Distribution Network Research Articles

Distribution network topology identification method based on state estimation with mixed integer programming and structural equation model

Distribution network topology identification (DNTI) is an important prerequisite for distribution system operation. Frequent topology changes and limited measurement equipment make identifying the correct topology unavoidably challenging. To this end, this paper proposes a state estimation method for distribution network topology identification using a small amount of phasor measurement unit (PMU) measurement data. Firstly, this paper introduces the application of the structural equation model (SEM) in distribution networks and explains the relationship between branch currents and topology. Then, auxiliary variables are introduced for the feasible domain problem present in the algorithm, which improves the solvability of the algorithm. In addition, this paper reconsiders the operation mode constraints in terms of nodes from the perspective of distribution network operation mode. The Structural Equation Modeling-based Mixed Integer Programming (SEM-MIP) method proposed in this paper can be solved using existing commercial solvers, and its effectiveness has been verified by simulation in IEEE 33-node test system and IEEE 123-node test system.

Optimal size and allocation of wind distributed generation in distribution network using particle swarm optimization

Optimal size and allocation of wind distributed generation in distribution network using particle swarm optimization

Direct Grid Connection of a Prototype with Real-Time Control for Energy Recovery and Pressure Control in a Water Distribution Network through Hydraulic Regulation

Direct Grid Connection of a Prototype with Real-Time Control for Energy Recovery and Pressure Control in a Water Distribution Network through Hydraulic Regulation

Eliminating Distribution Network Congestion Based on Spatial-Temporal Migration of Multiple Base Stations

Eliminating Distribution Network Congestion Based on Spatial-Temporal Migration of Multiple Base Stations

Impacts of Superconducting Fault Current Limiters on Undervoltage Protection for the Distribution Network

Impacts of Superconducting Fault Current Limiters on Undervoltage Protection for the Distribution Network

A Two-Stage Multi-Agent Deep Reinforcement Learning Method for Urban Distribution Network Reconfiguration Considering Switch Contribution

A Two-Stage Multi-Agent Deep Reinforcement Learning Method for Urban Distribution Network Reconfiguration Considering Switch Contribution

Research on Fault Current Limiting of Magneto-Biased Superconducting DC Current Limiter in DC Distribution Network

Research on Fault Current Limiting of Magneto-Biased Superconducting DC Current Limiter in DC Distribution Network

SMEs’ dependency on large corporations’ technology for sustainability in the United States

Introduction/Background: This study discusses how small and medium-sized firms (SMEs) depend on major corporations for key technology resources that support sustainability initiatives. While SMEs contribute considerably to economic development and innovation, they typically rely on bigger enterprises for access to new technology and market possibilities. This partnership gives SMEs benefits like greater operational efficiency and global market access but also creates obstacles such as budgetary limits and technical reliance. The essay explores techniques for SMEs to effectively exploit new technologies, balancing partnership with larger firms while retaining their autonomy and competitiveness in a quickly shifting business climate focused on sustainability. Materials and Methods: The materials used for this study were sourced from different databases which include scholarly journal articles, reports, and government gazette and policy papers in the areas of business, small and medium scale enterprises, entrepreneurship, management and economics. The inclusive criteria ensured that only those articles that investigated the merits and demerits of SMEs dependence on large corporation were utilised. The review of literature was conducted on 50 documents published between 2009 and 2024. Results: The findings as revealed from the reviewed literature revealed the different merits and demerits of SMEs dependence on large corporation. Case studies of Tesla, Microsoft, Walmart, and Amazon demonstrate the intricate reliance of SMEs on major firms in the United States. Tesla's reliance on SMEs for crucial components like batteries underlines the reciprocal advantages and dangers involved, since SMEs' stability rests on Tesla's performance. Microsoft helps SMEs through initiatives that stimulate innovation and technology adoption, boosting their capabilities. Walmart gives SMEs with access to huge distribution networks, enabling sales growth. Similarly, Amazon's platform gives SMEs tremendous market reach but also brings issues in competitiveness and price. Together, these instances show the potential and challenges SMEs confront in their relationships with bigger enterprises. Discussion: Encouraging supplier growth and enhancing supply chain resilience are vital for establishing a sustainable SME environment. Policymakers may promote SMEs through mentorship programs, financial aid, and tax incentives for partnership with large enterprises. Addressing financial impediments with targeted funding and boosting skills development through collaborations with educational institutions would help SMEs embrace new technology. Additionally, establishing technology-sharing activities and developing a legal framework that supports fair competition are vital. Fostering innovation among SMEs and supporting sustainable practices will eventually boost their competitiveness and contribute to broader economic and environmental goals, benefitting both SMEs and bigger organizations. Conclusion: The reliance of SMEs on big corporations provides noteworthy hurdles and possibilities that require for the adoption of effective government involvement. Through the elimination of financial obstacles, the upgrading of technical skills, the promotion of fair competition, and the encouragement of innovation and sustainability, governments may build a fair climate that enables SMEs to prosper alongside bigger firms. This approach not only allows SMEs access to resources and contemporary technology, but it also stimulates economic variety and creativity, which in turn contributes to continuous development and competitiveness in a dynamic market.

