Georgia Power is an electric utility headquartered in Atlanta, Georgia, United States. It was established as the Georgia Railway and Power Company and began operations in 1902 running streetcars in Atlanta as a successor to the Atlanta Consolidated Street Railway Company.Georgia Power is the largest of the four electric utilities that are owned and operated by Southern Company. Georgia Power is an investor-owned, tax-paying public utility that serves more than 2.4 million customers in all but four of Georgia's 159 counties. It employs approximately 9,000 workers throughout the state. The Georgia Power Building, its primary corporate office building, is located at 241 Ralph McGill Boulevard in downtown Atlanta.In 2006, the Savannah Electric & Power Company, a separate subsidiary of Southern Company, was merged into Georgia Power.
Introduction and Objective: Physical activity (PA) impacts glucose variability differently depending on the activity type. The DBLG1 System utilizes distinct target adjustments for Aerobic (+70 mg/dL), Mixed (+40), and Anaerobic (+20) activities. We evaluated the efficacy of these types by analyzing declared sessions, excluding generic “Sport” declarations to ensure physiological accuracy. Methods: We analyzed 8,358 PA sessions (Aerobic, Anaerobic or Mixed) from 982 adult users. We compared glycemic outcomes during the PA to matched non-PA control periods (shifted 24/48 hour). Metrics included Time Above Range (TAR >180 mg/dL) and Rescue Carbs (RC) consumption. Results: The algorithm demonstrated effective control for Anaerobic PA despite the lower target increase (Table 1): TAR increased by only 2.9 percentage points (pp) with minimal RC increase (+2.3 g). Conversely, Mixed PA was the most challenging: despite the intermediate target, they were associated with the largest TAR increase (+6.5 pp) and the highest RC increase (+4.1 g). Aerobic activities showed intermediate results (+3.6 pp TAR). Conclusion: The target strategy effectively prevents hypoglycemia. Mixed PA remains prone to hyperglycemia and high carbohydrate intake. The unpredictability of Mixed PA, combined with preventive snacking, could benefit from further algorithmic adaptations. Future improvements could consider PA type not just for target adjustment, but also to tailor RC recommendations to the specific PA type. Disclosure A. Adenis: Employee; Current; Diabeloop SA. S. Lachal: Employee; Current; Diabeloop SA. P. Gauthier: Employee; Current; Diabeloop SA. P. Gimenez: Employee; Current; Diabeloop SA. C. Desir: Employee; Current; Diabeloop SA. T. Le Roux-Mallouf: None. E. Huneker: Employee; Current; Diabeloop SA. P.Y. Benhamou: Employee; Current; Diabeloop SA. Advisory Panel; Ended; Eli Lilly and Company, Novo Nordisk.
Introduction and Objective: Physical activity (PA) is a well-known precipitant of hypoglycemia in type 1 diabetes (T1D), yet the temporal relationship between PA detection signals and hypoglycemic events remains incompletely characterized. We aimed to identify which PA indicators—patient declarations, heart rate elevation, or step counts—most frequently precede hypoglycemia, and quantify the timing between PA onset and hypoglycemia occurrence. Methods: We analyzed data from the T1-DEXI database, comprising 497 virtual adult patients with T1D (mean age 37 ± 14 years; mean HbA1c 6.6 ± 0.8%). PA was identified in the 120 minutes preceding hypoglycemia using three detection methods: patient declarations (intensity levels 1-3), heart rate exceeding a personalized threshold (resting heart rate + 0.4 × heart rate reserve) for >10 minutes, and step count >10 steps for >10 minutes. The maximum heart rate was calculated using the Tanaka formula. Results: Heart rate elevation was the most sensitive indicator, capturing 80.2% of PA cases. Patient declarations captured 53.5% of cases, while step counts captured 37.6%. With heart rate as the only active indicator, 37.2% of PA cases were detected, compared to 9.8% for patient declarations alone and 3.9% for step counts alone. PA detected using all indicators combined preceded 40.3% of hypoglycemic events, with a mean interval of 65.5 ± 0.4 minutes between PA onset and hypoglycemia. Conclusion: Heart rate monitoring demonstrates superior sensitivity for detecting PA preceding hypoglycemia compared to patient declarations or step counts alone. The observed delay between PA onset and hypoglycemia (65.5 minutes) aligns with the peak action window of rapid-acting insulin analogues (30-90 minutes), suggesting that timely insulin dose reduction upon PA detection could effectively prevent subsequent hypoglycemia. Integrating continuous heart rate data into automated insulin delivery systems may therefore enable proactive algorithmic intervention. Disclosure P. Gauthier: Employee; Current; Diabeloop SA. A. Adenis: Employee; Current; Diabeloop SA. S. Lachal: Employee; Current; Diabeloop SA. P. Gimenez: Employee; Current; Diabeloop SA. C. Desir: Employee; Current; Diabeloop SA. T. Le Roux-Mallouf: None. E. Huneker: Employee; Current; Diabeloop SA. P.Y. Benhamou: Employee; Current; Diabeloop SA. Advisory Panel; Ended; Eli Lilly and Company, Novo Nordisk.
