Source Of Electrical Energy Research Articles

Green hydrogen, power, and heat generation by polymer electrolyte membrane electrolyzer and fuel cell powered by a hydrokinetic turbine in low-velocity water canals, a 4E assessment

Thousands of kilometers of man-made low-velocity water transfer canals around the world can be used as a source of renewable energy for electricity and green hydrogen production. These canals have not been well investigated as an energy source, according to the literature. In the present paper, three technologies of Hydrokinetic Turbine (HKT), Polymer Electrolyte Membrane Fuel Cell, and Electrolyzer (PEM-FC/EL) are utilized to produce electricity, green hydrogen, and heat using these canals. Thermodynamic, economic, and environmental models of the cycle are presented, coded in the Engineering Equation Solver software, and finally validated with published research and manufacturers’ data. Two scenarios were examined, first HKT, PEMEL, and PEMFC were used for electricity generation (power-to-hydrogen-to-power, P2X2P) and second only HKT and PEMEL were used for green hydrogen production (power-to-hydrogen, P2X). While both scenarios are economical, the P2X scenario has a smaller payback period (less than 2 years) and a higher net present value. Practical correlations are derived to determine the rate of hydrogen production, power generation, and emission reduction as a function of water velocity. The round-trip energy and exergy efficiency of the system is 46.17% and 20.78% and it reduces carbon dioxide by 0.874 tons/year when water velocity is 1.5 m/s.

Retraction notice to ‘Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources’ [Energy 263 (2023) 125695

Retraction notice to ‘Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources’ [Energy 263 (2023) 125695

Metode Simple Additive Weighting (SAW) pada Pemilihan Jenis Lampu Penerang Jalan Berbasis Photovoltaic

The simple additive weighting method is a decision support system method for various decision selection problems. Many research topics use the SAW method as a reference method in a decision support system. The street lighting control system is very useful for residents of the village of Karanganyar, Paiton, Probolinggo. This lighting is the main spotlight for residents' activities at the village office. Such as August competitions, routine monthly recitations, competitions to commemorate Isra' Mi'raj and the Prophet's birthday. There are many types of lamps in stores. The criteria for lamps are that they have high light illumination, long-lasting light, large bulb size, so that they can help village residents carry out activities at night. This lamp also uses photovoltaic as an additional source of electrical energy so it can save electricity costs to PLN. The solar panel measures 550 x 360 x 25 mm, 20 volts and has a peak power of 20 watts

Energy and Environmental Aspects of the Sustainability of Clothing Production

The textile and clothing industries are very often lumped together when it comes to the environmental aspect such that the negative connotation of the textile industry from an environmental aspect is automatically transferred to the clothing industry. However, the two industries should be considered separately, particularly with regard to the machinery used and energy consumption in the production process. The energy consumption of electricity, compressed air, vacuum, steam, and other energy sources in the clothing industry is low compared to other related industries. Furthermore, no carcinogenic and allergenic waste is generated during the production of clothing, which has a low carbon footprint, i.e., it practically does not pollute the air, soil, and water. The waste produced during cutting is clean and unused and is immediately recycled. All of this contributes to the sustainability of the clothing industry from the energy and environmental aspects. This article describes the cutting and joining techniques used in the manufacture of clothing, from the energy and environmental aspects as well as aspects of the weaves, the necessary machine elements and mechanisms, and the energy used in all joining techniques, from which the above claims and facts can be seen.

A new framework for ultra-short-term electricity load forecasting model using IVMD–SGMD two–layer decomposition and INGO–BiLSTM–TPA–TCN

