@inproceedings{47fe7a8fab4e4ad684ef2466120ea1c0,
title = "Scenario Based Cost Optimization of Water Distribution Networks Powered by Grid-Connected Photovoltaic Systems",
abstract = "The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous study and is designed to reduce the economic costs associated with operating the WDN. To account for the uncertainty of the system, the problem is solved in a scenario optimization framework, where multiple scenarios are sampled from the uncertain variables related to PV power production. To accomplish this, a day-ahead PV power prediction method with a stochastic model is employed. The method is tested on a high-fidelity model of a WDN of a Danish town and the results demonstrate a substantial reduction in electrical costs through the integration of PVs, with PVs supplying 66.95% of the required energy. The study also compares the effectiveness of the stochastic optimization method with a deterministic optimization approach.",
author = "Mirhan {\"U}rkmez and Carsten Kalles{\o}e and Bendtsen, {Jan Dimon} and John-Josef Leth",
year = "2023",
doi = "10.1109/CCTA54093.2023.10252496",
language = "English",
isbn = "979-8-3503-3545-3",
series = "IEEE Conference on Control Technology and Applications (CCTA) - Proceedings",
publisher = "IEEE",
pages = "810--816",
booktitle = "2023 IEEE Conference on Control Technology and Applications, CCTA 2023",
address = "United States",
note = "2023 IEEE Conference on Control Technology and Applications, CCTA 2023 ; Conference date: 16-08-2023 Through 18-08-2023",
}