Dynamic restoration electricity price optimization method to enhance the resilience of distribution networks with multiple-microgrids

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Hongkun Wang, Yujie Gao, Hong Zhang, Dongmei Yan, Hongwei Li
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Abstract

Resilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post-disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple-microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33-node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network.

Abstract Image

提高多微电网配电网恢复能力的动态恢复电价优化方法
韧性是智能配电网的主要特征之一,微电网(MG)接入配电网为提高韧性提供了有效途径。微电网和配电网属于不同的利益主体,因此有必要通过价格杠杆积极引导微电网和灵活资源。这样,MG 就能参与灾后恢复,提高灾后恢复能力。首先,本文提出了极端灾害后的动态恢复电价响应机制,并构建了 MGs 内负荷和电动汽车的电力响应模型。其次,提出了多微网(MMG)配电网优化调度模型,以提高关键负荷恢复率(RRCL)。单个微电网实现了最大的微电网收益和恢复贡献,MMG 利用电力净空指标优化动态恢复电价,实现了最小的配电网购电成本。最后,通过双微电网接入 IEEE 33 节点配电系统,验证了考虑动态恢复电价响应机制的 MMG 配电网弹性增强优化调度方法。仿真结果表明,所提出的优化方法有效提高了配电网的 RRCL。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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