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In the hydrogen‐based integrated energy system (HIES), there exists a hydrogen trading market where hydrogen producers and consumers are distinct stakeholders. Current research in hydrogen trading predominantly focuses on high‐cost green hydrogen (GH), which is not aligned with the current trend of utilizing hydrogen from multiple sources. To address this, this paper proposes a hydrogen trading strategy between the virtual hydrogen plant (VHP) and electro‐hydrogen energy system (EHES) based on a bi‐level model, considering the synergy of GH produced from electrolyzers and blue hydrogen (BH) derived from natural gas in the HIES. In the VHP level, the objective is to maximize profit from hydrogen sales, allowing for the determination of hydrogen prices. In the EHES level, the goal is to minimize the cost of energy supply, leading to the formulation of GH and BH purchasing plans based on hydrogen prices. Additionally, this paper incorporates a risk‐averse model from the information gap decision theory (IGDT) to account for the impact of wind power output uncertainties in the VHP level. Subsequently, leveraging the Karush–Kuhn–Tucker (KKT) conditions of the EHES level, the bi‐level problem is transformed into a solvable single‐level mathematical program with equilibrium constraints (MPEC), with the non‐linear equilibrium constraints linearized. The proposed bi‐level optimization model is validated through case studies encompassing industrial and residential hydrogen utilization within the HIES. The outcomes confirm the rationality of the proposed model, demonstrating that, in comparison to exclusively trading GH, the coordinated GH and BH trading can increase the profit of the VHP by 2.7% and reduce the costs of the EHES by 8.5%. [ABSTRACT FROM AUTHOR]

This paper proposes an improved expanding interior algorithm (EIA) to estimate the region of attraction (ROA) of power systems with wind power generation based on sum of squares (SOS) programming. An ordinary differential equation (ODE) model is derived for the doubly‐fed induction generator‐based wind turbine (DFIGWT), which is named as an enhanced synchronous‐generator‐mimicking (ESGM) model. The ESGM model bridges the gap between the requirement of an ODE model in ROA estimation and the conventional differential‐algebraic equation (DAE) model of the DFIGWT system. The ESGM model is able to accurately reflect the low frequency dynamics of the DFIGWT. Moreover, an improved EIA is designed to estimate the ROA based on SOS programming, which has higher efficiency than the existing ROA estimation algorithms based on SOS programming. It is able to adaptively search for the Lyapunov function and obtain an optimal estimation of the ROA in an iterative process. The accuracy and efficiency of this algorithm are verified in three test systems composed of DFIGWTs and synchronous generators (SGs). The morphological changes in the ROA of the test systems caused by the penetration of DFIGWT are examined. [ABSTRACT FROM AUTHOR]

Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability. [ABSTRACT FROM AUTHOR]

The rapid growth of offshore wind power has resulted in a mismatch between generation and demand because of its variability. To quantify the maximum wind power penetration of the transmission network, the dispatchable region is defined as the largest region in the uncertainty space. The security distance is defined as the minimum distance from the operating point to the boundaries of the dispatchable region. System operators can use security distance as a metric to assess the flexibility of the power system. This paper proposes a method to construct the dispatchable region for the AC/DC hybrid system with VSC‐HVDC by outer convex relaxation firstly. The second‐order cone relaxation is employed to reformulate non‐convex and non‐linear power flow equations. Next, a polyhedral approximation is adopted to obtain the convex hull of the dispatchable region. Subsequently, an efficient algorithm known as adaptive constraint generation (Ad‐CG) is introduced to calculate the boundaries of the dispatchable region. Furthermore, solving the Chebyshev centre problem determines the minimal security distance. The modified IEEE 5‐bus and 39‐bus system is used for validating effectiveness of the proposed method and evaluating the impact of the converter reactive power compensation capacity and generation dispatch on the dispatchable region. [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]

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 fully distributed scheme to solve the day‐ahead optimal power scheduling of networked microgrids in the presence of different renewable energy resources, such as photovoltaics and wind turbines, considering energy storage systems. The proposed method enables the optimization of the power scheduling problem through local computation of agents in the system and private communication between existing agents, without any centralized scheduling unit. In this paper, a cloud‐fog‐based framework is also introduced as a fast and economical infrastructure for the proposed distributed method. The suggested optimized energy framework proposes an area to regulate and update policies, detect misbehaving elements, and execute punishments centrally, while the general power scheduling problem is optimized in a distributed manner using the proposed method. The suggested cloud‐fog‐based method eliminates the need to invest in local databases and computing systems. The proposed scheme is examined on a small‐scale microgrid and also a larger test networked microgrid, including 4 microgrids and 15 areas in a 24‐h time period, to illustrate the scalability, convergence, and accuracy of the framework. The simulation results substantiate the fast and precise performance of the proposed framework for networked microgrids compared with other existing centralized and distributed methods. [ABSTRACT FROM AUTHOR]

