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The actively commutated current source converter (CSC) does not need large energy storage capacitors and can realize the black start of wind farms, which provides a practicable scheme for the lightweight offshore wind power DC transmission system. However, resulted from utilizing pulse width modulation, the existing CSC has the disadvantages of high DC harmonics and high switching loss. To solve this problem, an offshore wind power transmission system based on the current source converter with fundamental frequency modulation employed (FFM-CSC) is investigated. Firstly, the topology and mathematical model are analyzed. Then, a steady-state control strategy coordinated by the offshore CSC and the onshore CSC is put forward, and the power operating range is researched. A black start control strategy for wind farms is also proposed. Finally, the offshore wind power transmission system is tested by simulation in PSCAD/EMTDC. The results demonstrate that the FFM-CSC-based offshore wind power transmission system can smoothly realize the black start, stably control the frequency and amplitude of AC voltage, adapt to output changes of wind turbines, and realize the fault ride-through. [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]

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]

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 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]

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 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]

We propose a power-based unit commitment formulation with capacity and intra-hour ramp reserves for dealing with intra-hour wind power variability and uncertainty. Although the formulation has an hourly resolution, the intra-hour ramp requirements capture wind power ramp excursions with a time duration below one hour, and thus allows the formulation to consider intra-hourly wind variability and uncertainty. This increases the security of the formulation compared to using hourly ramp reserves and allows more efficient scheduling of units with high ramp rates. We test the formulation with different durations for the intra-hour reserves, and using both hourly and intra-hourly ramp reserves, to find the best reserve formulation. The formulations are evaluated using a 5-min economic dispatch, which simulates the real-time operation of the system, for hundreds of out-of-sample realizations of wind power production. The proposed formulations are then compared to two hourly stochastic formulations and a stochastic formulation with 5-min time resolution. The proposed formulations outperform the stochastic formulations in terms of security, showing that they provide a scheduling which is more robust against intra-hour wind power variations. The proposed formulations also outperform the hourly stochastic formulations in terms of total costs, giving a better trade-off between scheduling costs and security. [ABSTRACT FROM AUTHOR]

A high proportion of wind power systems with correct as set weak network connections may suffer from cascading trip faults due to the lack of conventional power plants and voltage/var support, which brings about great challenges to secure wind farm operations. To accurately and efficiently evaluate the dynamic var margin for an online field application within large-scale integrated wind areas, the transient dynamic voltage processes of hundreds or thousands of wind turbines must all be considered due to the dramatic interior voltage fluctuation effect of wind units, which brings a heavy computational burden for solving the TSCOPF problem. Therefore, to address these large-scale challenges, this paper proposes an objective-oriented approximation dimensionality reduction equivalent (OOA-DRE) approach, which accurately transforms the high-dimensional differential system to a low-dimensional system with few differential equations and low-dimensional algebraic equations for the wind unit with the maximum electrical distance value. Meanwhile, this paper uses the Levenberg-Marquardt method to obtain the global pre-evaluation error, so that the evaluation error reaches the minimum value. Finally, an actual wind power system verifies that the calculation time is reduced by approximately 2/3 compared with the original model on the premise of ensuring accuracy for large-scale systems. [ABSTRACT FROM AUTHOR]

The uncertainty of wind power and electricityprice restrict the profitability of wind-storage integrated system (WSS) participating in real-time market (RTM). This paper presents a self-dispatch model for WSS based on deep reinforcement learning (DRL). The designed model is able to learn the integrated bidding and charging policy of WSS from the historical data. Besides, the maximum entropy and distributed prioritized experience replay frame, known as Ape-X, is used in this model. The Ape-X decouples the acting and learning in training by a central shared replay memory to enhance the efficiency and performance of the DRL procedures. Besides, the maximum entropy framework enables the designed agent to explore various optimal possibilities, thus the learned policy is more stable considering the uncertainty of wind power and electricity price. Compared with traditional methods, this model brings more benefits to wind farms while ensuring robustness. [ABSTRACT FROM AUTHOR]

