Koksal A.Sezgin L.Ozdemir A.2024-03-132024-03-1320229781665460194https://doi.org/10.1109/ICPEI55293.2022.9987031https://hdl.handle.net/20.500.12662/28572022 International Conference on Power, Energy and Innovations, ICPEI 2022 -- 19 October 2022 through 21 October 2022 -- -- 185591This paper presents a practical approach for demand forecasting and generation scheduling in a power system with solar PV sources and a Compressed Air Energy Storage System. The demand-forecasting method is improved using an artificial neural network in the first stage. After forecasting the demand for several representative days, the cost-effective generation scheduling is achieved for PV generation units and storage systems at the second stage. The proposed forecasting and generation scheduling approaches are tested in IEEE-RTS-96 test system, and the results are discussed from a technical and economic point of view. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccesscompressed air energy storage systemcost-effective operationGeneration schedulingload forecastingsolar generation unitsConference Object10.1109/ICPEI55293.2022.99870312-s2.0-85146432575N/A