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Conventional cyber-security intrusion detection systems monitor network traffic for malicious activity and indications that an adversary has gained access to the system. The approach discussed here expands the idea of a traditional intrusion detection system within electrical power systems, specifically power distribution networks, by monitoring the physical behavior of the grid. This is achieved through the use of high-rate distribution Phasor Measurement Units (PMUs), alongside SCADA packets analysis, for the purpose of monitoring the behavior of discrete control devices. In this work we present a set of algorithms for passively learning the control logic of voltage regulators and switched capacitor banks. Upon detection of an abnormal operation, the operator is alerted and further action can be taken. The proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site.

With the rapid development of distributed renewables, DC transmission, and active loads, increasingly more power electronics are and will be introduced into distribution grids. This has led to an increasing need for a new synchronized measurement system that covers different scenarios and applications to improve the stability and reliability of power-electronics-enabled power grids. In this paper, a full-view synchronized measurement system (SYMS) for the renewables, controls, loads, and waveforms of power-electronics-enabled power distribution grids is established, and is composed of different types of synchronized measurement devices (SMDs) and a data center. A general design method for the synchrophasor estimation of SMDs in different scenarios is proposed by deducing phasor measurement error models. This is further applied in combination with the design of hardware and a communication protocol to develop SMDs for the measurement of the renewables, controls, loads, and waveforms of power-electronics-enabled power distribution grids. Then, a framework of the data center of the SYMS, as well as several potential applications based on real-time measurements, are presented and discussed. The SYMS is a comprehensive, shared, and open power system data platform for use by future researchers. [ABSTRACT FROM AUTHOR]

The paper presents the protection design, stability, and control challenges associated with and insights drawn from two real-world microgrids. The presented microgrids were built within the electric utility distribution systems with the existing interconnected electrical loads and Distributed Energy Resources (DERs). Detailed analysis of the observed site protection and stability challenges, supported by an extensive set of site measurements and simulations, along with a systematic approach to troubleshooting the identified issues, is presented. The efficacy of the proposed and or implemented solutions is also discussed. [ABSTRACT FROM AUTHOR]

A new method is proposed to identify the location of transient events, including incipient faults, in power distribution systems, by using synchronized measurements from an emerging class of sensors, called waveform measurement units (WMUs). WMUs capture the voltage and current waveforms in time domain. The proposed method consists of three steps. The first step is to characterize the oscillatory modes of the transient components of all the captured synchronized voltage and current waveforms from all WMUs, by conducting a multi-signal modal analysis. The second step is to construct a circuit model for the underlying distribution feeder at the identified dominant mode(s) of the transient event. The final step is to identify the location of the transient event with the means of a method that involves certain forward and backward analyses of the constructed circuit model. The proposed method requires installing as few as only two WMUs. It can also utilize several synchronized waveform measurements when several WMUs are available. The performance of the proposed method is assessed on the IEEE 33-bus test system; for different cases of transient events, such as sub-cycle incipient faults, multi-cycle incipient faults, permanent faults, as well as benign yet informative events such as capacitor bank switching. Both the accuracy and the robustness of the proposed method are verified. The analysis and results in this paper provide new insights on possible applications of synchronized WMU measurements; while they also address a highly challenging problem in power distribution networks. [ABSTRACT FROM AUTHOR]

With a rapid growth in the deployment of renewable and distributed energy resources (DERs) in active power distribution networks, especially the inverter-interfaced distributed generators (IIDGs), existing fault section estimation methods are facing additional challenges. This paper proposes an optimization model for fault diagnoses in active power distribution systems, which aims at simultaneous identifications of suspected fault line sections and potential false alarms sent by remote fault indicators (RFIs). First, mathematical expressions of expected and false alarms of oriented RFIs (ORFIs) and non-oriented RFIs (NRFIs) are formulated to construct a basic fault diagnosis model. Minimizing the number of false alarms is chosen as the objective function to find the most credible one from all possible fault hypotheses which represent different combinations of elements in the outage area. Next, considering IIDGs, a modified fault diagnosis model is established by eliminating misleading components from the objective function which are produced by incompetent RFIs and have failed to detect weak currents. The monitoring data provided by distribution level phasor measurement units (D-PMUs) for weak IIDG fault currents are leveraged to construct the equivalent RFI current, which is compared with the trigger threshold to find incompetent sensors. The proposed problem is formulated as a mixed-integer linear programming (MILP) model, which is solved by off-the-shelf commercial solvers. The effectiveness of the proposed fault diagnosis model is validated by test cases representing several DERs and false alarms applied to a utility feeder. [ABSTRACT FROM AUTHOR]

This article presents models and algorithms to simultaneously solve both the long-term grid planning and the distributed energy resource scheduling optimization problem for medium voltage grids. An emphasis is on evaluation of electric vehicle scheduling and demand side management. The main benefit of this new simultaneous optimization approach is that it converges towards a global optimum by considering not only infrastructure investments but also energy cost changes due to scheduling measures as for example curtailment or demand side management. The article firstly analyzes degrees of freedom in grid planning and in scheduling to derive models. The sections thereafter present the algorithm for simultaneous optimization. It integrates a fast scheduling optimization into a meta-heuristic grid-planning algorithm. The grid-planning algorithm uses Delaunay triangulation and an ant colony systems approach. The scheduling optimization combines dynamic programming and a fast heuristic to consider grid constraints. The last section presents exemplary results and illustrates effects of different regulatory regimes on costs. Results suggest that market-based scheduling will prevail due to energy cost-savings. However, purely market-based scheduling is unattractive when grid costs are considered. Distribution system operators can reduce long-term grid costs significantly, if they may perform demand side management. [ABSTRACT FROM AUTHOR]

