Harmful Gas Emissions Research Articles

The Optimal Arrangement of Boats in a Coastal Maritime Passenger Lines System Using Graph Theory

This paper presents research in the field of optimization in maritime passenger traffic that can ensure the long-term sustainability of coastal maritime passenger lines system. For the purpose of the research contained in this paper, it has been hypothesized that the optimal arrangement of boats within a coastal maritime passenger lines system will reduce the consumption of propulsion energy, the emission of harmful gasses and operating costs. The aim of this paper is to present an efficient algorithm for a reduction in propulsion energy consumption in coastal maritime passenger lines systems by reassigning boats to lines that they service. The problem is modeled using a bipartite graph and the solution is obtained by searching for optimal matching using Edmonds’ algorithm. The authors apply, for the first time, Edmonds’ algorithm to the problems of the optimization of assignments of boats to lines. The research results were confirmed by tests on a representative example. The optimization results on only 10 ships in the given example show yearly savings of 91,097.30 L of fuel (lowering costs by EUR 69,233.95) and reducing CO2 by 243.59 tons, which proves that this algorithm found a much more efficient arrangement that could result in a significant reduction in propulsion energy consumption, thus providing economic and ecological benefits.

Unit prediction horizon [formula omitted] based model predictive control for the fuel cell based plug-in hybrid electric vehicle with rule-based energy management system

Plug-in hybrid electric vehicles (PHEVs) provide a good alternative in achieving better performance and in the reduction of harmful gas emissions. The hybrid energy storage system (HESS) and the integrated charging unit constitute the PHEV under consideration. To meet the load demands, the proposed HESS is a coupled system comprising a fuel cell, high energy density battery, and high-power density super-capacitor. On-board charging involves the utilization of a DC–DC buck converter and an uncontrolled rectifier and two buck-boost converters are utilized to facilitate a smooth transfer of energy. A rule-based supervisory controller has been implemented for different load conditions which takes into account the state of charge of energy sources and also the total power inflow of the power sources. A model predictive control (MPC) technique is implemented to ensure that PHEVs function smoothly in terms of regulation of DC bus voltage and tracking of current. Unit prediction horizon i.e. only one step ahead looking into the future is considered for the MPC to make it computationally less expensive. MPC is designed in such a way that its performance is close to our favorite linear controller which in our case is the H∞ controller. The inverse optimal control technique is used for determining the weight matrices of the cost function. The proposed controller has been simulated using MATLAB. Also, the performance comparison is made between the designed MPC and the non-linear control approach i.e. integral sliding mode control and integral backstepping-based control to show that it achieves comparable or superior performance to the non-linear controller. The real-time performance of H∞ based MPC is verified using controller hardware in the loop experimental setup.

Adsorption and Sensing Properties of Ni-Modified InSe Monolayer Towards Toxic Gases: A DFT Study

The emission of toxic gases from industrial production has intensified issues related to atmospheric pollution and human health. Consequently, the effective real-time monitoring and removal of these harmful gases have emerged as significant challenges. In this work, the density functional theory (DFT) method was utilized to examine the adsorption behaviors and electronic properties of the Ni-decorated InSe (Ni-InSe) monolayer when interacting with twelve gases (CO, NO, NO2, NH3, SO2, H2S, H2O, CO2, CH4, H2, O2, and N2). A comparative assessment of adsorption strength and sensing properties was performed through analyses of the electronic structure, work function, and recovery time. The results show that Ni doping enhances the electrical conductivity of the InSe monolayer and improves the adsorption capabilities for six toxic gases (CO, NO, NO2, NH3, SO2, and H2S). Furthermore, the adsorption of these gases on the Ni-InSe surface is characterized as chemisorption, as indicated by the analysis of the adsorption energy, density of states, and charge density difference. Additionally, the adsorption of CO, NO, NO2, and SO2 results in significant alterations to the bandgap of Ni-InSe, with changes of 18.65%, 11.37%, 10.62%, and −31.77%, respectively, underscoring its exceptional sensitivity. Moreover, the Ni-InSe monolayer exhibits a moderate recovery time of 3.24 s at 298 K for the SO2. Consequently, the Ni-InSe is regarded as a promising gas sensor for detecting SO2 at room temperature. This research establishes a foundation for the development of an Ni-InSe-based gas sensor for detecting and mitigating harmful gas emissions.

