High Wind Power Penetration Research Articles

A Method for Evaluating the Maximum Capacity of Grid-Connected Wind Farms Considering Multiple Stability Constraints

Boosting the capacity of grid-connected wind farms will greatly contribute to increasing the share of sustainable energy in the global generation mix. It is imperative to study the way to quantitatively assess the maximum capacity of grid-connected wind farms in combination with power system stability characteristics. In this work, a method to evaluate the maximum capacity of grid-connected wind farms considering the joint constraints of frequency and voltage stability is proposed based on the global intrinsic property of frequency stability and the local characteristic of voltage stability. Firstly, the maximum capacity of grid-connected wind farms in the power grid with high wind power penetration is assessed globally based on the frequency stability constraints, and then locally considering the voltage stability constraints of each local power grid. Further on, a quantitative method to evaluate the capacity of grid-connected wind farms is proposed based on the correlation between the local static voltage stability margin and the local capacity of grid-connected wind farms, as well as the global constraint of the maximum capacity of grid-connected wind farms. Finally, the effectiveness of the proposed method is verified by the simulation results of an actual regional power grid.

Economic Dispatch with High Penetration of Wind Power Using Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers Considering Upside Potential and Downside Risk

The power system with high penetration of wind power is gradually formed, and it would be difficult to determine the optimal economic dispatch (ED) solution in such an environment with significant uncertainties. This paper proposes a multi-objective ED (MuOED) model, in which the expected generation cost (EGC), upside potential (USP), and downside risk (DSR) are simultaneously considered. The heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power, respectively. Then, the MuOED model is formulated as a tri-objective optimization problem, which is related to uncertain multi-criteria decision-making against uncertainties. After-wards, the tri-objective optimization problem is solved by an extreme learning machine (ELM) assisted group search optimizer with multiple producers (GSOMP). Pareto solutions are obtained to reflect the trade-off among the expected generation cost, the upside potential, and the downside risk. And a fuzzy decision-making method is used to choose the final ED solution. Case studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm.

Formulation of Low Voltage Ride-Through (Lvrt) Curve Using Discrete Optimization Strategy with Reliability Constraints for High Penetration of Wind Power - a Case Study in Taipower System

Formulation of Low Voltage Ride-Through (Lvrt) Curve Using Discrete Optimization Strategy with Reliability Constraints for High Penetration of Wind Power - a Case Study in Taipower System

Impact Assessment of Plug-in Electric Vehicle Charging Locations on Power Systems with Integrated Wind Farms Incorporating Dynamic Thermal Limits

The increased presence of electric vehicle charging locations in a power system with high penetration of intermittent wind power potentially leads to operation complexities resulting in abnormal impacts. This paper proposes an innovative framework for assessing the impact of plug-in electric vehicle (PEV) charging locations on a power system with integrated wind farms, incorporating dynamic thermal limits (DTLs). The framework comprises Monte Carlo simulation, which is embedded with stochastic modeling of various uncertainties under the key operating conditions. As part of the modeling framework, the transmission lines are ranked in accordance with the lowest level of expected energy not supplied. The PEV charging demand is then modeled by incorporating DTLs and applied to the least stressed transmission lines, following the IEEE 738-2006 standard. The new assessment framework is investigated using an IEEE 24-bus test system. The results demonstrate that applying DTLs on the least stressed transmission lines in conjunction with the integration of decentralized wind farms and strategic charging location of PEVs significantly improves the security of the energy supply and considerably reduces interruption costs, as opposed to not having such a framework.

Coordinated frequency control for isolated power systems with high penetration of DFIG-based wind power

Coordinated frequency control for isolated power systems with high penetration of DFIG-based wind power

Small Signal Stability Analysis and Optimize Control of Large-Scale Wind Power Collection System

