Wind Generator Output Power Research Articles

Nataf-KernelDensity-Spline-based point estimate method for handling wind power correlation in probabilistic load flow

Modern power systems integrated with renewable energies (REs) contain many uncertainties. The proposed method introduces a novel approach to address the challenges associated with wind power generation uncertainty in probabilistic load flow (PLF) studies. Unlike conventional methods that use wind speed as an input, the paper advocates for utilizing wind generator output power (WGOP) as an input to the point estimate method (PEM) in solving PLF. The uniqueness lies in recognizing the distinct behavior of wind power uncertainty, where not all random samples of wind speed contribute to actual wind power production. The paper suggests a Nataf-KernelDensity-Spline-based PEM, combining the Nataf transformation, Kernel density estimation (KDE), and cubic spline interpolation. This innovative integration effectively manages wind power correlation within the analytical framework. By incorporating spline interpolation and kernel density estimation into the traditional PEM, the proposed method significantly enhances accuracy. To validate the effectiveness of the proposed approach, the method is applied to IEEE-9 and IEEE-57 bus test systems, considering uncertainties related to load, wind power generation (WPG), solar power generation (SPG), and conventional generator (CoG) outages. Comparative analysis with Monte Carlo simulation (MCS) results demonstrates that the proposed method outperforms the conventional PEM in terms of accuracy. Overall, the paper contributes a pioneering solution that not only highlights the importance of using WGOP as an input in PLF but also introduces a sophisticated method that surpasses traditional approaches, improving accuracy in power system studies involving renewable energy integration. The accuracy of the proposed method is validated by comparing its results with those obtained through Monte Carlo simulation (MCS), where the proposed method yields more accurate results than the conventional PEM.

Offshore wind power output prediction based on convolutional attention mechanism

ABSTRACT To solve the problem that offshore wind power generation is susceptible to extreme weather conditions that result in low accuracy of monitoring data, this work proposes a method for predicting offshore wind power generation output by combining convolution and attention mechanisms. The method combines bidirectional long and short-term memory (BiLSTM) network with attention mechanism (AM). The input data are first weighted with the AM for reducing the predictive weight of the interference data, and then the attention mechanism-weighted data are fed into the BiLSTM network. The bi-directional propagation neural network can effectively utilize all the input information, resulting in higher prediction accuracy. The method of combining BiLSTM network with AM is compared with the method employing long and short-term memory network, gated recurrent unit, and bidirectional long and short-term memory network alone through simulation. The mean square error (MSE) of the BiLSTM combined with AM method is 41.28% smaller than the MSE of the best LSTM method among the compared methods and 58.43% smaller than the average of the compared methods. The R2-R-Square is 14.21% larger than the R2-R-Square of the LSTM and 32.91% larger than the average of the compared methods. The results show that the proposed method of combining convolution and attention mechanism for offshore wind power generation output prediction has higher prediction accuracy.

Analysis of wind power output characteristics and output prediction

A fuzzy clustering method and a prediction correction method based on the Copula function are used to establish an analytical model of wind power generation output characteristics. It also predicts the expected and corrected values of its power generation to alleviate the pressure on the grid caused by the high proportion of random new energy sources connected to the grid. Based on the natural properties of wind, a new characteristic index based on the traditional load characteristic index and the new energy output curve is proposed to quantify and evaluate the characteristics of new energy power generation. The results show that the method proposed in this paper can effectively reduce the influence of climate change on output prediction; in comparison to the traditional index, the new index is more refined and instructive.

Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power

Since the future output of wind power generation is uncertain due to weather conditions, there is an increasing need to manage the risks associated with wind power businesses, which have been increasingly implemented in recent years. This study introduces multiple weather derivatives of wind speed and temperature and examines their effectiveness in reducing (hedging) the fluctuation risk of future cash flows attributed to wind power generation. Given the diversification of hedgers and hedging needs, we propose new standardized derivatives with higher-order monomial payoff functions, such as “wind speed cubic derivatives” and “wind speed and temperature cross-derivatives,” to minimize the cash flow variance and develop a market-trading scheme to practically use these derivatives in wind power businesses. In particular, while demonstrating the importance of standardizing weather derivatives regarding market liquidity and efficiency, we propose a strategy to narrow down the required number (or volume) of traded instruments and improve trading efficiency by utilizing the least absolute shrinkage and selection operator (LASSO) regression. Empirical analysis reveals that higher-order, multivariate standardized derivatives can not only enhance the out-of-sample hedge effect but also help reduce trading volume. The results suggest that diversification of hedging instruments increases transaction flexibility and helps wind power generators find more efficient portfolios, which can be generalized to risk management practices in other businesses.