The diode‐rectifier (DR)‐based high‐voltage direct current (HVDC) transmission is regarded as a low‐cost alternative to the HVDC transmission for offshore wind farms (OWFs) integration. However, the DR would generate harmonic voltages in the offshore grid, which greatly affects the power quality. To solve this problem, this paper utilizes a parallel‐connected small‐capacity auxiliary converter to suppress the harmonic voltages and provide black‐start energy for the OWFs. In addition, the submodule branches (SMBs) of the auxiliary converter can be used as part of the HVDC chopper. On this basis, a coordinated grid fault ride‐through (FRT) strategy is proposed in this paper. After a grid fault occurs, the HVDC chopper constructed by the auxiliary converter is able to constrain the DC‐link voltage. At the same time, the OWFs are controlled to reduce the output active power. As a result, the cost of the FRT can be significantly reduced. The proposed harmonic voltage suppression and the FRT method are verified through the simulation model in Simulink. [ABSTRACT FROM AUTHOR]

With the development of the energy structure of the power system, the characterization of the observed oscillation in the power system has steadily evolved. With the increasingly high penetration of distributed energy sources and power electronic devices, such as the photovoltaic, wind power and charging pile, the basic mechanism, frequency ranges and propagation of oscillation would become more dynamic and complex. Therefore, the traditional parameter estimation, as well as the suppression methods would also suffer from dynamic oscillations. To this end, this paper first investigates the oscillation propagation and typical events caused by the oscillations. Next, the oscillation model estimation method including the model‐based and data‐driven based methods is discussed. The benefits and difficulties of various parameter estimate techniques are highlighted. Under this context, the validity and limitations of the current power system stability are summarized. Furthermore, the different oscillation damping and mitigation measures are compared, and the major adaptability and limitations are listed for the specific application scenarios. Conclusions are drawn, and the wideband oscillation becomes the future research objective where the future work is presented at the end of the paper. [ABSTRACT FROM AUTHOR]

The ability to withstand and recover from disruptions is essential for seaport energy systems, and in light of the growing push for decarbonization, incorporating clean energy sources has become increasingly imperative to ensure resilience. This paper proposes a resilience enhancement planning strategy for a seaport multi‐energy system that integrates various energy modalities and sources, including heating, cooling, hydrogen, solar, and wind power. The planning strategy aims to ensure the reliable operation of the system during contingency events, such as power outages, equipment failures, or extreme weather incidents. The proposed optimization model is designed as a mixed‐integer nonlinear programming formulation, in which McCormick inequalities and other linearization techniques are utilized to tackle the model nonlinearities. The model allocates fuel cell electric trucks (FCETs), renewable energy sources, hydrogen refueling stations, and remote control switches such that the system resilience is enhanced while incorporating natural‐gas‐powered combined cooling, heating, and power system to minimize the operation and unserved demand costs. The model considers various factors such as the availability of renewable energy sources, the demand for heating, cooling, electricity, and hydrogen, the operation of remote control switches to help system reconfiguration, the travel behaviour of FCETs, and the power output of FCETs via vehicle‐to‐grid interface. The numerical results demonstrate that the proposed strategy can significantly improve the resilience of the seaport multi‐energy system and reduce the risk of service disruptions during contingency scenarios. [ABSTRACT FROM AUTHOR]

The multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed. [ABSTRACT FROM AUTHOR]

In order to solve the collaborative optimization scheduling of multi‐microgrid under the high penetration rate of new energy, this paper considered the energy interaction between micro‐grids in multi‐microgrid and the relationship between new energy consumption and electricity cost, constructed a collaborative scheduling model considering both micro‐grid load and main grid wind and optical energy storage, proposed objective function based on economic cost, and improved Genetic Algorithm (GA). The elite thought and catastrophe thought are used to optimize the selection operation, and the particle swarm optimization algorithm is used to optimize the mutation operation. Furthermore, three scenarios were selected to verify the effectiveness of the proposed model and the improved GA. The results show that the proposed model can ensure the stable operation of the multi‐microgrid system with a high proportion of new energy access and reduce the operation cost. In addition. the improved algorithm solves the problems of easy to fall into local optimum and slow iteration speed, and has better performance in solving the model. [ABSTRACT FROM AUTHOR]

Decision‐dependent (endogenous) uncertainties (DDUs), as a new type of uncertainties revealed recently, couple dispatch decisions with uncertainty parameters and thus render power system dispatch more challenging. However, most previous works handled various DDUs via stochastic programming (SP) or robust optimization (RO) in a two‐stage framework, which undoubtedly introduces the drawbacks of SP and RO, and cannot meet the nonanticipativity requirements in power scheduling. In this paper, a multistage distributionally robust optimization (DRO) method for generation dispatch with demand response (DR) is proposed considering the DDUs of deferrable loads and the decision‐independent (exogenous) uncertainties (DIUs) of wind power and regular loads. By analyzing the structure of decision‐dependency parameters, a novel data‐driven decision‐dependent ambiguity set is proposed, which provides a generic framework for formulating DDUs and DIUs simultaneously. Then a multistage DRO model with nested max‐min structure is developed to integrate the merits of DRO and nonanticipativity into generation dispatch. The proposed model is solved by tailored reformulation method and improved stochastic dual dynamic integer programming (SDDiP). Case studies illustrate the effectiveness of the proposed approach by comparing with the multistage SP, RO, and decision‐independent DRO methods. [ABSTRACT FROM AUTHOR]

