Automation Becoming the Backbone of Tomorrow's Clean Energy Ecosystems

[Semih Asil]

## The Role of Automation in Clean Energy Ecosystems

Industrial automation is revolutionizing electricity generation, storage, and distribution in the clean energy sector. The widespread use of automation in various clean energy technologies creates systems that operate autonomously and adapt without human intervention. This provides a critical advantage in solving safety and efficiency issues during the energy transition process.

### Sensors for Live Visibility and Control

Sensors provide continuous data on energy flow and system status, enabling operators to respond quickly to real-time changes. These sensors, connected via the Industrial Internet of Things (IIoT), form the foundation for intelligent monitoring and decision-making processes.

### Predicting Electricity Demand

Smart meters measure real-time electricity usage, voltage, and power quality, transmitting this data to energy providers. Thanks to advanced metering infrastructure (AMI), this data enables demand forecasting and dynamic pricing.
A multi-year study conducted in Colombia showed that hourly data from AMI sensors, combined with machine learning models, could predict weekly demand even without weather information. This provides operational-level demand forecasting support for distribution grid planning and grid security.

### CO2 Monitoring in Carbon Capture and Storage

Carbon capture and storage (CCS) is considered critical for reducing emissions in hard-to-abate sectors like cement and steel. Automation plays a significant role in process optimization. Sensors in factories continuously measure CO2 concentration, pressure, temperature, and flow rates, ensuring the system operates correctly and detecting potential leaks that could pose a hazard.

### Early Detection of Irregularities in Transformers

Distribution transformer monitors (DTM) continuously track current, voltage, and temperature, providing early warnings for issues such as overloading, imbalance, or overheating. This information is transmitted to SCADA systems, and actions are taken automatically to prevent equipment damage and unplanned outages.

### Turning Data into Action with Analytics

Advanced analytical methods transform sensor data into insights that form the basis for businesses' optimization decisions. IIoT eliminates human intervention delays, allowing machines and control systems to operate autonomously. For example, when a drop in efficiency is detected, machine speeds and process sequences can be automatically adjusted in real-time.

### Digital Twin Analytics in Microgrids

Microgrids are critically important in disaster areas and locations with limited energy access. Full automation of microgrids can be achieved using live sensor data with digital twin technology and machine learning. This increases energy security and resilience.

### Coordination of Distributed Energy Resources with Control Architectures

Modern automation systems enable the coordinated operation of different clean energy sources, offering high efficiency and reliability in energy management.

### Deep Reinforcement Learning in Hybrid Energy Storage Systems

Due to intermittent energy sources like wind and solar, energy supply and demand can sometimes be mismatched. Deep reinforcement learning algorithms balance resources such as batteries, supercapacitors, and green hydrogen in real-time, ensuring optimal energy management.

### Adaptive Solar Tracking Systems

To increase the efficiency of solar panels, automatic solar tracking systems adjust the panel angle according to irradiation and weather conditions. Single-axis systems can increase energy production by up to 35%, while dual-axis systems can increase it by up to 45%. Additionally, in cases of hail or strong wind risk, the systems move the panels into a protective position, safeguarding the hardware from damage.

### Predictive Maintenance in Wind Turbines

Gearboxes, the most critical and failure-prone parts of wind turbines, can be damaged prematurely due to varying loads. Machine learning models analyze vibration, temperature, and operational data from gearboxes to predict failures in advance and enable planned maintenance.

## Conclusion

Automation makes all processes in the green energy ecosystem visible, controllable, and optimizable. Expertise in integrating automated systems stands out as a competitive advantage for operational reliability and cost-effectiveness. Automation will form the backbone of the future energy infrastructure, and those who utilize this technology will achieve leadership in the clean energy sector by increasing their performance, resilience, and profitability.