Electric vehicle charge planning using Economic Model Predictive Control

R. Halvgaard, N. K. Poulsen, H. Madsen, J. B. Jørgensen, F. Marra, D. E. M. Bondy
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引用次数: 46

Abstract

Economic Model Predictive Control (MPC) is very well suited for controlling smart energy systems since electricity price and demand forecasts are easily integrated in the controller. Electric vehicles (EVs) are expected to play a large role in the future Smart Grid. They are expected to provide grid services, both for peak reduction and for ancillary services, by absorbing short term variations in the electricity production. In this paper the Economic MPC minimizes the cost of electricity consumption for a single EV. Simulations show savings of 50-60% of the electricity costs compared to uncontrolled charging from load shifting based on driving pattern predictions. The future energy system in Denmark will most likely be based on renewable energy sources e.g. wind and solar power. These green energy sources introduce stochastic fluctuations in the electricity production. Therefore, energy should be consumed as soon as it is produced to avoid the need for energy storage as this is expensive, limited and introduces efficiency losses. The Economic MPC for EVs described in this paper may contribute to facilitating transition to a fossil free energy system.
基于经济模型预测控制的电动汽车充电规划
经济模型预测控制(MPC)非常适合控制智能能源系统,因为电价和需求预测很容易集成到控制器中。电动汽车(ev)有望在未来的智能电网中发挥重要作用。预计它们将通过吸收电力生产的短期变化,提供减少高峰和辅助服务的电网服务。在本文中,经济MPC使单个电动汽车的电力消耗成本最小化。模拟显示,与基于驾驶模式预测的负载转移不受控制的充电相比,可节省50-60%的电力成本。丹麦未来的能源系统很可能以风能和太阳能等可再生能源为基础。这些绿色能源在电力生产中引入了随机波动。因此,能源应该一产生就被消耗掉,以避免需要能量存储,因为这是昂贵的,有限的,并且会带来效率损失。本文中描述的电动汽车经济MPC可能有助于促进向无化石能源系统的过渡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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