Wind Power Generation Model Research Articles

Weather as a driver of the energy transition – present and emerging perspectives of energy meteorology

Energy meteorology is an applied research field of meteorology that focuses on the study and prediction of weather conditions and events that affect energy production and use. This field has become increasingly important as the energy industry has become more dependent on weather conditions, especially in the areas of renewable energy sources such as wind energy, solar energy, and hydropower. The following paper has been written by experts of the Committee on Energy Meteorology of the German Meteorological Society summarizing their more than 30 years of experience and lessons learnt. It gives an overview of activities in energy meteorology that are already essential for the transformation of energy systems to systems with high shares of renewable energies. Building on this, the experts have created a vision of future topics that describe the future research landscape of energy meteorology. The authors explain that work in energy meteorology in recent years has primarily been concerned with the physically based modeling of wind and solar power generation and the development of short-term forecasting systems. In future years, a significant expansion of work in the areas of energy system modeling, digitalization, and climate change is expected. This includes the detailed consideration of regionally specified spatiotemporal variability for system design, the integration of artificial intelligence skills, the development of weather-related consumption based on smart meters, and the mapping of the effects of climate change on the energy system in planning and operating processes.

Operational characteristics of an integrated island energy system based on multi-energy complementarity

This study addresses the intermittent renewable energy supply and the large footprint of battery storage on an island reef in China by proposing an integrated energy system that incorporates hydrogen production, storage, and utilisation. Mathematical models for wind and photovoltaic power generation, energy storage, hydrogen production and utilisation, diesel generators, and energy management systems are established. Additionally, an integrated energy system is constructed using Simulink software to simulate and analyse its operational characteristics in various seasonal scenarios. The simulation results indicate that the surplus from wind power and photovoltaic systems, after supplying the load for over 70 % of the day, constitutes more than 40 % of the load demand. The energy storage system can maintain a maximum charging rate for more than 50 % of the day and a maximum discharge rate for less than 16.7 % of the day. The electrolysis tank produces hydrogen for more than 71 % of the day and simultaneously consumes up to 750 % of the load. The fuel cell consumes hydrogen for less than 12.5 % of the day and simultaneously provides up to 27 % of the load. Analysis revealed that the hydrogen system improves the energy utilisation, the control strategy realises stable supply and demand within the system.

Comparative study of time-series forecasting models for wind power generation in Gujarat, India

The rapid rate of transformation in the power sector of India has placed a significant emphasis on robust grids and distributed generation units. The observable shift in the energy sector, especially in wind and solar energy, also requires smooth integration of Distributed Generation units with the existing power grid. Precise wind power generation forecast, therefore, becomes an important and complex task for the strategic planning and management of the systems. We, thus, aim towards a system that can actually provide precise wind power forecasts by applying machine learning techniques. This work proposes a comparative and comprehensive analysis of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) model. The experimentations and modelling are performed considering meteorological and historical power generation data. The study is concentrated in Kutch, Gujarat and is validated on the data collected from the Central Electricity Authority (CEA), India for power generation data and weather data collected from regional weather centres. The findings show that ARIMA outperforms the other models for non-linear data in multivariate analysis, with a MAPE score of less than 5.5 on the prediction dataset.

Study on combined optimization technology of wind-solar-storage hybrid power generation based on electricity hydrogen complementarity

The wind-solar-storage hybrid power generation system is mainly composed of wind turbines, solar cell arrays, electrolytic cells, fuel cells, battery packs, intelligent controllers, multi-functional inverters, cables, supports and auxiliary parts to supply power to the load. The system utilizes wind and solar energy for power generation without an external power supply, and an energy storage module composed of an electrolytic cell, fuel cell, and battery is used to assist the power supply. In this paper, a fuel cell model, a wind power generation model and a solar power generation model are respectively constructed, and a small experimental platform was built to validate the model. The pulse width modulation (PWM) control method, combined with the auxiliary regulation of the system by the battery, reduces the impact of the volatility of wind power generation on the electrolytic cell and fuel cell, and makes the load current more stable under different working conditions. The simulation results show that the system has good compression resistance, fast response and good stability.

