100% clean and renewable Wind, Water, and Sunlight (WWS) all-sector energy roadmaps for 53 towns and cities in North America

Jacobson, Mark Z.; Cameron, Mary A.; Hennessy, Eleanor M.; Petkov, Ivalin; Meyer, Clayton B.; Gambhir, Tanvi K.; Maki, Amanda T.; Pfleeger, Katherine; Clonts, Hailey A.; McEvoy, Avery L.; Miccioli, Matthew L.; Krauland, Anna-Katharina von; Fang, Rebecca W.; Delucchi, Mark A.

doi:10.1016/j.scs.2018.06.031

Cited by 111 publications

(37 citation statements)

References 46 publications

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“…It is possible, for example, that capacity densities have changed significantly given that the data used in our analysis is about 5 years old. That said, the assumption by (Jacobson et al 2018) that urban rooftops can be retrofitted with a capacity density 4.5-times higher than the commercial-scale solar plants measured by (Ong et al 2013) seems highly unlikely, as does the resulting 24-27 W e m −2 power density (Jacobson et al 2018). It is also possible that capacity densities vary strongly with larger size installations (see figure 2(A)).…”

Section: Discussionmentioning

confidence: 94%

Observation-based solar and wind power capacity factors and power densities

Miller

Keith

2018

Environ. Res. Lett.

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Corrigendum abstractAn error in the estimate of wind plant area led us to underestimate wind power densities by about 40%. The error was our incorrect specification of the geometric projection in the calculation of the area of Voroni polygons in our GIS software. The severity of this error increased with latitude so errors were smaller in Texas than Montana. Our method used area to filter out plants with installed capacity densities <0.1 MW i km −2 , a step that generally removes plants with very small numbers of turbines, for which our Voroni method produces overly-large areas. Because the areas changed, the sample set also changed when the error was fixed. Finding the error motivated us to both provide a more detailed description of the method in the supplemental information is available online at stacks.iop.org/ERL/14/079501/mmedia and make data publicly available in an Addendum.The average wind power density changed to 0.90 W e m −2 (from 0.50 W e m −2 ) and the average installed capacity density changed to 2.8 MW i km −2 (from 1.5 MW i km −2 ). Yet while we are embarrassed to have made an error, these corrections do not affect the overall conclusions of the paper. Specifically: (a) wind plants with the largest areas have the lowest power densities; (b) wind capacity factors are increasing, and that increase is associated with a decrease in installed capacity densities, so power densities are stable or declining; and, (c) the observed average power densities are consistent with prior estimates that use physically-based models of turbine-atmosphere interaction and are inconsistent with many wind resource estimates that implicitly ignore these interactions. Corrections do change figures 3-7 and table 1, as well as text citing or comparing previously incorrect numbers. Paragraphs which required amendments are included below, with corrected numbers and text identifiable as bold-underscored text. We apologize for the inconvenience.Power density is the rate of energy generation per unit of land surface area occupied by an energy system. The power density of low-carbon energy sources will play an important role in mediating the environmental consequences of energy system decarbonisation as the world transitions away from high power-density fossil fuels. All else equal, lower power densities mean larger land and environmental footprints. The power density of solar and wind power remain surprisingly uncertain: estimates of realizable generation rates per unit area for wind and solar power span 0.3-47 W e m −2 and 10-120 W e m −2 respectively. We refine this range using US data from 1984 to 2016. We estimate wind power density from primary data, and solar power density from primary plant-level data and prior datasets on capacity density. The mean power density of 430 onshore wind power plants in 2016 was 0.90 W e m −2 . Wind plants with the largest areas have the lowest power densities. Wind power capacity factors are increasing, but that increase is associated with a decrease in capacity densities, so power densities ...

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Section: Discussionmentioning

confidence: 94%

Observation-based solar and wind power capacity factors and power densities

Miller

Keith

2018

Environ. Res. Lett.

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“…Among such studies that appeared relatively early, a few attracted significant attention by non-specialist media, such as Fthenakis et al's seminal "Solar Grand Plan" [8,9], while others seemed to polarize scientific opinion and generated strong controversy [10][11][12][13][14]. More recently, a string of similarly-framed studies respectively focusing on Europe [15], the Americas [16] or the whole world [17,18] have seemingly pointed to a degree of high-level agreement on the general desirability of such transition pathways, albeit with a number of caveats.…”

Section: Introductionmentioning

confidence: 99%

A Prospective Net Energy and Environmental Life-Cycle Assessment of the UK Electricity Grid

2020

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National Grid, the UK’s largest utility company, has produced a number of energy transition scenarios, among which “2 degrees” is the most aggressive in terms of decarbonization. This paper presents the results of a combined prospective net energy and environmental life cycle assessment of the UK electricity grid, based on such a scenario. The main findings are that the strategy is effective at drastically reducing greenhouse gas emissions (albeit to a reduced degree with respect to the projected share of “zero carbon” generation taken at face value), but it entails a trade-off in terms of depletion of metal resources. The grid’s potential toxicity impacts are also expected to remain substantially undiminished with respect to the present. Overall, the analysis indicates that the “2 degrees” scenario is environmentally sound and that it even leads to a modest increase in the net energy delivered to society by the grid (after accounting for the energy investments required to deploy all technologies).

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“…As was noted by previous authors, 42,44,63 the main barriers to adoption of such renewable district energy systems are more political and social than technological or economic. For example, in the case we describe here, retrofit of the existing systems using electrification and heat recovery for the heating and cooling system had the lowest cost of all the options considered.…”

Section: Discussionmentioning

confidence: 71%