Search

The air-drying of resin impregnated paper sheets in industrial lines, formed by a serial array of furnaces, presents a high number of different controllable operational parameters whose adjustment, usually done by the maintenance staff, leads to non-efficient configurations. A model-based numerical tool, which predicts accurately in a few seconds the evolution of the paper temperature and paper grammage along the line for a given combination of the input operational parameters (direct design), was used coupled to an optimization tool to select appropriate operational parameters (inverse design) that ensure a drying process quality (i.e., fulfills an objective grammage profile) with a minimum of energy consumption. The numerical tool was capable of selecting suitable configurations with an energy reduction of up to 50% for several tested industrial cases, making the model an essential tool in the framework of the increasingly relevant role of digital twins in industry. [ABSTRACT FROM AUTHOR]

Non-linear programming optimization method was used to optimize total steam and air consumption in the dryer section of multi-cylinder fluting paper machine. Equality constraints of the optimization model were obtained from specified process blocks considering mass and energy balance relationships in drying and heat recovery sections. Inequality constraints correspond to process parameters such as production capacity, operating conditions, and other limitations. Using the simulation, the process parameters can be optimized to improve the energy efficiency and heat recovery performance. For a corrugating machine, optimized parameters show the total steam use can be reduced by about 11% due to improvement of the heat recovery performance and optimization of the operating conditions such as inlet web dryness, evaporation rate, and exhaust air humidity, accordingly total steam consumption can be decreased from about 1.71 to 1.53 tonnes steam per tonne paper production. The humidity of the exhaust air should be kept as high as possible to optimize the energy performance and avoid condensation in the pocket dryers and hood exhaust air. So the simulation shows the supply air should be increased by about 10% to achieve optimal humidity level which was determined about 0.152 kgH2O/(kg dry air). [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

Abstract: Energy conservation is a central concern of the current industrial world, where increasing efficient energy usage is the only way of reducing a high energy demand. In the present study the optimization of a pulp and paper mill with a cogeneration plant has been carried out. The optimization was realized with a methodology which includes exergy, exergoeconomics, thermoeconomics and pinch analysis. The proposed methodology was useful in determining not only the best plant operating conditions but also establishing the components or subsystems with the highest irreversibilities. As a result of the study, operation changes in the recovery boiler, the turbogenerator, the thermal treatment and the deaerator were realized. Due to the higher irreversibility in the actual evaporator line, a new line of evaporators was proposed. Also, an innovative heat recycling technology as to the use of heat transformers was proposed in order to reduce waste heat discharged to the atmosphere. The results obtained with the proposed methodology, which integrates the different optimization methods, allowed reaching higher efficiencies and lower operational costs than those obtained with the optimization methods working separately. [Copyright &y& Elsevier]

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton's equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU. [ABSTRACT FROM AUTHOR]

Due to high energy consumption in the Chinese paper industry, this study considers higher-energy efficiency for the multicylinder dryer section of paper machines. A common situation in the Chinese paper industry is that energy is consumed in extensive mode. In order to improve the energy efficiency of the paper machine dryer section, deeper analysis and optimization of process parameters are necessary. A NLP optimization method is developed for integration of steam system and air system to reduce the steam consumption and decrease the loads of centrifugal blowers in the multicylinder dryer section of a paper machine. Equality constraints of the optimization model are extracted from different process modules based on material and energy balance. Inequality constraints are from the production capacity, operating condition, etc. Two illustrative examples are presented in this paper. The results show that the optimization model is adaptive and convenient for application. For a newsprint machine, less dry air and steam are used and the energy consumption can be reduced by about 8% in the dryer section. Applied on a linerboard machine which has surface sizing, the method can reduce the energy consumption by 5.6%. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) is a strategy that tries to save energy consumption by managing consumers' voltage. On the other hand, enhancing reactive power compensation in the power system gives rise to CVR implementation more effectively. This paper addresses how the power system planner could encourage investment to establish the above‐mentioned topics. Here, a model for encouraging industrial loads to dynamically invest in implementing the CVR strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. Industrial loads benefit from saving in their electricity bills and incentive payments that might be paid by utilities (if necessary). The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. The proposed planning problem is modelled as a non‐linear optimization problem that can be solved by off‐the‐shelf software, for example, GAMS. The proposed model is applied to the Ontario transmission system. The numerical results show the proposed model's effectiveness in designing an investment plan for industrial loads to implement the CVR strategy and upgrade PEVs' chargers. Here, a model for encouraging industrial loads to dynamically invest in implementing the conservation voltage reduction (CVR) strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to the fact that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. [ABSTRACT FROM AUTHOR]

