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Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed realtime electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating an cost function, it is suitably analysed and then a dynamic penalty function to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for the real-time energy management.

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The utility of a universal DNA ‘barcode’ fragment of 658 base pairs of the Cytochrome C Oxidase I (COI) gene for the recognition of all animal species has been a widely debated topic on theoretical and practical levels. Regardless of its challenges, large amounts of COI sequence data have been produced in the last two decades. To optimally use the data towards reliable species identification will require further steps to validate the method and reference libraries. The fruit fly tribe Dacini holds about a thousand species, of which eighty are pests of economic concern, including some of the world’s foremost fruit and vegetable pests, and there are many morphologically cryptic species complexes in the tribe. Where previous studies showed limited success in using COI to identify Dacini, our results with a highly curated morphological dataset indicate high congruence between morphology and COI: 90% of the species in our 5,576 sequences, 262-species global dataset can be identified with COI alone based on a monophyly criterion. However, in some key pest species belonging to complexes that were previously thought diagnosable with COI, we found that expanded sampling and independent validation of identifications using genomic data revealed introgression of mitochondrial DNA. We find that the informative SNPs are uniformly distributed across the COI gene, and we provide recommendations for standardization. We conclude that reliable molecular identifications with COI require extensive species coverage, population sampling, and genomics-supported reference identifications before they can be validated as a “diagnostic” marker for specific groups.

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