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This research uses data analysis and mining techniques to determine the technological expansion of measurement systems in a public service company. It integrates technical, economic, geographic, and social variables into the analysis using machine learning techniques to discover patterns and relationships in large data sets. The fuzzy logic methodology is applied using the MATLAB programming tool “Fuzzy Logic” to build algorithms that allow for the correct selection of measurement, achieving greater efficiency and precision in the assignment of meter types. The results show that 98% of the metering systems in the significant part are electronic meters, with the “Residential BT” rate being the most extensive data set. Implementing the “fuzzy logic” technique recognizes that more than 60% of the meters are electronic, with the registration of active energy, reactive energy, and demand, allowing for greater control over the marketing variables of the distribution system operator. This research suggests that a future restructuring of electrical metering systems benefits the company and its users. By applying the analysis, a portfolio of viable projects for the replacement of measurement systems is obtained, and they are grouped into two clusters based on the total cost of the technological change.

This study explores the feasibility of using impedance relays in electrical distribution systems, a context where their application is not as common as in transmission systems. Given the dynamic nature and complex topology of medium-voltage distribution systems, this work proposes an innovative methodology integrating clustering algorithms and metaheuristic techniques to optimize the placement of impedance relays and enhance system reliability and resilience. Using CYME simulation and the Ant Colony Optimization (ACO) method, case studies were designed to validate the effectiveness of the proposed methodology. The results demonstrated that strategic placement of impedance relays reduces failure frequency and significantly improves the system’s response to such failures. This approach allows for a more efficient configuration and quicker response, which is crucial for maintaining continuity and quality of power supply. A detailed analysis of system behavior under various fault scenarios illustrates the robustness and adaptability of the proposed solution, marking a significant advancement in the protection and optimization of electrical distribution systems.

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered.

The introduction of distributed generation (DG) into distribution systems is expanding due to carbon neutral policies. DG with intermittent output characteristics brings frequent voltage variations in distribution systems. Generally, a step voltage regulator (SVR) is installed in the long distribution line and controls its voltage, which has limits of stable voltage regulations due to the slow tap-changing rate, switching losses, and short life span. To solve those problems, this paper proposes an IGBT-based SVR using zero-voltage switching. It is verified that the stable operation can be obtained through simulation by PSCAD/EMTDC 4.6 software and empirical experiments.

A DC microgrid is an efficient way to combine diverse sources; conventional droop control is unable to achieve both accurate current sharing and required voltage regulation. This paper provides a new adaptive control approach for DC microgrid applications that satisfies both accurate current sharing and appropriate voltage regulation depending on the loading state. As the load increases in parallel, so do the output currents of the distributed generating units, and correct current sharing is necessary under severe load conditions. The suggested control approach raises the equivalent droop gains as the load level increases in parallel and provides accurate current sharing. The droop parameters were checked online and changed using the principal current sharing loops to reduce the variation in load current sharing, and the second loop also transferred the droop lines to eliminate DC microgrid bus voltage fluctuation in the adaptive droop controller, which is different and inventive. The proposed algorithm is tested using a variety of input voltages and load resistances. This work assesses the performance and stability of the suggested method using a linearized model and verifies the results using an acceptable model created in MATLAB/SIMULINK Software Version 9.3 and using Real-Time Simulation Fundamentals and hardware-based experimentation.

The reduction in GHG emissions has become one of the most important objectives in Europe. One of these clean technologies in the quest for large-scale decarbonisation is the blending of hydrogen and natural gas (H2NG). The significance of this article lies in conducting a technical–economic analysis that relies on simulating annual consumption patterns. This study focuses on the utilization of H2NG at different proportions within a distribution network in Bucharest, Romania’s largest city. The paper is a fallow up of another paper and presents a case study for the injection through all four entry points in the city of different percentages of hydrogen and the dynamic of the mix through the gas distribution network. The study found that the variation in H2 in the network is high and depends on the entry point levels and consumption. An economic analysis was conducted on seasonal real consumption, which confirmed the investment’s profitability.

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.

