Integration Of Wind Power Generation Research Articles

Research on the application of computer science in wind power generation and the future trends

In the context of the growing global demand for clean energy, wind power, as an important form of renewable energy, is gradually occupying a dominant position. At the same time, the continuous development of computer technology has also brought new breakthroughs in wind power generation. Traditional wind power generation suffers from low power generation efficiency and large fluctuations in power generation. However, through the integration of wind power generation with advanced computer technologies, the efficiency and cost of wind power generation have been greatly optimized. This paper seeks to provide an overview of how computer science is utilized in wind power generation, focusing on the integration of computational modeling, simulation, and machine learning. It highlights future trendsand emphasizes the significance of developing digital twins and integrating machine learning with physical models for efficient wind farm management. Combining findings from various existing studies, this paper delves into the profound implications for the future of energy, highlighting the transformative role of computer science in advancing sustainable power solutions and shaping the landscape of renewable energy sources.

Optimal location and operation of energy storage and transmission switching for minimizing wind power spillage

This paper proposes a bi-level multi-objective optimization model to improve the integration of wind power generators in electrical networks based on the optimal location and operation of the energy storage system and transmission switching strategies. The minimization of wind power spillage, load shedding, power losses, and the improvement of the voltage stability margin are the objectives of the upper-level subproblem. While the lower-level subproblem aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level subproblems that represent the market clearing conditions is used to constrain the upper-level subproblem. The effectiveness of the proposed algorithm is examined on a modified IEEE 24-bus test system. The results show that the applicability of the proposed algorithm in aiding power system improvement planning for minimizing wind power spillage to integrate wind energy with maximizing the social welfare and improving the loadability and the voltage stability.

Low-frequency oscillations damping with doubly-fed induction generators embodying grid-forming control with hardware-in-the-loop simulation

The integration of wind power generation into modern power systems necessitates the development of advanced control strategies to enhance grid stability and reliability. Among these strategies, grid-forming control has emerged as a promising solution, enabling converter-interfaced generation technologies to provide services traditionally supplied by conventional generation. This paper specifically addresses the challenge of low-frequency oscillations damping in power systems incorporating Doubly-Fed Induction Generators embodying grid-forming control. In this paper, two low-frequency oscillations damping strategies are proposed and tested, designed to complement a doubly-fed induction generator grid-forming control strategy. The first, called POD-Q, uses reactive power; while the second, POD-P, acts on the active power injection. Both strategies are analyzed through small-signal stability analysis and experimental testing. For the small-signal stability analysis, a dynamic model of a conventional system with a certain penetration of doubly-fed induction generation has been employed. Experimental results are obtained within a real-time simulation environment, using the hardware-in-the-loop technique to communicate a real controller board with the real-time simulator. The results indicate that the POD-P strategy is more efficient in low-frequency oscillations damping compared to a synchronous generator with a conventional power system stabilizer and also in comparison to the POD-Q strategy. However, both proposed strategies have demonstrated improvements in low-frequency oscillations damping.

Krill herd technique for dynamic economic dispatch problems with the integration of wind power generation

The krill herd (KH) approach is proposed in this article for tackling the dynamic economic dispatch (DED) problem with the integration of wind power generation, including constraints such as valve-point loadings and prohibited operating zones (PoZs). The purpose of resolving the DED problem is to determine the optimum feasible combination of power outputs for all allocated generating units within a given period while simultaneously meeting dynamic operational constraints and power demand. Due to the addition of valve points and PoZs to the cost characteristics of the generating unit, the DED issue becomes a highly non-linear and non-convex optimization issue. When transmission losses are taken into account, the DED problem becomes significantly more complex to solve. KH is a swarm-inspired algorithm based on krill individual herding behavior. KH employs two global and two local tactics, and these work in parallel, making KH a powerful algorithm. The effectiveness of the KH method is investigated and validated through extensive testing on various test systems, including 5-, 6-, 10-, 16-, and 30-unit test systems. Extensive studies are conducted to evaluate the efficacy of the proposed KH algorithm; the present research work compares the convergence properties of the suggested approach with those of the other recently published methods. In addition to reduced generation costs compared to other results published in recent literature, the numerical findings of the KH approach reveal that it has strong convergence qualities.

