Electrical Distribution Networks Research Articles

Multi-type energy conversion for managing the consumption by enhancing the resiliency of electrical distribution networks

The climatic circumstances of the world have altered due to the world warming up, and this issue has increased high-impact and low-probability (HILP) events more than before. Supplying energy requirements has turned into one of the main challenges of utilities especially electrical distribution companies considering the frequency and intensity of HILP events. On the other hand, developments in storing electricity have varied expectations and will change the solutions leading to resilient electrical distribution networks (EDNs). Some researchers have studied and analyzed numerous aspects of resilient EDNs but hybridization of different types of energy storage systems (ESSs) has not evaluated before. This paper considers energy management of emergency-operated EDNs equipped with two different types of energy storage systems which are batteries and flywheels. Convex equations in all parts of the problem, including different types of energy storage systems are proposed and modeled as an MIQCP to optimize the resilient networks considering all limitations. The proposed framework is developed in GAMS software and the results are provided in the form of Pareto optimal solutions. Applicability of the conducted model is evaluated by the IEEE 33-bus test system aiming at outstanding the effects of flywheels in improving the resiliency of electrical distribution networks. The proposed model analyzed by various energy storing scenarios based on technical and economical limitations. Results showed that among the considered case studies, the 50 % of the cases included with flywheel while batteries participated in 30 % that were the most expensive ones. On the other hand, the lowest amount of objective function belongs to the case that is only included with flywheels. Accordingly, by considering flywheels as a newly born energy storage system in the emergency-operated EDNs, the flexibility of energy management is facilitated and can be developed economically.

Spark: A Statistical Comparison and Evaluation of Classification Algorithms for Fault Prediction in Electrical Secondary Distribution Network

Managing faults in the electrical secondary distribution network is a challenging task given the nature, size, and complexity. Predicting faults early before they occur helps in increasing the safety and reliability of the power distribution system. Various statistical and machine learning techniques are being used to predict different types of faults. This study applies classification algorithms available in the big data framework Apache Spark through its python interface PySpark to predict electrical secondary distribution network faults. The study evaluates and compares nine algorithms: Decision tree, Gradient-boosted tree, Logistic regression, Naïve Bayes, Multilayer perceptron, Random forest, Linear Support Vector Machine, One-versus-rest and Factorization machines. The research uses Friedman’s test followed by the Nemenyi post hoc test to find the significance of performance differences among the algorithms. The results show significant differences among the algorithms. Gradient-boosted tree and One-versus-rest with Gradient-boosted tree had the best performance for binary and multiclass classification, respectively, while Naïve Bayes had the worst performance.

Robust network topology for unbalanced active distribution networks with uncertain injections

AbstractThis research paper introduces a comprehensive formulation for robust dynamic network reconfiguration (NR) of unbalanced active electric distribution networks (DNs). Network reconfiguration is a potent strategy to minimise active power loss in DN as it involves altering network topology through sectionalising (normally closed) and tie‐line switches (normally open). However, NR is usually a mixed integer NP‐hard non‐linear optimisation problem due to the discrete nature of the switches. Hence, including variable injection uncertainties (from generation or load) for an unbalanced active DN with all its attributes further poses a significant challenge in solving NR. The proposed formulation addresses these challenges in a robust optimisation (RO) framework to get a robust topology and power and voltage set points for dispatchable Distributed Generators (DGs). Also, Chance‐Constrained robust formulations are proposed to regulate the conservatism of RO. Numerical analyses demonstrate the impact of conservative robust NR on DG set points compared to the non‐robust NR method. Tests on a modified unbalanced IEEE 34‐bus system and comparison with previous formulations verify the efficacy of the proposed approach, showcasing its effectiveness.

Reviews on Load Flow Methods in Electric Distribution Networks

Reviews on Load Flow Methods in Electric Distribution Networks

Optimal Placement and Sizing of Distributed Generation in Electrical DC Distribution Networks Using a Stochastic Mixed-Integer LP Model

AbstractThis paper presents a stochastic mixed-integer linear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical demand and distributed renewable sources. The proposed model accurately represents the original mixed-integer nonlinear model, obtaining a globally optimal solution in less computational time with low errors. The mathematical model allows for considering constraints related to the maximum limits for the penetration of distributed generation, such as those specified by Resolution CREG 174 of 2021. Furthermore, the uncertainties of the electrical demand, wind energy-based distributed generation (DG), and solar energy-based DG are considered in the mathematical models using a two-stage stochastic programming approach. The accuracy and efficiency of the proposed model were tested and validated on a 21-node DC test system from the specialized literature, and the effectiveness and robustness were assessed on a 69-node DC test system. The obtained results show that the proposed stochastic mixed-integer linear mathematical model performs well.

Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study.

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

A novel variable neighborhood descent algorithm for service restoration in radial electrical distribution networks

Abstract This paper presents a variable neighborhood descent algorithm for the service restoration problem (SRP) in electrical distribution systems. The restoration problem appears when a permanent fault occurs in the system. The fault must be localized and isolated from the rest of the system. As a consequence of the isolation, the downstream area is de-energized and requires restoration. The variable neighborhood descent algorithm features its ability to solve the SRP for radial distribution systems and introduces a strategy that helps to deal with the system’s radiality. Tests are performed using a 53-node test system. The results show that the proposed algorithm can efficiently solve the SRP in a distribution system, and as a deterministic algorithm, the results can be used to help nondeterministic algorithms.

Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data

Green technologies such as solar photovoltaic systems and electric vehicles play a fundamental role in the global decarbonization effort. To enable their diffusion, electricity distribution networks need to be upgraded, which is a complex and expensive endeavor. However, utilities face budgetary constraints and seek to reduce planning uncertainty. Here, we utilize 7,527 real-world grid topologies and land cover data to develop a model for estimating conductor and capital investment needs for electrifying a specific area. Our work yields three main contributions: First, we demonstrate the important role of land cover data in power line planning. We show that, for medium and large networks, distinct methodologies are needed due to the significant impact of land cover, particularly buildings and roads. Second, we introduce a parsimonious model of power line length and identify the number of consumption points as the primary determinant of network investment costs. Third, we present a cost assessment model tailored for regulators and investors, offering valuable insights for network planning, policymaking, due diligence, and research. Our work highlights the importance of combining land cover data and operations research algorithms in distribution network planning and provides policymakers with a tool to ensure cost-efficient network expansion.

Towards full electrification of local public transport: A tool to guide strategies for implementing the electric charging network

This paper describes a methodology and a Decision Support Tool (DST) for the assessment of the impacts of future scenarios of public electric mobility on the electric Distribution Network (DN) of a city, aimed to support the implementation strategies of the electric charging network for the Local Public Transport (LPT).The objective of the proposed tool is supporting the decision makers in estimating the effect of the future development of electromobility on the urban electric grid considering the evolution of both electric buses and their charging infrastructure.Some scenarios for the development of electric charging infrastructure for LPT were identified together with the local public transport operator and the energy provider. The results, which are of interest to both the local Public Transport Operator (PTO) and the local Distribution System Operator (DSO), have been quantified for the city of Turin (Italy), but the methodology can be adopted in any urban context: DSOs and PTOs of each city can use the proposed tool as a support to define their public transport electrification strategies.Some results from the case study examined in this paper (Turin, Italy) show that the energy demand related to the electrification of LPT on a typical weekday is about 45 MWh in 2030 (corresponding to 40 % e-bus), and about 90 MWh when the entire bus fleet is electric. It is also shown how more distributed recharging during the day (with intermediate recharging at the terminus) can dampen the energy/power peaks required at the depot compared to overnight recharging alone: a reduction of up to 27 % of energy demand at the depot is observed due to opportunity charging.

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.

Distributed battery energy storage systems for deferring distribution network reinforcements under sustained load growth scenarios

Energy storage systems can be leveraged in electricity distribution network planning as mitigation alternatives to traditional grid reinforcements if they are strategically installed and operated to reduce congestion and voltage limit violations. This paper examines the technical and economic viability of distributed battery energy storage systems owned by the system operator as an alternative to distribution network reinforcements. The case study analyzes the installation of battery energy storage systems in a real 500-bus Spanish medium voltage grid under sustained load growth scenarios. The results show that, in general, dedicated battery energy storage systems are only a cost-efficient alternative in distribution system planning under very specific conditions, such as when low load growth rates are expected. Nevertheless, they are only required for peak shaving a few days per year. For the analyzed case study, the recoverable portion of their total cost through deferral of distribution system upgrades is higher than the fraction of cycles required for peak shaving under all sustained load growth scenarios. Therefore, it is also explored if mobile battery energy storage systems, capital grants, and revenue stacking can enable battery energy storage systems to become an efficient distribution system planning alternative.

