On the relevance of considering the uncertainty in renewables production forecasts to optimize hybrid power stations: a robust MILP approach

• Highlights the relevance of considering the uncertainty in renewables production forecasts to optimize HPSs. • Advances the research on RO by proposing a balanced approach to achieve a better trade-off among objectives. • Emphasizes the need to tune the bandwidth within scenarios required by RO are generated. • Proves the practical value of RO in outdated grids, boosting RES integration under stringent constraints. • Uses historical data and forecasts over a one-year period to provide an accurate assessment ofthe proposed strategies. Hybrid Power Stations (HPS), integrating RES with storage systems, offer a promising solution to increase the penetration of renewable energy sources (RES) by converting intermittent production into dispatchable power. This study underlines the importance of considering the uncertainty in forecasts of power production to improve the management of these systems and proposes a Mixed-Integer Linear Programming (MILP) framework able to account for multiple possible outcomes, instead of a single one. Using a one-year dataset of historical forecasts and actual production, the study develops and tests three dispatch strategies: rule-based, standard MILP using raw forecasts, and robust MILP. The latter considers several production scenarios to enhance the reliability of the dispatch plan, valuable for grids with strict operational constraints. Unlike previous studies in the literature, this work dives into the aspect of tuning the span in which prediction scenarios are generated and the relaxation of constraints of the robust optimization. Sensitivity analyses showed that a forecast scenario span of ± 20 % for wind and ± 30 % for solar, paired with a moderate divergence penalty of €12/MWh, offered the best balance between system reliability and economic performance. The rule-based strategy exported 235.9 MWh/year but suffered from high undershooting, reducing net earnings by 16.3 %. The standard MILP approach improved performance, increasing annual energy exports and reducing undershooting, resulting in net earnings of €213.2 k/year compared to €197.5 k/year for the rule-based strategy. The robust MILP approach further optimized performance, achieving only 21.8 MWh/year of undershooting and net earnings of €223 k/year (only 3.1 % reduction from gross earnings), demonstrating that this strategy can improve the reliability of HPS operation while reducing penalties associated with forecast errors.

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Publication Details

Journal
Energy Conversion and Management
Published
2025-06-16
DOI
https://doi.org/10.1016/j.enconman.2025.120078
Citations
2
Primary Topic
Electric Power System Optimization
Type
article
Field-Weighted Citation Impact
1.46
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article

On the relevance of considering the uncertainty in renewables production forecasts to optimize hybrid power stations: a robust MILP approach

Alessandro Bianchini, Francesco Superchi, Antonis Moustakis, George Pechlivanoglou
2 citations
Energy Conversion and Management
Electric Power System Optimization
1.46
article

On the relevance of considering the uncertainty in renewables production forecasts to optimize hybrid power stations: a robust MILP approach

Alessandro Bianchini, Francesco Superchi, Antonis Moustakis, George Pechlivanoglou
article en
2 citations

Abstract

• Highlights the relevance of considering the uncertainty in renewables production forecasts to optimize HPSs. • Advances the research on RO by proposing a balanced approach to achieve a better trade-off among objectives. • Emphasizes the need to tune the bandwidth within scenarios required by RO are generated. • Proves the practical value of RO in outdated grids, boosting RES integration under stringent constraints. • Uses historical data and forecasts over a one-year period to provide an accurate assessment ofthe proposed strategies. Hybrid Power Stations (HPS), integrating RES with storage systems, offer a promising solution to increase the penetration of renewable energy sources (RES) by converting intermittent production into dispatchable power. This study underlines the importance of considering the uncertainty in forecasts of power production to improve the management of these systems and proposes a Mixed-Integer Linear Programming (MILP) framework able to account for multiple possible outcomes, instead of a single one. Using a one-year dataset of historical forecasts and actual production, the study develops and tests three dispatch strategies: rule-based, standard MILP using raw forecasts, and robust MILP. The latter considers several production scenarios to enhance the reliability of the dispatch plan, valuable for grids with strict operational constraints. Unlike previous studies in the literature, this work dives into the aspect of tuning the span in which prediction scenarios are generated and the relaxation of constraints of the robust optimization. Sensitivity analyses showed that a forecast scenario span of ± 20 % for wind and ± 30 % for solar, paired with a moderate divergence penalty of €12/MWh, offered the best balance between system reliability and economic performance. The rule-based strategy exported 235.9 MWh/year but suffered from high undershooting, reducing net earnings by 16.3 %. The standard MILP approach improved performance, increasing annual energy exports and reducing undershooting, resulting in net earnings of €213.2 k/year compared to €197.5 k/year for the rule-based strategy. The robust MILP approach further optimized performance, achieving only 21.8 MWh/year of undershooting and net earnings of €223 k/year (only 3.1 % reduction from gross earnings), demonstrating that this strategy can improve the reliability of HPS operation while reducing penalties associated with forecast errors.

Energy Conversion and ManagementVol. 341
University of Florence (IT)
Affordable and clean energy
Openalex Percentile: Top 17%
Electric Power System Optimization
1.46
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