Search Results (3,210)

For nearly two centuries, electric drives have been used in transportation. Nevertheless, they were not always favored by designers. The century-long dominance of heat engines led to the disregard of numerous challenges associated with the operation of electric drive systems. One of these issues is the optimization of energy consumption by an electric vehicle. This publication proposes an electronic Energy Consumption Optimizer (ECO) that predictively uses information about the shape of the route and speed limits on its individual sections to control the motor speed and gear changes in the gearbox. This work presents the structure of the optimizer system and the developed control algorithms. Additionally, electric motor excitation control was used, which may have contributed to reducing the power and weight of the electric drive motor. Simulation studies carried out using WLTP test cycles and cycles from real road routes showed the potential to decrease energy consumption for vehicle movement by approximately 10%. Full article

The rapid expansion of renewable energy sources has led to increased instability in the power grid of Jeju Island, leading to the implementation of the plus demand response (DR) system, which aims to boost electricity consumption during curtailment periods. However, the frequency of curtailment owing to the increased utilization of renewable energy is outpacing the implementation of plus DR, highlighting the need for additional resources, such as energy storage systems (ESS). High initial investment costs have been the primary hindrance to the adoption of ESS by DR-participating companies but have not been fully considered in earlier studies on DR incentive determination. Therefore, this study proposes an algorithm for calculating appropriate incentives for plus DR participation considering the investment costs required for ESS. Based on actual load data, incentives are determined using an iterative mixed-integer programming (MIP) optimization method that progressively adjusts the incentive level to address the overall nonlinearity arising from both the multiplication of variables and the nonlinear characteristics of the internal rate of return (IRR), ensuring that the target IRR is achieved. A case study on the impact of factors such as IRR, ESS costs, and fluctuations in electricity rates on incentive calculations demonstrated that plus DR incentives required to achieve IRR targets of 5%, 10%, and 15% have increased linearly from 142.2 KRW/kWh to 363.0 KRW/kWh, confirming that the appropriate incentive level can be effectively determined based on ESS investment costs and target IRR. This result could help promote ESS adoption among DR companies and plus DR participation, thereby enhancing power grid stability. Full article

Electric-powered vehicles (EVs) allow for an environmentally friendly and economic alternative to fuel-running ones. However, such an alternative is expected to impose further usage hikes and periods of instability on cities’ power systems. From their perspective, cities need to scale their infrastructure grids to allow for adequate power resources to feed such new power-hungry consumers. Indeed, for such a green alternative to proceed, our power grids need to be ready to cope with any unexpected hikes in the power consumption rates without compromising the stability of the services provided to our homes and workplaces. Operators’ steps in this path are still modest, and the coverage of EV charging stations is still insufficient as they are trying to avoid any further costs for upgrading their infrastructures. The lack of price consideration for the charging services offered at charging stations may result in EV drivers paying higher costs compared to traditional fuel vehicles to charge their EVs’ batteries, hindering the economic incentive of owning such sorts of vehicles. Hence, it may take a while for sufficient coverage to exist. Although for drivers the adoption of EVs represents a city-friendly alternative with affordable expenses, it usually comes with range anxiety and battery charging concerns. In this work, we are presenting e-Fuel, a charge-sharing model that allows for preference-based mobile EV charging services. In e-Fuel, we are proposing a stable weight-based vehicle-to-vehicle matching algorithm, through which drivers of EVs will be capable of requesting instant mobile charge-sharing service for their EVs. In addition to being mobile, such charging services are customized, as they are chosen based on the drivers’ preferences of price-per-unit, charging speed, and time of delivery. The developed e-Fuel matching algorithm has been tested in various environments and settings. Compared to the benchmark price-based matching algorithm, the resulting matching decisions of e-Fuel come with balanced matching attributes that mostly allow for 6- to 7-fold shorter service delivery times for a minimal increase in service charges that vary between 9% and 65%. Full article

As advanced sensors and weapons require high power, naval vessels have increasingly adopted electric propulsion systems. This study aims to enhance the efficiency and operability of electric propulsion systems over traditional mechanical propulsion systems by analyzing the operational profiles of modern naval vessels. Consequently, a battery-integrated generator-based electric propulsion system was selected. Considering the purpose of the vessel, a specification selection procedure was developed, leading to the design of a hybrid electric propulsion system (comprising one battery and four generators). The power management control technique of the proposed propulsion system sets the operating modes (depending on the specific fuel oil consumption of the generators) to minimize fuel consumption based on the operating load. Additionally, load distribution control rules for the generators were designed to reduce energy consumption based on the load and battery state of charge. MATLAB/Simulink was used to evaluate the proposed system, with simulation results demonstrating that it maintained the same propulsion performance as existing systems while achieving a 12-ton (22%) reduction in fuel consumption. This improvement results in cost savings and reduced carbon dioxide emissions. These findings suggest that an efficient load-sharing controller can be implemented for various vessels equipped with electric propulsion systems, tailored to their operational profiles. Full article

