Wind Energy Generation Research Articles

The impact of induction generator and PWM inverter with energy storage on weak grids.

The impact of wind energy generation connected to a weak grid is studied with SIMULINK® as simulation platform. The wind energy conversion system is modelled by a squirrel cage asynchronous induction machine (ASM). The grid is modelled as an infinite bus. In the proposed system the ASM is connected to the grid through a DC-link with incorporated battery storage. An ultracapacitor (UCap) bank in parallel to the battery reduces the current ripple which is originated by the PWM – IGBT bridges and absorbs rapid power peaks. Different degrees of grid strength for 2 disturbing events (A: 3-phase fault and B: Voltage dip) are studied. The simulation model permits a sizing of the storage system according to the desired stabilizing capabilities of the system. From the simulation results can be concluded that under weak grid conditions the DC-link need to have a voltage control to ensure the stability of the system. Special attention must be paid to the power quality at the inverter output of the DC-link if renewable generation exceeds 5 % of the short circuit power at the coupling point. The simulated ASM generator is rated at 75 kW / 400 V. The DC-link operates at 700 V nominal voltage, the battery is rated at 340 kWh (C = 500 Ah), 34 kW at discharge rate C0.1 and 170 kW at discharge rate C0.5 with a UCap of 0.5 F in parallel, rated at 600 A discharge current

Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

A Techno-Economic Comparison of a Stand-alone Hybrid System for a Household: A Case Study for the Royal Commission in Saudi Arabia

It’s exciting today to use renewable off-grid sources for driving applications in the home sector (micro-grids). The current tendency is to use renewable energy sources to increase building sector energy efficiency and protect the environment. The economic element and the expense of building these grids is the biggest barrier to these applications. How to best choose the ratings and mix of certain renewable sources based on the location of the house is one of the crucial ways to lower the cost and boost the reliability of these grids. An efficient techno-economical design for a standalone hybrid system is presented in this research. A solar energy using photovoltaic (PV), wind energy, energy storage, and diesel generator are all parts of the system. This design is carried out by a thorough analysis of the techno-economical design of a standalone system to supply the required energy load for a typical residential building for the Saudi Arabian cities of Yanbu and Jubail. Yanbu, which is at latitude 24.0097 degrees north, and Jubail, which is at latitude 27.0726 degrees north, are the two cities’ respective geographical positions. These two cities were chosen for the study of the impact of metrological influences on building performance based on total energy cost, load needs, and energy consumption of the differences in their geographic positions. To improve the performance of the suggested hybrid system, some restrictions were chosen, including Total Annual Cost (TAC) and Net Present Cost (NPC). Results show that the proposed stand-alone hybrid systems can be used to increase the economics and energy efficiency of existing buildings. For Yanbu and Jubail cities, respectively, the optimal integration of a diesel generator, PV array, wind turbine, and energy storage results in a minimum energy cost of roughly $0.483/kWh and $0.487/kWh.

Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics

Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory policies must be determined for multiple locations in the network, and transportation of hydrogen from the production location to customers must be scheduled. At the same time, production patterns of hydrogen are intermittent, which affects the possibilities to realize the planned transportation and inventory levels. To provide policies for efficient transportation and storage of hydrogen, this paper proposes a parameterized cost function approximation approach to the stochastic cyclic inventory routing problem. Firstly, our approach includes a parameterized mixed integer programming (MIP) model which yields fixed and repetitive schedules for vehicle transportation of hydrogen. Secondly, buying and selling decisions in case of underproduction or overproduction are optimized further via a Markov decision process (MDP) model, taking into account the uncertainties in production and demand quantities. To jointly optimize the parameterized MIP and the MDP model, our approach includes an algorithm that searches the parameter space by iteratively solving the MIP and MDP models. We conduct computational experiments to validate our model in various problem settings and show that it provides near-optimal solutions. Moreover, we test our approach on an expert-reviewed case study at two hydrogen production locations in the Netherlands. We offer insights for the stakeholders in the region and analyze the impact of various problem elements in these case studies. Funding: This project received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under [Grant Agreement 875090]. The Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme, Hydrogen Europe and Hydrogen Europe Research. A. H. Schrotenboer received support from the Dutch Science Foundation (Nederlandse Organisatie voor Wetenschappelijk Onderzoek; NWO) through [Grant VI.Veni.211E.043].

