Duck Curve Management Using Deep Learning and Optimization Algorithms for Renewable Energy Integration

The paradox of the duck curve is that more solar and wind power entering the power grid means more duck curves. Efficient Unit Commitment (UC) is crucial for duck curve management, enabling stability and long-term sustainability of the power grid. In this paper, we analyze several approaches using UC to tackle the duck curve problem. After introducing the duck curve phenomenon and its influencing factors, the study reviews current UC strategies, identifying their pros and cons while discussing the conditions under which each strategy performs well. This approach combines Long Short-Term Memory (LSTM), One-Dimensional Convolutional Neural Network (1D-CNN), and a hybrid of both—LSTM-1D CNN and 1D CNN-LSTM—which are applied and compared to predict the day-ahead solar and wind power output. The results show that LSTM-1D CNN outperforms all other techniques, achieving maximum accuracy of 98.64% for solar and 98.87% for wind power. Additionally, three optimization algorithms are used and compared to plan the short-term performance of the power grid: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a hybrid of both (GA-PSO). The results confirm that GA-PSO surpasses the other methods, achieving the lowest operating cost of $319,876.7. This research can aid researchers, policymakers, and power grid managers create a more efficient and sustainable energy system.