The value of day-ahead forecasting for photovoltaics in the Spanish electricity market

Antonanzas, J.; Pozo-Vázquez, D.; Fernández-Jiménez, L. Alfredo; Martínez-de-Pisón, Francisco Javier

doi:10.1016/j.solener.2017.09.043

Cited by 51 publications

(34 citation statements)

References 19 publications

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“…In a detailed case study, Law et al (2016) discussed the benefits of improvements in irradiance forecasting for a concentrated solar thermal power plant in this context. An alternative view was given by Antonanzas et al (2017), where they compared the profit from different forecasting methods with respect to that from an ideally perfect forecast.…”

Section: Valuementioning

confidence: 99%

“…Considering that solar and other renewable energy sources are inherently variable, and that utility-scale energy storage is not economically viable globally yet, operational excellence of the power grids can benefit from accurate solar forecasts. 2 Consequently, reliable and well-characterized solar forecasting tools and methodologies are becoming essential, and are considered of high value (Martinez-Anido et al, 2016;Huang and Thatcher, 2017;Antonanzas et al, 2017;Klingler and Teichtmann, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Verification of deterministic solar forecasts

et al. 2020

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The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.

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Section: Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Verification of deterministic solar forecasts

et al. 2020

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“…Antonanzas et al [21] analyzed the imbalance economic valorization and the values of forecast improvement of a 1.86 MWp PV plant derived from the "dual pricing" rule on the Iberian balancing energy market. The authors used different PV power forecast methods, and as in [15], they pointed out the discrepancies between the standard accuracy metrics and economic metrics for choosing the best prediction method.…”

Section: Existing Literature and Novelty Of The Workmentioning

confidence: 99%

The Value of PV Power Forecast and the Paradox of the “Single Pricing” Scheme: The Italian Case Study

et al. 2020

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One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms’ generation is important both for the producers/traders to minimize the imbalance costs and for the transmission system operators to ensure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale photovoltaic (PV) plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of the forecast accuracy and accuracy improvement in the context of the Italian energy market’s regulatory framework. Through this study, we further point out several important criticisms of the Italian “single pricing” system that brings paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome the undesired effects of the current dispatching regulations.

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“…On the other hand, the development of short-term PV power forecasting models has followed a parallel path. Thus, some published works use classical time-series approaches [20], regression methods [21], fuzzy logic models [22], ANN-based models [23], ensemble methods [24,25], and support vector machines [25]. A comparative study of the forecasting performance of different models of the above-mentioned approaches for the same PV plant is presented in [26], and in which the best model, among those studied, changes according to the data available for the training process.…”

Section: Introductionmentioning

confidence: 99%

Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

et al. 2019

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The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.

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The value of day-ahead forecasting for photovoltaics in the Spanish electricity market

Cited by 51 publications

References 19 publications

Verification of deterministic solar forecasts

Verification of deterministic solar forecasts

The Value of PV Power Forecast and the Paradox of the “Single Pricing” Scheme: The Italian Case Study

Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

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