Research Highlights

Community Microgrids

We introduce the concept of “Community Energy Cells” (CEC). CEC refers to a group of DERs and controllable loads (PV, ESS, electric vehicle chargers, smart buildings, etc.) in close geographic proximity, which can be collectively represented via a Cell Aggregator (CA) as a single controllable entity with respect to the main power grid. With proper coordination with the utility, each CEC can potentially operate independently in a microgrid mode during service interruptions and provide backup power and telecommunications. It can also facilitate the deployment of renewable energy sources in Low-Moderate-Income (LMI) communities since they provide new revenue making renewable energy more reachable and affordable. Our main hypothesis is that decentralized deployment and operation of DERs within CEC is a key requisite to addressing climate change and grid resiliency while achieving energy justice

Transportation electrification

Our group, along with Con Edison, New York City Transit (NYCT), and New York State Energy Research and Development Authority (NYSERDA), explore how regenerative braking energy can provide strategic smart grid services, and hence improve the efficiency, reliability, and resilience of the New York power grid. In order to achieve this goal, we perform a thorough research study on NYCT’s traction power system and associated Con Edison systems, using real data, to investigate the feasibility, applicability, pros and cons, and barriers of deploying various regenerative energy recuperation techniques. This study aims at providing a comprehensive set of criteria and guidelines for decision-makers to evaluate the most suitable technology (i.e. best Value Proposition for a Con Edison/NYCT Collaboration). In addition, we develop algorithms that control the flow of recuperated regenerative energy, enabling the use of the application that provides the highest value (considering the impact on ConEd and NYCT operations) at any given time/location.

Power system resilience

Improving the protection and resilience of critical infrastructures (CIs) in the United States against natural disasters and manmade threats is an imperative short-term goal. An infrastructure is “a network of independent, mostly privately-owned, man-made systems and processes that function collaboratively and synergistically to produce essential goods and services”. Among those infrastructures, eight are considered critical (telecommunications, electric power systems, natural gas and oil, banking and finance, transportation, water supply systems, government services, and emergency services).

Network modeling of failure modes and propagation is needed by managers to devise strategies that mitigate the impact of failures across interdependent CIs. Although much progress has been made in network modeling of CIs to date, these efforts are incomplete representations and thus have limited utility: (1) top-down modeling efforts do not capture the complexity of CIs; (2) new and evolving situations and technologies invalidate dependency assumptions, such as the importance of geometric proximity in failure propagation; (3) methods are mostly empirical and statistically driven rather than based on principles of physics; and (4) most models only examine one CI and neglect interdependencies.

To improve failure and resilience modeling of interdependent CIs, we develop an innovative flow-based network model based on the influence graph concept and a novel failure index. This new methodology uses a bottom-up, hybrid physics-based/data-driven representation to capture unprecedented detail and greatly advance the modeling and mitigation of interdependent CIs. This work is then expanded using reinforcement learning to aid in decision-making. The objectives of this research are to develop the methodology, to verify and validate our proposed approach using test cases, and to use this framework within an intelligent CI planning and operation tool that will mitigate interdependent failure modes. Thus, the outcome of this research will be a framework to account for CI interdependencies that is critical for failure and disaster planning and recovery.

Food-Water-Energy Nexus

Many cities across the globe are facing difficult challenges in managing their food, water, and energy systems. The challenges stem from the fact that the issues of food, water, and energy are often tightly connected with each other, not only locally but also globally. This is known as the Food-Water-Energy (FWE) nexus. An effective solution to a local water problem may cause new local problems with food or energy, or cause new water problems at the global level. On a local scale, it is difficult to anticipate whether solutions to one issue in the nexus are sustainable across food, water, and energy systems, both at the local and the global scale. Innovative solutions that encompass the nexus are particularly important to enable cities to better manage their food, water, and energy systems and understand the benefits and tradeoffs for different solutions.

This project seeks to develop a shared urban data and modeling framework to help cities analyze and characterize FWE systems and nexus interrelationships. The framework will utilize a common urban 3D data model that will be shaped by urban stakeholder requirements and be applicable to regions and cities in Europe and the United States. This framework will help stakeholders identify, quantify and visualize cross-sectoral and cross-media impacts to FWE systems from various decisions — from urban development strategies to FEW infrastructure investments. The results will provide data that can help cities across the globe sustainably provide energy, water, and food supplies under healthy and economically productive conditions.

We gratefully acknowledge the support of our research sponsors and collaborators.