Modern smart grid relies on various sensor measurements for its operational control. In a successful false data injection attack, the attacker manipulates the measurements from the grid sensors such that undetected errors are introduced into the estimates of the system parameters leading to catastrophic situations. This article proposes a novel perturbation based false data injection attack detection mechanism that utilizes inverter based distributed energy resources (DERs) to create low magnitude perturbation signal in the distribution system voltage that is inconsequential to the normal grid operation. Two voltage sensitivity analysis based algorithms are designed to identify the optimal set of DERs that can create the voltage perturbation signal of desired magnitude. An analytical method of voltage sensitivity analysis is used to compute the magnitude of voltage perturbation signal at each node in a computationally efficient manner. Then, a detection mechanism is developed that checks for the presence of the perturbation sequence in each sensor measurement. A sensor measurement is deemed authentic if the voltage perturbation signal is present in the data. In case of sensor malfunction or cyber-attack, the perturbation signal will not be present in the measurement data. Performance of the proposed attack detection mechanism is validated via simulation of the IEEE 69 bus test system. [ABSTRACT FROM AUTHOR]

The recent advent of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, has introduced a wide range of new applications in power distribution systems. A sub-class of such emerging applications are called event-based methods. These methods focus on the analysis of events in the stream of micro-PMU measurements to achieve situational awareness, enhance load modeling, integrate distributed energy resources, etc. In this article, we explore a scenario, where a cyberattack compromises the micro-PMU measurements during an event. Such a targeted attack could be limited in scope but result in a major impact on the operation of the power grid by highly deviating the outcome of the event-based methods. First, we investigate and model two types of such attacks, event-unsynchronized (basic) attacks and event-synchronized (advanced) attacks. We then conduct a geometric analysis to understand each attack type, in a setting where the events are represented in the phasor domain in a differential mode. Next, we introduce a novel method to detect the presence of the attack and then identify which micro-PMUs are compromised so as to discard the compromised measurements as a defense mechanism. The proposed approach makes critical use of magnitude as well as phase angle measurements from micro-PMUs. The method is tested on the IEEE 33-bus power distribution test system. [ABSTRACT FROM AUTHOR]

With the rising electricity demand for freshwater production, power and water sectors are becoming increasingly interdependent. This vital link could create new opportunities for power and water distribution system operators to coordinate the operation of their systems and achieve operational cost and energy efficiency benefits. This paper proposes the flexible power-water flow (FlexPWF) model for co-optimizing the operation of flexible energy-intensive components in water systems with the operation of both power and water distribution systems. The FlexPWF model leverages the energy flexibility provided by water treatment and desalination plants in conjunction with variable speed pumps and water storage tanks to minimize the operating costs of interdependent power and water distribution systems. The proposed model takes into account the operational constraints of both power and water distribution systems, thus coordinating their operation without endangering the reliable supply of power and water to customers. The paper highlights the merits of integrating water treatment and desalination plants, where a detailed model is proposed to capture their dynamic operation for producing variable amounts of freshwater and providing energy flexibility. Simulations, conducted on the 33-bus test distribution system and a 15-node test water distribution system, show that the FlexPWF model effectively coordinates the operation, reduces the operating costs and improves the operational metrics of both power and water systems. [ABSTRACT FROM AUTHOR]

Incipient faults in power distribution systems potentially lead to catastrophic failures. Detection of incipient faults contributes to proactive fault management and predictive maintenance, which effectively improves power supply reliability. Since the faults are infrequent and transient, few samples can be procured in real applications. In this paper, a detection method based on human-level concept learning (HLCL) is proposed to address this problem. The method contains two steps: human-level waveform decomposition (HLWD) and hierarchical probabilistic learning (HPL). HLWD, inspired by human perception, decomposes waveform into primitives: segments of general shape and residuals, which identify a waveform. HPL learns waveform through a generative process based on primitives, where the probability for an abnormal event to be an incipient fault can be hierarchically calculated. Experiments on simulation data and field data, which contain subcycle incipient faults, multicycle incipient faults, permanent faults and transient disturbances, indicate that the proposed method outperforms other three generally used classifiers. [ABSTRACT FROM AUTHOR]

To efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixed-integer linear programing (MILP) model for the optimal disaster recovery of power distribution systems. In the proposed recovery scheme, the maintenance crews (MCs) are scheduled to repair damaged components, and the restoration crews (RCs) are dispatched to switch on the manual switches. Then, the MC and RC dispatch models are integrated into the disaster recovery scheme, which will generate an optimal sequence of control actions for distributed generation (DG), controllable load, and remote/manual switches. Besides, to address the time scale related challenges in the model formulation, the technical constraints for system operation are investigated in each energization step rather than time step, hence the co-optimization problem is formulated as an “event-based” model with variable time steps. Consequently, the disaster recovery, MC dispatch and RC dispatch are properly cooperated, and the whole distribution systems can be restored step by step. Last, the effectiveness of the co-optimization model is validated in the modified IEEE 123 bus test distribution system. [ABSTRACT FROM AUTHOR]

