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The Role of Temperature, Cloud Cover & Dew Points in Intraday Volatility

It is essential to have a grasp of the fundamentals underlying demand and weather forecasts in order to achieve an accurate preditction of power market prices. Figure 1. Winter DJF Load CurvesThis blog will focus on daily demand cycles, or the drivers of intraday volatility, which can be successfully explained through the volatility of the weather. This analysis will be focused on the role of three weather parameters (temperature, cloud cover, and dew point) in the ERCOT power market throughout the year. 

Winter Season Conditions: A Heading Load StoryFigure 2.

When temperature drops below 45F, heating load tends to be the dominant driver of volatility. Typically, the coldest temperature of the day occurs in the early morning following sunrise, as the earth radiates heat during the absence of solar radiation overnight. The mildest temperature of the day generally occurs late in the afternoon, lagging a few hours after the highest solar elevation angle. Between equinoxes (September 21 to March 21), there are less than 12 daylight hours, with the fewest daylight hours around the winter solstice in late December.

Figure 3a. Spring MAM Load CurvesTherefore, the strongest possible demand peak typically occurs around sunrise, with coincidental lighting load and human activities (getting up to work, go to school, etc.) adding to the strongest heating load of the day. Demand tends to drop over the afternoon hours, with decreasing heating load, creating an afternoon ‘valley.' The evening peak is slightly weaker, with similar lighting load, but decreased heating load compared to the morning hours, as seen in Figure 1.

However, demand shape differs when the temperature profile is atypical. For example, a cold front passage brings milder morning temperatures than evening temperatures and will result in the evening peak exceeding the morning peak in magnitude. Also, the presence of lighting load persisting during the day due to overcast, cloudy, or foggy conditions can fill the afternoon ‘valley’ and turn it into a ‘plateau' (Figure 2). 

Figure 3b. Fall SON Load Curves

Shoulder Season Conditions: Is it Dark or Clear Outdoors?

Between 45F and 75F, the main volatility factor tends to be lighting load, rather than the mostly absent significant heating load or cooling load. At the lower end of the temperature range, cloud cover during daylight hours is an upside factor, promoting stronger lighting load. At the highest end of the range, cloud cover during daylight hours is a downside factor, keeping temperatures at a comfortable level, reducing or preventing cooling demand. Daylight length changes and daylight time savings have reverse effects in the spring vs. fall season, with decreasing lighting demand in the spring outpaced by growing cooling load in the spring and the reverse (increasing lighting load and decreasing cooling load) in the fall.

Given that ERCOT is a southern region with significant cooling demand in late spring (April and May) as well as early fall (September and October), the effects of the daylight changes are best seen in the minimum load curves of Figure 3a and Figure 3b. In the spring curves, the evening peak ramp for the minimum load curve increases from HE 19 to HE 20, while in the fall curves, the evening peak ramp for the minimum load curve increases from HE 18 to HE 19. This is explained by Figure 4.Figure 4.

Summer Season Conditions: Turning on the Air Conditioners

Above 75F, cooling demand becomes the dominant factor, with overall demand becoming a bell curve (Figure 5). Heat index combining temperature and dew point typically better explains the volatility in demand, as it requires more work to cool a humid and hot air mass than a dry and hot air mass. Sunny, dry days typically feature morning downside risks (less cooling load) and afternoon upside risks due to increased solar elevation angle. In contrast, cloud cover is a downside factor as it impacts temperature with cooler risks, slowing down cooling load buildup and limiting direct sunshine into buildings. Thunderstorms moving over densely populated areas are a significant downside factor, as they typically drop down temperature and dew point very quickly. A dramatic example of this occurred on July 17, 2014, when rain dropped temperatures to the unusually comfortable upper 60’s – low 70’s in Northern Texas, including Dallas-Fort Worth. Thunderstorms developed by mid-day in the Houston Metro Area, crushing temperatures from upper 80’s to upper 70’s, as shown in Figure 6.

Figure 5. Summer JJA Load Curves

In summary, daily demand volatility is inherently tied to the weather, which differs from season to season, resulting in different dominating factors. In the winter, cold temperatures drive heating load and cloud cover impacts lighting load. In the shoulder seasons, temperature becomes secondary and lighting demand becomes the dominant volatility factor. Finally, in the summer, volatility is driven by the presence and magnitude of cooling load, with severe thunderstorms and hot and humid days bringing the highest downside and upside risks, respectively.

Genscape’s team of meteorologists enhances Genscape's Power Market Services by providing on-demand, up-to-date forecasts and a unique perspective on how weather conditions impact electricity demand, with accurate load forecasts up to 15-days out and analysis of weather conditions that drive demand for the major power markets in the contiguous United States.

Figure 6. ERCOT demand July 17, 2014

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