• Energy Research
• 2021-2025close
• other engineering and technologies close
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Abstract A method to analyze multi-area optimal power flow in novel energy management systems is proposed and studied in this paper. The studied power network consisted batteries, distributed generators and electric parking lots. A straightforward granular-based method is introduced to maintain the simplicity of the calculations while the independency of areas is preserved. In this case, a three-area power network as a multi-area power network problem is chosen to test and approve the applicability of the proposed method in terms of accuracy, simplicity, preserving independency, and not requiring any control or initial parameters. According to the results, maximum error is 0.0009 pu in the selected case study. Moreover, two valued variables namely operation cost and voltage deviation are selected as the two fundamental objective functions to be analyzed both multi-objectively and solely. Furthermore, in this paper, soon-to-be-indispensable electric vehicle parking lots and distributed generators are also utilized to optimize operation cost (up to 49.28%) as well as voltage deviation (up to 48.99%). Also, the influence of each area on one another in multi-area layout is studied. In one scenario, the third area is equipped with battery and fuel cell to improve voltage deviation of itself (40.42%) as well as first and second area 12% and 15.4%, respectively.

Smart cities, as well as smart homes research, are becoming of concern, especially in the field of energy consumption and production. However, there is a lack in the dataset that can be used to simulate smart city energy consumption and prediction or even smart homes. Therefore, this paper provides a carefully generated dataset for smart home energy management simulation. Five datasets are generated and analysed to ensure suitability, including 20, 50, 100, and 200 homes across 365 days. For more accurate data, energy consumption and production for 50 homes are generated based on real input taken from a dataset for homes in Saudi Arabia. Due to the unavailability of a comprehensive dataset related to the complex scenario of smart home sensors, energy consumption, and peer-to-peer data exchange, synthetic data was generated to support the simulation of smart home energy generation and consumption. This synthetic data plays a crucial role in situations where simulating uncommon events, ensuring data availability, facilitating extensive experimentation and model validation, and enabling scalability are paramount. It offers a valuable opportunity to incorporate these rare yet significant occurrences into the simulation, particularly in the context of infrequent events, such as abnormal energy consumption patterns observed in smart homes. The generated data is analysed and validated in this article, ready to be used for many smart home and city research.

PurposeCrucial transition of the Indian residential building sector into a low-emission economy require an in-depth understanding of the potentials for retrofitting the existing building stock. There are, however, limited studies that have recognised the interdependencies and trade-offs in the embodied energy and life cycle impact assessment of retrofit interventions. This research appraises the life cycle assessment and embodied energy output of a residential building in India to assess the environmental implications of selected retrofit scenarios.Design/methodology/approachThis study utilises a single case study building project in South India to assess the effectiveness and impact of three retrofit scenarios based on life cycle assessment (LCA) and embodied energy (EE) estimates. The LCA was conducted using SimaPro version 9.3 and with background data from Ecoinvent database version 3.81. The EE estimates were calculated using material coefficients from relevant databases in the published literature. Monte Carlo Simulation is then used to allow for uncertainties in the estimates for the scenarios.FindingsThe three key findings that materialized from the study are as follows: (1) the retrofitting of Indian residential buildings could achieve up to 20% reduction in the life cycle energy emissions, (2) the modification of the building envelope and upgrading of the building service systems could suffice in providing optimum operational energy savings, if the electricity from the grid is sourced from renewable plants, and (3) the production of LEDs and other building services systems has the highest environmental impacts across a suite of LCA indicators.Originality/valueThe retrofitting of residential buildings in India will lead to better and improved opportunities to meet the commitments in the Paris Climate Change Agreement and will lead to enhanced savings for building owners.

Abstract This paper examines the volatility spillover effects between precious metals futures (gold, palladium, platinum, and silver), Brent oil futures, and ASEAN stock markets (Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam) at multiple time horizons and during bear and bull market periods. The results show that precious metals, crude oil, and the Vietnamese stock market are net receivers of spillovers irrespective of time horizons and during bearish market conditions. In contrast, during the bullish market conditions, Brent oil futures and Vietnamese stock markets become net contributors to short-term spillovers. Furthermore, the stock markets of Indonesia, Malaysia, and the Philippines are the largest contributors to spillovers under bear and bull market conditions and regardless of the time investment horizons. Moreover, adding precious metals or oil futures to the ASEAN equity portfolio provides better hedging effectiveness during different considered crises. The role of Brent oil and precious metals futures serves as diversifier, hedge, and safe haven assets is discussed under major events.

