Penetration Level Of Wind Energy Research Articles

A novel method to estimate maximum wind energy penetration level considering potential frequency support of wind power plants

Wind turbines’ installed capacity is expected to increase significantly in future power grids due to environmental concerns. Reductions in the power system inertia and frequency response due to increasing penetration levels of wind energy lead to a decrement in the frequency stability margin. This paper focuses on the frequency stability issue in power grids with high penetration level of wind turbines. The maximum allowable wind generation penetration level is first formulated as a function of primary frequency response and system inertia with/without considering operating reserve limits. Following that, the possibility of participation of wind turbines in the frequency stability improvement is investigated and the minimum required wind turbine participation is estimated as a function of wind energy penetration level. In this trend, a time-independent formulation for power system frequency response is utilized to increase the accuracy and reduce the computational complexity. A comprehensive analysis is performed on IEEE 39-bus test system to investigate the proposed methodologies. As results confirm, participation of wind turbines in the frequency control task leads to more secure operation of the power system.

Power Quality Improvement in Rural Grid Using Adaptive Control Algorithm to Enhance Wind Energy Penetration Levels

The intermittency associated with Wind Energy causes Power Quality (PQ) disturbances, which in turn can limit the penetration levels. These penetration levels are further restricted due to the non-linear (NL) loads and the weakness of the grid (especially in the rural grid). This paper proposes a methodology to enhance wind power penetration levels into the rural grid by mitigating PQ disturbances using a DSTATCOM controlled by Adaptive Least Mean Fourth (ALMF) control. The proposed algorithm computes the active and reactive load current components with the help of updated weights by continuously adapting the changes in grid voltages, wind speed and DC-link voltage to generate reference currents, which are used to generate switching signals for the DSTATCOM. Various case studies establish the effectiveness of the proposed algorithm in MATLAB simulation and experimentally to enhance the wind energy penetration level up to 25% into the rural grid with a short circuit ratio as low as 2.74 in the presence of NL loads.

Active hybrid energy storage management in a wind-dominated standalone system with robust fractional-order controller optimized by Gases Brownian Motion Optimization Algorithm

Accessibility to sustainable and reliable electrical energy for remote area power supply (RAPS) systems under high penetration levels of wind energy requires adopting appropriate technologies and controllers. In recent years, energy storage has gained significant attention for smoothing inherent voltage and power fluctuations of standalone systems. This paper presents a filtration-based fractional-order PI (FOPI) controller optimized by Gases Brownian Motion Optimization (GBMO) algorithm to manage an active battery-supercapacitor hybrid energy storage system (HESS) in a wind-dominated RAPS system. The GBMO algorithm has a high accuracy and convergence rate among optimization algorithms introduced in recent years. Moreover, a fuzzy logic controller (FLC) guarantees the wind turbine generator's maximum power point tracking (MPPT) at any wind condition while mitigating its fluctuating torque. The proposed RAPS system uses the advantages of the FOPI controller, the GBMO optimization algorithm, the FLC-based MPPT algorithm, and a high-pass filter to manage precise coordination between different components of the RAPS system. The high-pass filter decouples high and low-frequency voltage oscillations to modify the HESS performance and relieve battery stress, thereby extending its lifespan. The proposed controller's performance and robustness are verified in comparison with an optimal PI controller based on GBMO under turbulent wind speed and load step changes in a detailed model of the system. The results demonstrate the superiority of the optimal FOPI controller over the optimal PI controller.

Techno-economic assessment of offshore wind-to-hydrogen scenarios: A UK case study

The installed capacity, electricity generation from wind, and the curtailment of wind power in the UK between 2011 and 2021 showed that penetration levels of wind energy and the amount of energy that is curtailed in future would continue to rise whereas the curtailed energy could be utilised to produce green hydrogen. In this study, data were collected, technologies were chosen, systems were designed, and simulation models were developed to determine technical requirements and levelised costs of hydrogen produced and transported through different pathways. The analysis of capital and operating costs of the main components used for onshore and offshore green hydrogen production using offshore wind, including alternative strategies for hydrogen storage and transport and hydrogen carriers, showed that a significant reduction in cost could be achieved by 2030, enabling the production of green hydrogen from offshore wind at a competitive cost compared to grey and blue hydrogen. Among all scenarios investigated in this study, compressed hydrogen produced offshore is the most cost-effective scenario for projects starting in 2025, although the economic feasibility of this scenario is strongly affected by the storage period and the distance to the shore of the offshore wind farm. Alternative scenarios for hydrogen storage and transport, such as liquefied hydrogen and methylcyclohexane, could become more cost-effective for projects starting in 2050, when the levelised cost of hydrogen could reach values of about £2 per kilogram of hydrogen or lower.