Applying the internet of things (IoT) for raising black soldier Fly (BSF) in closed system to minimize greenhouse gas emissions

The Black Soldier Fly (BSF), scientifically known as Hermetia Illucens L., effectively reduces organic waste and decreases harmful gas emissions more efficiently than landfill or composting methods. However, it can still produce harmful gases during the rearing of BSF larvae, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ammonia (NH3), which contribute directly and indirectly to the greenhouse effect. The pH level of the food provided to the larvae affects their biomass and leads to varying levels of gas emissions. This study aims to investigate the impact of greenhouse gas emissions when different pH levels of food are used, utilizing IoT technology to measure greenhouse gases and control the environmental conditions for rearing BSF larvae. The greenhouse gases monitored in this study include carbon dioxide (CO2) and methane (CH4), with environmental factors such as air temperature, humidity, soil moisture, and light intensity being controlled. The experiment tested pH levels ranging from 2.0, 4.0, 6.0, 7.0, and 8.0 to 10.0. The results show that different pH levels in food affect the emission of CO2 and CH4. Initially, a large amount of CO2 is released, gradually decreasing over time, while CH4 emissions steadily increase. In the early days, CH4 emissions are unstable, fluctuating before stabilizing. Food with a pH of 2.0 results in the lowest average CO2 emissions (430.53 PPM) from BSF larvae, and the lowest CH4 emissions (16.11 PPM) are also observed with a pH of 2.0.

Detection of CO2, H2S, NH3, and gas mixtures using a quartz crystal microbalance sensor array with five sensitive materials

The large-scale development of chicken farms has inevitably led to an increase in the emission of harmful gases, which has prompted rapid research development on the detection of harmful gases, such as quartz crystal microbalance (QCM) sensors. In this research, a QCM sensor array modified with ethyl cellulose (EC), polyethyleneimine (PEI), polypyrrole (PPy), tetramethylammonium fluoride tetrahydrate (TMAF), and Nitrogen-doped tungsten carbide supported on carbon nanosheets (WC@NC) was established to detect gas mixtures. By analyzing the relationship between the fundamental frequency shift and the number of sensitive material layers for the QCM sensor, the optimal number of layers was determined. The QCM sensor array consisted of a blank QCM sensor, an 8-layer EC sensor, a 4-layer PEI sensor, a 3-layer PPy sensor, an 8-layer TMAF sensor, and an 8-layer WC@NC sensor. The results showed that the PEI sensor exhibited maximum frequency shifts when detecting gases, while TMAF and WC@NC films had the highest contribution to NH3 detection. TMAF, PEI, and WC@NC played significant roles in the binary classification of exceeding the standard detection of NH3. Additionally, WC@NC and EC sensors played an important role in detecting H2S and CO2, respectively. The QCM sensor array could accurately classify the excessive state of CO2. Overall, the QCM sensor array demonstrated a correct recognition rate of 87.5 %, indicating its potential for classifying the exceedance of NH3, CO2, and H2S gases in gas mixtures. This work provides basic information for research on QCM sensor arrays in gas detection.

Enhancing the performance of biofilters with sorption ability for simultaneous purification of landfill gases using laterite soil-based substrate

Various harmful gas emissions are a major controversial issue globally, and open dumpsites have become a leading contributor to atmospheric pollution. The study aims to introduce a high-performance landfill gas purification substrate using laterite soil, an iron, and aluminum-rich, highly available soil type in tropical countries; the final suggested substrate contained uniformly mixed laterite soil and compost with a 4:1 fraction by volume. A self-design setup that can maintain moisture content and aerobic conditions was used with an active landfill gas collection system through the gas wells established on the Karadiyana landfill in Sri Lanka under a tropical climate. The elimination capacity of CH4, NH3, VOCs, and H2S were 202860 ± 51595, 91 ± 30, 75 ± 27, and 6 ± 3 mg m−3 h−1 with 91 ± 3, 96 ± 1, 93 ± 3 and 83 ± 4% of removal efficiencies respectively at a 9.4 min empty bed residence time (EBRT). Selected laterite soil acts as an adsorption material and provides a better bio-filtering substrate with compost due to its specific characteristics. The hybrid gas purification process, absorption, and biofiltration within the proposed material were the reasons for higher performance, and the setup may be successful under the industrial waste gas streams. Further investigation should be carried out to study the medium's effectiveness as the covering layer on the dump with the successful lowest thickness, regarding secondary pollutants, and industrial applicability under unique specific conditions.

