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Resilience of the power system against natural disasters is a vital need for sustainable energy supply. As a result of global warming, lakes and rivers have dried out, resulting in dust hubs that threaten the normal operation of outdoor power system equipment. Unlike other events like hurricanes and blizzards, the impact of extreme salt dust on power system insulator failures and network resilience in affected areas remains unexamined. In this paper, to avoid power curtailment caused by insulators breakdown in electricity distribution networks, the resiliency assessment and enhancement of these networks against salt dust is investigated. Failure mechanism analysis of insulators and fragility curves extraction of them in face of salt pollution and relative humidity are done using mathematical modelling and experimental tests to extract the breakdown probability; Experimental tests are conducted in the High Voltage Laboratory, University of Tehran (HVLUT) and a novel method is proposed to extract 3-dimensional fragility curves of insulators. A Monte Carlo-based resiliency assessment method is then employed to obtain resiliency curve against the salt dust. Some suitable indicators are introduced for this purpose. In addition, several resiliency enhancement measures are proposed and ranked using a benefit to cost ratio (BCR) index. Numerical simulations are conducted on two real distribution feeders in a distribution grid around Urmia Salt Lake, Iran. Numerical results confirm the effectiveness and applicability of the proposed method.

Abstract Unbalanced operation of distribution networks, as a crucial but inevitable aspect of power quality, brings about economic and technical issues. The increase in active power loss is more concerning due to the economic and technical implications. Economic sustainability of liberalized distribution networks is hugely tied to the availability of transparent and effective approaches to determine the stakeholders of this sort of phenomenon. This is done by awarding/penalizing them accordingly. Studies in the literature have not considered the incremental losses that unbalanced operations cause to distribution systems. Here, a mathematical approach based on vector analysis in the symmetrical components space is introduced. This approach quantifies the contribution of each unbalanced load to unbalanced operation and increased power losses. This technique discriminates between the mutual effects of different unbalanced loads on the power losses of the entire network. By means of the proposed approach, the best candidate unbalanced load(s)/location(s) to perform the corrective balancing actions and their ranking are precisely specified. Vector representation of the developed method simplifies its understanding, utilization, and expansion. For validation, the proposed method is applied to the IEEE 37‐bus test distribution network under various scenarios. The results confirm the sound results and practical merits of the developed method.

Abstract Emergence of different stakeholders with contrary objectives in active distribution network (ADN) requires for a fair network expansion planning that considers all of the contrary objectives. This paper provides an expansion planning model for ADN strategic visioning from electric distribution stakeholders' organization (EDSO) perspective, which takes into account the objectives of all stakeholders. The stakeholders are network owner, active customers (ACs), consumers, and environmental regulatory body (ERB). The proposed model is multistage bi‐level, where the upper‐level minimizes the present value of the total costs imposed to all ADN stakeholders and the lower‐level minimizes the total generation costs within the distribution‐level electricity market. The Karush–Kuhn–Tucker optimality conditions and the linearization techniques are employed through which the model is converted to mixed integer linear programming which can be tackled effectively. The model provides a visionary expansion plan that policymaker must legislate to realize it. Effectiveness of the model in providing a visionary expansion plan considering EDSO's perspective in making cost effective planning decisions per social welfare goals is verified through several case studies on 33‐bus standard test system. According to the simulation results, transition from DSO to EDSO in the presence of low‐capacity ACs improves social welfare by 5%. But this improvement is up to 70% in the presence of high‐capacity ACs.

Abstract The huge volume of data that is streamed from distribution phasor measurement units (DPMU) toward distribution management system (DMS) can rarely be used in the raw format. Data‐driven analysis to extract information out of massive raw data has revealed promising opportunities to overcome this challenge. This paper utilizes convolutional autoencoders (Conv‐AE) for the sake of anomaly detection based on the DPMU measurements in distribution systems. The Conv‐AE is unsupervised and independent of the event type. It dispenses preprocessing and feature extraction along with obtaining the essential information directly from the measured data. The anomaly detection is followed by a supervised classifier which is developed to identify the type of anomaly. This classifier is a convolutional neural network that is designed and fine‐tuned for the problem at hand. It uses an ensemble learning method to augment the fully‐labelled dataset. Performance of the proposed methodology is evaluated on modified IEEE 34‐node and IEEE 123‐node test feeders in various operating conditions, presence of noise, and different scenarios of DPMU outage. Moreover, a real‐world DPMU dataset is utilized to evaluate performance of the Conv‐AE model in practical conditions. Results confirmed effectiveness of the proposed technique to be used in future DMS platforms.

