Electricity Usage Research Articles

Predicting tropospheric nitrogen dioxide column density in South African municipalities using socio-environmental variables and Multiscale Geographically Weighted Regression.

Atmospheric nitrogen dioxide (NO2) pollution is a major health and social challenge in South African induced mainly by fossil fuel combustions for power generation, transportation and domestic biomass burning for indoor activities. The pollution level is moderated by various environmental and social factors, yet previous studies made use of limited factors or focussed on only industrialised regions ignoring the contributions in large parts of the country. There is a need to assess how socio-environmenral factors, which inherently exhibit variations across space, influence the pollution levels in South Africa. This study therefore aimed to predict annual tropospheric NO2 column density using socio-environmental variables that are widely proven in the literature as sources and sinks of pollution. The environmental variables used to predict NO2 included remotely sensed Enhanced Vegetation Index (EVI), Land Surface Temperature and Aerosol Optical Depth (AOD) while the social data, which were obtained from national household surveys, included energy sources data, settlement patterns, gender and age statistics aggregated at municipality scale. The prediction was accomplished by applying the Multiscale Geographically Weighted Regression that fine-tunes the spatial scale of each variable when building geographically localised relationships. The model returned an overall R2 of 0.92, indicating good predicting performance and the significance of the socio-environmental variables in estimating NO2 in South Africa. From the environmental variables, AOD had the most influence in increasing NO2 pollution while vegetation represented by EVI had the opposite effect of reducing the pollution level. Among the social variables, household electricity and wood usage had the most significant contributions to pollution. Communal residential arrangements significantly reduced NO2, while informal settlements showed the opposite effect. The female proportion was the most important demographic variable in reducing NO2. Age groups had mixed effects on NO2 pollution, with the mid-age group (20-29) being the most important contributor to NO2 emission. The findings of the current study provide evidence that NO2 pollution is explained by socio-economic variables that vary widely across space. This can be achieved reliably using the MGWR approach that produces strong models suited to each locality.

Prediction of Electric Vehicle Mileage According to Optimal Energy Consumption Criterion

In the field of electric vehicle usage, an inherent challenge lies in the restricted mileage capacity prior to requiring a recharge, hindering broader acceptance of electric vehicles. To alleviate this concern, enhancing the comprehension of vehicle energy consumption and range plays a pivotal role in easing the anxieties of electric vehicle drivers. Within this context, a novel model-based predictive approach is introduced for estimating electric vehicle energy consumption. This method considers the vehicle's specific parameters, the road network's topology, and actual traffic conditions. Through the macro model of electric vehicle energy consumption, real-time summary data can be extracted using conventional map-based web services. By representing the road network as a weighted directed graph tailored to the energy consumption model, an algorithm aids in mileage optimization by determining the optimal path for immediate use. The resultant motion range from this approach offers improved precision and dependability in contrast to conventional strategies based on average consumption and distance.

Safety in e-Scooters: a Machine-Learning Approach for Online Second Passenger Detection

Electric scooters are pivotal for urban mobility, yet safety concerns still prevent widespread adoption. Research identifes rider negligence, such as carrying a second passenger, as a major risk. To address this issue, we propose an autonomous system for real-time detection of second passengers. By analyzing vehicle dynamics through minimal sensors and employing an interpretable machine learning approach, our solution ensures accuracy and interpretability. Rigorous testing with diverse users validates its effectiveness, showcasing adaptability to user characteristics and road conditions, proving the potential of this approach to foster safer electric scooter usage.