Aggregated Load Modelling Approach to Study Impact of Heat Pumps Harmonic Distortion on the Low Voltage Distribution Network

Aggregated Load Modelling Approach to Study Impact of Heat Pumps Harmonic Distortion on the Low Voltage Distribution Network

Improving the distribution system capability by incorporating ZnO nanoparticles into high-density polyethylene cable materials

This paper introduces a novel insulated cable designed to enhance the distribution system’s capabilities. Accordingly, high-density polyethylene loaded with varying concentrations of zinc oxide (ZnO) nanoparticles (NPs) ranging from 0.0 to 5 wt% was prepared using the melt-blending technique. Zinc oxide (NPs) is synthesized by using sol–gel technique and their microstructure was examined by X-ray diffraction. The new insulated cable, HDPE nanocomposite loaded with 1 wt% of ZnO, demonstrates a 43% reduction in the relative dielectric constant and a 16.5% improvement in breakdown strength compared to pure HDPE. The observed changes in both the dielectric constant and breakdown strength offer several advantages in electrical applications. These benefits include a decrease in feeder current at the same loading level, mitigation of inrush transients during load switching, and a reduction in earth fault current values, particularly in unearthed distribution networks with ungrounded cables. A comparative study is conducted between the conventional insulating cable based on the original material (HDPE) and the new insulated cable incorporating ZnO nanomaterial at a ratio of 1.0 wt% of the total cable mass per unit length. This comparison utilizes data from two actual medium-voltage distribution feeders. Both actual feeders are simulated using the EMTP/ATP package. The obtained results prove the efficacy of the developed material cable (polymer doped with ZnO NPs) compared to the base material. The peak and duration of inrush current can be cut to 77.3% and 67% of their original values, respectively. The earth fault current can be reduced to 56.5% in ungrounded networks, while substation current under the normal operation can be cut to 84.3% with the same load currents.

Based on Deep Learning Model and Flink Streaming Computing Short Term Photovoltaic Power Generation Prediction for Suburban Distribution Network