Applying membrane techniques to separate hydrogen from natural gas for hydrogen technologies

Today, the world's energy industry is grappling with several issues arising from restrictions on harmful greenhouse gas emissions, related to climate and environmental protection. Methods of storing energy in various types of carriers are becoming increasingly important, the most promising of which is hydrogen. Various methods of storing it are being considered, where an interesting option is to store it temporarily, under certain conditions, in existing natural gas networks. The challenge, however, is its subsequent recovery. Membrane technology is receiving special attention among recovery methods because of its potential efficiency in hydrogen separation, safety and ease of operation, low energy requirements and environmental friendliness. The paper presents a literature study of the issue, preliminary analyses of membrane gas separation and the advisability of the direction of the new research that has been undertaken. The possibility of applying membrane techniques to the process of separating hydrogen from natural gas in transmission and distribution networks for hydrogen storage technology is presented. Preliminary research is geared toward exploring the scope of applicability of this method of membrane separation, in which the primary goal is to determine the possibility of separating hydrogen from methane. In this paper, within the framework of the adopted simplified structure of the polymeric membrane module, the basic parameters of the process (share of hydrogen in the feed, membrane surface area) were determined, as well as the influence and dependence of transmembrane pressure (permeate) on the values of membrane selectivity, recovery rate and purity of the obtained hydrogen.

Analysis of the Effect of More Fuel and Air Variations Excess Air on Combustion Process Simulation at a 10 kW Biomass Power Plant Using ASPEN PLUS

Abstract The utilization of biomass as an alternative energy can support the achievement of Net Zero Emissions. Each type of biomass fuel can provide different combustion effects from one another. This is due to the different heating values of each fuel. The calorific value is one of the things that affect the combustion performance of the biomass power plant. The main purpose of this study is to examine how different amounts of excess air used during the combustion process affect the outcomes. Combustion is a chemical reaction involving decomposition and gasification that simultaneously occurs in a space where a large amount of energy is released. The modeling and simulation of the decomposition and gasification processes were carried out using Aspen Plus. In this simulator, the decomposition process takes place in the Yields Reactor, while the gasification process occurs in the Gibbs Reactor. The biomass and ash components are characterized by their ultimate, proximate, and sulfur analysis. Then the excess air is varied to analyze its impact on the flame temperature from the reaction. The simulation findings emphasize crucial considerations for achieving complete combustion and reducing harmful gas emissions. Maintaining excess air close to the stoichiometric level ensures maximum combustion temperature at 1616.15 °C while minimizing excess air reduces NOx gas concentration. Approaching stoichiometric air level decreases CO gas and increases CO2, indicating more thorough combustion. However, excess air leads to lower adiabatic flame temperature due to nitrogen formation.

Assessment of harmful gases emission and its impact using IoT and geospatial technology

Numerous industries in our daily lives use a variety of dangerous chemical gases, and workers are frequently exposed to these gases. The losses due to harmful gases can be reduced by developing an automatic harmful gases detection and alerting system. This study aims to design, develop, and implement an automatic warning system using series of gas sensors to deduct. The designed system is implemented to monitor 3 locations to detect the hazardous pollutions parameters, namely Large-Scale industries, wood burning sites and residential housings and analysed to understand the pollution level. This study observed elevated temperatures (35–45 °C) and heat index (30–50 °C) in large-scale industries, along with higher harmful gases compared to wood-burning sites and homes. Interestingly, wood-burning sites exhibited higher humidity (60–70 %) and peak ozone and methane levels compared to other locations. These variations underscore the unique environmental characteristics of each site, emphasizing the need for tailored monitoring and safety measures.

