DGA is more sensitive and reliable technique for detecting and evaluating the incipient fault conditions in oil-immersed transformers. The most frequently interpretation technique that used in dissolved gas analysis is Duval triangle due to its simplicity and accuracy. Unfortunately the impacts of Ethane (C₂H₆) and Hydrogen (H₂) were not included in Duval triangle in spite of their importance in diagnosing certain faults types. In addition, the problem of no decision associated with some cases that lie out of the specified codes appeared in Doernenburg, Roger's and IEC techniques. Accordingly, this paper presented a new proposed graphical triangle technique using new three gas concentration ratios. These ratios were converted to new three ratio percentages of DGA based on the consideration of all five combustible gases. The aim of that was to overcome the conflict takes place in Duval triangle and other traditional techniques. The accuracy of the proposed triangle technique was evaluated using practical DGA database obtained from different transformers of different ratings life spans reported by Egyptian Electricity Holding Company, IEC TC 10 and related databases surveyed from actual incipient cases. The comparison with Duval and other traditional techniques showed good diagnostic accuracy of the new proposed triangle technique.

In this work, it is shown that it is possible to operate a wind energy conversion system (WECS) based on a doubly fed induction generator while operating in a different mode than maximum power tracking. Here, using the recent results on the steady state solution of the torque balance transcendental equation, analytical expressions for all the state variables of the WECS are obtained. In this fashion, closed-form online reference trajectories for the system variables are presented. These analytical formulas can be used to perform power tracking control into the safe operational region of WECS. Moreover, realistic generic case studies are described to depict how these results are useful in systematically assessing the secure operating regime for both, the electric and mechanic components of the WECS.

As vectors' similarity is defined on the basis of the hard boundary of Heaviside function in multiscale multivariate sample entropy, the two families of statistics show high sensitivity to the similarity tolerance. Multiscale multivariate fuzzy entropy (MMFE), which applied fuzzy membership function, overcomes the problem. Hence, the visualisation inrush current discrimination algorithm based on MMFE is proposed. The proposed algorithm divides the MMFE-τ plane into two parts: active and braking zones. The ratio of the current entropy area to the given braking zone area is designated as the entropy area ratio. The magnetising inrush and fault current of the transformer are identified on the basis of the numerical value comparison between the entropy area ratio and the fixed value of 1. Through the ATP/EMTP platform and dynamic testing system, the transformer currents under different operating conditions are analysed. Results demonstrate that the proposed algorithm can effectively distinguish the magnetising inrush from the fault current of the transformer and is superior to other methods.

In this study, optimised and coordinated operation planning of a generation company's (GenCo's) generation portfolio consisting of a wind farm, a gas turbine, and energy storage systems [battery and natural gas (NG) energy storage systems] is modelled. In this model, all the electrical energy (EE) and NG trades are performed through long-term contracts and spot markets in a deregulated environment. It is assumed that EE is traded through day-ahead market and bilateral contracts while NG is traded via a daily spot market in conjunction with baseline and intra-day mid-term contracts. The proposed framework enables the GenCo to maximise its profit in operation planning horizon by coordinating different types of contracts and energy arbitrage. The operation planning model is presented as a mixed-integer linear programming problem. Five case studies are implemented in GAMS software to evaluate the effectiveness of the proposed model and analyse different factors affecting benefits of the GenCo.

The penetration of renewable energy sources (RESs) in distribution systems faces many issues due to their output uncertainty resulted from climate conditions. The uncertainty impacts on the voltage fluctuations are reduced by using a proposed bi-stage method. At first, the system voltage is controlled by determining the optimal setting of voltage-regulating devices such as voltage regulators, transformer tap changers and static VAR compensator. Then, the dispatchable distributed generation (DDGs) units are accompanied by the voltage regulating devices to achieve more reduction in the voltage fluctuations. In this line, unbalanced backward–forward sweep load flow method is formulated to analyse the unbalanced operation of three-phase distribution systems. The main objectives of the proposed method are to reduce voltage fluctuations to maintain voltage profile within its permissible limits. In addition, the cat swarm optimiser (CSO) is implemented to obtain the optimal planning of voltage regulating devices and DDGs to achieve the lowest uncertainty influence on the voltage fluctuations. The proposed method is applied to a real unbalanced IEEE 34-bus distribution test system. The highest capability of CSO algorithm, i.e. CSO provides the highest reduction on the voltage fluctuations, is proven compared with particle swarm optimisation, harmony search and water cycle algorithms.

