Wind Energy In Power Systems Research Articles

Exergy, exergoeconomic optimization and exergoenvironmental analysis of a hybrid solar, wind, and marine energy power system: A strategy for carbon-free electrical production

In this research, aligned with global policies aimed at reducing CO2 emissions from traditional power plants, we developed a holistic energy system utilizing solar, wind, and ocean thermal energy sources, tailored to regions optimal for ocean thermal energy conversion (OTEC). The selected site, characterized by favorable wind and solar conditions close to areas with high OTEC potential, is designed to meet the electricity needs of a coastal community. The system's core components include an Organic Rankine Cycle, turbines, thermoelectric elements, pumps, a heat exchanger, a wind turbine, and a solar collector. A detailed system analysis and thermodynamic evaluation based on thermodynamic principles were carried out using the Engineering Equation Solver (EES) software. Key factors such as wind speed, solar radiation, and collector area were critical in determining system performance. To enhance the system's effectiveness, we conducted a comprehensive comparison of optimization algorithms, incorporating the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and utilizing a Pareto front for value optimization. This approach significantly outperformed other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of system efficiency and cost-effectiveness. The developed system achieved an exergy efficiency of 14.46 % and a cost rate of $74.98 per hour, demonstrating its suitability for its intended functions. Moreover, exergoenvironmental evaluation was conducted for the proposed plant. The findings revealed that key component HEX has a high exergoenvironmental factor due to their use of hot water, which has zero unit exergoenvironmental impact. Additionally, pumps demonstrated a zero exergoenvironmental impact factor, indicating negligible component-related environmental impacts. Sensitivity analysis further evaluated critical performance parameters, revealing that increases in solar irradiation lead to decreased total system cost rates, while higher turbine temperatures resulted in a remarkable 14.08 % reduction in the system's cost rate. These results underscore the economic viability of operating the system at higher temperatures and strengthen the argument for its adoption from a financial perspective.

Damping sub-synchronous resonance in DFIG wind farms: An innovative controller utilizing wide-area measurement systems

The use of series shunt compensators along with wind energy in power systems has increased significantly. One of the main disadvantages of wind energy and series compensation is the phenomenon of sub-synchronous amplification. In this paper, a control method with fuzzy system based on wide area measurement systems (WAMS) and considering time delay in order to increase the damping of sub-synchronous oscillations in compensated networks with capacitive compensators and wind turbines. The DFIG type is provided in the GSC converter’s voltage control loop for the system’s stable operation. The simulation results using MATLAB/Simulink software on Modified IEEE First Bench Mark test networks show the effectiveness of the proposed method in damping sub-synchronous oscillations in power systems compensated with series capacitors.

A combined wind speed prediction model based on data processing, multi-objective optimization and machine learning

The ability to estimate wind speed accurately is crucial for optimizing the utilization of wind energy in power systems. The randomness of wind speed and the performance of prediction models are the two issues that have the greatest impact on wind speed prediction. This paper starts from the two aspects of digging wind speed characteristics and optimizing model performance. A new wind speed combined prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved artificial bee colony algorithm (IABC) to optimize least squares support vector machine (LSSVM) performance. In order to reduce the randomness of the wind speed sequence and increase its predictability, CEEMDAN is first used to break down the wind speed sequence. Secondly, an LSSVM prediction model optimized by an improved artificial bee colony algorithm is established for each subsequence obtained by decomposition; finally, the wind speed forecast value is created by superimposing the results of each subsequence’s prediction. Case studies demonstrate that the combined prediction model put out in this study outperforms other models in terms of prediction performance.

Impact of components number selection in truncated Gaussian mixture model and interval partition on wind speed probability distribution estimation