Introduction and Objective: Exercise-induced hypoglycemia and hyperglycemia remain challenging for people with T1D. Variability in outcomes is often driven by physical activity (PA) characteristics and metabolic states. This study quantified the independent impact of declared intensity, duration, initial Insulin-On-Board (IOB) and initial glycemia on glycemic outcomes using Linear Mixed Effects Models (LMM). Methods: We analyzed 20,959 PA sessions from 1,720 adult users of the DBLG1 System. LMMs predicted Time Below Range (TBR <70 mg/dL) and Time Above Range (TAR >180 mg/dL) during activity. Fixed effects included initial glycemia, initial IOB, PA duration (<30 min, 30-60 min, >60 min), declared intensity (low, moderate, high), and the intensity-duration interaction. Patient ID was included as a random effect to account for repeated measures. Results: Initial metabolic conditions were the strongest predictors. Each unit of IOB at start increased TBR by 0.58 percentage points (pp) (p<0.001) and rescue carbohydrate intake by 0.82g. Conversely, higher initial glycemia protected against hypoglycemia (-0.41 pp TBR per 10 mg/dL) but significantly increased TAR (+4.9 pp per 10 mg/dL). A significant intensity-duration interaction was observed: short, high-intensity sessions (<30 min) were associated with the highest hypoglycemia risk (+4.2 pp absolute TBR), while long-duration sessions (>60 min) were associated with increased hyperglycemia (+5.1 pp absolute TAR). This suggests a more specific adaptation for short, intense effort for prolonged activity. Conclusion: While the system effectively personalizes treatment, this analysis identifies potential improvements. High IOB and short, intense exercise are a risk factor for hypoglycemia, while long-duration activities are a risk factor of hyperglycemia. Future algorithms could incorporate more specific adaptation to intensity-duration combination and dynamic IOB-aware adjustments to improve the glycemic control. Disclosure A. Adenis: Employee; Current; Diabeloop SA. S. Lachal: Employee; Current; Diabeloop SA. P. Gauthier: Employee; Current; Diabeloop SA. P. Gimenez: Employee; Current; Diabeloop SA. C. Desir: Employee; Current; Diabeloop SA. T. Le Roux-Mallouf: None. E. Huneker: Employee; Current; Diabeloop SA. P.Y. Benhamou: Employee; Current; Diabeloop SA. Advisory Panel; Ended; Eli Lilly and Company, Novo Nordisk.
Introduction and Objective: Physical activity (PA) increases insulin sensitivity in people with T1D, elevating nocturnal hypoglycemia risk. We evaluated the safety and efficacy of the DBLG1 System during the night following declared PA compared to nights following days without PA. Methods: We analyzed real-world data from 2,322 adults (61% female, median age 42 years, median HbA1c 7.4%). We compared 17,666 nights (12:00 PM-6:00 AM) following a PA session to matched control nights from non-PA periods (shifted 24/48 hours prior). Dependent variables included Time Below Range (TBR <70 mg/dL), Time in Range (TIR 70-180 mg/dL), total insulin delivery and rescue carbohydrate intake (RC). Differences were assessed using paired t-tests and Cohen’s d for effect size. Results: Nocturnal safety was equivalent between groups (Table 1): TBR remained identical at 1.07% (p=0.937). This stability was achieved through automated insulin reduction (-0.2 IU; p<0.001) rather than increased carbohydrate intake, as RC remained stable (2.5g vs 2.6g; p=0.47). TIR was maintained at a high level (81.3% vs 81.8%), with a negligible effect size (Cohen’s d = -0.019). Conclusion: The DBLG1 System effectively manages post-exercise nocturnal hypoglycemia risk. The algorithm successfully adapts to increased insulin sensitivity by modulating delivery rather than relying on additional rescue carbohydrates, ensuring that nights following activity are as safe as nights following sedentary periods. Disclosure A. Adenis: Employee; Current; Diabeloop SA. S. Lachal: Employee; Current; Diabeloop SA. P. Gauthier: Employee; Current; Diabeloop SA. P. Gimenez: Employee; Current; Diabeloop SA. C. Desir: Employee; Current; Diabeloop SA. T. Le Roux-Mallouf: None. E. Huneker: Employee; Current; Diabeloop SA. P.Y. Benhamou: Employee; Current; Diabeloop SA. Advisory Panel; Ended; Eli Lilly and Company, Novo Nordisk.
This article summarizes the three-year technical activities of the IEEE Task Force (TF) on behind-the-meter (BTM) distributed energy resources (DERs): estimation, uncertainty quantification, and control. The potential grid services from BTM DERs are discussed in detail. The paper also reviews the state-of-the-art for BTM DERs visibility, uncertainty quantification, and, optimization and control. Furthermore, different aspects of the market structures associated with BTM DERs are covered, including emerging market and business models. Finally, needs and recommendations are provided for additional areas such as system protection, computing capabilities, algorithm development, market structure design, cyberinfrastructure and security, and hardware and software developments.
