Many applications of Distribution Management Systems (DMSs) are based on power flow solutions. Fast and robust power flow methods capable of accommodating systems of general topologies and the most common models of Distributed Energy Resources (DERs) are, therefore, becoming indispensable. In this context, this paper proposes contributions for the symmetrical component-based three-phase power flow methods for distribution system analysis. The introduction of symmetrical components in the three-phase power flow problem allows it to be decomposed into three single-phase problems, which can be solved iteratively. Such decomposition significantly expedites the power flow solution problem, simplifies implementation complexity, and makes way for parallel computing techniques. The accuracy and validity of the proposed method were tested on distribution test feeders of different sizes and topologies and the results of several case studies were compared with those obtained by the Sequence Newton–Raphson method, and by the OpenDSS. Contributions of the paper include: (i) A new formulation of the Sequence-Decoupling Compensation method in terms of real-valued matrices; (ii) a novel modelling for PV buses; (iii) a simple procedure to tackle convergence issues related to delta and ungrounded-wye connected transformers; and (iv) a modelling for wye, closed- and open-delta connected step-voltage regulators in the sequence frame of reference.

This study proposes a time-based security (TBS) evaluation mechanism for radial microgrids (MGs) short-term operation. In the proposed framework, system security is modelled using time-based sensitivity analysis in the load points. Occurring branch contingencies in a radial MG creates some islands with oscillating frequencies. The settling time of frequency oscillation, as the successful islanding time, and the island's capability to continue the load supply are considered in TBS assessment. A system frequency response model and the stored energy of storages are used to calculate the two mentioned factors. Depending on the loads’ sensitivities to the successful islanding time and the service continuity duration, they are exposed to extreme damage. In the case of branches’ sudden outages, it may be impossible to operate the MG without any extreme damage of loads, due to the capacity shortage in the island, dynamic behaviours of the MG and inadequate stored energy of storages. Under these conditions, the load shedding unit participates considering the customer damage function. To evaluate the performance of the proposed security assessment mechanism, the 123-bus distribution test system is precisely analysed.

Power system stabiliser (PSS) widely used to enhance the stability of power systems is based on the feedback of rotor speed error with respect to the rated value. However in many occasions, after the disturbances, it is desirable to re-synchronise all generators first rather than to make all generators back to the rated 50/60 Hz immediately. This study proposed a distributed power system stabiliser (DPSS) that aims to synchronise generators by the feedback of relative rotor speed errors of each generator with respect to its neighbouring generators. The proposed DPSS is analysed on a double machine infinite bus system, and shows that the proposed DPSS can enhance the stability of power systems by increasing the synchronising torque. To evaluate the efficiency of proposed method, the proposed DPSS is tested on three benchmark systems. The simulation results verify the efficacy of theoretical analysis, show the outperformance of DPSS over other PSSs, and demonstrate the superiority of re-synchronising generators over making them back to rated value in the post-fault. Communication issues have also been evaluated, which demonstrates the robustness of DPSS against both communication failures and random delays.

This study presents a new method of forming emergency-responsive autonomous islands for restoring electric power supply using an electric vehicle (EV) idle power against the so-called cold load pickup (CLPU). Two stepwise discretisation methodologies are firstly proposed, which are used to discretise arbitrarily given cold-load models and aggregate discretised EV discharge models, respectively. Then, under the framework of mixed-integer linear programming, a methodology of forming particular responsive restoration islands is built, which aims to maximise the recovered energy of interrupted end-users given discretised CLPU and EV discharge models. Comparative analyses via the proposed method against existing algorithms are conducted on both a partial IEEE RBTS-BUS6 test feeder and a practical feeder. Specifically, the conducted analyses investigate the impacts of the CLPU on energy restoration and the assistance performance of discharge power from EV's on boosting the capability of responsive restoration islands to restore the interruption. Moreover, an especial relationship is studied, between the utilisation rate of total discharge power of an EV parking lot and the rate of restored energy concerning the total lost energy.

