Large-scale Integration Of Wind Energy Research Articles

Transnational experience exchange between countries towards speeding up EU renewable transition: example of Norway-Croatia

Abstract The RePowerEU plan aims to increase wind energy capacity to 480 GW by 2030, with 300 GW coming from offshore wind farms, to achieve climate neutrality by 2050. Due to Croatia’s substantial dependence on nautical and coastal tourism, along with significant concerns regarding negative environmental impacts, the installation of offshore wind farms has not been previously considered. However, due to increased demand for renewables, the offshore energy potential resurfaced. The recent Action Plan for the Uptake of Offshore Renewable Energy Sources in Croatia (May 2023) estimated offshore wind potential at 25 GW and identified more than 29 000 km2 of area available for offshore renewables. Norway, with its extensive experience in offshore engineering and pioneering developments in offshore wind, is a suitable partner in joint innovation and a resource for tried and tested best practices. In this collaborative research, a review of relevant documents, research articles, and interviews with industry experts from both from Croatia and Norway was conducted to obtain a broader perspective of the implications of offshore development in Croatia. As an outcome of this preliminary research, the paper outlines the required evaluations in the Croatian context, in terms of the social, environmental, legislative, and technical aspects that are to be considered for large-scale integration of wind energy into the Croatian grid. It also identifies the necessary grid improvements to achieve a reliable and sustainable grid of the future.

A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting

Large-scale integration of wind energy and large-amplitude wind power fluctuation in minutes to hours, imposes unprecedented challenges of retaining reliable and secure power systems. Proper detection and precise prediction of wind power ramp events could help power systems better optimize scheduling and mitigate the impact of extreme events. Thus, this paper proposes a novel ramp event forecasting approach named Informer Ordinal Regression Network with Label Diversity (IFORNLD), without distributional assumptions and increasing the computational complexity. The proposed framework retains the superiority of the Informer architecture which can easily handle very long sequences with efficient computation and effective gradient in training, and the strengths of the ordinal regression with label diversity (ORLD) method, which creates a multi-output model that creates diversity with similar computational complexity. The result of case studies on three wind power datasets demonstrate that the proposed framework is superior than some benchmark models, validating its effectiveness and advantages.

Wind Energy Resource Prediction and Optimal Storage Sizing to Guarantee Dispatchability: A Case Study in the Kenyan Power Grid

Kenya is experiencing a fast increase in grid-connected intermittent renewable energy sources (RESs) to meet its increased power demand, and at the same time be able to fulfill its Paris Agreement obligations of abating greenhouse gas emissions. For instance, Kenya has 102 MW of grid-tied solar power and 410 MW of grid-tied wind power. However, these sources are very intermittent with low predictability. Thus, after their installation and integration into the grid, they impose a new challenge for the secure, reliable, and economic operation of the system. To mitigate these and to ensure proper planning of the system operations, accurate and faster prediction of the generation output of the wind energy resources and optimal design and sizing of storage for the large-scale wind energy integration into the grid are of paramount importance. Artificial intelligence (AI) and metaheuristic techniques have proven to be efficient and robust in offering solutions to complex nonlinear prediction and optimization problems. Therefore, this study aims to utilize backpropagation neural network (BPNN) algorithm to conduct hourly prediction of the generation output of Lake Turkana Wind Power Plant (LTWPP), a 310 MW plant connected to the Kenyan power grid, and optimally size its battery energy storage system (BESS) using genetic algorithm (GA) to guarantee its dispatchability. The historical weather data, namely wind speed, ambient temperature, relative humidity, wind direction, and generation output from LTWPP, are employed in the training, testing, and validation of the neural network. LTWPP and BESS are modelled in MATLAB R2016a software. Thereafter, the developed BPNN and GA algorithms are applied to the modelled systems to predict the wind output and optimize the storage system, respectively. BESS optimization with neural prediction reduces the BESS capacity and investment costs by 59.82%, while the overall dispatchability of LTWPP is increased from 73.36% to 90.14%, hence enabling the farm to meet its allowable loss of power supply probability (LPSP) index of 0.1 while guaranteeing its dispatchability.

A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor

As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.

