Large-scale Wind Power Research Articles

Investments in green hydrogen as a flexibility source for the European power system by 2050: Does it pay off?

The European Union aims to deploy a high share of renewable energy sources in Europe’s power system by 2050. Large-scale intermittent wind and solar power production requires flexibility to ensure an adequate supply–demand balance. Green hydrogen (GH) can increase power systems’ flexibility and decrease renewable energy production’s curtailment. However, investing in GH is costly and dependent on electricity prices, which are important for operational costs in electrolysis. Moreover, the use of GH for power system flexibility might not be economically viable if there is no hydrogen demand from the hydrogen market. If so, questions would arise as to, what would be the incentives to introduce GH as a source of flexibility in the power system, and how would electrolyzer costs, hydrogen demand, and other factors affect the economic viability of GH usage for power system flexibility. The paper implements a European power system model formulated as a stochastic program to address these questions. The authors use the model to compare various instances with hydrogen in the power system to a no-hydrogen instance. The results indicate that by 2050 deployment of approximately 140 GW of GH will pay off investments and make the technology economically viable. We find that the price of hydrogen is estimated to be around €30/MWh.

Short-Term Optimal Operation Method for Hydro–Wind–Thermal Systems Considering Wind Power Uncertainty

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

Research on entropy weight variation evaluation method for wind power clusters based on dynamic layered sorting

This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes. A dynamic layered sorting allocation method is also proposed. The proposed evaluation method considers the power-limiting degree of the last cycle, the adjustment margin, and volatility. It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time, and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results. A case study of a large-scale wind power base in Northwest China was conducted. The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods. The results show that the three scoring trends are the same, and that the proposed evaluation method is closer to the average level of the latter two, demonstrating higher accuracy. The proposed allocation method can reduce the number of adjustments made to wind farms, which is significant for the allocation and evaluation of wind power clusters.

Two-Stage Optimal Configuration Strategy of Distributed Synchronous Condensers at the Sending End of Large-Scale Wind Power Generation Bases

The transmission end of large-scale wind power generation bases faces challenges such as high AC-DC coupling strength, low system inertia, and weak voltage support capabilities. Deploying distributed synchronous condensers (SCs) within and around wind farms can effectively provide transient reactive power support, enhance grid system inertia at the transmission end, and improve dynamic frequency support capabilities. However, the high investment and maintenance costs of SCs hinder their large-scale deployment, necessitating the investigation of optimal SC configuration strategies at critical nodes in the transmission grid. Initially, a node inertia model was developed to identify weaknesses in dynamic frequency support, and a critical inertia constraint based on node frequency stability was proposed. Subsequently, a multi-timescale reactive power response model was formulated to quantify the impact on short-circuit ratio improvement and transient overvoltage suppression. Finally, a two-stage optimal configuration strategy for distributed SCs at the transmission end was proposed, considering dynamic frequency support and transient voltage stability. In the first stage, the optimal SC configuration aimed to maximize system inertia improvement per unit investment to meet dynamic frequency support requirements. In the second stage, the configuration results from the first stage were adjusted by incorporating constraints for enhancing the multiple renewable short-circuit ratio (MRSCR) and suppressing transient overvoltage. The proposed model was validated using the feeder grid of a large energy base in western China. The results demonstrate that the optimal configuration scheme effectively suppressed transient overvoltage at the generator end and significantly enhanced the system’s dynamic frequency support strength.

A centralized power prediction method for large-scale wind power clusters based on dynamic graph neural network

The uncertainty of the wind process leads to the randomness of its regional propagation, and the spatial correlation between nearby wind farms also shows a dynamic change tendency under the influence of wind direction. To consider the influence of dynamic spatial correlation on wind power prediction modeling, a short-term wind power prediction method based on a dynamic graph neural network is proposed. First, the topology graph of the wind farm cluster was established to represent the correlation of wind farms based on graph theory. Then, a dynamic spatiotemporal graph neural network was constructed to adapt graph topology by node embedding, which can explore the changing characteristics of spatial correlation among wind farms. Finally, we proposed a decoupling error model that can quantify the proportion of errors caused by the modeling process, which can assist in evaluating the performance of predictive models. We conducted experiments using data provided by the wind farm cluster in Inner Mongolia, and the average normalized Root Mean Square Error for the 12 wind farms was 0.1424, which verified the effectiveness of the proposed model.

