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This manuscript presents a comprehensive review of recent advancements in electrical distribution networks, with a specific focus on the incorporation of direct current (DC) applications. The research aims to comprehensively address the current and future aspects of DC, spanning from the distribution level to the utilization level. The renewed interest in DC power systems has led to the investigation of several transitional challenges in recent years. A significant portion of these efforts has been dedicated to determining the feasibility of applying DC to specific use cases. Additionally, the literature has explored design considerations such as system architecture and voltage levels, the integration of DC into existing distribution networks, load flow (LF) computations, and the distinct safety concerns associated with DC power systems. In this paper, the various research endeavors are categorized, evaluated, and scrutinized to assess the current state of the transition from a purely alternating current (AC) distribution system to a solely DC or hybrid AC/DC distribution system. A bibliometric analysis is conducted, constructing a network of co-occurrence based on author-provided keywords, which reveals the primary research foci in this domain. The barriers hindering the widespread adoption of DC distribution systems and potential solutions are also discussed. Moreover, this article synthesizes ongoing efforts to address these obstacles and delineates future research directions by emphasizing the existing knowledge gaps.

Extreme weather, such as rainstorms, often triggers faults in the distribution network, and power outages occur. Some serious faults cannot be repaired by one team alone and may require equipment replacement or engineering construction crews to work together. Rainstorms can also lead to road damage or severe waterlogging, making some road sections impassable. Based on this, this paper first establishes a road network model to describe the dynamic changes in access performance and road damage. It provides the shortest time-consuming route suggestions for the traffic access of mobile class resources in the post-disaster recovery task of power distribution networks. Then, the model proposes a joint repair model with general repair crew (GRC) and senior repair crew (SRC) collaboration. Different types of faults match different functions of repair crews (RCs). Finally, the proposed scheme is simulated and analyzed in a road network and power grid extreme post-disaster recovery model, including a mobile energy storage system (MESS) and distributed power sources. The simulation finds that considering road damage and severe failures produces a significant difference in the progress and load loss of the recovery task. The model proposed in this paper is more suitable for the actual scenario requirements, and the simulation results and loss assessment obtained are more accurate and informative.

In the present work, a cuckoo search optimisation-based approach has been developed to allocate static shunt capacitors along radial distribution networks. The objective function is adopted to minify the system operating cost at different loading conditions and to improve the system voltage profile. In addition to find the optimal location and values of the fixed and switched capacitors in distribution networks with different loading levels using the proposed algorithm. Higher potential buses for capacitor placement are initially identified using power loss index. However, that method has proven less than satisfactory as power loss indices may not always indicate the appropriate placement. At that moment, the proposed approach identifies optimal sizing and placement and takes the final decision for optimum location within the number of buses nominated with minimum number of effective locations and with lesser injected VArs. The overall accuracy and reliability of the approach have been validated and tested on radial distribution systems with differing topologies and of varying sizes and complexities. The results shown by the proposed approach have been found to outperform the results of existing heuristic algorithms found in the literature for the given problem.

This paper develops a novel approach for distribution system monitoring and state estimation, where time synchronisation of smart‐meter measurements is carried out via the Precision Time Protocol (PTP). The approach is based on the concept of a Modified Smart Meter (MSM), a distribution system monitoring instrument that enables accurate time synchronisation of smart meter data. The design, application, communication technique and protocols of the MSM are described in detail. The proposed MSM device features PTP‐based time synchronisation of smart meter measurements, and the concept of unbundling is applied to collect measurements utilising the existing smart meter sensors. This is expected to reduce the overall implementation cost of an MSM‐based distribution network monitoring system compared to a system based on Phasor Measurement Units (PMUs). The problem of requiring open sky access for GPS links can potentially be solved by means of PTP synchronisation. Three‐phase state estimation simulations using the IEEE‐13 and 123 bus unbalanced test networks are employed to demonstrate the applicability of the MSM, and its performance is compared to standard PMU devices. The results indicate that the MSM may represent a workable monitoring solution for MV and LV distribution networks, with an acceptable trade‐off between cost and performance.

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi‐phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information‐theoretic approach to learn the topology of distribution grids. Specifically, multi‐phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow‐Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi‐phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi‐phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.

This study proposes a Pareto-based multi-objective distribution network reconfiguration (DNRC) method using discrete particle swarm optimisation algorithm. The objectives are minimisation of power loss, the number of switching operations and deviations of bus voltages from their rated values subjected to system constraints. Probabilistic heuristics and graph theory techniques are employed to improve the stochastic random search of the algorithm self-adaptively during the optimisation process. An external archive is used to store non-dominated solutions. The archive is updated iteratively based on the Pareto-dominance concept to guide the search towards the Pareto optimal set. The method is implemented on the IEEE 33-bus and IEEE 70-bus radial distribution systems, simulations are carried out and results are compared with other available approaches in the literature. To assess the performance of the proposed method, a quantitative performance assessment is done using several performance metrics. The obtained results demonstrate the effectiveness of the proposed method in solving multi-objective DNRC problems by obtaining a Pareto front with great diversity, high quality and proper distribution of non-dominated solutions in the objective space.

This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users.

The development and installation of Distributed Generators (DGs) in distribution power networks turned these networks from a passive into active ones in power systems. Proper management and control of these resources can help to improve power quality, and increase security and effectiveness of power networks. The power management of DGs considering their operating cost along with reconfiguration of distribution network can reduced the cost of operation, including cost of energy losses, cost of power purchasing from the upstream network and cost of power generation by dispatchable DGs. In this study a new method is presented for daily reconfiguration of distribution network in presence of dispatchable DGs and renewable DGs in order to reduce the total operating cost of distribution companies. Dynamic modeling of renewable DGs based on uncertainty of their output, switching costs and varying load are considered in this paper. Finally the method has been tested in three stages on 16-Bus IEEE to demonstrate the effectiveness of proposed method.

Many planning and operation studies that aim at fully assessing and optimizing the performance of the distribution grids, in response to the current trends, cannot ignore grid limitations. Modelling the distribution system, by including the electrical characteristics of the network (e.g., topology) and end user behaviors, has become complex, but essential, for all conventional and emerging actors/players of power systems (i.e., system and market operators, regulators, new market parties as service providers, aggregators, researchers, etc.). This paper deals with a methodology that, starting from publicly available open data on the energy consumption of a region or wider area, is capable to obtain reasonable load and generation profiles for the network supplied by each primary substation in the region/area. Furthermore, by combining these profiles with territorial and socio-economic information, the proposed methodology is able to model the network in terms of lines, conductors, loads and generators. The results of this procedure are the synthetic networks of the real distribution networks, that do not correspond exactly to the actual networks, but can characterize them in a realistic way. Such models can be used for all the kind of optimization studies that need to check the grid limitations. Results derived from Italian test cases are presented and discussed.