Reliable Distribution Network Research Articles

Frekuensi Gangguan dan Energi Tak Tersalurkan Sebagai Indikator keandalan Sistem Jaringan Tegangan Menengah

The increasing need for electrical energy must be balanced with improved customer service. Customers will feel comfortable if they can enjoy an uninterrupted electricity supply in the form of a disconnection of the electricity supply for a certain period of time or what is commonly called the ENS (Energy Not Supplied) index and the AENS (Average Energy Not Supplied) index. The reliability of the electric power system is one of the things that is very important in order to maintain the performance of the three main pillars of PT. PLN (Persero). A reliable distribution network can be seen from the number of disruptions that often occur in the distribution network. Disruptions that arise in the distribution network arise due to internal factors such as less reliable distribution network components. In addition, it can also be caused by external factors such as natural disasters or the influence of trees and animals. Based on the results of the research that has been conducted, it is known that the occurrence of disruptions and the length of the duration of the disruption cause the Energy Not Supplied (ENS) value to increase as in April and July, the high duration of the blackout also makes the ENS higher, namely 792.857804 kW and 789.046811 kW. However, the distribution network at the Wagom feeder is still considered reliable in distributing electrical energy based on the SAIDI and SAIFI indices. From the research results, the SAIDI index at the Wagom feeder reached 8.96 hours/customer/year. While the SAIFI index at the Wagom feeder reached 2.08 times/customer/year.

A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage

The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. This paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.

A Review of Research on The Reliability Assessment of Fresh Product Distribution Networks Considering Product Perishability Constraint

A reliable and stable distribution network is fundamental for the effective operation of fresh agricultural product logistics, and network reliability is a crucial metric for determining whether fresh agricultural product logistics can complete specified tasks promptly and smoothly. To further clarify the current research status on the reliability of fresh agricultural product distribution networks considering transportation losses, this study reviews the literature from three aspects: research on transportation losses of fresh agricultural products, reliability of stochastic distribution networks, and methods for assessing the reliability of stochastic distribution networks considering transportation losses. The studies indicate that few documents introduce both quantity deterioration and quality deterioration simultaneously into network reliability research, which tends to overlook the market demand under reduced freshness, and seldom considers the dynamic changes of deterioration factors in complex environments.

Probabilistic forecast-based portfolio optimization of electricity demand at low aggregation levels

In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of accuracy and computational efficiency.

Distribution Network Reconfiguration Using Chaotic Particle Swarm Chicken Swarm Fusion Optimization Algorithm

Aiming at the problems of traditional optimization algorithms for reconfiguring distribution networks, which easily fall into a local optimum, have difficulty finding a global optimum, and suffer from low computational efficiency, the proposed algorithm named Chaotic Particle Swarm Chicken Swarm Fusion Optimization (CPSCSFO) is used to optimize the reconfiguration of the distribution network with distributed generation (DG). This article works to solve the problems mentioned above from the following three aspects: Firstly, chaotic formula is used to improve the initialization of the particles and optimize the optimal position. This increases individual randomness while avoiding local optimality for inert particles. Secondly, chicken swarm optimization (CSO) and particle swarm optimization (PSO) are combined. The multi-population nature of the CSO algorithm is used to increase the global search capability, and, at the same time, the information exchange between groups is completed to extend the particle search range, which ensures the independence and excellence of each particle group. Thirdly, the node hierarchy method is introduced to calculate the power flow. The branching loop matrix and the node hierarchy strategy are used to detect the network topology. In this way, improper solutions can be reduced, and the efficiency of the algorithm can be improved. This paper has demonstrated better performance by CPSCSFO based on simulation results. The network loss has been reduced and the voltage level of each node in the optimal reconfiguration with distributed power supply has been improved; the network loss in the optimal reconfiguration with DG is 69.59% lower than that reconfiguration before. The voltage level of each node is increased, the minimum node voltage is increased by 3.44% and a better convergence speed is presented. As a result, the quality of network operation and the distribution network is greatly improved and provides guidance for building a safer, more economical and reliable distribution network.

A Self-Healing Strategy for Modern Distribution Networks

Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A self-healing strategy can be implemented for protection and control devices to work cooperatively, achieving the global purpose of automatic distribution system restoration. Thus, this work proposes a methodology for short-circuit fault detection, isolation of the faulted section, and restoration of downstream sections using neighbor feeders. The protection devices use standardized IEC and ANSI/IEEE functions to sensitize faults in the system and to promote adequate isolation, allowing the consequent restorative process. A genetic algorithm optimizes the devices’ parameters used in the protection scheme, making fastest the isolation process and ensuring the protection system coordination and selectivity. Results obtained using Simulink® allows for verifying the proposed methodology’s behavior and efficiency.