The frequency security problem of power system is highlighted as wind power penetration increases yearly, the eigenvalue analysis method based on the deterministic model is difficult to accurately evaluate the small signal stability of power system. The Weibull probability distribution is used to describe the uncertainty of wind speed. In this paper, the shape and scale parameters of the Weibull distribution are calculated by using the maximum likelihood approach and combining with the measured data, and the stability of small signal with large-scale wind power access is analyzed by the probability distribution method. Meanwhile, considering that the lack of inertia of the system caused by high-penetration wind power access can easily lead to the problem of frequency stability, it is proposed to take the maximum damping ratio of regional oscillation mode as optimization objective, virtual inertia and pitch angle control parameters as variables, and frequency stability as constraints, an small signal stability optimization model of wind power access to power system is established according to the proposed probabilistic small signal stability model. The sensitivity algorithm is used to obtain the optimal solution for the virtual inertia and pitch angle control parameters, and the validity of the proposed method and model is verified by two-area four-machine examples. Simulation results show that by optimizing the virtual inertia and pitch angle parameters of wind turbines, the small signal stability of the high-penetration wind power access system can be improved under the condition of ensuring sufficient inertia of the system, which provides a certain theoretical basis for the safe and reliable operation of wind power large-scale cluster access power system.

Forecasting high penetration of solar and wind power in the smart grid environment using robust ensemble learning approach for large-dimensional data

Forecasting enables the cost-effective integration of renewable energy sources such as solar and wind. Forecasting daily, monthly, seasonal, and annual data for different locations with varying observation/data samples remains challenging with a single forecasting algorithm. We presented the ensemble-based k-nearest neighbor algorithm for wind and solar power forecasting in this study to respond to the challenges mentioned above. The included k-NNC deep architecture provides nonlinear modeling complexity in solar and wind energy, enabling a more precise estimate of spatial solar and wind energy patterns. Makowski metric analysis was employed to train and optimize the k-nearest neighbor algorithm. The chi-square distance was used to theoretically assess the goodness of fit of the k-nearest neighbor algorithm between expected and observed values. The 5-fold cross-validation method was utilized to estimate the accurate lasso-penalty strength for high-dimensional solar/wind data. The ensemble forecasts were combined using 5-fold cross-validation to improve generalization performance, minimizing over-fitting induced by base model correlation. For daily, monthly, seasonal, and annual solar and wind power forecasting, two different climate areas and four distinct experimental setups are presented. The forecasting accuracy of the proposed k-nearest neighbor technique is validated using three performance assessment indices and four existing forecasting models. The mean absolute error of the k-nearest neighbor algorithm generalized linear regression model, one-step-secant backpropagation neural network, decision tree, and BFGS Quasi-Newton backpropagation neural network for solar and wind power forecasting was measured at 1.16, 5.59, 4.22, 1.70, 3.23, and 8.60, 15.81, 40.64, 0.56, and 15.04, respectively. Utilities can anticipate down- and up-ramps in variable renewable energy generation by introducing the k-nearest neighbor algorithm into power system operations, allowing them to cost-effectively balance renewable energy production and load on a daily, monthly, seasonal, and annual basis. This will improve power system dependability, reduce fuel costs, and limit the use of solar and wind resources.

A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations

Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of global power capacity comprising renewable-power-generating resources is increasing. However, due to the variability and uncertainty of wind resources, predicting the power output of these resources remains a key problem that must be resolved to establish stable power system operation and planning. In this study, we propose an ensemble prediction model for wind-power-generating resources based on augmented naïve Bayes classifiers. To select the principal component that affects the wind power outputs from among various meteorological factors, such as temperature, wind speed, and wind direction, prediction of wind-power-generating resources was performed using multiple linear regression (MLR) and a naïve Bayes classification model based on the selected meteorological factors. We proposed applying the analogue ensemble (AnEn) algorithm and the ensemble learning technique to predict the wind power. To validate this proposed hybrid prediction model, we analyzed empirical data from the wind farm of Jeju Island in South Korea and found that the proposed model has lower error than the single prediction models.

