Search

Wind power prediction is important in successfully integrating renewable energy sources into the grid. This study is focused on a sub-domain of wind power prediction and compares Bidirectional Long Short Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) architectures. Additionally, these models are enhanced by advanced pre-processing techniques, including such methods as Discrete Wavelet Transform (DWT) and Fourier Synchrosqueezed Transform (FSST), as well as hybrid models involving Convolutional Neural Network (CNN) and Random Forest (RF) together with BiLSTM and BiGRU Models. It was found that the hybrid model consisting of CNN and BiGRU performed better than other hybrids by returning an R2 score of 0.9093, RMSE of 0.1095, MSE of 0.0120 and MAE of 0.0466; this shows that our model had a much greater level of accuracy compared to others ones developed before. These model performance indices demonstrated its better trustworthiness and error level for further utilization in wind energy forecast applications required for efficiency improvements and reliability enhancement in wind energy management. The current study emphasizes the usefulness of combining deep learning approaches like BiLSTM and BiGRU for more accurate wind power predictions hence improving reliability and effectiveness in managing wind energy resources.

For power grids with a high penetration level of wind power generation, stochastic wind speed variation adversely affects the frequency stability. A variable-speed wind turbine generator would deloads its output by shifting the power operating point to provide frequency response. Because the wind speed variation directly affects the calculation of the operating point, difficulties arise in securing a reliable amount of the frequency response. This paper proposes a control scheme of a doubly fed induction generator (DFIG)-based wind generation providing the primary frequency reserve (PFR), which can improve its reliability and flexibility even with a continuously varying wind speed. To achieve these objectives, the proposed scheme employs a de-loaded operation control loop associated with the rotational inertia characteristics of the DFIG. In addition, a static gain-based droop control loop is employed to release the required amount of active power from the system operator. In the proposed scheme, the optimal operating point for de-loaded operation varies more smoothly than it does in a conventional scheme by using the rotor inertia characteristics. The performance of the proposed control scheme is demonstrated using an EMTP-RV simulator under various wind speeds. The simulation results indicate that the proposed scheme significantly improves the frequency support capability of the DFIG in both the process of securing and providing the PFR.

The suggested hybrid power plant combines conventional and renewable energy sources along with energy storage devices such as wind, pumped hydro storage (PHS), thermal, and solar-powered electric vehicles (EVs). Its major goal is to improve the electricity network’s revenue and profitability while maintaining grid frequency stability (fG). To achieve this purpose, the approach considers the projected wind velocity (WVProj) in a deregulated market and commits wind farms to meet energy demand appropriately. The functioning of PHS and solar-connected EV power components is closely monitored to reduce the detrimental impact of power system imbalances caused by discrepancies in true (WPTrue) and projected (WPProj) wind production. This operational strategy aims to reduce the unpredictability associated with renewable energy sources cost-effectively. Highlighting the resilience of this technique, the approach incorporates four energy stages of the PHS upper basin (EPHS,max, EPHS,opt, EPHS,low, and EPHS,min) and four energy stages of the EV battery (EEVB,max, EEVB,opt, EEVB,low, and EEVB,min). By considering these energy levels, the approach illustrates the strategy’s long-term effectiveness. Several optimization methods are used for implementation and comparison, including Sequential Quadratic Programming (SQP), BAT algorithm, Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), and Cuckoo Search Algorithm (CSA). The efficacy of the suggested approach is assessed by analyzing and evaluating the IEEE 14-bus power network. The proposed technique deviates from conventional methods by employing the PHS plant to overcome uncertainties related to wind power, guaranteeing that committed generation patterns are reached with the support of solar power and EV-battery storage. Hourly simulations demonstrate that the proposed approach significantly enhances the use of maximum reservoir constraints, which improves system stability and security. The study demonstrates that the proposed two-phase operating technique is successful at improving revenue and profit while stressing stability and security inside the hybrid system. It also provides a feasible option for effective energy management.

The successful integration of renewable energy resources into the power grid hinges on the development of energy storage technologies that are both cost-effective and reliable. These storage technologies, capable of storing energy for durations longer than 10 hours, play a crucial role in mitigating the variability inherent in wind and solar-dominant power systems. To shed light on this matter, a transparent, least-cost macro energy model with user-defined constraints has been utilized for a case study of California. The model addresses all included technologies, solving for both hourly dispatch and installed capacities. Real-world historical demand and hourly weather data have been utilized to do this analysis. A novel approach has been introduced to assess the significance of long-duration energy storage technologies (LDS) in terms of their energy and power capacity. This method explores the contributions of pumped hydropower storage (PHS), compressed air energy storage (CAES), and power-to-gas-to-power (PGP) storage toward minimizing the overall balance of system cost. Historical electricity demand, hourly weather data, and current technology costs are used to investigate high-level implications for California’s power system options. Increasing the storage capacity of each technology from 1 to 10 hours results in 29.6%, 14.4%, and 7.5% cost reduction for PHS, CAES, and PGP cases respectively. However, in studied simulations, maximum availability (maximum) of pumped hydropower storage reduces the balance of system costs by 72.3% followed by CAES (60.6%) and PGP (48.6%) and suggests that pumped hydropower storage in combination with CAES/PGP could play an important role in California’s electricity system, provided that suitable sites can be identified and constructed at reasonable costs.

Accurate wind power prediction helps to stabilize the operation of the power system, improve the utilization rate of renewable energy, reduce dependence on traditional energy, and achieve sustainable energy development. An ultra short-term wind power prediction method integrating EMD-EncoderForest-TCN is proposed to address the difficulty of predicting wind power due to frequent changes in wind speed. Firstly, the time-series input data of the model is decomposed into high-frequency and low-frequency components using Empirical Mode Decomposition. Then, based on the EncoderForest model and TCN model, differential information extraction is performed on the low-frequency and high-frequency components. The EncoderForest model regularizes low-frequency information and captures trend patterns in the data. The TCN model models the high-frequency components of time series to capture complex patterns and structures in wind power. Finally, based on convolutional neural networks, the output results of each part are calculated to achieve accurate prediction of wind power. Based on the operational data of an actual wind farm, conduct a case study analysis. The results show that the proposed model can achieve accurate prediction of short-term wind power, with a prediction accuracy improvement of 2.57%.