In this paper, a privacy-preserving decision-making framework is developed for a collaborative operation of networked microgrids and the local utility grid, where the distribution system operator (DSO) interacts with microgrid master controllers (MCs) in a leader-followers fashion. First, the paper models the decision-making in the collaborative operation presented as an integrated mixed-integer second-order cone programming problem which satisfies various technical and operational requirements in a look-ahead manner. Then, the integrated optimization problem is decomposed into a master problem and a set of subproblems in order to decentralize the decision-making process of DSO and MCs. The master problem is solved by DSO for optimizing the local utility grid operation, and the subproblems are solved independently by MCs for optimizing the operation of individual microgrids. We propose an enhanced Benders decomposition algorithm to guide the iterations between the master problem and subproblems. In particular, an enhanced approach is introduced in the Benders decomposition algorithm for generating valid cutting planes from mixed-integer linear programming subproblems. Mathematically, the proposed algorithm can find the globally optimal solution after a finite number of iterations. Accordingly, DSO and MCs manage to collaborate on making operational decisions that would maximize the social welfare without compromising independent decision-making and privacy provisions. Numerical experiments are conducted on a simplified two-bus system and a modified IEEE 123-bus system. The results validate the efficiency, the robustness, and the scalability of the proposed collaborative decision-making framework in power systems. [ABSTRACT FROM AUTHOR]

With the rising penetration of distributed energy resources, distribution system control and enabling techniques such as state estimation have become essential to distribution system operation. However, traditional state estimation techniques have difficulty coping with the low-observability conditions often present on the distribution system due to the paucity of sensors and heterogeneity of measurements. To address these limitations, we propose a distribution system state estimation algorithm that employs matrix completion (a tool for estimating missing values in low-rank matrices) augmented with noise-resilient power flow constraints. This method operates under low-observability conditions where standard least-squares-based methods cannot operate, and flexibly incorporates any network quantities measured in the field. We empirically evaluate our method on the IEEE 33- and 123-bus test systems, and find that it provides near-perfect state estimation performance (within 1% mean absolute percent error) across many low-observability data availability regimes. [ABSTRACT FROM AUTHOR]

The enhancement of load-point reliability level in power distribution systems is usually based on traditional methods including value-based planning (VBP), performance- based rates (PBR) and reliability insurance scheme (RIS). The implementation of these methods has shown that due to weaknesses in their structures, customer reliability requirements were fulfilled rather infrequently. However, satisfying a desired level of reliability for distribution level customers remains to be a major challenge for distribution utilities. In this paper, a new competition model that is based on game theory is introduced which can conclude to more satisfactory results for customers and utilities. The problem formulation shows that the proposed model is similar to the Cornout model in game theory. The presented model has been applied to a sample radial feeder and the results show how the proposed game model can be a more appropriate structure for providing customer reliability in power distribution systems. [ABSTRACT FROM AUTHOR]

This paper studies the problem of locating harmonic sources and estimating the distribution of harmonic voltages in unbalanced three-phase power distribution systems. We develop an approach for harmonic state estimation utilizing two types of measurements from smart meters and distribution-level phasor measurement units (DPMUs). It involves regression analysis for power flow calculation, prediction of demands using recurrent neural networks, and sparse Bayesian learning for state estimation. The proposed approach requires fewer DPMUs than nodes, making it more applicable to existing distribution grids. We show the effectiveness of the proposed estimator through extensive numerical simulations on an IEEE test feeder. We also investigate how the increased penetration of distributed energy resources could affect the performance of our state estimator. [ABSTRACT FROM AUTHOR]

The recent development of distribution-level phasor measurement units, also known as micro-PMUs, has been an important step toward achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to extract events from the extremely large collection of raw micro-PMU data. Subsequently, a data-driven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA, USA. In total, we analyze 1.2 billion measurement points and 10,700 events. The effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including ${k}$ -nearest neighbor method as well as decision-tree method. Importantly, two real-world use-cases are presented for the proposed data analytics tools, including remote asset monitoring and distribution-level oscillation analysis. [ABSTRACT FROM AUTHOR]

In power transmission systems, the false data injection (FDI) attacks against state estimation (SE) have been well studied. However, due to the unique features of power distribution systems including: the low x/r ratio, existence of one-and/or two-phase branches, unbalanced load distributions, and unsymmetrical line parameters; the research on FDI attacks against distribution system SE (DSSE) is still open. In this paper, we investigate the vulnerability of DSSE to FDI attacks. In particular, we first propose a local state-based linear DSSE for multiphase and unbalanced smart distribution systems, which can facilitate the construction of FDI attacks numerically with the least information of system states. Then, the construction of three-phase coupled FDI attacks is introduced. The consideration of the coupling among phases by the three-phase coupled FDI attacks may require the modification of a large number of measurements by the attackers. To reduce the number of required measurements, the perfect three-phase decoupled FDI attacks, which consider the weak couplings among phases, is investigated. The probabilities of successful three-phase decoupled FDI attacks in strongly three-phase coupled systems are also derived numerically. The performance of the proposed FDI attacks against DSSE is evaluated based on IEEE test feeders. The case study results indicate the feasibility of the FDI attacks against DSSE in practical multiphase and unbalanced smart distribution systems. Future research directions including potential countermeasures are also highlighted. [ABSTRACT FROM AUTHOR]

