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The conservation voltage reduction (CVR) and load modelling have acquired a renewed interest in reassessing the CVR capabilities and load model parameters for renewable-rich distribution networks. The underlying theoretical views behind the changes in load model parameters and subsequent CVR capabilities in the distribution networks caused by the high penetration of renewable generation have yet to be explored. This research has presented a mathematical analysis of equivalent network impedance with integrated distributed generations (DG) and further established a relationship of the equivalent network impedance with the static load model parameters and CVR to accurately assess the impact of DGs on the CVR. Then, this paper has demonstrated various case studies with IEEE 33 bus network to analyze and validate the changes in static load model parameters due to the integration of renewable DGs and batteries into the balanced and unbalanced power systems. The obtained results verify the increased CVR capabilities of a distribution network, caused by the higher active power exponent in the load model parameters, as affected by the integration of renewable DGs. [ABSTRACT FROM AUTHOR]

With the integration of intermittent renewable energy sources in the distribution network, the number of network reconfiguration events has increased significantly. In a medium voltage distribution network, most of the critical circuit breakers’ (CBs’) statuses are monitored by remote terminal units (RTUs). However, in many cases, some of the CBs’ status may not be correctly updated by the supervisory control and data acquisition (SCADA) system because of communication failure and data packet loss issues. Thus, the distribution system operator (DSO) cannot solely rely on CB status provided by the SCADA for topology detection. This paper proposes a data-driven topology tracking algorithm for active distribution networks. The topology of the distribution network is represented with a time-varying connectivity matrix. The changes in network topology are detected by estimating the elements of the bus connectivity matrix using voltage phase angle measurements provided by a sparse set of micro phasor measurement units ($\mu$ PMUs). The algorithm extracts information from previous network topology using an $l_1$ norm regularization on the difference of consecutive connectivity matrices. The changes in topology can be detected by observing a few $\mu$ PMU phasor samples. The algorithm is tested on IEEE benchmark test feeders with real load profiles. [ABSTRACT FROM AUTHOR]

Expansion of the DC distribution networks in the future power system has raised concerns over their stability assessment. To assess stability of the expanded DC distribution networks, the conventional zone aggregation method requires access to not only the nodes connected to the converters but also some other nodes within the DC passive network. However, access to the nodes within the DC passive network is not always feasible as some parts of the DC network may be buried or encapsulated. To address this issue, this paper proposes a new zone aggregation method for impedance-based stability assessment of the expanded DC-distribution networks. Compared with the conventional method, the main advantages of the proposed method are as follow: i) it only utilizes the impedance/admittance information of the nodes connected to the converters. Moreover, all the impedances/admittances can be measured directly without knowing the internal structure and/or parameters; ii) it can assess stability of expanded distribution networks with minimum number of nodes. Performance and effectiveness of the proposed method are evaluated and validated by simulation studies on a 10-bus DC distribution network and a modified IEEE 37-bus system in the Matlab/Simulink software environment. [ABSTRACT FROM AUTHOR]

The paper presents an approach for online centralized control in active distribution networks. It combines a proportional integral (PI) control unit with a corrective control unit (CCU), based on the principle of Model Predictive Control (MPC). The proposed controller is designed to accommodate the increasing penetration of distributed generation in active distribution networks. It helps in continuously satisfying the reactive power requirements of the transmission system operators (TSOs), while maintaining an acceptable voltage profile in the active distribution network, and simultaneously minimizing the total active power losses. The controller also ensures compliance to operation requirements of distribution network operators (DNOs). By replacing the full load flow (LF) calculation with sensitivities, derived from a linearized model of the network, the controller can work in real-time applications. Moreover, the computational burden of the proposed controller is reduced since the CCU is activated only when a voltage violation or considerable change of operation condition occurs. The performance of the proposed controller is demonstrated on a 11-kV test network with 75 buses and 22 distributed generators. [ABSTRACT FROM AUTHOR]

Soft normally open points (SNOPs) connected to distribution networks are instrumental in maintaining uninterrupted power supply and improving power quality during the fault period. In order to achieve the optimal service restoration scheme, a novel bi-level service restoration model based on the robust optimization method is proposed in this paper. The upper level of the proposed model aims at minimizing the risk of load loss. The switch statuses and range of power transmitted by SNOP terminals can be obtained. In the lower level of the model, the interval robust optimization method is adopted to cope with the uncertainties of DGs and loads. If there are no optimal solutions, the upper level model needs to be solved once more to adjust the network topology. Finally, a test system is established to verify the effectiveness of the proposed model and solution method. The effects of forecasting errors, the DG penetration, and the ratio of single terminal capacity of SNOP to line capacity on the optimization results are analyzed. [ABSTRACT FROM AUTHOR]

Maintaining balanced voltages across distribution networks is becoming more challenging with increasing deployment of single-phase distributed generation and larger single-phase loads. The paper develops a reactive power compensation strategy that uses distributed solar photovoltaic (PV) inverters to mitigate such voltage unbalance. The proposed strategy takes advantage of Steinmetz design and is implemented via both decentralized and distributed control. The latter coordinates PV inverters through a communication network. We demonstrate the performance of the controllers on the IEEE 13-node feeder and a much larger taxonomy feeder (617 nodes and 1196 triplex nodes), and consider different connections of loads and PV systems. Simulation results demonstrate the trade-offs between the controllers. It is observed that the distributed controller achieves greater voltage unbalance reduction than the decentralized controller, but requires communication infrastructure. [ABSTRACT FROM AUTHOR]