Offshore wind farm (OWF) is considered as a perfect zero‐carbon energy source for the future power system. However, the growing offshore distance and water depth of OWF make the OWF HVDC transmission technique a more promising solution than HVAC due to higher cost‐efficiency and reliability. In this paper, the current situation of OWF‐HVDC projects is introduced at first. Then, novel converter topologies with the higher power density and cost‐efficiency are presented, including the hybrid modular multilevel converter (MMC), alternative arm converter (AAC), and diode rectifier (DR). Next, several OWF HVDC transmission system topologies are introduced, including terminal‐hybrid, station‐hybrid and all‐DC delivered system. Furthermore, the key technologies for OWF HVDC operation and control are summarized, including grid‐forming control strategy for offshore wind turbines, stability analysis method, corresponding stability enhancement measures and frequency support control strategies. Additionally, the fault ride‐through and protection strategies for different fault locations have been presented. Finally, the main conclusions and prospects for OWF HVDC are summarized. [ABSTRACT FROM AUTHOR]

With the distribution of the large‐scale emergence of wind power connected to the power system, the stability problem of small disturbance has become increasingly prominent, especially the low‐frequency oscillation phenomenon caused by the weak or the negative damping. Aiming at the problem of probabilistic stability of small disturbance in power system connected with wind power, this paper proposes a method of probabilistic stability based on a variational Bayes and Lyapunov theory using PMU data. This method aims to minimize the computational efficiency when the number of wide‐area time series PMU data is large. Firstly, PMU data are classified by the Random Forest Gini classification index. Secondly, a variational Bayesian framework is introduced to improve the computational efficiency. And then, to obtain the probabilistic stability criterion, this framework is combined with the direct Lyapunov theory. Finally, simulation results show that the method proposed in this paper greatly improves the system probabilistic stability of small disturbance. The comparison between the traditional Monte Carlo based on the Markov chain method and the method in this paper has been achieved, which could provide the theoretical support for the study of the low‐frequency oscillation significantly. [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]

Based on a practical long‐distance large‐capacity transmission engineering case, the small‐signal stability of fractional frequency transmission system (FFTS) with Y‐connected modular multi‐level converter (Y‐MMC) is mainly researched in this paper. Different from the previous control strategies of Y‐MMC, this paper establishes a frequency decoupling mathematical model and proposes the decoupling control strategies. Then, the small‐signal model of the Y‐MMC system is obtained. Considering the existence of phase locked loop (PLL) will deteriorate the stability of the system under weak grid, this paper applies the short‐circuit ratio (SCR) for analysis and proposed a new structure of PLL, called phase‐shifted PLL (PS‐PLL), to improve the small‐signal stability of the system. In addition, for better stability and dynamic response performance of the closed‐loop control system, this paper proposes a new parameter optimization objective function for parameters adjustment. Controller parameters optimization is conducted by using the particle swarm optimization (PSO) algorithm and stability evaluation of the system is conducted based on the eigenvalue distribution. Finally, the feasibility and superiority of the proposed strategies and optimization are verified in MATLAB/ Simulink. [ABSTRACT FROM AUTHOR]

Fractional frequency transmission is a promising solution to offshore wind power integration. This paper proposes a new grid integration scheme of fractional frequency offshore wind power system (FFOWPS) based on Y‐connected modular multilevel converter (Y‐MMC). To get better power quality, a novel Y‐MMC modelling and control method considering frequency leakage is presented. Besides, in order to achieve satisfactory fault ride‐through (FRT) performance for FFOWPS, this paper proposes a novel FRT strategy. The strategy makes use of the onshore grid voltage and offshore grid frequency droop control to coordinately control the power of Y‐MMC and wind turbines. This strategy achieves power balance on both sides of Y‐MMC through coordinated control, thereby suppressing the increase of DC capacitor voltage during faults. It is also able to support the grid by injecting reactive power during voltage sags. Additionally, a new pitch angle control algorithm is designed to keep the balance between input mechanical power and output active power of wind turbines during FRT, avoiding the rapid rise of rotor speed. The simulation results in MATLAB/Simulink demonstrate good performance of FFOWPS during wind speed variations and validate the effectiveness of the proposed frequency leakage suppression strategy and FRT strategy. [ABSTRACT FROM AUTHOR]