The promotion of smart meters has led to the exponential growth of monitoring data. Consequently, challenges have emerged for utilities to analyse and interpret big data at the demand side. In this study, an adaptive weighted-feature K-means-affinity propagation (AP) clustering algorithm is proposed to analyse customer electricity consumption behaviours. First, a comprehensive feature set is constructed, i.e. the time-domain, frequency-domain and fluctuation features of load curves. Second, the feature set is applied to a divide-and-conquer framework to analyse customer behaviours. In the local modelling, adaptive K-means algorithm is adopted to cluster load curves by considering the time-domain and fluctuation features; the former is objectively weighed by entropy weighting. In the global modelling, AP clustering algorithm is introduced and clustering results are obtained by combining the weighted time-domain and frequency-domain features. The performance tests on the simulation data and electricity consumption behaviour analyses on the big dataset are conducted to verify the effectiveness of the proposed models and approaches. The divide-and-conquer framework is practical, and the proposed features are not only beneficial for clustering the load curves, but also conducive to consumption behaviour analysis.

Nowadays, demand response (DR) is used to modify the real-time load curve either directly through incentive-based centralised control or indirectly through inducing a particular load pattern by time-varying pricing schemes. Various DR schemes are available which encourage the participation of industrial, commercial and residential consumers in system operation. This study investigates the potential of residential consumers’ DR participation in reducing Deviation Settlement Charge (DSC) under frequency-linked pricing environment. An incentive-based DR model for household devices by categorising them into shiftable and interruptible devices; including customer-side Battery Energy Storage System has been presented. A particular category of devices participates in a centrally initiated DR program depending upon the real-time grid frequency. The simulations are carried out on a realistic system of Indian distribution utility having a large percentage of residential consumers. It has been observed that the residential community DR participation reduces the DSCs substantially under real-time frequency conditions.

This study proposes an agent-based approach to have a reliable service restoration scheme, which is vital to restore as many loads as possible after a permanent fault. The proposed multi-agent system (MAS) has four different types of agents: feeder agents, zone agents, switch agents and DG agents. The agents can communicate and cooperate with each other in order to supply services to out-of-service customers. An artificial neural network has been considered to handle DG uncertainties. The restoration plan is built in a distributed manner, based on local data considering system conditions, operational constraints and fault location. The result of the proposed MAS is a switching sequence. After defining this sequence, the command for opening will be received by circuit breaker agents. In case of one switch failure, the plan cannot be implemented, and hence the restoration plan will fail. To have a reliable restoration plan, the main feeder agent has to handle malfunction in order to operate the system at an acceptable range without violating system constraints. Different loading conditions have been considered under different scenarios and the results of the proposed approach for each scenario have been compared. Simulation results show the efficiency of the proposed agent architecture.

This work deals with an implementation of a unique control approach for the power quality enhancement in solar photovoltaic (PV) based grid interfaced system. In this work, a robust control technique with strong filtering capabilities is implemented. A single-phase two-stage grid interacted solar PV topology is considered with a second-order sequence filter as a test system for the validation of the power quality enhancement features. The system constitutes a PV array, a boost converter, a DC link capacitance, a voltage source converter (VSC), an interfacing inductor and a ripple filter. A boost converter is used at a primary stage of proposed topology, which supports the extraction of maximum power from the solar PV array. In addition, the VSC feeds the solar PV power into the grid. It also provides harmonics elimination and power factor correction. A new maximum power point tracking technique avoiding a drift feature is adopted for the fast dynamic performance. The implementation of the solar PV system is performed and the harmonic profile of grid current is observed in compliance to the IEEE-519 standard.

Extra high-voltage converter transformer resin impregnated paper (RIP) bushing is used as the outlet device of the converter transformer, its safe operation is directly related to the reliability of the power system. Under the long-term effects of high current, strong mechanical stress and abrasion, the contact resistance of connection structure inside the bushing is prone to deteriorate. When the local overheating increases to a certain level, the insulation performance of bushing will decline and may lead to penetrating discharge. Therefore, it is vital to develop strategies for predicting the overheating fault of RIP bushings. In this study, firstly, one typical overheating fault of RIP bushing induced by the contact deterioration was diagnosed according to the fretting corrosive model and SF₆ decomposition process. Secondly, using the three-dimensional electromagnetic-thermal-fluid finite element method, the temperature distributions of RIP bushing under the different overheating degrees were simulated. Finally, based on the maintenance experience and simulation results, the diagnosis strategies were proposed. The diagnosis principle and prediction strategies were successfully helped to maintain the faulted RIP bushings and wall bushings before a destructive overheating accident could occur, which provide a basic reference for the fault prediction and improvements of relative standards.