Integration of wind and solar energy sources in power systems causes frequency and voltage-related power quality issues, especially at high penetration levels. Battery energy storage systems help reduce fluctuations by absorbing excess power and delivering power deficits. However, batteries suffer from capacity fading and degradation due to frequent cycling. Storing energy in the form of a chemical fuel helps in overcoming these drawbacks. Since ammonia fuel cells address issues associated with hydrogen fuel cells, such as high flammability, poor volumetric density, and high storage costs, in this study the application of ammonia fuel cells for solar and wind power leveling is investigated. Excess wind/solar power is used in an electrochemical ammonia synthesizer (EAS) to produce ammonia, and a direct ammonia fuel cell (DAFC) converts the ammonia to electric power and supplies the power deficit. Smoothing filter concepts, such as moving average, moving median, Savitzky-Golay, moving regression (MR), and Gaussian filters, are employed to assess EAS and DAFC capacity requirements. Simulation results show that MR filter’s overall performance is superior to those of other smoothing filter approaches, resulting in reduced required capacities for ammonia production and fuel cell output, lowering system costs. The developed energy storage system is an effective compensation method for solar and wind power fluctuations.

The rapid expansion of wind power needs increased efforts in establishing rules for connecting wind power plants (WPPs) through grid codes and standards. This makes grid compliance testing and model validation more complex for wind turbine generator (WTG) manufacturers and WPP developers. This paper explores various challenges and solutions within the wind industry concerning grid compliance and the integration of large-scale WPPs. Traditionally, WTG original equipment manufacturers (OEMs) conduct grid compliance tests on full-scale prototype turbines, while WPP developers predominantly engage in studies for new WPPs based on offline EMT and RMS simulations. Up to this point, these approaches have sufficed to ensure grid compliance for WTGs and WPPs. However, as the wind sector progresses, new testing methods are required to meet the growing WT capacity, WPP complexity, and deployment pace, aiming to achieve society’s sustainability targets. The industry is actively exploring methodologies that address these challenges by testing new turbine subsystems and components at the development level, employing software-/hardware-in-the-loop real-time simulation for WTG/WPP interoperability assessment, and ensuring control and protection performance verification during design and operation. Additionally, digital twin approaches are being considered, encompassing the entire development and operation chain for grid compliance and connection aspects. Ultimately, this paper presents a fresh overview of these strategies, outlining definitions and the pros/cons for the stakeholders involved in the process.

Over the last twin decades, significant advancements have occurred in global electricity grids due to the widespread adoption of renewable energy resources (RES). While these sources play an essential role in total generation cost reduction, transmission power loss minimization, and reduction of environmental hazards related to traditional power plants. Still, however, the optimal planning and operation of the power system in the presence RES is considered a primary challenge due to the their stochastic natural and intermittency. One of the most complex and motivating issues in a power system is optimal power flow (OPF), a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to stochastic OPF problems while keeping system stability. To tackle this challenge, an effective optimization algorithm which mimics on the foraging behavior of dwarf mongooses’ in the nature is introduced. The objective function considers reserve cost for overestimation and penalty cost for underestimation of intermittent renewable sources. To show the robustness and efficacy of the recommended optimizer, case studies on the customized IEEE 30-bus system and a realistic power system DZA 26-bus (isolated grid) are undertaken. Numerical findings show that the proposed DMOA beats all previous published-results and performs better over a variety of objective functions while finding high-quality optimally viable solutions. The obtained results demonstrate that the DMOA realized exceptional performance for both the test networks, with total generation cost minimized values of 780.982 ${\$}$ /h and 8283.942 US ${\$}$ /h, respectively. These results highlight the precision and robustness of DMOA in effectively addressing various instances of the OPF problem Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study.

To use energy generated in wind farms (WFs), which contain uncertainty in their output, as a primary power source in a power system, a sophisticated balancing operation scheme is required. This study focuses on a balancing group (BG) scheme combining WFs and a variable-speed pumped-storage hydro generator (PSHG), which has a large capacity to compensate for the WF output. The proposed BG operational scheduling approach aims to maximize the expected revenue obtained in the day-ahead power market under the uncertainty in WF output and market price by considering the operational constraints of the PSHG, i.e., the water storage capacity and frequency of operation for switching pump-up/-down. In such a BG scheme, it is crucial to consider time-varying and time-dependent uncertainties in WF output to manage PSHG capacity constraints, as well as to derive a reasonable plan in practical computation time. The proposed BG operational scheduling scheme derives a set of WF output scenarios that represents the heterogeneous and time-dependent characteristics of real-world WF output behavior from probability density distributions derived by a cutting-edge prediction approach and implements the expected revenue maximization problem with scenario-based chance constraints of water storage transition by introducing computationally effective iterative optimization algorithm based on surrogate functions. Simulation results suggest that the proposed scheme provides an effective BG operation by considering the uncertainty in the WF output.

A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially rated converters. This mechanism has extensively been studied in recent years. To enhance the transient operation performance and low-voltage ride-through capacity of the proposed hybrid drive wind turbine, we aim to synthesize an advanced control scheme for the flexible regulation of synchronous generator excitation based on fractional-order sliding mode theory. Moreover, an extended state observer is constructed to cooperate with the designed controller and jointly compensate for parametric uncertainties and external disturbances. A dedicated simulation model of a 1.5 MW hybrid drive wind turbine is established and verified through an experimental platform. The results show satisfactory model performance with the maximum and average speed errors of 1.67% and 1.05%, respectively. Moreover, comparative case studies are carried out considering parametric uncertainties and different wind conditions and grid faults, by which the superiority of the proposed controller for improving system on-grid operation performance is verified.

