Electrical Distribution Networks Research Articles

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Flexible Energy Storage for Sustainable Load Leveling in Low-Voltage Electricity Distribution Grids with Prosumers

The sustainability of the energy sector is linked today with the diminishing of the reliance on fossil fuels and on the large-scale adoption of renewable generation. Medium- and low-voltage electricity distribution grids see the proliferation of microgrids that supply consumers able to generate electricity with local installations of PV panels. These consuming and generating entities, called prosumers, use the local generation for their own consumption needs and are exporting the surplus in the grid, modifying the typical steady state operation conditions. For mitigating this inconvenience, local storage equipment can be used, which also helps the prosumers to reduce their costs and preserve the sustainable operation of the distribution infrastructure. The literature shows that by optimally using storage in microgrids, the deterioration in quality and security of supply can be minimized in the presence of prosumers. This paper presents a study regarding local storage management in prosumer-enabled microgrids, seeking to find the optimal configuration of community (shared) storage systems that charge batteries overnight, during low consumption hours, providing load leveling opportunities and energy loss minimization. A study case performed on a real low-voltage electricity distribution network (LVEDN) shows the performance of the proposed optimization.

Low-carbon Economic Scheduling for AIES Considering Flexible Load Coordination and Multiple Uncertainties

The disordered energy use in traditional agricultural production has resulted in high energy purchase costs and carbon emissions. The coupling of integrated energy systems with agriculture offers the potential for flexible coordination of agricultural loads while agricultural loads are highly influenced by changes in the natural environment. In this context, this paper proposes a low-carbon and economic scheduling optimization method for agricultural integrated energy system (AIES), which comprehensively takes temperature, light intensity, and the coordination of agricultural loads into consideration. Firstly, solar and biomass resources are integrated through energy conversion equipment to support energy supply from both the electrical distribution network (EDN) and the gas network. At the same time, the modelling process of agricultural load is refined, which is divided into crop irrigation, greenhouse temperature control and residential consumption. The models of agricultural multi-energy complementary and energy storage equipment are established to achieve flexible conversion between different energy sources. In addition, the distributionally robust optimization (DRO) method based on multiple discrete scenarios is adopted to deal with the uncertainties arising from environmental condition fluctuations during the optimization process. Finally, the proposed model is tested on 4 various regions and 2 different seasons. Numerical results show that the proposed model can effectively enhance the economic and environmental performance of the AIES.

ANALYSIS OF NETWORKS IN ELECTRICAL DISTRIBUTION SYSTEMS USING FAULT TREE ANALYSIS METHOD IN PLN (PERSERO) UP3 BULUKUMBA ULP PANRITA LOPI

Distribution of electricity is often interrupted which can result in the process of distribution of power to the consumer becoming abnormal. Therefore, an analysis of the cause of disturbance of the electricity distribution network needs to be carried out. This research is conducted using the method of Fault Tree Analysis (FTA) because it is an effective method in determining the cause of the disturbance and with the help of Microsoft Exel as well as manual calculations. The results of the research obtained that which is the main factor that causes the occurrence of the disruption in the system of distribution of electric energy in PT PLN (Persero) UP3 Bulukumba ULP Panrita Lopi is due to disruptions in the distribution equipment, animal activity, human activity, material interference, error of installation and nature interferences. From the results of the study, it can be concluded that there are six factors that cause the disturbance of the process of electricity distribution to consumption, then become a priority for PLN based on the percentage of disruption occurred.

Design and Performance Evaluation of Fuzzy Controller based Energy Storage Enabled Statcom for Electrical Distribution Network

In order to fulfill the demand for power, the production of locally accessible resources, such as solar and wind energy, has increased dramatically during the past ten years. Their integration into the distribution of electrical network  (EDN) has affected the operation of the network. Variations in the real power flow and an increase in reactive power demand have impacted the EDN voltage/frequency profiles and distribution transformer strain. Hence to improve the voltage regulation and reduce the active power fluctuations, there is a need for a system network of electrical distribution (EDN). It has the ability to support both reactive and active power. Thus, the energy storage device with STATCOM is introduced i.e. an energy storage device linked to Statcom (EsSt) has been proposed in this paper. The Common Coupling Point (CCP) voltage can be adjusted by energy storage connected to Statcom (EsSt) to satisfy the amount of power required actively at EDN. The EsSt has an integrated battery storage system and may be controlled using a voltage source converter by employing the proper control strategy to deliver the necessary energy support and VAR compensation. The voltage at the dc-link has been integrated using DC-DC converter based half bridge. The use of conventional PI controllers provides poor transient response. To improve the transient performance, It is suggested that a fuzzy logic controller be used in place of the PI controller. Analysis of comparison using MATLAB. The rule-based fuzzy logic controller performs better than the PI controller, according to the Simulink environment.

