Characteristics Of Wind Power Research Articles

Offshore Wind Power: Progress of the Edge Tool, Which Can Promote Sustainable Energy Development

Offshore wind is renewable, clean, and widely distributed. Therefore, the utilization of offshore wind power can potentially satisfy the increasing energy demand and circumvent the dependence on fossil energy. Thus, offshore wind power is an edge tool for achieving sustainable energy development because of its potential in large-scale energy supply and its important role in reducing environmental pollution as well as carbon emission brought by fossil energy. The worldwide development of offshore wind power has entered the era of large-scale research and commercial application. It displays a trend of rapid development, continuous technological breakthroughs, and high-speed market growth. This article systematically introduces the structural components and technical characteristics of offshore wind power. Moreover, the current developmental status of offshore wind power is summarized. By reviewing the current development and application status of offshore wind power technology worldwide, large wind turbines and fixed and floating offshore wind power technologies are analyzed. Additionally, the development of the offshore wind energy market is overviewed. The policy condition and key aspects such as the construction, operation, and maintenance of offshore wind power plants are also summarized. Finally, the prospective challenges and development trends of offshore wind power and its significance in achieving sustainable society development are proposed. We consider that the article can provide reference and inspiration for researchers and developers dedicated to offshore wind power.

Optimal Coordinated Operation for Hydro–Wind Power System

The intermittent and stochastic characteristics of wind power pose a higher demand on the complementarity of hydropower. Studying the optimal coordinated operation of hydro–wind power systems has become an extremely effective way to create safe and efficient systems. This paper aims to study the optimal coordinated operation of a hybrid power system based on a newly established Simulink model. The analysis of the optimal coordinated operation undergoes two simulation steps, including the optimization of the complementary mode and the optimization of capacity allocation. The method of multiple complementary indicators is adopted to enable the optimization analysis. The results from the complementary analysis show that the hydraulic tracing effect obviously mitigates operational risks and reduces power losses under adverse wind speeds. The results from the analysis of capacity allocation also show that the marginal permeation of installed wind capacity will not exceed 250 MW for a 100 MW hydropower plant under random wind speeds. These simulation results are obtained based on the consideration of some real application scenarios, which help power plants to make the optimal operation plan with a high efficiency of wind energy and high hydro flexibility.

The balance issue of the proportion between new energy and traditional thermal power: An important issue under today's low-carbon goal in developing countries

The stability of the new power system depends on the balance between controllable flexible resources and uncontrollable uncertain resources. For many developing countries and regions lacking advanced flexible resources, thermal power is the main force in solving the problem of new energy consumption. Finding the balance between new energy and traditional thermal power is the key for today's low-carbon goal. To solve this problem, this paper calculates the power range and climbing rate range of thermal power based on physical principles and actual data, and characterizes the characteristics of wind power, photovoltaic power, load and net load based on the five proposed volatility indicators. Then, a logically clear method for determining the required number of thermal power units based on the volatility indicators of new energy from the mechanism and principle perspective is proposed. Finally, under the premise of meeting the power balance and flexibility balance of the power system, the reasonable ratios between the installed capacity of new energy and the number of thermal power units are calculated in four seasons. The results indicate that the judgment system can provide guidance for the energy structure of developing regions.

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.

Day-Ahead and Intraday Two-Stage Optimal Dispatch Considering Joint Peak Shaving of Carbon Capture Power Plants and Virtual Energy Storage

The anti-peaking characteristics of a high proportion of new energy sources intensify the peak shaving pressure on systems. Carbon capture power plants, as low-carbon and flexible resources, could be beneficial in peak shaving applications. This paper explores the role of carbon capture devices in terms of peak shaving, valley filling, and adjustment flexibility and constructs a virtual energy storage model utilizing various flexible loads on the demand side. Also, it proposes a joint peak shaving strategy involving carbon capture devices and virtual energy storage. Considering the predictive error characteristics of wind power and load across different time scales, this study establishes a day-ahead and intraday two-stage rolling regulation peaking model based on carbon capture power plants and virtual energy storage to fully leverage the diverse response speeds and peak shaving capabilities of various types of flexible loads. Initially, the model calculates the net load curve after implementing a demand response system, aiming to minimize the load peak–valley difference. Subsequently, within the intraday period, it seeks to minimize system operating costs by precisely allocating peak shaving resources as per demand, thus aiming for economic efficiency while ensuring the system’s peak shaving capability.