As wind power is volatile and intermittent, an increase in the proportion of wind power generation may affect the security of power systems. Accurate wind power generation and ramp forecasting are crucial for reducing operating costs and maintaining the power balance. This study proposed an enhanced performance evaluation metric for wind power ramp event forecasting, and analyzed the forecasting results using this metric. Experimental ramp event forecasting was conducted on wind farms located in Jeju, South Korea by employing an indirect forecasting method for wind power output forecasting. Considering the evaluation of forecasting performance, the accuracy (ACC) was observed to be 0.86 for Case #1 and 0.90 for Case #2. The results highlight the advantages of the proposed metric over the widely used confusion matrix for performance evaluation, which has more detailed and visually based analytical capabilities. The simulation demonstrates that the proposed approach is poised to make a noteworthy contribution in the advancement of tool development, enabling real-time curtailment forecasting and facilitating well-informed decision-making in high wind power scenarios.

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems. However, the volatility and intermittence of wind power pose uncertainties to traditional point prediction, resulting in an increased risk of power system operation. To represent the uncertainty of wind power, this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network (GNN) and an improved Bootstrap technique. Specifically, adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective. Then, the graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) are proposed to capture spatio-temporal features between nodes in the graph. To obtain high-quality prediction intervals (PIs), an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively. Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph, and the prediction results outperform popular baselines on two real-world datasets, which implies a high potential for practical applications in power systems.

As the steady-state frequency of an actual power system decreases from its nominal value, the composite load of the system generally responds positively to lower power consumption, and vice versa. It is believed that this load frequency damping (LFD) effect will be artificially enhanced, i.e., sensitivities of loads with respect to operational frequency will increase, in future power systems. Thus, for wind-integrated power systems, this paper proposes a frequency-dependent chance constrained unit commitment (FDCCUC) model that employs the operational frequency as a dispatching variable so that the LFD effect-based load power can act as a supplemental reserve. Because the frequency deviation is safely restricted, this low-cost reserve can be sufficiently exerted to upgrade the wind power accommodation capability of a power system that is normally confined by an inadequate reserve to cope with uncertain wind power forecasting error. Moreover, when the FDCCUC model is applied to a bulk AC/DC hybrid power system consisting of several independently operated regional AC grids interconnected by DC tie-lines, a hierarchically implemented searching algorithm is proposed to protect private scheduling information of the regional AC grids. Simulations on a 2-area 6-bus system and a 3-area 354-bus system verify the effectiveness of the FDCCUC model and hierarchical searching algorithm.

Battery energy storage system (BESS) will play important roles in the operation of future power systems integrated with high penetration of renewable energy sources. In this work, battery energy storage system (BESS) is equipped with a frequency controller to provide additional inertia support in a power system network made of wind power renewable energy and conventional sources. Several scenarios such as magnitude of power imbalance, transmission line length, variation in wind power penetration level, battery sizing, varying grid inertia were investigated to understand their impacts on the effectiveness of BESS in providing additional inertia support during power system contingencies. Time-domain simulation results of the studied network show that when the transmission line lengths are doubled, the rate of change of frequency (RoCoF) decreased by about 76% resulting in the minimum frequency rising from 49.39Hz to 49.85Hz during the power system disturbance. Besides, by increasing the size of BESS from 50MW to 130MW, the RoCoF was improved from −0.43876Hz/s to −0.30668Hz/s thereby raising the minimum frequency from 48.98Hz to 49.67Hz. Further results from the simulations show that when the magnitude of power imbalance was increased from 150 MW to 375MW, the BESS could not effectively provide additional support as the output power from the power converter remained fairly constant at about 60MW.

The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system. To coordinate the complicated optimal dispatching and rapid real-time control, this paper proposes a hierarchical cluster coordination control (HCCC) strategy based on model predictive control (MPC) technique. Considering the time-varying characteristics of wind power generation, the proposed HCCC strategy constructs an improved multi-time-scale active power dispatching model, which consists of five parts: formulation of cluster dispatching plan, rolling modification of intra-cluster plan, optimization allocation of wind farm (WF), grouping coordinated control of wind turbine group (WTG), and real-time adjustment of single-machine power. The time resolutions are sequentially given as 1 hour, 30 min, 15 min, 5 min, and 1 min. In addition, a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), wavelet thresholding (WT), and least squares support vector machine (LSSVM) is established. The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision. Simulation results show that the proposed HCCC strategy enables rapid response to active power control (APC), and significantly improves dispatching control accuracy and wind power accommodation capabilities.

Wind power’s increasing use and its integration into the utility grid have prompted scholars to focus more on the refinement of wind power harvesting systems, injecting stable power and providing ancillary services to the utility grid. This article introduces a novel hybrid approach termed as sine cosine algorithm and transient search optimization (HSCATSO) optimizer of better exploration and exploitation phases for optimal designing the power electronic converter control schemes (PECCS) of the grid-tied variable speed wind turbine (VSWT) system. The PECCS is simultaneously coordinated with a robust maximum power extraction algorithm (MPEA) to form enhanced control systems for achieving the best wind harnessing, improving the VSWT system performance, and supporting the utility grid stability. In this context, the HSCATSO optimally designs the PECCS parameters based on minimizing the summation of integral squared error (ISE) of multiple error signals in the developed control schemes in coordination with the MPEA. The superiority of the HSCATSO optimizer is validated using twenty benchmark functions and statistically analysed against four well-known optimization algorithms. Meanwhile, the effectiveness of the optimally designed PECCS using the HSCATSO is verified by the extensive simulation analysis in MATLAB/Simulink considering severe grid disturbance and real wind speed data taken from Kudat, Sabah, Malaysia to mimic realistic circumstances. The obtained results have been compared with that realized using the other algorithms-based design PECCS. The simulation outcomes affirmed that the PECCS designed by the HSCATSO and coordinated with MPEA resulted in higher power harvesting and enhanced the grid-tied VSWT system stability better than the competitive control schemes.

