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In the low-voltage distribution system of the USA, Canada and some countries of Central and South America, the most used configuration is the single-phase three-wire system (120 V/240 V) also known as the split-phase distribution system. When the neutral wire of the distribution system gets damaged or broken the current returns through the ground and a floating neutral condition arises. Service to the house continues without interruptions because no high over-currents come up. If the return path impedance is high enough, the equally balanced voltage system gets shifted, going out of boundaries and causing malfunctions in the appliances or even fire. A new classification-based detector is proposed to detect this condition, which only needs current measurements that the actual generation of form 2S meter gathers. Moreover, due to the simplicity of the algorithm, it can be embedded in the current generation of meters, which represents great potential of the detector. To that end, the low-voltage distribution system is modelled using a real database and some assumptions are made. The proposed novel detector approach shows zero false alarms in the houses tested and a detection time that allows the fault to be detected before significant damage occurs to the house.

This paper presents a methodology for optimizing the short-term operation of electric vehicle (EV) charging and discharging while considering the potential curtailment of active power due to volt-var control (VVC) prioritizing reactive power output. The proposed approach involves exchanging information between the EV aggregator and the distribution system operator (DSO). This approach allows the EV aggregator to optimize EV charge/discharge schedules while considering voltage-related constraints in the distribution system (DS). Initially, the aggregator shares the optimized schedule with the DSO to estimate the anticipated active power reduction through power flow analysis. Subsequently, the aggregator revises the constraint on active power output to avoid its expected curtailment and performs a second optimization for EV charging and discharging operation. Numerical simulations conducted on a realistic DS model in Japan validate the effectiveness of the proposed method in enhancing profitability in the day-ahead market while ensuring the quality of DS voltage. The results demonstrate an increase in profit by shifting the time of EV charging and discharging based on shared information from the DSO.

The high penetration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) into the grid introduces new challenges for Distribution Systems (DSs). The uncertainties related to these assets necessitate the development of real-time methodologies to optimize the operation of Low Voltage (LV) and Medium Voltage (MV) DSs. This paper aims to fill the gap in the literature by proposing a holistic real-time DS optimization model that considers the coupling of MV and LV DSs. Specifically, the methodology adopts a bottom-up three-layer approach. At the first layer an optimal EV Smart Charging Scheduling (SCS) methodology is applied for power losses minimization at the LV DSs, considering the characteristics of individual households (maximum rated power of the electrical installation, Photovoltaic generation, and load and EV charging demand). The second layer introduces a residential controller that fully exploits the flexibility of EVs, minimizing the impact of forecasting errors while satisfying limitations regarding households’ overloading protection. The third layer involves a real-time Network Reconfiguration (NR) methodology, considering real-time power transactions between MV and LV DSs, and determining the optimal topology through a cost-worth analysis of power loss reduction and switch operation costs. The overall design of the proposed methodology ensures broader adoption, repeatability, adaptability, and scalability across diverse DSs, including various types of LV DSs (residential, commercial, etc.) and different MV DS configurations. The proposed methodology can reduce DS power losses by up to 34.41% compared to the base scenario, which involves the operation of the DS without employing either EV SCS or NR.

The classical fast decoupled power flow method is an efficient, important, and commonly used method for solving load flow problems in power systems. Unfortunately, this method encounters convergence problems when used to obtain power flow solutions in radial distribution systems with high r/x ratio feeders. An efficient tool for calculating the complex voltages at all nodes is a prerequisite for reliable operation of radial distribution automation. To this end, research on a reliable fast decoupled power flow method for radial distribution systems remains an outstanding issue. This study presents an effective approach for implementing a fast decoupled-like algorithm to solve the power flow problem in a radial distribution system. The mathematical formulation of the problem was developed by utilizing node injection into a branch current matrix (NIBC). The nodal voltages and system parameters are described using rectangular coordinates. The mathematical model was described using a system of nonlinear algebraic real equations. These equations were numerically linearized by applying the Newton-Raphson iterative scheme. The elements of the Jacobian matrix were simplified and approximated to yield a decoupled constant Jacobian submatrix. These constant submatrices are inverted prior to the iterative process. The proposed algorithm was tested on several small- and large-scale radial distribution systems. The results revealed that the proposed method exhibits remarkable convergence characteristics for a wide range of system parameters. The proposed mathematical model can be used in other basic studies on radial distribution networks.

Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation (DG). However, the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available components. To improve the accuracy of DG capacity assessment, this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust approaches. The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart methodology. To achieve a tractable formulation, we first define uncertain chance constraints through distributionally robust conditional value-at-risk (CVaR), which is then reformulated into convex quadratic constraints. We subsequently solve the resulting large-scale, yet convex, model in a distributed fashion using the alternating direction method of multipliers (ADMM). Through numerical simulations, we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15% and 40%, respectively, in terms of total installed DG capacity.

Accurate distribution system models are becoming increasingly critical for grid modernization tasks, and inaccurate phase labels are one type of modeling error that can have broad impacts on analyses using the distribution system models. This work demonstrates a phase identification methodology that leverages advanced metering infrastructure (AMI) data and additional data streams from sensors (relays in this case) placed throughout the medium-voltage sector of distribution system feeders. Intuitive confidence metrics are employed to increase the credibility of the algorithm predictions and reduce the incidence of false-positive predictions. The method is first demonstrated on a synthetic dataset under known conditions for robustness testing with measurement noise, meter bias, and missing data. Then, four utility feeders are tested, and the algorithm’s predictions are proven to be accurate through field validation by the utility. Lastly, the ability of the method to increase the accuracy of simulated voltages using the corrected model compared to actual measured voltages is demonstrated through quasi-static time-series (QSTS) simulations. The proposed methodology is a good candidate for widespread implementation because it is accurate on both the synthetic and utility test cases and is robust to measurement noise and other issues.