Preventive maintenance is applied in distribution networks to prevent failures by performing maintenance actions on components that are at risk. Distributed generators (DGs) and batteries can be used to support power to nearby loads when they are isolated due to maintenance. In this paper, a novel short-term preventive maintenance method is proposed that explicitly considers the support potential of DGs and batteries as well as uncertainties in the power generated by the DGs. Two major issues are addressed. To deal with the large-scale complexity of the network, a depth-first-search clustering method is used to divide the network into zones. Moreover, a method is proposed to capture the influence of maintenance decisions in the model of the served load from DGs and batteries via generation of topological constraints. Then a stochastic scenario-based mixed-integer non-linear programming problem is formulated to determine the short-term maintenance schedule. We show the effectiveness and efficiency of the proposed approach via a case study based on a modified IEEE-34 bus distribution network, where we also compare a branch-and-bound and a particle swarm optimization solver. The results also show that the supporting potential of DGs and batteries in preventive maintenance scheduling allows a significant reduction of load losses. [ABSTRACT FROM AUTHOR]

State estimation (SE) of distribution networks heavily relies on pseudo measurements that introduce significant errors, since real-time measurements are insufficient. Interval SE models are regularly used, where true values of system states are supposed to be within the estimated intervals. However, conventional interval SE algorithms cannot consider the correlations of same interval variables in different terms of constraints, which results in overly conservative estimation results. In this paper, we propose a new interval SE model that is based on the relative distance measure (RDM) arithmetic. In the proposed model, measurement errors are assumed to be bounded in given sets and the state variables are described as RDM variables. Since the SE model is a non-convex, the solution's credibility cannot be guaranteed. Therefore, each nonlinear measurement equation in the model is transformed into dual inequality linear equations using the mean value theorem. The SE model is finally reformulated as a linear programming contractor that iteratively narrows the upper and lower bounds of the estimated state variables. Numerical tests on IEEE three-phase distribution networks show that the proposed method outperforms the conventional interval-constrained propagation, modified Krawczyk-operator and optimization based interval SE methods. [ABSTRACT FROM AUTHOR]

A large number of distributed energy resources (DERs) integrate into the distribution network, which changes the power flow, increases the power fluctuations, and complicates the scheduling of the distribution network. To cope with that, a multitimescale scheduling method, which considers the demand response as well as user satisfaction, is proposed in this paper. First, in the day-ahead stage, both the generation-side and demand-side are combined to minimize the operating costs and reduce the impact of DERs. Second, in the real-time stage, the model predictive control method is introduced, smoothing the power fluctuations and maximizing the consumed renewable energy. Finally, the user comprehensive satisfaction is considered, ensuring the users’ benefit and improving the flexibility of users to participate in scheduling while shifting electricity demands. By optimizing the generation-side and demand-side on both day-ahead and real-time timescales, the proposed method can improve the operation status for the distribution network effectively while ensuring the interests of users. Simulation on an improved IEEE-33 bus distribution system verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Routine inspection programs are procedures that utilities can use to improve their power quality standards and public perception. However, due to budget restrictions, it is usually not possible to inspect all elements in the whole concession area. In this scenario, strategies to efficiently allocate available teams at scheduled inspections are an interesting alternative. The challenge is to define which data are relevant and at hand to develop an strategy that is comprehensive, efficient and outperforms the current old fashioned way based on personal experience applied to automated spreadsheets. This paper proposes an approach that deals with a large amount of data from corporate systems as input to a proposed optimization engine based on Genetic Algorithm and heuristics. Primary feeders are split into segments among protection devices, referred to as metazones. The developed tool produces a set of prioritized metazones by maximizing the sum of merit indexes related to loss of energy, voltage and current violations, as well as outages, while satisfying teams’ inspections capacities. A case study with 56 primary feeders is presented, and results show that the yielded prioritized metazones are compatible with and improve current power utilities practices. [ABSTRACT FROM AUTHOR]

Low Voltage Direct Current (LVDC) distribution systems have recently been considered as an alternative approach to electrical system infrastructure as they provide the additional flexibility and controllability required to facilitate the integration of more low carbon technologies (LCTs). However, DC protection systems and, more specifically high accuracy DC fault location, have been recognised as a key challenge to facilitating post-fault network maintenance. Most of the existing fault location techniques rely on current derivative or communications-based methods that are either very sensitive to noise or require a high level of data synchronisation. Fault energy has been recognized as a reliable indicator of more accurate fault location estimations. Therefore, this paper develops a mathematical model for describing fault energy during the transient period of DC faults. The method subsequently proposes a new fault let-through energy based DC fault location working strategy to facilitate post-fault network maintenance. The proposed method does not require data synchronisation regardless of the voltage, current, and the size of the converters connected to the LVDC feeder. The capabilities of the proposed fault location strategy are validated against different faults applied on an LVDC test network in PSCAD/EMTDC and shown to be more reliable and accurate than existing methods. [ABSTRACT FROM AUTHOR]

Distribution network monitoring has the potential to improve service levels by reporting the origin of fault events and informing the nature of remedial action. To achieve this practically, intelligent systems to automatically recognize the cause of network faults could provide a data driven solution, however, these usually require a large amount of examples to learn from, making their implementation burdensome. Furthermore, the choice of input to such a system in order to make accurate classifications is not always clear. In response to this challenge, this paper contributes a means of using minimal amounts of historical fault data to infer fault cause from substation current data through a novel structural similarity metric applied to the associated power quality waveform. This approach is demonstrated along with disturbance context similarity assessment on an industrially relevant benchmark data set where it is shown to provide an improvement in classification accuracy over comparable techniques. [ABSTRACT FROM AUTHOR]

