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High energy consumption around the world and concerns over climate change and high greenhouse gas emissions have led to a significant increase in using renewable energy resources, particularly wind power plants. In spite of using small wind farms in distribution networks as distributed generation, the interest in employing large‐scale wind farms in transmission levels has grown significantly in recent years. The presence of large‐scale wind farms with a synchronous generator to supply the energy demand can affect the power swing parameters. These effects are in a way that can mislead power swing detection methods and cause undesirable tripping of transmission line due to mal‐operation of distance relay against power swing. Therefore, in this paper, a method has been presented based on modified empirical mode decomposition (EMD) method to differentiate fault from power swing in the networks connected to the large‐scale wind farms. The results prove the efficiency of the presented method in detecting the power swing in large‐scale wind farm networks. Finally, a comparison is conducted between the proposed method and similar methods which proves the efficiency and superiority of the proposed method in terms of noise tolerance, detection fault detection speed, and multi‐mode power swing detection compared to the current methods. [ABSTRACT FROM AUTHOR]

With the rapid increase of wind power penetration, the frequency indicators of power system, encompassing frequency deviation and rate of change of frequency (RoCoF), are prone to exceed the pertinent relay thresholds, thus leading to serious power outages. Wind farms can participate in primary frequency regulation (PFR) to alleviate the above problem. However, wind farms reserve capacity (WFRC) presented by overspeed control and pitch angle control is the important factor for frequency safety especially when large disturbance occurs, and it varies obviously in different conditions. Thus, WFRC should be utilized optimally to avoid insufficient or excessive response of wind farms in PFR. To overcome the challenge, this paper proposes frequency‐trajectory‐oriented control (FTOC) for wind farms based on a two‐fold optimization framework, seeking to obtain the best frequency control performances in PFR through realizing the optimal compromise between frequency results and the consumption of WFRC. First, when the frequency indicators deviate significantly from the normal values, the optimal frequency trajectory is planned periodically according to the local standards and operation conditions, thereby minimizing the total consumption of WFRC on the premise of frequency safety. Second, based on model predictive control and a real‐time power system parameters estimator, the total reference power of wind farms, which helps the frequency track the latest planned trajectory, is optimally distributed among wind generators according to their reserve capacity. In this case, through the optimal frequency trajectory planning and tracking, the new method in this paper not only effectively prevents the frequency results from exceeding the local standards under diverse conditions, but also finds the optimal balance among frequency indicators safety, frequency trajectory smoothness, and the reserve capacity consumption of wind power generators. Finally, the simulation results prove the effectiveness and advancement of FTOC. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system. [ABSTRACT FROM AUTHOR]

The grid‐following control‐based hybrid cascaded high‐voltage direct current (HC‐HVDC) links may contribute to the worsening of inertia decline and instability issues, especially in situations characterized by a weak grid. To address the challenge, this paper proposes a grid‐forming (GFM) based optimal frequency support scheme (OFSS) for HC‐HVDC integrated wind farms. Firstly, a GFM control for hybrid converter is proposed and its stability under weak grid conditions is evaluated through a small‐signal model. Then, DC current–voltage droop control is introduced to the line commutated converter (LCC). This control approach ensures the consistency of reactive power in the LCC by regulating the DC voltage and current in the same direction, thereby mitigating AC voltage fluctuations. It also facilitates active power control in the LCC in response to grid frequency variations. Furthermore, the OFSS can convey the frequency of receiving‐end grid to the rectifier and wind farms without communication for precise power control. Finally, to validate the effectiveness of OFSS, a point‐to‐point HC‐HVDC and a 4‐machine test model are constructed on PSCAD/EMTDC. The OFSS significantly enhances the frequency support capability of HC‐HVDC links and improves the robustness in weak grid. [ABSTRACT FROM AUTHOR]

The phenomenon of wind abandoned caused by fault of transmission lines has occurred frequently. Since fault current output by wind turbine after fault may induce the frequency variation of the fundamental frequency, fault location based on the fundamental resistance is easily invalid. In order to locate fault accurately and rapidly after wind farm transmission line faults, a time‐domain‐based novel fault location method for transmission line of wind farm is introduced in this paper. The main idea of the proposed scheme is to discretise the distributed‐parameter model of transmission line that is given in the form of partial differential equations by using the finite‐difference time‐domain. In addition, the phase current and voltage at both ends of lines are regarded as boundary conditions. Then, the variational iteration method is utilised to calculate transmission line equations to obtain line length function. At last, fault location function is constructed by a minimised objective function. This paper builds wind power transmission line system simulation model and carries out simulation experiments on PSCAD/EMTDC. To verify the performance of the algorithm, several factors are taken into account for simulation including different fault distances, different fault types, different fault resistances, and different fault inception angles. Simulation results reveal the performance of the proposed method to locate fault for transmission lines connected to wind farm with high accuracy. [ABSTRACT FROM AUTHOR]

This paper suggests a new control algorithm for On‐Load Tap Changer (OLTC) of wind farm main transformer that is responsible for voltage regulation of collector network. The conventional control scheme adjusts the voltage of LV side of transformer without considering the wind turbine voltages. The voltage of end‐string wind turbines may reach the upper or lower limits of Over or Under‐Excited operation regions. Voltages close to upper or lower limits bounds the reactive power capability (generation or consumption) of wind farm due to dependency of wind turbine reactive power capability to voltage. Operation of wind turbine at extreme voltages may stress its power electronic devices. Furthermore, minor disturbances at either grid or power plant may trigger the protection system and interrupt generation due to vicinity of operation point to protection thresholds. The suggested technique integrates the OLTC control into the power plant controller and considers both LV side voltage of transformer and wind turbine voltages. By monitoring the wind turbine voltages, the voltage setpoint of OLTC is adjusted to provide voltages closer to middle of range for wind turbines that prevents the wind turbines from reaching the voltage limits. Dynamical simulations conducted in DigSILENT PowerFactory software for both Under‐Excited and Over‐Excited operation regions demonstrate desired performance for the suggested technique. [ABSTRACT FROM AUTHOR]