In view of the stochastic nature of wind and solar energy, maintaining quality power in grid-connected hybrid energy system is crucial for efficient energy utilization, especially in the presence of non-linear loads. This paper deals with the control and performance assessment of grid-integrated hybrid energy system that comprises of doubly fed induction generator (DFIG) and photovoltaic (PV) system including battery energy storage (BES) as a stand-by supply to meet utility and load demand. In order to maintain power balance and address various power quality issues encountered with hybrid renewable integration along the source and the grid/load end, a dual-layer variable-step size adaptive control is implemented for the grid-interfaced converter of the proposed system. The variable step-size based dual-layer least mean square (LMS) adaptive control functions by extracting weights of various control signals (stator and load current) where the step-size parameter adjusts according to the conditions that limit the maladjustment error and increase the rate of convergence. The DC-link voltage control realized by bidirectional BES control reduces the steady-state error and fluctuations in the voltage. An artificial bee colony (ABC) optimization is adopted for optimal gain tuning of the DC-link voltage controller as a result of which the battery performance and grid-side converter performance is enhanced with the increased power exchange to the grid. The system model is developed in MATLAB/Simulink and controller performance is evaluated for diverse operating conditions including steady-state, unbalanced non-linear load conditions, wind-speed and solar irradiation variation and specified grid power modes. [ABSTRACT FROM AUTHOR]

Intelligent load scheduling for the smart city is essential due to the growth of industrialization, which directly consumes energy from the grid. The energy management system in industrialization is enhanced using the Internet of things in the same domain. The general need in a smart city is to maintain the energy utilization and cost that the industrial Internet of things could achieve for industry application. Researchers have also found a solution to this problem, but every time the generator cannot sell energy, the industry purchases the same. There is a chance for the industry to supply its energy to other industries or the grid. By keeping this constraint, an intelligent approach to load scheduling based on optimized Q-Net is proposed in this paper, comprising the supplies as renewable energy, battery, and industrial energy. Also, the reactive power flow of smart cities during scheduling is mitigated by classifying the energy available in the bus network. The proposed method scheduled demand power with 99% accuracy with the lowest cost of 13.7041$/KW for peak hr. scheduling with a demand of 6000KW/hr. [ABSTRACT FROM AUTHOR]

Cooperative Non-Orthogonal Multiple Access (Cooperative NOMA) is a communication technique used in wireless networks to enhance spectral efficiency and overall system performance. Non-Orthogonal Multiple Access is a multiple access scheme that allows multiple users to share the same time-frequency resources by allocating different power levels to each user. This paper presents a comprehensive performance validation of NOMA empowered by optimal relay selection and deep learning-based power allocation techniques. To achieve effective data transmission to far users, the optimal relay selection process employs the Maximum Energy Harvested Relay (MEHR) selection approach, which intelligently selects relays based on their energy harvesting capabilities. Furthermore, an enhanced Deep Convolutional Optimized Neural Network (DCOnN) is utilized for optimal power allocation among users in a cooperative NOMA system. To efficiently optimize the power allocation process with the DCOnN model, the Grey Wolf (GW) optimization is utilized to fine-tune the network weights. The presented approach significantly increases the energy efficiency in cooperative NOMA networks. The combination of optimal relay selection and deep learning-based power allocation enhances outage probability performance, ensuring better coverage and communication reliability while reducing the bit error rate for users in challenging channel conditions. Finally, the integration of optimal relay selection using DCOnN with GW optimization and cooperative cognitive NOMA provides a powerful framework to achieve superior communication performance in wireless networks. The proposed scheme achieves higher performance in terms of energy efficiency (519.257 Bits/J/Hz) and CPU time (0.012 seconds). The experimental results proved that the proposed performance is enhanced than the different existing schemes. [ABSTRACT FROM AUTHOR]

