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This paper presents a dynamic event data-based stability risk assessment method for power grids with high penetrations of inverter-based resources (IBRs). This method relies on obtaining the IBRs’ DQ admittance through dynamic event data and computing the system’s eigenvalues based on the admittance models. Two critical technologies are employed in this research, including time-domain and frequency-domain data fitting and $dq$ -frame voltage and current signal derivation. The first technology is key to obtaining the $s$ -domain expressions from the transient response data, and the $s$ -domain DQ admittance model from the frequency-domain measurements. The second technology is key to obtaining the $dq$ -frame voltage and current signals from either the three-phase instantaneous measurements or the phasor measurement unit (PMU) data. The method is illustrated using data generated from a Type-4 wind power plant modeled in PSCAD. This paper demonstrates the technical feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Cooperative control among the turbines has the potential to improve the power output. However, existing centralized power optimization methods are computationally expensive and does not scale well for large wind farms, limiting their practical use in real-time control for time-varying wind conditions and turbine configuration (with adding or maintaining of turbines). To address this problem, this paper proposes a fully distributed power optimization method for wind farms using alternating direction method of multipliers (ADMM). The proposed method allows the wind farm power output to be optimized in fully distributed manner with turbine-to-turbine message passing over a mesh network, guarantees the implemented control actions satisfy the control constraints of all turbines, and provably converges to a stationary point of the wind farm power optimization problem. Simulation results demonstrate that the proposed method can significantly reduce the computation time with hardly sacrificing the power gain compared with centralized method and thus is computationally efficient for real-time power optimization of large wind farms. [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 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]

Energy storage systems (ESSs) are being utilized to improve wind farms’ (WF) frequency support capability due to their high reliability, fast response and the dual role of energy users and suppliers. Nevertheless, the problem of how much capacity should each ESS possesses in order to better serve the WFs has never been investigated. With this perspective, this paper proposes an optimal ESS allocation (OEA) scheme for doubly fed induction generator (DFIG) based WFs to mitigate the impact of wake effect in frequency support. Firstly, the synchronous stability of wind turbines under frequency dips is analyzed with the concept of frequency support margin (FSM), and the detrimental impact of the wake effect is also investigated. Subsequently, the role of ESSs to improve wind turbines’ synchronous stability is demonstrated. To make the OEA scheme practical, the wind turbines in a WF are segmented into different clusters based on the received wind speed. Afterwards, the OEA problem is formulated, where the objective is to optimize the coherency of the wind turbine clusters’ FSM level. The simulation results show that ESS can provide secondary frequency support under major grid frequency drops, and the proposed OEA scheme can reduce the risk of loss of synchronous stability. [ABSTRACT FROM AUTHOR]

To integrate large-scale islanded onshore wind power with different sizes, this paper proposes an integration system based on the cascaded hybrid HVDC transmission system, which consists of LCC and several MMCs in series connection at the DC side of the rectifier. A large-scale wind farm is connected with one LCC and one MMC while several small-scale wind farms are connected with MMCs directly. Owing to the hierarchical integration arrangement, the operating flexibility can be improved with reduced capacity and the number of step-up interfacing transformers. A resilient DC voltage control is proposed for the integration system to adaptively redistribute power among the converters during wind power fluctuations. Firstly, the topology and operating characteristics of the wind power integration system are introduced. Then, a resilient DC voltage control is proposed to ensure stable operation during wind power curtailments. Finally, a simulation model of the hybrid cascaded HVDC transmission system is built in PSCAD/EMTDC to verify the effectiveness. The research results show that the system provides a new option for long-distance transmission of large-scale islanded wind power. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) has gradually become a key technology to the increasing penetration of renewable energy. The uncertainty and variability of renewable energy and load demand pose significant challenges to the efficiency and stability of VPP's operation. In this paper, a data-driven two-stage stochastic robust optimization (SRO) scheduling model is proposed for a VPP considering wind power, solar power, load demand, and market price uncertainties. The objective is to maximize the profit of the VPP in the energy market and minimize the total system cost of the VPP in the reserve market under the worst-case realization of the uncertainties. The Dirichlet process mixture model (DPMM) and variational inference algorithm are employed for constructing the data-driven uncertainty ambiguity set considering the correlations among multiple uncertainties. The tailored column-and-constraint generation algorithm is developed to solve the SRO model iteratively by reformulating the second stage with the application of the Karush-Kuhn-Tucker conditions. Results from a case study illustrate the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Case-by-case study in the literature has found from the results of numerical computation and simulation that a grid-connected virtual synchronous generator (VSG) can maintain the stability under the condition of extremely weak grid connection. This paper presents the theoretical proof to the finding and thus, generally concludes that the grid-connected VSG is immune to the instability risk under the condition of weak grid connection. In addition, the paper extends the theoretical proof to the case of a grid-connected PMSG wind farm with multiple VSGs. An example grid-connected PMSG wind farm with twenty VSGs is presented to demonstrate and evaluate the theoretical analysis and conclusion made in the paper. [ABSTRACT FROM AUTHOR]

The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%. [ABSTRACT FROM AUTHOR]

