Developing a Decision Support System for a Pumped Storage Hybrid Power Plant

Investments in renewable energy systems have been increasing in recent years in order to reduce climate-dependent fossil fuels and to supply the increased energy demand. One of the main difficulties with renewable resources is that they depend on meteorological conditions and produce intermittent energy. Energy storage systems are being developed to preserve the excess energy produced and to use it in situations where energy production is less than expected. Pumped Storage Hydropower Plants (PSHPs) have an important role to overcome these challenges. Operating PSHP in a hybrid manner together with another renewable energy source (solar, wind, etc.) becomes more advantageous for improving system efficiency. In this study, a hybrid system is developed as a Decision Support System (DSS) that wind and solar energy separately are the main energy generator, which stores the excess energy produced with PSHP. The energy production of the Wind Power Plant (WPP) is calculated using wind speed data acquired by the ERA5 global dataset. The energy production of the Solar Power Plant (SPP) is calculated by the solar radiation and cloudiness data. The study also investigates the effects of the developed hybrid system on both observed and predicted market prices. Hourly market prices are predicted via the Long Short-Term Memory (LSTM) model using the lagged market price data sets. According to the results, the hybrid system is superior to the conventional WPP/SPP regardless of observed and predicted market prices. The study is a pioneer application in Türkiye and provides a DSS to use energy resources management more efficiently by increasing the operational flexibility for the decision-makers. The preliminary results also promising as the total revenues increased by around %5.7 and %12.5 for the Hydro-Wind Hybrid System (HWHS) and the Hydro-Solar Hybrid System (HSHS), respectively.