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As wind power generation transits from centralized development mode to decentralized on-site consumption mode, microgrid (MG) can provide an efficient solution for wind power integration into the distribution network. However, the high-penetration wind power MG is the typical weak power grid system. The traditional wind turbine generator (WTG) participates in system frequency regulation through grid-following current source, which relies on the phase-locked loop for voltage phase synchronization and is unable to provide strong frequency support in weak power grid conditions. To fill this gap, this paper presents a decentralized grid-forming control strategy of high-penetration wind power MG. A wind power adaptive dynamic droop mechanism considering wind energy characteristics and rotor speed dynamic is proposed, cooperating with the implementation of wind maximum power point tracking (MPPT) for economical operation. A detailed small-signal model for voltage source wind power-based system considering electromechanical transients and adaptive droop mechanism is developed. The dominant modes are figured out and the critical control parameters are established and optimized. The proposed wind power adaptive droop mechanism can effectively provide frequency regulation and robust control performance. Theoretical analysis, time-domain simulation results and hardware-in-the-loop experiments under various scenarios verify the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

With the rapid growth of intermittent renewable energy sources, it is critical to ensure that renewable power generators have the capability to perform primary frequency response (PFR). This paper proposes a framework for using a shared battery energy storage system (BESS) to undertake the PFR obligations for multiple wind and photovoltaic (PV) power plants and provide commercial automatic generation control (AGC) service in the ancillary service market at the same time. Furthermore, an optimal bidding strategy in the hour-ahead energy and ancillary service markets is proposed to maximize the BESS' benefits, considering frequency regulation performance, battery degradation cost, and the state of charge (SoC) limits. The optimization model is first built as a chance-constrained optimization problem and then transformed into mixed-integer quadratically constrained programming using Wasserstein metric-based distributionally robust optimization technique. Simulation results show that the proposed optimal bidding strategy and solution method can effectively take advantage of a BESS to perform PFR and provide AGC services simultaneously and improve its economic benefits. [ABSTRACT FROM AUTHOR]

We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its non-affine characteristics. Over the training phase, the model sequentially maps input examples onto samples of base distribution, given the conditional contexts, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually maps samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art. [ABSTRACT FROM AUTHOR]

In the power system with high proportion of wind power, a day-ahead and intraday coordinated optimal scheduling model of wind power and thermal power units (TPUs) is established considering variable virtual inertia. Firstly, the frequency regulation characteristics of wind turbines (WTs) are analyzed, the feasibility of reserve of WTs is elaborated, and the dynamic deloading strategies of WTs in different wind speed zones are proposed. Then, the feasibility of variable virtual inertia realized by rotor overspeed control (ROC) of WTs is analyzed, which is combined with synchronous inertia of TPUs to deduce the deloading level constraints of WTs and minimum inertia constraints of power system considering inertia demand. Referring to the characteristics of reserve resources in different time scales on the generation side, the reserve capacities of TPUs are divided into minute and second levels according to response time, and the scheduling strategy of reserve capacity in different time scales is proposed, so as to improve the flexibility of TPUs and realize the refined management of reserves. Simulations indicate that the proposed model and method can effectively improve the inertial support capability of the system and reduce the operation cost. [ABSTRACT FROM AUTHOR]

Compared with commonly-used point forecasting, probabilistic forecasting provides quantitative information on the uncertainty associated with wind power output. However, most studies focus on modelling only a fixed set of quantiles or intervals, which cannot provide the entire information of wind power distribution. In this paper, we propose a non-parametric and flexible method for probabilistic wind power forecasting. First, the distribution of wind power output is specified by spline quantile function, which avoids assuming a parametric form and also provides flexible shape for wind power density. Then, autoregressive recurrent neural network is used to build the non-linear mapping from input features to the parameters of quadratic spline quantile function. A novel loss function based on continuous ranked probability score (CRPS) is designed to train the forecasting model. We also derive the closed-form solution of the integral required in computing the CRPS-based loss function in order to improve the computational efficiency in training. The effectiveness of our proposed method has been validated with public real-world data from Global Energy Forecasting Competition 2014. Compared with some advanced benchmarks, our proposed approach outperforms them at least six percent in terms of CRPS, indicating that it can provide probabilistic wind power forecasting with high quality. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic continuous-time optimization model for coordinating the operation of flexibility reserve in a hybrid hydro-thermal-wind power system. The proposed continuous-time model captures the sub-hourly uncertainty and variations of wind power and load, and accurately models and schedules the ramping flexibility of the hydrothermal system to match the uncertainty and variations of wind power and load. A simplified Northern European system is studied over a 30-hour period to examine the potential of using hydropower as a comprehensive flexibility provider. Norwegian hydropower is shown to be a significant source of flexibility used to mitigate wind power variations, especially during ramping constrained periods. The hydropower provides 73.5% of the balancing energy in the base case, which includes smoothing out longer wind power deviations as well as rapid ramping relief. The short-term implications of increasing the offshore wind power in the North Sea by 50% compared to 2020 was also studied in the Northern European test system. The increased wind power causes steeper ramping in the net load, which drives the hydropower to its full balancing potential to allow thermal units to operate within their ramping limits. [ABSTRACT FROM AUTHOR]

