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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.

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.

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix.

Transactive energy (TE) is a viable framework to tackle the load-generation mismatch in energy systems with high penetration of renewable energy resources (RERs). In this paper, we propose a TE framework for prosumers with heating, ventilation, and air conditioning (HVAC) systems to address real-time power shortage in a residential microgrid. Our framework consists of two phases. First, to mitigate load-generation mismatch, we develop an online appliance scheduling method to determine the optimal operation schedule of each prosumer’s appliances. In particular, we apply receding horizon optimization (RHO) to tackle the load and renewable generation uncertainties and to better match the real-time power consumption of the appliances with the priorly-purchased power from the day-ahead market. Second, in case that there still exists power shortage at the microgrid level, a TE market based on pay-as-market clearing price (MCP) is proposed among prosumers to reduce the power consumption of their HVAC systems. We capture the competition among the participating prosumers as a non-cooperative game and develop an algorithm to achieve the Nash equilibrium, while considering prosumers’ willingness to participate in the TE market. Extensive simulations are performed to demonstrate the efficiency of our proposed TE framework.

Power system protection and asset management have drawn the attention of researchers for several decades; but they still suffer from unresolved and challenging technical issues. The situation has been recently exacerbated in the wake of the ever-changing landscape of power systems driven by the growing uncertainty and volatility subsequent to the vast renewable energy integration, more frequent natural extreme events due to climate changes, increasing malicious cyberattacks, and more constrained transmission systems as the result of load growth and limited investments. On the opposite side, the proliferation of advanced measuring devices such as phasor measurement units, emerging electric and non-electric sensors, and Internet of Thing (IoT)-enabled data gathering platforms continually expand/nourish the databases; they hence offer unprecedented opportunities to take the advantage of data-driven techniques. Machine learning (ML) as a principal class of artificial intelligence is the perfect match solution to this need and has newly revoked many researchers’ interests to tackle the problems excluding their exact/detailed models. This paper aims to provide an overview on applications of ML techniques in power system protection and asset management. This paper elaborates on issues pertaining to (1) synchronous generators, (2) power transformers, (3) transmission lines, and (4) special and system-integrity protection schemes. In addition to the opportunities offered by the ML techniques, this paper discourses on the barriers and challenges to the wide-spread application of ML techniques in real-world practices.

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.

The electricity network is a very large-scale and widely distributed system that is highly interdependent with other large infrastructure systems (e.g., natural gas, telecommunication, water and wastewater, and transportation). Power grids are being stressed by severe loading conditions caused by the rising electrification of infrastructure (e.g., in creasing the use of electric vehicles) as well as the overall growth in world population and electricity usage. Any prolonged electricity interruptions can have a widespread effect on other systems, including disruptions in Internet and security equipment, traffic and street lights, pumping stations and public transportation, home appliances and tall building elevators, large industrial equipment and hospital devices, water supplies and natural gas delivery networks, and so on.

Energy systems, in general, and power systems, in particular, are undergoing a changing landscape in terms of generation and demand portfolios. These transitions, although offering new economic and labor market opportunities, bring forth a suite of unprecedented technical implications and challenges in the interim. According to experts, our planet's population will reach 9.3 billion in the next four decades. A fundamental and granular outlook on global energy systems (prior to the COVID-19 outbreak) predicted that electricity consumption will double by 2050, with electrified transportation and higher living standards in buildings being among the principal components of this increase.

Energy management systems (EMS) are viable techniques to reduce the customers billing cost as well as enhancing the grid reliability and efficiency. Uncertainty and information privacy of users are serious concerns in designing EMS of a residential nanogrid with renewable energy resources. This paper addresses a decentralized energy management algorithm for day-ahead scheduling of residential nanogrids using the concept of Mean Field (MF). The aim of each user is to find its optimal demand strategy by minimizing an objective function consisting of energy consumption cost, battery degradation cost, and cost of user discomfort while meeting a suite of constraints. The strategy of a user affects the objective functions of other users through the electricity price; such interaction among the users is hence modeled as a game problem. Each user, without any information exchange with other users, sends its electricity demand to the utility company. The utility company broadcasts the aggregated demand as a common information of the grid (MF term) to all users. The robustness of the algorithm in presence of uncertainties in decision making of the clients and non-residential load prediction error is analytically verified. In particular, it is shown that the algorithm converges to $\epsilon$ -Nash equilibrium point of the game and $\epsilon$ uniformly converges to its minimum value as the population size of the users goes to infinity. Simulation results show the advantages of the proposed method.

Integration of distributed energy resources (DERs) has salient technical benefits such as improving power system resiliency, reducing transmission network losses, and regulating voltage in distribution networks. In addition to technical benefits, DERs bring remarkable economic profits for their investor. Accordingly, a comprehensive and accurate planning study is urgently needed by an investor who is to build DERs in a distribution network. In this paper, a tri-level investment planning model is proposed to maximize the net present value (NPV) of DERs in distribution networks. Robust optimization is utilized to cope with physical and financial data uncertainty in load demand, DERs output, and energy selling price. An effective decomposition strategy called column and constraint generation (C&CG) method is adapted to link the first and second levels of the proposed optimization problem. An iterative AC optimal power flow (AC-OPF) is presented to model the distribution grid circuit equations. The developed AC-OPF is solved in the third level through a modified Newton's method lied within a Benders decomposition framework. The proposed model is formulated as mixed-integer linear programming (MILP) problem and its effectiveness is illustrated by the IEEE 33-bus distribution test system.