The increase in wind power penetration leads to the risk of energy supply interruption in integrated electricity‐heat energy systems (IHES). This paper presents an analytical‐based approach for the efficient operational reliability evaluation of the IHES. The wind power distribution characteristic model considering correlation is constructed based on Hidden Markov Model (HMM). To reduce computational complexity, a critical system state identification (CSSI) method is proposed that characterises both random device failures and wind power uncertainty. Under each critical system state, the analytical function relationship between the reliability index and the uncertainty factors is established based on the virtual stochastic response surface (VSRS) method. The analytical function can directly calculate the reliability index when the wind power output varies, avoiding a large number of repeated calculations of the optimal load‐shedding model of the system and reducing the time required for reliability assessment. This allows the operator to evaluate the operational reliability of the integrated energy system in real‐time. The numerical simulation of the test system combining the IEEE 33‐node distribution system and the existing 28‐node heating system proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of the Hunter‐prey optimization (HPO) method to enhance its search capabilities for solving the Optimal Power Flow (OPF) problem, which includes FACTS devices and wind power energy integration. The new algorithm is inspired by the behavior of predator and prey animals, such as lions, wolves, leopards, stags, and gazelles. The primary contribution of this study is to address the tendency of the original HPO approach to get trapped in local optima, by proposing an enhanced Hunter‐prey optimization (EHPO) approach that improves both the exploration and exploitation phases. This is achieved through a random mutation for exploration and an adaptive process for exploitation, which balances the transition between the two phases. The performance of the EHPO algorithm is compared with other optimization algorithms, and subsequently, it is used to solve the OPF problem incorporating FACTS devices and wind power. The results demonstrate the effectiveness and superiority of the proposed algorithm. In conclusion, this study successfully enhances the EHPO algorithm to provide better accuracy and faster convergence in finding optimal solutions for complex real‐world problems. [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]

Economic operation and reliable supply‐demand balance are problems of paramount importance in power grids with a massive share of intermittent renewable energy sources (RESs) of great interest. This paper sought an optimal coordinated generation scheduling for day‐ahead power system operation considering RESs and energy storage units. Renewable power generation, particularly, wind and photovoltaic are uncontrollable, whereas can be predicted using forecasting models. Within the proposed framework, a hyperparameter‐optimized long short‐term memory (LSTM) regression model is employed to forecast the day‐ahead weather from the historical time‐series weather data. Eventually, an empirical formula is used to estimate the power conversion from the day‐ahead weather forecasts for a selected PV module and wind turbine. The objective of the scheduling framework is to keep a delicate supply‐demand balance at the lowest possible cost of generation while maintaining the prevailing generation and system constraints. A variance measure uncertainty handling‐based grey wolf optimizer (GWO) technique is used to find the optimal day‐ahead generation schedules and dispatches under RESs forecast uncertainty. The proposed generation scheduling framework is examined on the IEEE 6 and 30‐bus systems. In the studied scenarios, the coordinated operation of generators can decrease the total day‐ahead operating cost for the modified IEEE 6‐bus system by 2.57% compared to supplying electricity generation with conventional generators alone. Likewise, the total operating cost from the coordinated operation of all generation portfolios was reduced by 6.93% from the operating cost of generation during base case simulation (supply only from dispatchable thermal units) on the modified IEEE 30‐bus system. Moreover, the case studies show that coordinated generation scheduling can mitigate the RESs power variability problem, provide secure supply‐demand operation, and minimize the operating cost of electricity generation. [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]

The damage of wind turbines suffered from lightning strikes has been a key issue for the safe and reliable operation of wind farms. Accurate determination of the annual lightning flash number to a wind turbine is essential for designing proper lightning protection measures. The interaction of downward and multiple upward leaders (MULs) is studied in this paper, which also considers the stochastic nature and branched behaviour of the lightning attachment phenomenon. Firstly, with an improved stochastic lightning model, the relationship among the striking distance, the height of wind turbines and the return stroke current is established. Moreover, a modified method for predicting the annual lightning flash number strikes to a wind turbine is proposed. The simulation results show that the striking distance and the collection area not only depend on the return stroke current, but also on the height and blade angle. Besides, the comparations between the calculation results and field statistics indicate that the conventional electric geometry model is not satisfied with the need of lightning protection for wind turbines. Note that there is only a difference of 4% between the modified method and the observation value. These quantitative researches provide the guidance of calculation results for the optimization of the blade lightning protection system (LPS) design. [ABSTRACT FROM AUTHOR]

With the fast development of cyber‐physical power systems, coordinated cyber‐physical attacks have become one of the main threats to the security of modern power grid. Considering that it is difficult for attackers to obtain full information of the entire power grid, the coordinated cyber‐physical attack problem for partitioned power grid is studied in this paper, and a tri‐level defender‐attacker‐dispatcher optimization model with consideration of wind power uncertainty in a specific coordinated cyber‐physical attack mode is proposed. The Karush‐Kuhn‐Tucker (KKT) condition and an improved implicit enumeration algorithm are applied to obtain the solution of the complicated optimization problem effectively. In addition, two risk assessment indices pertinent to the physical lines and load measurements are proposed according to the obtained total amount of optimized load shedding and coordinated attack‐defense strategy under valid attack‐defense scenarios. Taking a partitioned IEEE 39‐bus test system as example, the risks of physical transmission lines and load measurements in different areas under attacks are elaborately studied, and the impact of wind power uncertainty on attack‐defense strategies is discussed as well. Numerical simulation results illustrate the effectiveness of the proposed optimization model and risk assessment indices, and some significant conclusions are drawn. [ABSTRACT FROM AUTHOR]

