AC-coupled hybrid power system optimisation for an Australian remote community

Matthew Combe, Amin Mahmoudi, Mohammed H. Haque, Rahmat Khezri

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


This paper investigates the operation of various optimised AC-coupled hybrid power systems for an isolated community in South Australia. In addition to diesel generators, various combinations of distributed generators and storage technologies are considered to form different hybrid power systems. Optimal design of the hybrid power systems is implemented using particle swarm optimisation (PSO) algorithm. The performance of the PSO in optimizing the above hybrid systems is discussed in detail. Whilst meeting the power system operational constraints, the minimum levelised cost of electricity (LCOE) of all systems, with various combinations of system components, are calculated. Simulation results obtained for each optimized system are carefully described and analysed. The annual operations of various components in each system are then compared and discussed in detail. A sensitivity analysis by varying the cost of wind turbines and the photovoltaic is also conducted for all hybrid systems. It has been found that the inclusion of energy storage systems (battery and flywheel) can reduce the LCOE and CO2 emission significantly compared to the inclusion of renewable generations (solar and wind) alone. The diesel-wind-solar-battery system is the eco-friendliest with an annual CO2 emission of 451 t compared to the reference diesel system (1064 t). In addition, the hybrid system consisting of diesel, wind, solar, battery and flywheel is found to be the most economic with a LCOE of 58.94 ¢/kWh compared to the diesel system with a LCOE of 67.17 ¢/kWh.

Original languageEnglish
Article numbere12503
Number of pages19
JournalInternational Transactions on Electrical Energy Systems
Issue number9
Early online date10 Jun 2020
Publication statusPublished - 1 Sept 2020


  • energy storage
  • isolated remote area power system
  • optimal sizing
  • particle swarm optimisation
  • renewable energy


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