Impact of the Location and Energy Carriers Used on Greenhouse Gas Emissions from a Building

The growth in population increases greenhouse gas (GHG) emissions into the environment. High GHG emissions are attributed to meat production, due to its high energy demand. The largest carbon footprint in the production of poultry meat is generated by combustion. This paper deals with the problem of greenhouse gas emissions (total dust, CO, CO2, NOx, SOx and benzo(a)pyrene) resulting from the generation of energy for heating broiler houses located in different locations in Europe. The study includes continuous measurements of selected microclimate parameters: temperature and relative humidity inside and outside the building, floor temperature, wind speed and direction, and solar radiation intensity. Validation and calibration of the model, emission calculations, and analysis of the obtained results were conducted. Eighteen design variants were assumed, differentiated by the heating fuel used (hard coal, fuel oil, gaseous fuels), material and construction solutions for the floor and the location of the facility. The analysis showed that CO2 emissions for a facility located in northern Europe are 123,153 kg higher compared to the same building located in southern Europe. In addition, increasing the floor’s thermal resistance by 3.69 m2·K·W−1 reduced harmful gas emissions by an average of 5.7% for each of the locations analysed.

Application of the MAMCA Method in the Evaluation of Delivery Flows within City Centers: A Case Study of Rijeka

The aim of this paper was to propose a model for the evaluation of the flow of goods delivered to urban centers based on a systematic approach, generating possible scenarios of delivery activities and selecting those criteria for evaluation that contributed to the development of delivery activities and their evaluation by individual stakeholders. The number of users of delivery services is increasing, which leads to an increase in the volume of goods in the city center, creating additional traffic congestion—so-called bottlenecks—which results in an increased noise level and the emission of harmful exhaust gases, increasing dissatisfaction with the quality of life of the city population. It is, therefore, necessary for decision-makers to make a decision that achieves the best consensus among stakeholders. A test was carried out in the city of Rijeka. Based on a review of the literature and the spatial planning of the city of Rijeka, this paper proposed the following possible scenarios for the delivery of goods within the city center: status quo, delivery from one consolidation center, delivery from two consolidation centers, delivery by environmentally friendly vehicles from one consolidation center and livability. The criteria were evaluated by relevant stakeholders to determine the weight of each criterion for the development of goods deliveries in the city center. This paper used a multi-actor multi-criteria analysis (MAMCA) based on the optimization and ranking of scenarios in accordance with the given objective, the research problem, the defined criteria and the interests of the stakeholders. The solution of the MAMCA analysis evaluated the scenario with two consolidation centers best in accordance with the specified optimization objective.

Recent Advances in Biomimetic Methods for Tillage Resistance Reduction in Agricultural Soil-Engaging Tools

The high tillage resistance of agricultural soil-engaging tools (TASTs) in farmland operations (e.g., tillage, sowing, crop management, and harvesting) increases fuel consumption and harmful gas emissions, which negatively affect the development of sustainable agriculture. Biomimetic methods are promising and effective technologies for reducing the TASTs and have been developed in the past few years. This review comprehensively summarizes the typical agricultural soil-engaging tools (ASETs) and their characteristics and presents existing biomimetic methods for decreasing TASTs. The introduction of TAST reduction was performed on aspects of tillage, sowing, crop management, and harvesting. The internal mechanisms and possible limitations of current biomimetic methods for various ASETs were investigated. The tillage resistance reduction rates of ASETs, as affected by various biomimetic methods, were quantitatively compared under different soil conditions with statistical analyses. Additionally, three future research directions were recommended in the review to further reduce TASTs and encourage the development of sustainable agriculture.