In this paper, a post-event restoration method is proposed for power distribution systems with Internet data centers (IDCs) as critical loads. The proposed method is formulated as a mixed integer linear programming-based optimization model which considers emergency operation decisions in IDCs and restoration decisions in power distribution systems, and can minimize the utility loss of IDCs and other loads during the restoration process. Since the original form of the proposed method may face some problems in practical application, such as confidential information sharing and long computing time, a decoupling method is proposed to convert the integrated model into several sequential parts which are operated by power distribution systems or IDCs independently, so that the confidential information sharing issue is solved, and the computing time is significantly reduced. The proposed restoration method is tested on the IEEE 123-Bus Feeder with multiple IDCs to illustrate its feasibility and efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces a predictive method for distribution feeder vegetation management based on a risk framework. The state of risk is calculated for each feeder section using a variety of factors extracted from network parameters and historical outage data, historical weather data and weather forecasts, and a variety of vegetation indices. The framework implements the spatiotemporal correlation of all the collected data. The prediction model used is the Gaussian conditional random field, which takes into account spatial interdependencies between different feeder sections. This enables better prediction accuracy, and also offers the capability to deal with missing and bad data. Based on the calculated risk, the dynamic optimal tree trimming schedule, which minimizes the overall risk for the system under a given predetermined budget, is developed. Results obtained on a real utility network show that optimal tree trimming based on the developed risk framework for vegetation management could significantly decrease the overall risk of the feeder outages without increasing the budget. [ABSTRACT FROM AUTHOR]

Improvement of reliability indices and decreasing the energy not supplied are two main purposes of the power distribution systems’ operator. Installation of visual or remote fault indicators (FIs) can significantly contribute to achieving these goals. However, installation of these types of FIs in areas with limited or no communication coverage is not practical. This paper presents a novel conceptual structure for application of unmanned aerial vehicles (UAVs) in distribution systems. In the proposed framework, UAV-based FIs (UFIs) which are installed in the non-coverage areas, communicate with the UAVs and send their signals to them when a fault occurs in downstream. We formulate an optimization problem to find the optimal number of UFIs and UAVs as well as their locations, taking both communication and investment constraints into consideration. To evaluate the effectiveness of the presented framework, simulation results are presented and discussed for bus 6 of the IEEE-RBTS. [ABSTRACT FROM AUTHOR]

Traffic lights play a critical role in mitigating traffic congestions, which can be energized by distributed generators (DGs) when power outages occur in urban areas. This paper studies the resilience enhancement strategy by line hardening and DG placement when outages occur in distribution lines and traffic lights in the coupled power distribution system and urban transportation system (PDS-UTS). A tri-level optimization model is formulated with a limited budget for line hardening and DG placement to minimize the cost of load shedding and aggregated vehicle travel time. The first level determines line hardening and DG placement strategies, the second level searches for the worst case of faulted lines that would maximize load shedding and aggregated vehicle travel time, and the third level minimizes the corresponding costs of load shedding and travel. In urban transportation system, a dynamic user equilibrium model is established in a cell transmission model and solved by a linear complementarity approach. As the number of unavailable lines and traffic lights are definite in the inner-most level, the coupled PDS-UTS is considered as two decoupled systems. Accordingly, the tri-level model is converted into an equivalent bi-level model through Karush–Kuhn–Tucker conditions, which is then solved by a greedy search method. Case studies corroborate the effectiveness of the proposed model and relevant solution method for the coupled PDS-UTS. [ABSTRACT FROM AUTHOR]

The power flow problem can be compactly formulated using complex variables; its solution is however commonly computed using the real variable Newton-Raphson algorithm, where the complex variables are replaced by their real valued rectangular or polar coordinates. A Newton-Raphson solution in complex variables is hindered by the fact that the power flow equations are non-analytic in their phasor voltages, i.e., they do not have a Taylor series expansion in terms of these complex variables alone. By using Wirtinger calculus, the power flow equations can be expanded in terms of the phasor voltages and their conjugates, thus allowing the implementation of the complex variable Newton-Raphson algorithm. This paper specializes the solution of the complex variable Newton-Raphson power flow to distribution networks, and includes the particularities of modeling line drop compensators, reactive power limited generation, and voltage dependent loads. Numerical results presented on distribution networks with up to 3139 nodes show that the complex implementation is better suited to modern processors that employ single instruction multiple data (SIMD) processor extensions; in particular SIMD instructions result in a complex variable formulation that is faster than the classical real variable formulation, yet it has the distinct advantage of a simple software implementation. [ABSTRACT FROM AUTHOR]

Active consumers who engage in energy consumption, production and provide ancillary services in a dynamic and interactive manner will be an integral part of the future grid. This paper models and analyzes interaction between active consumers and aggregators with a specific focus on consumer actions in response to real-time electricity pricing and their impacts on grid voltage. First, this paper introduces a unique prospect theory-based consumer behavior model that captures wide range of consumers each with their individual preferences. Second, the interaction between consumers and aggregators is modeled as a Stackelberg game. However, unlike existing game theoretic efforts that assume rational behavior of consumers, the prospect theory-based models systematically incorporate realistic consumer behavior including irrationality. Another unique aspect of the formulation is the inclusion of the physical grid constraints (in terms of voltage violations) within the analysis. In contrast to prior approaches that limit themselves to economic aspects, the proposed techno-economic perspective provides an understanding of the impact of large scale penetration of active consumers on the physical grid. The proposed model is analyzed using simulations on the IEEE 69 bus test system and the impact of irrationality on both consumer/aggregator payoffs and voltage violations is quantified. [ABSTRACT FROM AUTHOR]