A Cross-Country Examination of Mineral Import Demand and Wind Energy Generation: Empirical Insights from Leading Mineral Importers

The relevance of this research lies in the utilization of mineral goods as critical industrial inputs for the manufacture of renewable energy machinery, which has sparked an increase in demand and prices for essential minerals. The purpose of the study is to examine the import-demand function for metallic mineral goods by applying the quantiles via moments (MM-QR) approach, considering the potential heterogeneity across the sample of the top 5 mineral-consuming (importing) nations. The dataset, covering the years 1996-2021, is analysed to test the hypothesis regarding the influence of wind production capacity on mineral-import requirements, considering the price of mineral goods, exchange rates, and income growth. We observe a monotonic favourable response of mineral import demands to wind power generation across all quantiles. However, when considering the quadratic form of wind energy generation, the mineral import demand shows a monotonic reverse trend as the size of wind energy generation expands. Our results reveal the unexpected finding of a monotonic positive effect of copper prices on mineral import demand, which contradicts the Marshallian price theorem. Conversely, the reaction of mineral imports to exchange rates remains consistently positive without modulation. Additionally, we observe a non-monotonic association between the income factor and mineral imports, indicating that the mineral import response to economic growth remains positive until a specific threshold is reached, beyond which it tends to stabilize. The theoretical and practical significance of these findings lies in boosting mineral goods trade to advance the clean energy transition goal for a decarbonized global environment.

Research on combined power generation of wave energy and wind energy

Research on combined power generation of wave energy and wind energy

Performance Evaluation of Thailand’s 8 MW Wind Farm Feeder Trip, Energy Generation, and Loss Using 5 MWh BESS—A Statistical and Economic Approach

Performance Evaluation of Thailand’s 8 MW Wind Farm Feeder Trip, Energy Generation, and Loss Using 5 MWh BESS—A Statistical and Economic Approach

Enhancing decentralized frequency regulation approach in mixed source of generation diversified with wind and PV integration deploying artificial gorilla troops algorithm

Nowadays, the penetration of renewable energy sources, mainly wind and solar photovoltaic systems, into the existing power systems, destabilizes the grid, especially in the aspect of frequency regulation. AGC is one of the most important tasks in this mixed-generation environment to maintain the balance between the generated and consumed electricity, thus keeping the system frequency at an acceptable level. Therefore, AGC needs to be optimized for the noisy and volatile output of RESs. AGTA is a recent optimization method developed based on the social foraging behavior of gorillas, being a sophisticated way of exploration and exploitation. Therefore, the method is implemented in AGC. By mimicking the social behaviour and foraging strategy, the group gives rise to a new technique for enhancing the efficiency and response of the AGC system in light of wind and PV energy generation variation. A two-area power system model has been formulated characterized by the existence of wind and PV generation in addition to the conventional sources of the power system. This model is meant to simulate diverse situations to give an idea as regards the capability of the new algorithm to enhance grid stability and adaptability. The simulated results show that the proposed AGTA significantly surpasses other conventional optimization methods for AGC and results in an effective frequency control strategy. it confirms the potential of the AGTA from a new perspective in providing a feasible option for decentralized frequency regulation in a multi-generation landscape.

Control Strategies of DFIG Technology-based Variable-Speed Wind Turbines-A Review

This review paper examines the advancements and limitations of wind energy technology, while concentrating on the utilization of Doubly Fed Induction Generators (DFIG) to capture maximum power in variable speed winds. The paper evaluates the efficacy of several control strategies for DFIG relying on WT (wind turbines), on the basis of their simulation results, key features, and control objectives. The paper highlights the potential areas for investigation to improvise the performance as well as efficiency of wind energy generation.

Six-phase DFIG-MPPT synergy: pioneering approaches for maximising energy yield in wind energy generation system

Six-phase DFIG-MPPT synergy: pioneering approaches for maximising energy yield in wind energy generation system