As the wind power penetration increases, wind farms are required by the grid codes to provide frequency regulation services. This article develops a fully data-driven model predictive control (DMPC) scheme for the wind farm to provide temporal frequency support. The main technical challenge is the complexity and the nonlinearity of wind turbine dynamics that make the DMPC intractable. Based on Koopman operator (KO) theory, a specialized dynamic mode decomposition (SDMD) algorithm is proposed, which fits a global linear dynamic model of the wind turbines. The performance of learning dynamics is powered through integrating the physical knowledge of the wind turbine into the specialized observables of KO. To stabilize the rotor speeds in frequency regulation, the active power contribution is optimally dispatched in a moving horizon fashion. Simulation results show that the DMPC can efficiently learn and predict the wind turbine dynamics. During the frequency response process, the proposed method can effectively track the frequency support order specified by the utility grid operator while significantly stabilizing the rotor speeds. [ABSTRACT FROM AUTHOR]

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Nowadays, taking into account the intermittency of renewable energy resources such as wind farms and uncertainty of load demand seems helpful to obtain a more reliable strategy for the power systems. Nevertheless, information gap decision theory (IGDT) as a nonprobabilistic method has been employed in numerous papers to address the uncertain behavior of input parameters, the simultaneous optimal values of main objective functions (OF) such as cost and radius of uncertainty cannot be guaranteed. To overcome this issue, Fuzzy- IGDT is used, on the other hand, this approach reports the same value for the radius of uncertainty related to the uncertain resources. To cope with this problem, this article presents an algorithm considering uncertainties of multiple wind farms and load demand by acquiring different uncertain bands based on the decision-maker's preferences. Moreover, it is appropriate for uncertain resources with direct and inverse effects on the OF. In other words, it is more flexible than fuzzy-information gap decision theory. The precision of the proposed framework is evaluated by Monte Carlo simulation, meanwhile, its effectiveness and performances are proved. To guarantee global optimal results with reliable precision, the linearized approximation of the original mix integer nonlinear programming model has been accomplished in the GAMS platform. The performance of presented method is illustrated by utilizing IEEE 30 BUS and IEEE 62 bus systems. [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]

This article aims to address the wind-farm power tracking problem, which requires the farm's total power generation to track time-varying power references and, therefore, allows the wind farm to participate in ancillary services such as frequency regulation. A novel preview-based robust deep reinforcement learning (PR-DRL) method is proposed to handle such tasks which are subject to uncertain environmental conditions and strong aerodynamic interactions among wind turbines. To our knowledge, this is for the first time that a data-driven model-free solution is developed for wind-farm power tracking. Particularly, reference signals are treated as preview information and embedded in the system as specially designed augmented states. The control problem is then transformed into a zero-sum game to quantify the influence of unknown wind conditions and future reference signals. Built upon the $H_\infty$ control theory, the proposed PR-DRL method can successfully approximate the resulting zero-sum game's solution and achieve wind-farm power tracking. Time-series measurements and long short-term memory networks are employed in our DRL structure to handle the non-Markovian property induced by the time-delayed feature of aerodynamic interactions. Tests based on a dynamic wind-farm simulator demonstrate the effectiveness of the proposed PR-DRL wind-farm control strategy. [ABSTRACT FROM AUTHOR]

This article proposes a risk-based contingency-constrained optimal power flow model by leveraging the methods of both adjustable uncertainty set and distributionally robust optimization. In the proposed model, an adjustable uncertainty set of wind power is developed with network contingencies explicitly incorporated. Based on this uncertainty set, the proposed model is capable of securing the network against both wind power fluctuations and contingencies in a probabilistic manner with the optimal balance between operation cost and risk. Meanwhile, a data-driven L1-norm-based ambiguity set is employed so that the proposed model is distributionally robust to the ambiguous probability distribution of wind power and the size of the model remains unchanged as the available wind power data increases. A decomposition-based algorithm is also derived so that the proposed model is solvable by off-the-shelf solvers. Numerical studies on IEEE 14- and 118-bus systems are conducted to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Chance constrained economic dispatch (CCED) is widely used to handle uncertainties in power systems with high-proportion renewable energy sources (RESs). However, in such system, the upper and lower limits of reserve or transmission line constraints may be violated simultaneously, which makes single-side CCED (SS-CCED) inapplicable. This letter develops a two-side CCED (TS-CCED) model, in which the Gaussian mixture model (GMM) is used to characterize the correlated non-Gaussian distribution of RESs. We propose a hyperbolic tangent function based approximation method to approach GMM and make the TS-CCED tractable. Numerical studies show the proposed method is more applicable than SS-CCED with high efficiency. [ABSTRACT FROM AUTHOR]