High penetration of wind energy has intensified system pressure in balancing supply and demand due to the uncertainty nature of wind velocity. To mitigate the negative impacts brought along by wind power integration, an optimal power regulation scheme for a wind farm is proposed in this work. Specifically, the benefits of the designed wind power regulation scheme is innovatively quantified via the reduction of balancing cost. To handle wind uncertainty while determining generation profile for the wind farm, a robust strategy is introduced in the proposed control. In addition, the aerodynamic interactions among wind turbines are considered. To bypass the nonlinearity and non-convexity involved in the optimization problem and achieve online application, an artificial intelligence aided control is newly designed. Simulation results demonstrate the good performance of the proposed control scheme under multiple operation scenarios. [ABSTRACT FROM AUTHOR]

In this paper, an analytical two-machine equivalent method of DFIG-based wind power plants (WPPs) is proposed. Through simulations of a 49.5 MW WPP, the root causes of errors of the single-machine equivalent are identified. Taking a four-machine WPP as an example, analytical expressions of fault-ride through (FRT) behaviors of the WPP model and its single-machine equivalent model are derived, and root causes of errors are revealed in theory including: inconsistent transient power behaviors in the initial FRT stage; unequal quasi-steady-state active power during a fault; and mismatching initial points and ramp rates of active power after the fault clearance. To eliminate equivalent errors, an improved equivalent method is proposed with two steps: 1) Group wind turbines into two clusters by the crowbar actions and represent them as two equivalent machines; 2) Correct their controller reference settings based on the derived FRT behavior expressions of the detailed WPP. Simulation results verify that the proposed method can accurately reproduce FRT behaviors of the WPP under various wind scenarios and voltage dips. [ABSTRACT FROM AUTHOR]

To address the uncertainties of renewable energy and loads in transient stability assessment with credible contingencies, this letter proposes a stacked denoising autoencoder (SDAE)-based probabilistic prediction method. The correlations among wind farms have been effectively considered through the variable transformation via the Cholesky decomposition. SDAE allows learning the mapping relationship between operational features and the transient stability margin. The possible operation scenarios are sampled under different confidence levels to generate appropriate inputs for SDAE to assess the probabilistic transient stability distribution. Results on the modified IEEE 39-bus system show that our proposed method can achieve a similar level of accuracy as the benchmark and improved Monte Carlo simulations-based methods while having much higher computational efficiency. [ABSTRACT FROM AUTHOR]

In this letter, the voltage sensitivity-based coherent reactive power control is proposed to perform the online voltage security enhancement for insecure buses in the multi-area wind power generation systems. The proposed approach is realized in the two-level optimization problem, containing a master problem and two subproblems. The master problem is responsible for the equal reactive power adjustments in multiple regional clustering wind farms regarding to the voltage security enhancement while the subproblems proceed the further improvement of the voltage security based on the analysed result of the master problem. Case studies on IEEE 57-bus test system are presented to validate the cost-effectiveness of the proposed method. Also, the time-domain simulation is conducted to illustrate the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical analysis of how wind generation impacts the slow coherency property of power systems. Slow coherency arises from time-scale separation in the dynamics of synchronous generators, where generator states inside a coherent area synchronize over a fast time-scale due to stronger coupling, while the areas themselves synchronize over a slower time-scale due to weaker coupling. This time-scale separation is reflected in the form of a spectral separation in the weighted Laplacian matrix describing the swing dynamics of the generators. However, when wind farms with doubly-fed induction generators (DFIG) are integrated into the system then this Laplacian matrix changes based on both the level of wind penetration and the location of the wind farms. The modified Laplacian changes the effective slow eigenspace of the generators. Depending on the penetration level, this change may result in changing the identities of the coherent areas. We develop a theoretical framework to quantify this modification, and propose an equivalent Laplacian matrix to compute the modified coherent areas. Results are validated using the IEEE 68-bus test system with one and multiple wind farms. The model-based slow coherency results are compared with measurement-based principal component analysis to substantiate our derivations. [ABSTRACT FROM AUTHOR]

A novel concurrent control scheme of optimal re/active power regulation in a wind farm is proposed in this letter, of which the objective is to minimize the detrimental shortfall of active power during low-voltage-fault events. Distinguished from the existing works, the proposed control scheme incorporates an efficient optimization module into the wind turbine controllers. Specifically, it features the merit that the practical feasibility of tracking the resultant references can be ensured via considering multiple constraints in presence of complex wake interactions. Case studies on a permanent-magnet-synchronous-generator based wind farm built in a joint DIgSILENT-MATLAB platform are carried out to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Chance-constrained program (CCP) is a popular stochastic optimization method in power system planning, and operation problems. Conditional Value-at-Risk (CVaR) provides a convex approximation for chance constraints which are nonconvex. Although CCP assumes an exact empirical distribution, and the optimum of a stochastic programming model is thought to be sensitive in the designated probability distribution, this letter discloses that CVaR reformulation of a chance constraint is intrinsically robust. A pair of indices are proposed to quantify the maximum tolerable perturbation of the probability distribution, and can be computed from a computationally-cheap dichotomy search. An example on the coordinated capacity optimization of energy storage, and transmission line for a remote wind farm validates the main claims. The above results demonstrate that stochastic optimization methods are not necessarily vulnerable to distributional uncertainty, and justify the positive effect of the conservatism brought by the CVaR reformulation. [ABSTRACT FROM AUTHOR]