Flexibility Analysis of O&G Platform Power System with Wind Energy Integration

Many researchers and operators are assessing the impact of wind energy integration into the gas turbines based conventional power system due to the intermittent and variable nature. The flexibility characteristics of the gas turbines are vital to guarantee adequate performance at different levels of wind energy penetration to meet the demand of the O&G platform. This study aims to verify the impact of increasing flexibility of the offshore O&G platform’s power system. Therefore, the conventional O&G platform power system is modelled and compared with the post-flexibilization or state-of-the-art power system at different dynamic restrictions of the Open-Cycle Gas Turbines (OCGT) like ramp rates, minimum loading, uptime and downtime, and start-up/shut-down costs. Subsequently, the conventional and state-of-the-art power system model are then simulated at different levels of wind energy penetration, to analyze the system response of the O&G platform, as the intermittent wind energy can generate critical power system instability and imbalance. The proposed model has 4 OCGTs of 33.3MW (locally installed at the O&G platform) and 4 offshore floating wind turbines of 15MW that is satisfying 2 different load profiles of O&G platform (68MW and 34MW average load). The simulation results highlighted that the state-of-the-art power system accommodated higher shares of wind energy as compared to the conventional power system due to the flexible constraints. Also, the flexible power system achieved higher levels of fuel saving, when simulated for 100 hours. The same case study was considered for 25 years and the hours of fuel saving at 5% was 1733 hours and 20% of wind penetration resulted in 1857 hours of fuel saving. The study was performed in the modified Python for Power System Analysis (PyPSA), a python based free simulation toolbox for optimizing the power dispatch.

Intermittent power control in wind turbines integrated into a hybrid energy storage system based on a new state-of-charge management algorithm

Wind technology has developed sustainably in recent decades, becoming the primary source of renewable energy integrated into the power system. The high penetration levels of wind energy involve technical problems because of the intermittent power injected by wind turbines, caused by the variability of wind speed. This means a significant challenge in the operation of power grids reliably. This paper presents the intermittent power control in wind turbines based on direct-drive permanent magnet synchronous generator technology (DD-PMSG) integrating through the DC link a hybrid energy storage system (HESS) composed of lithium-ion electrochemical batteries (BESS) and supercapacitors (SESS), to convert a wind power plant into a dispatchable energy source. To ensure the stable operation of the wind turbine and HESS system, a new algorithm is developed to manage the state-of-charge (SOC) of the HESS to avoid its full charge/discharge. This is achieved by considering the mutual support between the batteries and the supercapacitors. The simulation results illustrate the power variation of the wind turbine integrated to the HESS, with different wind speed profiles and different initial state-of-charge for the BESS and SESS. Furthermore, it is shown how the algorithm manages the state-of-charge of the HESS under both normal and critical operation, demonstrating a good performance during the system operation. In this way, the power output of the wind turbine becomes stable and controllable, even in adverse situations, thanks to the control of the algorithm. Finally, new concepts related to wind turbine inertia and wind turbine rotational speed have been introduced from the point of view of the generator and the grid, which could be useful for electrical studies. The whole dynamic model has been developed in MATLAB/Simulink.

Carbon Value Assessment of Hydrogen Energy Connected to the Power Grid

Hydrogen energy plays a fundamental role during the decarbonization transition of electricity systems. However, there is still a lack of a systematic framework analyzing the carbon-reduction capability of integrating a hydrogen-energy storage system (HESS) into a power grid. This research combines the grid dispatch model and storage-control model to define the metric and formulate a framework for assessing the HESS’s value of reducing carbon emissions in a power system. A dynamic programming algorithm is developed to estimate the maximal carbon-reduction value of HESS, which is a nonconvex optimization problem. We apply our model to assess the carbon value of HESS in the ERCOT market and discover the tradeoff between using HESS to reduce carbon and fuel costs. Using the HESS can even cause an increase in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$CO_2$</tex-math></inline-formula> emissions in ERCOT if the charging strategy is designed only to reduce the economic cost. We further identify the factors determining the carbon value of the HESS. The wind energy penetration level decisively determines how much carbon emissions the HESS can reduce. The technical characteristics of HESS also affect its carbon value.

