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.     

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.     

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.     

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.     

A stochastic approach for the operation of a wind and pumped storage plant under a deregulated environment

To improve the competiveness in the energy market, it is necessary that the wind power plants provide guaranteed power generation, although, it is not possible to forecast power availability from wind power plant accurately. This paper presents a stochastic model and solution technique for the combined operation of wind and pumped storage power plants to improve the power availability and increasing the profit considering uncertainties of wind power generation. In this model, uncertainties in wind data have been forecasted for grid connected day-ahead market using Weibull distribution model. The imbalances in the forecasted wind data and the market demand have been reduced by operating the pumped storage power plant. In this stochastic mixed integer problem, pumped storage plant can take the supply either from the grid or from the wind power plant for the pumping operation to store the energy in order to utilize this energy during peak hours for increasing the overall revenue. The reliability of the pumped storage is improved by replacing the conventional unit with the adjustable speed type pumped storage unit. In order to prove the optimality of the solution, two case studies were considered. In case study–I, scheduling is provided by operating the conventional pumped storage unit, whereas in case study–II, adjustable speed pumped storage unit has been used. It has been found that the adjustable speed pumped storage unit has further reduced the imbalance between generated power and demand. The complete approach has been formulated and implemented using AMPL software.     

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 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.     

Best location of the small wind turbine around a single high-rise building

This research is a three-dimensional investigation about the aerodynamic interaction between the wind flow and a single high-rise building. In order to find location(s) with high potential of velocity around the building, a wide variety of wind speeds ranging from 2 to 10 m/s is studied. On the other hand, a high-rise building with the ratio of height to width of H/W = 3 is considered. Computations are performed numerically by means of the finite volume approach. Several results are obtained in the present numerical study. For example, it is found that due to wind-structure vertical interaction, locations with enhanced velocities are developed on the building roof in which the rate of this enhancement increases with increasing the wind speed. In addition, over the building, “lines C and D” are realized as the best locations having high power potentials and low turbulence intensities. In addition, lateral wind-structure interaction revealed that for all wind speeds, location of L/W = 0.5 is the best for the small wind turbine installation.     

Discourses of on-farm wind energy generation in the UK farming press

As the owners of the majority of land in the U.K., farmers are well placed to contribute to renewable energy targets. Media coverage can both drive and reflect farmers’ views about renewable energy but has been largely unexplored to date. This article uses discourse analysis to examine the evolution of coverage of one form of renewable energy – on-farm wind – in the U.K. farming press from 1980 to 2013. We identified a diverse debate with five major discourses. On-farm wind turbines are alternatively represented as: profitable farm diversification opportunities; producers of clean energy; important for rural development and sources of conflict. Although press coverage predominantly encourages wind energy production, a further discourse advises farmers to ‘Proceed with Caution’. While emphasising images and values which have widespread affinity among U.K. farmers, the press have increasingly employed an economic frame, constructing wind energy generation as a farm diversification strategy. The most recent farming press coverage predominantly encourages an instrumental approach to wind energy, crowding out other (non-economic) rationales and marginalising local community concerns. This appears to reflect the financial orientation of recent policy support (particularly Feed-in Tariffs), and may have long-term costs in enabling sustainable energy production systems.     

Location wise comparison of mixture distributions for assessment of wind power potential: A parametric study