Stochastic power management strategy for hybrid energy storage systems to enhance large scale wind energy integration

The strong variability of renewable energy sources (RES) often hinders their integration in power systems. Hybrid energy storage systems (HESS), based on complementary storage technologies, enable high RES penetration towards modern and sustainable power generation, improving energy systems performances and stability, while reducing CO2 emission. This paper introduces a novel power management strategy for a HESS consisting of a flywheel and a LiFePO4 battery coupled to a 2 MW wind turbine operating in interconnected mode. The power management strategy is based on the simultaneous perturbation stochastic approximation (SPSA) principle and targets a smoother power profile at the point of interface to the grid and, at the same time, a reduced solicitation of the battery. The underlying algorithm falls within the gradient-based optimization category, being able to pursue the envisaged goals without requiring a detailed model of the objective function. The main and novel contribution of this research aims to extend the SPSA recognized advantages, demonstrated in control applications, in the field of real-time HESS power management. Real datasets are employed to size an economic storage section and define representative simulation scenarios in order to validate the suitability of the proposed approach. Simulations are performed over one day timeframe in Matlab/Simulink for the most representative days extracted from the wind turbine yearly generation profile, employing a 1 s timestep. Results obtained prove that the proposed strategy ensures a substantially reduction of the power profile fluctuations at the point of interface to the grid, by more than 80% compared to the wind profile. Moreover, a power ramp mitigation of 65% on average towards the battery if compared to the flywheel.

Analysis of pumped hydroelectric energy storage for large-scale wind energy integration

Renewable energy sources are becoming a popular choice of energy, due to their sundry advantages and more convenient environmental impacts. Wind certainly emerges as one of the most plausible energy sources in modern power generation. However, large-scale wind energy is associated with fluctuations in voltage and power due to its intermittent nature. The proposed pumped hydroelectric energy storage system offers one of the best solutions in solving this problem. It is shown that large-scale integration of wind energy becomes more feasible and efficient when a proper energy storage system is added to achieve appropriate energy charging or discharging. Upon comparing the potential of different storage systems, pumped hydroelectric energy storage is found to be highly promising for wind integration. In order to demonstrate the behavior of the integrated system with respect to time constant and to verify the feasibility of the energy storage in different case studies, simulations are performed in Matlab/Simulink. This work would open new doors toward the investigation of large-scale wind power integration. It is also expected that pumped hydroelectric energy storage would become a possible alternative for energy reserves for large-scale wind power applications.

Smart Wire Placement to Facilitate Large-Scale Wind Energy Integration: An Adaptive Robust Approach

Enhanced utilization of the existing transmission grid is a cheaper and paramount way to have a high penetration of large-scale wind energy resources. Smart wire devices (SWDs) are a new technology to enhance the transfer capability through power flow control. The SWDs are distributed, cheap, self-governing smart assets of FACTS technologies, which can adjust the power flow in an interconnected transmission network. Accordingly, this paper presents a comprehensive three-stage robust SWD placement model, which minimizes the generation and investment costs while guaranteeing that the adaptive and secure robust solution is accustomed to cover the wind uncertainty interval. Since the proposed robust model is not solvable via an off-the-shelf optimization package and to reduce the computation burden of the solution process, an effective solution strategy is proposed to solve it. Detailed simulation results on the IEEE 24-bus system verify the effectiveness of the proposed robust SWD placement model.

Wind speed forecasting based on Quantile Regression Minimal Gated Memory Network and Kernel Density Estimation

As a renewable and clean energy, wind energy plays an important role in easing the increasingly serious energy crisis. However, due to the strong volatility and randomness of wind speed, large-scale integration of wind energy is limited. Therefore, obtaining reliable high-quality wind speed prediction is of great importance for the planning and application of wind energy. The purpose of this study is to develop a hybrid model for short-term wind speed forecasting and quantifying its uncertainty. In this study, Minimal Gated Memory Network is proposed to reduce the training time without significantly decreasing the prediction accuracy. Furthermore, a new hybrid method combining Quantile Regression and Minimal Gated Memory Network is proposed to predict conditional quantile of wind speed. Afterwards, Kernel Density Estimation method is used to estimate wind speed probabilistic density function according to these conditional quantiles of wind speed. In order to make the model show better performance, Maximal Information Coefficient is used to select the feature variables while Genetic Algorithm is used to obtain optimal feature combinations. Finally, the performance of the proposed model is verified by seven state-of-the-art models through four cases in Inner Mongolia, China from five aspects: point prediction accuracy, interval prediction suitability, probability prediction comprehensive performance, forecast reliability and training time. The experimental results show that the proposed model is able to obtain point prediction results with high accuracy, suitable prediction interval and probability distribution function with strong reliability in a relatively short time on the prediction problems of wind speed.

An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization

Large-scale integration of wind energy into electric grid is restricted by its inherent intermittence and volatility. So the increased utilization of wind power necessitates its accurate prediction. The contribution of this study is to develop a new hybrid forecasting model for the short-term wind power prediction by using a secondary hybrid decomposition approach. In the data pre-processing phase, the empirical mode decomposition is used to decompose the original time series into several intrinsic mode functions (IMFs). A unique feature is that the generated IMF1 continues to be decomposed into appropriate and detailed components by applying wavelet packet decomposition. In the training phase, all the transformed sub-series are forecasted with extreme learning machine trained by our recently developed crisscross optimization algorithm (CSO). The final predicted values are obtained from aggregation. The results show that: (a) The performance of empirical mode decomposition can be significantly improved with its IMF1 decomposed by wavelet packet decomposition. (b) The CSO algorithm has satisfactory performance in addressing the premature convergence problem when applied to optimize extreme learning machine. (c) The proposed approach has great advantage over other previous hybrid models in terms of prediction accuracy.