With large‐scale wind farms connected to AC/DC network, the transient stability assessment (TSA) of the power system becomes more and more difficult. Among them, estimating the region of attraction (ROA) of the equilibrium point is a traditional but still challenging problem. Based on the Lyapunov stability theory, this paper proposes a new approach to obtain the enlarged estimation of the ROA. The optimization and updating strategy of shape function in the sum of squares (SOS) optimization problem are studied to reduce the conservatism of estimation result. In the proposed method, the gravitational search algorithm (GSA) is employed to optimize the coefficients of initial shape function to improve estimation performance. Based on the time‐domain simulation (TDS) of expected faults, the fitness value for shape function optimization is calculated using the values of state variables at the fault clearing time and the system stability information. Furthermore, the optimal Lyapunov function is computed by introducing the update condition of shape function and adjusting the iteration strategy of the ROA estimation algorithm. Finally, the proposed method is applied to a two‐machine‐infinite‐bus system and a more complex nine‐bus AC/DC system with wind power. And the effectiveness of the proposed method is verified by comparing with the existing ROA estimation methods. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) diagnosis is a crucial tool to assess the insulation condition of wind farm cables. Among PD diagnosis techniques, PD localisation is promising as it can provide target maintenance indicators on the insulation weak points of the cables. Accordingly, this paper developed a portable PD detection and location system for wind farm inter‐array cables. The system consists of two non‐invasive and lightweight testing units, which can be conveniently deployed on an energised cable, enabling highly efficient online PD diagnosis of the widely distributed inter‐array cables. The system achieves accurate PD localisation of the energised cable via an improved double‐sided travelling wave method. The method exhibits two superior features: the double‐sided testing units are accurately synchronised via the joint application of Global Position Systems and a pulse‐based interaction process, and a windowed phase difference method is proposed and integrated into the system to robustly estimate the time‐of‐arrival difference in low signal‐to‐noise ratio environment. Validation experiments were conducted on both a 10‐kV cable in the laboratory and a real 35‐kV cable in an on‐shore wind farm. [ABSTRACT FROM AUTHOR]

Nowadays, due to the simplicity of operation and proper coordination characteristics, distance relays are being extensively used in the protection of power transmission lines. Despite many advantages, the distance relay is exposed to a maloperation when a power swing phenomenon occurs. Power swings may occur due to major disturbances, such as switching lines, generator interruptions, and connecting or disconnecting a large part of the loads, causing outages in healthy lines. Detecting power swing and preventing distance relay maloperation has always been a challenge. In addition, different parameters can have a great impact on the performance of power swing detection methods. In this paper, these parameters and their effects are examined along with various methods for detecting power swings. Accordingly, this comprehensive review covers all aspects of power swing phenomenon and its detection, including causes of power swings, power swing detection methods, determining threshold values in power swing methods, effecting of compensated lines and wind farms in power swing detection methods, and testing power swing function in the distance relays. It is hoped that this study will be helpful for researchers in power system protection to find relevant references and develop new methods for power swing function. [ABSTRACT FROM AUTHOR]

The invention of doubly‐fed induction generator (DFIG) brings the wind energy one step ahead as renewable power generation. But the performance of the grid‐connected DFIGs are greatly affected by grid disturbances as their stator windings are interfaced to the grid directly. Different fault current limiters are capable of improving fault ride through capability during short circuit faults. Nonlinear controller based fault current limiters (FCLs) are superior to deal with the nonlinearity of the power systems. This paper proposes a nonlinear adaptive backstepping controller (ABSC) based capacitive bridge‐type FCL (CBFCL) to enhance the fault ride through capability of a DFIG‐based wind farm connected to a multi‐machine power system. At first, a complete modelling of the CBFCL is derived to understand its behaviour during the normal and fault period more accurately. Then, the ABSC is designed based on that dynamic model, along with the backstepping controller (BSC) and sliding mode controller (SMC) for comparison purpose. Finally, the performance of the ABSC to control the CBFCL has been analysed and verified by comparing with that of the BSC and the sliding mode controller. All the graphical and mathematical analyses favour the ABSC based CBFCL under symmetrical and asymmetrical fault (both temporary and permanent) scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a WECS based on a back to back diode‐clamped multilevel inverter systems (DCMLI) fired comparatively by sinusoidal pulse width modulation (SPWM) and third harmonic injection pulse width modulation (THIPWM) techniques. DFIG performance is compared using these technologies for different wind speed under normal operation condition. The proposed approach shows that the DCMLI systems generate a nearly sinusoidal voltage with reduced total harmonic distortion (THD) thus upgrading the power quality of that produced by DFIG. As SPWM and THIPWM MLI are most common usage techniques in research, we applied them in the system to discuss a comparison of using those techniques to determine the number of levels that achieve the IEEE 519 criteria of THD which is less than 5% for both techniques (5th level for SPWM and 4th level for THIPWM) in all operation wind speed. Compared with simple PWM, and THIPWM have many advantages as lower THD, minimal number of switching to decrease switching losses and the output fundamental voltage is increased. Lastly, the paper investigates the variation of the frequency of induced rotor voltage and the active power flow due to the wind speed change when the rotor speed changes from super‐synchronous to sub‐synchronous speeds. [ABSTRACT FROM AUTHOR]