The increasing availability of used Electric Vehicle Batteries (EVBs), as well as the environmental concerns associated with their disposal, have led to increased interest in reusing them in grid-scale stationary energy storage systems. This paper proposes the utilization and control of repurposed EVBs in Community Energy Storage Systems (CESSs) in Low-Voltage (LV) distribution networks. A real-time Community Energy Management System (CEMS) is proposed for the control of the CESS, which uses repurposed EVBs and a power electronic interface comprised of a four-quadrant ac/dc converter and a bidirectional dc/dc converter together with the corresponding controllers. To assess the capability of the controller in providing CESS services in the form of active and reactive power control in an LV residential distribution network, four case studies are carried out using Hardware-in-the-Loop (HIL) simulations. The HIL simulations use an actual battery pack and a four-quadrants converter system. The impact of the proposed controller on the repurposed EVBs’ cycling performance is evaluated using a Rainflow Counting Algorithm (RCA). The distribution transformer Loss of Life (LoL) is also estimated. The HIL simulation results demonstrate the effectiveness of the proposed real-time CEMS in slowing down the degradation of the battery and LoL of the distribution transformer, while improving the LV distribution system’s voltage profile.

The integration of solar photovoltaic systems into low-voltage distribution networks is witnessing significant global growth. While solar photovoltaic generation offers numerous benefits, exceeding the hosting capacity limits in these networks remains a major technical challenge for network operation, particularly in terms of voltage management. Modern smart inverters are equipped with reactive power, active power, and Power Factor (PF) control features, which can assist in regulating network voltage levels. This paper presents a comparative evaluation of smart inverter control methods (reactive power and PF) to achieve maximum solar PV system penetration without impacting the voltage profile at the Point of Common Coupling (PCC). Additionally, a Battery Energy Storage System (BESS) is employed to enhance the system’s hosting capacity. The active power, reactive power, and bus voltage of the system are analyzed under different control methods using MATLAB/Simulink to determine the most effective approach for achieving maximum hosting capacity without compromising bus voltage. The modeling includes a PV system connected to the grid with various control strategies. The results demonstrate an increase in the Hosting Capacity (HC) of the network, thereby improving grid characteristics. The integration of smart inverter functionalities will greatly facilitate the integration of PV solar installations into electricity grids.

Electric Vehicles (EVs) are known to be future mode of transportation because of their environment-friendly nature. The increase in electric vehicle (EV) penetration needs to set up the new charging stations to meet the demand. The EV charging shows the negative impact on distribution system and system failures lead to the unavailability of power to charge EVs. The EVs not charging due to system failure is to be considered but ignored in the previous studies. Incorporating the Vehicle-to-Grid (V2G) technologies into charging station (CS) improves the system reliability. In this paper, solar rooftop PV units are integrated with CSs to overcome the negative impacts of EV charging and further enhance the reliability of the system. To extracts the maximum benefits from the solar PV integrated charging stations (PVCS), optimal placement is done with objective of reliability improvement. EV reliability is evaluated by using a novel index called as expected energy not charged (EENC). The reliability of both distribution system and EVs are considered as objective functions simultaneously, hence, placement problem becomes multi-objective. The optimal placement is done by considering different EV penetration levels. A multi-objective Grasshopper optimization algorithm (MOGOA) is applied to solve the optimal placement problem of PVCS. The EENS value is improved by 6.18% and 13.9% as compared to base case for case 1 and case 2 respectively. The EENC is improved by 11.51% in case 2 as compared to case 1.

The aggregation of distributed energy resources (DERs) enables them to provide various grid services as a virtual power plant (VPP). Utilities use enterprise control solutions, such as advanced distribution management systems (ADMS) and distributed energy resource management systems (DERMS), to efficiently integrate DERs and realize the benefits of a VPP. These control solutions can complement each other to offer additional benefits. This paper evaluates the coordinated operation of an ADMS and a DERMS that collectively implements a VPP to provide peak demand reduction and voltage regulation through the simulation of an actual distribution feeder. A commercial ADMS reduces the peak demand through conservation voltage reduction (CVR). A prototype DERMS dispatches residential battery energy storage systems (BESS) based on real-time optimal power flow to provide additional peak demand reduction. The DERMS also maintains voltage regulation across the feeder by controlling both residential batteries and rooftop PV systems. The results from the controller-hardware-in-the-loop (CHIL) real-time simulations conducted in a realistic laboratory environment show that the coordinated operation of the ADMS and the DERMS effectively achieves peak demand reduction while enforcing voltage regulation across the feeder. Specifically, the ADMS dynamic voltage regulation (DVR) application and DERMS working together achieved a peak demand reduction of nearly 500 kW, whereas the ADMS DVR application alone obtained a reduction of approximately 100 kW. The DERMS VPP control in this work relies on the residential BESS for the demand reduction; the demand reduction accomplished depends on the BESS capacity available in the distribution system.

Weather-related failures significantly challenge the reliability of distribution systems. To enhance the risk management of weather-related failures, an interpretable extra-trees based weather-related risk prediction model is proposed in this study. In the proposed model, the interpretability is successfully introduced to extra-trees by analyzing and processing the paths of decision trees in extra-trees. As a result, the interpretability of the proposed model is reflected in the following three respects: it can output the importance, contribution, and threshold of weather variables at high risk. The importance of weather variables can help in developing a long-term risk prevention plan. The contribution of weather variables provides targeted operation and maintenance advice for the next prediction period. The threshold of weather variables at high risk is critical in further preventing high risks. Compared with the black-box machine learning risk prediction models, the proposed model over-comes the application limitations. In addition to generating predicted risk levels, it can also provide more guidance information for the risk management of weather-related failures.

With the increasing use of renewable resources and electric vehicles (EVs), the variability and uncertainty in their nature put forward a high requirement for flexibility in AC distribution system incorporating voltage source converter (VSC) based multi-terminal direct current (MTDC) grids. In order to improve the capability of distribution systems to cope with uncertainty, the flexibility enhancement of AC-MTDC distribution systems considering aggregated EVs is studied. Firstly, the charging and discharging model of one EV is proposed considering the users' demand difference and traveling needs. Based on this, a vehicle-to-grid (V2G) control strategy for aggregated EVs to participate in the flexibility promotion of distribution systems is provided. After that, an optimal flexible dispatching method is proposed to improve the flexibility of power systems through cooperation of VSCs, controllable distributed generations (CDGs), aggregated EVs, and energy storage systems (ESSs). Finally, a case study of an AC-MTDC distribution system is carried out. Simulation results show that the proposed dispatching method is capable of effectively enhancing the system flexibility, reducing renewable power curtailment, decreasing load abandonment, and cutting down system cost.

