Abstract
Recently, with the large scale of power grids and the increase in frequency of extreme weather, the safe and stable operation of power systems is facing great challenges. Therefore, mobile emergency power source (MEPS) are a promising and feasible way to deal with extreme weather and reduce economic losses. However, the current urban power grid and transportation network are closely coupled, and the congested traffic hinders the rapid configuration of MEPSs. Therefore, this paper proposes an MEPS configuration scheme considering real-time traffic conditions. Firstly, the dynamic road traffic index (DRTI) is defined, which can fully describe the dynamic characteristics of traffic. The wavelet neural network (WNN) is used to predict the traffic flow. Then, combined with the knowledge of graph theory, an A-Star algorithm (AS) is used to determine the optimal path. Secondly, the optimal installation location of MEPSs is determined by forward–backward sweep method in distribution network. Finally, the feasibility, accuracy, and time cost of the proposed method are verified by numerical simulations, which can meet the requirements of online application.
Highlights
When an urban power system is in a great power outage caused by an extreme climate disaster [3,4], the daily life of residents will be greatly affected, and a large number of economic losses and serious consequences will occur at the same time
After the first stage that determines the optimal route in an urban transportation network, mobile emergency power source (MEPS) can quickly reach the power grid
An wavelet neural network (WNN) is used to predict traffic flow and the traffic flow data of an urban transportation network in China is used for training and testing
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Some extreme weather, such as typhoons, hail, and cold tides will greatly affect the safety level of power systems. The load is mainly concentrated in the distribution network, especially the urban power grid [2], so the requirements for reliable power supply are more stringent. When an urban power system is in a great power outage caused by an extreme climate disaster [3,4], the daily life of residents will be greatly affected, and a large number of economic losses and serious consequences will occur at the same time. Extreme weather is uncontrollable, with the development of information technology, the occurrence time and place of extreme weather, such as typhoons and cold tides, can be predicted in advance, so as to guide the relevant operators of power systems to implement risk aversion action and emergency repair.
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