Wind energy (WE) is emerging as a green and clean energy source. The power quality (PQ) significantly deteriorates when WE sources are connected to the weak ac grid. This is due to the variable wind speed, the impedance of weak grids, and nonlinear loads. This article proposes a methodology to improve the PQ in the weak grid with WE penetration using additional distribution static compensator (DSTATCOM). Fuzzy logic (FL) controller is implemented in the conventional synchronous reference frame (SRF) scheme to minimize the oscillations in the voltage signals. Also, a feedforward block of the WE source is incorporated in the FLSRF control scheme for adapting changes associated with the wind speed. The proposed control scheme accommodates the changes related to the grid’s strength, wind speed, load currents, and dc-link voltage dynamics to estimate the switching signals for the DSTATCOM. The MATLAB simulation and experimental studies established the effectiveness of the proposed method for enhancing the PQ performance of weak grid-connected WE sources in the presence of nonlinear loads. [ABSTRACT FROM AUTHOR]

Firms’ knowledge base is one of the key antecedents of firm innovation. While extant literatures identify diversification and depth of firms’ knowledge base as the important inputs of firm innovation, coherence of firms’ knowledge base reflecting the relatedness of knowledge base is largely neglected. This article goes a further step and points out that coherence of firms’ knowledge base will co-affect with diversification or depth of firms’ knowledge base to foster firm innovation, because it improves the efficiency of knowledge combination and integration. Moreover, degree centralization of industrial knowledge networks, as a characteristic of industrial knowledge base, moderates the abovementioned relationships by offering technological combination opportunities in the industry and helping firms break cognitive inertia. Based on patent data of 76 global firms from three-dimensional printing, information and communication technology, wind energy, and lithium battery industries during the period of 1993–2013, the analysis results show that coherence of firms’ knowledge base positively moderates the relationship between diversification of firms’ knowledge base and firm innovation. Furthermore, degree centralization of industrial knowledge networks, together with coherence of firms’ knowledge base, strengthens the positive relationship between knowledge diversification and firm innovation. Theoretical and practical suggestions about firms’ knowledge management were discussed. [ABSTRACT FROM AUTHOR]

Servitization is recognized as a key business strategy for original equipment manufacturers willing to move up the value chain. However, several barriers have to be overcome in order to successfully integrate products and services. Many of these barriers are caused by the technical challenges associated with the design and management of the product-service systems (PSSs), such as life cycle service level and cost estimation, risk management, or the system design and pricing. Asset management (AM) presents itself as a key research area in order to overcome these barriers as well as to integrate PSSs within the manufacturers’ operations management. It is the scope of this article to provide theoretical and practical insights with regards to the alignment of AM and PSS research areas. To support the alignment between both areas, a management framework which gathers specific technologies, including reliability analysis, simulation modeling, and multiobjective optimization algorithms, is presented. The purpose of the framework is to provide manufacturers with a decision-support tool that facilitates the main managerial challenges faced when implementing a servitization strategy. The article contributions are successfully applied to case studies in the railway and wind energy sectors based on real field data, thereby demonstrating their suitability for both facilitating manufacturer’s decision-making process and better satisfying stakeholders’ interests. [ABSTRACT FROM AUTHOR]

The equivalent circuit is an indispensable tool for analyzing the steady-state characteristics of the brushless doubly fed induction machine (BDFIM). However, the most commonly used equivalent circuits are not available to analyze some special operation modes, such as the natural synchronous mode with one set of winding powered by dc current. In order to overcome the limitation, in this article, a novel equivalent circuit is proposed. At first, the electromagnetic relations within BDFIMs are described by a new approach in which the cause of working magnetic fields is refined. Then, the equivalent circuit is built by the rigorous derivation of electromagnetic relations. And a few features of BDFIMs in some typical operation modes including the natural synchronous mode are introduced in details. Finally, the validity of the equivalent circuit is made by comparison of model and experimental results based on a wound-rotor BDFIM prototype. The proposed model with clear physical concept can cover all operation modes. And both power relationship and torque components reflected in the proposed model is more detailed and clearer than those reflected in the traditional one. [ABSTRACT FROM AUTHOR]

In this article, a voltage-fed isolated matrix-type ac/dc converter (VF-MC) is proposed for wind energy conversion system (WECS). Instead of the line-frequency transformer, the high-frequency transformer (HFT) is used for isolation between the generator and the grid. The direct matrix converter (MC) is employed to connect the wind power generator and the HFT, and the intermediate dc capacitors are saved in the ac/ac conversion. Moreover, the proposed VF-MC can work without use of filter capacitors on generator side, and enable more compact size. The dedicated space-vector-modulation scheme and commutation method are proposed for safe operation of the MC. The voltage spikes due to the leakage inductance of HFT are thus reduced and the additional snubber circuit is avoided in the proposed system. A 1-kW laboratory prototype of the proposed VF-MC-based WECS is built to verify the effectiveness of the system experimentally. [ABSTRACT FROM AUTHOR]

The current source converter (CSC) based conversion system is a good candidate for next-generation series-connected wind energy conversion systems. In this article, a new power converter is proposed for the CSC-based series-connected wind system. In addition, three versions of the proposed converter are developed. Compared with the existing CSC-based converters, the proposed one provides a practical solution for the series-connected wind system, achieves superior current harmonic performance, and features inherent power balancing, simple control, and high scalability, in addition to keeping all the advantages of the existing CSCs. The operation principle of the proposed converter is discussed, and its performance is verified by the simulation and lab-scaled experiments. [ABSTRACT FROM AUTHOR]

