Wind Energy Consumption Research Articles

Aerodynamic stability and vibration mechanism of long-span flat roofs with various span to eaves-height ratios

Computational fluid dynamics (CFD) based on large eddy simulation (LES) is used to investigate the aerodynamic stability of long-span flat roofs with various span/eaves-height ratios (L/H) in this study. Forced vibration tests, where vibrations are induced in the first antisymmetric mode with various frequencies, are conducted on vibrating as well as rigid roofs with L/H = 3, 6, and 9 to obtain distributions of the wind pressure coefficients. The primary focus is the effect of L/H and that of the approach flow turbulence on the aerodynamic stability of the roof, which is quantified using energy consumption. The work done by the unsteady aerodynamic forces on the vibrating roof during the period of vibration was computed using the time histories of the wind pressures at various points on the roof. Furthermore, fluctuating wind pressures and energy consumption are characterized using complex proper orthogonal decomposition (CPOD) analysis. Finally, the vibration mechanism is discussed based on the energy consumption and convection of the wind pressures obtained from CPOD analysis. The results indicate that smooth flow has a higher probability of inducing aerodynamically unstable vibration than turbulent flow because of the phase excitation.

Wind energy, industrial-economic development and CO2 emissions nexus: Do droughts matter?

This study empirically explored the nexus of wind energy (with regards to the effect of the drought), industrial-economic development, and emitted CO2 in 41 World's top countries in wind energy consumption from 1997 to 2018. Cross-sectional augmented distributed lag estimators (CS-DL, CS-ARDL, CCE-P) and newly updated estimation packages to effectively assess the relationships between variables. Our results are the following: First, severe droughts were not a significant matter in wind energy, and consuming wind energy reasonably contributes to reducing emitted CO2, while industrial and economic development positively promotes CO2 emissions in sampled countries. Second, industrial development significantly promotes economic growth, while wind energy use has an insignificant positive effect on economic growth. Moreover, wind energy negatively affects industrial development. Third, two-way directional causal relationships were noted between CO2 and other covariates, this hypothesis was also noted between industrial development and economic growth and wind energy use. We, therefore, suggested policy implications to reduce CO2 across the globe and country-specific and consider the positive effect of wind energy on growth.

The impact of hydro-biofuel-wind-solar energy consumption and coal consumption on carbon emission in G20 countries.

Globally, as a renewable source of energy, biofuel, wind, solar, and hydro energy have been increasingly valued by a range of businesses. There is no denying the advantages of using renewable energy in terms of preventing climate change and protecting the environment. Therefore, this article investigates the relationship among hydro, solar, biofuel, wind, coal consumption, and CO2 emissions in 19 G20 member countries between 2000 and 2019. The results of dynamic fixed effects and the system-generalized method of moments suggest that solar energy and biofuel usage have a negative and statistically significant influence on carbon emissions. On the other hand, the impact of wind energy consumption on carbon dioxide emissions is positive and statistically significant. However, hydro energy consumption is statistically insignificant. The significance of this study is to include wind energy, solar energy, and hydro energy as new indicators for a group of 19 G20 member countries. Furthermore, this research paper also includes coal consumption in the existing model to confirm that CO2 emissions may rise if more coal is consumed. Growing the solar-hydro-biofuel-wind industry can reduce nonrenewable energy consumption and has tremendous potential to diminish carbon dioxide emissions.

The role of wind energy towards sustainable development in top-16 wind energy consumer countries: Evidence from STIRPAT model

Wind energy is the second most extensively used source of renewable energy. This has the potential to maintain long-term economic growth by meeting rising global energy demand while also addressing climate issues. This study explores the factors that drive wind energy consumption and emission reduction in the top-16 wind energy consumer countries from 1992 to 2020. China (38.5 %), the United States of America (16.1 %), and Germany (8.5 %) have the highest shares in terms of global wind turbine capacity. The main implication of this study is that it uses the cubic form of income to explain the technique, scale, and cumulative effects on wind energy and carbon emissions. The study evaluates the mediation effect of income and foreign investment to promote wind energy consumption and reduce emissions. This study explored whether the increment of wind energy capacity is derived from economic growth. The results show an inverted N-shaped curve between income and wind energy consumption. The carbon emissions model shows an N-shaped environmental Kuznets curve. The feedback hypothesis is supported by causality analysis between wind energy and growth. Energy policies aimed at accelerating the transition from fossil fuels to renewables will affect wind energy consumption and wind capacity.