Confronted with considerable intermittence and variability of wind power, prediction intervals (PIs) serve as a crucial tool to assist power system decision-making under uncertainties. Conventional PIs rely on predetermining the lower and upper quantile proportions and therefore suffer from conservative interval width. This paper innovatively develops a chance constrained extreme learning machine (CCELM) model to generate quality nonparametric proportion-free PIs of wind power generation, which minimizes the expected interval width subject to the PI coverage probability constraint. Due to the independency on the preset PI bounds proportions, the proposed CCELM model merits high adaptivity and taps the latent potentialities for PI shortening. The convexity of extreme learning machine renders the sample average approximation counterpart of stochastic CCELM model equivalent to a parameter searching task in parametric optimization problem with polyhedral feasible region. A novel difference of convex functions optimization based bisection search (DCBS) algorithm is proposed to efficiently construct the CCELM model, which successfully realizes machine learning by means of solving linear programming problems sequentially. Comprehensive numerical experiments based on actual wind farm data demonstrate the significant effectiveness and efficiency of the developed CCELM model and DCBS algorithm. [ABSTRACT FROM AUTHOR]

Unsteady motion of the atmosphere incurs nonlinear and spatiotemporally coupled uncertainties in the wind power prediction (WPP) of multiple wind farms. This brings both opportunities and challenges to wind power probabilistic forecasting (WPPF) of a wind farm cluster or region, particularly when wind power is highly penetrated within the power system. This paper proposes an Improved Deep Mixture Density Network (IDMDN) for short-term WPPF of multiple wind farms and the entire region. In this respect, a deep multi-to-multi (m2m) mapping Neural Network model, which adopts the beta kernel as the mixture component to avoid the density leakage problem, is established to produce probabilistic forecasts in an end-to-end manner. A novel modified activation function and several general training procedures are then introduced to overcome the unstable behavior and NaN (Not a Number) loss issues of the beta kernel function. Verification of IDMDN is based on an open-source dataset collected from seven wind farms, and comparison results show that the proposed model improves the WPPF performance at both wind farm and regional levels. Furthermore, a laconic and accurate probabilistic expression of predicted power at each time step is produced by the proposed model. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic coordination control strategy to enhance the reactive power capability of wind power plants (WPPs) which deploy doubly-fed induction generator-based wind turbines (DFIG-WTs). The proposed control architecture seeks to maximize reactive power availability during grid faults without violating the stable and thermal operational limits of the generator. To achieve this objective, the presented control topology calculates the maximum available reactive power that can be provided by the DFIG-based WPP under any disturbed operation of a power system. The reference value of the required reactive power compensation is thereafter calculated using an adaptive proportional-integral (PI) regulator which outperforms the traditional PI controller. The computed reference value is directly applied to the outer control of the rotor side converter (RSC) to regulate reactive power generation of the stator circuit. Furthermore, the $P-V$ droop characteristics of the induction generator is employed to adjust the active power reference of the RSC converter to increase the reactive power capability during grid faults. Meanwhile, de-rating the active power generation of the DFIG-based WT is accompanied with modifying the pitch angle of the rotor blades to balance the mechanical and electromagnetic torques and to avoid any stresses excreted on the mechanical parts of the WT. If rotor current limits are reached and more reactive power support is still required, the responsible control loop of the grid side converter (GSC) will be activated and augmented to enhance the overall reactive power capability of the DFIG-based WPP. Finally, the proposed control strategy is verified using Two area four machines and IEEE 68 bus test systems. [ABSTRACT FROM AUTHOR]

The construction of the uncertainty set is a key aspect of robust unit commitment (RUC) for the power systems with wind power integrated. The existing uncertainty sets based on the convex hull of historical data are limited to the specific shape of the historical data set or with high computational burden. In this letter, we propose the partition-combine method to build the minimal uncertainty set with the irregularly-distributed historical data. The partition of the box set is used to reduce the size of the uncertainty so that the set can properly adapt the data provided and is no longer limited to a specific shape. The combination of non-empty subsets is used to formulate the model of uncertainty set instead of the extreme points. The scale of uncertainty variables is reduced with quick identification of inner subsets which contain no extreme points. The simulation results validated the enhanced performances of the proposed method from both the conservativeness and computational burden perspectives. [ABSTRACT FROM AUTHOR]

This paper proposes a novel control approach to reduce the rated power of energy storage system (ESS) in the smoothing of wind power output. Wind power generation causes frequency fluctuations in power systems. Therefore, ESSs are used to absorb the fluctuating power of wind generators. Generally, the controller in an ESS is composed of a first-order low-pass filter (FLF) with a large time constant. However, the FLF incurs a high cost because its phase-lag induces an unnecessary increase in the rated power of ESS. This study addresses the reduction in the rated power of ESS without using a complex controller. First, the requirement of the controller is analyzed and its structure is proposed. Then, the absorption of the short-period wind power output is reduced by tuning a gain in the controller until the grid code is violated. The effectiveness of the proposed controller is verified by comparing it to the conventional controllers via scenario simulations using real wind data. [ABSTRACT FROM AUTHOR]

