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Renewable energy sources are likely to play a major role in meeting the future energy requirement of a developing country like India. Among the various renewable energy sources, the bio‐energy plays a key role for the power generation. In this paper, an attempt is made to develop a fuzzy based linear programming optimal electricity allocation model (OEAM) that minimizes the cost and determines the optimum allocation of different energy sources for the centralized and decentralized power generation in India with special emphasis to bio‐energy.

The OEAM model optimizes and selects the appropriate energy options for the power generation on the factors such as cost, potential, demand, efficiency, emission and carbon tax. The objective function of the model is minimizing the cost of power generation. The other factors are used as constraints in the model. The fuzzy linear programming optimization approach is used in the model.

The extents of energy sources distribution for the power generation in the year 2020 would be 15,800 GWh (4 per cent) from the coal based plants, 85,400 GWh (20 per cent) from the nuclear plants, 191,100 GWh (44 per cent) from the hydro plants, 22,400 GWh (5 per cent) from the wind mills, 45,520 GWh (11 per cent) from the biomass gasifier plants, 14,112 GWh (3 per cent) from the biogas plants, 8,400 GWh (2 per cent) from the solid waste, 33,600 GWh (8 per cent) from the cogeneration plants and 11,970 GWh (3 per cent) from the mini hydel plants, respectively.

The OEAM has been developed for the electricity demand allocation for the year 2020. An extensive literature survey revealed that carbon tax and emission constraints were never used in the previous models and they are considered in the present model.

The clean development mechanism (CDM) provides industrialized countries with an incentive to invest in emission reduction projects in developing countries to achieve a reduction in CO₂ emissions at lowest cost that also promotes sustainable development in the host country. Windmill pumps could be of interest under the CDM because they directly displace greenhouse gas emissions while contributing to sustainable rural development. This study aims to assess the maximum theoretical as well as the realistically achievable CDM potential of windmill pumps in India.

To estimate the CDM potential of windmill pumps in case of electricity replacement (by the substitution of electric motor pumps) regional baseline(s) has been used whereas in case of diesel replacement (by the substitution of diesel engine pumps) the IPCC emission factor(s) has been used. The authors have analyzed the financial feasibility of five designs of windmill pumps at ten select locations in India to justify the financial additionality of windmill pump‐based CDM projects. To analyze the future dissemination levels of windmill pumps logistic growth model has been used.

The annual CO₂ emissions mitigation potential through windmill pumps in India could theoretically reach five million tones. Under more realistic assumptions about diffusion of windmill pumps based on past experiences with the government‐run programmes, annual CER volumes by 2012 could reach 13,000‐46,000 and 0.07‐0.3 million by 2020. This would require that the government sets the subsidy level for windmill pumps at a level that allows them to become viable with the CER revenue. From a macro‐economic point of view, this makes sense if the sustainability benefits are deemed sufficiently high to warrant promotion of this project type.

The paper illustrates the vast theoretical potential of CO₂ mitigation by using windmill pumps for irrigation water pumping in India.

This paper aims to present a forecast for renewable energy production in the USA. Growth curves are used to conduct the forecasts.

The analysis is based upon a literature review, supplemented by collection of secondary data. The study then focuses on applying the Pearl growth curve.

The authors' results show that biomass energy production is growing the fastest followed by geothermal and wind. Additionally, the forecast for solar energy production shows little to no growth over the next two decades.

If the US government hopes to achieve its goals in renewable energy, considerable funding and incentives will have to be put forth to accelerate the growth of renewable energy. Since the biomass technology is already growing nicely it makes sense to put the additional resources behind the other three technologies to close the 10.3 percent gap being forecasted. The government also needs to put more funding into dual renewable plants such as wind or solar combines with pumped hydro, this will ensure environmental and reliability are both maintained. Finally, for renewable energies to be competitive in the long term, considerable research needs to go into driving down the cost so there is not a need for subsidies.

This study provides value in providing a forecast for expected future growth for renewable energy sources.

The purpose of this paper is to address the question of how a developing country, like Mexico, can reform its electricity industry at the same time as addressing climate change issues.

The objective is to provide a tool that policy makers could use to make better and more informed decisions if they decide to liberalise the power sector in Mexico. The problems they would face are difficult to address in an analytically tractable way using conventional economic models. Also, these problems are too idiosyncratic to solve by translating empirical experience from other markets. In response, a system dynamics model has been developed in order to test the impact of a range of different environmental and energy policies.

The paper finds that a reform setting where competition is allowed in fossil fuel technologies; while the public company (CFE) keeps control of hydro and nuclear capacity produce the most well rounded scores in terms of efficiency, CO2 emission reductions and political feasibility. The results could improve the policy implementation process by shedding light on the circumstances and policy choices that can exacerbate or minimise effects.

A simulation of this kind would increase the understanding of these kinds of policies by providing structured insights into key uncertainties. They can also foster new ideas – in the energy sector have underpinned major policy initiatives. But results should be taken with caution, as complete validation of models is impossible.

