Abstract
The strong growth of the solar power generation industry requires an increasing need to predict the profile of solar power production over a day and develop highly efficient and optimized stand-alone and grid-connected photovoltaic systems. Moreover, the opportunities offered by battery energy storage systems (BESSs) coupled with photovoltaic (PV) systems require an ability to forecast the load power to optimize the size of the entire system composed of PV panels and storage devices. This paper presents a sizing and control strategy of BESSs for dispatching a photovoltaic generation farm in the 1-h ahead and day-ahead markets. The forecasting of the solar irradiation and load power consumption is performed by developing a predictive model based on a feed-forward neural network trained with the Levenberg–Marquardt back-propagation learning algorithm.
Highlights
IntroductionS OLAR power generation using Photovoltaics (PVs) has become widely employed, especially in places where power grids are inconvenient or unreasonably expensive to connect, but its use is increasing significantly in gridconnected situations as a way to feed low-carbon energy into the grid [1].The increasing use of solar energy introduces new technical challenges due to the intermittency of this source, which is influenced by natural and meteorological conditions, as well as other factors [2].Due to this uncertainty and the intermittency of solar energy, any grid-connected solar PV plant is considered to be an uncontrollable and non-dispatchable power source with fluctuations in its power output that affect the stability of power systems [3].In Europe, the growth in the numbers and power rating of photovoltaic plants has been relatively high
This study focuses on the energy management and it can be adapted to different technologies used for the storage systems
The 1-hour ahead forecasting is obtained by means of the three-layer Artificial Neural Network described earlier
Summary
S OLAR power generation using Photovoltaics (PVs) has become widely employed, especially in places where power grids are inconvenient or unreasonably expensive to connect, but its use is increasing significantly in gridconnected situations as a way to feed low-carbon energy into the grid [1].The increasing use of solar energy introduces new technical challenges due to the intermittency of this source, which is influenced by natural and meteorological conditions, as well as other factors [2].Due to this uncertainty and the intermittency of solar energy, any grid-connected solar PV plant is considered to be an uncontrollable and non-dispatchable power source with fluctuations in its power output that affect the stability of power systems [3].In Europe, the growth in the numbers and power rating of photovoltaic plants has been relatively high. The increasing use of solar energy introduces new technical challenges due to the intermittency of this source, which is influenced by natural and meteorological conditions, as well as other factors [2]. Due to this uncertainty and the intermittency of solar energy, any grid-connected solar PV plant is considered to be an uncontrollable and non-dispatchable power source with fluctuations in its power output that affect the stability of power systems [3]. In Europe, the growth in the numbers and power rating of photovoltaic plants has been relatively high. There is a need to dispatch renewable resources so they can be controlled
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