Wind Generator Output Power Smoothing by Using Pitch Controller

It is well known that pitch controller can maintain output power of wind generator at rated level when wind speed is above the rated speed. Moreover, it can also enhance the transient stability of wind generator when severe network disturbance occurs in power system. In this paper, another new feature of pitch controller is analyzed. As wind speed varies randomly the output power of wind generator fluctuates continuously. This fluctuation can be minimized significantly by changing the blade pitch angle of wind turbine. Our proposed pitch controller can do this well. The wind generator output power loss and smoothness level are analyzed when the proposed pitch controller is used with wind turbine. Comparative studies are carried on using three types of input command power of the controller. Some technical points of mechanical servo system of wind turbine are emphasized to reduce the load of wind turbine. Moreover, different types of wind speed patterns are used to validate the effectiveness of the proposed controller. Simulation results show that the wind power fluctuation can be reduced significantly by using the proposed fuzzy logic based pitch controller.

Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future.

Research on the Internal Flow Characteristics of Pump Turbines for Smoothing the Output Fluctuation of the Wind–Photovoltaic Complementary System

Wind and photovoltaic (PV) power generation and other distributed energy sources are developing rapidly. But due to the influence of the environment and climate, the output is very unstable, which affects the power quality and power system stability. Pumped hydroelectric energy storage (PHES) systems are suitable as peaking power sources for wind and photovoltaic (Wind–PV) complementary systems because of their fast start–stop and long life. The mathematical models and operational characteristics of the three subsystems in the wind–PV–PHES complementary system are analyzed to improve the generation efficiency and access capacity of wind and PV power. The peaking characteristics of the PHES system are used to balance the maximum benefit and minimum output fluctuation of the wind–PV complementary system. The stable operation of the pump turbine is an important guarantee for the smooth output of the wind–PV complementary system. Three operating points are selected from the net load curve and converted to the pump turbine model parameters. The internal flow characteristics and laws of the pump turbine under different guide vane opening conditions are summarized through the analysis of the computational fluid dynamics (CFD) numerical simulation post-processing results. The study shows that the output of wind and PV power generation varies with the changes in wind speed and solar radiation, respectively. The output of the wind–PV complementary system still has large fluctuations, and the PHES system can effectively suppress the power fluctuation of the wind–PV complementary system and reduce the abandoned wind and light rate. CFD technology can accurately and efficiently characterize the internal flow characteristics of the pump turbine, which provides a basis for the design, optimization, and transformation of the pump turbine.

Day-Ahead Economic Dispatch of Renewable Energy System considering Wind and Photovoltaic Predicted Output

To alleviate the issues of global warming and energy crisis, countries are vigorously developing renewable energy technology. The integration of large-scale renewable energy, including wind energy, hydropower, and photovoltaic (PV), has a great impact on system operation scheduling and economic dispatch. This paper presents an economic dispatching method of wind-PV-CSP-hydro-battery system with wind and photovoltaic power generation as the main energy sources. The long short-term memory (LSTM) neural network is applied to predict wind and PV power, besides, the Latin Hypercube Sampling (LHS) method and the synchronous reduction algorithm are used to obtain 10 typical wind and PV power scenarios. A day-ahead economic dispatch model of wind-PV-CSP-hydro-battery mathematical model is established, and relevant constraints are considered. Concentrated solar power (CSP), hydropower stations, batteries, and transferable loads are used as flexible resources to increase the penetration rate of wind and photovoltaic power generation. Finally, three cases are tested to demonstrate the feasibility of the proposed model. The results show that: (1) LSTM neural network can well predict the output power of wind and photovoltaic power generation with a small root mean square error (RMSE). (2) The introduction of transferable loads and CSP power station into the renewable energy power system can effectively reduce the fluctuation and curtailment rates of wind power and PV power generation.