This paper presents an innovative method for fault location in distribution networks based on classical circuit analyses. Two synchronized and few nonsynchronized pre- and during-fault voltages are required at few buses along with the impedancematrix. A new impedance matrix manipulation procedure is proposed so that the distribution system is investigated in partitions across multiple subsystems. This procedure allows the fault location process to be performed by solving systems of determined equations, making the method more technically accessible. The fault is located by analyzing the voltage sag at the terminal-bus of each subsystem separately. The proposed method is validated on the IEEE 33-bus, 12.66 kV distribution system with/without distributed generation (DG). Simulation results show the robustness and accuracy of the method under several pre- and during-fault scenarios and measurements errors. [ABSTRACT FROM AUTHOR]

As rooftop solar PVs installed by residential customers penetrate in low voltage distribution network (LVDN), some issues, e.g. over/under voltage and unbalances, which may undermine the network's operational performance, need to be adequately addressed. To mitigate unbalances in LVDN, phase-switching devices (PSDs) and static var compensator (SVC) are two equipment that is cost-effective and efficient. However, most existing research on operating PSDs is based on inflexible heuristic algorithms or without considering the network formulation, which may lead to strategies that violate operational requirements. Moreover, few pieces of literature have been reported on mitigating unbalances in LVDN via SVC and PSDs together. This paper formulates the decision-making process as a mixed-integer non-convex programming (MINCP) problem after developing an SVC model for dispatch purpose. Compared with existing work, the proposed method aims at minimizing current unbalance based on their phasor values and takes the network's operational requirements into account. To efficiently solve the challenging problem, the MINCP is reformulated as a mixed-integer second order-cone programming (MISOCP) problem based on either exact reformulations or accurate approximations, making it possible to employ efficient off-the-shelf solvers. Simulations based on two modified IEEE systems and a practical Australian LVDN demonstrates the efficiency of the proposed method in mitigating unbalances in LVDN. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal location planning of renewable distributed generation (RDG) units by taking into account the random uncertainties of renewable generation and load demand. In presence of the random uncertainties, location planning problem is naturally a two-stage stochastic mixed integer nonlinear programming problem, which is hard to solve efficiently. Instead of using traditional sampling methods or robust optimization methods, we propose a novel analytical approach in this paper to solve the problem efficiently and optimally. In particular, analytical expressions are derived for efficiently evaluating the performance of a candidate RDG placement decision. In this way, the stochastic mixed integer nonlinear programming problem is equivalently transformed into a deterministic integer problem, which can be solved efficiently using off-the-shelf tools. Numerical results show that the optimal RDG placement can save up to $4.2\%$ of the long-term average cost and $80.59\%$ of the line losses on the IEEE 13-bus test feeder. In addition, our proposed approach effectively reduces the computational time by $99.51\%$ on the IEEE 123 node test feeder compared with other traditional sampling-based metaheuristic approaches. [ABSTRACT FROM PUBLISHER]

Load modelling plays a key role in assessing peak load reduction and has attracted renewed interest recently due to increasing diversity and uncertainty in load characteristic resulted by emergence of new types of loads and more penetrations of distributed renewable generations onto electricity networks. This paper presents a methodology for the development of a static load model based load characteristic profile in medium voltage distribution networks from year-long field measurements. High-resolution load monitoring devices with the 1 s sampling rate installed at 60 primary substations in the U.K. distribution network are used to collect the data for the purpose of load modelling. Load models at these 60 substations comprising domestic, commercial/industrial, and mixed-type load demand are presented in the paper together with relevant model parameters. Field trials (which involve triggering transformer taps to initiate voltage changes) were performed throughout the year on 15 selected substations aimed at load profile validation. The developed load profiles take into account seasonal, weekly, and daily variations of P–V and Q–V characteristics. Representative 24 h (0.5 h) load matrices are developed for all 60 substations and provide an insight into the operational flexibility and network resilience to voltage variations. [ABSTRACT FROM AUTHOR]

Increased monitoring of distribution networks and power system assets present utilities with new opportunities to predict and forestall system failures. Although automated pattern recognition methodologies have given other industries significant advantage, power system operators face additional challenges before these can be realized. The effort of apportioning ground truth to fault data creates a knowledge bottleneck that can make utilizing automatic classification techniques impossible. Surrogate approaches using operational process outputs such as maintenance tickets as labels can be challenging owing to the causal ambiguity of these written records. To approach a solution, this paper demonstrates utilizing natural language processing techniques to disambiguate the free text in maintenance tickets for onward use in supervised learning of fault prediction and classification techniques. A demonstration of this approach on an established power quality fault data set is provided for illustration. [ABSTRACT FROM AUTHOR]

Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time; however, tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This paper develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines, our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions, and in particular Gaussian, for injections. We validate the algorithm through extensive experiments on ac three-phase IEEE distribution grid test cases. [ABSTRACT FROM AUTHOR]

