High Penetration Of Wind Energy Research Articles

A data-driven online approach for detection and localization of forced oscillation in wind turbine integrated power system

This article introduces an innovative approach for online detection and localization of forced oscillation (FO) in power systems with significant integration of wind turbine generation (WTG). The synergistic combination of Multichannel Prony analysis with Periodogram and dissipating energy flow (DEF) approach is proposed in this paper to overcome the challenges inherent in detecting FO amidst the complexities introduced by WTG integration. An adaptive version of multichannel Prony analysis is proposed in this paper that dynamically estimates Prony’s system order via the recursive process. It estimates the frequency and damping ratio of all oscillatory modes, which is used to identify low damping modes that are potentially attributed to FO. Leveraging this information, the proposed method enhances both FO detection and source localization capabilities. The proposed method is verified on measurements taken from IEEE benchmark 4-machine, 11-bus system and 10-machine, 39-bus system with high wind energy penetration. Comprehensive testing demonstrates the proposed approach’s effectiveness across various FO scenarios, including governor valve malfunction, exciter malfunction, and cyclic loads. Furthermore, the methodology is evaluated under resonant conditions and scenarios involving the simultaneous identification of multiple disturbances.

Minimizing the Effect of Integrating Wind Farms on Transmission Power Systems

Expanding the renewable energy sources usage in power systems will help the government meet its energy policy goals of lowering emissions, ensuring high power supply reliability, and promoting spot markets with high competitivity. However, due to the intermittent nature of these sources, this will necessitate the mitigation of network vulnerabilities. High wind energy penetration in the grid could have an impact on the system frequency, voltage, real and reactive power response, and power quality of the system. The main objective of this paper is to investigate how the integration of wind farms in the Jalan Bani Bu Ali (JBBA) area will affect Oman's main interconnected system (MIS). Results show that the effect is minor on voltage and frequency stability by all disturbances taken into account.

Geospatial optimisation of wind and solar energy generation: A framework for optimised resource allocation

Wind and solar power generation profiles exhibit a high degree of power output variability and uncertainty compared to conventional fossil fuel generation plants. However, it is generally accepted that this variability and uncertainty can be mitigated by geographic siting and dispersion of the renewable energy plants, particularly with the view to leverage aggregation effects. In this context, this paper proposes a framework for the geospatial optimisation of wind and solar PV generation for scenarios with a high penetration of wind and solar renewable energy. The proposed optimisation framework represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, the wind and solar resources are categorised in terms of the statistical properties of the associated spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time of Use periods. It is thereby recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time of Use periods are more important than others. The proposed framework is implemented for a number of baseline case studies and optimisation case studies, using wind and solar resource data sets covering South Africa. Overall, it is concluded that this approach can leverage geospatial site optimisation to substantially reduce the risks associated with high residual load events. Furthermore, the proposed framework is highly flexible in that the formulation of the minimum and maximum allocation constraints allows application for real-world scenarios whereby stringent capacity allocation constraints can be applied.

Line protection in presence of high penetration of wind energy: a review on possible solutions

Line protection in presence of high penetration of wind energy: a review on possible solutions

Quantitative Frequency Security Assessment of Modern Power System Considering All the Three Indicators in Primary Frequency Response

The primary frequency response scale is deteriorating in the modern power system due to the high penetration of different power devices. Frequency security assessments are essential for the operation or stability-checking of the power system. Firstly, this paper establishes the Unified Transfer Function Structure (UTFS) of power systems with highly penetrated wind turbines. Based on the UTFS, this paper analyzes the three indicators of the primary frequency responses. Secondly, to better assess the security of the frequency, the secondary frequency drop (SFD) is avoided, with the frequency response parameters of the wind turbines calculated. Moreover, considering all three indicators of the primary frequency response, this paper proposes a frequency security margin index (FSMI). The FSMI divides the system stability margin into three levels, quantitively and linearly representing the frequency response capability of different power devices. Finally, to show the effectiveness and practicability of the FSMI, this paper establishes a simulation model with high wind energy penetration, including four machines and four zones in DigSILENT. Based on the FSMI, the frequency stability margins in different typical operating scenarios are divided into three zones: “Absolut secure”, “Secure” and “Relative secure”. The FSMI also shows the dominant frequency stability problem and the risk of system frequency instability for each zone. Considering the checking principles, the frequency stability margin is equivalently expanded by calculating the energy storage’s minimum frequency response capacity.

