The uncontrolled charging behaviors of large-scale electric vehicles (EVs) increase the security risk of the power grid and bring a new challenge for the computing ability of the power system. Using vehicle to grid (V2G) technology, most control systems coordinate the power interaction between EVs and power grid by minimizing the load fluctuation and user cost, but their optimization results are often achieved at the expense of reducing personal travel time. EVs should first meet basic travel needs and then obey the scheduling arrangement. Based on this idea, a four-objective optimal control method for EV charging and discharging schedules considering travel convenience is proposed, including minimization of the load fluctuation and user cost and maximization of the flexible travel time and state of charge (SOC). To solve this large-scale many-objective problem, a resource allocation-based preference-inspired coevolutionary algorithm (PICEAg-EV) is presented. Taking the IEEE 33-node system as an example, the simulation and analysis verify the effectiveness of the proposed control strategy and optimization algorithm. The experimental results show that PICEAg-EV outperforms seven popular intelligence algorithms under EV participation rate setting of 10%, 25%, 50%, 100%. Compared with 2- and 3-objective optimization models, the 4-objective optimization model can provide sufficient flexible travel time and a higher SOC for traveling, which is a better match for the user needs.

大规模电动汽车（EV）不可控的充电行为增加了电网的安全风险，给电力系统的计算能力带来了新的挑战。大多数控制系统使用车辆到电网（V2G）技术，通过最小化负载波动和用户成本来协调电动汽车与电网之间的电力交互，但其优化结果往往以减少个人出行时间为代价。电动汽车应先满足基本出行需求，再服从调度安排。基于此思路，提出了一种考虑出行便利性的电动汽车充放电调度四目标优化控制方法，包括最小化负载波动和用户成本，最大化灵活出行时间和荷电状态（SOC）。为了解决这个大规模的多目标问题，提出了一种基于资源分配的偏好启发协同进化算法（PICEAg-EV）。以IEEE 33节点系统为例，仿真分析验证了所提出的控制策略和优化算法的有效性。实验结果表明，在 10%、25%、50%、100% 的 EV 参与率设置下，PICEAg-EV 的性能优于七种流行的智能算法。与2目标和3目标优化模型相比，4目标优化模型可以提供足够灵活的出行时间和更高的出行SOC，更好地匹配用户需求。仿真和分析验证了所提出的控制策略和优化算法的有效性。实验结果表明，在 10%、25%、50%、100% 的 EV 参与率设置下，PICEAg-EV 的性能优于七种流行的智能算法。与2目标和3目标优化模型相比，4目标优化模型可以提供足够灵活的出行时间和更高的出行SOC，更好地匹配用户需求。仿真和分析验证了所提出的控制策略和优化算法的有效性。实验结果表明，在 10%、25%、50%、100% 的 EV 参与率设置下，PICEAg-EV 的性能优于七种流行的智能算法。与2目标和3目标优化模型相比，4目标优化模型可以提供足够灵活的出行时间和更高的出行SOC，更好地匹配用户需求。