Techno-economic simulation and optimization of residential grid-connected PV system for the Queensland climate

By Gang Liu, M. Rasul, M. Amanullah, M. M. Khan. 2012

160 citations recorded by the source corpus.

With the environmental advantages of solar energy, the use of solar photovoltaic (PV) in residential electricity generation is encouraged by Australian governments incentives, however, what number of residents benefit from installing a grid-connected PV system and how much electricity generated by such a system is not clearly understood yet. This study aims to investigate the economic, technical and environmental performance of residential PV system running under the Queensland (Australia) climatic conditions, and optimize the size and slope of PV array in the system. The solar irradiation data of the 4 typical climate zones of Queensland, including tropical, sub-tropical, hot arid, and warm temperature zone, are investigated. Using global solar irradiation as solar energy resource data, the price of PV devices, batteries, converters, and grid electricity tariff and sale-back tariff as economic analysis inputs, the system is simulated and optimized by HOMER software. The optimized system not only satisfies the typical residential load of 23 kWh per day but also meets the requirement of minimizing the total costs of system investment and electricity consumption during the system's lifetime. It is found that under the specific climatic conditions of the eleven main cities of Queensland, a PV system is an effective way to decrease electricity bills and mitigate carbon dioxide emission. In particular, a 6 kW PV system in Townsville is able to deal with 61% of the total electricity load and conserves more than 90% of electricity payments and reduce approximately 95% of carbon dioxide emission. It is also found that for all the cities the systems with 20–25 degrees of slope have the best performance including the least cost of energy (COE) and the least carbon dioxide emission.

Subfields: Artificial Intelligence, Energy Engineering and Power Technology, Pollution

View Paper: https://www.semanticscholar.org/paper/12e8f2b793f8a093c2bc434e2473a04524e96333

Citations in the corpus