How Can Wind-Solar Hybrid Power Be Smarter? Practical Applications of AI in System Optimization and Control

Intelligent Control of Wind-Solar Hybrid Renewable Power Systems Using Artificial Intelligence

Wind-solar hybrid renewable energy systems leverage the sustainability and complementarity of wind and solar resources. However, the intermittent and fluctuating nature of these energy sources leads to unstable power output, negatively impacting supply reliability and power quality. Optimizing system control through advanced technologies to enhance generation stability and efficiency has become a critical challenge—key to expanding clean energy adoption and achieving sustainable energy development.

1. Research Background: Challenges in System Control

Wind-solar hybrid systems are heavily influenced by natural conditions, posing significant control challenges. The intermittency and volatility of wind and solar energy undermine generation stability. In coastal regions, marine weather conditions affect wind direction and speed. During typhoon passages, wind speeds can surge from the normal operating range of 5–7 m/s to over 15 m/s within minutes—exceeding the safe operational limits of wind turbines and forcing shutdowns, resulting in power interruptions.

In plateau regions, large day-night temperature differences reduce solar panel performance at night, decreasing photovoltaic (PV) efficiency by 30%–40%. On cloudy or hazy days, solar radiation intensity drops sharply, reducing PV output by 60%–70% compared to sunny days. This causes significant fluctuations in system output, making stable power delivery difficult.

Traditional power distribution strategies have clear limitations. Relying on fixed empirical parameters and simple threshold rules, they fail to adapt to real-time changes in energy availability. For example, in an urban-rural fringe hybrid power station, during early morning with light winds and gradually increasing sunlight, traditional control keeps wind turbine output at only 30%–40% of rated capacity due to unmet wind speed thresholds, wasting abundant wind resources. Meanwhile, due to suboptimal initial PV configuration, solar generation exceeds load demand as soon as irradiance rises, wasting approximately 25% of generated energy. When weather changes abruptly—such as rapid wind shifts from thunderstorms or sudden cloud cover—traditional strategies cannot respond quickly, degrading power stability and failing to meet the stringent power quality requirements of modern industrial equipment and precision electronics, hindering broader application of hybrid systems.

2. Artificial Intelligence Applications

2.1Energy Forecasting

Machine learning algorithms, with their powerful data processing and pattern recognition capabilities, lay the foundation for stable system operation. A large coastal wind-solar farm, facing complex meteorological conditions and high resource variability, collected five years of historical data—including wind speed, wind direction, solar radiation, cloud thickness, and corresponding generation outputs. By training a Long Short-Term Memory (LSTM) network on this data, a robust energy forecasting model was developed. Validation showed that during summer typhoon seasons, wind energy prediction errors were reduced to 10%–15% for 6-hour forecasts—a 30%–40% improvement over traditional methods. Under cloudy conditions, solar radiation prediction errors remained within 15%–20%, enabling proactive power planning and dynamic equipment adjustments to mitigate instability risks.

2.2 Power Distribution Optimization

Optimizing power allocation is crucial for improving system efficiency, where intelligent algorithms play a central role. The Particle Swarm Optimization (PSO) algorithm, inspired by bird flocking behavior, searches complex solution spaces to find optimal power distribution between wind and solar sources. At a mountainous hybrid station with abundant daytime sunlight but highly variable wind due to terrain, traditional control struggled. After implementing PSO, the system continuously monitored energy forecasts and load demands. When it detected an impending increase in valley wind speeds and a drop in solar irradiance due to cloud movement, PSO dynamically adjusted the power mix—increasing wind output by 30%–40% while reducing solar contribution. Real-world testing showed a 20%–30% improvement in energy utilization under complex weather, minimizing waste and ensuring stable power for local villages and small industries.

2.3 Equipment Monitoring and Fault Diagnosis

Convolutional Neural Networks (CNN) excel in equipment condition monitoring and fault diagnosis. In large wind farms with harsh operating environments, blade wear and gearbox failures are common. Traditional monitoring often fails to detect such issues early. By applying CNN to analyze vibration, temperature, and current data from sensors on critical components, significant improvements were achieved. For vibration signals, the CNN model could detect early-stage blade wear up to one week in advance, with 90%–95% accuracy. At a solar plant, the same model identified partial shading and hot-spot faults with 92%–96% accuracy. This drastically reduced fault detection time, minimized downtime, lowered maintenance costs, and enhanced overall system reliability and efficiency.

3. Evaluation of Application Results

AI-driven optimization has delivered remarkable results in real-world projects. In a remote off-grid project in western mountainous regions—where conventional grid extension is costly and difficult—abundant wind and solar resources were previously undermined by rugged terrain and volatile weather. Before AI integration, the power supply was highly unstable, with residents experiencing an average of 35 hours of outage per month, disrupting daily life and halting small agro-processing businesses.

After deploying AI technologies:

• An LSTM model accurately predicted local weather patterns with low error rates.

• PSO dynamically optimized power allocation based on forecasts and real-time loads.

• A CNN model provided real-time equipment health monitoring and early warnings.

Results showed a dramatic improvement: monthly outages dropped to fewer than three incidents, totaling around 3 hours. Energy utilization increased by 30%, and resident satisfaction rose from 35% to 90%. Local industries stabilized, e-commerce emerged, and over 30 new jobs were created, significantly boosting regional economic growth.

From an industry-wide perspective, AI adoption in wind-solar hybrid systems is reshaping the sector. Over the past three years, the number of AI-optimized projects has grown by 45%. These projects have achieved 25%–35% higher generation efficiency and 20%–30% lower maintenance costs. In large hybrid plants, intelligent scheduling and accurate forecasting have reduced curtailment rates by 20%–25% and improved grid integration capacity for renewables by about 20%.

However, challenges remain. High initial investment in hardware and model training makes deployment difficult for economically disadvantaged areas. Rapid technological updates and a shortage of skilled personnel further slow widespread adoption. Future efforts must focus on R&D to reduce costs, strengthen talent development through university-industry collaboration, and unlock AI’s full potential to drive high-quality growth in the clean energy sector.

4. Conclusion

The future of AI in wind-solar hybrid renewable systems is promising. As technology advances, more efficient and energy-efficient AI models and algorithms will emerge. These innovations will not only refine energy forecasting and power allocation but also overcome bottlenecks in data acquisition and processing, enabling AI to perform effectively in diverse and complex environments. This progress will elevate clean energy systems to new heights, providing strong support for global sustainable energy development.