Rural electrification in Nigeria: A review of impacts and effects of frugal energy generation based on some of e-waste components.

Nigeria is the seventh most populous country in world being the highest in Africa. The country is blessed with vast natural resources and is one of the highest producers of oil in the world. However, the inadequate supply of electrical energy is a major setback in the nation's economic development. Thus, there is need for an urgent and immediate solution to address the electricity access situation in Nigeria. It is in view of this that we first present an overview of the electrical energy situation of Nigeria (especially in the rural areas). The benefits of rural electrification and it impacts are discussed to buttress the need for electrifying rural areas and an overview of the abundant renewable energy resources in Nigeria is presented. As a proposed solution to improve the electricity situation, the concept of a reuse solar photovoltaic system based on e-waste components and old materials is presented. The system comprises repurposed Power Supply Unit (PSU) from old desktop computers, old thermal car Lead-acid batteries, old solar panels and Uninterruptible Power Supply (UPS) units. The possibility of adopting this solution in Nigeria depends on the amount of e-wastes generated annually thus necessitating the need for an analysis to see the annual impact of this system on electricity access based on the amount of available e-waste. Using the huge amount of e-waste generated/received annually in Nigeria, the feasibility of our solution is assessed by estimating the possible number of households that could be electrified by the second life renewable energy systems we propose. Due to the lack of official data in this field, certain constraints and assumptions were defined for the purpose of this analysis which resulted in obtaining a range of results that showed the possible impacts of adopting the reuse system. The analysis showed the minimum and maximum impacts the reuse solution could have on electricity access in Nigeria, based on best and worst case scenario respectively. The results further showed that an average of 287,000 households can be electrified annually if this solution is adopted, causing 2.2 % increment in population with electricity access in a year (between 620 thousand and 4.1 million individuals). Thus, the result is an indication that it is possible to achieve immediate growth in electricity access based on renewable energy integration, frugal innovation and reuse/repurposing of e-waste materials. In addition, this extension of their lifespan reduces their ecological footprint. It is expected that the energy demands of the continuously growing population can be met by strict adherence to set targets including adoption of smart-grids, generation diversification and focusing on rural electrification.

Investigating the impact of alumina nanoparticles in coconut oil distillate biodiesel to lessen emissions in direct injection diesel engine.

Petroleum fuels are commonly used for automobiles. However, the continuous depletion and exhaust gas emission causes serious problems. So, there is a need for an alternative eco-friendly fuel. Biodiesel is a type of fuel manufactured through a process called transesterification, which involves converting vegetable oils into a usable form. The process parameters of the transesterification process were optimized using the Taguchi method to achieve maximum biodiesel yield. However, the main problem of biodiesel is its high cost which could be reduced by using low-cost feedstock. To address this challenge, biodiesel (BCFAD) is derived from coconut fatty acid distillate (CFAD), a by-product obtained from refining coconut oil. This work uses BCFAD and BCFAD with Alumina nanoparticles as fuels. Alumina nanoparticles in the mass fraction of 25 ppm, 50 ppm, and 100 ppm are dispersed in BCFAD. The investigation results reveal an increase of 6.5% in brake thermal efficiency for BCFAD with 100 ppm nanoparticles when compared to BCFAD. There is a reduction of 29.29% of hydrocarbon and 34% of Carbon monoxide emissions with BCFAD100 in comparison with diesel. However, there is a marginal increase in NOx emission with the increase in nanoparticles. The heat release rate and cylinder pressure of BCFAD100 are comparable to diesel fuel. It was concluded that the utilization of BCFAD with a nanoparticle dispersion of 100 ppm is suitable for direct use as fuel in diesel engines.

Reliable renewable energy forecasting for climate change mitigation.

