By Karthik Yenduru, TGS

The U.S. Inflation Reduction Act (IRA) of 2022 sparked strong momentum for clean energy by offering significant incentives to accelerate adoption, strengthen energy independence, and fuel economic growth. However, proposed legislation like the “One Big Beautiful Bill” risks introducing uncertainty and slowing progress. In this evolving landscape, many solar and storage asset owners are expected to double down on optimizing the performance and profitability of their existing portfolios. Here, success depends on leveraging high-quality, real-time data to drive smarter decisions, streamline operations, and unlock greater value from every asset. 

Impact of Good Quality Data on Solar and BESS Asset Management 

High-quality, reliable data is the backbone of successful renewable energy operations. With accurate, real-time monitoring and continuous analysis, operators can quickly detect issues, predict maintenance needs, and allocate resources more effectively, reducing unexpected downtime by up to 50%, as noted by the IEA [2]. NREL reinforces this by highlighting how dependable data improves day-to-day decision-making, system oversight, and revenue tracking. Asset performance management (APM) platforms bring everything together, combining historical and real-time insights into one unified view. This empowers teams to forecast more accurately, stay compliant, and make smarter investment choices. For independent power producers (IPPs), data-driven operations aren’t just helpful, they’re essential to staying competitive and achieving long-term success. 

  • Real-time monitoring & issue resolution: Continuous data streams enable real-time anomaly detection and rapid response, as seen in a Prediktor/Scatec case where centralized monitoring preserved peak production by quickly addressing high-severity issues, often through automated controls or alerts that reduce costly site visits. 
  • Predictive maintenance: Machine learning on clean historical data enables predictive maintenance that minimizes downtime and costs, maximizes energy output, and prevents failures by accurately forecasting issues and optimizing intervention timing. 
  • Revenue optimization & market participation: High-granularity data enables precise dispatch and forecasting, allowing solar-plus-storage assets to optimize market participation, reduce curtailment, and unlock new revenue streams through smarter bidding, demand response, and ancillary services, amongst other strategies. 

Risks & Limitations of Poor-Quality Data 

Poor data quality can quietly erode the performance and profitability of renewable energy assets. When readings are inaccurate, missing, or inconsistent, they often trigger false alarms, mislead diagnostics, and lead to inefficient maintenance, resulting in unnecessary downtime and lost revenue. These issues don’t just stay isolated; they ripple through performance models, skew reports, and compromise decision-making. Industry experts and NREL’s O&M guidelines consistently stress the importance of catching data anomalies early to avoid these costly setbacks [3]. That’s why modern APM platforms are built with robust validation tools, like outlier detection and gap filling, to ensure the data you rely on is clean, consistent, and actionable. In high-stakes operations, trustworthy data isn’t a “nice-to-have”; it’s a must. 

  • Analytics breakdown: Data analysis is a powerful tool, but when sensors are miscalibrated or data goes missing, it can quickly undermine forecasting accuracy, drive up maintenance costs, and limit optimization, forcing operators to make cautious decisions that impact performance and reliability. 
  • Operational risks: Poor data quality or inconsistent formats limit timely O&M decisions, forcing costly manual inspections, delaying issue detection, and reducing confidence in automation, ultimately leading to diminished operational insight and lost revenue [3]. 

Latency, Standardization and Reliability 

High-quality data isn't just about accuracy; speed, consistency, and compatibility are also required. When data from solar or BESS assets is delayed or unstable, it can disrupt real-time decisions like curtailment, market dispatch, or emergency shutdowns. As the U.S. Department of Energy highlights, low latency is critical to keeping grid operations responsive and stable. At the same time, inconsistent data formats make it harder to merge and analyze information across different systems. That’s why industry leaders, including NREL, recommend using open standards like IEC 61724, IEC 61850, OPC UA or other equivalent protocols to streamline integration and ensure all data speaks the same language [3]. Tools like Sandia’s PECoS framework and platforms like Prediktor PowerView™ take it a step further by automating quality checks, detecting outliers, and layering in validation to ensure operators always work from a clean, reliable data foundation [6]. This kind of data infrastructure doesn’t just support better performance; it builds trust in every decision. 

Case Studies & Examples 

  • Prediktor & Scatec ASA: A leading IPP with over 3 GW of solar, wind, and storage assets, relies on Prediktor PowerViewTM at its Global Operations Center to monitor real-time data quality and asset performance. By standardizing data collection and using live dashboards to detect issues instantly, Scatec ensures peak production, accurate forecasting, and timely interventions that directly boost revenue and operational efficiency [5][7]. 
  • Battery + Solar Arbitrage: In a commercial case study, a solar-plus-storage site used high-resolution market and weather data with smart forecasting and dispatch tools to charge during low-price periods and sell during peaks [1]. This data-driven strategy boosted revenue, improved efficiency, and unlocked new value streams through optimized battery use and participation in ancillary services. 
  • Predictive Maintenance: Academic trials show that solar plants using well-integrated sensors and machine learning for predictive maintenance achieved significantly higher uptime. These studies highlight that consistent, high-quality data, covering metrics like irradiance and inverter status, are essential for accurate anomaly detection and cost-saving insights, while poor or inconsistent data limits model effectiveness. 

In today’s uncertain policy landscape, the ability to operate efficiently and profitably has never been more important for renewable asset owners. Across all examples, from Scatec’s centralized monitoring to predictive maintenance trials and real-time market participation, one truth remains constant: the quality of your data defines the quality of your decisions. Clean, timely, and standardized data unlocks powerful insights, enables proactive operations, and drives revenue growth. In contrast, poor data breeds inefficiency, reactive maintenance, and lost opportunity. As renewable portfolios grow in scale and complexity, building a strong foundation of trustworthy, real-time data isn’t just good practice; it’s a strategic imperative for resilience, performance, and long-term success. 

 

APA References 

  1. Arcus Power. (n.d.). *Optimizing solar and battery storage revenue* \[Case study]. Retrieved from https://www.arcuspower.com/case-studies-details/optimizing-solar-battery-storage-revenue 
  2. International Energy Agency (IEA). (2020). Digitalization and Energy. Retrieved from https://www.iea.org/reports/digitalisation-and-energy 
  3. National Renewable Energy Laboratory (NREL). (2019). Best Practices for Operation and Maintenance of Photovoltaic and Energy Storage Systems; 3rd Edition. NREL/TP-7A40-73822. https://www.nrel.gov/docs/fy19osti/73822.pdf 
  4. National Renewable Energy Laboratory (NREL). (2021). Data Quality and Performance Metrics for Solar Power Systems. NREL/TP-6A20-77627. https://www.nrel.gov/docs/fy21osti/77627.pdf 
  5. Prediktor AS. (2023). PowerView™ – Real-Time Performance Monitoring for Solar & BESS Assets. [White Paper]. 
  6. Sandia National Laboratories. (2018). Performance and Energy Characterization of Photovoltaic Systems (PECoS). SAND2018-3255. https://www.sandia.gov 
  7. Scatec ASA. (2022). Operational Excellence through Digitalization: Global Operations Center Use Case. [Internal client case study, provided by Prediktor].