Temperature control load-regulation strategy based on multi-dimensional state queue method

The development of temperature control load is inevitable for people to improve the quality of life, but it has the same characteristics as the output of wind power generation, that is, randomness and intermittence. Therefore, the continuous access of temperature control load has an unprecedented challenge to the stability of power grid. However, the instability of the power grid caused by the input of wind power can be balanced by the temperature control load acting on the power grid. The research direction of this paper is the regulation of temperature controlled load household air source heat pump equipment on the power grid on the load side, which is constrained by meeting the user’s comfort. It is intended to achieve the goal of cutting peak and filling valley for the power grid side and reducing the wind rejection rate. The user side gets the economic compensation to cooperate with the power grid dispatching. At the same time, the power grid side improves the economic benefits by reducing the peak valley difference and reducing the occurrence of fan suspension. The main research contents are as follows: the thermodynamic equivalent model of single air source heat pump is established, and the aggregation model is established combined with Monte Carlo method. The simulation analysis shows that the air source heat pump group can participate in the regulation of load side; Using the prediction model of wind power based on meta learning, combined with single time series model and single linear regression model, the wind power is reasonably predicted; The multi-dimensional state queue method is used to improve the load characteristics and reduce the air rejection rate on the premise of meeting the user comfort; This paper expounds the distribution network level load regulation system, establishes the hierarchical load control structure, and makes the two-way interaction between the family compliance control system and the power grid a reality. In order to study the influence of user selection on regulation effect, according to the differences of user needs, we established three regulation strategies. Scheme A can achieve the most ideal regulation effect, and scheme B is slightly worse than scheme A. The most ideal control effect parameters can be obtained by scheme A, and the worst control effect parameters can be obtained by scheme C. Comparing the regulation effect parameters after scheme a and scheme C, based on the parameters of scheme C, the peak valley difference of scheme A is reduced by 18.30%, the peak to average ratio is reduced by 17.61% and the load variance is reduced by 18.12%. It is proved that the user can reduce the peak valley difference by using scheme A. Comparing the regulation effect parameters after scheme B and scheme C, compared with scheme C, the peak valley difference of scheme B is reduced by 11.80%, the peak to average ratio is reduced by 11.81%, and the load variance is reduced by 14.77%. It is proved that the regulation effect of scheme B can be reduced, but the effect is not as good as scheme A.

Microgrid Energy Management Strategy Base on UCB-A3C Learning

The uncertainty of renewable energy and demand response brings many challenges to the microgrid energy management. Driven by the recent advances and applications of deep reinforcement learning a microgrid energy management strategy, i.e., upper confidence bound based advantage actor-critic (A3C), is proposed to utilize a novel action exploration mechanism to learn the power output of wind power generation, the price of electricity trading and power load. The simulation results indicate that the UCB-A3C learning based energy management strategy is better than conventional PPO, actor critical and A3C algorithm.

Scheduled Operation of Wind Farm with Battery System Using Deep Reinforcement Learning

With increasing amounts of wind power generation installed, the steep fluctuation of wind power generation output, called ramp events, causes serious problems for power system operation. Controlling fluctuations is an important issue for increasing the amount of wind power generation as a wind farm (WF) in the future. The authors reported the scheduled operation method of WF using a battery energy storage system (BESS) and forecast data of wind power generation output. In this paper, the authors propose a new scheduled operation method of WF. In particular, we propose the application of deep reinforcement learning to decide the output schedule of WF. Moreover, we compare the conventional method, the reinforcement learning method, and the deep reinforcement learning method in terms of the number of ramp events. In addition, we calculate the reducing effect of the storage capacity of BESS. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Stochastic optimization model for coordinated operation of natural gas and electricity networks

Renewable energy sources will anticipate significantly in the future energy system paradigm due to their low cost of operation and low pollution. Considering the renewable generation (e.g., wind) intermittency, flexible gas-fired power plants will continue to play their essential role as the main linkage of natural gas and electricity networks, and hence coordinated operation of these networks is beneficial. Furthermore, uncertainty is always found in gas demand prediction, electricity demand prediction, and output power of wind generation. Therefore, in this paper, a two-stage stochastic model for operation of natural gas and electricity networks is implemented. In order to model uncertainty in these networks, Monte Carlo simulation is applied to generate scenarios representing the uncertain parameters. Afterwards, a scenario reduction algorithm based on distances between the scenarios is applied. Stochastic and deterministic models for natural gas and electricity networks are optimized and compared considering integrated and iterative operation strategies. Furthermore, the value of flexibility options (i.e., electricity storage systems) in dealing with uncertainty is quantified. A case study is presented based on a high pressure 15-node gas system and the IEEE 24-bus reliability test system to validate the applicability of the proposed approach. The results demonstrate that applying the stochastic model of gas and electricity networks as well as considering integrated operation strategy in the presence of flexibility provides different benefits (e.g., 14% cost savings) and enhances the system reliability in the case of contingency.

