scholarly journals Capacity and Location Effects of Wind Turbine Energy Systems on Power Systems Stability

2013 ◽  
Vol 4 (1) ◽  
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Energy Systems

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

scholarly journals Eco-Emission Analysis of Multi-Carrier Microgrid Integrated with Compressed Air and Power-to-Gas Energy Storage Technologies

2021 ◽  
Vol 13 (9) ◽  
pp. 4681
Author(s):  
Khashayar Hamedi ◽  
Shahrbanoo Sadeghi ◽  
Saeed Esfandi ◽  
Mahdi Azimian ◽  
Hessam Golmohamadi
Keyword(s):  
Energy Storage ◽  
Power Systems ◽  
High Efficiency ◽  
Objective Assessment ◽  
Energy Systems ◽  
Vital Role ◽  
Compressed Air ◽  
Emission Analysis ◽  
Multi Objective ◽  
Power To Gas

Growing concerns about global greenhouse gas emissions have led power systems to utilize clean and highly efficient resources. In the meantime, renewable energy plays a vital role in energy prospects worldwide. However, the random nature of these resources has increased the demand for energy storage systems. On the other hand, due to the higher efficiency of multi-energy systems compared to single-energy systems, the development of such systems, which are based on different types of energy carriers, will be more attractive for the utilities. Thus, this paper represents a multi-objective assessment for the operation of a multi-carrier microgrid (MCMG) in the presence of high-efficiency technologies comprising compressed air energy storage (CAES) and power-to-gas (P2G) systems. The objective of the model is to minimize the operation cost and environmental pollution. CAES has a simple-cycle mode operation besides the charging and discharging modes to provide more flexibility in the system. Furthermore, the demand response program is employed in the model to mitigate the peaks. The proposed system participates in both electricity and gas markets to supply the energy requirements. The weighted sum approach and fuzzy-based decision-making are employed to compromise the optimum solutions for conflicting objective functions. The multi-objective model is examined on a sample system, and the results for different cases are discussed. The results show that coupling CAES and P2G systems mitigate the wind power curtailment and minimize the cost and pollution up to 14.2% and 9.6%, respectively.

scholarly journals Numerically and Experimentally Verified Design of a Small Wind Turbine with Injection Molded Blade

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 776
Author(s):  
Byunghui Kim ◽  
Sang-June Park ◽  
Seokyoung Ahn ◽  
Myung-Gon Kim ◽  
Hyung-Gun Yang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Power Systems ◽  
Wind Turbine ◽  
Wind Power ◽  
Offshore Wind ◽  
Power Coefficient ◽  
Manufacturing Method ◽  
Small Wind Turbine ◽  
Verification Methods ◽  
Wind Power Systems

Although mega-watt class onshore and offshore wind power systems are used to generate power due to their cost-effectiveness, small wind power systems are important for household usages. Researchers have focused on aerodynamic characteristics as a conceptual design from their previous studies on Archimedes spiral wind turbines. Here, we verified the design of a small wind turbine AWM-750D (100 W capacity) via both numerical simulation and experimentation. We used commercial code ANSYS CFX for numerical simulation and compared turbulence models and surface roughness for determining the performance. To obtain reliable and robust blades, we analyzed the effective manufacturing method with Moldflow. Through a test with an open-suction type atmospheric boundary layer wind tunnel, we varied wind speed from 4.0 m/s to the rated value of 12.5 m/s and obtained 106 W, equivalent to a power coefficient of 0.205. In addition, we compared the numerical and experimental power vs. rotational speed and found the former is 6.5% lower than the latter. In this study, we proved that numerical simulations can act as design verification methods to predict wind turbine performances and reliable manufacturing. Through our research, we provided the prototype of a small wind turbine with 100 W to act as an efficient electric power supplier for households and also the stable manufacturing process for complex spiral blades using injection molding.