One of the effective methods to reduce the risk of load shedding and its subsequent blackout is to evaluate the risk of cascading outages and detection of key links in a power system. This risk assessment is getting more complicated considering the plans for integration of high wind energy. Therefore, in this study, a new sequential time approach has been proposed to evaluate risk of cascading outages considering the high penetration of correlated wind farms. The cascading processes are investigated using the extended OPA model using the graph traversal algorithm. Therefore, the problem of non-convergence situations caused by the island formation is resolved. To deal with the future wind farms integration, a correlation is defined between them by a proposed method based on the Copula theory. The numerical results confirm that in evaluation of risk, the limited scenarios cannot always be reliable. The extended OPA model is then compared with a simple OPA to confirm the importance of islanding detection in the cascade simulation. Different aspects of cascading outages, i.e. fractional load shedding, cascade propagation level and number of line interruptions have been investigated. Finally, the impact of wind penetration and correlation of wind farms on results have been presented.

Use of Battery in standalone system can offer number of benefits in terms of energy management, reduction of dependency on fossil source and voltage regulation. In this study, a power management technique considering conservation voltage reduction (CVR) along with droop control method is proposed for a standalone system supplied from Photovoltaic (PV) source and battery. The proposed power management scheme ensures that under all operating conditions, the battery is neither overcharged nor completely discharged. It further ensures that under no condition, the power limit of battery charging as well as battery discharging controller is exceeded. The proposed method also aims to minimize the size of overall Photovoltaic battery (PVB) system to reduce the total investment and operation cost considering energy saved achieved by CVR, while satisfying system operational constraints. The proposed method also reduces the number of charging/discharging operations of battery to enhance its life. The validity of the proposed power management scheme has been extensively tested on a European distribution system through PSCAD/EMTDC simulation studies as well as on real time digital simulator (RTDS). The simulation results indicate that the combination of CVR and Droop control can achieve appreciable energy savings.

A new approach for differential protection based on the mother wavelets which match in shape with inrush and fault current waveforms is proposed in this work. It is demonstrated that if the matched wavelet is used for processing inrush and fault waveforms of transformer then its detection capability improves. In case of false tripping of transformer, the equipment is to be opened for inspection for faults. This leads to equipment outage because there is no mechanism to ascertain that there was no fault. It is needed that inrush and fault be detected independently not only for the purpose of faster and better detection but also for studying mal-operations and post-analysis. For a faster and better detection, a new protection scheme is also proposed making use of independent detection of fault and inrush waveforms. To find matched inrush and fault wavelets, differential evolution algorithm is used. Out of various possible solutions for different number of wavelet coefficients, a choice is made by further testing on various switching angles. The effectiveness of the proposed method is tested through PSCAD simulations on transformers of different ratings.

Potential critical risks of cascading failures in power systems can be identified by exposing those critical electrical components on which certain initial disturbances may cause maximum disruption to the systems. The authors investigated the cascading failures in power systems described by the direct current power flow equations, where the initiating disturbances (natural or anthropic factors) give rise to changes in admittances of one or multiple transmission lines. The disruption is quantified with the remaining transmission power at the end of the cascading process. In particular, identifying the critical branches and the corresponding initial disturbances causing the worst-case cascading blackout is formulated as a dynamic optimisation problem (DOP) in the framework of optimal control theory, where the entire propagation process of cascading failures is considered. An Identifying Critical Risk Algorithm based on the maximum principle is proposed to solve the DOP. Simulation results on the IEEE 9-Bus and the IEEE 14-Bus test systems are presented to demonstrate the effectiveness of the algorithm.

The promotion of electric vehicles (EVs), triggered by environmental concerns, can also increase the flexibility of power systems through ancillary services, such as frequency regulation among others. Nevertheless, EV penetration has increased the concern regarding voltage drops in distribution grids. This concern has motivated researchers to examine EV reactive power provision to mitigate such problems. This work proposes a local market perspective to promote EV reactive power provision, enabling distribution system operators to control the voltage level using cost-effective solutions. The authors propose the extension of a centralised control framework that schedules EV frequency regulation to optimise the reactive power provided by the same EVs. Additionally, extra power losses in chargers while EVs provide reactive power were investigated, and the associated cost in the economic evaluations were considered. A test-case with the IEEE 33-node distribution grid is used to assess the market potential of EV reactive power provision. This new service extends EV penetration in a cost-efficient way without causing voltage problems. The simulation concludes that it is economically feasible to use EVs for reactive local provision with efficient chargers. These outcomes sustain the potential of promoting such new EV services through a proper market in distribution grids.

