Multi-objective Scheduling of Electric Vehicle Charging/Discharging with Time of Use Tariff

TL;DR

This paper presents a multi-objective optimization framework for EV charging/discharging scheduling that balances costs, profits, and grid impact, using a novel evolutionary algorithm tailored for mixed variables.

Contribution

It introduces a comprehensive MOEVCS framework with a custom MVMOEA to optimize EV charging schedules considering multiple stakeholders and dynamic load scenarios.

Findings

Effective balancing of stakeholder objectives demonstrated

Proposed algorithm outperforms traditional methods

Scenario analysis shows adaptability to load variations

Abstract

The increased uptake of electric vehicles (EVs) leads to increased demand for electricity, and sometimes pressure on power grids. Uncoordinated charging of EVs may result in stress on distribution networks, and often some form of optimization is required in the charging process. Optimal coordinated charging is a multi-objective optimization problem (MOOP) in nature, with objective functions such as minimum price charging and minimum disruptions to the grid. In this manuscript, we propose a general multi-objective EV charging/discharging schedule (MOEVCS) framework, where the time of use (TOU) tariff is designed according to the load request at each time stamp. To obtain the optimal scheduling scheme and balance the competing benefits from different stakeholders, such as EV owners, EV charging stations (EVCS), and the grid operator, we design three competing objective functions including EV owner cost, EVCS profit, and the network impact. Moreover, we create four application scenarios with different charging request distributions over the investigated periods. Due to different types of decision variables in this MOOP, we develop a constraint mixed-variable multi-objective evolutionary algorithm (MVMOEA) to implement the proposed MOEVCS framework. Our results demonstrate the effectiveness of MOEVCS in making a balance between three competing objectives.