Wind Power Utilization Research Articles

MITIGATION OF CURRENT HARMONICS IN WIND POWER PLANT USING FUZZY BASED SHUNT APF CONTROLLER

In the present power scenario depending more on conventional sources of power generation creating trouble for the environment and the other side, as coal reserves are vanishing coal based power plants becomes inadequate. Effective utilization of wind power will swipe the problems mentioned earlier. Fortunately wind energy is the cheapest and clean source of energy. But completely relaying on wind power is still a sensible question because of its power fluctuations due to variable wind speeds. Doubly fed Induction Generator (DFIG) is an added advantage to wind farms, but it frequently encounters with power quality problems caused by internal, external faults also due to speed fluctuations. Rotor side and grid converters play a crucial role to maintain the output power constant under all the operating conditions. Hence, protecting these converters from current harmonics caused by various disturbances is carried out in this paper. Throughout this paper, Fuzzy controller is used to diagnose the rotor converters to eliminate current harmonics. MATLAB/Simulink is used to validate the proposed method.

Analysis and Quantification of the Impact of Energy Storage on Wind Power Utilization

Energy storage plays a significant role in accommodating the rapidly increasing wind power in power system, and its two important parameters, maximum charging/discharging power and total capacity, have important impacts on wind power utilization. In this paper, we propose two characterization models to quantify the relationship between wind power curtailment rate and energy storage parameters, in the sense of expectation and robustness for wind uncertainty. By adopting typical wind power scenarios and duality theory, the original characterization models are reformulated into a series of linear programs with different parameter sets for efficient solving. Case studies demonstrate the effectiveness of the proposed models and methods.

Low-carbon economic dispatching model of electric-heating combined system considering multi-type energy storage

In order to reduce the wind abandonment rate during the winter heating period in the “Three North” region and the environmental pollution caused by carbon emissions during power generation, this paper proposes a low-carbon economic dispatching model that takes into account multiple types of energy storage for electric-heating combined systems. First, a multi-type energy storage model with electric boilers, electric energy storage, and heat storage as the core is proposed, and its internal energy conversion, storage, and output links are refined. Then, a carbon trading mechanism is introduced into electric heating. The combined system, taking into account the cost of carbon trading, and aiming at the lowest overall operating cost of the system, construct a low-carbon economic dispatching model for wind-power, conventional thermal power units, combined heat and power units, and multiple types of energy-storage combined heat and power systems. Yalmip/Gurobi solved the model and analysed the wind power consumption effect and carbon emission situation in the dispatching mode through numerical simulation. Simulation results show that under carbon trading, participation of multiple types of energy storage can effectively improve wind power utilization and the economic and environmental benefits of the system.

Research on Wind Power Ultra-short-term Forecasting Method Based on PCA-LSTM

Wind power ultra-short-term forecasting can provide the support for adjusting the intraday power generation plan, carrying out the incremental spot trading of wind power, and improving the utilization of wind power. In order to improve the forecast accuracy of wind power, a wind power ultra-short-term power forecast method based on long-term-term memory (LSTM) network is proposed. First, the principal component analysis method is used to reduce the multivariate meteorological time series dimension. Then by using the cyclic memory characteristics of LSTM network to model multi-dimensional time series, the nonlinear mapping relationship between meteorological data and power data is established, and the wind power forecast is finally realized. The actual data of the eastern China wind farm is used to verify the results. It shows the method established in this paper can effectively use the meteorological and power data to forecast the wind power, and compared with the traditional time series, BP neural network method, the method in this paper has higher forecast accuracy and has broad application potential.

Point and interval forecasting for wind speed based on linear component extraction

As a renewable energy, wind power attracts more and more attention. However, the intermittence and randomness of wind speed make the utilization of wind power a challenging task. Therefore, it is essential to improve the ability of wind speed forecasting. This paper proposes a new hybrid system for wind speed forecasting, which includes three modules: data pre-processing module, deterministic point prediction module and probability interval prediction module. Empirical mode decomposition (EMD) and singular spectrum analysis (SSA) are conducted to extract the linear component of the initial wind speed series in data pre-processing module, autoregressive integrated moving average model (ARIMA) and back propagation neural network (BPNN) are employed to produce the prediction points (PPs) of the initial data in deterministic point prediction module, and ARIMA and improved First Order Markov Chain (IFOMC) model are provided to make the uncertainty analysis of wind speed in probability interval prediction module. A case study is selected to test the performance of the new system. The simulation results demonstrate that the new system can achieve better precision and higher efficiency than several benchmark methods.