Fault Detection and Classification to Design a Protection Scheme for Utility Grid with High Penetration of Wind and Solar Energy

This paper designed a protection scheme for utility grid with high share of renewable energy (RE) generated from wind energy and solar energy plants. This is based on extraction of features from the current using Stockwell transform (ST), Hilbert transform (HT), and alienation coefficient. A Stockwell index (SI) is designed by extracting current features using ST, a Hilbert index (HI) is designed by extracting current features using HT, and an alienation index (AI) is designed by extracting current features using an alienation coefficient. A fault index (FI) is formulated by multiplying the SI, HI, AI, and WF (weight factor). This FI is implemented for fault detection. Fault classification is achieved considering number of fault phases and ground fault index (GFI). This GFI is designed by processing zero sequence currents applying ST. GFI effectively identifies the ground involved during event of a fault. A designed protection scheme is effective to identify faults in the scenario of high RE share and during various cases of study which includes the variations of fault impedance, different fault occurrence angles (FOA), fault incident at different nodes, and noisy condition. This protection scheme effectively discriminates the fault events from the operational events such as feeder operation, load, and capacitor switching. Performance of hybrid protection method formulated in this paper is better relative to alienation coefficient-based protection scheme (ACPS) reported in literature. The ACPS has maximum error and mean error of fault detection equal to 9.54% and 5.99%, respectively, which is relatively high compared to the respective values for the proposed method which are 1.89% and 0.978%, respectively. ACPS is effective for detecting the fault events in noise level of 30 dB SNR (signal-to-noise ratio) whereas the proposed method effectively identifies the faults in the high noise scenario of 20 dB SNR.

Performance Improvement of Grid-Integrated Doubly Fed Induction Generator under Asymmetrical and Symmetrical Faults

The doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) suffers from voltage and frequency fluctuations due to the stochastic nature of wind speed as well as nonlinear loads. Moreover, the high penetration of wind energy into the power grid is a challenge for its smooth operation. Hence, symmetrical faults are most intense, inflicting the stator winding to low voltage, disturbing the low-voltage ride-through (LVRT) functionality of a DFIG. The vector control strategy with proportional–integral (PI) controllers was used to control rotor-side converter (RSC) and grid-side converter (GSC) parameters. During a symmetrical fault, however, a series grid-side converter (SGSC) with a shunt injection transformer on the stator side was used to keep the rotor current at an acceptable level in accordance with grid code requirements (GCRs). For the validation of results, the proposed scheme of PI + SGSC is compared with PI and a combination of PI with Dynamic Impedance Fault Current Limiter (DIFCL). The MATLAB simulation results demonstrate that the proposed scheme provides superior performance by providing 77.6% and 20.61% improved performance in rotor current compared to that of PI and PI + DIFCL control schemes for improving the LVRT performance of DFIG.

Optimal energy management of battery with high wind energy penetration: A comprehensive linear battery degradation cost model

Lithium-ion batteries have drawn considerable attention due to their different applications in smart grids. Nevertheless, various factors, including the charging/discharging process, can cause battery capacity degradation and reduce its lifetime. Considering the high investment cost of the battery, employing an appropriate approach to integrate the battery degradation cost into the scheduling problem is vital to optimizing the battery performance. This paper proposes a novel degradation cost model for optimal battery scheduling. A linear model based on the semi-empirical approach is introduced to model the battery capacity degradation process. Moreover, a novel linear algorithm based on the rain-flow algorithm is presented to count complete and incomplete cycles. Then, a degradation cost model is presented based on the amount of battery capacity fade and engineering economics principles. The battery scheduling problem is formulated as a mixed-integer linear programming (MILP) model. In the next stage, a novel approach based on model predictive control (MPC) is utilized to decline the degradation cost and improve battery energy management. The simulation results show that integrating the battery degradation cost into the battery scheduling problem significantly influences the charge/discharge strategy and achieves more benefits for battery owners. In this regard, the proposed scheme can decrease the battery degradation cost and the amount of capacity fade by 31.62% and 37.23%, respectively. Also, considering the battery degradation process in the optimization problem leads to a more optimal and accurate outcome.

Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization

Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration.

Modified Shapley Value-Based Profit Allocation Method for Wind Power Accommodation and Deep Peak Regulation of Thermal Power

Peak regulation ancillary service plays a significant role in the safe and economic operation of power system with high wind energy penetration. However, the interest conflict in wind power accommodation and thermal power schedule results in insufficient initiative for peak regulation service. To promote the initiative for peak regulation service, the paper is devoted to formulating an effective interest guarantee scheme. First, we introduce the deep peak regulation optimization model of wind power and thermal power based on cooperative game theory. Next, considering cooperative fairness and the encouragement to coalition improvement, a novel profit allocation method is specially modified based on Shapley value. The ratio of deep peak regulation ability and demand (ADR) is introduced to recognize the main limiting reason of current coalition and overcome the short-sight of original Shapley value allocation method. Since the parameters are adjustable according to the specific pursuit, modified Shapley value method for profit allocation can be regarded as the comprehensiveness of the original Shapley value method for profit allocation and cost allocation. Finally, numerical study clarifies the effectiveness of modified Shapley value method with ADR.

