Presented by: Amro M. Farid
Since its first formulation in 1962, the Alternating Current Optimal Power Flow (ACOPF) problem has been one of the most important optimization problems in electric power systems. Its most common interpretation is a minimization of generation costs subject to network flows, generator capacity constraints, line capacity constraints, and bus voltage constraints. The main theoretical barrier to its solution is that the ACOPF is a non-convex optimization problem that consequently falls into the as-yet-unsolved space of NP-hard problems. To overcome this challenge, the literature has offered numerous relaxations and approximations of the ACOPF that result in computationally suboptimal solutions with potentially degraded reliability. While the impact on reliability can be addressed with active control algorithms, energy regulators have estimated that the sub-optimality costs the United States ~$6-19B per year. Furthermore, and beyond its many applications to electric power system markets and operation, the sustainable energy transition necessitates renewed attention towards the ACOPF. This webinar relays a new profit-maximizing security-constrained current-voltage AC optimal power flow (IV-ACOPF) model and globally optimal solution algorithm. More specifically, it features a convex separable objective function that reflects a two-sided electricity market. The constraints are also separable with the exception of a set of linear network flow constraints. Collectively, the constraints enforce generator capacities, thermal line flow limits, voltage magnitudes, power factor limits, and voltage stability. The optimization program is solved using a Newton-Raphson algorithm and numerically demonstrated on the data from a transient stability test case.
Presented by: Jay Caspary, Bruce Tsuchida, Jenny Erwin, Joey Alexander, and Kaveh Aflaki (Moderator)
Unlocking the Queue study was conducted by The Brattle Group, with input from the WATT Coalition and funding from GridLab, EDF Renewables North America, NextEra Energy Resources, and Duke Energy Renewables. The study’s sophisticated model demonstrates that dynamic line ratings, advanced power flow control, and topology optimization could enable Kansas and Oklahoma to integrate 5,200 MW of wind and solar generation currently in interconnection queues by 2025, more than double the development possible without the technologies.
Presented by: Rodrigo Daniel Trevizan
Energy Storage Systems (ESS) are an increasingly important asset in power grids, capable of providing several essential services to systems dominated by intermittent renewable energy resources. Such relevance turns ESS into a potential target for attacks. Some of their applications require these distributed devices to communicate with remote entities, such as grid operators, manufacturers and other field equipment, which poses a challenge for cybersecurity. This talk will present risks an overview of physical and cyberattacks on power grids, as well as current research, standards, and industry best practices that can make ESS more secure.
IEEE Smart Grid Socieity Participating members:
Presented by: Daiwon Choi and Vish Viswanathan
This webinar will be focused on reliability of electrochemical energy storage system (ESS) based on Li-ion batteries, which is currently the most widely deployed system. In this webinar, the authors focus on two specific areas, 1) fundamentals of Li-ion battery operation and degradation mechanisms, 2) examples of efficiency, performance and comparison of different Li-ion battery chemistries under standardized frequency regulation (FR), peak shaving (PS) and electric vehicle (EV) drive cycles. The lifecycle and degradation mechanisms derived from capacity, round trip efficiency (RTE), resistance, charge/discharge energy and total utilized energy of the battery chemistries will be compared. Performance and safety of electrical energy storage systems will also be addressed, reviewing applicable standards and gridscale storage data.
Presented by: Steven E. Collier, VP Business Development, Conexon
Inadequate electric grid capacity, reliability, stability, and quality of service have traditionally been addressed by expansion of utility network infrastructure, specifically generation, transmission, and distribution facilities (i.e., “wires” components). Environmental and other barriers, capital and O&M costs, and functional limitations increasingly inhibit their deployment and operational effectiveness. Nonutility distributed energy resources (DER), which are proliferating at an ever-increasing pace, can be helpful. These include wind generators, solar PV, combined heat and power (CHP), batteries, microgrids, premises energy management systems, smart appliances and equipment, even conservation and energy efficiency. To leverage these, new utility and nonutility monitoring and control systems (e.g., demand side management or DSM, automated distribution management systems or ADMS, and DER management systems or DERMS) are being deployed. Utilization of these and other new components and systems can reduce or even eliminate the need for additional utility wires infrastructure. Learn about these non-wires alternatives (NWA) and how they may change the planning, construction, and operation of the electric utility grid.
