Optimisation of a Hybrid Energy Storage System for Autonomous Photovoltaic Applications

Abstract

As the world's population grows and becomes more dependent on technology the demand for energy increases. Alongside the increasing energy demand there is a reduction in the availability of natural resources. The production of energy from fossil fuels has environmental implications, which has lead policymakers throughout the world put clean energy targets in place. Solar energy as a clean technology can be employed to meet these targets. Due to the nature of solar energy, autonomous photovoltaic (PV) systems require an energy buffer to match the generation with the time distribution of demand. Generally the most common storage technology utilised is the Valve Regulated Lead Acid (VRLA) battery, because of its low cost, maturity, and wide availability. PV panels are not an ideal source for battery charging; the output is unreliable and heavily dependent on weather conditions. Therefore, an optimum charge/discharge cycle cannot be guaranteed. The demand profile experienced by the PV system also influences the battery storage. Some load applications require high power for a short period of time, for example the operation of devices which involves starting motors. VRLA batteries in this situation are large in order to deal with the high power requirement. To overcome these issues a combination of VRLA batteries and ultracapacitors in a Hybrid Energy Storage System (HESS), which increases the power density of the overall system, is developed. Operating the ultracapacitor bank under high power conditions reduces the strain of large current extraction from the battery bank. The addition of the ultracapacitor bank presents the need for a methodology to optimise the PV system in order to prevent excess battery storage. A methodology to optimise the PV system ensures the demand can be met, while preventing an excessively large and expensive energy storage system. The optimisation process takes into account the solar radiation at the system location and the demand profile over the course of a year.