Big data driven optimal sizing of stand-alone photovoltaic energy systems

Abstract

The issues of global climate change and energy security stimulate significant boost of renewable energy (RE) integration in the past decades. However, the mismatch between the intermittent renewable energy and time-varying load demand may cause power system operation instability and power supply unreliability. Energy storage devices (e.g. battery bank) is widely used to mitigate such supply-demand mismatch. It is a challenging topic to optimize the size of energy storage devices for efficient, reliable and cost-effective power supply in the presence of the intermittent renewable energy. Instead of complex intelligent algorithms, a big data driven approach is proposed to optimize the size of the battery bank in the standalone photovoltaic (SAPV) energy systems in this paper. The big data simulation based case studies which employ a mess of worldwide solar irradiation data on the earth's surface indicates that, there exists a cut-off value for the battery bank capacity in the SAPV energy systems — below the cut-off capacity, both reliability and efficiency of RE systems will grow rapidly with the increase of the battery bank capacity; above the cut-off value, both reliability and efficiency will intend to saturate with the increase of the battery bank capacity. Such big data driven approach provides a key to the optimal size of SAPV energy systems.