Obtaining the real-time state of the distribution system is the basis for intelligent operation of the power system. With the surge in new energy generation and the volatility in load demands, traditional state estimation methods face significant challenges. The distribution system requires frequent topology reconfigurations to maintain stable operation. However, current data-driven methods typically cater only to specific topologies. To address this issue, a complete distribution system state estimation (DSSE) framework is proposed to adapt to frequent topology reconfigurations. To ensure data quality, a measurement device configuration algorithm using node importance was designed. Then, a convolutional neural network-based topology identification model is utilized to provide real-time topology data to the DSSE, which uses measured data pre-processed by the Gramian corner field. Finally, we use graph attention network to model the DSSE as a node-level regression prediction problem on a graph abstracted from the distribution system. Simulation results on IEEE 33-bus and IEEE 118-bus distribution systems illustrate the feasibility and efficiency of the proposed framework. Further experiments show that the proposed framework has good robustness.

The growing integration of distributed energy resources into distribution systems poses challenges for voltage regulation. Dynamic VAR Compensators (DVCs) are a new generation of power electronics-based Volt/VAR compensation devices designed to address voltage issues in distribution systems with a high penetration of renewable generation resources. Currently, the IEEE Std. 1547-based Volt/VAR Curve (VV-C) is widely used as the local control scheme for controlling a DVC. However, the effectiveness of this scheme is not well documented, and there is limited literature on alternative control and placement schemes that can maximize the effective use of a DVC. In this paper, we propose an optimal dispatch and control mechanism to enhance the conventional VV-C based localized DVC control. First, we establish a multi-objective optimization framework to identify the optimal dispatch strategy and suitable placement for the DVC. Next, we introduce two supervisory control strategies to determine the appropriate instances for adjusting the VV-C when the operating condition changes. The outlined scheme comprises two primary stages: time segmentation and VV-C fitting. Within this framework, each time segment aims to produce optimized Q-V trajectories. The proposed method is tested on a modified IEEE 123-bus test system using OpenDSS for a wide range of operating scenarios, including sunny and cloudy days. Simulation results demonstrate that the proposed scheme effectively reduces voltage variations compared to the standard VV-C specified in IEEE Std. 1547.

The variability of the output power of distributed renewable energy sources (DRESs) that originate from the fast-changing climatic conditions can negatively affect the grid stability. Therefore, grid operators have incorporated ramp-rate limitations (RRLs) for the injected DRES power in the grid codes. As the DRES penetration levels increase, the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service (AS). Energy storage systems (ESSs) coordinated by RR control algorithms are often applied to mitigate these power fluctuations. However, no unified definition of active power ramps, which is essential to treat the RRL as AS, currently exists. This paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS (BESS) in distribution systems (DSs). The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system (TS). The proposed control is based on the direct control of the ΔP/Δt, which means that the control parameters are directly correlated with the RR requirements included in the grid codes. In addition, a novel method for restoring the state of charge (SoC) within a specific range following a high ramp-up/down event is proposed. Finally, a parametric method for estimating the sizing of central BESSs (BESS sizing for short) is developed. The BESS sizing is determined by considering the RR requirements, the DRES units, and the load mix of the examined DS. The BESS sizing is directly related to the constant RR achieved using the proposed control. Finally, the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.

High Impedance Faults (HIFs) are recurring events in electrical Distribution Systems (DSs) and occur by the contact between energized conductors and high impedance surfaces. HIFs may pose hazards to living beings and cause bushfires. However, the HIF protection has not been completely solved due to the small fault current and varying impedance, inhibiting traditional protection techniques from functioning correctly. In the literature, researchers have mainly focused on detection techniques. Thus, the development of HIF Location Methods (HIFLMs) is recent, and evidences for conclusive solutions are still lacking. Moreover, to this date, no existing study reviews the main challenges concerning HIFLMs in DSs. This paper proposes a systematic analysis of the common stages to design the main existing HIFLMs. The strategy is evaluating the similar characteristics that pose a common research path regarding challenges faced in real-world conditions. Additionally, this paper proposes a case study to assess the best input signals, metrics, and machine learning-based decision algorithms of a new HIFLM. The results are promising, with high identification rates, even in noisy conditions. The methodology can help to select the datasets for supervised learning-based HIFLM. Highlighting the state-of-art of current methods and support development of HIFLMs are this paper’s main contributions.

Increasing intermittent renewable energy sources (RESs) intensifies the imbalance between demand and generation, entailing the diversification of the deployment of electrical energy storage systems (ESSs). A large-scale biogas plant (LBP) installed with heating devices and biogas energy storage (BES) usually exhibits a storage-like characteristic of accommodating an increasing penetration level of RES in rural areas, which is addressed in this paper. By utilizing the temperature-sensitive characteristic of anaerobic digestion that enables the LBP to exhibit a storage-like characteristic, this paper proposes a bi-level energy trading model incorporating LBP and demand response aggregator (DRA) simultaneously. In this model, social welfare is maximized at the upper level while the profit of DRA is maximized at the lower level. Compared with cases only with DRA, the results show that the proposed model with the LBP improves the on-site accommodation capacity of photovoltaic (PV) generation up to 6.3%, 18.1%, and 18.9% at 30%, 40%, and 50% PV penetration levels, respectively, with a better economic performance. This nonlinear bi-level problem is finally recast by a single-level mathematical program with equilibrium constraints (MPEC) using Karush-Kuhn-Tucker (KKT) conditions and solved by the Cplex solver. The effectiveness of the proposed model is validated using a 33-bus test system and a sensitivity analysis is provided for analyzing what parameter influences the accommodation capacity most.

The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances. Though the dynamic pricing environment results in several benefits to the utilities and consumers, it also poses some challenges. The crowding among residential customers is one of such challenges. The scheduling of loads at low-cost intervals causes crowding among residential customers, which leads to a fall in voltage of the distribution system below its prescribed limits. In order to prevent crowding phenomena, this paper proposes a priority-based demand response program for local energy communities. In the program, past contributions made by residential houses and demand are considered as essential parameters while calculating the priority factor. The non-linear programming (NLP) model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding phenomena. Since the NLP model does not guarantee global optima due to its non-convex nature, a second-order cone programming model is proposed, which captures power flow characteristics and guarantees global optimum. The proposed formulation is solved using General Algebraic Modeling System (GAMS) software and is tested on a 12.66 kV IEEE 33-bus distribution system, which demonstrates its applicability and efficacy.