The impact of residence location and the accessibility of alternative electric micro-mobility vehicles on electric scooter usage patterns in Hungary

The impact of residence location and the accessibility of alternative electric micro-mobility vehicles on electric scooter usage patterns in Hungary

A novel reinforcement learning framework for disassembly sequence planning using Q-learning technique optimized using an enhanced simulated annealing algorithm

Abstract The increase in Electrical and Electronic Equipment (EEE) usage in various sectors has given rise to repair and maintenance units. Disassembly of parts requires proper planning, which is done by the Disassembly Sequence Planning (DSP) process. Since the manual disassembly process has various time and labor restrictions, it requires proper planning. Effective disassembly planning methods can encourage the reuse and recycling sector, resulting in reduction of raw-materials mining. An efficient DSP can lower the time and cost consumption. To address the challenges in DSP, this research introduces an innovative framework based on Q-Learning (QL) within the domain of Reinforcement Learning (RL). Furthermore, an Enhanced Simulated Annealing (ESA) algorithm is introduced to improve the exploration and exploitation balance in the proposed RL framework. The proposed framework is extensively evaluated against state-of-the-art frameworks and benchmark algorithms using a diverse set of eight products as test cases. The findings reveal that the proposed framework outperforms benchmark algorithms and state-of-the-art frameworks in terms of time consumption, memory consumption, and solution optimality. Specifically, for complex large products, the proposed technique achieves a remarkable minimum reduction of 60% in time consumption and 30% in memory usage compared to other state-of-the-art techniques. Additionally, qualitative analysis demonstrates that the proposed approach generates sequences with high fitness values, indicating more stable and less time-consuming disassembles. The utilization of this framework allows for the realization of various real-world disassembly applications, thereby making a significant contribution to sustainable practices in EEE industries.

Review on li-ion battery model used in electrical vehicle

In recent years, battery models have become increasingly crucial, particularly with the surge in electric vehicle usage within the transportation sector. Accurate models are essential for assessing battery performance and devising effective battery management systems. Various modeling approaches exist in literature, each carrying its own set of advantages and drawbacks. Typically, more intricate models offer precise results but demand greater computational resources and entail time-consuming, costly laboratory tests for parameter determination. Hence, for first stage assessments and battery managing system designs, models employing simpler parameter documentation procedures are the most viable and suitable options. The presence of a precise battery model within a simulation platform holds immense significance in crafting efficient battery-powered systems. Absolutely! The lithium-ion battery model is a standout option owing to its potential as an energy storage system, particularly for renewable technology applications. This is largely due to its remarkable power compactness and energy storage capabilities.

Comparative Economic Study of Electric Cars in Indonesia

As a step to reduce C02 emissions, the Indonesian Government has issued various regulations, one of the aims of which is to encourage the use of electric cars as environmentally friendly vehicles. According to Gaikindo (2022), the number of electric cars sold in 2022 of only 15,437 units. The figure has increased by 383.46% compared to 2021 however, the number of electric car users in Indonesia is still quite low when compared to the Indonesian Government’s target of 2 million units for switching from conventional/Internal Combustion Engine (ICE) cars to electric-based cars by 2030. Economic considerations are a significant barrier to efforts to electrify conventional cars. This research aims to provide an economic analysis of electric cars and conventional cars in Indonesia through Total Cost of Ownership (TCO) and Equipment Uniform Annualized Cost (EUAC) calculations. According to the TCO calculation, the best ownership time for an electric car at a distance of 10000-25000 KM is between 7 to 9 years. The deeper investigation through EUAC calculation showed that the economic lifetime of electric cars at a distance of 10000-25000 KM is 9 years compared with other ICE cars in the same segment.

Assessing performance and satisfaction of micro-mobility in smart cities for sustainable clean energy transportation using novel APPRESAL method