Accurate prediction of electricity generation from diverse renewable energy sources (RES) plays a pivotal role in optimizing power schedules within RES, contributing to the collective effort to combat climate change. While prior research often focused on individual energy sources in isolation, neglecting intricate interactions among multiple sources, this limitation frequently leads to inaccurate estimations of total power generation. In this study, we introduce a hybrid architecture designed to address these challenges, incorporating advanced artificial intelligence (AI) techniques. The hybrid model seamlessly integrates a gated recurrent unit (GRU) and a ResNext model, and it is tuned with the modified jaya algorithm (MJA) to capture localized correlations among different energy sources. Leveraging its nonlinear time-series properties, the model integrates meteorological conditions and specific energy source data. Additionally, principal component analysis (PCA) is employed to extract linear time-series data characteristics for each energy source. Application of the proposed AI-infused approach to a renewable energy system demonstrates its effectiveness and feasibility in the context of climate change mitigation. Results reveal the superior accuracy of the hybrid framework compared to more complex models such as decision trees and ResNet. Specifically, our proposed method achieved remarkable performance, boasting the lowest error rates with a normalized RMSE of 6.51 and a normalized MAPE of 4.34 for solar photovoltaic (PV), highlighting its exceptional precision in terms of mean absolute errors. A detailed sensitivity analysis is carried out to evaluate the influence of every element in the hybrid framework, emphasizing the importance of energy correlation patterns. Comparative assessments underscore the increased accuracy and stability of the suggested AI-infused framework when compared to other methods.

Towards CO2 valorization in a multi remote renewable energy hub framework with uncertainty quantification.

In this paper, we propose a multi-RREH (Remote Renewable Energy Hub) based optimization framework. This framework allows a valorization of CO2 using carbon capture technologies. This valorization is grounded on the idea that CO2 gathered from the atmosphere or post combustion can be combined with hydrogen to produce synthetic methane. The hydrogen is obtained from water electrolysis using renewable energy. Such renewable energy is generated in RREH, which are locations where RE is cheap and abundant (e.g., solar PV in the Sahara Desert, or wind in Greenland). We instantiate our framework on a case study focusing on Belgium and 2 RREH, and we conduct a techno-economic analysis under uncertainty. This analysis highlights, among others, the interest in capturing CO2 via Post Combustion Carbon Capture (PCCC) rather than only through Direct Air Capture (DAC) for methane synthesis in RREH. By doing so, a notable reduction of 10% is observed in the total cost of the system under our reference scenario. In addition, we use our framework to derive a carbon price threshold above which carbon capture technologies may start playing a pivotal role in the decarbonation process of our industries.

Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration.

Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China's green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.

Assessing the drivers and solutions of green innovation influencing the adoption of renewable energy technologies.

The degradation of the environment in China is accelerating along with economic expansion. Adoption of renewable energy technologies (RETs) is crucial for reducing the adverse impacts of economic growth on the environment and fostering sustainable development. This study attempts to identify the green innovation drivers and sub-drivers that affect the adoption of RETs in China and provide solutions for boosting their implementation. The study prioritized the drivers, sub-drivers, and strategies of green innovation by combining the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. In the study, the triple bottom line (TBL) approach has been used to determine the economic, societal, and environmental driving forces. The study also suggests strategies for encouraging the use of RETs. The results of the AHP method revealed that economics is the most crucial driver, with a weight of 0.376, followed by environmental (0.332), and social (0.291) drivers. The findings of the SAW method indicated that government green innovation initiatives, consumer initiatives, and industry initiatives are the most significant strategies for deploying RETs in China. This study has important theoretical and practical ramifications for encouraging China to adopt RETs. The suggested approaches can help researchers, business professionals, and policymakers promote sustainable development in China.

Urbanization and renewable energy consumption in the emerging ASEAN markets: A comparison between short and long-run effects.

The emerging markets in the ASEAN region, such as Indonesia, Malaysia, the Philippines, Thailand and Vietnam, have put great effort into achieving dual objectives: (i) supporting economic growth and (ii) combating environmental degradation simultaneously. These objectively depend on the fast urbanization taking place in these countries. While increased energy consumption from urbanization supports economic growth, urbanization is a key driver of environmental degradation. This paper examines a potential link between urbanization and renewable energy consumption, primarily ignored in current literature, particularly in the ASEAN-5 countries. Findings from this paper indicate that, despite the adverse effect of urbanization on renewable energy consumption in the short run, a positive effect is found in the long run for these emerging ASEAN markets, except Malaysia. The Philippines appears to balance well between urbanization and renewable energy consumption in the short and long run. Policy implications have emerged based on the findings of this paper.