Performance of Pd and Pt noble metal impregnated on Lapindo mud-based mesoporous silica on hydrotreatment of waste cooking oil into biogasoline

In the present work, an alternative pathway to produce liquid biogasoline with low aromatic value was done via catalytic hydrotreatment of waste cooking oil (WCO) over noble metal (Pd and Pt) loaded on cost-effective mesoporous silica (MS) synthesized from Lapindo mud. The implementation of 1.82 and 1.53 SiO2/CTAB (cetyltrimethylammonium bromide) weight ratio successfully produced Lp-MS with average diameters of 5.1 (Lp-MS1) and 4.7 nm (Lp-MS2), respectively. The XRD analysis showed a better dispersion for Pt with a considerably smaller particle size and TEM image revealed that while Pt was shown to occupy both external and internal surface of Lp-MS1, Pd was only present on the outer part of Lp-MS2. During the hydrotreatment of WCO using a semi-batch reactor, Pd/Lp-MS2 exhibited a superior deoxygenation and hydrogenation capacity than Pt/Lp-MS1 by generating over 60.9% liquid biofuel with 86.75% selectivity towards gasoline-range hydrocarbon. The liquid product obtained from the catalytic hydrotreatment contained very low aromatic compound (<4%) which was known to be responsible in the emission of harmful gas during fuel combustion. This result can be maintained for at least 4 consecutive runs. This study offers another efficient pathway to produce a cleaner source of energy in the form of biogasoline fuel.

Optimization of high-temperature thermal pretreatment conditions for maximum enrichment of lithium and cobalt from spent lithium-ion polymer batteries

Due to their increasingly high demand, lithium-ion batteries (LIBs) will encounter a scarcity of essential resources if recycling is not prioritized. The current pretreatment methods for spent LIBs used in industrial production are inefficient, costly, and hazardous. The study aimed to maximize the yield of lithium and cobalt from the black mass of spent Lithium-ion batteries through an optimized high-temperature thermal pretreatment process, which combined mechanical (direct crushing) and thermal treatments to facilitate the subsequent recovery of these valuable metals. Sieve analysis showed that direct crushing for 2 min resulted in 40.81 % of the anode material in the 4750 + 2500 μm size range, while 5 min of crushing led to a more even distribution, with 28.57 % in the smallest size range (−75 μm). Heat treatment followed by 2 min of crushing concentrated 52.38 % of the anode material in the 4750 + 2500 μm range. For the cathode material, 2 min of direct crushing distributed 24.49 % in the −2500 + 1250 μm range, while 5 min of crushing accumulated 38.80 % in the −75 μm range. Heat treatment and 2 min of crushing resulted in 56.05 % of the cathode material in the −75 μm range, indicating improved liberation. The combination of heat treatment and mechanical treatment effectively liberates and reduces the size of the active materials, resulting in a higher cumulative mass fraction of finer particles (≤630 μm). Heat treatment at 600 °C for 15 min, followed by 2 min of crushing, dissociated current collectors (Al) from the cathode material, minimized the harmful gas emissions and produced fine particles (630 μm) with minimal Al content (0.8 wt%). The optimal heat treatment condition of 600 °C for 35 min achieved complete decomposition of PVDF and enriched 73.49 wt% cobalt and 5.41 wt% lithium in the black mass, superior to previous studies.

Identification of the Problem in Controlling the Air–Fuel Mixture Ratio (Lambda Coefficient λ) in Small Spark-Ignition Engines for Positive Pressure Ventilators

The air–fuel ratio is a crucial parameter in internal combustion engines that affects optimal engine performance, emissions, fuel efficiency, engine durability, power, and efficiency. Positive pressure ventilators (PPVs) create specific operating conditions for drive units, characterized by a reduced ambient pressure compared to standard atmospheric pressure, which is used to control carburetor-based fuel supply systems. The impact of these conditions was investigated for four commonly used PPVs (with internal combustion engines) in fire services across the European Union (EU), using a lambda (λ), carbon dioxide (CO2), carbon monoxide (CO), and hydrogen carbon (HC) analyser for exhaust gases. All four ventilators were found to operate with lean and very lean mixtures, with their lambda coefficients ranging from 1.6 to 2.2. The conducted tests of the CO2, CO, and HC concentrations in the exhaust gases of all four fans show dependencies consistent with theoretical analyses of the impact of the fuel–air mixture on emissions. It can be observed that as the amount of burned air decreases, the values of CO and HC decrease, while the concentration of CO2 increases with the increase in engine load. Such an operation can accelerate engine wear, increase the emission of harmful exhaust gases, and reduce the effective performance of the device. This condition is attributed to an inadequate design process, where drive units are typically designed to operate within atmospheric pressure conditions, as is common for these engines. However, when operating with a PPV, the fan’s rotor induces significant air movement, leading to a reduction in ambient pressure on the intake side where the engine is located, thereby disrupting its proper operation.