Dynamic performance improvement of wind-diesel power system through robust sliding mode control of hybrid energy storage system

Off-grid users can be provided with electricity via a hybrid integration of wind power generators and a diesel system functioning as a backup supply. However, due to wind power fluctuations and rapid load changes, system voltage and frequency variances may exceed permitted limits, resulting in aberrant system behavior. Therefore, to improve the dynamic performance of the wind-diesel power system, a hybrid energy storage system (HESS) made of battery and superconducting magnetic energy storage is installed with the system via a converter interface. Based on the switching manifold design, a sliding mode controller with the super-twisting feature is developed over the hybrid energy storage system (HESS) to carry out the required amount of power exchanges with the system accomplished through the control of converter operation. Lyapunov stability analysis is conducted to guarantee the asymptotic stability of the system. MATLAB simulations are performed to validate the improved performance of the system with the proposed scheme.

Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors

Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

Due to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria.

Integration of wind power generation through an enhanced instantaneous power theory

This study deals with the operation of the wind power generation system (WPGS) having active filter capabilities using the grid-interfacing converter (GIC) control approach. A novel current decomposition technique based on enhanced instantaneous power theory (EIPT) is suggested for controlling the GIC to realise an improved power quality (PQ) in WPGS and to provide the requisite reactive power support. To reduce the sluggish response of the numerical filters, a novel mathematical average algorithm is proposed. Furthermore, to reduce the non-linear load effects at GIC, a 13-level reduced switch multi-level inverter (RSMLI) is integrated. Real-time application point of view in the proposed approach, to improve the GIC performance, a novel indirect current control (ICC) technique-based EIPT approach is proposed. ICC technique is used to decompose the power component into a much more detailed component by which it is easier to cancel out the non-linear components and make the system more flexible and reliable for practical applications. The rotor interfacing converter control approach is used to attain the optimum power. Considering different test conditions, the performance of the proposed EIPT-based 13-level RSMLI approach is investigated through MATLAB/Simulink software.

Continuous-Time Co-Operation of Integrated Electricity and Natural Gas Systems With Responsive Demands Under Wind Power Generation Uncertainty

This paper studies the role of electricity demand response program (EDRP) in the co-operation of the electric power systems and the natural gas transmission system to facilitate integration of wind power generation. It is known that time-based uncertainty modeling has a critical role in co-operation of electricity and gas systems. Also, the major limitation of the hourly discrete time model (HDTM) is its inability to handle the fast sub-hourly variations of generation sources. Accordingly, in this paper, this limitation has been solved by the operation of both energy systems with a continuous time model (CTM). Also, a new fuzzy information gap decision theory (IGDT) approach has been proposed to model the uncertainties of the wind energy. Numerical results on the IEEE Reliability Test System (RTS) demonstrate the benefits of applying the continuous-time EDRP to improve the co-scheduling of both natural gas and electricity systems under wind power generation uncertainty.

C-Vine pair copula based wind power correlation modelling in probabilistic small signal stability analysis

The increasing integration of wind power generation brings more uncertainty into the power system. Since the correlation may have a notable influence on the power system, the output powers of wind farms are generally considered as correlated random variables in uncertainty analysis. In this paper, the C-vine pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated. Monte Carlo simulation is performed to analyze the small signal stability of a test system. The probabilistic stability under different correlation models and different operating conditions scenarios is investigated. The results indicate that the probabilistic small signal stability analysis adopting pair copula model is more accurate and stable than other dependence models under different conditions.

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.

Robust Energy and Reserve Scheduling Under Wind Uncertainty Considering Fast-Acting Generators

The large-scale integration of wind power generation has significantly boosted the role played by conventional fast-acting generating units in power system operation. Due to their short response time, these relatively expensive devices are particularly suited to cope with the intermittency and volatility featured by uncertain wind-based generation. Within the context of network-constrained generation scheduling under wind-related uncertainty, this paper presents a new robust approach wherein the operation of both slow- and fast-acting generating units is precisely characterized in a day-ahead co-optimized energy and reserve market. As salient modeling features, the ramping-related effect of dispatch nonanticipativity and nonspinning reserve offers are both accounted for. The resulting robust counterpart is formulated as a challenging trilevel program with lower-level binary variables that is effectively solved by an exact nested column-and-constraint generation algorithm. Numerical results including a practical out-of-sample validation procedure demonstrate the operational advantages and the computational effectiveness of the proposed approach.