Accurate forecasting of electricity loads is crucial for the development of electricity scheduling and supply services. With the increase in distributed electricity energy sources and the complexity of electricity systems, the strong volatility of load changes brings more challenges to the reliability of load forecasting. Therefore, we propose a novel ultra-short-term electricity load forecasting model using improved two-layer decomposition and an improved deep learning model with temporal pattern attention based on improved northern goshawk optimization (INGO). First, the load data were decomposed thoroughly using the two-layer decomposition model of improved variational mode decomposition (IVMD) with parameter optimization by INGO and symplectic geometry mode decomposition (SGMD) to improve the interpretability of the subsequences. Subsequently, INGO is used to optimize the parameters in bidirectional long short-term memory (BiLSTM). Temporal pattern attention (TPA) is added to BiLSTM, which extracts complex relationships from the hidden neurons of BiLSTM and selects relevant information from different time scales. After predicting and reconstructing the subsequences, the temporal convolutional network (TCN) prediction model is used to perform error correction to improve the final prediction accuracy. Because many government reports and policy information are summarized and published quarterly, to provide information support, we divide the electricity load datasets of two countries by quarters before forecasting. By performing multiple sets of experiments on the two datasets, it is demonstrated that the proposed model has high precision and robustness, and the obtained ultra-short-term electricity load forecasting results can accurately fit the load fluctuation trend.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Awareness of Campus Building Users to Achieve Energy Efficiency in Facing the Implementation of Green Buildings (Case Study: Politeknik Negeri Pontianak Integrated Lecture Building)

Abstract Building users’ awareness is a very crucial factor in efforts to achieve efficient energy use in buildings, especially in the green building concept. However, research that examines the awareness of building users specifically in the campus environment is still very rarely conducted, especially in Indonesia. This research, which uses a case study at Politeknik Negeri Pontianak integrated lecture building, investigates the criteria for awareness of building users and their behavior in using energy in the building. This research tries to identify social parameters in achieving building energy efficiency using a quantitative approach. The series of statistical tools used includes ranking of average values. The results of this research show that the focus of improvement can be done by increasing awareness of several things, namely recognizing the problem of decreasing electrical resources, dividing resource use in order to reduce electricity consumption, and understanding efforts and making preparations in the face of reduced electrical energy sources.

Optimal Control Strategy for Power Management Control of an Independent Photovoltaic, Wind Turbine, Battery System with Diesel Generator

The need for a greater supply of energy from sustainable sources is growing because of increasing energy prices, concerns about nuclear power, climate change, and power grid disruptions. This research offers a method for the balance of power management of a combination of multi-source DC and AC supplier systems that enables sources of clean energy based on an independent grid to function economically and with the highest levels of system predictability and stability possible. The DC microgrid's hybrid generation source consists of a diesel power source, wind, photovoltaic (PV) power, and a battery bank. The energy system can fulfill the load demand for electricity at any moment by connecting various renewable sources. It can function both off and on the grid. The microgrid may occasionally not be able to provide sufficient electricity, while every green energy source's electricity contribution is based on how its supply varies and how much power is needed to meet demand. As a result, a diesel generator is required as additional backup power, particularly while operating off-grid. This paper designs and implements an MPPT technique for a PV system based on the GWO algorithm. By creating PWM pulses in response to variations in the PV panel voltage, this method modifies the converter's duty cycle, while wind turbines using MPPT based on P&O, to get the most out of hybrid energy sources that are renewable while simultaneously enhancing the quality of power. The priority sources of electricity for the grid are photovoltaics and wind power. Based on the results of simulations and experiments, the proposed control method for DC, which uses the MPPT approach, can dynamically switch between all of the system's various modes of operation, independent of the battery's condition or environment, ensuring safe operation and constant bus voltage. An analysis was conducted on the suggested system's performance. It has been noted that compared to the conventional approaches, the suggested GWO-based MPPT methodology is quicker and produces fewer MPP oscillations. It offers a more effective reaction to quickly shifting atmospheric conditions. Results of simulation for the recommended control scheme with MATLAB/Simulink.

Assessing the Performance of a Proton Exchange Membrane Green Hydrogen Generation System through Stack and Balance of Plant Modeling

The integration of Proton Exchange Membrane electrolyzers into hydrogen generation systems offers significant potential for sustainable energy solutions. Integrating hydrogen production with electricity, heat, and renewable energy sources enhances sustainability. PEM electrolyzers effectively address renewable energy intermittent by converting energy into hydrogen, leveraging their ramp rate and load-following capabilities. However, widespread commercialization hinges on cost reductions through new material development and a comprehensive understanding of the system's heat, mass, and electrochemical processes. This study presents a zero-dimension model developed using the Aspen Custom Modeler, detailing the electrochemical phenomena. A holistic PEM electrolysis model, including the stack and the Balance of Plant, is developed in Aspen Plus, demonstrating an overall system efficiency of 50%, with the BoP accounting for 8% of total electricity consumption. This research underscores the critical role of optimizing BoP subsystems and integrating grid-based backup solutions to enhance the economic viability and resilience of PEM electrolysis systems.