In a high proportion of new energy grids or a 100% new energy source of power grid, the dynamic performance of PMSG under fault conditions is very important. However, the current small signal model lacks detailed and deep analysis of a permanent‐magnet synchronous generator (PMSG)‐based wind power generation under LVRT control. Here, a small signal model of a PMSG under pre‐fault, fault and after fault failure recovery stages are established. This paper employs linear analyses to characterize the dynamic performance of a PMSG‐based wind power generation system under LVRT control and practical generator characteristics. In particular, the conflict between frequency regulation and LVRT is discussed based on the constructed model. On this basis, the impact of dc‐link bandwidth on the system performance during failure recovery stage is discussed, and the active damping method is analysed to improve the performance of PMSG during failure and failure recovery stages. Finally, time‐domain simulations are utilized to verify the analyses and models discussed. The research results in this paper are helpful to promote and improve the performance of PMSG during LVRT conditions. [ABSTRACT FROM AUTHOR]

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]

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 security of energy systems has always been one of the most important issues of energy systems operation. The multi‐carrier multi‐microgrid (MCMMG) is one of the novel structures in the power system. High penetration of renewable resources and their uncertainty, their ability to operate in isolation mode, and the presence of energy hubs in MCMMG systems increase the importance of security assessment in this type of system. This paper presents a decentralized risk‐based security‐constrained optimal power flow model for an MCMMG system. The stochastic nature of wind energy, photovoltaic generation, and electrical and heating loads are considered in the proposed model. In the model, the risk level of the system is determined by considering both the electrical and natural gas (NG) networks' contingencies. The proposed model is decomposed into three types of subproblems with the augmented Lagrangian relaxation (ALR) method: The subproblem related to the multi‐microgrid operator level, the NG network subproblem, and the individual microgrid level subproblem. The decomposed problem is solved using MATLAB software. According to the results, the proposed model can secure the system from high costs that may impose on the system due to the contingency occurrence. [ABSTRACT FROM AUTHOR]

Renewable energy and energy storage are essential technologies for decarbonizing energy systems. Expansion planning of the two technologies considering source‐side carbon responsibility has been well studied. However, expansion planning considering both source‐side and load‐side carbon responsibility, which may simultaneously stimulate the carbon reduction potential of source and load, has been rarely studied. To fill this research gap, this paper proposes a bi‐layer wind turbine generator (WTG) and demand‐side battery (DSB) coordinated planning framework considering wind power and load uncertainties. Moreover, a novel source‐load bilateral carbon trading (BCT) mechanism based on the Aumann–Shapley method is proposed to stimulate the WTG and DSB configuration. In the bi‐layer planning framework with BCT mechanism, WTG and DSB capacity planning with the two‐stage stochastic optimization are conducted in the upper and lower layer, respectively. Finally, test results on a modified IEEE 24‐bus system demonstrate that the proposed framework with BCT mechanism can effectively motivate the planning configuration of WTG and DSB and carbon mitigation. Compared with the models with unilateral carbon trading mechanisms, the proposed model has advantages in stimulating configuration of WTG and DSB, reducing carbon emissions, and balancing source‐load economic burden from carbon trading. [ABSTRACT FROM AUTHOR]

With the rapid increase of the penetration rate of renewable energy sources in power system, the decrease of system inertia will threaten the frequency stability, and the dispersion of frequency response cannot be ignored. This paper proposes a method to estimate area inertia of power system containing wind power considering dispersion of frequency response. Firstly, the impact of inertia distribution between areas on the frequency stability and the inter‐area oscillation are analysed. Secondly, the system is divided based on the spectral clustering algorithm and the frequency similarity index is defined according to the Pearson correlation coefficient to determine the measurement locations for area frequency. Thirdly, the measured tie‐line power and frequency deviation are used to estimate the area inertia based on the numerical integration method. Finally, the case study is conducted based on the modified IEEE 39‐bus system. The simulation results revealed that the proposed method is accurate and reliable in different scenarios. [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]