Design and Optimization of Integrated Distributed Energy Systems for Off-grid Buildings

2021 ◽  
pp. 1-27
Author(s):  
Jian Zhang ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Energy Storage ◽  
Optimal Design ◽  
Power Systems ◽  
Thermal Energy ◽  
Waste Heat ◽  
Carbon Dioxide Emission ◽  
Energy Systems ◽  
Solar Thermal ◽  
Solar Thermal Energy ◽  
Distributed Energy

Abstract Off-grid concepts for homes and buildings have been a fast-growing trend worldwide in the last few years because of the rapidly dropping cost of renewable energy systems and their self-sufficient nature. Off-grid homes/buildings can be enabled with various energy generation and storage technologies, however, design optimization and integration issues have not been explored sufficiently. This paper applies a multi-objective genetic algorithm (MOGA) optimization to obtain an optimal design of integrated distributed energy systems for off-grid homes in various climate regions. Distributed energy systems consisting of renewable and non-renewable power generation technologies with energy storage are employed to enable off-grid homes/buildings and meet required building electricity demands. In this study, the building types under investigation are residential homes. Multiple distributed energy resources are considered such as combined heat and power systems (CHP), solar photovoltaic (PV), solar thermal collector (STC), wind turbine (WT), as well as battery energy storage (BES) and thermal energy storage (TES). Among those technologies, CHP, PV, and WT are used to generate electricity, which satisfies the building's electric load, including electricity consumed for space heating and cooling. Solar thermal energy and waste heat recovered from CHP are used to partly supply the building's thermal load. Excess electricity and thermal energy can be stored in the BES and TES for later use. The MOGA is applied to determine the best combination of DERs and each component's size to reduce the system cost and carbon dioxide emission for different locations. Results show that the proposed optimization method can be effectively and widely applied to design integrated distributed energy systems for off-grid homes resulting in an optimal design and operation based on a trade-off between economic and environmental performance.

scholarly journals A Review on Fault Current Limiting Devices to Enhance the Fault Ride-Through Capability of the Doubly-Fed Induction Generator Based Wind Turbine

2018 ◽  
Vol 8 (11) ◽  
pp. 2059 ◽  
Author(s):  
Seyed Naderi ◽  
Pooya Davari ◽  
Dao Zhou ◽  
Michael Negnevitsky ◽  
Frede Blaabjerg
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Induction Generator ◽  
Fault Current ◽  
Doubly Fed Induction Generator ◽  
Advantages And Disadvantages ◽  
Fault Ride Through ◽  
Current Limiting ◽  
Two Factors ◽  
Doubly Fed

The doubly-fed induction generator has significant features compared to the fixed speed wind turbine, which has popularised its application in power systems. Due to partial rated back-to-back converters in the doubly-fed induction generator, fault ride-through capability improvement is one of the important subjects in relation to new grid code requirements. To enhance the fault ride-through capability of the doubly-fed induction generator, many studies have been carried out. Fault current limiting devices are one of the techniques utilised to limit the current level and protect the switches, of the back-to-back converter, from over-current damage. In this paper, a review is carried out based on the fault current limiting characteristic of fault current limiting devices, utilised in the doubly-fed induction generator. Accordingly, fault current limiters and series dynamic braking resistors are mainly considered. Operation of all configurations, including their advantages and disadvantages, is explained. Impedance type and the location of the fault current limiting devices are two important factors, which significantly affect the behaviour of the doubly-fed induction generator in the fault condition. These two factors are studied by way of simulation, basically, and their effects on the key parameters of the doubly-fed induction generator are investigated. Finally, future works, in respect to the application of the fault current limiter for the improvement of the fault ride-through of the doubly-fed induction generator, have also been discussed in the conclusion section.

scholarly journals Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

2019 ◽  
Vol 9 (20) ◽  
pp. 4417 ◽  
Author(s):  
Sana Mujeeb ◽  
Turki Ali Alghamdi ◽  
Sameeh Ullah ◽  
Aisha Fatima ◽  
Nadeem Javaid ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Power Systems ◽  
Prediction Model ◽  
Wind Power ◽  
Fossil Fuel ◽  
Energy Systems ◽  
Accurate Estimation ◽  
Wind Power Forecasting ◽  
Power Forecasting

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

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