Global energy and emissions scenarios for effective climate change mitigation—Deterministic and stochastic scenarios with the TIAM model

The achievement possibilities of the EU 2 °C climate target have been assessed with the ETSAP TIAM global energy systems model. Cost-effective global and regional mitigation scenarios of carbon dioxide, methane, nitrous oxide and F-gases were calculated with alternative assumptions on emissions trading. In the mitigation scenarios, an 85% reduction in CO₂ emissions is needed from the baseline, and very significant changes in the energy system towards emission-free sources take place during this century. The largest new technology groups are carbon-capture and storage (CCS), nuclear power, wind power, advanced bioenergy technologies and energy efficiency measures. CCS technologies contributed a 5.5-Pg CO₂ annual emission reduction by 2050 and 12 Pg CO₂ reduction by 2100. Also large-scale forestation measures were found cost-efficient. Forestation measures reached their maximum impact of 7.7 Pg CO₂ annual emission reduction in 2080. The effects of uncertainties in the climate sensitivity have been analysed with stochastic scenarios.     

Availability estimation of a multi-state wind farm in fuzzy environment

Wind is one of the fastest growing renewable energy resources in the electric power system. Availability of wind energy is volatile in nature due to the stochastic behavior of wind speed and non-linear variation of the wind power curve of wind turbine generator. Because of this impression and uncertainty, the availability estimation of wind power has become a challenging issue. In this paper, Markov Fuzzy Reward technique has been proposed for finding out the reliability of wind farm by assessing the availability of wind power. According to this technique, availability of the wind power has been estimated considering wind farm and demand both as a multi-state system. In addition to the availability, different reliability indices such as the number of absolute failures, mean time to deficiency, and probability of failures of a wind farm have been assessed in a time horizon, which can provide useful information for the power system planner at wind farm installing stage. A comparison of this study reveals the efficacy of the proposed Markov Fuzzy Reward approach over the conventional Markov Reward approach.     

Game theory and hybrid genetic algorithm for energy management and real-time pricing in smart grid: the Tunisian case

ABSTRACT

Microgrids are the key for integrating renewable energy from different sources into smart grid, that is why power grid evolves into a combination of interconnected microgrids. In fact, future power grids are undergoing this groundbreaking change that will help meet the increasing demand of electric power and reduce carbon emission. In this sense we study in this paper, based on measured data, a real case of energy management in the area of Beja located in Tunisia. Indeed, we propose a model for the power exchange which proves the potential of applying game theory in the development of both real-time pricing and energy management mechanism for an open electricity market. We also introduce a hybrid genetic algorithm to compute the Nash Equilibrium. Results show that the proposed smart energy management can decrease the real cost of power up to 20%, to divide the energy transmission losses by a factor of two and to reduce the carbon emission in the area of Beja.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

Blade thickness effect on the aerodynamic performance of an asymmetric NACA six series blade vertical axis wind turbine in low wind speed

ABSTRACT

Vertical axis wind turbine (VAWT) is an economic and widely used energy converter for converting wind energy into useful form of energy, like mechanical and electrical energy. For efficient energy conversion in low wind speed and to have improved power coefficient of asymmetric blade VAWT, selection of optimum blade thickness is needed thus entailing its detailed investigation with respect to different operating wind speed conditions. Present study methodically explores the impact of thickness to chord (t/c) ratio on aerodynamic performance of a three bladed asymmetrical blade H-Darrieus VAWT at different low wind speed conditions by using 2D unsteady CFD simulations. The optimal t/c is obtained on the basis of maximum power coefficient and average moment coefficient of the turbine. The aerodynamic performance curves are obtained at different operating and t/c conditions and the performance insights are corroborated with the findings from the flow physics study to come to some concrete conclusions on the effects of the thickness to chord ratio. The present study identifies large blade curvature to create a large diverging passage on the blade suction surface as the prominent reason for aerodynamic performance drop at a high t/c ratio.     

An Analysis of the Wind Energy Potential of Elazig,Turkey

Abstract

In this study, the wind energy potential of Elazig is statistically analyzed based on hourly measured wind speed data over the five-year period from 1998 to 2002. The probability density distributions are derived from cumulative distribution functions. Two probability density functions are fitted to the measured probability distribution on a yearly basis. The wind energy potential of the location is studied based on the Weibull and Rayleigh distributions. It was found that the numerical values of both Weibull parameters (k and c) for Elazig vary over a wide range. The yearly values of k range from 1.653 to 1.878 with an average value of 1.819, while those of c are in the range of 2.757–2.994 m/s with an average value of 2.824 m/s. In addition, yearly mean wind speed and mean power density of Elazig is found as 2.79 m/s and 38.76 W/m², respectively. The wind speed distributions are represented by Weibull distribution and also by Rayleigh distribution, with a special case of the Weibull distribution for k = 2. As a result, the Rayleigh distribution is found to be suitable to represent the actual probability of wind speed data for Elazig.     