With the ever‐increasing penetration of wind power into power systems, wind turbine generators (WTGs) are required to provide frequency regulation services to maintain the system frequency stability. For variable‐speed WTGs, the adaptability of dissimilar frequency control strategies to different operating regions is distinct, which impacts the system frequency response. However, existing under‐frequency load shedding (UFLS) schemes do not take that impact into account, causing improper load shedding after a loss of generation. To this end, this paper proposes an adaptive UFLS scheme for power systems with high wind power penetration considering operating regions. Based on the WTG operating characteristics in different operating regions, the appropriate frequency control strategy for each operating region is elaborated. The simplified frequency response model of the power system considering WTGs participating in frequency regulation is then established. According to the WTG active power response characteristics in different operating regions, the impact of WTGs participating in frequency regulation on UFLS is analysed. Accordingly, the method of accurately estimating the magnitude of the power deficiency and calculating the amount of load to be shed is proposed. Finally, the feasibility and effectiveness of the proposed UFLS scheme are verified through simulations on the IEEE 39‐bus test system. [ABSTRACT FROM AUTHOR]

In the active distribution networks (ADNs), the emerging trend of high penetration of sustainable energy sources such as wind turbines, photovoltaics, and the forthcoming integration of energy storage devices and additional dynamic loads like plug‐in‐electric vehicles (PEVs) influences to change the nature of electric distribution networks. Consequently, it deteriorates power quality and reliability in terms of unacceptable voltage rise‐drop, harmonic distortion, voltage unbalance, and excessive power loss. In this context of transition into the active distribution network, the probabilistic optimal power flow (P‐OPF) in the ADN is considered an important tool that helps the distribution grid operators for defining the optimal settings of the system's control variables. However, the optimal solution of P‐OPF becomes highly complex while considering the multiple variables, time‐varying characteristics and operational uncertainties associated with sustainable resources, consumer behaviour and electricity market prices. This review aims at promoting the research and discussion of P‐OPF studies in the context of ADNs characteristics and challenges. First, we perform a data‐driven and visualized scientometric analysis of 1988 quality documents retrieved from the Scopus during the last ten years using VOSviewer software. Second, an in‐depth investigation of the recent and emerging techniques for solving the P‐OPF problem is presented. Third, a set of recommendations and highlights of future challenges for P‐OPF in ADNs are offered. The review findings showed high interest among the scientific community in sustainable energy source integration, given the recent rise in publications and citations. Also, there is still a lack of innovative optimization techniques that can adequately handle the current issues associated with P‐OPF and objectives with fair computational efficiency and accuracy. Finally, this paper offers a guide for P‐OPF researchers to navigate the recent studies considering the sustainable sources, spot the new research frontiers, and identify the most impactful countries and publishers. [ABSTRACT FROM AUTHOR]

The operational flexibility of electric energy systems is one of the essential requirements for integrating a high share of renewable resources. The operational flexibility significantly impacts the mix of new power generation technologies. In this paper, a low‐carbon generation expansion planning (GEP) model is presented to investigate the impacts of flexibility requirements of power systems with a high share of wind energy. An improved clustered unit commitment (CUC) formulation is proposed to capture the flexibility limitation of thermal generating units fully. In this regard, clustered 10‐min ramp up/down limits for operational reserves, flexible‐ramp reserves, and contingency reserves, are introduced. The yearly variations of load and renewable generations preserving the chronological time correlations are included, considering 36 representative days obtained by the clustering approach. Besides, two types of BES devices are considered to investigate the role of BES in the provision of flexibility. The proposed flexible low‐carbon GEP model is formulated as a mixed‐integer programming model, and an optimal expansion plan is obtained using the CPLEX algorithm. By incorporating improved CUC formulation into the low‐carbon GEP model, more profound insight into power systems' flexibility requirement with high wind generation penetration is obtained. [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]

Nowadays, with the development of the global economy, traditional non‐renewable energy resources can not only meet the increasing energy demand but also bring severe ecological and environmental problems. Wind power, as one of the most economical types of renewable energies source, has become one of the essential resources for clean energy production. Because of the randomness, intermittency, and fluctuation of natural wind, the output power of wind turbines is greatly affected by wind uncertainty. Therefore, wind power generation forecasting and scheduling may be challenging. In this paper, for the successful and accurate presence of wind power producers in the electricity energy market, a method based on forecasting wind power production, the electricity price, and Financial Loss/Gain (FLG) in coordination with energy storage is proposed. To predict the electricity price, wind power production, and FLG for the next 24 h, the hybrid method based on deep learning time series prediction based on the LSTMs method and input selection based on the MRMI method has been used. For this purpose, first, based on historical data, the wind power producer forecasts the electricity price and wind power production for the next 24 h. According to the same predicted values, initial offers are set for participation in the day‐ahead electricity market. After that, wind power producers modify their wind power production based on the FLG prediction method. Since the FLG signal has highly volatile behaviour, therefore it is not efficient to forecast and apply it directly to the proposed wind power production offers. Therefore, classifying FLG by the FCM method and predicting the FLG class labels is much more helpful in improving the proposed bid of wind power producers to the electricity market. Finally, the wind power producer can improve its profit from participating in the electricity market by interacting with the energy storage unit. The presented numerical results demonstrate the efficiency of the proposed method. For example, if the initial offers are modified based on the FLG method, it has caused the expected profit to improve by 4.44–27.69% in the desired months of the year 2018. Also, in three months, the total profit of the wind power producer and energy storage will be higher than if the profit of the wind power producer participates in the day‐ahead market with 100% accuracy. [ABSTRACT FROM AUTHOR]