Evaluation of available transfer capability (ATC) is a complicated process involving the determination of the total transfer capability (TTC) and the existing transfer commitments (ETC). Considering the uncertain renewable generation such as wind power will bring more challenge to this process. Previously the uncertainty of wind power is considered either in the ED model or the following TTC calculation, but not included in two process simultaneously. Therefore, the ATC output may not be accurate since the uncertainty impact two problem simultaneously. To consider the uncertainty and correlation of wind power in both ISO's ED and ATC evaluation, this paper proposes a bi‐level optimization framework in which the ATC evaluation is formulated as the upper level problem and the ED is the lower level. The bi‐level model is first converted to a mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush‐Kuhn‐Tucker (KKT) optimality conditions, and then transformed to a mixed‐integer linear programming (MILP) problem which can be solved by existing optimization tools. Case studies on the PJM 5‐bus and IEEE 118‐bus systems are presented to verify the proposed methodology and the impacts of correlation of wind power on ATC are analysed. [ABSTRACT FROM AUTHOR]

Large‐scale integration of renewable energy sources (RESs) such as wind power can impact on the existing power system's stability. For example, there is a possibility that the wind farm (WF) cannot be maintained stable and then it is disconnected from the grid when the instantaneous voltage sag caused by a grid disturbance occurs. Moreover, the increasing integration of RESs in the power system can reduce the number of conventional synchronous generators (SGs) connected to the grid, and lead to a reduction of system inertia in the grid. The system inertia reduction can cause larger and faster frequency fluctuations during the transient period. Hence, this paper proposes a new control strategy composed of the voltage control for suppressing the voltage sag and the virtual synchronous generator (VSG) control based on the proportional‐integral‐differential (PID) fuzzy logic control (FLC) for maintaining the frequency stability which is performed by high voltage direct current (HVDC) interconnection line. The aim of the new control strategy is to improve the stability of the entire power system with large‐scale integration of RESs. The effectiveness of the proposed strategy is confirmed through the simulation analyses by PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

In this study, a new short‐term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short‐term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. The results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model. [ABSTRACT FROM AUTHOR]

Generally, wind speed alters continuously during a day and leads to fluctuating output power of a wind farm (WF). Connecting such a WF to the transmission line of grid causes a malfunction in the performance of the backup zone of the distance relays due to the random nature of generation and the number of connected wind turbines (WF penetration). In this study, a new adaptive setting groups‐based method has been proposed to set the second zone of the distance relay in the grid with a WF equipped with a doubly fed induction generator. In this method, the authors have also taken into account the uncertainty of wind fluctuations and the availability of WT. A Markov model is developed to implement these features. To determine the impedance setting of each of the set groups of the second zone in the distance relays of the remote bus, the obtained impedances are clustered in four clusters by using the combination of K‐means and particle swarm optimization algorithms. Finally, the details of the analysis results of this issue have been compared with other methods available in the literature. The validity and accuracy of the proposed approach also have been assayed and confirmed. [ABSTRACT FROM AUTHOR]

With the real-time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs' output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs' DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF. [ABSTRACT FROM AUTHOR]

Due to high wind power penetration into power system, synchronous generators may no longer be the dominant generation, which implicitly requires participation of wind power plants (WPPs) in the load-frequency control (LFC). Frequency, a common variable throughout the entire power system, is the key variable for LFC, which causes equal contribution of all distant and nearby WPPs in LFC. Power transfer from distant WPPs to the event location may overload the middle transmission lines leading to cascading failures. This study proposes an online dispatch rescheduling algorithm for WPPs participating in the LFC task to prevent over loading of transmission lines and hence cascading failures following severe frequency contingencies. The WPP dispatch limits are calculated based on line ampacity for each generation unit participating in the LFC task. Numerical simulations carried out in DigSilent PowerFactory on 39 bus IEEE standard test system, demonstrates the efficiency of suggested online dispatch rescheduling technique for keeping the loading rate of transmission lines, bus voltages and system frequency in range. [ABSTRACT FROM AUTHOR]

With the increasing penetration rate of renewable energy sources, variability impacts, aside from uncertainty, will increase the need for operational flexibility resources in power systems. Accordingly, in this study, flexibility provision from both generation and demand sides are considered in a day-ahead scheduling problem. Fast start gas-fired units, due to their agility in starting-up and high ramping capabilities are considered as generation side flexibility providers. However, the normal operation of these units strictly depends on a secure gas supply through connected gas pipelines. Thus, gas-supply uncertainty is considered in the proposed model to mimic the impacts of deviations in gas volume through pipelines. On the other hand, demand response programs (DRPs) with their specific characteristics are among powerful demand side flexibility providers. However, unlike the existing literature that considers a supplementary role for DRPs, this study introduces the main role for DRPs as flexibility providers through comprehensive modelling of versatile DRPs. The proposed day-ahead scheduling problem is a multi-resolution robust security constrained unit commitment that the robustness comes from the decision making against sub-hourly gas-supply uncertainty and wind power variability. The applicability of the proposed model is tested on both an 8-bus and IEEE-118-bus standard test systems. [ABSTRACT FROM AUTHOR]

Integration of renewable energies such as wind and solar with an energy storage system (ESS) in a distribution network is the interest of current studies in power system engineering. Wind and battery ESS (BESS) are known for their complement and efficient approaches into the distribution networks. The promising of renewable energies for wind and solar in Afghanistan is a motivation for stepping up the power sector of the country by enhancing the power quality as well as selfdependency in electricity production. In this study, a multi-objective optimisation technique, non-dominated sorting genetic algorithm II (NSGA-II) is proposed for an extensive distribution network in Kabul city considering technical, environmental, and financial control schemes for the network improvement. Three different scenarios with various objective functions are deemed to evaluate their impact on decision variables and network parameters. Furthermore, optimum allocation of the wind turbine and charge/discharge scheduling of BESS are revealed with improvement in performance of the power system. Simulations are deployed in MATLAB® with its application on developed 162-bus real-distribution network to demonstrate the effect of different objective function arrangements in each scenario as well as confirming the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