This paper presents a novel modular voltage control algorithm for optimal scheduling of a distribution system’s load tap changers to minimize the number of tap changes while maintaining a voltage deviation (VD) around a desired target. To this end, a bi-objective optimal voltage regulation (OVR) problem is addressed in two distinct stages. First, the operational constraint on the load tap changer is removed to form a single-objective OVR problem relating to the voltage. The solution obtained in this stage is ultimately utilized to determine the penalty value assigned to the distance from the optimal (solely in terms of voltage) control value. In the second stage, the optimal scheduling problem is formulated as a minimum-cost-path problem, which can be efficiently solved via dynamic programming. This approach allows the identification of optimal scheduling that considers both the voltage-related objective as well as the number of load tap changer switching operations with no added computational burden beyond that of a simple voltage optimization problem. The method imposes no restriction on the load tap changer’s operation and is tested under two different target functions on the standard IEEE-123 test case. The first attains a nominal voltage with a 0.056 p.u. voltage deviation and the second is the well-known conservation voltage reduction (CVR) case with a 0.17 p.u. voltage deviation. The method is compared to an evolutionary-based algorithm and shows significant improvement in the voltage deviation by a factor of 3.5 as well as a computation time acceleration of two orders of magnitude. The paper demonstrates the effectiveness and potential of the proposed method as a key feature in future cutting-edge OVR methods.

Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids.

Incorporating photovoltaic (PV) inverters in power distribution systems via static synchronous compensators (PV-STATCOM) during the nighttime has lately been described as a solution to improve network performance. Hunter prey optimization (HPO) is introduced in this study for efficient PV-STATCOM device allocation in distribution systems. HPO generates numerous scenarios for how animals could act when hunting, some of which have been expanded into stochastic optimization. The PV-STATCOM device allocation issue in distribution networks is structured to simultaneously minimize the electrical energy losses and improve the voltage profile while accounting for variable 24 h loadings. The impacts of varying the number of installed PV-STATCOM devices are investigated in distribution systems. It is tested on two IEEE 33-node and 69-node distribution networks. The effectiveness of the proposed HPO is demonstrated in comparison to the differential evolution (DE) algorithm, particle swarm optimization (PSO), artificial rabbits algorithm (ARA), and golden search optimizer (GSO). The simulation results demonstrate the efficiency of the proposed HPO in adequately allocating the PV-STATCOM devices in distribution systems. For the IEEE 33-node distribution network, the energy losses are considerably decreased by 57.77%, and the voltages variance sum is significantly reduced by 42.84%. The energy losses in the IEEE 69-node distribution network decreased by 57.89%, while voltage variations are reduced by 44.69%. Additionally, the suggested HPO is highly consistent than the DE, PSO, ARA, and GSO. Furthermore, throughout the day, the voltage profile at all distribution nodes surpasses the minimum requirement of 95%.

Non-Technical Losses (NTL) represent a serious concern for electric companies. These losses are responsible for revenue losses, as well as reduced system reliability. Part of the revenue loss is charged to legal consumers, thus, causing social imbalance. NTL methods have been developed in order to reduce the impact in physical distribution systems and legal consumers. These methods can be classified as hardware-based and non-hardware-based. Hardware-based methods need an entirely new system infrastructure to be implemented, resulting in high investment and increased cost for energy companies, thus hampering implementation in poorer nations. With this in mind, this paper performs a review of non-hardware-based NTL detection methods. These methods use distribution systems and consumers’ data to detect abnormal energy consumption. They can be classified as network-based, which use network technical parameters to search for energy losses, data-based methods, which use data science and machine learning, and hybrid methods, which combine both. This paper focuses on reviewing non-hardware-based NTL detection methods, presenting a NTL detection methods overview and a literature search and analysis.

The ever-growing participation of Renewable Energy Sources (RES) in modern distribution networks is replacing an important portion of Conventional Generation (CG), which brings along new challenges in the planning and operation of distribution grids. As RES such as Photovoltaic Energy (PV) and Wind Power Generation (WPG) increase in distribution networks, studies regarding their integration and coordination become more important. In this context, the purpose of this paper is to propose a Multi-period Optimal Power Flow (MOPF) model for the optimal coordination of Battery Energy Storage Systems (BESSs) with PV, WPG, and CG in modern distribution networks. The model formulation was developed in A Modeling Language for Mathematical Programming (AMPL) and solved through the Knitro solver within a time horizon of 24 h. A distinctive feature and one of the main contributions of the proposed approach is the fact that BESSs can provide both active and reactive power. The proposed optimization model reduces power losses and improves voltage profiles. To show the applicability and effectiveness of the proposed model, several tests were carried out on the 33-bus distribution test system and a real distribution system of 141 buses located in the metropolitan area of Caracas. Power loss reductions of up to 58.4% and 77% for the test systems of 33 and 141 buses were obtained, respectively, when BESSs provided both active and reactive power. The results allow us to conclude that the proposed model for optimal coordination of BESSs with RES is suitable for real-life applications, resulting in important reductions of power losses and flattening of voltage profiles.