The power flow calculation of the multiterminal high voltage direct current (MTDC) system is essential for planning sustainable energy sources and power flow analysis for the MTDC system. However, the traditional unified methods require a large system of non-linear equations leading to low calculational efficiency. Also, for a large DC grid, there is a concern about the convergence of sequential methods. This paper proposes a general power flow calculation method for voltage source converter (VSC) based MTDC systems. Based on an extended topology of an AC grid, a generalized calculation model of the MTDC power flow is proposed. Then, a novel sensitivity analysis-based power flow (SAPF) method is proposed, in which the state variables of the extended AC grid are calculated via sensitivity analysis. With a smaller system of equations, the proposed SAPF method has less computational burden than the traditional unified methods and causes no convergence problem compared to sequential methods. To further improve the calculational efficiency, the sensitivity-based variable updating is adopted to accelerate the iterative process. By comparing with the existing methods in calculating power flow for different MTDC systems, including large systems with multiple AC/DC grids, the effectiveness and scalability of the proposed methods are verified. [ABSTRACT FROM AUTHOR]

Determining the flow structure in the flanged diffuser shrouding designed to be used for a wind turbine, plays a vital role in improving efficiency in small-scale wind turbine technology. In this study, the flow structure in the curved type flanged diffuser was investigated in terms of water flow velocity increase, by using particle image velocimetry (PIV) measurements. The dimensionless magnitude of the resultant velocity determined at downstream and up radial regions as well as at the flange downstream of the curved type wind turbine shroud, revealed that resultant velocity was increased by a factor of 1.5. This increase of the wind velocity will result the wind energy interacting with the rotor blades to be enhanced by 3.38 times more. By the designed shrouding component to be used in micro wind turbines, it is also aimed to start the power generations in these types at a lower value of cut-in wind speed value.

The article presents the concept of a new design of a multi-phase doubly-fed induction generator (DFIG). The innovative design approach uses a five-phase power supply from the rotor side of the generator with a three-phase classic stator power supply. Modern three-phase doubly-fed induction generators are the dominant choice for Wind Energy Conversion Systems (WECS). Solutions of this type are sensitive to the loss of at least one phase of the power supply from the rotor side due to the failure of the rotor side inverter. The proposed design solution in the form of a multi-phase power supply in the rotor circuit aims to extend the range of possible failure-free operation of the generator system, thus reducing system downtime due to the failure of power electronic systems. The correctness of the adopted conceptual assumptions was confirmed by the results of laboratory tests. The main contribution is to prove that using five-phase rotor winding improves the overall reliability of the proposed electrical energy generation system significantly.

A ship’s wind energy utilization device with multi-mode arc-shaped sails is designed, which have different working modes for sail-assisting or wind power generation according to the ship’s navigation. The structural characteristics and working principles of this device are firstly described in this paper. Three sets of arc-shaped sails with different thickness (4.5 cm, 11.3 cm, 21.7 cm) were designed. Wind tunnel experiments were carried out in the respects of sail-assisting performance and wind-power generation to determine the best sail blade shape and to verify the energy-saving effect of this device. Experiments show that the sail with the smallest thickness (4.5 cm) has a better boosting effect than others, and the sail with the largest thickness (21.7 cm) has the best wind power generation performance. Considering the lateral force and the structural strength of the support, in the case of the comprehensive evaluation for the boosting and power generation performance, it is considered that the intermediate thickness (11.3 cm) is the best choice. The device has a good comprehensive energy utilization effect and has development and application value.

Wind energy resource assessments at two islands in the Cook Islands are carried out in the present work. The wind data were collected for one year from sites on Mauke and Rarotonga Islands in the Cook Islands and the daily, monthly and seasonal average wind speeds, the diurnal variations of the wind shear coefficient, average temperature and turbulence intensity were estimated. Eleven frequentist methods and a Bayesian technique were used to determine the Weibull parameters and the wind power density (WPD) for each site. The best method was determined using the goodness of fit test and error measures. The average wind speeds were 4.65 m/s and 3.86 m/s at 34 m above ground level for the sites on Mauke and Rarotonga respectively. Based on the goodness of fit tests and error measures, the Least Squares Method performed best for estimating the Weibull parameters at the Mauke site, while for the Rarotonga site, the median and quartiles method performed the best. For both the sites, the Bayesian method, which is being used for the first time for wind resource assessments, ranked second of the twelve methods, indicating good potential for this method. The annual energy production (AEP) was also determined which was calculated to be 2192.34 MWh from a total of ten Vergnet 275 kW turbines at the two sites. Finally, an economic analysis, carried out for the two sites, indicated a payback period of 7.72 years.

