Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Abstract

The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

Key technologies for hybrid energy system planning and operation

“…With the continuous development of artificial intelligence technology, application of some machine learning methods, such as XGBoost algorithm, is gradually becoming feasible for runoff hydropower prediction Kumar et al, 2021). These methods use the historical data as training samples, utilize the intelligent processing and self-learning mode of the algorithm, learn the mapping relationship between the historical data and the influencing factors, and apply the algorithm to predict the future load data after strengthening learning and improving the accuracy (Bordin et al, 2020;Bernardes et al, 2022;Lai et al, 2020). The XGBoost method shows the nonlinear mapping ability and strong selfadaptation ability and is expected to become an effective means to solve the problem of distributed small hydropower generation capacity prediction.…”

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

“…The multi-criteria factors forecast for a dam reservoir gives a hint about the sustainable exploitation of the hydrologic resources [53]. Bernardes et al [54] reviewed the studies that present machine learning in hydropower production and clustered them. The findings show a large area of applications (supervising, operation management, river flow, etc.)…”

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

“…Review of ML techniques for hydropower operation optimization, with a focus on optimal dispatch of hydropower plants in the context of energy production enhancement. [34] Scheduling problem. Review of hydropower optimization R&D activities using a metaheuristic approach, also considering scheduling problems.…”

“…Operation strategies for hydropower plants [24][25][26][27][28][29]; • Methods for the solution of scheduling problems in hydroelectric power plants [30][31][32][33][34][35]; • Modelization and control in hydroelectric power plants [26,27,[36][37][38]; • Hydropower case study collection [39].…”