Scientific literature discussed various types of mixture models and models derived from maximum entropy principle using short-term wind speed data for their relative assessment. The literature on suitability of these mixture models for long-term data is rarely available. However, for correct assessment of wind power potential both wind speed and wind direction are equally important. Therefore, in this paper, both wind speed and wind direction are simultaneously analyzed using several types of mixture distribution and compared the same with conventional Weibull distribution. For wind speed and wind power density assessment, the mixture distributions such as Weibull--Weibull distribution, Gamma--Weibull distribution, Truncated Normal--Weibull distribution, Truncated Normal--Normal distribution, proposed Truncated Normal--Gamma distribution and Gamma--Gamma distribution along with MEP-distribution are compared with conventional 2-parameter Weibull distribution. Similarly, for wind direction analysis, the finite mixtures of von-Mises distribution are compared with conventional von-Mises distribution. Judgment criteria include R², RMSE, Kolmogorov--Smirnov test and relative percentage error in wind power density. The sites selected are the three onshore locations of India, viz., Calcutta, Trivandrum, and Ahmedabad. The results show that for wind speed assessment, mixture distribution performs better than the conventional Weibull distribution for analyzing wind power density. However, location wise comparison of all mixture distribution is of prime importance. For wind direction analysis, finite mixture of two von-Mises distributions proved to be a suitable candidate for Indian climatology.     

Development of new monthly global and diffuse solar irradiation estimation methodologies and comparisons

In assessing and deciding the prediction schemes of solar irradiation countrywide, better the accuracy means better the management of energy transition toward renewables. Consequently, the present study is on the development of new models to make the most accurate possible estimations of the global and diffuse solar irradiation based on ground measurements. Such analysis produces the most accurate estimations for the input of solar energy systems. This is utmost significant for deciding the investments on solar energy systems and their design periods. Turkey is a high-potent country whose solar energy market has been growing rapidly. She doesn’t have adequate reliable measurement network and there is no estimation methodology developed for each and every point within its territory. Moreover, installing such a measurement system network doesn’t seem to be economically feasible and technically possible, inter alia. Accordingly, this study defines a methodology to make the most accurate estimations of monthly mean daily solar irradiation on horizontal surface and its diffuse and beam components. For the global and diffuse estimations, new methodologies in linear and quadratic forms are developed, compared, and discussed. The comparison is applied by using mean bias error and root mean square error statistical comparison methods. The measured data values used for modeling and comparisons are provided from the State Meteorological Service of Turkey responsible authority for solar irradiation measurements. The results revealed that the methodologies explained in this study give very high accurate values of total solar irradiation on a horizontal surface and its diffuse component.     

Research on Coupling Mechanism of China’s Wind Power Industry Chain

ABSTRACT

Firstly, on the basis of literature research, sort out and summarize the critical coupling relationship among the upstream, middle, and downstream enterprises in the wind power industry chain. Secondly, the evaluation index system of coupling coordination degree of China’s wind power industry chain was established. Based on entropy weight method and subsystem efficiency function, the capacity coupling (CC) coefficient model of wind power industry chain subsystem was established. The coupling coordination degree between the upstream subsystem and the midstream subsystem of the wind power industry chain, and between the midstream subsystem and the downstream subsystem is dynamically evaluated, and the coupling coordination degree evaluation model of the wind power industry chain in China is proposed. Thirdly, according to the relevant statistical data of China from 2010 to 2017, this paper conducts an empirical study on the coupling of the upstream, middle and downstream subsystems of the wind power industry chain. Finally, based on the collaborative coupling study of China’s wind power industry chain, this paper analyzes the key factors influencing the collaborative development of wind power industry chain, and puts forward Suggestions on the optimization of the collaborative development of China’s wind power industry chain.     

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.     

Wind energy potential analysis for Thailand: Uncertainty from wind maps and sensitivity to turbine technology

