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The Volt/Var optimization (VVO) enables advanced control strategy development for voltage regulation. With the recent advancement of data-driven approaches and communication infrastructure, realtime decision-making through VVO can effectively address distributed energy resources (DERs) uncertainties without relying on models and topologies of distribution networks. In this paper, a comprehensive review on data-driven VVO in distribution networks is presented, focusing on statistics and machine learning (supervised/unsupervised, ensemble, and reinforcement learning (RL)). State-of-the-art monitoring devices essential in data-driven VVO frameworks are firstly discussed. How data-driven structures serve as primary or supplementary tools in VVO frameworks is then detailed. Since RL is increasingly used, RL-based algorithms (value-based, policy-based, actor-critic-based, and graph-based algorithms) are reviewed. Decision-making processes for RL-based VVO frameworks, such as the Markov decision process (MDP), Markov game, constrained Markov decision process, constrained Marko game, and adversarial Markov decision process, are also surveyed. Future research directions in this area are recommended in the paper.

The management of electricity distribution faces numerous challenges, including energy losses arising from technical and non-technical issues such as electricity theft. Energy losses due to theft have a significant global impact, including in South Africa (SA). SA rural areas are suffering from poor voltage, and technical and non-technical losses due to old power system systems. Hence, this research project is a proposal for the upgradation of an old and technically weak network of Nottingham Road, Kwa-Zulu Natal in South Africa. To reduce non-technical losses, this research examines the influence and potential solutions of using smart meters. The paper focuses on modeling real low voltage (LV) distribution systems and the energy meters that measure various parameters of the smart meters. This research also examines the effect of illegal links on the network. However, implementing the smart metering technology for lesser power consumers in developing countries such as South Africa can be challenging because of the greater initial costs. Therefore, this paper investigates the possibility of various technology choices and their return on investment in both urban and rural regions.

This paper proposes a fault location and protection method for power distribution system with Distributed generation (DG) resources, considering fault impedance. A multi-layer perceptron (MLP) Artificial Neural Network (ANN)-based approach using the Levenberg-Marquardt algorithm to train the neural network for fault location and identification within distribution networks with DGs is developed. The proposed method emphasizes the incorporation of fault impedance in the analytic process. It also leverages the robust computational capabilities and inherent simplicity of ANNs to accurately detect the fault type and subsequently identify the fault location. Furthermore, it implements a zoning strategy, which partitions the distribution system into multiple independent sections to facilitate a more organized and effective fault management process. Simulation results and conducted analyses confirmed the effectiveness of the proposed approach in localizing faults within the network thereby minimizing downtime during faulty conditions and improving power system’s capabilities and reliability. Among the key advantages of the proposed fault detection approach are its ability to adeptly handle the complexities introduced by the wide integration of DGs in distribution networks.

Fuse saving protection is usually applied in distribution networks, through auto-reclosing, to prevent fuse burning under transient faults. The modern fault ride-through (FRT) standards, which require distributed generators (DGs) to remain connected during the fault, may lead to the miscoordination of recloser-fuse. This paper proposes a discrete coordinate-descent (DCD) approach that optimizes the FRT of DGs so that the coordination between recloser and fuse is preserved. First, the method performs a short-circuit calculation (SCC) considering the currents contributed by the DGs, according to the FRT standards of each country. Then, if a miscoordination between recloser-fuses is observed, it optimally scales down the DG currents, until the coordination is restored. The proposed method has simple implementation, low communication requirements and offers adequate voltage support during the fault. Simulations were carried out in the IEEE 13-bus and the IEEE 8500-node network to highlight the distinct features of the proposed approach against the state-of-the-art current limiting approaches.

The excessive integration of renewable distributed generation (RDG) and electric vehicles (EVs) could be considered the two most problematic elements representing the greatest threat to the distribution network (DN) technical operation. In order to avoid going beyond technical limitations, the term hosting capacity (HC) was proposed to define the highest permitted amount of distributed generation (DG) or EVs that can be integrated safely into the DN. The connection of RDGs was first brought to the attention of researchers and DN operators since it accounts for the most notable portion of these technical issues. Hence, the phrase ‘DG-HC’ was initially proposed and evolved significantly over the last few years. Currently, EV integration in most DNs worldwide is still low, but given the worldwide support for clean transportation options, expectations are raised for a significant increase. As a result, it is anticipated that over the next years, the effect of EV integration on the DN will be highly noticeable, requiring greater attention from researchers and DN operators to define the accepted limits of EV penetration levels, ‘EV-HC,’ which is expected to pass along the same line of DG-HC. This article provides an in-depth review of both DG-HC and EV-HC. It first analyses how the DG-HC research has grown over the years and then studies the published EV-HC papers, illustrating to what extent there is a similarity between them and, finally, employs these analyses to expect future development in the EV-HC research area. This article includes the different uses of the term HC, the most common performance indices of DG-HC, the various methods for assessing DG-HC, the different techniques for DG-HC enhancement, the effects of integrating EVs on the DG-HC, and finally, calculating and enhancing methods for EV-HC.