Owing to global environmental concerns in recent years, wind power has garnered significant attention as a clean and renewable energy source. The probability distribution of the wind speed plays a pivotal role in assessing the wind energy potential and power system operation. As a single Gaussian may be powerless in capturing features of real wind speed distributions owing to the randomness of wind speed, this study aimed to use a truncated Gaussian mixture (TGM) distribution with determined optimal Gaussian components number and suitable interval partitions as a possible alternative model to estimate the wind speed probability distribution (WSPD). In particular, two expectation-maximization algorithms were employed to determine the parameters of the TGM models. Subsequently, the continuous ranked probability score loss theory related to TGM models with two arbitrary truncated points was developed and employed to detect the optimal number of Gaussian components in the TGM models, in addition to casually or empirically setting the number of Gaussian components. Subsequently, the WSPD is estimated using the detected optimal Gaussian component number and the corresponding parameter determination results. Considering that different interval partitions may result in diverse accuracy results, to enhance the flexibility of the above TGM models, another technique is designed using the False-Discovery-Rate-inspired algorithm. This algorithm performs well in the case of large-scale hypothesis tests and can determine the suitable interval partition number. The feasibility and effectiveness of the proposed model were validated using real wind speed data collected from two stations: Haikou and Wulumuqi stations. Comparison of the different goodness-of-fit evaluations demonstrates that the estimation accuracy of WSPD of the proposed approach is comparable to those of the truncated Gaussian mixture distribution with arbitrary Gaussian component numbers and some other comparison models.

Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions

The use of renewable energy techniques is becoming increasingly popular because of rising demand and the threat of negative carbon footprints. Wind power offers a great deal of untapped potential as an alternative source of energy. The rising demand for wind energy typically results in the generation of high-quality output electricity through grid integration. More sophisticated contemporary generators, power converters, energy management, and controllers have been recently developed to integrate wind turbines into the electricity system. However, a comprehensive review of the role of converters in the wind system’s power conversion, control, and application toward sustainable development is not thoroughly investigated. Thus, this paper proposes a comprehensive review of the impact of converters on wind energy conversion with its operation, control, and recent challenges. The converters’ impact on the integration and control of wind turbines was highlighted. Moreover, the conversion and implementation of the control of the wind energy power system have been analyzed in detail. Also, the recently advanced converters applications for wind energy conversion were presented. Finally, recommendations for future converters use in wind energy conversions were highlighted for efficient, stable, and sustainable wind power. This rigorous study will lead academic researchers and industry partners toward the development of optimal wind power technologies with improved efficiency, operation, and costs.

A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting

Wind speed forecasting plays an important role in the stable operation of wind energy power systems. However, accurate and reliable wind speed forecasting faces four challenges: how to reduce the data noise; how to find the optimal model inputs; how to describe the complex fluctuations in wind speed; and how to design a suitable loss function to tune the forecasting model. This study proposes a novel forecasting model to address the four challenges mentioned above. First, it uses a wavelet soft threshold denoising method to reduce noise in the original wind speed time series. Second, it uses the maximal information coefficient, which measures the linear and nonlinear relationships between historical wind speed data and forecasted targets, to determine the optimal model inputs. Third, a novel convolutional Transformer-based truncated Gaussian density network is designed to characterize the complex fluctuations in wind speed. The multi-scale information from different convolutional layers is weighted using the self-attention mechanism and then fed into the Transformer network to extract temporal information. The outputs are mapped into the forecasted targets with several fully connected layers. Fourth, considering the non-negativity of wind speed, the truncated Gaussian distribution, which shows a probability of zero when the wind speed is less than zero, is employed to model the uncertainty of wind speed forecasts. This leads to designing a truncated Gaussian distribution-based loss function to train the forecasting model. The forecasting results on three real-world datasets show that the proposed model not only provides accurate deterministic wind speed forecasts but also produces reliable probabilistic wind speed forecasts. The hypothesis testing also illustrates the effectiveness of the proposed model for deterministic and probabilistic wind speed forecasting.

Hybrid Model-Based BESS Sizing and Control for Wind Energy Ramp Rate Control

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly.

Energy Storage Capacity Planning Method for Improving Offshore Wind Power Consumption

This paper proposes a method of energy storage capacity planning for improving offshore wind power consumption. Firstly, an optimization model of offshore wind power storage capacity planning is established, which takes into account the annual load development demand, the uncertainty of offshore wind power, various types of power sources and line structure. The model aims at the lowest cost of investment, operation and maintenance of the system, and takes lower than a certain abandoned wind level as the strict constraint to obtain two parameters of power capacity and energy capacity of energy storage on the source side. Secondly, taking a coastal power grid as a typical case, the energy storage capacity planning method is verified. Finally, the key factors affecting offshore wind power consumption are summarized, and the sensitivity analysis is carried out from the point of view of the transmission protocol of the transmission lines outside the province and the capacity allocation of the tie lines in the province. This study will be helpful for the planning and operation of the high-proportion of offshore wind energy power systems.