To capture the stochastic characteristics of renewable energy generation output, chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical reformulation for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenario-based methods are used to approximate probability with heavy computational burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including reserve sufficiency and branch power flow bounds are ensured to be satisfied with predetermined probability. To solve this CCUC problem, we propose a Newton method based procedure to acquire the quantiles and transform chance constraints into deterministic constraints. Therefore, the CCUC model is efficiently solved as a mixed-integer quadratic programming problem. Numerical tests are performed on several systems to illustrate efficiency and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a derivation of the small-signal stability limit of a grid-connected wind farm with M permanent magnet synchronous generators (PMSGs) as determined by the dynamics of phased locked loops (PLLs). The stability limit reveals the mechanism whereby the PLLs can destabilize the wind farm in the case of a weak grid connection. Computation of the derived stability limit is simple and obviates the need to establish a parametric model of the wind farm. The stability limit can approximately indicate the potential instability risk collectively posed by the PLLs in the wind farm. Case studies are presented to evaluate the analytical conclusions obtained and the derived small-signal stability limit. Application of the stability limit during the planning and operating stages of a grid-connected wind farm is also demonstrated. Various factors that can affect the stability of the wind farm, and that must be considered during planning and operation, are examined and useful conclusions are obtained. These factors include the length of the transmission line connecting the wind farm to the AC grid, the topology of the wind farm, the wind speed, and its distribution over the wind farm, as well as the parameters of the PLLs. [ABSTRACT FROM AUTHOR]

The rapid expansion of wind power has triggered significant wind curtailment because the power system lacks flexibility to deal with the uncertainty and variability of wind power. The operational flexibility of coal-fired power plants is limited by the minimum stable firing rate in the boiler. Steam extraction and thermal energy storage could enable power output adjustment without changing the firing rate in the boiler. Thus, retrofitting existing coal-fired power plants with steam extraction and thermal energy storage is a promising option to accommodate the high penetration of wind power in the power system, especially in coal-dominated power system. This study investigated the operational flexibility of coal-fired power plants retrofitted with steam extraction and thermal energy storage. First, a linear operation model is proposed for retrofitted coal-fired power plants considering new characteristics and technical constraints. Second, a simulation framework based on stochastic unit commitment and rolling economic dispatch is developed to explore the benefits of steam extraction and thermal energy storage in wind-integrated power systems. The forecasted wind power data is used in stochastic unit commitment to evaluate start-up and shut-down cost, while real wind power data is adopted in rolling economic dispatch to calculate the actual production cost. The simulation results on the modified IEEE 24-bus system demonstrate the effectiveness of retrofitting coal-fired power plants with steam extraction and thermal energy storage for mitigating wind curtailment. [ABSTRACT FROM AUTHOR]

In recent years, the continuous growth of grid-integrated wind power generation causes reduction in the inertia of power system, which deteriorates primary frequency regulation (PFR) of grid. Therefore, wind farms (WFs) are recommended to operate at deloaded condition, rather than maximum power point, to provide sufficient primary reserve for improvement of the PFR. To improve PFR, the droop control uses this reserve and the inertia support emulates stored kinetic energy from the rotor of wind turbines. In this paper, a distributed synchronized control (DSC) technique is proposed which uses optimum power share ratio (PSR) and the frequency of each WF at the point of common coupling to calculate the droop. The optimum PSR is based on the ratio of primary reserve at the WF and required change in rotor speed for the WF. An iterative method is proposed to obtain the PSR of each WF. To reduce delay effect on proposed DSC performance, the synchronized droop varies differently with increasing communication delays. The effectiveness of proposed DSC is tested in WF integrated 39-bus New England system and compared with distributed Newton method. The performance of proposed DSC is verified for different false data injection scenarios. [ABSTRACT FROM AUTHOR]

It is hard to obtain precise wind power forecasting due to the chaotic nature of weather systems. Prediction intervals become an efficient tool to quantify the uncertainty involved in wind power forecasting. Traditional central prediction intervals are widely produced by forecasters, however, which might be conservative with respect to interval width and not well fit the practical conditions. This paper develops a novel adaptive bilevel programming (ABP) model, with extreme learning machine based quantile regression as the follower's problem and tuning hyperparameters of quantile proportions as the leader's problem. The proposed ABP model aims at minimizing the average interval width subject to well calibration. In order to overcome the difficulties in disposing of the intractable nested structure of bilevel programming, the ABP model is equivalently transformed into a single-level nonlinear programming problem with bilinear terms. An improved spatial branch-and-bound (ISBB) algorithm is proposed to efficiently solve the reformulated bilinear programming problem. In the ISBB algorithm, an innovative bounds tightening method is developed to tighten the convex relaxation of the bilinear constraint and enhance the convergence. Experimental studies based on actual wind farm of USA under four seasons show the significant effectiveness and high robustness of the developed ABP model, as well as the excellent global optimum attainment and convergence performance of the proposed ISBB algorithm. Moreover, the widely used traditional interval scores are first verified to be prejudiced in probabilistic wind power forecasting evaluation. [ABSTRACT FROM AUTHOR]