With the advancement of global energy internet construction, accurate prediction of new energy generation power such as photovoltaic is an important foundation for ensuring the safety and economic working of new power systems. A short-term photovoltaic power generation prediction method for suburban distribution networks based on deep learning model fusion and Flink flow calculation is proposed to address the challenges of complex power grids, diversified disturbance factors, and isolated monitoring points. This method uses Bi directional Long Short Term Memory(BiLSTM) to extract cross sequential nonlinear characteristic of photovoltaic power generation time series data. Compared with standard LSTM, BiLSTM can consider both historical and future information simultaneously, thus extracting richer extracted features from power generation time series data. This method also integrates attention mechanism to capture the importance distribution of historical temporal features for power generation prediction, effectively solving the problem of long-term temporal dependence in standard LSTM models. The Flink streaming computing framework embeds a trained BiLSTM-Attention photovoltaic power generation prediction model, enabling real-time prediction and monitoring analysis of photovoltaic power generation at various monitoring points in the suburban distribution network. This article uses a dataset of a suburban photovoltaic power station for validation, and trains the model with historical power generation data, meteorological factors, weather types, seasons, and other information as inputs. The BiLSTM-Attention fusion model studys the temporal characteristics of power generation, and has high accuracy in predicting short-term photovoltaic power generation in different scenarios. The Flink streaming computing platform can not only process high throughput predicted power data, but also has low time delay.

Water–Energy Nexus-Based Optimization of the Water Supply Infrastructure in a Dryland Urban Setting

Managing water supply systems is essential for developing countries to face climate variability in dryland settings. This is exacerbated by high energy costs for pumping, water losses due to aging infrastructures, and increasing demand driven by population growth. Therefore, optimizing the available resources using a water–energy nexus approach can increase the reliability of the water distribution network by saving energy for distributing the same water. This study proposes a methodology that optimizes the Water Distribution Network (WDN) and its management that can be replicated elsewhere, as it is developed in a data-scarce area. Indeed, this approach shows the gathering of WDN information and a model to save energy by optimizing pump schedules, which guarantee water distribution at minimal operational costs. The approach integrates a genetic algorithm to create pumping patterns and the EPANET hydraulic simulator to test their reliability. The methodology is applied for a water utility in the dryland urban setting of Lodwar, Turkana County, Kenya. The results indicate a potential reduction in energy costs by 50% to 57% without compromising the supply reliability. The findings highlight the potential of WEN-based solutions in enhancing the efficiency and sustainability of data-scarce water utilities in dryland ecosystems.

Ensuring Stable Operation of Wind Farms Connected to Distribution Networks

Wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The low voltage ride through (LVRT) characteristic makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1–2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rises in the loads for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. This article presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes.

Evaluating the performance of water distribution network deterioration using customer-oriented performance indices

ABSTRACT Water distribution networks (WDNs) are an essential urban infrastructure, with their performance directly influencing societal well-being. Our study applied to a real network model that employs a 24-h simulation with 1-h time steps and evaluates the impact of leaks and loss in the pipe cross-sectional area on WDN hydraulic performance as experienced by end users. Adhering to a UK utility's 20-m head pressure requirement as the water main benchmark, we present two new customer-oriented performance indices (CPIs) centred on network reliability and pressure deficit severity. The new CPIs adeptly quantify network performance degradation due to pipe deterioration. This degradation translates directly to a poor customer experience, highlighting the potential for these CPIs to pinpoint areas of the network where performance levels are compromised. Furthermore, the CPIs identify individual pipes within the network where defects would severely impact network performance and the sets of pipes which, when simultaneously experiencing defects, would lead to a more severe loss in network performance. Results show that the CPIs capture relatively small performance declines and identify sensitive pipes impacting network performance, providing insights for optimised inspection and maintenance intervention to provide better customer service.

Sustainability and Capacity Estimation of Photovoltaic Based Distribution Network Integrated with Demand Response Using Arithmetic Optimizer Algorithm

Sustainability and Capacity Estimation of Photovoltaic Based Distribution Network Integrated with Demand Response Using Arithmetic Optimizer Algorithm