Optimal Allocation of Hybrid Renewable Distributed Generation with Battery Energy Storage System Using MOEA/D-DRA Algorithm

The need for energy and environmental sustainability drives the adoption of renewable energy systems to reduce greenhouse gas emissions. Efficient planning of energy sources is crucial for a reliable distribution network. This article solves the optimal allocation of hybrid energy sources (i.e., Renewable Distribution Generators (RDGs) and Battery Energy Storage System (BESS)) problem, aiming to minimize the annual energy loss and investment costs simultaneously. The proposed formulation includes the uncertainties such as forced outages of RDG’s, intermittence in RDG power generation, Expected Energy Not Served (EENS) by DG units, and time-varying demand altogether in the existing formulation. To evaluate the effectiveness of the proposed formulation, a Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamic Resource Allocation (MOEA/D-DRA) is used. Simulation studies are conducted on IEEE 33-node and TNEB 84-node Radial Distribution Systems (RDSs), comparing results with the Rider Optimization Algorithm (ROA) and Hybrid Nelder Mead-Particle Swarm Optimization (HNMPSO) respectively. The proposed formulation for a Multi-objective Optimization of Hybrid Energy Sources allocation problem solved by the MOEA/D-DRA algorithm provides improved benefits like minimum annual energy loss, investment cost, CO2 emission, EENS by the DG units, and enhanced system voltage stability and voltage profile.

Providing an optimal demand response program through placement of automatic switches and energy storage systems to improve the reliability of power distribution networks

AbstractCreating highly reliable distribution networks is becoming increasingly vital in today's society. In a similar vein, utilities have issues in properly planning and developing distribution networks, both to reduce their imposed costs and to meet the demands of their consumers. In the presence of Demand Response Program (DRP), this research provides a coordinated architecture that considers automated switches and Energy Storage Units (ESUs) placement with the uncertainty of repair time to enhance the reliability of power distribution network. The suggested objective for optimal placement of automatic switches, ESUs and DRPs is to minimize a combination of reliability index (SAIDI) and Total Cost of system (TC). Customer interruption cost, energy not supplied cost, automated switch investment, ESU investment, ESU participation cost, and DRP cost are all considered as parts of the total cost. The proposed method is used in four different cases on a common reliability test system, followed by numerous sensitivity studies, in order to illustrate applicability and efficiency of suggested method.

Graph attention neural network for water network partitioning

Partitioning a water distribution network into several district metered areas is beneficial for its management. Partitioning a network according to its node features and connections remains a challenge. A recent study has realized water network partitioning based on node features or pipe connections individually. This study proposes an unsupervised clustering method for nodes based on a graph neural network, which uses graph attention technology to update node features based on the connections and a neural network to cluster nodes. The similarity between nodes located in each area and the balance of the total water demand between areas are optimized, and the importance of the boundary pipes is calculated to determine the installation position of flowmeters and valves. Three water distribution networks with different structures and sizes are used to verify the proposed model. The results show that the average location differences (LocDiffs) within the areas of the three networks completed by partitioning are 0.12, 0.07, and 0.06, and the total demand differences (DemDiffs) between areas are 0.13, 0.27, and 0.29, respectively. The LocDiff and DemDiff of the proposed method decreased by 6% and 55%, respectively, when compared to the traditional clustering method. Additionally, the proposed method for calculating the importance of boundaries provides an objective basis for boundary closure. When the same number of boundaries are closed, the comprehensive impact of the proposed method on the pipe network decreases by 17.1%. The proposed method can be used in practical applications because it ensures a highly reliable and interpretive water distribution network partitioning method.

Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy

Global transactive energy has become more complex due to the extension of its efficiency. Thus, multi-residential demand responses pose a new challenge in the critical infrastructure of power transition networks in energy planning and management. By encompassing optimal scheduling with strategic energy decentralization, a reliable distribution network can suggest a resolution that contributes to optimize energy efficiency. This research proposed a holistic bidirectional distribution network with optimal scheduling-based demand-side management (DSM) for superior power sharing and beneficial performance. A DSM was developed to maintain equal power sharing between various mix-energy sources, providing a sub-level residential demand response program. Among several scenarios considered, the DSM-based scheduling distribution network resulted in optimum and equivalent sharing of power loads for residential demands. Moreover, the application of self-production-based renewable energy source (RES) is a novel strategy to improve imbalanced power sharing. The energy deficit and surplus can be solved by establishing a conservation strategy with self-installation-based photovoltaic panels by end-users. CO2 emissions were also estimated to elucidate the effect of the energy distribution network on the environment according to different category of energy sources. Considering satisfaction analysis based on appliance energy consumption, the energy distribution network was efficient in improving the end-user expenditure, sustaining a clean energy loading profile, and maximizing the benefit of cost.

