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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.

Under the dual pressure of environmental pollution and energy crisis, the global energy consumption structure reform deepens unceasingly and the concept of energy internet has developed rapidly. The widespread volatility, randomness, and uncertainty of distributed new energy generation impose new requirements on distribution systems. The zigzag transformer is used as the coupling and isolating device for simultaneous AC–DC transmission. The basic principle and structure of simultaneous AC–DC power distribution network is analyzed. The topology structure of the simultaneous AC–DC hybrid microgrid and basic operating principle of the microgrid under different operating modes are proposed for the distributed power grid technology. Combined with power electronic technology, a modular multi-interface structure of power routers applied to AC–DC hybrid microgrid and its control strategy are proposed to realize the power routing control of microgrid and ensure reliable operation control of the microgrid. By building the model of simultaneous AC–DC hybrid microgrid and its power router, the rationality and effectiveness of the power router for microgrid routing control are verified.

This work shows the quantification of the flexibility in power distribution systems in the scenario in which non-conventional renewable sources are connected to it. From a set of metrics available in the literature, one is selected based on its applicability to operational and distribution system planning scenarios. The theoretical foundation and detail of its computational implementation is shown. On the basis of this, its calculation is addressed for a distribution system in which non-conventional renewable sources and storage systems are present. From the results it is possible to identify quantifiable characteristics of flexibility to the variation in the operation of this type of systems.

An increasing share of electric vehicles can mean excessive peak loads in low‐voltage power distribution networks. Introducing peak shaving mechanisms to the charging systems, such overloads can be mitigated significantly. The first contribution of this study is to quantify the amount of flexibility that electric vehicles can contribute to peak load reduction so that the drivers can still fully charge the batteries of their vehicles. The second contribution is that the study presents and compares two optimisation strategies for peak load reduction. The work is based on real charging data covering about 25,000 charging sessions at various charging sites in the metropolitan area of the Finnish capital city. The main finding is that the peak loads at charging sites can be reduced by up to 55%. Another important result is that load reduction through low‐power charging is achievable only if the average parking time at the charging site is >3 h, without affecting the user experience negatively. It is also found out that the average parking time is over 2 h longer than the average charging time, which indicates the enormous potential of electric vehicles in peak shaving.

The scope of this study is the optimal siting and sizing of distributed generation within a power distribution network considering uncertainties. A probabilistic power flow (PPF)-embedded genetic algorithm (GA)-based approach is proposed in order to solve the optimisation problem that is modelled mathematically under a chance constrained programming framework. Point estimate method (PEM) is proposed for the solution of the involved PPF problem. The uncertainties considered include: (i) the future load growth in the power distribution system, (ii) the wind generation, (iii) the output power of photovoltaics, (iv) the fuel costs and (v) the electricity prices. Based on some candidate schemes of different distributed generation types and sizes, placed on specific candidate buses of the network, GA is applied in order to find the optimal plan. The proposed GA with embedded PEM (GA–PEM) is applied on the IEEE 33-bus network by considering several scenarios and is compared with the method of GA with embedded Monte Carlo simulation (GA–MCS). The main conclusions of this comparison are: (i) the proposed GA–PEM is seven times faster than GA–MCS, and (ii) both methods provide almost identical results.

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field.

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.

The increased penetration of distributed generation (DG), renewable energy utilization, and the introduction of the microgrid concept have changed the shape of conventional electric power networks. Most of the new power system networks are transforming into the DG model integrated with renewable and non-renewable energy resources by forming a microgrid. Islanding detection in DG systems is a challenging issue that causes several protection and safety problems. A microgrid operates in the grid-connected or stand-alone mode. In the grid-connected mode, the main utility network is responsible for a smooth operation in coordination with the protection and control units, while in the stand-alone mode, the microgrid operates as an independent power island that is electrically separated from the main utility network. Fast islanding detection is, therefore, necessary for efficient and reliable microgrid operations. Many islanding detection methods (IdMs) are proposed in the literature, and each of them claims better reliability and high accuracy. This study describes a comprehensive review of various IdMs in terms of their merits, viability, effectiveness, and feasibility. The IdMs are extensively analysed by providing a fair comparison from different aspects. Moreover, a fair analysis of a feasible and economical solution in view of the recent research trend is presented.

Defending power systems against a high-altitude electromagnetic pulse (HEMP) requires accurately assessing its impact on critical equipment. This paper presents a method integrating theoretical analysis, deep neural networks (DNNs), critical thresholds for primary equipment, and the minimum path method to quantitatively assess the failure probabilities of critical equipment and their effects on a 10 kV distribution network. The analysis of HEMP impact on power distribution networks can be completed within several tens of seconds. Results indicate that the failure probabilities of unreinforced transformers and insulators can reach up to 96% and 12.7%, respectively. These failures can cause typical 10 kV overhead line distribution networks to experience power outages over distances exceeding a thousand kilometers. The maximum power interruption probability reaches 41.6%, with a maximum load loss ratio of 48.6%, even with the proportion of unreinforced transformers of 5%. The spatial distribution of power interruption probability and load loss ratio exhibits an “eye” shape. The results also identify insulator failure as the primary cause of system failures, and corresponding protective suggestions are provided.

The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region.The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks.The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.