Climate change and electricity demand in California

Franco, Guido; Sanstad, Alan H.

doi:10.1007/s10584-007-9364-y

Cited by 179 publications

(95 citation statements)

References 13 publications

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“…Additional emission scenarios are modeled to account for changes in ozone precursor emissions with temperature, where emission rate changes are scaled to the temperature in the base case scenario of 313 K. Although anthropogenic NO x emissions will likely increase as a result of increased energy demand [estimated to be approximately 1200 MW K −1 (25)], much of the state's electricity is generated out of state during peak demand periods (26), which results in a highly uncertain link between local NO x emissions and warmer temperatures. However, anthropogenic VOC emissions (e.g., evaporative emissions, industrial processes) are local and affected by warmer temperatures.…”

Section: Resultsmentioning

confidence: 99%

Observed suppression of ozone formation at extremely high temperatures due to chemical and biophysical feedbacks

Steiner

Davis

Sillman

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

150

143

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Ground level ozone concentrations ([O 3 ]) typically show a direct linear relationship with surface air temperature. Three decades of California measurements provide evidence of a statistically significant change in the ozone-temperature slope (Δm O3-T ) under extremely high temperatures (>312 K). This Δm O3-T leads to a plateau or decrease in [O 3 ], reflecting the diminished role of nitrogen oxide sequestration by peroxyacetyl nitrates and reduced biogenic isoprene emissions at high temperatures. Despite inclusion of these processes in global and regional chemistry-climate models, a statistically significant change in Δm O3-T has not been noted in prior studies. Future climate projections suggest a more frequent and spatially widespread occurrence of this Δm O3-T response, confounding predictions of extreme ozone events based on the historically observed linear relationship. (4-10), and several studies have attempted to isolate the drivers of this relationship, as summarized in ref. 11. Early studies investigating the ozone-temperature relationship noted the impact of peroxyacetyl nitrate (PAN) decomposition on ozone formation (4, 12). The PAN sink for NO x and odd hydrogen (HO x ) decreases exponentially as temperatures increase, implying a saturation of ozone formation from PAN decomposition as temperatures increase above ∼310 K. Sillman and Samson (10) found that m O3-T is a function of multiple chemical processes, including the reaction rate of PAN, emissions of biogenic volatile organic compounds (VOC), photolysis rates, and water vapor concentrations. Therefore whereas absolute temperature is a strong predictor of the effects of incremental temperature change on ozone (2), chemical kinetics and temperature-dependent emission rates further complicate this relationship. For example, prior studies have noted that m O3-T varies between regions with different NO x ∕VOC ratios (13), and can decrease following significant NO x emissions reductions (6). The m O3-T relationship has been called a climate change "penalty," signifying that emissions reductions will need to be more stringent to counteract the effects of warming temperatures (6,14). However, the stationarity of this ozone-temperature relationship has yet to be evaluated using observations over a broad range of temperatures.High concentrations of tropospheric ozone ([O 3 ]) are an indicator of poor air quality, and adversely affect the health of humans and ecosystems (15,16). A suite of chemical and meteorological factors contributes to the formation of ozone. Photochemically driven reactions of VOC in the presence of nitrogen oxides (NO x ) can form ozone at the surface (17), whereas stagnant meteorological conditions promote and maintain ozone events.Changes in the frequency of certain meteorological features such as fewer midlatitude cyclones (18,19) or shallower boundary layer depths (20) (Fig. S1). Daily maximum surface air temperature (T max ) data are obtained from a statistically interpolated gridded product of ground-based National Oceanic a...

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Section: Resultsmentioning

confidence: 99%

Observed suppression of ozone formation at extremely high temperatures due to chemical and biophysical feedbacks

Steiner

Davis

Sillman

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

150

143

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“…The weather factor include the following [4], [5]  Temperature  Humidity  Precipitation  Wind speed and wind chill index  Cloud cover and light intensity Let us define all these factors and then we will explain their effect on short term load forecasting.…”