As theincrease of wind power penetration, the fluctuations of wind power results in serious power system frequency turbulences. This article proposes a low-pass virtual filter (VF) for wind energy conversion systems (WECSs) to smoothen output power. First, a linearized model is established for a WECS. Then, a low-pass VF is derived for the active power control loop in WECS to modulate the kinetic energy stored in rotating mass for output smoothing. The principle is to modify the traditional wind power controller to emulate a physical energy storage system controlled as a low-pass filter to smooth the wind power output. The proposed low-pass VF does not need the physical deployment of energy storage system but still can filter the high-frequency fluctuations in the output wind power. Furthermore, in order to ensure operation stability of the wind turbine (WT), a stability-constrained coefficient is determined according to the proposed stability criteria to ensure the stability of WTs. Simulation and experiment results verify the effectiveness of the proposed VF in WECS for output wind power fluctuation reduction and the stability-constrained coefficient for stability ensuring. [ABSTRACT FROM AUTHOR]

Recent studies have shown that the grid connection of offshore wind farms through modular multilevel converter–high voltage direct current (MMC-HVdc) transmission system has brought low inertia issue. It is an urgent requirement for offshore wind farms to compensate for the inertia. After analysis and evaluation in this article, it is found that integrating an ultracapacitor energy storage system (UESS) into an MMC has an advantage comprehensively considering cost, size, and dynamic. Based on the evaluation, this article proposes an approach to inertial compensation of HVdc offshore wind farms by MMC-UESS integration. An ultracapacitor is selected as an energy storage element (ESE) for its high power density since the inertial response is a short-term response and it is hoped that added ESE does not significantly increase the size of an MMC submodule. Batteries with high energy density but low power density are not suitable for such applications. The optimal operating voltage of a UESS and the control scheme between MMC loops and inertial emulation are proposed in this article. Experimental results from real-time simulation and a downscaled prototype are presented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents the DC fault transient study of a meshed DC grid with high-temperature superconducting (HTS) HVDC cables to integrate offshore wind power. A four-terminal meshed DC grid model with a ±100 kV DC voltage rating is developed. DC circuit breakers (DCCBs) are implemented in the DC grid to deal with DC cable faults. To conduct the fault transient study, the fault scenarios with different fault locations and protection delays are studied. The sensitivity study versus different DC fault locations reveals that the faults on the faulted cable will not cause the quenching of healthy cables. The sensitivity study considering different DC fault protection delays demonstrates that the HTS cables have a current-limiting effect and the long delay of DC fault protection does not result in large fault current and the quenching of healthy cables. Extensive simulations in PSCAD/EMTDC validate the founding. [ABSTRACT FROM AUTHOR]

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]

An integrated generator–rectifier architecture is a promising candidate for harvesting energy from offshore wind. The most attractive feature of this architecture is that the majority of the power is processed by reliable, efficient, and inexpensive diodes operating at the generator frequency. The use of dc collection grids (medium-voltage or high-voltage dc) is an emerging trend in a wind farm. This article proposes a dc grid interface circuit for the integrated generator–rectifier and a control strategy for maximum power point tracking. Using a 10-MW illustrative design, it is shown that the proposed architecture reduces the total switch volt–ampere rating requirement by 22.8%, which translates into overall loss reduction ranging between 28.3% and 71.7%, depending on the extracted power. The maximum power point tracking algorithm is implemented based on the relationship between the active rectifier $d$ -axis current and the generated power. Experimental results obtained from a laboratory prototype validates the effectiveness of the proposed converter architecture and its control strategy. This approach opens up opportunities for integrating dc collection networks with offshore wind farms through an efficient, reliable, and cost-effective energy conversion system. [ABSTRACT FROM AUTHOR]

The fault-tolerant control method of dual three-phase permanent-magnet synchronous motor (PMSM) drives has gained more and more attention in some applications, such as aircraft system and offshore wind energy system. In this letter, the reason for the torque ripple caused by the standard control method has been investigated in view of the frequency domain after the occurrence of a single open-phase fault. Based on the theoretical analysis, a universal control scheme has been proposed for the dual three-phase PMSM drives with two isolated neutral points, which can be implemented under either healthy condition or fault condition. The key is to modify with a closed-loop controller on harmonic subspace with the notch filter. The proposed method not only realizes the seamless transition between two operation conditions without fault diagnosis but also avoids the reconfiguration of the control structure. Moreover, the system optimization of the minimum copper loss under postfault condition has been integrated into the proposed method. The experimental results are presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The wind–thermal-bundled transmission system is a feasible way to transmit wind power generation; however, the stability of the system should be paid more attention under high wind power penetration. In this article, the impact of wind energy penetration on torsional vibration and electrical characteristics is investigated for wind–thermal-bundled transmitted by high-voltage direct current system. The torsional vibration responses of generator shaft with different wind–thermal-bundled ratios are analyzed by time and frequency methods. The electrical characteristics of wind–thermal-bundled transmission system are disclosed under the influence of the wind farm penetration. The analysis illustrates that the penetration of doubly fed induction generation makes the torsional vibration of turbo generator and wind turbine shaft present different mode characteristics, and the risks of different modes are different. The current and voltage of wind–thermal-bundled transmission system show regular evolution with the change of wind–thermal-bundled proportion. Analytical results provide the value for the design and control of the wind–thermal-bundled transmitted system, which contributes to the improvement of power system stability. [ABSTRACT FROM AUTHOR]

The power system with high penetration of wind power is gradually formed, and it would be difficult to determine the optimal economic dispatch (ED) solution in such an environment with significant uncertainties. This paper proposes a multi-objective ED (MuOED) model, in which the expected generation cost (EGC), upside potential (USP), and downside risk (DSR) are simultaneously considered. The heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power, respectively. Then, the MuOED model is formulated as a tri-objective optimization problem, which is related to uncertain multi-criteria decision-making against uncertainties. After-wards, the tri-objective optimization problem is solved by an extreme learning machine (ELM) assisted group search optimizer with multiple producers (GSOMP). Pareto solutions are obtained to reflect the trade-off among the expected generation cost, the upside potential, and the downside risk. And a fuzzy decision-making method is used to choose the final ED solution. Case studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm.