A shared pool of grid-scale storage resources called Cloud Energy Storage (CES) can bring substantial benefits to the economical and reliable operation of MGs. However, the investment cost of CES may not be affordable for a single microgrid (MG). As a solution, we propose an approach in which neighboring microgrids in a distribution network collaborate and form a multi-microgrid (multi-MG) to install a shared CES to increase their profit and improve their reliability. Different investment scenarios are evaluated by considering the yearly reward from TSO and DSO. For each of the investment scenarios, TSO and DSO give a yearly reward based on the contribution of CES in peak-shaving and distribution network operation yearly cost reduction. Afterward, a decision table is provided in which, for all investment scenarios, profit, reliability index based on expected energy not supplied (ENS), and TSO-DSO yearly reward are determined. Finally, the microgrids select one of the investment scenarios using a multi-attribute decision-making approach. Simulation results of a case study validate the effectiveness of the proposed collaborative decision-making framework in increasing the economic value of CES investment, reliability enhancement in multi-MG, and peak-shaving.

Load modeling is essential to distribution system analysis, planning, and control. Therefore, in this work, effect of non-linear load models has been considered for the optimal site and size of DG and SC allocation. A new and efficient modified branch and bus ordering-based forward-backward load flow method has been applied to solve load flow problem in radial distribution system. Recently implemented several state-of-the-art evolutionary algorithms (EAs) are employed to solve optimal site and size of DG and SC allocation problems and it is shown that performance of multi-operator/multimethod is better than other algorithms that are based on a single operator and/or algorithm. Therefore, a new hybrid EA based on various state-of-the-art operators such as GA, DE, and PSO is designed and applied to solve optimal site and size of DG and SC allocation problems. Various technical objective functions (index of active and reactive power loss and voltage deviation index) are considered to show the impacts of non-linear load models. From the simulation results, it is shown that DG and SC allocation problem is multi-objective. Therefore, further weighted sum multi-objective technical, economic, and environmental functions are formulated to find the solution to DG and SC allocation problems. The gathered results demonstrate that the proposed methodology significantly minimizes the cost of energy supplied by grid, total operating cost, and active and reactive power losses. Consequently, it can be stated that the suggested methodology has considerable economic and technological benefits and may be used to address many optimization issues in various distribution networks.

The increasing expansion of devices such as electric vehicle charging stations, renewable energy resources, and electric subways, will cause an imbalance between load and production in the distribution network, which will lead to an increase in power losses, a decrease or increase in voltage and ultimately impose more costs on the independent system operator (ISO). In this paper, we have presented a mixed integer quadratic programming (MIQP) optimization model to improve the distribution network performance in the presence of these devices. The proposed model is a mixed integer model including demand side management modeling, energy storage system, battery to subway (B2S) system, optimal control of on-load tap changer (OLTC), step voltage regulator (SVR), fossil generation resources, renewable energy and capacitors, and shunt reactors. The considered multi-objective function is a scenario-based stochastic model, which accurately models the uncertainties in renewable energy resources. Paying attention to the nature of the proposed model will guarantee globally optimal solutions. The proposed model runs on the standard network of 33 buses, and the simulation results guarantee the optimal and accurate performance of the proposed model. The simulation results demonstrate that the outage of three distributed generation units from the network causes an average increase of 55% in energy losses, and the outage of three renewable units leads to an increase in emissions of about 40%. Another point received from the results illustrated that with the implementation of the proposed model under any load level of the network, as well as the outage of fossil and renewable distributed generation (DG) resources, no load shedding will occur in the network during the 24 hours. Finally, due to the short execution run time, the proposed model can be used for real and online grids.

The building sector is responsible for about 7% of overall greenhouse gas emissions in the US. Cutting the emissions by electrifying heating and cooling supply through heat pumps (HPs) leads to an increase in electricity demand and potential overloading of lines and transformers in electricity distribution systems. Although many studies investigate the maximum potential for HPs in existing distribution systems in Europe, they neglect a potential relieving effect of combining HPs with rooftop photovoltaic (PV) systems as well as the consequence of coupling the electricity and gas system at distribution level. Hence, we investigate the effect of HPs and rooftop PV systems in a representative distribution system in Northeast US and the potential of coupling electricity and gas distribution systems. We show that generally no overloading in average US electric distribution systems occurs even under high realistic HP and PV adoption rates, 40% and 26% respectively. Moreover, our results show that combining HPs and rooftop PV reduces the impact on the distribution system throughout the year with the greatest reduction in spring and fall. In contrast, the potential for injecting hydrogen on distribution level is technically very limited and not economic. Under a 20vol.-% blending strategy, CO textsubscript 2 emissions would barely decrease 7%, while energy costs would skyrocket due to the high cost of producing green hydrogen from electrolysis against blue hydrogen from steam methane reforming. Moreover, electrolyzers (ELZs) at distribution level are neither able nor needed to reduce congestion in the electricity system.

Switches (SWs) and renewable distributed generations (RDG) are subject to failure as well as other components of a system that are usually presumed fully reliable in the literature. It can lead to an overestimation of their contribution to reliability enhancement disregarding their dysfunction. This study considers the failure rate of SWs and RDGs to assess their impact on the reliability cost of the system and their effect on the optimal placement using a Markov-based approach. The optimal placement of SWs and RDGs in radial distribution systems is carried out simultaneously aim at minimizing the overall cost including investment, maintenance of SWs, and RDGs alongside the interruption cost. The performance of the model is verified through different scenarios and tested on RBTS bus 2. The results show that high values of the failure rates lead to fewer allocations to prevent plummeting the reliability of the system and increasing the capacity of the RDG results in fewer SWs allocation.

Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems. However, traditional intelligent methods limit the use of the physical structures and data information of power networks. To this end, this study proposes a fault diagnostic model for distribution systems based on deep graph learning. This model considers the physical structure of the power network as a significant constraint during model training, which endows the model with stronger information perception to resist abnormal data input and unknown application conditions. In addition, a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability. This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults. In addition, a multi-task learning framework is constructed for fault location and fault type analysis, which improves the performance and generalization ability of the model. The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model. Finally, different fault conditions, topological changes, and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model. Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.

