Fuzzy-based adaptive pricing rules for a typical microgrid energy management system

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.