CMI Integration

header-integration.jpg

CMI Integration introduces new conceptual frameworks that are useful to governments and citizen groups considering climate change policies. One current effort seeks to make the emerging statistical analyses of extreme events, such as urban heat waves, more accessible. Another initiative involves the study of renewable energy intermittency, lulls in windpower in particular, and examines potential implications for power management. 

Research Highlights – At a Glance

Michael Oppenheimer: Heat waves (HWs) are among the most damaging climate extremes to human society. For urban residents, the urban heat island (UHI) effect further exacerbates the heat stress resulting from HWs, and these risks are even greater if HWs interact synergistically with UHIs. Combining climate model simulations and a new analytical framework, the Oppenheimer group has investigated the synergistic effects, sometimes positive and sometimes negative, between UHIs and HWs at a large scale under climate change. The study also uncovered the physical mechanisms underpinning these synergistic effects.

Robert Socolow: Intermittency—variability in output—bedevils wind and solar energy. Presented here is a fresh approach to intermittency that focuses on the statistics of lulls (periods of low output), especially the longest lulls. Long lulls are extreme events and should be at the center of attention in grid management. The longest lulls are unlikely to elicit the same strategies as shorter lulls. In particular, batteries (and other storage strategies that have costs roughly proportional to the energy they store) will be ill-suited to compensate for the rare, longest lulls.

 


 

Modeling Current and Future Heat Waves Across US Cities
Principal Investigator: Michael Oppenheimer

At a Glance 

Heat waves (HWs) are among the most damaging climate extremes to human society. For urban residents, the urban heat island (UHI) effect further exacerbates the heat stress resulting from HWs, and these risks are even greater if HWs interact synergistically with UHIs. Combining climate model simulations and a new analytical framework, the Oppenheimer group has investigated the synergistic effects, sometimes positive and sometimes negative, between UHIs and HWs at a large scale under climate change. The study also uncovered the physical mechanisms underpinning these synergistic effects.

Research Highlight

Among the many damaging environmental extremes, including hurricanes, floods, and tornadoes, heat waves (HWs) are the deadliest in the US. Assuming no acclimatization and adaptation, extreme heat stress in a changing climate has the potential to cause a substantial increase in human mortality, morbidity, energy demand, and perhaps civil conflicts. 

In recognition of these concerns, better understanding is needed of the risks of future HWs including physical mechanisms, temporal structure, and potential adaptation and mitigation strategies to better manage changing risks over time.

Climate models consistently project that HW frequency, severity, and duration will increase markedly over this century. For urban residents, the heat stress resulting from HWs are amplified by the urban heat island (UHI) effect, and these risks are even greater if HWs interact synergistically with UHIs (Figure 3.1.1). Future impacts from such events can be mitigated through adaptation and risk management efforts informed by an improved understanding of the response of UHIs to HWs. 

3.1.1.png
Figure 3.1.1. Schematic of positive (a) and negative (b) synergistic effects between UHIs and HWs.

STEP postdoctoral research associate Lei Zhao, with his advisor, Michael Oppenheimer, used an Earth system model to investigate the interactions between UHIs and HWs in 50 US cities under current climate and future warming scenarios. The results show significant sensitivity to local background climate and warming scenarios. Sensitivity also differs between daytime and nighttime (Figure 3.1.2). In today’s climate, there is a positive synergy in all regions day and night, significantly stronger in some cities at night. These synergistic effects, however, change in complex ways in future warmer climates. For cities in the eastern half of the US, in the daytime, positive synergies will become negative synergies, while at night, many positive synergies will become more strongly positive. For cities in the western half of the US, which consist mostly of dry climates, synergies are generally weak in today’s climate, but will become stronger in a future warmer one.

3.1.2.png
Figure 3.1.2. Maps of average synergistic effects between UHIs and HWs during 1975−2004 (a, b) and 2071−2100 (c, d) under RCP 8.5 scenario for selected cities in the US.

