Enhancing Sustainability and Efficiency in Offshore Oil and Gas Engineering through the Integration of Chaotic Local Search and Particle Swarm Optimization for Microenergy Systems Optimization

The offshore oil and gas industry is under increasing pressure to reduce carbon emissions while maintaining energy reliability. Offshore oil and gas platforms (OOGPs) face significant challenges in integrating low-carbon operations with their energy systems. This study introduces an optimized scheduling approach for offshore microintegrated energy system (OMIES) that incorporates a hybrid energy storage system, including a floating power-to-gas associated gas storage (FP2G-AGS) module, to address the intermittency of renewable energy sources. An economic optimization model is formulated, accounting for carbon emissions, operational costs, and the status of gas turbine generator sets. To solve the complex optimization problem, this study develops a hybrid chaotic local search and particle swarm optimization (CLPSO) algorithm. The CLPSO algorithm synergizes the global search ability of PSO with the local refinement of chaotic local search, enhancing the convergence to optimal solutions. Experimental results demonstrate that the proposed CLPSO algorithm effectively achieves optimal solutions within a range of 48.2–51.7. Case studies validate the model’s capability to promote new energy integration, reduce operational costs, and decrease CO₂ emissions across various scenarios. This research significantly contributes to achieving low-carbon operations on OOGPs and promotes the sustainable development of marine resources.