Temperature impacts on utility-scale solar photovoltaic and wind power generation output over Australia under RCP 8.5

Climate change has the potential to impact the generation of renewable energy significantly subject to location and equipment specifications. As the penetration of renewable energy in the energy systems keeps increasing, this impact needs be systematically assessed so that investment and reliability information is accurate. Australia represents an ideal study case characterized by its frequency of extreme weather events and the recent and planned growth in the renewable energy sector. In this study, we model and quantify the long-term temperature de-rating impact of utility-scale solar photovoltaic and wind power generation over Australia. Using climate projections simulated by six Global Circulation Models and the CSIRO's Cubic Conformal Atmospheric Model, we analyze half-hourly time series of key weather variables such as temperature, surface solar irradiance, and wind speed for 1980–2060 at two sites where variable renewable generators are located, or are likely to be located in the future based on the current Integrated System Plan by the Australian Energy Market Operator. We also built power conversion models for the temperature de-rating of solar and wind power with added focus on high temperature scenarios. We found that the general temporal trends in annual solar and wind power generation due to climate change are small, being at the order of 0.1% of their average production per decade. However, for peak temperature events, which coincide with the peak power demand and, generally, high prices, the temperature de-rating impact can be much more substantial and disruptive.

Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

Coordination of Energy Storage and Wind Power Plant considering Energy and Reserve Market for a Resilience Smart Grid

Summary The intermittency of wind power generation causes some challenges in scheduling normal operation and emergency states. The presence of Pumped Storage (PS) power plants along with wind power plants can neutralize the negative impacts of varying production. It can also improve the resilience performance of the power system. The main components of this paper are to investigate the effect of coordination between wind and PS power plants and to consider the energy market and reserve on normal operation and emergency (resilience) of the power system. Since the output power of wind generation is intermittent, the scheduling of wind and PS is stochastic problem scheduling which is named stochastic Security-Constrained Unit Commitment (SCUC). The uncertainty of wind power generation and load are modeled by the Monte Carlo simulation method. Non-convexity and nonlinearity are two important points that prevent the global optimal solution to the problem. The proposed scheme is a Mixed-Integer Nonlinear Programming (MINLP) problem. The proposed model by Lagrange relaxation will become a Mixed-Integer Linear Programming (MILP) problem. One important factor that problem makes nonconvex is nonexistence affine state of equality constraints. Since AC load flow constraints make the problem a complex non-convex and non-linear problem. Because convexity and faster solving (the system should restore very quickly in an event.) is used DC load flow. Therefore, the optimal solution is guaranteed in this article. The obtained results demonstrate the effectiveness of the proposed method in normal and resiliency operation.

Closed Loop Control of Wind Power Based on Trans Quasi-Zsourceinverter

The paper presents a wind generator output power appropriately monitored using a closed loop controller engaging the buck-boost competency of Trans qZSI and is fed to grid. The converter getting all benefits of conventional ZSI and exchanges the traditional inverter in the arrangement. Moreover, the Trans qZSI has the exceptional benefits of minimum component count and lower component ratings. The feed forward controller cautiously controls the wind generator output power. It monitors the short circuit duty cycle of inverter using third harmonic injected control technique to create a boosted output. Simulation of the complete circuit is performed on MATLAB and results are discussed. It offers an exclusive voltage regulation by creating proper choice of shoot through duty ratio under variable wind speed. The proposed control system is suitable for transient and steady state operation retaining the preferred level of output.