Wind has been admitted as one of the most promising renewable energy resources in multinational regionalization policies. However, due to the randomness and volatility of wind energy and the rigid transmission of traditional wind turbines, it is difficult to control the active power of wind turbines with high precision. Thus the hydraulic wind turbines (HWT) is taken as the research object, and the stable operation of wind turbines at a demand power point under certain wind speed conditions is realized. The mathematical model of HWTs is established, and the HWT operation characteristics are analyzed. The small signal linearization method is adopted to solve the multiplying nonlinear problems in the system, and the working area of the power transmission system to adjust the transmission power stably is analyzed. According to the demand of active power control for wind turbine, the HWT active power control strategy is proposed. However, the power response of the HWT is inconsistent with the time-varying operating point and the proposed controller fixed parameters. The controller parameter online adjustment method of system pressure and power gain is proposed. The effectiveness of the optimized control method is verified based on the semi-physical simulation experiment platform of HWT, and the consistency of active power response is achieved. At the same time, the power response characteristics are analyzed, and the HWT active power response time can satisfy the demand of electrical grid under the optimal control strategy.

Sufficient fault ride-through (FRT) of large wind power plants (WPPs) is essential for the operation security of transmission system. The majority of studies on FRT do not include all disturbances originating in the transmission system or the disturbances irrelevant to the operation security. Based on the knowledge of power quality, this paper provides a guide to stakeholders in different aspects of FRT for wind turbines (WTs) and WPPs. This paper details the characteristics of the most common disturbances originated in the transmission system, how they propagate to the WT terminals, and how they impact the dynamic behavior of a large WPP. This paper shows that the details of the voltage disturbances, not only in the transmission system, but also at the WT terminals, should be taken into consideration. Moreover, a detailed representation or characterization of voltage dips is important for FRT studies, despite that the simplified models used in the literature are in-sufficient. This paper strongly recommends that distinct events and additional characteristics such as the phase-angle jump and oscillations in the transition segments should be considered in FRT analysis.

The design, operational planning, and integration of wind power plants with other renewables and the grid face challenges attributed to the intermittent nature of wind power generation. Addressing this issue necessitates the development of a smart wind power (and in particular wind speed) forecasting approach. This is a complex task due to substantial fluctuations in wind speed. To overcome the inherent stochastic nature of wind speed and mitigate related challenges, traditionally, numerical weather prediction (NWP) models are employed for wind speed forecasting. However, the applicability of NWP models is limited to short-term forecasting due to their computational constraints. In this study, a hybrid AI-based approach is proposed to improve forecast accuracy over a 48 h horizon for the city of Montreal. The results demonstrate that by integrating the probability distribution of wind speed with a deep learning model, the forecasted values align closely with the observed values in terms of seasonality and trend, exhibiting enhanced accuracy. Evaluation metrics reveal a substantial reduction in the root mean squared error (13–31%) across three prediction horizons (summer, fall, and winter) compared to a single long, short-term memory model. Furthermore, integrating the improved model with the numerical weather prediction model yields increased accuracy and decreased error compared to the LSTM–Weibull model.

The power control can not meet the control target due to the prediction error of wind power generation. Based on the difference of power prediction error and confidence interval between different new energy power stations, an optimal control strategy for active power of wind farms was proposed. The strategy used historical data to evaluate the prediction error distribution and confidence interval of wind power, and introduced confidence interval constraints to establish a wind power active optimization model to realize active power distribution, so as to realize the complementary prediction errors among wind farms with asymmetric error distribution. Combined with the actual data of a wind power based in a province in China, a simulation example was designed to verify the rationality and effectiveness of the proposed strategy. The results show that the proposed method can effectively improve the control performance of wind power active power, and the control deviation in the simulation example can be reduced by about 10%, which is helpful to reduce the risk of wind power active power control deficiency.

The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy.

The variability of power production from renewable energy sources (RESs) presents serious challenges in energy management (EM) and power system stability. Power forecasting plays a crucial role in optimal EM and grid security. Then, accurate power forecasting ensures optimum scheduling and EM. Therefore, this study proposes an artificial neural network- (ANN-) based paradigm to predict wind power (WP) generation and load demand, where the meteorological parameters, including wind speed, temperature, and atmospheric pressure, are fed to the model as inputs. The normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) criteria are used to evaluate the forecasting technique. The performance of ANN was compared to four machine learning methods: LASSO, decision tree (DT), regression vector machines (RVM), and kernel ridge regression (KRR). The obtained results show that ANN provides high effectiveness and accuracy for WP forecasting. Furthermore, ANN has proven to be an interesting tool in ensuring optimum scheduling and EM.

