Multi-heterogeneous renewable energy scheduling optimization based on time series algorithm and green computing-driven sustainable development

Abstract

The integration of heterogeneous renewable energy sources, such as wind and solar, poses significant challenges to the dynamic economic and environmental dispatch of power systems due to their intermittent and uncertain nature. Efficient coordination between generation and consumption is crucial to ensure stability, reduce emissions, and lower costs. Accurate forecasting of renewable outputs is a critical prerequisite for achieving optimal dispatch decisions. To address this, we propose a hybrid prediction and scheduling framework that leverages time series forecasting to support real-time dispatch optimization. Specifically, we develop a novel prediction model based on a Completely Input and Output-connected Long Short-Term Memory (CIAO-LSTM) network, whose parameters are optimized using an Improved Fruit Fly Optimization Algorithm (IFOA). This approach enhances the model’s ability to capture both linear and nonlinear temporal features and improves convergence through adaptive search strategies. The predicted outputs are then incorporated into a rolling real-time scheduling model that jointly minimizes generation costs and pollutant emissions. Simulation results on a six-unit power system demonstrate that our approach significantly improves prediction accuracy and dispatch performance, reducing average generation costs and emissions by over 8 % and 16 %, respectively. These results confirm the effectiveness of the proposed method in promoting green and sustainable power systems.