Technical and economic assessment of hydrogen-based electricity generation from PV sources in tertiary buildings: a case study of a hospital building in Algeria.

The largest anthropogenic source of carbon dioxide emissions is the global energy system, which means transforming the global energy system is one of the most significant ways to reduce greenhouse gas emissions and mitigate climate change. Buildings play a critical role in our transition to a lower-carbon future, accounting for approximately 47% of global energy consumption and about 25% of global greenhouse gas emissions. Renewable hydrogen represents one of the most environmentally friendly options for energy generation. This study presents an energetic, economic, and environmental impact of a self-sufficient system for energy production from renewable energy sources in buildings. To achieve this objective, a hydrogen-based generation system was selected to meet all the electrical requirements of tertiary building in Algeria throughout the year. The results indicate that the hybrid renewable energy system can avoid the emission of approximately 1056 tons of carbon dioxide per year. Furthermore, the payback period is 7 years. These results clearly demonstrate that the integration of hydrogen energy in buildings is the optimal choice for environmental sustainability.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia.

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Energy use in residential buildings for sustainable development: The fifth Solar Decathlon Europe revelations.

This research focuses on achieving sustainable development in residential buildings with energy use. Under the influence of the energy crisis and related problems, research on residential buildings for less energy use has great potential. The literature review, according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, and including VOSviewer analysis, shows the research is increasing and meaningful. Solar Decathlon buildings are used as the main objects in this research. The fifth Solar Decathlon Europe energy use technologies are examined through onsite investigation and online searching. The Analytic Hierarchy Process method for multi-criteria decision analysis is used for sustainability assessment. Moreover, the Ladybug and ClimateStudio plugins simulated respectively the annual solar radiation and the best angle for receiving it. The main findings show that 34 kinds of technologies used in these buildings can be classified into two categories in three directions. Passive technologies should be applied and prioritized, but generating renewable energy is also important. Some infrequently used technologies are not insignificant. The research shows that the combination of technologies decides sustainability performance, but the quantity used does not. Furthermore, energy use also needs to be balanced and coordinated in combination with architectural aesthetics. This research on energy use in residential buildings is beneficial for achieving sustainable development.

Cumulative Effects of Watershed Disturbances and Run-of-river Dams on Mercury Cycling: Case Study and Recommendations for Environmental Managers.

Run-of-river power plants (ROR) represent the majority of hydroelectric plants worldwide. Their environmental impacts are not well documented and are believed to be limited, particularly regarding the contamination of food webs by methylmercury (MeHg), a neurotoxin. RORs are typically installed in small rivers where combined effects of watershed disturbances with dam construction can complicate environmental management. We report a multi-year case study on the Saint-Maurice River (Canada) where an unpredicted temporary increase in MeHg accumulation in predator fish was observed after the construction of two ROR plants. The associated pondages acted as sedimentation basins for mercury (Hg) and organic matter from a watershed disturbed by a forest fire and by logging. This fresh organic carbon likely fueled microbial MeHg production. Hg methylation was more associated with environmental conditions than to the presence of Hg, and main methylating microbial groups were identified. A constructed wetland was a site of significant Hg methylation but was not the main source of the fish Hg increase. Organic carbon degradation was the main driver of MeHg accumulation at the base of the food chain whereas trophic levels explained the variations at the top of the food chain. Overall, carbon cycling was a key driver of Hg dynamics in this system, and ROR plants can cause temporary (ca. 12 years) Hg increase in food webs when developed in disturbed watersheds, although this increase is smaller than for large reservoirs. Recommendations for future ROR construction are to establish a good environmental monitoring plan with initial high temporal resolution and to consider recent and potential watershed disturbances in the plan.

The risk paradox: Exploring asymmetric nexus between climate policy uncertainty and renewable energy technology budgets.