Offshore wind farms (OWF) are being rapidly integrated to the power systems, which can be primarily attributed to saturation of the mainland onshore area, and high wind speed in offshore sites. However, to maintain stable grid operation, OWFs are required to comply with Low Voltage Ride Through (LVRT) capability. OWF during LVRT period should not only stay grid connected, but also support the grid during the disturbance. In this paper, a new communication-independent LVRT control scheme for HVDC connected OWF has been proposed. Under the proposed strategy, OWFs not only successfully ride through a fault induced voltage dip, but also comply with active power recovery ramp rate during the fault recovery period. Besides complying with LVRT requirement and ramp rate limit, the proposed strategy improves voltage dip induced frequency (VDIF) issue by increasing active power injection from the OWF during LVRT period. The proposed LVRT strategy also exploits the stored energy in HVDC link capacitors in order to reduce the impact of VDIF issue. The proposed and the conventional LVRT strategies for HVDC connected OWF have been implemented and evaluated on IEEE 39 bus benchmark system. [ABSTRACT FROM AUTHOR]

In a deregulated environment, the renewable energy producers will face the challenge of how to increase their revenues under uncertainties of power generation and time-varying electricity price. In traditional power network scheduling, prediction and optimization are two independent processes, which easily leads to information loss and modeling error. To deal with the uncertainty and realize an end-to-end controller, this article proposes an energy storage system control model (ESSCM) in the scene of the combined wind-solar storage system. The proposed ESSCM using deep reinforcement learning (DRL) algorithm is trained by interacting with the massive environment of a power grid without requiring the assumption on the uncertainties. It learns from scratch to realize the coordination operation of with wind power and photovoltaic power in a combined system, further maximize the benefits of the combined system in the electricity market. One state-of-the-art DRL algorithm, namely double deep Q-network, is used to formulate the proposed ESSCM. Numerical results illustrate that the proposed approach can effectively accommodate the uncertainty and bring high revenues to the combined system. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy, such as wind power, has brought great challenges to the power system operation due to its uncertainty. Flexibility, which measures the ability of power system to deal with the uncertainties, is critical for power system to adapt to the new era of renewable energy. The rising electrical demand of the water distribution system (WDS) creates opportunities for power system to leverage the flexibility provided by WDS. This paper investigates the water-energy relationship between these two systems and makes full use of the water pumps and tanks in the WDS to enhance the power system flexibility. Current WDS designs did not consider well the role WDS plays in the power system. This hinders the WDS from being fully used by power system to provide flexibility. An optimization model is proposed to determine the optimal tank size of WDS, which may provide the maximum available flexibility of power system. The effect of tank size on water quality is also investigated to ensure that the supplied water quality is not compromised. Moreover, a Benders-based heuristic algorithm is proposed to find the optimization solution more efficiently and to protect the data of each energy system. Results of case study highlight the merit of the proposed optimization design and the advantage of using WDS to provide flexibility for power system. [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 integration of wind power into the power system causes difficulties in system operations due to its associated uncertainties. Spatial correlation is one aspect of uncertainties and derives from geographically dispersed distribution. However, spatial correlation is ignored by most studies, which decreases the modeling accuracy. In this paper, a high-dimensional t-copula model is first proposed to preserve spatial correlation characteristics. Through this model, adequate simulated data are sampled, which forms scenario generation. K-means clustering method is further used to reduce the number of scenarios generated. A case of 4 wind farms selected from AEMO dataset has been implemented to illustrate the establishment of the spatial correlation model. Then, a modified IEEE 24-bus test system is utilized to conduct optimal power flow (OPF) with and without the consideration of spatial dependence. The comparison results show that with spatial correlation accessing, the generation cost would have a certain increase. This implies, if the spatial correlation is ignored, the overall cost of OPF is underestimated. Furthermore, with the penetration level of wind power increasing, a lager cost gap is obtained, thus proving there exist benefits with the inclusion of spatial correlation during system modeling. [ABSTRACT FROM AUTHOR]

