• Energy Research
• 2021-2025close
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• 12. Responsible consumption close
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Abstract The increasing energy demand in developing countries has jeopardised energy security, necessitating the employment of solar energy to augment conventional energy sources. It is important to assess the annual performance parameters of solar power plants to understand its place in energy generation. This study analyses the performance and economic viability of a large-scale solar power park located in India. A 50 MWp Solar Photovoltaic Power Park (SPPP) located at Sakunala, in the State of Andhra Pradesh, is one of the largest solar power park in India, and the site receives an average solar radiation of 5.5–6.0 kWh/m2/day. The design, performance analysis, economic feasibility, and greenhouse gas mitigation of the 50 MWp SPPP is presented. The energy yields, performance ratio (PR), capacity utilization factor (CUF), and losses are assessed based on the IEC 61724 standards for two consecutive years (2018–2019 and 2019–2020). The performance results obtained are compared with the PVsyst simulation results. The PR, CUF, and energy yields are estimated as 0.779, 0.24, and 107,326.4 MWh in 2018–2019 and 0.691, 0.22, and 96,707.336 MWh in the year 2019–2020, respectively. The PVsyst simulator evaluates the PR, CUF, and energy yields as 0.80, 0.24, and 106,022 MWh, respectively. Further, the effect of power curtailment policies on solar PV projects is also discussed and the effect of curtailment policy on the economy of the solar power park in terms of payback period and emissions are analysed. The performance of the SPPP is also compared with those of other PV plants installed all over the world.

Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

Purpose With the growing acceptance of renewable energy sources in the world, new energy sources are pursued and investigated by customers. In India, residential PV technology is in an early stage of development and very less in demand among households. Therefore, the purpose of this study is to identify the major variables and its impact on customers purchase intention towards solar PV technology, especially in the context of central India. Design/methodology/approach Based on literature reviews, this study identifies six major independent variables having a relationship with purchase intentions. Using convenience sampling method, 413 customers’ data has been collected by the researcher and was investigated through structural equation modelling, using SmartPLS 2.0 and SPSS-20 software. Findings The study findings suggested that the construct promotional strategies, societal influence, customer awareness and government initiative plays an important role in generating customers purchase intention towards solar PV technology. While the constructs environmental concern and availability & cost having relationship, showed insignificant influence. Research limitations/implications The study outcomes provide some valuable insights to the government and policymakers in designing their policies and strategies to increase customer involvement in solar PV technology. This study suggested that the service providers need to offer more benefits in the form of subsidies and schemes that motivate customers to willingly show their purchase intention. Originality/value The major contribution of this study is the empirical analysis of six independent variables, which affects the customer purchase intentions towards solar PV technology over available conventional energy sources in an emerging Indian market.

Abstract Recently, many studies have exploited the potential of deep learning to forecast energy demand, but they cannot explain the results. They only analyze the simple correlations between the input and output to discover the most important input features, or they depend on the manual investigation of the latent space embedded with power demand patterns. In this paper, to overcome these shortcomings, we propose a deep autoencoder that can explain the prediction results by manipulating the latent space. It consists of 1) a power encoder that embeds power information, 2) an auxiliary encoder that embeds auxiliary information for an interpretable latent space in two dimensions, 3) a predictor that predicts power demand by using concatenated values of the latent variables extracted from the two encoders, and 4) an explainer that provides the most important input features in predicting the future demand by utilizing the interpretable latent variables. Several experiments on a dataset of household electric energy demand show that the proposed model not only performs better than conventional models, with a mean squared error of 0.376 in predicting electricity demand for 60 min, but also provides the capacity to explain the results by analyzing the correlation of inputs, latent variables, and energy demand predicted.