South Korea signed the Global Methane Pledge to reduce methane emissions by 30% by 2030, updated its Nationally Determined Contribution (NDC) to target a 40% reduction in GHGs (Green House Gas) from 2018 levels by 2030. In addition, South Korea submitted its Long-Term Strategy (LTS) to achieve carbon neutrality by 2050. Nowadays, to mitigate the GHG emission every country explicitly planning to reduce non-renewable energy resource vehicles. The electric vehicle (EV) markets growing rapidly with various technologies, strategies and innovations to support decarbonization. In 2022, by the transportation CO2 (Carbon dioxide) emission report of International Energy Agency (IEA), the cars and vans contributes 48% in the over all CO2 emissions. Increasing the electric micro-mobility service would be one of the best effective approach in accordance to reduce CO2 emission. This research study support to reduce the GHG emission by increasing the clean energy vehicle. A case study conducted to analyze firm-wise e-scooter sharing service performance and its satisfaction in smart cities of South Korea. Lacking in the consistent good quality and performance affects the number of electric vehicle users. Hence, electric vehicle firm should focus into their product better performance and quality. This research supports to analyze the e-scooter based on the various criteria from the user’s perspective. This research study helps to firms to identify their lacking criteria and improve their quality and performance. We considered the performance, accessibility, tangibility, reliability and responsiveness factors which affects user’s perspective in e-scooter sharing services. A survey was conducted over sixty user’s for different e-scooter services with twenty four factors. Generally, MCDM (Multi-Criteria Decision-Making) techniques holds two phases to determine the weight of the criteria and another phase to ranking the alternatives. The existing MCDM techniques lacks to handle the user’ satisfaction index analysis and not considering the influence grade of each factors in the analysis. To overcome this draw back and analyze the micro-mobility services based over the user’s point of view, we introduced a novel fuzzy based MCDM method name as Approach for Preference, Performance and Ranking Evaluation with SAtisfaction Level (APPRESAL) approach. The findings show that Lime, a micro-mobility firm, outperformed other firms with high-quality service and user satisfaction, followed by Wind, XingXing, Alpaca, and Beam. The influences of defected factors from the accessibility, reliability, responsiveness, and assurance dimensions had an adverse effect on quality of firm’s micro-mobility service with unsatisfactory performance.

A Scientometric Analysis of electric vehicle-based text mining

The United Nations Climate Change Conference (COP 21) held in Paris on December 12, 2015, agreed on the Paris Agreement, which aims to address climate change and its negative impacts as part of the challenge of Global Warming. The transportation sector contributed 23% of CO2 emissions of a global contributor in 2018. Electric vehicles themselves have benefited from an environmental and economic perspective. The development of electric vehicles in Indonesia has increased the number of electric vehicle users as well as research related to customer awareness, opinions, preferences, and satisfaction, identification of markets and potential for technology and innovation, predictions, new policies, literature reviews and topics, keywords and fields related to electric vehicle. From the results of this study, it is expected to be able to find out customer characteristics, customer concerns, market penetration, model social impact data, predict market growth and technology developments, and formulate policies and regulations according to the needs of stakeholders Therefore, a systematic review is needed regarding the publication of electric vehicles based on text mining, then analyzes scientometric analysis Bibliographic database files from 2011 – 2023.

A Market Strategy for Joint Profitability of Electric Vehicle Charging Stations and Retailers: Considering Electricity Price Fluctuations and Users’ Service Fee Response

With the introduction of the “carbon peaking and carbon neutralizing” initiative, China is undergoing a positive energy transition, emphasizing the replacement of traditional fossil fuels with clean energy sources. As part of this transition, numerous megacities are progressively endorsing electric vehicles (EVs) as substitutes for traditional fuel‐based vehicles. The expanding use of EVs, however, has presented challenges related to the randomness and volatility of EV charging. In response to this, many regions in China have mandated the implementation of the time‐of‐use (ToU) scheme. This scheme divides the day into distinct periods such as “valley,” “flat,” “peak,” and “critical peak,” each associated with varying charging prices. According to the management regulations, the fees collected from vehicle owners at the charging station are divided into two parts: ToU electricity value and service fees. The former is jointly determined by the power company and the government, while the latter is set by the charging station operator. To overcome the limitation on profitability, this paper proposes a scheme enabling charging stations and retailers to participate in the day‐ahead (DA) market. The proposed approach involves a two‐stage decision‐making method. By analyzing the charging habits of local residents, the paper constructs a quadratic curve for dynamic service fees and charging rates. The objective function is then utilized to adjust the anticipated charging load curve. Prior to the DA‐bidding process, an uncertain set of scenarios for electricity prices and charging loads is generated. The conditional value‐at‐risk (C‐VaR) method is employed to balance expected benefits and risks amid uncertain scenarios.