Low carbon economic dispatch of integrated energy systems considering utilization of hydrogen and oxygen energy

Power-to-gas (P2G) facilities use surplus electricity to convert to natural gas in integrated energy systems (IES), increasing the capacity of wind power to be consumed. However, the capacity limitation of P2G and the anti-peaking characteristic of wind power make the wind abandonment problem still exist. Meanwhile, the oxygen generated by P2G electrolysis is not fully utilized. Therefore, this study proposes a low-carbon economic dispatch model considering the utilization of hydrogen and oxygen energy. First, the two-stage reaction model of P2G is established, and the energy utilization paths of hydrogen blending and oxygen-rich deep peaking are proposed. Specifically, hydrogen energy is blended into the gas grid to supply gas-fired units, and oxygen assists oxygen-rich units into deep peaking. Subsequently, the stochastic optimization is used to deal with the uncertainty of the system, and the objective function and constraints of the IES are given to establish a low-carbon dispatch model under the energy utilization model. Finally, the effectiveness of the proposed method is verified based on the modified IEEE 39-node electric network, 20-node gas network and 6-node heat network models.

Optimal Operation Scheduling of Multi-energy Complementary Systems Including Offshore Wind Power

Background: With the rapid development of offshore wind power in China, offshore wind power is accounting for an increasing proportion of the whole installed power generation in China, which put forward higher requirements for operation scheduling of multi-energy complementary systems including offshore wind power. However, existing studies have paid little attention to the complementary output scheduling of multi-energy systems with a high proportion of offshore wind power, this phenomenon is not conducive to the realization of reasonable operation scheduling. Objective: To realize the reasonable operation scheduling of multi-energy systems with a high proportion of offshore wind power. Methods: Firstly, the output characteristics of offshore wind power were analyzed and summarized, and modeling was conducted from the perspectives of time distribution, spatial correlation, volatility and randomness, which would serve as the theoretical basis for subsequent researches. Secondly, the complementary rule among offshore wind power and the multi-energy system was analyzed, and the complementary characteristic index was put forward. Then the safety operation was taken as the constraint, and the two-stage operation model of the integrated energy system was established, which considers the auxiliary service costs. In the first stage, the initial generation scheduling was developed according to the day-ahead forecast values of the offshore wind power; in the second stage, the more accurate intra-day operation scheduling was further achieved by considering the intra-day forecast values of offshore wind power and the day-ahead scheduling results. Costs of rotating reserve capacity could be reduced by correcting the deviation of generating capacity, then the revenue-maximizing scheduling strategies were obtained and the output of the pumped-storage power plants was optimized. Results: The effectiveness of the model proposed in this paper was verified based on the simulation examples. Compared with the traditional scheduling strategy, the scheduling strategy considering auxiliary service cost proposed in this paper can promote the consumption of offshore wind power, reduce the wind curtailment proportion and improve the electricity sales revenues in the end. Conclusion: The research results in this paper provide a reference for the reasonable consumption of multi-energy complementary systems with a high proportion of offshore wind power and improving electricity sales revenues

Short-term prediction of wind power based on grey wolf optimization Elman neural network algorithm

The intermittence and fugitiveness of wind power is one of the significance ekements affecting the quality of wind power. Aiming at the random characteristics of wind power, the output power of short-term wind electricity is forecasted by collecting historical wind speed, power and other parameters of wind farm. A model of Elman network optimized by grey wolf arithmetic (GWO) is proposed to realize short period accurate forecast for wind power. The pattern is verified and analyzed on matlab. Compared with other prediction models, the prediction accuracy is higher, which shows the effectiveness and advancement of the model.