In the context of global ecological degradation and the gradual depletion of fossil energy sources, the importance of new energy power in production and development is rising year by year. In order to reduce the impact of fluctuations in wind power systems on the grid due to various reasons during grid connection, this paper proposes an improved Linear Active Disturbance Rejection Control (LADRC) combined with a neural network based grid-side inverter control method, which, based on the analysis of the characteristics of permanent magnet motors and the current situation of their use, enables the converter to show better control characteristics in terms of DC-side voltage fluctuation amplitude and recovery time during operation. Simulation results show that the controller is more robust and adaptable to the uncertainty and external disturbance changes of the converter under different operating conditions than the traditional control method. The system has good dynamic response performance. [ABSTRACT FROM AUTHOR]

With the large-scale wind power integration into the power system, the inertia and damping characteristics of the system have been weakened, which is not conducive to fulfill the grid-friendly operation of wind power. Aiming at this, an adaptive control scheme combining virtual inertia control (VIC) and supplementary damping controller (SDC) is proposed in this paper. Firstly, the mathematical model of wind grid-connected power system with VIC is established. Secondly, the polytopic linear parameter varying (LPV) system is built to describe the uncertainty of system operating points, and the gap metric-based nonlinear evaluation method is used for polytope vertex configuration. Next, Youla-Kucera parametrization are extended to polytopic LPV system to design an adaptive damping control, in which VIC and SDC are coordinated to provide damping and virtual inertia simultaneously. Finally, the proposed adaptive control scheme is compared with several existing control techniques to validate its effectiveness and advantages in suppressing system oscillation in the modified IEEE 39-bus system integrated with 2 wind turbines. [ABSTRACT FROM AUTHOR]

In this paper, an approximate aggregated impedance model (AAIM) of a grid-connected wind farm with multiple wind turbine generators (WTGs) for the small-signal stability study is derived. The derivation is conducted under the condition that the WTGs inside the wind farm are of the same type and from the same manufacturer. The AAIM derived can be seen as the impedance model (IM) of a grid-connected aggregated WTG. The IM of the aggregated WTG is the weighted average of the IMs of the individual WTGs in the wind farm and hence is simple to be built. The reactance of the line connecting the aggregated WTG to the external AC grid is the maximum eigenvalue of the network reactance matrix of the grid-connected wind farm. In addition, the derivation justifies the representation of a wind farm by an aggregated WTG in the IM-based small-signal stability analysis. Finally, two example grid-connected wind farms are used to evaluate and demonstrate the effectiveness of the AAIM proposed in the small-signal stability analysis. It is also shown that the computational burden of the stability analysis of a large-scale wind farm could be effectively reduced when the AAIM is used. [ABSTRACT FROM AUTHOR]

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

This paper presents a wind turbine (WT) control strategy to deliver frequency response (FR) that is similar to conventional synchronous generators, i.e., to deliver accurate and efficient FR. This is achieved by the coordination of WT’s rotor speed control (RSC) and pitch angle control (PAC), wherein the RSC is the summation of the proposed deloading power point tracking curves and FR signal; the PAC is kept as adjusting its control law according to the WT rotor speed. Through the above coordination, the strategy pre-reserves a portion of wind power and releasing it during underfrequency events. Compared with existing methods, the strategy has two significant advantages: 1) it does not need the switching of WT controls according to the real-time wind speed (WS), nor the identification of the boundaries among high, medium and low WSs according to the required reserved power, ultimately reducing the complexity of applying the strategy to practice; 2) its active power response during FR period is independent of the deviated rotor speed and is equal to the FR signal, ultimately enabling the WT to provide accurate and efficient FR. The simulations focusing on the grid frequency side demonstrated the advantages of the strategy over existing methods. [ABSTRACT FROM AUTHOR]

The integration of large-scale renewables into the multienergy system has brought a series of problems and challenges to the optimal dispatch of system, especially how to effectively realize the dispatch and accommodation of wind power. In this article, a dynamic economic dispatch (DED) model of wind/combined heat and power (CHP) hybrid energy system considering uncertainty of multiple wind farms and different dispatch time scales is proposed, which aims to minimize expected total energy generation cost based on the typical scenarios of upper bound of wind farm output. The spatial dependence of hourly output of multiwind farm is modeled by copula-based model and improved scenario reduction technique, and the different adjustment time scales of heating system and power system are modeled as well to achieve more reasonable and accurate system dispatching. In addition, an improved adaptive cuckoo search algorithm (ACSA) is proposed for the solution of the DED model of wind/CHP hybrid energy system with highly nonlinear constraints, and a shared parallel computing platform is employed to realize the acceleration of the ACSA. Finally, the corresponding numerical simulation and comparative analysis are performed on two test systems to verify the validity of the proposed model and method. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid development of hydrogen powered vehicles, the demand for hydrogen refueling stations (HRSs) in the transportation field is growing. The renewable energy, e.g., wind energy, is used to produce and store hydrogen on site for HRSs, which may be a relatively cheap and clean solution. Considering the wind farm (WF) and HRSs belong to different entities, a new cooperative operation model of the WF-HRSs combined system is proposed. Specifically, the robust optimization methods are implemented to characterize the uncertainties of wind power and market electricity price, respectively, to alleviate risk. To ensure the fairness of profit allocation, the bargaining power is measured by various contribution levels of each stakeholder based on the Nash–Harsanyi bargaining game theory. Furthermore, the model is transformed into two sequential subproblems: energy trading problem (SP1) and the payment bargaining problem (SP2). Accordingly, a solving technique adopting column and constraint generation algorithm is provided to solve the SP1. In addition, for the sake of privacy protection, a distributed approach based on data-centric mode is developed to solve SP2. Finally, the numerical results can validate the effectiveness and scalability for the proposed scheme and algorithms. [ABSTRACT FROM AUTHOR]