This study considers the dynamic state estimation of power systems with model uncertainties that might be caused by the unknown noise statistics or unpredicted changes to the model parameters. To deal with these issues, an innovation-based estimator that is able to dynamically revise the statistics of system and measurement noise is proposed firstly. Then, based on the criteria for bounding the adverse influences on the estimation error of model uncertainties and unscented transform technique, an adaptive strategy is developed to adjust the estimation error covariance matrix under various conditions. Finally, by incorporating the proposed approaches and filter theory, a novel adaptive unscented filter is established to realise dynamic state estimation of power system against model uncertainties. Extensive simulation results obtained from the IEEE-39 bus test system are presented to illustrate the effectiveness and robustness of the proposed method.

Distribution networks include thousands of sensors and a large number of controllable devices such as flexible loads, batteries and distributed generators with an arbitrary configuration that can be parametrically uncertain or topologically unknown. In this study, a new robust non-linear voltage-control strategy based on backstepping and fractional-order (FO) sliding mode control (SMC) is presented for an islanded microgrid (MG). Then for the problem of determining the controller parameters, particle swarm optimisation algorithm is used. The primary objective in this study is to improve robust performance and disturbance rejection of the closed-loop control system. A FO backstepping SMC is proposed in this study. The proposed controller robustly regulates the MG voltages in the presence of parametric uncertainties, unmodelled dynamics, imbalanced and non-linear loads with harmonic. Finally, the performance of the proposed technique is investigated under disturbance in an islanded MG. An in-depth comparative study is done to highlight the merits of the proposed controller. Simulation results revealed that the total harmonic distortion and the steady-state error of the output voltage are successfully decreased and the dynamic performance and the capability of perturbation rejection are efficiently improved.‏

The focus of this work is to analyse the performance of automatic generation control (AGC) of two area four units interconnected deregulated power system for implementing modified sine cosine algorithm (MSCA) to design the conventional PID and fuzzy-PID (FPID) controllers. Each area consists of thermal generating unit with reheat turbine and a gas generating unit. Thermal generating units of both areas are employed with appropriate generation rate constraint. At first, sine cosine algorithm (SCA) is modified to implement MSCA technique and superiority of MSCA is proved over SCA using four benchmark functions. Then the work is extended to design the conventional PID and FPID controllers using both the algorithms. Three different situations of deregulated markets are considered. For the design of optimal controller gains integral time absolute error is taken as the objective function. To analyse the dynamic behaviour of the deregulated power system undershoot, overshoot and settling time are considered as transient parameters. After the analysis, it is found that MSCA-based FPID controller exhibits a better transient response in terms of less undershoot, overshoot and settling time. Robustness analysis is performed by varying some of the important parameters of the power system to show the proposed FPID controller is less sensitive to parametric variations.

A new enhanced probabilistic approach for substation reliability assessment is presented. The proposed technique evaluates substation reliability using a combinative approach that considers not only the impact of substation equipment failures but also the effect of the system conditions in which the substation resides. The approach uses the analytical contingency enumeration technique and uses a commercial program to perform power flow analysis in order to assure the accuracy of the reliability evaluations. The application of the proposed approach is illustrated by performing probabilistic reliability assessment on an actual utility substation. The technique presented will enable more realistic assessment of the reliability performance of substations and/or switching stations using probabilistic approaches.

Electric spring (ES) as a device to solve the voltage fluctuation problem caused by the connection of a large number of renewable energy sources to the power grid has developed quickly. ES which involves a power inverter with a capacitor in DC side (ES-C) with the simple structure and low cost has great potential for application with strict control requirements and some operation details are not been discussed. In this study, based on the analytical solution of ES-C's operating points, the operation region of ES-C is divided into four parts. Also, a piecewise control strategy was proposed to ensure the safe and stable operation of ES-C. With the piecewise control, ES-C can participate in system frequency regulation while maintaining load voltage as rated voltage. Also, the simulations in PSCAD/EMTDC proved that the proposed piecewise control can switch between operation regions and can make full use of the operating points of ES-C.

