Publications

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This report explores the reliance on communication systems for bulk grid operations and considers selected options as a supplement to cyber security. The extreme scenario of a complete loss of communications for power grid operation is assessed, presenting a bounded, worst-case perspective. The paper explores grid communications failures and how a system modifications can, at an increased cost, retain a moderate level of preparedness for a loss of communications and control when used in partnership with cyber security protocols. Doing so allows the increased economic and secure operation that communication based controls affords, but also ensures a level of resilient operation if they are lost. The motivation of this paper is due to the proliferation of photovoltaic (PV) resources, and more generally, smart-grid resources within the US grid, which are requiring more and more active and wide-area controls. Though the loss of communication and control can affect nearly any grid control system, the risk of losing load at large scales requires a broad view of system interconnectivity, so it has been evaluated from a transmission perspective in this report.

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

Power system utilities continue to strive for increased system resiliency. However, quantifying a baseline system resilience, and deciding the optimal investments to improve their resilience is challenging. This paper discusses a method to create scenarios, based on historical data, that represent the threats of severe weather events, their probability of occurrence, and the system wide consequences they generate. This paper also presents a mixed-integer stochastic nonlinear optimization model which uses the scenarios as an input to determine the optimal investments to reduce the system impacts from those scenarios. The optimization model utilizes a DC power flow to determine the loss of load during an event. Loss of load is the consequence that is minimized in this optimization model as the objective function. The results shown in this paper are from the IEEE RTS-96 three area reliability model. The scenario generation and optimization model have also been utilized on full utility models, but those results cannot be published.

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This report documents the use of wind turbine inertial energy for the supply of two specific electric power grid services; system balancing and real power modulation to improve grid stability. Each service is developed to require zero net energy consumption. Grid stability was accomplished by modulating the real power output of the wind turbine at a frequency and phase associated with wide-area modes. System balancing was conducted using a grid frequency signal that was high-pass filtered to ensure zero net energy. Both services used Phasor Measurement Units (PMUs) as their primary source of system data in a feedforward control (for system balancing) and feedback control (for system stability).

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This report characterizes communications network latency under various network topologies and qualities of service (QoS). The characterizations are probabilistic in nature, allowing deeper analysis of stability for Internet Protocol (IP) based feedback control systems used in grid applications. The work involves the use of Raspberry Pi computers as a proxy for a controlled resource, and an ns-3 network simulator on a Linux server to create an experimental platform (testbed) that can be used to model wide-area grid control network communications in smart grid. Modbus protocol is used for information transport, and Routing Information Protocol is used for dynamic route selection within the simulated network.

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In 2012, Hurricane Sandy devastated much of the U.S. northeast coastal areas. Among those hardest hit was the small community of Hoboken, New Jersey, located on the banks of the Hudson River across from Manhattan. This report describes a city-wide electrical infrastructure design that uses microgrids and other infrastructure to ensure the city retains functionality should such an event occur in the future. The designs ensure that up to 55 critical buildings will retain power during blackout or flooded conditions and include analysis for microgrid architectures, performance parameters, system control, renewable energy integration, and financial opportunities (while grid connected). The results presented here are not binding and are subject to change based on input from the Hoboken stakeholders, the integrator selected to manage and implement the microgrid, or other subject matter experts during the detailed (final) phase of the design effort.

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This report proposes a reformulation of U.S. ISO/RTO-managed wholesale electric power mar- kets for improved reliability and e ciency of system operations. Current markets do not specify or compensate primary frequency response. They also unnecessarily limit the participation of new technologies in reserve markets and o er insu cient economic inducements for new capacity invest- ment. In the proposed market reformulation, energy products are represented as physically-covered rm contracts and reserve products as physically-covered call option contracts. Trading of these products is supported by a backbone of linked ISO/RTO-managed forward markets with planning horizons ranging from multiple years to minutes ahead. A principal advantage of this reformulation is that reserve needs can be speci ed in detail, and resources can o er the services for which they are best suited, without being forced to conform to rigid reserve product de nitions. This should improve the business case for electric energy storage and other emerging technologies to provide reserve. In addition, the facilitation of price discovery should help to ensure e cient energy/reserve procurement and adequate levels of new capacity investment.

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