Fault Ride-Through Operation Analysis of Doubly Fed Induction Generator-Based Wind Energy Conversion Systems: A Comparative Review

In present electrical power systems, wind energy conversion systems based on doubly fed induction generators represent one of the most commonly accepted systems in the global market due to their excellent performance under different power system operations. The high wind energy penetration rate makes it challenging for these wind turbines to follow grid code requirements. All operations of a wind energy system during a dip in voltage require special attention; these operations are critically known as fault ride-through and low voltage ride-through. In this paper, various fault ride-through techniques of doubly fed induction generator-based wind energy conversion systems, such as protective circuitry, reactive power injection, and control methods for transient and steady state operations, have been presented to improve the performance. During system disturbances, protective circuitry or control mechanisms are typically used to limit the over-current of the rotor and the generated inappropriate DC link over-voltage. Simultaneously, the reactive power injection system overcomes the reactive power scarcity and enhances the transient response, further limiting the DC bus voltage and rotor current. This review paper compares and suggests appropriate FRT methods that are driven by external modifications and internal system improvements. Furthermore, typical case studies are discussed to illustrate and support the FRT system. The impact of each case study was evaluated and analyzed using the results obtained from the MATLAB/Simulink application and the OPAL-RT (OP4500) real time simulator (RTS).

Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.

Support Vector Regression Approach for Wind Forecasting

The government policies to fully support the G20 sustainability emphasize increased use of renewable energy. The high penetration of wind energy into power systems poses many challenges for energy system operators, primarily due to the unpredictability and variability of wind energy production. Wind power may not be provided, but accurate forecasting of wind speed and power generation helps grid operators reduce the risk of reduced electricity reliability. Accurately predicting wind speeds over 1 to 24 hours based on these conditions is important for predicting potential energy supply. These short-term forecasts are important to support wind power planning, so the required base load supply for the grid is always guaranteed (even if the wind power output fluctuates significantly). This task demonstrates that the relative forecasting performance of a support vector regression (SVR) wind forecasting system can be improved by systematically selecting and combining related input functions that affect wind speed. Shows the results of data collected in Sidrap, Indonesia, during the six months of 2019. This paper explained key methods of wind forecasting, based on the evaluation of wind speeds and wind speed prediction methods. The RMSE from the SVR shows an 8% - 9% improvement on the RMSE of the persistence forecast every 1 hour. Wind speed estimation using a support vector regression approach has the potential for further development, one of which is determining the potential location of wind-based renewable sources and Wind Energy Conversion System (WECS) can make more efficient.

Support Vector Regression Approach for Wind Forecasting

The government policies to fully support the G20 sustainability emphasize increased use of renewable energy. The high penetration of wind energy into power systems poses many challenges for energy system operators, primarily due to the unpredictability and variability of wind energy production. Wind power may not be provided, but accurate forecasting of wind speed and power generation helps grid operators reduce the risk of reduced electricity reliability. Accurately predicting wind speeds over 1 to 24 hours based on these conditions is important for predicting potential energy supply. These short-term forecasts are important to support wind power planning, so the required base load supply for the grid is always guaranteed (even if the wind power output fluctuates significantly). This task demonstrates that the relative forecasting performance of a support vector regression (SVR) wind forecasting system can be improved by systematically selecting and combining related input functions that affect wind speed. Shows the results of data collected in Sidrap, Indonesia, during the six months of 2019. This paper explained key methods of wind forecasting, based on the evaluation of wind speeds and wind speed prediction methods. The RMSE from the SVR shows an 8% - 9% improvement on the RMSE of the persistence forecast every 1 hour. Wind speed estimation using a support vector regression approach has the potential for further development, one of which is determining the potential location of wind-based renewable sources and Wind Energy Conversion System (WECS) can make more efficient.

Stability Improvement of a Transmission Grid With High Share of Renewable Energy Using TSCOPF and Inertia Emulation

This paper proposes a Transient Stability Constrained Optimal Power Flow (TSCOPF) formulation that models non-synchronous renewable generation equipped with synthetic inertia. The proposed optimization problem calculates the optimal operating point of the system, accommodating high shares of non-synchronous renewable generation while ensuring transient stability in the event of critical incidents. Synthetic inertia controllers are used to improve the dynamic stability of the system in cases of very high share of renewable generation. The proposed tool is tested in the North-West Spanish system, a network with a high penetration of wind energy that causes a reduction in the total system inertia. The results of the study show that 1) synthetic inertia in renewable power plants can diminish electromechanical oscillations after a severe contingency, reducing the cost of ensuring transient stability; 2) using synthetic inertia the system becomes more stable when conventional generation is decommissioned following de-carbonization and renewable promotion policies; and 3) the proposed model can be used to calculate the parameters of the synthetic inertia control.

