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Multi Objective Optimization for Multi-Microgrid Energy Management: A Lexicography Approach

机译:多微电网能源管理的多目标优化:一种词典方法

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Microgrids (MGs) are known as the optimal solution for using distributed generations in smart grids. With increasing number and capacity of MGs, the Multi-Microgrids (MMG) concept is introduced. This paper presents multi-objective energy management for MMGs. The MMG has various local energy resources such as Photovoltaic (PV) panels, Wind Turbines (WT), Diesel Generators (DG), Fuel Cells (FC), Micro Turbines (MT), and Battery Energy Storages (BES). The MMG operator tries to minimize daily cost for MGs and MMG can participate in Demand Response (DR) programs. DR programs help to cost minimization, and reliability improvement for MMG but a non-coordinated DR creates a new peak in the distribution systems. In this paper, a multi-objective optimization is presented that it minimizes MMG cost and Deviation from Mean (DFM) load simultaneously. A lexicography approach is used and at first, the MMG cost is minimized. After that DFM is minimized in the second level of optimization. The proposed model will not increase the MMG cost but it improves load curve characteristics. This model is tested on a standard case study and it solved by GAMS software. The results show the efficiency proposed model.
机译:微电网(MGs)是在智能电网中使用分布式发电的最佳解决方案。随着MG的数量和容量的增加,引入了多微电网(MMG)概念。本文介绍了MMGs的多目标能源管理。 MMG具有各种本地能源资源,例如光伏(PV)面板,风力涡轮机(WT),柴油发电机(DG),燃料电池(FC),微型涡轮机(MT)和电池储能(BES)。 MMG运营商试图将MG的每日成本降到最低,并且MMG可以参与需求响应(DR)计划。 DR计划有助于最小化成本并提高MMG的可靠性,但是不协调的DR会在配电系统中创造一个新的高峰。在本文中,提出了一种多目标优化,该优化可同时最小化MMG成本和平均偏差(DFM)负载。使用词典编纂方法,首先,将MMG成本降至最低。之后,在第二级优化中将DFM最小化。提出的模型不会增加MMG的成本,但是会改善负载曲线的特性。该模型在标准案例研究中进行了测试,并通过GAMS软件进行了求解。结果表明了提出的效率模型。

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