• Energy Research
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Renewable energy has become more popular since it is cost-effective and more efficient than conventional energy sources. Biomass-based renewable energy is primarily used in emerging economies to ensure environmental sustainability. This study examines the asymmetric correlation between biomass energy consumption and CO2 emissions in the top-10 biomass energy consumer countries (Brazil, Canada, Thailand, China, Italy, India, Germany, USA, UK, and Japan). A new approach "Quantile-onQuantile (QQ)" is employed by utilizing the data from 1991 to 2018. Biomass energy consumption, with the exception of Thailand, significantly mitigates CO2 emissions at various quantiles in selected countries. As a robustness check, we used the quantile regression test, whose findings are consistent with the outcomes from the quantile-on-quantile method. However, the degree of asymmetry in the biomass energy-CO2 nexus varies by country, necessitating extra attention and government vigilance when developing biomass energy and environmental policies.

Abstract Hydrogen is considered a potential game changer for world energy systems and a solution to climate change concerns, as it generates zero waste and it is suited for power generation and transportation. Despite its several advantages, there are significant technical challenges in deploying a stable hydrogen economy including improving its process efficiencies, lowering production costs, maintaining cost-effective transmission and distribution, and exploiting inexpensive and sustainable feedstocks. In this context, a detailed study was conducted to analyze the production sources, technologies, storage and transport systems, and global potential exportable feedstocks to produce hydrogen. A comprehensive analysis of current hydrogen production technologies with their energy efficiencies and hydrogen selling prices was reported in this study. Various hydrogen production technologies with their capital investments and CO2 emissions were also presented. Potential feedstocks for hydrogen production were identified and analyzed through a product space model, which characterizes a network of global exportable products based on their similarities and productive knowledge. It was established that the hydrogen production feedstocks and sources currently used are primarily available in six countries: the United States of America, France, Russia, Sweden, the Netherlands, and Spain. Broadly, the results revealed that the United States of America and Russia shared the highest hydrogen feedstock exports, indicating a higher probability of hydrogen production in these countries. Except for Russia, all the studied countries fell in the most desired quadrant, indicating that they can move in all product space directions to exploit unexplored hydrogen feedstocks for better sustainable economic growth.

Abstract This paper presents a conceptual study of a solar PV integrated refrigeration system for a cold storage facility based on the conventional vapor compression system for banana fruit. In the first stage, the conventional system has been thoroughly analyzed. Mathematical modelling has shown that the condenser is oversized, while the evaporator was undersized. Furthermore, the air-conditioning compressor was also found to be oversized by approximately 12 kW after an evaluation at no-load and full-load conditions. In the second stage, the optimized system with reduced consumption was integrated to a solar PV field. The solar field sizing, and performance optimization of the proposed PV hybrid refrigeration system was accomplished in PV*SOL tool. The simulations demonstrated that with a 170 m2 solar field, an optimized PV hybrid refrigeration system can achieve 58.1% solar fraction at a performance ratio of 59.2%, under given climatic conditions. With net metering, the hybrid system shows a high economic feasibility with the payback period of 5.2 years. The comprehensive methodology and all-inclusive results are valuable to generate explicit recommendations for repowering along with renewable integration of cold storage facilities operating in the region.

Abstract Theft of electricity is a problem in many developing countries. But AMI is paving the way for data-centric architecture to help in theft detection. However, a smart grid or even AMR is a long shot for many developing countries due to the costs involved in its large-scale deployment. This paper presents a technique to detect outliers among electricity users that further investigates electricity theft using data analytics on monthly usage data available to every utility company. Using this technique, we have reduced the search space for theft identification to as low as 3.4% of the total customer base.

Adaptive transparent system (ATS) is an aspiring and effective building system that can adapt itself through environmental changes in spontaneous and reversible ways. It proposes an automatic and real-time response to indoor and outdoor conditions by increasing building energy efficiency and user comfort. This research presents a critical analysis to provide a preference order of the smart materials being used in the adaptive transparent systems. The approach of using smart material technologies in the building façades is discussed as the promising research direction for the future. A comprehensive literature review is conducted to identify the smart materials used in adaptive transparent systems along with their properties. Seven types of smart materials that are aerogel (AG), phase change material (PCM), photovoltaic (PV), electrochromic (EC), thermo-chromic (TC), thermo-tropic (TT), and liquid crystal polymers (LCP) are selected based on evaluation criteria determined by the literature. The identified criteria are bulk density (BD), thermal conductivity (TCd), sound insolation (SI), fire retardation (FR), heat transfer coefficient (HTC), solar transmittance (ST), air purification (AP), annual energy saving (AES), cost saving (CS), ultraviolet (UV)/near infrared (NIR) control (UNC), and embodied carbon (EC). The identified seven smart materials are investigated with the help of two multi-criteria decision-making (MCDM) techniques named analytical hierarchical process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) for analysis and ranking of materials. Results of the analysis demonstrate that PV is the most optimum material for adaptive transparent systems while others are ranked according to their performance.