Optimal Scheduling of Regional Integrated Energy System Considering the Integration of Electric Vehicles and the Life Cycle Assessment Method

With the increasing scale of renewable energy and electric vehicles (EVs), the optimization scheduling of the regional integrated energy system (RIES) is facing new challenges due to the integration of EVs and new energy into the system. To realize the low-carbon economic operation of the RIES, a bi-level optimization scheduling strategy for low-carbon economic operation of the RIES is proposed, which considers EVs' participation and lifecycle carbon emissions, and integrates the electricity market and carbon trading market. Firstly, there are many factors affecting the carbon emissions of EVs and the difficulties in prediction. From the indirect carbon emissions during the use of EVs, the relationship between the carbon emissions of EVs and the charging mode and carbon emission sources is comprehensively analyzed. A carbon emission model of EVs based on the Life Cycle Assessment (LCA) is established in this paper. Secondly, the charging model of EVs based on demand response is combined with the electricity market; The carbon emission model of EVs based on the LCA is combined with the carbon trading market, and a bi-level scheduling strategy based on the flexible interaction between carbon and electricity is proposed. Then the strategy is solved to minimize system economic costs and carbon transaction costs. Finally, simulation example analysis is conducted to demonstrate that the proposed strategy can effectively reduce the total charging costs of electric vehicle users, improve the utilization rate of renewable energy, and reduce carbon dioxide emissions, with good economic and environmental benefits.

Electromobility: Safety or a Threat to Poland’s Transport?

The trend following the purchase of electric-powered vehicles is currently the biggest challenge of the Polish automotive industry. Increasing restrictions and looming deadlines for selling electric-only cars contribute to their increasing purchase numbers. It is known that electric vehicles require a stable charging infrastructure. From 2020 to 2022, infrastructure development is significant in Poland, but is it sufficient? Is this the main drawback of owning an electric vehicle? More and more users are charging their cars locally at their places of residence, but not everyone has this option. This raises the question of whether it is safe to own an electric vehicle. Is it possible to cover the route from point A to point B without much trouble? Awareness of the need to stop longer to charge an electric vehicle is not a problem among the Polish public; a bigger and more significant issue is the number of publicly available charging points. Therefore, these topics will be analyzed more extensively in this article; the possibilities of alternative charging and energy harvesting methods will be presented, as the growing interest in electric-powered vehicles and the indicated questions regarding the development of charging infrastructure and safety for electric vehicle users will be answered.

Shifting Gears: Insights into Electric Vehicle Adoption and User Satisfaction in Southern Kerala, India

This study examines how consumers in Kerala, India, think about EV adoption and satisfaction levels with conventional automobile ownership. The research indicates a significant preference for electric vehicles (EVs) among youthful, educated men, especially in the two-wheeler market controlled by companies such as Ather and Ola, based on a thorough examination of demographic characteristics and vehicle preferences. Due mostly to reduced operating costs and government incentives, long-time drivers of conventional vehicles are switching to electric vehicles (EVs). In comparison to economic issues, environmental concerns are less influential in shaping decisions, despite their importance. Particularly in terms of financial gains and operational efficiency, the results point to increased levels of satisfaction among EV users. In addition to outlining opportunities and challenges in fostering widespread EV adoption, this report highlights Kerala’s changing sustainable transportation scenario.

Alleviating range anxiety with ‘Enroute’: A real-time mobile solution and infrastructure innovation for electric vehicle users

Amidst the growing adoption of electric vehicles (EVs), this study addresses the prominent issue of "range anxiety" encountered by EV users when confronted with critical charging situations during their journeys. The research background emphasises the constraints of the existing charging infrastructure, primarily designed for urban areas, leaving EV drivers susceptible to unforeseen charging emergencies in remote or less-travelled regions. To tackle this challenge, an innovative mobile application that provides a real-time solution by harnessing crowd-sourced data and forging partnerships with charging infrastructure providers was developed. This app assists users in locating available charging stations. It offers the option to book a service that can bring the necessary charging equipment to a specified location (similar to a porter service), ensuring timely assistance. The research methodology involved user surveys and data analysis, specifically emphasising seamless integration with EVs’ onboard systems, making support practical and readily accessible. The results demonstrate the effectiveness of this app in alleviating range anxiety and enhancing EV users’ confidence in long-distance travel. In conclusion, this paper outlines a promising solution to a pressing issue in electric mobility. It underscores the importance of addressing the charging infrastructure gap for the widespread adoption of EVs. The future scope of this research involves further refinement of the app, expanded partnerships with charging networks and Porter service providers, and continued collaboration with EV manufacturers to ensure seamless integration and user satisfaction, ultimately advancing the cause of sustainable transportation.