This article addresses the problem of the optimal selection of conductors in asymmetric three-phase distribution networks from a combinatorial optimization perspective, where the problem is represented by a mixed-integer nonlinear programming (MINLP) model that is solved using a master-slave (MS) optimization strategy. In the master stage, an optimization model known as the generalized normal distribution optimization (GNDO) algorithm is proposed with an improvement stage based on the vortex search algorithm (VSA). Both algorithms work with discrete-continuous coding that allows us to represent the locations and gauges of the different conductors in the electrical distribution system. For the slave stage, the backward/forward sweep (BFS) algorithm is adopted. The numerical results obtained in the IEEE 8- and 27-bus systems demonstrate the applicability, efficiency, and robustness of this optimization methodology, which, in comparison with current methodologies such as the Newton metaheuristic algorithm, shows significant improvements in the values of the objective function regarding the balanced demand scenario for the 8- and 27-bus test systems (i.e., 10.30% and 1.40% respectively). On the other hand, for the unbalanced demand scenario, a reduction of 1.43% was obtained in the 27-bus system, whereas no improvement was obtained in the 8-bus grid. An additional simulation scenario associated with the three-phase version of the IEEE33-bus grid under unbalanced operating conditions is analyzed considering three possible load profiles. The first load profile corresponds to the yearly operation under the peak load conduction, the second case is associated with a daily demand profile, and the third operation case discretizes the demand profile in three periods with lengths of 1000 h, 6760 h, and 1000 h with demands of 100%, 60% and 30% of the peak load case. Numerical results show the strong influence of the expected demand behavior on the plan’s total costs, with variations upper than USD/year 260,000.00 between different cases of analysis. All implementations were developed in the MATLAB® programming environment.

In this paper, the role of soft open point (SOP) is investigated with and without system re-configuration (SR) in reducing overall system power losses and improving voltage profile, as well as the effect of increasing the number of SOPs connected to distribution systems under different scenarios using a proposed hybrid water cycle algorithm (HWCA). The HWCA is formulated to enhance the water cycle algorithm (WCA) search performance based on the genetic algorithm (GA) for a complex nonlinear problem with discrete and continuous variables represented in this paper by SOP installation and SR. The WCA is one of the most effective optimization algorithms, however, it may have difficulty striking a balance between exploration and exploitation due to the nature of the proposed nonlinear optimization problem, which mostly causes slow convergence and poor robustness. Consequently, the HWCA proposed in this paper is an efficient solution to improve the balance between exploration and exploitation, which in turn leads to improving the WCA’s overall performance without the possibility of getting trapped in local minima. Several cases are studied and conducted on an IEEE 33-node and the IEEE 69-node to investigate the real benefit gained from using SOPs alone or simultaneously with the SR. Based on the obtained results, the proposed HWCA succeeds in enhancing the performance of the proposed test systems considerably in terms of loss reduction (e.g., 31.1–63.3% for IEEE 33-node and 55.7–82.1% for IEEE 69-node compared to the base case) and voltage profile when compared to the base case while maintaining acceptable voltage magnitudes in most cases. Furthermore, the superiority of the proposed method based on the HWCA is validated when compared with the GA and WCA separately for both test systems. The obtained results show the outperformance of the proposed HWCA in attaining the best optimal solution with the least number of iterations.

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access.

In this paper, the electricity network automation based on Power Network Reconfiguration (PNR) is implemented to improve the operational reliability of distribution systems using jellyfish search algorithm. For this purpose, system average interruption frequency index (SAIFI), system average interruption unavailability index (SAIUI) and total energy not supplied (TENS) are critical measures. In this paper, a new optimization technique of jellyfish search (JFS) algorithm is employed for distribution network reconfiguration for reliability improvement. It is concerned with the moving patterns of jellyfish. They are divided into three categories. The jellyfish could flow towards the ocean current or between its own swarm. Meanwhile, whenever the food supply is adequate, the jellyfishes are attracted to its location. It is formulated considering the three reliability indices of SAIFI, SAIUI and TENS, simultaneously in a multi-objective model based on the weight factors. The proposed methodology based on the JFS optimizer is implemented on an IEEE 33-node distribution network. According to the numerical results, the SAIFI, SAIUI and TENS is improved by 36.44%, 34.11% and 33.35%, compared to the initial condition. For comparison purposes, tuna swarm optimizer and tunicate swarm algorithm, besides the JFS algorithm, are implemented as well. The simulation results declare the significant outperformance of the JFS algorithm compared to TUNA and TSA in terms of the obtained improvements and the regarding convergence properties.

