Environmentally Responsible Supply Chains by Atalay Atasu

Author:Atalay Atasu
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

As discussed in Hu et al. (2015), the level of data granularity used in the renewable energy producing technology can lead to very different solutions. To show this, we computed the optimal solution for different levels of granularity in demand and solar yield, that is, aggregating data in adjacent 15-min intervals into one or more hours. There are different solutions for each granularity level, depending on the “starting time” for aggregation. For example, for a granularity level of 2 h, there are two possibilities for aggregating the data, depending on the starting time: (1) 12 a.m.–2 a.m., 2 a.m.–4 a.m., and so forth; and (2) 1 a.m.–3 a.m., 3 a.m.–5 a.m., and so forth. The results are shown in Fig. 10.4. The optimal solution does not change significantly if one uses 15 min or 1 h for demand-yield data granularity. As the granularity level lowers from 2 to 12 h however, the spread in the possible solutions increases. In general, for a given granularity level, better solutions (i.e., closer to the optimal of 13.6 kW, computed with 15-min granularity) occur when there is a better match between solar supply and demand during the aggregated time intervals.

Fig. 10.4Impact of data granularity for demand and solar yield on optimal solution and cost (Note: There is more than one capacity choice (filled triangle) and corresponding percentage cost deviations (multiplication symbol) for each granularity level, depending on how aggregation is performed. The lines plot their respective averages.)

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