Low-carbon hydrogen could be an important component of a net-zero carbon economy, helping to mitigate emissions in a number of hard-to-abate sectors. The United States recently introduced an escalating production tax credit (PTC) to incentivize production of hydrogen meeting increasingly stringent embodied emissions thresholds. Hydrogen produced via electrolysis can qualify for the full subsidy under current federal accounting standards if the input electricity is generated by carbon-free resources, but may fail to do so if emitting resources are present in the generation mix. While use of behind-the-meter carbon-free electricity inputs can guarantee compliance with this standard, the PTC could also be structured to allow producers using grid-supplied electricity to qualify subject to certain clean energy procurement requirements. Herein we use electricity system capacity expansion modeling to quantitatively assess the impact of grid-connected electrolysis on the evolution of the power sector in the western United States through 2030 under multiple possible implementations of the clean hydrogen PTC. We find that subsidized grid-connected hydrogen production has the potential to induce additional emissions at effective rates worse than those of conventional, fossil-based hydrogen production pathways. Emissions can be minimized by requiring grid-based hydrogen producers to match 100% of their electricity consumption on an hourly basis with physically deliverable, `additional' clean generation, which ensures effective emissions rates equivalent to electrolysis exclusively supplied by behind-the-meter carbon-free generation. While these requirements cannot eliminate indirect emissions caused by competition for limited clean resources, which we find to be a persistent result of large hydrogen production subsidies, they consistently outperform alternative approaches relying on relaxed time matching or marginal emissions accounting. Added hydrogen production costs from enforcing an hourly matching requirement rather than no requirements are less than $1/kg, and can be near zero if clean, firm electricity resources are available for procurement.

The problem of wheTher, where, when, and whaT Types of transmission facilities to build in terms of minimizing costs and maximizing net economic benefits has been a challenge for the power industry from the beginning-ever since Thomas edison debated whether to create longer dc distribution lines (with their high losses) or build new power stations in expanding his urban markets. Today's planning decisions are far more complex, as grids cover the continent and new transmission, generation, and demand-side technologies emerge.We Must Consider Our Uncertain Future magnifying the complexity is our highly uncertain future. In fact, uncertainty has never really been a "new" problem for transmission planners, but they had more urgent problems to address up to now. over the past three decades, planners have been busy working out the expansion of the transmission network so it can effectively play its indispensable role in developing economically efficient and environmentally sustainable markets. Tools have been created to estimate the benefits of grid enhancements in terms of greater system reliability, increased energy trade, and decreased pollution emissions. many of these tools take the form

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A framework to quantify the value of model enhancements (VOME) in transmission planning models is proposed and applied to a case study of the large-scale, long-term planning of the Western Electricity Coordinating Council (WECC) system. The VOME, which is closely related to the concept of the value of information from decision analysis, quantifies the probability-weighted improvement in the system performance resulting from changes in decisions that result from model enhancements. The WECC case study shows that it is practical to quantify VOME and illustrates the type of insights that can be obtained. The values of four types of model enhancements are compared. The results show major benefits from considering long-run uncertainty using multiple scenarios of technology, policy, and economics; these benefits are as much as 14% of total benefits of new transmission built in the first ten years. But less benefit is obtained from more temporal granularity, more complex network representations, and inclusion of unit commitment constraints and costs. This framework can be applied to quantify the value of model enhancements in any planning context, such as integrated resource planning. Nomenclature () Expected present worth of system cost of making decision , based on the model with all enhancements () Binary parameter: if () = 1, then enhancement i is included in the model with setting ; if zero, then the enhancement is excluded. For instance, if there are three candidate enhancements, then 1 (*) = 1, 2 (*) = 0, 3 (*) = 1 indicates a model with only Enhancements 1 and 3 implemented. Set of enhancements, indexed by i, j = 1…n Optimal first stage transmission investments ("decision") from a model with enhancements setting specified by. (E.g., ω * , where 1 (*) = 1, 2 (*) = 0, 3 (*) = 1 , indicates investments from a model with only Enhancements 1 and 3 implemented.) Decision of no transmission investments in stage 1 Optimal decision from the model with all enhancements (i.e., () = 1 for all i). Model enhancement setting, describing what enhancements are included in the model formulation. Ω The set of all possible permutations of enhancements other than i

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