This paper presents an on-line operational optimization approach to determine the optimal reactive power/volt-ampere reactive (VAr) set points for open unified power quality conditioner (UPQC-O) under varying load demand of a distribution network. The UPQC-O consists of series and shunt inverters. The modeling of UPQC-O is done to provide VAr support to a network, to mitigate supply voltage sag to the downstream buses, and to eliminate harmonics present in the line current. A non-linear optimization problem is formulated to determine the optimal VAr set points for the inverters of UPQC-O in each loading condition. The objective function is the minimization of energy loss. The limits on bus voltage magnitude, line current flow, and maximum VAr support capacities of inverters are considered to be the operational constraints. The optimization is performed with the data of hourly varying load demand for different seasons in a year. The optimization problem is solved using CONOPT solver of general algebraic modeling system. The cost-benefit analysis and the infrastructure required for the real time implementation of the proposed methodology are also provided. The simulation results show the effectiveness of the proposed approach in energy loss reduction. [ABSTRACT FROM AUTHOR]

This paper presents a modeling approach for capturing closed-loop load behavior in distribution networks using bottom-up time-variant load models and incorporating load-flow feedback for thermal loads. Traditional load models are useful for providing insights into instantaneous changes in power, however, they do not capture the time-variant response to changes in voltage due to the effects of appliances with closed-control loops such as those which are thermostatically controlled. This paper proposes a modeling and co-simulation platform to simulate closed-loop load-modeling using state-of-the-art bottom-up load modeling techniques for the residential sector with a feedback model for time series steady-state load-flow. It incorporates time-variant load models and discrete state-space representation of thermostatically controlled appliances. The developed methodology is used in a conservation voltage reduction case study on an urban distribution network, allowing detailed assessment of the voltage response and analysis of changes in active and reactive power and network losses. [ABSTRACT FROM AUTHOR]

This paper presents a novel probabilistic approach to distribution network observability. The observability analysis is an important part of the state estimation process. Traditionally, it determines whether the state of a network can be estimated based on the available set of measurements. This works well in transmission networks, where numerous metering devices are normally available. In distribution networks, however, only a few metering devices are usually installed. Pseudo measurements with large margins of error are often used in the absence of real measurements to perform state estimation. This implies that if a large number of pseudo measurements is used to make a network observable, the estimated state can be significantly different from the actual state even if the network is classified as observable. To overcome this limitation, a new approach to observability is proposed in this paper. The proposed method takes the uncertainty of the state estimation into account, and therefore, assesses the network observability depending on the accuracy of the estimated network state. This paper also demonstrates how a meter placement method based on the proposed observability assessment can be implemented. The presented methods have been tested on a modified 34-bus IEEE test feeder. Results are compared with existing methods. [ABSTRACT FROM PUBLISHER]

Conservation voltage reduction (CVR) has been traditionally applied adopting moderate settings at primary substations and when distributed generation was uncommon. However, as new infrastructure is deployed across European-style medium voltage (MV) and low voltage (LV) networks, driven by increasing photovoltaic (PV) penetration levels, the opportunity arises to develop more advanced CVR schemes. This paper proposes a centralized, three-phase AC optimal power flow (OPF)-based CVR scheme that, using monitoring, on-load tap changers and capacitors across MV and LV, actively manages voltages to minimize energy consumption, even with high PV penetration, while considering MV–LV constraints. To tackle scalability issues brought by discrete variables, a two-stage approach is proposed to solve the OPF as a non-linear programming problem (relaxing integer variables). A process that continuously checks customer voltages is adopted to trigger the optimization only when needed. Moreover, CVR benefits are not only quantified at a network level but also for customers, providing useful insights to policy makers. The proposed control is assessed using a realistic, unbalanced U.K. residential MV–LV network (2,400+ customers) with high PV penetration, and 1-min resolution time-varying profiles and load models. Results demonstrate that the proposed control effectively coordinates voltage regulation in MV and LV levels throughout the day, minimizing energy imports for all customers. [ABSTRACT FROM AUTHOR]

Load flow methods for distribution networks, such as backward forward sweep (BFS) have a good computational performance and can find solutions with accuracy. However, some studies may demand the determination of low voltage solutions, and this poses a problem for these methods since they cannot find these solutions due to convergence issues. This paper presents a load flow method based on a novel complex-valued formulation developed for distribution networks, which works well on radial topologies by using an incidence matrix to avoid complicated series element models, allow high-performance and low-voltage solution capability. The formulation is solved by Newton's method via Wirtinger's calculus. To prove the low-voltage solution capability, both sides of QV curves, i.e., unstable and stable regions were traced on balanced and unbalanced networks. Performance tests in the IEEE test feeders show that the runtime is less than or equal to the runtime of the BFS method. Furthermore, the line R/X ratio and the number of controlled voltage node or volt-var functions do not affect the computational performance, yielding advantages over the classic Newton and BFS methods. [ABSTRACT FROM AUTHOR]

The rapid adoption of residential-scale photovoltaic (PV) systems in low-voltage (LV) networks combined with the falling prices of residential-scale battery energy storage (BES) systems is paving the way for a future in which customers could locally supply most of their energy needs. However, off-the-shelf (OTS) storage systems operate for the sole benefit of the customer (reducing grid imports). This means that charging might not occur during times of high PV generation, resulting in technical issues on LV and medium-voltage (MV) networks. This paper proposes an adaptive decentralized control strategy for residential-scale BES systems to reduce voltage and thermal issues whilst still benefiting customers. With this strategy, the power charging and discharging rates constantly adapt throughout the day based on clear-sky irradiance, PV generation, demand, and state of charge; significantly reducing reverse power flows and ensuring adequate storage capacity the next morning. A real Australian MV feeder with realistically modeled LV networks is studied using smart meter data. Results demonstrate that the proposed control strategy overcomes the limitations of the OTS BES. It is also shown that it can be as effective as an ideal optimization-based approach, being able to manage all technical issues without significantly affecting customers. [ABSTRACT FROM AUTHOR]

