Multi-agents based optimal energy scheduling technique for electric vehicles aggregator in microgrids

This paper presents multi-agents based optimal energy scheduling technique at microgrid level, aiming to minimize overall costs allied with the domestic energy consumption and electric vehicles charging during the particular market price and battery degradation costs. Firstly, agents-based optimal technique is presented for the distributed resource management, where local agents operate and accomplish their tasks autonomously that making the microgrid system more intelligent and reliable. Secondly, to model the actual grid voltage and price uncertainties, the proposed technique is applied in a low distribution network considering the upper and lower limits of the grid prices instead of the average/estimated prices. The problem is solved by linear programming considering the generation capabilities of the renewable energy resources and electric vehicle state of charge during the day-ahead period of 24 h. Besides, to deal with the domestic load and electric vehicles state of charge uncertainties, the simulation is carried out based on their energy consumption periods during the day while the electric vehicles initial state of charges is estimated on their daily mileage. For validation, the proposed technique is employed at a low voltage residential area and compared, which shows that the proposed technique total profit raised by 16.92% and 5.60% in comparison with the uncoordinated and stochastic techniques respectively, and guarantee the optimal energy scheduling that satisfies the consumers load demands efficiently.

Automated summary

This paper proposes a multi-agents based optimal energy scheduling technique at microgrid level, using distributed resource management and linear programming to address actual grid uncertainties and electric vehicle states.