Impact on the Spanish electricity network due to the massive incorporation of electric vehicles

Electric vehicles, along with renewable energy, are two of the most important components for achieving a more sustainable and cleaner future. This paper study the Spanish electricity demand at the Iberian Peninsula level during the eleven-year period 2007-2018 with daily data from the Spanish electricity network, calculating the monthly daily average for each year as actual data on the use of the electricity distribution network. Having in mind this information, the number of electric vehicles (EVs) that could be charged in Spain is being studied in order to reorganize the Spanish production system. Three different scenarios are analyzed (slow, accelerated and fast charging) according to the capacity conditions of the electric distribution network, previously determining the available electric energy that varies according to the electric demand. Results obtained reveals the need of a complex reorganization of the Spanish electricity production system due to the geographical seasonality of electricity demand.

Optimal electric distribution network configuration using adaptive sunflower optimization

Network reconfiguration (NR) is a powerful approach for power loss reduction in the distribution system. This paper presents a method of network reconfiguration using adaptive sunflower optimization (ASFO) to minimize power loss of the distribution system. ASFO is developed based on the original sunflower optimization (SFO) that is inspired from moving of sunflower to the sun. In ASFO, the mechanisms including pollination, survival and mortality mechanisms have been adjusted compared to the original SFO to fit with the network reconfiguration problem. The numerical results on the 14-node and 33-node systems have shown that ASFO outperforms to SFO for finding the optimal network configuration with greater success rate and better obtained solution quality. The comparison results with other previous approaches also indicate that ASFO has better performance than other methods in term of optimal network configuration. Thus, ASFO is a powerful method for the NR.

Detection of Non-Technical Losses in Electric Distribution Network by Applying Machine Learning and Feature Engineering

Non-technical losses (NTL), which occur up to 40% of the total electric transmission and distribution power, create many challenges worldwide. These losses have a severe impact on distribution utilities and adversely affect the performance of electrical distribution networks. Furthermore, the depreciation of these NTL reduces the requirement of new power plants to fulfill the demand-supply gap. Hence, NTL is an emerging research area for electrical engineers. This paper proposed a model for the detection of non-technical losses based on machine learning and feature engineering. Experimental results check the performance of the proposed model. These results clearly show that this proposed detection model has better accuracy, precision, recall, F1 score, and AUC score than other existing approaches.