Part 1: Why NWA are needed
Part 2: Examples of NWA and how they work
Presented by: Mike Zhou, Chief Scientist, State Grid EPRI China
A new fast online analysis system development project, sponsored by the State Grid of China, was started in 2006. The primary goal of the project was set to reduce the online analysis overall round-trip time, from data acquisition to complete the analysis, from the current proximate 10 minutes to less than 60 seconds. The project development work has been completed at the end of 2018. A pilot new online analysis system has been deployed and running in a provincial dispatching center in China. The preliminary testing data indicates that the new online analysis system can achieve sub-second response speed.
Digital Twin (DT) has been in the Gartner’s Top 10 Strategic Technology Trends list every year since 2017. In the project development, an Online Analysis Digital Twin (OADT) has been implemented. In this presentation, the DT concept will be introduced in the context of application to power grid online analysis. A high-level overview of the project and some of the preliminary performance testing results will be presented. The overview will include the project high-level solution architecture, the DT concept used in framing the solution architecture, the OADT implementation to support the solution architecture realization, and the pilot online analysis system implementation details.
Presented by: Rui Yang, Research Engineer, National Renewable Energy Laboratory
Increasing amounts of heterogeneous sensor data and information are becoming available in energy grids from sources such as smart meters, distributed generation, and smart home energy management systems. Being able to collect, curate, and create actionable information with these data will be crucial to power systems operations with the increasing penetrations of distributed energy resources. In this webinar, we will present NREL’s latest work on developing predictive analytics to facilitate the real-time decision making in power systems operations. In this work, a high-precision predictive state estimator is first developed which employs sparse measurement data to provide system-wide awareness in distribution systems, while traditional state estimation techniques have difficulty coping with the low- observability conditions often present on the distribution systems due to the paucity of sensor measurements. Based on the predicted system conditions, grid operators can proactively control all the flexible resources by employing coordinated optimization techniques. The developed technologies allow grid operators to manage power systems with lean reserve margins while maintaining and enhancing grid reliability with high penetrations of renewable energy resources.
Presented by: Grace Gao, University of Illinois at Urbana-Champaign, Sriramya Bhamidipati, Doctoral student - Aerospace Engineering Department, University of Illinois at Urbana-Champaign, and Tara Yasmin Mina, Graduate student - Electrical and Computer Engineering Department, University of Illinois at Urbana-Champaign
In the future Smart Grid, Phasor Measurement Units (PMUs) will monitor the power grid state in real-time by synchronizing measurements across the network using GPS. However, because civilian GPS is unencrypted, PMUs are susceptible to spoofing. This webinar presents a spoofing detection algorithm using a wide-area, hybrid communication architecture: Each PMU securely transmits conditioned signal fragments containing the military P(Y) signal, which serves as an encrypted signature in the background of all authentic GPS signals. This signature is then verified amongst several, distant receivers, strategically picked with a subset selection algorithm. The algorithm has been demonstrated to successfully evaluates the authenticity of a widely dispersed receiver network, using real- world data recorded during a government-sponsored, live-sky spoofing event.
Presented by: Qun Zhou, Assistant Professior, University of Central Florida
Deep learning is powerful in data-driven applications, such as computer vision and natural language processing. Power system state estimation is data-driven in nature as the amount of measurement data is rapidly increasing with emerging sensing technology. This research explores the possibility of applying deep learning for power system state estimation. The proposed adaptive hybrid deep learning model, which incorporates the physical power flow mode, is both data driven and first principle based. It is trained in real time and intended for online state estimation.
Conventional state estimation is considered as single-snapshot, which estimates system variables by only using the measurement data at the moment. In fact, power system states are intrinsically correlated in time, but the true dynamics are very challenging to model. In this research, deep learning is employed to learn this dynamics and temporal correlations are explored among historical measurements. Two deep learning networks, feedforward neural net and long short-term memory net, are investigated. Results are presented in IEEE 14-bus and118-bus systems in terms of accuracy and robustness. The applicability and potential challenges are discussed.
Qiuhua Huang, Pacific Northwest National Laboratory
Jason Hou, Pacific Northwest National Laboratory
Part II of this webinar will focus on the-state-of-the-art applications with some R&D work from Pacific Northwest National Laboratory as examples. These include PMU data analytics, uncertainty quantification(UQ), tie-line exchange prediction, adaptive Remedial Action Scheme (RAS) settings using machine learning, power system emergency control using deep reinforcement learning, which powered AlphaGo to beat human Go champions. The ultimate goal of these projects is to leverage state-of-the-art machine learning technologies to make decision-makings in power system control centers—the “brain” of the grid— adaptive, robust and smart.
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