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L. Antia Guisasola
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Forecast Uncertainty Propagation in Wind-Storage Hybrid Power Plant Dispatch
From wind forecast error to operational and economic performance
Hybrid power plants (HPPs) with storage can support renewable integration and capture electricity-market arbitrage revenue by shifting stored energy towards higher-value hours, but this flexibility depends on uncertain forecasts of future wind generation. This thesis investigates how wind-power forecast errors propagate into revenue losses in a wind–storage HPP operated with rolling-horizon dispatch. Forecast uncertainty is represented through a SARIMAX-based scenario ensemble fitted to historical wind-power output, and the uncertainty-induced losses are obtained by comparing forecast-based outcomes with a perfect-information benchmark across four one-week periods covering different wind regimes.
The results show a traceable propagation sequence: forecast errors change dispatch decisions, cause the battery SoC to diverge from its perfect-information trajectory, and become costly when the battery reaches a high-value or export-constrained period in the wrong state. Forecast error magnitude alone is therefore a poor predictor of revenue loss; errors are harmful mainly when they affect battery positioning before such critical events. Losses are concentrated in a small number of hours and are only partly recovered later. Four recurring mechanisms are identified, explaining how forecast errors create overcharged or undercharged battery states. In the studied weeks, undercharge mechanisms dominate, indicating that insufficient battery preparation before price peaks is the dominant loss mechanism. The thesis contributes a diagnostic framework for interpreting forecast uncertainty at the level of individual dispatch decisions, suggesting that forecast evaluation for storage operation should prioritise the timing and operational context of errors rather than average forecast accuracy alone. ...
The results show a traceable propagation sequence: forecast errors change dispatch decisions, cause the battery SoC to diverge from its perfect-information trajectory, and become costly when the battery reaches a high-value or export-constrained period in the wrong state. Forecast error magnitude alone is therefore a poor predictor of revenue loss; errors are harmful mainly when they affect battery positioning before such critical events. Losses are concentrated in a small number of hours and are only partly recovered later. Four recurring mechanisms are identified, explaining how forecast errors create overcharged or undercharged battery states. In the studied weeks, undercharge mechanisms dominate, indicating that insufficient battery preparation before price peaks is the dominant loss mechanism. The thesis contributes a diagnostic framework for interpreting forecast uncertainty at the level of individual dispatch decisions, suggesting that forecast evaluation for storage operation should prioritise the timing and operational context of errors rather than average forecast accuracy alone. ...
Hybrid power plants (HPPs) with storage can support renewable integration and capture electricity-market arbitrage revenue by shifting stored energy towards higher-value hours, but this flexibility depends on uncertain forecasts of future wind generation. This thesis investigates how wind-power forecast errors propagate into revenue losses in a wind–storage HPP operated with rolling-horizon dispatch. Forecast uncertainty is represented through a SARIMAX-based scenario ensemble fitted to historical wind-power output, and the uncertainty-induced losses are obtained by comparing forecast-based outcomes with a perfect-information benchmark across four one-week periods covering different wind regimes.
The results show a traceable propagation sequence: forecast errors change dispatch decisions, cause the battery SoC to diverge from its perfect-information trajectory, and become costly when the battery reaches a high-value or export-constrained period in the wrong state. Forecast error magnitude alone is therefore a poor predictor of revenue loss; errors are harmful mainly when they affect battery positioning before such critical events. Losses are concentrated in a small number of hours and are only partly recovered later. Four recurring mechanisms are identified, explaining how forecast errors create overcharged or undercharged battery states. In the studied weeks, undercharge mechanisms dominate, indicating that insufficient battery preparation before price peaks is the dominant loss mechanism. The thesis contributes a diagnostic framework for interpreting forecast uncertainty at the level of individual dispatch decisions, suggesting that forecast evaluation for storage operation should prioritise the timing and operational context of errors rather than average forecast accuracy alone.