Smart grid infrastructures enable consumers with technologies, such as electric vehicles and energy storages, to participate in electric regulation services. Usually with such technologies, the implementation of large-scale regulation services confronts high interruption cost, uncertainties in availability, and batteries’ degradation cost. This motivates us to explore an alternative solution by participating energy hubs with energy conversion technologies to adjust the conversion of natural gas into electricity if the electric grid calls for demand shaping and regulation services. To exploit the potential of energy hubs, we propose an auction for their participation in regulation services. The energy hubs’ interaction in the auction is modeled as a non-cooperative game with coupling constraints. To study the existence and uniqueness of the generalized Nash equilibrium (GNE) we show that the underlying game admits a best response potential function, whose global minimum corresponds to the GNE. We also design a distributed algorithm to achieve that equilibrium. Simulations are performed to illustrate the convergence properties and scalability of the proposed algorithm. Results show that if a participant becomes an energy hub, its profit increases by 60% on average. The electric system operator also benefits from 31% payment reduction to the participants.

The swift technological progress of inverter-based distributed energy resources (IBDERs) has led to proliferation of such DERs in electric power grids. Despite sequel of advantages, IBDERs integration render serious impacts pertaining to the protection, control, operation, and planning, of power system, particularly at distribution level. Accordingly, this paper aims at exploring the challenges, opportunities, and future trends of IBDER-integrated distribution grids. Via illustrative examples, the new insights on different aspects of protection, control, operation, and planning solutions are drawn and discussed which paves the way for future researches in this area. The struggle is, however, by no means over and extensive future research efforts are to be conducted for leveraging advanced techniques and technologies to address techno-economic concerns.

Traditionally, conventional units were able to handle the load variations in the power system. Ever swelling installation of wind farms renders power system to be influenced by the lack of enough ramping capability. To overcome this shortcoming, it is necessary to incorporate ramp requirement within the market in an explicit manner. The flexible ramp constraints have inter-temporal nature and necessitate application of a dynamic game model to specify the equilibrium. Uncertainties imposed by wind farms along with consideration of two-floor markets, day-ahead and real-time market, can be tackled by a multi-stage stochastic programming. To do so, a multi-stage stochastic bi-level equilibrium is addressed here where, besides day-ahead and real-time market, flexible ramp market is included. Modeling process comes across through explicit techniques in which Mathematical Program with Equilibrium Constraints (MPEC) and Equilibrium Program with Equilibrium Constraints (EPEC) carry the heavy burden of the modeling operation. The performance of the proposed model is examined on a two-player environment system. The results show that incorporation of flexible ramp market into the traditional power markets affects equilibrium and guarantees the feasibility of the problem in the term of ramping capability.

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.

Proactive preparedness is the key necessity of power systems to successfully cope with high-impact rare (HR) events. A resilience-oriented proactive methodology is proposed in this paper, which aims at enhancing the preparedness of multiple energy carrier microgrids (MECMs) against an approaching hurricane. First, the hurricane-originated contingency chain of MECM is characterized, which is different from that of single energy carrier distribution networks. The contingency chain includes natural gas interruption within the MECM, islanding event, and hurricane landfall on MECM. The pre-event scheduling horizon is then defined from the first alert declaration of hurricane to the worst-case (the earliest instant) of hurricane landfall on MECM. The preparedness index is defined as the sum of electric and thermal energy storage at the end of the scheduling horizon. Enhancing the proposed preparedness index will be at the expense of additional load curtailment during the scheduling horizon. Thus, a compromise is made between the two conflicting objectives (preparedness index and load supply) via a multi-objective optimization problem. An integrated gas and electricity power flow is proposed in a linear computationally efficient fashion capable of modeling gas interruption and islanding event. The effectiveness of the proposed methodology is examined on a real-scale MECM.

Due to the unpredictable nature of wind energy and non-coincidence between wind units output power and demand peak load, wind units is deemed as an unreliable source of energy. In order to compensate for the short-term fluctuation of wind energy, deployment of energy storage (ES) units in various types have been introduced as a viable solution. This paper develops a stochastic mathematical model for the optimal allocation of ES units in active distribution networks (ADNs) in order to reduce wind power spillage and load curtailment while managing congestion and voltages deviation. Nonlinearities of the original formulation are converted to linear equivalents and the final model lies within the computationally tractable mixed-integer linear programming (MILP) fashion. The IEEE 33-bus 12.66 kV radial distribution test system is utilized to illustrate the effectiveness of the proposed methodology. It was found that the rated power and capacity of ES units are depends on wind units' location and penetration level, in such a way that ES units are allocated near wind units to absorb excessive wind energy as much as possible. Furthermore, the results indicate ES units are useful for other purposes such as voltage management issue even if the wind units are not allocated.