In the existing multi-period robust optimization methods for the optimal power flow in radial distribution systems, the capability of distributed generators (DGs) to regulate the reactive power, the operation costs of the regulation equipment, and the current of the shunt capacitor of the cables are not considered. In this paper, a multi-period two-stage robust scheduling strategy that aims to minimize the total cost of the power supply is developed. This strategy considers the time-of-use price, the capability of the DGs to regulate the active and reactive power, the action costs of the regulation equipment, and the current of the shunt capacitors of the cables in a radial distribution system. Furthermore, the numbers of variables and constraints in the first-stage model remain constant during the iteration to enhance the computation efficiency. To solve the second-stage model, only the model of each period needs to be solved. Then, their objective values are accumulated, revealing that the computation rate using the proposed method is much higher than that of existing methods. The effectiveness of the proposed method is validated by actual 4-bus, IEEE 33-bus, and PG 69-bus distribution systems.

This paper presents a comprehensive grid impact analysis design and corresponding mitigation strategies for heavy-duty electric vehicle (EV) charging stations. The charging load of heavy-duty charging station can reach several megawatts, which could induce adverse impacts on the distribution grid if not effectively mitigated. To analyze the impacts and provide corresponding solutions, we select four representative distribution systems—including both single-feeder cases and a multi-feeder case—and design thorough test metrics for the impact analysis. The charging load profiles used in the analysis are derived from realistic conventional heavy-duty vehicle travel data. Based on the analysis results, charging stations are placed at three different representative locations in each distribution system: best, good, and worst locations. Mitigation strategies using a combination of smart charger functionality, on-site photovoltaic (PV) generation, and on-site energy storage (ES) are proposed and tested. A sizing method is also proposed to find the optimal PV-ES-charger capacity that minimizes the capital cost.

Within the distribution system operator (DSO) framework, there could be alternative arrangements to enable market participation by all entities, including the privately owned distributed energy resources (DERs). In many cases, the retailers and similar aggregating agencies facilitate this. Even before finalizing the modalities of the market at the distribution level, it is essential to know about the possible market outcome and its impact on the operational decisions of DSO and vice-versa. This paper presents a simulation framework and associated case studies, where the market outcome and DSO’s operational activities are carried out in tandem to establish a realistic and feasible outcome. The market model is represented by a non-cooperative game where an efficient and fair solution is obtained using Nash equilibrium. For this, multi-player power transaction problem (MPTP) is solved using complementarity modeling. The proposed MPTP is further compounded with network reconfiguration and loss considerations which are the part of DSO’s operations. The inclusion of reconfiguration and loss allocation to entities makes the outcome realistic and feasible one. The proposed simulation framework is implemented on various test cases with a heterogeneous set of participants to establish its effectiveness.

Fast and accurate power-flow methods are of great importance, especially in near real-time optimal operation of power systems. This importance will be even more highlighted in the presence of more repetitions of power-flow calculations, which cause more computational complexities in optimization problems. As a solution, in this paper, a novel fast and accurate approach of linear power-flow formulation is proposed. Principles of the proposed approach are based on dividing power-flow calculations into base and variable parts. To this aim, at first, system modeling of base and variable parts are presented. For the base-part modeling, utilizing a nonlinear power-flow, an accurate base power-flow (BPF) is extracted. Afterwards, by linearizing the power system around the BPF, variable-part model which is the result of a linear fitting process, is obtained. Then, it is shown that the variable-part of the operating point is always a function of the obtained base-part and variable-part models. In this paper, by focusing on the stochastic application of the proposed approach, different uncertainties in a distribution system are considered. Finally, numerical results carried out in the Matlab environment, for a IEEE 34-bus standard distribution system and then a 1486-bus case study, verify the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

As the penetration of Distributed Energy Resources (DERs) increases, it becomes critical to model these resources adequately in bulk power system (BPS) operation and planning studies. Adequate in this context refers to capturing the characteristics of the distribution system to the required degree such that reasonable estimates about BPS stability can be ascertained. Historically, performing such studies on a large transmission system connected to multiple distribution systems have been limited due to the unavailability of software capable of executing high fidelity simulation at the required scale on personal workstations i.e. not having to resort to high performance computing resources. In this paper, we present TDcoSim, an open source software that allows user to perform large scale high fidelity simulation for BPS planning and operation studies and (a) outline the necessary details required to build such software, (b) present the practical challenges faced during the development of TDcoSim.

High penetration of renewable energies enlarge the peak-valley difference of the net load of the distribution system, which puts forward higher requirements for the operation scheduling of the distribution system. From the perspective of leveraging demand-side adjustment capabilities, an optimal scheduling method of the distribution system with edge computing and data-driven modeling of price-based demand response (PBDR) is proposed. By introducing the edge computing paradigm, a collaborative interaction framework between the control center and the edge nodes is designed for the optimization of the distribution system. At the edge nodes, a classified XG-Boost-based PBDR modeling method is proposed for large-scale differentiated users. At the control center, a two-stage optimization method integrating pre-scheduling and re-scheduling is proposed based on demand response results from all edge nodes. Through the information interaction between the control center and edge nodes, the optimized scheduling of the distribution system with large-scale users is realized. Finally, a case study is implemented on the modified IEEE 33-node system, which verifies that the proposed classified modeling method has lower errors, and it is beneficial to improve the economics of the system operation. Moreover, the simulation results show that the application of edge computing can significantly reduce the calculation time of the optimal scheduling problem with PBDR modeling of large-scale users.

Adaptive protection is defined as a real-time system that can modify the protective actions according to the changes in the system condition. An adaptive protection system (APS) is conventionally coordinated through a central management system located at the distribution system substation. An APS depends significantly on the communication infrastructure to monitor the latest status of the electric power grid and send appropriate settings to all of the protection relays existing in the grid. This makes an APS highly vulnerable to communication system failures (e.g., broken communication links due to natural disasters as well as wide-range cyber-attacks). To this end, this paper presents the addition of local adaptive modular protection (LAMP) units to the protection system to guarantee its reliable operation under extreme events when the operation of the APS is compromised. LAMP units operate in parallel with the conventional APS. As a backup, if APS fails to operate because of an issue in the communication system, LAMP units can accommodate a reliable fault detection and location on behalf of the protection relay. The performance of the proposed APS is verified using IEEE 123 node test system.