Machine learning based renewable energy generation and energy consumption forecasting

ABSTRACT Renewable energy generation sources are the need of the hour to mitigate the challenges of fossil fuels, environmental impacts, large transmission and distribution losses, and variable consumption pattern. Renewable energy such as solar PV generation or wind generation is most unpredicted generation and depends upon weather constraints. A promising prediction of generation is a suitable solution for this problem. A machine learning-based conventional neural network algorithm is proposed to forecast the generation and calculate the errors in generation forecast. The generated energy is supplied to consumers, but the consumers consume electricity by their choice and an energy management model is required at the consumer end to supply the available predicted generation to develop a self-sustainable microgrid. The energy consumption at consumer end is essential to predict, and based on the predicted consumption at hourly bases, the cost model for the consumers is developed. The consumption profile is forecasted using long short-term memory as it is a good tool for time-series forecasting using available sequential load consumption data. The demand is forecasted and analyzed for selected load categories (i.e. fixed, non-shift-able, and shift-able loads). The evaluation matrix mean square error (MSE), mean absolute error, and root mean square error (RMSE) are used for the error calculations. The RMSE for solar and wind power generation forecasting have been 2.472 and 3.034, respectively. The RMSE for fixed, non-shiftable, and shiftable demand forecasting have been 0.027, 0.067, and 0.21, respectively. The results show that the proposed methods for predictions of solar, wind power generation and energy consumption forecast achieve better accuracy. The presented methodology can be implemented with energy storage for renewable energy production and demand management for energy from sustainable energy resources.

Optimization and analysis of an integrated energy system based on wind power utilization and on-site hydrogen refueling station

An integrated energy system coupled with wind turbines and an on-site hydrogen refueling station is proposed to simulate the future scenario, which can meet the demands of cooling, heating, power and hydrogen. The system was modeled to calculate the capacity and annual operation of each equipment with the total annual cost as the optimization objective. This study evaluates the performance of the system based on the results. When the system is configured with 0–10 wind turbines, the economics, energy consumption and carbon emissions improve as the scale of wind turbines increases. Energy utilization and wind power utilization are above 66.79% and 99.73%, respectively. The on-off coefficient of the power generation unit can affect energy efficiency. When the system contains 5 turbines, 91% of the hydrogen can be self-produced with the minimum amount of energy redundancy.

Low-carbon optimal dispatch of virtual power plant based on time-of-use ladder carbon emission rights exchange mechanism

In view of the problems of abandoning wind and solar energy, and carbon emissions, inevitable reduce of thermal power generation caused by the consumption of wind and solar energy, and the changes in the proportion of wind and solar energy consumption to thermal power at different times under the influence of time-of-use price, a time-of-use ladder carbon emission rights exchange mechanism is proposed, and on this basis, a low-carbon virtual power plant optimal dispatching model with ‘wind-solar-gas-storage’ is built. The maximum daily operating income of the virtual power plant is taken as the objective function in the model. The IBM commercial solver CPLEX is employed to optimize the solution, so as to obtain the optimal economic dispatching strategy of the virtual power plant, and compares it with the set scenarios. The simulation results reveal that the proposed mechanism can effectively enhance the enthusiasm and operating benefits of virtual power plants for renewable energy consumption, and reduce carbon emissions, providing new methods for low-carbon dispatching, and wind and solar energy consumption of virtual power plants.

Deep learning model-transformer based wind power forecasting approach

The uncertainty and fluctuation are the major challenges casted by the large penetration of wind power (WP). As one of the most important solutions for tackling these issues, accurate forecasting is able to enhance the wind energy consumption and improve the penetration rate of WP. In this paper, we propose a deep learning model-transformer based wind power forecasting (WPF) model. The transformer is a neural network architecture based on the attention mechanism, which is clearly different from other deep learning models such as CNN or RNN. The basic unit of the transformer network consists of residual structure, self-attention mechanism and feedforward network. The overall multilayer encoder to decoder structure enables the network to complete modeling of sequential data. By comparing the forecasting results with other four deep learning models, such as LSTM, the accuracy and efficiency of transformer have been validated. Furthermore, the migration learning experiments show that transformer can also provide good migration performance.

