Optimization of Hybrid power system with on site meteo station with integrated prediction methods.
02 HYB24-87
Presented by: Jan Liguš, Slavomír FILIP
Climate changes introduce heightened fluctuations in weather patterns, posing significant challenges to the operational efficiency of renewable energy systems. In this context, the optimal operation of hybrid systems, designed to work complementarily with various renewable sources, becomes critical. Meteorological (meteo) stations equipped with integrated prediction capabilities play a pivotal role in this domain. These local stations leverage diverse sources of meteorological datasets, combining them with local historical data and real-time measurements, to provide comprehensive insights into prevailing weather patterns.
The optimization of hybrid renewable systems hinges on a multitude of meteorological parameters, each influencing power generation. Solar irradiance, ambient temperature, wind speed and direction, humidity, air pressure, and precipitation collectively shape the energy output of solar photovoltaic panels, wind turbines, and other renewable sources. As such, accurate forecasting of these parameters is essential for maximizing operational efficiency, especially for island operations.
Our integrated prediction system is tailored to deliver forecasts spanning up to 24 hours into the future, with an emphasis on the next 24 to 48 hours. By harnessing advanced modeling techniques and machine learning algorithms, it automatically generates predictions for power production based on anticipated meteorological conditions, including longer-term forecasts. This proactive approach empowers stakeholders to make informed decisions regarding system operation, maintenance, and resource allocation, considering both short-term and long-term planning horizons.
Furthermore, the integration of Battery Energy Storage Systems (BESS) adds complexity to system optimization. Our predictive model considers forecasted energy generation, demand, and storage requirements over extended periods. This holistic approach ensures optimal BESS utilization, facilitating load balancing, grid stability enhancement, and integration of customer off-take requirements.
Moreover, our approach underscores the importance of online availability, enabling seamless integration with other cooperating systems, such as energy management platforms. By providing real-time access to prediction outputs, stakeholders can make informed decisions promptly, enhancing overall system performance and grid stability.
In conclusion, the integration of meteorological prediction capabilities into hybrid renewable systems represents a significant advancement in sustainable energy solutions. Leveraging cutting-edge technology and data-driven insights, we aim to enhance the resilience, reliability, and performance of renewable energy infrastructure, particularly in island operations, where forecasting accuracy is crucial for effective planning and management.
The optimization of hybrid renewable systems hinges on a multitude of meteorological parameters, each influencing power generation. Solar irradiance, ambient temperature, wind speed and direction, humidity, air pressure, and precipitation collectively shape the energy output of solar photovoltaic panels, wind turbines, and other renewable sources. As such, accurate forecasting of these parameters is essential for maximizing operational efficiency, especially for island operations.
Our integrated prediction system is tailored to deliver forecasts spanning up to 24 hours into the future, with an emphasis on the next 24 to 48 hours. By harnessing advanced modeling techniques and machine learning algorithms, it automatically generates predictions for power production based on anticipated meteorological conditions, including longer-term forecasts. This proactive approach empowers stakeholders to make informed decisions regarding system operation, maintenance, and resource allocation, considering both short-term and long-term planning horizons.
Furthermore, the integration of Battery Energy Storage Systems (BESS) adds complexity to system optimization. Our predictive model considers forecasted energy generation, demand, and storage requirements over extended periods. This holistic approach ensures optimal BESS utilization, facilitating load balancing, grid stability enhancement, and integration of customer off-take requirements.
Moreover, our approach underscores the importance of online availability, enabling seamless integration with other cooperating systems, such as energy management platforms. By providing real-time access to prediction outputs, stakeholders can make informed decisions promptly, enhancing overall system performance and grid stability.
In conclusion, the integration of meteorological prediction capabilities into hybrid renewable systems represents a significant advancement in sustainable energy solutions. Leveraging cutting-edge technology and data-driven insights, we aim to enhance the resilience, reliability, and performance of renewable energy infrastructure, particularly in island operations, where forecasting accuracy is crucial for effective planning and management.