Wind generation and power system interaction analysis using probabilistic techniques

. Energy power from renewable sources, especially wind turbine generators, are being considered as an important generation alternative in the electrical power systems around the world due to their non contaminant nature and low environmental effects. In particular, the power supplied by wind generators is widely random following the random nature of weather conditions; therefore a probabilistic approach during planning seems much more appropriate than the deterministic approach actually used in the electrical utilities. This paper presents a methodology that allows the application of probabilistic analysis, thru the use of programming tools, in the load flow planning studies. The methodology described provides the opportunity to obtain the steady state response in systems where wind power exists using a wind power generation model (WPGM) and an iterative routine to the load flow analysis considering each value of generation of the wind sources. A wind power generation model (WPGM) was developed using a random wind speed generator. The random wind speed generator describes the Weibull distribution of wind measurements, and then using Montecarlo simulation techniques, a program in Visual Basic executes the automatic evaluation of random samples of speed of wind considering the characteristic power curve of a wind turbine, obtaining a widely database of wind generation conditions. As a result of the investigation, a computational tool was developed in order to use it, in combination with the PSS/E load flow analysis software, for steady state power system analysis. The methodology was used as an assessment tool to identify the effects of wind turbines generation into the substation voltages and power flow profiles in the power network, for a wind farm project intended to be installed in Margarita Island. The analysis aimed to identify the transmission adjustments, reinforcements and expansions needed to guarantee the wind farm project integration successful into the power system.

The Doubly-fed Wind Power Generator Virtual Synchronous Control

Given the high proportion of wind power frequency/voltage stability problems, the power system based on synchronized coordinates (DFIG) mathematical model of doubly-fed motor, studies a current source containing inertial link type wind turbine virtual synchronous control strategy, and the virtual synchronous control loop and the current control design of the inner ring. Established in the Matlab/Simulink simulation model of doubly-fed wind power generator, the virtual synchronous generator realized virtual synchronous generator inertia. Simulation results confirm the validity of the theoretical analysis and the effectiveness of the proposed control strategy.

Study on a Development Model of Wind Power Generation Integrating an Artificial Neural Network

Study on a Development Model of Wind Power Generation Integrating an Artificial Neural Network

Retracted: Benefit Modeling and Analysis of Wind Power Generation under Social Energy Economy and Public Health.

[This retracts the article DOI: 10.1155/2022/5635853.].

Multi-objective operation optimization analysis based on distributed energy sources

In the context of the determination and implementation of the dual carbon goal, it is an important trend of China’s energy strategy to incorporate a considerable proportion of renewable energy into the power grid. Therefore, energy consumption is gradually turning to new energy sources such as wind energy and wind energy. Considering the limitations of random, intermittent and anti-peak regulation of energy forms such as wind and light, a multi-objective optimization model of wind, light, fire and storage combined power generation and heating is built. This model can fine-tune and fill the valley by using the peak regulating capacity of thermal power plants and pumped storage devices, so that the whole system has stable power generation and heating capacity. The model is solved by multi-purpose particle swarm optimization algorithm based on maximum total generation and heating rotation and minimum generation and heating slope fluctuation. The calculation results show that the established model can effectively improve the wind and light consumption rates and reduce the peak set pressure of the thermal device.