In this article, power flow transfer entropy (PFTE) is proposed as a criterion to evaluate the power flow transfer characteristics of the multiterminal HVdc (MTdc) wind power integration grid. This proposed criterion can detect and locate the ac three-phase to ground (TPG) faults in the MTdc grid by its sensitivity to power flow change to avoid communication. The transient energy dissipation control strategy is then designed according to the variety of PFTE, which reflects the power flow reversal characteristic of the MTdc grid during ac faults. Experimental results show that the system based on the proposed communication-free method can ride through ac TPG faults without blocking any converter. [ABSTRACT FROM AUTHOR]

With the rapid growth of wind power penetration in power grid, the system operators need to implement the grid specification of wind farm participating in primary frequency regulation (PFR). In this paper, a double-layer control framework based on permanent magnet synchronous generator (PMSG) is proposed, which can simulate the inertia and droop characteristics similar to thermal units. The top-layer control adopts centralized model predictive control (MPC), while the bottom layer carries out distributed real-time correction control in case of inaccurate MPC prediction. In order to realize fast distributed correction among wind turbines (WTs), a distributed Newton method is proposed, which only requires WTs to exchange limited information with their neighbors on sparse communication network, and has a super-linear convergence rate. By introducing an index of state of energy, this method adequately exploits the kinetic energy of all WTs without loss of security. Compared with the centralized schemes, the dependence on prediction accuracy and data synchronization is greatly reduced. Compared with the distributed schemes, the overall characteristics and computing efficiency can be guaranteed. The simulation results show that the method has satisfactory dynamic performance and reliability, and the system frequency stability is significantly improved under the synchronized control method. [ABSTRACT FROM AUTHOR]

The temporal dependencies of wind power are significant to be involved in the modeling of short-term wind power forecasts. However, different time series inputs will contribute differently to the forecasting performance and bring in challenges to the selection of the relevant driving information. In this paper, a Multi-Source and Temporal Attention Network (MSTAN) is proposed for short-term wind power probabilistic prediction. The MSTAN model introduces multi-source NWP and makes three specific designs to improve prediction performance. Firstly, a novel multi-source variable attention module is proposed to select the driving variables of NWP. Secondly, a temporal attention module is used to capture the implicit temporal dependency hidden in the historical measurements and multi-source NWP sequence. Thirdly, the residual module is wrapped in MSTAN to skip some unnecessary nonlinear transformations and provide adaptive complexity to the entire model. After training, multi-horizon density forecasts for the next 48 hours are yielded by MSTAN. The MSTAN is compared with state-of-the-art machine learning schemes in the wind power forecasting system using the operation data from 3 wind farms. We demonstrate that MSTAN outperforms other counterparts on both deterministic and probabilistic prediction. The structure design scheme of MSTAN has been proven effective. [ABSTRACT FROM AUTHOR]

Wind farm multiple aggregated power curve modeling plays an important role in reducing the complexity of analyses in wind farm management and annual power prediction. There is a trade-off between the complexity and accuracy of aggregated power curves. In this paper, $K$ -Means clustering is utilized to classify turbines in a wind farm into homogeneous groups according to a new set of features based on the overall performance of turbines. We apply multilevel modeling methods, including random intercept and random slope models on turbine clusters, to take into account the hidden correlation among different clusters. Results show that the accuracy of our proposed methods are higher than the single aggregated method alongside an equal complexity. The proposed multiple aggregated power curve model can be utilized to analyze wind farm behavior and wind farm power simulations to forecast wind power. [ABSTRACT FROM AUTHOR]