Taking into account the international policies in the field of environmental protection in the world in general, and in the European Union in particular, the reduction of greenhouse gas (GHG) emissions, and primarily of carbon dioxide, has become one of the most important objectives. This can be obtained through various renewable energy sources and non-polluting technologies, such as the mixing of hydrogen and natural gas. Combining hydrogen with natural gas is an emerging trend in the energy industry and represents one of the most important changes in the efforts to achieve extensive decarbonisation. The importance of this article consists of carrying out a techno-economic study based on the simulation of annual consumptions regarding the construction and use of production capacities for hydrogen to be used in mixtures with natural gas in various percentages in the distribution network of an important operator in Romania. In order to obtain relevant results, natural gas was treated as a mixture of real gases with a known composition as defined in the chromatographic bulletin. The survey presents a case study for the injection of 5%, 10%, and 20% hydrogen in the natural gas distribution system of Bucharest, the largest city in Romania. In addition to conducting this techno-economic study, the implications for final consumers of this technical solution in reducing greenhouse gas emissions—mainly those of carbon dioxide from combustion—are also presented.

The use of power-electronics-based devices in distribution generation seeks to improve energy quality and reduce costs. The inverter-based distributed generator, that works in different operation modes, has emerged as a promising technology. In a high distributed generation penetration scenario it is important to know the voltage profile and fault information due to the uncertainty in the generator operation and the impact that have on the network. This study aims to use two proposed methods of analysis: for power flow, based on backward/forward sweep method, and short-circuit, based on hybrid impedance matrix, that considers the inverter operation modes and represents each generator as a voltage-controlled current source. The chosen network is the IEEE 34-Node Test Feeder with a generator on each load per phase. The voltage profiles obtained will be validated with a Simulink/Matlab phasorial model. The results show an average error of 2.39% and a gain in voltage profile processing time of 2185.24%, making its use consistent for larger systems.

The aim of this study is to improve the three-phase unbalanced voltage at the secondary side of a distribution transformer. The proposed method involves compensation sources injecting three different single-phase currents into the connected point of a grid. The computations of optimal single-phase currents are performed using the circuit analysis method and particle swarm optimization algorithm. An unbalanced three-phase power distribution system model is constructed, including a transformer Δ–Δ connection, V–V connection, load balance, load unbalance combination, and three single-phase compensation current sources. The results show that the voltage unbalance rate of the electricity user side is improved to less than 1%, and the three-phase total compensation apparent power is approximately 0 VA. In the future, the application of the model as an auxiliary service could be achieved by adding an energy storage system.

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, and permission for electricity sales to the upper utility on decreasing RES curtailment and distribution system operation costs. Noticeably, the RES curtailments became zero with the permission of bidirectional power flow. In addition, the adopted BESS model excluding binary variables was also validated. Finally, the effectiveness of the developed simulation analysis platform for day-ahead scheduling was demonstrated.

High penetration levels of Plug-in Electric Vehicles (PEVs) could cause stress on the network and might violate the limits and constraints under extreme conditions, such as exceeding power and voltage limits on transformers and power lines. This paper defines extreme conditions as the state of a load or network that breaks the limits of the constraints in an optimization model. Once these constraints are violated, the optimization algorithm might not work correctly and might not converge to a feasible solution, especially when the complexity of the system increases and includes nonlinearities. Hence, the algorithm may not help in mitigating the impact of penetrating PEVs under extreme conditions. To solve this problem, an original algorithm is suggested that is able to adapt the constraints’ limits according to the energy demand and the energy needed to charge the PEVs. Different case scenarios are studied for validation purposes, such as charging PEVs under different state of charge levels, different energy demands at home, and different pricing mechanisms. Results show that our original algorithm improved the profiles of the voltage and power under extreme conditions. Hence, the algorithm is able to improve the integration of a high number of PEVs on the distribution system under extreme conditions while preserving its stability.