Large-scale wind power cluster with distributed wind farms has generated the active power dispatch and control problems in the power system. In this paper, a novel hierarchical model predictive control (HMPC) strategy based on dynamic active power dispatch is proposed to improve wind power schedule and increase wind power accommodation. The strategy consists of four layers with refined time scales, including intra-day dispatch, real-time dispatch, cluster optimization, and wind farm modulation layer. A dynamic grouping strategy is specifically developed to allocate the schedule for wind farms in cluster optimization layer. In order to maximize wind power output, downward spinning reserve and transmission pathway utilization are developed in wind farm modulation layer. Meanwhile, a stratification analysis approach for ultra-short-term wind power forecasting error is presented as feedback correction to increase forecasting accuracy. The proposed strategy is evaluated by a case study in the IEEE Network with wind power cluster integration. Results show that wind power accommodation has been enhanced by use of the proposed HMPC strategy, compared with the conventional dispatch and allocation methods. [ABSTRACT FROM AUTHOR]

With regard to wind farms connected to the main system via dedicated overhead transmission lines, the available wind power absorbed by the system relies on not only the power generation capacity but also the power transmission threshold. Thus, this study centers on the close relationship between environmental conditions and power transfer limits that involve the thermal limit and steady-state stability limit. Simultaneous equations representing both overhead conductors’ heat balance and power transmission operation are established. In addition, formulations of the maximum transmission power for the steady-state stability limit are derived and then expressed as functions of environmental factors and the line length. The presented approach allows one to analyze the threshold considering not only the thermal limit but also the steady-state stability limit, which could be remarkably different under certain environmental and transmission distance conditions. Then, a scheme to identify the primary limitation and obtain the corresponding threshold value is proposed. Moreover, a simple treatment of wind speed correlation is deployed because of the close distance between a wind farm and its tie-line. Case studies demonstrate that, for a certain wind speed level, the process of transmission rather than generation could become the key factor determining the threshold value. [ABSTRACT FROM AUTHOR]

The injection of a significant amount of wind power tends to increase the rate of change of the grid frequency. Therefore, it is a trend for wind farm to participate in the grid frequency regulation. However, during the frequency control process, individual wind generators in a wind farm may prone to instability due to possible over-deceleration. To address this issue, this paper presents a new nonlinear model predictive control (NMPC) scheme for the wind farm frequency response. By incorporating the nonlinear dynamics of each individual wind generator into the NMPC design, it achieves both objectives of dynamically optimal frequency response and wind generator stability. This scheme has a three-layer structure. Based on the linear model predictive control and moving horizon estimation, a top-layer controller computes the overall wind farm power reference to support the frequency control. This overall power reference is fed to the middle-layer NMPC, and is further distributed among multiple wind generators. The dispatched power references are then sent to the bottom-layer wind generators local controllers for execution. Simulation results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Experimental results and performance analyses of wind farms providing secondary frequency regulation is lacking. This is despite the increasing share of variable generators in electrical energy production. As variable generators displace traditional generators that provide ancillary services, there is a growing need for alternate providers of these ancillary services. Wind generators are technologically capable of providing ancillary services, such as frequency regulation. This paper presents experimental results of a secondary frequency regulation test carried out on a 10 MW wind farm with Type-V wind turbines. This is a 5 h test of the wind farm's ability to follow an external, historical automatic generation control signal. 1 MW of power is offered on the regulation market with up-regulation provided via a fixed curtailment value. The performance of the farm is evaluated using the performance score method developed by the Pennsylvania–New Jersey–Maryland (PJM) system operator. Performance scores are used to perform an economic analysis using 2017 hourly PJM price data. This results in an estimated additional income of $\$$ 7200 from participating in the regulation market versus providing energy alone. A sensitivity analysis reveals that profitability is more influenced by the turbine's performance scores than regulation and energy prices. This paper also examines some practical aspects of providing secondary regulation, such as pitch activity, power error, and operating power ranges. [ABSTRACT FROM AUTHOR]

This paper proposes a novel model for the day-ahead self-scheduling problem of a virtual power plant trading in both energy and reserve electricity markets. The virtual power plant comprises a conventional power plant, an energy storage facility, a wind power unit, and a flexible demand. This multi-component system participates in energy and reserve electricity markets as a single entity in order to optimize the use of energy resources. As a salient feature, the proposed model considers the uncertainty associated with the virtual power plant being called upon by the system operator to deploy reserves. In addition, uncertainty in available wind power generation and requests for reserve deployment is modeled using confidence bounds and intervals, respectively, while uncertainty in market prices is modeled using scenarios. The resulting model is thus cast as a stochastic adaptive robust optimization problem, which is solved using a column-and-constraint generation algorithm. Results from a case study illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Substantial challenges in power systems operation and control as a result of the intermittent and stochastic nature of wind power generation can be significantly alleviated by proficient very short-term wind power prediction interval (WPPI) models. In WPPI models, minimization of cost functions is conducted to train prediction engines and consequently tune their parameters. The prevalent cost functions of prediction engines in WPPI models are mainly non-differentiable and non-convex, and therefore the training process becomes problematic. To transcend such a crucial barrier, this paper addresses a new very short-term WPPI framework based on a bi-level formulation and benefiting from a differentiable and convex cost function. The prediction engine is trained by classical global optimization of the cost function in the lower-level problem, while hyperparameters that control the quality of the WPPIs are injected thereto from the upper-level problem. The hyperparameters can be tuned such that the most useful WPPIs are constructed from the lower-level problem depending on the power system operator's preferences. Lessening the need to heuristically tune a large number of prediction engine parameters is the foremost contribution of this work to the WPPI literature. The superior performance of the proposed WPPI is verified in the multistep ahead prediction of real wind power generation data in comparison to well-tailored benchmark models. [ABSTRACT FROM AUTHOR]

