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首页> 外文期刊>Smart Grid, IEEE Transactions on >Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach
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Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach

机译：用于微电网中可更新集成的实时能量存储管理：一种离线优化方法

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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.
机译：通过集成分布式可再生发电机和存储系统以有效满足本地需求，微电网是未来智能电网的关键支持解决方案。然而，由于可再生能源的随机性和间歇性，对微电网的可靠运行提出了新的挑战。为了解决此问题，我们在本文中研究了单个微电网系统的实时能源管理，该系统构成了可再生发电系统，储能系统和总负荷。我们对负载随时间推移产生的可抵消的可再生能源建模（称为净能曲线），在实践中是可以预测的，但是误差可以任意分配。我们的目标是通过共同优化随时间推移从存储系统充放电的能量来最小化从有限范围内从主电网汲取的常规能量的总能量成本（建模为时变的严格凸函数的总和）受实际负载和存储限制。为了实时解决这个问题，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曲线是提前完美预测或已知的，然后我们得出封闭形式的最优离线能量调度解决方案。接下来，在最佳离线解决方案的启发下，我们提出了一种基于滑动窗口的在线算法，用于在带有任意误差的嘈杂预测净能量分布的实际设置下进行实时能量管理。最后，我们与爱尔兰启发式设计的算法以及传统的基于动态规划的解决方案相比，基于爱尔兰电力系统的真实风力发电数据进行了仿真，以评估我们提出的算法的性能。

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