In the last decade, logistic systems based on small aerial vehicles (drones) have become attractive for urban delivery operations as a sustainable alternative to ground vehicles because they are not affected by the congestion of the road network, thus allowing for faster and more reliable services. Aerial logistic systems, however, require a substantially different approach to operations management and need specifically designed supportive infrastructures. While the research on urban aerial delivery mostly focuses on the optimization vehicle operations, the appropriate design of supportive infrastructures is seldom considered. This paper focuses on the energy efficiency of aerial logistic systems, and proposes a new original methodology to obtain a realistic estimate of the overall energy consumed by a swarm of drones employed for urban delivery, taking into account the extension of the area served and its specific features. The methodology proposed offers relevant information for the decision problems related to the appropriate sizing of the infrastructures, the dimensioning of the swarm of drones and the capacity of the energy storage system. The paper also reports a comparison with ground vehicles in the same scenarios, showing the relevant tradeoffs. The results obtained demonstrate how an appropriate design of the supportive infrastructures for urban aerial logistics may significantly impact the overall efficiency of the delivery system.

The deployment of low-carbon hydrogen in gas grids comes with strategic benefits in terms of energy system integration and decarbonization. However, hydrogen thermophysical properties substantially differ from natural gas and pose concerns of technical and regulatory nature. The present study investigates the blending of hydrogen into distribution gas networks, focusing on the steady-state fluid dynamic response of the grids and gas quality compliance issues at increasing hydrogen admixture levels. Two blending strategies are analyzed, the first of which involves the supply of NG–H2 blends at the city gate, while the latter addresses the injection of pure hydrogen in internal grid locations. In contrast with traditional case-specific analyses, results are derived from simulations executed over a large number (i.e., one thousand) of synthetic models of gas networks. The responses of the grids are therefore analyzed in a statistical fashion. The results highlight that lower probabilities of violating fluid dynamic and quality restrictions are obtained when hydrogen injection occurs close to or in correspondence with the system city gate. When pure hydrogen is injected in internal grid locations, even very low volumes (1% vol of the total) may determine gas quality violations, while fluid dynamic issues arise only in rare cases of significant hydrogen injection volumes (30% vol of the total).

In this paper, a chance-constrained (CC) framework is developed to manage the voltage control problem of medium-voltage (MV) distribution systems subject to model uncertainty. Such epistemic uncertainties are inherent in distribution system analyses given that an exact model of the network components is not available. In this context, relying on the simplified deterministic models can lead to insufficient control decisions. The CC-based voltage control framework is proposed to tackle this issue while being able to control the desired protection level against model uncertainties. The voltage control task disregarding the model uncertainties is firstly formulated as a linear optimization problem. Then, model uncertainty impacts on the above linear optimization problem are evaluated. This analysis defines that the voltage control problem subject to model uncertainties should be modelled with a joint CC formulation. The latter is accordingly relaxed to individual CC optimizations using the proposed methods. The performance of proposed CC voltage control methods is finally tested in comparison with that of the robust optimization. Simulation results confirm the accuracy of confidence level expected from the proposed CC voltage control formulations. The proposed technique allows the system operators to tune the confidence level parameter such that a tradeoff between operation costs and conservatism level is attained.

The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other six locations were employed.

Today, the pace of development of decentralized transactive management systems has increased significantly due to growing renewable energy source technologies and communication infrastructure at the distribution system level. Such bilateral energy transactions have changed the structure of electricity markets and led to the emergence of a local energy market in electricity distribution. While examining this change of attitude, this paper analyzes the effects of local market formation on the performance and performance of distribution companies. Accordingly, the technical requirements in the three areas of operation, network control, and ICT in the new workspace are thoroughly examined. The hardware requirements will be presented in two parts for the end-user and the distribution systems. Then, the proposed local distribution market framework will be introduced, and finally, the conclusion will be presented.

Battery systems bring technical and economic advantages to electrical distribution systems (EDSs), as they conveniently store the surplus of cheap renewable generation for use at a more convenient time and contribute to peak shaving. Due to the high cost of batteries, technical and economic studies are needed to evaluate their correct allocation within the EDS. To contribute to this analysis, this paper proposes a stochastic mathematical model for the optimal battery allocation (OBA), which can be guided by the optimization of two different economic metrics: net present value (NPV) and internal rate of return (IRR). The effects of the OBA in the EDS are evaluated considering the stochastic variation of photovoltaic generation and load. Tests with the 33-node IEEE test system indicate that OBA results in voltage profile improvement (~1% at peak time), peak reduction (31.17%), increased photovoltaic hosting capacity (18.8%), and cost reduction (3.06%). Furthermore, it was found that the IRR metric leads to a different solution compared to the traditional NPV optimization due to its inherent consideration of the relation between cash flow and investment. Thus, both NPV and IRR-based allocation alternatives can be used by the decision maker to improve economic and technical operation of the EDS.

