Power Grids as Complex Networks: Formulating Problems for Useful Science and Science Based Engineering - Titles & Abstracts
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Ian Dobson
How can we model power systems for cascading blackouts?… considerable complications, cutting corners, and validation with data.
Abstract: Traditional detailed modeling procedures in power systems engineering are overwhelmed by the extreme complications of cascading blackouts, and the fine analytic advances in complex networks do not yet apply to power systems because of modeling problems. After briefly reviewing the full horror of the modeling difficulties, I will discuss how the nature of cascading blackouts shapes modeling choices, and how one might proceed to cut corners but nevertheless move forward towards some science and engineering applicable to cascading blackouts with validated models.
Adilson E. Motter
Synchronization in Oscillator and Power-Grid Networks
An imperative condition for the functioning of a power-grid network is that its power generators remain synchronized. Power grids deliver a growing share of the energy consumed in the world and will soon undergo substantial changes owing to the increased harnessing of intermittent energy sources, the commercialization of plug-in electric automobiles, and the development of real-time pricing and two-way energy exchange technologies. These advances will further increase the economical and societal importance of power grids, but they will also lead to new disturbances associated with fluctuations in production and demand. Disturbances can prompt desynchronization of power generators, which is a leading cause of large power outages. In this talk, after reviewing some pertinent advances in the study of synchronization of coupled oscillators, I will discuss a condition under which the desired synchronous state of a power grid is stable. This condition can be used to specify tunable parameters of the generators that enhance spontaneous synchronization. Because these results concern spontaneous synchronization, they are relevant both for reducing dependence on conventional control devices, thus offering an additional layer of protection, and for contributing to the development of networks that can recover from failures in real time.
In the second part of the talk, I will discuss our recent contributions to the control of power-grid dynamics through compensatory perturbations. A fundamental property of networks is that the perturbation of one node can affect other nodes, in a cascading process that may cause the entire or a substantial part of the system to change behavior and possibly collapse. Here, I will show how damage caused by external perturbations can often be mitigated or reversed by the application of compensatory perturbations that conform to the constraints of being physically admissible and amenable to implementation on the network. Our approach accounts for the full nonlinear behavior of real complex networks and can bring the system to a desired target state even when this state is not directly accessible. The effectiveness of this methodology is demonstrated through the identification of interventions that can control desynchronization instabilities in power-grid networks.
Zhifang Wang
Power Grid Network Analysis for Smart Grid Applications
Smart Grid is an umbrella term which refers to the modernization of electricity delivery systems with enhanced monitoring, analysis, control, and communication capabilities in order to improve the efficiency, reliability, economics, and sustainability of electricity services. The modernization happens at both the High Voltage transmission and the Medium or Low Voltage distribution grids.
This talk will present our investigation results on the statistical properties of power-grid based on a number of synthetic and real-world power systems, including the Small-World Properties, the Nodal Degree Distribution, the Graph Spectrum and Connectivity Scaling Property, and the Distribution of Line Impedance. It will also introduce an algorithm which is able to generate random topology power grids featuring the same topology and electrical characteristics found from the real data.The second part of this talk will cover various electrical centrality measures for power grids (which have been proposed in our past studies) and discuss their potential advantages in Smart Grid applications, such as the decentralized monitoring/controls and the implementation of Synchronous Phasor Measurement Units (Syn-PMUs).
Anna Scaglione
Cascading Overload Failures in Power Grid
Cascading failure in power grids has long been recognized as a sever security threat to national economy and society, which happens infrequent but results in costly damage. The triggering causes of cascading phenomena can be extremely complicated due to the many different and interactive mechanisms such as transmission overloads, protection equipment failures, transient instability, voltage collapse, etc. In the literature a number of models have been proposed to study the cascading overload failures in power grids. The first part of this talk will discuss the power flow distribution/redistribution in power grids through a couple of visualized examples, so that one may understand the special properties of power grids and realize the limitations of graph theoretic approaches.
Then we will introduce a stochastic Markov model of cascading overload failures in power grids, whose transition probabilities are derived from a stochastic model for the flow redistribution, and that can potentially capture the progression of cascading failures and its time span. Finally we wish to trigger an open discussion on the affecting factors that contribute to the grid vulnerability to such kind of cascading failures.
David P. Chassin
GridLAB-D: A Smart Grid Simulation Tool
ABSTRACT GridLAB-D is a new power distribution system simulation and analysis tool that provides information to those who design and operate smart grid systems. It is an agent-based simulation environment that incorporates advanced modeling techniques and high-performance algorithms to couple with powerflow, market, control system and load models. GridLAB-D™ was developed by the U.S. Department of Energy (DOE) at Pacific Northwest National Laboratory (PNNL), under funding from the Office of Electricity, in collaboration with industry and academia. David will discuss how GridLAB-D works and how it has been applied to the study of smart grid business cases, volt-VAR control, distribution automation, energy storage, renewable integration, and demand response.
BIOGRAPHY: David Chassin is the manager for Electric Power Systems Engineering team at Pacific Northwest National Laboratory and has more than 25 years of experience in the research and development of computer applications software for the architecture, engineering and construction industry. His research focuses on non-linear system modeling, high-performance simulation of energy and power systems, controls, and diagnostics. He is the principle investigator and project manager of DOE's SmartGrid simulation environment, called GridLAB-D and was the architect of the Olympic Peninsula SmartGrid Demonstration's real-time pricing system. He was granted several U.S. and international patents relating to Grid Friendly(TM) appliance technology. He is a Senior Member of IEEE, a member of the NASPI Data Network Management Task Team, the Western Electric Coordinating Council (WECC) Load Modeling Task Force and Market Integration Committee, and the North American Electricity Reliability Council (NERC) Load Forecasting Work Group.
Title: Looking for trouble – and finding it! Presenters: Maggie Eppstein and Paul Hines
Power systems are generally operated such that the failure of a single component (an “n-1 contingency”) does not result in a blackout. However multiple simultaneous component outages (“n-k contingencies”) can trigger large cascading blackouts, and grid operators are now required to protect against such problems. Identifying which combinations of component failures will cause large blackouts, however, is a hard problem due to the combinatorial size of the search space, and because of the complicated paths that cascading failures can take. We describe a stochastic ``Random Chemistry algorithm that can identify large sets of n-k contingencies that initiate cascading failures in a simulated power system. The method requires only O(log n) simulations per contingency identified, which is orders of magnitude faster than random search. We applied the method to a model of cascading failure in a power network with n=2896 transmission lines and identify 148,243 unique, minimal n-k branch contingencies (2 ≤ k ≤ 5) that cause large cascades, many of which would not be found by using pre-contingency flows, linearized line outage distribution factors, or performance indices as screening factors and which cannot be predicted from simple graph-theoretic metrics. Within each n-k collection, the frequency with which individual branches appear follows a power-law (or nearly so) distribution, indicating that a relatively small number of components contribute disproportionately to system vulnerability. We discuss a number of implications for modeling and assessing cascading failure risk in power systems.