Wind Sources Research Articles

A grid frequency control on small scale power system by introduction of variable speed diesel‐driven power plant

AbstractThis paper proposes the application of an adjustable speed diesel engine‐driven power plant employing a doubly‐fed induction generator to an isolated small‐scale power system including renewable power sources. This type of power plant can contribute to fast and flexible power balancing regulations under vacillating power supplies such as wind, solar, and other renewable power sources. Installation of a battery system is also considered, which can assist coordinately with the power plant to augment renewable power sources in the isolated power system.

Dynamics of the Atmospheric Boundary Layer over two middle-latitude rural sites with Doppler lidar

The Atmospheric Boundary Layer (ABL) over two middle-latitude rural sites was characterized in terms of mean horizontal wind and turbulence sources using a standard classification methodology based on Doppler lidar. The first location was an irrigated olive orchard in Úbeda (Southern Spain), representing one of the most important crops in the Mediterranean basin and a typical site with Mediterranean climate. The second location was PolWET peatland site in Rzecin (Northwestern Poland), representing one of the largest natural terrestrial carbon storages that have a strong interaction with the climate system. The results showed typical situations for non cloud-topped ABL cases, where ABL is fully developed during daytime due to convection, with high turbulent activity and strong positive skewness indicating frequent and powerful updrafts. The cloud-topped cases showed the strong influence that clouds can have on ABL development, preventing it to reach the same maximum height and introducing top-down movements as an important contribution to mixing. The statistical analysis of turbulent sources allowed for finding a common diurnal cycle for convective mixing at both sites, but nocturnal wind shear driven turbulence with marked differences in its vertical distribution. This analysis demonstrates the Doppler lidar measurements and the classification algorithm strong potential to characterize the dynamics of ABL in its full extent and with high temporal resolution. Moreover, some recommendations for future improvement of the classification algorithm were provided on the basis of the experience gained.

Editorial: Experimental and modelling approaches for clean combustion technologies

Despite the rapid growth in alternative energy sources utilization, combustion technologies will still 14 play a major role in the years to come. Indeed, the energy mix of the next decades will see a 15 coexistence of wind and solar sources and of combustion of e-fuels or synthetic fuels derived from 16 renewable energies or produced from electricity excess, biofuels produced from non-edible biomass 17 conversion (Kohse-Höinghaus 2022; Elishav et al. 2020, Dryer 2015 or unconventional sour fuels 18 (Gupta et al. 2016 The formation of particulate matter in fuel-rich combustion systems has been the object of numerous 65 studies in consideration of their negative effects on the human health and on the environment. In this 66 regard, soot oxidation is particularly relevant for mitigating the amount of carbon particulates 67 released from a combustion process (Niessner, 2014

Forecasting of non-renewable and renewable energy production in India using optimized discrete grey model.

Renewable energy delivers reliable power supplies and fuel diversification, enhancing energy security and lowering fuel spill risk. Renewable energy also helps conserve the nation's natural resources. Solar and other renewable energy sources have become increasingly prominent in recent years. India has achieved the 20 GW capacity solar energy production target before 2022. It is presently producing the lowest-cost solar power at the global level. Thermal energy has dominated the energy market. Countries have decided on energy generation from renewable sources and adopting green energy. This study forecasted non-renewable and renewable energy from multiple sources (hydropower, solar, wind and bioenergy) using grey forecasting model DGM (1,1,α). The comparative analyses with the classical models DGM (1,1) and EGM (1,1) revealed the superiority of the DGM (1,1,α). We also used CAGR for 2009-2019 to compare the actual and predicted data growth rate. The results show that non-renewable and renewable energy production is expected to increase. However, renewable energy generation wind sources continue to increase faster than hydropower, solar and bioenergy.

Fat Tails due to Variable Renewables and Insufficient Flexibility: Evidence from Germany

The large-scale integration of renewable energy sources requires flexibility from power markets in the sense that the latter should quickly counterbalance the renewable supply variation driven by weather conditions. Most power markets cannot (yet) provide this flexibility effectively as they suffer from inelastic demand and insufficient flexible storage capacity or flexible conventional suppliers. Research accordingly shows that the volume of renewable energy in the supply system affects the mean and volatility of power prices. We extend this view and show that the level of wind and solar energy supply affects the tails of the electricity price distributions as well and that it does so asymmetrically. The higher the supply from wind and solar energy sources, the fatter the left tail of the price distribution and the thinner the right tail. This implies that one cannot rely on symmetric price distributions for risk management and for valuation of (flexible) power assets. The evidence in this paper suggests that we have to rethink the methods of subsidizing variable renewable supply such that they take into consideration also the flexibility needs of power markets.