Two-Stage Distributed Robust Optimization Scheduling Considering Demand Response and Direct Purchase of Electricity by Large Consumers

The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases.

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Virtual impedance optimization control strategy for wind turbine grid-forming converters in weak grid

Abstract Grid forming(GFM) technology is key to the effective consumption and grid-friendly integration of large-scale wind power. However, as the new type of power system sees a high penetration of new energy sources and a decrease in circuit capacity, leading to systems exhibiting weak grid characteristics, this paper studies the adaptive characteristics of grid-forming wind turbine converters under weak grid conditions. It investigates the dynamic relationship between power coupling characteristics and the system impedance ratio based on the virtual impedance method and proposes an impedance optimization method to improve the power decoupling accuracy and dynamic response capability of grid-forming converters. The research results show that the grid-forming controller can operate stably under weak grid conditions, an increase in grid impedance ratio leads to stronger power coupling intensity, and the voltage support capability of grid-forming converters weakens. The simulation verifies that the virtual impedance optimization strategy proposed in this paper can effectively improve the power coupling characteristics of the grid-forming converter and enhance the converter’s dynamic response performance under weak grid conditions.

Distance to offshore wind farms, onshore wind power spillover relationships, and the willingness to pay for farshore, large-scale wind power development

ABSTRACT As demands for clean energy mount to meet international and national climate goals, constant tension and trade-offs regarding where to site large-scale offshore wind farms (OWF) remain. Using data from a choice experiment to elicit preferences for large-scale, 3.6 gigawatts OWFs in Denmark, we find that respondents living far from a proposed or existing offshore wind power projects have a lower willingness to pay (WTP) to locate new OWFs at 12 km, 18 km, or 50 km relative to 8 km from the coast. Furthermore, based on the estimated densities of existing onshore wind turbines at a given respondent’s postal (ZIP) code level, we find evidence of a significant relationship between existing onshore wind power development and preferences for proposed OWF locations.

Pilot protection based on the similarity of the traveling waves between the quadratic fitting curve and the calculated curve for the All-DC wind power transmission system

The All-DC wind power generation system can effectively solve the harmonic resonance problem caused by large-scale wind power AC collection and transmission, gradually becoming a hot topic in future research. However, there are few studies on the relay protection of the All-DC wind power transmission systems. In the All-DC wind power systems, there are differences in topology, control strategies, fault characteristics, boundary characteristics, etc. At the same time, existing pilot protection has problems such as strict information synchronization requirements and selection of setting values through simulation. Therefore, the proposed pilot protection based on the similarity of the traveling wave between the quadratic fitting curve and the calculated curve for the All-DC wind power system transmission lines. Firstly, the difference of the 1-mode initial current waveform characteristics is analyzed in the time domain at both ends. Then, the least squares method is introduced to fit the differential curves of the current forward and backward traveling waves at two terminals. The differential characteristics of the differences between the fitted curve, the calculated curve, and the measured curve under the influence of internal and external errors are analyzed. Finally, the calculated value of traveling wave current during external metallicity fault is used as the standard. By comparing the fitting curve of current traveling waves and the difference in the calculation curve to distinguish internal faults from external faults. The simulation results show that the proposed protection principle can fault resistance of 2200 Ω and noise interference of 20 dB. In addition, the proposed method does not require strict data synchronization, higher sampling frequency, and setting value through simulation.

A Low-Frequency Oscillation Suppression Method for Regional Interconnected Power Systems with High-Permeability Wind Power.