Short-term wind power prediction based on spatial model

Large-scale integration of wind energy into power systems may cause operational problems due to the stochastic nature of wind. A short-term wind power prediction model based on physical approach and spatial correlation is proposed to characterize the uncertainty and dependence structure of wind turbines' outputs in the wind farm. Firstly, continuous partial differential equation of each wind turbine has been developed according to its specific spatial location and the layout of its neighboring correlated wind turbines. Then, spatial correlation matrix of wind speed is derived by discretizing differential equation at each wind turbine using a finite volume method (FVM). Wind speed at each turbine is acquired by solving the relevant differential equation under given boundary conditions. Finally, the wind speed is converted to wind power production via a practical power curve model. Prediction results showed that the spatial correlation model can accurately characterize the correlations among outputs of wind turbines and reduce the error of short-term wind power prediction.

Offshore wind farms energy injection in the electrical grid - Lithium battery to mitigate power fluctuations

The wind farm energy injection in the electrical grid generates the economic and technical challenges which must to be solved for successful large scale wind energy integration in electrical grids. Due to wind speed variations, the wind farm output power presents the fluctuating components which are not compatible to the electrical grid requirements. Today, this problem is a major challenge with the wind farm projects increasing in the world. Large power variations cause the deviations of rms voltage and the frequency compared to nominal values in point of the coupling. These deviations cause generally the protective equipment activation which causes a disconnection of wind farms from the grid. To avoid these problems, the electrical grid managers use the grid code requirements to regulate the wind energy injection process in the grid. Using energy storage devices such as a lithium battery or ultracapacitors to compensate the power fluctuations from wind farm is an interesting solution. This paper deals this issue to improve the injected energy in electrical grid by the wind farm using lithium battery. Proposed approach allows compensating the wind energy fluctuations, so that the energy destined to electrical grid becomes compatible to electrical grid code requirements. Studied system configuration includes a wind farm with rated power of 300MW, a lithium battery module with a maximum power of 30MW and electrical grid. Lithium battery is connected to grid through an inverter and the power fluctuations from wind farm are assigned to battery. To show the performances of the control strategy, some simulation results are presented and analyzed using Matlab/SimPowerSystem software.

Decentralized Multiarea Robust Generation Unit and Tie-Line Scheduling Under Wind Power Uncertainty

The growing interconnections of regional power systems and the large-scale integration of wind energy bring about the critical need to coordinate multiarea generation unit and tie-line scheduling (MAUTS). It is recognized that because of the limitations on private data exchange and model management, it is suitable to address the multiarea power scheduling problem in a decentralized way. In this paper, the MAUTS problem is formulated using the adaptive robust optimization (RO) scheme to account for uncertain wind energy. Our model is decomposed into regional subproblems by augmented Lagrangian decomposition (ALD), which enables a fully distributed computation within an alternating direction multiplier method framework. To address the nonconvexity issue, a tractable alternating optimization procedure (AOP) is developed to obtain high-quality solutions with finite convergence for the nonconvex mixed-integer problem. Simulations on different test systems are conducted to show the computational performance, the solution quality, and scalability of the proposed method.

Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration.

The emergence of smart grids has posed great challenges to traditional power system control given the multitude of new risk factors. This paper proposes an online supplementary learning controller (OSLC) design method to compensate the traditional power system controllers for coping with the dynamic power grid. The proposed OSLC is a supplementary controller based on approximate dynamic programming, which works alongside an existing power system controller. By introducing an action-dependent cost function as the optimization objective, the proposed OSLC is a nonidentifier-based method to provide an online optimal control adaptively as measurement data become available. The online learning of the OSLC enjoys the policy-search efficiency during policy iteration and the data efficiency of the least squares method. For the proposed OSLC, the stability of the controlled system during learning, the monotonic nature of the performance measure of the iterative supplementary controller, and the convergence of the iterative supplementary controller are proved. Furthermore, the efficacy of the proposed OSLC is demonstrated in a challenging power system frequency control problem in the presence of high penetration of wind generation.

Lithium-Ion Capacitor Energy Storage Integrated With Variable Speed Wind Turbines for Power Smoothing

Utilization of wind energy in modern power systems creates many technical and economical challenges that need to be addressed for successful large scale wind energy integration. Variations in wind velocity result in variations of output power produced by wind turbines. Variable power output becomes a challenge as the share of wind energy in power systems increases. Large power variations cause voltage and frequency deviations from nominal values that may lead to activation of protective relay equipment, which may result in disconnection of the wind turbines from the grid. Particularly community wind power systems, where only one or few wind turbines supply loads through a weak grid such as distribution network, are sensitive to supply disturbances. Energy storage integrated with wind turbines can address this challenge. In this paper, Li-ion capacitors are investigated as a potential solution for filtering power variations at the scale of tens of seconds. A novel topology and control technique has been introduced to integrate capacitors and power conversion circuitry. Modeling and scaled-down experimental results are provided to verify the theoretical analyses.