Section: Weathermentioning

confidence: 99%

Factor Affecting Short Term Load Forecasting

Fahad¹,

Arbab²

2014

JOCET

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Abstract-The basic objective of short term load forecasting is to predict the near future load for example next hour load prediction or next day load prediction etc. The total system load is the load seen at the generating end of the power system, which includes the sum of all types of loads connected to the system plus the losses. To design efficient and accurate forecasting model one must have good understanding of the characteristics of the system. There are various factors which influence the behavior of the consumer load and also impact the total losses in transmission lines. These factors can be categorized as Time factor, weather, economy and random disturbances. In this research paper these factors and their impact on consumption of electric power and their significance in short term load forecasting is evaluated.Index Terms-ANN, factors of STLF, load forecasting, load prediction. I. TIME FACTORTime is the most important factor in short term load forecasting because its impact on consumer load is highest. From observing load curve of several different grid stations it is found that the load curve has "time of the day" property, also it has "day of week", "week of month" and "month of season" property. This means that load curve is periodic in nature. Fig. 1(a)- Fig. 1(c) shows the load curve of 3 Fridays of the April 2012.As can be seen in Fig. 1(a)- Fig. 1(c) that although the average load is different because of the difference of temperature during each day but the pattern of the load is nearly same. In each curve there arises three peaks First peak at 8 o clock, 2 nd peak at 12:30 pm, And third at 8 pm and in curves first peak is lowest 2 nd a bit taller and third the highest. This shows periodicity in terms of day of the week. Manuscript received August 17, 2013; revised December 16, 2013. This work was carried out as a final year project for the award of MS degree in electrical engineering.Muhammad Usman Fahad is with the University of Engineering and Technology, Peshawar,.Naeem Arbab is with the Electrical Engineering Department of University of Engineering and Technology, Peshawar, Pakistan (e-mail: mnarbab01@yahoo.com). From the observation of daily load curve of any grid station it can be seen that load variation follows certain rules with the "time point" of the day.In Fig. 2 hourly load curves is shown for 24 hours of a day in winter season. The interval length is one hour, so there are 24 intervals in the figure. It can be seen in figure that the load is low and stable from 0 am to 6 am (0am means 12 o clock at night), the load start rising at 7 o clock till 9 am and then it is flattened till 12 am and after that it descends till 5 pm (17:00 in figure) and after 5 pm it start rising till 8 pm (19:00), after 8 pm load gradually decrease again until the end of the day.It can be seen in Fig. 2 that maximum demand occurs at 8pm, and minimum load demand occurs after mid night. So if we closely observe this load curve it can be seen that load demand reflects the consumer's daily life style. At mid...

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“…Note, however, that although the bulk of electricity used in Washington is from hydroelectric plants, marginal effects may affect thermoelectric plants. 51 Studies of particular locations (Amato and others 2005;Franco and Sanstad 2008;Ruth and Lin 2006) provide interesting insights about local or regional conditions but may not be useful for large-scale analyses because they employ differing methodologies or omit information that would allow their results to be integrated with those from studies of other locations. 52 Data for one of the eight states, Louisiana, were not used because the information appeared to be an outlier.…”

Section: Chapter 7: Conclusionmentioning

confidence: 99%

Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA Assessment

Foti¹,

Ramı́rez²,

Brown³

2012

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Abstract:Comparison of projected future water demand and supply across the conterminous United States indicates that, due to improving efficiency in water use, expected increases in population and economic activity do not by themselves pose a serious threat of large-scale water shortages. However, climate change can increase water demand and decrease water supply to the extent that, barring major adaptation efforts, substantial future water shortages are likely, especially in the larger Southwest. Because further global temperature increases are probably unavoidable, adaptation will be essential in the areas of greatest increase in projected probability of shortage. Keywords SummaryThe likelihood of future water shortages depends on how water supply compares with demands for water use. Comparison of supply and demand within a probabilistic framework yields an estimate of the probability of shortage and thus a measure of the vulnerability of the water supply system. This comparison was performed for current conditions and for several possible future conditions reflecting alternative socio-economic scenarios and climatic projections. Examining alternative futures provides a measure of the extent to which serious future risks of water shortage must be anticipated.Water supply was quantified by first estimating freshwater input as precipitation minus evapotranspiration for each point in a grid covering the study area. These water inputs were then allocated to major river basins and made available to meet basic in-stream flow requirements, satisfy off-stream demands including those from downstream basins or those reached by trans-basin diversions, and add to reservoir storage. Off-stream demands were estimated as threshold quantities of desired water use based on extending past trends in water use under the assumption that water supply would be no more constraining to future water withdrawals than in the recent past. Modeling water supply and demand in this way does not provide a forecast of future shortage levels. Rather, it provides a projection of the degree to which water shortages would occur in the absence of adaptation measures to either increase supply or decrease demand.On a per capita basis, aggregate water withdrawal in the United States has been dropping since at least 1985. This reduction has occurred largely because of changes in the irrigation, thermoelectric, and industrial water use sectors. In the West, agricultural acreage has been decreasing and water withdrawal efficiency has been improving. Water withdrawal per kilowatt hour produced at thermoelectric plants has been steadily dropping as production has moved to more water-efficient plant types. And industrial water use has been dropping as industrial capacity has moved overseas and water recycling has become more common at remaining plants.Despite the reductions in per-capita water withdrawal, total U.S. withdrawal rose from 1985 to 2000, largely in response to population growth of roughly 2.7 million persons per year. However, the most recent ...

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Climate change and electricity demand in California

Cited by 179 publications

References 13 publications

Observed suppression of ozone formation at extremely high temperatures due to chemical and biophysical feedbacks

Observed suppression of ozone formation at extremely high temperatures due to chemical and biophysical feedbacks

Factor Affecting Short Term Load Forecasting

Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA Assessment

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