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

• Published in: Sustainable computing informatics and systems Vol. 47; p. 101173
• Main Authors: Ma, Chaoran, Hou, Puguang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2025
• Subjects: Connected LSTM; Fruit fly optimization algorithm; Green computing; Heterogeneous renewable energy of wind and solar; Sustainable Development; Time series prediction; Green computing; Connected LSTM; Time series prediction; Fruit fly optimization algorithm; Sustainable Development; Heterogeneous renewable energy of wind and solar
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2025.101173

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. •Proposed a green computing-driven framework for renewable energy scheduling.•Introduced an Improved Fruit Fly Optimization Algorithm for time series forecasting.•Optimized CIAO-LSTM parameters to improve prediction accuracy and generalization.•Verified the significant prediction advantages of the model.