Continuous regulation of wind energy power system permitting protection of the system components against wind over-speed

In this paper is presented a methodology of design and control of an electromagnet braking system dedicated to the protection of the wind energy production systems against the strong rise of the current in the power chain attached to the strong elevation of the wind speed. This methodology focuses on the dimensioning of the electromagnet acting as a mechanical brake and its integration into the wind energy generation system. The electromagnet is designed jointly by the analytical method offering the advantages of compatibility with large-scale optimization approaches and the finite element method for verifying and increasing the accuracy of the analytical model. This study essentially involves the design and modeling phase of the electromagnet dissociated from the wind turbine and then by the phase of its integration with the wind turbine. The global model associating the electromagnet and the wind turbine controlled with a view to ensuring a continuous recharging of energy accumulator at a constant current equal to the nominal current of which the wind turbine is sized, is implanted under the Matlab-Simulink simulation environment. The results of simulations are encouraging and open the research trail to optimization work of the entire chain.

Load Shedding in High-Integrated Wind Energy Power Systems Using Voltage Electrical Distance

This paper presents a load shedding method for power systems with high integration of wind energy, considering their frequency response. The minimum load shedding power needed to restore system frequency to operational limits can be determined by using the modified frequency response model along with secondary frequency control. The voltage electrical distance method can then be applied to appropriately distribute the shedding power to load buses. This method brings selectivity to the problem and minimizes the impact caused by load shedding. The proposed method was validated using simulations on the IEEE 37-bus test system with a modified wind power generator model.

Heterostructured Ov‐Mn2O3@Cu2SnS3@SnS Composite as Battery‐Type Cathode Material for Extrinsic Self‐Charging Hybrid Supercapacitors

AbstractIn this work, self‐charging pouch‐type hybrid supercapacitor (HSCs) is fabricated by extrinsic integration of wind and solar energy power systems. Initially, the synthesized mesoporous 3D‐rhombohedral Mn2O3 shows a specific capacity of 37.61 mA h g−1 in KOH plus redox additives electrolyte. Further, the oxygen vacancy of Mn2O3 is tuned by fast‐reduction (FR) and mild‐reduction (MR) techniques. The rich oxygen vacancy of FR‐Mn2O3 (rich Ov‐Mn2O3) exhibits a specific capacity of 66.64 mA h g−1. On the other hand, the different morphologies of Cu2SnS3@SnS are synthesized by time‐dependent solvothermal technique. The 3D‐microsphere Cu2SnS3@SnS shows higher electrochemical performance than the others. In order to further improve the electrochemical performance, the heterostructure of rich Ov‐Mn2O3@Cu2SnS3@SnS composite is prepared that provides a maximum specific capacity of 126.07 mA h g−1 at 8 mA cm−2 and rate capability of 66.09% with retention of 93.89% after 10 000 cycles. This is ascribing to high electrical/ionic conductivity, larger faradaic reactions, lower interfacial resistance, and synergistic effect. Then, the fabricated pouch‐type HSC delivers a maximum specific energy density of 47.97 Wh kg−1 and power density of 5120 W kg−1. Finally, the pouch HSCs are employed for practical self‐charging applications.

Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)

Estimating the wind speed correctly and reliably plays a key role in managing and operating wind energy power systems. Therefore an novelty adaptive estimation model (NAEM) combined with deep learning-based mode discretization has been developed for use in wind speed estimation in this study. This developed model consists of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the continuous wavelet transforms (CWT), the contrast limited adaptive histogram equalization (CLAHE), the particle swarm optimization (PSO), and convolutional neural network (CNN). Adaptive estimation model using decomposition method was presented as an alternative to the traditional data matrix transformation used in data preprocessing stage. Thus, both the usage firstly of this model in the data preprocessing stage and the creation of a hybrid structure by combining the methods included in this model for the first time constitute the most important innovative aspect of the study. Proposed model (NAEM) was tested in different case studies and RMSE, MAPE, and R2 were used as performance metrics. In the comparison with commonly used deep learning models (CNN-RNN, GRU, LSTM) the root means square error (RMSE) values decrease by 25.80%, 61.17% and 63.60% respectively. In addition, the power density value of the actual wind speeds was approached by 95.1% with the proposed NAEM.