The self‐scheduling horizon is an important schedule parameter in the self‐scheduling problem. A more reasonable self‐scheduling horizon can lead to higher benefits of the wind farm (WF) and large‐scale compressed air energy storage (CAES) combined system. However, very few studies have been reported about the optimisation of self‐scheduling horizon for a WF paired with a CAES plant. In this study, a rolling day‐ahead self‐scheduling framework for a WF and CAES combined system is first proposed. After that, the self‐scheduling horizon optimisation model is developed in the formulation of a bilayer stochastic chance‐constrained optimisation problem. The proposed model is converted into its equivalent deterministic linear formulation and then is solved. Based on the developed model, the impacts of self‐scheduling horizon on the profit of the combined system are analysed. Numerical simulation results indicate that the profit of the combined system increases after using the optimal self‐scheduling horizon, and the profit increment is more obvious with the increase of the CAES's energy storage capacity. [ABSTRACT FROM AUTHOR]

This study, based on a novel control strategy, proposes a sizing method for battery energy storage systems (ESSs), which makes the wind power system more dispatchable. The main objective of the proposed control‐based sizing method is to facilitate robust unit commitment by smoothing the output power of wind according to a desired reference. Owing to the energy conversion loss, the controller closely monitors the battery state of charge (SOC) to prevent the battery from being fully discharged during the smoothing procedure. The proposed controller can automatically revise the battery dispatching reference maintaining the SOC constant. In each dispatch interval, model predictive control is used in real time to ensure that the output power follows the generated reference. The ESS is sized according to the simulation results solved by the proposed control strategy. Meanwhile, the performance of the proposed control strategy is validated over actual wind power data. [ABSTRACT FROM AUTHOR]

Power‐to‐gas (P2G) technology opens up another perspective for effective, long‐term, large‐scale energy storage, so that it can promote large‐scale wind power integration. Under this background, this study introduces P2G technology into the economic dispatch model of wind power integrated systems. First, an uncertainty interval model of wind power is built based on the probability distribution of wind power prediction error. Then, considering P2G's function of absorbing surplus wind power and undertaking downward spinning reserve, the factors that influence P2G's ability in enhancing the accommodation of wind power are analysed through theoretical derivation. Consequently, the relationship between the maximum wind power integration and the power delivered to the P2G facilities under a given capacity of P2G can be further understood. Moreover, an economic dispatch model incorporating wind power, P2G facilities and battery energy storage systems is established and a feasible region relaxation algorithm is proposed to deal with the non‐smooth functions in the proposed model. Case studies are conducted on a modified IEEE 39‐bus system. The results verify the correctness of theoretic analysis and show that the proposed model can help to reduce wind power curtailment and enhance the security and economy of the power system. [ABSTRACT FROM AUTHOR]

Based on the analysis on the uncertain nature of wind power output, the Weibull distribution parameters of regional wind speed at different periods are calculated to obtain the probability density function (PDF) and the cumulative distribution function (CDF) of the wind power. The spinning reserve (SR) requirements for wind power incorporation are determined according to the obtained PDF and CDF, and by converting the wind power into a chance constrained form, a model for dynamic economic emission dispatch with wind power is constructed. A hybrid multi‐objective algorithm that integrates differential evolution (DE) and particle swarm optimisation (PSO) algorithm is put forward to solve the proposed model. The algorithm is implemented based on the Pareto dominance theory and a dynamic external archive set, and fully exploits the advantages of DE and PSO. An improved calculation method of crowding distance and Pareto solution set reduction rule are also employed to enhance the performance of the proposed algorithm. Also, three performance indicators are introduced to evaluate the performance of the algorithm. Two distinct test systems are performed to verify the proposed model and algorithm, and the results show that they are effective and reasonable. [ABSTRACT FROM AUTHOR]

A large number of distributed generations (DG) integrated into a distribution network challenge the planning for traditional power systems making traditional deterministic power flow calculations not applicable. Based on the interval and credibility of the blind number theory, this study is about the uncertainty of DG and the correlation between DGs. The interval models for wind power and solar power are established based on the characteristics of the wind power generator output and the photovoltaic cell output. The improved analytic hierarchy process is applied to determine the credibility of the intervals. The blind number theory and Cholesky decomposition are applied to study the correlation between wind speed and light intensity. According to the result of the power flow calculation with uncertainty, the voltage operational quality index is proposed to measure the impact of the DG. Through a modified IEEE 33‐bus system, the feasibility and effectiveness of the blind number model for the DG and the necessity of considering the correlation between wind speed and light intensity are verified. [ABSTRACT FROM AUTHOR]

Large‐scale day‐ahead wind power forecasting (WPF) for wind farm clusters (WFCs) can enable dispatching agencies to formulate scientifically sound power generation plans and enhance the robustness of power grids. Most available WPF methods for WFCs only involve mathematical models and rarely consider spatial correlation factors. This necessitates further improvements to forecasting systems. In this study, to increase the day‐ahead WPF accuracy for WFCs, fractal transform theory is introduced to optimize the process of WPF for WFCs through spatial upscaling and establish a day‐ahead WPF model for WFCs based on an improved spatial upscaling method. First, a WFC is partitioned into subclusters. Then, using fractal transform theory, an affine relation is established between the local output of each subcluster and the overall output of the WFC. Finally, a day‐ahead power forecast is produced for the WFC through spatial upscaling of the forecast for the subcluster with the highest grey relational grade with the output of the WFC. The applicability of the proposed forecasting model is examined using historical measured data for a large WFC in north‐eastern China. The case study results show that the proposed forecasting model outperforms the summation method and the statistical upscaling method in terms of forecasting accuracy. [ABSTRACT FROM AUTHOR]

This study considers the optimal reactive power dispatch strategy that minimizes electrical loss while working within the constraints of the internal layout voltage. Wind turbines (WTs) with a power converter system can promptly generate reactive power, and when a reactive power order arrives from the connected grid, an operator can assign a reactive power reference to each wind turbine. Recently, multiple objective functions in use have been based on centralized control, but their flexibility has been determined as inadequate for situations in which the voltage range of the wind turbine is also a factor. The designated constraints of the voltage fluctuation for each section have thus been considered by designing and applying an adaptive method. The main purpose of such a method is to use general dispatch optimization while maintaining the voltage of each connection point within operating range. The included case studies are designed using an electromagnetic transients program that checks both the voltage and loss of the entire wind farm. We tried to identify voltage violations and confirmed the effectiveness of the proposed method. This work thus attempts to confirm whether an objective function can be implemented while also adhering to a certain grid code. [ABSTRACT FROM AUTHOR]

