Enhanced PSO-based optimisation with probabilistic analysis for standalone DC microgrid design

Offshore industries face significant challenges in integrating renewable energy sources (RES) to achieve a sustainable and reliable energy supply, due to the intermittency and unpredictable offshore weather conditions, which hinder the reliability of standalone microgrids. To address this issue, this study explores the integration of a hydrogen gas energy storage station within a standalone DC microgrid, evaluating its potential to enhance stability and reduce emissions in offshore maritime operations. The research investigates the effectiveness of hybrid energy storage systems (HESS) in mitigating RES intermittency, incorporating solar PV, wind, and wave energy as primary generation sources. Using an enhanced particle swarm optimisation (PSO) method, the study compares various energy storage configurations, with results indicating that a battery-supercapacitor HESS achieves the lowest levelised cost of electricity (LCOE), which is 19.63 US Cents /kWh, making it the most cost-effective solution. A probabilistic model is further developed to validate the microgrid's resilience under real-world conditions, bridging the gap between theoretical design and practical implementation. Additionally, the study assesses the feasibility of integrating wave energy, concluding that current market dynamics render it financially unviable for offshore microgrid applications. The proposed enhanced PSO algorithm demonstrates superior performance compared to commonly used heuristic optimisation methods such as Genetic Algorithm (GA), standard PSO, and Ant Colony Optimisation (ACO). This improvement is attributed to the integration of quadratic interpolation and extended local search mechanisms. Additionally, the study introduces an energy storage system (ESS) degradation algorithm that outperforms the traditional Rainflow counting method in both accuracy and computational efficiency, particularly in modelling partial charge–discharge cycles. Overall, this work provides critical insights into optimising standalone microgrids for offshore industries, alongside technical performance and economic viability. • Propose an enhanced particle swarm optimization (PSO) algorithm for standalone DC microgrid design. • Establish a framework to quantify the degradation of energy storage systems. • Develop a mathematical model for wave energy to assess the feasibility of wave energy integration in offshore standalone microgrids. • Conduct a scenario-based probabilistic analysis to evaluate the impact of input variable uncertainties.