The role of hydrogen-based energy storage in the support of large-scale wind energy integration in island grids

To confront problems concerning large-scale integration of renewable energy sources, introduction of energy storage constantly gains ground. Benefits stemming from the adoption of energy storage include exploitation of otherwise rejected energy, increased reliability of energy supply and improved operation of a given power system overall. In this regard, contribution of such systems in achieving large-scale integration of wind energy into island grids is currently considered. More precisely, fuel cells and hydrogen storage (FC–HS) are investigated, in comparison with conventional batteries. For this purpose, a simulation algorithm is developed to study the energy performance of different FC–HS configurations used to recover wind energy curtailments. The developed algorithm is then applied to a representative Aegean island of medium–high quality wind potential. Results obtained indicate that FC–HS may become attractive in comparison with conventional batteries, only in the case that the use of hydrogen surplus to cover other energy flows is also put forward.

Operational constraints and hydrologic variability limit hydropower in supporting wind integration

Climate change mitigation will require rapid adoption of low-carbon energy resources. The integration of large-scale wind energy in the United States (US) will require controllable assets to balance the variability of wind energy production. Previous work has identified hydropower as an advantageous asset, due to its flexibility and low-carbon emissions production. While many dams currently provide energy and environmental services in the US and globally, we find that multi-use hydropower facilities would face significant policy conflicts if asked to store and release water to accommodate wind integration. Specifically, we develop a model simulating hydroelectric operational decisions when the electric facility is able to provide wind integration services through a mechanism that we term ‘flex reserves’. We use Kerr Dam in North Carolina as a case study, simulating operations under two alternative reservoir policies, one reflecting current policies and the other regulating flow levels to promote downstream ecosystem conservation. Even under perfect information and significant pricing incentives, Kerr Dam faces operational conflicts when providing any substantial levels of flex reserves while also maintaining releases consistent with other river management requirements. These operational conflicts are severely exacerbated during periods of drought. Increase of payments for flex reserves does not resolve these operational and policy conflicts.

Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy

The recent literature on applied energy has emphasized the role of energy storage in the electricity supply chain. However, absence of an integrated valuation framework for services provided by energy storage technologies, owed to its limited scope so far, hinders investments in such capital intensive systems. To this end, the feasibility of an alternative operation strategy for energy storage systems (ESSs), based on the use of wind energy surplus, is currently investigated. More precisely, valuation of the policy options available to promote wind-based ESSs is carried out, by employing, for the first time, a comprehensive socioeconomic cost-benefit model that takes into account – among others – initial investment subsidies and feed-in tariffs (FiTs) for ESSs. Pumped hydro and compressed air energy storage covering peak demand are examined, demonstrating that such systems may prove cost-effective if “socially just” FiTs are applied, with our findings however, also supporting the notion that a portfolio of policies can more effectively facilitate investments in the sector.

The flexible grid operation with large-scale wind energy integration in Germany,based on the feed-in data analysis

The flexible grid operation with large-scale wind energy integration in Germany,based on the feed-in data analysis

Analysis of 2030 Large-Scale Wind Energy Integration in the Eastern Interconnection Using WINS

A simulation of the 2030 load forecast in the Eastern Interconnection suggests that large-scale wind energy integration will have a major impact on the hourly commitment and dispatch of gas and coal units, especially at off-peak load hours. While fuel price alterations will have major impacts on the system production cost, load variation will have a larger impact and potential carbon costs will have the greatest impact.

Cluster analysis of wind turbines of large wind farm with diffusion distance method

Understanding the dynamics of power output of a wind farm is important for integration of large-scale wind energy. In a large complex dynamic system, such as a wind farm, clustering is a way to reduce the model complexity and improve the understanding of the model dynamics. This study presents a methodology for clustering wind turbines of a wind farm into different groups. A weighted graph is firstly designed to represent the complex relationships between the power outputs of wind turbines. Such graph is used to construct a Markov chain for estimation of the likelihood of wind turbines belonging to the same cluster. The spectral properties of the constructed Markov chain are analyzed to identify the number of clusters, so the elements of each cluster can be identified in a feature space. Theoretical study shows that the proposed methodology can simplify the dynamic model of power output of wind farm without compromising the overall dynamic characteristics of the original system asymptotically. The methodology of clustering is demonstrated and tested based on observations of power output of wind turbines in an actual wind farm. The methodology can be used to simplify controller design, and operation and forecast of wind farm output.