The volatile nature of wind power generation creates challenges in achieving secure power grid operations. It is, therefore, necessary to accurately predict wind power and its uncertainty quantification. Wind power forecasting usually depends on wind speed prediction and the wind-to-power conversion process. However, most current wind power prediction models only consider portions of the uncertainty. This paper develops an integrative framework for predicting wind power density, considering uncertainties arising from both wind speed prediction and the wind-to-power conversion process. Specifically, we model wind speed using the inhomogeneous Geometric Brownian Motion and convert the wind speed prediction density into the wind power density in a closed-form. The resulting wind power density allows quantifying prediction uncertainties through prediction intervals. To forecast the power output, we minimize the expected prediction cost with (unequal) penalties on the overestimation and underestimation. We show the predictive power of the proposed approach using data from multiple operating wind farms located at different sites. [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]

Wind is the dominant factor for wind power generation. However, wind, which is intermittent and fluctuating all the time, is hard to be accurately forecasted, especially only by single-source numerical weather prediction. This article presents a short-term wind power prediction method based on multisource wind speed fusion. First, the relationships among the weather factors and wind power, and the characteristics of three independent forecasted wind speed (FWS) provided by three organizations are analyzed. Next, a weighted naive Bayes method is described to fuse the three FWSs in order to estimate an accurate wind speed according to their characteristics. Then, a backpropagation neural network is designed to predict the wind power based on the fused wind speed. Finally, application results show that the method accurately predicts wind power generation, and the accuracy is much higher than that predicted by conventional methods. [ABSTRACT FROM AUTHOR]

Sub/Super-synchronous oscillations have occurred in wind farms consisting of doubly-fed induction generators (DFIGs) or permanent magnet synchronous generators (PMSGs). This paper investigates the impact of synchronous condenser (SC) on sub/super-synchronous oscillations in wind farms with different types of generator and compares it with STATCOM. Impedance model is adopted in this paper to explain the mechanism of oscillations in wind farms and its mitigation using SC. Compared to STATCOM, SC is more effective in oscillation mitigations of the two types of wind farms. Eigenvalue analysis and time domain simulations in PSCAD/EMTDC are used to check the influence on sub/super-synchronous oscillations from different parameters of SC, such as its capacity, leakage reactance and inertia constant. The results indicate that SC would improve the system stability while a larger capacity will induce a greater improvement. A smaller leakage reactance of SC also benefits the mitigation of oscillations while its inertia constant can hardly influence system stability. [ABSTRACT FROM AUTHOR]

Previous robust transmission expansion planning (RTEP) studies have rarely considered the important question of how large the uncertainty set should be, and hence, how the uncertainty budget can be determined objectively. This study addresses this issue by proposing a novel adaptive optimization method that optimizes the value of the uncertainty budget to minimize the size of the uncertainty set while considering the underlying risk for wind power generation (WPG) fluctuations residing outside of the proposed uncertainty set. As such, the proposed method ensures a good tradeoff between the robustness and costs of RTEP solutions. In addition, the proposed method optimizes investment strategies under forecasted WPG scenarios, while providing a security guarantee under extreme WPG scenarios. The variable-limit integral terms introduced by the proposed method are addressed in the solution process by applying the piecewise linearization approximation method combined with the quadratic Newton-Gregory interpolating polynomial technique, which allows the solution process to be cast as a mixed integer linear programming problem. The good performance and effectiveness of the proposed adaptive uncertainty set optimization method are verified by numerical results. [ABSTRACT FROM AUTHOR]

