Searcharxiv⌕ Search

arXiv subjects

Ioanna Kalospyrou

Publications and source records attributed to Ioanna Kalospyrou.

2 recordsLinked to original sources

Distribution Network Congestion Management via Strategic Aggregator Intervention in Local Energy Markets

High penetration of distributed energy resources increasingly creates congestion in low-voltage distribution networks, while local energy markets (LEMs) optimise community welfare without explicitly internalising network constraints. This paper investigates whether a profit-seeking aggregator embedded within a welfare-oriented LEM can partially internalise distribution-level congestion through market participation. We develop a post-clearing, price-protected intervention in which the aggregator injects additional supply and triggers re-clearing, with network feasibility validated using nonlinear AC power flow subject to a non-deterioration constraint on maximum line loading. The mechanism is benchmarked against Distribution System Operator (DSO)-only corrective control and a hybrid regime with residual DSO action following aggregator intervention. Results on a UK LV feeder show that aggregator participation reduces thermal loading and preserves community welfare relative to DSO-only control, though it does not fully restore compliance under severe stress. The hybrid regime achieves the strongest technical performance while maintaining lower welfare loss. Overall, aggregator intervention remains privately profitable, indicating partial incentive alignment.

eess.SY↗

Multiple Gaussian process models based global sensitivity analysis and efficient optimization of in vitro mRNA transcription process

The in vitro transcription (IVT) process is a critical step in RNA production. To ensure the efficiency of RNA manufacturing, it is essential to optimize and identify its key influencing factors. In this study, multiple Gaussian Process (GP) models are used to perform efficient optimization and global sensitivity analysis (GSA). Firstly, multiple GP models were constructed using the data from multiple experimental replicates, accurately capturing the complexities of the IVT process. Then GSA was conducted to determine the dominant reaction factors, specifically the concentrations of reactants NTP and Mg across all data-driven models. Concurrently, a multi-start optimization algorithm was applied to these GP models to identify optimal operational conditions that maximize RNA yields across all surrogate models. These optimized conditions are subsequently validated through additional experimental data.

q-bio.QM↗