Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach
Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach
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DOI：
10.1109/tsg.2014.2359004
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发表时间：
2015
影响因子：
9.6
通讯作者：
Katayoun Rahbar;Jie Xu;Rui Zhang
中科院分区：
文献类型：
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作者：
Katayoun Rahbar;Jie Xu;Rui Zhang
微电网是未来智能电网的关键使能解决方案，它通过集成分布式可再生发电机和存储系统来有效地满足当地需求。然而，由于可再生能源的随机性和间歇性，微电网的可靠运行面临着新的挑战。为了解决这一问题，本文研究了构成可再生发电系统、储能系统和综合负荷的单个微电网系统的实时能量管理问题。我们对可再生能源随时间的负荷补偿进行建模，称为净能量分布，使其具有实际可预测性，但误差有限，可以任意分布。我们的目标是在满足实际负荷和存储约束的情况下，通过联合优化在一段时间内进出存储系统的能量，最小化从主电网获取的常规能量在有限范围内的总能量成本(模型为时变的严格凸函数之和)。为了实时解决这一问题，我们提出了一种新的离线优化方法来设计在线算法。在这种方法中，我们首先假设净能量分布是完全可预测的或提前知道的，在此基础上，我们以闭合形式推导出最优离线能量调度解。其次，受最优离线解的启发，提出了一种基于滑动窗口的在线实时能量管理算法，该算法适用于实际设置的任意误差噪声环境下的净能量管理。最后，我们利用爱尔兰电力系统的实际风力发电数据进行了仿真，与其他启发式设计的算法以及传统的基于动态规划的算法进行了比较，以评估该算法的性能。
Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the random and intermittent characteristics of renewable energy, new challenges arise for the reliable operation of microgrids. To address this issue, we study in this paper the real-time energy management for a single microgrid system that constitutes a renewable generation system, an energy storage system, and an aggregated load. We model the renewable energy offset by the load over time, termed net energy profile, to be practically predictable, but with finite errors that can be arbitrarily distributed. We aim to minimize the total energy cost (modeled as sum of time-varying strictly convex functions) of the conventional energy drawn from the main grid over a finite horizon by jointly optimizing the energy charged/discharged to/from the storage system over time subject to practical load and storage constraints. To solve this problem in real time, we propose a new off-line optimization approach to devise the online algorithm. In this approach, we first assume that the net energy profile is perfectly predicted or known ahead of time, under which we derive the optimal off-line energy scheduling solution in closed-form. Next, inspired by the optimal off-line solution, we propose a sliding-window based online algorithm for real-time energy management under the practical setup of noisy predicted net energy profile with arbitrary errors. Finally, we conduct simulations based on the real wind generation data of the Ireland power system to evaluate the performance of our proposed algorithm, as compared with other heuristically designed algorithms, as well as the conventional dynamic programming based solution.