The production cost modeling (PCM) plays an important role in the mid/long-term power system planning. With the increasing penetration of renewable power, it becomes solving a series of unit commitment (UC) problems for time periods up to one or multiple years. This process is greatly challenged by the UC solution efficiency which hinders its practical use. In this paper, a fast UC method is proposed based on the load state transition curve (LSTC). Firstly, LSTC is proposed and obtained via load clustering. It simplifies the load model as well as maintains the necessary temporal characteristics. Secondly, the LSTC based UC model is developed by accommodating the constraint formulation to LSTC. Compared with the conventional ones, the scale of LSTC-based UC is dramatically reduced. The stochastic and volatile wind power is also taken into consideration. The case studies on the modified IEEE-RTS 1979 have verified the validity and feasibility of the proposed method in which the solution time is reduced from ∼300s to ∼12s with the cost error lower than 0.2%. It provides an efficient tool for the optimal planning and operation of large-scale power systems. [ABSTRACT FROM AUTHOR]

This paper presents Weighted Dynamic aggregation (WD agg) method to obtain an equivalent Wind Turbine Generator (WTG) for an induction machine-based wind farm using its dynamic model. The suggested approach obtains the equivalent d-q model of the induction generators considering the contribution of each unit in the model. The challenges in the aggregation of a large-scale wind farm are the variation of wind speeds at different zones and differences in the WTGs parameters. Compared with the existing methods such as Full aggregation (Full agg), Zone aggregation (Zone agg), and Semi aggregation (Semi agg), the suggested WD agg method provides an accurate single unit equivalent model for a large-scale wind farm while taking into account various wind speed zones and unequal WTG parameters. The proposed method is evaluated through time-domain simulation of a 4-WTG and a large-scale 20-WTG Doubly-Fed Induction Generator (DFIG) wind farms and their aggregated models. These simulations cover combinations of different wind speeds and WTGs parameters. Also, a 4-WTGs fixed-speed wind farm is studied to show the generality of the proposed method. Comparing WD model with the detailed response of the wind farm verifies the accuracy of the method in both steady-state and transient behaviors. [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]

In the future power systems, a large number of offshore wind farms will be connected to the AC grids through high voltage DC (HVDC) and multi-terminal DC (MTDC) grids. As wind power penetration level increases, complex grid codes and regulations will be imposed on wind turbines for frequency support. To follow any grid code and requirement for frequency support, two important features should be included in the wind turbine frequency support: i) It should be able to adjust the maximum additional power that the wind turbine temporarily provides for frequency support; ii) It should be capable of adjusting the time interval in which the wind turbine provides additional temporary power. The first feature is mainly important for reducing rate of change of frequency (RoCoF) and improving the frequency nadir while the second one is mainly important for fast frequency recovery from its nadir and improving the second frequency drop. This paper indicates that the conventional method cannot offer both of the two aforementioned features. To address this issue, two approaches are proposed for frequency support by wind turbines. The first one uses P-ωr and P-ƒWF droops in each wind turbine controller, where P, ωr, and ƒWF represent the wind turbine power, rotor speed, and wind farm frequency. The second method employs P-ωr and P-ƒWF droops in each wind turbine controller. Performance and effectiveness of the proposed methods are evaluated by time-domain simulation studies on an MTDC grid in the PSCAD/EMTDC software environment. [ABSTRACT FROM AUTHOR]

Due to the long-distance transmission, external grid cannot effectively support the access point voltage (APV) of the large-scale wind farm (WF), which thus entails DFIG-based WF to provide its ancillary services on reactive power generation. Considering factors of limited down-sized converter capacity, various operating modes of wind turbines (WTs), and the large pressure of large-scale WF communication on the central governor, this paper proposes a distributed active power and reactive power coordination scheme for APV support. A reactive power self-allocation scheme is developed and operated in each WT for reactive power dispatch. Based on the mechanism of distributed consensus control and aligning with the principle of preferential and proportional utilization of spare reactive power capacity (RPC), the proposed method collaborates the rotor side converters and the grid side converters of all WTs but without the necessity of knowing the total RPC. Further, in case that the total RPC is insufficient for the demand, a coordinating factor for RPC extension is generated to weaken the active power. Through a current-enforced PQ coordinating loop, it guarantees a high utilization of converter capacity as well as an exact compensation on the reactive power imbalance, which thereby reduces the wind power curtailment for the RPC release. Several case studies verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article addresses the challenging task of developing a procedure for the day-ahead dispatch planning of wind power which emanates from a wide geographical area. Using the complete ensemble empirical mode decomposition technique, it is shown that the low-frequency components of the area aggregated wind power account for the largest proportions of the perturbing energy harnessed from the wind. By taking advantage of the slow-varying characteristics of the low-frequency components, accurate forecast of these components is readily obtained and incorporated into the developed dispatch planning procedure. The dispatchability of the wide-area wind generation is facilitated by the buffering actions offered by a centralized power dispatch energy storage system, operating under a proposed power flows control strategy. The efficacy of the developed procedure is illustrated using a pumped hydroelectric system as the dispatch energy storage medium. [ABSTRACT FROM AUTHOR]

