Improving Energy Reliability by Co-Optimization Planning for Interdependent Electricity and Natural Gas Infrastructure Systems
FUNDED BY DOE/CLARKSON UNIVERSITY (2016 - 2019)
The electricity grid and the natural gas network are two essential infrastructure systems in the U.S. energy industry, which were originally designed and managed independently. However, because of the planned retirement of many coal-fired generators, the deeper penetration of renewable energy sources, and the commercially sustainable gas price, their interactions have intensified over the last five years. Hence, in order to ensure environmentally friendly, reliable, and cost-effective electricity and gas production and delivery, it is important to jointly optimize these two systems. However, due to their scales, complexities, and requirements/regulations, such a co-optimization planning problem is very challenging in both modeling and computation aspects. To address this critical challenge, this project will build analytical decision support models and design efficient solution methods to aid the energy industry in formulating and computing practical-scale co-optimization problems.
FUNDED BY DOE/CLARKSON UNIVERSITY (2016 - 2019)
The electricity grid and the natural gas network are two essential infrastructure systems in the U.S. energy industry, which were originally designed and managed independently. However, because of the planned retirement of many coal-fired generators, the deeper penetration of renewable energy sources, and the commercially sustainable gas price, their interactions have intensified over the last five years. Hence, in order to ensure environmentally friendly, reliable, and cost-effective electricity and gas production and delivery, it is important to jointly optimize these two systems. However, due to their scales, complexities, and requirements/regulations, such a co-optimization planning problem is very challenging in both modeling and computation aspects. To address this critical challenge, this project will build analytical decision support models and design efficient solution methods to aid the energy industry in formulating and computing practical-scale co-optimization problems.
UNDED BY NSF (2013 - 2019, WITH NON-COST EXTENSION)
The objective of this CAREER proposal is to study the impacts of short-term variability and uncertainty of renewable generation (RG) and demand response (DR) as well as hourly chronological operation details of energy storage (ES) and generators on the long-term planning via the proposed co-optimized generation, transmission, and DR planning solutions. The interaction among variability, uncertainty, and constraints from long-term planning and hourly chronological operation will be quantified for enhancing security and sustainability of power systems with significant RG, DR, and ES. This research can be used to evaluate effective load carrying capability (ELCC) of variable energy sources, and to study policies on portfolios of energy production and storage techniques. The research and educational findings would help educate engineers to meet challenges of the secure and sustainable electricity infrastructure.