Analysis of the influence of large‐scale offshore wind power on the steady‐state voltage of the vicinity region of grid connection point

AbstractThe power characteristics of offshore wind power will change the regional power flow distribution and affect the regional voltage. Here, the direct‐drive wind turbine generator controlled by unity power factor is selected as the research object, and the influence of change of wind power on voltage at the grid connection point is analyzed from the simple integration model. Based on the two critical states of the external equivalent power of the grid connection point, an analytical expression of the output of wind power in the corresponding state is obtained, and the relationship among the critical state outputs, the output of wind power and the parameters at the grid connection point is drawn, hence the related influence laws is analyzed. The influence of change of wind power on voltage and regional reactive power and the reactance distribution of transmission lines near the grid connection point is considered to analyze the influence of offshore wind power on the vicinity region of grid connection point. The rationality of the conclusion is verified by study the cases of the integration of offshore wind power into Guangdong power grid, and some suggestions are put forward for the planning of offshore wind power integration.

Experimental investigation of wind turbine stress and power characteristics under dynamic changes in wind direction

ABSTRACT The dynamic inlet conditions of variable wind direction had an obvious influence on the response of wind turbine stress and power, and accurately analyzing their changing characteristics is of great significance to enhancing wind turbines’ stability and efficient operation. Experiments were performed in a wind tunnel, where the wind turbine was mounted on a rotating platform to simulate dynamic changes in wind direction at prescribed speeds and angles. The wind turbine’s stress and power were measured. Results show that the stress was minimally reduced by 4.4% (tower) and 1.7% (blade) when the wind direction change angle was small. Maximum stress reduction of 92% (tower) and 93% (blade) with increasing wind direction change angle. As the wind direction change speed increased, the magnitude of the drop in blade stress decreased by 6% ~32%. The decrease in power coefficient and rotational speed at small wind direction change angles and large wind direction change speeds were not significant, and the fluctuations were more obvious. The coupling of aerodynamic deterioration and inertial effects during dynamic changes in wind direction was one of the main reasons for the variations in the wind turbine power and stress response.

Optimal scheduling of electric-gas-heat system considering dual virtual energy storage of gas network management and heat network storage

Aiming at the problem of wind curtailment caused by the lack of system flexibility, an optimal scheduling strategy for improving the flexibility of the electricity-gas-heat interconnection system by the coordinated operation of the gas network management and storage characteristics and the heat storage characteristics of the heat network is proposed. Firstly, the influence of gas network storage and heat network storage on improving flexibility is analyzed respectively. Then, an electricity-gas-heat interconnected system scheduling model considering the dynamic characteristics of gas network management and t the delay characteristics of heat storage in heat network, which greatly improves the imbalance between supply and demand of flexibility in space and time brought by the anti-peak regulation characteristics of wind power to the system. Finally, the IEEE-24 nodes power system, Belgium’s 20-node natural gas network and 6-node thermal network are used, for example, analysis. The results show that the proposed scheduling scheme improves the flexibility of the system while its operating cost and wind curtailment cost are the lowest, which promotes the consumption of wind power.

Optimal operation of electric–freshwater energy system considering load regulation strategy of individual hydrogen electrolyzer

AbstractIn view of the existing literature, only the optimization operation of hydrogen energy–wind/light new energy is considered, and the research problems such as comprehensive utilization of water resources and load control strategies are ignored. Based on the analysis of load characteristics of offshore wind power, this paper has established an optimal operation model of power‐fresh water energy system based on “wind‐hydrogen‐water‐electricity” interaction. Meanwhile, an electrolytic hydrogen individual load control strategy is proposed to match wind power fluctuations from the perspective of internal load regulation of electrolytic hydrogen system. From the economic characteristics, operation characteristics, accommodation situation and other simulation analysis, it can reduce the total operating cost by about 3.1%, improve the utilization rate of electrolytic cell capacity, and meet the water demand of coastal users. It shows that the individual control strategy and optimal operation model have advantages, which is of great significance for realizing low‐carbon, safe, and economical operation of power grid in the future.