Energy management systems (EMSs) play a central role in improving the performance of microgrids by ensuring their efficient operation while minimizing operational costs and environmental impacts. This paper presents a comprehensive study of mixed integer linear programming (MILP) based EMSs developed and implemented in MATLAB 2021a using YALMIP software for the energy management of a new positive energy district in the city of Savona, Italy, as part of the Interreg Alpine Space Project ALPGRIDS. The main objective of this research is to optimize the functioning of the microgrid, focusing on cost efficiency and environmental sustainability. In pursuit of this objective, the EMS undergoes comprehensive testing and analysis, replicating actual conditions and addressing the diverse demands of end-users across typical days throughout the year, considering real electricity selling and purchase prices. The EMS also accounts for the reactive power capabilities of the various technologies integrated into the microgrid. The levelized cost of electricity (LCOE) serves as a metric for assessing curtailment costs, while penalties related to reactive power absorption from the distribution network are appraised in alignment with prevailing regulatory guidelines. The case study provides valuable insights into the practical implementation of EMS technology in microgrids and demonstrates its potential for sustainable energy management in complex urban energy districts. In all scenarios, the battery energy storage system (BESS) and combined heat and power (CHP) are pivotal for load satisfaction and microgrid resilience. BESSs balance supply and demand, which are crucial in periods of low renewable energy availability, while the versatile CHP efficiently addresses energy demands, contributing significantly to overall microgrid effectiveness. Their synergy ensures reliable load satisfaction, showcasing the dynamic and adaptive nature of microgrid energy management across diverse scenarios. [ABSTRACT FROM AUTHOR]

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP. [ABSTRACT FROM AUTHOR]

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications. [ABSTRACT FROM AUTHOR]

This paper provides recommendations for improved analyses of the performance of ground-coupled heat pumps. Most research on ground-coupled heat pumps focuses on improving the performance of borehole heat exchangers (BHE) and reducing system costs. However, the potential improvements are mainly assessed at the BHE level rather than considering the entire system incorporating a heat pump, circulation pump, and building needs. This paper shows that such an approach can be misleading, and improvements in BHE are significantly overestimated if the operation of the entire system is not simulated. For instance, improvements in pipe thermal conductivity (from 0.4 to 3 W/(m K)) result in a 7.57% improvement in BHE performance when simulating only a BHE (constant inlet temperature assumed). However, a more realistic simulation of the entire system shows that improvements at a system level are only 0.15%. Other important simulation aspects are also investigated, focusing on different choices regarding the sensitivity analysis method, flow condition type, and optimization strategy. The results suggest that modifications to individual BHE parameters have a limited impact on the overall system performance, while modifying all parameters simultaneously can lead to more significant reductions in total system energy consumption (6% in this study). Furthermore, the research also shows that the potential savings in investment costs (by reducing the borehole depth) outweigh potential savings in operational costs. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the future digitalization of microgrids to meet the increasing energy demand. It begins with an overview of the background of microgrids, including their components and configurations, control and management strategies, and optimization techniques. It then discusses the key digital technologies that can be used to improve the performance of microgrids, including distributed energy resources management systems, the Internet of Things, big data analytics, blockchain technology, artificial intelligence, digital twin technology, cloud computing, and augmented reality. The paper also highlights the importance of cybersecurity in microgrids, identifying the potential security vulnerabilities and threats to microgrid cybersecurity, as well as strategies for addressing these challenges. Finally, the paper discusses the barriers and challenges regarding the digitalization of microgrids, including technical complexity, high implementation costs, regulatory barriers, data privacy and security concerns, lack of standardization, interoperability issues, limited technical expertise, and integration with the main grid. Overall, this paper demonstrates the significant potential for digital technologies to transform the future of microgrids. By leveraging advanced technologies and implementing effective cybersecurity measures, microgrids can become more efficient, reliable, and resilient, enabling them to meet the growing demand for energy and contribute to a sustainable energy future. [ABSTRACT FROM AUTHOR]