In this paper, a study of the day-ahead unit commitment problem with stochastic wind power generation is presented, which considers conditional, and correlated wind power forecast errors through a distributionally robust optimization approach. Firstly, to capture the characteristics of random wind power forecast errors, the least absolute shrinkage, and selection operator (Lasso) is utilized to develop a robust conditional error estimator, while an unbiased estimator is used to obtain the covariance matrix. The conditional error, and the covariance matrix are then used to construct an enhanced ambiguity set. Secondly, we develop an equivalent mixed integer semidefinite programming (MISDP) formulation of the two-stage distributionally robust unit commitment model with a polyhedral support of random variables. Further, to efficiently solve this problem, a novel cutting plane algorithm that makes use of the extremal distributions identified from the second-stage semidefinite programming (SDP) problems is introduced. Finally, numerical case studies show the advantage of the proposed model in capturing the spatiotemporal correlation in wind power generation, as well as the economic efficiency, and robustness of dispatch decisions. [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]

This article proposes a method based on data clustering for the optimal placement and sizing of wind farms (WFs) in radial distribution systems considering the wind uncertainty and the wake effect. The stochastic output of wind turbines makes the optimization problem more challenging. Using the probabilistic methods for such analyses proves efficient to yield more reliable results. The data clustering-based Monte Carlo simulation is used to bunch the output powers of wind farm into clusters. The network loss and voltage profile are then evaluated to achieve the optimal candidate bus for the placement of WF with the optimal number of turbines. To accelerate the procedure, a technical constraint is used so as to eliminate the nonimportant samples in WF size evaluation, which results in a reduction in computational burden. In addition, two methods for load flow calculations are investigated, namely the direct load flow and indirect backward/forward sweep load flow methods to evaluate the time burden of load flow method on the proposed problem. The effectiveness of the proposed methodology is illustated by conducting case studies. [ABSTRACT FROM AUTHOR]

Different from power prediction for a single wind farm, the regional wind power prediction is to predict the total power of multiple wind farms located in the specific region. The regional wind power prediction involves more data that implicate abundant information on spatiotemporal correlations and nonlinearity. So that addressing the massive data and extracting the representative features became the crucial issues to construct an effective regional wind power prediction model. This article proposes a spatiotemporal quantile regression (QR) algorithm to perform the short-term nonparametric probabilistic prediction of regional wind power, incorporating the advantages of the hybrid neural network (HNN) and QR. In the approach, the high-dimensional input data are reorganized into a feature graph that is ready for feature extraction by the HNN. Therefore, the advantages of HNN can be utilized to extract the representative features and construct nonlinear regression models. Meanwhile, by following the QR rules, the model obtains quantiles and perform probabilistic prediction. By properly addressing the explanatory variable selection issue, the approach provides a specific solution for regional wind power probabilistic prediction with the massive input data. The test results in a region with ten wind farms demonstrate the effectiveness of the proposed approach. [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]