Integrating management systems in the energy sector: the case of the electricity industry in New Zealand

The New Zealand Electricity Engineers Association (EEA) asked for advice about development of a guide for a public safety management system (PSMS) for electricity distribution networks based on ISO45001 Occupational health and safety management systems. This would help eliminate overlap between legislation and standards while maintaining compliance with legal requirements. At Victoria University of Wellington we have been working on such an integrated approach and this article reports on the project and suggests how the results can be extended to include other areas of management while retaining the ability for appropriate elements to be audited and certified in accordance with systems required by legislation. The project reviewed an earlier report and literature review on PSMS for the Electricity Networks Association. Development of the legal requirements for a PSMS were reviewed and use of the International Standards Organization (ISO) framework for all management system standards proposed to encompass ISO45001 and similar standards. Relevant management content was incorporated in the high-level structure and key stakeholders contributed technical content. Use of the ISO Annex SL as a template enabled development of an integrated management system incorporating other management system standards, that would still support legal compliance while enabling elimination of duplication and clutter, and waste of resources at a time of technology change. The work was carried out in New Zealand (NZ) and may not apply in other countries with different legal requirements. Auditors may resist integrated management systems although this may not be a limitation if documentation is appropriately written. The electricity networks businesses in NZ may move to implementation of the PSMS guide but this will be an internal decision and results may take some years to declutter documentation and reduce overlapping audits under management system standards. Over the next decade the context of the New Zealand electricity industry will undergo major shifts with the need to adapt to increasing expectations for occupational health and safety and public safety, adaptation to climate change and accelerated implementation of new technologies. The electricity industry will need to apply its resources to those changes while avoiding duplicated efforts to implement multiple management systems. Developing an integrated management system that incorporates several management systems and public safety may aid protection of non-workers who cannot otherwise be “controlled” by a business.

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

Internet of Things (IoT) System for Diagnosing Power Transformer Faults using the IEC 60076-1 Standard: Review

The evaluation of Internet of Things (IoT), which are contracted to research the adoption or acceptance of IoT technology, is deemed to be a subject of vital importance. Studying the adoption or admissibility of IoT technology is not a brand-new study area, and numerous scholars have undertaken the task. The current and upcoming generations of Internet are being shaped by the Internet of Things (IoT). The main aim of this survey, contrasted with previous surveys, is to systematically evaluate the IoT technology research in condition monitoring of power transformers to proffer a thorough cursor that can facilitate scholars to accomplish additional research in IoT acceptance. Almost all appliances may now be remotely monitored and connected to the internet thanks to the Internet of Things. Power transformers play a crucial role in electrical distribution networks, and their reliable operation is paramount. The existing diagnostic tests outlined in the IEC 60076-1 standard are essential for ensuring transformer health but suffer from manual procedures, leaving them susceptible to human error. This traditional method not only introduces the potential for inaccuracies due to human involvement but also incurs significant operational costs and delays. The need to physically dispatch technicians to transformer sites for routine diagnostics hampers timely fault detection and preventive maintenance. Therefore, there exists a pressing need to modernize and enhance the diagnostic procedures by integrating emerging technologies like the Internet of Things (IoT). This paper will review the most recent techniques for transformer health tracking system using IoT and highlight the contemporary approaches. A total of 50 primary research literature that was published between 2018 and 2024 were surveyed in this review paper.

Impact of electric vehicle charging demand on power distribution grid congestion

California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state's electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs' impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption.

