Multi-objective economic operation of smart distribution network with renewable-flexible virtual power plants considering voltage security index

可再生能源 索引(排版) 计算机科学 电压 虚拟发电厂 网络安全 功率(物理) 电力网络 环境经济学 计算机安全 电气工程 电力系统 分布式发电 万维网 经济 工程类 物理 量子力学
作者
Ehsan Akbari,Ahad Faraji Naghibi,Mehdi Veisi,Amirabbas Shahparnia,Sasan Pirouzi
出处
期刊:Scientific Reports [Springer Nature]
卷期号:14 (1) 被引量:22
标识
DOI:10.1038/s41598-024-70095-1
摘要

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47-62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35-47%, and 44%, respectively, compared to the power flow model.
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