Large-scale Wind Power Penetration Research Articles

Dynamic Economic Dispatch Problem in Hybrid Wind Based Power Systems Using Quasi Oppositional Based Chaotic Honey Badger Algorithm

This article proposes a dynamic economic load dispatch (DELD) including wind power involvement process, which is capable to produce least possible generation cost of electrical systems by integrating large-scale wind power penetration. The primary component of hybridized power system comprises of conventional thermal generators and wind generators. To expedite the convergence flexibility and escape the clarifications from the confined optimal purpose, quasi-oppositional based learning (Q-OBL) is combined with HBA, which results in quasi oppositional chaotic honey badger algorithm (QOHBA). Moreover, chaotic strategy is integrated with QOHBA to boost the convergence speed as well as enhance the performance of the basic honey badger algorithm (HBA). The proposed QOCHBA is suggested to explain wind-based shared DELD problem to reduce thermal-wind generation energy cost. The superiority and expediency of the prospective technique is proved through its operation in two benchmark investigation systems. We use probability density functions (PDF) for wind energy (WE) forecasting to assessment dynamic working reserve necessities, based on the readjust of uncertainty in the prediction. To establish the efficacy of the recommended QOCHBA method in explaining non-linear, non-convex, and constrained DELD problem, the suggested structures are implemented on benchmark of 10-unit in addition to 15-unit test methods.

Impact of large-scale wind power penetration on incentive of individual investors, a supply function equilibrium approach

This paper investigates two problems: 1) the decision-making approach for an individual investor in a power market with the uncertainty of wind power generation, and 2) the impacts of expanding the penetration of wind energy on the profitability of the investment. To answer these questions, a comparison framework of two distinct investments is designed in which one is deterministic in a power market including only fossil-fuel generators, and another is uncertain in a power market with wind energy production. The power market is modeled as a supply function equilibrium (SFE), and a scenario-based model is implemented on the IEEE 30-bus system to deal with the uncertainty of wind energy. A novel approach, including error correction and the sensitivity analysis method, is introduced. The concept of conditional value at risk is applied to measure the investment risk. The results show that the wind speed prediction error and the wind probability density function variation create a very low impact on the final result. However, the load duration curve has a high impact on the decision-making problem. The proposed approach can address the ensured gained profit of an investment in an uncertain power market.

Impacts of large-scale penetration of wind power on day-ahead electricity markets and forward contracts

Utilization of renewable energy resources, especially wind power, for producing electric energy is growing fast in the world. Besides the environmental advantages, the variability, unpredictability, and uncontrollability of the wind turbines’ output power face the market players with different financial risks. Producers and consumers prefer to have a stable income in the power system and try to avoid the uncertainties and fluctuations in their profits. In this situation, forward contracts are used as efficient tools for helping the market players to hedge themselves against these risks. Since market players participate in both forward and day-ahead markets, their actions in each market affect the other market. So, day-ahead and forward markets affect each other. In this paper, the behavior of market players in the forward and day-ahead electricity markets in the presence of large-scale wind farms are studied. To this end, first, the contracting period is modeled considering different outcomes for the delivery period and then, the delivery period is modeled considering the contracting period outcomes. Equilibrium models are presented for each model. Both uniform and pay-as-bid pricing models for the day-ahead market are considered in the modeling procedure. A recently introduced risk management method called concern scenarios is upgraded and applied to model the risk management preferences of market players. Simulation results are presented, analyzed, and compared for models by applying them to a test system case study.

Source-load Dispatch Model for Electricity Day-ahead Market with Wind Power Penetration

Due to the intermittent and volatility of wind power output, the traditional power grid’s operation and dispatching mode and the adjustment capability of conventional units were unable to meet the demands of large-scale wind power penetration. Based on the characteristics of the day-ahead dispatching link in the spot power market, this paper combines the auxiliary service with the day-ahead power market transaction to form the day-ahead trading plan. At the same time, considering the conventional thermal power peak shifting and interruptible load peak shifting, a new market peak shifting optimization model reflecting the will of the source-load role is formed. On this basis, electricity day-ahead market dispatching model considering source-load and peak shifting is established, aiming at the maximum expected income of wind power plant. Through practical examples and scenario analysis, it is verified that the source-load peak shifting scheduling model will effectively improve the expected income and absorption rate of wind power plant.

Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms

The intermittent and uncertain nature of wind places a premium on accurate wind power forecasting for the reliable and efficient operation of power grids with large-scale wind power penetration. Herein, six-month-ahead wind power forecasting models were developed using tree-based learning algorithms. Three models were developed to investigate the impact of input data on forecasting accuracy. The first model was trained with the average and standard deviation of wind speed values measured at a height of 40 m with a 10-min sampling time. To evaluate the impact of sampling time on model performance, a second model was trained with wind speed values measured at a height of 40 m with 1-h, 12-h, and 24-h sampling times. To assess the effect of measuring height on model accuracy, the third model was trained with wind speed values measured at 40 m extrapolated from values measured at heights of 30 m and 10 m. Experiments revealed that using longer time intervals and height extrapolation leads to considerable accuracy degradation in forecasted models. Finally, to study the generalization ability of the forecasted models, they were tested against wind data measured at heights and locations different from what the models had been trained with. Simulation results substantiated that tree-based learning algorithms can be successfully adopted not only for long-term wind power forecasting, but for potential wind power forecasting at different heights and geographical locations.

Stochastic economic dispatch considering the dependence of multiple wind farms using multivariate Gaussian kernel copula

Wind farms usually gather in areas with abundant wind resources. Therefore, spatial dependence of wind speeds among nearby wind farms should be taken into account when modeling a power system with large-scale wind power penetration. This paper proposes a novel non-parametric copula method, multivariate Gaussian kernel copula (MGKC), to describe the dependence structure of wind speeds among multiple wind farms. Wind speed scenarios considering the dependence among different wind farms are sampled from the MGKC by quasi-Monte Carlo (QMC) method, so as to solve the stochastic economic dispatch (SED) problem, for which an improved mean-variance (MV) model is established, which targets at minimizing the expectation and risk of fuel cost simultaneously. In this model, confidence interval is applied in the wind speed to obtain more practical dispatch solutions by excluding extreme scenarios, for which the quantile-copula is proposed to construct the confidence interval constraint. Simulation studies are carried out on a modified IEEE 30-bus power system with wind farms integrated in two areas, and the results prove the superiority of the MGKC in formulating the dependence among different wind farms and the superiority of the improved MV model based on quantile-copula in determining a better dispatch solution.

Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network

With the large-scale wind power penetration, probabilistic power flow plays an important role in power system uncertainty analysis. This paper proposes a novel Gaussian Mixture Model to fit the probability density distribution of short-term wind power forecasting errors with the multimodal and asymmetric characteristics. Cumulants are used to calculate mean value and deviation of state variables for each random combination result of Gaussian components. Probabilistic power flow is acquired by summing up all the Gaussian probability density functions with weights counted by the product of Gaussian components in each random combination. Parallel probabilistic power flow computation by use of the Gaussian Mixture Model and cumulants could simplify the calculation procedure in large scale of integrated wind power network. Case studies are carried out in modified IEEE 57-bus test system to verify advantages of the novel approach. Results show that the computational efficiency and accuracy are well improved in the proposed method.

Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

Wind speed/power prediction plays an important role in large-scale wind power penetration because of the wind volatility and uncertainty. In this paper, an accurate forecast model is presented based on improved wavelet transform, informative feature selection and hybrid forecast engine. The proposed forecasting engine is based on support vector machine which is an appropriate prediction forecast engine due to its ability to discover natural structures of wind speed/power variation. The mentioned forecast engine is equipped with an intelligent algorithm and enhances its prediction accuracy. For this purpose, we applied a new version of enhanced particle swarm optimization in this work as the optimization algorithm. Effectiveness of the proposed forecast model is extensively evaluated by real-world electricity market through comparison with well-known forecasting methods. Obtained numerical results and analysis demonstrate the validity and superiority of the proposed method.

Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System

Abstract As a promising renewable energy source, wind power has environmental benefits, as well as economic and social ones. Due these characteristics, wind farm has grown fast in the last five years, and in some countries, it has already surpassed conventional sources, such as hydro and coal plants. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. This study proposes a hybrid approach that combines the Singular Spectrum Analysis (SSA), which rarely presents application in literature on wind speed forecasting, and a Computing Natural paradigm called Adaptive Neuro Fuzzy Inference System (ANFIS). The SSA decomposes the original wind speed into various components, so these components are pre-processed regarding to the level of original wind series information remained. The main components selected to reconstruct the original series have in their structure the information about trend and harmonic components. The final remaining components grouped are labeled as noise. The ANFIS model uses these two information to construct the model applied to forecasting the next wind speed value. In this way, the hybrid model can learn the trend and the harmonic structure of the wind time series. Experimental results show that prediction errors are significantly reduced using the proposed technique to perform 10min one-step-ahead and k -step-ahead wind speed forecast.

Regional variations of environmental co-benefits of wind power generation in China

The rapid development and large-scale penetration of wind power would bring about substantial environmental benefits by substituting fossil fuel-based thermal power generation. In this paper, the environmental co-benefits synergized with energy saving benefit of wind power penetration in China were quantified. Moreover, the regional distribution of environmental co-benefits was mapped by considering the baseline emission factor of each region. Based on the co-benefits analysis results, the wind power deployment pathways were designed to enlarge the environmental co-benefits in the whole country level. The results show that the CO2, SO2, NOx, and PM2.5 emissions reduced and water consumption saved due to wind power penetration account for respectively 2.58%, 2.71%, 2.66%, 1.58%, and 2.34% of total environmental emissions and water consumption from the whole power generation sector in China. In view of regional distribution, the environmental co-benefits of wind power to the power system is not proportional to the amount of fossil-fueled power that it displaces. The penetration of wind power results in the largest environmental co-benefits in the North China grid. The Northwest China grid has a higher environmental alleviating potential compared with the Northeast China grid even though the two regions has an equal magnitude of wind power penetration. Scenario analysis suggests that lowering the proportion of thermal power in power generation structure and the transmission of bundled wind power and thermal power from the north to the load centers are promising ways in enlarging the environmental co-benefits of wind power penetration.

Control optimisation for pumped storage unit in micro‐grid with wind power penetration using improved grey wolf optimiser

As the large-scale wind power penetration and distributed generation become popular in modern power systems, the governing control of pumped storage unit has attracted many attentions in recent years for its flexible power adjustment capability. To provide a research platform for dynamic analysis of the hybrid power system, a micro-grid mainly including a pumped storage unit and a wind power plant is introduced in detail and taken as the system plant. Refined mathematical models of pump turbine, synchronous generator and wind turbine generator have been established considering the complicated non-linear dynamic characteristics of the multi-energy power system. Furthermore, a novel chaotic grey wolf optimiser algorithm is proposed to select the optimal control parameters of the pump turbine governing system for the sake of maintaining frequency stability and enhancing control performances under the complicated operating conditions. Simulation experiments have been conducted in the micro-grid under representative operating conditions to validate the effectiveness of the proposed control method and its preponderance in comparison with traditional ones.

Nonlinear Observer-Based Robust Passive Control of Doubly-Fed Induction Generators for Power System Stability Enhancement via Energy Reshaping

The large-scale penetration of wind power might lead to degradation of the power system stability due to its inherent feature of randomness. Hence, proper control designs which can effectively handle various uncertainties become very crucial. This paper designs a novel robust passive control (RPC) scheme of a doubly-fed induction generator (DFIG) for power system stability enhancement. The combinatorial effect of generator nonlinearities and parameter uncertainties, unmodelled dynamics, wind speed randomness, is aggregated into a perturbation, which is rapidly estimated by a nonlinear extended state observer (ESO) in real-time. Then, the perturbation estimate is fully compensated by a robust passive controller to realize a globally consistent control performance, in which the energy of the closed-loop system is carefully reshaped through output feedback passification, such that a considerable system damping can be injected to improve the transient responses of DFIG in various operation conditions of power systems. Six case studies are carried out while simulation results verify that RPC can rapidly stabilize the disturbed DFIG system much faster with less overshoot, as well as supress power oscillations more effectively compared to that of linear proportional-integral-derivative (PID) control and nonlinear feedback linearization control (FLC).

Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula

Wind power forecasting is a key role for large-scale wind power penetration on conventional electric power systems by understanding stochastic nature of winds. This paper proposes an empirical statistical model for forecasting monthly offshore wind speeds as a function of remotely sensed sea surface temperatures over the seas around the Korean Peninsula. The model uses the optimal lagged multiple linear regression method, and predictors are characterized by mixed periodicities derived from the autocorrelation between spatially variable satellite-observed sea surface temperatures and wind speeds at all grid points over a period of about ten years (2001 to 2008). Offshore wind speeds were found to be correlated with sea surface temperatures within a seasonal range of two- to four-month lags. In particular, offshore wind speeds were closely associated with the sea surface temperature at lag 4 M, followed by lag 3 M and lag 2 M. Correlation is less at lag 1 M as compared lag 2 M, lag 3 M and lag 4 M. The results demonstrate that this approach successfully produces accurate depictions of monthly wind speeds at the gridded network. The hindcast offshore wind speeds and wind power density showed slightly improved skills compared to the seasonally varying climatology with the value of root-mean square errors, +18% and +23%, respectively. The spatial distributions of the monthly gridded wind speed and wind power density remained fairly stable from one month to another, whereas individual regions displayed slight differences in variability. The results of this study are expected to be useful in establishing guidelines for operating and managing nascent offshore farms around the Korean Peninsula.

Techno-economic analysis of wind curtailment/hydrogen production/fuel cell vehicle system with high wind penetration in China

Wind curtailment/hydrogen production/fuel cell vehicle system (WCHPFCVS) is the use of curtailment to electrolyze water to produce hydrogen, which then provides energy for hydrogen fuel cell vehicles. In this paper, a techno-economic analysis of WCHPFCVS is proposed using the HOMER software. Large-scale wind power penetration is expected to lead to serious wind curtailment, and therefore, the hydrogen fuel cell vehicle will play an important role in future renewable energy storage, energy internet sharing, and electric transport areas. A system model of wind curtailment/hydrogen production/fuel cell vehicle is presented and analyzed using HOMER software to optimize the capacity and cost of the system. An annual revenue and profit of the system is then calculated and analyzed for energy conservation, emissions reduction, and environmental benefits. A technoeconomical evaluation of the system when cost of producing hydrogen and hydrogen load (fuel cell vehicle quantities) changes is also presented, taking into consideration the future progress of the technology and its market development. Techno-economic analysis of WCHPFCVS is shown as an effective method through a case study using actual data of curtailment from a wind farm in Jilin province in northeast China.

Finding Equilibria in the Pool-Based Electricity Market With Strategic Wind Power Producers and Network Constraints

This paper proposes a model to find the equilibria in the short-term electricity market with large-scale wind power penetration. The behavior of each strategic player is modeled through a two-stage mathematical problem with equilibrium constraints (MPEC), where the upper-level problem maximizes the profit of the strategic player and the lower-level problem describes the clearing processes of the day-ahead and real-time markets while considering the network constraints. The joint solution of all the MPECs constitutes an equilibrium problem with equilibrium constraints (EPEC). The uncertain wind power production and demand are represented by a set of plausible scenarios. By using the duality theory and Karush–Kuhn–Tucker condition, each MPEC is transferred into a mixed-integer linear programing problem. The Nash equilibria of the electricity market are obtained by solving the EPEC using Game theory and the diagonalization algorithm. Case studies are performed to show the effectiveness of the proposed model.

Multi-Scale Amplitude Modulation Effect of Wind Speed Random Fluctuation

Wind power is widely used as a type of clean and renewable energy source in recent years. However, large-scale wind power penetration brings lots of challenges due to the uncertainty of wind speed. As a result, the research of wind speed uncertainty plays an important role in large scale wind power integration. In this paper, the multi-scale amplitude modulation effect of wind speed random fluctuation is found based on actual wind speed data. And the physical mechanism of multi-scale characteristics are presented from the perspective of turbulence. This research has both certain physical academic meaning and engineering application value in wind speed uncertainty research.

