Gamze Yetimler Kostur

Natural disasters can cause severe damage to power systems, leading to outages with economic and societal impacts. Ensuring supply of critical loads is therefore a requirement for mitigating the consequences of such events. Microgrids (MGs) can operate in islanded mode, ensuring electricity supply to critical loads through localized energy resources. This study focuses on the formation of MGs by optimally placing distributed generation (DG) units and energy storage systems (ESSs) during outages. The problem is formulated as a mixed-integer conic programming (MICP) model, explicitly accounting for seasonal variations in load demand and season-dependent generation profiles of different DG technologies. The objective function minimizes the total cost, while prioritizing critical loads based on their importance. The model determines the optimal locations and capacities of DGs and ESSs while satisfying the power balance, operational, and topological constraints required for MG formation. The optimization is performed over a 24-hour horizon to capture temporal variations. The framework is applied it to the IEEE 33-bus test system under different seasonal conditions representing the occurrence of a disaster in various periods of the year. Simulation results demonstrate that incorporating seasonal demand and renewable generation characteristics significantly influences DG and ESS allocations.

Objective: Six Sigma-SS and the DMAIC-Define, Measure, Analyze, Improve, Control cycle are employed to enhance the success of the launch of a new built-in oven with a digital panel. Method: Following the define and measure phases, to identify and eliminate potential failures, Failure Mode and Effect Analysis-FMEA is conducted and control plans were composed during the analyse phase. In the improvement phase, the Risk Priority Numbers-RPNs obtained through FMEA and recommendations were listed. Using Design of Experiments-DOE, the lifespan of bulbs in ovens was maintained at the target value while reducing procurement costs. In the control phase, the results of validation experiments and the gains were reported. Findings: After reporting the RPNs related to high-risk failure modes and providing improvement recommendations, DOE and Analysis of Variance were employed to determine which type of oven and brand of bulb could be used. If implemented, the expected annual savings are approximately 20,000 Euros. Originality: This article focuses on an integrated approach for a product that is set for a new launch. It aims to improve design and production processes using the SS while also targeting cost reduction in procurement processes. To the best of our knowledge, no similar study has been encountered in the literature.