Due to the growing proportion of uncertain and fluctuating renewable energy sources in the power system, reasonable generation expansion considering the uncertainties is desperately needed. This paper proposes a novel three‐stage wind‐thermal generation expansion planning model based on representative day unit commitment that incorporates the uncertainty of wind power and load and the probabilistic reliability criteria. The probabilistic reliability criteria are considered by simulating random outage events of thermal units using a robust approach. The consequent robust reliability model is reformulated as mixed‐integer linear programming models and integrated into the multi‐stage generation expansion planning model. A decomposed strategy based on the Benders decomposition is adopted to tackle with the large‐scale original model. Case studies are performed on both the revised IEEE 14‐bus and IEEE 118‐bus test systems to validate the effectiveness of proposed model and method. Numerical results show that the proposed generation planning method balances the economy and reliability of the system. [ABSTRACT FROM AUTHOR]

Wind power is one of the most important renewable energy sources that is widely used in many developed and developing countries. However, it is generally stated in the literature that providing accurate forecasts for large‐scale planning purposes is not a simple task, especially by single models. It is the main reason for this fact that why researchers in recent years have sought to propose hybrid models for increasing the accuracy of predictions. In general, choosing the appropriate type and the number of components, as well as the proper type of hybridization methodology, are the most effective factors in the performance of the developed hybrid models. Although in the literature, numerous attempts have been made in order to answer these questions, there is no general consensus on this matter. For this reason, the main idea of this paper is to concurrently combine different hybrid methodologies as well as different single models in order to benefit from the advantages of these models and methodologies, simultaneously. In this way, three well‐known and widely used hybrid methodologies, including the preprocessing, the series, and the parallel methodologies, are combined together by incorporating the linear/nonlinear and certain/uncertain components. In addition, in the proposed model, a new process is proposed based on the complex/uncertain modelling to model the preprocessing phase residuals, which have been ignored in the modelling procedures. In this way, in the first stage of the proposed model, the data is preprocessed by the Kalman filter as a preprocessing approach in order to divide data into two groups of trend and residual patterns. The trend data provided in the previous step, with the original data, are simultaneously considered input data of an autoregressive integrated moving average as the certain linear model and a multilayer perceptron as the certain nonlinear model for certain linear and nonlinear modeling of patterns. This step is repeated for the residual data by the series hybridization of models in the previous stage by the fuzzy models for the uncertain linear and nonlinear modelIng of patterns. Finally, each component's weight is optimally calculated by the least square algorithm, and then the results are combined together in a parallel process. Empirical results of two benchmarks of wind domain indicate that the proposed method has averagely improved the performance of its component used separately, parallel‐based hybrid models, and series‐based hybrid models 46%, 22%, and 19%, respectively, for predicting wind power time series. [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]

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]

Optimal charging scheduling of electric vehicles with renewable energy could greatly save the electricity cost in parking lots, and also helps for environment protection. However, uncertainties of renewable energy and electric vehicles charging demand bring great challenges to obtaining the optimal charging policy. To this end, in this paper, a state aggregation based dynamic programming method is presented for such a stochastic charging scheduling problem. Specifically, first, a new Markov decision process based formulation is established for the multi‐stage stochastic programming problem. Second, a novel state‐aggregation method is proposed to relieve the dimension curse due to large state‐action space without sacrificing the optimality of the charging policy. Besides, the consistency of the problem before and after state aggregation also could be verified by proposed theorems both on feasibility and optimality. Moreover, economic efficiency and computational efficiency are both improved in numerical testing, which shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized robust dispatch method for multi‐area AC/DC system considering wind power uncertainty. The accommodated wind power interval model is adopted to handle the inherent uncertainty and volatility of wind generation, which is modelled based on the affinely adjustable robust optimization method. The accommodated wind power interval and the automatic generation control participation factors are optimized simultaneously to accommodate more wind energy and ensure operational reliability of the entire multi‐area system. The flexible adjustment model of HVDC converter station is also presented to accommodate more wind power. Then, the resulting economic dispatch model for multi‐area system is solved in a fully decentralized and parallel manner using accelerated alternating direction method of multipliers algorithm to preserve the information privacy and dispatch independence of each region. Case studies illustrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