Security-constrained economic dispatch is executed based on consecutive dispatch intervals in real time. The short-term variations of wind power can cause the system to enter an unsecured operating region inside a dispatch interval, which is referred to as the intra-interval security (IIS) problem in this paper. IIS may not be addressed by existing dispatch methods as the traditional dispatch is to ensure the security at the interval end. This can lead to IIS risks in an interval when a high-wind-penetrated system encounters large intra-interval variations (IIVs) of wind power. To address this issue, an IIS preventive dispatch approach is proposed in this study. This approach models the wind power IIVs and develops a preventive dispatch to ensure the IIS over the interval in terms of line overloads while taking into account the tradeoff between security and economics. The proposed approach is implemented in a linear-programming model whose global optimum can be efficiently obtained by adding Benders-like cuts. Case study on the IEEE 118-bus system proves that a high level of nonlinear IIVs can cause the IIS issue (inside of an interval) even with accurate wind forecasts at both interval ends. The simulations show that some lines can have severe overloads inside the dispatch interval in spite of having flows below their capacity limits at both interval ends, and demonstrate that the proposed approach can mitigate such intra-interval overloads even under the worst case IIV scenario. [ABSTRACT FROM AUTHOR]

The large scale integration of wind energy generation adds a new burden to frequency regulation because of the volatility of wind power. Incorporating wind turbines (WTs) into automatic generation control (AGC) is a new approach for maintaining a satisfactory frequency regulation performance, which requires the active power control (APC) of WTs. Conventionally, the APC of WTs is achieved by pitch angle control (PAC) or rotor speed control (RSC), which respectively show a slow response and a narrow regulating range. To improve the regulation performance of variable speed WTs, a coordinated APC strategy that includes the simultaneous activation of PAC and RSC and takes full advantage of rotor kinetic energy to absorb or release extra mechanical energy is proposed in this paper. The simulations focusing on both the WT and power system levels have shown the effectiveness of proposed control strategy. [ABSTRACT FROM AUTHOR]

To improve the energy system resilience and economic efficiency, the wind power as a renewable energy starts to be deeply integrated into smart power grids. However, the wind power forecast uncertainty brings operational challenges. In order to provide a reliable guidance on operational decisions, in this paper, we propose a short-term wind power probabilistic forecast. Specifically, to model the rich dynamic behaviors of underlying physical wind power stochastic process occurring in various meteorological conditions, we first introduce an infinite Markov switching autoregressive model. This nonparametric time series model can capture the important properties in the real-world data to improve the prediction accuracy. Then, given finite historical data, the posterior distribution of flexible forecast model can correctly quantify the model estimation uncertainty. Built on it, we develop the posterior predictive distribution to rigorously quantify the overall forecasting uncertainty accounting for both inherent stochastic uncertainty and model estimation error. Therefore, the proposed approach can provide accurate and reliable short-term wind power probabilistic forecast, which can be used to support smart power grids real-time risk management. [ABSTRACT FROM AUTHOR]

The inherent uncertainty in predicting wind power generation makes the operation and control of power systems very challenging. Probabilistic measurement of wind power uncertainty in the form of a reliable and sharp interval is of utmost importance, but construction of such high-quality prediction intervals (PIs) is difficult because wind power time series are nonstationary. In this paper, a framework based on the concept of bandwidth selection for a new and flexible kernel density estimator is proposed. Unlike previous related works, the proposed framework uses neither a cost function-based optimization problem nor point prediction results; rather, a diffusion-based kernel density estimator (DiE) is utilized to achieve high-quality PIs for nonstationary wind power time series. Moreover, to adaptively capture the uncertainties of both the prediction model and wind power time series in different seasons, the DiE is equipped with a fuzzy inference system and a tri-level adaptation function. The proposed framework is also founded based on a parallel computing procedure to promote the computational efficiency for practical applications in power systems. Simulation results demonstrate the efficiency of the proposed framework compared to well-known conventional benchmarks using real wind power datasets from Canada and Spain. [ABSTRACT FROM AUTHOR]