Development of Low-Speed Generator for Rural Energy Self Power Plant

This study investigates the potential for wind energy generation in various regions of Indonesia, which have been identified to potentially produce over 100 megawatts (MW) of electrical energy. In response to the need for rural electrification and the utilization of this wind potential, we developed a low-speed generator suitable for rural, energy-independent power plants. The objectives were twofold: to design a low-speed generator optimized for low rotational speeds reflective of local wind conditions, and to assess its performance in electricity generation. Our research methodology encompassed an analytical comparison of exploratory data and literature to devise a generator design compatible with rural wind patterns. This involved calculating the necessary wind acceleration to actuate the designed generator. Subsequently, a prototype was designed, constructed, and tested, examining variables such as iron core composition, winding wire, magnetic strength, and electrical output under wind-driven conditions. The performance tests revealed that the Low-Speed Generator produced an average voltage output scaling with rotational speed–from 5.3V at 100rpm to 21.1V at 500rpm. The peak voltage output at 500rpm rotation reinforces the generator’s efficacy in higher rotational regimes. Such findings advocate for the Low-Speed Generator’s application as a sustainable solution to the rural energy crisis, aligning with the broader objectives of equitable energy distribution and the Sustainable Development Goals (SDGs).

Generalized Predictive Control of the Active and Reactive Stator Powers of the DFIG for Wind Energy Generation

This paper proposes a Generalized Predictive Control (GPC) strategy for Wind Power Generation (WPG) based on a doubly fed induction generator (DFIG). The objective is to study and apply a robust active and reactive power control strategy based on GPC of the DFIG, this is likely to optimize the energy production and improve the quality of the energy produced. In order to maximize the amount of WPG taken even when the turbine is uncertain or the wind speed varies abruptly, the design is built utilizing the Maximum Power Point Tracking (MPPT) theory. Through numerical modeling with the aid of the Matlab/Simulink software, the predictive control (GPC) of the active and reactive stator powers of the DFIG was validated. A comparison between the GPC of the GADA and the indirect method based on a typical PI controller is developed in order to illustrate the viability of the suggested method. According to the simulation results of this comparison, the suggested method is viable and has promising results.

Quantifying the Effects of Energy Infrastructure on Bird Populations and Biodiversity.

Shale oil and gas production and wind energy generation both expanded rapidly across the United States between 2000 and 2020, raising concerns over impacts on wildlife. I combine longitudinal microdata from the National Audubon Society's Christmas Bird Count with geolocated registries of all wind turbines and shale wells constructed in the contiguous U.S. during this period to estimate the causal effects of these contrasting types of energy infrastructure on bird populations and biodiversity, which are key bellwethers of ecosystem health. Results show that the onset of shale oil and gas production reduces subsequent bird population counts by 15%, even after adjusting for location and year fixed effects, weather, counting effort, and land-use changes. Wind turbines do not have any measurable impact on bird counts. Negative effects of shale are larger when wells are drilled within important bird habitats.

PROPOSAL FOR PUBLIC POLICIES TO PROMOTE AND ENCOURAGE THE GROWTH OF WIND ENERGY GENERATION IN THE UNITED STATES.

This paper proposes novel public policies aimed at incentivizing wind energy generation in the United States. In response to the imperative of transitioning to sustainable energy sources, the study explores innovative approaches to promote and accelerate the adoption of wind power. Furthermore, the research employs the LEAP (Long-range Energy Alternatives Planning) software for energy planning to simulate the forecasted impact of these proposed policies on the wind energy supply in the USA. By integrating policy recommendations with simulation results, this study provides a comprehensive analysis to guide policymakers and stakeholders in fostering the growth of wind energy as a pivotal component in the country’s renewable energy landscape.

Optimization of Vertical Axis Darrieus Wind Turbine As Aerator Generator

Research has focused on a wind turbine system with a vertical-axis Darrieus design with a capacity of 15 watts, serving as an aerator for aeration or adding air to water. The Darrieus turbine, made of aluminium with three corners and a modified NACA 0018 model optimized to work at low wind speeds of 2 m/s, effectively powers a 4-watt DC aerator. Generator voltage readings show a minimum value of 2 volts at a wind speed of 1 m/s and a maximum value of 12.8 volts at a wind speed of 6.2 m/s. The calculated power coefficient is 0.3. Charging the accumulator with a 12 volts/12 Ah takes 23 hours with a generator voltage of 13 volts at a wind speed of 6 m/s. This research reflects Indonesia's progress in optimizing wind energy for sustainable power generation.

Designing risk-free service for renewable wind and solar resources

In all world geographies, renewable energy investment is viewed as a major component of the solution to meet the global energy demand. However, key renewable energy resources, such as wind and solar, are inherently stochastic, and thus, pose significant risk and reliability challenges. We apply financial engineering asset securitization principles in this paper to carve out risk-free generation capability from wind and solar energy generation. A market driven, dynamically evolving design and pricing of a risk-free tranche is developed that would allow renewable generators to competitively bid and participate in day-ahead power markets. We apply the framework to wind and solar resources located in different US geographies and assess the risk-free performance of the tranche against a risk-free benchmark established in this paper.

