Scale Wind Power Integration Research Articles

Large scale integration of wind power – influence of geographical allocation

This paper investigates the influence of geographical allocation of wind power generation in Northern Europe, assuming large scale integration of wind power. The work applies a linear cost optimization model of the heat and power sector with a 1-hour time resolution. The model minimizes the sum of running costs to meet the heat and power demand and the wind power and transmission investment costs. Wind data are taken from modelled wind speed data from the Swedish Meteorological and Hydrological Institute. The Nordic countries and Germany were divided into regions and the 200 sites with the highest yearly output were chosen to represent the region. The model gives the most favourable distribution of wind power between the regions. In addition, the paper provides an assessment of the effect of geographical distribution of wind power with respect to influence on the aggregated wind power production (only considering the wind power generation itself). The modelling results show that the largest investments in wind power are made in the windy region of Southern Norway. However, depending on the cost of transmission allocating wind power near large load centers in Germany may also be favourable. As for the assessment of distribution of wind power, the wind data gives that if the 400 best sites in Europe were used, this would result in a capacity factor of 38.5% and a lowest output of 2.5 % of rated power (applying 2009 wind data).

Dynamic Voltage Stability of an Electric Power Network with Double Fed Induction Wind Power Generators

In recent years, the large penetration of wind power poses new challenges for the voltage stability analysis of an electric power network. In this paper is studied the effect of large scale integration of wind power in the dynamic voltage stability of a power network under a fault condition. The automatic voltage regulators of the generating units, the turbine speed governors and the double fed induction generator were modeled. Different load models were used and the under load tap changers were also taken into account. The simulation results were obtained using the EUROSTAG software package. Finally, some conclusions that provide a better understanding of the dynamic voltage stability in a system with a large amount of wind power generation are pointed out.

SWSA transformer: A forecasting method of ultra-short-term wind speed from an offshore wind farm using global attention mechanism

Accurate ultra-short-term wind speed forecasting is great significance to ensure large scale integration of wind power into the power grid, but the randomness, instability, and non-linear nature of wind speed make it very difficult to be predicted accurately. To solve this problem, shifted window stationary attention transformer (SWSA transformer) is proposed based on a global attention mechanism for ultra-short-term forecasting of wind speed. SWSA transformer can sufficiently extract these complicated features of wind speed to improve the prediction accuracy of wind speed. First, positional embedding and temporal embedding are added at the bottom of the proposed method structure to mark wind speed series, which enables complicated global features of wind speed to be more effectively extracted by attention. Second, a shifted window is utilized to enhance the ability of attention to capture features from the edge sequences. Third, a stationary attention mechanism is applied to not only extract features of wind speed but also optimize the encoder-decoder network for smoothing wind speed sequences. Finally, the predicted values of wind speed are obtained using the calculation in the decoder network. To verify the proposed method, tests are performed utilizing data from an real offshore wind farm. The results show that the proposed method outperforms many popular models evaluated by many indexes including gated recurrent unit, Gaussian process regression, long-short term memory, shared weight long short-term memory network, and shared weight long short-term memory network -Gaussian process regression, in terms of mean absolute error, mean square error (MSE), root mean square error, mean absolute percentage error, mean square percentage error, and coefficient of determination (R2).

GA−Reinforced Deep Neural Network for Net Electric Load Forecasting in Microgrids with Renewable Energy Resources for Scheduling Battery Energy Storage Systems

The large−scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling difficulties, and reliability reduction. The microgrid concept has been proposed to locally control and manage a cluster of local distributed energy resources (DERs) and loads. If the net load power can be accurately predicted, it is possible to schedule/optimize the operation of battery energy storage systems (BESSs) through economic dispatch to cover intermittent renewables. However, the load curve of the microgrid is highly affected by various external factors, resulting in large fluctuations, which makes the prediction problematic. This paper predicts the net electric load of the microgrid using a deep neural network to realize a reliable power supply as well as reduce the cost of power generation. Considering that the backpropagation (BP) neural network has a good approximation effect as well as a strong adaptation ability, the load prediction model of the BP deep neural network is established. However, there are some defects in the BP neural network, such as the prediction effect, which is not precise enough and easily falls into a locally optimal solution. Hence, a genetic algorithm (GA)−reinforced deep neural network is introduced. By optimizing the weight and threshold of the BP network, the deficiency of the BP neural network algorithm is improved so that the prediction effect is realized and optimized. The results reveal that the error reduction in the mean square error (MSE) of the GA–BP neural network prediction is 2.0221, which is significantly smaller than the 30.3493 of the BP neural network prediction. Additionally, the error reduction is 93.3%. The error reductions of the root mean square error (RMSE) and mean absolute error (MAE) are 74.18% and 51.2%, respectively.

Real-time impact of power balancing on power system operation with large scale integration of wind power

Highly wind power integrated power system requires continuous active power regulation to tackle the power imbalances resulting from the wind power forecast errors. The active power balance is maintained in real-time with the automatic generation control and also from the control room, where regulating power bids are activated manually. In this article, an algorithm is developed to simulate the activation of regulating power bids, as performed in the control room, during power imbalance between generation and load demand. In addition, the active power balance is also controlled through automatic generation control, where coordinated control strategy between combined heat and power plants and wind power plant enhances the secure power system operation. The developed algorithm emulating the control room response, to deal with real-time power imbalance, is applied and investigated on the future Danish power system model. The power system model takes the hour-ahead regulating power plan from power balancing model and the generation and power exchange capacities for the year 2020 into account. The real-time impact of power balancing in a highly wind power integrated power system is assessed and discussed by means of simulations for different possible scenarios.

