Distributed Economic Model Predictive Control for the Joint Energy Dispatch of Wind Farms and Run-of-the-River Hydropower Plants

In the transition towards sustainable power systems, integrating multiple renewable energy sources has become a key strategy for maximizing efficiency and reliability. Wind farms and run-of-the-river hydropower plants, when operated together, offer complementary benefits—wind power is variable and weather-dependent, while run-of-the-river hydropower provides steady, flexible output. However, coordinating their joint operation is complex, requiring advanced control strategies that can balance economic efficiency with system stability.

This is where Distributed Economic Model Predictive Control (DEMPC) comes into play. Unlike traditional centralized approaches, DEMPC divides the overall optimization task into smaller, coordinated sub-problems. Each renewable plant makes local decisions based on its own constraints and forecasts, while also sharing critical information with other plants to achieve system-wide optimal performance. This distributed approach reduces computational burden, enhances scalability, and ensures resilience against communication failures or data uncertainties.

By applying DEMPC to the joint energy dispatch of wind and hydropower plants, operators can significantly improve resource utilization. The strategy allows wind farms to maximize their contribution when wind availability is high, while hydropower plants can adjust output to stabilize fluctuations and meet demand. This not only minimizes operational costs but also ensures grid stability and increases renewable energy penetration without relying heavily on fossil-based backup power.

Ultimately, DEMPC paves the way for smarter, green energy systems where diverse renewables work together seamlessly. For regions rich in both wind and hydropower potential, this control approach offers a practical and forward-looking pathway toward achieving long-term sustainability and energy security.

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