The deployment of new sensors and devices on electric distribution systems is increasing the awareness of phenomena characterized by intermittent periods of highly dynamic activity that occur within extended periods of relatively static behavior. The deployment of new devices has enabled the observation of these phenomena; however, the currently available simulation methods cannot accurately reproduce the entire system behavior. Existing simulation methods, and their associated models, are able to capture portions of these phenomena, but there is not a method for efficiently modeling the entire event in a single simulation. This paper presents a novel method of adaptive simulation that enables automated transitions between quasi-static time-series and electromechancial simulation modes, as necessary to capture relevant system dynamics. The transitions between the simulation modes are triggered automatically during the running simulation based on the evolution of the system variables, utilizing multistate modes for generators and motors. This method allows for a single simulation that spans the entire time-frame, has the ability to capture dynamic events, and includes all relevant power system controls. The method of adaptive simulation can support the direct analysis of dynamic power system events, co-simulation of transmission and distribution systems, the development of control systems, and the development of reduced-order models. [ABSTRACT FROM AUTHOR]

This paper presents a long-term impact evaluation algorithm to assess advanced under frequency load shedding (UFLS) schemes on distribution systems with intentional islanding of distributed generation (DG). The algorithm is based on a combined discrete-continuous simulation model which is utilized to verify the effect of the schemes on reliability indices such as the system average interruption frequency index, system average interruption duration index, and energy not supplied. Moreover, a polynomial neural network-based approach to advanced load shedding is implemented to support DG islanding in order to illustrate the applicability of the evaluation. Simulation results highlight the long-term effect of employing UFLS to support intentional islanding of DG using an actual network from the South of Brazil. [ABSTRACT FROM AUTHOR]

Low-voltage distribution networks are emerging as an increasingly important component of power system operations due to the deployment of distributed renewable resources (e.g., rooftop solar supply) and the need to mobilize the flexibility of consumers that are connected to the low-voltage grid. The pricing of electric power at distribution nodes follows directly from the theory of spot pricing of electricity. However, in contrast to linearized lossless models of transmission networks, an intuitive understanding of prices at the distribution level presents challenges due to voltage limits, reactive power flows, and losses. In this paper, we present three approaches toward understanding distribution locational marginal prices by decomposing them: 1) through a duality analysis of the problem formulated with a global power balance constraint; 2) through a duality analysis of a second-order cone program relaxation; and 3) through an analysis of the impact of marginal losses on price. We discuss the relative strengths and weaknesses of each approach in terms of computation and physical intuition, and demonstrate the concepts on a 15-bus radial distribution network. [ABSTRACT FROM AUTHOR]

With increasing utilizations of distributed energy resources and smart energy apparatuses, power distribution systems are undergoing a transition from passive to active networks, which challenges distribution system operators in many aspects of energy management and system control. In this paper, a short-term control strategy for active power distribution systems is proposed to regulate voltages into statutory ranges. The voltage control strategy is modeled as a convex optimization problem by applying a second-order cone relaxation. After the network decomposition, the alternating direction method of multipliers is employed to solve the established optimization model in a decentralized mode. Finally, the IEEE 33-bus and 123-bus test systems are employed to demonstrate the performance of the proposed voltage control strategy in active power distribution systems. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage method for the outage management of power distribution systems. The first stage is to cluster repair tasks of damaged power generation and delivery components based on their distances from the depots (central crew stations) and the availability of resources, to improve the computational efficiency in solving outage management problems for large distribution systems. The second stage is to co-optimize the repair, reconfiguration, and distributed generation dispatch to maximize the picked-up loads and minimize the repair time. The distribution system repair and restoration problem is formulated as a mixed integer linear program, considering constraints of system operation and routing repair crews. Crews are dispatched considering equipment resources, traveling time, and repair time. The proposed method is tested on modified IEEE 34 and 123-bus distribution test systems with multiple damages. The results demonstrate the advantages of co-optimizing repair and restoration. [ABSTRACT FROM AUTHOR]

Network operators and utilities are challenged with increasing extreme weather conditions, resulting in interrupted power supply to critical loads. Resiliency metrics, which can capture the level of preparedness to resist adverse impact of extreme conditions on a distribution system, can be leveraged in multiple ways to provide better operation of the network and design of the future systems. In this paper, a methodology to quantify resiliency and maintain power supply to critical loads (CLs) during extreme contingencies has been proposed. Resiliency evaluation of power distribution system has been defined as a multi-criteria decision making problem and quantified using graph theoretic approach and Choquet integral. The algorithm proposed in this paper to calculate the resiliency for all feasible network configurations supplying CLs in a network is useful in planning as well as operation of the distribution network. The application of the proposed algorithm is demonstrated through several case studies using two proximal CERTS microgrids and IEEE 123 node distribution system. Simulation studies are also provided for planning of resilient network, by placing additional switches in the considered distribution systems with microgrid. [ABSTRACT FROM AUTHOR]