One of the major challenges in evacuating power from the offshore renewable energy source is reducing the number of equipment involved in the energy conversion process. In the strategy presented in this article, the output of a permanent magnet synchronous generator coupled with a wind turbine is converted to a low voltage DC by an AC–DC converter and then directly connected to a Medium Voltage Direct Current (MVDC) corridor using a new fault tolerant soft switched high gain DC–DC converter. During the fault in the MVDC corridor, the DC–DC converter is blocked forcing the fault current to zero, the faulty MVDC corridor is bypassed using the simple DC disconnectors, and then the DC–DC converter is connected to the healthy MVDC corridor. In this article, the efficacy of the proposed scheme is discussed in detail and substantiated through extensive simulation studies done in PSCAD/EMTDC. The theoretical and simulation analysis is also verified by experimental results. [ABSTRACT FROM AUTHOR]

This article presents an investigation and evaluation of an integrated flux-modulated machine for wind power generation. The integrated flux-modulated machine has two rotors which function as two contrarotating rotors connected to two sets of turbine blades. Hence, compared to conventional wind generators, more wind energy could be captured by this wind power generation system. Moreover, the integrated machine consists of two sets of stator windings. By regulating the currents in these windings, dual maximum power point tracking control strategy could be achieved. As a result, the wind power conversion efficiency is further improved. Moreover, this wind power generation system exhibits the advantage of high torque/power density due to the enhanced magnetic-gearing effect involved in the integrated flux-modulated machine. Hence, this machine is more suitable for direct-drive wind power generation, where the reliability is improved without the maintenance issues related to mechanical gearboxes. The topology and operating principle of the investigated machine are demonstrated in detail. Decoupled design of the two sets of windings is investigated, and a general rule to achieve decoupled windings by appropriate slot-pole combination selection is illustrated. The advantages of the investigated machine are confirmed in comparison to a benchmark machine. Finally, the investigated flux-modulated machine is prototyped. The validity of the operating principle and the simulation results are verified by experimental results. [ABSTRACT FROM AUTHOR]

The low-frequency AC (LFAC) transmission is a competitive scheme for offshore wind power integration, in which the modular multilevel matrix converter (M3C) is the key equipment. In this brief, the selection methods of the M3C main circuit parameters are proposed. First, the maximum arm voltage is derived and the selection method of the sub-module (SM) number is established. Then, according to the switching function approach, the analytical expressions of the average SM capacitor current and the average SM capacitor voltage are derived. On this basis, the selection method of the SM capacitance is proposed. Additionally, the design principle of the arm inductance is proposed. Finally, a case is elaborated to illustrate the proposed M3C main circuit parameter selection methods and provide a reference for practical projects. [ABSTRACT FROM AUTHOR]

Wind speeds in geographically adjacent areas are highly correlated, which correspondingly leads to wind power correlation. It is essential to consider the wind power correlation for steady-state related calculations in the modern power system with high wind power penetration. This short article presents a novel continuous permutation method for wind power correlation analysis. With the novel eigen-permutation, the proposed method is applicable to tackle the correlation analysis even the correlation matrix of random variables is not positive definite. A continuous permutation is then proposed to reduce the permutation deviation and, hence, to reduce the error of steady-state related calculations. Simulation results show the effectiveness of the proposed method in dealing with the nonpositive definite correlation matrix of the wind speeds. Also, compared to the traditional methods, the proposed method achieves more than an 80% reduction of the general error in the correlation analysis, which also contributes to higher accuracy in the power flow calculation. [ABSTRACT FROM AUTHOR]

This article proposes a hybrid method to find the optimal place of dc power flow controllers (DC-PFC)s in HVdc grids. This hybrid method consists of max flow-min cut theory with Ford–Fulkerson augmenting paths and combining sensitivity analysis to find the most important HVdc lines and the best placement. In this article, series DC-PFC is modeled and get placed. A significant advantage of this method is, reducing the computational burden of calculations by finding the most important and susceptible HVdc lines to get overloaded without focusing on other unnecessary HVdc lines. The static security of the power system strongly depends on the placement of DC-PFCs, which has become an important issue because of rapid-developing of HVdc grids. Static security studies have been done on an eight-terminal HVdc grid incorporating double scenarios. [ABSTRACT FROM AUTHOR]