An analytical method was used to disentangle the mechanisms behind the interactions between UHIs and HWs that explain the spatiotemporal patterns of the interactions. Results show that evaporation plays a key role. Over a water-sufficient surface (such as moist soils or wet surfaces), an increase of air temperature favors increasing latent heat flux, a change in phase such as evaporation, rather than increasing sensible heat flux, a change in temperature. Over a water-limited surface (such as dry soils or concrete surfaces), sensible heat flux dominates. 

In the present-day climate, despite ample precipitation in temperate regions, cities in these regions are usually water limited due to the large fraction of impervious surfaces; whereas their surrounding rural surfaces are water sufficient. Therefore, during HWs (i.e., temperature increases) evaporation increases less over urban surfaces than over rural surfaces, resulting in an enhanced UHI effect, which can be measured as the urban-rural gap in air or surface temperature. In dry regions where both urban and rural surfaces are water limited, the UHI effect due to differences in evaporation is weaker. 

Under future warming scenario, for cities in the temperate region, the enhancement of evaporative contribution to the urban-rural difference during HWs is diminished near the end of this century under RCP 8.5 (a greenhouse gas concentration trajectory adopted by the Intergovernmental Panel on Climate Change). The reason lies in the model-projected increase in precipitation in this region in future warmer climates. The increase in precipitation, to some extent, turns cities into water-sufficient surfaces, so that evaporation over cities can increase as much as over their surrounding rural surfaces when HWs come in. The enhanced anthropogenic heat release during HWs, present in temperate and dry climate regions, is primarily the result of higher air-conditioning energy use to cope with the heat extremes. 

At night, the enhanced release of stored heat (in built structures) and anthropogenic heat during HWs are the primary contributors to synergistic effects.

This work highlights the heat risks that urban residents face now and in the projected future. HWs have detrimental impacts on human society and natural ecosystems. The synergistic effects that were found in the current study augment these impacts. The daytime synergistic effect alone leads to a 3.2% increase in mortality risk in the current climate for temperate cities. The nighttime synergistic effect in the temperate region leads to an increase of 2.2% in mortality risk in the current climate and is projected to increase to 4.3% by the end of this century under RCP 8.5.

 


 

Lull Analysis to Characterize Windpower Intermittency
Principal Investigator: Robert Socolow

At a Glance 

Intermittency—variability in output—bedevils wind and solar energy. Presented here is a fresh approach to intermittency that focuses on the statistics of lulls (periods of low output), especially the longest lulls. Long lulls are extreme events and should be at the center of attention in grid management. The longest lulls are unlikely to elicit the same strategies as shorter lulls. In particular, batteries (and other storage strategies that have costs roughly proportional to the energy they store) will be ill-suited to compensate for the rare, longest lulls.

Research Highlight

From 2004 to 2017, installed solar and wind power grew spectacularly on a global scale: from 3 to 300 GW for solar and from 40 to 500 GW for wind. It is far from clear that solar and wind will continue to surge, but it is time to take seriously the possibility that the world will swap the current fossil energy system for one where solar and wind dominate. 

Relative to the current fossil energy system, a wind-solar energy system is more different than two other contending low-carbon systems, one dominated by fossil fuels with CO2 capture and storage (CCS) and the other by nuclear (fission and fusion) power. The intermittency and unpredictability of both solar and wind power creates fundamentally new challenges associated with long periods of low power output, or lulls. Long lulls should be conceptualized as a class of extreme events, needing dedicated attention and deserving names like those of hurricanes. 

This approach explored a study of a single database: total hourly windpower production in 2016 in the region governed by the Electricity Reliability Council of Texas (ERCOT), a region approximately coincident with the state of Texas. This is a work in progress, conducted jointly by CMI PI Robert Socolow and Pedro Haro. Haro was a Visiting Fellow at Princeton University in the winter of 2017-18 and is now a professor at the University of Seville.

The question that has generated Socolow and Haro’s inquiry is: How long do periods of low power last? To answer that question, their general method is to:

  1. Start with a time sequence (in our case, hourly data for a year). 
  2. Choose a threshold (in our case, less than half the annual average power). 
  3. Measure the duration of low power (the length of the lull) from the hour the power first drops below the threshold to the hour the power first returns above the threshold. 
  4. Create a histogram that bins these lulls by duration. 
  5. Pay special attention to the longest lulls, treating them as distinct events.

