Collaborative Research: Improving Energy Reliability by Co-Optimization Planning for Interdependent Electricity and Natural Gas Infrastructure Systems

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The electricity grid and the natural gas network are two essential infrastructure systems in the U.S. energy industry. They are 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. The effectiveness and benefit of co-optimization planning will be demonstrated and evaluated through an actual microgrid project and industrial collaborations. In addition to including doctoral students in research and creating educational materials for the next generation energy system planners and operations researchers, concrete projects will be designed to involve underrepresented students on utilizing analytical/computational tools to address real energy problems. Previous research on co-optimization planning of electricity and gas systems is limited, while also often neglecting critical reliability considerations, key random/uncertain factors, or the long-term multi-stage nature of planning problems. This research project will address these shortcomings by (1) investigating key interactions between electricity and gas systems with different spatial-temporal granularities as well as multiple planning and operation levels; (2) building a set of co-optimization planning models that simultaneously consider the multi-stage planning horizon, the hourly chronological operation details with critical random/uncertain factors, and the requirements of long-term reliability and short-term flexibility; (3) designing and implementing high-performance computational methods and tools through advanced decomposition strategies, strong approximation approaches, and effective hybrid methods; and (4) validating, demonstrating, and promoting the developed models and computational tools through an on-going microgrid project and established industry connections.