Integrated Energy Planning for Energy Management for Sustainable Development in Rural Areas: A Case Study of Jharkhand Village

Abstract: The goal of an energy audit is to balance overall energy inputs with total energy outputs, as well as to locate all energy streams in a site. Recent years have seen a rise in concern about environmental degradation and energy security, which has increased the importance of new and renewable energy sources. The problem will be exacerbated by the rapid depletion of fossil fuel reserves, the poor quality of fuels, the high cost of basic materials plus transportation costs, and, above all, the harm to the environment caused through the use of conventional sources of energy. Clean and green energy, which are non-conventional and renewable energy sources that are environmentally friendly and pollution-free, have arisen as major alternatives to traditional energy sources in such circumstances. Alternative energy sources such as wind, solar energy (thermal and photovoltaic), biomass, and hybrid sources all will contribute significantly to increase power capacity. Private investments, which are encouraged by policies created by the Central and State governments, make up the majority of investment. The Integrated Rural Energy Planning (IREP) Program's goals are to provide rural residents' minimal household energy demands for lighting, heating, and cooking. This paper presents a case study of IREP Programme implemented in the village of Ranchi and Hazaribag districts of Jharkhand state and future energy planning with available resources.

Stabilizing the Unstable: Chromium Coating on NiMo Electrode for Enhanced Stability in Intermittent Water Electrolysis.

Hydrogen production through water electrolysis is a promising method to utilize renewable energy in the context of urgent need to phase out fossil fuels. Nickel-molybdenum (NiMo) electrodes are among the best performing non-noble metal-based electrodes for hydrogen evolution reaction in alkaline media (alkaline HER). Albeit exhibiting stable performance in electrolysis at a constant power supply (i.e., constant electrolysis), NiMo electrodes suffer from performance degradation in electrolysis at an intermittent power supply (i.e., intermittent electrolysis), which is emblematic of electrolysis powered directly by renewable energy (such as wind and solar power sources). Here we reveal that NiMo electrodes were oxidized by dissolved oxygen during power interruption, leading to vanishing of metallic Ni active sites and loss of conductivity in MoOx substrate. Based on the understanding of the degradation mechanism, chromium (Cr) coating was successfully applied as a protective layer to inhibit oxygen reduction reaction (ORR) and significantly enhance the stability of NiMo electrodes in intermittent electrolysis. Further, combining experimental and Molecular Dynamics (MD) simulations, we demonstrate that the Cr coating served as a physical barrier inhibiting diffusion of oxygen, while still allowing other species to pass through. Our work offers insights into electrode behavior in intermittent electrolysis, as well as provides Cr coating as a valid method and corresponding deep understanding of the factors for stability enhancement, paving the way for the successful application of lab-scale electrodes in industrial electrolysis powered directly by renewable energy.

Greening the Internet of Things: A Comprehensive Review of Sustainable IoT Solutions from An Educational Perspective

Green IoT, or the use of Internet of Things (IoT) technologies to promote sustainability and reduce environmental impact, has gained increasing attention in recent years. The purpose of this paper is to increase awareness about the environmental effect of IoT and to share insights and ideas for making IoT more sustainable and contributing to a more environmentally friendly future. The paper discusses the benefits and challenges of using solar, wind, and other renewable energy sources in IoT applications, as well as the use of sensors, data analytics, and machine learning to optimize resource usage and monitor environmental conditions. The key findings of this paper indicate that Green IoT has the potential to play a significant role in addressing environmental challenges, such as climate change and resource depletion. However, there are still many challenges that need to be addressed, including the high cost of renewable energy sources and the complexity of managing large -scale IoT networks. Overall, this paper emphasizes the importance of Green IoT and the need for further research to develop more efficient and sustainable IoT solutions.

Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.