Energy storage system optimization based on a multi-time scale decomposition-coordination algorithm for wind-ESS systems

Wind power is one of the most important renewable energy sources to build a sustainable power system. Energy storage technologies provide an effective control method for the operation of power systems with high penetration wind power. In existing energy storage system (ESS) optimization methods for wind-ESS systems, different ESS devices are deployed for several typical scenarios separately, including wind output power fluctuation smoothing, power imbalances mitigating, and peak load shaving. But the operational coupling of different ESS devices among these scenarios is usually omitted or simplified improperly. In this paper, the wavelet analysis algorithm was used to obtain three decomposed components of the wind output power with different time scales, and then the decomposition-coordination ESS optimization method was used to solve the constructed corresponding three submodels for different ESS application scenarios. Several coupling variables are designed to coordinate these sub-optimization models with different time scales. In the meantime, the emerging battery ESS has also been discussed in our optimization process based on the battery cost and the net earnings of the whole system to enhance the effectiveness of our proposed optimization method. Compared with traditional ESS deployment methods, our proposed method can reduce the total ESS equivalent investment annual cost by 20% in the selected regional power system in China. Our proposed multi-time scale decomposition-coordination algorithm is suitable for the optimal ESS devices deployment because the coordinated charge–discharge operation of different ESS devices can be inherited taken into consideration for short-term power fluctuation smoothing, power balancing during several hours, and peak load shaving in awhole day.

Optimal node selection of damping controller for power systems with high wind power penetration

With the increasing penetration of doubly fed induction generator (DFIG) wind turbines in power systems, they should be capable of damping the power oscillations. DFIGs equipped power oscillation damper (POD) combing synchronous generators equipped with power system stabilizer (PSS) are being deployed to damping power system oscillations. However, a major gap in this field is the selection of the optimal controller node in the system. This paper aims to propose an optimal node selection approach for POD and PSS in power systems, assisted by an event triggered node augmentation scheme to cope with cascading system contingencies. A submodular function is constructed to evaluate the distance between the system unstable eigenvectors and the controllability Gramian, and the base input set as well as the candidate nodes are selected. Subsequently, an event triggered condition is formulated to allow the candidate controller nodes to participate in damping the system oscillation. Four case studies are conducted and the results indicate that the proposed approach can effectively select the optimal PSS/POD nodes to improve the performance of the controllers.

Coordinated Frequency Regulation of Smart Grid by Demand Side Response and Variable Speed Wind Turbines

Frequency stability of the power system is impacted by the increasing penetration of wind power because the wind power is intermittent. Meanwhile, sometimes the demand side loads increase quickly to require more power than total power produced. So balancing the active power in the power system to maintain the frequency is the main challenge of the high penetration of wind power to the smart grid. This paper proposes coordination rotor speed control (RSC), pitch angle control (PAC) and inertial control (IC) to control wind turbines, together with demand side response (DSR) participating in frequency regulation to balance active power in the power system. Firstly, the model of a single area load frequency control (LFC) system is obtained, which includes variable-speed wind turbines (VSWT) and DSR containing aggregated air conditioners and plug-in electric vehicles (PEVs). Then the RSC, PAC and IC, which controls wind turbines participating in frequency regulation in the power system, are introduced, respectively. Finally, the coordination of these three methods for wind turbines in different wind speeds is proposed. Case studies are carried out for the single area LFC system with a wind farm and DSR supported grid frequency. Coordination RSC and PAC combined IC are used to control wind turbines with DSR to balance active power in the power system. The proposed method used in the power system with high penetration of wind power and fluctuation of demand load is tested, respectively. Coordinated RSC or PAC with DSR can increase penetration of wind power and reduce peak load.

Large-scale wind farm control using distributed economic model predictive scheme

The reliable control of the large-scale wind farm is crucial for the stability and security of the renewable power system with high wind power penetration. Due to the uncertain and variable nature of wind power, the traditional control strategy is difficult to work. Regarding the large-scale, geographically dispersed wind farm, an efficient distributed economic model predictive control strategy is proposed, which integrates the power tracking and economic optimization of the wind farm into one optimal control framework. By adopting the global economic cost function, the Nash optimal solutions under distributed framework approach the Pareto optimum. Thus, the reference power from the transmission system operator is accurately tracked, while the global dynamic economic optimality is guaranteed. The simulation results under step wind speed and practical wind speed variations verify the efficiency and reliability of the proposed control strategy.

Decentralized Data-Driven Load Restoration in Coupled Transmission and Distribution System With Wind Power

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance.