Uncertainty of the wind power output is the main influence factor for wind power utilization in the bulk power system. Because of the large capacity and the load characteristic, energy-intensive load holds great potentials to share the burden of wind power fluctuation smoothing. In this paper, a direct control scheme of the energy-intensive load power is proposed to smooth the wind power fluctuation. Based on the characteristic of the energy-intensive load production technology, the feasibility of energy-intensive load control and its influence on the production are discussed. A feedback control is designed to regulate the saturable reactor and transformer tap changer when the power support from the bulk power system is limited. Regulation capacity is analyzed quantitatively based on the energy-intensive load equivalent circuit model. A grid-connected energy-intensive enterprise driven by self-generation wind power plants and thermal power plants is studied as an example case. By the proposed energy-intensive load control scheme, power fluctuation of the wind farms can be smoothed with the minimum control cost. Besides, the regulation requirement of the bulk power system is eased correspondingly so that the utilization capacity of the power system can be improved significantly. Simulation is done on the real-time digital simulator to verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper focuses on economic dispatch (ED) in power systems with intermittent wind power, which is a very critical issue in future power systems. A stochastic ED problem is formed based on the recently proposed versatile probability distribution of wind power. The problem is then analyzed and proved to be strictly convex. Although such convex optimization is tractable in many cases, it may take a long time to solve due to its large scale. This paper proposes a dual decomposition method to decompose the large problem. Then, two methods are employed to solve the decomposed problem, namely, the subgradient method and a faster method, limited-memory BFGS with box constraints (L-BFGS-B, a quasi-Newton method). Case studies were conducted to verify the efficiency of the dual decomposition and L-BFGS-B method for solving the stochastic ED problem. [ABSTRACT FROM AUTHOR]

Frequency control of isolated power systems with high penetration of wind power is a challenging task for system operators. The difficulty results from the limited frequency response capability of thermal power units to keep up with the fluctuations of wind power. This paper develops a field verification architecture to leverage aluminum smelting loads (ASLs) to participate in primary frequency control. A new method is proposed to coordinate ASLs with thermal power units to quickly stabilize the isolated power system with significant wind power fluctuations or curtailments. Moreover, a closed-loop control system for the ASLs controller is developed and tested on a real-world isolated power system in Inner Mongolia, China. Hardware-in-the-loop simulations, as well as field tests, are conducted under various scenarios of wind power fluctuations or curtailments to verify the proposed architecture for frequency control. The highly consistent simulation results and field tests demonstrate the good frequency control capability of the ASL. The regulated power from the ASL is demonstrated to be able to reach 6% of its rated power within 4 s. Working jointly with thermal power units, the ASL helps maintain the stable operation of the isolated power system, significantly improving the frequency response characteristic. [ABSTRACT FROM AUTHOR]

Series FACTS devices are one of the key enablers for very high penetration of renewables due to their capabilities in continuously controlling power flows on transmission lines. This paper proposes a bilevel optimization model to optimally locate variable series reactor and phase shifting transformer in the transmission network considering high penetration of wind power. The upper level problem seeks to minimize the investment cost on series FACTS, the cost of wind power curtailment, and possible load shedding. The lower level problems capture the market clearing under different operating scenarios. Due to the poor scalability of $B\theta$ formulation, the shift factor structure of FACTS allocation is derived. A customized reformulation and decomposition algorithm is designed and implemented to solve the proposed bilevel model with binary variables in both upper and lower levels. Detailed numerical results based on 118-bus system demonstrate the efficient performance of the proposed planning model and the important role of series FACTS for facilitating the integration of wind power. [ABSTRACT FROM AUTHOR]

In this study, a novel approach for look-ahead dispatch (LAD) considering large-scale wind power integration is proposed. An index designated the conditional value-at-risk of wind power (CVaR-WP) is introduced to evaluate the risk of wind power accommodation. A flexible LAD model is developed to balance the operational costs and the CVaR-WP based on robust optimization (RO). According to the proposed model, the base points, participation factors, and flexible capacity of automatic generation control units are co-optimized. In addition, a reasonable admissible region of wind power on each node can be obtained correspondingly. An efficient algorithm based on the big-M method and the decomposition method is presented to solve the resulting bilinear programing model. The proposed approach combines the advantages of RO and stochastic programing, which can avoid the overconservativeness of RO while maintaining its high computing efficiency. Test results illustrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

This work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability, and capacity limits of the transmission system are duly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to identify critical equipment for operation in each scenario. The proposed method is applied to the original IEEE RTS system and to a modified configuration with insertion of wind power plants. A subsystem of the Brazilian interconnected network is also used to illustrate the practicality of the proposed method. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on operational reliability studies of modern power systems taking into consideration the effects of energy storage systems (ESSs). The aim is to develop a new evaluation tool to assess the effects of different factors such as penetration rate, operational strategies, and capacities of the ESSs in determining the role of these systems as a source of operating reserve. In this regard, at first, some modifications are made to the Pennsylvania-New Jersey-Maryland (PJM) method aimed to precisely model the short-term variability in output generation of wind farms in reliability studies. Then, this algorithm is employed to examine the effects of ESSs operating strategies in operational reliability level of the system. Finally, a new formulation for ESSs scheduling problem is proposed to optimally gain the benefits of ESSs in improving the mid-term reliability level of the system. The usefulness of the proposed framework is illustrated using different case studies. [ABSTRACT FROM AUTHOR]

An improved single-machine equivalent method is proposed for wind power plants (WPPs) by calibrating the postfault power recovery behaviors. A real WPP is simulated with extensive wind scenarios to evaluate the traditional single-machine equivalent model, and it is found that its equivalent error is mainly resulted from the postfault recovery process. The simulation analysis further indicates that a wind turbine operating at different wind speeds restores to its prefault active power at a certain ramp rate after fault clearance, with different starting power and different recovery time. Taking a two-machine WPP as an example, the analytical expression for active power of the WPP is derived for the fault ride through process, and thus the equivalent error of traditional single-machine model is found to be resulted from two aspects. One is the mismatch of the starting power of the postfault recovery process, and another is the mismatch of the power recovery rates between the complete WPP and its equivalent model. The analytical expressions are extended to multimachine WPP to calibrate the starting power and the ramp rates of the postfault recovery process. Simulation results show that the proposed method has good performance for various voltage drops, wind scenarios, and grid characteristics. [ABSTRACT FROM AUTHOR]