The low inertia renders renewable-based microgrids ( $\mu$ Gs) more susceptible to incipient faults and makes it more difficult for distributed systems to maintain a reasonable margin for dynamic security. This paper studies the $\mu\text{G}$ security using a three-stage approach to the power system protection. The first stage considers offline analyses of synchronous generator-based distributed energy resources (SGBDERs) and inverter-based distributed energy resources (IBDERs) for establishing dynamic security models in $\mu$ Gs. The required data and settings are also determined at this stage. The second stage uses the first stage models for the online calculation of equilibrium points, regions of attraction, and protection zones for SGBDERs and IBDERs operations. This stage adapts to different conditions for $\mu\text{G}$ operations. The third stage is responsible for the real-time protection of $\mu$ Gs. This stage uses the real-time data for the fast detection of dynamic security status and protection of $\mu$ Gs. Simulation results presented in the paper demonstrate the adaptive features of the proposed scheme.

Microgrids as the local area power systems are changing the power system landscape due to their potential of offering a viable solution for integrating renewable energy resources into the main grid. From the operational point of view, microgrids should have the appropriate power electronic interfaces, control schemes, as well as monitoring and automation infrastructures to provide the required flexibility and meet the related IEEE 1547 standard requirements. This paper describes some of the efforts made in the smart microgrid educational laboratory to provide these facilities and create a real-world conditions needed to conduct researches and teach the related courses. Laboratory works not only increase the practical skills of the students but also can motivate them to pursue theoretical courses with more passion. The introduced facilities are somehow unique for the integration of both electric and communication infrastructures which overcome the shortcomings of not considering data transfer challenges in the studies. Complete hardware design of power plant components, and incorporation of solar photovoltaic (PV) and two types of wind turbine generations are some of the efforts made to bring the real-world conditions in the laboratory. In the load-related side, dynamic behaviors of the various types of motors are modeled.

Increasing penetration of wind power generation makes system operators face challenges in balancing supply and demand in real-time. The emerged energy imbalances in distribution networks can be addressed through energy trade with real-time balancing market or making use of flexible behavior of independent units such as flexible loads with ability to shift energy demand in time and small scale distributed generators. To this end, we propose a decentralized energy flexibility trading framework amongst multiple Microgrids (MGs) and Distribution System Operator (DSO) based on Alternating Direction Method of Multipliers (ADMM) to manage unforeseen imbalances in real-time. We show that MG's trading problem is an exact potential game with pure strategy Nash equilibrium. The results demonstrate, participating in the proposed trading mechanism results in considerable cost reduction for MGs in comparison with cases without trading with DSO or trading with real-time balancing market in order to balance energy consumption and generation in real-time.

Conventional auto-reclosing algorithm (CRA) jeopardizes the transient stability of synchronous generator-based distributed energy resource (SGBDER) and, accordingly, the transient stability of entire microgrid ( $\mu \text{G}$ ). The fixed dead time associated with CRA is the origin of this deficiency. In this paper, an adaptive auto-reclosing algorithm is devised to overcome the transient stability problem in SGBDER-integrated $ \mu $ Gs. The proposed method aims at reclosing the interrupted feeder in the least possible time while preserving the transient stability of $ \mu \text{G}$ even during permanent faults. To do so, the most appropriate reclosing instant (MARI) is determined based on the SGBDER potential energy assessment. MARI is an instant where the potential energy associated with SGBDER is minimum. Two indices are introduced to specify MARI. The indices are forecasted to determine the time to MARI for readiness purposes. To realize this, the least square error approach is deployed to elicit the temporal characteristics of the indices and their future behavior. The evaluation of proposed indices necessitates the availability of rotor angle of SGBDER as well as the associated rotor angular velocity deviation and its first order derivative. These signals are provided using the dynamic equations of the SGBDER which are fed to the algorithm.

The governments of various countries continue to be alarmed by the adverse environmental impact of fossil energy, which could result in additional pollution, polar glaciers further melting, and intensified natural disasters. Around the world, the consumption of fossil energy is a socioeconomic and sociopolitical calamity. However, remedies in such circumstances depend, to a great extent, on the availability of cleaner energy resources and governmental policies on energy pricing, consumption, and conservation. According to a recent World Bank report, air pollution and its effects on the global economy cost roughly US$225 billion in 2013. The American Lung Association has also announced that more than 50% of the U.S. population lives within regions with serious air pollution concerns. Additionally, global warming stemming from increased levels of fossil energy consumption has resulted in fewer rainy days, more droughts, and the higher usage of air conditioning appliances, causing even greater fossil energy usage. Despite having clear perspectives of the perceived challenges, certain countries with a substantial number of energy consumers could soon be facing the adverse consequences of global warming. Continuing the current level of energy consumption in developing countries as well as consumption patterns in populated regions of the world could also result in a deep shortfall of energy supply, leading to higher energy costs. The U.S. Energy Information Administration has reported that, in 2015, roughly 40% of U.S. energy consumption was in residential and commercial buildings. In many office spaces, building operators' intentions are to offer employees comfort; however, improper settings for room temperature, area lighting, air conditioning, ventilation, and other services in offices, laboratories, and meeting rooms result in the inefficient use of electricity. Consequently, corrective countermeasures and effective policies are essential to cope with energy production and consumption.