Figure 3.2.1 displays 2016 hourly data for the total electricity production from the wind farms in the ERCOT service area. During 2016, the installed wind capacity for these wind farms climbed from 16 GW to 17.5 GW and averaged 17.1 GW, while the average windpower output was 6.0 GW. Taking into account all sources of electricity, the average ERCOT demand in 2016 was 40.0 GW, so average windpower production was 15% of average demand.

Socolow’s and Haro’s choice for the threshold defining “low” output (horizontal line in Figure 3.2.1) is half of the annual average value, or 3.0 GW.

3.2.1.png
Figure 3.2.1. Hourly total electricity production from the wind farms in the ERCOT service area in 2016. Three values are identified on the vertical axis: 17 GW is the total capacity of the wind farms; 6 GW is the annual average windpower production: and 3 GW is half of the annual average—the threshold we have chosen for the illustrative analysis here. The events A, B, C, and D are the year’s longest lulls.

Figure 3.2.2 arranges all lulls by their duration, and plots the total time of lulls for each duration. The four longest lulls are labeled A, B, C, and D in the histogram, and these events are also identified in Figure 3.2.1. Event A lasts roughly four days, and events B, C, and D last roughly two days—a total of 10 days for the four events. 

Figure 3.2.3 reveals that altogether in 2016 there were 219 lulls accounting for 2059 hours (nearly one quarter of a year), and the average duration of a lull was nine hours. However, averages over lulls carry very little information. More importantly, 75% of the lulls lasted for less than 12 hours and 95% for less than a day. There were only 12 long lulls, defined as lasting more than a day, yet these accounted for 481 hours (20 days).

3.2.2.png
Figure 3.2.2. A histogram showing total time contributed by lulls of different durations; same data as Figure 3.2.1. A lull is defined here as a time interval when total windpower is below half its annual average. For lull lengths with a single occurrence, which include the longest lulls of the year, the fraction of the year associated with that lull (y-axis) is proportional to the length of the lull; the lower diagonal dashed line passes through the tops of all these one-lull bars. Lull lengths that occurred more frequently, which include many of the shorter lulls, form steeper trend lines. Total time spent in lulls: 2059 hours (23% of the year).

 

Number of Events

Cumulative Hours

% of Total Hours

Lull Length (hrs)

<12

164

862

42

13-24

43

716

35

25-36

8

247

12

>36

4

234

11

Total

219

2059

100

Figure 3.2.3. A table of summary statistics for these lulls.

Other data sets. We examined the same ERCOT data using a lower threshold, 25% of annual average power, or 1.5 GW. The longest lull with this tighter definition lasted less than half as long, 20 hours. 

The researchers also examined one other database, total hourly windpower production in mainland Spain in 2016, with a threshold of 50% of average power, and found shorter lulls than for ERCOT: the maximum lull was 17 hours, and the second longest was 11 hours. 

For sure, this methodology could also reveal important features of “solar lulls,” stretches of cloudy days. If applied to solar-wind lulls (the periods of low output when both solar and wind power are contributing to a single power system), it would show the degree to which solar and wind in combination could shorten and make less deep the lulls that either would produce independently.

Implications for the management of intermittency. Lulls need to be partitioned for optimal management. The longest lulls are unlikely to elicit the same strategies as the shorter ones. In particular, batteries, which have costs roughly proportional to the energy they store, will be ill-suited to compensate for the longest lulls. In this example, credibly, lulls under 12 hours, and perhaps in the future under 24 hours, would be matched to batteries, while the eight events lasting between 25 and 36 hours, the three two-day events, and the single four-day event would require other solutions. 

Ongoing analysis. Prolonged low wind is the obvious explanation for long lulls in windpower output. However, it is proving not at all straightforward to identify events directly from wind speed data, even when data are combined for several locations in ERCOT territory. Two explanations that Socolow and Haro have been able to exclude are: 1) edicts from ERCOT leading to curtailments, and 2) extensive storms creating winds exceeding turbine cut-out speeds.