The resilience in critical infrastructures context indicates the capability to anticipate, absorb, resist and recover from an event or adversity. In a power distribution system, as the frequency of severe weather event increases, this characteristic becomes mandatory, considering the impact in the society when a blackout occurs. This study proposes the analysis of power distribution systems resilience through two stochastic resilience indexes. Given several simulated weather scenarios and several numbers of available repair crews, the resilience indexes estimate the probability of a waiting crew time greater than a threshold as well as for the impact in the total time to restore the entire distribution system. These indexes, together with a costs and losses estimation methodology help the utilities in the decision support process as it demonstrates how the number of available repair crews will impact in the restoration process and costs for each possible weather scenario. The result for a real Brazilian power distribution system indicates its feasibility and benefits for the utilities.

Electric vehicle (EV) as dynamic energy storage systems could provide ancillary services to the grids. The aggregator could coordinate the charging/discharging of EV fleets to attend the electricity market to get profits. However, the aggregator profits are threatened by the uncertainty of the electricity market. In this study, an EV aggregator bidding strategy in the day-ahead market (DAM) is proposed, both reserve capacity and reserve deployment are considered. The novelty of this study is that: (i) The uncertainty of the reserve developments is addressed in terms of both time and amount. (ii) Scenario-based stochastic programming method is used to maximise the average aggregator profits based on one-year data. The proposed method, jointly considers the reserve capacity in the DAM and the reserve deployment requirements in the real-time market (RTM). (iii) The risk of the deployed reserve shortage is addressed by introducing a penalty factor in the model. (iv) An owner-aggregator contract is designed, which is used to mitigate the economic inconsistency issue between the EV owners and the aggregator. Results verify the performance of the proposed strategy, that is the average aggregator profits are guaranteed by maximising reserve deployment payments and mitigating the penalties in RTM and thus the reserve deployment requirements uncertainty is well managed.

The growth of utilisation of wind power plants (WPP) in power systems placed at long distance of the transmission power system has shown that the best connecting alternatives are by means of high-voltage direct current lines (HVDC) using voltage source converter (VSC) stations. Generally, the bus closest to the WPP and with the highest level of short circuit is selected as the best option to connect the VSC-HVDC link, mainly due to economic criteria. This paper proposes a methodology to select the best connection point in the AC system, taking advantage of the potential increase of transmission capability due to the reactive power controlled at the VSC converter stations; measured as the increment of the loadability margin computed by means of PV curves. Nevertheless, a trade-off between this benefit and the cost of overhead HVDC transmission line as consequence of the length differences among alternatives is required. This trade-off is computed as the cost of the equivalent STATCOM required to get the same loadability margin. Thus, a technical-economical comparison of connection alternatives allows the selection of the best option for connecting the VSC-HVDC line. The methodology has been applied to connect a WPP of 500 MW to the NETS.

This study proposes a modified formulation for the popular fast decoupled load flow method to facilitate automatic incorporation of reactive power flow related constraints, i.e. generator reactive power and in-phase transformer tap limits. This formulation has been developed by viewing each device constraint as a set of complementary conditions and adopting the complementarity framework to define one equation to represent this set. The original set of load flow equations is augmented with this set of new equations representing the device constraints and solved to get the adjusted solutions directly. This approach is a significant departure from all the earlier approaches. The earlier approaches generally adopt a two-stage scheme. In the first stage, an approximate unadjusted solution is obtained and in the next stage, adjustments are attempted by ad hoc schemes. The new formulation presented here permits handling both the reactive power related constraints simultaneously. However, it can also be used in situations where only one type of adjustment is needed. Two alternate algorithms in complementarity framework based on mixed complementarity problem and non-linear complementarity problem formulations are presented here. Simulation results are presented to provide evidence of the impressive performance of the new algorithms as compared with the existing ones.

Corona is a critical effect that must be considered during the design and optimisation stages of high-voltage hardware such as substation connectors, since due to the harmful effects, corona threats power systems reliability. Visual corona tests allow detecting and identifying the critical corona points on the surface of substation connectors, so corrective actions can be applied for product optimisation. This article focuses on reduced-scale (RS) visual corona tests intended to verify and optimise the behaviour of such high-voltage hardware. RS visual corona tests allow reducing the voltage to be applied, laboratory size, instrumentation requirements, assembly, and test times, and finally the overall costs of the tests compared to standard corona tests carried out in large-size high-voltage laboratories. A hybrid approach combining experimental tests and finite element method (FEM) simulations is presented, which allows obtaining the equivalent visual corona onset voltage between RS and full-scale tests. Although the article focuses on the analysis of aluminium substation connectors, the proposed approach can be applied to many other hardware intended for high-voltage applications.