This paper presents a novel planning strategy for distribution system planners (DSPs) to enhance the resilience of distribution systems confronting emergencies. The problem is formulated as a two-stage stochastic programming model under emergency and normal scenarios. The decisions on line hardening, distributed generation (DG) placement, mobile emergency generators (MEG) allocation, and tie switch placement are made in the first stage to maximize the system resilience. In the second stage, the operation costs of the system pertaining to the DSP power purchase from the upstream network, DG power production, and forced load shedding in emergency conditions are minimized to achieve a techno-economic compromise of investment costs and enhanced operation/resilience benefits over both planning and operation scales. Since access to dependable distribution functions for probabilistic approaches is a notable challenge in resilience studies, an uncertainty modeling approach is presented based on the thresholds for the line damage in the worst-case event. The proposed approach can drastically decrease the number of line damage scenarios for resilience studies. The efficiency and effectiveness of the new approach are validated on two distribution system testbeds with 33 and 118 nodes.

Zone-3 of distance relays might maloperate during stresses frequently encountered in power systems, such as power swing, load encroachment, and voltage instability. This paper proposes a new protection algorithm for discrimination between short-circuit faults and other stresses in the transmission networks. The proposed method compares the sum of currents at the predetermined buses before and after the disturbance occurrence using synchronized current phasor measurements. The faulted area and line are identified as well. The optimal placement of phasor measurement units (PMUs) is tackled using a mathematical model. One of the main advantages of the proposed algorithm is decreasing the number of required PMUs in comparison with those of existing wide-area back-up protection schemes. In virtue of its computational speed, the proposed method can be exploited as a practical back-up protection cooperating with conventional protection schemes. The extensive simulation studies carried out on the IEEE 57-bus test system verify applicability of the proposed algorithm as a reliable back-up protection scheme for lines.

This paper develops a generalized energy management approach for smart microgrids (MGs) with the collaboration of plug-in electric vehicle (PEV) charging facilities in both active and reactive powers exchange. In addition to the monetary saving and economical benefits, this feature reveals a promising opportunity for power quality improvement in modern MGs. To do this, an efficient multi-objective framework is proposed for the optimal operation scheduling of MGs. The optimization model incorporates the full potential benefits of PEV fleets in balancing both active and reactive powers supply and demand. The multi-objective formulation lies within a mixed-integer linear programming fashion, which is tackled by $\varepsilon $ -constraint method. A quadruple linear operating area is envisioned for PEV fleet inverters caped with the maximum apparent power limit. A suitable voltage-dependent load model, with negligible error, is deployed to tailor the proposed approach for real-world applications. The new methodology is successfully applied to a 33-bus test MG with illustrative case studies. The obtained results are reported in terms of voltage profile improvement, released capacity of generating units, moderated operation of under-load tap changers, and MG operation cost reduction.

Transmission line parameters are indispensable prerequisites for power system studies such as power flow analysis, state estimation, congestion management, protective relaying, etc. Since the line parameters vary in time due to various reasons such as environmental condition changes, online estimation of line parameters is thus crucial to assure reliable outcome of system studies. Phasor measurement units (PMUs), as the basis of measurement infrastructure in a smart transmission grid, turn out the online estimation of transmission line parameters feasible. In this paper, a practical method for online estimation of series-compensated transmission lines parameters is devised in which PMU and supervisory control and data acquisition data are jointly used. To do so, the nonlinear weighted least square error algorithm is employed. Additionally, the proposed method is extended to set forth the parameter estimation of three-phase model of a transmission line needed for unbalanced system studies. Unlike the existing methods, which necessitate PMU installation at both sides of the lines, the proposed method works with few PMUs and has admirable practical merits in nowadays power systems. Case studies through software and hardware simulation efforts demonstrate validity of the modeling approach.

This paper presents the application of cybersecurity to the operation and control of distributed electric power systems. In particular, the paper emphasizes the role of cybersecurity in the operation of microgrids and analyzes the dependencies of microgrid control and operation on information and communication technologies for cybersecurity. The paper discusses common cyber vulnerabilities in distributed electric power systems and presents the implications of cyber incidents on physical processes in microgrids. The paper examines the impacts of potential risks attributed to cyberattacks on microgrids and presents the affordable technologies for mitigating such risks. In addition, the paper presents a minimax-regret approach for minimizing the impending risks in managing microgrids. The paper also presents the opportunities provided by software-defined networking technologies to enhance the security of microgrid operations. It is concluded that cybersecurity could play a significant role in managing microgrid operations as microgrids strive for a higher degree of resilience as they supply power services to customers.

Power system resilience is the ability of an electric power grid to withstand, absorb, and rapidly recover from low-probability high-impact disasters. In recent years, several major incidents all around the world have highlighted the need for operative solutions in order to materialize the resilience of the power grid in response to such catastrophic events. With the advent of synchrophasor technology in modern power systems, it has emerged as a promising measure to improve the resilience of power network. At the event of widespread outages, synchrophasor technology could facilitate the restoration procedure, reduce total outage duration, and mitigate damaging impacts to critical loads. This paper proposes a two-stage hierarchical approach to the transmission-level load restoration problem based on synchrophasors. The first stage determines the optimal amount and location of load pickups, and in the second stage, the predetermined decisions are implemented and the network stability is monitored until reaching a viable equilibrium point. The whole problem is decomposed into a set of tractable subproblems in order to improve the computational efficiency. The effectiveness of the proposed approach is further illustrated through a case study based on the IEEE 39-bus system.