The independent system operators (ISOs) usually offer financial transmission right (FTR) as a financial instrument for electricity market participants to hedge against the transmission congestion cost. Meanwhile, the development of demand response (DR) provides load serving entities (LSEs) opportunities to perform coupon based demand response (CBDR) programs, and thus LSEs can behave as strategic bidders in the whole‐sale market by adjusting its demand level. In the existing approaches for modelling CBDR, the potential impact of FTR which leads LSEs to obtain the congestion compensation under a high load level is overlooked. Therefore, this study proposes a comprehensive strategic CBDR model in which the LSE's profit is maximised by providing CBDR to customers and the congestion compensation from the LSE bidder's FTR holding is also considered. The proposed model is formulated as a bi‐level optimisation problem with the LSE's net revenue maximisation as the upper level and the ISO's economic dispatch considering wind power uncertainty as the lower level problem. The bi‐level model is addressed with mathematic program with equilibrium constraints technique and mixed‐integer linear programming, which can be solved using available optimisation software tools. In addition, the case studies of an illustrative two bus system, the PJM 5‐bus system, and IEEE 39‐bus system verify the proposed method. [ABSTRACT FROM AUTHOR]

Rapid expansion and integration of wind energy is restrained due to transmission capacity constraints and conventional generation technologies limited operational flexibility in today's power systems. Energy storage is an attractive option to integrate and utilise more renewable energy without major and timely upgrade of existing transmission infrastructure. Moreover, it can be considered as a means for differing the reinforcement plans. The evaluation of energy storage deployment projects is a challenging task due to severe uncertainty of wind power generation. In this study, a robust techno-economic framework is proposed for energy storage evaluation based on information gap decision theory for handling wind generation uncertainty. The total social cost of the system including conventional generators' fuel and pollution cost and wind power curtailment cost is optimised considering generators operational constraints and transmission system capacity limitations based on the DC model of the power grid. The effect of storage devices on system performance is evaluated taking into account wind power uncertainty. The proposed method is conducted on the modified IEEE reliability test system and the modified IEEE-118-bus test system to assess its applicability and performance in mid-term robust evaluation of energy storage implementation plans. [ABSTRACT FROM AUTHOR]

This study presents the distributed model predictive control (D-MPC) of a wind farm equipped with fast and short-term energy storage system (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines (WTs) and the ESS unit, the WT mechanical loads are alleviated. With the fast gradient method, the convergence rate of the D-MPC is significantly improved which leads to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger WT load reduction, compared with the conventional wind farm control without ESS. Moreover, the efficiency of the developed D-MPC algorithm is independent from the wind farm size and is suitable for the real-time control of the wind farm with ESS. [ABSTRACT FROM AUTHOR]

With increasing large-scale wind farms being integrated into the power grids, transmission expansion planning (TEP) increasingly requires more flexibility to account for the intermittency as well as other uncertainty factors involved in the process. In this study, a probabilistic TEP model is proposed for planners to tackle the variability and uncertainty factors associated with grid connected wind farms. Both load forecast and wind power output uncertainties are considered in the proposed model. Other factors considered in the model include the forced outage rates of transmission lines and generators, and the wind speed correlation between wind farms. Moreover, the incentive-based demand response (IBDR) program is introduced as a non-network solution instead of the conventional network expansion approaches. The utilities will pay IBDR providers for their contributions to peak demand reduction. The proposed TEP model can find the optimal trade-off between transmission investment and demand response expenses. The hierarchical Bender's decomposition algorithm integrated with Monte Carlo simulation is employed to solve the proposed model. Case studies are given using the Garver's six-bus system and the IEEE-reliability test system to show the effectiveness of the method. [ABSTRACT FROM AUTHOR]

With increasing large‐scale wind farms being integrated into the power grids, transmission expansion planning (TEP) increasingly requires more flexibility to account for the intermittency as well as other uncertainty factors involved in the process. In this study, a probabilistic TEP model is proposed for planners to tackle the variability and uncertainty factors associated with grid connected wind farms. Both load forecast and wind power output uncertainties are considered in the proposed model. Other factors considered in the model include the forced outage rates of transmission lines and generators, and the wind speed correlation between wind farms. Moreover, the incentive‐based demand response (IBDR) program is introduced as a non‐network solution instead of the conventional network expansion approaches. The utilities will pay IBDR providers for their contributions to peak demand reduction. The proposed TEP model can find the optimal trade‐off between transmission investment and demand response expenses. The hierarchical Bender's decomposition algorithm integrated with Monte Carlo simulation is employed to solve the proposed model. Case studies are given using the Garver's six‐bus system and the IEEE‐reliability test system to show the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Wind farms (WFs) contribution in frequency deviations curtailment is a grey area, especially when WFs replace large conventional generation capacities. This study offers an algorithm to integrate hydro‐pumped storage station (HPSS) to provide inertial and primary support, during frequency drops by utilising stored wind energy. However, wind turbines follow maximum power tracking, and do not apply frequency support methods, thus the wasted wind energy is mitigated. First, HPSS rated power and energy capacity are determined based on several givens, including wind speed and load characteristics. Thus, HPSS major aspects are estimated [e.g. pump(s), reservoir layout and generator(s)]. Second, offered algorithm coordinates energy storage, and releasing through several dynamic and static factors. HPSS output is continuously controlled through a timed approach to provide frequency support. A hypothetical system is inspired from Egyptian grid and real wind speed records at recommended locations to host WFs. Case studies examine the algorithm impact on frequency recovery, at 40% wind power penetration. The responses of thermal generation and HPSS are analysed to highlight the influence of tuning the parameters of the proposed algorithm. The assessment of several frequency metrics insures the positive role of HPSS in frequency drops curtailment. Simulation environments are MATLAB and Simulink. [ABSTRACT FROM AUTHOR]