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

Modern power systems must provide efficient, reliable, and environmentally responsible energy. Recently, the inclusion of Microgrids (MGs) has allowed us to overcome some difficulties and face important challenges in this direction, especially related to the use of alternative energy sources. Increased and probabilistic demand, as well as limited energy supply, pose the need to evaluate the reliability of any distribution system (DS) when MGs are introduced. Here we reviewed and classified the state-of-the-art of reliability assessment (RA) in MGs. Initially, we contextualize RA in distribution systems. Next, each of the MGs subsystems components are introduced and the questions (1) why is it important to evaluate the reliability in Microgrids? (2) how do Microgrids influence the reliability of distribution systems? and (3) how does each of its subsystems influence the reliability of the Microgrids? are addressed. A total of 1395 research studies were published between 2002 and 2020. Using the PRISMA model, 147 met the inclusion criteria (71% correspond to research papers and 29% to reviews; 62% were published in journals, 34% were conference papers and 4% were books). The first study dates from 1971. Despite immense advances in MGs, we identified that (1) real test systems constitute an emerging trend; (2) although new MG configurations sound promising, the development and application of new RA techniques are a necessary step towards the identification of potential pitfalls of such architectures; (3) new RA methods or variations of the existing ones, whether analytical or simulation-based, are constantly being proposed, but their comparison for a particular DS, including MGs, are yet to be performed; and (4) more research studies are needed to assess how new control strategies and information and communications technology impact MGs reliability. Future lines of research could build upon these gaps to enhance reliability, especially when alternative energy resources are available.

Home energy management systems (HEMS) are a key technology for managing future electricity distribution systems as they can shift household electricity usage away from peak consumption times and can reduce the amount of local generation penetrating into the wider distribution system. In doing this they can also provide significant cost savings to domestic electricity users. This paper studies a HEMS which minimizes the daily energy costs, reduces energy lost to the utility, and improves photovoltaic (PV) self-consumption by controlling a home battery storage system (HBSS). The study assesses factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policy. Two management strategies have been used to control the HBSS; (1) a HEMS based on a real-time controller (RTC) and (2) a HEMS based on a model predictive controller (MPC). Several methods have been developed for home demand energy forecasting and PV generation forecasting and their impact on the HEMS is assessed. The influence of changing the battery’s capacity and the PV system size on the energy costs and the lost energy are also evaluated. A significant reduction in energy costs and energy lost to the utility can be achieved by combining a suitable overnight charging level, an appropriate sample time, and an accurate forecasting tool. The HEMS has been implemented on an experimental house emulation system to demonstrate it can operate in real-time.

State estimation of distribution systems typically relies on measurement sets with very low redundancy levels. In this paper, this fact is exploited by first solving a conventional load flow, using exclusively a critical set of measurements, and then compensating the solution to account for the few redundant measurements available. This leads to a suboptimal but sufficiently accurate estimate. It is shown how the sparse triangular factorization of the load flow Jacobian matrix can be fully exploited throughout the compensation-based procedure, preventing in this way the ill-conditioning associated with the gain matrix arising in the conventional least-squares formulation. Simulation results are provided for measurement configurations customarily found in distribution systems, showing the potential advantages of the proposed methodology.

This paper presents a new linear state estimation model based on a current summation load flow method for three-phase distribution systems. The developed estimator may be applied to both the supervision of distribution systems under normal operating conditions and the fault location in cases of low and high impedance faults. Our studies were conducted using a real distribution feeder. For fault location analysis, the system was modeled using the ATP (alternative transient program) in order to emulate measurements of voltages and currents at the substation, and voltage magnitudes registered by other meters during the fault. We used the MATLAB™ software to process the algorithms. The main contributions that arose after integrating the current method into system supervision in case of network failures under normal operating conditions and the fault location are as follows: (i) estimation of system losses; (ii) modeling of loads in real time to consider their contributions in the fault location process; and (iii) low influence of fault resistance in the location algorithm. The results show that the proposed method has good precision, low computational processing time, and is promising for distribution system supervision with a reduced number of meters.