Open-delta connected step voltage regulators (SVRs) have been widely installed into distribution networks, and their characteristic model (auto-transformer model) has been established over the years for power system analysis. However, these models are not fully developed to a complete bus admittance matrix form, which can be readily used for unbalanced load flow programs. Further, the debate on the neutral shift after implementation of open-delta SVRs has added difficulties on the modeling. There have been confusions on what is the neutral shift for a three-wire network without neutral and where the neutral is shifted to. Recently, accurate modeling of such a network component has become exceptionally important especially with the rapid proliferation of photovoltaic (PV), and there is a strong requirement of evaluating PV integration impacts on distribution systems. Propelled by all these necessity of modeling, this paper develops a new mathematical model of the open-delta SVR in a bus admittance matrix form which can be easily incorporated into suitable unbalanced load flow programs. Further, the neutral shift issue is also clarified during the model development, and in addition a method for tap position estimation is derived. All the established models are validated by field measurement. [ABSTRACT FROM PUBLISHER]

This paper presents a novel hierarchical multilevel decentralized optimal power flow (OPF) for power loss minimization in three-phase unbalanced large-scale distribution systems via optimal reactive power scheduling of rooftop photovoltaics generators. The system is decomposed into three levels corresponding to: 1) the primary; 2) the lateral; and 3) the secondary feeder subnetworks of distribution systems. A distributed sequential coordination scheme based on analytical target cascading method is developed to minimize power losses while considering the operational constraints. Results based on the proposed method are compared with centralized OPF for validation. Control based on the proposed method is compared with no reactive power control and local reactive power control to identify its effectiveness. Further, a virtual feeder is introduced to separate the coupled subnetworks into decomposed layers, which enables parallel processing of the optimization problems to reduce computational complexity and provide faster solution. A 559-node large-scale distribution network built based on the IEEE 37 node test system is used to demonstrate performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

The presence of uncertain parameters in electrical power systems presents an ongoing problem for system operators and other stakeholders when it comes to making decisions. Determining the most appropriate dispatch schedule or system configuration relies heavily on forecasts for a number of parameters such as demand, generator availability and more recently weather. These uncertain parameters present an even more compelling problem at the distribution level, as these networks are inherently unbalanced, and need to be represented as such for certain tasks. The work in this paper presents an information gap decision theory based three-phase optimal power flow. Assuming that the demand is uncertain, the aim is to provide optimal and robust tap setting and switch decisions over a 24-hour period, while ensuring that the network is operated safely, and that losses are kept within an acceptable range. The formulation is tested on a section of realistic low voltage distribution network with switches and tap changers present. [ABSTRACT FROM PUBLISHER]

Cyber-enabled operation is needed for smart distribution system implementation. The interaction of cyber and power components will affect system reliability. This paper focuses on developing an analytical reliability model for fault detection, isolation, and service restoration for smart distribution feeders. The impact of end-to-end outage probability of data communication along with sending, receiving, and relaying communication node failures is incorporated into the model. Vulnerability of system to cyber attack as an emerging cause of reliability degradation is also investigated. An optimal placement of fault detectors and switching devices are determined in this work to improve reliability. The sum of customer service interruption cost and investment cost is considered as an objective function to be minimized. Bus 2 of the Roy Billinton test system and a typical 27-node distribution network are used to illustrate the role of communication infrastructure malfunction on the planning problem in a distribution feeder. Results and discussions show the necessity of incorporating communication infrastructure failure into the power distribution system planning problem. [ABSTRACT FROM PUBLISHER]

The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. Traditionally, OPF is solved in a centralized manner. With increasing penetration of renewable energy in distribution system, we need faster and distributed solutions for real-time feedback control. This is difficult due to the nonlinearity of the power flow equations. In this paper, we propose a solution for balanced radial networks. It exploits recent results that suggest solving for a globally optimal solution of OPF over a radial network through the second-order cone program relaxation. Our distributed algorithm is based on alternating direction method of multiplier (ADMM), but unlike standard ADMM-based distributed OPF algorithms that require solving optimization subproblems using iterative method, our decomposition allows us to derive closed form solutions for these subproblems, greatly speeding up each ADMM iteration. We illustrate the scalability of the proposed algorithm by simulating it on a real-world 2065-bus distribution network. [ABSTRACT FROM PUBLISHER]

This paper describes high-level findings from an innovative network of high-precision phasor measurement units (PMUs), or micro-PMUs ( \mu PMUs), designed to provide an unprecedented level of visibility for power distribution systems. We present capabilities of the technology developed in the course of a three-year ARPA-E funded project, along with challenges and lessons learned through field deployments in collaboration with multiple electric utilities. Beyond specific applications and use cases for \mu PMU data studied in the context of this project, this paper discusses a broader range of diagnostic applications that appear promising for future work, especially in the presence of high penetrations of variable distributed energy resources. [ABSTRACT FROM PUBLISHER]