Wind farm industrial control systems (ICSs) play an important role in the operation of wind turbines (WTs). To increase revenues, a corrupt wind power producer tends to compromise the ICSs of the opponents’ wind farms by launching false data injection (FDI) attacks. In this article, we investigate a novel profit-oriented FDI (POFDI) attack against wind farms and study corresponding attack detection schemes. Specifically, we proposed a potential FDI attack model against ICSs of wind farms considering the power distribution of wind farms. Based on the characteristics of the proposed attack model, a profit-oriented attack strategy is discussed as a composition of attack detection. Considering the characteristics of the compromised data and those of normal data, we develop countermeasures to detect the POFDI attack, through the relationship between the characteristics of WTs in wind farms. A relation-based detection network is used to evaluate the similarity between the data to be tested and the historical data. Simulation results show the impact of the proposed attack strategy and the effectiveness of the attack detection scheme. [ABSTRACT FROM AUTHOR]

Under-frequency load shedding (UFLS) is an important measure for tackling low-frequency events caused by load-generation imbalance. However, the uncertainty of wind power amplifies power imbalances and can potentially impair frequency stability. Electric vehicles (EVs) present a more effective means for addressing this issue compared to load shedding. However, EVs have several limitations such as commute randomness. To ensure frequency stability and simultaneously reduce load shedding, a bi-level confidence-interval-based optimal strategy is proposed to enable the participation of EVs in UFLS, where the uncertainties of wind power and the commute randomness of EVs are estimated using a non-parametric kernel density estimation (KDE) method. In bi-level optimization, the upper level reduces the dependency on commute randomness and the wind power uncertainty during load-shedding events. Further, the upper-level solutions are sent to EV charging stations for emergency dispatch. By contrast, at the lower level, an approximation-function-based priority is proposed to optimize the task allocation. Simulation results show the advantages of the proposed approach in maintaining a stable frequency compared with traditional and adaptive UFLS schemes. [ABSTRACT FROM AUTHOR]

The green hydrogen provides a pathway to the 100% sustainable energy future, rendering hydrogen energy service providers (HESPs) an important role in energy supply. Different from the conventional electricity service providers, HESP is not only responsible for the production, purchase and retailing but also the transportation of hydrogen, which makes its operation a challenging problem. To this end, this paper proposes a bi-level strategic operation model for HESPs. In the operation level, the bidding, hydrogen production and hydrogen transportation are coordinated to minimize the overall cost of HESPs. In the market level, the market clearing is simulated to estimate the influence of HESP behavior on electricity prices in the market. Then, a model reformulation technique is developed to connect the discrete-time based hydrogen production and continuous-time based hydrogen transportation. The bi-level optimization model is transformed into a single-level mixed-integer linear programming (MILP) problem with Karush-Kuhn-Tucker (KKT) conditions. The case studies with the modified IEEE-RTS-79 system were given to validate the proposed model and confirm the necessity to coordinate the hydrogen production and transportation in the strategic operation of HESPs. Sensitivity analysis is also conducted to study the potential influencing factors. [ABSTRACT FROM AUTHOR]

This article is concerned with the stability analysis of time-varying hybrid stochastic delayed systems (HSDSs), also known as stochastic delayed systems with Markovian switching. Several easy-to-check and less conservative Lyapunov-based sufficient criteria are derived for ensuring the stability of studied systems, where the upper bound estimation for the diffusion operator of the Lyapunov function is time-varying, piecewise continuous, and indefinite. It should be stressed that our results can be directly used to analyze the stabilization of HSDSs via aperiodically intermittent control (AIC). Compared with the existing results about AIC, the restrictions on the bound of each control/rest width and the maximum proportion of rest width in each control period are removed. Thus, the conservativeness is reduced. Finally, two examples, together with their numerical simulations, are provided to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

Direct-drive generators are an attractive candidate for wind power application since they do not need a gearbox, thus increasing operational reliability and reducing power losses. However, this is achieved at the cost of an increased generator size, larger inverter and decreased thermal performance. The associated cooling system is therefore crucial to keep the generator and inverter sizes down and to operate within the safe thermal limits. Various cooling techniques suitable for generators are therefore reviewed and analyzed in this paper. The performance and maintenance requirements are unavoidable compromises that need to be investigated together, especially for large generators. The location of the wind turbine is also important and dictates critical issues such as accessibility and maximum size. The key novelty in this paper is the assessment of the cooling methods based on generator size, reliability and maintenance requirements. [ABSTRACT FROM AUTHOR]