A big data-based ensemble for fault prediction in electrical secondary distribution network

A big data-based ensemble for fault prediction in electrical secondary distribution network

Impact of Prosumers’ Operation on Selected Parameters of Low-Voltage Distribution Network Operation

This article presents the issue of the impact of the operation of prosumer installations on the operation of the distribution network. The emergence of a very large number of distributed generation units in electric distribution networks (in particular, photovoltaic installations in low-voltage grids) can be associated with both positive and negative consequences. Positive consequences can include, among others, the environmental aspect of electricity generation. Negative consequences, on the other hand, can include technical problems of power grid operation, manifested, for example, in changes that can be observed in steady states, in the values of such parameters of grid operation as voltage level, values of branch currents, or power and energy losses. The issue discussed will be illustrated by a calculation example considering, among other things, the size and location of the prosumer installation.

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.

Using Simplified Distflow-Based Mixed-Integer Quadratically Constrained Programming Formulation for Optimum Selection of Conductor Size in Electrical Distribution Networks

Determining conductor size is a subproblem that plays a vital role in the planning process of distribution systems. The problem of selecting the conductor cross-sectional area is usually made a model of mixed-integer non-linear programming (MINLP). It is important to note, however, that the MINLP model is not always capable of ensuring convergence to a solution that is globally optimum. In this research, a mixed-integer quadratically constrained programming (MIQCP) formulation is developed as a method for determining the conductor cross-sectional area in power distribution networks in an optimal manner. The goal function aims at minimizing the lifetime cost of lines, including initial capital cost together with operational expenses. The suggested optimization model’s constraints consist of power balance equations, thermal loading capacity of branches, nodal voltage restrictions, budget limitations for investment, and the need to have the same wire size in the main feeder. By using the simplified DistFlow approach for electrical distribution networks and precisely linearizing the product of a binary variable and a continuous variable, the MIQCP formulation is derived from the MINLP model. It is likely possible to solve the MIQCP formulation efficiently in the GAMS environment by using available commercial solvers like CPLEX. The developed MIQCP model is validated using three medium voltage distribution grids of IEEE 33 buses, IEEE 85 nodes, and the Vietnamese real 102 buses. The calculation results revealed the accuracy along with the efficacy of the proposed optimization procedure.

Optimizing Capacitor Placement in Distribution Systems Under Variable Loading Conditions with Golden Jack Optimization (GJO)

In modern society, the demand for electricity is ever-growing, making the minimization of power losses in distribution systems paramount. One significant aspect contributing to these losses is the strategic placement of capacitors within the distribution network. Efficient capacitor placement not only reduces power losses but also enhances the overall performance and reliability of the system. In today's world, where electricity is indispensable, minimizing power losses in the distribution system holds significant importance. This research introduces the Golden Jack Optimization (GJO) algorithm as a novel approach to address the challenge of capacitor placement in distribution systems. GJO, inspired by the foraging behavior of jackals, exhibits unique characteristics such as adaptability and efficiency in finding optimal solutions this paper proposes an innovative algorithm specifically designed for this purpose. To validate the effectiveness of the algorithm, extensive testing and experimentation have been conducted on the IEEE 69-bus system. This study aims to provide a practical and efficient solution to the challenge of power loss reduction within distribution systems. By harnessing the power of GJO and the insights gained from testing on the IEEE 69-bus system, we contribute to the ongoing efforts to enhance the efficiency and sustainability of electricity distribution networks. The results are compared with MPSO, TSM, GA-Fuzzy algorithms and proposed algorithm shows superior performance.

Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters.

The integration of renewable energy systems into electricity grids is a solution for strengthening electricity distribution networks (SEDNs). Renewable energies such as solar photovoltaics are suitable for reinforcing a low-voltage line by offering an electrical energy storage system. However, the integration of photovoltaic systems can lead to problems of harmonic distortion due to the presence of direct current or non-linear feedback in networks from other sources. Therefore, connection standards exist to ensure the quality of the energy before injection at a point of common coupling (PCC). In this work, particle swarm optimization (PSO) is used to control a boost converter and to evaluate the power losses and the harmonic distortion rate. The test on the IEEE 14 bus standard makes it possible to determine the allocation or integration nodes for other sources such as biomass, wind or hydrogen generators, in order to limit the impact of harmonic disturbances (LIHs). The evaluation of the harmonic distortion rate, the power losses as well as the determination of the system size is done using an objective function defined based on the integration and optimization constraints of the system. The proposed model performs better since the grid current and voltage are stabilized in phase after the photovoltaic source is injected.

