This study proposes a method for optimizing the cable sizes in the collection systems of wind turbines on offshore wind farms. Although cable size optimizations for offshore wind farms may require a high initial investment, the power losses are considerably reduced over the system lifespan, thereby justifying the high investment. The proposed method employs a simple binary integer programming model that can determine optimum cable sizes and related cable costs (installation and loss costs) with minimal inputs regarding the topology of the wind-turbine collection system. It can be employed to design the electrical infrastructure of offshore wind farms using heuristic or metaheuristic approaches, as it can automatically calculate the power flows in arcs using bus injection to branch current matrices, even if the system topology changes. Furthermore, we have introduced a method of calculating the power losses by accounting for the diverse capacities and availabilities of WTs to reflect the actual characteristics of WTs. Our method was validated at two offshore wind-farm sites in the Southwestern Sea of Korea, one 60 MW and the other 400 MW, and this study provides some simple examples and the related parameters. Owing to the optimal cable size, the proposed technique (combined with K-clustering based genetic algorithm) demonstrated cost savings in the range of 1.55%-2.21% within the wind-turbine collection system when compared with the conventional metaheuristic methods. Moreover, the cost-based topology modifications achieved cost savings in the range of 0.18%-0.66%.

In South Korea, Renewable Energy Sources (RES) have been increasing with the application of energy policies, such as Feed in Tariff (FIT) and the Renewable Portfolio Standard (RPS). However, a rapid increase in RES supply leads to an uncertain power supply due to the intermittent output of RES. A representative example is the curtailment of Wind Turbines (WT), which frequently occurs in Jeju Island, South Korea. The proportion of RES power on Jeju Island is 67%, and there are cases where WT is curtailed among the operable sections to maintain the balance of power supply and demand. This paper applies Power-to-Gas (P2G) technology to hydrogenate, store, and utilize unused power to solve this problem. In this paper, Aewol-eup in Jeju Island is selected as a target site for case study. An Integrated Energy System (IES) for various energy operations is designed to control RES output. This paper proposes the optimal facility configuration and finally drives the optimal design and operation solution of IES by analyzing the objective functions and focusing on the Independent Power Producer (IPP) perspective.

Recently, it has been suggested that microgrids (MGs) can improve the resilience of distribution systems. However, predictions about future faults are uncertain. This makes calculating the exact value of the benefits of system resilience enhancement close to impossible at the time of MG planning. Therefore, this paper proposes a framework for MG planning, which focuses on resilience estimation. To consider the uncertainties of future failure events, the proposed method for estimating the resilience utilized the Monte Carlo simulation. In addition, an optimal scenario was estimated using a cost–benefit analysis and constraints on the expected value of resilience enhancement. In the case study, an actual MG installation at D-university was evaluated to obtain the optimal MG planning scenario. The results show that the capacity and installation locations of the distributed generators (DGs) impact the resilience enhancement. The proposed method can effectively derive the optimal MG planning scenario by evaluating the possibility of future operations based on the segmentation of both the system configuration and type of DG to improve the resilience of distribution systems.

At a time when interest in improving the resilience of the distribution system is increasing, this paper proposes an optimal method for reinforcing the distribution system with regards to resilience. In this study, resilience is derived from the empirical distribution function, which is used as a constraint in the distribution facility reinforcement plan. The resilience calculation process utilizes Monte Carlo simulation and considers various uncertainties such as the number of facility failures, location, and recovery time for post-contingency. The case study identifies the minimum investment cost and optimal location for the facility reinforcement considering resilience for the modified 13 node test feeder. The results of this study demonstrate that the proposed method is more effective than the existing facility reinforcement plans based on several fault scenarios, because the calculated results for multiple unpredictable resilience indices are used as constraints for the reinforcement plan.

In power system resilience, the dynamic state of a system can be assessed via the sectionalization process which is necessary to search for available and unavailable load points (ALP, ULP) in a system configuration that changes over time. To develop an automatic and quick search for ALP and ULP in a dynamic system configuration, this paper proposes an ALP searching (ALPS) method with formulation. In the case study, the accuracy and computational performance of the proposed method are analyzed based on the recovery strategies considered during the resilience assessment process. The proposed method has the ability to exactly analyze ALP and ULP in radial, loop, and mesh system configurations, which can be applied to both transmission and distribution systems. The results of this paper demonstrate that the proposed method yields an excellent performance for various system configurations and recovery strategies.