Rapid development of wind energy has been witnessed in Thailand. However, different wind resource maps (over land) have brought great uncertainty to wind energy planning. Here, four important mesoscale wind maps were considered: DEDP (2001), World Bank (2001), Manomaiphiboon et al. (2010) of JGSEE, and DEDE (2010). The wind maps were first harmonized to a common grid at 100 m and then compared. The earlier wind maps (DEDP and World Bank) are shown to represent the lower and upper limits of predicted speed, respectively, while JGSEE and DEDE tend to be more moderate with predictions statistically closer to observations. A consolidated wind map was constructed based on their median and shown to have the best prediction performance. It was then used for the technical potential analysis, in which three large (2-MW) turbine models (two conventional and one designed for low wind speed) were considered. By GIS techniques, any land areas not feasible for large wind turbines were excluded, and the corresponding overall onshore technical potential ranges between 50 and 250 GW, depending on map and turbine model. Considering only economically feasible turbines (with capacity factors of 20%) and the median-based map, the final technical potential equals 17 GW when using the low-wind-speed model but is reduced to 5 GW with the conventional models, adequately meeting the national wind energy target of 3 GW by the year 2036. The results suggest a strong sensitivity of estimated technical potential to turbine technology and a suitability of low-wind-speed turbines for wind conditions in Thailand.     

Performance evaluation of a horizontal axis wind turbine in operation

The operation of modern horizontal axis wind turbine (HAWT) includes a number of important factors, such as wind power (P), power coefficient (C_P), axial flow induction factor (a), rotational speed (Ω), tip speed ratio (λ), and thrust force (T). The aerodynamic qualities of these aspects are evaluated and discussed in this study. For this aim, the measured data are obtained from the Sebenoba Wind Energy Power Plant (WEPP) that is located in the Sebenoba region in Hatay, Turkey, and a wind turbine with a capacity of 2 MW is selected for evaluation. According to the results obtained, the maximum turbine power output, maximum power coefficient, maximum axial flow induction factor, maximum thrust force, optimum rotational speed, probability density of optimum rotational speed, and optimum tip speed ratio are found to be 2 MW, 30%, 0.091, 140 kN, 16.11 rpm, 46.76%, and 7, respectively. This study has revealed that wind turbines must work under optimum conditions in order to extract as much energy as possible for approaching the ideal limit.     

浅谈新疆风电场水土保持措施配置——以新疆华电小草湖风电场二场一期工程为例

风能作为一种清洁无污染的可再生能源，已经被广泛运用到电力事业的发展中来。近年来，新疆的风力发电发展迅速，风电场建设过程中不可避免地会对土壤及植被造成一定的破坏，人为地加剧了水土流失。概述了风电项目水土流失特点，以新疆华电小草湖风电场工程为例，结合项目区具体特点，分区设置防治措施。通过工程措施、植物措施与临时措施的有机结合，有效地防止水土流失。     

Optimal power flow model incorporating wind,solar, and bundled solar-thermal power in the restructured Indian power system

A new optimal power flow model for wind, solar, and solar-thermal bundled power scheduling and dispatch is proposed, incorporating the deviation incentive/penalty charges for renewable energy introduced in India. The multiobjective function is solved using the flower pollination algorithm; the scheme is successfully tested on the IEEE 30-bus and Indian utility 30-bus systems. The forecasting error constraints introduced in renewable energy scheduling and dispatch are demonstrated to be beneficial in several aspects. Solar-thermal bundling is shown to create win-win situations for thermal and solar generators. The effectiveness of the flower pollination algorithm in solving optimal power flow models is proved.     

On the wind resource mapping of Burkina Faso

In this work, mesoscale wind resource maps, at 5-km resolution, of the country of Burkina Faso (274,200 km²) were developed using the Anemoscope and mesoscale compressible community models. Results show that the northeast region of Burkina Faso has a good wind regime at 80 m above ground level (agl), while the wind regime in other parts of the country is generally low, even at 80 m agl. In addition, the technical power potential and the potential annual energy production that can be generated from the wind in Burkina Faso are identified using analysis tools based on geographical information systems and economic constraints. Results from the technical power potential at 80 m agl show that a total of 312 MW of wind farms, generating annually a total of 741 GWh of energy, could be installed in Burkina Faso. On the other hand, a total of 4411 MW of small wind turbines (50 kW) could be installed over the territory, corresponding to an annual energy production of 7843 GWh. The Wind Atlas of Burkina Faso provides an opportunity for local stakeholders to consider wind energy for the electricity portfolio of the country.     

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.