Abstract An effective robust fuzzy model predictive control (RFMPC) method for secondary voltage control in islanded microgrids (μGs) is presented here. In contrast to the existing techniques, which require a detailed model of μG and an ideal communication network between μG central controller and primary local controllers, RFMPC is synthesized for a non‐linear model of the μG with various time delays, uncertainties, and bounded disturbances. The famous Takagi‐Sugeno fuzzy approach is adopted to approximate the inherently non‐linear model of μG by locally linear dynamics. The Lyapunov–Razumikhin functional method is exploited to deal with time delays. In this regard, sufficient conditions are provided in the form of linear matrix inequalities (LMIs). Then, a sequence of control laws corresponding to a set of terminal constraints is computed offline. Doing so, the online stage is reduced to solving a convex problem with LMI constraints considering the sequence of constraint sets obtained in the offline stage, thereby reducing the computational burden significantly. Robust positive invariance and input‐to‐state stability property concerning communication network deficiency are then speculated. The effectiveness of the proposed RFMPC is verified via a comprehensive suite of simulations in the MATLAB/SimPowerSystems environment.

Abstract Natural disasters threaten the sustainability of electric power supply. This fact highlights the importance of enhancing technological resourcefulness to handle the upcoming events. Once a natural disaster occurs, the most urgent undertaking is to rapidly pinpoint and assess component damages and dispatch the repair crews towards the most critical impaired elements. Practical efforts confirm that utilising a drone, which is an unmanned aerial vehicle, can notably reduce the duration of post‐disaster distribution system damage assessment (DA) and increase the resilience of power systems. This study presents an optimisation model to determine the optimal number and type of drones required for DA. The main goal is to minimise the time duration of the DA mission, which includes scanning the grid and identifying the damaged components. As a matter of fact, the financial resources are limited, so the problem is subject to the available budget constraint. Besides, the technical capacity of purchased drones for inspecting the grid is considered. The optimisation problem is formulated as a mixed‐integer programme with a guaranteed optimum solution. Case studies confirm the applicability of the model and the optimality of the results. As the major conclusion, the best allocation is a compromise between the speed and price of drones.

Abstract Having a robust and accurate grid synchronization unit for distributed generators (DGs) has been accentuated due to rapidly growing integration of inverter‐based DGs to the autonomous microgrids. Although most of the synchronization methods are based on the phased locked loops (PLL) technique, digital filters could result in more salient dynamic performance in non‐ideal sinusoidal situations. This paper presents a novel digital filter as a grid synchronization method that overcomes the flaws of conventional PLLs under harmonic distorted and unbalanced cases as well as large delay of window‐based digital filters. For the sake of computational advantages, the proposed method is executed in two distinct offline and online stages. In the offline stage, the second‐order Taylor series expansion approximation of the bandpass oscillatory signal calculates the state‐transition matrices and state vectors. Then, by applying the concept of Kalman filter, the state vectors are updated in the online calculation section much easier and faster compared to all‐online approach. Compared to the existing methods, the proposed method generates a dynamic response with less settling time, more accuracy, and immunity against harmonic distortion, imbalances, and noise.

Abstract Unexpected imbalances between supply and demand during real‐time operation in the wake of high penetration of renewable energy resources necessitate system operators to improve flexibility of the system. Moreover, increasing growth in flexible distributed resources installation in distribution networks with multiple MicroGrids (MGs) such as fuel‐based distributed generators, storage units, and controllable loads makes distribution system operators be able to manage energy imbalances locally through exploiting potentials of aforementioned resources in flexibility enhancement. This paper proposes a receding horizon data‐driven chance‐constrained energy flexibility trading framework for active distribution networks with multiple MGs. Specifically, a distance‐based confidence set is constructed in a data‐driven manner to apply net load uncertainty into this modelling. According to this proposed data‐driven approach, the energy flexibility is modelled using flexibility envelope notion to demonstrate its dynamics in different scenarios in an imminent horizon. Furthermore, MGs' flexibility trading problem is modelled as an exact potential game that possesses a pure strategy Nash Equilibrium (NE). An iterative decentralized algorithm based on alternating direction method of multipliers is developed to achieve the NE strategy of MGs. Numerical results are also represented to show the performance efficiency of the proposed energy flexibility trading framework.