Utilizing hydraulic modelling and Geographical Information System (GIS) in developing a water distribution network for reclaimed water

Rapid development has increased water demand, combined with population growth, water scarcity, climate change, and rapid economic growth, are driving factors for water reclaim. This paper reviews previous research on developing a water distribution system for delivering reclaimed water in order to mitigate the industry’s growing water demand. As a result, reclaimable wastewater should be identified in order to establish links between urban and industrial infrastructure planning and water reclamation. Correspondingly, it is tasked with the responsibility of developing low-cost water treatment for industrial water reclamation. Simultaneously, a reliable water distribution network is required to deliver reclaimed water. This can be accomplished through the use of hydraulic modelling and GIS to analyse, manage, and develop the water distribution network in response to a growing supply demand. Hydraulic modelling is used to calculate and analyse the demand for the water supply over a specified time period based on daily water use. Additionally, it computing the appropriate parameter for the pipeline in order to deliver an adequate amount of pressure to the consumer efficiently. The developed hydraulic model was then integrated into GIS to facilitate the network layout and visualization of the water distribution system. Apart from that, in order to fully implement the concept of reclaimed water and its delivery via a water distribution system, several factors must be considered, including public acceptance, economic viability, environmental stewardship, technical operation, and health risk.

Reliability analysis of complex water distribution systems: the role of the network connectivity and tanks

Abstract A reliable water distribution network (WDN) can provide an adequate supply service to customers under both normal and abnormal working conditions. The WDN reliability analysis, therefore, is a keystone to improve the supply service efficiency. Strategies for reliability analysis are usually proved on small WDNs, which do not compare with large real complex systems in terms of number of water tanks, pressure reduction valves, variable speed pumps, controlled devices and possible alternative water supply schemes. The topological changes due to pipeline interruptions impact on emptying–filling of water tanks and network pressure status. This work proposes a two-level procedure for mechanical reliability assessment, suited for large real WDNs. It leverages a path/connectivity-based approach to set up reliability indicators for global-level analysis and local screening of the most critical scenarios. The employed advanced hydraulic model includes the automatic detection of topological changes and the robust modelling of water level in tanks using the generalized global gradient algorithm. The extended period simulation enables the reliability assessment of alternative water supply schemes and the sensitivity of tanks and controlled devices to single failure events. The procedure is demonstrated on a real complex network, being consistent with the ongoing digital transition in the WDN management sector.

Modelling Electricity Consumption Forecasting Using the Markov Process and Hybrid Features Selection

Given the problem of electrical energy storage, it is critical to predict the amount of load required in order to have a reliable and stable power distribution network. Predicting electricity consumption of subscribers and analyzing their consumption behavior under the influence of various factors and time variables is important. Given the large volume of subscriber consumption data and the effective factors, it is only possible to analyze the data using new information technology tools such as data mining. In this paper, feature selection, clustering and Markov process techniques are used to model and predict the power consumption data of subscribers. First, the selection of a subset of effective features is based on the combined PCA approach and the Firefly algorithm. Subscribers are then clustered based on the features selected by the K-means. Finally, subscriber behavior patterns are modeled to predict consumption using the Markov process on high-risk clusters. This study is simulated based on the data of electricity subscribers in Bushehr-Iran Power Distribution Company. The simulation results show the superiority of the proposed model over other similar algorithms such as LASSO-QRNN and HyFIS. The accuracy of power consumption prediction in the proposed method is about 1% compared to LASSO-QRNN and about 0.5% compared to HyFIS.

Contextual dimensions for cache replacement schemes in information-centric networks: a systematic review.

In recent years, information-centric networks (ICNs) have gained attention from the research and industry communities as an efficient and reliable content distribution network paradigm, especially to address content-centric and bandwidth-needed applications together with the heterogeneous requirements of emergent networks, such as the Internet of Things (IoT), Vehicular Ad-hoc NETwork (VANET) and Mobile Edge Computing (MEC). In-network caching is an essential part of ICN architecture design, and the performance of the overall network relies on caching policy efficiency. Therefore, a large number of cache replacement strategies have been proposed to suit the needs of different networks. The literature extensively presents studies on the performance of the replacement schemes in different contexts. The evaluations may present different variations of context characteristics leading to different impacts on the performance of the policies or different results of most suitable policies. Conversely, there is a lack of research efforts to understand how the context characteristics influence policy performance. In this direction, we conducted an extensive study of the ICN literature through a Systematic Literature Review (SLR) process to map reported evidence of different aspects of context regarding the cache replacement schemes. Our main findings contribute to the understanding of what is a context from the perspective of cache replacement policies and the context characteristics that influence cache behavior. We also provide a helpful classification of policies based on context dimensions used to determine the relevance of contents. Further, we contribute with a set of cache-enabled networks and their respective context characteristics that enhance the cache eviction process.