With the serious energy crisis and speedy development of natural gas utilization, regional integrated electrical and gas energy system is an efficient way to improve energy potency. This paper studies the energy scheduling of integrated electrical and gas energy system with bi‐directional energy conversion at distribution network level, considering wind power uncertainty. To realize independent autonomy of energy networks, meantime adjusting scheduling strategies of integrated energy system from system‐level perspective, innovative edge‐cloud collaborative operation architecture is proposed. In edge servers, electrical and gas networks are optimized severally for scheduling strategies. In cloud server, the uncertainty of wind power are processed with robust optimization method, strategies of energy conversion units are updated for re‐optimization. Cooperated with edge‐cloud architecture, a novel multi‐time scales rolling optimization framework is proposed, electrical and gas networks are optimized under day‐ahead time scale in corresponding edge servers, and electrical network is further adjusted under intraday time scale in electrical edge server. Case studies show that edge‐cloud collaborative operation architecture can ensure the practical feasibility of optimization, which is more suitable for energy scheduling of integrated electrical and gas energy system. Multi‐time scales rolling optimization framework have significant efficiency on reducing the impact of wind power uncertainty. [ABSTRACT FROM AUTHOR]

Inertial and droop control are common methods for improving grid frequency control. Using the first-order derivative of the frequency in the inertial control loop only investigates the rate of frequency variation, while other useful information is not taken into consideration. Accordingly, in this study, the application of a fractional-order derivative (between 1 and 2) in the inertial control loop is proposed to have more useful frequency-based information. To achieve this goal, firstly continued fraction approximations and secondly, the discretization is adopted by using the Tustin Method with Prewarping. Inertia loop and droop loop gain have a significant impact on the operation of the wind turbines and the power system frequency control. Therefore, in this paper, a novel method for the adaptive adjusting of droop and inertia control loops gains in DFIG is proposed. To utilize more frequency error information, fractional-order adaptive law is proposed to update the loop gains and the inertia control loop. In order to have a robust system, which is permanently stable for every fractional order (FO), is used parameter space method. Simulation results demonstrate the proper performance of the FO-based adaptive approach to increase the frequency nadir (FN) and decrease the frequency variations. [ABSTRACT FROM AUTHOR]

With the increasing proportion of wind power connected to grid, power system dispatching is facing more and more challenges from uncertainty. To cope with this uncertainty, robust optimization has been applied in unit commitment (UC) problem. In this paper, a multi-band uncertainty set considering the temporal correlation (MBUSCTC) of wind/load prediction error is proposed firstly, which has two characteristics: (1) The MBUSCTC rigorously and realistically reflect the distribution characteristics of uncertainties in uncertainty intervals, thereby effectively reducing the conservatism of the traditional singe-band uncertainty set; (2) the temporal correlation constraints of wind power/load prediction errors in MBUSCTC could limit the realization of uncertainties fluctuating frequently in uncertain intervals, thereby eliminating scenarios with lower probability in uncertainty sets. Then the proposed MBUSCTC is applied to UC problem, leading a robust UC model based on MBUSCTC, which is solved by Benders decomposition method and C&CG method. Finally, case studies based on the modified IEEE-118 bus system and an actual power system of China demonstrate that the proposed method can effectively reduce the conservativeness of the robust UC model and ensure the robustness of the unit commitment solution. [ABSTRACT FROM AUTHOR]

In this paper, real-time parameter estimation based intelligent controller has been proposed for the optimum automatic generation control (AGC) operation of multi-area interconnected power system. In earlier AGC studies, supplementary controllers were designed for presumed power system dynamic conditions, such as constant system loadings, fixed values of power system dynamic model parameters. However in practical AGC system, these power system model parameters, namely frequency bias parameter B, steam chest and reheater time constants TSC & TRH, power system time and gain constants Tp & Kp, load sensitivity factor D, etc., continuously varies depending upon the consumer's load demand and number of power generating units participating in AGC. The controller's operation no longer remains optimum as these system parameters changes from their initial values. In view of the above, parameter estimation based real-time intelligent controller has been designed which automatically adjusts its controller gain settings to its optimum conditions in real-time after sensing changes in power system model parameters. At first, different methodologies are presented to estimate dominant power system model parameters. Then, presented methodologies are utilised in the design of intelligent controller for AGC system. The proposed controller is successfully tested upon IEEE 39 bus system with various case studies. [ABSTRACT FROM AUTHOR]

This paper introduces an intelligent extraction of optimum power and its dispatch by using fuzzy logic from a grid tied hybrid generation system comprising of a permanent magnet synchronous generator based wind turbine and a low concentration photovoltaic generator. For photovoltaic generator, maximum power point tracking control is implemented using fuzzy logic under varying solar irradiance. Power extracted from wind turbine is designed as a fuzzy function of the dc link voltage error, its rate of change and error in the direct axis current of the inverter. This reduces high frequency oscillations in the wind extracted power. Such an extraction is considered as a novelty of this paper. A failure mode and effect analysis is done for power converters and possible mitigation schemes are suggested for different faults. A 1:1 delta wye‐grounded transformer is used at the inverter output to eliminate the triplet harmonics. Further, dynamic performances of both fuzzy proportional‐derivative and integral (PD+I) controller and classical proportional integral (PI) controller, to control the inverter currents, are compared. The proposed method results in an enhanced power dispatch and improvement in distortions and oscillations in the converter currents. This also reduces the probability of failures in the converter switches and other passive components. [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]