The drone-assisted variable speed asymmetric traveling salesman problem

We introduce and solve the Drone-Assisted Variable Speed Asymmetric Traveling Salesman Problem, which considers different flight times for the drone depending on the selected speed level and weather conditions, as a result of the natural asymmetry intrinsically involved in the delivery operations. Moreover, we provide an extension that introduces an additional decision regarding the speed of the drone influencing energy consumption. We formulate mixed-integer linear programming models (MILP) and develop two metaheuristic approaches: a variable neighborhood descent (VND) and a multi-neighborhood tabu search (MTS) algorithm. To assess their performance, a set of instances based on existing benchmarks are proposed. Results point out that delivery systems are strongly sensitive to the features considered in this research, showing that the symmetric approach is not able to find feasible solutions when incorporating operational aspects, such as wind conditions and energy consumption. Furthermore, we demonstrate that the option to fly at lower speeds results in a decreasing makespan due to the increased use of the drone. Finally, we show that for all larger instances, the heuristics VND and MTS outperform the MILP solutions by at least 48% and 53%, respectively.

Assessment of role of green bond in renewable energy resource development in Japan

The main purpose of this research is to investigate the effects of issued green bonds and energy price volatility on the consumption of three types of green energy (wind, solar, and hydro) in Japan during 1990–2020. For this purpose, an autoregressive distributed lag (ARDL) estimation method was used. The main findings revealed the long-term positive impacts of issued green bonds and energy prices and the negative impact of geopolitical risk on the wind energy consumption. In contrast, geopolitical risk and energy prices have no significant short-term impacts on solar energy consumption in Japan. Solar energy and hydro energy experienced the most considerable impact from issuance of green bonds in Japan. Moreover, a unidirectional causal relationship runs from green bonds and geopolitical risk to wind, solar, and hydro-energy consumption. At the same time, a bidirectional linkage exists between energy prices and green energy (solar, wind, and hydro) consumption in Japan. As practical policies, Japan should try to develop green bond market and promote digital green financing tools that are more resilient and adaptable to the post–COVID-19 era.

Asymmetric linkages between wind energy and ecological sustainability: Evidence from quantile estimation

Renewable energy is gaining popularity since it is cheaper and more efficient than conventional energy sources. New green energy technologies, like wind energy, are primarily utilized to maintain ecological sustainability. This study looked at the asymmetric link between wind energy and ecological footprint in the top-10 wind energy-consumer economies (China, USA, India, UK, Germany, Spain, France, Canada, Brazil, and Italy) by using the data from the years 1991–2020. Most past research utilizes panel data techniques, which yield common findings on the wind energy-environment nexus, despite the reality that some nations have no evidence of such association on their own. This research, on the other side, employs a novel approach “Quantile-on-Quantile”, which can explore time-series interdependence in each economy separately to gain worldwide yet economy-related evidence for the linkage between the variables. The results imply that wind energy consumption minimizes ecological footprint at distinct quantiles in sample nations, with the exception of Spain and India, which show mixed findings. However, the degree of the asymmetric association of the variables varies by country, necessitating individual vigilance and caution on the behalf of governments when designing wind energy and ecological sustainability policies.

Does renewable energy consumption reduce energy ecological footprint: evidence from China

The modern economic growth paradigm relies heavily on natural endowments. Renewable energy as a permanent energy source has the potential to reduce the ecological footprint (EF). We adopt the Vector Autoregressive model to examine the impact of renewable energy consumption on the energy EF and use the quantile regression method to test the heterogeneity and asymmetry between energy EF and photovoltaic, wind energy, and biomass energy. The results show that renewable energy has a long-term negative impact on the EF, and for every 1% increase in renewable energy consumption, the energy EF will decrease by 2.91%. The contribution of renewable energy consumption to reducing the EF is 1.34% on average. There is no two-way Granger causality between renewable energy consumption and energy EF. The reduction effect of wind energy consumption on the energy EF varies the most, followed by biomass energy and photovoltaic. In addition, under different energy EF distribution conditions, the impact of photovoltaic or wind energy or biomass energy consumption on the energy EF is different.

