Offshore wind farms (OWFs) have a high utilization potential; however, their initial investment cost is very high. This study proposes a binary integer programming-based optimization framework for designing OWF inter-array systems. To alleviate the combinatorial growth of candidate connections, the k-nearest neighbors scheme and the Bentley–Ottmann algorithm were applied to generate a reduced candidate-arc set and identify crossing arc pairs. Based on the reduced candidate set, the proposed model simultaneously determined the inter-array topology and submarine cable types while minimizing the life-cycle total cost. The model accounted for the coupling between wind turbine failures and cable-loss reduction and considered spare cables to reflect practical maintenance practices. Case studies on 60 and 400 MW OWFs demonstrated that the proposed preprocessing reduced the number of arc pairs requiring crossing checks from 3240 to 50 and from 16,110 to 49 (reductions of 98.5% and 99.7%, respectively). The coupled loss model reduced the power-loss cost by 10.91% and 10.72%, respectively, and the inclusion of the spare-cable cost led to a lower total life-cycle cost through topology and cable-type reconfiguration. In conclusion, the proposed methodology may achieve economic efficiency and reliability of OWF inter-array system design.

This paper presents an optimal-design methodology for small-scale Integrated Energy Systems (IESs) that couple electricity and heat in distributed networks. A hybrid reliability assessment integrates probabilistic state enumeration with scenario-based simulation. Mathematically, the design is cast as a stochastic, reliability-driven ranking: time-sequential Monte Carlo (MC) produces estimators of Loss of Load Probability (LOLP), Expected Energy Not Supplied (EENS), and Self-Sufficiency Rate (SSR), which are normalized and combined into a Composite Reliability Index (CRI) that orders candidate siting/sizing options. The case study is the D-campus microgrid with Photovoltaic (PV), Combined Heat and Power (CHP), Fuel Cell (FC), Battery Energy Storage Systems (BESSs), and Heat Energy Storage Systems (HESSs; also termed TESs), across multiple siting and sizing scenarios. Results show consistent reductions in LOLP and EENS and increases in SSR as distributed energy resource capacity increases and resources are placed near critical nodes, with the strongest gains observed in the best-performing configurations. The CRI also reveals trade-offs across intermediate scenarios. The operational concept of the campus Energy Management System (EMS), including full operating modes and scheduling logic, is developed to maintain a design focus on reliability-driven decision making. Probability-based formulations, reliability metrics, and the sequential MC setup underpin the proposed ranking framework. The proposed method supports Distributed Energy Resource (DER) sizing and siting decisions for reliable, autonomy-oriented IESs.