4.6 Article

Value of deterministic day-ahead forecasts of PV generation in PV plus Storage operation for the Australian electricity market

Journal

SOLAR ENERGY
Volume 224, Issue -, Pages 672-684

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2021.06.011

Keywords

Large-scale PV; Energy storage; Solar forecasting assessment; Operation scheduling; Optimization; Electricity market; Forecast value

Categories

Funding

  1. Region Reunion
  2. European Regional Development Fund (FEDER) under the POE-FEDER 2014-2020 [2018-18030017548]

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This study compares the economic value of different operational solar forecasts for a specific application, finding a linear relationship between error metrics and economic gains. An improvement of 1% point in mean absolute error (MAE) results in approximately a 2% increase in economic gain for a large-scale PV farm with Li-ion batteries in the Australian energy market context.
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.

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