| dc.description.abstract | One of the largest existential challenges the US and other countries face is climate change. And maybe no other system is more crucial to combatting climate change than the grid. Increasingly more requirements have been put onto the transmission and distribution grids to play an even larger role than they have in the past; consider AI, EV, residential solar, electrification of heat, decarbonization of buildings, increasing energy rates, old infrastructure. Improving the grid is a necessity to decarbonize and innovate. However, utilities, backed up by state regulation, usually, but not always, use traditional techniques to expand grid capacity and increase resiliency as opposed to investing in modern grid technology that would more quickly allow for future innovations and decarbonization. These technologies, or techniques, are broadly called grid enhancing technologies, or GETs. There are rational reasons why GETs are not used more often. Utilities are correctly, highly risk averse because they must safely and adequately supply power directly to people. Utilizing new technologies, even if proven, can be a risk that utilities are unwilling, or not allowed, to take given their role and responsibility. But these risks are largely avoided with the technologies discussed in this paper and one could argue these technologies could not only make the grid cheaper to expand but also give the grid more resilience. This paper explores how a particular grid section can increase its solar penetration by avoiding traditional hosting capacity limitations and use not even innovative GETs but GETs that are largely tested and proven. Traditionally, at some limit, the utility will stop allowing solar in an area due to various grid constraints. This paper explores how a utility may solve these constraints using new methods to avoid large grid expansion CAPEX costs and utilize new technologies or techniques. Some of the techniques explored here are commercial scale energy storage support at substations, PV curtailment, and volt-var optimization control. | |
