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India is endowed with a lot of solar radiation as a result of its location. The Indian government therefore intends to maximize the usage of its solar energy resources through the development of solar power plants across the country. The concentrated solar power plant (CSP) is one of the technologies that rely on solar energy for its electricity generation. The type of condenser model in the CSP technology has the potential to affect its techno-economic viability. In this paper, a 100 MW solar tower power plant (STPP) with two different condenser models, i.e., the dry-cooled STPP and wet-cooled STPP models, are studied using the System Advisor Model (SAM) at six different geographical areas in India. The study employed the optimization of the thermal energy storage and the solar field size to identify the minimum levelized cost of electricity (LCOE) for all six locations. Results from the simulation show that the LCOE will range between 13 and 17 cents/kWh under the optimization conditions for the STPP dry-cooled condenser model, while that of the wet-cooled condenser model will range between 12.40 and 12.96 USD cents/kWh for the study locations. It was also observed that the optimized solar multiple (SM) for the dry-cooled STPP model ranges between 1.4 and 1.8, whereas that of the wet-cooled model ranges between 1 and 1.8. The study identified Bhopal as the best location for installing the STPP plant for both condenser models. In addition, this paper also discusses major potential barriers and government policies that are needed to develop CSP technologies in India. The outcome of the study is expected to help both government and other stakeholders in decision making and policy formulation for the sector. [ABSTRACT FROM AUTHOR]

Contrary to conventional fossil fuel-based electricity generation technologies, renewable energy centered technologies, specifically small hydropower, release a lesser amount of anthropogenic greenhouse gases but are normally more expensive. A major segment of the capital investment in the current small hydropower scenario accounts for equipment and construction process costs. The construction and cost administration process are generally limited to analysis of the capital cost of civil constructions, electro-mechanical equipment works, neglecting the costs related to operating and maintaining the plant, replacement or refurbishment, certified emission reductions, among others. Contemporary studies indicate that these costs form a substantial fraction of the total capital investment. Consequently, for cost management and investment decision making, small hydropower plant developers are drawing increased attention in recent years towards conducting life cycle costing studies that take into account the ignored costs. In addition, small hydropower plants in developing nations can become more competitive by trading the emission reductions achieved under the provision of the Clean Development Mechanism, an outcome of the Kyoto Protocol proposed at the United Nations Framework Convention on Climate Change. In this paper, a modest attempt has been made to determine the Levelized cost of electricity generation using life cycle costing methodology, which accounts for all the costs over operating lifetime on a range of small hydropower plants and the results are analyzed. [ABSTRACT FROM AUTHOR]

This work intends to perform technical and 2E (economic & environmental) analysis for the proposed hybrid energy generating system for a part load at SRM IST at the Delhi-NCR campus, India. The investigation has been done for electricity generation and hydrogen production through renewable energy sources, mainly solar energy. It is in line with the Indian Government's initiatives. The proposed hybrid system has to meet the electric load demand of 400 kWh/day with a peak load of 74.27 kW and hydrogen load demand of 10 kg/day with a peak demand of 1.86 kg/h. The analysis has been performed for both on-grid and off-grid conditions. As a result, optimum results have been obtained off-grid condition, with $0.408 per kWh cost of energy, $16.6 per kg cost of hydrogen, low O&M cost ($21,955 per year), a high renewable fraction (99.8%), and low greenhouse emissions (247 kg/year). In addition, sensitivity analysis has been performed between—(1) the solar PV array size & the number of battery strings, with NPC, renewable fraction & CO2 emissions as sensitivity variables, and (2) reformer capacity & hydrogen tank capacity, with NPC as sensitivity variable. [ABSTRACT FROM AUTHOR]

Agriculture residue is a promising resource of energy. It can be seen as a source of power production. In India, there is a huge amount of biomass available, but it cannot be used in proper ways, and with the help of GIS it can be customised. In the present paper, it is estimated that biomass reserves are available for power generation. The biomass produced by the surplus agricultural crops is reflected as a source of fuel for electricity generation. The data taken by satellite are useful for assessment of the areas with the help of satellite images taken in high resolution, which increases the preciseness of estimation. An agriculture cropland map with agricultural statistics has been analyzed in GIS to discover the agricultural straw potential for bioenergy generation. Due to unawareness about the benefits and uses of GIS, the modern farming sector bears a loss of huge bioenergy potential every year. To overcome the above mentioned challenges, the agricultural system needs a major shift from conventional farming to smart farming practices with the help of GIS. Agricultural waste is the best source for bioenergy production, and it can be used as biomass for meeting renewable energy goals in the country. [ABSTRACT FROM AUTHOR]

This study focuses on the life cycle assessment (LCA) of an onshore wind farm in India. The study is conducted on 10 Vestas 1.65 MW wind turbines situated in the Karnataka state of India. Following the ISO 14044 standard, SimaPro LCA software is used to model the process. The functional unit is chosen as 1 MWh sent out electricity. The results of the life cycle-based emissions of wind farm are compared with those of the coal power plant. The global warming potential is found to be 11.3 g CO2-eq/MWh for wind power, which is 98.8% lower than that for the coal power plant. A comparison of data available in SimaPro LCA software was carried out with data in GaBi software. There is a small difference between the two databases. This may be due to different boundary and inclusion of input items. Steel, aluminium, and concrete contributed 86%, 84%, 84% and 85% of total CO2, NOx, SO2 and PM2.5 emissions, respectively. Recycling the materials of a wind turbine at the end of its life can reduce the environmental impact. Higher capacity factors can increase the electricity generation with reduced environmental impact. A 22% increase in capacity factor can reduce environmental impact by 19%. In addition, the increase in the life of wind turbines reduces the environmental impact, as a wind turbine only has a few moving parts and requires minimum regular maintenance. [ABSTRACT FROM AUTHOR]

With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India's power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022). [ABSTRACT FROM AUTHOR]