Constructing statutory energy goal compliant wind and solar PV infrastructure pathways

Concerns over climate change have led governments around the world to establish a range of renewable, low-carbon energy goals. Plans for meeting these targets vary widely in their ambition, specificity, and time horizons. Wind and solar electricity generation will feature prominently in future energy systems that meet these renewable, low-carbon energy goals. Implementing largescale wind and solar PV infrastructure configurations in a timely fashion will require cooperation between and among electric grid stakeholders and communities that host the infrastructure. This paper presents methods for constructing a diverse range of wind and solar PV energy infrastructure pathways that meet statutory energy goals, measuring their land use impacts, and assessing their performance relative to electricity demand. A case study on the state of Vermont’s statutory energy goals from its 2016 Comprehensive Energy Plan is presented as an example. While total wind and solar PV infrastructure requirements would increase several-fold, Vermont’s statutory energy goals can be met while occupying less than 1% of the state’s land area. Vermont electricity demand was most effectively met by balanced configurations of wind and solar PV similar to the state’s present wind and solar PV resources, while 100% wind or 100% solar PV configurations were less effective.

generate large quantities of electricity on relatively small, widely separated parcels of land. This 27 dynamic leads to significant land use and related environmental landscape impacts in the few 28 areas that host the generators themselves, leaving most other areas of the landscape essentially 29 unaffected. A future wind and solar PV powered grid will likely draw energy from electricity 30 generation infrastructure that is distributed much more widely across the landscape than incumbent 31 generators thanks to their reliance on prevailing weather conditions for electricity generation and 32 their inherent modularity [17] [18]. In turn, the infrastructure siting processes that attend electricity 33 system decarbonization driven by wind and solar PV will not only rise sharply in number but will 34 also frequently trigger opposition from those who oppose the landscape disruption that wind and both in time and in scope. In North America, regional transmission organizations (RTOs) and surface. Using [24]'s method, R b is calculated as follows: where: 127 cos θ z = cos φ sin δ + cos φ cos ω cos δ For overnight hours, R b is set to zero. R b is capped at 4 to limit artificial overproduction of solar 128 power in hours very near sunrise and sunset. R b is then used to calculate solar panel capacity 129 factors, CF P V , as follows: where S JDS is the solar irradiance at the surface in W/m 2 from the JDS and S CS is the estimated interpolating generation between hours on a minutely basis, the general trends of the wind and sun 145 resources intra-hour are captured, though some variability is undoubtedly missing as compared 146 to the real-world meteorological conditions. Capturing this variability would require higher time 147 resolution data which is not yet available.  1 Wind turbines cannot be placed directly next to one another as solar PV panels can due to the inherent spacing required between wind turbines to maintain operational safety and downwind wake effects on neighboring wind turbines. This spacing is referred to in this work as indirect land use. The modeling restriction of 9 wind turbines per 9km 2 imposed here thus means that indirect land use is incurred at a rate of 1/3 km 2 per MW AC of wind turbine capacity. that weight infrastructure type and infrastructure siting method to the user's specifications are also where other infrastructure of its own type is already located (hereafter referred to as clustering), 196 and randomly. Finally, the model randomly selects the grid box which will receive the new  to the electricity sector, it is likely that some fraction of presently non-electric energy consumption 225 in Vermont and elsewhere will be electrified even under business-as-usual conditions. This study 226 will therefore consider, in general terms, the potential increase in electricity demand in Vermont

Annual electricity imports, in-state generation, and consumption
Vermont relies on a range of in-state and out-of-state electricity generation capacity to meet its load increases and decreases. The sharp load increase between 4AM and 7AM and corresponding 290 load decrease between 6PM and 10PM are particularly challenging for grid operators to manage.