Investigating capacity credit sensitivity to reliability metrics and computational methodologies

Assigning capacity value to renewable energy sources (RES) is a challenge faced in planning their integration with the grid. The difficulties stem from the natural characteristics of variability and intermittency of wind and solar sources. The capacity credit (CC) analysis evaluates the system’s actual power output compared with a constant capacity generator, i.e., conventional generator and determines an effective capacity to use for planning and operation. This paper presents different factors that could affect the CC of a system. Two methods are proposed to determine the CC, namely equivalent firm capacity (EFC) and effective load carrying capability (ELCC). Since these methods are based on satisfying reliability criteria, daily loss of load expectation (LOLE), hourly loss of load (LOLH), and expected energy not served (EENS) have been employed as indices. To obtain the CC value, both methods apply two techniques: traditional and optimization. Genetic algorithm (GA) is the optimization approach used in this paper. Then, this work compares the two techniques and shows the superior performance of the optimization approach. Two hybrid systems, stand-alone (SA) and grid-connected (GC) modes, are proposed and used as case studies. The hybrid systems consist of photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS). In this work, three different scenarios are used to compare capacity credit: system as a whole, only wind, and no batteries. Finally, sensitivity analysis is carried out to examine the impact of varying the wind speed, solar irradiation, and load. It is demonstrated that the choice of reliability index plays an important role in determining the capacity credit and it is shown that EENS is a more comprehensive and consistent index of reliability.

Predicting infrasound transmission loss using deep learning

SUMMARY Modelling the spatial distribution of infrasound attenuation (or transmission loss, TL) is key to understanding and interpreting microbarometer data and observations. Such predictions enable the reliable assessment of infrasound source characteristics such as ground pressure levels associated with earthquakes, man-made or volcanic explosion properties, and ocean-generated microbarom wavefields. However, the computational cost inherent in full-waveform modelling tools, such as parabolic equation (PE) codes, often prevents the exploration of a large parameter space, that is variations in wind models, source frequency and source location, when deriving reliable estimates of source or atmospheric properties—in particular for real-time and near-real-time applications. Therefore, many studies rely on analytical regression-based heuristic TL equations that neglect complex vertical wind variations and the range-dependent variation in the atmospheric properties. This introduces significant uncertainties in the predicted TL. In the current contribution, we propose a deep learning approach trained on a large set of simulated wavefields generated using PE simulations and realistic atmospheric winds to predict infrasound ground-level amplitudes up to 1000 km from a ground-based source. Realistic range dependent atmospheric winds are constructed by combining ERA5, NRLMSISE-00 and HWM-14 atmospheric models, and small-scale gravity-wave perturbations computed using the Gardner model. Given a set of wind profiles as input, our new modelling framework provides a fast (0.05 s runtime) and reliable (∼5 dB error on average, compared to PE simulations) estimate of the infrasound TL.

New Insights into the First Two PSP Solar Encounters Enabled by Modeling Analysis with ADAPT-WSA

Parker Solar Probe's (PSP's) unique orbital path allows us to observe the solar wind closer to the Sun than ever before. Essential to advancing our knowledge of solar wind and energetic particle formation is identifying the sources of PSP observations. We report on results for the first two PSP solar encounters derived using the Wang–Sheeley–Arge (WSA) model driven by Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model maps. We derive the coronal magnetic field and the 1 R ⊙ source regions of the PSP-observed solar wind. We validate our results with the solar wind speed and magnetic polarity observed at PSP. When modeling results are very reliable, we derive time series of model-derived spacecraft separation from the heliospheric current sheet, magnetic expansion factor, coronal hole boundary distance, and photospheric field strength along the field lines estimated to be connected to the spacecraft. We present new results for Encounter 1, which show time evolution of the far-side mid-latitude coronal hole that PSP corotates with. We discuss how this evolution coincides with solar wind speed, density, and temperature observed at the spacecraft. During Encounter 2, a new active region emerges on the solar far side, making it difficult to model. We show that ADAPT-WSA output agrees well with PSP observations once this active region rotates onto the near side, allowing us to reliably estimate the solar wind sources retrospectively for most of the encounter. We close with ways in which coronal modeling enables scientific interpretation of these encounters that would otherwise not have been possible.

