Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems
Weiguo Huang, Wen Li, Xuewen Pan, Qiming Liu, Jie Yang
Abstract
As the global energy landscape shifts and sustainability becomes crucial, the offshore oil and gas sector confronts significant challenges and opportunities. This paper addresses the issues of energy efficiency and environmental impact of optimizing offshore micro-energy systems (OMIES) by proposing a multi-objective optimization model that integrates chaotic local search and particle swarm optimization (PSO). The model aims to achieve optimal scheduling of the energy system by comprehensively considering operational costs, carbon emissions, energy utilization efficiency, and energy fluctuation risks. The research results indicate that the optimization model can significantly improve energy utilization efficiency, reduce operational costs, and decrease environmental pollution. This study also explores the practicality of incorporating renewable energy into OMIES, tackling operational challenges to support low-carbon and secure energy operations on offshore platforms. These findings not only provide a new perspective on energy management for offshore oil and gas platforms but also contribute valuable strategies to the sustainable development of global energy.