Power prediction of a wind farm cluster based on spatiotemporal correlations

Accurate power prediction of wind farm clusters is important for safe and economic operation of power systems with high wind power penetration. Current superposition and statistical scaling methods used in wind power prediction systems do not fully consider the relationships among wind farms in a cluster, thereby leading to insufficient power prediction accuracies. To improve the power prediction accuracy of wind farm clusters, a new method based on spatiotemporal correlations is proposed herein. First, three correlation coefficients are used to represent spatiotemporal correlation characteristics of wind farms in a wind cluster. The Shapley value method is used to weight these coefficients, and a standard wind farm is found by combining the nominal capacities of the wind farms. Then, considering the spatiotemporal factors that affect wind power generation, a characteristic matrix of the wind farm cluster is constructed, and the key characteristics are extracted using a convolutional neural network (CNN). Considering the time series characteristics of wind power generation, a long and short term memory (LSTM) neural network is used to establish the mapping relationship between key characteristics and power generation, and power prediction of a wind farm cluster is performed. Finally, by utilizing the actual operating data of wind farm clusters in North China as an example, feasibility and effectiveness of the proposed method are verified. The proposed system provides a new high-precision method for future wind farm cluster power predictions.

Probabilistic power output model of wind generating resources for network congestion management

With the growing importance of renewable energy generating resources around the world, the government is working towards expanding the share of renewable energy to 20% of total power generation considering the limitation of coal-fired and nuclear generation by 2030 in South Korea. However, the growing number of wind generating resources being connected to the electrical power system presents a challenge for transmission grid planning to increase the penetration of wind power while maintaining high levels of reliability and security of the electrical power system. In this paper, we propose a probabilistic power output model of wind generating resources for network congestion management. We use the historical data from the wind farms located on Jeju Island in South Korea to fit the Weibull distribution and implement Monte Carlo simulations. The simulation results, which represent network congestion with probabilistic values, are applied to the empirically modeled power grid of Jeju Island and a steady-state security evaluation is performed. The proposed probabilistic approach will be a key role to reduce the risk of over-investment in power transmission facilities compared to the deterministic approach to develop the generation mix scenarios with high wind power penetrations.

Modelling and control of vanadium redox flow battery for smoothing wind power fluctuation

Due to the negative impact of a highly stochastic wind power fluctuation on the power quality and stability during high penetration of wind power in power systems, there is growing interest in power smoothing and energy redistribution in wind power systems by using large-scale energy storage technologies. The aim of this work is to use a vanadium redox flow battery as an energy storage system (ESS) to smooth wind power fluctuation with two system configurations and corresponding control strategies. As the first step, a vanadium redox flow battery (VRFB) mathematical model, underlain by electrochemical theories, is built to describe the battery behaviours in charge/discharge cycles. Accordingly, the aforementioned system configuration and power fluctuation smoothing schemes are proposed. At the end of this work, simulations are conducted on a 3.6 MW doubly-fed induction generator (DFIG) at a diversity of wind speeds for an assessment of feasibility that this work can be applied to a high penetration of wind power in practice. Simulation results reveal that power fluctuations can be improved by power smoothing. The root mean square deviation (RMSD) percentage can be reduced to less than 1%.

Short-Term Wind Power Forecasting Using Mixed Input Feature-Based Cascade-connected Artificial Neural Networks

Accurate short-term wind power forecasting is crucial for the efficient operation of power systems with high wind power penetration. Many forecasting approaches have been developed in the past to forecast short-term wind power. Artificial neural network-based approaches (ANNs) have become one of the most effective and popular short-term wind speed and wind power forecasting approaches in recent years. However, most researchers have used only historical data from a specific station to train the ANNs without considering meteorological variables from many neighboring stations on the forecasting performance. Using additional meteorological variables from neighboring stations can contribute valuable surrounding information to the forecasting model of the target station and improve ANNs performance. In this paper, a mixed input features-based cascade-connected artificial neural network (MIF-CANN) is used to train input features from many neighboring stations without encountering overfitting issues caused by many input features. Multiple ANNs train different combinations of input features in the first layer of the MIF-CANN model to produce preliminary results, then cascading into the second phase of the MIF-CANN model as inputs. The performance of the proposed MIF-CANN model is compared with the ANNs-based spatial correlation models. Simulation results show that the proposed MIF-CANN has better performance than the ANNs-based spatial correlation models.

Comparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Power

Today, the power system operation represents a challenge given the security and reliability requirements. Mathematical models are used to represent and solve operational and planning issues related with electric systems. Specifically, the AC optimal power flow (ACOPF) and the DC optimal power flow (DCOPF) are tools used for operational and planning purposes. The DCOPF versions correspond to lineal versions of the ACOPF. This is due to the fact that the power flow solution is often hard to obtain with the ACOPF considering all constraints. However, the simplifications use only active power without considering reactive power, voltage values and losses on transmission lines, which are crucial factors for power system operation, potentially leading to inaccurate results. This paper develops a detailed formulation for both DCOPF and ACOPF with multiple generation sources to provide a 24-h dispatching in order to compare the differences between the solutions with different scenarios under high penetration of wind power. The results indicate the DCOPF inaccuracies with respect to the complete solution provided by the ACOPF.

Sunflower optimization based fractional order fuzzy PID controller for frequency regulation of solar-wind integrated power system with hydrogen aqua equalizer-fuel cell unit

ABSTRACT The rapid advancement of electric consumer demand enforces challenging situations to manage high penetration of Solar PV and Wind Power into the hydrothermal system resulting in load frequency control (LFC) problem. This article introduces an advanced strategy to manage the LFC issue in an integrated power system (PS) with the impact of hydrogen aqua equalizer (HAE)-based Fuel cells (FC). A transformative fractional-order fuzzy-based PID (FOFPID) controller regulated by the Sunflower optimizing (SFO) technique has been proposed for frequency regulation of the proposed integrated PS. The preeminence of the FOFPID controller has been validated over PID, Fuzzy-PID (FPID), and fractional order PID (FOPID) controller. The effectiveness of the SFO technique has been established over teaching learning-based optimization (TLBO) and Genetic Algorithm (GA). The supremacy of the SFO approach authenticated by some recent frequency regulation research outcomes. The influence of HAE-FC for frequency regulation in an integrated PS has been studied with the proposed SFO-tuned FOFPID controller. From the observation, it is perceived that the values of reduction in ITAE with suggested SFO-tuned FOFPID controller as compared to GA & TLBO are 84.61% & 56.04% and further 35.16% is reduced with the occurrence of HAE-FC element. It is also perceived that the improvement in settling time for ∆f1, ∆f2, ∆Ptie12 with SFO approach related to GA are 78.07%, 76%, and 83.87% and compared to TLBO are 9.97%, 38.25%, and 68.23%. Furthermore, 26.45%, 18.64%, and 31.21% are reduced with the presence of HAE-FC unit. Lastly, the sturdiness of the anticipated strategy was authenticated by a sensitivity test and a real-time validation test using OPAL-RT.

Decentralized computation method for robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power

Large-scale integrated energy systems often encompass several subsystems divided by geographic areas. Coordinated operation of multi-area integrated energy systems can enhance operational flexibility, which is crucial for systems with high wind power penetration. Traditional centralized methods have some inherent defects, such as privacy protection, communication burden, and computational capability, etc. To address such challenges, this paper proposes a decentralized computation method for the robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power. The interconnected multiple areas can cooperate for improved system flexibility and wind power integration capability. Using the decentralized method, the area operator optimizes its operation decisions independently without transferring its dispatch rights. The shared information is limited to exchanged energy flows. The proprietary information is thus preserved while the communication burden is significantly reduced. To solve the robust operation problem distributedly, the decentralized method is enhanced to cope with both deterministic and robust problems. A tri-level algorithm composed of the iterative alternating direction multiplier method and the column constraints generation method is utilized to realize the decentralized optimization. Numerical results for three test systems show the effectiveness, computational quality, and scalability of the proposed method. Compared to the isolated method, the proposed method fully utilizes the coordination effect of multi-area RD-IES and has obvious economic advantages, especially in scenarios with high wind power penetration levels. The proposed method brings a 12.4% cost reduction in the two-area system and a 22.4% cost reduction in the eight-area system.