Unlocking the Power of Renewable Energy: How Maximo Renewables Enables Smarter Asset Management

As the world continues its rapid transition to sustainable energy, optimizing the performance of renewable energy assets is more critical than ever.

Renewables will account for almost half of global electricity generation by 2030, with the share of wind and solar PV doubling to 30%.¹ At the end of this decade, solar PV is set to become the dominant renewable source, surpassing both wind and hydropower, which are currently the largest renewable generation sources by far.¹

By 2030, solar and wind penetration is set to reach close to 70% in countries such as Chile, Germany, the Netherlands and Portugal.²

Imagine a solution that not only monitors but intelligently analyzes vast amounts of data across an enterprise's solar, wind and battery energy storage assets, helping to identify inefficiencies, minimize downtime and maximize energy production.

That’s exactly what IBM® Maximo® Renewables delivers—an asset performance management solution that is tailored for the renewable energy sector. 

The Maximo Renewables analyze module helps to optimize your renewable energy assets and achieve your sustainability goals. With advanced analytics, AI-driven insights and seamless integration, you can empower your renewables operations with actionable intelligence to help stay competitive in the increasingly dynamic energy landscape.

The renewable energy sector is growing fast, but operational challenges are cutting into revenue and production. Wind turbines, solar panels and battery systems require constant maintenance, and as the industry scales, so do costs and the risk of downtime.

Advanced asset lifecycle management (ALM) and AI can help find the root cause of underperformance, optimize performance, and extend asset lifespans. By analyzing historical data and real-time insights, ALM can help identify issues before they cause downtime, while AI automates workflows for greater efficiency.

Managing renewable assets comes with major hurdles—unexpected failures, maintenance delays and data silos that reduce efficiency and profitability. Environmental factors and reactive maintenance drive up costs, while fragmented data from SCADA systems and IoT sensors makes it difficult to get a unified operational view.

Without centralized insights, decision-makers struggle to maximize ROI and prevent revenue loss. AI-driven predictive maintenance offers a smarter approach, helping to reduce risks, improve performance and ensure that assets operate at peak efficiency.

IBM Maximo Renewables is a robust AI-powered SaaS solution that collects plant data, applies data science models to identify causes for underperformance, and recommends actions to increase generation. With near real-time performance insights, root-cause analysis and AI-driven analytics, organizations can now monitor, analyze and manage operations for their renewable energy assets more efficiently.

Maximo Renewables provides near real-time visibility across the portfolio, plant and device levels, tracking key performance indicators (KPIs) and generating anomaly alerts to support data-driven decision-making. By using advanced AI analytics, it identifies inefficiencies and underperformance, such as patterns in wind turbine performance that might indicate gearbox issues, inverters in a solar farm operating below specifications or trackers that fail to follow the sun.

Additionally, Maximo Renewables automates repetitive operations and maintenance (O&M) tasks, such as those related to reporting and compliance, while proactively generating work orders to address identified issues, allowing enterprise teams to focus on high-value activities.

At the heart of Maximo Renewables APM is the analyze module—a tool that provides a gateway to data-driven insights and intelligent decision-making across renewable energy assets. This robust tool offers a range of essential features.

Comprehensive loss waterfall

The Maximo Renewables analyze module visualizes energy losses across the entire operational workflow through a cascading loss waterfall diagram. This representation provides a holistic view of how various factors contribute to overall energy loss, enabling operators to pinpoint inefficiencies at every stage, from generation to delivery.

14 loss buckets with deep insights

Generation losses are categorized into predefined buckets, for example: equipment downtime, weather variability, grid unavailability, curtailment and shading issues.

Each bucket provides granular data, allowing operators to isolate and analyze specific devices contributing to underperformance. With the ability to drill down into root causes, targeted corrective actions can be implemented to mitigate losses on a device-by-device basis.

In-depth inverter performance analysis for solar

Understanding inverter efficiency is crucial for optimizing energy production. Maximo Renewables offers detailed insights into individual inverter performance, detecting clipping losses, thermal derating and grid outages.

Operators can use this data to resolve inverter-specific problems quickly, ensuring optimal energy conversion and minimal downtime. The analysis applies to both centralized and string inverters.

