A multi-objective optimisation framework for a standalone hybrid offshore renewable energy system with electrical and hydrogen loads
Sumit Kumar, Natee Panagant, Ehsan Arzaghi, Til Baalisampang, Vikram Garaniya, Rouzbeh Abbassi
Abstract
Offshore renewable energy systems (ORESs) have substantial potential to decarbonise industries and clean energy generation. However, they face challenges, including high costs and risks due to uncertain and hazardous marine environments. To address this challenge, this study presents a multi-objective design and optimisation framework for a hybrid standalone ORES powering industrial and domestic green electricity and hydrogen loads simultaneously. The hybrid ORES model integrates offshore wind (OWF) and floating solar (FPV) farms with large-scale battery energy storage systems (BESS) and hydrogen storage systems (HSS) as a firming technology. The framework optimises three conflicting objectives, including minimising cost and potential energy waste and maximising system reliability, subject to various technical and economic constraints. To regulate optimal energy flow and protect ORES components, a simple and efficient power management strategy is presented and utilised. Five recent state-of-the-art multi-objective metaheuristics are applied to solve the hybrid ORES design and obtain Pareto solutions. The comparative analysis utilises widely employed Pareto front hypervolume (HV) metric statistics and Friedman's rank test. A case sensitivity analysis is conducted to evaluate the model's robustness, reliability, and effectiveness. Validation of the methodology is conducted through a real-world case study in Australia's offshore region, showcasing its capacity to supply clean energy to industrial and domestic loads. Results indicate SHAMODE-WO's superior diversity and convergence traits in Pareto-optimal sets, with SHAMODE closely trailing. Furthermore, the average Friedman rank across all three cases designates SHAMODE-WO as the top performer. The proposed framework can facilitate decision-makers in addressing complex multi-objective ORES optimisation problems and choosing optimal solutions.