Assessment of post-contingency congestion risk of wind power with asset dynamic ratings

Abstract Large scale integration of wind power can be deterred by congestion following an outage that results in constrained network capacity. Post outage congestion can be mitigated by the application of event control strategies; however they may not always benefit large wind farms. This paper investigates this problem in detail and proposes an advanced mathematical framework to model network congestion as functions of stochastic limits of network assets to capture post contingency risk of network congestion resulting through the constrained network capacity that limits high penetration of wind. The benefit of this approach is that it can limit the generation to be curtailed or re-dispatched by dynamically enhancing the network latent capacity in the event of outages or as per the need. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The case study results with large and small network models suggest that the following an outage, wind utilization under dynamic line rating can be increased considerably if the wind power producers maintain around a 15% margin of operation.

Wind Power Integration to the Transmission Grid: The Egyptian Perspective

The Government of Egypt has recognized that the availability of reliable power supply is essential for economic and social prosperity and human development as well as for attracting private sector investments in the country. Egyptian Economic reform and growth have triggered a rapid increase in electricity demand. Peak load growth rate averaged 7 % p.a. in 2005–2008 and reached as high as 12% in 2008/09. In response to the rapid growth in demand, the supply capacity has expanded through an ambitious power sector investment program that has been under implementation since 2002. The Government's new energy strategy is aiming to increase the share of renewable energy to 20 % by the year 2020. 12% of this value, i.e 7200 MW, is from wind resources. The complexity of the interconnection of wind farms to the power grid stems from the fact that wind energy is rather unstable and available only during certain hours of the day. By its nature the wind power generation may drop suddenly. To keep the grid operation stable, there is a need to ensure that other power generation sources are available to compensate for the drop. Another common characteristic of the wind farms is that they are located somewhat remote from the power grid. Therefore, the utility has to ensure the sufficiency of power transmission capacity. A rather unique feature of wind power in Egypt is that most of the wind resources are concentrated in the Gulf of Suez and Eastern and Western bank of Nile River areas. This raises the additional issue of the impact on the system when large amount of wind power are interconnected at certain points of the network and not distributed over along the country. The effects of having 3000 MW in Gulf of Suez and 4200 MW at Western Nile River areas depend very much on the local wind climate and the typical hour-to-hour load curve (electricity demand). Some important issues needed to be considered are therefore: how should wind farms be expected to behave and perform, and which requirements should be imposed in order to expect wind farms to support the system? Is it realistic to expect wind turbines and wind farms to behave as any other power plant? What is the Impact on thermal loading of lines/transformers? What are the Power Quality Aspects should be mitigated? What is the behaviour during grid faults/Fault ride through? Reactive power compensation and voltage control…..etc. Grid codes are set up to specify the relevant requirements which have to be met in order to integrate wind turbines into the grid. This paper will focus on The Egyptian approach to integrate wind power to the Egyptian Power System and the important issues related to system operation, stability, protection and the associated technical issues. Grid code requirements for large scale integration of wind power will also be illustrated.

Small-Signal Stability Analysis of Wind Power System Based on DFIG

This paper focuses on the small-signal stability of power system integrated with DFIG-based wind farm. The model of DFIG for small-signal stability analysis has built; the 3-generator 9-bus WECC test system is modified to investigate the impacts of large scale integration of wind power on power system small-signal stability. Different oscillatory modes are obtained with their eigenvalue, frequency and damping ratio, the results from eigenvalue analysis are presented to demonstrate the small-signal stability of power system is enhanced with the increasing output of the wind farm.

Large scale integration of wind power: moderating thermal power plant cycling

AbstractPower plant cycling in thermal plants typically implies high costs and emissions. It is, therefore, important to find ways to reduce the influence of variations in wind power generation on these plants without forsaking large amounts of wind power. Using a unit commitment model, this work investigates the possibility to reduce variations by means of a moderator, such as a storage unit or import/export capacity. The relation between the reduction in CO2‐emissions and the power rating of the moderator is investigated, as well as the benefit of a moderator which handles weekly variations compared with a moderator which has to be balanced on a daily basis.It is found that a daily balanced moderator yields a decrease in emissions of about 2% at 20% wind power grid penetration. The reduction in emissions is mainly due to an avoidance of start‐up and part load emissions and a moderator of modest power rating is sufficient to achieve most of this decrease. In the case of a weekly balanced moderator, emissions are reduced as the moderator power rating increases. At 40% wind power grid penetration, a weekly balanced moderator reduces emissions with up to 11%. The major part of this reduction is due to the avoidance of wind power curtailment.The simulated benefit (CO2‐emissions and costs) from adding a general moderator is compared with emissions from Life Cycle Assessment (LCA) studies and cost data of five available moderator technologies; transmission capacity, pumped hydro power, compressed air energy storage, flow batteries and sodium sulphur batteries. Copyright © 2010 John Wiley & Sons, Ltd.

SAR-Satellite for Offshore and Coastal Wind Resource Analysis, with Examples from St. Lawrence Gulf, Canada

This paper illustrates the benefits of using remote sensing methodology as an intermediate step to assess offshore and coastal wind resources. Results are based on an ongoing research to understand wind patterns in the St-Lawrence Gulf. This area combines two advantages for wind power development in Canada: a) very good wind, b) high potential of the large scale integration of wind power with the hydro-wind concept. Advantages and drawbacks of satellite techniques in such a complex environment are reviewed. Our approach of satellite data selection for dominant wind conditions reduces the weakness of Synthetic Aperture Radar (SAR) satellite temporal resolution. Wind fields are extracted from sixteen scenes provided by RADARSAT-1. Results are compared with two main sources: in situ measurements and QuickSCAT scatterometer computations. Among interesting findings, it appears that a relative small sample of scenes can already indicates the best wind sites to be investigated for further analysis. The proposed approach to obtain a global wind map of the gulf and the advantages of such high-resolution wind maps to wind resource assessment are discussed.