On the extension of streamwise turbulence intensity profile beyond the atmospheric surface layer under neutral to unstable stratifications

Turbulence intensity (TI) is a key factor that affects both the energy production and the performance of individual wind turbines and wind farms. In particular, accurate energy production estimation from wind sources requires a reliable TI model at turbine hub height. Modern multi-megawatt wind turbines operate at heights well above the atmospheric surface layer (ASL). However, most existing analytical TI models are merely valid in the ASL or under neutral atmospheric conditions. Therefore, this paper aims to derive an analytical expression for streamwise TI under neutral to unstable stratifications, which can reasonably model TI at heights beyond the ASL. To this end, the Monin-Obukhov similarity theory, Townsend's attached eddy hypothesis, and a modification proposed in the literature to the eddy population density of Townsend for unstable conditions are utilized. The proposed models are validated against datasets from four tall meteorological towers in Denmark, Sweden, and China. The sites constitute a considerable difference in Coriolis parameter and surface roughness. Results show that the proposed models reasonably follow the measured velocity variance and TI data under neutral to unstable conditions in the ASL and beyond.

A SWOT Analysis for a Roadmap towards Sustainable Electric Power Generation

During the last decade, the abundance of wind, solar, and conventional energy sources have been widely utilized in Sindh, Pakistan, to meet the energy demand-supply gap of the country. Some of these energy projects have been approved, are under constructional and operational phases and have connected to the National grid successfully. However, these energy sources are being influenced by several constraints, which keep the project developmental phases away from their technological upgradation. Therefore, the aim of the present study is to perform a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of existing energy projects in Sindh to examine its current conditions and future roadmap to promote its valuable utilization. The outcomes of the study have identified several socio-economic and environmental impacts on the sustainable development of renewable energy sources. The study concludes that energy strategic policy must contain financial profits, energy security, and environmental protection. This SWOT study will encourage local and foreign investors, researchers, and engineers to invest their financial and technical abilities towards Sindh to get benefit from the hub of energy sources. They can initiate independent power generation and distribution companies on stated sites and can provide cost-effective tariffs from green energy sources with more revenue and less payback period. It can ultimately increase profitable business as well as can boost the state’s economy efficiently.

Treynor Ratio guided risk-return model for TSO-DSO mutualism considering energy and price volatility

Treynor Ratio (TR), a reward to volatility measure in finance, is used to quantify excess return per unit risk taken by the investor, where risk implies volatility in returns relative to market portfolio return. In this paper, TR is modified (TREP) to suggest the hourly weight of each risky energy asset at which it is worth using in the transmission and distribution levels to sustain the volatility of the market and to yield reasonable returns to both TSO and DSO. Uncertainties in the wind and solar energy sources pose energy-risk and fluctuations in grid power price poses financial risk, and both impact the TSO-DSO profit trade-off. Here, beta is used as the risk measure that evaluates the effect of uncertainty in each energy source on operators’ profit relative to the collective effect of all the energy sources available in the system. When risk is modelled using absolute measures like standard deviation or variance which are independent of market portfolio returns, it discourages risky assets more, to keep a risk-averse profile. By co-optimizing TREP values of TSO and DSO, more realistic portfolios are built by maximizing their expected profits per unit risk in relative terms. The resultant portfolio utilizes higher share of distributed energy resources (DERs) and renewable energy, thereby improving the role of DSOs in the market. The operators’ returns are found to be closer to the market portfolio returns at risks well within the market portfolio risk level.

Evaluation of wind power generation projects to reduce air pollution using multi-criteria decision-making methods in Saudi Arabia.

Currently, Saudi Arabia has very limited renewable energy generation capacity, as most of the country's electricity sector is dependent on cheap fossil fuels. However, in recent years, the Saudi government has announced a national development program called "the Saudi Vision 2030," whereby the country intends to increase the share of renewable energies in its total power generation to 20% by 2030. This research is aimed on the possibility of developing wind farms in Saudi Arabia's Al-Jawf area, which is known to be rich in wind sources. The potential of wind energy in the region was examined in the first phase of the research, which focused at the environmental, economic, and technical aspects. For this goal, the two-parameter Weibull function was used to model wind energy in the area. The economic assessment was performed in terms of the Levelized Cost of Energy and payback period. Multi-criteria decision-making approaches were employed in the second phase of the study to determine the most proper sites for harvesting wind energy in the study region based on eight factors including technical, economic, environmental, and social aspects. The most proper site for wind farms was identified by the combined use of Stepwise Weight Assessment Ratio Analysis and Weighted Aggregated Sum Product Assessment. The results showed that the most proper site for locating wind farms in the study area is the city of Al-Qurrayyat, where, using 1 MW turbines, it will be possible to produce 2357 MWh/year of electricity at a cost of 0.092 $/kWh, resulting in a payback period of 8.1 years. From the environmental perspective, wind power generation in Al-Qurrayyat will result in 1124.15 ton/year of CO2 emissions reduction.