With the integration of large-scale wind power into the power grid, the impact on system stability, especially the issue of low-frequency oscillations caused by small disturbances, is becoming increasingly prominent. Therefore, this paper proposes a damping quantitative analysis method for regional interconnected power systems incorporating large-scale wind power. Using the cross-entropy particle swarm optimization (CE-PSO) algorithm, the control parameters of wind turbines are optimized to suppress low-frequency oscillations in interconnected systems. The method begins with the state equation of the interconnected power system in two regions; it deduces the characteristic polynomial of the interconnected system, including wind farms, and takes into account the influence of wind power integration on the electrical connectivity of the system. Subsequently, the influence of wind turbine control parameters on the system is quantified, and a quantitative analysis model of the impact of wind power integration on system damping characteristics is constructed. Based on this, an optimization model for wind turbine control parameters is established, and the CE-PSO algorithm is utilized to achieve suppression of low-frequency oscillations in interconnected power grids with wind power integration. Finally, the accuracy and effectiveness of the proposed method are verified through a typical electromagnetic transient simulation model of the two-region interconnected power system.

Frequency Coordination Control Strategy for Large-Scale Wind Power Transmission Systems Based on Hybrid DC Transmission Technology with Deep Q Network Assistance

Wind power is currently the most mature representative of sustainable energy generation technology, which has been developed and utilized on a large scale worldwide. The random and fluctuating nature of wind power output poses a threat to the secure and stable operation of the system. Consequently, the transmission of wind power has garnered considerable attention as a crucial factor in mitigating the challenges associated with wind power integration. In this paper, an artificial-intelligence-aided frequency coordination control strategy applicable to wind power transmission systems based on hybrid DC transmission technology is proposed. The line commutated converter (LCC) station at the sending end implements the strategy of auxiliary frequency control (AFC) and automatic generation control (AGC) to cooperate with each other in order to assist the system frequency regulation. The AFC controller is designed based on the variable forgetting factor recursive least squares (VFF-RLS) algorithm for system identification. First, the VFF-RLS algorithm is used to identify the open-loop transfer function of the system. Then, the AFC controller is designed based on the root locus method to achieve precise control of the system frequency. The DC line power modulation quantity is introduced in the AGC to automatically track the active power fluctuation and frequency deviation of the system. The AGC utilizes the classical proportional-integral (PI) control. By selecting the integrated time absolute error (ITAE) performance index to construct the reward function, and using a deep Q-network (DQN) for controller parameter optimization, it achieves improved regulation performance for the AGC. The voltage source converter (VSC) station at the receiving end implements an adaptive DC voltage droop control (ADC)strategy. Finally, the effectiveness and robustness of the proposed frequency control strategy are verified through simulation experiments.

Technical and economic feasibility of a small vertical axis wind turbine in low wind conditions compared to other power sources for pumping water

In a global context, the significance of transitioning to renewable energy sources is paramount for sustainable development. This relevance is particularly evident in Brazil, where significant strides have been made in microgeneration of photovoltaic solar power and on-shore large-scale horizontal-axis wind power. Despite the country's extensive energy capacity from hydroelectric facilities, still approximately 38.5 million people reside in rural areas and surrounding areas, emphasizing the need for effective and affordable energy solutions for these regions. Based on this social and business opportunity of small-sized devices used in sustainable development, this practical experimentation explores the viability of initial studies from a prototyped small vertical-axis wind turbine (VAWT) system for water pumping, comparing its technical and economic feasibility with commercial off-the-shelf (COTS) photovoltaic solar pump (PV) and conventional electrical pump systems (EP). Two scenarios are investigated: (1) a patented transmission mechanism connected to a double effect pump and (2) the VAWT system designed to be connected to the same mechanism. Compared to PV ‘systems, scenario (1) offers 1.4x higher flowrate at a significantly lower cost (137% less) and consumes half the power. It offers a lower flowrate (2.6x) than EP systems but at a lower cost (36% less) and consumes one-third the power. Scenario (2) provides 2.7x less water flow than PV at a slightly lower cost (41% less). While less efficient in water flow (10 x) compared to EP, it has a slightly higher cost (15% more). This research builds upon existing knowledge and offers valuable insights for future small VAWT applications in low-wind remote areas for water pumping.

