Energy Systems Research | ECE | Virginia Tech

Research Areas

Energy Systems researchers develop devices, models, simulations, and algorithms to study and control the power distribution system, with the goal of preventing blackouts, integrating renewable energy, and making this critical infrastructure more reliable and more secure around the world.

Associated Labs & Facilities

Current Research

Demand management

One of our groups has developed Building Energy Management Open Source Software (BEMOSS) to improve sensing and control of equipment, reduce energy consumption, and help implement demand response in small- and medium-sized commercial buildings. According to the Energy Information Administration, about half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size. Medium-sized commercial buildings (between 5,001 and 50,000 square feet) constitute another 44 percent. These buildings typically do not use automation systems to monitor and control their buildings from a central location.

Integrating renewable energy

Integrating renewable energy increases the time-variability and uncertainty. Our work suggests that stochasticity might couple with inherent system flexibilities to enhance grid efficiency. For instance, voltage magnitudes can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement this opportunistic mode of operation, we are developing ergodic energy management schemes for dispatching resources in real time.

We are also making big strides in resolving issues with the variability of renewable energy sources. We have developed a new measure of solar variability, called the Probability Of Persistence (POP). The POP framework can determine the probability that the forecasted solar variability is within the ramping capability of the local power system, and provide the power and energy ratings of the additional accommodating resource if needed. For wind, we have developed a game-theoretic framework to determine the conditions for cooperation between energy storage operators and wind power producers.

Preventing blackouts

Recently, we developed a risk-based sequential Monte Carlo method to evaluate the vulnerability of composite power systems to cascading failure leading to blackouts. It was found that combining importance sampling and antithetic-variate techniques yields the largest decrease in the number of samples to be investigated while maintaining the accuracy of the results at a desired level.

Another group has designed and applied a microgrid system to improve the resilience and operational efficiency of the electric power system in Rwanda.

Smart Grid models/simulators

ECE researchers are working on a collaborative modeling and analysis capability for the International Smart Grid Action Network (ISGAN). Using a model of a seven-substation, 14-feeder system, we are investigating inverter-based control strategies for high-penetration solar generation and analyzing flow control for Silicon Valley Power.

Another group developed co-simulation techniques for the networking and communications of smart grids, based on a Global Event-driven Co-simulation (GECO) framework. GECO provides a platform to study hybrid system dynamics with smart grid and cybersecurity implications. Power system dynamics get influenced by the underlying communication infrastructure and studying the two systems as a single distributed cyberphysical system can lead to improved system reliability and improved resilience to breeches in cybersecurity.

We are developing an open-source GPS-synchronized Synchrophasor Simulator with event/simulation playback features. The simulator will be used by researchers to study the operation and effect of communication systems in wide-area measurement.

We recently developed the world's only synchrophasor-based three-phase linear state estimator. Instead of every few seconds, the estimator can deliver the state of the system up to 60 times per second, providing a more dynamic status of the power system.

Devices and systems

Virginia Tech developed the first smart grid technology for wide-area measurement: the Phasor Measurement Unit (PMU). We continue to develop PMU technology and analysis as PMUs become more integrated in the national grid. Recently, ECE researchers developed a technique to automatically calibrate the current and voltage transformers on a network that has PMUs installed. We are now investigating the possibility of determining transmission line parameters from PMU measurements at the same time that the instrument transformer calibration is carried out.

Grid security

Can the topology of the power transmission network be discovered using energy prices publicly available online? Although publishing wholesale locational electricity prices promotes a healthy market, it may involve cyberphysical security risks--our work shows that a third party could possibly unveil the topology of the underlying physical grid using publicly available market data. We are investigating the use of alternate market data in a batch or streaming manner and countermeasures to reduce the risk.

Microgrid & EV integration

Power distribution microgrids are being challenged by voltage fluctuations due to solar generation, demand response, and Electric Vehicles (EV). We are developing algorithms, models, and schemes to safely and efficiently integrate these growing technologies.

One team is exploring augmenting conventional voltage-regulating equipment with the reactive power capabilities of distributed generation units. We are currently developing accelerated control rules enabling plug-and-play operation of PV inverters under balanced and unbalanced conditions in distribution grids.

We are also developing distributed and coordinated multi-agent schemes to control a host of microgrids aimed at providing the main grid with a desired level of robustness and resiliency when subject to major disturbances that may lead to blackouts. In addition, we are designing vibrant retail energy markets that foster the adoption of these innovative technologies.

The impact of the growing EV penetration will first be visible at the electric power distribution level. An ECE team is developing a set of algorithms to optimize the operation of an electric power distribution network through a combination of demand response, solar photovoltaics, and energy storage to absorb the new crop of EVs, without a significant system upgrade.