How Analytics Enable Acute Problem-Solving for Distributed Energy Storage and Utility-Scale Bess
Автор: Energy Storage News
Загружено: 2025-10-07
Просмотров: 101
Описание:
Distributed energy storage delivers a wide variety of services that help end-users reduce electricity costs and CO2 emissions through peak shaving, self-consumption, EV charging, and backup power, while also supporting the grid with ancillary services. This diverse range of applications creates numerous challenges that must be identified and solved to optimize both the safety and performance of these assets.
Socomec and PowerUp share several use cases demonstrating how advanced battery analytics have successfully uncovered and resolved complex issues for end-users. These insights apply not only to smaller distributed energy storage systems but also to utility-scale assets, where the potential impact of problems is magnified due to their larger size. This on demand webinar will benefit all industry professionals working across both behind-the-meter and front-of-the-meter applications.
The Dual Challenge for BESS Owners and Operators
BESS Owners and Operators must succeed in two critical areas - maximizing revenue from their assets while ensuring the highest levels of safety.
Revenue Optimization:
Reduce revenue loss from system downtime, underperformance, and abnormal degradation
Safeguard warranty coverage by verifying and proving that operations remain within compliance parameters
Lower O&M costs by detecting issues early and reducing urgent and unnecessary service visits
Safety Assurance:
Detect thermal-runaway precursors before they escalate and require urgent intervention
Predict HVAC and thermal-management failures by identifying anomalies and drifts early
Identify BMS and control issues that could lead to unsafe operation
The combination of Socomec's high-quality BESS equipment features and PowerUp's comprehensive battery analytics provides an excellent foundation for success in both areas. The webinar presents detailed use cases demonstrating how battery failures become predictable and preventable through deep analytics in various domains, including module-to-rack health, thermal management, telemetry, and controls.
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