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A dedicated grid-tied offshore hybrid energy system for hydrogen production is a promising solution to unlock the full benefit of offshore wind and solar energy and realize decarbonization and sustainable energy security targets in electricity and other sectors. Current knowledge of these offshore hybrid systems is limited, particularly in the integration, component control, and allocation aspects. Therefore, a grid-integrated analytical model with a power dispatch allocation strategy between the grid and electrolyzer for the co-production of hydrogen from the offshore hybrid energy system is developed in this paper. While producing hydrogen, the proposed offshore hybrid energy system supplies a percentage of its capacity to the onshore grid facility, and the amount of the electricity is quantified based on the electricity market price and available total offshore generation. The detailed controls of each component are discussed. A case study considers a hypothetical hybrid offshore energy system of 10 MW situated in a potential offshore off the NSW of Australia based on realistic metrological data. A grid-scale proton-exchange membrane electrolyzer stack is used and a model predictive power controller is implemented on the distributed hydrogen generation scheme. The model is helpful for the assessment or optimization of both the economics and feasibility of the dedicated offshore hybrid energy farm for hydrogen production systems. • Modeling of a grid-integrated analytical model of an OHES for hydrogen co-production. • Development of a power dispatch allocation strategy for the proposed OHES. • Development of detailed controls of each component used in the proposed OHES. • Demonstrations of a case study considering 10 MW OHES situated in offshore off the NSW of Australia. [ABSTRACT FROM AUTHOR]

Australia has significant offshore wind resources that can support ever-increasing power demand and hence provide a long-term contribution to the net-zero target by 2050. Although some offshore wind farm projects are proposed in Australia, the industry is not mature enough to justify the benefits and display economic opportunities. More specifically, the techno-economic analysis of offshore wind energy has not been systematically mapped in Australia. Therefore, the investigation of the spatial variation of technical feasibility and economic potential of offshore wind is vital to identify the potential regions and to develop strategies for future offshore wind farm developments in Australia. This paper presents detailed mappings of the techno-economic potential of offshore wind energy in Victoria-Tasmania's exclusive economic zone. Offshore wind energy resources are assessed in the paper in terms of energy availability and variability and the feasibility of various offshore wind technologies is investigated, including turbines, foundation types and transmission topologies. In addition, the high-fidelity life-cycle cost models and two innovative evaluation matrices are developed to assess the economic potential of offshore energy farms in South-eastern Australia. The impact of local power system regulation costs and future carbon prices on the viability of offshore wind is also investigated in this paper. It is concluded that the detailed mapping approach and analysis in the paper can provide a systematic tool for industry partners, investors and policymakers at the pre-planning stage of developing offshore renewable energy systems. • The technical feasibility of offshore wind is mapped under various constraints. • The novel LACE and high-fidelity LCC assessment models are developed. • The techno-economic potential is systemically mapped in South-eastern Australia. • The impacts of electricity market regulation and future carbon prices are studied. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]