These results add to the policy implementation literature. For the case of Mexico that has not engaged in a specific model of deregulation, the use of simulation model would be very useful to predict flaws in the design of the new regulation and to prevent unwanted scenarios to happen.

A judicious implementation of an electricity reform can help achieve lower carbon emissions reductions which would be beneficial against the climate change problem.

To the author's knowledge, the approach of analysing ex ante the environmental outcome of electricity reform using a simulation model in a developing country has not been treated in literature.

This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA.

The study used quarterly data that span over the period of 1980Q1-2015Q4 to develop and validate the models. Eight input parameters were used for modeling the demand for energy. Hyperparameter optimization was performed to determine the ideal parameters for configuring each country’s model. To ensure stable forecasts, a repeated evaluation approach was used. After several iterations, the optimal models for each country were selected based on predefined criteria. A multi-layer perceptron with a back-propagation algorithm was used for building each model.

The results suggest that the validated models have developed high generalizing capabilities with insignificant forecasting deviations. The model for Australia, China, France, India and the USA attained high coefficients of determination of 0.981, 0.9837, 0.9425, 0.9137 and 0.9756, respectively. The results from the partial rank correlation coefficient further reveal that economic growth has the highest sensitivity weight on energy demand in Australia, France and the USA while industrialization has the highest sensitivity weight on energy demand in China. Trade openness has the highest sensitivity weight on energy demand in India.

This study incorporates other variables such as financial development, foreign direct investment, trade openness, industrialization and urbanization, which are found to have an important effect on energy demand in the model to prevent underestimation of the actual energy demand. Sensitivity analysis is conducted to determine the most influential variables. The study further deploys the models for hands-on predictions of energy demand.

Sustainability is an accepted measure of business performance, with reductions in energy demand a commonly practised sustainability initiative by multinational corporations (MNCs). Traditional energy models have limited scope when applied to the entire MNC as the models exhibit high data and time intensity, high technical proficiency, specificity of application and omission of non-manufacturing activities. The purpose of this paper is to propose a process centric energy model (PCEM), which adopts a novel approach of applying business processes for business energy assessment and optimisation. Business processes are a fundamental requirement of MNCs across all sectors. The defining features of the proposed model are genericity, reproducibility, minimum user input data, reduced modelling time and energy evaluation of non-manufacturing activities. The approach forwards the adoption of Industry 4.0, a subset of which focuses on business process automation or part thereof.

A quantitative approach is applied in development of the PCEM. The methodology is demonstrated by application to the procure to pay and electroplating business processes.

The PCEM quantifies and optimises the business energy demand and associated carbon dioxide emissions of the procure to pay and electroplating business processes, validating the application of business processes. The application demonstrates minimum user inputs as only equipment operational parameters are required and minimum modelling time as business process models and optimisation options are pre-defined requiring only user modification. As MNCs have common business processes across multiple sites, once a business process energy demand is quantified, its inputs are applied as the default in the proceeding sites, only requiring updating. The model has no specialist skills requirement enabling business wide use and eliminating costs associated with training and expert’s services. The business processes applied in the evaluation are developed by the researchers and are not as comprehensive as those in actual MNCs, but is sufficiently detailed to accurately calculate an MNC energy demand. The model databases are not exhaustive of all resources found in MNCs.

This paper provides a new approach to MNC business energy assessment and optimisation. The model can be applied to MNEs across all sectors. The model allows the integration of manufacturing and non-manufacturing activities, as it occurs in practice, providing holistic business energy assessment and optimisation. The model analyses the impacts of the adoption of Industry 4.0 technologies on business energy demand, CO₂ emission and personnel hours.

The purpose of this paper is to explore whether the private sector consider voluntary involvement in efforts to combat the impacts of climate change in the lines mitigation approaches and adaptation approaches. Today’s world has increasingly become aware of the adverse effects of climate change and its impact on the poor, though the latter impact is not that well known. To address these impacts, recommendations exist that follow two different though interrelated approaches – mitigation and adaptation.

Using a survey questionnaire as the research instrument and a sampling frame of 350 junior corporate executives, an empirical study was conducted in the Chennai area in southern part of India to evaluate/measure the linkages between awareness to climate change, its impact on the poor and the willingness of private sector to act on adaptation as well as mitigation strategies.

From the data analysis, it emerges that there is significant awareness about the impacts of climate change, though the awareness to vulnerability of the poor is not yet significant in Chennai area in the private sector. However, the study concludes that there does exist a significant linkage between awareness and the willingness to support adaptation strategies on the part of junior corporate executives.

The study is country specific because the research was carried out in a defined region in India.

Because the study brought out the result that private sector was willing to participate in adaptation strategies, extensive awareness building can be carried out for corporate executives and plan out activities which will enable them to participate in adaptation strategies which would help the poor in India to help address the devastations caused by Climate Change from time to time.