This paper focuses on the role of networked microgrids as distributed systems for enhancing the power system resilience against extreme events. Resilience is an intrinsically complex property which requires deep understanding of microgrid operation in order to respond effectively in emergency conditions. The paper first introduces the definition and offers a generic framework for analyzing the power system resilience. The notion that large power systems can achieve a higher level of resilience through the deployment of networked microgrids is discussed in detail. In particular, the management of networked microgrids for riding through extreme events is analyzed. In addition, the merits of advanced information and communication technologies (ICTs) in microgrid-based distributed systems that can support the power system resilience are presented. The paper also points out the challenges for expanding the role of distributed systems and concludes that networked microgrids in particular provide a universal solution for improving the resilience against extreme events in Smart Cities.

The manifested merits of microgrids (μGs) are well-known in the era of modern power systems; however, μGs are in challenge with new technical issues pertaining to their operation, control, and protection. Maintaining dynamic security in μGs is one of the challenges addressed in this paper.

Proliferation of distributed generations (DGs) and power-electronics-based loads is bringing about more harmonic-polluted power signals. While some failures may occur as a consequence of nonsinusoidal current or voltage waveforms, existing reliability models of protection relays have not taken the harmonic-related failures into consideration. This paper develops a comprehensive Markov reliability model to categorize the possible functional states of a component, say a transmission line, protected by a relay, operating in a harmonic-polluted environment. In order to make the proposed model practically tractable, it is further simplified in two ways, through merging the states of the same consequence. The first simplified model focuses on the power component reliability assessment; while the other is built for reliability analysis of a protection system. Calculation of transition rates and how the resultant model is mathematically evaluated are then discussed. Numerical analyses and outcomes are based on the real-world data taken from Canadian substations. Accordingly, the results and conclusions drawn in this paper would be interesting to both academia and industry. Moreover, the proposed reliability assessment methodology is readily applicable in scrutinizing the impacts of harmonic pollution on the protection devices of other power components.

In recent years, natural disasters around the world have underscored the need for operative solutions that can improve the power grid resilience in response to low-probability high-impact incidents. The advent of microgrids (MGs) in modern power systems has introduced promising measures that can fulfil the power network resiliency requirements. This paper presents a two-stage stochastic programing approach to the optimal scheduling of a resilient MG. The impact of natural disasters on the optimal operation of MGs is modeled using a stochastic programming process. Other prevailing uncertainties associated with wind energy, electric vehicles, and real-time market prices are also taken into account. The proposed hourly scheme attempts to mitigate damaging impacts of electricity interruptions by effectively exploiting the MG capabilities. Incorporating AC network constraints in the proposed model offers a better solution to the security-constrained operation of MGs. The proposed model is linearized which offers robustness, simplicity, and computational efficiency in optimizing the MG operation. The effectiveness of proposed approach is illustrated using a large-scale MG test bed with a realistic set of data.

This paper presents an adjustable interval optimization model for the energy and reserve clearance while treating the wind power variation and uncertainty. Instead of conventional predicted intervals (PIs), adjustable intervals (AIs) as subsets of PIs are proposed as more judicious choices for economically covering wind uncertainties. Ramp-capability reserve is modeled to ensure the existence of sufficient ramp capability needed to follow up wind power variation within AIs. However, realization of wind power beyond AIs, which is no longer compensable by ramp-capability reserve, would necessarily lead to either load shedding or wind spillage. In order to optimally determine AIs, apart from the energy and reserve procurement costs, the costs associated with load shedding and wind spillage are incorporated in the objective function. The confidence level theory is employed as well for tuning the robustness and conservatism of the final solution. Performance of the proposed method is examined on several case studies on an 8-bus and modified IEEE 118-bus test systems. Sensitivity analyses are conducted to specify the impacts of important parameters on the obtained solution. The results confirm the applicability and effectiveness of the proposed methodology.

Electricity markets, without any doubt, have been witnessing the times when inflexible generating units have brought the outcome of markets into questions. This negative aspect is more manifested when a generating unit benefits from its own inflexibility. Emerging such kind of deficits in electricity markets may demotivate or even dissuade the generation companies to invest on flexible units. In this paper, an entire analytical methodology based on duality theorem is proposed to discriminate the share of each binding constraint on individual firms’ income. The proposed formulation is able to concurrently determine the Decomposed Dual Variables (DDVs) in the market clearing process. The post-process or a fresh round of problem solving is hence not needed for calculation the impact of units physical constraints on the outcome of market. The settlement process is then adjusted to assure fairness in remunerating generating units and hence alleviate the power system planners’ concerns regarding the flexibility issue. The effectiveness of the proposed methodology is verified through comprehensive studies on the 24-bus and 57-bus IEEE test systems. Owing to DDVs, the positive effect of the generating units constraints on its own incomes is detected and the scheduled generating levels stem from such positive effects are subsequently settled based on a proposed mechanism. The simulation shows that the new analytical scheme is capable of detecting and mitigating the potential market power induced by units inflexibility.