Assessing the Performance of a Proton Exchange Membrane Green Hydrogen Generation System through Stack and Balance of Plant Modeling

The integration of Proton Exchange Membrane electrolyzers into hydrogen generation systems offers significant potential for sustainable energy solutions. Integrating hydrogen production with electricity, heat, and renewable energy sources enhances sustainability. PEM electrolyzers effectively address renewable energy intermittent by converting energy into hydrogen, leveraging their ramp rate and load-following capabilities. However, widespread commercialization hinges on cost reductions through new material development and a comprehensive understanding of the system's heat, mass, and electrochemical processes. This study presents a zero-dimension model developed using the Aspen Custom Modeler, detailing the electrochemical phenomena. A holistic PEM electrolysis model, including the stack and the Balance of Plant, is developed in Aspen Plus, demonstrating an overall system efficiency of 50%, with the BoP accounting for 8% of total electricity consumption. This research underscores the critical role of optimizing BoP subsystems and integrating grid-based backup solutions to enhance the economic viability and resilience of PEM electrolysis systems.

Experimental Study on a Liquid Hydrogen Tank for Unmanned Aerial Vehicle Applications

A lightweight, 12 L liquid hydrogen fuel tank has been designed, fabricated, and tested with the aim of optimizing boil-off rates while minimizing the weight of a complete propulsion system intended to be integrated into an unmanned aerial vehicle. After looking at several different insulation schemes, the system was optimized as two concentric, lightweight titanium cylinders with high vacuum and multilayer insulation in between. The thickness of the multilayer insulation and the design of support structures were optimized to reduce the overall weight of the tank. This paper focuses on characterizing the boil-off rate of a small liquid hydrogen tank in relation to ambient temperature and fuel level. Additionally, it addresses the analysis of the instrumented transfer line between the reservoir and the operating fuel cell, presenting pressure and temperature measurements for each component of the transfer line. The efficiency of the heat exchanger under natural and forced convection is also discussed. The key contribution of the experimental campaign lies in demonstrating an average boil-off rate of 19.5 g/h over 45 h. This significantly surpasses the limits of ultra-long-range flight achievable with alternative electric energy sources. Furthermore, the paper highlights the importance of the variable boil-off with time and its impact on aircraft performance.

KELURAHAN PETANI HIJAU MODERN: SMART FARMING MENGGUNAKAN TEKNOLOGI PANEL SURYA UNTUK MEWUJUDKAN KAWASAN PERTANIAN MODERN DI KELURAHAN BAKUNG JAYA

The development of automatic plant watering devices has become a major focus in increasing efficiency and effectiveness in plant cultivation. In this context, automatic plant watering devices that use electrical energy sources from solar panels have shown great potential in saving energy and reducing environmental impacts. This tool is designed to replace manual work in watering plants, both in hydroponic systems and sprinkler systems. In hydroponic systems, automatic plant watering devices use soil moisture sensors to detect soil moisture and send commands to the water pump to water periodically. This allows hydroponic plants to receive nutrients continuously and maintain air humidity with automatic fogging. Meanwhile, in sprinkler systems, automatic plant watering devices use solar panels as a source of electrical energy. These solar panels generate electrical energy that is used to power the water pump and sprinkler, thus watering plants automatically without the need for human intervention. The advantages of using solar panel in automatic plant watering devices include the use of renewable energy, easy maintenance, and no greenhouse gas emissions. However, this tool also has disadvantages, such as dependence on sunlight intensity. Thus, the development of automatic plant watering devices using solar panel can help increase plant productivity, save energy, and reduce environmental impacts. This tool can also be integrated with various types of plant cultivation systems, thus providing many benefits in the development of modern agricultural technology.

