Abstract
This paper delves into the challenges encountered in decision-making processes within Hybrid Energy Systems (HES), placing a particular emphasis on the critical aspect of data integration. Decision-making processes in HES are inherently complex due to the diverse range of tasks involved in their management. We argue that to overcome these challenges, it is imperative to possess a comprehensive understanding of the HES architecture and how different processes and interaction layers synergistically operate to achieve the desired outcomes. These decision-making processes encompass a wealth of information and insights pertaining to the operation and performance of HES. Furthermore, these processes encompass systems for planning and management that facilitate decisions by providing a centralized platform for data collection, storage, and analysis. The success of HES largely hinges upon its capacity to receive and integrate various types of information. This includes real-time data on energy demand and supply, weather data, performance data derived from different system components, and historical data, all of which contribute to informed decision-making. The ability to accurately integrate and fuse this diverse range of data sources empowers HES to make intelligent decisions and accurate predictions. Consequently, this data integration capability allows HES to provide a multitude of services to customers. These services include valuable recommendations on demand response strategies, energy usage optimization, energy storage utilization, and much more. By leveraging the integrated data effectively, HES can deliver customized and tailored services to meet the specific needs and preferences of its customers.
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Acknowledgement
This work is supported by the Kamprad Family Foundation for the project Models of Distributed Information Processing in Smart Grid Systems, and partially by the Knowledge Foundation (Stiftelsen för kunskaps-och kompetensutveckling) for the project Intelligent Management of Hybrid Energy Systems under Grant No. 20220111-H-01, as well as the project Intelligent and Trustworthy IoT Systems under Grant No. 20220087-H-01. The authors would like to thank Thomas Höglund at Crossbreed AB and Patrick Isacson at Crossbreed AB and Cetetherm AB for valuable insights in the management of hybrid energy systems.
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Boiko, O., Shendryk, V., Malekian, R., Komin, A., Davidsson, P. (2024). Towards Data Integration for Hybrid Energy System Decision-Making Processes: Challenges and Architecture. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_14
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