Wind-Thermal-Energy Storage System Optimization: Evidence from Simulations of the Economical Consumption of Wind Energy

To realize the economical consumption of wind energy (WE), an optimal dispatch strategy for wind-thermal-energy storage systems (WTESSs) is proposed. The scheduling model is divided into two stages. In the first stage, the strategy aims to shave peaks and fill valleys in the load curve using a time-of-use (TOU) electricity price and to reduce the variance of the net load and use the energy time-shift characteristics of energy storage systems (ESSs) to optimize their charging and discharging power. In the second stage, the strategy minimizes the cost of WTESSs, obtaining the output power of the thermal power units (TPUs) in each period. Considering the actual need for carbon reduction, a method for calculating the TPUs’ life loss cost under different variable load amplitudes is introduced, and a thermal power peaking cost model considering the ladder-type carbon trading model is constructed to calculate the cost accurately. In addition, to account for the fact that connecting all wind power outputs to the grid will significantly increase the grid peak regulation pressure and operational risk, a mathematical model for WE utilization is established to find the optimal power value for wind power grid connection in each period, which enables economical and practical WE scheduling. According to the simulation results, the overall peak-shaving cost of the system can be reduced by up to 23.95%, and the thermal power deep peak regulation cost can be reduced by up to 90.06%.

The Nexus between Wind Energy Consumption, Economic Growth and Financial Development: Evidence from Panel Causality and Cointegration Test with Fourier Function

It is known that many countries have made intensive researches and made important technological developments in order to benefit from the wind energy potential, and even in today's conditions, they use some of this potential economically. In the light of these developments, it is important to investigate the relationship between wind energy, especially in the financial markets, and therefore with the economies of the countries. This study aimed to the relationship among wind energy consumption (WIND), economic growth (GDP) and financial development (FIN) for 26 selected OECD countries in period of January-2016 and November-2020. The long-term nexus between the panels has been investigated with fourier panel cointegration and fourier panel causality tests, which detect smooth transition multiple structural breaks using trigonometric functions. According to the panel cointegration test results, the panels move together in the long run, positively for WIND-FIN and negatively for WIND-GDP. Moreover, there is causality relations from both FIN and GDP to WIND. This is a support indicate that wind energy use is costly. Therefore, it would be beneficial to achieve both FIN and GDP to increase the use of renewable energy. When the country-based results are examined, it is understood that there is no causality relationship for 16 countries and that there is unidirectional (neutrality hypothesis) or bidirectional causality for the remaining 6 countries (growth or conservation hypothesis). The findings provide valuable information for policymakers about wind energy investment. In addition, bureaucratic difficulties and deficiencies related to wind energy should be reduced, the private sector should contribute more to renewable energy investments, and individuals should be made aware that renewable energy sources can replace non-renewable energy sources.

The impact of wind and geothermal energy consumption on economic growth and financial development: evidence on selected countries

The aim of this study is to investigate the impacts of wind and geothermal energy consumption on economic growth and financial development over the period 2016:M1 and 2020:M11. The data obtained from Germany, Iceland, Italy, Japan, Mexico, New Zealand, Portugal, Turkey, and the United States of America, which consume wind and geothermal energy, were utilized in the research study. To this end, after determining the cointegration relationship between the variables, the long-term elasticity coefficients were estimated by employing the FMOLS (2000) and DOLS (2001) models. As a result of the analysis, it was determined that geothermal energy consumption had a positive impact on financial development. Nonetheless, wind energy had no impact on financial development, whereas it had a negative impact on economic growth. According to the Dumitrescu–Hurlin causality test results, it was determined that a unilateral causality existed from wind and geothermal energy consumption to economic growth. These obtained findings, in favor of the conservative hypothesis, yielded important signals for investments to be made in the renewable energy sector as well as policymakers.

Integrated unit commitment and economic dispatch of combined heat and power system considering heat-power decoupling retrofit of CHP unit

Low-carbon development of a power system is realised via the flexibility retrofit of thermal power units with heat-power decoupling (HPD) of combined heat and power (CHP) units providing the necessary transformation pathway. First, this study examined the primary measures of HPD of CHP units and their impact on unit flexibility. Thereafter, aiming to realise flexibility retrofit of thermal power units, a detailed HPD model on the power side was proposed. In addition, the heat delay and heat loss in the process of heat transmission were also considered, followed by the establishment of a simulation method for unit commitment and economic dispatch (UCED) of CHP system considering HPD. Subsequently, based on IEEE RTS-79 test system, the effectiveness of the model and method proposed in this study was proven. Moreover, the proposed UCED method was applied to systematically analyse the comprehensive benefits achieved via the use of the HPD retrofit of the CHP units. The results showed that HPD retrofit has significant benefits in reducing dispatch costs, enhancing units' flexibility, and promoting wind and solar energy consumption. Finally, combined with the actual power supply and heating in Zhangjiakou, China, the HPD effect in the area was analysed to provide guidance and suggestions for the flexibility retrofit of local thermal power plants.

