Evolving power systems with increasing renewables penetration, along with the development of the smart grid, calls for improved communication networks to support these distributed generation sources. Automatic and optimal placement of communication resources within the advanced metering infrastructure is critical to provide a high-performing, reliable, and resilient power system. Three network design formulations based on mixed-integer linear and non-linear programming approaches are proposed to minimise network congestion by optimising residual buffer capacity through the placement of data concentrators and network routeing. Results on a case study show that the proposed models improve network connectivity and robustness, and increase average residual buffer capacity. Maximising average residual capacity alone, however, results in both oversaturated and underutilised nodes, while maximising either minimum residual capacity or total reciprocal residual capacity can yield much-improved network load allocation. Consideration of connection redundancy improves network reliability further by ensuring quality-of-service in the event of an outage. Analysis of multi-period network expansion shows that the models do not deviate significantly from optimal when used progressively (within 5% deviation), and are effective for utility planners to use for smart grid expansion.
Methods of polymer reaction engineering were used to develop a mechanistic model of nonenzymatic polynucleotide replication. The model is made possible by the rate constants developed by Rajamani et al. This model treats the ideas of stalling proposed by Rajamani in a direct kinetic mechanism. When one assumes that multiple incorrect insertions in a row are unlikely, the model returns estimates of the error threshold very much in agreement with the modified Eigen model (based on probability arguments) proposed by Rajamani. In addition, the current model gives average lengths of polynucleotides, categorized by ultimate nucleoside (type and correct/incorrect) as well as the average lengths of interior and terminal sequences of correct and incorrect insertions in a row. When the insights of Leu et al. (specifically, that incorrect additions following an incorrect addition are likely) are included into the model, the error threshold will drop to unrealistic levels. To compensate for this, a mechanism for the “recycle” of incorrect strands (via hydrolysis and repolymerization) was included. Calculations indicate that even with a “correction” mechanism (hydrolysis of chain containing errors), the error threshold drops to an unacceptable value.
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