Improving contaminant removal and inhibiting CH4 and H2S emissions from septic tanks: Nitrified human urine as a source of electron acceptor

Septic tanks are widely adopted in decentralized household wastewater treatment systems serving billions of people globally. Due to the lack of effective electron acceptors, insufficient nutrient removal and the emission of harmful gases, e. g. H2S, CH4, etc., are the common drawbacks. In the present work, we attempted to supplement nitrite into septic tanks as an electron acceptor, via nitrifying human urine source-separated from blackwater, to overcome these drawbacks. Partial or complete nitritation of source-separated urine was achieved in a sequencing batch reactor. The addition of nitrified urine into septic tanks improved organic and nitrogen removals in blackwater up to 90 % and 70 %, respectively. The emission of harmful gases from the septic tanks was stably diminished, with more than 75 % of CH4, CO2 and H2S reductions. Nitrite addition significantly reduced the abundance of hydrogenotrophic methanogens in septic tanks. Though the activity of sulfate-reducing bacteria recovered after the initial inhibition upon nitrite addition, the bio-generated H2S was retained in water since the increased wastewater pH after nitrite addition promoted the disassociation of H2S in aqueous solution.

Charging Station Power System Data Prediction Model Based on Deep Learning

The increase growth and adoption of electric vehicles (EVs) are playing a crucial role in advanced transportation system, helping to minimizing the emissions of harmful greenhouse gases and enhancing environmental sustainability. The need for EVs to be charged immediately has gained significant attention due to the rise in EVs sales over the last years. As a result of this, need for electric car charging is important to reducing the impact of electric network and providing minimum charging fares. In order to calculate the demand for charging EVs, a novel Deep Learning (DL)-based Long-Short Term Memory (LSTM) recurrent neural network predictor model is attempted to be developed in this research study. The Modified Aquilla Optimizer Algorithm (MAOA) is used to optimizing the parameter of the new Deep LSTM (DLSTM) neural predictor models and Independent Component Analysis (ICA) is utilized to solve the input time series data while conserving its properties. In this research, a novel ICA—AOA—DLSTM neural predictor model was developed to addressed the challenges of vanishing and exploding gradient in basic recurrent neural learnings. The predictor model was tested on the EV charging dataset from Georgia Tech, Atlanta, USA, indicating its performance. During simulation, the predictor achieved a prediction accuracies of 96.24% with a mean absolute errors of 0.1083 and a RMSE errors of 3.0629 × 10^-5, outperforming previous techniques. Additionally, the mean absolute error was found to be 0.2083, with a mean square error of 3.25516 × 10^-10. These results highlight the effectiveness of the novel deep learning LSTM neural predictor for this dataset compared to existing techniques.

The Evaluation and Prediction of Flame Retardancy of Asphalt Mixture Based on PCA-RBF Neural Network Model.

Warm mix flame retardant asphalt mixture can reduce the energy dissipation and harmful gas emissions during asphalt pavement construction, as well as mitigate the adverse effects of road fires. For this, this paper studies the design and performance of a mixture modified with a combination of warm mix agent and flame retardant, and the pavement performance and flame retardancy of the modified mixture are evaluated. Additionally, a flame retardancy prediction model based on the radial basis function (RBF) neural network model is established. On this basis, the principal components analysis (PCA) model is used to analyze the most significant evaluation indicators affecting flame retardancy, and finally, a three-dimensional finite element model is developed to analyze the effects of loading on the pavement structure. The results show that compared to virgin asphalt mixture, the modified mixture shows a reduction in mixing and compaction temperatures by approximately 12 °C. The high-temperature performance of the mixture is improved, while the low-temperature performance and moisture stability slightly decrease, but its flame retardancy is significantly enhanced. The RBF neural network model revealed that the established flame retardancy prediction model has a high accuracy, allowing for precise evaluation of the flame retardancy. Finally, the PCA model identified that the combustion time has a significant effect on the flame retardancy of the asphalt mixture, and the finite element model revealed that the displacements of the warm mix fire retardant asphalt mixture were lower than virgin asphalt mixture in all directions under the loading.