Optimal management strategies of renewable energy systems with hyperexponential service provisioning: an economic investigation

The current research proposes optimal management strategies for queueing modeling-based renewable energy systems with hyper-exponentially distributed maintenance/repair under the assumption of an admission control policy. Using the concept and steps of the matrix-analytical method, the steady-state probability distribution associated with energy systems is explicitly presented. A relatively straightforward computation that can help with modeling wind energy generation, investigating wind farm performance, optimizing energy based on system storage, reliability inspection, service maintenance planning, and numerous other purposes can be employed to mathematically derive several system performance indicators. The investigation findings are validated via quantitative outcomes, illustrative possesses, and a step-by-step recursive methodology for efficient management of the renewable energy system. Additionally, considering multiple governing parameter values, the nature-inspired optimization technique, Cuckoo Search (CS), is employed to demonstrate the optimum anticipated cost of renewable energy system. A comparison with other metaheuristics and semi-classical approaches is also presented to establish the best convergence results. In order to help system designers, policymakers, engineers, and researchers, several numerical examples are also provided to construct more practical strategies based on the production of energy, storage, and system management. The economic, parametric, and performance investigation findings are highlighted, and the opportunities and recommendations for further research are provided. In a nutshell, the outcomes of the present analysis can be adopted to formulate the most effective economic strategies and regulate decision-making processes in the energy sectors.

Cost-Effective Hybrid Wind-Photovoltaic Generation System for Isolated Critical Loads: A Case Study

This study provides a technical and economic analysis of integrating a microgrid structure for power generation with wind energy and photovoltaic (PV) generation. The study mainly focuses on providing optimal generation from renewable energy sources for supplying isolated critical loads with optimized cost. Using HOMER software, a financial study of the proposed wind-PV-based microgrid is presented. A model is built with artificial neural network (ANN) to further tune the simulated economic optimization results obtained for further economic optimization and validation analysis. A reduction of about 22% in cost of energy is obtained from the ANN based optimization of system cost. This indicates that the proposed hybrid generation system is a profitable one serving localized loads in isolation with 100% renewable energy utilization. The proposed system can modelled and established in an onshore area in eastern India with optimized utilization of renewable sources for generation with minimal cost as observed.

Grid Connected Wind Energy System Power Quality Improvement Using STATCOM

The wind energy generation, utilization and its grid penetration in electrical grid are increasing worldwide. Injection of the wind power into an electric grid affects the power quality. The wind generated power is always fluctuating due to its time varying nature and causing stability problems. This weak interconnection of wind generating source in the electrical network affects the power quality and reliability. This paper demonstrates the power quality problem due to installation of wind turbine with the grid. In this proposed scheme Static Compensator (STATCOM) is connected at a point of common coupling with a battery energy storage system to mitigate the power quality issues. The STATCOM gives reactive power support to wind generator and also load. The battery energy storage is integrated to sustain the real power source under fluctuating wind power. The STATCOM control scheme is simulated using MATLAB/SIMULINK in power system block set.

Controlling PMSG Wind Turbine Connected to Three Level Neutral Point Clamped Inverter Based on MPC

Wind energy generation systems are facing increasing importance in terms of harmonic distortion control and power quality standards. In recent years, variable speed has also risen rapidly due to advancements in power electronics. The three key elements of a wind power conversion system (WECS) are electrical generators, machine-side converters, and grid-side converters. It should be noted that the permanent magnet synchronous generator is one of the most often used types of electrical generators due to its special qualities. When wind energy is converted to the proper electrical power, it may be injected into the utility grid using either synchronous generators or grid-side converters. Therefore, an investigation of several topologies and control strategies is required to determine which is the best for the machine-side converter. The use of model predictive control (MPC) for a surface mount permanent magnet synchronous machine wind turbine is the main topic of this work. The machine's electrical model is the one that was utilized. The power converters are selected considering the intricate and highly nonlinear aerodynamic system of the wind turbines. MPC is a promising control technique despite being a relatively recent application in power electronics. In addition, MATLAB and Simulink are used to build the MPC code and offer a simplified model of the turbine.