Title: Ensuring Long-Term Availability and Bankability of Offshore Wind Through Hurricane Risk Assessment and Mitigation
Abstract: We expect this proposal to remove a significant amount of the uncertainty associated with conjectures about hurricane risk with a targeted, industry-driven series of investigations that span from basic atmospheric science to highly applied offshore wind engineering. The team plans to examine the project through the following lens:
Are the risks large enough that the grid should take special precautions to provide backup power in case of such an event? Or are the risks relatively small compared to the vulnerability of the present power system to outages due to freezing or heat waves that increases electricity demands while at the same time reducing the capability of traditional power plants?
Title: Foundational Assistance to ISO/RTOs under Electricity Market Transformation
Abstract: ISOs and RTOs face numerous challenges in maintaining reliability, resiliency, and affordability in an evolving power system. A consortium of researchers that includes Argonne National Laboratory, National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory, Electric Power Research Institute, and Johns Hopkins University has been collaborating on a project to provide technical assistance and research to guide the Independent System Operator (ISO) and Regional Transmission Organizations (RTO), and their stakeholders, on the evolution anticipated as supportive of future electricity markets. The team has been working closely with a set of advisors from the U.S. ISOs and RTOs to advise on the needed R&D to support a reliable and economically efficient electricity market of the future.
Award Number: C18/SR/12676686
Title: Energy management system for smart sustainable buildings: planning, operation and optimal integration in the smart energy system (gENESiS)
Abstract: Striving to mitigate CO2 emissions in a cost-effective manner, the European Union (EU) has unprecedentedly emphasized, through its directive 2010/31/EU (revised in 2016), energy efficiency improvement in the building sector, responsible for 36% CO2 emissions in the EU. This directive supports renewable energy sources (RES) integration, imposing that, by the end of 2020, new buildings must be nearly zero-energy buildings (nZEBs), meaning that their on-site RES energy production and consumption be yearly nearly balanced. To achieve this goal, buildings are being equipped with RES and possibly energy storage devices. To ensure nZEB stakeholders compliance and investment profitability, it is vital to design an efficient energy management system (EMS) to optimally steer RES, storage, and deferrable loads (smart appliances, electric vehicles, heat pumps).
Funder: National Science Foundation | 2017-2021
Award Number: 1711850
Title: A Global Algorithm for Quadratic Nonconvex AC-OPF Based on Successive Linear Optimization and Convex Relaxation
Abstract: Non-convex programming involves optimization problems where either the objective function or constraint set is a non-convex function. These kinds of problems arise in a broad range of applications in engineering systems. Despite the substantial literature on convex and non-convex quadratic programming (general classes of optimization problems), most available optimization techniques are either not scalable or work efficiently only for convex quadratic programming and do not provide adequate results for non-convex quadratic programming. This project focuses on fundamental research on an integrated approach which the research team expects will lead to powerful solution methods for classes of non-convex programming problems. The new approach will be applicable for non-convex problems arising in many areas, such as power and energy systems, transportation, and communications. The project will involve students from underrepresented groups and will positively impact engineering education.
Award Number: 1622877
Title: A Fast and Efficient Power System Dynamic Simulator
Abstract: This project will develop a software program for dynamic analysis of power grids. In electric power industries, grid simulators are vital tools to evaluate and analyze dynamic behavior of the grid. Power grids are large-scale networks that are mathematically modeled by thousands of variables and equations. Analysis of such a large number of variables and equations is very time-consuming. Integration of intermittent renewable energy resources as well as small-scale low-inertia generators to modern power grids make the network analysis even more challenging. In the proposed algorithm (to become a software program) a novel mathematical solver is proposed that significantly reduces the computations required to solve the well-known differential-algebraic equations describing power systems. The proposed algorithm contributes to the reduction of the size of the Jacobian matrix and removal of the error loop in the power grid dynamic simulations. These improvements significantly increase the speed of the proposed software program in analyzing dynamic behavior of power grids when compared to the available methods. Preliminary studies show that the proposed power grid simulator can perform at least 30 times faster than the standard Newton-Raphson algorithm when simulating large grids with significant number of generators.
Funder: Louisiana Board of Regents | 2016-2018
Grant Number: LEQSF(2016-19)-RD-A-10
Title: A System of Systems Based Distributed Algorithm for Collaborative Power Transmission and Distribution Planning
Abstract: In smart electric power grids, the system’s physical structure is becoming more distributed, and different entities take the control responsibility of different parts of transmission and distribution systems. Although these entities might be independent, any decision made by a control entity affects decisions made by other entities as they are physically interconnected. The objective of the proposed research is to develop a system of systems engineering (SoSE) frameworks for collaborative power transmission and distribution planning in smart grids.