Improving the accuracy of forecasting unplanned power extinction (outage) time of power distribution network using ARIMAX time series model (Case study: Yazd power distribution network)

Electric power and power distribution are prominent infrastructures for economic development in any developing country like Iran. Also, the power distribution network is a very important supply chain that combines a variety of processes. Smart electrical energy distribution networks are one of the latest technologies in the world. The main goal of these networks is to provide reliable electricity, increase the reliability factor and network stability, and respond to the growing needs of customers with minimal damage to the environment, profit, and high efficiency. In the last three decades, the rapid evolution and prevalent adoption of information systems, distribution analysis tools, computational models, and more recently, the emergence of smart grid technologies have given utilities access to the data and tools required for improving these analyses and the possibility of increasing the efficiency of power distribution systems (by, for example, reducing losses and optimizing voltage profiles). Forecasting the future state of the network with the least error brings us closer to the smart network. Because electricity is a mortal product, a comprehensive approach to unplanned power extinction (outage) time is very valuable in preventing any power distribution losses. Various accidents disrupt (cause breakdowns in) the power distribution network, which can be repaired and restored without a hotline. One of the main reasons for customers' power outages is the blackouts in the distribution field, which are affected by technical and non-technical events in the electricity distribution networks. Forecasting these events and managing them can be effective in reducing unplanned power extinction (outage) time. The purpose of this article is to present a model for predicting the duration of unplanned power extinction (outage) and unsold energy based on the data recorded from 121 systems (controllers), the urban network of the three power distribution companies in Yazd province. The final result shows that the ARIMAX model(s) shows less error than the ARIMA model(s) and presents better prediction. Therefore, using exogenous variables in predictions and not being satisfied with the fluctuations of a variable can improve predictions. The model proposed for predicting unsold energy is ARIMAX(1,0,1)(0,0,0) considering the number of incidents and the time of unplanned outages as exogenous variables. The model also shows that in July 2022, the unplanned power extinction (outage) time of this network will be approximately ten hours and also the unsold power will be approximately 6 MWH. On the other hand, the community is without electricity and dissatisfaction has arisen, which lies in economic and social losses. Therefore, with this warning, managers should re-examine the factors of disruption and lack of electricity supply and think of measures to reduce these blackouts when planning for this month of the year