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Future Distribution Networks: A Review
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.
Journal Article
Multi-objective collaborative optimization of active distribution network operation based on improved particle swarm optimization algorithm
ADN (Active distribution network) is easily disturbed during its operation, resulting in problems such as power supply quality degradation and operation safety deterioration. Therefore, the research and simulation of multi-objective collaborative optimization of ADN operation based on improved particle swarm optimization algorithm are proposed. An objective function of multi-objective collaborative optimization configuration for ADN operation is constructed. According to this objective function, the improved particle swarm optimization algorithm is used to optimize the collaborative optimization configuration, and the population particles are mutated, and the obtained result is the optimal energy storage capacity configuration result of power system. The architecture of the simulation platform for cooperative operation of ADN is constructed, and the load grades of distribution system are divided. Based on the hierarchical management of loads in distributed systems, multi-objective collaborative optimization of ADN operating voltage in both frequency and time domains has been achieved. The experimental results show that during peak periods, the system’s load capacity is only twice that of before optimization or other situations, achieving stable power supply for peak power demand. Multi-objective collaborative optimization in frequency domain and time domain has the best effect. Under the conditions of reactive power and active power, the multi-objective collaborative optimization method of ADN operation has good results.
Journal Article
Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks
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.
Journal Article
Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters
Coastal cities often face typhoons and urban water logs, which can cause power outages and significant economic losses. Therefore, it is necessary to study the impact of these disasters on urban distribution networks and improve their flexibility. This paper presents a method for predicting power-grid failure rates in typhoons and water logs and suggests a strategy for improving network elasticity after the disaster. It is crucial for the operation and maintenance of power distribution systems during typhoon and water-logging disasters. By mapping the wind speed and water depth at the corresponding positions in the evolution of wind and water logging disasters to the vulnerability curve, the failure probability of the corresponding nodes is obtained, the fault scenario is generated randomly, and the proposed dynamic reconstruction method, which can react in real-time to the damage the distribution system received, has been tested on a modified 33-node and a 118-node distribution network, with 3 and 11 distribution generators loaded, respectively. The results proved that this method can effectively improve the resiliency of the distribution network after a disaster compared with the traditional static reconstruction method, especially in the case of long-lasting wind and flood disasters that have complex and significant impacts on the distribution system, with about 26% load supply for the 33-node system and nearly 95% for the 118-node system.
Journal Article
An Enhanced Artemisinin Optimization Algorithm for the Optimal Position and Size of Energy Storage System on the Distribution Networks With Multi‐Objectives
Integrating renewable energy into the radial distribution network (RDN) poses challenges related to stability, reliability, and network operation. An energy storage system (ESS) incorporated into the RDN is a potential approach to addressing these challenges. Thus, we aim to optimize the allocation and operation of the ESS in this study. The multi‐objective framework aims to optimize system costs, improve voltage regulation, reduce peak demand, and minimize power losses, thereby enhancing the performance of the RDN. To achieve this, an enhanced artemisinin optimization (EAO) is proposed by incorporating the chaotic local search (CLS) into the original artemisinin optimization (AO) algorithm. The CLS enhances the optimization performance by exploring a large search space during the early run phase to prevent premature convergence and by exploiting a smaller region in the later run phase to refine the final solutions. Furthermore, the EAO parameter settings are adaptively adjusted to enhance search capability. The proposed EAO was applied to the IEEE 33‐bus and 69‐bus RDN, with various case studies to validate its performance. The results show that properly integrating an ESS can significantly enhance the performance of RDNs. Additionally, the proposed EAO method is compared with other methods to confirm its effectiveness in solving optimization problems.
Journal Article
Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin
Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μPMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on a densely-noded distribution network.
Journal Article
Impact of Distributed Generators Penetration Level on the Power Loss and Voltage Profile of Radial Distribution Networks
The Distributed Generator types have different combinations of real and reactive power characteristics, which can affect the total power loss and the voltage support/control of the radial distribution networks (RDNs) in different ways. This paper investigates the impact of DG’s penetration level (PL) on the power loss and voltage profile of RDNs based on different DG types. The DG types are modeled depending on the real and reactive power they inject. The voltage profiles obtained under various circumstances were fairly compared using the voltage profile index (VPI), which assigns a single value to describe how well the voltages match the ideal voltage. Two novel effective power voltage stability indices were developed to select the most sensitive candidate buses for DG penetration. To assess the influence of the DG PL on the power loss and voltage profile, the sizes of the DG types were gradually raised on these candidate buses by 1% of the total load demand of the RDN. The method was applied to the IEEE 33-bus and 69-bus RDNs. A PL of 45–76% is achieved on the IEEE 33-bus and 48–55% penetration on the IEEE 69-bus without an increase in power loss. The VPI was improved with increasing PL of DG compared to the base case scenario.
Journal Article
A Post-Disaster Fault Recovery Model for Distribution Networks Considering Road Damage and Dual Repair Teams
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.
Journal Article
Multi-Objective Optimal Planning for Distribution Network Considering the Uncertainty of PV Power and Line-Switch State
With the construction of the smart grid, the distribution network with high penetration of the photovoltaic (PV) generator relies more and more on cyber systems to achieve active control; thus, the uncertainty of PV power and the line-switch state will inevitably affect the distribution network. To avoid the situation, a min–max multi-objective two-level planning model is proposed. Firstly, the uncertainty of PV power is considered, and a multi-time PV power model is established. Followed by the analysis of the line-switch state uncertainty in the distribution network, and according to Claude Shannon’s information theory, the line-switch state uncertainty model is established under multiple scenarios. After the distribution network reconfiguration, the Latin hypercube sampling (LHS) method is used to determine the line-switch state when the uncertainty budget is different. Finally, considering the worstcase by the uncertainty of PV power and line-switch status, the control model is proposed to improve the stability of the distribution network with the minimal maintenance cost. The model feasibility is verified by the test system and the characteristics of PV power uncertainty, the line-switch state uncertainty is analyzed, and the influence of the scheduling strategy is discussed, thus providing practical technical support for the distribution network.
Journal Article