New powertrain technologies, such as those used in Hybrid Electric Vehicles, have a price premium which is often offset by lower running costs. Total Cost of Ownership (TCO) combines these purchase and operating expenses to identify the most economical vehicles overall. This is a valuable assessment for both private and fleet purchasers. To date, no studies have compared Total Cost of Ownership across more than two vehicle markets or analysed historic costs. To address this gap, this research provides a more extensive assessment of TCO in three industrialized countries — the U.S.A. (California specifically), the UK, and Republic of Korea — for the time period from 2015 ∼ 2019. As an early analysis of the regional characteristics of TCO, this study aims to examine how TCO varies across different geographic regions and develop an analysis methodology for future analysis of environmental vehicles. To analyze the TCO under identical conditions for each region, the TCO result is calculated using data from conventional vehicles by utilizing sufficient statistical data. The TCO model includes registration and road taxes as well as insurance, fuel, financial interest, depreciation, and maintenance costs. To represent the local features, the research considers four vehicles from different manufacturers. Using the proposed TCO model, the regional characteristic is analyzed and a sensitivity analysis is performed with the parameters constituting the TCO model. This research has implications for both fleet purchasers and private owners considering new vehicles. The findings are also relevant for carmakers aiming to develop vehicle sales strategies optimized for specific regions.

This paper presents new results on the gasification of spent pot lining (SPL) waste material generated in the primary aluminium smelting industry. The main objective is to test the performance of the gasification process of treated SPL materials and to develop an optimization method to maximize the quality of syngas fuel. The novelty of this study is the development of statistical models to predict the syngas composition and the gasification performance indicators during the SPL waste materials thermal conversion process. Modelling and simulation analysis are performed to convert the SPL solid materials to syngas fuel. The percentage of hydrogen (H2) and carbon monoxide (CO) in the syngas fuel, the cold gasification efficiency (CGE) and the carbon conversion (CC) are determined. The response surface methodology (RSM) is used for the optimization of the performance of the gasification process. The effects of the input factors such as the temperature, the equivalence ratio and the steam to fuel ratio on the output variables (H2 and CO in the syngas, the CGE and the CC) are determined. The optimization results show that the optimized operating parameters to maximize the H2, CO, CGE and CC were T = 1200 °C, ER = 0.1 and SFR = 1.29, respectively. The optimum values for the H2, CO, CGE and CC were 37.2%, 22.2%, 79.75% and 97.7%, respectively. New correlations for the variation of the output variables versus the input factors are also presented.

A common narrative is that growth based on fossil-fuel-based energy source is not as ecologically benign as non-renewable energy. Green energy promotes economic growth in a more environmentally friendly manner. This paper examines the relationship between economic growth and electricity production by public and private power plants in Asia. The development of more private power plants is expected to allow the energy sector to drive greater economic growth. Threshold estimation is used to analyze the relationships between the variables. We employ oil price as the threshold variable. Our sample covers eight selected Asia countries, grouped into ASEAN and non-ASEAN countries. We conclude that the public power plants are driving more economic growth when the oil price is high (oil price > USD 37.89). On the other hand, the private power plants are performing well in triggering economic growth when the oil price is low (oil price < USD 37.89). We recommend that the government improve the efficiency of the grid's transmission system that aims to reduce the transmission and distribution loss. We also recommend the government to offer various incentives to encourage the construction of more private power plants to increase the electricity supply.

DATA AVAILABILITY : Data will be made available on request. ; Please read abstract in the article. ; http://www.elsevier.com/locate/seta ; 2024-09-30 ; hj2023 ; Mechanical and Aeronautical Engineering

This study evaluated the effectiveness of shrimp shell biochar (SBC) and nickel (Ni) loaded SBC in increasing methane yield during anaerobic digestion of food waste. The results indicated that the methane yields of control (without SBC), SBC, SBC loaded with the low concentration of Ni, and SBC loaded with the high concentration of Ni were 81.8, 116.1, 134.7, and 99.2 mL/(g·VS), respectively. SBC promoted the efficiency and stability of the whole anaerobic digestion process including hydrolysis, volatile fatty acid conversion and methanogenesis. While the invigorating effect of loaded Ni at the low concentration of 0.88 mg/g was mainly concentrated in methanogenesis, the inhibition effect of the high Ni concentration was comprehensive. SBC helped Methanosarcina proliferation, and low concentration Ni promoted the number and activity of Methanosarcina and Methanosaeta. The results show that biochar loaded with a low level of trace elements such as Ni can promote the anaerobic digestion process.