The results show a traceable propagation sequence: forecast errors change dispatch decisions, cause the battery SoC to diverge from its perfect-information trajectory, and become costly when the battery reaches a high-value or export-constrained period in the wrong state. Forecast error magnitude alone is therefore a poor predictor of revenue loss; errors are harmful mainly when they affect battery positioning before such critical events. Losses are concentrated in a small number of hours and are only partly recovered later. Four recurring mechanisms are identified, explaining how forecast errors create overcharged or undercharged battery states. In the studied weeks, undercharge mechanisms dominate, indicating that insufficient battery preparation before price peaks is the dominant loss mechanism. The thesis contributes a diagnostic framework for interpreting forecast uncertainty at the level of individual dispatch decisions, suggesting that forecast evaluation for storage operation should prioritise the timing and operational context of errors rather than average forecast accuracy alone.
Hybrid power plants, combining power generation and/or energy storage, are a promising solution to increase the share of renewable energy by taking advantage of shared infrastructure and increasing the utilization of limited grid capacity. In particular, the inclusion of a storage system allows the power delivery to be shifted in time to periods of high demand. The storage system can be charged during periods of high power production and low demand, and discharged when the demand is high and the production is low. As such, the decision to charge or discharge needs to be planned based on an estimate of future power production and demand.
However, estimating the power production is based on forecast information and thus includes uncertainties in the storage dispatch. These uncertainties reduce the performance of the power plant, both in terms of revenues from the electricity markets and reliability of electricity supply. This challenge becomes more important when the hybrid power plant includes a high share of wind energy, since wind power is particularly difficult to forecast.
Previous studies in the field examined the impact of wind power forecast uncertainties on hybrid power plant performance under various case studies. However, most studies primarily evaluate the performance of the dispatch under uncertainty at the level of performance metrics such as revenues. How input uncertainty translates into realized operational behavior once decisions are implemented is rarely examined in detail. Results are often reported as single expected values, without analyzing their variability. The same dispatch strategy can produce a wide range of realized outcomes when uncertain inputs deviate from forecasts, but that dispersion is rarely quantified.
This work aims to understand how power forecast uncertainties propagate into battery dispatch decisions. In particular, we identify the characteristics of the most impactful forecast uncertainty on the system operation.
We study a wind farm coupled with a storage system located in Spain. A large number of wind power forecast scenarios are generated using the SARIMAX method and used to evaluate the forecast uncertainty characteristics. Each forecast scenario is used to simulate the dispatch operation of the hybrid power plant to maximize revenues on the day-ahead market. The analysis of the ensemble of dispatch scenarios shows that deviations in storage operations are systematically caused by the same mechanisms. We classify them into four types: (i) missed discharge due to under-forecasting, (ii) missed charge due to over-forecasting, (iii) false anticipation due to under-forecasting, and (iv) false anticipation due to over-forecasting. Our study further shows that the first type is the most common and accounts for the greatest revenue losses. ...
However, estimating the power production is based on forecast information and thus includes uncertainties in the storage dispatch. These uncertainties reduce the performance of the power plant, both in terms of revenues from the electricity markets and reliability of electricity supply. This challenge becomes more important when the hybrid power plant includes a high share of wind energy, since wind power is particularly difficult to forecast.
Previous studies in the field examined the impact of wind power forecast uncertainties on hybrid power plant performance under various case studies. However, most studies primarily evaluate the performance of the dispatch under uncertainty at the level of performance metrics such as revenues. How input uncertainty translates into realized operational behavior once decisions are implemented is rarely examined in detail. Results are often reported as single expected values, without analyzing their variability. The same dispatch strategy can produce a wide range of realized outcomes when uncertain inputs deviate from forecasts, but that dispersion is rarely quantified.
This work aims to understand how power forecast uncertainties propagate into battery dispatch decisions. In particular, we identify the characteristics of the most impactful forecast uncertainty on the system operation.
We study a wind farm coupled with a storage system located in Spain. A large number of wind power forecast scenarios are generated using the SARIMAX method and used to evaluate the forecast uncertainty characteristics. Each forecast scenario is used to simulate the dispatch operation of the hybrid power plant to maximize revenues on the day-ahead market. The analysis of the ensemble of dispatch scenarios shows that deviations in storage operations are systematically caused by the same mechanisms. We classify them into four types: (i) missed discharge due to under-forecasting, (ii) missed charge due to over-forecasting, (iii) false anticipation due to under-forecasting, and (iv) false anticipation due to over-forecasting. Our study further shows that the first type is the most common and accounts for the greatest revenue losses. ...