The production and consumption of non-renewable energy resources have disrupted the environment's biodiversity cycle. Global climate change, including worldwide warming, has made human life both now and in the future. The construction industry in the world has a significant share in the demand for energy consumption in these challenges. Therefore, the primary purpose of this paper is to implement standards to save and prevent energy loss to control and limit the demand for energy requested from the power network. Constructing a building with self-sufficient energy production that meets its energy needs by producing clean energy becomes more important. It also sells the excess energy to the grid, known as zero energy buildings. In the present paper, the issue is a constrained optimization problem that aims to minimize the total annual cost, including the initial investment cost for PV and batteries and their maintenance costs, as well as the cost of network exchanges. Among the limitations, the proposed model can mention the restrictions governing the battery, such as the limitations of the battery state of charge (SoC). The problem under optimization is a mixed integers nonlinear programming (MINLP) that will be solved by a particle swarm optimization (PSO) algorithm considering the total cost minimization. [ABSTRACT FROM AUTHOR]

Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

The energy consumption of a battery‐powered train in an interstation depends on the running time and state of energy (SOE) at departure. In this paper, we develop an optimization method of train timetables to minimize energy consumption in line with several stations. The variables in this proposed optimization model are running, dwell, and charging times as real numbers and places of charging facilities as binaries. Additionally, we conducted a case study using the real‐world light rail transit (LRT) route, vehicle, and onboard battery model to confirm the effectiveness. In the case study, the proposed method can optimize the timetable and placement of charging facilities by considering the track gradient and battery SOE. [ABSTRACT FROM AUTHOR]

In wireless-sensing networks (WSNs), the energy economy has lately emerged as the main problem. Since sensor networks run on batteries, they eventually run out of power. To increase the packet transmission ratio for sensing devices, it becomes more difficult to enhance data loss in an energy-efficient manner. In WSNs, the mobile drain causes high network energy usage and data delay. This paper suggests an Improved Ant Colony Clustering-Based Data Transmission Algorithm (EACODT) that first develops the network nodes' energy density function before allocating sensing nodes with higher residual energy as cluster leaders using the energy density function. The EACODT is thoroughly modeled for different WSN situations with variable numbers of sensing nodes and CHs, and the findings are contrasted with some recently developed meta-heuristic algorithms. As a consequence, it is discovered that EACODT gets 34% of energy usage, 98.8% of network lifespan, 95% of packet delivery ratio, 854 kbps of transmission, and a 98% convergence rate. [ABSTRACT FROM AUTHOR]

This paper considers subsonic turbojet aircraft fuel consumption minimization problem during cruise phase, assuming fixed time of arrival. The problem takes into account real atmosphere data. We utilize tailwind/headwind component values at various flight levels, as well as air temperatures and atmospheric pressures at various altitudes. The solution to the altitude and speed flight profile optimization problem is through constrained coordinate descent method. The paper considers optimizing the fuel consumption of a medium-haul aircraft during the cruise phase using sample data set on temperature, pressure, and wind speed. The proposed approach achieves a decrease in fuel consumption of 1.2% when optimizing with regard to real atmosphere. [ABSTRACT FROM AUTHOR]