The ups and downs of climate policy uncertainty (CPU) cast a captivating shadow over the budgets allocated to renewable energy (RE) technologies, where strategic choices and risk assessment will determine the course of our green environmental revolution. The main intention of this investigation is to scrutinize the effect of CPU on the RE technology budgets (RETBs) in the top 10 countries with the highest RE research and development budgets (the USA, China, South Korea, India, Germany, the United Kingdom, France, Japan, Australia, and Italy). Although former researchers have typically employed panel data tools to contemplate the connection between CPU and RE technology, they repeatedly ignored variations in this connection throughout different economies. In contrast, our research adopts a unique approach, "quantile-on-quantile," to check this association at the country-to-country level. This approach offers a comprehensive worldwide perspective while procuring tailor-made perceptions for individual economies. The outcomes suggest that CPU significantly decreases RETBs across several data quantiles in our sample nations. In addition, the outcomes underscore that the connections between our variables differ among nations. These outcomes highlight the significance of policymakers implementing thorough appraisals and skillfully governing plans relevant to CPU and RETBs.

Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods.

Forecasting is of great importance in the field of renewable energies because it allows us to know the quantity of energy that can be produced, and thus, to have an efficient management of energy sources. However, determining which prediction system is more adequate is very complex, as each energy infrastructure is different. This work studies the influence of some variables when making predictions using ensemble methods for different locations. In particular, the proposal analyzes the influence of the aspects: the variation of the sampling frequency of solar panel systems, the influence of the type of neural network architecture and the number of ensemble method blocks for each model. Following comprehensive experimentation across multiple locations, our study has identified the most effective solar energy prediction model tailored to the specific conditions of each energy infrastructure. The results offer a decisive framework for selecting the optimal system for accurate and efficient energy forecasting. The key point is the use of short time intervals, which is independent of type of prediction model and of their ensemble method.

Examining the dynamic impact of carbon emissions, renewable energy and economic growth on healthcare expenditure in Asian countries.

This study aims to examine the impact of renewable energy, carbon emissions, and economic growth on healthcare spending in 36 Asian countries during 2000-2019. Fully Modified Ordinary Least Square (FMOLS) and Dynamic Ordinary Least Square (DOLS) models have been applied to the panel data for 36 Asian countries. The study's findings show that CO2 emissions in Asia increased due to public and private health spending, with the commercial health sector having a larger negative influence on CO2 emissions than the public sector. According to FMOLS and DOLS findings, carbon emissions and GDP are positively related to health spending, indicating that high economic growth through energy-intensive production processes leads to increased carbon emissions, but on the contrary, renewable energy consumption has decreased healthcare expenditure. This study advocates new policies to reduce carbon emissions and hospitalisation without jeopardising national economic growth. In order to achieve sustainable health services and an environmentally friendly future in Asia, health administrators must raise state and private healthcare spending while implementing an effective cost-service and energy-efficient management plan.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production.

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Pure lignin induces overexpression of cytochrome P450 (CYP) encoding genes and brings insights into the lignocellulose depolymerization by Trametes villosa.

Trametes villosa is a remarkable white-rot fungus (WRF) with the potential to be applied in lignocellulose conversion to obtain chemical compounds and biofuels. Lignocellulose breakdown by WRF is carried out through the secretion of oxidative and hydrolytic enzymes. Despite the existing knowledge about this process, the complete molecular mechanisms involved in the regulation of this metabolic system have not yet been elucidated. Therefore, in order to understand the genes and metabolic pathways regulated during lignocellulose degradation, the strain T. villosa CCMB561 was cultured in media with different carbon sources (lignin, sugarcane bagasse, and malt extract). Subsequently, biochemical assays and differential gene expression analysis by qPCR and high-throughput RNA sequencing were carried out. Our results revealed the ability of T. villosa CCMB561 to grow on lignin (AL medium) as the unique carbon source. An overexpression of Cytochrome P450 was detected in this medium, which may be associated with the lignin O-demethylation pathway. Clusters of up-regulated CAZymes-encoding genes were identified in lignin and sugarcane bagasse, revealing that T. villosa CCMB561 acts simultaneously in the depolymerization of lignin, cellulose, hemicellulose, and pectin. Furthermore, genes encoding nitroreductases and homogentisate-1,2-dioxygenase that act in the degradation of organic pollutants were up-regulated in the lignin medium. Altogether, these findings provide new insights into the mechanisms of lignocellulose degradation by T. villosa and confirm the ability of this fungal species to be applied in biorefineries and in the bioremediation of organic pollutants.