The Correlation between the Power Quality Indicators and Entropy Production Characteristics of Wind Power + Energy Storage Systems

The Correlation between the Power Quality Indicators and Entropy Production Characteristics of Wind Power + Energy Storage Systems

Enhancing envrionmental sustainability via interval optimization for low-carbon economic dispatch in renewable energy power systems: Leveraging the flexible cooperation of wind energy and carbon capture power plants

In response to pressing environmental challenges, the power system is gradually developing towards a more efficient, clean, and sustainable direction. Carbon capture power plants (CCPPs) and wind power generation will play a significant role in this eco-friendly transformation. However, the high energy consumption of CCPPs and the anti-peak regulation characteristics of wind power lead to contradictions in their coordinated operation during peak shaving, restricting the system's low-carbon economic development. Aiming at the above challenges, an interval optimization method is proposed to coordinate wind power and CCPPs. Firstly, a flexible operation model of CCPPs is established, and the coordinated scheduling mechanism of wind power joint carbon capture operation and load-side demand response is introduced to optimize the system carbon emission through low carbon scheduling on both sides of the source and load. Secondly, interval numbers are used to manage the uncertainty of wind power output, and the interval-optimized scheduling model of wind power combined CCPPs is established. To solve the proposed model, the improved interval possibility method is adopted to transform the uncertain model into a deterministic one, which is solved by the CPLEX solver. Finally, the simulation outcomes using the IEEE 30-bus system framework reveal that the proposed approach significantly enhances the coordination between CCPPs and wind power. Notably, when compared to scenarios that solely focus on CCPPs, the proposed method achieves a substantial reduction in total costs by 16.67 % and decreases carbon emissions by 26.53 %. This sets a significant reference for low-carbon economic dispatch within power systems, providing key insights and foundational guidelines for future implementations, as well as novel strategies and technological support for the sustainable transformation of the power system towards lower carbon emissions.

Temporal collaborative attention for wind power forecasting

Wind power serves as a clean and sustainable form of energy. However, its generation is fraught with variability and uncertainty, owing to the stochastic and dynamic characteristics of wind. Accurate forecasting of wind power is indispensable for the efficient planning, operation, and grid integration of wind energy systems. In this paper, we introduce a novel forecasting method termed Temporal Collaborative Attention (TCOAT). This data-driven approach is designed to capture both temporal and spatial dependencies in wind power generation data, as well as discern long-term and short-term patterns. Utilizing attention mechanisms, TCOAT dynamically adjusts the weights of each input variable and time step based on their contextual relevance for forecasting. Furthermore, the method employs collaborative attention units to assimilate directional and global information from the input data. It also explicitly models the interactions and correlations among different variables or time steps through the use of self-attention and cross-attention mechanisms. To integrate long-term and short-term information effectively, TCOAT incorporates a temporal fusion layer that employs concatenation and mapping operations, along with hierarchical feature extraction and aggregation. We validate the efficacy of TCOAT through extensive experiments on a real-world wind power generation dataset from Greece and compare its performance against twenty-two state-of-the-art methods. Experimental results demonstrate that TCOAT outperforms existing methods in terms of both accuracy and robustness in wind power forecasting. Moreover, we conduct a generality study on an additional real-world dataset from a different climate condition and wind power characteristics. The results show that TCOAT can achieve comparable or better performance than the state-of-the-art methods, confirming the generalization ability of TCOAT.

