Grid

University of Texas at Austin (UT Austin) will use novel nanotechnology to develop a power transformer capable of operating for 80 years, increasing U.S. grid reliability. Key elements of UT- Austin’s research includes (1) the development and synthesis of cellulosic material and nano-additives (boron nitride, oxides) for paper and pressboard, (2) use of validated high-fidelity models to predict the thermal and electrical performance and life of transformers, (3) refurbishing a transformer to assess the impact of new materials, and (4) scale-up manufacturing of down-selected nanomaterials.

The key cause of transformer failure is overheating, which becomes more likely over time due to breakdown of mineral oil, an important transformer heat dissipation and insulating component. C-Crete Technologies will integrate advanced surface chemistry, colloidal engineering with high-throughput characterization, and standardized testing to develop insulating nanofluids for large power transformers (LPTs), with a projected lifetime greater than 80 years.

The University of Illinois at Urbana-Champaign (UIUC) has developed and prototyped a new architecture for distributed control and coordination of generation and load assets within a microgrid to provide frequency regulation services to the connected bulk power grid. The architecture’s decision-making capability relies on distributed computations over a cyber network, which is a radical departure from commercially available microgrid control solutions.

The power industry sees risk as statistically independent of today's operational practices and regulations. The challenge is convincing the industry to proactively and explicitly study, quantify, price, and incorporate risk into dispatch and response algorithms. Columbia University will develop a risk dashboard to address this challenge that will enable independent system operators (ISOs) of the electrical grid to compute and analyze engineering and financial risks occurring on operational time scales ranging from several minutes to days.

Modern electricity markets face new sources of uncertainty and risk due to a growing adoption of renewable resources, such as wind and solar power. Princeton will quantify the impact of uncertainty on daily system operation and will ascribe risk and costs to each asset’s contribution to overall system cost. The team will develop methods to quantify the stochasticity and variability in load, renewable generation, outages, and other uncertainties, and incorporate these to yield a probabilistic distribution of the system-wide operational cost.

The Energy Trading Analytics team will redesign a novel, state-of-the-art stochastic market tool to restructure wholesale real-time energy and reserve markets for improved information aggregation, reliability and security, and consumer choice. The design will be coupled with intelligent energy-portfolio risk management tools that enable consumers to prioritize flexible demand assets (e.g., air conditioners, water heaters, energy storage) to offer flexibility into markets as reliable resources.

The National Renewable Energy Laboratory (NREL)-led project team will develop an operating paradigm that leverages flexibility from distributed and bulk resources to cost-effectively manage delivery risk of intermittent resources, like solar and wind. Flexibility from a limited number of Distributed Energy Resources (DERs) is offered in wholesale energy markets today, and the value of flexibility is not yet recognized for economic hedging of delivery risk.

Tabors Caramanis Rudkevich’s (TCR) Stochastic Nodal Adequacy Platform (SNAP) will determine the value of resource adequacy for the electric power industry given significant penetration of intermittent and distributed generation. TCR and IBM’s The Weather Company are developing algorithms and software to stochastically value system adequacy by taking into account the weather-driven stochasticity of intermittent solar (utility and residential) and wind generation and weather-dependent variation in demand.

Duke University is developing a Grid that’s Risk-Aware for Clean Electricity (GRACE) energy management system (EMS) framework for characterizing the uncertainty of electric power system assets to optimize their performance. GRACE determines the scheduling, dispatch, and compensation of different resources in organized wholesale electricity markets and vertically integrated utilities by building upon industry-implemented market structure and algorithms to incorporate risk considerations.