电网连接型多能系统的整体优化：生物质与灵活储能的集成

Abstract This study tackles the pivotal challenge of optimizing the capacity and flexible energy storage of Multi-Energy Systems (MES) in Northern Jiangsu, with a focus on integrating biomass, wind, and photovoltaic power sources. This research is vital for enhancing the efficient and sustainable use of green renewable energy, which is essential for addressing energy crises and environmental issues. This study adopted a Biomass and Flexible Storage (BMFS) strategy, which encompasses the integration of wind turbines, photovoltaic generators, biomass power supply units, and energy storage systems. Employing an Ensemble Empirical Mode Decomposition-Symbiotic Organisms Search-Radial Basis Function (EMD-SSA-RBF) optimization algorithm, the system’s performance was simulated under a variety of load conditions, aiming to achieve a balance between grid supply and demand. This algorithm combines the strengths of EMD for data decomposition, SSA for optimizing the parameters of the RBF neural network, and RBF for its high precision in function approximation, making it a robust choice for the complex optimization challenges presented by the multi-energy system. Key findings include the consistent supply of 525 kW from the Gas Boosted Gas Turbine (GBGT), with peak hour output ratios of about 20 %, crucial for grid stability. The Comprehensive Sustainability Index (CSI) of the system saw significant improvements, rising by 7.57 % in the summer and by 21.96 % in the winter. The optimized MES not only demonstrated a reduction in the Levelized Cost of Electricity (LCOE) but also achieved increased CO2 savings, along with a significant reduction in annual payments and improved capital cost efficiency. The novelty of this research lies in its holistic BMFS approach, which not only optimizes MES configurations but also enhances system sustainability through the strategic enhancement of green energy resources. This study lays a theoretical foundation and offers practical guidance for the optimization of MES capacity and the development of flexible energy storage technologies, providing invaluable insights into sustainable energy management.