This study introduces the implementation of intrinsic time decomposition (ITD) based passive islanding detection technique for distributed generation (DG) system. The proposed method utilises voltage signals and decomposes it into a baseline signal and residual signal. The performance index in terms of energy computed from baseline signal is defined to discriminate between islanding and non-islanding events. The proposed ITD based technique for islanding detection has been analysed on different types of DGs connected to the system. The performance of the proposed technique is validated via simulation of distribution system modelled in EMTDC/PSCAD. The different critical operating conditions: lower end active and reactive power mismatch and events like capacitor switching, load switching, inception of fault, have been simulated to test the performance of the proposed scheme. The obtained results suggest that the proposed technique exhibits high reliability and has quick response time to detect islanding situation as compared to other passive islanding detection methods.

Generally, the owners of microgrids are not identical; therefore, each microgrid tries to optimise its own profit and maximise its utilisation of the point of common coupling (PCC) capacity to boost its profitability, while being ignorant of its neighbouring microgrids. In the absence of information concerning neighbouring microgrids, data limitations in the PCC may be troublesome, especially in load peak hours. Hence, existence of an agent is vital to manage energy exchanges between microgrids and grid. This study introduces a bi-level hierarchical structure to manage energy in a system composed of multimicrogrids while considering PCC congestion. In the first level, each microgrid implements its day-ahead scheduling and declares its probable energy mismatch to an agent, entitled microgrid aggregator (MGA). In the second level, the MGA aims to maximise profit of microgrids considering PCC constraint through a two-stage optimisation problem. In the first stage, Karush-Kuhn-Tucker conditions are used to present a novel virtual pricing algorithm exerted by aggregator for rescheduling of microgrids. After regulating generation of microgrids through a reciprocal process, the resultant profit is fairly divided among microgrids via Shapley value in the second stage. The simulation results reveal the efficiency and reliability of the proposed method.

While approaching towards smart(er) grids, the volume of data increases significantly and thus, data management becomes extremely challenging. Power system operational data can be compressed effectively by differential binary encoding algorithm (DBEA) and extended-DBEA (E-DBEA). In the proposed smart DBEA encryption (S-DBEAE) algorithm, data is compressed either by DBEA or by E-DBEA and the obtained string is encrypted to ensure data security. Data management is done by appending few identity characters containing different data fields before the encrypted information. Though the compression ratio obtained with S-DBEAE is slightly lower than that obtained with DBEA (or E-DBEA), provision of data management makes S-DBEAE superior than DBEA or E-DBEA. The cloud-based real-time test bench developed for managing power system operational data comprises four personal computers connected through Internet. While managing generation scheduling information, inertia weight-particle swarm optimisation algorithm was employed to solve economic load despatch problem for two generating station system. As S-DBEAE can reduce the volume of system monitoring data considerably, the system can be realised for managing those data as well. Owing to the simplicity of the proposed system, it is possible to extend the work even with low-level microcontrollers by incorporating certain modifications.

Dynamic VAR compensation (DVC) planning is important in power system planning to enhance short-term voltage stability. However, since its optimisations models include differential algebraic equations (DAEs), existing methods cannot directly solve the problem as that in the optimal power flow problem. In this study, the authors design an integer quadratic programming (IQP) model for the DVC planning problem which can simultaneously optimise the sites and size of DVCs. On the basis of the defined dynamic correlation indices, a quadratic programming objective function is defined to minimise the total investment of DVCs as well as to avoid redundant installation of DVCs on adjacent buses. Subsequently, sensitivity coefficients about the control effect of DVCs are introduced to form the linear inequations as the substitute for DAEs in constraints. Then, an iterative process is applied to solve the IQP. The proposed method is applied on a modified NE 39-bus system and a real power grid. Comparisons to the existing methods are carried out to show its superiority in model complexity and solving efficiency.

With the development of synchronised measurement technique, online dynamic security assessment (DSA) is of great significance to prevent power system blackout. Recently, based on the phasor measurement unit (PMU) data, intelligent data-driven techniques have been rapidly developed for online DSA owing to their fast decision speed, less data requirement, and decision rule discovery ability. The interpretable decision rules provide useful information for preventive control and post-event auditing. However, in case of incomplete measurement events, such as PMU failure, communication loss, and phasor data concentrator failure, the accuracy and the transparency of the intelligent models can be significantly impaired by such incomplete data. To overcome this problem, this paper proposes a robust white-box model that can sustain DSA accuracy and survive the model interpretability and transparency against incomplete PMU data. The proposed model is tested on New England 39-bus system and demonstrates higher robustness over existing methods.

