This paper introduces a method for the techno-economical sizing of a pool of Renewable Energy Converters (RECs), Fuel-Based Generators (FBGs), and Energy Storage Systems (ESSs) to meet the load power demand of a target Isolated Power System (IPS) offshore. The problem is formulated as Mixed Integer Linear Programming (MILP), with a cost function accounting for the risk aversion of decision makers. The framework considers already mature technologies, such as Wind Turbines (WTs), Gas Turbines (GTs), Diesel Generators (DGs) and Battery Energy Storage Systems (BESSs), as well as innovative ones, such as floating Photo-Voltaic Panels (PVs), Wave Energy Converters (WECs) and Hydrogen Storage Systems (HSSs). A load side Demand Response (DR) mechanism is also proposed. The methodology is applied to the test case of offshore Oil and Gas (O&G) platforms, in which the reduction of the emissions produced by FBGs supplying electricity and heat is urgent. The results show that, for current costs, the 100% Fuel-Based Generator (FBG)-based solutions are cheaper than any other hybrid solution, but the deployment of RECs alongside just one Gas Turbine (GT) reduces the emissions by 70% while increasing the costs by only 6%. It is also shown that a tenfold carbon tax increase is required to make a combined FBG/Renewable Energy Converter (REC)-based solution financially advantageous compared to a solely FBG-based one. Furthermore, the flexible management of 10% of the load can lead to a cost reduction of around 4%. Sensitivity of the solution from Photo-Voltaic Panel (PV), Wave Energy Converter (WEC), and BESS costs, user’s risk aversion, and platform deployment locations are also analyzed. • Optimal sizing of multi-resource isolated power system for offshore O&G. • 100% REC is feasible today, but costs about 8 current fuel-based setups. • Combining renewable and thermal units cuts the emissions with a limited cost increase. • Findings inform decision makers on the present and future decarbonization potential. • Open-source tool enables extended analyses beyond the study’s boundaries.
Andreetta et al. (Sun,) studied this question.