An existing Romanian hydro system (two reservoirs/peak hydropower plants), where the addition of a pumped storage plant with a weekly emptying/filling cycle was planned, was selected as a case study. For the main upstream reservoir/hydropower plant, an operational policy with a monthly time step was derived using a stochastic dynamic programming model. The optimal weekly scheduling problem of the whole system was solved by a genetic algorithm-based model. The monetary income maximization, by 2014 market energy prices for produced/consumed energy, was chosen as the objective function; any gain from ancillary services was ignored. An original methodology for coupling the monthly and weekly time-step results was used to obtain the annual performance estimation within the mean hydrological year conditions. At that level, the inclusion of the pumped storage plant would lead to a gain to nearly double the annual performance of the existing hydro system.

In the hydropower sector, accurate estimation of short-term reservoir inflows is an essential element to ensure efficient and safe management of water resources. Short-term forecasting supports the optimization of energy production, prevention of uncontrolled water discharges, planning of equipment maintenance, and adaption of operational strategies. In the absence of data on topography, vegetation, and basin characteristics (required in distributed or semi-distributed models), data-driven approaches can serve as effective alternatives for inflow prediction. This study proposes a novel hybrid approach that reverses the conventional LSTM (Long Short-Term Memory)—ARIMA (Autoregressive Integrated Moving Average) sequence: LSTM is first used to capture nonlinear hydrological patterns, followed by ARIMA to model residual linear trends.The model was calibrated using daily inflow data in the Izvorul Muntelui–Bicaz reservoir in Romania from 2012 to 2020, tested for prediction on the day ahead in a repetitive loop of 365 days corresponding to 2021 and further evaluated through multiple seven-day forecasts randomly selected to cover all 12 months of 2021. For the tested period, the proposed model significantly outperforms the standalone LSTM, increasing the R² from 0.93 to 0.96 and reducing RMSE from 9.74 m³/s to 6.94 m³/s for one-day-ahead forecasting. For multistep forecasting (84 values, randomly selected, 7 per month), the model improves R² from 0.75 to 0.89 and lowers RMSE from 18.56 m³/s to 12.74 m³/s. Thus, the hybrid model offers notable improvements in multi-step forecasting by capturing both seasonal patterns and nonlinear variations in hydrological data. The approach offers a replicable data-driven solution for inflow prediction in reservoirs with limited physical data.

In the context of the need for an increasing share of renewables in electricity mixes, the paper presents the existing RES mix, PV and wind, for partially covering the electricity consumption of a research institute, ICSTM, and proposes a solution for completion with a third form of RES, a small hydropower plant. Moreover, it is envisaged to include the proposed small hydropower plant as a new real-scale laboratory attached to ICSTM. The method includes the presentation of an existing proposal for increasing installed capacity in new PV panels and propose to install an SHPP to a weir situated a few hundred meters from the institute. The hydropower potential for two possible arrangements is assessed and some types of turbines suitable for this location are presented. The main results demonstrate that building an SHPP is a better solution for completion of PV and wind as source of electricity for ICSTM. The main conclusion of the paper is that by installing new RES capacities, ICSTM can build a real-scale laboratory for new technologies, at the same time fully covering its own electricity consumption and even supplying a green electricity mix into the national power system.

Large hydropower developments are not considered to be environmentally friendly due to large landscape flooded by their reservoirs. There is a certain carbon footprint related to this, in the phase of construction and of the operation of the reservoirs. This paper presents GHG emissions related to an iconic reservoir from Romania, Vidraru, and demonstrate that values are close to those determined around the world and that comparing with other conventional power plants producing electricity is the best environmental option. Furthermore, must be considered the other water uses related to reservoirs as flood mitigation, water supply for population, irrigation, and industry, where reservoirs cannot be replaced by anything else.

This paper investigates the integration of hydropower and solar energy within the Lower Olt River cascade as a pathway toward sustainable energy generation in Romania. The study focuses on the conceptual design of future hybrid power plants consisting of existing hydropower facilities where floating photovoltaic panels are proposed to be installed on the reservoir’s surfaces. An estimation of electricity production from both sources was performed, followed by the formulation of a trading strategy for the July–September 2025 period. The paper also explores the interaction between tactical and strategic management in hydropower operation and planning, describing how forecasting and decision-making processes are structured within the institutional framework. Finally, results for the selected hydropower plants demonstrate the positive influence of floating photovoltaic deployment on company performance, the national energy mix, and the overall sustainability of energy generation in Romania.

In this paper, the forecast of the daily inflow water volumes in the Asomata reservoir, on the Aliakmon River, Greece, based on long short-term memory was realized. A MATLAB program was developed for one day ahead prediction; the model was calibrated for the period 2011-2021 based on the historical values and used in a repetitive loop for 365 days corresponding to the testing year 2022. An improvement in the forecasting was observed when the daily index associated with the forecasted variable was used as input. This improvement was much more important than when the associated precipitation values were used in the absence of the day index. A possible explanation would be the operating schedule of the plants upstream, Asomata reservoir being the fourth in a five reservoirs cascade. More than that, the upstream hydropower plant, Sfikia, is a pumped storage plant, Asomata being lower reservoir for this one. The results are in good agreement with the measurements, confirming the fact that long-short term memory networks are widely used in the hydrological forecasting with good results.