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Fault diagnosis and location play a pivotal role in expediting fault restoration and enhancing power system resilience. However, integrating distributed generation and diverse load profiles has led to more complicated distribution networks, intensifying fault diagnosis and location challenges. Hence, there is a need to transform traditional distribution networks into resilient active ones that actively guide and utilize distributed energy resources, possessing active control and operational capabilities. This paper comprehensively reviews fault diagnosis and location techniques in distribution networks, focusing on faulted line selection, fault section estimation, and accurate fault location. It provides an in-depth analysis of current fault diagnosis and location techniques, along with an assessment of their challenges, summarizing and evaluating the current research status at both local and international levels. To address existing fault diagnosis and location technologies limitations, a method utilizing micro-phasor measurement units (μPMUs) to enhance situational awareness and state estimation of active distribution networks is proposed. The paper also provides a comprehensive analysis of the optimized configuration strategy for achieving network observability and fault diagnosis using limited μPMU, in addition to comparing and analyzing the advantages and disadvantages of typical solution algorithms for the optimal PMU placement (OPP) problem. In response to extreme disturbance scenarios in active distribution networks, resilience enhancement strategies are proposed from three perspectives: fault prevention, fault response, and fault restoration. Finally, a summary and outlook on current research methods for fault diagnosis and location, as well as the challenges and future research directions in resilience enhancement strategies, are provided. [Display omitted] • Review fault diagnosis, section estimation, and accurate fault location methods. • Improving power system resilience through fault diagnosis and location. • Strategies using limited μPMUs to enhance distribution network situation awareness. • Comprehensive methods for power system resilience in extreme disasters. [ABSTRACT FROM AUTHOR]

Currently, distribution systems are continuously evolving towards modern and flexible structures while integrating promising renewable energy resources (RERs). In this regard, an optimal network constraint-based expansion planning model combined with an optimal integration framework of intermittent RERs is proposed in this work to improve the topological flexibility in modern distribution networks (DNs). Specifically, the best investment locations and times of substations, lines, and RER-based distributed generations (DGs) are jointly taken into consideration. Additionally, the uncertainty-based multiple scenarios are modeled by probability distribution functions to strengthen the robustness and reliability of DNs influenced by the stochastic of renewable energy and load behavior. The novel network constraint is combined with three levels, where the first level introduces the graph theory to guarantee the radiality topology of modern DNs. In the second level of the network constraint, graph theory and fictitious load theory are collaboratively applied to ensure that each subsystem has a reserve connection interconnected to other subsystems. The third level is modifying the conventional fictitious load theory to ensure each subsystem is linked with at least one DG. The proposed planning model is driven by the minimum present value of total cost, including investment cost of branches, DGs, and substations, cost of substations operation, the electricity purchasing cost of substations and DGs, power losses cost, and environmental penalty cost of conventional generators. Numerical results are presented to verify that a more flexible and resilient topology of the DN system is obtained, and criteria evaluation is introduced to validate its higher performance with respect to existing procedures from power supplied quality, environmental burden, and supplied flexibility three aspects. [ABSTRACT FROM AUTHOR]

Achieving the goal of sustainable development is dependent on the widespread integration of renewable energy sources, energy storage systems (ESSs), and electric vehicles (EVs). However, a continuous increase in the penetration of such elements would bring more complexities to the distribution network. Accordingly, this paper presents a unified planning model comprising renewable energy-based distributed generation (DG), ESS, and electric vehicle charging stations (EVCSs). In this regard, a Latin Hypercube Sampling method is utilized to generate multi-scenario for describing the uncertainty of renewable energy and load demand. The stochastic EV charging behaviors are represented by various probability density functions (PDF). In addition, an exploitable capacity loss of ESS and EV batteries is calculated by the battery degradation model based on the depth of discharge (DOD). Furthermore, the battery degradation cost is incorporated into the objective of the planning model to identify the optimal decision for candidate assets. A piecewise linearization approach is introduced to convert the problem into a mix-integer linear programming (MILP) model. Numerical results demonstrate that the exploitable capacity loss of batteries plays a key role in asset planning and provides potential contributions to the optimal decisions of the distribution network. In the meantime, by considering battery degradation in the optimization model, the sustainability and lifetime of the battery can be preserved. • Uncertainties include the renewable energy, load demand, and behaviors of electric vehicles are considered. • Renewable energy-based DG, ESS, and EVCS are involved in the unified planning model. • The degradation model is proposed to analyze the exploitable capacity loss of ESS and EV battery. • The enhancement of investment decision while maintaining the lifetime of battery is achieved. [ABSTRACT FROM AUTHOR]

• Establishing a realistic unbalanced harmonic power flow calculation model. • Establishing DG line loss optimization model with harmonic constraint. • Analyzing several DG line loss optimization schemes in distribution network with multi-type harmonic loads. • The harmonic contents have been decreased but also increased the line loss. • Under the same line loss, the optimal capacity of double DG is less than that of single DG, • The optimization of double DG is better without considering the cost. With the large penetration of power electronic devices in distribution networks, power quality problems such as line loss and harmonics have become the main factors affecting the access of distributed generation (DG). At present, the research on DG integration, such as the optimization of the DG capacity using particle swarm optimization algorithm and ant colony algorithm, is mainly focused on the traditional distribution network. In contrast, the research on the power electronic distribution networks, which include a large number of harmonic sources, is not sufficient. This paper presents a novel optimization method for connecting DG to power electronic distribution network to reduce line loss. Firstly, based on the frequency domain model of the distribution network, this paper establishes the harmonic model of distribution network components, including generators, transformers, lines, harmonic sources and non-harmonic sources. Then the location and capacity of DG are optimized using genetic algorithm with network loss reduction as a target and voltage and harmonic as constraints. Finally, the power electronic distribution network is modelled based on the IEEE 34 - node standard model. The obtained results confirmed that the optimization model with harmonic constraints can effectively reduce the line loss by 108.26 kW and the line loss rate by 4.67 % using single DG. Also, when using the double DG, the line loss and the line loss rate are decreased by 118.53 kW and 5.14 %, respectively. In addition, the optimization effect of dual DG is better than that of single DG without considering the cost. [ABSTRACT FROM AUTHOR]