DC collection and transmission is one of the main developing directions of the large-scale offshore wind power system. This article proposes a multifunctional dc collector (MDC) to construct a low-cost, high reliability, and high flexibility all-dc offshore wind power system. By introducing the MDC to achieve energy collection and cascade boost, not only the system cost, size, and intermediate links of power conversion are reduced but also the coupling among wind turbines (WTs) is greatly weakened. Accordingly, the maximum power point tracking operation can be achieved independently for each WT unit. Furthermore, a constant voltage ratio control with first harmonic elimination iterative algorithm is proposed for dc collector to link the HVdc bus voltage and discretized dc voltage for triggering the energy consumption operation and suppressing the inductor current ripple, and the detailed control parameter design is provided. Meanwhile, the various operating modes are considered for MDC, including practical engineering configuration, fault isolation, dynamic cut-in/out, and energy consumption operation, which provides a practical solution for the construction of a long-distance and large-capacity offshore system. Finally, the comprehensive experimental results are given to illustrate the effectiveness of MDC. [ABSTRACT FROM AUTHOR]

A power decoupling control for wind power converter based on series-connected modular multilevel converter (SCMMC) and open-winding permanent magnetic synchronous generator (OW-PMSG) is proposed in this article. The power converter based on SCMMC and OW-PMSG achieves medium voltage direct conversion and transmission and has higher dc-bus voltage compared with conventional MMC, which makes it a promising solution for copper consumption reduction of cable, low power transmission loss, and the elimination of the step-up converter. The proposed power decoupling control realizes independent power control of each phase in the wind power converter by decoupling the negative-sequence and zero-sequence current and guarantees the phase dc-link voltages balance of the SCMMC and the robustness to restrain the imbalance power disturbance. Instantaneous three phase dc-link voltages balance of the SCMMC is achieved especially when the imbalance of phase dc-link voltages occurs. The feasibility of the wind power converter based on SCMMC and OW-PMSG and the proposed power decoupling control is verified through simulations and a scaled-down laboratory prototype. [ABSTRACT FROM AUTHOR]

This article proposes a comprehensive coordinated suppression strategy for DC overvoltage to achieve the receiving-end fault ride-through of DC grid connected large-scale wind power. For the monopolar DC overvoltage, by reasonably switching the control mode of bipolar modular multilevel converter (MMC), the power transfer capability of the healthy pole MMC can be improved while maintaining DC voltage stability, and the precise load-shedding control of wind farm and its coordination scheme with dissipative resistors are designed, which can effectively reduce the input capacity of dissipative resistors and the risk of wind turbines being cut off. For the bipolar DC overvoltage, the load-shedding control strategy and its parameter selection principles are proposed, which can make wind farm and capacitors in each MMC absorb excess power together, to reduce the change rate and amplitude of DC voltage. Then its cooperation scheme with the dissipative resistors is designed to achieve fault ride-through in the full power range of wind farm. Finally, based on the simulation model of four- terminal DC grid, the effectiveness of the proposed coordinated control methods is verified. [ABSTRACT FROM AUTHOR]

This article presents a novel reconstructed model for the delayed load frequency control (LFC) schemes considering wind power, which aims to improve the computational efficiency for PID controllers while retaining their dynamic performance. Via fully exploiting system states influenced by time delays directly, this novel reconstructed method is proposed with a controller isolated. Hence, when the PID controllers are unknown, the stability criterion based on this model can resolve controller gains with less time consumed. For given PID gains, this model can be employed to establish criteria for stability analysis, which can realize the tradeoff between the calculation accuracy and efficiency. The case study is first based on a two-area traditional LFC system to validate the merits of a novel reconstructed model, including accurately estimating the influence of time delay on system frequency stability with increased computational capability. Then, under traditional and deregulated environments, case studies are carried out on the two-area and three-area schemes, respectively. Through the novel reconstructed model, the efficiency of obtaining controller parameters is highly improved while their robustness against the random wind power, tie-line power changes, inertial reductions, and time delays remains almost unchanged. [ABSTRACT FROM AUTHOR]

This article focuses on quasi-synchronization of fuzzy heterogeneous complex networks. An aperiodically intermittent control strategy based on discrete-time state observations (aperiodically intermittent discrete-time state observations control for short) is designed. Different from intermittent control strategies referred in existing literatures, the control duration of the control strategy used in this article is based on discrete-time state observations, which makes the control used in this article somewhat less demanding and more effective. Under the aperiodically intermittent discrete-time state observations control strategy, a valid approach combining Lyapunov method with graph theory is proposed in this article. Throughout this article, a theorem and a corollary to the quasi-synchronization criterion of fuzzy heterogeneous complex networks are established. The results show that when the control gain is larger, the convergence domain is smaller. Finally, an illustrative example is presented and the simulation of this example shows the feasibility and validness of the obtained results. [ABSTRACT FROM AUTHOR]

