A Two-Layer Hybrid Optimization Approach for Large-Scale Offshore Wind Farm Collector System Planning

Constructing large-scale offshore wind farms (OWF) has become the main direction of utilizing the wind power to help realize the energy transformation. Traditionally, the planning of OWF collector system would rely on either heuristic or deterministic optimization algorithms, which respectively suffers from unstable outputs and a lack of freedom in searching for global optimal solution. This paper innovatively designs a hybrid optimization approach combining algorithms in these two categories to achieve a balance between improved economic efficiency and stable outputs. The whole design consists of two layers of hybrid optimizations. The outer layer is to partition wind turbines (WT) into groups, where each group locates an offshore substation (OS) for power collection and transmission. This partitioning and locating optimization is solved through a combination of deterministic Fuzzy C-means (FCM) clustering method and the genetic algorithm (GA). The inner layer is to arrange optimal connections using proper cable ratings among WTs within each group, and GA is properly integrated into the deterministic Two-Phase Clark and Wright's saving algorithm to solve the problem. The collector system planning in this paper concerns on the investment costs for the internal medium voltage (MV) network that gathers power from WTs and the external high voltage (HV) cabling to the onshore grid, and also the long-term power loss costs. The proposed design is tested on a benchmark OWF collector system, and the test result verifies its achievements in higher economic efficiency with stable outputs.