Multipoint Layout Planning Method for Multienergy Sources Based on Complex Adaptive System Theory

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy.

Optimal scheduling method of integrated energy system in parks considering wind and solar energy consumption

Aiming at the problem that the renewable energy cannot be efficiently utilized and the energy consumption cost is high in the production process of the park with wind and solar energy, an integrated energy system (IES) for the park that takes into account the wind and solar energy consumption is proposed. Optimize the scheduling method. First, an output model of the park’s comprehensive energy system including wind-solar renewable energy was established, and biogas was used as the gas source. Then, considering a variety of energy constraints, and taking the lowest energy consumption cost and the highest wind and light absorption rate as the goal, the improved multi-objective particle swarm algorithm is used to optimize the solution. Finally, the proposed method is verified by taking an integrated energy system of a park as a practical example. The results show that the proposed optimal scheduling method can effectively reduce the daily operating cost of the park and improve the local consumption rate of renewable energy.

CO2 emissions in the USA: new insights based on ANN approach.

The paper’s main aim is to forecast the carbon dioxide (CO2) emissions in the USA and its related components, analysing the contributions of each of those components to CO2 total volume. The empirical ground is a mix of non-linear tools, combining the artificial neural network (ANN) parametric method with a vector autoregressive (VAR) estimator. ANN includes 1 layer and 20 neurons, forecasting being based on the economic growth and net trade effects doubled by different types of renewable energy consumption. The accuracy of estimations for 14 targeted categories of CO2 emissions is ensured by 4360 observations, with 10 types of inputs over 1984M01–2020M04. ANN seems to offer superior forecasting accuracy compared with the widely used autoregressive methods, such as VAR model, but seems to be weak in capturing the output ‘spike’ forms. The main findings show that, although economic growth and net trade have an important contribution to the targeted outputs, the more prominent ones are wind, solar and total biomass energy consumption. Therefore, the CO2 emissions can be better controlled through non-polluting capacities, in parallel with the use of wind, solar and total biomass energies. The tool excellently predicts the CO2 emissions during pandemic crises being a good instrument in policy decisions. Modest contributions to CO2 prediction seem to have energy consumption generated by waste, hydroelectric power and renewable geothermal systems. This underlines an unclear current status given their collateral effects in environmental damages and high investment costs. The paper contributes to the literature in several ways. It is one of the first works focused on CO2 emissions forecasting in the USA based on a mixed approach by ANN and VAR types, considering an extended pallet of inputs to predict the volume of total CO2 emissions but also its components. As a novelty, the inputs combine both economic and environmental determinants. Not at least, the estimations are performed based on a large span, with monthly frequency.

Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine

To avoid the harmful effects of global warming on the earth planet and its atmosphere, the expansion of wind energy consumption based on new control techniques leads to improved energy capacity. The development of wind power plants on the sea is more efficient owing to the stronger wind flows in comparison with the onshore structure. Furthermore, stable installation of the offshore wind energy base meets the energy management requirements of today's consumers. As a result, dependency on fossil fuels as the main source of global warming decreases. The feedback control system and on-line sensor signals provide the stability and increased efficiency of the floating wind turbine. The aggressive environment and large size structure of the turbine lead to entering uncertainty in the turbine model and exogenous noises that should not be effectively compensated by conventional control methods. Therefore, a radial based functional neural network (RBFNN) controller is proposed to estimate and compensate the effect of uncertainty on feedback control of the wind turbine. For comparison purposes, a linear quadratic regulator (LQR) state feedback is also developed for optimal control of the floating wind turbines. The neural network adaptively determines the upper bound of uncertainty/noise. Therefore, conservative high-gain control actions to achieve robustness of the classical feedback controllers against structural deviations of the turbine body are decreased. In our newly proposed adaptive control algorithm, using a basic raised cosine function guides to restore the computational efficiency of RBFNN. With the Lyapunov-based stability analysis, the final limits of the closed-loop system and the convergence caused by the terminal sliding mode (TSM) tracking error are determined. Detailed software simulations of both the LQR and the designed RBFNN control systems indicate the superiority of the neural network-based approach.