Institution of Engineering and Technology
Faculty of Computing, Health and Science
School of Engineering / Centre for Communications Engineering Research
This paper details the implementation of adaptive pricing rules for a typical autonomous microgrid. The proposed rules aim at generating competitive prices based on monitoring the microgrid's operation conditions, thus maximising the profit from selling electricity to the utility grid throughout the microgrid's lifetime. A decision tree based linear programming and fuzzy system are developed to generate the proposed rules. Microgrid's operation conditions such as electricity demand, generation price and amount of generation are considered in the generated pricing rules. To simulate the behaviour of electronic competition, we have implemented a Multi-Agent System (MAS) to represent the performance of two competitive sellers and one buyer under uniform and discriminatory pricing rules. As a case study, our proposed pricing rules are tested on the power grid of the Joondalup campus of Edith Cowan University in Western Australia. Simulation studies for 30-minutes operation intervals for the developed virtual market pool with the adaptive microgird pricing strategies have recorded beneficiary sale prices with a reasonable number of electricity trade participations.