–Madison)
Abstract:
The optimal power flow is an optimization problem commonly solved in operation of electric power systems, and obtaining tight convex relaxations (i.e., good convex outer approximations) of this problem to obtain globally optimal solutions has recently received significant attention. However, since power systems are critical infrastructure, ensuring safe operations is of paramount importance and in many settings feasibility is a more important goal than optimality. In this talk, we describe convex restrictions, which represent safe convex inner approximations of the optimal power flow feasible region. In particular, these restrictions ensure that a feasible solution to the non-convex equality constraints representing the AC power flow equations for ranges of power injections exists. We discuss how this allows us to solve robust AC optimal power flow and guarantee feasible system trajectories.
Biography:
Line A. Roald is an assistant professor and Grainger Institute for Engineering fellow in the Department of Electrical and Computer Engineering at the University of Wisconsin–Madison. Prior to joining UW Madison, she obtained her Ph.D. at ETH Zurich in Switzerland and worked as a post-doctoral fellow at Los Alamos National Laboratory in New Mexico. Her research interests include optimization, risk assessment, and probabilistic methods for electric power systems.
Optimization and Control of a Smart Neighborhood
Speaker: Michael Starke (Oak Ridge National Laboratory)
Abstract:
Today, buildings consume 74% of the total electricity produced within the United States. Residential heating ventilation and air-conditioning (HVAC) and water heating (WH) systems represent a significant portion of this load and enabling flexibility can improve overall electric grid efficiency. In this presentation, a microgrid with transactive framework for optimization of HVAC and WH will be discussed. Implementation strategies, software architecture, and results of controls will also be discussed. Lessons learned on human interactions, cyber security challenges, and wide system controls will also be presented.
Biography:
Michael Starke is a power system systems integration researcher at the Oak Ridge National Laboratory (ORNL). He has been at ORNL for more than 10 years performing research in different areas of power systems analysis. He received his B.S, M.S., and Ph.D. in electrical and computer engineering at The University of Tennessee in 2004, 2006, and 2009 respectively. He is a member of IEEE and of the Power and Energy Society with over 50 publications in power systems and power electronics. His research areas have been primarily focused on energy storage, load control, and microgrids but he has been actively engaged in wind and solar generation research as well. In the microgrid area of research, he has led a team that developed an open-source microgrid controller called CSEISMIC, which is currently being utilized in a number of demonstration projects. Starke led a team that successfully constructed a secondary use energy storage system composed of Nissan batteries for deployment with Habitat for Humanity. He has also worked on several demand response projects with industrial load partners and received a patent on a tool developed to estimate the demand response potential of industrial plants.
Optimal Electricity Dispatching with Hourly Health Considerations
Speaker: Valerie Thomas (Georgia Institute of Technology)
Abstract:
Integrating accurate air quality modeling with decision making is hampered by complex atmospheric physics and chemistry and its coupling with atmospheric transport. Existing approaches to model the physics and chemistry accurately lead to significant computational burdens in computing the response of atmospheric concentrations to changes in emissions profiles. By integrating a reduced form of a fully coupled atmospheric model within a unit commitment optimization model, we allow a fully dynamical approach toward electricity planning that accurately and rapidly minimizes both cost and health impacts. The reduced-form model captures the response of spatially resolved air pollutant concentrations to changes in electricity-generating plant emissions on an hourly basis with accuracy comparable to a comprehensive air quality model. The integrated model allows for the inclusion of human health impacts into cost-based decisions for power plant operation. We use the capability in a case study of the state of Georgia over the years of 2004–2011, and show that a shift in utilization among existing power plants during selected hourly periods could have provided a health cost savings of $175.9million dollars for an additional electricity generation cost of $83.6 million in 2007 US dollars (USD2007). The case study illustrates how air pollutant health impacts can be cost-effectively minimized by intelligently modulating power plant operations over multi-hour periods, without implementing additional emissions control technologies.
Biography:
Valerie M. Thomas is the Anderson Interface Professor of Natural Systems at the Georgia Institute of Technology, with appointments in the H. Milton Stewart School of Industrial and Systems Engineering and in the School of Public Policy.
Immediately prior to coming to Georgia Tech, Thomas was the 2004-05 APS Congressional Science Fellow. Previously she worked at Princeton University at the Center for Energy and Environmental Studies, the Princeton Environmental Institute, and at the Woodrow Wilson School of Public and International Affairs; and at the Department of Engineering and Public Policy at Carnegie Mellon University.
Thomas is Associate Editor of the Journal of Industrial Ecology, board member of the Southeast Energy Efficiency Alliance, and a member of the US DOE/USDA Biomass Research R&D Technical Advisory Committee. She was a Member of the US EPA Chartered Science Advisory Board from 2003 to 2009.