Design, construction, and testing of generation of electric energy using microbial fuel cell

A microbial fuel cell (MFC) is a bio-electrochemical system that converts chemical energy from organic compounds/renewable energy sources to electrical energy/bio-electrical energy through microbial catalysis at the anode under anaerobic conditions. The process is becoming an attractive and alternative methodology for the generation of electricity. The scope of this paper was to design, construct, and test microbial fuel cells that source their fuel from organic waste, using microbial fuel cells to power LED lights. The research concluded that microbial fuel is a prospective energy source for future electricity that can be produced and used by all consumers. Single Chamber Microbial fuel cells were set up using material readily available around i.e. the slurry and, in the market, i.e. the other materials, in batches. The studies showed that the single chamber MFC can produce a maximum voltage of over 500mV and can last for a minimum of two and half months. Out of the 30 MFCs produced 18 of them which produce stable and progressive voltage were connected in series in the frame. The whole setup successfully powered five red LED lights. This shows that more of the MFCs set up can produce more electricity such as that can power a control system.

Quantifying and enabling the resiliency of a microgrid considering electric vehicles using a Bayesian network risk assessment

With the increase in the number of natural disasters, caused by climate change, the role of emission and resilience has become more outstanding. In this paper, a novel model for quantifying and enabling the resiliency of a microgrid, using a Bayesian network risk assessment approach is presented. The three objective functions of cost, pollution, and resilience are optimized simultaneously using an improved lexicographic augmented ɛ-constraint method. It should be mentioned that these items are included in the proposed model due to the irreplaceable role of electric vehicles (EVs) and renewable energy sources (RESs) in distribution systems. Moreover, with the Bayesian network, the interaction between random variables is considered and a graphic view of casual relationships between stochastic and probabilistic variables is investigated in this study. The Monte Carlo scenario-based technique has handled the uncertainties of EVs and RESs. The proposed mixed integer linear programming (MILP) is implemented in the GAMS software environment under the CPLEX solver and finally, the results are discussed.

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

Implication of the EU Countries’ Energy Policy Concerning Scenarios Affecting the Air Quality Improvement

Energy policy has a significant impact on the state of the environment and, therefore, on residents’ health and life expectancy, especially in highly urbanized areas. Reducing emissions is currently one of the necessary actions that must be taken at the scale of individual countries to ensure sustainable development. The article aims to identify the best ways to shape energy policy by evaluating development scenarios for air protection and their environmental impact. The realization of the goal is based on the data included in three groups: (1) Economic factors, Health factors, and Demographic factors; (2) Clima-e related economic losses, Renewable Energy sources in electricity, heating, and cooling, Premature deaths due to exposure to fine particulate matter (PM2.5), Health impacts of air pollution, Population change; (3) Demographic balance and crude rates at the national level, GDP per capita in purchasing power PPS, GDP, and principal components; covering 36 EU countries in 2019 and 2021. The study proposes an advanced methodology for assessing development strategies by integrating the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Bayesian networks (BN) and incorporating them into a multicriteria decision-making (MCDM) support system. The TOPSIS model based on BN allowed for the illustration of the features of many criteria and the identification of relationships between scenarios, allowing for selecting the best way to develop energy policy. The results showed a 60.39% chance of achieving success in extending the life of residents by five years. At the same time, the most favorable development path was the scenario promoting activities aimed at reducing air pollution by introducing renewable energy sources to produce energy used for lighting and preparing domestic hot water urban areas. By presenting possible scenarios and the probability of success, it is possible to achieve the goal of practical energy policy at the level of the country and individual European cities and also by extending the life of city inhabitants, as presented by the authors in this study.

Enhancing PI controller performance in grid-connected hybrid power systems

The optimal operation of a microgrid was buildup of both uncontrollable (solar, wind) and controllable (batteries, diesel generators) electrical energy sources are enclosed in this paper. By replacing controllers, the variations in wavering power supply caused by load fluctuations are managed. The objective of the research paper is to optimize these controller gain settings for effective use of electrical energy. In this paper integral time square error principle is combined along with the Cuckoo search algorithm (CSA) and particle swarm algorithm (PSA) to obtain the accurate, precise and appropriate results. It enhances the microgrid's steady-state sensitive responsiveness in comparison to trial-and-error techniques, assuring a stable supply of electricity to the load.