Enhancement of Wind Energy Penetration Levels in Rural Grid Using ADALINE-LMS Controlled Distribution Static Compensator

Wind Energy (WE) penetration levels in rural grids are limited due to the Power Quality (PQ) issues owing to the inconsistency in the wind speed and low short circuit ratios. This is further aggravated due to the day-by-day increase in non-linear loads. This paper proposes a methodology to enhance WE penetration levels by mitigating PQ issues using distribution static compensator (DSTATCOM) controlled by the Adaptive Linear Element-Least Mean Square (ADALINE-LMS) algorithm. The proposed adaptive control algorithm estimates the active and reactive weight components of load currents by considering the changes in wind generation, DC-link voltage and grid voltage. These weights are used to generate reference current signals to switch the DSTATCOM to mitigate PQ disturbances. The proposed method has been implemented for the enhancement of WE penetration in the rural grid under the non-linear load. The results obtained by simulation in MATLAB studies have been validated using the experimental studies. These studies established the effectiveness of the proposed method for enhancing WE penetration level in rural grid up to 25%.

Investigating the Need for Real-Time Adjustment Cost in Unit Commitment Framework for Wind-Integrated Power Systems

The best day-ahead schedule is essential for a secured operation of wind-integrated power systems. This article investigates the role of real-time adjustment cost in the unit commitment framework and analyzes its impact on the scheduling of generating units. The work is accomplished in two steps: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> , developing a unit commitment framework with real-time adjustment costs in a deterministic and stochastic manner for a wind-integrated system; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">second</i> , carrying out a comprehensive comparison between unit commitment models with and without real-time adjustment cost. This article ascertains the need and impact of real-time adjustment cost on the contribution of different generating units in providing the generation and flexibility at various levels of wind energy penetration. Its competence is demonstrated on the modified IEEE RTS-96 system.

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented.

Frequency Stability of AC/DC Interconnected Power Systems with Wind Energy Using Arithmetic Optimization Algorithm-Based Fuzzy-PID Controller

This article proposes an intelligent control strategy to enhance the frequency dynamic performance of interconnected multi-source power systems composing of thermal, hydro, and gas power plants and the high penetration level of wind energy. The proposed control strategy is based on a combination of fuzzy logic control with a proportional-integral-derivative (PID) controller to overcome the PID limitations during abnormal conditions. Moreover, a newly adopted optimization technique namely Arithmetic optimization algorithm (AOA) is proposed to fine-tune the proposed fuzzy-PID controller to overcome the disadvantages of conventional and heuristic optimization techniques (i.e., long time in estimating controller parameters-slow convergence curves). Furthermore, the effect of the high voltage direct current link is taken into account in the studied interconnected power system to eliminate the AC transmission disadvantages (i.e., frequent tripping during oscillations in large power systems–high level of fault current). The dynamic performance analysis confirms the superiority of the proposed fuzzy-PID controller based on the AOA compared to the fuzzy-PID controller based on a hybrid local unimodal sampling and teaching learning-based optimization (TLBO) in terms of minimum objective function value and overshoots and undershoots oscillation measurement. Also, the AOA’s proficiency has been verified over several other powerful optimization techniques; differential evolution, TLBO using the PID controller. Moreover, the simulation results ensure the effectiveness and robustness of the proposed fuzzy-PID controller using the AOA in achieving better performance under several contingencies; different load variations, the high penetration level of the wind power, and system uncertainties compared to other literature controllers adjusting by various optimization techniques.

Hybrid PSO-GSA algorithm-based optimal control strategy for performance enhancement of a grid-connected wind generator

&lt;span&gt;Due to the great level of wind energy penetration in the existing network, huge efforts have been directed to enhance the grid-connected wind generator performance. This paper shows an application of a hybrid algorithm of the particle swarm optimization and the gravitational search algorithm (PSO-GSA) to enhance the transient stability of the grid-tied wind energy conversion system. The variable-speed wind turbine (VSWT) direct-drive permanent-magnet synchronous generator is connected to the network through a full-scale converter. The generator- and grid-side converters are controlled by utilizing an optimum proportional-integral (PI) controller. The criterion of the integral squared error is utilized as an objective function. The PSO-GSA based-PI controller efficacy is validated by comparing its results with that are obtained by utilizing the genetic algorithm (GA)-based-PI controller. The performance of the suggested control scheme is checked during various fault conditions. The control scheme quality is legalized by the simulation results that are obtained using MATLAB/Simulink program&lt;/span&gt;

Power system frequency control enhancement by optimization of wind energy control system

