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Environmental and health impacts of alternative grid configurations under large load growth scenarios
One-line summary
A solar energy research paper on Environmental and health impacts of alternative grid configurations under large load growth scenarios.
Engineering notes
Engineering notes will be added by the Power for Solar editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
Abstract This work investigates the health impacts of actively debated policy pathways targeting specific generation technologies. Exploring capacity expansion pathways that maximize or minimize the use of these technologies, this work explores novel directions for grid modeling by pairing an open-source capacity expansion model with a health impacts screening and mapping tool, exploring multiple grid configurations derived from a near least-cost optimal solution space, and leveraging forward-looking emissions inventory and demographic data. Using Texas as a case study, the Electric Reliability Council of Texas (ERCOT) grid system is modeled under large load growth scenarios through 2040, first in a cost-optimized case and then in a set of policy-informed, cost-constrained scenarios with objectives to minimize or maximize the capacity or dispatch of certain generation technologies. The NOx, SO2, PM2.5, and VOC emissions of the modeled ERCOT system are compared across scenarios and used as inputs for a health impacts screening and mapping tool. Associated health-related costs are subsequently compared to alternative grid-related costs. While objectives to limit renewable capacity or support dispatchable capacity result in significant health costs, limiting fossil fuel capacity and dispatch yields substantial health savings. Maximizing the expansion of renewable capacity can counterintuitively increase emissions and health costs due to a related expansion of high-emitting combustion turbines. This work presents a promising direction for comparing costs of alternative grid systems, highlighting paths forward for capturing technological, environmental, and health priorities in energy systems models.
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