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The Integrated Design of a Novel Secondary Control and Robust Optimal Energy Management for Photovoltaic-Storage System Considering Generation Uncertainty

机译:考虑到不确定性的光伏存储系统的新型二级控制和强大的最佳能量管理的集成设计

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摘要

Due to the generation uncertainty of photovoltaic (PV) power generation, it has been posing great challenges and difficulties in maintaining the stability, security, and reliability of PV-storage systems (one kind of microgrid). To overcome these challenges and difficulties, this paper is concerned with secondary control and robust energy management for PVs in a grid-connected microgrid (MG) considering uncertainty. In our designs, to maintain the stable operation of PVs in MG, a novel secondary control method combining an event-triggered finite time sliding mode controller (FTSMC) and consensus controllers is proposed. Furthermore, a robust optimization framework is established to minimize the total cost of grid-connected MG involving the operation cost of multi-battery Energy Storage Systems (BESSes) and the electricity purchased from the main grid. To eliminate the effects of PV uncertainty, the optimization problem with uncertain constraints is converted into a new optimization problem with only deterministic constraints by using the box theory to represent the PV outputs. In other words, the robust optimization strategy makes uncertain boundaries easier to be represented by setting all uncertain parameters into an uncertain domain involving all typical extreme cases. Then, a particle swarm optimization (PSO) method is employed to solve the newly converted optimization problem. Finally, the experimental results validate the effectiveness of the proposed integrated framework.
机译:由于光伏(PV)发电的不确定性,它在维持PV储存系统(一种微电网)的稳定性,安全性和可靠性方面都会产生巨大的挑战和困难。为了克服这些挑战和困难,本文涉及考虑不确定性的网格连接的微电网(MG)中PVS的二次控制和强大的能量管理。在我们的设计中,为了保持MG中PVS的稳定运行,提出了一种结合事件触发的有限时间滑模控制器(FTSMC)和共识控制器的新型二次控制方法。此外,建立了一种稳健的优化框架,以最小化涉及多电池储能系统(BESSES)的运营成本的网格连接的MG的总成本以及从主电网购买的电力。为了消除PV不确定性的影响,通过使用盒子理论表示PV输出,仅使用盒子理论转换为新的优化问题的新优化问题。换句话说,稳健的优化策略使得不确定的边界更容易通过将所有不确定参数设置为涉及所有典型的极端情况的不确定域来表示。然后,采用粒子群优化(PSO)方法来解决新转换的优化问题。最后,实验结果验证了拟议的综合框架的有效性。

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