Large-scale off-grid wind power hydrogen production multi-tank combination operation law and scheduling strategy taking into account alkaline electrolyzer characteristics

This paper proposes a multi-electrolyzer switching scheduling strategy based on the Pelican Optimization Algorithm (POA) to enhance the efficiency of large-scale wind power hydrogen production systems. To validate the effectiveness of the proposed strategy, we analyzed wind power data from three typical days in northern Hebei, China, with a 2.5 MW wind turbine output. We designed three strategies for comparative analysis: a simple start-stop strategy, a rule-based multi-electrolyzer switching strategy, and a POA-based multi-electrolyzer switching strategy. The study results demonstrate that the POA-based strategy exhibits higher hydrogen production efficiency and system stability under various wind conditions. Particularly, in extreme wind scenarios, this strategy significantly reduces the start-stop cycles of electrolyzers, thereby lowering operational costs and improving overall system performance. The main contribution of this study lies in the novel optimization algorithm and its validation through real-world data, demonstrating its effectiveness in large-scale wind power hydrogen production systems. Our findings provide new insights for enhancing the utilization of renewable energy and the economics of hydrogen production systems.

Consuming the redundant renewable energy by injecting hydrogen into the gas network: A case study of electricity-gas-hydrogen coupled system of Ireland

Converting surplus electricity into hydrogen is considered a promising way for accommodating large-scale wind or solar power. However, it is expensive to establish a new-built hydrogen network or storage facilities at the current stage. Hydrogen-enriched compressed natural gas (HCNG), a mixture of natural gas and hydrogen, can be an alternative approach to consuming surplus renewable energy in the short-medium term. This paper investigates the feasibility of establishing an electricity-gas-hydrogen coupled energy system by modeling the HCNG network, power grid, and hydrogen electrolyzer in Ireland with consideration of pipeline characteristics. The economic analysis based on the planning results of the coupled system is presented in detail. When the hydrogen injection ratio reaches 20% at the HCNG stations, the system operating cost and carbon emission can be reduced by 24.2% and 6.4%, and 100% wind can be consumed.

Identification of vulnerable lines in power grids with wind power integration based on topological potential

Vulnerable transmission lines are one of the weak links in the operation of the power system, as their damage can trigger widespread blackouts and cascading failures. The integration of large-scale wind power generation into the grid has resulted in significant changes to both the topology and distribution patterns of power flows inside the system, exacerbating line vulnerabilities. Identifying these lines can enhance power system safety through vigilant monitoring or augmenting their carrying capacity. In this paper, a method for identifying vulnerable lines based on the theory of topological potential is proposed. The method is based on the N-1 fault model of optimal DC power flow, and a corresponding network is constructed for the power system. The task of pinpointing weak points in a complicated network is converted into an assessment problem of important nodes. The proposed index comprehensively considers the in-degree and out-degree topological potentials of nodes, and uses the entropy weight method for weight allocation. Meanwhile, the TLS distribution is used to fit the wind power prediction error. Finally, simulation experiments are conducted on the IEEE 39-bus system. Simulation results indicate that the most vulnerable transmission lines tend to be located at the hubs of the system’s topological network and in close proximity to wind farms.

