Multi-Agent Based Optimal Scheduling and Trading for Multi-Microgrids Integrated With Urban Transportation Networks

This paper investigates multi-period optimal energy scheduling and trading for multi-microgrids (MMGs) integrated with an urban transportation network (UTN). Specifically, an optimization based multi-period traffic assignment model is built to characterize the vehicular flows considering rational drivers with time-flexible travel demand. Meanwhile, each microgrid (MG) is modeled to independently schedule its operation by trading energy with other MGs and charging electric vehicles (EVs) with its fast charging stations (FCSs). The EV charging prices could further influence traffic flow distribution of the UTN. To co-optimize the coupled system, a multi-agent based optimal scheduling and trading scheme is proposed, which is facilitated by peer-to-peer communication of transactive energy information among the agents, thus can preserve privacy of the market participants. Within the scheme, the MGs and EVs take their optimal decisions in response to MG energy trading and EV charging prices updated via multi-bilateral negotiation based on the demand-supply relationship. When an equilibrium of the coupled system is reached, each MG can minimize its operation cost, and the traffic condition could also be improved. Case studies of a MMG system coupled with a double-ring UTN are conducted to validate the effectiveness of the scheme.

Automated summary

This paper investigates multi-period optimal energy scheduling and trading for multi-microgrids integrated with an urban transportation network. A multi-agent based optimal scheduling and trading scheme is proposed to co-optimize the coupled system, facilitated by peer-to-peer communication of transactive energy information among the agents. Case studies validate the effectiveness of the scheme in improving energy efficiency and traffic conditions.