This research paper presents an effective approach to reducing marine pollution and costs by determining the optimal marine alternative fuels framework for short-sea shipping vessels, with a focus on energy efficiency. Employing mathematical models in a Python environment, the analyses are tailored specifically for conventional and fully autonomous high-speed passenger ferries (HSPFs) and tugboats, utilizing bottom-up methodologies, ship operating phases, and the global warming potential approach. The study aims to identify the optimal marine fuel that offers the highest Net Present Value (NPV) and minimal emissions, aligning with International Maritime Organization (IMO) regulations and environmental objectives. Data from the ship’s Automatic Identification System (AIS), along with specifications and port information, were integrated to assess power, energy, and fuel consumption, incorporating parameters of proposed marine alternative fuels. This study examines key performance indicators (KPIs) for marine alternative fuels used in both conventional and autonomous vessels, specifically analyzing total mass emission rate (TMER), total global warming potential (TGWP), total environmental impact (TEI), total environmental damage cost (TEDC), and NPV. The results show that hydrogen (H2-Ren, H2-F) fuels and electric options produce zero emissions, while traditional fuels like HFO and MDO exhibit the highest TMER. Sensitivity and stochastic analyses identify critical input variables affecting NPV, such as fuel costs, emission costs, and vessel speed. Findings indicate that LNG consistently yields the highest NPV, particularly for autonomous vessels, suggesting economic advantages and reduced emissions. These insights are crucial for optimizing fuel selection and operational strategies in marine transportation and offer valuable guidance for decision-making and investment in the marine sector, ensuring regulatory compliance and environmental sustainability. Full article

Blockchain technology is an important technical basis for ensuring carbon trading and plays a fundamental role in maintaining fairness in the carbon trading market. This paper proposes a carbon emission metering and settlement method and a system based on blockchain technology which creates the digital identity of electric meters and stores it in the blockchain. Verifiable credentials are generated based on the digital identity, energy data, and time stamp. The system records the energy data read by the verified meter to the blockchain cloud platform for carbon emission statistics. In the payment and settlement stage, through application of the blockchain and its combination with a digital payment wallet, the regional energy network consumption settlement value is generated according to the regional power supply and electricity consumption, and the settlement value is used as the benchmark to measure the carbon emissions in the region. Through the data analysis of practical application cases in an industrial park in China, this study concludes that the carbon emission statistical settlement method based on blockchain technology solves the problems of untrustworthiness, unreliability, and inconsistency in the statistical and settlement methods during the statistical settlement of electric energy statistics and energy consumption carbon emissions. Full article

The transport sector heavily relies on the use of fossil fuels, which are causing major environmental concerns. Solutions relying on the direct or indirect use of electricity through e-fuel production are emerging to power the transport sector. To ensure environmental benefits are achieved over this transition, an accurate estimation of the impact of the use of electricity is needed. This requires a high temporal resolution to capture the high variability of electricity. This paper presents a previously unseen temporal analysis of the carbon intensity of e-fuels using grid electricity in countries that are members of the European Network of Transmission System Operators (ENTSO-E). It also provides an estimation of the potential load factor for producing low-carbon e-fuels according to the European Union legislative framework. This was achieved by building on top of the existing EcoDynElec tool to develop EcoDynElec_xr, a python tool enabling—with an hourly time resolution—the calculation, visualisation, and analysis of the historical time-series of electricity mixing from the ENTSO-E. The results highlight that, in 2023, very few European countries were reaching low carbon intensity for electricity that enables the use of grid electricity for the production of green electrolytic hydrogen. The methodological assumptions consider the consumption of the electricity mix instead of the production mix, and the considered time step is of paramount importance and drastically impacts the potential load factor of green hydrogen production. The developed tools are released under an open-source license to ensure transparency, result reproducibility, and reuse regarding newer data for other territories or for other purposes. Full article