A Dual-Step Integrated Machine Learning Model for 24h-Ahead Wind Energy Generation Prediction Based on Actual Measurement Data and Environmental Factors

Wind power generation output is highly uncertain, since it entirely depends on intermittent environmental factors. This has brought a serious problem to the power industry regarding the management of power grids containing a significant penetration of wind power. Therefore, a highly accurate wind power forecast is very useful for operating these power grids effectively and sustainably. In this study, a new dual-step integrated machine learning (ML) model based on the hybridization of wavelet transform (WT), ant colony optimization algorithm (ACO), and feedforward artificial neural network (FFANN) is devised for a 24 h-ahead wind energy generation forecast. The devised model consists of dual steps. The first step uses environmental factors (weather variables) to estimate wind speed at the installation point of the wind generation system. The second step fits the wind farm actual generation with the actual wind speed observation at the location of the farm. The predicted future speed in the first step is later given to the second step to estimate the future generation of the farm. The devised method achieves significantly acceptable and promising forecast accuracy. The forecast accuracy of the devised method is evaluated through several criteria and compared with other ML based models and persistence based reference models. The daily mean absolute percentage error (MAPE), the normalized mean absolute error (NMAE), and the forecast skill (FS) values achieved by the devised method are 4.67%, 0.82%, and 56.22%, respectively. The devised model outperforms all the evaluated models with respect to various performance criteria.

Adaptive Control Strategy for Time-Varying Power System Based on Continuous Markov Model

Concerning the variation of power system operating condition caused by time-varying factors such as wind generator output power fluctuation and load change, an adaptive control strategy for time-varying power system based on continuous Markov model is proposed in this paper. First, the Lyapunov functional containing the continuous Markov time-varying power system model is constructed, and the robust stochastic stability theorem with H∞ norm bound γ is derived using the Dynkin lemma. On this basis, according to the Schur complement theorem, the robust stochastic stability LMI (linear matrix inequality) which satisfies the disturbance attenuation degree γ and the minimum variance constraint is derived considering the system operating conditions steady-state variance constraint. And then, through transforming the LMI to the minimization problem of linear objective function, the matching controller of each operating condition is solved. Finally, the stochastic gradient method is used to identify the system operating condition and determine the weighting coefficient of each controller, thus the adaptive control strategy is established. Time-domain simulation tests show that, the proposed control strategy could effectively suppress cascading disturbances as well as avoid the problem of mismatch between current operating condition and the fixed fault set. Besides, the impact on power system caused by controller switching is reduced.

Integration Capability Evaluation of Wind and Photovoltaic Generation in Power Systems Based on Temporal and Spatial Correlations

With the rapid growth of renewable energy generation, it has become essential to give a comprehensive evaluation of renewable energy integration capability in power systems to reduce renewable generation curtailment. Existing research has not considered the correlations between wind power and photovoltaic (PV) power. In this paper, temporal and spatial correlations among different renewable generations are utilized to evaluate the integration capability of power systems based on the copula model. Firstly, the temporal and spatial correlation between wind and PV power generation is analyzed. Secondly, the temporal and spatial distribution model of both wind and PV power generation output is formulated based on the copula model. Thirdly, aggregated generation output scenarios of wind and PV power are generated. Fourthly, wind and PV power scenarios are utilized in an optimal power flow calculation model of power systems. Lastly, the integration capacity of wind power and PV power is shown to be able to be evaluated by satisfying the reliability of power system operation. Simulation results of a modified IEEE RTS-24 bus system indicate that the integration capability of renewable energy generation in power systems can be comprehensively evaluated based on the temporal and spatial correlations of renewable energy generation.

Consensus-Based SOC Balancing of Battery Energy Storage Systems in Wind Farm

Multiple battery energy storage systems (BESSs) are used to compensate for the fluctuation in wind generations effectively. The stage of charge (SOC) of BESSs might be unbalanced due to the difference of wind speed, initial SOCs, line impedances and capabilities of BESSs, which have a negative impact on the operation of the wind farm. This paper proposes a distributed control of the wind energy conversion system (WECS) based on dynamic average consensus algorithm to balance the SOC of the BESSs in a wind farm. There are three controllers in the WECS with integrated BESS, including a machine-side controller (MSC), the grid-side controller (GSC) and battery-side controller (BSC). The MSC regulates the generator speed to capture maximum wind power. Since the BSC maintains the DC link voltage of the back-to-back (BTB) converter that is used in the WECS, an improved virtual synchronous generator (VSG) based on consensus algorithm is used for the GSC to control the output power of the WECS. The functionalities of the improved VSG are designed to compensate for the wind power fluctuation and imbalance of SOC among BESSs. The average value of SOCs obtained by the dynamic consensus algorithm is used to adjust the wind power output for balancing the SOC of batteries. With the proposed controller, the fluctuation in the output power of wind generation is reduced, and the SOCs of BESSs are maintained equally. The effectiveness of the proposed control strategy is validated through the simulation by using a MATLAB/Simulink environment.