Hydroelectric power plants’ operational decisions are associated with several factors, such as generation planning, water availability and dam safety. One major challenge is to control the water spillage from the reservoir. Although this action represents a loss of energy production, it is a powerful strategy to regulate the reservoir level, ensuring the dam’s safety. The decision to use this strategy must be made in advance based on level and demand predictions. The present work applies supervised machine learning techniques to predict the operating condition of spillage in a hydroelectric plant for 5 h ahead. The use of this method, in real time, aims to assist the operator so that he can make more assertive and safer decisions, avoiding waste of energy resources and increasing the safety of dams. The Random Forest and Multilayer Perceptron methods were used to define the architecture compared to the forecasting capacity. The proposed methodology was applied to a 902.5 MW Hydroelectr... [more]

Construction time and time overruns for infrastructure projects have been frequently studied; however, the construction time of power plants has not been studied. This lack of study is problematic, as more renewable energy power plants, such as wind and solar, are planned for many jurisdictions. Accurately estimating the construction time of a power plant will assist construction planning, budget estimates, and policy development encouraging the use of more renewable sources. The construction times of utility scale power plants in Canada were studied using publicly available data. Multiple linear regression analysis techniques were applied to the data to generate construction time estimation functions for all power plants together, and for individual technologies. The analyses reveal that construction time is sensitive to jurisdiction and the decade of construction, indicating that decisions made by individual Canadian provincial governments at different times had statistically signifi... [more]

To achieve sustainable development goals (SDGs), it is necessary to solve the problem of assessing and measuring energy sustainability performance. A popular indicator used for this purpose is the World Energy Council (WEC) energy sustainability index, or the Energy Trilemma Index, which is based on such key metrics as energy security, energy equity, environmental sustainability, and country context. Each of the four metrics, or dimensions, includes many indicators that depend on both internal and external factors. By combining a variety of indicators into integral ones, WEC ranks countries in terms of energy sustainability. However, what is not taken into account is how countries differ in terms of economic development, income, energy mix, renewables use, ownership in the energy sector, and other factors, and neither is the methodology itself disclosed by the developers. As a provider for all other sectors of the economy, the energy sector plays an important role in developing countri... [more]

This study applied regionalization techniques on future climate change scenarios for the precipitation over the Xingu River Basin (XRB) considering the 2021−2080 horizon, in order to assess impacts on the monthly flow rates and possible consequences for electricity generation at the Belo Monte Hydroelectric Power Plant (BMHPP). This is the fourth largest hydroelectric power plant in the world, with a generating capacity of 11,233 MW, and is located in the Brazilian Amazon. Two representative concentration pathways (RCP 4.5 and RCP 8.5) and an ensemble comprising four general circulation models (CanESM2, CNRM-CM5, MPI-ESM-LR and NORESM1-M) were used. The projections based on both scenarios indicated a considerable decrease in precipitation during the rainy season and a slight increase during the dry season relative to the reference period (1981−2010). According to the results, a reduction in the flow rates in Altamira and in the overall potential for power generation in the BMHPP are al... [more]