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As controllable generation sources are replaced by intermittent generators like wind and solar PV, 292 it will be increasingly difficult for grid operators to meet load reliably and safely. Measuring

Modeling assumptions and parameters
This paper applies a number of modeling assumptions and parameterizations to the REGS in order  We therefore elect to leave the JDS' wind speed data unchanged. Irradiance data were 311 reduced by 15% to offset the sunny bias present in the northeastern CONUS as depicted    Table 2 shows the corresponding mean annual electricity generation performance of the 397 two alternative wind and solar PV infrastructure siting methods and of the initial Vermont wind 398 and solar PV infrastructure configuration. As expected, the maximum generation siting methods 399 produce infrastructure configurations that outperform Vermont's actual configuration. Mean 400 annual solar power production is approximately 6% higher in the maximum generation scenario 401 as compared to the initial Vermont configuration while wind power generation nearly doubles.

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The random placement scenario also yields slight improvements in both wind and solar PV mean  Table 2: Mean annual electricity generation (TWh) from hypothetical alternative Vermont wind and solar PV infrastructure arrangements. NOTE: For modeling simplicity, 150 MW AC of wind turbine capacity (fifty 3 MW AC wind turbines) were sited in the maximum generation and random placement scenarios. This puts the 'maximum generation' scenario and 'random placement' scenario at a 1 MW AC advantage against Vermont's initial wind turbine nameplate capacity.

Land use impacts of Vermont SEG-compatible deployments
The rest of section 4 presents modeled expansions of Vermont wind and solar PV infrastructure 407 using three siting methods. The first two siting methods used are the maximum generation and 408 random placement methods described above; the third siting method used is named 'clustering'.

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The clustering siting method weights each grid box according to how much land is already

Assessing wind and solar PV deployments versus hourly load
Finally, we examine each wind and solar PV infrastructure deployment scenario for its performance 517 relative to real hourly Vermont load data. Modeled hourly electricity generation data for the years

Discussion
The foregoing case study demonstrates how more granular modeling of wind and solar PV compare and contrast with Vermont were hampered by the lack of datasets equivalent to [32]. The 566 diversity of potential pathways for meeting SEGs and broader goals like the "rapid and far-reaching 567 transitions" called for by the IPCC means that this work only represents one part of the process 568 for finding and delivering a consensus electricity system decarbonization solution [1]. utility of this information is then unlocked when its findings are used to inform and initiate 579 further analyses and stakeholder discussions. It is from these processes that the ultimate electricity 580 system decarbonization pathways will be determined. To that end, we will now discuss a range of 581 additional topics that interlock with and overlap the work undertaken here.

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As noted, the Vermont case study shows that, among the three infrastructure types modeled,  While infrastructure replacement means that more efficient wind turbines or solar PV panels can 608 be installed, it also allows for land leases to expire and generation capacity to be lost. Capturing 609 these factors in future modeling activities could also enhance the utility of this work. impacts of electricity systems as a whole. 636 We have elected not to incorporate energy storage in this work as we feel it would 637 significantly extend the scope of the work, add substantial modeling complexity, and stray from 638 the paper's core purpose of assessing SEGs 4 . Instead, we feel this paper best serves as an enabler of further modeling and analysis in more focused areas, particularly power systems analysis, by type, distribution, land use impacts, and performance relative to load. 648 We have also elected not to undertake explicit mathematical optimization analyses in this 649 paper for similar reasons. As with the energy storage case, introducing optimization methods

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to the suite of test scenarios represents a significant extension of this paper's scope. Identifying 651 optimal placements of new wind and solar PV infrastructure to meet SEGs with respect to one or 652 more geospatial parameters, the electric grid, economic criteria, or other constraints is a worthy 653 task, but one which can easily stand on its own in a separate paper. We believe this paper's 654 outcomes and methods can be used to facilitate and more richly inform these efforts, particularly 655 those undertaken by RTOs and ISOs. Specifically, we also believe that optimization with respect 656 to certain parameters (e.g. maximizing electricity generation) could lead to overfitted solutions 657 that are unlikely to be feasible to implement. For example, if a strictly optimal solar PV panel