In this study, a ring-type multi-microgrid (MG) system comprising of three MGs with solar energy-based generation, wind energy-based generation, and static synchronous compensator as primary sources along with batteries as emergency back-up is proposed. The voltage-source converters at the individual MG level serve as the interface between the neighbouring MGs and the grid. An augmented proportional sliding-mode-control-based approach is proposed in the primary control level to improve the power quality of the system during balanced and unbalanced loading conditions. A negative-sequence controller is also included to mitigate the negative-sequence voltage from the inverter output voltage. The multi-MG (MMG) system is capable of operation in grid-connected, islanded, or interconnected mode with balanced and unbalanced loads. The bidirectional DC -DC converters interfacing the batteries at the DC link is controlled by a hysteresis-function-based sliding-mode control. A comparison of the proposed inverter primary control with proportional -integral controller, quasi-proportional -resonant controller, and hysteresis-band current controller is carried out. The comparison reveals that the total harmonic distortion and voltage unbalance factor improves by employing the proposed control when compared with conventional control. The proposed MMG system with its control strategies is modelled in MATLAB/Simulink environment and is experimentally verified using OP4510 from OPAL-RT.

Meeting electricity demand in remote communities and non-electrified regions in the poor developing world is a challenge. Power generation is in shortage compared to electricity demand. Electric utilities either would enforce grid's zonal load curtailment or not electrify regions. Controlling electricity demand can play a vital role in enabling electricity access; however, weather uncertainty drives electricity demand variability. This article provides an overview of current demand-side management research, identify research gaps, and propose a more promising approach to enable electricity access. Also, it proposes manipulating appliances models to fit their operation in applications where power supply shortage is an issue. The proposed work considers the effect of the probabilistic nature of weather and meeting AC grid codes of operation.

Population dispersion necessitates grid expansion to meet electricity demand. For many developing countries and remote communities, meeting electricity demand is a challenge due to a power generation shortage and load variability that is highly driven by weather uncertainty. Electric utilities’ practical planning solutions are to disable electricity access from new residential regions, supply at least 10% of the non-electrified regions, or follow a rotating feeder curtailment such that the new regions are electrified for a few hours daily. This study proposes an alternative framework to plan electricity access more efficiently in developing countries. A probabilistic multi-stage optimisation framework that first incorporates in-depth analysis of appliance operational models, second accounts for AC grid codes of operation and third anticipates consumers’ actions is deployed. The framework is formulated to account for climate/weather uncertainty factors. Results show that energy efficiency can reach up to 97%, and the computation time can be improved by 99.6% with respect to the existing current state-of-the-art approaches.

In an active network, as a virtual power plant (VPP), periodic maintenance of distributed generators (DGs) is critically vital for the reliable operation of the power system. To prevent unexpected failure of DGs and avoid deterioration of the grid's reliability, coordination of maintenance scheduling is indispensable. In this study, maintenance management of a VPP is proposed for scheduling the planned outage of DGs, in order to preserve their useful lifespan. In addition to conventional DGs and the upstream power grid, renewable generation including wind turbines and photovoltaic systems, energy storage systems, and curtailable loads are considered as components of the VPP. The proposed maintenance scheduling scheme provides different advantages in viewpoints of cost and reliability. Moreover, risk management is also investigated to lower the risk of maintenance scheduling due to the uncertainty in price in an energy market by adopting the conditional value at risk as a measure of risk. The overall cost is minimised considering the power loss in the grid as well as the security constraints such as DGs operational constraints, voltage magnitude, and transmission lines’ power flow limit. The effectiveness of the proposed scheme is illustrated using numerical studies with short- and long-term scheduling.

In a bulk power system, generators can be divided into groups based on the variation of their swing curves after disturbance occurrence. In this regard, generators that have the same variations are placed in the same group and are called ‘coherent’. Identifying coherent generators is an important step in creating a reduced model of bulk power systems for dynamic wide-area analysis. The aim of this study is to provide a comprehensive and up-to-date bibliographic review of the literature related to coherency detection and power system partitioning after disturbance occurrence. In this study, coherency detection methods are classified into model-based and measurement-based categories. Furthermore, existing methods for power system partitioning based on coherency concept are also reviewed and categorised using two different criteria. Finally, the future trend toward coherency and coherent areas detection is discussed.