Bang-Bang Charging of Electrical Vehicles by Smart Grid Technology

TL;DR

This paper proposes a bang-bang charging strategy combined with model predictive control to optimize electric vehicle charging and power grid operation, reducing costs and fluctuations with a novel solution method ensuring global optimality.

Contribution

It introduces a new optimization approach for joint PEV charging and grid management using MPC and a mixed integer nonlinear programming model, ensuring globally optimal solutions.

Findings

The proposed method achieves globally optimal solutions in simulations.

It effectively minimizes grid fluctuations and power generation costs.

The bang-bang charging strategy simplifies implementation while maintaining optimality.

Abstract

The success of the transportation electricification in this century particularly requires the penentration of the internet of plug-in electric vehicles (PEVs) into the smart power grid. Beside the function of serving the traditional residential power demand, next-generation power grids also aim to support the internet of PEVs at the same time. The distinct difference between the traditional power demand and PEVs' power demand is that while the statistics of the former is rich enough for treating it as inelastic/known before hand, the latter is unknown until random PEVs' arrivals. Massive penentration of PEVs certainly causes the grid unpredictable fluctuation. The present paper considers the joint PEVs charging coordination and grid power generation to minimizing both of the negative impact of PEVs' integration and the cost of power generation while meeting the grid operating constraints and all parties' demand. The bang-bang PEVs charging strategy is adopted to exploit its simple implementation. By using a recently developed model predictive control (MPC) model for this problem, the online compuation is based on a predictive mixed integer nonlinear programming (MINP). A new solution computation for this optimization problem is developed. Its capacity of achieving the globally optimal solution is shown by numerical comparison between its performance and that by an off-line optimal solution.