Future Electricity Network Infrastructures

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging stations and empowers PEV users to manage their charging demand by using smart charging solutions. This makes PEV grids assets that provide flexibility to the power grid. The Internet of Things (IoT) feature can make smooth EVGI possible through a supporting communication infrastructure. In this regard, the selection of an appropriate communication protocol is essential for the successful implementation of EVGI. This study assesses the efficacy of the UK’s 4G network with TCP and 4G UDP protocols for potential EVGI operations. For this, an EVGI emulation test bed is developed, featuring three charging parking lots with the capacity to accommodate up to 64 PEVs. The network’s performance is assessed in terms of data packet loss (e.g., the data-exchange capability between EVGI entities) and latency metrics. The findings reveal that while 4G TCP often outperforms 4G UDP, both achieve latencies of less than 1 s with confidence intervals of 90% or greater for single PEV cases. However, it is observed that the high penetration of PEVs introduces a pronounced latency due to queuing delays in the network including routers and the base station servers, highlighting the challenges associated with maintaining efficient EVGI coordination, which in turn affects the efficient use of grid assets. Full article

With the recent development in power electronic devices, HVDC (High Voltage Direct Current) systems have been recognized as the most prominent solution to transmit electric power economically. Today, several HVDC projects have been implemented physically. The conventional HVDC systems use grid commutation converters, and its commutation relies on an AC system for the provision of voltage. Due to this reason, there are possibilities of commutation failure during fault. Furthermore, once the DC (Direct Current) system power is interrupted momentarily, the reversal of work power is likely to cause transient over-voltage, which will endanger the safety of power grid operation. Hence, it is necessary to study the commutation failure and transient over-voltage issues. To tackle the above issues, in this paper, the dynamic and transient characteristics of Pakistan’s first HVDC project, i.e., the Matiari–Lahore ±660 kV transmission line has been analyzed in an electromagnetic transient model of PSCAD/EMTDC. Based on the characteristics of the DC and the off-angle after the failure, a new control strategy has been proposed. The HVDC system along with its proposed control strategy has been tested under various operating conditions. The proposed controller increases the speed of fault detection, reduces the drop of AC voltage and DC and suppresses the commutation failure probability of LCC-HVDC (line commutated converter- high voltage direct current). Full article

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches. Full article