Impacts of renewable and disaggregated non-renewable energy consumption on CO2 emissions in GCC countries: A STIRPAT model analysis.

This research investigates the effects of renewable (REC) and disaggregated non-renewable energy consumption (coal, oil, and natural gas) on CO2 emissions (CO2) in GCC countries, employing the STIRPAT model. The research also compares the impact of various non-renewable energy (NREC) sources to identify their contributions to CO2 emissions. Demographic factors like population and economic growth are considered main determinants of CO2. Panel data econometric methods are used, including diagnostic tests and unit root tests, to found long-run relationships among the variables. The study reveals significant positive associations between coal, natural gas, oil consumption and CO2, with oil having the highest impact. Conversely, REC shows a significant negative correlation with CO2. Economic growth and population are also linked to increased CO2. The findings emphasize the need for strategies promoting renewable energy usage, energy efficiency, public transportation, carbon pricing, and research in green technologies to alleviate CO2 and enhance sustainable development in the GCC countries.

Investigating the role of nuclear power and battery storage in Hungary's energy transition using hourly resolution electricity market simulations.

Electricity supply in European countries faces a number of challenges, such as achieving carbon neutrality, tackling rising prices, reducing dependence on fossil fuels, including fossil fuel imports. To achieve these goals, the electricity systems of all European countries will have to undergo major changes, while taking into account technical, environmental, economic and social objectives. Our simulations provide essential data for this transition by analyzing different power plant portfolios and electricity consumption scenarios. The analyses focus on the cooperation of nuclear power and weather-dependent renewables, and on the possible role that battery-based electricity storage can play in the Hungarian electricity system. In this paper, we present the experience gained in setting up an electricity market model and the results of running the model on the electricity systems of Hungary and its six neighboring countries (Slovakia, Romania, Serbia, Croatia, Slovenia and Austria), taking into account the constraints of the cross-border capacities. The results of the sensitivity analysis for the 2030 power plant portfolios, battery capacities and renewables analyzed in this paper cover Hungary's import/export position, the energy source structure of its electricity generation, battery operation, CO2 emissions from electricity generation, expected prices in the system and the utilization parameters of nuclear power plants.

Techno-economic and environmental analysis of renewable energy integration in irrigation systems: A comparative study of standalone and grid-connected PV/diesel generator systems in Khyber Pakhtunkhwa.

Water is an essential requirement for agricultural productivity. In the agriculture sector, electricity generated by conventional sources contributes to a substantial amount of carbon footprints for pumping water through tube wells. Over the past few decades, a transitional shift towards renewable resources has increased leading to decarbonizing the environment and is considered as a viable solution for electricity production. To assist and provide a road map for this paradigm shift, the proposed study presents a techno-economic and environmental analysis of irrigation systems by carrying comparative analysis of both standalone and grid-connected systems based on four independent sites in a developing country. PV system integrated with grid enabling both energy purchase and sale (PV + G(P+S)), proved to be the most optimal configuration with cost of energy (COE) of $0.056/kWh, $0.059/kWh, $0.061/kWh, and $0.068/kWh while having net present cost (NPC) of $7,908, $20,186, $25,826, and $34,487 for Peshawar, Khyber Agency, Mardan, and Charsadda respectively, over a useful life span of 25 years. Furthermore, sensitivity analysis has been carried out based on uncertain variables such as Grid power purchase (GPP) and average solar radiation (GHI) to check the optimality behavior of the system. Results from environmental analysis revealed that (PV+ G(P+S)) system has a relatively low carbon impact as compared with conventional sources. This configuration also has the ability to prevent excess water extraction by selling any excessive solar PV energy to the grid. This study provides a policy framework insight for the entities for future optimization.