To tackle the high stochastic characteristic of renewable energy resources, such as wind power, sufficient flexibility is of great necessity to the security and economic operation of power systems. With increasing wind power penetration in the power system, the flexibility demand and the possibility of wind spillage are on the rise. Under the tight power balance state, the flexibility solely provided by thermal units will reduce the system flexibility, resulting in load shedding. It is imperative to utilize flexible resources to reduce wind spillage and load shedding. To address the above problems, the power system flexibility supply and demand model and electric vehicle (EV) charging stations operation model are first constructed in this paper. On this basis, a disposal strategy for tight power balance with EV charging stations providing flexible ramping capacity is established. Finally, numerical simulations are conducted on the modified IEEE 118-bus system. Results show that the integration of EV charging stations reduces the operation cost of the system by 11.72%, wind spillage by 17.88%, load shedding by 59.42% and provides flexibility to the system.

Integrating more renewable energy sources into the grid leads to a more vulnerable power system and challenges for power system planners. This paper proposes a probabilistic overload constraint based AC transmission expansion planning model. In terms of economic dispatch, generation adequacy and network constraints are assessed using a probabilistic participation factor power flow. Generation participation factors, which are handled in the AC power flow equations, are used to manage mismatch power that comes from uncertainties as well as transmission loss of the system in a distributed slack bus concept. Combinations of load, wind and N-1 contingency uncertainties are evaluated with Monte Carlo Simulation and the loading limits for the existing and candidate lines and violations on bus voltages are enforced as probabilistic constraints using chance constraint programming. The investment decisions taken under various operating conditions are re-evaluated under a new set of wind power uncertainty that differs from those utilized in the optimization process, and the resulting TEP decisions are analyzed from a risk perspective. The proposed method allows for the uncertain operating conditions to be easily and accurately adapted to the ACTEP optimization. The optimization results show that the participation factor power flow is a promising tool for evaluating probabilistic operating conditions, which can be used to explore the possibility of violations under different uncertainty conditions.

With the ever growing environmental concerns, renewable energy sources emerge as a promise of clean and abundant energy, enabling long-term sustainable development. In this context, wind power gained significant interest due to its relative low cost and availability. Switched reluctance generators (SRGs) are suitable candidates for wind energy conversion systems, as they present a simple structure, robustness, a wide range of speed and are capable of operating in harsh environments. The machine, however, poses challenges such as high torque ripple, acoustic noise production and highly nonlinear behavior. Nonetheless, with the use of adequate control strategies, high dynamic performance SRG-based wind energy conversion systems can be achieved. As a result, this article presents a state of the art review of SRGs in wind power applications. First, the fundamentals of the SRG are presented. Next, two categories of firing angle control are reviewed: optimization and closed-loop control. Then, voltage and power control strategies are discussed, being divided in model-independent and model-based approaches. After that, a review on grid-tied SRG-based wind energy conversion systems is carried out. The most common filter topologies as well as the employed control strategies are detailed. Lastly, an outline of the discussed topics is presented and future trends as well as suggestions for future investigation are listed.

As the use of renewable energy is continuously increasing, power systems are currently exposed to greater uncertainty and variability, which can lead to severe power system stability issues. Therefore, a power system analysis tool should be devised to assess the impact of renewable energy integration along with an accurate modeling of their stochastic characteristics. In this study, an advanced probabilistic power flow (PPF) method is developed using vine copulas that captures the complex dependency of the stochastic wind power generated from multiple wind sites. The proposed method also involves the use of a function for selecting the probability models of wind speeds by regions in a sophisticated manner. The effectiveness of the proposed method is tested on an IEEE bus system as well as, on a South Korean power system with thousands of buses and transmission lines using PSS/E with Python API. The simulations demonstrate that the proposed method can more accurately evaluate the power system risks with the sophisticated modeling of wind power in multiple sites as compared to the deterministic approach or the PPF with independent sampling.

The rapid growth of the share of variable renewable energy (VRE) may make it difficult to operate power systems incorporating these sources, due to fluctuations in VRE output. In this paper, we focus on the short-term fluctuations (STFs) in wind power total outputs in several balancing areas (BAs) in Japan. We propose five methods to mitigate STFs, utilizing innate functions of wind turbines that use neither battery systems nor any other additional systems or equipment. In addition, the methods suggested do not require predictions of the wind power output. The efficiency of the method was measured based on the relationship between the mitigation of STFs and associated energy loss. Historical wind power output data from three BAs in Japan (the Hokkaido, Tohoku, and Kyushu BAs) were used to conduct numerical simulations. One of the proposed methods effectively mitigated STFs in the total wind power output. The proposed approach is applicable to solar power and will help overcome challenges on the road toward 100% renewable energy.

This study proposes a statistical measure and a stochastic optimization model for generating risk-seeking wind power offering strategies in electricity markets. Inspired by the value at risk (VaR) to quantify risks in the worst-case scenarios of a profit distribution, a statistical measure is proposed to quantify potential high profits in the best-case scenarios of a profit distribution, which is referred to as value at best (VaB) in the best-case scenarios. Then, a stochastic optimization model based on VaB is developed for a risk-seeking wind power producer, which is formulated as a mixed-integer linear programming problem. By adjusting the parameters in the proposed model, the wind power producer can flexibly manage the potential high profits in the best-case scenarios from the probabilistic perspective. Finally, the proposed statistical measure and riskseeking stochastic optimization model are verified through case studies.