Assessment of wind and wave power characteristic and potential for hybrid exploration in Australia

Offshore wind and wave power are abundant energy sources and could provide long term contributions to our future energy supply. The combined exploration of wind and wave power has been proposed as an effective way to mitigate the non-negligible power intermittency and variability of offshore renewables. However, the assessments of wind and wave resources have been developed separately in Australia, and the potential of diversified wind and wave power has not been studied systematically. This study investigates offshore wind and wave energy sources in multiple locations around the Australian coastline and their potentials for integration in terms of energy availability, power variability, coherence and correlation, and annual and seasonal variability over the last seven years. In addition, wind and wave mixed energy farms are studied using commercial wind turbine models and various wave energy converter prototype models. The energy availability, power smoothing effect, capacity factor and downtime of these mixed energy farms are also discussed. Moreover, this paper proposes an effective matrix for assessing the potential of hybrid energy farms in multiple sites in terms of power availability, power variability and combination performance and the sensitivity of selecting various wave energy converter (WEC) models is also investigated to provide a general guideline for future work. The regional comparative results indicate that the swell wave dominated sites in Western and Southern Australia present merits for combining wind and wave power, while wind–wave dominated regions, such as Eastern Australia, are not preferable for this diversified system. It can be found that power variability and downtime can be significantly reduced for the specific wind–wave capacity mix if a lower correlation or longer lag time exists between the two renewables. The results also illustrate that the combinations with different WEC systems present varying benefits in the different locations in Australia.

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.

Multi-Objective Optimal Power Flow including Wind and Solar Generation Uncertainty Using New Hybrid Evolutionary Algorithm with Efficient Constraint Handling Method

A new hybrid decomposition-based multiobjective evolutionary algorithm is proposed for optimal power flow (OPF) including wind and solar generation uncertainty. This study recommends a novel constraint-handling method, which adaptively adds the penalty function and eliminates the parameter dependency on penalty function evaluation. The summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of multiobjective optimization algorithms. The OPF problem is modeled as a multiobjective optimization problem with four objectives such as minimizing (i) total fuel cost (TC) including the cost of renewable energy source (RES), (ii) total emission (TE), (iii) active power loss (APL), and (iv) voltage magnitude deviation (VMD). The impact of RESs such as wind and solar energy sources on integration is considered in optimal power flow cost analysis. The costs of RESs are considered in the OPF problem to minimize the overall cost so that the impact of intermittence and uncertainty of renewable sources is studied in terms of cost and operation wise. The uncertainty of wind and solar energy sources is described using probability distribution functions (PDFs) such as Weibull and lognormal distributions. The efficiency of the algorithm is tested on IEEE 30-, IEEE 57-, and IEEE 118-bus systems for all possible conditions of renewable sources using Monte Carlo simulations.

Comparison of solving probabilistic optimal power flow methods in the presence of wind and solar sources

Power system control and operation studies have been experienced essential changes due to increasing the penetration of uncertain renewable energy resources. One of the most critical issues in this regard is the optimal power flow (OPF). As a result, the deterministic methods do not have the capability of different modeling uncertainties raised in new power systems, and there is a need to investigate the effective models in this regard. This paper focuses on probabilistic optimal power flow (POPF) methods applied to power systems with uncertain wind and Photovoltaic power generations. In this paper, the Monte Carlo simulation (MCS) and analytical methods such as the three-point estimation method (3PEM), unscented transformation (UT), and Interior-Point method (IPM) are applied to solve the probabilistic optimal power flow problem. MCS has been widely applied as a framework to assess the ability of analytical methods. The mentioned techniques are applied to a sample case study extracted from the IEEE 300-bus system. The main contribution of this work is the comparison of analytical methods concluding 3PEM, UT, and IPM, and with MCS as well. The obtained results on the studied networks by the suggested techniques show that in the 3PEM, due to the limited points, the optimal solution is achieved in less calculation time than the other methods. From another perspective, the voltage changes at the buses would be more stable in the IPM. Also, this method is much faster than the MCS method in terms of the convergence rate. To show the effectiveness of the mentioned methods, this paper presents probabilistic load flow method based on the statistical methods to deal with fluctuations because of large-scale renewable energy integration. The proposed methods are validated on the improved industrial 85–bus system of Kermanshah region (in the west of Iran) by adding solar and wind farms.