• Energy Research
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Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.

Pakistan is an energy deficient country with depleting energy reserves and increasing energy demand. Due to excessive population growth, the domestic and commercial energy sectors are experiencing rising demand. To meet the requisite demand, renewables are favored rather than conventional counterparts. In this study, we model hybrid power systems using solar, wind and biomass resources for electrifying remote areas. The four locations are chosen for the study around a developing country, Pakistan, where each site is designed according to an isolated microgrid with maximum indigenous resources potential as per the requisite demands. A survey is conducted for the load demand and biomass availability. Optimization is conducted across objectives of minimum levelized cost of the generated energy, least the net present cost and lesser payback period. The optimal results were achieved in-terms of required objectives across southern sites as compared to northern counterparts. The cost of generated energy is comparable to grid electricity and ensures 24 h power supply without cut off and load shedding to the un-electrified rural area. The hybrid power system has a low carbon footprint across emissions due to the use of renewable resources. All the estimated load of rural communities is met with the available resources and mid-career impact has also been conducted across 10 years of the project life to fulfill the increasing load demand of the communities after installation. The results are validated via comparative analysis and show the effectiveness of the proposed study.

Analyzing the efficiency of the water, energy, and food (WEF) nexus is critical for effective governance strategies. Therefore, three-stage data envelopment analysis (DEA) was used to measure the efficiency level of WEF in the 36 districts of Punjab, Pakistan, for the period from 2015 to 2021. Furthermore, the stochastic frontier was used to analyze the effect of external environmental factors on these efficiency scores of the WEF nexus. The results of the DEA showed that the number of frontier efficiency districts decreased, and most districts experienced rank change over time. Overall, the performance of 50% of the districts declined over time. The relative decline in efficiency was found to be higher in districts Bahwalnaghar and Rahim Yar Khan. The performance of districts Multan and Sheikhupura increased over time, while districts Vehari and Sargodha were the most complete and efficient in actual performance. According to the SFA’s findings, the WEF nexus efficiency of South Punjab districts was negatively impacted by external environmental factors (urbanization rate, manufactured industry output, population), leading to severe stress across WEF sectors. Districts in central and southern Punjab, however, were more likely to have lower rankings because of the positive impact of external environmental factors on the efficiency of the WEF nexus. The substantial rise of external environmental variables focused on scale expansion rather than quality improvement, which created a wide gap in WEF inputs and, hence, reduced the efficiency of the WEF nexus in the districts. The findings of this study provide valuable insights for developing governance strategies based on external environmental factors and WEF resource endowment, and they complement the efficiency calculation of WEF nexus research. Future research should focus on the Baluchistan region, the most deprived area in terms of water, energy, and food.

Expansion of modern power systems due to increasing energy demands face the challenges of grid reinforcement cost, size and complexity, transmission losses, and environmental factors. Placement of renewable energy sources (RES) based generation systems addresses these challenges. However, the size and placement location of RES-based system require optimization of installation and operational cost with better return on investment and reduction of greenhouse gas emissions. This paper presents an optimized solution for RES-based generation system to be installed with the existing power system of Azad Jammu and Kashmir (AJK) region that is facing power shortfall and load shedding. The weather and climate data from NASA and National Renewable Energy Laboratory (NREL) have been used and various models of on-grid hybrid renewable energy system (HRES) are compared to highlight their techno-economic benefits. An optimal hybrid photovoltaic, wind, and hydroelectric energy-based generation system is proposed with a significant reduction in cost of energy, net present cost, initial costs, and GHG emissions. Installation of the proposed hybrid RES-based generation system guarantees reduction in system power losses and line flows with an improved voltage profile of the system.

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.

The Moroccan government has always advocated reducing energy intensity so as to reach the target of the sustainable development. The current study presents the connection between CO2 emission, energy intensity (EI), natural resource rents (NRRs), energy productivity (EP), and renewable energy (RE) by employing annual time-series data from 1990–2020 for Morocco. Using the ARDL model, the empirical findings illustrate that (i) increasing EI significantly contributes to carbon emissions, (ii) higher consumption of natural resources adversely affects CO2 emissions, and (iii) EP and RE are the key factors to mitigate carbon emissions in both the short term and long term, suggesting that these two factors strengthen the considerable impact of EI and the consumption of natural resources on carbon emissions. Nevertheless, the negative environmental effects are moderately neutralized by adopting significant clean and green energy consumption within the country. The outputs of the robustness test verify the reliability of the regression results. Moreover, a one-way Granger causality running from EI, EP, RE, and NRR to CO2 emissions indicates that any variation in these variables will cause CO2 emissions. The present study offers the latest insights by adding EP and RE into country development and will support government policy makers in decisions related to efficiently abating CO2 emissions.

Using survey information of 150 randomly selected households across 21 villages of the forest-rich district of Swat, Pakistan, this study assessed households’ decision-making behaviors in depending on income from nearby forested land using socio-economic attributes. The evidence from the study may aid in making the existing policies be better targeted toward families that depend on the forest for income. Descriptive statistics and econometric techniques such as logit and tobit were used to analyze the data. Respondent households obtained the highest share of their income from off-farm activities (37%) and least from forest activities (16%). Fuelwood constitutes the biggest share (66%) of forest income, followed by medical plants (20%) and fodder (13%). We found that households with more physical assets, more family members working in off-farm jobs, and households earning more income from off-farm jobs were significantly and negatively associated with households’ decision to depend on forest income and total income obtained. We also found that households with less distance to the market and membership to joint forest management committees (JFMCs) were significantly and negatively associated with households’ total income obtained. However, household size was significantly and positively related to households’ decision of forest dependency. The study recommends the creation of off-farm opportunities and inclusion of local people in the management of forests through establishment of JFMCs, particularly for large and poor families.

This article demonstrates a new topology for optimization of the electrical variables and enhancement of low-voltage-ride-through (LVRT) capacity of a grid-tied photovoltaic system (PS) during asymmetrical faults. The proposed topology comprises a fuzzy-logic controller (FLC) based on gradient descent (GD) optimization in association with parallel-resonance-type fault current limiter (PRFCL) as an LVRT circuitry. Gradient descent is an iterative process to minimize the objective function by updating the variable in the opposite direction of the gradient of the objective function. The PRFCL optimizes the fault variables, specifically preventing voltage sag without any transitional spikes. A 100-kW detailed model of grid-tied PS is used in MATLAB/Simulink to analyze the response of the proposed topology at the point of common coupling (PCC) and PV side variables. A keen comparative study of the conventionally adopted proportional-integral (PI) controller in association with crowbar circuitry is carried out for the justification of the proposed topology. The simulation findings of the proposed topology authenticate the optimal response of variables in terms of stability, robustness, smoothness, and fault tolerance at both the grid side and the PV side.