Distribution network protection is becoming more sophisticated in the wake of ever-changing landscape of power systems driven by the vast renewable energy integration mostly sited behind the meters, growing uncertainty and volatility subsequent to smart demand response and renewable energy integrations, further fusion of power electronics-interfaced equipments, and more constrained distribution branches as the result of load growth and limited investments. In the opposite side, the massive deployment of smart meters, proliferation of advanced measuring devices such as phasor measurement units, emerging electric and not-electric sensors, and IoT-enabled data gathering platforms continually expand/nourish the databases; they hence offer unprecedented opportunities to take the advantage of data-driven techniques. Machine learning (ML) as a principal class of artificial intelligence is the perfect match solution to this need and has newly revoked many researchers’ interests to tackle the problems excluding their exact/detailed models. This paper discusses applications of ML techniques in protection and dynamic security assurance of active distribution network, microgrids, and power electronics-based systems.

The need for cleaner and more efficient electrical power generation along with the limited budget for construction of bulkygeneration and transmission facilities have motivated power system utilities toward deployment of distributed energy resources (DERs). On-site generation by DERs offers various economic and environmental benefits for the end customers, utilities, and society. Among those benefits we can include enhancement of reliability and resilience of power delivery systems as well as deferring new investment for bulky generation, transmission, and distribution expansions. Furthermore, DER integration may lead to lower losses at the transmission system thus reducing emissions of large fossil-fueled power plants [1], [2]. DER integration has modified conventional centralized bulk generation concept and has introduced new notions, such as microgrid (μG) [3].

In low-voltage autonomous micro-grids, preserving both frequency and voltage stabilities at event of severe contingencies is an unsolved technical challenge. The conventional control algorithms are designed based on either one of frequency and voltage stabilities and do not bring satisfactory performance for real world practices. As per experiences in the transmission level, load shedding (LS) can be an effective countermeasure to overcome this challenge. This study introduces an under voltage-frequency LS (UVFLS) scheme to achieve proper load shed amounts (LSAs). The voltage effect is accounted for in two online and real-time mode stages. A quick computational algorithm is proposed in the online stage to obtain ‘nose’ curves of the network as a linearised piecewise function of voltage/load level. Then in the real-time operation stage, according to these curves and the present operating points of the system, optimum LSA is calculated. The frequency droop characteristics of distributed generation units are employed to incorporate the effect of frequency on the LSA. Simulation studies are carried out on two distribution systems, a 5-bus autonomous micro-grid and the modified standard IEEE 37-bus system. Validation of the proposed method shows that frequency and voltages are restored to their desired ranges using the novel UVFLS algorithm.

To diminish damaging consequences of power system major disturbances, under frequency load shedding (UFLS) plans are devised so as to prevent power networks from collapse. Since load shedding scenarios in conventional UFLS plans are specified regardless to voltage stability criteria, they cannot bring desired outcomes at the event of severe and combinational contingencies. In these circumstances, the location of load drops, in addition to their amounts, should be determined as well. This paper presents a two-unit wide-area adaptive load shedding scheme based on synchrophasors. In the first unit, an adaptive precise system frequency response (SFR) model is developed, by the help of which, the appropriate amount of load rejections is determined such that the dynamic and steady state frequency limitations are fulfilled. Incorporation of both static and dynamic loads into the adaptive SFR model results in a negligible error in calculations. Specifying the best location of load drops based on suitable voltage stability criteria is the main task of the second unit. These two units are executed in a parallel way; the computational burden of the proposed approach is thus tractable for real world applications. The new method is successfully testified in the IEEE 39-bus system and Khorasan network.

This paper deals with the short-circuit level constrained transmission expansion planning (TEP) problem through a mixed-integer linear programming (MILP) approach. The proposed framework is outlined by a master problem and three subproblems based on the Benders decomposition technique. The master problem incorporates the optimal investment planning model. System security and short-circuit level constraints are examined by subproblems I and II, respectively. In case of any violation, infeasibility cuts are derived to reflect the appropriate modification in the master problem solution. The short-circuit study is inherently a nonlinear analysis and hard to concurrently be tackled in power system studies. To overcome this difficulty, a linear approximation is developed for the short-circuit analysis which not only mitigates the computational burden of the problem, but even is efficient for taking the advantage of decomposed schemes. Subproblem III examines optimality of the investment solution from the operation point of view and, through optimality cuts, steers the master problem toward the optimal solution. The proposed model is tested on the IEEE 24-bus reliability test system and its effectiveness is assured by comprehensive simulation studies.

Owing to the increasing electricity load demand on one hand and in order to have a reliable electric network on the other hand, constructions of new transmission lines and generation sources are extremely essential. Such expansions, however, raise the fault current level which can even exceed the maximum short-circuit level rating of the circuit breakers (CBs). High fault current, in addition to the adverse effect on the CB function, can damage other substation equipment suddenly or over the time. To this end, severity of the fault current is a key factor in the substation reliability assessment. In this context, fault current limiters (FCLs) which are employed to decline the fault current level need to be precisely modelled and their impact should be extensively studied. In this study, the effect of FCL on the failure rate of substation equipment is first investigated. A computational technique for assessing the substation reliability is accordingly devised. As the location of FCL installation in a given substation or in substations with different arrangements would not be the same, various credible configurations are incorporated here. The results show that from the perspective of reliability indices, double-bus double-breaker and one breaker and a half configurations are more suitable for FCL installation.