Multistep Forecasting Method for Offshore Wind Turbine Power Based on Multi-Timescale Input and Improved Transformer

Wind energy is highly volatile, and large-scale wind power grid integration significantly impacts grid stability. Accurate forecasting of wind turbine power can improve wind power consumption and ensure the economy of the power grid. This paper proposes a multistep forecasting method for offshore wind turbine power based on a multi-timescale input and an improved transformer. First, the wind speed sequence is decomposed by the VMD method to extract adequate timing information and remove the noise, after which the decomposition signals are merged with the rest of the timing features, and the dataset is split according to different timescales. A GRU receives the short-timescale inputs, and the Improved Transformer captures the timing relationship of the long-timescale inputs. Finally, a CNN is used to extract the information of each time point at the output of each branch, and the fully connected layer outputs multistep forecasting results. Experiments were conducted on operation data from four wind turbines located within the offshore wind farm but not near the edge. The results show that the proposed method achieved average errors of 0.0522 in MAE, 0.0084 in MSE, and 0.0907 in RMSE on a four-step forecast. This outperformed comparison methods LSTM, CNN-LSTM, LSTM-Attention, and Informer. The proposed method demonstrates superior forecasting performance and accuracy for multistep offshore wind turbine power forecasting.

Stochastic and Extreme Scenario Generation of Wind Power and Supply–Demand Balance Analysis Considering Wind Power–Temperature Correlation

In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand balance analysis based on it. Firstly, the influence of temperature on wind power output is analyzed via Pearson coefficient to obtain the correlation between wind power and temperature. Secondly, based on the historical wind power curve, a large number of wind power output scenarios are randomly generated while fully preserving its characteristics, and probabilistic supply–demand analysis is carried out. Thirdly, for the extreme case of continuous multi-day extreme heat without wind, extreme scenarios are selected from the generated scenarios for supply–demand balance analysis. Finally, a practical example in a province in central-eastern China is used to verify the effectiveness of the proposed method. The results indicate that the scenario generation method can effectively capture the historical wind power characteristics and can be better applied to the diversified supply and demand balance analysis to obtain more accurate analysis results.

Low-carbon economic dispatching strategy based on feasible region of cooperative interaction between wind-storage system and carbon capture power plant

The high penetration of new energy into the grid is an effective method for reducing carbon emissions. However, the randomness and uncertainty of large-scale wind power (WP) output pose challenges to the grid dispatch operation and increase the requirements for carbon emission control. This paper proposes a low-carbon economic dispatching strategy based on the feasible region (FR) of cooperative interaction between WP and flexible resources to promote the consumption of WP and reduce carbon emissions. In particular, energy storage (ES) and carbon capture power plant (CCPP) are used as a set of adjustable flexible resources, and the power FR of WP, ES and CCPP is constructed to provide a visualization reference for grid scheduling. Then, a two-stage robust optimization (RO) dispatching FR-based model considering WP consumption capacity is proposed to cope with the operational risk of WP output stochasticity for power grid. Finally, the column constraint generation algorithm (C&CG) is used to solve the problem and evaluate low carbon level in the system according to the carbon flow theory. The simulation results verified the effectiveness of the suggested approach demonstrated by the modified IEEE 39-bus and IEEE 118-bus test cases.

Design of Series-Connected Novel Large-Scale Offshore Wind Power All-DC System with Fault Blocking Capability

The utilization of wind power all-DC systems with DC collection and transmission is an effective solution for the extensive development of wind power in deep-sea areas. However, in the event of faults occurring in wind power all-DC systems, the fault propagation speed is extremely rapid, with a wide-ranging impact, and to date, there are no complete DC engineering references available. It is crucial to research the topology and fault isolation methods applicable to large-scale offshore wind power all-DC systems in deep-sea areas. This paper proposes a novel series-connected all-DC system topology and presents corresponding fault isolation methods for internal faults in wind turbine units and faults in high-voltage DC transmission lines. The system simulation model was constructed using PSCAD/EMTDC (v4.6.3), and simulations were conducted for internal faults in the wind turbine units and DC transmission line short-circuit faults. The simulation results demonstrate that the proposed system can isolate various DC faults while maintaining stable operation, thereby validating the effectiveness of the control strategies and fault isolation methods proposed in this paper.