A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

Nonlinear control of a wind turbine based on nonlinear estimation techniques for maximum power extraction

This work proposes nonlinear estimators with nonlinear controllers, for variable speed wind turbine (VSWT) considering that either the wind speed measurement is not available or not accurate. The main objective of this work is to maximize the energy capture from the wind and minimizes the transient load on the drive train. Controllers are designed to adjust the generated torque for maximum power output. Estimation of effective wind speed is required to achieve the above objectives. In this work the estimation of effective wind speed is done by using the Modified Newton Rapshon (MNR), Neural Network (NN) trained by different training algorithms and nonlinear time series based estimation. Initially the control strategies applied was the classical ATF (Aerodynamic torque feed forward) and ISC (Indirect speed control), however due their weak performance and unmodeled WT disturbances, nonlinear static and dynamic feedback linearization techniques with the above wind speed estimators are proposed.     

A GIS-based technique for linking landscape characteristics to non-point source nitrogen export potential: implications for contributing areas management

A Nitrate-N Leaching Index (NLI) is calculated and the results indicate that nitrogen loss in the study area occurs through both leaching and surface runoff. A non-linear regression model of trapping efficiency was combined with a first order decay model to examine the impact of soil characteristics, slope, vegetative cover, land use and distance to streams on the spatial pattern of non-point source nitrogen inputs to streams. The model evaluates the statistical significance of each landscape factor and provides an easy interpretation of the landscape delivery ratio of nitrogen based on a pixel-based characterisation of the watershed. The model was validated by comparing the distributions of the observed and estimated monthly nitrogen concentrations. The exploratory GIS-based method presented here can improve the understanding of the impact of landscape characteristics on nitrate-nitrogen contributing areas and therefore assist watershed management efforts.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

An economic and environmental assessment of carbon capture and storage (CCS) power plants: a case study for the City of Kiel

In the not too distant future several power plants throughout Europe will have to be replaced and the decision has to be made whether to build coal-fired power plants with carbon capture and storage (CCS). In a study for the city of Kiel in northern Germany only an 800 MW coal power plant reaches a required minimum for rentability. This study looks at an additional economic and environmental evaluation of a coal plant with CCS. We find that in two out of three carbon and energy price scenarios integrated gasification combined cycle (IGCC) plants with CCS have the greatest rentability. Pulverised coal (PC) plants with CCS can only compete with other options under very favourable assumptions. Life-cycle emissions from CCS are less than 70% of a coal plant – compared with at least more than 80% when only considering direct emissions from plants. However, life-cycle emissions are lower than in any other assessed option.     

A well to wire LCA model development and sensitivity analysis for carbon footprint of combined cycle power plants in Iranian electricity network

The present research introduces a well to wire pseudo comprehensive carbon footprint model for combined cycle power plants. The mentioned model integrates land use change model, operational model and transmission and distribution model into one comprehensive model. The parameters which their effects are considered in the integrated model are: fuel type, fuel transmission type, emission for fuel extraction and processing, own consumption of the plant, degradation, site ambient condition, transmission and distribution losses. For quantifying the effectiveness of each parameter, sensitivity analyses based on different life cycle scenarios are performed. The result shows that the effect of land use change is negligible. The carbon footprint of electrical energy produced in combined cycle plant until it is delivered to the end users varies from 321 to 522 g CO₂ eq/kWh.