The growing evolution of regional electricity markets and proliferation of wind power penetration underline the prominence of coordinated operation of interconnected regional power systems. This paper develops a parallel decentralized methodology for multi-area energy and reserve clearance under wind power uncertainty. Preserving the independency of regional markets while fully taking the advantages of interconnection is a salient feature of the new model. Additionally, the parallel procedure simultaneously clears regional markets for the sake of acceleration particularly in large-scale systems. In order to achieve the optimal solution in a distributed fashion, the augmented Lagrangian relaxation along with alternative direction method of multipliers are applied. The wind power intermittency and uncertainty are tackled through the interval optimization approach. Opposed to the conventional wisdom, adjustable intervals, as subsets of conventional predefined intervals, are introduced here to compromise the cost and conservatism of the solution. The confidence level approach is employed to accommodate the stochastic nature of wind power in a computationally efficient deterministic manner. The effectiveness and robustness of the proposed method are evaluated through several case studies on a two-area 6-bus and the modified three-area IEEE 118-bus test systems.

power grids are supposed to be fault-tolerant, but you doubt the premise when you consider daily outages in more populated parts of the world and more recent brownouts and blackouts globally. Power engineers have been pushing forward the frontiers of power systems, and the hard-fought battle against the darkness is supposed to have been won, but the struggle is by no means over. Indeed, the electricity grid today is faced more intensely with challenging issues concerning cyber and physical system security. The recent blackout in Turkey on 31 March 2015, which knocked out the transportation infrastructure in major cities, causing massive traffic jams, is a testimony to this claim. It also shut down vital services to thousands of offices for several hours, inflicting business and social losses (see Figure 1). Although widespread blackouts are regarded as low-probability events, they carry immense socioeconomic costs. In such events, the burden of responsibility often lies with extreme weather events, cascading failures, or similar low-probability, high-impact incidents. According to a report from the White House Council of Economic Advisors, severe weather is the major cause of power grid outages in the United States. Meanwhile, it is estimated that weather-related power outages cost the American economy more than US$300 billion between 2003 and 2012. The report calls for increased investment to enhance the resilience of the power grid against rising incidences of extreme weather such as thunderstorms, hurricanes, and blizzards predicted by climate models.

Proliferation of private distributed energy resources (DERs) necessitates devising effective energy management frameworks. This paper aims at establishing a decentralized model for the optimal dispatch of DERs in smart grids. The objective function is to minimize expected procurement cost of demand while profit of private DER owners is not sacrificed. Unit management modules (UMMs) embedded in private DERs and a central agent are autonomous agents of the model. The approach is iterative. In each iteration, the central agent announces to UMMs its procurement strategy, which contains purchased powers and their prices. Then, UMMs optimize and send back their output power and selling price proposals in the hope of achieving higher profits. The energy service provider (ESP) gathers the bids and modifies the procurement strategy whenever bids are in line with the cost minimization objective. The service provider releases the last procurement strategy in response to which UMMs synchronously update and send back their bids. The mutual interaction between UMMs and ESP is continued to the point in which no further modification in ESP’s procurement strategy is experienced. The effectiveness of the proposed algorithm is demonstrated by its implementation in a system with a few DERs from different types.

Summary This paper addresses the design and simulation validation of a direct drive surge wave energy converter (DDSWEC) as an efficient device for harnessing marine wave energy in near-shore locations. The proposed DDSWEC is composed of a rigid rectangular plate producing linear motions in response to surge forces of ocean waves. A translator is devised to transfer the wave energy to a linear permanent magnet synchronous generator, which all together form the power take-off (PTO) system. In the proposed scheme, the mechanical simplicity of PTO system is a superior feature in reducing power losses in the wave-electric power conversion chain. Additionally, suitable mechanical degrees of freedom lead to an impressive increase in the power conversion efficiency. Besides, mathematical modeling of the mechanical and electrical systems governing DDSWEC is interrogated in depth. In addition, through a comprehensive frequency contents analysis, the capture width of DDSWEC and the device efficiency are scrutinized to validate its well performance. Because the stochastic behavior of waves induces voltages with variable frequency and magnitude, which are undesirable features for grid integration, a full-scale back-to-back converter with both generator and grid-side controls is accordingly implemented. To validate the overall performance of the proposed DDSWEC, a test system is designed in Matlab/Simulink platform by which several time-domain simulations for both the monochromatic and irregular waves are conducted. Copyright © 2015 John Wiley & Sons, Ltd.

Distributed static series compensator (DSSC) is a member of the family of distributed flexible AC transmission system devices that can be attached directly to conductors of transmission lines. DSSCs are able to compensate for and adjust the line reactance; thus, they could be widely used in stability-limited transmission systems. Owing to economic matters, the optimal placement of DSSCs is of a great interest from practical viewpoints. This study presents an approach to find the optimal locations of DSSCs to enhance the system loadability and reliability using the DC load flow model. The optimisation problem is solved using the mixed-integer linear programming. A compromise between improving the system loadability and reliability is fulfilled and a wide range of non-dominated optimal solutions is calculated. The final solution is then selected using the fuzzy satisfying method. Numerical studies are conducted on the IEEE reliability test system (IEE-RTS).

