Value of deterministic day-ahead forecasts of PV generation in PV + Storage operation for the Australian electricity market.
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| Title: | Value of deterministic day-ahead forecasts of PV generation in PV + Storage operation for the Australian electricity market. |
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| Authors: | David, Mathieu1 (AUTHOR) mathieu.david@univ-reunion.fr, Boland, John2 (AUTHOR) john.boland@unisa.edu.au, Cirocco, Luigi2 (AUTHOR) lui.cirocco@unisa.edu.au, Lauret, Philippe1 (AUTHOR) philippe.lauret@univ-reunion.fr, Voyant, Cyril3 (AUTHOR) |
| Source: | Solar Energy. Aug2021, Vol. 224, p672-684. 13p. |
| Subjects: | Electricity markets, Numerical weather forecasting, Photovoltaic power generation, Energy storage, Lithium-ion batteries, Work values, Forecasting |
| Abstract: | • Comparison of day-ahead deterministic forecasts of solar irradiation and of production of a large-scale PV plant. • Use of Numerical Weather Predictions, post-processing methods and benchmark models. • Optimization of the operation of an energy storage system associated to a PV farm using the solar forecasts and a receding horizon approach. • Evaluation of the economic gain brought by the forecasts in order to increase the revenue offered by the energy arbitrage of the Australian electricity market. • Highlight of relationships between quality and economic value of solar forecasts. During the last decade, numerous solar forecasting tools have been developed to predict the energy generation of photovoltaic (PV) farms. The quality of solar forecasts is assessed by comparing predictions with measured solar data. However, this methodology does not consider the added value of the forecasts for their applications. As a consequence, what value could be given to the improvement of forecasts considering this evaluation framework? To answer this question, this work compares the value of different operational solar forecasts for a specific application. The aim is to look for relationships between the economic value and the error metrics defined to evaluate the forecast quality. A new generation of large-scale PV plants integrates ESS. The aim is to add flexibility to the injection of the production into the grid and thus to maximize the profit by taking advantage of the possibilities offered by the electricity market, such as energy arbitrage. To optimize the operation of these specific ESS, forecasting of the solar production is of paramount importance. The study case considered in this work is a large-scale PV farm of several megawatts associated with Li-ion batteries in the Australian energy market context. For this specific case study, the results show that the metrics used to evaluate the forecast quality based on the mean absolute error (MAE) have an almost linear relationship with the economic gain brought by applying the forecast. More precisely, an improvement of 1% point in MAE results approximately in an increase of 2% points in economical gain. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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