While the world’s power distribution system resembles an intricate web-like structure, the most conventionally implemented distribution mechanism is the radial distribution system (RDS) where connection points for each distribution line are normally kept open. However, disadvantages regarding the traditional system have led to active research on establishing the networked distribution system (NDS), in which multiple circuits are interconnected with electricity as well as high-speed communication systems. The NDS offers multiple advantages including increased facility utilization, increased hosting capacity, and higher terminal voltage. Conversely, an unsolved issue prevails as the existing protection coordination method designed for the RDS is inadequate for fault occurrences in the NDS due to inaccurate fault direction identification. Hence, there is an urgent need for an alternative protection coordination method that allows high precision fault direction identification through communication. Moreover, the application of various existing technologies is hindered as the distance relay protection coordination algorithm malfunctions in situations where distribution lines are of short length, loads are dispersed onto multiple lines, and integration of distributed generation (DG) is frequent. Therefore, this paper presents a fault direction identification method that uses the waveform of the fault current based on long short-term memory (LSTM) neural network and a communication-based protection coordination scheme that can be applied in fault situations within an NDS.

Reliable and resilient grid operations with high penetration levels of distributed energy resources (DERs) can be achieved with improved situational awareness and seamless integration of DERs with utility enterprise controls. This paper presents the details of the development of a data-enhanced hierarchical control (DEHC) architecture and the results of its evaluation. The DEHC is a hybrid control framework that enables the efficient, reliable, and secure operation of distribution grids with extremely high penetrations of solar photovoltaic (PV) generation by seamlessly integrating centralized utility controls, distributed controls for DERs, and autonomous grid-edge controls. In the DEHC architecture, the advanced distribution management system (ADMS) controls the legacy devices (such as load tap changers and capacitor banks), the PV smart inverters are dispatched by real-time optimal power flow, and the grid-edge devices regulate local voltages in coordination with each other. The DEHC is demonstrated using a commercial ADMS platform, real utility distribution feeder models, and grid-edge devices. The performance of the DEHC architecture is evaluated using simulations and hardware-in-the-loop experiments with voltage regulation as the control objective. The results show that the DEHC enables high penetration levels of PV in distribution feeders by effectively managing system voltages through the synergistic operation of ADMS, distributed PV smart inverter controls, and secondary-level grid-edge device control.

Aiming at the high-dimensional uncertainties of restoration process, an optimization model for distribution system restoration control is proposed considering expected restoration benefits, expected restoration costs, and security risks of the overall restoration scheme. In the proposed model, the effect of security control on restoration process is actively analyzed considering the security control costs of preventive, emergency, and correction controls. A two-layer decision support framework for distribution system restoration decision support system (DRDSS) is also designed. The upper layer of the proposed framework generates the pre-adjustment schemes of operation mode for energized power grid by load transfer and selects the optimal pre-adjustment scheme and the corresponding partitioning scheme based on the partition adjustment results of each pre-adjustment scheme. In addition, it optimizes the spatial-temporal decision-making of the inter-partition connectivity. For each partition, the lower layer of the proposed framework pre-selects the units and loads to be restored according to the pre-evaluated restoration income, generates the table of alternative restoration scheme for coping with uncertain events through simulation and deduction, and evaluates the risk and benefit of each scheme. For the uncertain events in the actual restoration process, the current restoration scheme can be adaptively switched to a sub-optimal scheme or re-optimized if necessary. Meanwhile, the proposed framework provides an information interaction interface for collaborative restoration with the related transmission system. A 123-node test system is built to evaluate the effectiveness and adaptability of the proposed model and framework.

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs). This paper presents a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements. The method is demonstrated using an actual feeder from an electrical utility with high penetration of DERs. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, and data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The ACOPF uses the measured voltage magnitudes, derived active power measurements, and the feeder head measurements to obtain a complete active power and reactive power capture of the feeder loads. Additionally, the method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed is verified in two stages: by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification automatically.

This paper presents a novel bi-level multi-objective model for planning solar PV-battery storage (PV-BS) based DERs in Smart grid distribution system (SGDS). The planning of Solar PV and battery storage (BS) in the distribution system presents severe complexities for the system planner due to their inherent attributes. The detailed analysis considering all three planning aspects, namely technical, economics, and environmental, is presented, assisting in the efficient and reliable planning operation. The four test cases are formulated based upon the life cycle cost, unserved energy, penetration level, and social welfare. The level-1 proceeds with evaluating the DERs sizing along with penetration level and serves as an input to level-2 for computing the optimal placement for DERs. The proposed model is implemented on the IEEE-33 bus distribution system and solved using Butterfly-PSO (BF-PSO) algorithm. The detailed parametric and sensitivity analysis presented may aid the system planners in terms of planning preview. The simulation results validate the effectiveness of the proposed work by showing significant improvement in bus voltage profiles (up to 4%) and reduction in total power losses (up to 45%). The highest penetration level is observed for Case 2 (70%), which also corresponds to the highest LCC (k $\$ $ 147094) and thus results in the lowest emission cost (k $\$ $ 4920).

With the increased usage of different types of renewable energy based on variable output characteristics, power curtailments have become a major problem in grid operation. The advanced controllers in distribution networks are significant in reducing the uncertainties caused by renewable generators. Along with this, at the operational level, the determination of the appropriate amount of curtailment for the stability and economics of the system was taken into account The paper proposes an adaptive curtailment method in order to improve the accuracy of the power flow coming from the distribution network, with the signal created according to the system operator based on the response of the electric vehicle (EV). The method covers the economic loss caused by the curtailment process through the compensation profits. The main determinant reference proposal concerns the sensitivity and ability to increase the stability and economic feasibility of the interconnected power system. The benefits generated by the EV charging are provided to wind turbine operators in the scenario to compensate for excessive constraints. The whole method is applied in the IEEE-69 bus system to verify the effect with MATLAB.