Thomas’ research is on the environmental impacts and costs of energy systems, the environmental impacts of biofuels and other products and services, and the effects of policies and technologies on the development of energy systems.
Thomas has a PhD in high energy physics from Cornell University and a BA in physics from Swarthmore College. Her Ph.D. thesis work was on the catalysis of proton decay in grand unified theories, and her post-doctoral research was on the verification of nuclear arms control treaties. She was a participant in 1989 Black Sea Experiment on the detection of nuclear warheads, and was one of the founders of the International Summer Symposium on Science and World Affairs, now in its 26th year. She has more than 80 technical publications spanning energy, environment, optimization, physics, and nuclear arms control.
At Georgia Tech, Thomas teaches a graduate course in Energy Technology and Policy, and undergraduate courses in Energy, Efficiency and Sustainability, Engineering Economics, and Senior Design.
Optimization and Power Systems Resilience – Overview, Challenges, and Opportunities
Speaker: Jean-Paul Watson (Sandia National Laboratories)
Abstract:
Resilience in power systems operations and planning addresses high-consequence, low-probability events – in contrast to reliability, which addresses low-consequence, high-probability events. This talk explores various aspects of power systems resilience in relation to computational optimization – most of which represent open research challenges to the community. We will frame the discussion in the context of the US Department of Energy’s recently initiated North American Energy Resilience Model (NAERM) program, and discuss some of the both short- and longer-term research needs identified by the NAERM effort to date.
Biography:
Dr. Jean-Paul Watson is a Distinguished Member of Technical Staff in the Cyber Analytics and Data Science Department at Sandia National Laboratories in Livermore, California. He has over 17 years of experience applying and analyzing algorithms for solving difficult combinatorial optimization and informatics problems, in fields ranging from logistics and infrastructure security to power systems and computational chemistry. His research currently focuses on methods for approximating the solution of large -scale deterministic and stochastic mixed-integer and non-linear programs, with applications in the domain of electricity grid operations, planning, and resiliency. Previously, he developed solutions for real-world stochastic optimization problems in logistics (Lockheed Martin and the US Army) and sensor placement (US Environmental Protection Agency). He has published over 65 journal articles, several books, and is a co-developer of the widely used Pyomo (www.pyomo.org) open source Python package for expressing and solving mathematical optimization problems.
Indirect Mechanism Design for Efficient Integration of Uncertain Resources in Power System Operations
Speaker: Yue Zhao (Stony Brook University)
Abstract:
A two-settlement power market mechanism in which both conventional generators and renewable power producers (RPPs) participate is studied. The locational marginal prices at both stages are used for paying the RPPs. A key advantage of the mechanism is that the independent system operator (ISO) does not need to consider the renewable’s uncertainty when solving the economic dispatch problems in congestion-constrained power networks. Under this market mechanism, efficient methods for computing the Nash Equilibrium (NE) among the strategic price-making RPPs are developed. It is shown that this market equilibrium converges to the social optimum as the number of RPPs increases. The analytical derivation of the NE offers an elegant characterization of the market power of the competitive RPPs.
Biography:
Yue Zhao is assistant professor of electrical and computer engineering at Stony Brook University. He received a B.E. in electronic engineering from Tsinghua University, Beijing, China, in 2006 and an M.S. and Ph.D. in electrical engineering from the University of California, Los Angeles (UCLA), in 2007 and 2011, respectively. His current research interests are in the areas of mechanism design, machine learning, renewable energy integration, and smart grid.
Managing Risk in Market Operation – Practice and Challenges
Speaker: Tongxin Zheng (ISO New England)
Abstract:
Electricity market operation faces many uncertain events – ranging from loss of generation and transmission elements in the electric system to fluctuation of renewable generations to contingencies in the fuel delivery system. Each type of event can potentially cause disruption in the system operation, changing the outcome of electricity markets. Managing risk and maintaining system reliability becomes an important task for the market and system operators. Under the current environment, risk management is more related to satisfying a set of standards defined by North America Electric Reliability Corporation without much consideration of the potential costs of maintaining these standards. The integration of renewables and demand-side market participation also pose new challenges to the reliability standards and require new techniques for risk management. This presentation will discuss the existing practice of risk management in the market operation, its pro and cons, and potential research needs.
Biography:
Tongxin Zheng is currently the technical director at ISO New England. He manages research and development projects for the regional wholesale electricity market and research collaborations with the research community. He provides technical consultation on market and system operations to senior management and oversees the development of the market clearing engine and the market simulation software. His research interests are power system operation, optimization, and electricity market.