High volatility renewable penetration increases the security risk of the market participants. In order to ensure the security of the reserve market under uncertainties, the authors have proposed a novel optimal bidding strategy based on bivariate pair copula-based security-constrained unit commitment economic dispatch (SCUCED) for the generation companies (GENCOs) while considering the volatility of wind power generations and load variations under different (N − k) contingencies in ancillary service market. The expected profit of GENCOs are based on the ramp-up and ramp-down optimal reserve curves in each time interval are submitted to the independent system operator. The profit of wind farms is obtained and the risks due to uncertainties with wind farms are also analysed while participating in the reserve market. Conditional value-at-risk is considered for the optimal placement of wind farms in the network. The risk-based SCUCED model is solved by mixed integer linear programming. Moreover, a factor called joint Pareto tail factor has been also developed to provide information regarding the fat tails in the lower end of the bivariate marginal distribution of different GENCOs. The effectiveness of the proposed approach has been tested on a modified IEEE 30 bus system. [ABSTRACT FROM AUTHOR]

A novel islanding detection technique by hybridising empirical mode decomposition (EMD) and multi-scale mathematical morphology (MMM) is proposed to detect the islanding condition in a distributed generation system to ensure personnel and equipment safety. The proposed method first uses EMD to efficiently separate the collected raw signal into the number of intrinsic mode functions (IMFs) with different frequency scales and the signal is reconstructed considering important IMFs which carry transient features for further analysis using their correlation coefficients. MMM is used for determining a ratio index named as MMMRI, the threshold value of the proposed MMMRI decides islanding and other power quality (PQ) disturbances. The proposed hybrid method name coined as EMD-MMMRI. The main motivation behind hybridising two signal processing techniques is to reduce detection time and improve accuracy. The efficacy of the method is demonstrated for different PQ disturbances and islanding events simulated on a grid-connected, heavily wind energy penetrated distributed generation system using MATLAB/Simulink environment. The test bench validation of the proposed technique is obtained through TMS 320C6713 Starter Kit (DSK) in digital signal processor platform. The efficacy of proposed work is demonstrated with large number of simulation studies. [ABSTRACT FROM AUTHOR]

The first swing rotor angle stability is still an important stability challenge for modern power systems integrated with a large number of renewable energy-based sources, such as wind farms and solar-photovoltaic farms. Therefore, innovative strategies must be developed to exploit the full potential of doubly fed induction generators (DFIGs) to improve the first swing rotor angle stability in the system. This study presents a new active power logic (APL) controller for DFIGs which can reduce the active power during the fault and slowly recover it after the fault to allow synchronous generators to increase the electrical power during and after the fault; thus, enabling synchronous generators to improve the first swing rotor angle stability. The feasibility of the proposed control scheme is investigated via theoretical analysis and simulation studies. The reliability and voltage stability test system is used to demonstrate the effectiveness of the proposed scheme for local and remote faults and increased DFIG penetration conditions. The comparative results show that the proposed APL controller of the DFIG improves the first swing rotor angle stability, specifically when the DFIGs are located near the synchronous generators. [ABSTRACT FROM AUTHOR]

As a result of the increasing penetration of renewables in power systems, wind farms (WFs) have to satisfy the low-voltage ride-through (LVRT) requirement to stay connected to the power grid under fault conditions. This study proposes a novel method that uses the static synchronous compensator (STATCOM) to improve the LVRT capability of WFs during grid faults. The proposed method is based on the Taguchi method which involves orthogonal experiments. The allocation (locations and sizes) and fuzzy control gains of STATCOM are determined by the analysis of mean (ANOM) technique as part of the Taguchi method to achieve a robust design that is insensitive to variations of operating conditions and faults. The optimal STATCOM that is obtained by the proposed method is validated in both a single WF-infinite bus power system and the IEEE 39-bus power system. The simulation results show that the proposed method can enhance the LVRT capability of WFs. [ABSTRACT FROM AUTHOR]

This study investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in smart distribution system integrated with electric vehicles (EVs) and wind power generation. A two-stage framework is proposed for real and reactive power management and DFR. In the first stage, demand dispatch of EVs is coordinated to utilise available wind power and minimise the total load variations. In the second stage, a comprehensive problem formulation is developed to minimise the losses in the system with simultaneous EVs reactive power management and DFR. A genetic algorithm is used to solve this two-stage optimisation problem. A case study of 33-bus radial distribution system is presented to highlight the efficacy of proposed approach. The obtained results are quantitatively assessed in terms of losses and node voltages. A comparative benchmarking analysis is presented to substantiate the role of DFR and reactive power capability of EVs in improving the system performance. [ABSTRACT FROM AUTHOR]

The low and variable inertia due to the integration of large-scale wind generation poses severe challenges to the frequency control of the power grid. In this study, a novel model predictive control (MPC)-based load frequency controller (LFC), which can adapt to the changes in system inertia, is proposed to improve the frequency control effect. First, the inertia estimation scheme based on measurements is designed. Then, the state-space model, which is the key that determines the control effect of MPC, is corrected in time based on the estimated inertia. The proposed method in this study can avoid the problem of MPC-based LFC failure caused by inaccurate model parameters. The performance of the proposed strategy is demonstrated in two scenarios, i.e. wind power fluctuation and load multi-step, using a two-area interconnected power system with time-variant inertia. [ABSTRACT FROM AUTHOR]