Executives taking up the Climate Change adaptation strategy would help protect and benefit all communities especially the poor in the country. Companies operating in India would find an avenue to reach out in their efforts to touch communities around them. Employees in such companies may be organized and gathered together to participate in such reach-out activities on the part of the companies.

This paper fulfils urgent need to inspire the corporate executives to take up initiatives related to climate change. The paper lays the groundwork on which an array of corporate activities can be developed to implement the adaptation strategies. Further extensive thinking can follow this research as to where and how exactly private sector can help.

This study aims to investigate the unsteady magnetohydrodynamic mixed convective nanofluid flow by using Buongiorno two-phase model to achieve an appropriate mechanism to improve the efficiency of solar energy systems by mitigating the energy losses.

The transport phenomena occurring in this physical problem are modelled using nonlinear partial differential equations and are non-dimensionalised by using non-similar transformations. The quasilinearisation technique is used to solve the resulting system with the help of a finite difference scheme.

The study reveals that the effect of the applied transverse magnetic parameter is to increase the temperature profile and to reduce the wall heat transfer rate. The Brownian diffusion and thermophoresis parameters that characterise the nanofluids contribute to the reduction in wall heat transfer rate. The presence of nanoparticles in the fluid gives rise to critical values for the thermophoresis parameter describing the behaviour of the wall heat and mass transfer rates. Wall heating and cooling are analysed by considering the percentage increase or percentage decrease in the heat and mass transfer rates in the presence of nanoparticles in the fluid.

The investigation on wall cooling/heating leads to the analysis of control parameters applicable to the industrial design of thermal systems for energy storage, energy harvesting and cooling applications.

The analysis of the control parameters is of practical value to the solar industry.

In countries, such as South Africa, daily power cuts are a reality. Any research into improving the quality of energy obtained from alternate sources is a national necessity.

From the literature survey in the present study, it is found that no similar work has been reported in the open literature that analyses the time-dependent mixed convection flow along the exponentially stretching surface in the presence of the effects of a magnetic field, nanoparticles and non-similar solutions.

This paper aims to present an analytical investigation of energy and exergy performance on a solar flat plate collector (SFPC) with Cu-CuO/water hybrid nanofluid, Cu/water and CuO/water nanofluids as collector running fluids.

Heat transfer characteristics, pressure drop and energy and exergy efficiencies of SFPC working on these nanofluids are investigated and compared. In this study, a comparison is made by varying the mass flow rates and nanoparticle volume concentration. Thermophysical properties of hybrid nanofluids are estimated using distinctive correlations available in the open literature. Then, the influence of these properties on energy and exergy efficiencies of SFPC is discussed in detail.

Energy analysis reveals that by introducing the hybrid nanoparticles in water, the thermal conductivity of the working fluid is enhanced by 17.52 per cent and that of the individual constituents is enhanced by 15.72 and 15.35 per cent for Cu/water and CuO/water nanofluids, respectively. This resulted in 2.16 per cent improvement in useful heat gain for hybrid nanofluid and 1.03 and 0.91 per cent improvement in heat gain for Cu/water and CuO/water nanofluids, respectively. In line with the above, the collector efficiency increased by 2.175 per cent for the hybrid nanofluid and 0.93 and 1.05 per cent enhancement for Cu/water and CuO/water nanofluids, respectively. Exergy analysis elucidates that by using the hybrid nanofluid, exergy efficiency is increased by 2.59 per cent, whereas it is 2.32 and 2.18 per cent enhancement for Cu/water and CuO/water nanofluids, respectively. Entropy generation is reduced by 3.31, 2.35 and 2.96 per cent for Cu-CuO/water, Cu/water and CuO/water nanofluids, respectively, as compared to water.

However, this is associated with a penalty of increment in pressure drop of 2.92, 3.09 and 2.74 per cent for Cu-CuO/water, Cu/water and CuO/water nanofluids, respectively, compared with water.

It is clear from the analysis that Cu-CuO/water hybrid nanofluids possess notable increment in both energy and exergy efficiencies to use them in SFPCs.

Conditions monitoring system (CMS) is a tool for describing the present condition of the components of a system. To achieve this objective, there is a need to develop an efficient fault prediction algorithm. This paper seeks to address this issue.

The paper analyses four real wind farms with control charts of indices derived from UNE EN15341:2008 standard indicators, as the main CMS algorithm to define which index must be considered for improving wind farm maintenance and related costs.

The findings show that climatic conditions are related to maintenance cost indices. Employing the statistical control process of various wind energy converter (WEC) indices proposed by wind farm operators is an adequate procedure to monitor and control wind farm performance. In particular, only the maintenance cost index and the hourly maintenance cost index presented a clear relationship with respect to weather conditions.

Climatic conditions must form the basis for organising maintenance activities. Despite this, future maintenance models must be centred on indices obtained from experience, like the maintenance cost index and hourly maintenance cost index, and not solely in general indicators defined by standards.

A practical case study of wind farm maintenance based in the new UNE EN15341:2008 standard and wind farm operators experience is shown, defining real indices to be employed in future maintenance procedures.