Accurate and robust estimation of dynamic states is essential for monitoring and controlling a power grid. To achieve higher accuracy and robustness, this study proposes a hybrid estimation approach to estimate the dynamic states of synchronous generators using multiple models, classified into a set of local models and a wide-area model. The proposed estimation approach, first, estimates dynamic states using the extended Kalman filter based on a local model and a wide-area model. Then, at each time step, probability indexes, which quantify the likelihood of both local and wide-area models, are determined using hypothesis testing based on measurements innovations. Finally, using the weighted fix approach, the states estimated from the local and wide-area models are combined based on their probability indexes. Simulation studies using the two-area four-machine system show that the proposed approach can improve estimation accuracy and increase robustness against model errors.

Traditional expansion planning models reflect usage-based practices and do not consider the complete utilisation of existing resources. This problem arises due to non-inclusion of price-based incentives provided to the consumers who incur additional network reinforcement or expansion cost. The paper proposes a generation and transmission expansion planning framework that includes price-based incentives to define the future nodal load growth. The framework implements long-run incremental cost (LRIC)-based pricing signals for economically distributed demand to minimise consumer usage charges as well as network investment. Peak demand conditions by consumers are further minimised by the time-of-use-based demand response mechanism. The combined effects of price-based response and demand response are reflected in the future demand which serves as the basis for combined generation and transmission expansion planning. The case studies highlight the drawbacks of the conventional approaches which does not maximise the utilisation of existing grid resources while the proposed framework results in delayed investment strategies as a collective effort of the users and planner to optimise grid resources and the cost of assessing the electricity.

Considering the increased interactions between power grids and natural gas grids, this study presents a chance-constrained maintenance scheduling model for integrated gas-electric grids with wind energy integration. Given the uncertainties of wind power, the loss of wind power probability is modelled as a chance constraint, ensuring the high utilisation of wind power. To overcome the adversities caused by the non-linear and non-convex models of natural gas systems, a piecewise linear approximation method is employed to transform the non-linear models into a group of mixed integer linear models. A big-M formulation method is used to construct inequality constraints for lines/pipelines to be under maintenance. In addition, unit commitment is also coordinated to achieve the best maintenance strategies. The proposed chance-constrained stochastic programming model is converted into an equivalent deterministic programming model via a strong extended formulation for the sample average approximation by leveraging the star-inequalities. Several tests on a four-node natural gas system with a six-bus power system and a 20-node natural gas system with a modified IEEE 118-bus power system demonstrate the effectiveness of the proposed model.

Electrical load monitoring techniques are valuable to consumer site for energy saving, permitting reduction in electricity costs. Nowadays, smart grid technology incorporates advanced load monitoring applications, enabling efficient consumption of electrical energy. Non-intrusive load monitoring (NILM) is a moderately new practice to identify the power consumption of individual appliances of a consumer from the aggregated household at a single point of measurement. In this study, an improved NILM technique is proposed by using a shunt passive filter installed at the source side of any residential complex. The proposed method can be realised in two steps. The first step is to determine the harmonic impedance at the load side for different groups of loads for a single household. The second step is to implement a fuzzy rule-based approach for identification of different loads at the consumer end. Suitable simulations backed by experiments are demonstrated in this study to validate the viability of the proposed methodology.

Secure and reliable operation of power systems is a crucial factor to the security of power supply, and security assessment is an effective way to evaluate the quality of security. In order to evaluate the specific security status of a power system, a novel method for security-level classification (SLC) based on power system partitioning is proposed here. In this method, power system is partitioned into different subareas satisfying different N-k contingencies. Then, the mutual power supply between each subarea is coordinated to obtain the total supply capacity (TSC) under N-k contingencies. The security margin (SM) index, average system disequilibrium (ASD) index, and comprehensive safety index (CSI) are applied to assess the security of power system. Besides this, the threshold crossing (TC) index and the loss rate of load (LRL) index are applied to assess the unsafe conditions of power systems. According to the above procedures, the power system security states are classified into five levels, and a quantitative criterion to determine the exact security level is also given. Finally, a practical power system and the IEEE 118-bus test system are adopted to validate the feasibility of security classification based on N-k contingencies partition.