A Novel DFIG Damping Control for Power System with High Wind Power Penetration

Aiming at the fact that large-scale penetration of wind power will to some extent weaken the small signal stability of power systems, in this paper, the dynamic model of a doubly fed induction generator (DFIG) is established firstly, to analyze the impact of wind generation on power oscillation damping. Then, based on the conventional maximum power point tracking control of variable speed wind turbine, a supplementary control scheme is proposed to increase the damping of power system. To achieve best performance, parameters of the damping control are tuned by using a genetic algorithm. Results of eigenvalue analysis and simulations demonstrate the effectiveness of supplementary damping control with fixed wind speed. At last, due to the problem that fluctuation of output power of wind generators would cause the unstable performance of the DFIG damping controller above, a new algorithm that adapts to the wind variation is added to the supplementary damping control scheme. Results of the simulation show that an improved damping control scheme can stably enhance system damping under various wind speeds and has higher practical value.

Day-Ahead Prediction of Wind Speed with Deep Feature Learning

Day-ahead prediction of wind speed is a basic and key problem of large-scale wind power penetration. Many current techniques fail to satisfy practical engineering requirements because of wind speed's strong nonlinear features, influenced by many complex factors, and the general model's inability to automatically learn features. It is well recognized that wind speed varies in different patterns. In this paper, we propose a deep feature learning (DFL) approach to wind speed forecasting because of its advantages at both multi-layer feature extraction and unsupervised learning. A deep belief network (DBN) model for regression with an architecture of 144 input and 144 output nodes was constructed using a restricted Boltzmann machine (RBM). Day-ahead prediction experiments were then carried out. By comparing the experimental results, it was found that the prediction errors with respect to both size and stability of a DBN model with only three hidden layers were less than those of the other three typical approaches including support vector regression (SVR), single hidden layer neural networks (SHL-NN), and neural networks with three hidden layers (THL-NN). In addition, the DBN model can learn and obtain complex features of wind speed through its strong nonlinear mapping ability, which effectively improves its prediction precision. In addition, prediction errors are minimized when the number of DBN model's hidden layers reaches a threshold value. Above this number, it is not possible to improve the prediction accuracy by further increasing the number of hidden layers. Thus, the DBN method has a high practical value for wind speed prediction.

Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression

Wind speed or wind power forecasting plays an important role in large-scale wind power penetration due to their uncertainty. Support vector regression, widely used in wind speed or wind power forecasting, aims at discovering natural structures of wind variation hidden in historical data. Most current regression algorithms, including least squares support vector regression (SVR), assume that the noise of the data is Gaussian with zero mean and the same variance. However, it is discovered that the uncertainty of short-term wind speed satisfies Gaussian distribution with zero mean and heteroscedasticity in this work. This kind of task is called heteroscedastic regression. In order to deal with this problem, we derive an optimal loss function for heteroscedastic regression and develop a new framework of $\nu$ -SVR for learning tasks of Gaussian noise (GN) with heteroscedasticity. In addition, we introduce the stochastic gradient descent (SGD) method to solve the proposed model, which leads the models to be trained online. Finally, we reveal the uncertainty properties of wind speed with two real-world datasets and test the proposed algorithms on these data. The experimental results confirm the effectiveness of the proposed model.

Compensating active power imbalances in power system with large-scale wind power penetration

Large-scale wind power penetration can affect the supply continuity in the power system. This is a matter of high priority to investigate, as more regulating reserves and specified control strategies for generation control are required in the future power system with even more high wind power penetration. This paper evaluates the impact of large-scale wind power integration on future power systems. An active power balance control methodology is used for compensating the power imbalances between the demand and the generation in real time, caused by wind power forecast errors. The methodology for the balance power control of future power systems with large-scale wind power integration is described and exemplified considering the generation and power exchange capacities in 2020 for Danish power system.