HELOS: Heterogeneous Load Scheduling for Electric Vehicle-Integrated Microgrids
HELOS: Heterogeneous Load Scheduling for Electric Vehicle-Integrated Microgrids
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DOI：
10.1109/tvt.2016.2636874
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发表时间：
2017-07
影响因子：
6.8
通讯作者：
Gaoxiang Li;Di Wu;Jiefeng Hu;Yong Li;M. S. Hossain;Ahmed Ghoneim
中科院分区：
文献类型：
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作者：
Gaoxiang Li;Di Wu;Jiefeng Hu;Yong Li;M. S. Hossain;Ahmed Ghoneim
随着对全球环境状况的日益关注和可再生能源技术的快速发展，微电网已被视为一种有前途的解决方案，以减少基于基础设施的电力系统的负担。然而，由于现有可再生能源固有的间歇性特征，沿着随机的居民行为模式，电动汽车（EV）的不可预测的插入或拔出动作以及随时间变化的电价，微电网运营商有效地执行负载调度和能源管理是具有挑战性的。在本文中，我们提出了一种在线算法进行成本感知调度的电动汽车负载和能源供应的微电网。我们将这个问题转化为一个随机优化问题，目标是最小化微电网的时间平均成本，包括从主电网购买电力的成本，充电和放电电池的成本，可再生能源的收获成本和生命周期温室气体排放成本。为了解决这个问题，关键思想是利用电力价格的动态来进行电池充电和放电操作，可再生能源收集，并适当地调度EV负载。我们的方法是基于李雅普诺夫优化技术，具有较低的计算复杂度，只需要有限的预测价格信息。对算法的理论分析表明，该策略能在显式约束下达到最优。通过进行广泛的真实数据驱动的模拟，我们证明了我们提出的算法可以实现更低的成本，比其他替代解决方案更环保。
With increasing concerns about worldwide environmental conditions and rapid development of renewable energy technologies, microgrids have been regarded as a promising solution to reduce the burden of infrastructure-based power systems. However, due to the intrinsically intermittent features of existing renewable energy, along with random residential behavior patterns, unpredictable plugged-in or unplugged actions of electric vehicles (EVs) and the time-varying price of electricity, it is challenging for microgrid operators to efficiently perform load scheduling and energy management. In this paper, we propose an online algorithm to conduct cost-aware scheduling of EV loads and energy supplies for microgrids. We formulate this problem into a stochastic optimization problem with the objective of minimizing the time-average cost of a microgrid, including the purchase cost of electricity from the main grid, the cost of charging and discharging batteries, renewable harvesting costs, and life-cycle greenhouse-gas emission costs. To solve this problem, the key idea is to exploit the dynamics of the price of electricity to conduct battery charging and discharging operations, renewable energy harvesting, and schedule EV loads properly. Our method is based on the Lyapunov optimization technique, which has low computational complexity and only requires limited prediction of price information. The theoretical analysis of our algorithm confirms that the proposed strategy can achieve optimality with explicit bound. By conducting extensive real-data driven simulations, we demonstrate that our proposed algorithm can achieve much lower cost and be more eco-friendly than other alternative solutions.