Probabilistic evaluation of a power system's capability to accommodate uncertain wind power generation

With the rapidly growing integration of wind power generation (WPG), it is of great importance for an operator to grasp the ability of the power system to accommodate uncertain WPG. This study proposes two probabilistic methods to assess such capability of a power system based on the level of data availability. If the probability distribution type (PDT) of wind power prediction error (WPPE) is known, the total accommodation probability is calculated as the sum of a fully guaranteed probability and a partially guaranteed probability. The former one leads to a tri-level max-max-min optimisation problem which is solved via a dichotomy procedure, and the latter one can be obtained based on the geometrical analysis of the dispatchable region of WPG. If the PDT of WPPE is not exactly known, the authors tackle the problem via a data-driven uncertainty quantification method. More precisely, they consider a family of ambiguous probability distributions around the empirical distribution described by historical data in the sense of Wasserstein metric. The probability of failure in the worst-case distribution is calculated from a linear programming. The proposed method is tested on modified PJM-5 and IEEE-118 bus systems. Comparison with the traditional Monte Carlo Simulation method demonstrates its efficacy and efficiency.

Optimal transmission switching in the stochastic linearised SCUC for uncertainty management of the wind power generation and equipment failures

The following article published in IET Generation, Tranmission and Distribution, Nikoobakht, A., Aghaei, J., Mardenah, M., ‘Optimal transmission switching in the stochastic linearised SCUC for uncertainty management of the wind power generation and equipment failures’, Gener. Transm. Distrib., 2017, 11, (10), pp. 2664 – 2676, doi:10.1049/iet-gtd.2016.1956, has been withdrawn by agreement between the authors, the Editors-in-Chief, Innocent Kamwa and Christian Rehtanz, and the Institution of Engineering and Technology. This is because the incorrect text was uploaded due to a technical error. The correct version has now been published as: Nikoobakht, A.,Aghaei, J., Mardenah, M. et al, 'Moving beyond the optimal transmission switching: stochastic linearised SCUC for the integration of wind power generation and equipment failures uncertainties', Gener. Transm. Distrib., 2018, 12, (15), pp. 3780 – 3792, 10.1049/iet-gtd.2017.0617.

Moving beyond the optimal transmission switching: stochastic linearised SCUC for the integration of wind power generation and equipment failures uncertainties

This study recommends a stochastic optimization model for the security constrained unit commitment (SCUC), which incorporates the optimal transmission switching (OTS) for managing the uncertainty of wind power generation and equipment failures, i.e. unit/line outages. Also, this study presents a technique in stochastic SCUC model with the OTS action using the AC optimal power flow (AC OPF). The AC OPF provides a more accurate picture of power flow in the power system compared to the DC optimal power flow that is usually considered in the literature for the stochastic SCUC models and the OTS action. While the stochastic SCUC model with the OTS action based on AC OPF is a mixed-integer non-linear programming model, this study transforms it into a mixed-integer linear programming (MILP) model. The MILP approach uses a piecewise linear model of AC OPF, which allows the reactive power and voltage to be considered directly in power flow model. The proposed stochastic SCUC problem is evaluated on the 6 bus, IEEE 118-bus and 662-bus test systems in pre- and post-OTS action. Obtained results demonstrate the effectiveness of the proposed model.

Robust Coordinated Transmission and Generation Expansion Planning Considering Ramping Requirements and Construction Periods

Two critical issues have arisen in transmission expansion planning with the rapid growth of wind power generation. First, severe power ramping events in daily operation due to the high variability of wind power generation pose great challenges to multi-year planning decision making. Second, the long construction periods of transmission lines may not be able to keep pace with the fast growing uncertainty due to the increasing integration of wind power generation. To address such issues, we propose a comprehensive robust planning model considering different resources, namely, transmission lines, generators, and FACTS devices. Various factors are taken into account, including flexibility requirements, construction period, and cost. We construct the hourly net load ramping uncertainty (HLRU) set to characterize the variation of hourly net load including wind power generation, and the annual net load duration curve uncertainty (LDCU) set for the uncertainty of normal annual net load duration curve. This results in a two-stage robust optimization model with two different types of uncertainty sets, which are decoupled into two different sets of subproblems to make the entire solution process tractable. Numerical simulations with real-world data show that the proposed model and solution method are effective in coordinating different flexible resources and rendering robust expansion planning strategies.

Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting

Accurate wind speed forecasting is a fundamental requirement for large-scale integration of wind power generation. However, the intermittent and stochastic nature of wind speed makes this task challenging. Artificial neural networks (ANNs) are widely used in this area; however, they may fail to provide the accuracy that may be required. This is due to applying shallow architectures with error-prone hand-engineered features. This paper proposes a deep neural network (DNN) architecture with stacked autoencoder (SAE) and stacked denoising autoencoder (SDAE) for ultrashort-term and short-term wind speed forecasting. Autoencoders (AEs) are applied for unsupervised feature learning from the unlabeled wind data and a supervised regression layer is applied at the top of the AEs for wind speed forecasting. Several uncertain factors exist in the wind data that degrade the accuracy of current methodologies. In order to improve the accuracy, rough neural networks are incorporated in the proposed deep learning models to develop novel rough extensions of SAE and SDAE that are robust to wind uncertainties. Experimental results show that the proposed rough DNN models outperform classic DNNs and previous models that apply shallow architectures in the view of lower RMSE and mean absolute error measurements.

Addressing and assessing the issues related to connection of the wind turbine generators to the distribution grid

Integration of wind power generation into power distribution grid may result in stability and power quality challenges. In this way, this paper first reviews the grid integration issues of wind power generation. Then, as the main contribution, the paper develops in-depth theoretical analyses for examination the grid integration issues such as voltage rise, output power fluctuations, and static and dynamic voltage variations created due to grid integration of wind turbines and corresponding aerodynamic power fluctuations. Besides, the paper addresses these issues by proposing elaborate control approaches and by using the static compensator (STATCOM). The paper first addresses the problem of voltage rise by active power curtailment through pitch control. However, this method may result in a large waste of wind energy that may not be cost effective. Next, the STATCOM is implemented and efficient control approaches are proposed for addressing the problem of voltage rise, improvement of voltage profile and suppression of voltage and power fluctuations. At the end, simulation studies for the study distribution grid are given. The study system is an actual distribution grid in Iran with approximately 7.9MW wind power generation and 21.9MW load.

A source–grid–load coordinated power planning model considering the integration of wind power generation

Power system planning approach should meet the new requirements brought by continuous development of power systems. In recent years, wind power generation capacity keeps a rapid growth, but the problem of wind power curtailment becomes increasingly serious in some countries. The limitations of wind power integration produced by power systems, such as the inconsistency between installed generation capacity and transmission capacity and the insufficiency of regulation capacity, are drawing the attention in both academia and industry. This paper proposes a source–grid–load coordinated planning model for power systems with regulation capacity constraints being taken into account. In this novel model, traditional generation expansion planning and transmission expansion planning are integrated, and the peak-load regulation capacity and flexible regulation capability of power systems are considered. Meanwhile, the positive impacts of demand response are also taken into consideration. The numerical study verifies that the source–grid–load coordinated planning model can not only reduce the overall cost of the system, but also improve the wind power integration capacity and guarantee the sustainable development of wind power generation.

Risk Assessment for Power System Operation Planning With High Wind Power Penetration

Integration of wind power generation (WPG) is increasing rapidly worldwide. The variability and uncertainty of wind energy may lead to significant load-generation imbalances resulting in large frequency deviations, hence increasing system operational risks, especially in small and isolated power systems with low inertia and limited capabilities of providing frequency responses. This raises the need for investigating alternatives to current power system operation planning approaches to cope with the uncertain nature of the intermittent generation. This paper presents a risk assessment approach to analyze power system security for operation planning under high penetration of wind power generation. The proposed approach deals with not only steady-state voltage and overload evaluations, but also frequency response adequacy. For fast identification of operational limit violations in the proposed risk assessment method, we develop an analytical procedure for approximating frequency response and assessing the consequences of limit violations without performing dynamic simulations. As a result, the frequency response adequacy assessment can be run simultaneously with the steady-state voltage and overload evaluations. The proposed risk assessment approach is illustrated via its application to a model of a power system with high wind power penetration.