A multi-objective site selection of electric vehicle charging station based on NSGA-II

The planning of charging infrastructure is crucial to developing electric vehicles. Planning for charging stations requires considering several variables, including building costs, charging demand, and coverage levels. It might be advantageous to use a multi-objective optimization method based on the NSGA-II. We need to address the current problems in choosing the location of electric vehicle charging stations. Firstly, urban land use is divided into five functional areas, and the TF-IDF algorithm is applied to the division of functional areas. A combined clustering algorithm is proposed to cluster POIs in functional areas into several clusters and determine the cluster centers as charging demand points. We Analyze charging practices and travel patterns of electric car users, fit the charging likelihood of various functional regions, and calculate the charging demand of each charging demand point in the study area. Introduce the NSGA-II algorithm and consider the charging station's progressive coverage to fit the actual area covered by the charging station.Taking the maximization of system benefits and the maximization of the minimum coverage level as the optimization objectives to carry out multi-objective optimization. Finally, we take the charging station planning in the urban area of Hohhot as an example and provide different site selection planning schemes. The planning schemes for different numbers of charging stations are analyzed to obtain a charging station planning scheme that takes into account both objectives.

Robo-Chargers: Optimal Operation and Planning of a Robotic Charging System to Alleviate Overstay

Charging infrastructure availability is a major concern for plug-in electric vehicle users. Nowadays, the limited public chargers are commonly occupied by vehicles which have already been fully charged. Such phenomenon, known as overstay, hinders other vehicles' accessibility to charging resources. In this paper, we analyze a charging facility innovation to tackle the challenge of overstay, leveraging the idea of Robo-chargers - automated chargers that can rotate in a charging station and proactively plug or unplug plug-in electric vehicles. We formalize an operation model for stations incorporating Fixed-chargers and Robo-chargers. Optimal scheduling can be solved with the recognition of the combinatorial nature of vehicle-charger assignments, charging dynamics, and customer waiting behaviors. Then, with operation model nested, we develop a planning model to guide economical investment on both types of chargers so that the total cost of ownership is minimized. In the planning phase, it further considers charging demand variances and service capacity requirements. In this paper, we provide systematic techno-economical methods to evaluate if introducing Robo-chargers is beneficial given a specific application scenario. Comprehensive sensitivity analysis based on real-world data highlights the advantages of Robo-chargers, especially in a scenario where overstay is severe. Validations also suggest the tractability of operation model and robustness of planning results for real-time application under reasonable model mismatches, uncertainties and disturbances.

Flux potentials of local talc in low voltage electro-porcelain insulator production

Increasing demand for accessories like porcelain electric insulators have continued in developing countries causing overdependence on imported products. Meanwhile, raw materials for their local production are available in commercial quantities with high firing temperature being the major challenge. Hence, the need for fluxing agents that can reduce the working temperature led to the investigation of Talc as a partial replacement of conventional Feldspar. An optimum replacement of 10% Feldspar with Talc was adopted, and this recipe showed microstructurally enriched glassy phase, primary and secondary Mullite with other crystalline phases like Cristobalite, Microcline and Albite at 1200oC with micropores evident in both the Talc and Feldspar formulations. The produced insulator fired at 1200oC offered an optimum thermal conductivity of 0.2768W/mK with reductions in water absorption, apparent porosity and linear shrinkage by 78.29%, 76.02% and 19.75% respectively with a 9.48% rise in bulk density. The electrical performance comprised Inception, Withstand and Breakdown Voltages of 10.2 kV, 14 kV and 20 kV respectively at leakage currents of 0.2 mA, 1.2 mA and 2 mA accordingly. With a withstand voltage above 11 kV and breakdown voltage below 22 kV, this pin-type porcelain insulator will find wide application in both electric substations and end user load points like commercial and residential electric power lines. The novelty lies on the fluxing and filler potentials of Talc as a locally-sourced mineral in porcelain insulator production which increases the market value of Nigerian Talc while reducing importation, costs of energy and raw materials in porcelain production.