Power-to-gas (P2G) technology is an effective way to provide additional flexibility to electricity systems with high penetration of renewable energy. Effective P2G bidding strategies in electricity markets can bring in arbitrage opportunities. This article proposes a coordinated bidding strategy for wind farms and P2G facilities using a cooperative game approach. A coordination framework for optimizing the profit of wind farms and P2G facilities is presented, in which wind power participates in day-ahead, real-time and reserve markets, and the P2G facilities participate in day-ahead and reserve markets for arbitrage. Wind farms strategically purchase reserve to avoid high imbalance penalties. As a coalition, wind farms can provide energy to P2G facilities and receive reserve capacity in return. The coordinated bidding strategy is modeled on stochastic programming to maximize the expected joint profit of wind farms and P2G facilities. Nucleolus and Shapley-value based methods are proposed to allocate profits to each player in the coalition. Numerical results based on real world market data demonstrate that the proposed bidding strategy can increase the profits of both wind farms and P2G facilities. Strategically purchasing reserve can reduce the imbalance penalties of wind farms, and participating in reserve markets of P2G facilities can increase profit. [ABSTRACT FROM AUTHOR]

This article proposes an analytical target cascading (ATC) based distributed optimal voltage control (DOVC) scheme for the voltage-source-converter high-voltage-direct-current (VSC-HVDC) connected large-scale wind farm cluster (WFC). The aims are to minimize the voltage fluctuations of the point of connection (POC), collector buses, and wind turbine (WT) terminal buses inside the WFC while regulating the bus voltages close to the rated voltage and smooth the reactive power outputs of the WTs. With the DOVC scheme, the large-scale strongly coupled sensitivity-based voltage optimization problem is decomposed and solved in the WFC voltage-source-converter (WFCVSC) controller and sub-wind farm controllers in parallel. The DOVC scheme considers the $N-1$ principle to improve the reliability, and distributes computation burden to several controllers to achieve better scalability of the WFC. The optimization subproblems are solved with local constraints and local measurements while guaranteeing the optimality of the primal optimization problem. A WFC with 5 wind farms and each wind farm consisting of 20 WTs is used to validate the proposed DOVC scheme. [ABSTRACT FROM AUTHOR]

Regional wind power prediction (WPP) is conducive to achieving scientific dispatch of the power system as well as improving its reliability. Data mining based on a large number of samples is an effective way to improve the accuracy of WPP. A new regional power forecasting method in the context of data mining is proposed in the article. First, a previously described regional WPP method, single feature similarity matching (SFSM) method, is analyzed and a new multifeature similarity matching (MFSM) method is proposed. Second, the optimization and analysis processes of four key parameters in the MFSM method are proposed and the impact of each key parameter on prediction error and the applicability of the method under different regional scales is analyzed. Finally, the new method and the optimization and analysis processes are verified by wind farm clusters, composed of 52 wind farms in northeast China, and the new method is compared with the conventional method, such as additive method, statistical up-scaling method, and the SFSM method. The results show that the improved MFSM method is superior to the conventional prediction method, whereby the root mean square error (RMSE) of 0 to 2 h is reduced by 3.65% and the RMSE of 0 to 4 h is reduced by 1.56%. The new method has been proved to be an effective WPP method for wind farm clusters and has potential industrial application prospects. [ABSTRACT FROM AUTHOR]

This article presents a two-stage adaptive robust coordinated generation and transmission expansion planning model for a wind farm-integrated power system. Also, dynamic thermal rating (DTR) systems, energy storage systems, and optimal line switching maneuvers are considered as various flexible sources to enhance the flexibility of the power system in response to uncertain variations of net system demand. The proposed approach characterizes the uncertainty of demands, wind power, and DTRs in each representative day by a polyhedral uncertainty set. Additionally, the $k$ -means clustering technique is used to obtain upward/downward variations of correlated uncertain parameters in each representative day and to construct the uncertainty set. The proposed model is inherently intractable as it includes infinite constraints modeling enforced techno-economic limitations for all realizations of uncertain parameters. To resolve this limitation, the proposed intractable model is recast as a tractable mixed-integer linear programming problem using affine policies. The proposed approach is implemented on the Garver 6-bus and IEEE 73-bus test systems. Simulation results illustrate its flexibility, practicality, and tractability. [ABSTRACT FROM AUTHOR]