Dynamics of direct hydrocarbon PEM fuel cells

Hydrocarbon fuels contain approximately 50 times more energy per unit mass than commercial batteries, thus converting even 10% of the energy contained in hydrocarbon fuels to electrical energy could present a more mass-efficient electrical energy source than batteries. Considering the storability of hydrocarbon fuels compared to hydrogen, the viability of direct hydrocarbon polymer electrolyte membrane fuel cells was examined. With extremely pure (> 99.99%) propane, the cell Open-Circuit Voltage (OCV) was only 0.05 V and produced negligible power. However, with addition of trace quantities of unsaturated hydrocarbons, the cell had an OCV of 0.85 V and produced power, even after the unsaturated hydrocarbon addition was discontinued. At sufficiently high current densities, power output gradually decreased then the cell rapidly “extinguished” but by periodically shutting off the current for short time intervals the average power density could be increased significantly. Chemical analysis revealed that no significant amounts of hydrocarbon intermediates or CO were present in the effluent and that conversion of the hydrocarbon fuel to CO2 and H2O was nearly complete. An analytical model incorporating the relative rates of conversion of active anode catalyst sites to inactive sites and vice versa was developed to interpret this behavior. The model predictions were consistent with the experimental observations; possible physical mechanisms are discussed.

Design and Implementation of Smell Agent Optimizer for Parameters Estimation of Single and Double Diode in PV System: A Comparative Analysis

One of the most important and desirable options for moving toward clean electric energy sources is solar energy. Therefore, a PV system's characteristics play a significant role in determining how effective it is across a range of temperature and radiation scenarios. One can consider the PV model's parameter estimation to be a nonlinear optimization situation. This work makes use of a novel application of the smell agent optimizer (SAO) created to forecast the undefined parameters of the PV model's single- and two-diode equivalent circuits. The goal of this effort is to create an accurate photovoltaic model that can accurately represent its performance under variable operating conditions. The square of the mean squared error between the actual measured curve and the current-voltage curve derived from the model defines the intended objective function. The suggested system is constructed and tested experimentally in a range of temperature and light conditions. Next, the MATLAB software is used to create the simulated PV model integrated with the SAO. The PV parameters are then predicted by comparing the experimental data with the convergence of the SAO based on the PV model. Based on the observed properties, the suggested approach for determining the parameters of an actual solar cell has been put into practice and contrasted with eight other optimization techniques. The outstanding efficacy of the method utilized compared with alternate methods is demonstrated by the statistical comparison of the ideal objective function resulting from the difference in the current-voltage curve produced from the optimized circuit model and the measurement.

Biodiesel production using various methanol sources and catalytic routes: A techno-environmental analysis

The primary objective of this research is to investigate the biodiesel production via the transesterification reaction between triolein and methanol in the presence of a basic and acidic catalysts, namely sodium hydroxide and sulfuric acid, using CHEMCAD simulation software as a process modelling and simulation tool. Methanol, one of the raw-materials used in biodiesel manufacturing, was synthesized using reforming technology applied to: i) coke oven gas and carbon dioxide, and ii) glycerol that represents an important by-product of the biodiesel synthesis. Four different cases were developed and compared, with detailed operating parameters and equipment designs for each of the investigated scenarios. The production capacity was set to 1000.00 kg/h of biodiesel and a purity higher than 98 % was registered in all investigated cases. Based on the material and energy balances derived from modelling and simulation section, an environmental analysis using the Life Cycle Assessment methodology was performed with the aid of the GaBi software. Ten environmental impact categories (i.e., Global Warming Potential, Fossil Depletion Potential, Human Toxicity, Water Depletion, etc.) are generated and relevant discussions about the sub-processes that exhibit the highest influence are given. The environmental evaluation shows that the most environmentally friendly scenario consists of the methanol production using coke oven gas and carbon dioxide, and its subsequent utilization for the biodiesel production following the alkali catalysis. A scenario analysis regarding several electric and thermal energy sources (i.e., natural gas, biogas, biomass, grid mix) was performed. According to this scenario analysis, while evaluating the synthesis of biodiesel through the alkali catalysis pathway, using biomass as a renewable source improves the environmental performances across certain impact categories.