The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization of faults and proper actions of the protection system. In this paper, we propose a data-driven fault localization strategy based on multi-level system regionalization and the quantification of fault detection results in all subsystems/subregions. This strategy relies on the tree segmentation criterion to divide the entire system under study into several subregions, and then combines Support Vector Data Description (SVDD) and Kernel Density Estimation (KDE) to find the confidence level of fault detection in each subregion in terms of their corresponding p-values. By comparing the p-values, one can accurately localize the faults. Experiments demonstrate that the proposed data-driven fault localization can greatly improve the accuracy of fault localization for distribution systems with high DER penetration.

In recent years, distributed generation and a large number of sensitive AC and DC loads have been connected to distribution networks, which introduce a series of challenges to distribution network operators (DNOs). In addition, the advantages of DC distribution networks, such as the energy conservation and emission reduction, mean that the voltage source converter based multi-terminal direct current (VSC-MTDC) for AC/DC distribution systems demonstrates a great potential, hence drawing growing research interest. In this paper, considering losses of the reactor, the filter and the converter, a mathematical model of VSC-HVDC for the load flow analysis is derived. An AC/DC distribution network architecture has been built, based on which the differences in modified equations of the VSC-MTDC-based network under different control modes are analyzed. In addition, corresponding interface functions under five control modes are provided, and a back/forward iterative algorithm which is applied to power flow calculation of the AC/DC distribution system including VSC-MTDC is proposed. Finally, by calculating the power flow of the modified IEEE14 AC/DC distribution network, the efficiency and validity of the model and algorithm are evaluated. With various distributed generations connected to the network at appropriate locations, power flow results show that network losses and utilization of transmission networks are effectively reduced.

The integration of renewable distributed generation into distribution systems has been studied comprehensively, due to the potential benefits, such as the reduction of energy losses and mitigation of the environmental impacts resulting from power generation. The problem of minimizing energy losses in distribution systems and the methods used for optimal integration of the renewable distributed generation have been the subject of recent studies. The present study proposes an analytical method which addresses the problem of sizing the nominal power of photovoltaic generation, connected to the nodes of a radial distribution feeder. The goal of this method is to minimize the total energy losses during the daily insolation period, with an optimization constraint consisting in the energy flow in the slack bus, conditioned to the energetic independence of the feeder. The sizing is achieved from the photovoltaic generation capacity and load factors, calculated in time intervals defined in the typical production curve of a photovoltaic unit connected to the distribution system. The analytical method has its foundations on Lagrange multipliers and relies on the Gauss-Jacobi method to make the resulting equation system solution feasible. This optimization method was evaluated on the IEEE 37-bus test system, from which the scenarios of generation integration were considered. The obtained results display the optimal sizing as well as the energy losses related to additional power and the location of the photovoltaic generation in distributed generation integration scenarios.

In order to systematically shift existing control and management paradigms in distribution systems to new interoperable communication supported schemes in smart grids, we need to map newly developed use cases to standard reference models like Smart Grid Architecture Model (SGAM). From the other side, any new use cases should be tested and validated ex-ante before being deployed in the real-world system. Considering various types of actors in smart grids, use cases are usually tested using co-simulation platforms. Currently, there is no efficient co-simulation platform which supports interoperability analysis based on SGAM. In this paper, we present our developed test platform which offers a support to design new use cases based on SGAM. We used this platform to develop a new scheme for wide area monitoring of existing distribution systems under growing penetration of Photovoltaic production. Off-the-shelf solutions of state estimation for wide area monitoring are either used for passive distribution grids or applied to the active networks with wide measurement of distributed generators. Our proposed distribution state estimation algorithm does not require wide area measurements and relies on the data provided by a PV simulator we developed. This practical scheme is tested experimentally on a realistic urban distribution grid. The monitoring results shows a very low error rate of about 1 % by using our PV simulator under high penetration of PV with about 30 % error of load forecast. Using our SGAM-based platform, we could propose and examine an Internet-of-Things-based infrastructure to deploy the use case.