As the global energy transforms to renewable-based power system, the wind power generation has experienced a rapid increase. Due to the loss of synchronous machines and its frequency control mechanisms, the gradual evolution leads to critical challenges in maintaining the frequency stability. Under post-fault condition, the wind power generation has a slow recovery due to the fault ride-through (FRT) control strategy and may cause a larger frequency deviation due to the power imbalance between the supply and demand. Then, the impacts of the frequency deviations would further cause inaccuracy and instability in the control system for wind power generation. Considering the long parking time of electric vehicles (EVs), the demand-side response is provided to support the power grid via load-to-grid technology. Thus, a power-balancing coordinated control strategy of the wind power and the demand-side response is developed. It can significantly mitigate the power imbalance, thereby resulting in the enhanced frequency stability. Finally, the simulation results are provided to validate the power-balancing coordinated control strategy.

Accelerating the development of renewable energy and reducing CO2 emissions have become a general consensus and concerted action of all countries in the world. The electric power industry, especially thermal power industry, is the main source for fossil energy consumption and CO2 emissions. Since solvent-based post-combustion carbon capture technology would bring massive extra energy consumption, the application of solar-assisted carbon capture technology has attracted extensive attention. Due to the important role of coal-fired combined heat and power plants for serving residential and industrial heating districts, in this paper, the low-carbon operation benefits of combined heat and power integrated plants based on solar-assisted carbon capture (CHPIP-SACC) are fully evaluated in heat and power integrated energy system with a high proportion of wind power. Based on the selected integration scheme, a linear operation model of CHPIP-SACC is developed considering energy flow characteristics and thermal coupling interaction of its internal modules. From the perspective of system-level operation optimization, the day-ahead economic dispatch problem based on a mix-integer linear programming model is presented to evaluate the low-carbon benefits of CHPIP-SACC during annual operation simulation. The numerical simulations on a modified IEEE 39-bus system demonstrate the effectiveness of CHPIP-SACC for reducing CO2 emissions as well as increasing the downward flexibility. The impact of different solar field areas and unit prices of coal on the low-carbon operation benefits of CHPIP-SACC is studied in the section of sensitivity analysis.

Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.

This paper presents the solution to the security-constrained unit commitment (SCUC) problem for hybrid voltage source converter (VSC) based multi-terminal DC/AC (VSC-MTDC/AC) power systems with high penetration of wind generation. The formulated SCUC model presents a detailed representation of the VSCs with two different VSC control strategies considered, constant DC voltage control (master-slave control) and DC voltage droop control. In addition, Grid Code for wind farm connection is considered. Benders decomposition is used to solve the formulated SCUC problem by decomposing it into a master problem for solving unit commitment (UC) and hourly transmission security check subproblems. The final SCUC solution provides an economic and secure operation and control strategy for the meshed MTDC/AC system. Numerical tests illustrated the efficiency of the proposed SCUC model.

Complementarities of geographically dispersed wind resources have been well recognized and evaluated based on the statistics of wind data. This paper investigates the effect of spatiotemporal complementarities of grid-connected offshore wind farms on operational cost and stability of meshed DC/AC grids with VSC-based MTDC networks. The investigation is carried out for a real power system in Jiangsu Province, China, and has used the real hourly wind data provided by the China Meteorological Administration (CMA) in terms of the unit commitment (UC) as well as the probabilistic small-signal angular stability. Several distributed offshore wind farms are interconnected via a VSC-based MTDC network to send power to the southern area in Jiangsu Province. Computational results of the UC and system probabilistic stability demonstrate that significant saving of operational cost and stability improvement can be brought about from the complementarities of geographically distributed offshore wind farms.

Producing energy from a variety of sources in a power system requires an optimal schedule to operate the power grids economically and efficiently. Nowadays, power grids might include thermal generators and renewable energy sources (RES). The integration of RES adds complexity to the optimal power flow problem due to intermittence and uncertainty. The study suggests a Multi-Objective Search Group Algorithm (MOSGA) to deal with multi-objective optimal power flow integrated with a stochastic wind and solar powers (MOOPF-WS) problem. Weibull and lognormal probability distribution functions (PDFs) are respectively adopted to describe uncertainties in wind speed and solar irradiance. The MOSGA incorporates crowding distance strategies, fast non-dominated sorting, and an archive selection mechanism to define and preserve the best non-dominated solutions. The total cost, real power loss, and emission were defined as the objectives for the MOOPF-WS problem. In the economic aspect, formulated cost modelling includes both overestimation and underestimation situations related to wind and solar power prediction. Further, uncertainty in load demand is represented by a normal PDF and is considered as a special study case due to its novelty. The effectiveness of MOSGA was validated on the IEEE 30-bus and 57-bus systems considering various combinations of objective functions as well as different loading scenarios. Its performance was comprehensively compared with the other three well-regarded multi-objective optimization algorithms including NSGA-II, MOALO, and MOGOA in terms of Spread metric, Hypervolume metric, and the best compromise solutions for all scenarios. The comparisons showed MOSGA was capable of obtaining well-distributed Pareto fronts and producing better quality solutions compared to the others in all tested scenarios. In addition, MOSGA also obtained better solution quality than significant research in the literature for all the comparable cases. These show the superiority of the MOSGA in dealing with the MOOPF-WS problem.

This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for a wind power producer using virtual bidding, which is a kind of financial tool available in most electricity markets of the United States. In the proposed model, virtual bidding is used to improve the wind power producer's market power in the day-ahead (DA) market by trading at multiple buses, which are not limited to the locations of the wind units. The optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization model. The upper-level problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk (CVaR) for risk management. The lower-level problem represents the clearing process of the DA market. By using the Ka-rush-Kuhn-Tucker (KKT) conditions, duality theory, and big-$M$ method, the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints (MPEC) model and then a mixed-integer linear programming (MILP) model, which can be solved by existing commercial solvers. To reduce the computational cost of solving the proposed stochastic optimization model for large systems, a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual bidding. Case studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual bidding. The impacts of the transmission limits, wind unit location, risk aversion parameters, wind power volatility, and wind and virtual capacities on the price-maker trading strategy are also studied through case studies.