This is the second part of a two-part paper on data-based distributionally robust stochastic optimal power flow. The general problem formulation and methodology have been presented in Part I (Y. Guo, K. Baker, E. Dall’Anese, Z. Hu, and T.H. Summers, “Data-based distributionally robust stochastic optimal power flow—Part I: Methodologies,” IEEE Trans. Power Syst., 2018.). Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance. On the distribution side, the method mitigates overvoltages due to high photovoltaic penetration using local energy storage devices. On the transmission side, the method reduces $N-1$ security line flow constraint risks due to high wind penetration using reserve policies for controllable generators. In both cases, the data-based distributionally robust model-predictive control algorithm explicitly utilizes forecast error training datasets, which can be updated online. The numerical results illustrate inherent tradeoffs between the operational costs, risks of constraints violations, and out-of-sample performance, offering systematic techniques for system operators to balance these objectives. [ABSTRACT FROM AUTHOR]

This paper develops a tractable formulation for optimal placement and sizing of inverter-based renewable systems in multi-phase distribution networks. The goal of the formulation is to minimize the cost of inverter installation, average power import, and average distributed generation curtailment. Three-phase and single-phase inverter models are presented that preserve the underlying mappings between renewable uncertainty to power injection. The uncertainty of distributed generators (DGs) and loads are characterized by a finite set of scenarios. Linear multi-phase power flow approximations are used in conjunction with scenario reduction techniques to arrive at a tractable two-stage stochastic formulation for optimal DG placement and sizing. First-stage decisions are locations for DG deployment and capacity sizes, and second-stage decisions include DG real power curtailment, reactive power support, as well as feeder voltage profile. The resulting formulation is a mixed-integer second-order cone program and can be solved efficiently either by existing optimization solvers or by relaxing the binary variables to the [0,1] interval. Simulation studies on standard multi-phase IEEE test feeders promise that optimal stochastic planning of DGs reduces costs during validation, compared to a scheme where uncertainty is only represented by its average value. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel transactive energy trading (TET) framework to deal with the economic issues in energy trading and the technical issues in distribution system operation in a holistic manner. In particular, we innovatively integrate a bilateral energy trading mechanism with the optimal power flow (OPF) technique to increase economic benefits to individual participants, and meanwhile ensure the reliability and security of the system operation. In order to resolve the inherent conflict of interests, Nash bargaining theory is used to model the TET problem, which is further decomposed into a multiperiod OPF problem and a payment bargaining problem. Moreover, we develop an efficient distributed algorithm for solving the TET problem base on alternating direction method of multipliers (ADMM). Instead of directly solving optimization subproblems like most ADMM-based distributed algorithms, we derive closed-form solutions to all subproblems to significantly improve the computational efficiency. Finally, numerical tests on the IEEE 37-bus and 123-bus distribution systems demonstrate the effectiveness of our proposed framework and the efficiency of our distributed algorithm. [ABSTRACT FROM AUTHOR]

Due to the lack of available tests and equipment, the ability of master stations to process faults in power distribution systems cannot be evaluated systematically or comprehensively during their commissioning. As a solution, this paper presents an injection methodology that consists of a research system and an evaluation technique. The same system is also employed for on-site tests. Standardized test networks and corresponding cases are integrated into the system, where a traversal fault-setting scheme can specify the location for short-circuit faults anywhere along the test network. The Tabu Search is adopted to optimize network reconfiguration for complex fault scenarios. Additionally, the system can validate the configuration and settings of peripheral equipment, including relays. More than one-hundred master stations have been tested and graded by the proposed methodology, leading to acceptance of the injection test. [ABSTRACT FROM AUTHOR]

To effectively manage the growing number of distributed energy resources, more extensive monitoring is needed at distribution network levels, where a challenging combination of constraints and cost-effectiveness requirements must be taken into account. In this paper, we consider the idea of combining accurate phasor measurement with the capability to analyze harmonic and interharmonic phasors. This creates the need to deal with multiple phasor components having different amplitudes, including interharmonics with unknown frequency locations. For this purpose, we propose an enhanced compressive sensing-based algorithm for the estimation of Taylor–Fourier coefficients in a multifrequency dynamic phasor model. We characterize its accuracy in harmonic and interharmonic phasor measurement and discuss reporting rate and latency performance. We show that very good accuracy can also be achieved as a standard phasor measurement unit, allowing to combine multiple functions into a promising new-generation measurement, monitoring, and diagnostic device. [ABSTRACT FROM AUTHOR]

This paper proposes a novel contribution factor (CF) approach to predict diversified daily peak load of low voltage (LV) substations. The CF for each LV template developed in part I of the paper is determined by a novel method—clusterwise weighted constrained regression (CWCR). It takes into account the contribution from different customer classes to substation peaks, respecting the natural difference in time and magnitude between LV substation peaks and the variance within the templates. In CWCR, intercept and coefficients are constrained to ensure that the resultant coefficients do not lead to reverse load flow and can respect zero-load substations. Cross validation is developed to validate the stability of the proposed method and prevent over fitting. The proposed method shows significant improvement in the accuracy of peak estimation over the current status quo across 800 substations of different mixes of domestic, industrial and commercial (I&C) customers. The work in the two parts of the paper is particularly useful for understanding the capabilities of LV networks to accommodate the increasing penetration of low carbon technologies without large-scale monitoring. [ABSTRACT FROM PUBLISHER]