An Innovative Reliability Oriented Approach for Restructured Power System Considering the Impact of Integrating Electric Vehicles and Renewable Energy Resources

Attributing to the irreplaceable quality of nil carbon footprints, the power system considering the integration of electric vehicles and renewable energy resources are appealing growing attention in the recent time, but the reliability of their associated components, is a main matter of concern nowadays. Therefore, in this paper, a novel incentive based fuzzy fault tree analysis (NIBFFTA) approach for restructured power system considering the influence of integrating Electric vehicles and Renewable energy resources-based hybrid wind-solar energy is presented. The approach combines the impacts of different components failure rate and the incentive Gaussian distribution effects under the inducement fuzzy fault tree atmosphere for the grid integrated Renewable energy resources and Electric vehicles configurations. In the basic fault tree analysis, the vague and inaccurate events such as system switches and low power component failures could not be identified competently. Moreover, the probability values of fault occurrences in the complete power system are not considered into account. Additionally, it is quite hard to have a precise assessment of the grid-connected wind energy power systems and EV configuration failure chances or the possibility of occurrence of undesired actions in the complete system because of data deficiency. To overwhelm these demerits, a novel incentive based fuzzy fault tree analysis based on the Gaussian distribution and fuzzy set model is recommended and used for the restructured power system considering the impact of integrating Electric vehicles and Renewable energy resources. Besides, the probability analysis of fault occurrence is also proposed to determine the impact of each basic event of the proposed system on the top event. Furthermore, the prediction analysis of fault occurrence is also done to know the effect of every basic action of the system on the top action. Whereas, prediction analysis factors for the different basic events of the proposed systems are evaluated and these can be used to obtain the real consequence of basic events on the proposed system. It is found that a novel incentive based fuzzy fault tree analysis approach is more significant and efficient than the conventional fault tree method for risk assessment of restructured power system with integrating the electric vehicles and renewable energy resources.

A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration

With increasing proportion of wind energy in power systems, the intermittence of such energy makes the system run a wide range of operating conditions. In this context, ordinary power system stabilizers (PSS) tuned based on the linearized model of the system at one operating condition may not be able to effectively damp low frequency oscillations (LFO), which brings great challenges to the stability of the system. To this end, this paper proposes a novel sparsity promoting adaptive control method for the online self-tuning of the PSS parameter settings. Different from the existing adaptive control methods, the proposed method combines deep deterministic policy gradient (DDPG) algorithm and sensitivity analysis theory to train an agent to learn the sparse coordinated control policy of multi-PSS. After training, the well-trained agent can be employed for online sparse coordinated adaptive control, and the control signal is only applied, when it is required and only to the key PSS parameters that have the maximum influence on the system stability. Simulation results verify that the proposed method can make the PSS achieve the better performance of damping oscillation and robustness against the change of wind energy in comparison with other methods.

Current State of the Worldwide Renewable Energy Generation: a Review

The purpose of this study is to undertake a global review of the renewable energy generation's current state, specifically in the area of photovoltaic (PV) solar energy, wind energy, bioenergy and hybrid power systems for electricity production. This article presents the most relevant aspects of the progress and the experiences of countries aiming at the construction and implementation of these technologies. It also quotes studies that include the advances of countries seeking to contribute to the decrease of greenhouse gas emission or that due to their geographic and environmental conditions are attempting to improve the access and the quality of the electric energy generation, involving especially remote communities that could hardly connect to the national power grid because of techno-economic reasons. These renewable energy studies could lead off to evaluate the impact of clean energy in the global energy supply and demand. Although in general, these technologies require high investments hardly competitive with traditional energy, with the support of stable policies and government subsidies, these become a choice that grows exponentially every day concerning efficiency and the reduction of costs, which can build trust in new investors.