Fault location in electrical energy distribution networks is an important task, as faults in distribution grids are among the main causes of electricity supply disruption. Fault location in the distribution systems, however, is a challenging task because of the topology of the distribution networks, as well as the main and side branches. Therefore, it is necessary to address these challenges through an intelligent approach to fault location. In this paper, fault location in electric energy distribution networks is addressed considering the changes in fault distance and fault resistance in the presence of different fault types. A new method for fault location is developed for conditions where the minimum information is available and only information at the beginning of the feeder is used. This facilitates wide adoption of the technique as it does not require significant investments in instrumentation and measurement. The proposed intelligent method is based on the impedance and transient state estimation. This technique employs a specific impedance analysis for determining possible fault locations considering the unbalanced performance of distribution systems, distances, and different fault resistances. To determine the real faulty section, real fault frequency component analysis and the simulated faults at possible fault locations are used. At this stage of the process, it is possible to eliminate multiple estimations with the help of comparison and identification of the similarities. Therefore, a real faulty section is determined. It is observed that some conditions of electric energy distribution networks affect the accuracy and performance of the proposed method significantly; thus, a detailed investigation is conducted to neutralize these conditions. Simulation results and calculations based on MATLAB along with a practical test of the proposed method in power network simulator confirm a satisfactory performance.

This paper introduces a brief analysis on hosting capacity and related concepts as applied to distribution network systems. Furthermore, it addresses the applicability of hosting capacity study methodologies to harmonic voltage distortion caused by photovoltaic panels (PV) connected at a low-voltage (LV) side of a university campus grid. The analysis of the penetration of new distributed generation technologies, such as PV panels, in the distribution grid of the campus was carried out via measurement processes, and later by computer simulations analyzing a new concept of the hosting capacity approach in relation to voltage harmonics distortion. The voltage rise due to harmonic injection is analyzed and discussed with the aim of validating the discussed model and also putting forward recommendations for connecting PV generation across other network systems.

Feeding biomass-fueled gas engines (BFGEs) with olive tree pruning residues offers new opportunities to decrease fossil fuel use in road vehicles and electricity generation. BFGEs, coupled to radial distribution systems (RDSs), provide renewable energy and power that can feed electric vehicle (EV) charging stations. However, the combined impact of BFGEs and EVs on RDSs must be assessed to assure the technical constraint fulfilment. Because of the stochastic nature of source/load, it was decided that a probabilistic approach was the most viable option for this assessment. Consequently, this research developed an analytical technique to evaluate the technical constraint fulfilment in RDSs with this combined interaction. The proposed analytical technique (PAT) involved the calculation of cumulants and the linearization of load-flow equations, along with the application of the cumulant method, and Cornish-Fisher expansion. The uncertainties related to biomass stock and its heating value (HV) were important factors that were assessed for the first time. Application of the PAT in a Spanish RDS with BFGEs and EVs confirmed the feasibility of the proposal and its additional benefits. Specifically, BFGEs were found to clearly contribute to the voltage constraint fulfilment. The computational cost of the PAT was lower than that associated with Monte-Carlo simulations (MCSs).

This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

We present a framework for the design and simulation of electrical distribution systems and short term electricity markets specific to the UK. The modelling comprises packages relating to the technical and economic features of the electrical grid. The first package models the medium/low distribution networks with elements such as transformers, voltage regulators, distributed generators, composite loads, distribution lines and cables. This model forms the basis for elementary analysis such as load flow and short circuit calculations and also enables the investigation of effects of integrating distributed resources, voltage regulation, resource scheduling and the like. The second part of the modelling exercise relates to the UK short term electricity market with specific features such as balancing mechanism and bid-offer strategies. The framework is used for investigating methods of voltage regulation using multiple control technologies, to demonstrate the effects of high penetration of wind power on balancing prices and finally use these prices towards achieving demand response through aggregated prosumers.

With the penetration of distributed generators (DGs), operation planning studies are essential in maintaining and operating a reliable and secure power system. Appropriate siting and sizing of DGs could lead to many positive effects forthe distribution system concerned, such as the reduced total costs associated with DGs, reduced network losses, and improved voltage profiles and enhanced power-supply reliability. In this paper, expected load interruption cost is used as the assessment of operation risk in distribution systems, which is assessed by the point estimate method (PEM). In light with the costs of system operation planning, a novel mathematical model of chance constrained programming (CCP) framework for optimal siting and sizing of DGs in distribution systems is proposed considering the uncertainties of DGs. And then, a hybrid genetic algorithm (HGA), which combines the GA with traditional optimization methods, is employed to solve the proposed CCP model. Finally,the feasibility and effectiveness of the proposed CCP model are verified by the modified IEEE 30-bus system, and the test results have demonstrated that this proposed CCP model is more reasonable to determine the siting and sizing of DGs compared with traditional CCP model.