A novel multi-objective security-constrained power management for isolated microgrids in all-electric ships
A novel multi-objective security-constrained power management for isolated microgrids in all-electric ships
复制标题
全电动船舶孤立微电网的新型多目标安全约束电源管理
DOI：
10.1109/ests.2017.8069273
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
H. Livani
中科院分区：
文献类型：
--
作者：
V. Sarfi;H. Livani
由于孤立微电网，比如全电船舶（AES），发电能力有限且惯性有限，这些微电网中容易出现频率和电压违规情况。因此，有必要建立一个电力管理框架，以便在实现稳定实时运行的同时对微电网进行优化调度。本文介绍了一种基于高效计算优化技术——帕累托凹性消除变换（PaCcET）的新型多目标安全约束电力管理（MO - SCPM）框架，用于全电船舶。PaCcET将多目标空间转换为变换后的目标空间，然后利用单目标优化器找出原始多目标空间的所有非劣解。这个新框架在一个统一的框架内有效地整合了稳态和动态安全限制，比如功率平衡、有功发电、无功发电、电压幅值、线路潮流、频率以及电压暂态。这种新的电力管理方法被应用于一个概念性的全电船舶模型，以找到最佳折衷方案，该方案不仅使单个有功功率设定点调整最小化，而且在满足运行和安全约束的同时使电压设定点调整最小化。然后将结果与基于非支配排序遗传算法II（NSGA - II）的计算成本高昂的多目标技术进行比较，NSGA - II是一种著名的多目标优化基准。
Since isolated microgrids, such as all-electric ships (AES), have limited generation and finite inertia, frequency and voltage violations are prone to happen in these microgrids. Therefore, it is necessary to have a power management framework for optimal scheduling of microgrids while achieving stable real-time operation. This paper introduces a novel multi-objective security constrained power management (MO-SCPM) framework for AES, based on a computationally-efficient optimization technique, Pareto Concavity Elimination Transformation (PaCcET). PaCcET transforms a multi-objective space to a transformed objective space and then utilizes a single-objective optimizer to find all the non-dominated solutions of the original multi-objective space. The new framework efficiently integrates steady-state and dynamic security limits, such as power balance, active power generation, reactive power generation, voltage magnitude, line flows, frequency, and voltage transient in a unified framework. The new power management method is applied to a notional AES model to find the best compromise solution that not only minimizes the individual active power set-point adjustments but also minimizes voltage set-point adjustments while keeping the operating and security constraints satisfied. The results are then compared to the computationally-expensive multi-objective technique based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) as a well-known multi-objective optimization benchmark.