The considerable heat generated in electronic devices, resulting from their high-power consumption and dense component integration, underscores the importance of developing effective thermal interface materials. While composite materials are ideal for this application, the random distribution of filling materials leads to numerous interfaces, limiting improvements in thermal transfer capabilities. An effective method to improve the thermal conductivity of composites is the alignment of anisotropic fillers, such as hexagonal boron nitride (BN). In this study, the repeat blade coating method was employed to horizontally align BN within a polydimethylsiloxane (PDMS) matrix, followed by flipping and cutting to prepare BN/PDMS composites with vertically aligned BN (V-BP). The V-BP composite with 30 wt.% BN exhibited an enhanced out-of-plane thermal conductivity of up to 1.24 W/mK. Compared to the PDMS, the V-BP composite exhibited outstanding heat dissipation capacities. In addition, its low density and exceptional electrical insulation properties showcase its potential for being used in electronic devices. The impact of coating velocity on the performance of the composites was further studied through computational fluid dynamics simulation. The results showed that increasing the coating velocity enhanced the out-of-plane thermal conductivity of the V-BP composite by approximately 40% compared to those prepared at slower coating velocities. This study provides a promising approach for producing thermal interface materials on a large scale to effectively dissipate the accumulated heat in densely integrated electronic devices. Full article

With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result. Full article

The power split plug-in hybrid electric bus (PHEB) boasts the capability for concurrent decoupling of rotation speed and torque, emerging as the key technology for energy conservation. The optimization of energy management strategies (EMSs) and powertrain parameters for PHEB contributes to bolstering vehicle performance and fuel economy. This paper revolves around optimizing fuel economy in PHEBs by proposing an optimization algorithm for the combination of a multi-layer rule-based energy management strategy (MRB-EMS) and powertrain parameters, with the former incorporating intelligent algorithms alongside deterministic rules. It commences by establishing a double-planetary-gear power split model for PHEBs, followed by parameter matching for powertrain components in adherence to relevant standards. Moving on, this paper plunges into the operational modes of the PHEB and assesses the system efficiency under each mode. The MRB-EMS is devised, with the battery’s State of Charge (SOC) serving as the hard constraint in the outer layer and the Charge Depletion and Charge Sustaining (CDCS) strategy forming the inner layer. To address the issue of suboptimal adaptive performance within the inner layer, an enhancement is introduced through the integration of optimization algorithms, culminating in the formulation of the enhanced MRB (MRB-II)-EMS. The fuel consumption of MRB-II-EMS and CDCS, under China City Bus Circle (CCBC) and synthetic driving cycle, decreased by 12.02% and 10.35% respectively, and the battery life loss decreased by 33.33% and 31.64%, with significant effects. Subsequent to this, a combined multi-layer powertrain optimization method based on Genetic Algorithm-Optimal Adaptive Control of Motor Efficiency-Particle Swarm Optimization (GOP) is proposed. In parallel with solving the optimal powertrain parameters, this method allows for the synchronous optimization of the Electric Driving (ED) mode and the Shutdown Charge Hold (SCH) mode within the MRB strategy. As evidenced by the results, the proposed optimization method is tailored for the EMSs and powertrain parameters. After optimization, fuel consumption was reduced by 9.04% and 18.11%, and battery life loss was decreased by 3.19% and 7.42% under the CCBC and synthetic driving cycle, which demonstrates a substantial elevation in the fuel economy and battery protection capabilities of PHEB. Full article

With the implementation of policies to promote renewable energy generation on the supply side, a micro-energy grid, which is composed of different electricity generation categories such as wind power plants (WPPs), photovoltaic power generators (PVs), and energy storage devices, can enable the local consumption of renewable energy. Energy storage devices, which can overcome the challenges of an instantaneous balance of electricity on the supply and demand sides, play an especially key role in making full use of generated renewable energy. Considering both minimizing the operation costs and maximizing the renewable energy usage ratio is important in the micro-energy grid environment. This study built a multi-objective optimization model and used the NSGA-III algorithm to obtain a Pareto solution set. According to a case study and a comparative analysis, NSGA-III was better than NSGA-II at solving the problem, and the results showed that a higher renewable generation ratio means there is less electricity generated by traditional electricity generators like gas turbines, and there is less electricity sold into the electricity market to obtain more benefits; therefore, the cost of the system will increase. Energy storage devices can significantly improve the efficiency of renewable energy usage in micro-energy grids. Full article