Explainable Optimisation through Online and Offline Hyper-heuristics

Abstract - Research in the explainability of optimisation techniques has largely focused on metaheuristics and their movement of solutions around the search landscape. Hyper-heuristics create a different challenge for explainability as they make use of many more operators, or low-level heuristics and learning algorithms which modify their probability of selection online. This paper describes a set of methods for explaining hyper-heuristics decisions in both online and offline scenarios using selection hyper-heuristics as an example. These methods help to explain various aspects of the function of hyper-heuristics both at a particular juncture in the optimisation process and through time. Visualisations of each method acting on sequences provide an understanding of which operators are being utilised and when, and in which combinations to produce a greater understanding of the algorithm-problem nexus in hyper-heuristic search. These methods are demonstrated on a range of problems including those in operational research and water distribution network optimisation. They demonstrate the insight that can be generated from optimisation using selection hyper-heuristics, including building an understanding of heuristic usage, useful combinations of heuristics and heuristic parameterisations. Furthermore the dynamics of heuristic utility are explored throughout an optimisation run and we show that it is possible to cluster problem instances according to heuristic selection alone, providing insight into the perception of problems from a hyper-heuristic perspective.

TeleHelp Ukraine: A distributed international telemedicine response to the ongoing war.

Humanitarian crises frequently garner solidarity and robust volunteer recruitment among health care communities. However, a common obstacle is matching providers to those in need across geographic and other barriers. We examined the application of a decentralised governance strategy in establishing an emergency telemedicine response, TeleHelp Ukraine (THU). Using a case study approach, we explored how global networking and technological advancements empower organisations to generate, access, disseminate, and utilise knowledge for sustainable health care delivery. Preliminary results suggest that a non-profit, decentralised model strengthened by robust team dynamics may optimise the distribution of clinical workload and scheduling procedures. Institutional and cultural diversity among health care providers and volunteers fosters the mobilisation of knowledge resources, synergistic collaboration, and tailored care standards that align with both provider and patient expectations. By integrating these diverse, distributed networks, a synergistic effect is achieved, combining effective learning mechanisms with intellectual capital. Our study provides insights into the structure, implementation strategies, dissemination methodologies, and initial results of THU's operation. These findings may inform future emergency telemedicine responses in humanitarian scenarios, thereby reinforcing the practical implementation of health as a human right.

Buildings as Batteries

Aggregators are increasingly using portfolios of various end-user resources such as local renewable generation, batteries, EVs, and demand-side management to participate in the electricity markets as virtual power plants. Whilst the smart control of energy within buildings is well established for cost optimization, the prospect of an aggregator collaborating with the energy managers of buildings to trade electricity in the wholesale markets is under researched. This paper uses a state-of-the-art architectural simulator to model the thermal inertia of a typical high-rise building in London and represent the thermodynamics of its hourly occupancy and weather responses. The building is simulated as a virtual battery by manipulating the HVAC settings. Price arbitrage on the electricity wholesale market and contracting with the local distribution network for flexible reserve services are both feasible and profitable. It is concluded that aggregators can apparently use buildings as additional assets within their trading portfolios. JEL Classification: L85 and L94.

Optimisation of Integrated Heat Pump and Thermal Energy Storage Systems in Active Buildings for Community Heat Decarbonisation

The electrification of residential heating systems, crucial for achieving net-zero emissions, poses significant challenges for low-voltage distribution networks. This study develops a simulation model to explore the integration of heat pumps within active building systems for community heating decarbonisation. The model optimises heat pump operations in conjunction with thermal energy storage units to reduce peak demand on low-voltage networks by using real-time measured electricity demand data and modelled heat demand data for 76 houses. The study employs an algorithm that adjusts thermal storage charging and discharging cycles to align with off-peak periods. Three scenarios were simulated: a baseline with unoptimised heat pumps, a fixed threshold model, and an active building model with daily optimised thresholds. The results demonstrate that the active building model achieves a 21% reduction in peak demand on the low-voltage substation compared to the baseline scenario; it also reduces the total electrical energy consumption by 12% and carbon emissions by 17%. The fixed threshold scenario shows a 16% improvement in peak demand reduction, but it also shows an increase in energy consumption and emissions. These findings highlight the potential of active buildings to enhance the efficiency and sustainability of residential energy systems, marking a significant step toward decarbonising residential heating while maintaining grid stability.

Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning

This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.