This paper presents a fault location method for power distribution networks with the presence of distributed generation (DG) units along their feeders. The proposed fault location method is based on voltage and current phasor quantities, calculated from measurements provided by intelligent electronic devices installed only at the substation bus, and on information about the electrical parameters of the feeders, stored in the utility’s database. It is capable of correctly locating the fault point using detailed models of the feeder’s line sections, information about the impedances of the loads, and the equivalent circuits of the DG units connected to the feeder. The proposed method relies on an optimization process that estimates the fault point using the Pattern Search method. A typical distribution feeder was implemented using EMTP/ATP and test data produced during the simulations were used to evaluate the method’s performance. The simulations considered variations of the fault type and phases involved, variations of the fault resistance, and input data errors. The results indicate that the method is robust and provides a high level of accuracy. [ABSTRACT FROM PUBLISHER]

As an emerging fast-growing technology, smart grid networks (SGNs) have been dramatically accepted by the current power supply industry for achieving high performance power governance system. The wireless SGN (WSGNs) have enabled numerous flexible power management solutions without the restrictions of the wired infrastructure. The cognitive radio network (CRN) is one of the widely deployed wireless networking approaches. The communication security is a major concern while CRN is used in WSGNs. Currently, jamming and spoofing are two common attack approaches that are active in the deployment of WSGNs when using CRNs. This paper proposes an attack strategy, maximum attacking strategy using spoofing and jamming (MASS-SJ), which utilizes an optimal power distribution to maximize the adversarial effects. Spoofing and jamming attacks are launched in a dynamic manner in order to interfere with the maximum number of signal channels. Our proposed approach has been evaluated by our experiments and the results have shown the positive performance of using MAS-SJ. [ABSTRACT FROM PUBLISHER]

Feeder volt-var control (VVC) has been widely promoted and adopted in power distribution industry. This paper presents a technique to “measure” the true benefits of such schemes using the continuous data collected from the advanced metering infrastructure system and the statistical data analytics techniques. The VVC schemes under consideration are switchable capacitor based. The proposed method is able to estimate, statistically, the amount of feeder losses saved due to switched capacitors during a given test period such as a week. Feasibility of the method is demonstrated by simulation studies on the IEEE 123 node test feeder. The studies reveal that the method can still perform by even a 20 min time resolution of measurements, so that the proposed approach is promising even with regard to existing metering technologies in distribution systems. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel implementation of a multiphase distribution network state estimator which employs industrial-grade modeling of power components and measurements. Unlike the classical voltage-based and current-based state estimators, this paper presents the implementation details of a constrained weighted least squares state calculation method that includes standard three-phase state estimation capabilities in addition to practical modeling requirements from the industry; these requirements comprise multiphase line configurations, unsymmetrical and incomplete transformer connections, power measurements on \triangle -connected loads, cumulative-type power measurements, line-to-line voltage magnitude measurements, and reversible line drop compensators. The enhanced modeling equips the estimator with capabilities that make it superior to a recently presented state-of-the-art distribution network load estimator that is currently used in real-life distribution management systems; comparative performance results demonstrate the advantage of the proposed estimator under practical measurement schemes. [ABSTRACT FROM PUBLISHER]

Weather impacts are one of the main causes of distribution outages. To devise strategies to mitigate weather impacts, a fuzzy logic system for decision making is introduced. It allows utility operators to achieve more precise outage predictions and optimize real time operation and maintenance scheduling. A novel approach for weather-driven risk framework is applied to process the data and produce risk maps for better decision making. The use of weather data in reducing fault location time, an important performance improvement in outage management, is also presented. [ABSTRACT FROM PUBLISHER]

A resilience-oriented service restoration method using microgrids to restore critical load after natural disasters is proposed in this paper. Considering the scarcity of power generation resources, the concept of continuous operating time (COT) is introduced to determine the availability of microgrids for critical load restoration and to assess the service time. Uncertainties induced by intermittent energy sources and load are also taken into account. The critical load restoration problem is modeled as a chance-constrained stochastic program. A Markov chain-based operation model is designed to describe the stochastic energy variations within microgrids, based on which the COT is assessed. A two-stage heuristic is developed for the critical load restoration problem. First, a strategy table containing the information of all feasible restoration paths is established. Then the critical load restoration strategy is obtained by solving a linear integer program. Numerical simulations are performed on the IEEE 123-node feeder system under several scenarios to demonstrate the effectiveness of the proposed method. The impacts of fault locations, available generation resources, and load priority on the restoration strategy are discussed. [ABSTRACT FROM PUBLISHER]

Agent-based modeling of active distribution network helps to understand the dynamics and to design the control strategies for overall system efficiency. There is, however, a lack of generic and multipurpose agent definitions in existing studies. In this paper, a multi-agent system-based modeling of an active distribution network is presented using cooperative agents. A method to solve a network-wise objective of state estimation is explained with the proposed model. The network component agents are defined to be cooperative to meet the overall objectives and greedy to fulfil individual objectives such as energy cost minimization. A token-ring protocol is deployed for the agent communication among themselves, as well as with market and network operator agents. Furthermore, a MATLAB/Simulink model of active distribution network is used to simulate the emerging stochastic loading scenario, while the autonomous prosumer agents optimize their total energy cost responding to market price variations. [ABSTRACT FROM PUBLISHER]