Privacy-preserving decentralized power system economic dispatch considering carbon capture power plants and carbon emission trading scheme via over-relaxed ADMM

This paper proposes a decentralized power system economic dispatch (DPSED) model considering carbon capture power plants (CCPPs) and carbon emission trading scheme (CETS) under a low-carbon economy. First, CETS is introduced and analyzed, and a time-varying carbon emission quota allocation method is proposed. The centralized power system economic dispatch model (CPSED) under a low-carbon economy is established with the objective function to minimize the total operation cost in which CCPPs are introduced that can capture CO2 to reduce carbon emission trading cost. Then, the centralized economic dispatch problem is reformulated into a decentralized model. It is composed of a set of sub-problems in individual areas that can preserve privacy and be solved independently. The alternating direction method of multipliers (ADMM) algorithm is employed to solve the decentralized optimization model, reducing the amount of exchanged information and preserving the privacy of information shared among participating areas. Moreover, the over-relaxed ADMM algorithm is further adopted to accelerate the convergence. Finally, case studies are performed on a 6-bus system and an IEEE 118-bus system to validate the effectiveness of the proposed model and algorithm. Numerical results demonstrate that CCPPs could reduce carbon emissions, help wind power utilization, and achieve economic operation.

Optimal sizing of wind-hydrogen system considering hydrogen demand and trading modes

In order to make full use of renewable energy and improve the utilization of wind power, a new joint optimization scheme of the wind-hydrogen system coupled with transmission project is proposed in this paper, in which wind power is reasonably allocated for grid integration and for hydrogen production. Aiming at maximize the annul wind-hydrogen system benefit, the optimal sizes of wind power transmission project and hydrogen system are obtained under different hydrogen production modes, hydrogen trading modes and hydrogen demand levels. In addition, the penalty cost of wind curtailment and hydrogen supply shortage and the system environmental benefits are taken into account. Results show: during the long-term of insufficient of wind power, it is better to produce hydrogen using wind power and grid-assisted power to avoid hydrogen supply shortage; considering the future increase of hydrogen demand, the optimal supply number of hydrogen refueling stations in the wind-hydrogen system is two. Also, the low utilization of fuel cells means that the benefit from regeneration cannot offset the high cost, which leads to the abnegation of fuel cells in the wind-hydrogen system.

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Wind power generation has been widely deployed in the modern power system due to the issues of energy crisis and environment pollution. Meanwhile, the microgrid is gradually regarded as a feasible way to connect and accommodate the distributed wind power generations. Recently, more research studies also focus on incorporating various energy systems, for example, heat and gas into the microgrid in terms of satisfying different types of load demands. However, the uncertainty of wind power significantly impacts the economy of the integrated power-heat-gas microgrid. To deal with this issue, this paper presents a two-stage robust model to achieve the optimal day-ahead economic dispatch strategy considering the worst-case wind power scenarios. The first stage makes the initial day-ahead dispatch decision before the observation of uncertain wind power. The additional adjustment action is made in the second stage once the wind power uncertainty is observed. Based on the duality theory and Big-M approach, the original second-stage problem can be dualized and linearized. Therefore, the column-and-constraint generation algorithm can be further implemented to achieve the optimal day-ahead economic dispatch strategy for the integrated power-heat-gas microgrid. The experimental results indicate the effectiveness of the presented approach for achieving operation cost reduction and promoting wind power utilization. The robustness and the economy of the two-stage robust model can be balanced, of which the performances significantly outperform those of the single-stage robust model and the deterministic model.

Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing

Secure operation of the power system is challenged by the high level of uncertainty and fluctuation introduced by renewable energy sources. More flexibility is needed to cope with the uncertainty and improve the utilization of renewable energy. A prominent solution to provide flexibility, and simultaneously increase the efficiency of the system, is the integration of different energy sectors. This paper proposes a two-stage stochastic scheduling scheme of an integrated multi-energy system, which considers the wind power uncertainty and the synergy of different energy sectors to achieve the optimal economic operation of the whole system with minimum curtailment of wind power. In the first stage, energy and reserve scheduling of generating units is performed, while accommodation of wind power production is realized through reserves in the second stage. In the proposed scheme, the electric power system, natural gas system, and district heating system are coordinated to achieve more flexibility, both in the day-ahead and real-time stage. The stochasticity of the wind power uncertainty is represented by realistic scenarios with corresponding probabilities, which are obtained from a scenario generation algorithm based on historical observations taking into account the temporal correlation of wind power. The simulation results on a small-scale test system show that both, the economic efficiency and wind power utilization, have been improved with more flexibility and more reliable scenario set. It is shown, that the total system cost is reduced and reserves are optimized.