With the spotlight on smart grid development around the world, it is critical to recognize the key factors contributing to changing power system characteristics. This is more apparent in distribution systems with the integration of renewable energy sources, energy storage, and microgrid development. Utilities are also focusing on the reliability and resiliency of the grid. These activities require distribution automation (DA) strategies that take advantage of available technologies while promoting newer solutions. It is necessary to create a roadmap for holistic DA strategies in a smarter grid. Sustainable and resilient grid development is a paradigm shift requiring a new line of thinking in the engineering, operation, and maintenance of the power system. International perspectives on DA are also addressed, with the understanding that one solution will not fit all. Integrating technical, business, and policy decisions into the challenges will generate the development of technologies, standards, and implementation of the overall solution. The challenges in the development of industry standards are also discussed. This paper explores the challenges and opportunities in the changing landscape of the distribution systems. Evolution of technologies and the business case for infrastructure investment in distribution systems are covered in another paper by the same authors.

This paper focuses on the parameter estimation of transmission lines which is an essential prerequisite for system studies and relay settings. Synchrophasor measurements have shown promising potentials for the transmission line parameter estimation. Majority of existing techniques entail existence of phasor measurement units (PMUs) at both ends of a given transmission line; however, this assumption rarely holds true in nowadays power networks with few installed PMUs. In this paper, a practical technique is proposed for the estimation of transmission line parameters while the required data are phasor measurements at one end of a given line and conventional magnitude measurements at the other end. The proposed method is thus on the basis of joint PMU and supervisory control and data acquisition (SCADA) measurements. A non-linear weighted least-square error (NWLSE) algorithm is employed for the maximum-likelihood estimation of parameters. The approach is initially devised for simple transmission lines with two terminals; then, it is extended for three-terminal lines. Numerical studies encompassing two- and three-terminal lines are conducted through software and hardware simulations. The results demonstrate the effectiveness of new technique and verify its applicability in present power networks.

This paper presents a fast and robust wide-area backup protection scheme to detect the faulty condition and to identify the faulted line in a large power network. The proposed methodology uses positive-sequence synchrophasor data captured by either digital relays with synchronization capability or phasor measurement units dispersed over the network. The basic idea behind the new protection scheme is the comparison of bus voltage values calculated through dissimilar paths. Upon occurrence of a fault, the faulty condition is first detected and the bus(es) connected to the faulted line is(are) determined. Among transmission lines connected to the suspected bus(es), the faulted one is thereafter identified. In addition to two-terminal transmission lines, multiterminal lines are also incorporated. The performance of the proposed method is validated on the IEEE 57-bus test system in different fault conditions (fault type, fault location, and fault resistance). Discrimination of faulty and normal conditions is simulated by examining various stressed conditions, for example, load encroachment, generator outage, and power swing. The data requirement of the proposed technique is analyzed as well. To do so, a mathematical model for the optimal placement of measurement devices is developed and applied to different IEEE standard test systems.

The evolving wide-area measurement system technology offers new opportunities for backup protection of transmission lines to avert the misoperation of conventional relays against system disturbances and prevent system instability. Since wide-area backup protection (WABP) is to operate at the range of a few hundred milliseconds, the delays associated with data and command communications and algorithm execution can be a troublesome issue in a large-scale power system. This paper briefly reviews a WABP scheme based on positive-sequence voltage phasor direct and indirect measurements. To overcome the problem of WABP delays, an integer linear programming (ILP) model is proposed to divide the power system into several protection regions. This process is accomplished in three stages in which the number and location of measurement devices (MDs), the number and boundary of protection regions, and the location of protection rooms, as well as the total number of communication links between MDs and protection rooms are optimized. The proposed WABP scheme is implemented in each region and individually protects the associated transmission lines. Execution of the proposed ILP model on different test systems reveals that with regionalization of the systems, WABP schemes can reliably be used in practice.

This paper presents a rule-based approach using synchrophasor measurements for generating unit model validation and calibration. The proposed method utilizes frequency and three-phase voltage signals recorded by phasor measurement unit at the point of common coupling of unit transformers to the high voltage grid. In the simulation setup, by taking the hybrid data simulation technique in use, the signals are being injected to the subsystem of generating units through a voltage source, which models the truncated external grid. The measured and simulated active power oscillations are then compared to each other and the effects of generating unit parameters on the oscillation mismatches are extracted. The maximum amplitude and damping ratio of oscillations are two main aspects of the observed mismatches. To recognize the most effective parameters on these aspects, a sensitivity analysis technique is applied. Next, identified parameters are adjusted to calibrate the generating unit dynamic model. Finally, quantitative and qualitative perspectives of the proposed method are investigated through different sorts of actual disturbances. The obtained results demonstrate that the final calibrated model outperforms the existing one, and it can be used for power system planning and operation studies. Time domain simulations are performed by DIgSILENT power factory software.