Abstract The optimal controlled islanding of power systems is a practical solution to prevent system blackouts if the boundary of islands and corrective control actions in each island are carefully specified. This paper establishes a model for the controlled islanding problem by a proposed mixed integer linear program (MILP). The frequency‐arresting (FA) and frequency‐stabilising (FS) constraints are linearised and incorporated in our FA‐FS‐constrained MILP model to prevent triggering of load shedding (LS) relays. This achievement makes our model capable of handling low‐inertia networks. Intentional LS and stepwise generation curtailment are corrective actions accommodated for frequency control and power mismatch considerations. These corrective actions increase the degree of freedom and broaden the feasible space of the MILP model under the envisaged tough operating conditions. Our model's computational efficiency is improved using a proposed graph theory‐based network reduction technique. The basic groups of coherent generators, determined in the offline mode, are aggregated as equivalent buses using an extended Steiner tree method. A graph‐path determination technique is also proposed to generate disconnection constraints (between equivalent buses of incoherent areas) and bus‐allocation constraints. Simulation results on the IEEE 39‐bus test system and a 76‐bus case study verify the proposed network reduction technique's effectiveness and the MILP model.

Abstract High penetration of renewable distributed generations is one of the most current challenges of electric distribution networks due to resources uncertainties, and hosting capacity limitation of the networks. The challenges may be more critical when uncertainties are highly correlated. The effect of correlated uncertainties of wind speed and load on the distributed generation hosting capacity of distribution networks is evaluated. A combination of point estimation method, and inverse Nataf transformation is proposed for correlated uncertainties modelling. The efficiency of some ANM schemes on the hosting capacity improvement is also studied in the presence of correlated uncertainties. To do so, an optimisation framework with the objective of maximising the installed capacity of distributed generation subject to network operational constraints such as voltage deviation is proposed. The proposed optimisation problem is formulated in a mixed‐integer quadratically constrained program form, and is solved via CPLEX solver. The proposed method is implemented on the 33‐bus standard test system. The results demonstrate the significant effect of the correlation between uncertainties on the distributed generation hosting capacity of network.

Although synchrophasor measurement technology (SMT) has become a somewhat mature and operational tool used in transmission-level power networks, its use in active distribution networks (ADNs) is still in very early stages. Due to distinct features (such as unbalanced operation, very low phase angle differences, greater harmonics pollution, and lower signal-to-noise ratios in distribution networks), transmission-level devices and applications may not be fully effective at the distribution level. Extensive research and development efforts are in progress to bring distribution-level phasor measurement units (DPMUs) to an operational stage in ADNs. This essential work is made even more urgent by changing ADNs that are widely integrating distributed energy resources and enabling demand-side entities as active network players, in some cases considering authorizing them for peer-to-peer energy trading. This paper focuses on SMT/DPMU applications in ADNs and microgrids. We specifically elaborate on how SMT/DPMUs are enabling 1) effective situational awareness, 2) enhanced protection and fault location functions, 3) novel control algorithms and augmented energy management applications, and 4) long-term baselining and analysis. Various barriers and challenges that come with rapid and broad deployment of SMT/DPMUs and their associated applications are also discussed.

Power systems are evolving to the networks with proliferated penetration of renewable energy resources to leverage their environmental and economic advantages. However, due to the stochastic nature of renewables, the management of the rapidly increasing uncertainty and variability in power system planning and operation is of crucial significance. This paper represents a comprehensive overview of power system flexibility as an effective way to maintain the power balance at every moment. Definitions of power system flexibility from various aspects are explained to reach the reliable and economic planning and operation of the power system. The effects of the high penetration of variable energy resources on power systems and the evolution of flexibility in response to renewables are studied. A variety of resources during the flexibility evolutionary transition are introduced and discussed. As an influential flexibility solution in current power systems integrated with renewable resources, market design improvement is widely reviewed in this paper, and required modifications in market design mechanisms are investigated pertaining to various time horizons.