The upsurge in higher education is considered a key determinant for enhancing green growth. Moreover, ICT development is also the main catalyst of green growth. This research explores the role of higher education and ICT on green growth for China from 1995 to 2020. The study employs auto-regressive distributive lag (ARDL) approach for short-run and long-run estimates of green growth. The effect of higher education and ICT on green growth is significantly positive in the long run and short run. The outcomes of the empirical models reveal that financial inclusion is positively associated with green growth in both long run and short run. Moreover, renewable energy consumption is found to have a positive impact on green growth. The findings thus point to the need for policies that promote human capital and ICT infrastructure as a way of accelerating green growth.

The relationship between exchange rate volatility (ERV) and other macroeconomic factors, including trade flows, domestic production, inflation, money demand, and economic growth, has remained a topic of a large number of studies in international finance. However, the research question that the past empirics have overlooked is whether ERV has any role in helping an emerging economy like China in its journey toward a renewable energy transition. To answer this question, this analysis intends to scrutinize the nexus between ERV and renewable energy investment (REI) in China over 1991Q1-2021Q4. Moreover, due to the asymmetric behavior of ERV, we have based our analysis on the asymmetric assumption. For analyzing the short and long-run impacts of ERV on REI in China, we used the linear autoregressive distributed lag (ARDL) and nonlinear ARDL methods. In the long term, the ARDL model predicts that stricter environmental policies and higher GDP will lead to more investment in renewable energy. As far as the nonlinear model is concerned, the long-term negative change in ERV does not affect REIs, whereas a long-term positive change in ERV reduces such investments. Likewise, unfavorable short-term exchange rate shocks encourage REI while positive short-term shocks discourage such development. Moreover, investment in renewable energy is bolstered by GDP, environmental policy strictness, and financial development, but is dampened by CO2 emissions in the short term in both models. On the basis of these results, we suggest that policymakers should consider implementing measures to stabilize exchange rates to promote investment in renewable energy.

Insulated aerial bundled cables (ABCs) are preferred over conventional bare conductor cables in electrical distribution system as ABCs are more safe, less prone to electricity pilferage and offers higher reliability. However, the degradation phenomenon of ABCs is sudden as compared to conventional cables especially in coastal areas. Sudden failures of ABCs in coastal areas make the maintenance planning challenging. Hence, accurate reliability estimation is required which can enable timely maintenance planning and in turn reducing the chances of failures. A novel reliability model is reported in this work which is derived from historical failure data of particular type of ABCs coupled with the degradation models. The models are based upon the actual environmental conditions experienced by the cables under study. The actual loading data as well as environmental data of two sites of varying distance from Seashore are used to develop the respective reliability models. The reliability prediction from proposed reliability model is then validated using time to failure computation through comparison of historical infrared thermography based Non-destructive testing (NDT) data, acquired at the sites under study, with reference to NDT measurements acquired from the ruptured/failed cable. The validation indicates the efficacy of the proposed reliability model.

Abstract Photovoltaic (PV) modules are serially connected in a PV string to provide a high-voltage input for grid-connected inverters. One of the stumbling blocks in maximum power extraction is the detrimental effects of partial shading condition (PSC) on series-connected PV modules/submodules. There are different types of approaches proposed in the literature to mitigate or minimize the effect of PSCs. However, in this paper, the study focused on the review of voltage equalizer (VE) topologies, a segment of circuit-based shading mitigation approaches. VE topologies increased the maximum power extraction capability during PSCs in comparison with the activation of bypass diodes by equalizing the voltages across all modules connected in series and avoids the multiple peaks generation on P–V and I–V curve. Twenty-three VE topologies were reviewed and classified into seven categories in a manner that each subsequent topology improved the prior. Each topology has been elaborated from a design perspective. Further, the topologies were quantitatively and qualitatively analyzed. Additionally, the optimized topology from each category was evaluated for improved efficiency and loss percentage using a PowerSIM software tool. For a fair comparison between each type, the same set of PSCs was considered. An experimental performance comparison between one of the efficient VE topology and conventional solar PV optimization controller proves the power efficiency improved by 60.12% for the same PSC. The conclusion was drawn based on the literature review and evaluation findings. This study provides a brief idea to researchers and practitioners about the ongoing topologies of VEs. This work also suggests future recommendations on which further research can be focused.