With an increasing emphasis on energy conservation, emission reduction, and power consumption management, port enterprises are focusing on enhancing their electricity load forecasting capabilities. Accurate electricity load forecasting is crucial for understanding power usage and optimizing energy allocation. This study introduces a novel approach that transcends the limitations of single prediction models by employing a Binary Fusion Weight Determination Method (BFWDM) to optimize and integrate three distinct prediction models: Temporal Pattern Attention Long Short-Term Memory (TPA-LSTM), Multi-Quantile Recurrent Neural Network (MQ-RNN), and Deep Factors. We propose a two-phase process for constructing an optimal combined forecasting model for port power load prediction. In the initial phase, individual prediction models generate preliminary outcomes. In the subsequent phase, these preliminary predictions are used to construct a combination forecasting model based on the BFWDM. The efficacy of the proposed model is validated using two actual port data, demonstrating high prediction accuracy with a Mean Absolute Percentage Error (MAPE) of only 6.23% and 7.94%. This approach not only enhances the prediction accuracy but also improves the adaptability and stability of the model compared to other existing models. Full article

The subject of this study is the optimization of the paper production process in one of Poland’s leading paper mills. In addition to its primary objective of paper production, the company generates heat and electricity for internal consumption and external clients, including the local municipality. Surplus energy may be sold on the power exchange; however, this requires forecasting the quantity of energy to be sold 24 h in advance, which introduces an element of uncertainty. Production stoppages, often caused by random events such as paper breakage, force a power decrease in the CHP system, further complicating energy forecasting. To minimize the occurrence of such events, numerical taxonomy methods were employed to determine the optimal screen speed (V_s) and winding speed (V_n) for two paper machines, based on the type and weight of the paper produced. This analysis utilized detailed daily data collected by the company over the period 2015–2020. The findings contribute to minimizing the occurrence of paper roll tearing, thereby reducing the risk of inaccurate forecasts of the energy and heat produced by the CHP system. Furthermore, the methodology employed in this study may be effectively applied to other optimization problems in industrial processes. Full article

Agricultural water scarcity has become a global issue. Optimizing irrigation water quality and effectively utilizing non-conventional water resources are essential strategies to alleviate pressure on agricultural water use and achieve sustainable development. This study employed Italian lettuce as the test crop to explore the effects of magnetization treatment (M) at a magnetic field strength of 0.2 T and various irrigation water sources (T) on its growth. The following six treatments were established: fresh water irrigation (M0T1), recycled water irrigation (M0T2), saline water irrigation (M0T3), magnetized fresh water irrigation (M1T1), magnetized recycled water irrigation (M1T2), and magnetized saline water irrigation (M1T3). The results showed that the magnetization treatment increased the electrical conductivity (EC), power of hydrogen (pH), and dissolved oxygen (DO) of the three water sources compared to the non-magnetized treatment. Furthermore, magnetized irrigation with fresh water, recycled water, and saline water increased the contents of nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) in lettuce. It also led to increases in the contents of soluble proteins (by 9.27% to 22.25%), soluble sugars (by 13.45% to 20.50%), and vitamin C (VitC) (by 4.18% to 19.33%) in lettuce. Additionally, it enhanced the above-ground fresh weight of lettuce (by 9.36% to 8.81%) and water productivity (WPc) (by 5.85% to 10.40%), while reducing water consumption. Among these treatments, magnetized fresh water irrigation was the most effective in improving quality, fresh weight, and WPc, followed by magnetized recycled water. Gene expression analysis revealed that differentially expressed genes (DEGs) were primarily enriched in metabolic pathways such as the MAPK signaling pathway—plant, phytohormone signaling, and cysteine and methionine metabolism. In summary, magnetized irrigation significantly enhanced DO levels in irrigation water, along with the fresh weight, quality, and WPc of lettuce, demonstrating its effectiveness as an efficient method for agricultural irrigation. Full article

As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A key issue is how to adopt charging strategies suited to varying conditions to reduce the operational capacity loss caused by charging. This paper proposes a simulation-based optimization method for AGV charging strategies in U-shaped ACTs based on an improved Proximal Policy Optimization (PPO) algorithm. Firstly, Gated Recurrent Unit (GRU) structures are incorporated into the PPO to capture temporal correlations in state information. To effectively limit policy update magnitudes in the PPO, we improve the clipping function. Secondly, a simulation model is established by mimicking the operational process of the U-shaped ACTs. Lastly, iterative training of the proposed method is conducted based on the simulation model. The experimental results indicate that the proposed method converges faster than standard PPO and Deep Q-network (DQN). When comparing the proposed method-based charging threshold with a fixed charging threshold strategy across six different scenarios with varying charging rates, the proposed charging strategy demonstrates better adaptability to terminal condition variations in two-thirds of the scenarios. Full article