Reliability analysis in distribution system by deep belief neural network

Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate.

АНАЛІЗ РЕЗУЛЬТАТІВ ЕКСПЕРИМЕНТАЛЬНИХ ДОСЛІДЖЕНЬ НЕОДНОРІДНОСТІ МЕРЕЖІ ПОСТІЙНОГО СТРУМУ

The modes of breakage of one line of network sections with parallel lines in electrical distribution networks of power supply to consumers of direct current electric transport have been studied. An analysis of the experimental data was carried out, the results of which indicate the presence of a dependence of the value of the variable component in the rectified current on the distance to the damaged area. Mathematical modeling of the breakage mode of one of the line sections in the network with a serial connection of three sections, each of which has parallel lines, has been carried out. The obtained results of mathematical modeling of the modes of operation of a direct current network with heterogeneity of different sections also indicate the possibility of using the sixth harmonic of the current to determine the occurrence of heterogeneity and the section with this heterogeneity. To reduce the influence of load changes when determining an inhomogeneous section, it is proposed to control the input impedance of the network at a frequency of 300 Hz. It has been established that in these regimes the resistance angle changes the most. A method for determining the occurrence of local damage to a direct current line and the distance to this damage location is proposed, which is based on moni-toring the value of the line current and the input resistance of the line at a frequency of 300 Hz. References 7, Figures 4, Tables 2.

Coupling fire and energy in the Anthropocene: Deploying scale to analyze social vulnerability to forced electricity outages in California

Extreme events such as wildfires and winter storms result in disruptions to grid-based electricity delivery. Electricity supply disruptions are both reactive, whereby specific events cause damage to physical infrastructure, or anticipatory where electricity suppliers—namely electric utility companies—preemptively de-energize sections of an electrical grid or distribution network based on elevated potential of extreme conditions that may cause wildfire ignition. De-energization has been promoted as a strategy to mitigate risk of wildfire ignition and spread when active fires may encounter distribution/transmission lines. Provision of basic energy services such as electricity are necessary for maintaining a range of essential functions such as communication, which become critical during extreme events. In recent years, Public Safety Power Shutoffs (PSPS) have increasingly been deployed by utility companies in Western U.S states as wildfire risk increases due to combined impacts of anthropogenic climate change, fuel accumulation, and expansion of development in fire prone lands. While the PSPS policy was designed to reduce liability of utilities in igniting fires, there is a dearth of research critically analyzing how the policy affects social vulnerability for populations subjected to periods of de-energization during high-risk fire conditions. This article aims to deepen current understandings of the way scale can be deployed to illustrate the highly spatial nature of relationships coupling electricity supply outages with demographic data to advance limited knowledge on social vulnerability characteristics for specific communities subjected to PSPS. The research engages scale to compare social vulnerability to outages experienced both in Butte County, located in northern California and the state as a whole.

Power line Communication: Revolutionizing data transfer over electrical distribution networks

Power Line Communication (PLC) is an emerging technology that utilizes existing electrical power infrastructure for data transmission. It enables communication over power lines, allowing devices to exchange information and access the internet through the power grid. This method offers numerous advantages, including widespread availability, cost-effectiveness, and easy deployment without the need for additional cabling. PLC has found applications in various domains, including smart grid management, home automation, industrial control systems, and Internet of Things (IoT) connectivity. This abstract provides an overview of PLC, highlighting its benefits, challenges, and key technologies. It also discusses the potential of PLC in transforming power distribution networks into intelligent and interconnected systems. Additionally, emerging trends and future directions for PLC research and development are explored, emphasizing the potential for enhanced reliability, speed, and efficiency in data transmission over power lines. This paper proposed a two way communication from source to load sides using BPSK and QPSK modulation techniques. Monte Carlo simulation is used to predict the transformer theoretical channel (AWGN) and compare the efficiency of the proposed methods. Randomly selected data from 10 k to 50 k size are used to compare the performance curve. Moreover, a test sound data is sent from the source side to the load side and it is observed that using the QPSK modulation technique has almost zero Bit Error Rate (BER).