Wind power generation has developed rapidly in recent decades due to low carbon emissions. However, the significant uncertainty makes it uneconomic or even unreliable to be consumed, which hinders the development of wind energy. To guarantee the minimum wind utilisation level without jeopardising system reliability and cost-effectiveness, this study proposes a concept of optimal wind power consumption point. Based on that, a two-stage chance-constrained unit commitment model is presented to co-optimise the day-ahead energy and reserve schedules, which achieves a reasonable trade-off between robustness and costs. The battery energy storage is also investigated to enhance system flexibility and promote wind consumption. The joint chance constraint is dealt with through a sample average approximation method in bilinear forms. The resulting large-scale mixed-integer programming is decomposed into the master and subproblem formulations and then solved iteratively by the developed bilinear Benders decomposition (BBD) method. To achieve computational tractability, several techniques are used to enhance the convergence property of BBD and accelerate the solution process, with a novel optimality-check-only bilinear Benders decomposition method proposed. Case studies on six-bus, IEEE 118-bus and 236-bus systems demonstrate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

This study presents the structural, operational and control aspects of doubly fed induction generator (DFIG) based wind integrated power systems. The automatic generation control (AGC) of a meshed power system including DFIG-based wind turbines has been framed and investigations under various system perturbation are presented. The two-area system consisting of non-reheat thermal turbines with DFIG and interconnected through parallel AC/DC tie-lines is considered for the study. The system non-idealities such as governor lag and generation rate constraints are taken into consideration. An AGC strategy using a layered recurrent artificial neural network (ANN) is proposed in this work. The gains of the proposed AGC are obtained by effectively training the ANN using a set of reliable data obtained from a widespread range of operating system conditions using robust control strategy. The study also incorporates the design of AGC for the power system using the fuzzy logic concept and other AGC actions such as integral (I), proportional–integral (PI) and proportional–integral–derivative (PID) calculated via the means of particle swarm optimization (PSO). The results obtained with the proposed ANN created AGC are linked and demonstrated their superiority over fuzzy logic PI and traditional PSO-based I/PI/PID AGC strategies under numerous system operating conditions. [ABSTRACT FROM AUTHOR]

With the large-scale wind power, centralised access to high-voltage direct current (HVDC) asynchronous interconnected sending system, the random fluctuation of wind energy increases the uncertainty of frequency regulation. A frequency coordinated control (FCC) strategy based on situation awareness is proposed to make full use of the frequency regulation potential of wind turbines and HVDC transmission. Firstly, the influence of wind power permeability and HVDC power ratio on frequency regulation capability is analysed, and the mathematical model of FCC is established. Secondly, based on the information of FCC obtained by wide-area measurement system and data acquisition system, a non-linear mathematical prediction model is constructed, and the frequency response in finite time domain is predicted according to the frequency deviation and its change rate. According to the results of the prediction, the parameters of FCC are dynamically adjusted to realise the whole process variable objective optimal control on the time scale of primary frequency regulation. Finally, the simulation results show that the maximum frequency deviation of the proposed control method is reduced by about 46.6% and the frequency callback time is reduced by about 11.6% compared with the traditional control method with fixed parameters under the same disturbance. [ABSTRACT FROM AUTHOR]

Wind power application in producing electrical energy is an integral part of the increased eco-friendly generation. Accordingly, wind power producers may have a dominant position in some electricity markets. This study mainly concerns a strategic wind power investor who owns a number of units and seeks to optimise its expansion plans on sizes and sites of new generation capacities in a horizon year. Here, the investor is a price-maker in both day-ahead (DA) and intraday (ID) markets, while acts as a deviator in the balancing market. A stochastic bi-level model is proposed wherein the upper-level, the investor maximises the expected profit. Both DA and ID market clearings are considered in the lower-level (LL). The model is formulated as a mathematical program with equilibrium constraints (MPEC) by replacing the LL problem with its Karush–Kuhn–Tucker (KKT) conditions. The MPEC model is then converted to a mixed-integer linear programming model. A three-bus illustrative example and IEEE 24-bus reliability test system are used to demonstrate the effectiveness of the proposed model. The results confirm that the presence of the ID market in the investment model has increased the expected profits of the investor and also increases invested wind capacity. [ABSTRACT FROM AUTHOR]

Dynamic line rating (DLR) technologies are fast becoming a key instrument in power systems with high penetration of wind farms, because power companies can reduce the value of wind power spillage and also can postpone the investments of transmission lines by using DLR equipment. However, what the authors know about DLR is largely based on empirical studies that investigate how to change the capacity of transmission lines with changing the weather data. Therefore, lack of a mathematical model for estimating the capacity of lines by considering the weather conditions across the line and wind farm generations can be accounted as a research gap of DLR problems. This study proposes a solution for transmission expansion planning considering DLR technologies. Also, this study seeks to develop a model, which will help to address the research gap of DLR problems. The geostatistical analysis is used to develop a mathematical model, which can provide the relationship between wind farms as well as the capacity of lines calculated based on the weather conditions across the line. The efficiency of the proposed model is examined on a modified IEEE 118-bus test system. [ABSTRACT FROM AUTHOR]

There are rising opportunities and prospects for integration of a large‐scale energy storage system in the electric power system to mitigate the challenges arising from wide‐spread growth in variable and uncertain sources of renewable energy generation. Compressed air energy storage (CAES) is one of the promising large‐scale energy storage technologies that is being explored. This study presents a novel probabilistic framework to evaluate the reliability benefit of CAES in the wind integrated power system. The developed framework is based on a hybrid approach which is a combination of Monte Carlo simulation (MCS) based state of charge model and analytical method based reliability evaluation. An equivalent average model is developed within the hybrid framework to assess the adequacy benefit of CAES operated to seasonally accumulate and transfer energy. This hybrid method brings together advantages of both MCS and analytical method in reliability evaluation resulting in a comprehensive and computationally efficient framework. A detailed Markov model for CAES component reliability is developed and integrated into the hybrid framework. Case studies are conducted to demonstrate the effectiveness of the proposed framework. The results presented quantify the reliability benefit from diurnal and seasonal energy management in CAES in addition to the environmental and financial benefits. [ABSTRACT FROM AUTHOR]