Optimal consumer capacity investment in solar panel system under supply and demand uncertainty

Our paper analyzes the optimal capacity investment in solar panels system for consumers. During the daytime, solar panel system absorbs energy from the sun and supplies the energy generated to the building structure. Our model takes into consideration the variations in the daily consumption of electricity and the variations in the daily available sun energy. We model these variations in our paper as uncertainty in energy demand and uncertainty in energy supply. Excessive usage of electricity that is beyond the energy generation of the solar panels are bought at a higher price from the utility company than the payment received from the utility company for excess energy sold to the grid. We analytically solve for the optimal quantity of solar panels to purchase and install. Our paper fills the research gap in quantifying the impact changes in solar energy yield rate, consumption, and sell-back rate has on the consumer’s optimal investment decision in solar panels. We find that the optimal quantity of solar panels increases with the cost of energy during the daytime and payment for excess energy sold to the grid. Interestingly, when solar yield increases, the optimal solution only increases initially, and then it will eventually decrease. Furthermore, our analytical models provide consumers with a decision support system to determine the optimal number of solar panels to purchase and install while taking into consideration the random yield of solar panels, variations in energy consumption, and potential for sell back of excess unused energy generated under complex billing structures from the utility company. Our mathematical models are adaptable to policy changes such as rebates and purchase and sell back rates for electricity. Our numerical study validates the mathematical models and further analyzes the impact changes in solar energy yield rate, energy consumption, and sell-back coefficient have on the consumer’s capacity investment decision in solar panels. Our numerical studies find that the lower the reliability factor of solar yield, the more impact the daytime energy cost has on the optimal quantity. When the sell-back coefficient exceeds a certain threshold, the optimal quantity of solar panels increases exponentially.

Research on low-carbon operation mode of coal mine based on life cycle

Based on the theoretical framework of the whole life cycle theory and the specific production process of underground mines, this research focuses on a comprehensive analysis of the carbon emission accounting boundaries and sources within the entire life cycle of underground mining. Furthermore, a robust carbon emission accounting model is developed specifically for the entire life cycle of coal production. Subsequently, a case study involving an actual coal mine production scenario is conducted, wherein the model is utilized for analysis and calculation purposes. The comprehensive analysis and calculation results reveal that electricity usage constitutes the primary source of carbon emissions within coal mines, closely followed by methane escape and greenhouse gas emissions resulting from fossil fuel combustion. Based on these findings, the research then proposes strategies for low-carbon operations and the implementation of carbon reduction technologies in coal mines. By conducting a detailed analysis of the carbon emission accounting within the whole life cycle of underground mining and providing concrete findings and practical recommendations, this paper contributes to the understanding and management of carbon emissions in the coal production sector.

Solar power control system on smart green home

The need for fossil fuel electricity usage is increasing every year. If used for a long period of time, it will eventually run out. To reduce the use of fossil fuel energy, other alternatives are needed to maintain the availability of natural resources, one of which is by utilizing renewable energy. Utilization of solar power as an electrical energy source using photovoltaic panels. In its application, a solar power plant requires a controller that can regulate the energy produced. This controller is responsible for regulating the process of selecting the battery to be used. The working principle of this device is that when the battery reaches the minimum voltage threshold, the battery usage will be diverted to another battery with a voltage above the minimum threshold. The results of the research show that the system is capable of performing switching processes well using the smart switching method with a 100% success rate. The system uses an INA219 sensor for reading voltage and current from the battery. This research uses a 10-watt DC lamp, 20-watt DC lamp, and 30-watt DC lamp as loads. The battery can last for 4,8 hours by using a 10-watt DC lamp as a load, 2,4 hours by using 20-watt DC lamps as a load, and 1,6 hours by using 30-watt DC lamp as a load.