This paper proposes a new impedance-based technique to locate all fault types in distribution networks with/without distributed generators. A new procedure to form an impedance matrix using only series impedances of the distribution lines is introduced. The impedance matrix along with the prefault and during-fault voltage phasors at few buses is used to estimate the injection fault current via the least-squares technique. Linear least-squares estimator is utilized if microphasor measurement units (\mu \rmPMUs) are installed along the network. However, a nonlinear least-squares problem solved by the trust-region-reflective algorithm is used when only the voltage magnitudes are provided by smart meters. The operation of the standard protective devices in the distribution networks is used to reduce the computational burden of the proposed method. Also, a generalized measurement placement algorithm is studied using the discovered features of the impedance matrix. In addition, the Sobol's sensitivity analysis is conducted to quantify the importance of different input factors on the fault location accuracy. The effectiveness of the proposed method is validated on a real 134-bus, 13.8 kV distribution network under several fault scenarios and noisy measurements. [ABSTRACT FROM AUTHOR]

Renewable distributed generation (DG) is likely to be actively controlled in future distribution networks to mitigate voltage issues resulting from high penetrations. This requires understanding the corresponding dependencies between voltage magnitudes and DG active/reactive power outputs. One approach to compute these dependencies is to use classical sensitivity methods such as those based on the Jacobian matrix inverse. However, updating the latter involves extensive remote monitoring. This paper presents a novel approach to produce voltage sensitivities applying the surface fitting technique on data based solely on the knowledge of network characteristics; making it suitable for decentralized DG control. To assess the benefits, comparisons with classical methods are carried out using the 16-bus UK GDS test network (1-min resolution simulations) considering a decentralized voltage control algorithm that simultaneously caters for the active and reactive power outputs of a single DG plant. The robustness of the proposed approach is also investigated considering changes in network parameters. Finally, the use of coordinated time delays is proposed to cater for multiple DG plants. Comparisons with a centralized optimization demonstrate that the combined use of the proposed voltage sensitivity approach and decentralized control algorithm is an effective and implementable candidate to actively manage renewable DG plants. [ABSTRACT FROM PUBLISHER]

Conservation voltage reduction (CVR) plays an important role in energy savings and shaving peak demand. This paper quantifies energy savings for refrigeration loads (RLs) with experimentally validated air conditioner and refrigerator models. It is demonstrated that peak demand reduction and energy savings exist simultaneously during the implementation of CVR. A 1666-bus real meshed network with residential, small, and large commercial customers is simulated for the calculation of energy savings. Different working patterns: weekday and weekend; weather conditions: hottest and average summer days during peak load of the year; and load types: mixed load (ZIP and RLs) and ZIP load are studied. In addition, the IEEE 8500-node radial system with 100% residential customers is also used to show the energy savings of CVR. It is concluded that the economic benefits of CVR are larger for meshed networks than for radial systems. It is also shown experimentally and by simulation that CVR produces significant savings for both utilities and customers. [ABSTRACT FROM PUBLISHER]

Insulation monitoring is crucial to personal safety and equipment operation in the power distribution system. Recently, with the rapid development of distributed generators, the three-phase voltage-source PWM converter (VSC) is widely used as the bidirectional interface between the dc microgrids and the distribution networks. This paper presents the relationship between the response of insulation monitoring device (IMD) and the neutral-point voltage deviation (NPVD) of VSC, by analyzing the disturbances of the switching function and the dc-side insulation balancing. Then, a method of detecting the NPVD for grid-connected VSC is proposed, based on the dc signal injection and the Duffing oscillator. Furthermore, the proposed method takes the mean and spectrum of the neutral-point voltage as the primal characteristics. Thus, the dc signal injection is adopted to detect the mean of the neutral-point voltage, and the Duffing oscillator is adopted to determine the frequency disturbance of the neutral-point voltage, respectively. The test result confirms the existence of the neutral-point voltage deviation and verifies the availability of the proposed method. Hence, the proposed method could be used to improve the response of IMD. [ABSTRACT FROM AUTHOR]

Plug-in electric vehicle (PEV) charging equipment incorporates protection that ensures that grid disturbances do not damage the charger or vehicle. When the grid voltage sags below 80% of nominal, undervoltage protection is likely to disconnect the charging load from the grid. Most PEV charging will occur overnight, when non-PEV load is at a minimum. This paper argues that PEV voltage-sag response, when synchronized across large numbers of PEVs, could result in the loss of a significant proportion of the total load. It is shown that this load loss can lead to unacceptably high voltages once the initiating event has been cleared. This paper explores the nature of this voltage-rise phenomenon. Analysis tools are developed to assist in determining PEV loading conditions that demarcate acceptable postdisturbance voltage response from unacceptable outcomes. Two examples, based on standard distribution test systems, are used to illustrate PEV-induced overvoltage behavior, and demonstrate applications of the analysis tools. [ABSTRACT FROM AUTHOR]

In this paper, for the first time, a high-impedance fault indicator (HIFI), to be mounted on the poles of distribution feeders, is proposed to detect and track down the location of high-impedance faults (HIFs). The main contribution of this paper is the investigation of employing the magnetic-field strength signal, measured in the vicinity of the conductors of a feeder, to detect HIFs based on a novel pattern recognition method. The proposed HIFIs should be installed so that the faulted branch identification becomes possible and simple (i.e., at the sending ends of branches). Applying the data for HIFs, insulator leakage current and harmonic load from field tests and for other similar phenomena from simulations has shown high security and dependability of the proposed method as a computational model for HIFI. [ABSTRACT FROM AUTHOR]