Cooperative fault management for resilient integration of renewable energy

Cooperative fault management (CFM) is designed herein to control different types of renewable energy resources cooperatively during electrical faults. This paper studies systems with a high penetration of photovoltaic (PV) energy and wind energy. First, CFM leverages power converters of PV farms to boost the ride-through capability of nearby doubly-fed induction generators (DFIGs). By controlling PV farms’ output voltages to change smoothly during both fault initiation and fault clearance, the widely used crowbar in DFIGs is less likely to be activated. Crowbar activation adversely makes DFIGs lose controllability and absorb reactive power. The second contribution is the development of a software-defined CFM controller and a controller-in-the-loop demonstration of the real-time performance of this optimization-based CFM. CFM capitalizes on distributed optimization formulation to enable flexibility, plug-and-play, and privacy-preserving. Computation time, however, is a major concern for optimization-based dynamics control. Real-time controller-in-the-loop simulation results show optimization-based CFM can output reference values around 60 ms and is quick enough for dynamic control.

Recent development and future trends of resonance in doubly fed induction generator system under weak grid

In recent years, the wind energy conversion system faces significant challenges for stable and secure operations, especially in the weak grid due to high penetration of wind energy into the grid. Doubly fed induction generator (DFIG) is one of the most popular wind turbine generators due to its cost-effective variable speed and low-cost partial size power converter. Impedance interaction between series or parallel compensated weak grid and the DFIG system causes sub-synchronous resonance (SSR) and high-frequency resonance (HFR). A comprehensive review of the state-of-the-art SSR and HFR in the DFIG system under a weak grid is presented. First, based on features, such as supplementary hardware, linear and non-linear control strategies, the resonance damping techniques are classified into different types. Then, the advantages and disadvantages of SSR analysis and damping techniques techniques are put forward. Moreover, the latest development of state-of-the-art SSR and HFR analysis and damping techniques are supplemented. Finally, the contemporary and future trends of the SSR and HFR techniques are discussed.

Artificial Neural Network-based Flexibility Assessment of the Electric Power System with High Wind Energy Penetration

One of the ways to resolve the environmental protection issue is to increase electricity production based on renewable energy sources (RES). High penetration of RES in an electric power system (EPS) induces great uncertainty in EPS operation. In this case, the normal and secure operation of EPS requires information on its flexibility. Algorithms for determining the EPS flexibility in real time are developed in the study. The acceptable time of obtaining the results is ensured by splitting the flexibility computing process into online and offline ones. Offline computation of operating state with minimum flexibility relies on an exact method. Online calculation of operating state with minimum flexibility employs trained artificial neural networks (ANNs). The two machine learning techniques (supervised and unsupervised) are explored and compared. These are the feedforward neural network and Kohonen neural network. In this study, the ANN architecture which fits in with solving the considered problem most is chosen. A new way of interpreting the ANN response is presented. To this end, another ANN is trained to recognize the operating state with minimum flexibility.

Wind power ramp event detection using a multi-parameter segmentation algorithm

The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy.

Combined hydro-wind frequency control scheme: Modal analysis and isolated power system case example

Wind speed fluctuations and the lack of Variable Speed Wind Turbines (VSWTs) synchronous inertia make difficult grid frequency control, mainly in isolated power systems with high penetration of wind energy. To solve this drawback, a combined wind-hydro frequency control scheme is proposed and evaluated. The VSWT rotational speed variations are included as an additional input of the hydro-power governor units, determining the hydro-power frequency control response in accordance with such VSWT rotational variations and grid frequency oscillations. This new combined control scheme is introduced in two cases: VSWTs providing or not inertial control. A modal analysis based on a linear reduced-order model is used to discuss the oscillation modes, participation factors and power system stability. An isolated power system located in El Hierro (Canary Island, Spain) is proposed as case example. Results, discussion and comparative to previous frequency control approaches based on twenty-two different schedules are included in the paper. The proposed combined frequency control scheme reduces both VSWTs rotational speed and frequency deviations, regardless VSWTs frequency contribution. Additionally, VSWTs inertial control becomes more effective under imbalances, mainly due to the minor rotational speed excursions.