Russian scientists develop high-precision method for managing port energy supply
New approach optimises electricity consumption in port infrastructure while improving environmental sustainability
Researchers at the N. S. Solomenko Institute of Transport Problems of the Russian Academy of Sciences (RAS) have developed an algorithm capable of adapting to changing operating modes of maritime transport equipment while reducing the carbon footprint in coastal areas. The development was announced by the Ministry of Science and Higher Education of the Russian Federation.
Energy consumption in ports often depends on non-steady-state power sources and is influenced by seasonal factors, as well as sharp fluctuations associated with vessel berthing cycles and crane operations. According to the researchers, the efficiency of port infrastructure management can be improved through the introduction of intelligent forecasting methods.
The research team developed a mathematical model and used it to create computer software capable of managing energy flows in ports with a high degree of accuracy. The method's key advantage is that it does not rely on fixed parameters. Instead of using rigid templates, the system dynamically identifies the appropriate number of neighbouring energy consumers and relevant temporal changes in real time, adapting to local data characteristics.
The new method is expected to support a wide range of factors affecting fluctuating electricity demand for shore-side power supply to vessels. It could help optimise the operation of diesel-generator and hybrid power systems, reduce operating costs and harmful emissions in coastal areas, improve the reliability of electricity supply for critical consumers, and facilitate the integration of renewable energy sources, including wind, tidal and solar power, without compromising the stability of port microgrids.
The developers note that the method does not require high-performance computing systems, is highly energy-efficient, and has outperformed a number of modern forecasting models in terms of accuracy.
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