Integration of Photovoltaic and DSTATCOM In the Distribution Network Using Rat Swarm Optimization Technique

Power loss minimization and improved voltage profile have been major challenges faced by the electrical distribution network (DN) mainly because of the long length of the feeders and the high resistance to reactance (R/X) ratio of the DN. A lot of techniques have been investigated to solve these problems. One of the most prominent is the optimal integration of distributed generation (DG) such as photovoltaic (PV) as well as the integration of Distribution Static Synchronous Compensator (DSTATCOM) into the network. The main challenge with this solution has been the determination of the optimal sizes and sites of the DG and/or DSTATCOM. This paper seeks to optimize the simultaneous allocation of multiple DSTATCOMs and PVs in the DN for power loss reduction and voltage profile improvement using the rat swarm optimization (RSO) technique, which is a simple, yet robust optimization technique. The optimization problem is formulated to minimize power loss, voltage deviation index, and maximize the voltage stability index. The IEEE 33 node DN is used as a test network and the simulation results show the effectiveness of the RSO technique in finding the best sizes and locations of the PVs and the DSTATCOMs. The power losses of the network are reduced from 210.996 kW, and 143.129 kVAr when there is no DSTATCOM nor PVs in the network to 26.155 kW, and 19.128 kVAr when DSTATCOM and PVs are simultaneously allocated into the network. A remarkable improvement in the voltage profile of the network is also observed with the minimum node voltage being 0.98 p.u. compared to 0.9038 p.u. when there are no DSTATCOMs or PVs. The RSO results were compared with other techniques from the literature, and it proved its superiority.

Deployment of an IoT based sensor node for faults detection and classification in electrical secondary distribution networks

Deployment of an IoT based sensor node for faults detection and classification in electrical secondary distribution networks

A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network

AbstractThe adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1.

Estimating primary substation boundaries and the value of mapping Great Britain's electrical network infrastructure

Localised data aggregation in many countries including Great Britain (GB) is typically performed at a geographical level with polygon boundaries that have a robust and trusted governance system in place. This means there is confidence in a process to create a set of polygons that have unique identifiers coupled to geographical areas, and the ability to have these updated through a defined code of practice. Examples found across many countries are in the delivery of post, such as post codes and zip codes, and of the definition of census areas and municipal boundaries. The confidence in these boundaries allows different data to be aggregated by third parties, which itself provides greater amounts of data over comparable geographical areas to enhance wider analysis and decision making. As helpful as these polygons are for certain types of analyses, they are not specifically defined for energy systems analysis. Here we combine publicly available datasets published from the six regional electricity Distribution Network Operators of GB to produce a new geospatial dataset with 4436 unique polygons defining the areas served by electrical primary substations. An example demonstrating the value of these polygons is given to link these polygons with postcode level open government datasets on domestic energy consumption (2015–2020) from the Department of Energy Security and Net Zero (DESNZ). The resulting new data reflects energy statistics aggregated to the geographical areas served by each primary electrical substation across Great Britain. The significant value of the generated data is demonstrated by the quantification of the domestic annual heating demand presently met by natural gas across different primary substation areas which varies from an average of 5100 kWh to 28,900 kWh per property. This highlights how the rate of electrical capacity expansion will have to differ by geography under an electrified heating scenario. Therefore, we believe there is a compelling argument for all countries to set up processes to create and update polygons that have a meaningful relationship to energy systems. This would allow more accurate energy systems analysis to be performed, ultimately leading to an accelerated or potentially lower cost transition to a net zero world.

Study of the Management of the Electrical Energy Production and Distribution System Within the National School of Teachers of Mamou, Guinea

With the energy transition, marked essentially by the mass integration of energy production based on renewable resources, the missions and challenges of electrical energy distribution networks are evolving. This study is part of this dynamic, its objective is the study of the management of the production and distribution system of electrical energy within the National School of Teachers of Mamou. It emerges from this study that the supply of electrical energy to the National School of Teachers of Mamou is ensured by a hybrid system of three power sources: photovoltaic solar fields, Generator Group and Electricity of Guinea. The current electrical energy requirements of the Mamou NST are 40 kW. The total power of the installed photovoltaic solar fields is 70 kWp; the Generator used has a power of 10 kVA; the site’s Electricity of Guinea network is made up of transformers, cabin substations and protective equipment. The electricity distribution network is characterized by: Four (4) 250 A circuit breakers; a 32 A circuit breaker for the departure of lamps, sockets and fans; a 10 A circuit breaker for the lamps; a 10 A circuit breaker for the fans; a 16 A circuit breaker for the sockets and an 800 A mechanical inverter. The study shows that the power of photovoltaic solar fields is largely sufficient to cover the current electrical energy needs of the National School of Teachers of Mamou.