In this study, a three-settlement electricity market including the day-ahead, risky, and real-time trades is analysed. A wind power producer faces generation uncertainty and needs to cover its real-time generation deviation unless it would be penalised. To do so, wind power producers participate in an intraday risky power market, as well as in the day-ahead and realtime markets. Mathematical program with equilibrium constraints (MPEC) is used to model the behaviour of each generation company in its profit maximisation process. The complementarity constraints describe the market clearings and constitute a bilevel optimisation problem for each company. The companies have a combination of wind and thermal power plants in their generation portfolios. Wind power uncertainty is modelled using plausible scenarios. The joint solution of all companies' MPECs makes an equilibrium problem with equilibrium constraints (EPEC). This EPEC is linearised and solved using mixed integer linear programming techniques. EPEC solution gives the Nash equilibrium. Results show the effectiveness of the risky power trades on reducing the reserve requirements, increasing the wind power producers' profit, and reducing the wind power spillage. [ABSTRACT FROM AUTHOR]

Reductions in system inertia due to increasing penetration levels of wind power may result in frequency stability issues. Thus, it is necessary to provide a comprehensive method to estimate the maximum level of wind power that can maintain a system's frequency within allowable security limits following a major disturbance. Here, a fast method to estimate maximum wind power penetration level (WPPL) that considers frequency cumulative effect is proposed. The method first establishes an average system frequency response (ASFR) model of integrated wind power and then calculates the corresponding frequency response trajectory characteristics. Subsequently, a new transient frequency security index (TFSI) based on the frequency cumulative effect is expressed. The physical basis for the TFSI is that it can objectively reflect the dynamic security state of the system frequency. The quantitative functional relationship between the WPPL and the TFSI can be obtained by combining the frequency response characteristics and the TFSI definition. A numerical expression for the maximum WPPL integrated into the grid can be obtained, and the maximum WPPL of the integrated wind power system can be instantaneously and quantitatively estimated. Finally, the feasibility and effectiveness of the proposed analytical method is demonstrated on a 10-machine 39-bus system. [ABSTRACT FROM AUTHOR]

This study establishes a multi‐objective model for comprehensive energy system scheduling optimisation with wind farms, combined heat and power units, power to gas technology, circulating fluidised bed boilers and gas boilers. The goal is to minimise the operating costs and minimise the discharge of pollutants while meeting the constraints of the material balance of the circulating fluidised bed boiler. The improved weak robust optimisation theory is used to deal with the uncertainty of wind power, and the weak robust coefficient is introduced. By adjusting the coefficient, the optimal solution under the different tolerances of the deterioration of the objective function is obtained. The improved bacterial population chemotaxis algorithm is used to optimise the comprehensive energy system scheduling model, and a feasible solution with certain robustness is obtained. Sensitivity analysis under different risk levels is done. The simulation verifies the correctness and effectiveness of the optimised scheduling model and the improved algorithm. [ABSTRACT FROM AUTHOR]

Due to the intermittent power generation, wind farms integrated into the grid may cause a problem of large frequency fluctuation, when the capacity of load frequency control (LFC) is not enough to compensate the unbalance between the generation and the load demand. In this study, a new robust LFC strategy against load disturbance and wind power fluctuation is proposed to improve the disturbance-resistant performance of the power system with wind farms. First, a simplified model of the power system with wind farms is established without considering the accurate model information. Then, a control scheme based on the compensation of equivalent input disturbance (EID) is introduced, and a method of EID estimation is presented. The robust LFC design addresses various disturbances and internal/external uncertainties of power systems. Finally, to evaluate the effectiveness of the proposed method, two case studies are carried out and the performance comparisons with the classical proportional-integral-derivative control scheme are used to confirm the proposed method. [ABSTRACT FROM AUTHOR]

A regional power grid containing high‐density wind power is generally connected to a load centre through long‐distance transmission lines. Voltage problems, caused by wind speed fluctuation, in the sending‐end grid have become increasingly serious. The variable speed wind turbine has been used to compensate the deficit in reactive power. However, the available methods could not meet the requirement because the controllable range of reactive power contributed by wind farms cannot be adjusted. A new idea of reactive power control realised by the active control before wind speed fluctuation was proposed to prevent the voltage variation. Firstly, the influence of wind speed fluctuation on voltage was analysed, and the power controllable range of wind turbines was studied. The principle and strategy of the active control of reactive power were proposed based on the model predictive control theory. Then, the active control model was established according to the system dynamic demand for reactive power. According to wind speed forecasting, the reactive control capability of wind turbine was excavated to meet the grid demand by adjusting active power before wind speed variation, and the reduced active power of wind farms is optimally compensated by thermal powers. Finally, the method was proven to solve voltage problems. [ABSTRACT FROM AUTHOR]

Wind and solar have major share among the growing renewable penetration, due to their extensive availability and improved technologies. Both wind and solar generation are highly uncertain and intermittent as compared to system load. The increased number of such uncertain and intermittent variables necessitates complex multivariate operational strategies for system operation. A compilation of different uncertain and intermittent variables such as load, wind and solar generation, to a single uncertain variable called net load, reduces system operational planning complexity. Net load is the difference between total load and renewable generation. Thus, conventional generation units have to be scheduled for net load. Prior knowledge about net load can help optimum operational planning such as generation scheduling and power system flexibility estimations. There have been significant advancement in load and renewable generation forecasting over the last decades. Still, there is little attention towards net load forecasting (NLF). This study proposes a novel NLF model using Gumbel copula based joint probability distribution for load, wind and solar generation forecasting error aggregation. Gumbel copula covers all extreme forecasting errors due to max‐stable property. Proposed model uses modified Grey index models for forecasting. Results show that proposed model has strong potential in very short‐term NLF. [ABSTRACT FROM AUTHOR]