In the dynamic world of energy, where renewable sources are increasingly taking center stage, the concept of Virtual Power Plants (VPPs) is gaining significant traction. These VPPs aggregate small-capacity and large-volume distributed energy resources (DERs), such as wind and solar, into a unified system that can participate in electricity market transactions. However, the inherent volatility of these resources poses challenges in terms of power output and revenue distribution. Researchers led by SONG Duoyang from the College of Electricity and New Energy at Three Gorges University in Yichang, China, have tackled this issue head-on with a groundbreaking cooperative game scheduling model.
The research, published in ‘电力工程技术’ (Power Engineering and Technology), introduces a novel approach to managing the uncertainties associated with wind power output. “The unpredictability of wind power has always been a thorn in the side of renewable energy integration,” says SONG. “Our combined prediction model, which leverages variational modal decomposition (VMD) and an improved bidirectional multi gated long short-term memory (Bi-MGLSTM) network, significantly enhances the accuracy of wind power forecasting.”
But the innovation doesn’t stop at prediction. The researchers have developed a cooperative game scheduling model that allows different types of DERs to form alliances within the VPP. These alliances aim to maximize revenue from power sales while ensuring fairness in revenue distribution among members. This is achieved through a multifactor improvement Shapley value method and a two-stage refinement of the revenue distribution scheme based on the parity cycle kernel method. “Our model not only improves the operational efficiency of the VPP but also ensures that all participants, from individual generators to large-scale alliances, receive a fair share of the revenue,” explains SONG.
The implications of this research are far-reaching. As the energy sector continues to transition towards renewable sources, the ability to predict and manage the output of these resources more accurately will be crucial. The cooperative game scheduling model proposed by SONG and his team offers a robust solution to this challenge, paving the way for more efficient and equitable energy markets. The researchers’ work could potentially shape future developments in the field, encouraging more widespread adoption of VPPs and fostering a more stable and profitable renewable energy landscape.
For energy companies and market participants, this research represents a significant step forward in navigating the complexities of renewable energy integration. By providing a framework that enhances prediction accuracy and ensures fair revenue distribution, the model could revolutionize how VPPs operate, making them more attractive to investors and more reliable for consumers. As the world continues to seek sustainable energy solutions, innovations like this one will be pivotal in driving the transition to a greener future.