Differentiating DC underperformance from soiling

Sophisticated loss bucketing algorithms help highlight DC underperformance at the inverter level. Drone analytics or string-level analysis can then be used to further isolate solar panels with different types of defects.

String analysis helps optimize asset performance by providing insights into individual strings, the most granular entity monitored in a solar plant. Key features include:

•       Ranking strings by performance: Strings are ranked based on KPIs such as uptime, failure rates and current levels, helping prioritize maintenance efforts.

•       Comparing strings: Operators can benchmark different strings, correlating underperformance with weather and operational data to validate root causes and generate targeted work orders.

Using power curve performance, Maximo Renewables benchmarks the actual performance of wind turbines against a warranted or site-specific power curve, helping to quickly determine underperformance. Key benefits include:

•       Turbine prognosis for root cause identification (yaw, pitch, weather sensors, rotor, generator, gearbox and others)

•       Availability management for managing contractually mandated availability

•       General availability data system (GADS) reporting—specific to US North American Electric Reliability Corporation (NERC) compliance

A battery energy storage system (BESS) typically consists of many underlying cells or modules that are connected in a series and in parallel to create utility-scale equipment. With data-driven insights, organizations can optimize energy storage, reduce downtime, and improve overall efficiency. Key features include:

•       Weak module detection to detect underperformance.

•       Battery replacement planning by tracking battery health and performance to proactively plan replacements of parts, avoiding unplanned outages and extending system efficiency.

•       Warranty claims management by tracking module performance and warranty periods, simplifying warranty claims management by providing accurate data to support timely claims.

Automated cycle detection automatically detects charge and discharge cycles in a BESS, enabling improved monitoring of BESS performance during charging and discharging and better measurement of BESS cycle efficiency. Sophisticated battery chemistry-specific algorithms can be used during these charge and discharge regions, paving the way for better battery analytics at container, rack and module levels.

Drone analytics with string analysis is a comprehensive solution for solar panel defect detection and energy loss quantification. This capability integrates AI and computer vision to automate routine inspections and deliver critical insights for efficient asset management.

Thermographic data and heat map

The thermographic data that is captured by drones is converted into heat maps that visually highlight problem areas. This allows operators to quickly identify and address systemic losses by providing a clear, visual representation of the asset's health. IBM Maximo Renewables can also quantify energy loss due to each of the defects by combining drone analytics with string analysis.

Defect categories identified by thermography

With AI, Maximo Renewables categorizes defects into 8 common types to streamline diagnostics and maintenance. These categories are key to understanding the root causes of performance losses and taking swift corrective actions:

1.    Bypass diode failure: Issues with diodes causing loss of efficiency in solar panels

2.    Dirt or shadow: Reduced energy production due to dirt or shading on panels

3.    Vegetation: Overgrowth obstructing sunlight or airflow, leading to performance losses

4.    Hotspot: Overheating in specific panel regions, often due to malfunctioning cells or connections

5.    Cable point heating: Hotspots forming due to faulty or damaged cable connections

6.    String hot: Excessive heat in string connections indicating potential issues with panel wiring

7.    Module short circuit: Short circuits that can significantly impact energy output

8.    Module hot: Overheating of entire panels, often caused by internal defects or degradation

By categorizing defects into specific types, operators can quickly identify the root causes of performance issues and take targeted action. For example, a detected hotspot might signal a failing module, while dirt or shadows might be reducing energy output. With these insights, operators can prioritize repairs that restore maximum efficiency and prevent further losses.

The recommendation engine goes even further—analyzing hidden trends, uncovering the causes of underperformance, and delivering clear, data-backed actions to minimize production losses. Instead of reacting to failures, operators can make proactive decisions to keep energy flowing and assets running at peak performance.

In addition to the readily available analytics built into IBM Maximo Renewables, energy providers can build on top of and extend these models.

As the energy landscape continues to shift toward renewables, data-driven decision-making is the key to operational excellence. By integrating IBM Maximo Renewables APM into their operations, energy providers gain a competitive edge, reduced downtime, optimized maintenance schedules and improved energy output.

Are you ready to transform your renewable asset performance? Explore IBM Maximo Renewables today and harness the power of advanced analytics for a sustainable energy future.

To see how Maximo Renewables can optimize your renewable energy portfolio, book a live demo today!

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