Due to a variety of social and economic reasons, renewable energy resources (RES) have grown exponentially in the recent years. Although, with the inclusion of RES into the electrical grid, multiple predicaments like voltage stability are to be unequivocally foreseen. This paper proposes to use decentralised measurements gathered from the distribution grid. Exemplarily, an adapted Cigré low-voltage benchmark network is used for simulation on a real-time simulator (RTS). The voltage and VAR control (VVC) is implemented on a standardised measurement device, in addition to other smart grid functionalities. The VVC algorithm is executed on the measurement devices and uses real-time analogue signals from the RTS for validating the implemented VVC functionality. The distributed measurements are then communicated to a central controller device using IEC 61850 architecture and the decisions of the VVC algorithm are processed back to the client devices for co-ordinated VVC control strategies. These include transformer tapping, reactive power control, and network reconfiguration. The results also indicate the advantages of implementing VVC using decentralised measurements against the existing control strategy at the transformer with no decentralised measurements.

This study reports on the results from efforts by the Institute of Electric and Electronics Engineers (IEEE) Distribution Reliability Working Group (DRWG) and the U.S. Energy Information Administration (EIA) to improve the usefulness of reliability metrics by developing and then applying consistent, yet distinct measures of the continuity of supply based on the portion of the electric power system from which power interruptions originate: the lower voltage distribution system versus the high-voltage bulk power system. The modified metrics better support reliability planning in the US because they separately measure the effectiveness of actions to improve reliability made by the two distinct groups of firms (and their regulators or oversight bodies) that are responsible for planning and operating each portion of the US electric power system. The authors then present for the first time quantitative information on the reliability of each portion of the US electric power system. When reliability is measured using the system average interruption duration index and the system average interruption frequency index, they find that the distribution system accounts for at least 94 and 92%, respectively, of all interruptions. They also find that these relationships have been stable over the recent past.

Accurate fault-cause identification is highly important to the fault analysis of overhead transmission lines (OTLs). In order to improve the efficiency and accuracy of fault identification, this study proposes a fault identification method based on the ADBN (adaptive deep belief network) model and the time–frequency characteristics of a travelling wave. According to the mechanisms of different OTL faults, the appropriate time–frequency characteristic parameters of the fault current travelling wave were selected as the input of the ADBN model, and the fault-type labels were selected as the output. The ADBN model introduces the idea of adaptive learning rate into CD (contrastive divergence) algorithm and improves its performance with self-adjusting learning rate. The parameters of the ADBN model were pre-trained with the improved CD algorithm and adjusted by back propagation algorithm with the labels of the samples. The performance of the ADBN model was verified by field data, and the accuracy of fault identification was analysed under different model parameters, characteristic parameters, and sample sizes. The results showed that the model helps to characterise the inherent relationship between characteristic parameters and fault causes, and the proposed method can effectively identify different fault causes in OTLs.

Power system security assessment is an important and challenging problem. Large variations in loads and power generation present increased risks to the secure operation of power systems. This study proposes a distributed deep network structure for power system security knowledge discovery based on multitask learning to monitor and control power grids more properly and effectively. First, a deep neural network structure based on the deep belief network (DBN) is designed to non-linearly extract deep and abstract features layer-by-layer for total transfer capability (TTC) regression tasks. Then, a distributed training algorithm for the deep structure is developed to accelerate the training process. Furthermore, multitask learning is adopted by grouping and training-related tasks together to improve the task performance. Finally, the accuracy and efficiency of the deep structure are evaluated using the Guangdong Power Grid in China. The simulation results demonstrate that the proposed model can outperform the existing shallow models in terms of accuracy and stability and can meet the requirements of online computing efficiency.