With the emergence of virtual power plants (VPPs) in distribution systems, distribution companies (DISCOs) can manage distributed generators scattered across their systems. Interactions between each DISCO and VPP are expected to be crucial for securing system reliability without increasing operational complexity. This article proposes a bi-level scheduling model for these interactions, considering the ramping flexibility of the distribution system. In the upper level, the DISCO minimizes the distribution system’s operational cost and the cost of procuring flexibility by varying the price of the power traded with the VPP. In the lower level, the VPP maximizes its profit by managing the amount of power it trades with the DISCO. We transform the bi-level model into a single-level problem by using Karush–Kuhn–Tucker optimality conditions and linearization techniques. We present case studies of interactions between the DISCO and VPPs, with the assumption that the DISCO can purchase active power from a wholesale market. The results demonstrate that a win–win situation for both the DISCO and VPP can be achieved by managing the power exchanged while procuring flexibility.

Dynamic state estimation (DSE) plays an important role in the real-time control and monitoring of distribution systems, which are high-dimensional space–time systems. The degree of nonlinearity of distribution system increases drastically with the availability of sustainable energy and the diversification of load types, thereby reducing the accuracy of the standard linear model. Under the existing schemes, the measurement noise may not follow a Gaussian distribution. Therefore, a Koopman operator-based Kalman particle filter (KKPF) is proposed herein for estimating the dynamic states of a distribution system. The KKPF performs data-driven dynamic state estimation based on the Koopman operator theory, which does not rely on the distribution system model and can be applied to high-dimensional systems. Furthermore, this method can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions. The KKPF method provides accurate estimation results when only a small number of measurements are available. IEEE 141 system and an improved 141 system were used to test the performance of the proposed KKPF compared to the EKF, CKF, and PF. The test revealed that the proposed KKPF can obtain accurate results under high-dimensional and non-Gaussian noise environments.

The increasing penetration of photovoltaic (PV) generators has led to a shift of the operational policy of the distribution system operator (DSO) from passive to active intervention in distribution networks (DNs), where a centralized controller governs the operation of voltage regulation devices. However, the PV output uncertainty hinders the verification of the impact of PVs on DNs. Therefore, the hosting capacity (HC) analysis framework for PV penetration should reflect both the operational benefit of the DSO and the PV output uncertainty. Thus, in this study, a two-stage optimization-based framework is proposed to analyze the probabilistic HC for PV under active network management (ANM) of DNs. In the first stage, the optimal PV base capacity (PVBC) to maximize the sum of the HC for PVs is determined based on a heuristic optimization method; in the second stage, using the predefined load and PV output profile, the maximum available power generation curve for PVBC is calculated, and the maximum HC for PV is derived by the calibration of PVBC through a comparison with the actual PV generation profile curve. The proposed method considers the time-series-based load flow results, reflecting the time-scheduling strategy by the DSO. Moreover, the uncertain characteristics of PV output are stochastically considered using a Monte Carlo simulation-based repetitive calculation approach. Case studies were implemented using the modified IEEE-123 test system, and the simulation results provided a quantitative comparison of the effect of the probabilistic HC improvement on the utilization of controllable resources and the centralized ANM by the DSO.

Along with the battery technology advancements and government policy support, the penetration level of electric buses (EBs) in the urban public transportation system has been increasing in recent years. Considering the potential influence of the increasing EB charging demand on power systems, estimating the real-time energy consumption of EBs has become a principal issue. In this work, a data-driven approach for EB energy consumption estimation is proposed. In particular, a detailed physical model of EB is constructed to model its energy consumption considering the randomness in EB operation, including speed, acceleration, and passenger count. In order to improve the estimation accuracy, the conventional Kalman filter (KF) is modified involving EB mass estimation considering stochastic real-time passenger count, motion data dimension deduction based on EB operation route. To estimate the EB acceleration accurately and reduce the noise caused by the unimportant features, we extended the feature discarding algorithm of decision trees to the regression trees. In the case study, an Android application is developed to collect the EB motion data so that any general Android smartphone can be used for data collection. The performance of the proposed approach is evaluated based on real-world EB operation data collected from St. Albert Transit, AB, Canada. According to the results, our APP can track the real-time EB trace, and the proposed modified KF can filter most of the noises caused by the GPS data collection process and stochastic passenger count. Also, with the extended random forest algorithm, the unimportant features can be discarded and the real-time EB acceleration is estimated efficiently with a small sum of square error (SSE). Compared with the existing approaches, the proposed approach achieves a more accurate real-time energy consumption estimation of EBs, which in turn, provides a better characterization of power system loading and voltage variation. [ABSTRACT FROM AUTHOR]

The time required to restore distribution systems following an extreme event is highly dependent on damage assessment. Waiting for field assessors patrolling the feeders to identify fault locations is a bottleneck in improving restoration efficiency. This paper proposes an optimal service restoration model for resilient distribution systems considering the coordination with damage assessment, as a contribution to earlier studies. The restoration actions such as fault isolation, network reconfiguration, crew mobilization and fault repair are brought forward to the damage assessment stage and the restoration schedules are dynamically updated with the reveal of the damage status. The relationship between fault location, switch status and node status is established to optimize the network topology and guarantee crew operation safety under the condition that the network has multiple faults or unchecked potential faulted areas. Moreover, the crew routing formulations are modified to enable fault isolation and load island reconnection by manual switches during the restoration process. Case studies validate the effectiveness of the proposed model in reducing load shedding and restoration duration. [ABSTRACT FROM AUTHOR]

This article studies the planning of swapping electric vehicle (SEV) battery centralized charging station (BCCS) based on EV spatial-temporal load forecasting. First, according to the road topology of the planning area, the road topology model is built. The driving energy consumption process and endurance mileage model of EVs are developed. Then, we analyze the travel demand of people, the driving segments of SEVs (e.g., swapping private EVs and swapping taxis) are analyzed. Next, the spatial distribution model of SEVs is built. Thus, the spatial-temporal load model of SEVs is established. The sizing and locating planning model based on operation analysis is established for BCCSs. In addition, the photovoltaic (PV) configuration model in the urban grid is also established. Finally, considering the coupling relationship between urban distribution system and power line pipes, a grid planning method considering power line pipes is proposed. The planning model of BCCS aimed at minimizing annual comprehensive cost considering distribution system with PV is constructed. The MATLAB/YALMIP toolbox is applied to optimize the planning model in this article, and the feasibility and effectiveness of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Topology detection and state estimation in real-time are critical for modern distribution systems management and control. However, the number of sensors in distribution networks is limited, and communication links between switch devices and distribution management system are not well-established. In this regard, this article proposes mixed-integer quadratic programming formulations to determine the topology of distribution network and estimate distribution system states simultaneously using micro-phasor measurement units and smart meter data. Two approaches based on ac optimal power flow are proposed and analyzed: polar power-voltage (PPV) formulation and rectangular current-voltage (RIV) formulation. The proposed models can identify multiple simultaneous switching actions and different topology configurations including radial and meshed networks. Only measurement data at each time interval are needed to identify the topology and states of the system correctly. The proposed models are tested on a modified IEEE-33 bus system with realistic load data from the Pecan Street Inc. database and a real distribution feeder of an electric utility. The results confirm that both models can identify system topology and states with remarkable accuracy in real-time, whereas the RIV model outperforms the PPV model. Moreover, the impact of missing data on the performance of the model is evaluated. [ABSTRACT FROM AUTHOR]