Abstract Future trends are increasingly directed towards replacing traditional energy sources by new and renewable energy ones, with Wind Energy coming first in the row of these sources worldwide. The Egyptian electric power system still has a relatively low penetration level, around 3%, of Wind Energy to its generation mix. However, it is planned to increase this ratio to 14.6% by the year 2035. This paper studies the frequency dynamics associated with significant penetration levels of Wind Energy into the Egyptian grid, which leads to reducing the overall system inertia. The control strategy adopted depends on maximizing the wind energy contribution to frequency control, in order to compensate that reduction of system inertia. The Real data of the Egyptian power system model used is verified by the National Electricity Control Centre, updated to the latest values declared and tested under the lowest and highest loads recorded. A modified control loop is added to the control system in order to allow the Wind Energy Conversion System to significantly contribute to the Load Frequency Control. The PI controller gains used in that loop are tuned using three methods; Local Unimodal Sampling, Harmony Search Algorithm and Equilibrium Optimizer (EO). The Equilibrium Optimizer (EO) proved its superiority to the other methods, as it gave the best dynamic response. The simulated results are presented using MATLAB/SMULINK.

Contribution of Variable Speed Wind Turbine Generator based on DFIG using ADRC and RST Controllers to Frequency Regulation

In the interconnected power systems, there must always be a balance between the amount of electricity produced and the amount of electricity consumed in order to keep the grid frequency stable and nearby to its nominal value. Therefore, any change in consumption can lead to imbalance, instability, load shedding and system frequency disturbances. With the increase of wind energy penetration level in the electrical network, it has become necessary to make wind generators participate in frequency regulation. The fundamental objective of this research paper is to investigate the ability of wind turbine at variable speed equipped with doubly fed induction generator (DFIG) to contribute in the grid frequency regulation. For the purpose of the contribution in the frequency regulation, the wind turbine system must inject a supplementary power support into the electrical network. To do that, two control strategies are adopted: the first one is the synthetic inertia control which uses the kinetic energy reserved and stocked in the turbine and generator rotors of the wind system. The second one is the droop frequency control which includes the deloading method that allows to the VSWT to create a certain reserve active power. Then, each of these control techniques is implemented and their performances are evaluated in case of frequency variation. Moreover, the control structure that uses the combination of these two techniques is also implemented and studied. The active disturbance rejection control strategy (ADRC), the classical PI controller and the polynomial RST controller have been adopted to command the rotor side converter (RSC) of the DFIG which allows it to provide, in case of frequency fault, a support for the power system into the grid by adjusting the rotor speed. The performance of these controllers are tested and compared and the simulation is performed by MATLAB/Simulink simulation software.

Modified demagnetisation control strategy for low‐voltage ride‐through enhancement in DFIG‐based wind systems

The large-scale wind energy conversion systems (WECSs) based on doubly-fed induction generators (DFIGs) are very popular in recent years due to the numerous technical and economic benefits. With the increasing penetration level of wind energy, the latest grid codes require the DFIG-based WECSs to remain connected to the grid under grid fault scenarios and deliver the required reactive power into the grid. However, the direct connection of the stator of the DFIG to the grid makes it prone to grid disturbances, especially to voltage sag. This study proposes a modified demagnetisation control strategy to enhance the low-voltage ride-through (LVRT) capability of the DFIG under grid faults. The proposed control strategy is implemented in a coordinated approach by using the existing demagnetisation control and the addition of an external resistance in the stator side of the DFIG. The demagnetisation control damps the direct current component of the stator flux and the external resistance accelerates the damping of the transient flux by decreasing the time constant and hence, enhancing the LVRT capability of DFIG. The effectiveness of the proposed control strategy is demonstrated under both symmetrical and asymmetrical grid faults simulated system through MATLAB/Simulink®. The comparative results justify the merits of the proposed methodology.

Analysis of the Implementation of the Primary and/or Inertial Frequency Control in Variable Speed Wind Turbines in an Isolated Power System with High Renewable Penetration. Case Study: El Hierro Power System

With high levels of wind energy penetration, the frequency response of isolated power systems is more likely to be affected in the event of a sudden frequency disturbance or fluctuating wind conditions. In order to minimize excessive frequency deviations, several techniques and control strategies involving Variable Speed Wind Turbines (VSWTs) have been investigated in isolated power systems. In this paper, the main benefits and disadvantages of introducing VSWTs—both their inertial contribution and primary frequency regulation—in an exclusively renewable isolated power system have been analyzed. Special attention has been paid to the influence of the delays of control signals in the wind farm when VSWTs provide primary regulation as well as to the wind power reserve value which is needed. To achieve this objective, a methodology has been proposed and applied to a case study: El Hierro power system. A mathematical dynamic model of the isolated power system, including exclusively renewable technologies, has been described. Representative generation schedules and wind speed signals have been fixed according to the observed system. Finally, in order to obtain conclusions, realistic system events such as fluctuations in wind speed and the outage of the generation unit with the higher assigned power in the power system have been simulated.