The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the problem of economic dispatch of energy of an electrical system is solved, based on three different approaches: first is deterministic, a second is stochastic and a third given by bilevel programming. It is observed that when solving the deterministic economic energy dispatch, results follow the principle of order of merit, likewise under some scenarios availability of a large flexible capacity during the power balance stage becomes necessary. In another hand, the stochastic model becomes a tool that optimizes flexible capacity by sacrificing the order of merit which is not the case of bilevel model that adopts both, order of merit and optimization of flexible capacity by choosing an optimum of wind power to dispatch. In terms of profit, it has been observed that under the stochastic approach those flexible participants must fall into losses under certain scenarios. However, under the bilevel approach, the aforementioned issue is improved. Finally and in order to illustrate the impact of the bilevel approach a 24-node system serves as a test system to obtain results and demonstrate the economic advantages of an bilevel dispatch. [ABSTRACT FROM AUTHOR]

This letter proposes a novel transient stability criterion for power systems with multi-wind farms, which is deduced from power flow and inertia distribution. The variations of acceleration and deceleration power with and without wind integration, instead of the acceleration and deceleration areas themselves, are calculated, in which the power angle swing direction, the transient stability control of wind turbines, the position of wind farms access, and the unbalanced output of synchronous clusters are also considered. The simulation results show the effectiveness of the proposed criterion to distinguish transient stability. [ABSTRACT FROM AUTHOR]

As an efficient tool for uncertainty quantification of renewable energy forecasting, prediction intervals (PIs) provide essential prognosis to power system operator. Merely improving the statistical quality of PIs with respect to calibration and sharpness cannot always contribute to the operational value for specific decision-making issue. In this paper, the cost-oriented prediction intervals are firstly proposed to achieve the joint improvement of forecasting quality and decision performance. In order to bridge the gap between forecasting and decision, a novel cost-oriented machine learning (COML) framework is established, which unifies nonparametric renewable power PI construction and decision-making. Formulated as a bilevel programming model, the COML minimizes the operational costs of decision-making process by adaptively adjusting the quantile proportion pair of PIs resulting from extreme learning machine based quantile regression. The hierarchical optimization model of the COML is equivalently simplified as a single level nonlinear programming problem. Then an enhanced branch-and-contract algorithm with innovative bounds contraction strategy is devised to efficiently capture the optimum of the single level problem with bilinear nonconvexity. Numerical experiments based on actual wind farm data simulate the online forecasting and decision process for wind power offering. Comprehensive comparisons verify the substantial superiority of the proposed COML methodology in terms of forecasting quality, operational value, as well as computational efficiency for practical application. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy, accurate and efficient probabilistic optimal power flow (POPF) calculation becomes more and more important to provide decision support for secure and economic operation of power systems. This paper develops a novel nonparametric probabilistic optimal power flow (N-POPF) model describing the probabilistic information by quantiles, which avoids any parametric probability distribution assumptions of random variables. A novel critical region integral method (CRIM) which combines the multiparametric programming theory and discrete integral is proposed to efficiently solve the N-POPF problem. In the CRIM, the critical region partitioning algorithm is firstly introduced into the POPF model to directly establish the mapping relationship from wind power to optimal solutions of the POPF problem. Besides, a discrete integral method is developed in the CRIM to achieve the probability convolution calculation based on quantiles. Comprehensive numerical experiments verify the superior performance of the proposed CRIM in estimation accuracy and computational efficiency, and demonstrate that N-POPF model significantly improves the accuracy of uncertainty analysis. In general, the proposed N-POPF model and CRIM form a new framework of POPF problem for power system analysis and operation. [ABSTRACT FROM AUTHOR]

Power electronic devices interfaced doubly fed induction generators (DFIGs) cause a reduction in the power system inertia and further the power system becomes vulnerable to severe disturbances. DFIGs provide fast frequency response (FFR) strategies to the power system by increasing the output power. Nevertheless, these strategies produce a large frequency second drop or a slow rotor speed restoration. This article suggests a FFR strategy of a DFIG based on variable power point tracking control to boost the frequency support capability with grid-friendly rotor speed recovery. Furthermore, the benefit of the proposed strategy when participating in FFR is calculated. The test results illustrate that the proposed FFR strategy enhances the performance of the frequency nadir, size of SFD, and benefits of auxiliary FFR service. Hence, the proposed strategy provides a promising solution of ancillary service for FFR from a wind turbine generator to power systems. [ABSTRACT FROM AUTHOR]

In this article it is proposed that permanent magnet Vernier generators be used for its new and unknown application in a small-scale uncontrolled passive wind generator system. Various aspects, such as the machine selection, conventional versus split-tooth topology, power matching, torque quality, and the overall feasibility of the proposal are investigated by evaluating optimized generator designs. It is found that the conventional permanent magnet Vernier generator with a magnetic gearing ratio of eight is the superior generator choice for this application. The performance of the optimum permanent magnet Vernier generator is validated through test results of a manufactured prototype. Other designs in the article are confirmed by comparing 2-D and 3-D finite element results. [ABSTRACT FROM AUTHOR]