Research on the realization of air intake device based on zero energy consumption complementary to wind energy and solar energy

The device studied in this paper is to solve the problem of water shortage in islands and mountainous areas. The area is developed by using traditional water abstraction technologies, such as seawater desalination and direct air condensation, which have the advantages of large investment, high energy consumption and low efficiency. With new technology, the project has developed a device that uses wind and solar energy to draw water directly from the air. The content of this product is mainly reflected in: 1) The device can synergistically utilize wind power to realize wind power generation and automatic inhalation of humid air, and then utilize high-efficiency water-absorbing material, and can collect moisture in the air, and then use the energy generated by wind energy and solar power generation device, and finally The water-absorbing material is heated to evaporate water vapor, and finally the semiconductor refrigeration technology is used to condense the water vapor to collect and obtain liquid water. 2) The device adopts aerodynamic simulation analysis and experimental verification, and this paper proposes a spiral vertical fan and an induced draft flow path. The structural design realizes the integration of wind power generation and humid air collection functions; 3) The design of the circulating suction mechanism of the suction and dewatering module of the device enables the device to realize the process of regeneration and recycling of the water absorbing material.

Minimizing Wind Power Curtailment and Carbon Emissions by Power to Heat Sector Coupling—A Stackelberg Game Approach

This paper proposes a Stackelberg game approach to minimize both the wind power imbalances and carbon emissions by harnessing demand response (DR) of residential heating loads fed by electricity or district heating (DH) options. The problem is formulated as a bilevel optimization model. An aggregator (leader) at the upper level aims at minimizing the mismatch between electricity demand and wind power while mitigating the carbon emissions arising from the DH system. The aggregator owns a wind farm and is responsible for controlling the DH generation through a combustion-based source and a deep well heat pump system (DWHP), which converts power into heat by utilizing wind power and replacing combustion-based DH. The aggregator submits bonuses to the households (followers) at the lower level incentivizing them to modify their consumption profile. The households receive the bonus offers and consequently decide how to optimize their net electricity and DH energy payments via an upward or downward DR strategy. The uncertainties associated with wind power and heating loads are considered using a stochastic programming framework. Long term thermal performance of the DWHP is studied separately. Results prove that the proposed bilevel framework enables significant reductions in wind power imbalances, carbon emissions, and energy payments.

Resurs latitudinalnog vetra u priobalnom pojasu države Tamil Nadu u Indiji

Globally, the wind power capacities are growing every passing year, which is an indicative of social and commercial acceptance of this technology by a larger section of the populations. In Indian perspective, the wind power capacities are also increasing with annual additions of new capacities and most of the development work is taking place in the southern part and that too in Tamil Nadu state. Research work in the area of accurate wind power assessment is being conducted to optimize the utilization of wind power and at the same time efforts are being exerted to enhance the operation and maintenance capabilities of the local skilled and semi-skilled work force. This study utilizes 38 years of hourly mean wind speed data from seven locations for providing the accurate wind power assessment and understanding the longitudinal behavior of its characteristics. The wind speed is found to be increasing with decreasing latitudes and having lesser variation in wind direction fluctuations, simply means conversing wind direction to narrower bands. Kanyakumari is identified as the most probable wind power deployment site with annual energy yield of 227.55 MWh and capacity factor of 34% followed by Vedaranyam, and Thoothukudi, as second and third priority sites with respective annual yields of 223.36 MWh and 218.73 MWh.