Power Quality Assessment and Event Detection in Distribution Network With Wind Energy Penetration Using Stockwell Transform and Fuzzy Clustering

Power quality (PQ) is a vital issue in the present power systems integrated with large renewable energy sources since more power electronics devices are incorporated in the system. This article proposes a novel method for assessing PQ associated with wind energy integration. This method is effective to recognize PQ issues in power systems with high penetration of wind energy with a low computational burden. Furthermore, it detects different operational issues in the distribution network. Stockwell transform (S-transform) is utilized to decompose the voltage signal and calculate the S-matrix. To assess the PQ, a plot is developed from this matrix. The features of this matrix such as mean, standard deviation, and maximum deviation are further utilized for detecting the operational issues such as wind speed variation, islanding, synchronization, and outage of the wind generation by using clustering with fuzzy C-means. A modified IEEE 13-bus test system is utilized to validate the proposed method, which is also supported by hardware and real-time digital simulator results. The quality of power is graded with the help of a proposed PQ index under various operational events with different levels of wind energy penetration. The proposed method is effective for the identification and grading of different operational events in terms of PQ and recognizing a wide range of PQ issues with a high share of wind energy. The performance of the proposed scheme is established by comparing its results with other approaches.

Heuristic Optimization of Virtual Inertia Control in Grid-Connected Wind Energy Conversion Systems for Frequency Support in a Restructured Environment

In the work reported in this paper, a novel application of the artificial bee colony algorithm is used to implement a virtual inertia control strategy for grid-connected wind energy conversion systems. The proposed control strategy introduces a new heuristic optimization technique that uses the artificial bee colony (ABC) algorithm to calculate the optimal gain value of an additional derivative control loop added to the control scheme of the machine side converter in a wind energy system to enable wind farms to participate in frequency control as specified by recent grid codes. This helps to minimize the frequency deviations, reduce active power deviation in the system, and increase the penetration level of wind energy in power systems. The study was performed in a restructured power system environment. The proposed control scheme and its robustness were evaluated using load–frequency analysis for three real-life transaction scenarios that can occur in an interconnected open-energy market and the validation was carried out using eigenvalue analysis. The results in this study show that the optimal gain of the proposed controller reduces the frequency deviations and improves stability and overall performance of the system.

An Efficient Control Strategy for Enhancing Frequency Stability of Multi-Area Power System Considering High Wind Energy Penetration

This paper proposes an efficient control strategy to enhance frequency stability of three-area power system considering a high penetration level of wind energy. The proposed strategy is based on a combination of a Proportional Integral Derivative (PID) controller with a Linear Quadratic Gaussian (LQG) approach. The parameters of the proposed controller (i.e., PID-LQG) are optimally designed by a novel natural physical based-algorithm called Lightning Attachment Procedure Optimization (LAPO). The main objective is to keep the frequency fluctuation at its acceptable value in the presence of high penetration of wind energy, high load disturbance and system uncertainties. The superiority of the proposed PID-LQG controller is validated by comparing its performance with optimal Coefficient Diagram Method (CDM) controller, conventional CDM controller, optimal PID controller-based LAPO, and integral controller. Moreover, the exhaustive results completely demonstrate that the proposed controller gives better performance in terms of overshoot, undershoot, and settling time as well as provides reliable frequency stability for interconnected power systems considering high wind penetration and system uncertainties.

Withholding strategies for a conventional and wind generation portfolio in a joint energy and reserve pool market: A gaming-based approach

This work considers a strategic producer whose generation portfolio consists of conventional and wind power production. Based on the single leader-follower game, a bi-level complementarity model is constructed to derive optimal capacity withholding strategies for this portfolio in a pool-based market. The upper level of the model represents the maximization of the strategic producer’s expected profits while the lower level represents the security-constrained economic dispatch conducted by the independent system operator. The market clearing scheme refers to energy-only markets optimizing jointly scheduled energy and reserves through a two-stage stochastic programming. The first stage illustrates the day-ahead market clearing and the second stage illustrates the balancing market clearing taking into consideration the wind generation uncertainty. With the use of the Karush-Kuhn-Tacker optimality conditions the initial bi-level model is recast into a mathematical programming with equilibrium constraints model which is then reduced into an equivalent mixed integer linear programming using the strong duality theorem and disjunctive constraints. The proposed algorithm derives optimal scheduled thermal and wind energy as well as reserve deployments. It also provides optimal offers based on the endogenous formation of local marginal prices under network constraints and different wind energy penetration levels.