Electro deionization using microbial communities has been proven as a competent method for desalination and abatement of water pollution by removing ionic chemicals from the target waters. Microbial Desalination Cell (MDC) facilitates microbial deionization which can either support or be a substitute for the conventional desalination methods. Generation of electricity is accomplished by the bio electrochemical oxidation of organic compounds present as contaminants in wastewater which in turn attribute to the migration of ions in MDC system. The present review aims to elucidate the theory, principles and the application of microbial desalination cell and microbial fuel cell (MFC) in treatment of saline and wastewaters. Air cathode MDC and stacked MDC for purification of saline water are found to give promising results. Air pump assisted microbial desalination cell reported 150.39 ppm h-1 of salt removal with an operational time period of 80 h and showed consistent results. Hence the air cathode assisted MDC showed dominant capacity of salt removal compared to stacked MDC. Also, three major types of microbial fuel cell, namely photosynthetic biofilm MFC, constructive wetland MFC and ceramic membrane supported MFC are reviewed for their potentials in wastewater treatment by deionization method and electricity generation. Complete (100%) removal of chemical oxygen demand was reported by photosynthetic microbial fuel cell operated for 16 days having 435.8 Ω of external resistance. When constructive wetland microbial fuel cell was operated for 10 days with 1000 ohms of external resistance, it exhibited complete (100%) removal of chemical oxygen demand from the wastewater. About 92% of chemical oxygen demand removal was demonstrated by ceramic membrane supported microbial fuel. Compared to ceramic membrane microbial fuel cell, photosynthetic and constructive wetland microbial fuel cell displayed better performance in terms of pollutant removal capacity and economical factor. Ability of the electrogenic species, namely Geobacter, Shewanella, Clostridium and Bacillus and the photosynthetic species, namely Chorella Vulgaris Rhodopsuedomonas, and Scenedesmus abundans in microbial deionization methods and their performance levels reported by several researchers are presented.

Given the alarming level of climate change, policymakers across the globe are seeking strategies to mitigate environmental pollution to achieve sustainable development. In this context, renewable energy and technological advancements have emerged as an effective way to lower pollution and attain sustainable development. This study evaluates the effect of financial inclusion, technological innovation, and renewable energy on the load capacity factor (LCF) in European countries from 2004 to 2018. LCF is considered the most comprehensive indicator of ecological sustainability, combining both the biocapacity factor and ecological footprint. Hence, the present work fills the literature gap by exploring, for the first time, the effect of financial inclusion on the LCF. Applying the advanced Method of Moment Quantile Regression (MMQR), the study demonstrates that technological innovation and economic growth have adverse effects on LCF while renewable energy and financial inclusion promote LCF. The study indicates that technological innovation and economic growth undermine ecological excellence in European nations while green energy and financial inclusion enhance it. Moreover, the findings of the causality analysis reveal a causal association between financial inclusion, renewable energy, and LCF. Our study recommends prioritizing financial inclusion alongside investments in renewable energy to enhance ecological sustainability.

This article reviews existing research and discussions on urban policy in response to the global COVID-19 pandemic. The analysis focuses on the first pandemic period in the European Union and the Middle East. A search was conducted in available databases and search engines (Scopus, Google Scholar). A critical bibliometric analysis of publications from the first pandemic period was carried out. The most frequent topics covered were spatial organization, transport, environment, and social issues. The analysis showed that the quantitative scope and depth of the selected topics in the European Union and the Middle East differ. Activities defined as “exploitative” should be considered a particularly interesting point of reference in both analyzed regions.

The aim of this paper is to investigate agricultural sustainability as a collective issue involving multiple rather than individual farms. Through the utilization of energy consumption as a proxy, we propose a novel methodology that evaluates the impact of farm consolidations on agricultural sustainability while accounting for resource preferences. Our approach incorporates the ordered weighted averaging (OWA) operator within an inverse data envelopment analysis (IDEA) model to identify post-merger farms that meet preset efficiency targets. We employ a DEA cross-efficiency (DEA-CE) procedure to select merger plans that maximize agricultural sustainability for each preference scenario. By analysing a case study of 43 tomato greenhouse farms in Biskra, northern Algeria, our findings demonstrate that mergers can significantly enhance agricultural sustainability, surpassing the potential of individual farms by a factor of over 15. Additionally, the adoption of the most sustainable merger plan can lead to energy savings of more than 69%. Irrespective of the preference scenario, substantial energy savings in machinery, fertilizers, diesel, and electricity ranging from 22.92% to 73.73% were observed. These results emphasize the strategic role of merger processes in promoting agricultural sustainability and optimizing resource utilization.