With the increasing interdependence of various energy carriers, the operation of power systems is found to correlate closely with the limitations on the other energy infrastructures. This paper presents a mixed-integer linear programming (MILP) model for the microgrid (MG) optimal scheduling considering technical and economic ties between electricity and natural gas (NG) systems. In the proposed methodology, different energy converters and storages, including combined heat and power (CHP) units, electricity/heat storage units, and distributed energy resources (DERs) are considered. The proposed model allows the MG operator to minimize the operation cost of the MG while different operational limitations on the energy hub are satisfied. The model is developed based on AC power flow constraints so as to respect reactive power and voltage security constraints. The efficiency and robustness of the proposed MILP formulation are successfully verified using a large-scale test MG.

This study proposes a single-leader-multi follower game to model a bilevel retail market among an aggregator and multiple microgrids to determine the optimal demand scheduling of the consumer, as well as price-power bidding strategies of microgrids in an interactive scheme. In the lower level, microgrids which include several distributed energy resources and energy storage units, compete with each other and offer the optimal energy-price bids such that their individual profit is maximised, while energy dispatch among their energy resources is also determined. Then, in the upper-level problem, the aggregator leads the competition taking advantages of demand-side management including interruptible and shiftable loads to minimise its energy payment for real-time pricing of generation units. By means of Karush–Kuhn–Tucker optimality condition, the bilevel optimisation of Stackelberg game is reduced to a single-level mixed-integer linear programming problem. Moreover, impact of microgrids’ integration on the retail market clearance mechanism, as well as required incentives for such integration has been discussed in a separate scenario.

Abstract This paper reveals that the existing techniques have some deficiencies in the proper estimation of voltage stability margin (VSM) when applied to a power system with different load change scenarios. The problem gets worse when credible contingencies occur. This paper proposes a real-time wide-area approach to estimate VSM of power systems with different possible load change scenarios under normal and contingency operating conditions. The new method is based on an artificial neural network (ANN) whose inputs are bus voltage phasors captured by phasor measurement units (PMUs) and rates of change of active power loads. A new input feature is also accommodated to overcome the inability of trained ANN in prediction of VSM under N−1 and N−2 contingencies. With a new algorithm, the number of contingencies is reduced for the effective training of ANN. Robustness of the proposed technique is assured through adding a random noise to input variables. To deal with systems with a limited number of PMUs, a search algorithm is accomplished to identify the optimal placement of PMUs. The proposed method is examined on the IEEE 6-bus and the New England 39-bus test system. Results show that the VSM could be predicted with less than 1% error.

This paper analyzes the notion of resilience in power systems from a fundamental viewpoint and thoroughly examines its practical implications. This paper aims to describe and classify different high-impact rare (HR) events, provide a more technical definition of power system resilience, and discuss linkages between resilience and other well-established concepts, such as security and reliability. Most relevant decisions of system operators in the face of HR events involve a significant level of stress and strain. In order to make informed decisions within this context, it is crucial to have an all-inclusive picture of the state of the system. This paper provides an appropriate framework that not only characterizes the various states of the system but also derives informed decisions from a resilience-oriented perspective. It also describes and analyzes diverse resilience improvement strategies. Comprehensive models and classifications are provided to clearly capture various aspects of power system resilience.

Phasor measurement units (PMUs) are rapidly being deployed in electric power networks across the globe. Wide-area measurement system (WAMS), which builds upon PMUs and fast communication links, is consequently emerging as an advanced monitoring and control infrastructure. Rapid adaptation of such devices and technologies has led the researchers to investigate multitude of challenges and pursue opportunities in synchrophasor measurement technology, PMU structural design, PMU placement, miscellaneous applications of PMU from local perspectives, and various WAMS functionalities from the system perspective. Relevant research articles appeared in the IEEE and IET publications from 1983 through 2014 are rigorously surveyed in this paper to represent a panorama of research progress lines. This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.