Interactions of wind energy project siting, wind resource potential, and the evolution of the U.S. power system

The interactions of wind energy project siting, resource potential, and power system evolution are critical to understand given interest in high renewable energy systems, particularly as wind costs have fallen and grid operators implement solutions to manage variable generation. This study applies an integrative approach that combines spatially-explicit resource potential analysis with high spatial resolution U.S. electricity system modeling. Multiple wind supply curves, representing variations in siting regimes that account for interactions with built infrastructure, regulatory, physical, and social land use factors, are evaluated to determine how siting could influence prospective wind development. Different siting regimes lead to variations in future wind capacity, with the greatest impacts observed under scenarios with high demand for clean energy. With the tightest emissions limit modeled, 2050 onshore wind capacity varied by +7% (49 GW) in the least-restrictive siting regime to a decrease of 37% (270 GW) under the most-constrained case. More-stringent siting restrictions lead to higher electricity prices and emissions that should be weighed with local impacts. Under restrictive siting regimes, wind deployment is also sensitive to transmission availability and wind plant design. Overall, the findings highlight the importance of local land use considerations in regional and national power system planning.

Transient Stability Assessment of IEEE 9-Bus System Integrated Wind Farm

The use of wind energy is increased due to the high demand for sustainable energy. The penetration of wind energy in electrical networks might have several effects on load flow and power system stability. In this research, the transient stability of the IEEE 9-Bus system integrated with Doubly Fed Induction Generator (DFIG) is analyzed. Additionally, different penetration levels of a wind farm are considered. With a 5% penetration of wind energy, the maximum power angle of the synchronous generator is around 129 deg, which is quite similar to the existing system. In contrast, the power angle increases to 140 deg after adding more wind turbines with 15% wind farm penetration. Then, the system loses stability with a 25% penetration of wind energy. The results indicate that the high penetration of wind energy has a destabilizing impact on the studied network. Moreover, the location of the wind farm affects transient stability. This research intends to contribute towards assessing the stability of the power system integrated DFIG. Hence, this study will support the increase of using wind energy in power systems rather than conventional power plants and evaluate the stability to enable the reliability of alternative energy sources in the grid.

Many-Objective Robust Optimization for Dynamic VAR Planning to Enhance Voltage Stability of a Wind-Energy Power System

Growing integration of wind power and increasing use of induction motor loads dramatically affect a power system's dynamic performance. To address the challenges, this paper proposes a robust dynamic VAR planning method to enhance the voltage stability of wind energy power systems under uncertainty. Firstly, two indices are developed to evaluate the steady-state and the short-term voltage stability of the system. Then, a robustness index is proposed to quantitatively evaluate the robust optimality of the solutions, considering the uncertainties from wind power output and STATCOMs initial state before a contingency. Four objectives are optimized simultaneously: 1) total planning cost, 2) short-term voltage stability index, 3) steady-state voltage stability index, and 4) robustness of the solutions. Finally, an Adaptive Non-dominated Sorting Genetic Algorithm-III based on Latin Hypercube Sampling (A-NSGA-III-LHS)is designed to solve this many-objective optimization problem with 3 improvements over the standard NSGA-III: 1) adaptive mutation rate, 2) Differential Evolution operator, 3) Latin Hypercube Sampling based initial population generation. The proposed method is tested on the New England 39-bus system with an industry-standard complex load model, showing high robust optimality and computational efficiency over conventional methods.

Quantification of operating reserves with high penetration of wind power considering extreme values

The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.

Multi‐stage coordinated dynamic VAR source placement for voltage stability enhancement of wind‐energy power system

Increased wind power penetration and induction loads have deteriorated the voltage stability performance of the power system. This study proposes a multi-stage multi-objective dynamic VAR source placement method for wind energy power system, where three sub-objectives are optimised simultaneously: (i) investment cost and operational control cost; (ii) steady-state voltage stability index; (iii) short-term voltage stability index. This method coordinates the dynamic VAR allocation decisions with preventive and corrective stability controls, including wind power curtailment, modification of the line drop compensation, and load shedding, to improve both steady-state and short-term voltage stability of the system. A robust parameter design technique called Taguchi's Orthogonal Array Testing is used to model the wind power uncertainty. Pareto optimal solutions are obtained for flexible decision-making. The computation burden is mitigated by candidate buses selection and critical contingency identification. The proposed model is demonstrated on the New England 39-bus test system using PSS®E. Compared with conventional approaches, it can achieve higher voltage stability level with less overall cost and moderate wind power curtailment, and at the same time avoid the conservativeness of planning decisions.