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

In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered. [ABSTRACT FROM AUTHOR]

Large-scale blackouts and extreme weather events in recent decades raise the concern for improving the resilience of electric power infrastructures. Distribution service restoration (DSR), a fundamental application in outage management systems, provides restoration solutions for system operators when power outages happen. As distribution generators (DGs) and remotely controllable devices are increasingly installed in distribution systems, an advanced DSR framework is needed to perform optimally coordinated restoration that can achieve maximal restoration performance. This paper introduces a DSR modeling framework, which can generate optimal switching sequences and estimated time of restoration in the presence of remotely controllable switches, manually operated switches, and dispatchable DGs. Two mathematical models, a variable time step model and a fixed time step model, are presented and compared. The proposed models are formulated as a mixed-integer linear programming model, and their effectiveness is evaluated via the IEEE 123 node test feeder. [ABSTRACT FROM AUTHOR]

Distribution service restoration (DSR) is critical for improving the resilience and reliability of modern distribution systems by strategically and sequentially energizing the system components and customer loads. Restoring electricity service to affected customers also requires multiple crews with different skill sets to perform multiple tasks that are procedurally interdependent with safety guaranteed. However, in existing DSR practices, switch operations and crew dispatch are scheduled separately, and their interdependence is not fully considered. As advanced technologies are enabling remote communication, control, and dispatch, utilities now desire an integrated DSR framework to achieve seamless coordination among multiple DSR tasks such as switch operation, crew dispatch, and component repair. In this paper, we introduce a synthetic model that integrates the service restoration model and the crew dispatch model based on a universal routing model. The proposed model can provide the estimated time of restoration for each load, the switching sequence for safely operating remotely/manually operated switches, and dispatch solutions for crews with different skill sets. The proposed synthetic model is formulated as a mixed-integer linear programming model, and its effectiveness is evaluated via the IEEE 123 bus test feeder and several large-scale test feeders (EPRI Ckt5, Ckt7, Ckt24, and IEEE 8500 node test feeder. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to co-optimize the distribution system operation and repair crew routing for outage restoration after extreme weather events. A two-stage stochastic mixed integer linear program is developed. The first stage is to dispatch the repair crews to the damaged components. The second stage is distribution system restoration using distributed generators, and reconfiguration. We consider demand uncertainty in terms of a truncated normal forecast error distribution, and model the uncertainty of the repair time using a lognormal distribution. A new decomposition approach, combined with the progressive hedging algorithm, is developed for solving large-scale outage management problems in an effective and timely manner. The proposed method is validated on modified IEEE 34- and 8500-bus distribution test systems. [ABSTRACT FROM AUTHOR]

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, the measurement-based approach is one of the main methods in load modeling but it has some defects in practice, mainly, it only utilizes the data recorded during three-phase symmetric disturbances which rarely happen in power systems. In this paper load modeling during asymmetric disturbances (LMAD) is proposed. LMAD may collect data during asymmetric disturbances which occur more often in power systems. Some measured data are chosen to testify LMAD with satisfying results which not only validate the method but also are applied in power system simulation now. LMAD is promising in load modeling since it does not need extra field tests and special data acquisition instruments, both of which hardly get approval of utilities. Furthermore, the conditions on the uniqueness of identified load model parameters are discussed. Some characteristics like electro- magnetic torque of the load model are studied as well. [ABSTRACT FROM AUTHOR]

The load models play an important role in the simulation and evaluation of power systems performance. This paper first proposes a new load model, which is based on a particular form of artificial neural networks we denote as adaptive back-propagation (ABP) network for nonparametric models. ABP can overcome some shortcomings of common back-propagation (BP), and the ABP load models offer several advantages over traditional load models as they are nonstructural and can be derived quickly. The application of the method is illustrated using actual field test data from Northeast China to Shanghai, one of the biggest cities in China. The load models so obtained are shown to replicate the test measurements more closely than those based on traditional load models. Second, extension of the method to the determination of the parameters of the traditional load models is also proposed. It is based on a linear back-propagation (LBP) network. The proposed LBP for parametric load model is incorporated in a transient stability program to show that not only the computational time is significantly reduced, but also the accuracy of identification is improved. [ABSTRACT FROM AUTHOR]

This paper describes a novel power injection model (PIM) of interline power flow controller (IPFC) for power flow analysis. In this model, the impedance of the series coupling transformer and the line charging susceptance are all included. In this situation, it is proved that the original structure and symmetry of the admittance matrix can still be kept, and thus, the Jacobian matrix can keep the block-diagonal properties, and sparsity technique can be applied. The IPFC state variables are adjusted simultaneously with the network state variables in order to achieve the specified control targets. Furthermore, the model can take into account the practical constraints of IPFC in Newton power flow. Numerical results based on the IEEE 57-bus and IEEE 300-bus systems are used to demonstrate the effectiveness and performance of the IPFC model. [ABSTRACT FROM AUTHOR]