Comprehensive energy system optimization using developed coyote algorithm for effective management of battery, heat source, and thermal storage

The modern building sector faces the challenge of meeting energy demands while minimizing environmental impacts and promoting energy efficiency. This research paper presents a comprehensive approach to optimizing building energy systems using a modified metaheuristic, the Developed Coyote Algorithm (DCO). The increasing importance of storage equipment in energy organizations, driven by changes in peak-load demand and the growing adoption of renewable energy sources, necessitates efficient storage solutions. Battery Energy Storage (BES) and Thermal Energy Storage (TES) are commonly used to store excess energy generated from renewable sources and supply it during peak demand. By applying the DCO algorithm, the operational plans of energy systems comprising BES, air-source heat pumps, and TES can be efficiently optimized with minimal computational requirements. The proposed method aims to enhance the productivity and sustainability of energy systems while providing valuable insights for policymakers and stakeholders involved in renewable energy development. The discharge of batteries and thermal energy storage occurs during low electricity prices, with charging power fluctuating between 100 and 150 kW. GWO and IWWO results show that charging is done at night with 150 kW, with a reduced amount used during other times. Thermal energy storage can store energy from 500 to 800 kW during night time, while DCO generates 700–900 kW. The results of this research contribute to the effective management of batteries, heat sources, and thermal storage in building energy systems, further advancing the utilization of renewable energy resources

Comparative Analysis of Arctic-Related Strategies at the National Level: Competition and Collaboration

The melting of ice creates favorable conditions for the development of the Arctic by elevating the Arctic’s geostrategic value and stoking national competition. As a result, many states, both within and beyond the Arctic region, have recently released Arctic strategies or updated existing ones, clarifying their roles, interests, priorities, and policies for Arctic international cooperation. In this study, a database is generated with a collection of Arctic strategies from 16 states, and then grounded theory is applied to code the texts in these strategy documents. Using this code, word-frequency analysis, cross-tabulation, correlation analysis, and cooperative-network analysis are implemented to explore competition and collaboration in the Arctic among these 16 states. The results indicate that Arctic states are significantly concerned with governance, economic and social development, and security and stability, while environmental protection and scientific research are given more attention by non-Arctic states. In addition, the highest value of the Pearson correlation coefficient is identified as 0.85 for collaboration and Arctic public security affairs, which indicates that collaboration on Arctic security will dominate the development of Arctic affairs in the near future

Microstoma Ningshanica, a new species of Microstoma based on molecular, light and scanning electron microscopy analyses from Shaanxi Province, China

The Qinling mountains are one of the richest diversity hotspots in the world. This area is remote, and new species remain to be discovered, including macrofungi. In this study, Qinling Mountain of the Shaanxi province was surveyed for macrofungus, and one new species of macrofungi Microstoma ningshanica (Sarcoscyphaceae) was discovered and analyzed by light microscopy and scanning electron microscopy as well as DNA sequence analysis. The description, photographs, and comparisons with morphologically similar and phylogenetically related species are provided. Conclusions of its phylogenetic relationships with similar species are also drawn based on the sequence of the nuclear internal transcribed spacer region, as well as the large subunit rRNA