Document Type
Conference Proceeding
Publisher
Modelling and Simulation Society of Australia and New Zealand
Editor(s)
Chan, F., Marinova, D. and Anderssen, R.S.
Faculty
Faculty of Computing, Health and Science
School
School of Engineering
RAS ID
13313
Abstract
Australia has a vast land mass characterised by more than 35,000km of shore-line and an abundance of renewable sources (e.g., solar and wind en ergy). Despite the existence of much potential to utilise sustainable pathways of power generation, ther e remains a general reliance on electricity generated from larger plants which are mostly grid-connected but fossil-fuel operated. In Western Australia, only a fraction of its coastal areas and inland mass is serviced by the South West Interconnected (grid) System. For the majority of its regional commun ities, decentralised power generati on forms the prime source of power provision. This exacerbates the situation with regard to accessing el ectricity due to the elevated cost of obtaining fuel (for power generation) as well as relian ce on smaller, less-efficient, generator sets. For many small (remote) or coastal communities’ access to potable water is limited alongside good availability of renewable energies. This provides opportunity for utilis ing renewably powered stand-alone energy systems to help deliver the power needed to directly run utilities, operate desalination systems and reduce the associated emissions footprint. This investigation uses modelling to analyse the perf ormance of small-scale stan d-alone (energy) systems incorporating Reverse Osmosis (RO), providing up to 15litres/day potable water. Through inclusion of physical models representing different hardware components in a Solar-Photovoltaic (PV) system, this research provides an insight into the interaction between the availability of solar energy, energy conversion into DC electric power via PV panels, power conditioning (DC to AC), battery charging/discharging and the power needed for desalination. This paper not only highlights a modelling methodology for such systems but also demonstrates how individual (system) components may be characterised and seasonal variations (of solar irradiance, localised wind speed and ambient temperature) included in the simulations. Simulations undertaken include consideration for unit quantities (solar irradiance per square metre and temporal resolution of predicted irradiance to 1hour). Such approa ches provide a basis for future studies into energy system scalability, energy efficiency in small-scale (stand-alone, renewably powered) desalination systems as well as the deployment of other (non-battery) energy storage media to increase renewable energy utilisation. Modelling yields solar irradiance predictions which are co mpared to measured data at locations typical of Perth, West Australia. The models also accommodate co nsiderations for the effects of localised wind speed and ambient temperature on predicted PV panel pe rformance. This yields mo re accurate conversion characteristics of PV and helps provide better resolved (dynamic) renewable energy (input) data for the simulations. Laboratory based experiments are used to verify the efficiency, water recovery ratio and power characteristics of RO as well as other energy system components. Simulations undertaken using MATLAB help analyse the energy system over a yearly period. Results allow predictions of total (renewable) energy availability, excess (renewable) energy (not captured due to battery capacity) and total potable water production (under two different amounts of daily water demand). Outcomes are discussed with regard to the benefits of incorporating more advanced predictive m odelling methodologies and alternate means of (battery- free) energy storage for stan d-alone PV energy systems.
Access Rights
free_to_read
Comments
This is an Author's Accepted Manuscript of: Clarke, D. P., Al-Abdeli, Y. M., & Kothapalli, G. (2011). Modelling Small-Scale Stand-Alone (PV) Energy Systems with Reverse Osmosis Integration. Paper presented at the International Congress on Modelling and Simulation (MODSIM). Perth, Australia. Available here