Enabling a High Performing Smart Distribution Grid through Effective Recuperation of Electric Rail Regenerative Braking Energy
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 application that provides the highest value (considering the impact on ConEd and NYCT operations) at any given time/location.
The Rising Interdependencies between the Power Grid, E-Transportation, and ICT
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 be 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.
Microgrids have the potential to prominently increase grid resiliency, in the face of natural disasters. A microgrid -according to the definitions adopted by the US department of Energy- refers toa group of interconnected loads, distributed energy resources (DERs), and energy storage systems (ESS), which act as a single controllable entity with respect to the grid. Micogrid control can be generally achieved using one of two approaches: (1) communication based control; and (2) voltage-based droop control.
Communication-based control has received considerably less attention than droop control in the literature, since there has been a consensus that dependence on communication networks would lead to compromised reliability. In addition, utilizing droop control for microgrids seems more convenient since it resembles and builds upon our experiences with controlling the main grid. With advances in communication technologies (e.g. the transition to 5G and Internet of Things), we envision the smart grid of the future to consist of a myriad of microgrids that continuously coordinate with each other and with the main grid. Therefore, we hypothesize that increased reliance on communication will become inevitable.
We develop communication-based hybrid state/event driven control schemes for microgrids. These schemes are hierarchical, such that, on the primary control level, they encompass state driven local controllers, and on the secondary control level, an event driven MG centralized controller (MGCC) is used. The hybrid state/event driven design aims at reducing the communication network complexity and requirements. We develop mathematical models for the proposed control schemes using Finite State Machines (FSM), and use them to cover all possible modes/sub-modes of operation and assure seamless transitions among them, during various events.
Many cities across the globe are facing difficult challenges 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.