The growing tendency of wind energy technology usage has induced researchers to seek more studies on the improvement of wind energy harvesting systems with highly reliable power injected into the electric grid. The amount of extracted wind energy by a wind turbine depends on the accuracy of the maximum power point tracking (MPPT) algorithm associated with the control scheme of the wind energy conversion system (WECS). This paper aims to present a novel development of MPPT algorithm based on parabolic prediction techniques (PPT) for variable speed wind turbines (VSWT) so as to achieve maximum wind power extraction and efficiency enhancement. The developed PPT-MPPT algorithm uses parabolic convex functions to estimate the parabola wind turbine output curve and locate the maximum power point on that curve. Simultaneously the algorithm establishes a systematic procedure for adjusting the concavity and the optimal part of the estimated parabola curve to make sure that the proposed technique is in continuous convergence. The feasibility of the developed PPT-MPPT algorithm has been validated under rapid and random changing wind speed profiles via the MATLAB/Simulink environment. The performance of the proposed PPT-MMPT algorithm is evaluated by comparing the simulation finding to those obtained using the benchmark perturb and observe(P&O) algorithms. The simulation outcomes affirm that the proposed PPT-MPPT algorithm outperformed the other algorithms in terms of tracking efficiency and extracted power which can be regarded as an efficacious manner to improve the VSWT system.

Wind power is a new energy source, for which the forecasting accuracy is related to the feasibility of various decision-making schemes for the power grid. The random, fluctuating and uncertain characteristics of wind power make accurate point forecasting very difficult and result in low practical application value. Therefore, a novel wind-power interval prediction method is proposed in this paper. First, variational model decomposition (VMD) is applied to the historical time sequence for wind power, followed by using an extreme learning machine (ELM) to establish a prediction model for the intrinsic mode function (IMF). An autoregressive integrated moving average (ARIMA) is used to establish a prediction model for the residual components, and the predicted IMF and residual components are reconstructed to predict the wind power. Finally, iterative self-organizing data analysis technique algorithm (ISODATA) are used with the error sequence to perform clustering segmentation, and adaptive diffusion kernel density estimation (ADKDE) is used to fit the probability density function for the prediction error of each segment and thereby obtain the cumulative distribution function and the final interval prediction under a given confidence level (CL). The results of a simulation based on real wind farm data from Hunan Province, China, from January to April 2020 show that the proposed method can significantly improve interval coverage.

The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market. The impact of mutual bidding strategies on market prices, traded volumes, and revenues has been quantified. The coordination assumes that hydro power generation is scheduled mainly to compensate the differences between actual and planned wind power outputs. The potential of this coordination in achieving and utilizing of market power is explored. The market equilibrium of asymmetric generation companies is analyzed using a game theory approach. The assumed market situation is imperfect competition and non-cooperative game. A numerical approximation of the asymmetric supply function equilibrium is used to model this game. An introduced novelty is the application of an asymmetric supply function equilibrium approximation for coordinated hydro-wind power generation. The model is tested using real input data from the Croatian power system.

Utility-scale and small scale wind and solar power installations along with electric vehicle charging stations, and other active sources of energy are increasing at the medium and lower voltage levels in the distribution grid. This situation requires a better understanding of the impact of high penetration of weather-dependent renewable energy sources on the operating conditions of the distribution network at both medium and low voltage levels. Despite the need, a multi-voltage level distribution network model, based on real network data and weather-dependent renewable generation data, has not been presented for distribution grid studies. This paper presents a comprehensive multi-voltage level active distribution network model based on real network data along with load and generation time-series for about a year. The network topology is modelled based on geographical data for various rural, semi-urban, and urban locations. The distribution network is embodied with a large share of renewable generation sources, with generation time-series simulated from meteorological data. The network is also flexible to incorporate other assets such as electric vehicle charging stations, storage, etc. The presented active distribution network model can be used to study, optimize, and control the effects of weather-dependent generation and other network assets in the distribution grid. [ABSTRACT FROM AUTHOR]

An optimal static voltage stability margin (SVSM) control model considering the uncertain wind farm output is proposed, in which the probability distribution of SVSM is required to satisfy the chance and variance constraints. A probabilistic distribution control method is proposed to solve the model. First, a linear weighted sum of B-spline basic functions is introduced to approximate the analytical function between the probability density function (PDF) of SVSM and control variables. Next, the analytical expression of the PDF of SVSM corresponding to a control variables’ sample is derived, and the sample of corresponding B-spline function weights is obtained, thus forming a series of ‘control variables-weights’ samples. Then, the analytic functions between weights and control variables are obtained using the generated samples to train a radial basis function neural-network, and the analytical function between the PDF of SVSM and control variables is obtained. After that, the chance and variance constraints are transformed into general nonlinear constraints with respect to control variables. Moreover, a method of selecting key control variables by calculating sensitivities is proposed to increase computational efficiency. Test results on the IEEE-39 bus system and an actual provincial power grid demonstrate the feasibility and effectiveness of the proposed method. [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]

The loss of small signal stability under parameter variation (e.g., fluctuation of loads and wind powers) can be ascribed to local bifurcations, i.e., saddle-node, Hopf, singularity-induced, and limit-induced bifurcations. Classic bifurcation calculation methods like the direct method and continuation method can only provide a bifurcation point or two-parameter bifurcation curve. Based on Galerkin method and the implicit function theorem, this paper proposes accurate polynomial approximations of bifurcation hypersurfaces in the multi-dimensional parameter space of interest. The proposed method can ensure high accuracy in this parameter space, and thus is preferable to the existing Taylor expansion-based local approximation method, given the intrinsic large-variation characteristic of wind powers. Acquired bifurcation hypersurfaces are immediately used to construct the small signal stability region of the continuous system, and then compute the stability margin for the operating point subjected to uncertainty of wind generation. Besides, the validity scope of the proposed method is analyzed, and the handling of limit-triggered equation switching as well as its computational difficulty is discussed. Computational results on the two-parameter 11-bus two-area and six-parameter IEEE 145-bus test systems validate the high accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]