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Case Study

Yes Energy Demand Forecasts Help Improve Day-Ahead Trading for Arizona Public Service

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Project Information

With an average of more than 300 days of sunshine a year, solar generation, both utility scale and rooftop PV, comprises a large part of Arizona’s generation mix.

Significant challenges occur when cloudy weather limits the amount of energy generated by rooftop systems, especially during monsoon season. From APS’ perspective, power generation from rooftop PV is essentially negative demand, contributing to the duck curve in APS’ daily load profile.

Case Study, Project Information

Temperature Sensitivity

During monsoon season in the southwest US, temperatures can drop by 0-30° Fahrenheit (~0-17° Celsius) within an hour. Yes Energy expanded the Temperature Sensitivity feature for APS, so they can see the effect of a -15° F (~-8° C) temperature deviation from the forecast.

Arizona Duck Curve

Forecast Vendor Selection

APS utilizes multiple load forecasts. Their web application helps traders with forecast contrast and selection by displaying both the Artificial Neural Network Short Term Load Forecast (ANNSTLF) and Yes Energy Demand Forecasts. 

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Conclusion

APS easily integrated Yes Energy Demand Forecasts within its workflows, giving team members access to the industry's most accurate forecasts, especially on days with unpredictable weather.

With the Temperature Sensitivity feature, APS can see how Yes Energy Demand Forecasts behave when the temperature projection is changed. This allows traders to prepare for large power demand drops and to adjust their strategy. 

“Yes Energy yields exceptional results when compared to other vendors during inclement weather.”

Danny Dayal,Senior Business Analyst, APS

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