Uncertainties associated with wind power integration challenge the operational adequacy of conventional power systems. A set of wind power ramp limits (WPRLs) is proposed in this paper to evaluate the operational adequacy of power systems with high wind power penetration and to provide operating references to wind farms in the form of a ramp power limit (RPL) and ramp rate limit (RRL). The RPL is used to evaluate the minimum and maximum allowable generation of a wind farm by considering the power reserve capacities of generators and power flow constraints of transmission lines. A robust second-order cone programming RPLs formulation with AC power flow constraints and a column-and-constraint generation based solution method are proposed to maximize the total operating range of all wind farms. Meanwhile, a Pareto optimality based RPLs evaluation approach is proposed to handle the coupled relationship among the operating ranges of the wind farms to achieve a balanced RPLs solution for each wind farm. The RRL is used to evaluate the most rapid wind power ramp behavior that can be handled by system frequency regulation without exceeding the designated frequency range. A comprehensive criterion is proposed to evaluate the RRLs by considering primary and secondary frequency regulation. Finally, the effectiveness of the proposed evaluation approach is verified through case studies. [ABSTRACT FROM PUBLISHER]

As wind power makes an increasing  contribution to power systems, the problems associated with wind power curtailment have become a concern in recent years. Battery energy storage (BES) can reduce the effects of wind power curtailment by peak shaving and wind power forecast error compensation. Accordingly, the operational constraints of power systems and wind power uncertainty should be considered in the optimization of BES capacity installed at wind farms. This paper proposes a two-stage method to determine the optimal power and capacity of BES in systems including thermal plants, wind farms, and BES. In the first stage, the unit commitment of thermal generators and scheduled wind farm output are optimized with ac power flow constraints modeled by second-order cone programming. Time series of the wind farm output generated by Monte Carlo simulations are used for BES optimization. In the second stage, operational strategies for BES are designed. The simulation results indicate that cooperation between the BES and wind farm using the proposed method can reduce the costs of both wind farms and thermal plants. Finally, a sensitivity analysis is performed to assess the influence of the BES unit cost and wind power penetration on the optimal power and capacity of BES. [ABSTRACT FROM PUBLISHER]

Recently, an emerging subsynchronous resonance (SSR) issue was observed in series-compensated systems comprising multiple wind farms. However, only simplified single-machine-infinite-bus system models were adopted to investigate the characteristics of this issue in the previous work. As a result, it is unable to reflect the impact on SSR from some critical factors, such as the network topology, the spatial distribution of wind farms, the diversity of wind turbine generators, and the distributed wind speed. To fill in this gap, this paper proposes an impedance network model (INM) based SSR stability analysis method. The impedance models of each wind farm and transmission line are established, and then interconnected according to system topology to form the whole INM. Such INM is further aggregated into lumped impedance. By analyzing the impedance-frequency features of the latter, a new stability criterion is developed to quantify the stability of SSR. The application of the criterion is demonstrated on a practical power system containing multiple wind farms. Both field measurements and time-domain simulations have been presented to validate its effectiveness and accuracy. The proposed method is of great potential in analyzing the SSR issue for very large-scale wind power systems. [ABSTRACT FROM PUBLISHER]

With the rapid growth of wind power penetration, system operators are required to enforce grid codes that implement frequency regulation of wind farms. This paper presents a distributed cooperation framework for a doubly fed induction generator based wind farm to participate in primary frequency regulation, including imitated inertia and droop characteristics similar to conventional plants. Compared with centralized schemes, the control efficiency is significantly improved. To realize fast distributed coordination and optimal power distribution among wind turbines (WTs), a distributed Newton method is developed, which only requires WTs to exchange limited information with their neighbors over a sparse communication network, and has a super-linear convergence rate. By introducing an index of state of energy, this method can adequately exploit the kinetic energy of all WTs without loss of security. The simulation results indicate that the method exhibits satisfying dynamic performance and reliability, and the system frequency can be stabilized faster when the wind farm controlled by the proposed method participates in frequency control. [ABSTRACT FROM PUBLISHER]

In industrial-scale wind farms, interactions among multiple turbines alter the power generation efficiency of turbines. In general, turbines in downwind rows are impacted by wind deficits, producing less power, compared to upstream turbines. Therefore, the generation performance of multiple turbines differs from one another. Moreover, the power curve of each turbine becomes heterogeneous when changes in wind direction cause some upstream turbines to become downstream turbines. This study proposes an integrative methodology that quantifies the heterogeneous wake effects over a range of wind direction by utilizing the concept of canonical models and similarity functions. We model the direction-dependent multi-turbine power curves in a Bayesian hierarchical framework and validate the performance of the proposed approach with multiple real-world data sets from industrial-scale wind farms. [ABSTRACT FROM AUTHOR]