Hybrid power plants, combining power generation and/or energy storage, are a promising solution to increase the share of renewable energy by taking advantage of shared infrastructure and increasing the utilization of limited grid capacity. In particular, the inclusion of a storage system allows the power delivery to be shifted in time to periods of high demand. The storage system can be charged during periods of high power production and low demand, and discharged when the demand is high and the production is low. As such, the decision to charge or discharge needs to be planned based on an estimate of future power production and demand.
However, estimating the power production is based on forecast information and thus includes uncertainties in the storage dispatch. These uncertainties reduce the performance of the power plant, both in terms of revenues from the electricity markets and reliability of electricity supply. This challenge becomes more important when the hybrid power plant includes a high share of wind energy, since wind power is particularly difficult to forecast.
Previous studies in the field examined the impact of wind power forecast uncertainties on hybrid power plant performance under various case studies. However, most studies primarily evaluate the performance of the dispatch under uncertainty at the level of performance metrics such as revenues. How input uncertainty translates into realized operational behavior once decisions are implemented is rarely examined in detail. Results are often reported as single expected values, without analyzing their variability. The same dispatch strategy can produce a wide range of realized outcomes when uncertain inputs deviate from forecasts, but that dispersion is rarely quantified.
This work aims to understand how power forecast uncertainties propagate into battery dispatch decisions. In particular, we identify the characteristics of the most impactful forecast uncertainty on the system operation.
We study a wind farm coupled with a storage system located in Spain. A large number of wind power forecast scenarios are generated using the SARIMAX method and used to evaluate the forecast uncertainty characteristics. Each forecast scenario is used to simulate the dispatch operation of the hybrid power plant to maximize revenues on the day-ahead market. The analysis of the ensemble of dispatch scenarios shows that deviations in storage operations are systematically caused by the same mechanisms. We classify them into four types: (i) missed discharge due to under-forecasting, (ii) missed charge due to over-forecasting, (iii) false anticipation due to under-forecasting, and (iv) false anticipation due to over-forecasting. Our study further shows that the first type is the most common and accounts for the greatest revenue losses.
However, estimating the power production is based on forecast information and thus includes uncertainties in the storage dispatch. These uncertainties reduce the performance of the power plant, both in terms of revenues from the electricity markets and reliability of electricity supply. This challenge becomes more important when the hybrid power plant includes a high share of wind energy, since wind power is particularly difficult to forecast.
Previous studies in the field examined the impact of wind power forecast uncertainties on hybrid power plant performance under various case studies. However, most studies primarily evaluate the performance of the dispatch under uncertainty at the level of performance metrics such as revenues. How input uncertainty translates into realized operational behavior once decisions are implemented is rarely examined in detail. Results are often reported as single expected values, without analyzing their variability. The same dispatch strategy can produce a wide range of realized outcomes when uncertain inputs deviate from forecasts, but that dispersion is rarely quantified.
This work aims to understand how power forecast uncertainties propagate into battery dispatch decisions. In particular, we identify the characteristics of the most impactful forecast uncertainty on the system operation.
We study a wind farm coupled with a storage system located in Spain. A large number of wind power forecast scenarios are generated using the SARIMAX method and used to evaluate the forecast uncertainty characteristics. Each forecast scenario is used to simulate the dispatch operation of the hybrid power plant to maximize revenues on the day-ahead market. The analysis of the ensemble of dispatch scenarios shows that deviations in storage operations are systematically caused by the same mechanisms. We classify them into four types: (i) missed discharge due to under-forecasting, (ii) missed charge due to over-forecasting, (iii) false anticipation due to under-forecasting, and (iv) false anticipation due to over-forecasting. Our study further shows that the first type is the most common and accounts for the greatest revenue losses.