Wind power potential assessment for three buoys data collection stations in the Ionian Sea using Weibull distribution function

The present article utilizes wind measurements from three buoys data collection stations in Ionian Sea to study the wind speed and power characteristics using the Weibull shape and scale parameters. Specifically, the site dependent, annual, and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been analyzed. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy, in most of the cases. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites, as the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear. At all these sites, no definite increasing or decreasing trends in annual mean wind speed values could be detective over the data reporting period. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time in Pylos and Zakynthos. Moreover, Pylos and Zakynthos were found to be the best sites from wind power harnessing point of view.     

A coordinated control strategy using supercapacitor energy storage and series dynamic resistor for enhancement of fault ride-through of doubly fed induction generator

Power fluctuation and fault-related complication are the two major issues for doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). The occurrence of fault leads to the rotor over current, stator over current, and DC-link overvoltage as well. These uncertainties may damage the rotor circuit, converter circuit and force the disconnection of wind system from the grid. To get rid of these issues, a supercapacitor energy storage element along with a passive series dynamic resistor (SDR) is suggested in this paper. Supercapacitor energy storage system (SCESS) is located across the DC-link, which able to handle the power fluctuation and the SDR is placed in rotor circuit, which will reduce the overcurrent possibility. Simulation is carried for a DFIG-based WECS for three phase to ground fault and two phase to ground fault. During symmetrical fault as well as asymmetrical fault, various operational disorders appeared such as rotor overcurrent, stator overcurrent and DC-Link overvoltage are found to be within their permissible limits. The results reveal the effectiveness of the proposed strategy over the conventional vector control scheme and SCESS as well.     

A novel blade design for Savonius wind turbine based on polynomial bezier curves for aerodynamic performance enhancement

ABSTRACT

Advanced wind turbine designs and technologies have been evolved to take advantage of wind energy. Despite the significant progress already attained, the need for a dependable wind energy converter particularly devoted to small-scale applications remains a challenging issue. Due to its design simplicity, Savonius wind turbine is the most suitable candidate for such applications. It operates at low wind speed, with the necessary starting capacity and insensitivity to wind directions. Moreover, in the literature related to wind energy, the Savonius rotor is known for its low performance compared to other types of wind turbines. In this paper, we present a study into the utilization of Bézier curves and transient computational fluid dynamics (CFD) to optimize the conventional Savonius blade design. The k-ω SST turbulence model is employed to perform a series of CFD simulations in order to assess the power coefficient of each generated design. A validation of optimization results using the Taguchi method was carried out. The comparative analysis of the torque and power coefficients shows a significant increase in the power coefficient (C_p). The optimal C_p is 0.35 and is 29% higher than the conventional Savoniu wind turbine (SWT). Subsequently, the effectiveness of the innovative geometry is proved by improved pressure and velocity distributions around blades of novel design.     

Optimization of grid tied hybrid power system in smart premises

Tapping of renewable energy sources like solar and wind is given great priority by power producers all over the world. Technical problems of linking them to the grid are solved. The cost constraints of utilizing renewable energy at specific locations are to be determined. In this work, a model is developed for grid tied hybrid power system (HPS) consisting of photovoltaic (PV) module and wind mill at the roof top of smart premises. The grid is capable of delivering and receiving energy. Objective function is formed with constraints taking into account the cost of PV module, wind mill, and grid tied inverter with controller. The constraints are rating of HPS and energy that can be delivered to the grid. Using this model, case studies were conducted in three locations in India, each location having two different demands. The results are presented. With the optimal rating of HPS, results shows that, conventional energy cost is higher.     

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.     

A review on the potential of citrus waste for D-Limonene,pectin, and bioethanol production

Citrus peel waste is a valuable lignocellulosic feedstock for bioethanol production due to its richness in fermentable sugars and low lignin content. Citrus peel contains two major value-added products: d-limonene and pectin. d-Limonene is widely used in food, cosmetics, and pharmaceutical industries. However, it acts as a microbial growth inhibitor for yeast during the fermentation process and hence it has to be removed prior to fermentation. Pectin is used as thickening agent, gelling agent, and stabilizer in the food industry. Since pectin increases the viscosity of the fermentation medium and makes fermentation troublesome, it has to be either extracted or degraded into galacturonic acid using pectinase enzyme. Thus, the removal and recovery of both D-limonene and pectin from citrus peel are essential for better fermentation. For bioethanol production, pretreatment plays a crucial role in the utilization of citrus peels since the reduction of d-limonene concentration to less than 0.05% is necessary. This review solely describes the potential of citrus waste for value added products such as d-limonene and pectin and the production of bioethanol from citrus peel waste is discussed in detail.     

Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

A convective wind resource model for Solar Vortex power generation

ABSTRACT

Climate change has increased the need for clean, nonpolluting energy sources to decrease dependence on fossil fuels. Alternative energy sources, mainly solar and horizontal wind, have been the primary focus for producing clean energy. New technologies are being developed, such as the Solar Vortex (SoV), which was developed at the Georgia Institute of Technology, and relies on a vertical wind resource to generate power. The National Renewable Energy Lab (NREL) has resource models representing solar and horizontal wind resources across the 48 United States. This research developed a vertical wind resource model that is comparable in resolution to NREL’s solar and horizontal wind resource models and uses the model for estimating power output for the SoV. This model complements NREL’s existing resource models and supports the deployment of an additional clean energy generation technology. The model was applied to Mesa, Arizona to find feasible sites for a small-scale vertical wind farm.     

A Weibull and finite mixture of the von Mises distribution for wind analysis in Mersing,Malaysia

Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R² determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m², and SSW is the most common wind direction for this data.     

Hybrid input-output analysis to evaluate economic impacts of biomass energy

ABSTRACT

The introduction of the new biomass industry exerts influences that change the interactions among economic sectors by drawing the resources for generating electricity, pricing alterations from the existing market and competitively selling electricity to the power grid. These influences should be described and identified to ensure the benefits to the local economy. In this article, we deem to test the potential of hybrid I-O analysis to analyze the economic impacts and address the change in characteristics of the economic impacts of the biomass power plant. The resource utilization data from the existing biomass power plant located in Kochi Prefecture (Japan) is collected and then analyzed by a hybrid input-output (I-O) analysis. We found that (1) the use of cutoff function could determine the new economy’s structure included the biomass power plant where the allocation of resource in the economy is changed according to the consumption and production of the biomass power plant, (2) The power plant increases the total production of Kochi prefecture’s economy, and this benefit overthrows the negative effect of the loss of resource demand of the existing economic sectors. The use of the hybrid I-O to forecast the economic impacts on the local economy could enhance the decision made by the policymaker.     

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.     

Use of experience curves to estimate the future cost of power plants with CO2 capture

Given the dominance of power plant emissions of greenhouse gases, and the growing worldwide interest in CO₂ capture and storage (CCS) as a potential climate change mitigation option, the expected future cost of power plants with CO₂ capture is of significant interest. Reductions in the cost of technologies as a result of learning-by-doing, R&D investments and other factors have been observed over many decades. This study uses historical experience curves as the basis for estimating future cost trends for four types of electric power plants equipped with CO₂ capture systems: pulverized coal (PC) and natural gas combined cycle (NGCC) plants with post-combustion CO₂ capture; coal-based integrated gasification combined cycle (IGCC) plants with pre-combustion capture; and coal-fired oxyfuel combustion for new PC plants. We first assess the rates of cost reductions achieved by other energy and environmental process technologies in the past. Then, by analogy with leading capture plant designs, we estimate future cost reductions that might be achieved by power plants employing CO₂ capture. Effects of uncertainties in key parameters on projected cost reductions also are evaluated via sensitivity analysis.     

Performance enhancement of a stand-alone induction generator-based wind energy system using neural network controller

ABSTRACT

The limitation of self-excited induction generator (SEIG) when used in the stand-alone wind energy system (WES) is poor voltage regulation at variable speed. The indirect vector control (IVC) technique is employed for both the generator-side converter (GSC) and load-side converter (LSC) to regulate the variation of SEIG speed, DC link voltage, and electromagnetic torque independently. Further performance of the proposed IVC technique has been analyzed independently with neural network controller (NNC) and fuzzy logic controller (FLC) as its components. The FLC is replaced by an NNC to improve the performance of the proposed system. IVC of SEIG-based WES has been simulated in MATLAB/SIMULINK software, and the prototype model of the proposed WES is developed to experimentally validate the performance using dSPACE DS-1104 R&D controller board.     

Investigation of sustainable energy alternatives for powering remote communities in northern Ontario

ABSTRACT

Remote communities in the North of Ontario survive in isolation as their proximity to the southern industrial sector of the province limits their accessibility to the major grid. The lack of grid connection has led to antiquated methods of power generation which pollute the environment and deplete the planet of its natural resources. Aside from the primary means of electricity generation being by diesel generators, generation infrastructure is deteriorating due to age and the stagnation of the power supply has led to communities facing load restrictions. These challenges may be resolved by introducing clean energy alternatives and providing a fuel blend option. The primary energy sources investigated in this research are solar, wind, and hydrogen. To assess the viability of these energy production methods in Northern communities, an exergy analysis is employed as it utilizes both the first and second law of thermodynamics to determine systems’ efficiency and performance in the surroundings. Local weather patterns were used to determine the viability of using wind turbines, solar panels and/or hydrogen fuel cells in a remote community. Through analysis of the resources available at the community, it was determined that the hydrogen fuel cell was best suited to provide clean energy to the community. Wind resulted in low efficiency in the range of 2–3% while solar efficiencies resulted in ranges of 18 – 19%, as the seasonal variations between the three years is not very great. Due to the higher operating efficiencies observed of the PV panels it would also be an attractive alternative to diesel generators however, the lack of consistent operation above 30% efficiency throughout the year, resulted in hydrogen fuel cells being a better alternative.