Application of Multimedia Quality Evaluation Relying on Intelligent Robot Numerical Control Technology in New Energy Power Generation System

New energy (NE) power generation technology has become an important means to solve the problems of global energy demand and environmental governance due to its sustainability and cleanliness. However, restricted by climate and other factors, the instability of its power generation capacity has a serious impact on the voltage multimedia quality evaluation of small microgrids, which even causes damage to important equipment. This paper proposed a multimedia quality evaluation method in the new energy power generation system relying on intelligent robot numerical control technology. According to the state characteristics of different units, the expanded representation models of conventional units, wind turbines, and photovoltaic units were established, respectively. Based on intelligent robot numerical control technology, a multimedia quality monitoring model of the new energy power generation system in random production simulation was established. The planned outage condition of the unit, which was ignored by the current stochastic simulation method, has been fully considered to ensure the rationality of the simulation of intelligent robot numerical control technology. The intelligent robot numerical control technology was used to explore the expected production power change of the unit and to improve the calculation speed and accuracy. In this paper, an appropriate wind and solar power generation model was constructed based on the output characteristics of wind and solar. Secondly, the influence of random faults between units on the subsequent loading rate was studied through probability calculation. The start-stop factors of conventional units were comprehensively analyzed. Then, the expected value of unit start-stop times was obtained. The average relative error of the expected value of the power shortage of the new energy electricity generation system (EGS) was 2.45%, and the error was lower than 2.98%. In the intelligent robot numerical control technology in this paper, the planned outage condition of the unit was considered, which showed the correctness and superiority of the multimedia quality evaluation of the proposed model.

Current Harmonic Control Method of Wind Turbine Based on PIR Controller

Wind power equipment technology is the basis for the development of wind power generation. Aiming at the problem that the traditional PI controller is difficult to control the AC component of the current in the DQ axis, resulting in a large number of current harmonics in the process of power generation, which seriously affects the efficiency of power generation. This paper proposes a current harmonic control method for wind turbines based on a PIR controller. The mathematical model of wind power generation is constructed based on the synchronous rotating coordinate system. The current harmonic components are calculated according to the DQ rotor speed, shaft component, and other parameters. The PIR current harmonic controller is established by improving the ordinary PI controller to track and control the AC current signal. And finally, a current harmonic control target value is obtained according to different main control targets, and a specific harmonic component is eliminated. The experimental results show that the harmonic content is 3.12%, 2.06%, 1.17%, and 1. 02%, respectively, when the harmonic current order is 5, 7, 11, and 13. It is better than the comparison method. Therefore, this method can greatly reduce the harmonic content of the current in the process of different current orders, reduce the harmonic loss, and improve the generating efficiency of the unit.

Risk assessment of power system voltage security for operational planning

The growing penetration of renewable energy sources causes significant increase of system uncertainties and system operation complexity. It is of considerable importance to conduct accurate and fast risk assessment with probabilistic load flow analysis to ensure secure operation of power grids. Thus, Monte Carlo simulation with Newton-Raphson method is undesirable as it requires a long computation time and suffers from convergence problem. To overcome these challenges, an analytical probabilistic approach using non-iterative Holomorphic Embedding Load Flow method is proposed to obtain explicit rational probabilistic distribution function expressions for bus voltages assuming that the complex power injections at some of the buses depend on one wind power generation model while other buses have deterministic power injections. Computational time and accuracy of the proposed approach are evaluated to validate the performance and effectiveness of proposed algorithm. This paper is a preliminary trial for analytical method of probabilistic load flow study and provides insights into the impacts of renewable energy sources on power system operational security. It helps system operators to evaluate and control the potential risks associated with voltage fluctuation by considering the severity of the consequence and the likelihood of the occurrence.

커널 밀도 추정 기법을 이용한 풍력발전량에 대한 확률론적 모델

As the proportion of renewable energy sources that are intermittent and variability increases, system uncertainty is increasing, and a probabilistic analysis that can reflect these characteristics is required. In this paper, we propose a method for generating a probablistic wind power generation model using kernel density estimation. The boundary bias problem caused by using kernel density estimation for wind power generation data is eliminated through the boundary correction method, and a bandwidth suitable for the data can be selected through the bandwidth selector. Scenarios were created from probabilistic models made in various ways, and the accuracy of the models was validated by comparing them with real data. As a result, the accuracy was improved when the scenario was created by generating the model directly from the wind power generation data by estimating the kernel density estimation.

Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization

Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution networks, the uncertain nature of both solar and wind resources results in power quality and system stability issues. The randomization behavior of solar and wind energy resources is controlled through the precise development of a power prediction model. Fuzzy-based solar PV and wind prediction models may more efficiently manage this randomness and uncertain character. However, this method has several drawbacks, it has limited performance when the volumes of wind and solar resources historical data are huge in size and it has also many membership functions of the fuzzy input and output variables as well as multiple fuzzy rules available. The hybrid Fuzzy-PSO intelligent prediction approach improves the fuzzy system's limitations and hence increases the prediction model's performance. The Fuzzy-PSO hybrid forecast model is developed using MATLAB programming of the particle swarm optimization (PSO) algorithm with the help of the global optimization toolbox. In this paper, an error correction factor (ECF) is considered a new fuzzy input variable. It depends on the validation and forecasted data values of both wind and solar prediction models to improve the accuracy of the prediction model. The impact of ECF is observed in fuzzy, Fuzzy-PSO, and Fuzzy-GA wind and solar PV power forecasting models. The hybrid Fuzzy-PSO prediction model of wind and solar power generation has a high degree of accuracy compared to the Fuzzy and Fuzzy-GA forecasting models.The rest of this paper is organized as: Section II is about the analysis of solar and wind resources row data. The Fuzzy-PSO prediction model problem formulation is covered in Section III. Section IV, is about the results and discussion of the study. Section V contains the conclusion. The references and abbreviations are presented at the end of the paper.

Smart Micro-grid Functional Features and Planning Indicators Evaluation

This paper focuses on the comprehensive perspective of society and analyses the operating costs and income of microgrids in the balanced power network mode in order to comprehensively evaluate the economy of microgrids. It is critical for the rational planning of the micro-grid and developing a scientific evaluation indicator is an effective way to test the rational test. Research on the functional characteristics, evaluation standards, evaluation indicators, and economics of microgrids, we analyze the functional characteristics and the cost benefits of micro-grid. Meanwhile, we also establish an evaluation indicator feature library model, and build a data model of regional weather wind, light, and water power generation, simulating production. The indicator strategy and complete process will establish a solution assessment method based on the indicator space distance from a multi-dimensional space angle. Verify the effectiveness of this method through an example of an independent microgrid for the island.

Construction of Power Supply Stability Control Model for Wind Connected Power Grid

There are many problems in the power supply stability control of wind power generation system, such as large fluctuations, poor control effect and so on. Therefore, a new stability control model of wind power grid connected is designed. Determine the DC grid connection mode when the wind farm is connected, convert the DC power into AC power through the converter station, and transmit it to the final AC system to realize the grid connection of wind power and power grid; According to the determined wind power access mode, calculate the mechanical operation power, mechanical torque and wind energy utilization coefficient collected by the wind turbine, complete the best collection of wind energy, and determine the shafting according to the mass block model of the wind turbine and generator, so as to realize the research on the mathematical model of wind power generation. By analyzing the power flow direction of the stator and rotor of the wind turbine generator set, the unstable state of the power supply voltage of the wind turbine generator set after grid connection is determined. The PV curve method is used to calculate the steady-state voltage stability of grid connected wind turbines, and a power supply stability control model based on the voltage stability of grid connected wind turbines is established. The nonlinear objective function method is used to optimize the critical point of power supply stability, calculate the maximum load and maximum power of the system, establish the static power supply and transient power supply stability model after wind power grid connection, and realize the power supply stability control research of grid connected wind power through the analysis of power supply characteristics. The experimental results show that the model is closer to the stability of the actual power supply in the test of improving the stability of the power supply, ensuring the quality of power supply, while the test results of the other two methods have large fluctuations. In the analysis of the change of power supply after grid connection, the experimental results obtained by the model are very close to the actual data values. Therefore, this method can effectively improve the performance of power system.

