## 1 Introduction  
In recent years, offshore oil-gas platforms have tended to reduce carbon emissions. Currently, the joint power-supply technology of renewable energy and traditional energy is being explored. As an abundant but unstable renewable-energy source, offshore wind power has the advantages of high average wind speed, high utilization hours, and easy large-scale development. Since offshore solar energy complements wind energy [1, 2], offshore wind turbine (WT) and photovoltaic (PV) units have become global hotspots at the frontier of renewable energy development [3, 4, 5]. As the number of WT/PV units increases, energy storage (ES) systems with a two-way energy-flow capacity must be properly introduced to reduce the impact of the instability of WT/PV output on a power system [6]. Therefore, to achieve the low-carbon-emission goal for offshore oil-gas platforms, it is necessary to introduce WT/PV ES into the existing offshore oil field power grid to form a power-supply mode of new-energy access, multi-energy complementation, and low-carbon operation, which is necessary for the future construction of \"carbon-neutral\" oil fields. Recently, many scholars studied the method of WT/PV ES for distribution networks and proposed a variety of distributed generation (DG) and ES optimization methods. In [7], the siting and sizing of DG were divided into two levels. The first level considered line losses and voltage fluctuations and the second level considered the income of operators. In [8], an PV ES-capacity-optimization model was established with the goal of optimizingthe loss-of-load probability and the energy-overflow ratio, but the influence of line loss was not considered in the capacity planning. In [9], a distribution network with numerous nodes was divided into clusters. That study proposed an initial planning of the capacity of each cluster, with the cluster as the unit and a reduction in the comprehensive cost as the goal, followed by the planning of the capacity of each node, with the node as the unit and reducing line loss as the goal. In [10], a heuristic strategy based on voltage sensitivity was proposed to realize the optimal planning of ES to solve the voltage-limit violation of a distribution network upon the connecting of DG to the distribution network. A particle swarm optimization (PSO) algorithm was employed in [11; 12; 13; 14] to determine the location and capacity of a single DG unit or multiple DG units, focusing on optimizing voltage fluctuation and line losses. However, ES was not considered. A model was developed in [15] to optimize ES capacity planning by considering investment costs and battery lifespan to achieve overall economic efficiency. An improved particle swarm optimization (IPSO) algorithm was used in [16] to optimize energy storage capacity, with the objectives of minimizing voltage fluctuations and enhancing economic factors. However, the siting of ES was not considered. A model based on multiple scenarios for siting and sizing for PV and ES planning was established in [17], and the planning problem was solved by a centroid opposition-based learning-particle swarm optimization (COBL-PSO) algorithm; however, that study did not consider multiple forms of DGs. These studies established reasonable planning methods for the connection of DG to a distribution network and effectively improved the penetration rate of new energy in a distribution network, but they did not consider the offshore situation. The marine environment is very harsh, with a high salt-fog concentration, high humidity, and the possibilities of extreme cold, high heat, typhoons, tsunamis, and other disastrous events [18]. When WT, PV, and other power-generation equipment are located offshore in a marine environment, corrosion and aging are significantly accelerated by the harsh conditions. Compared with land equipment, offshore equipment is more likely to fail, leading to equipment downtime.