This study aimed to evaluate the fermentation quality, bacterial community, and nitrate content of sorghum-sudangrass silage with two ensiling densities [550 kg fresh weight (FW)/m3 (low density, LD) and 650 kg FW/m3 (high density, HD)] stored at two temperatures [10°C (low temperature, LT) and 25°C (normal temperature, NT)] for 60 days. The fermentation parameters, microbial counts, bacterial community, nutritional composition, and nitrate and nitrite levels were assessed. The pH and ammonia nitrogen (N) in all silages were below 4.0 and 80 g/kg total N, respectively. Compared with LT treatments, NT treatments had lower pH and lactic acid (LA) bacteria and yeasts counts and contained higher LA and LA/acetic acid (LA/AA) (p < 0.05). The LT-LD contained more ammonia–N than LT-HD (p < 0.05) and had higher nitrate and lower nitrate degradation than other treatments (p < 0.05). Lactobacillus was the most dominant genus with all treatments (57.2–66.9%). The LA, LA/AA, and abundances of Pantoea, Pseudomonas, and Enterobacter in the silage negatively correlated with nitrate concentration and positively correlated with nitrate degradation (p < 0.05). Moreover, pH and ammonia–N were positively correlated with nitrate concentration and negatively correlated with nitrate degradation (p < 0.05). Overall, all silage had satisfactory fermentation quality, and the silage with HD and NT had better fermentation quality and higher nitrate degradation. The bacterial communities in all silages were dominated by Lactobacillus. The nitrate degradation during the fermentation process might be related to the fermentation quality and the activity of Pantoea, Pseudomonas, and Enterobacter in silage.

This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the threephase branch flow is modeled to characterize the unbalanced nature of the ADN, schedule DER for three phases, and derive a realistic DER allocation. Then, both active and reactive power resources are co-optimized for voltage regulation and power loss reduction. Second, the battery degradation is considered to model the aging cost for each charging or discharging event, leading to a more realistic cost estimation. Further, copulabased uncertainty modeling is applied to capture the correlations between renewable generation and power loads, and the twostage SP method is then used to get final solutions. Finally, numerical case studies are conducted on an IEEE 34-bus three-phase ADN, verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.

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