Research Projects

Avangrid has piloted world leading flexible interconnection technologies to facilitate the low-cost integration of solar farms into the distribution power grid. Compared to traditional "Static Interconnection," this flexible technology offers several advantages: 1) lowering interconnection costs for solar developers, 2) clustering solar connections to streamline the congested interconnection queue, and 3) reducing interconnection timelines (in New York, the average solar project interconnection takes about 3 years!). However, the current technology relies on the static capacity of grid assets (e.g., transformer/conductor ratings), without incorporating dynamic asset features in response to the changing meteorological conditions. Additionally, the impacts of climate change, including rising temperatures and regional variations in meteorological conditions, have yet to be fully considered.

This research project aims to assess the solar hosting capacity of distribution grids with dynamic asset ratings and evaluate the negative impact on this hosting capacity from climate change. Under the guidance of engineers at Avangrid and faculty at Clarkson, the student researcher will investigate: 1) temperature trends driven by climate change, 2) dynamic asset ratings in response to changing meteorological conditions, and 3) solar hosting capacity of distribution grid assets under climate change.

New York has established ambitious climate goals, including a mandate to achieve 6,000 MW of solar power, 6,000 MW of energy storage by 2030, 9,000 MW of offshore wind by 2035, and 100% decarbonization of the electricity system by 2040. The increasing integration of variable renewable energy sources exposes the state's power grid to unprecedented uncertainties and variability, particularly during extreme events. To maintain grid reliability, system operators rely on both in-market and out-of-market tools to re-dispatch generation resources in order to meet peak demand and mitigate the risk of load loss. Recently, NYISO developed the Capacity Analysis Commitment Tool (CACT) to recommit generation resources when a reliability risk is anticipated. However, operational risk metrics have not yet been defined to guide system operators, and the most cost-effective risk mitigation strategies have yet to be explored.

The student researcher will collaborate with engineers from NYISO and faculty at Clarkson University to develop operational risk management technologies for the power grid operation. Specifically, the student will: 1) integrate Clarkson’s operational energy adequacy tool into the NYISO CACT prototype; 2) calculate risk metrics based on real-time grid conditions; and 3) assist in selecting the most cost-effective generation commitments to mitigate the risk of load loss in grid operation.

River ice can significantly hinder the efficient operation of hydropower plants by clogging water intakes and increasing water head, potentially leading to multi-million-dollar generation losses at hydropower facilities operated by the Ontario Power Generation (OPG) and the New York Power Authority (NYPA) along the St. Lawrence River. A forecasting tool could provide hydropower operators with early warnings about potential river ice issues, allowing plant operators to take proactive measures such as adjusting flow control and refining generation bidding strategies.

This project aims to further develop an advanced machine learning-based ice forecasting tool that uses weather variables to identify and learn behavioral patterns. The student will expand upon a preliminary version of this tool developed by the research team by incorporating additional weather parameters—such as water temperature, wind direction, cloud cover, and irradiance—expected to improve the accuracy and confidence of the river ice forecasts.

Data centers are critical infrastructure in the digital world, yet their operation is increasingly challenged by electrical load shedding and power supply instability. This project explores integrating system loss of load risks into workload scheduling and migration strategies for distributed data centers, enhancing resilience against extreme events and power constraints. The integration of stochastic models for assessing operational vulnerability, developed in power grid resilience studies, is proposed to optimize workload scheduling and migration under constrained power conditions.

Electric power outages can disrupt daily life, business operations, and essential services, leading to economic losses for customers. The Value of Lost Load (VOLL) quantifies these economic impacts by estimating the cost of power outages for different customer segments, such as residential, commercial, and industrial users. Despite its importance, accurate and region-specific VOLL estimates remain scarce. This research project aims to estimate the VOLL for New York customers using economic models. Under the guidance of faculty and industry professionals, the student researcher will 1) analyze outage data and economic variables to identify outage patterns and their consequences, and 2) estimate regionally differentiated VOLL estimates for New York load zones. This project will provide valuable insights into the economic impact of power outages and support more effective grid planning and investment decisions.

As the demand for electricity grows, driven by electrification and renewable energy integration, increasing grid capacity is critical to meeting energy needs and ensuring reliability. However, expanding grid capacity requires significant investment, and understanding its economic value is essential for optimizing resource allocation. This research project aims to estimate the economic value of increased grid capacity by evaluating its impact on regional economic growth, reliability, and integration of renewable energy. The student researcher will 1) use economic models to quantify the monized benefits of increased grid capacity in New York, 2) examine case studies of grid expansion projects, and 3) develop a framework to evaluate the investment costs and social benefits, providing decision-makers with actionable insights for future grid planning. By assessing the economic value of grid capacity, this project will contribute to understanding how infrastructure investments can drive sustainable economic growth and energy resilience.

Electric power utilities have been granted monopoly privileges to serve customers within their service territory. In exchange for receiving this privilege, they have agreed to be regulated by state public service commission boards. This includes the obligation to serve customers within their territory. This obligation is interpreted differently from company to company as well as from state to state. In this project, the undergraduate researchers will conduct research to determine practices across a number of companies and states. They will identify differences in practices and develop recommendations as to the consequences of these different interpretations.

Northern New York has significant hydroelectric, wind and photovoltaic generation. It also has a high voltage dc tie with Quebec and relatively light load. This results in the export of power to southern New York. In this project, undergraduate researchers will build dynamic models of the Northern NY power grid and investigate potential for control interactions between these generation sources in the North Country and the downstate power grid. Background in differential equations and Matlab/Simulink desirable.

New York State has set ambitious climate goals, including requiring all new light-duty vehicles to be zero-emission by 2035 and all new medium- and heavy-duty vehicles by 2045. Additionally, the state aims to significantly reduce greenhouse gas emissions from transportation, which currently accounts for about one-third of New York’s total emissions. As a result, accurately forecasting electric vehicle (EV) charging demand has become a critical challenge for long-term power system generation and transmission resource planning.

This project seeks to develop an advanced deep learning-based tool for long-term EV charging demand forecasting. The tool will incorporate New York State socio-economic variables, transportation electrification trajectories, and existing EV charging station data to identify and learn key EV charging demand patterns.

A challenge in the design of large-scale power grids is that they often form sparse, and complex network structures in order to service customers. The complexity of these structures can lead to surprising effects, for instance during regular maintenance on the German power grid, crews caused a cascading failure by taking down a single line. Adding to the challenges, we now have a new mix of power sources coming online which is more heavily reliant on less reliable sources (that cannot necessarily be produced 24 hours a day) in wind and solar. This project will focus on modeling the large-scale design, and optimizing the network structures while also accounting for the new renewable power sources which are being added to the grid.