The knowledge of the topology of a wired network is often of fundamental importance. For instance, in the context of power line communications networks it is helpful to implement data routing strategies, while in power distribution networks and smart microgrids it is required for grid monitoring and for power flow management. In this paper, we use the transmission line theory to shed new light and to show how the topological properties of a wired network can be found exploiting admittance measurements at the nodes. An analytic proof is reported to show that the derivation of the topology can be done in complex networks under certain assumptions. We also analyze the effect of the network background noise on admittance measurements. In this respect, we propose a topology derivation algorithm that works in the presence of noise. We finally analyze the performance of the algorithm in a setup that is typical of power line distribution networks. [ABSTRACT FROM AUTHOR]

Neutral-to-ground overvoltage may occur in noneffectively grounded power systems because of the distributed parameters asymmetry and resonance between Petersen coil and distributed capacitances. Thus, the constraint of neutral-to-ground voltage is critical for the safety of distribution networks. In this paper, an active grounding system based on a single-phase inverter and its control parameter design method is proposed to achieve this objective. Relationship between its output current and neutral-to-ground voltage is derived to explain the principle of neutral-to-ground voltage compensation. Then, a practical current detection method is proposed to specify the reference of compensated current. A current control method consisting of proportional resonant and proportional integral (PI) with capacitive current feedback is then proposed to guarantee sufficient output current accuracy and stability margin subjecting to large range of load change. The PI method is taken as the comparative method and the performances of both control methods are presented in detail. Experimental results prove the effectiveness and novelty of the proposed grounding system and control method. [ABSTRACT FROM AUTHOR]

This paper presents a modified power control to integrate inverter-based distributed generation (DG) in distribution networks. This control is designed for mitigating the maloperation of the overcurrent protection coordination, due to the DG current contribution during the fault. The proposed power control introduces a new approach to perform the inverter control by using new orthogonal components of the grid voltages that provide a modified controlled power. Using the proposed methodology, the injected current to the grid during faults is limited to the DG-rated level. By controlling the reference power based on a proposed function of the per-unit terminal voltage, the injected current is limited to a desired value. The results of the simulated system corroborate the control capability to completely limit the inverter-based DG effects on protection coordination in the distribution level. [ABSTRACT FROM AUTHOR]

Wind power is the source of electrical energy that has grown more over the last years, with annual rate in installed capacity around 20%. Therefore, it is important to optimize the production efficiency of wind farms. In a wind farm, the electrical energy is collected at a central substation from different wind turbines placed nearby. This paper addresses the optimal design of the cable network interconnecting the turbines to the substation aiming to minimize not only the infrastructure cost but also the cost of the energy losses in the cables. Although this problem is nonlinear, different integer linear programming models are proposed considering the wind farm technical constraints. The models are applied to three real cases Portuguese wind farms. The computational results show that the proposed models are able to compute the optimal solutions for all cases. [ABSTRACT FROM PUBLISHER]

State estimation (SE) in distribution networks is not as accurate as that in transmission networks. Traditionally, distribution networks (DNs) are lack of direct measurements due to the limitations of investments and the difficulties of maintenance. Therefore, it is critical to improve the accuracy of SE in distribution networks by placing additional physical meters. For state-of-the-art SE models, it is difficult to clearly quantify measurements' influences on SE errors, so the problems of optimal meter placement for reducing SE errors are mostly solved by heuristic or suboptimal algorithms. Under this background, this paper proposes a circuit representation model to represent SE errors. Based on the matrix formulation of the circuit representation model, the problem of optimal meter placement can be transformed to a mixed integer linear programming problem (MILP) via the disjunctive model. Comprehensive numerical studies show the effectiveness and efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

The paper presents a procedure for fault location in distribution networks, based on the use of the integrated time-frequency wavelet decompositions of the voltage transients associated with the fault-originated travelling waves. The proposed analysis of time-frequency wavelet decompositions has been found to improve the identification accuracy of the frequencies associated to the characteristic patterns of a fault location with respect to a sole frequency-domain wavelet analysis. Several laboratory fault tests, carried out by means of a reduced-scale model of a distribution feeder, are used to illustrate the characteristics and assess the performances of the proposed improved procedure. The paper also illustrates the application of the proposed procedure to a transient, originated by a permanent phase-to-phase fault, measured in a real distribution network in which a post-test analysis has identified the faulted branch. [ABSTRACT FROM PUBLISHER]

This paper addresses the allocation of electrical losses in distribution networks with embedded generation, in a liberalized environment. The nonlinear nature of the issue, the loss changes due to voltage variation and, specially, the contribution of embedded generation to loss variation are considered. The proposed method is based on tracing the real and imaginary parts of the currents and has two steps. First, the loses in the distribution network, in the absence of embedded generation, are allocated to the consumers (or their providers). Second, the variations in the losses that result from the influence of embedded generation are allocated to the generators. These variations are a measure of the avoided or added costs related to losses. In the allocation process, made in a branch basis, both real and reactive powers ate considered. The methodology presented in this paper can be used to evaluate embedded, generation incentives or to design tariffs for the use of the distribution network. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of utilizing dc links to merge heavily meshed urban distribution networks in dense-load areas to increase reliability and expand operational flexibility. It provides a cost-benefit evaluation of utilizing dc-link technology to interconnect three segments of New York City electric distribution networks with complex grid configurations. The outcome of this work highlights the advantages provided by dc links, such as increased reliability and power quality, improved voltage support, and demand relief for feeders at or above capacity limitations. Furthermore, the study shows that dc links may provide a better alternative to transformer installations, feeder upgrades, and/or capacitor additions, and offer the opportunity to postpone large capital investments for system upgrades (such as building a new substation) due to demand increase. The study was carried out with power-flow simulations using field-validated power-flow data. [ABSTRACT FROM AUTHOR]