Modeling of Direct-Drive Permanent Magnet Synchronous Wind Power Generation System Considering the Power System Analysis in Multi-Timescales

The dynamics of wind power generation cannot be neglected in the modern power system and could have a great impact on the system dynamics, even raising the risk of a blackout. Because of this, power system simulation has to include the model of wind power generation. However, due to the high order of the full model of the wind power generator, it is impossible to model them in detail in the use of the power system dynamic simulation considering the thousands of wind generators in the grid. In this context, a simplified model is normally used with the trade-off in lower accuracy. As a direct-drive permanent magnet synchronous wind power generation system (D-PMSG) would take up a certain occupation in the modern power system, a proper D-PMSG simplified model is needed in the power system simulation. For a different research purpose in a different timescale, a different complexity of the model can be used to maximize the accuracy, in the meantime speeding up the simulation. This paper proposes a set of simplified models of the direct-drive permanent magnet synchronous wind power generation system (D-PMSG) and classifies them according to the timescale of the dynamics and the use cases, i.e., faults (transient stability analysis), system contingencies (voltage and frequency stability analysis) and wind speed variations (energy transformation). The accuracy of the proposed simplified models is verified by comparing them with the detailed D-PMSG electromagnetic transient mode in Matlab/Simulink, and their use case of the power system simulation is validated based on the case study of the IEEE 39-bus system considering the above scenarios.

Benefit Modeling and Analysis of Wind Power Generation under Social Energy Economy and Public Health.

Wind is a renewable energy source. Overall, using wind to produce energy has fewer effects on the environment than many other energy sources. Wind and solar energy provide public health and environmental benefits to the social. Wind turbines may also reduce the amount of electricity generation from fossil fuels, which results in lower total air pollution and carbon dioxide emissions. In order to better optimize the effect of social energy economic management and facilitate the multiobjective decision making of coordinated development of energy and socioeconomic environment, a modeling and analysis method of economic benefits of wind power generation based on deep learning is proposed. In this paper, based on the principle of deep learning, the evaluation indicators of wind power economic benefits are excavated, a scientific and reasonable economic benefit evaluation system is constructed, a wind power economic benefit analysis model supported by public management is constructed, and the steps of wind power economic benefit analysis are simplified. It is concluded that the modeling and analysis method of wind power economic benefits based on deep learning has high practicability in the actual application process, which is convenient for the prediction and analysis of energy demand for social and economic development.

Data-driven stochastic optimization for power grids scheduling under high wind penetration

To address the environmental concern and improve the economic efficiency, the wind power is rapidly integrated into smart grids. However, the inherent uncertainty of wind energy raises operational challenges. To ensure the cost-efficient, reliable and robust operation, it is critically important to find the optimal decision that can correctly and rigorously hedge against all sources of uncertainty. In this paper, we propose data-driven stochastic unit commitment (SUC) to guide the power grids scheduling. Specifically, given the finite historical data, the posterior predictive distribution is developed to quantify the wind power prediction uncertainty accounting for both inherent stochastic uncertainty of wind power generation and input model estimation error. For complex power grid systems, a finite number of scenarios is used to estimate the expected cost in the planning horizon. To further control the impact of finite sampling error induced by using the sample average approximation (SAA), we propose a parallel computing based optimization solution methodology, which can quickly find the reliable optimal unit commitment decision hedging against various sources of uncertainty. The empirical study over six-bus and 118-bus systems demonstrates that our approach can provide more cost-efficient and robust performance than the existing deterministic and stochastic unit commitment approaches.

Design of an adaptive controller for double-fed induction wind turbine power

The doubly-fed induction wind turbine has gradually become the mainstream model of wind power generation. Power control is one of the advantages of doubly-fed induction wind generators and has been widely studied. However, existing power control strategies for these turbines have issues with low efficiency. An adaptive control method is proposed in this work, aiming at the power control system of doubly-fed induction wind turbines. Firstly, the power model is obtained, and then the adaptive controller is designed according to the model. Finally, simulation results verify the effectiveness of this method.