The uncertainty of renewable generation makes the operating status of distribution systems more volatile, as fully controllable resource at the distribution level is rare. A dispatchable region refers to the set that consists of all admissible patterns of nodal renewable power injections under which the power flow is solvable without violating security bound constraints. This article studies the dispatchable region in distribution networks under alternating current power flow model. A rank minimization problem is proposed to test power flow solvability under a fixed nodal power injection pattern, providing basic operation to construct the exact dispatchable region. A sequential low-order semidefinite programming procedure is developed to solve the problem. Furthermore, based on a global outer approximation of the second-order conic relaxation of the distflow model, a linear programming-based polyhedral projection algorithm is developed to calculate an outer approximation of the dispatchable region. The projection algorithm is also applied to the traditional linearized distflow model. Combining the feasibility test procedure, it is shown that the intersection of the respective dispatchable regions obtained from two linearized power flow models produces a fairly accurate approximation for the true dispatchable region under the exact nonlinear distflow model. The proposed method is an extension of existing studies on security assessment for distribution systems under uncertainty. [ABSTRACT FROM AUTHOR]

Recently, due to the restructuring of power systems and the high penetration level of local renewables, distribution systems have encountered with the complexity of power management. Therefore, the modern systems would be operated in a multi-agent structure which facilitates the power management as well as privacy protections of independent entities. In this structure, the distribution system is assumed to compose of several agents who independently schedule their local resources in order to maximize their own profits. Consequently, this paper provides an efficient peer-to-peer (P2P) active power management framework in a multi-agent distribution system while considering network constraints (i.e., line loadings and losses). In this context, in the proposed P2P scheme, the distribution system operator (DSO) model the network constraints in the form of line-usage costs within the transactive signals. Respectively, the developed transactive control signals enable the DSO to model the power loss as well as alleviate the congestion in the grid. Therefore, the agents automatically consider the network constraints in their power transactions management procedure without any direct interferences of the DSO in their resource scheduling. Finally, the proposed model is implemented on the modified-IEEE-37-bus-test system in order to investigate its effectiveness in the energy management of multi-agent systems. [ABSTRACT FROM AUTHOR]

This paper deals with the short-circuit analysis of distribution systems dominated by Voltage Source Converters (VSCs). A novel methodology has been proposed in this paper for short-circuit calculation of power systems populated with VSCs where the control modes and potential current-saturated operation of power converters are considered. The studied distribution system has been modeled using an element-based steady-state formulation that includes the converter equations in both normal operation and fault scenarios. Then, equilibrium points of the studied distribution system are identified by solving the established systems of equations for different potential current-saturation states of the VSCs. The methodology identifies possible solutions involving different saturation states of different converters. The proposed methodology is applied to a specific case study of a distribution system penetrated with VSCs in both grid-forming and grid-following control. The short-circuit analysis results indicate that VSCs operation shows a significant impact on the short-circuit equilibrium points of the studied system. In addition, multiple equilibrium points may exist depending on whether the grid-forming converter is saturated or not. The existence of multiple equilibrium points is validated through dynamic simulations. [ABSTRACT FROM AUTHOR]

Electric water heaters (EWHs) are opportune appliances for implementing demand-side control. EWH models serve as a fundamental step toward accurately estimating EWH flexibility potential and designing proper control strategies. Existing studies have adapted numerous modeling approaches in evaluating the potential of EWHs for a variety of grid applications. This article presents an analytical study that evaluates the performance of state-of-art EWH models in terms of accuracy and computational complexity for adaptation in evaluation studies for transactive systems. The work also proposes a novel transactive control strategy that optimally utilizes the thermal inertia of EWHs for providing grid services. The performance of the control strategy and the impact of modeling accuracy are evaluated for device-level and feeder-level use cases using the IEEE 123 node distribution system. The simulation results illustrate the effectiveness of the control strategy in reducing the feeder demand during the peak period by 15% and also quantifying the impact of using simplified modeling approaches for determining the potential of EWHs for providing grid services. [ABSTRACT FROM AUTHOR]

Most of the available directional relaying schemes use the phase angle information of sequence components of voltage and current with a cycle or more as the decision time. Converter interfaced solar photovoltaic plants are subjected to follow grid codes, leading to significant voltage and current modulation compared to conventional synchronous machines based sources. This may result in malfunction of existing directional relaying. This paper presents a high-speed time-domain directional relaying method using only phase fault current for distribution systems in the presence of solar plant. The method proposes a fault direction discrimination function, and its sign provides the direction of fault. The proposed method is tested for various control options embedded in solar plant, fault parameters, and noisy signals in CIGRE 14-bus distribution system integrating solar plant. Results validate the ability of the proposed method to decide within one-fourth of a cycle following fault detection for both PV plant side as well as grid end. Comparative assessment reveals the superiority of the proposed current based direction relaying method over the existing technique. [ABSTRACT FROM AUTHOR]

In this letter, a simplified voltage sensitivity analysis technique that can provide accurate estimates of voltage change across the network for a given change in bus power injections in a three-phase unbalanced distribution network is proposed. This technique is derived from the first-order approximation of the Neumann series, which allows maintaining the accuracy of the solution while the computational effort is reduced. The proposed technique is tested on a 559-bus unbalanced distribution system with multiple distributed generation resources. The results show that the average error in the voltage estimates with the proposed method is not more than 0.3% with the execution time of similar order relative to the state-of-the-art sensitivity analysis methods. [ABSTRACT FROM AUTHOR]