АНАЛІЗ ПОКАЗНИКІВ ЯКОСТІ ПОСЛУГ ПОСТАЧАННЯ ЕЛЕКТРИЧНОЇ ЕНЕРГІЇ

The object of the study is the processes in distribution electric networks and their influence on technical and economic characteristics aimed at achieving the main indicators of reliability, which is characterized by the continuity of electricity supply, namely, indicators of the quality of services: the index of the average duration of long interruptions and the index of the average frequency of long interruptions ( SAIDI, SAIFI), as well as compliance with the guaranteed quality standards of electric energy supply services, which are established by the Resolution of the NCRECP. The problem to be solved is a set of factors aimed at reducing the index of the average duration of long interruptions and the index of the average frequency of long interruptions (SAIDI, SAIFI), taking into account and applying effective measures of stimulating tariff formation (Rab - tariff) aimed at providing quality services to consumers of electric energy in accordance with the guaranteed standards established by the Resolution of the NCRECP. The analysis of the main indicators of the quality of SAIDI and SAIFI services for the period from 01.01.2022 to 03.31.2022 was carried out on the example of the operator of the distribution system JSC "Kharkivoblenergo". The impact of natural and unnatural factors on the formation of these indicators is analyzed, taking into account force majeure circumstances and military aggression on the territory of the Kharkiv region. The changes in the quality indicators of SAIDI and SAIFI services and their compliance with the normative values established by the NCRECP were analyzed. The conducted analysis of SAIDI and SAIFI service quality indicators allows us to state that achieving the target values of electricity supply continuity indicators is a fundamental factor for improving the quality of electricity supply services to consumers. The provision of the necessary guaranteed quality standards for the supply of electric energy to consumers is substantiated taking into account the stimulating tariff formation (Rab - tariff). It is shown that the introduction and development of stimulating tariff formation lays a reliable foundation for a strong and competitive Ukraine on the territory of the European Union and introduces dynamic development for a strong and independent Ukrainian economy. Ref. 19, figure, table.

СКЛАДОВІ ПОТУЖНОСТІ ТИРИСТОРНОГО РЕГУЛЯТОРА НАПРУГИ З АКТИВНИМ НАВАНТАЖЕННЯМ

A study of the power balance of the semiconductor voltage regulator on an active load with phase control by two-operation thyristors was carried out. The suitability of its use for simultaneous obtaining of the reactive power resource for distribution electric networks has been confirmed. According to the concept of intelligent electrical networks, such devices belong to the class of flexible alternating current transmission systems, and they can be the basis for building virtual energy sources. The power components for individual components of the regulator, including the thyristor block, were studied. The spectral method of describing currents and voltages at the regulator sections is applied. The obtained results of theoretical studies were confirmed by an example with numerical calculations, which made it possible to clarify the physical essence of energy transformations during voltage regulation. Bibl. 8, fig. 3.

Reducing non-technical losses in electricity distribution networks: Leveraging explainable AI and three lines of defence model to manage operational staff-related factors

This study presents a multidisciplinary approach involving Explainable Artificial Intelligence (ExAI) and operational risk management to reduce Non-Technical Losses (NTL) in electricity distribution. It empirically explores how the activities of employees of utility companies contribute to NTL, a phenomenon often overlooked in existing empirical research. An ensemble classification algorithm is used to analyse utility operations data, and the SHAP explainability technique establishes the predictive significance of staff activities for NTL. Subsequently, these staff activities are mapped into risk cells using the BASEL II and III operational risk definitions, and the Three Lines of Defence (3LoD) model is developed for optimizing electricity distribution. The paper makes three original contributions to the literature: first, it empirically links staff operations to NTL; second, it maps NTL causes to Basel II/III operational risk categories; and finally, to the best of the authors’ knowledge, it is the first study to use the 3LoD model for electricity distribution optimization.