SARS-CoV-2 vaccines: fast track versus efficacy

SARS-CoV-2 vaccines: fast track versus efficacy

МИКРОСЕТИ ДЛЯ ЛОКАЛЬНОГО ЭНЕРГОСНАБЖЕНИЯ ДЕЦЕНТРАЛИЗОВАННЫХ ПОТРЕБИТЕЛЕЙ: ОБЗОР МЕЖДУНАРОДНОГО ОПЫТА

Local microgrids implemented in existing power grids are an effective solution to the problem of providing power to decentralized consumers. Due to the high penetration of distributed energy resources into power systems, microgrids demonstrate enormous advantages of providing efficient and reliable distribution networks with high flexibility and reliability. However, their implementation is complex, and institutional changes are needed to unlock their full potential. The purpose of this article is to review the existing literature and analyze institutional changes affecting the development of local microgrids.

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

The application of statistical and Machine Learning models plays a critical role in planning and decision support processes for efficient and reliable Water Distribution Network (WDN) management. Failure models can provide valuable information for prioritizing system rehabilitation even in data scarcity scenarios, such as developing countries. Few studies have analyzed the performance of more than two models, and examples of case studies in developing countries are insufficient. This study compares various statistical and Machine Learning models to provide useful information to practitioners for the selection of a suitable pipe failure model according to information availability and network characteristics. Three statistical models (i.e., Linear, Poisson, and Evolutionary Polynomial Regressions) were used for failure prediction in groups of pipes. Machine Learning approaches, particularly Gradient-Boosted Tree (GBT), Bayes, Support Vector Machines and Artificial Neuronal Networks (ANNs), were compared in predicting individual pipe failure rates. The proposed approach was applied to a WDN in Bogotá (Colombia). The statistical models showed an acceptable performance (R2 between 0.695 and 0.927), but the Poisson Regression was the most suitable for predicting failures in pipes with lower failure rates. Regarding Machine Learning models, Bayes and ANNs exhibited low performance in the prediction of pipe failure condition. The GBT approach had the best performing classifier.

Optimal Location of Transformers in Electrical Distribution Networks Using Geographic Information Systems

This research shows a heuristic model for the design of scalable and reliable electrical distribution networks. The algorithms presented allow to optimize the location of transformation centers using on their database geographic information systems from which it is possible to define user locations, candidate sites, possible routes for the deployment of the electricity grid and, in general, data for the reconstruction of the scenario. The model employs clustering and triangulation methods, as well as algorithms for creating a minimally expanding tree and the consequent site assignment for transformer placement. After setting the optimal locations for the transformer site, the algorithms compute voltage drops in secondary circuits, required transformation capability, execution times, and coverage achieved. The results obtained are adjusted to the requirements of an actual distribution power grid and show a good performance on the proposed scenario.

Load shedding optimization for economic operation cost in a microgrid

Distributed generation and demand-side participation have been widely deployed for secure, reliable and economic power distribution networks. Microgrids have been merged in power systems to meet this increase in distributed generation and to provide more control on the massive demand expansion. This paper presents an optimization model for scheduling and operating a microgrid considering the participation of the end-user customers in the electricity market. Mixed-integer programming is used in the proposed model to procure the minimum operation cost of the microgrid and to apply load shedding (LS) optimization on the local responsive loads. The objective function is described by piecewise linear functions. The proposed model allows the implementation of high number of constraints related to generation units, power exchange with the main grid, energy balance and LS. The operation decisions are based on binary variables that represent the status of the generation units, grid-connection and the responsive loads. Furthermore, the proposed model demonstrates the relationship between pick-up/drop-off rates of the responsive loads and the hourly operation cost of the microgrid. Example studies have illustrated the performance of the optimization model in finding the minimum operation cost with multiple technical and economic constraints.

Distribution Network Model Platform: A First Case Study

Decarbonisation policies have recently seen an uncontrolled increase in local electricity production from renewable energy sources (RES) at distribution level. As a consequence, bidirectional power flows might cause high voltage/ medium voltage (HV/MV) transformers to overload. Additionally, not-well-planned installation of electric vehicle (EV) charging stations could provoke voltage deviations and cables overloading during peak times. To ensure secure and reliable distribution network operations, technology integration requires careful analysis which is based on realistic distribution grid models (DGM). Currently, however, only not geo-referenced synthetic grids are available inliterature. This fact unfortunately represents a big limitation. In order to overcome this knowledge gap, we developed a distribution network model (DiNeMo) web-platform aiming at reproducing the DGM of a given area of interest. DiNeMo is based on metrics and indicators collected from 99 unbundled distribution system operators (DSOs) in Europe. In this work we firstly perform a validation exercise on two DGMs of the city of Varaždin in Croatia. To this aim, a set of indicators from the DGMs and from the real networks are compared. The DGMs are later used for a power flow analysis which focuses on voltage fluctuations, line losses, and lines loading considering different levels of EV charging stations penetration.