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Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds a model that predicts the amounts of solar power generation using weather information provided by weather agencies. This study proposes a two-step modeling process that connects unannounced weather variables with announced weather forecasts. The empirical results show that this approach improves a base approach by wide margins, regardless of types of applied machine learning algorithms. The results also show that the random forest regression algorithm performs the best for this problem, achieving an R-squared value of 70.5% in the test data. The intermediate modeling process creates four variables, which are ranked with high importance in the post-analysis. The constructed model performs realistic one-day ahead predictions.

Among the many components of material delivery operations, packaging is one of the foundations of secure and cost-efficient on-time delivery. Current environmental concerns have increased the popularity of returnable packaging over disposable packaging. This study considers a supply chain in the automotive industry where a single supplier adopts returnable packages for delivery operations to a single recipient. If a returnable package is not available, then an expendable package will be used as a more expensive alternative. Thus, an investment decision on the number of returnable packages must be made prior to launching a returnable packaging system. Using the actual data from an automotive supply chain, this study conducts simulated experiments, under the uncertainty of future demand and required lead time of reverse logistics, to identify the optimal quantity of returnable packages. Sensitivity analysis is then performed by varying the assumptions on operation duration, demand variability, and lead time variability. In general, the results indicate that a greater initial purchase of returnable packages is desirable for longer operation duration, higher demand variability, and higher lead time variability. However, if operation duration is short and the uncertainty is high, then there may be little benefit in using reusable packages. These results generalize the findings from previous studies. This approach and solution can enhance reliable and efficient supply chain operations in the automotive industry and may be applied to other industries where packaging is important and expensive.

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This Paper deals with the optimum energy management of Microgrid (MG) having Energy-Storage System(ESS)s. Recently, the importance of retaining the profits of MG owners and the needs of providing additional requirements to the electric grid are rising. To accommodate these needs systematically, the Quadratic Programming (QP), one of the simplest and effective optimization method, is gaining attention. The QP has been used for similar cases before, but unlike the known advantages of early QP studies, some of the subsequent papers have been conducted in an inappropriate direction and may be overshadowed. Therefore in this paper, an extended and more practical QP cost function considering the realistic operating conditions is proposed, and the advantages of the original methods are revisited with comparisons. As a result, the proposed method retains the genuine features of QP, such as peak power shaving and assuring the power reserve rate, and can be simply extended to include Electric Vehicle (EV)s into the optimization. Additionally, the practical issues of implementing the QP in real-time have been discussed and resulted in both improved optimization speed by 58% using the cost function reformulation and the robustness with the forecast mismatching.

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