Solar energy generation from residential buildings, transition of the energy sector from fossils to carbon-free energy and meeting UN SDG

Purpose This study aims to focus on the transition of Kosovo’s energy generation sector from fossil fuels (94%), to renewable sources. The installation of 10 kW photovoltaic (PV) panels in individual houses will mitigate CO2 emissions from electrical energy generation and contribute meeting the sustainable development goals (SDGs; 7, 11 and 13) set by United Nations General Assembly. This study case is based on the installation of PV panels on the roofs, and where possible on the facades of the private residential buildings in seven, the most populated towns of Kosovo (Prishtina, Prizren, Mitrovica, Peja, Gjakova, Ferizaj and Gjilan). Design/methodology/approach This study used the data, in regard to direct normal irradiation, altitude, coordinates, PV system configurations, specific PV power output and optimum tilt of PV panels specific for the selected locations,retrieved from Global Solar Atlas, which is a web-based-tool, as provided by “Solargis,”a company that provides online and commercial solar data resources, selected by The World Bank and the International Finance Corporation. The second software was RETScreen Expert, which is more sophisticated and allows input of more variables with regard to the proposed 10 kW PV system. With the use of RETScreen Expert software, the financial viability of the project, the equity payback period, and the reduction in greenhouse gas (GHG) emissions compared to the base case were assessed. Based on the gained data, the feasibility and outcome of the study case were assessed in terms of power generation, cost and comparison with the present PV installed capacities in Kosovo. Findings Small-scale solar energy generated from individual buildings can make great impact of country’s policies toward lowering CO2 emission as one most influential greenhouse gas in rising average global temperature, improving air quality in towns by lowering emission of harmful gases and particulate matter (PM). As the study foresees installation of 10 kW of PV in residential houses, the calculated yearly energy generation would be around 15 MWh, which is twice of the average of real consumption of a household in Kosovo. This calculated energy generation from private houses is equal in capacity with generation of present PV parks that are connected on grid as reported from Transmission, System and Market Operator of the Republic of Kosovo. This proves that, if implemented, the study outcome would make Kosovo to meet the goal for a carbon free energy and meeting targets of at least three out of 17 SDG set by UNSC. Originality/value This paper’s model provides a ground for a transition of national energy sector from 90% fossils dependence to renewable energy sources (RES). Despite of some barriers such as cost of initial investment, energy storage, lack of government’s incentives and legislative base for households to become prosumer or at best energy self-sufficient buildings, this solution will make Kosovo harness its unused RES and meet targets of Paris Climate Agreement for net zero CO2 emissions from energy production by 2050 and SDG targets.

Electric vehicle charging by use of renewable energy technologies: A comprehensive and updated review

The majority of the vehicles in the world consuming fossil fuels that causes emissions of harmful greenhouse gases. In order to mitigate the emissions regarding the transport sector, Electric Vehicles (EVs) have attracted attentions. One of the main concerns in the development of EVs is supply of required power for the charging stations. Renewable energy systems would be clean and attractive options for supply of required power of charging stations. In recent years, several studies have investigated applications of renewable energy systems for charging stations of EV and analyzed different aspects of these technologies. This article reviews the research works on the design, optimization and performance investigation of charging stations coupled with renewable energy systems. Studies on EV charging systems powered by stand-alone or hybrid renewable energy systems are considered in the present article. According to the reviewed works, significant potential of renewable energy systems for supply of charging stations can be concluded. Different factors such as the applied technology and components, meteorological data of the installation site and operating condition influence the performance of these systems. Furthermore, it can be concluded that control design system can influence various aspects of EV charging technologies powered by renewable energy systems such as share of renewables in the grid-connected configurations and performance reliability. Challenges and opportunities associated with the development of these systems are provided in the current works and some recommendations are highlighted for the forthcoming studies and projects.