Bilateral Coordinated Dispatch of Multiple Stakeholders in Deep Peak Regulation

Driven by high penetration renewable energy roadmaps, making full use of wind power is still an important work for wind industry and the goal of China. Considering the multiple stakeholders in power supply side and the unbalanced development of them, coordinated dispatch can maximize the wind power utilization. Deep peak regulation (DPR) market in China now has facilitated the coordination. However, wind farms in the market are involved passively for the cost sharing which is not conducive to wind farms to make full play of it advantages. Besides, the electricity of wind farm benefiting from deep peak regulation market cannot be clearly calculated. To solve this problem, a bilateral coordinated dispatch of wind power and other power generation companies (GENCOs) can be introduced in deep peak regulation. In this paper, the bilateral coordination of wind power and other GENCOs in DPR is studied. The potential economic feasible regions in bilateral coordination are analyzed and the compensation pricing ranges are given according to the individual rationality (IR) constraints. The contributions of different participants are clearly calculated using Shapley value, according to which the fair profit allocation is ensured. An affine decision rule is utilized for the simplification and fast computation. A real system in northeast China is utilized for case study to verify the potential economic feasible region. The results can provide reference for the implement and profit allocation of bilateral coordination.

Design and Fabrication of Vortex Bladeless Turbine

ABSTRACT - In present situation, India is one of the top growing economies. The various sectors contributing to this,need electricity for its functioning. Non-renewable resources being depleted day by day, importance is given to develop power from renewable sources of energy like wind, solar, hydro energy etc. In the year 2017-2018 the total utility power generated in India is 1,303,493 GWh and captive power generated is 183,000 GWh making a total of 1,486,493 GWh. Out of 1,303,493 GWh, 52,666 GWh(4% utility power) is generated using wind power. The aim of this project is to utilize wind power to its maximum potential to generate electricity. The region of high speed wind is limited and the area required for installation of conventional windmill is high due to the wake effect. Research is done to find new innovative methods that can operate under optimum wind conditions, under less area by minimizing wake effect and provide an efficient output. One such technology is Bladeless turbine that provides a quiet, safe, simple and efficient alternative to the conventional bladed turbines. Bladeless turbine is not actually a turbine, since it does not rotate. This new approach captures wind energy based on the phenomenon of aeroelastic resonance. Harnessing energy from the vortexes, a process called vortex shedding or Vortex Street. This causes the device to oscillate with little movement which is perfect to be placed anywhere without lubricants and without disturbing wildlife. Aeroelastic resonance phenomenon is usually considered as a problem but this has been used as basic technology for power generation. Bladeless turbines are also the only ones with almost no harmful effects on the environment. Another major advantage of this turbine is that it has less moving parts, thereby reducing losses to a minimum. This is a new age turbine with improved performance that is economic, eco-friendly and less complex with wear prone transmission being eliminated.

Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning

The utilization of wind power is influenced by the fluctuation of the wind, to further strengthen the prediction accuracy of wind speed, two novel hybrid models uniting signal processing, deep learning and ensemble learning were proposed. Firstly, the wind speed series was disaggregated by the wavelet transform (WT). Then to enhance the forecasting precision of the subseries, the deep belief network (DBN) was applied to extract the high dimensional features. Besides, to overcome the limitation of the conventional DBN, the forecasts for each subseries processed by DBN were executed by the light gradient boosting machine (LGBM) and the random forest (RF). Some experiments have been accomplished, where the promotion of high dimensional feature extraction through DBN was explored. Meanwhile, the development of forecasting accuracy by applying tree-based models was confirmed, and the differences between these two hybrid models were discussed. It is shown that: (1) In comparison with the persistence method, the Elman neural network (ENN), DBN, LGBM, and RF, the hybrid models show a great boost in prediction accuracy. (2) The high dimensional feature extraction through DBN is in favor of improving the predicting accuracy of tree-based models, and tree-based models would facilitate the prediction. (3) Between the proposed models, the hybrid model integrated with RF slightly outperforms the other with LGBM in prediction accuracy, but the one with LGBM gets more stable predictions.

A Review of Wind Energy Conversion System

Wind power has been utilised as a supply of power for several centuries. The wind electricity experienced a noteworthy pace within the past few decade due to depleting archetypal capitals and increasing environmental concerns. Wind power generation has become a trustworthy and economical for power generation. The life span of contemporary wind power turbines are currently 20-25 years, which is reminiscent of several different standard power generation technologies. The researchers and scientist are gone through arduous study and experimentation to find out the clarification for abundant utilization of wind power. Our everyday activity is deeply based upon energy, hereafter the effort towards research is extremely sensitive and important. The outcome of extensive research, wind power is being extensively used for power generation. This paper make a short review of wind energy system with highlighting aerodynamics structure, mechanical aspects, and different electro-mechanical control strategies. The decisive survey of ongoing research and upcoming possible perfections for wind energy system have been talk over.

Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network

Given the intermittency and randomness of wind energy, the mass grid connection of wind power poses great challenges in power system and increases the threat in power system balance. Wind power forecasting can predict the fluctuation of output wind power in wind farms, which can effectively reduce wind power uncertainty. Improving the accuracy of wind power is indispensable for enhancing the efficiency of wind power utilization. To improve the forecasting accuracy, this research proposed a novel wind power forecasting method based on singular spectrum analysis and a new hybrid Laguerre neural network. First, singular spectrum analysis was used to analyze the wind power series, which decomposes the series into two subsequences, namely, trend and harmonic series and noise series. Then, Laguerre neural network and new Laguerre neural network were proposed to build the hybrid forecasting model optimized by the opposition transition state transition algorithm. The two decomposed signals were used for forecasting the future wind power value by using a forecasting model. Finally, the proposed hybrid forecasting method was investigated with respect to the wind farm in Xinjiang, China. Prediction performance results demonstrated that the proposed model has higher accuracy than the Laguerre neural network, hybrid Laguerre neural network, hybrid Laguerre neural network with singular spectrum analysis, hybrid Laguerre neural network with opposition transition state transition algorithm and singular spectrum analysis, and other popular methods.

Analysis of risks that are based on the aerial photography used in photogrammetric monitoring maps for environmental wind power energy plant projects.

Currently, the role of energy is important to sustain and even improve the development and prosperity of a nation. Specifically, in the information age that we are living in with the increasing demands for ever-improving technology, the needs for this critical energy power grow each day. In sustainable electrical energy production, which has a substantial share among energy types, utilizing wind power instead of fossil fuels constitutes a prime role among alternative energy sources. To maintain these preferred wind energy power plant projects in more efficient and sustainable ways, their conformity with the relevant field is also very important. Field monitoring and map-producing studies could be performed with geodetic measurement methods with the aid of aerial photography methods. Once the aerial photography production method is implemented in a careful manner, ultimately, sensitive field and environmental monitoring maps can be obtained; however, some potential failures and risk factors could also be encountered during the production of these maps. In this research, potential failures and risk factors, relevant precautionary measures taken against these risks that could be encountered during the production of aerial photography monitoring maps (photogrammetry), and measurements that are used for producing environmental field models for wind power plant projects to be used for the production of electrical energy are studied with the FMEA (failure mode effect analysis) and the systematic Pareto analysis.

Design and generating energy as a car alternator to be an alternative electricity

The alternator is a critical component. The car alternator issued AC electric power by utilizing wind power rotation is then converted into DC electrical power supplied to the accumulator before. Therefore, it is not required of operators to operate the working of the alternator. The voltage generated cells in a standard battery is 2V / cell. However, the working voltage is higher. As is known, in order to drain current through a battery, alternator voltage should be higher than the voltage of the batteries themselves. On the other hand must be maintained so that the voltage is not too high to prevent boiling of (gas) from the batteries. As the voltage regulator safe at 200 ° C should be maintained to 2.35 V / cell until 2.4 V / cell. For 12V battery it means that the working voltage of 14.1 V-14.

Modeling and Analysis of Maximum Power Tracking of a 600 kW Hydraulic Energy Storage Wind Turbine Test Rig

An innovative wind turbine with a particular hydraulic transmission and energy storage system is proposed in this paper. The purpose of applying the hydraulic transmission is to remove the gearbox and power converter of traditional wind turbine and cooperate on wind resource storing with the energy storage system. To overcome the volatility and intermittence shortcomings of wind and improve the output power quality, hydraulic accumulators are used as the energy storage device for wind energy regulation. The original gearbox and generator in the nacelle of a Micon 600 wind turbine were removed and replaced with a hydraulic pump to make a test rig for the investigation into maximum power point tracking (MPPT) of this hydraulic wind turbine concept. The mathematical model of the entire test system is established according to the four function modules. The MPPT control strategy based on the tip speed ratio (TSR) is adopted and a control system containing three closed-loop controls is designed to achieve maximum wind power extracting and produce constant frequency power generation. Ultimately, the dynamic response of rotor speed control is revealed under step change of wind speed and the maximum power tracking performance of the 600 kW hydraulic energy storage wind turbine test bench is simulated and analysed by subjecting to turbulent speed condition. The simulation results demonstrate that the rotor of the wind turbine can run at the expected optimal speed depending on wind speed, and the wind power utilization coefficient of the unit is stabilized at about the maximum value.