Orderly charging and discharging dispatching strategy for electric vehicles based on microgrid

With the increasing penetration of electric vehicles (EVs) in the power grid, their disordered charging characteristics pose a challenge to the economic and stable operation of the power grid. To cope with this problem, this paper proposes a microgrid-based scheduling strategy for orderly charging and discharging of EVs, aiming to reduce the impact of EVs on the grid and improve the efficiency of grid operation. The strategy firstly personalises the scheduling of EVs by modelling the users’ travel patterns, and secondly considers the users’ charging needs and assigns different charging modes. In order to reduce the dimensionality of the model, this paper employs Minkowski summation to aggregate the EV population into a generalised energy storage device. Subsequently, dynamic tariffs are employed to better accommodate the grid’s base load profile fluctuation patterns. Finally, by integrating EVs into the microgrid and coordinating the EV population with the generator set, base load, wind-photovoltaic and storage batteries, the individual differential evolution quantum particle swarm optimisation algorithm is employed to minimise the unit fuel cost, pollutant emission cost, start-up cost, user charging cost, and the penalty cost for wind and photovoltaic abandonment. The experimental results show that this microgrid-based EV orderly charging and discharging strategy can effectively reduce the user charging cost and microgrid operation cost, and realise the efficient use of renewable energy and energy complementarity. This study proves the stability and economy of the proposed scheduling strategy, and provides a new solution for the intelligent integration of EVs and microgrids.