Efficient management and coordination of distributed energy resources with advanced automation schemes requires accurate distribution system modeling and monitoring. Big data from smart meters and photovoltaic (PV) micro-inverters can be leveraged to calibrate existing utility models. This paper presents computationally efficient distribution system parameter estimation algorithms to improve the accuracy of existing utility feeder radial secondary circuit model parameters. The method is demonstrated using a real utility feeder model with advanced metering infrastructure (AMI) and PV micro-inverters, along with alternative parameter estimation approaches that can be used to improve secondary circuit models when limited measurement data is available. The parameter estimation accuracy is demonstrated for both a three-phase test circuit with typical secondary circuit topologies and single-phase secondary circuits in a real mixed-phase test system. [ABSTRACT FROM PUBLISHER]

A new multiagent system (MAS) design for fault location, isolation, and restoration in power distribution systems (PDSs) is presented. When there is a fault in the PDS, MAS quickly isolates the fault and restores the service to the fault-free zones. Hierarchical coordination strategy is introduced to manage the agents, which integrate the advantages of both centralized and decentralized coordination strategies. Proposed MAS is composed of zone agents (ZAs), feeder agents (FAs), and substation agents. In this framework, ZA locate and isolate the fault based on the locally available information, and assist the FA for reconfiguration and restoration. FA can solve the restoration problem using the existing algorithms for the 0-1 knapsack problem. A novel Q -learning mechanism is also introduced to support the FA in decision making for restoration. The design is illustrated by the use of case studies of fault location, isolation, and restoration on West Virginia super circuit. The results from the case studies indicate the performance of proposed MAS design. [ABSTRACT FROM PUBLISHER]

Distribution systems are undergoing many enhancements and developments to enable the future smart grid, and distribution system state estimation (DSSE) provides the control centers with the information necessary for several of its applications and operational functions. However, the quality of DSSE typically suffers from a lack of adequate/accurate measurements. Recently, many electric utilities have started to install fairly accurate smart meters throughout their distribution networks, which create an opportunity to achieve higher quality DSSE. However, the signals provided by smart meters are generally not synchronized and the difference between the measurement times of smart meters can be significant. Therefore, a complete snapshot of the entire distribution system may not be available. This paper proposes a method to deal with the issue of nonsynchronized measurements coming from smart meters based on the credibility of each available measurement and appropriately adjusting the variance of the measurement devices. To illustrate the effectiveness of the proposed method, two IEEE benchmark systems are used. The results show that the proposed method is robust and improves the accuracy of DSSE compared with the traditional DSSE approach. [ABSTRACT FROM PUBLISHER]

Large-scale integration of rooftop solar power generation is transforming traditionally passive power distribution systems into active ones. High penetration of such devices creates new dynamics for which the current power distribution systems are inadequate. The changing paradigm of power distribution system requires it to be operated as cyber-physical system. A goal-based holonic multiagent system (HMAS) is presented in this paper to achieve this objective. This paper provides details on design of the HMAS for operation of power distribution systems. Various operating modes and associated goals are discussed. Finally, the role of HMAS is demonstrated for two applications in distribution systems. The first one is associated with control of reactive power at solar photovoltaic installations at individual homes for optimal operation of the system. The second deals with the state estimation of the system leveraging different measurements available from smart meters at homes. [ABSTRACT FROM PUBLISHER]

Distribution utilities are becoming increasingly aware that their networks may struggle to accommodate a large number of plug-in electric vehicles (PEVs), especially at times of peak loading. In this paper, a centralized scheduling scheme is formulated to coordinate charging of a heterogeneous PEV fleet, which is then divided into load groups, each with a set of local objectives, constraints, and a decentralized control algorithm. Realistic energy tariffs are used to formulate probable “real-world” objectives for each load group, and a distributed dynamic program is used to regulate the net load of each load group. It is shown that charging coordination not only reduces peak network loads, but can also significantly reduce the customer’s PEV charging cost. Potential improvements from the perspective of communication and modeling are also highlighted by adopting a systematic approach to divide the PEV fleet into load groups. [ABSTRACT FROM PUBLISHER]

The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. We demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure. [ABSTRACT FROM PUBLISHER]

Minimization of the de-energized consumers load and reduction of the service restoration costs are considered in the proposed heuristic graph-based approach for finding the solution of the service restoration in distributed networks. The approach is based on the logic of the modified Prim’s algorithm that finds the minimum spanning trees (i.e., a forest). Operation costs including an actual status (“open” or “close”) of the switching equipment in a de-energized area is represented by the switching cost coefficients. The cost coefficients are assigned to all branches in a de-energized area. The switching action is performed by switching equipment with minimal switching costs coefficient in each iteration of the algorithm. The results confirm that the service restoration costs are decreased by the consideration of the actual status of the switching equipment. [ABSTRACT FROM PUBLISHER]

This paper presents a method for the real-time management of electric vehicles (EVs) charging processes. The proposed method aims to limit the peak load and to increase the number of rechargeable EVs with respect to the scenario in which no coordination action is performed, while achieving given constraints on the power distribution system. The approach is based on a tight interaction between a scheduling algorithm and a power-flow evaluation procedure. The scheduling algorithm finds the best charging periods for each EV. The power flow procedure checks the achievement of electrical constraints and evaluates the operational parameters of the grid. Simulations are carried out on a real electricity distribution system of a medium-sized Italian city. The results show that the proposed approach increases the number of rechargeable EVs up to 33%. At the same time, the peak load is reduced by 25%. The scheduling algorithm requires an average of 50 ms to evaluate each charge request on an ordinary computer, therefore allowing its use in real-time conditions. [ABSTRACT FROM PUBLISHER]