In this paper, distributed energy-storage systems (ESSs) are proposed to solve the voltage rise/drop issues in low-voltage (LV) distribution networks with a high penetration of rooftop photovoltaics (PVs). During the peak PV generation period, the voltages are mitigated by charging the ESSs, and the stored energy is discharged for voltage support during the peak load period. The impact of storage devices integrated with the PV source on feeder voltages is investigated in detail. A coordinated control method, which includes distributed and localized controls, is proposed for distributed ESSs. The distributed control using the consensus algorithm regulates the feeder voltages within the required limits, while the localized control regulates the state of charge (SoC) of each ESS within the desired SoC range. The entire control structure ensures voltage regulation while effectively utilizing storage capacity under various operation conditions. The proposed control method is evaluated in LV distribution networks, and the simulation results validate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for single and simultaneous fault location in distribution networks by means of a sparse representation (SR) vector, Fuzzy-clustering, and machine-learning. The method requires few smart meters along the primary feeders to measure the pre- and during-fault voltages. The voltage sag values for the measured buses produce a vector whose dimension is less than the number of buses in the system. By concatenating the corresponding rows of the bus impedance matrix, an underdetermined set of equation is formed and is used to recover the fault current vector. Since the current vector ideally contains few nonzero values corresponding to fault currents at the faulted points, it is a sparse vector which can be determined by \ell^1-norm minimization. Because the number of nonzero values in the estimated current vector often exceeds the number of fault points, we analyze the nonzero values by Fuzzy-c mean to estimate four possible faults. Furthermore, the nonzero values are processed by a new machine learning method based on the k-nearest neighborhood technique to estimate a single fault location. The performance of our algorithms is validated by their implementation on a real distribution network with noisy and noise-free measurement. [ABSTRACT FROM PUBLISHER]

This paper discusses the need for short circuit analysis in real-time applications of modern distribution networks and presents a short circuit tool that builds on recent advances in Fortescue-based current injection power flow. The proposed short circuit computation (SCC) method is fundamentally based on the symmetrical components transformation of three-phase, two-phase, and one-phase systems. Unlike the classical symmetrical components SCC method that postulates a structurally symmetrical three-phase pre-fault network with balanced loading, the proposed method accounts for multiphase networks that are comprised of three-phase, two-phase, and one-phase network parts; given a pre-fault power flow solution, it requires a maximum of three current injection iterations to compute the short circuit current flow in the entire network. Numerical results show that the Fortescue SCC approach with multiphase lines exhibits significant computational performance improvement on large-scale networks as compared to classical SCC in phase coordinates. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel method for fault location in distribution networks using compressive sensing. During fault and prefault voltages are measured by smart meters along the feeders. The voltage sag vector and impedance matrix produce a current vector that is sparse enough with one nonzero element. This element corresponds to the bus at which a fault occurs. Due to the limited number of smart meters installed at primary feeders, our system equation is underdetermined. Therefore, the \ell ^1-norm minimization method is used to calculate the current vector. Primal-dual interior point (PDIP) and the log barrier algorithm (LBA) are utilized to solve the optimization problem with and without measurement noises, respectively. Our proposed method is implemented on a real 13.8-kV, 134-bus distribution network when single-phase, three-phase, double-phase, and double-phase-to-ground short circuits occur. Simulation results show the robustness of the proposed method in noisy environments and satisfactory performance for various faults with different resistances. [ABSTRACT FROM AUTHOR]

The expected impact of distributed generation (DG) into Smart Grid represents a great challenge of the future for power systems. In particular, the integration of DG based on renewable energy sources (RESs) in distribution networks, without compromising the integrity of the grid, requires the development of proper control techniques to allow power delivery to customers in compliance with power quality and reliability standards. This paper proposes a coordinated local control approach that allows distribution system operator (DSO) and independent power producers (IPPs) to obtain benefits offering the voltage regulation ancillary service to DSO and maximizing allowable active power production for each RES unit belonging to the same IPP. The control is based on a cooperation of data transfer between DSO and IPPs. In order to realize such cooperation, a nonlinear constrained optimization problem is formulated and solved by sequential quadratic programming (SQP) method. The validation of the proposed control technique has been conducted through several time series simulations on a real MV Italian distribution system. [ABSTRACT FROM PUBLISHER]

The increasing uptake of electric vehicles suggests that vehicle charging will have a significant impact on the electricity grid. Finding ways to shift this charging to off-peak periods has been recognized as a key challenge for integration of electric vehicles into the electricity grid on a large scale. In this paper, electric vehicle charging is formulated as a receding horizon optimization problem that takes into account the present and anticipated constraints of the distribution network over a finite charging horizon. The constraint set includes transformer and line limitations, phase unbalance, and voltage stability within the network. By using a linear approximation of voltage drop within the network, the problem solution may be computed repeatedly in near real time, and thereby take into account the dynamic nature of changing demand and vehicle arrival and departure. It is shown that this linear approximation of the network constraints is quick to compute, while still ensuring that network constraints are respected. The approach is demonstrated on a validated model of a real network via simulations that use real vehicle travel profiles and real demand data. Using the optimal charging method, high percentages of vehicle uptake can be sustained in existing networks without requiring any further network upgrades, leading to more efficient use of existing assets and savings for the consumer. [ABSTRACT FROM PUBLISHER]