Short-term Wind Power Prediction based on CEEMDAN-TCN

In order to improve the accuracy of wind power prediction, a wind power prediction method based on time series decomposition and error correction is proposed in this paper. Firstly, the maximum information coefficient (MIC) method is used to select the features with strong correlation with wind power, so as to reduce the complexity of the original data; Then, according to the non-stationary characteristics of wind power, the wind power is decomposed into several stationary subsequences by using adaptive noise complete set empirical mode decomposition (CEEMDAN); Finally, the time convolution network (TCN) is used to dynamically model the multivariable time series of wind power; In order to further improve the prediction accuracy, a light quantization hoist (LightGBM) is introduced to correct the error of the prediction value. The simulation results show that the proposed method has higher short-term wind power prediction accuracy than other prediction models.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of wind- driven generators, a min-max-min two-stage robust optimization model is presented, considering the unit commitment, source-network load collaboration, and control of the load demand response. After the constraint functions are linearized, the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method. The minimum-maximum of the original problem was continuously maximized using the iterative method, and the optimal solution was finally obtained. The constraint conditions expressed by the matrix may reduce the calculation time, and the upper and lower boundaries of the original problem may rapidly converge. The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately; otherwise, it is easy to cause excessive accommodation of wind power at some nodes, leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power. Thus, the most economical optimization scheme for the worst scenario of the output power of the generators is obtained, which proves the economy and reliability of the two-stage robust optimization method.

Research on Two-Stage Regulation Method for Source–Load Flexibility Transformation in Power Systems

Under the premise of continuously increasing the grid-connected capacity of new energy, the fluctuation and anti-peak shaving characteristics of wind power have always constrained the development of green power systems. Considering the characteristics of power system flexibility resources, this paper introduces a two-stage regulation approach for power systems with enhanced source–load flexibility. In the day-ahead stage, an advanced peak regulation transformation is employed, leveraging the combined heat storage device of conventional thermal power units to enhance their peak regulation capability. Additionally, the Energy Intensive Load (EIL) is integrated into the regulation system. A two-level coordinated optimization model is developed, incorporating wind power integration and dispatching power allocation, with the aim of optimizing wind power integration and achieving the optimal allocation of dispatching power. In the intra-day stage, the connection of wind plants and energy storage devices is utilized to minimize the wind power fluctuations and improve the control ability over wind power variations. Compared with traditional methods, the wind power consumption in Scenario 1 and Scenario 2 increases by 2741.1 MW/h and 2478.5 MW/h respectively. Furthermore, the inclusion of an energy storage device in the intra-day stage significantly reduces the wind power fluctuations, maintaining a stable fluctuation rate within ±1%. Therefore, this method can effectively improve the level of wind power consumption and reduce the impact of real-time fluctuations on the power system.

Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation

The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction.

Wind Power-Photovoltaic-Hydropower Optimization in Watershed Based on Wind Power-Photovoltaic Cluster Convergence

With the construction of wind power-photovoltaic-hydropower (WVH-system) clean energy base in the river basin, the complex geographical distribution characteristics of WVH-system along the river basin and the randomness and fluctuation of their output bring challenges to the basin on a large scale. How to consider the complementary characteristics of wind-photovoltaic (WV-power) output and cluster effect to smooth the WV-power output curve is an important way to solve this challenge. For this reason, a WVH-system bundling optimization method based on WV-power cluster convergence is proposed in this paper. Considering the complementary coupling characteristic of wind power and photovoltaic output, the output characteristic index of the WV-power cluster is constructed to evaluate the output complementarity, output stationarity and load tracking characteristics of the WV-power cluster. Based on the characteristic index, the clustering method of the WV-power cluster is constructed to divide the WV-power power cluster along the river basin. A basin WVH-system bundling optimization model is developed based on the division of clusters and efficient utilization of pumped storage units’ regulation capacity. Finally, the WVH-system data of a river basin in southwest China are taken as an example to verify the effectiveness of the proposed method.