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

arXiv: https://arxiv.org/abs/2108.05062 · HTML: https://mlsanity.com/paper/2108.05062/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization](https://mlsanity.com/paper/2311.14563/) (2023)
- [EVTradeMatch: A Mobility-Aware Multi-Objective Matching Framework for EV--EV Energy Trading](https://mlsanity.com/paper/2609.10551/) (2026)

## Abstract (arXiv)

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.