Future work. It will be important to understand the extent to which long lulls can be predicted in advance; having warnings of long lulls would improve preparedness (use of reserve capacity, expansion of imports, and shutdown of non-essential activity). Investigations of spatial correlations specific to long lulls will improve assessments of the value of long-distance transmission capacity and clarify the trade-offs between back-up power (e.g., provided by natural gas or diesel generators), storage, and demand-side management. Long-lull analysis should also provide insight into likely behavioral responses to these stresses.

Acknowledgments

Pedro Haro is a full co-author, with Robert Socolow, not only producing clever data displays but also contributing key hypotheses. Both authors wish to emphasize that this work is at an early stage.

The inspiration for this work was a field trip in May 2017 to BP’s Sherbino 2 wind farm in west Texas, hosted by Jason McDonald of BP and involving eight Princeton participants (see Figure 3.2.4). That particular day the team experienced dramatically variable wind: the wind, having died down after midnight, was recovering briskly as we arrived in the early morning. McDonald has continued to be helpful and resourceful. Haro and Socolow are also benefitting from discussions with Paul Wattles, Aaron Townsend, Pengwei Du, and Kevin Hanson at ERCOT.

3.2.4.png
Figure 3.2.4. The eight Princeton visitors to BP’s Sherbino 2 wind farm, May 2017. Left to right: Robert Socolow, Minjie Chen, Robert Williams, Ryan Edwards, Levi Golston, Greg Davies, Elie Bou-Zeid, and Hossein Hezaveh.

References

Haro and Socolow found two earlier papers that investigated the frequency of lulls vs. their duration, for lulls in either wind velocity or windpower:

Kaldellis, J.K., 2002. Optimum autonomous wind-power system sizing for remote consumers, using long-term wind speed data. Applied Energy. 71: 215–233. doi: 10.1016/S0306-2619(02)00005-3.

Poder, V., T. Peets, K. Toom, and A. Annuk, 2010. The estimation of wind lull and consumption factor influence on autonomous wind energy system. Agronomy Research. 8: 226-235, doi: 10.3176/oil.2009.3S.10. 

 


 

Integration Publications

Budolfson, M., F. Dennig, M. Fleurbaey, A. Siebert, and R.H. Socolow, 2017. The comparative importance for optimal climate policy of discounting, inequalities, and catastrophes. Climatic Change. 145(3-4): 481-494. doi: 10.1007/s10584-017-2094-x.

Rand, B.P., F. Meggers, W.C. Witt, M. Gokhale, S. Walter, and R.H. Socolow, 2017. Sunlight to Electricity: Navigating the Field, an Energy Technology Distillate from the Andlinger Center for Energy and the Environment. https://acee.princeton.edu/distillates/sunlight-to-electricity/.

Scovronick, N., M.B. Budolfson, F. Dennig, M. Fleurbaey, A. Siebert, R.H. Socolow, D. Spears, and F. Wagner, 2017. Impact of population growth and population ethics on climate change mitigation policy. Proc. Natl. Acad. Sci. 114(46): 12338-12343. doi: 10.1073/pnas.1618308114.

Socolow, R.H. and J. Mecklin, 2017. [In] The Experts on Trump’s Climate Decision. Bulletin of The Atomic Scientists. https://thebulletin.org/experts-trumps-climate-decision10809.

Socolow, R.H., 2017. The Limited Domain of Carbon Capture and Use. Carbon Mitigation Initiative Annual Report 2016. 44-47. http://cmi.princeton.edu/annual_reports/2016/integration/index.php#cio_3.

Zhao, L., M. Oppenheimer, Q. Zhu, J.W. Baldwin, K.L. Ebi, E. Bou-Zeid, K. Guan, and X. Liu, 2017. Interactions between urban heat islands and heat waves. Environmental Research Letters. 13(3): 034003. doi: 10.1088/1748-9326/aa9f73.

 

<< Previous  |  Table of Contents  |  Next >>