This paper focuses on enhancing the energy collection efficiency of an integrated collector storage solar water heater (ICS SWH) by vee‐corrugating the absorber and optimizing the design for the vee‐included angle through simulation and experimental study. This paper presents an efficient algorithm for analyzing a vee‐corrugated absorber ICS SWH using Engineering Equation Solver Software. For validating this algorithm, two models of ICS SWH systems have been fabricated: one with a flat absorber and the other with a 60° vee‐included angle corrugated absorber with eight corrugations. The basic purpose of the fabrication of the flat absorber ICS SWH system was to approximate the absorptivity of the absorber. A typical value of 0.68 was estimated for the absorptivity of the aluminum absorber coated with nonselective black paint. After the experimental investigation of the 60° vee‐included angle ICS SWH system, it was found that the real‐time readings were in close agreement with the numerical model readings. For comparison with the previous work, a five‐corrugation system with an approximate 90° vee‐included angle with the same projected dimensions was modeled and the efficiencies of both the models for the time from 7 a.m. to 3 p.m. were calculated. The efficiency of the eight‐corrugation model was 42.56%, which was better than the previous work of the five‐corrugation model, with a 38.86% efficiency for the same ambient conditions. Also, it was theoretically deduced that we had an optimized system at 18 corrugations and a vee‐included angle of 28.78°. [ABSTRACT FROM AUTHOR]

2E-MDCVRP is a multi-depot, capacity, twoechelon vehicle routing issue. Satellites make it possible to collect and sort orders. In this paper, a 2E-MDCVRP model will be designed to be applied to e-commerce. In the completion stage, the researcher developed a 2E-MDCVRP solution with two stages of completion. The first stage is finding the best route for the satellite to travel to the customer. In the first stage of work, satellites are plotted as centroids in clusters using the k-Means algorithm to obtain k-set clusters. To determine route scheduling using the RNN algorithm in determining travel routes in the second echelon. The second stage uses the trial-anderror method to determine the first echelon's routing schedule. This research aims to minimize the operational application, fuel consumption, vehicle maintenance, vehicle oil change, and handling costs. Moreover, at the end of the paper, the researcher simulate the model that has been built and test the model's effectiveness by comparing it with the classic MDCVRP model, which is similar to the Same-Day Delivery model. The test results show that the 2E-MDCVRP model can make better total distance and cost improvements than the classic MDCVRP model carried out by Same-day Delivery. [ABSTRACT FROM AUTHOR]

Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to evaluate the performance of an electric vehicle hybrid power system consisting of a fuel cell and a supercapacitor. The study compares the results to those obtained for a battery electric vehicle. The methodology extends to three driving modes, ECO, NORMAL, and SPORT, corresponding to conservative, moderate, and aggressive acceleration, and three driving conditions, low, medium, and high energy demand. We develop a simulation process to evaluate the energy consumption and the energy rate of a specific electric vehicle used as a prototype for the study. The methodology applies to a driving route that includes acceleration, deceleration, braking, and constant speed segments, reproducing standard driving conditions in urban journeys. The proposed method considers combined driving modes, ECO, NORMAL, and SPORT, in each acceleration process, with variable fractions, from 0% to 100%, for each mode. This methodology optimizes the simulation results for the current driving patterns in urban environments. The simulation results show an average reduction in energy consumption of 37% and 27.1% in vehicle weight, contributing to lower energy use. The study concludes that using a hybrid power system, a fuel cell/supercapacitor, instead of a battery in electric vehicles is beneficial, especially in journeys with frequent acceleration processes. [ABSTRACT FROM AUTHOR]

[Display omitted] • There is no reported work on 3D printed catalysts for biomass-biooil conversion applications. To fill this gap, this paper reviewed the key technologies that will be needed including 1) typical catalysts used for biomass conversion applications, 2) 3D printed catalysts, and 3) design and optimization of structured catalysts. • Desired catalysts performance in current biomass-biooil conversion research is mostly achieved by altering catalyst material composition rather than structural properties. • 3D printed catalysts research is still in its infancy and lacks full considerations of the design potentials of 3D printing, such as hierarchical complexity and material complexity. • Current work on design and optimization of structured catalysts shows deficiency in manufacturing constraints, for example, resolution of 3D printers, which may lead to coke formation issues in catalytic process. The devastative effects of traditional fossil fuel consumption have made biomass (e.g., plants, wood, and waste) a widely used renewable source of energy. The variety in biomass properties makes tunable catalysts an essential ingredient to promote reactions for desired molecule outputs. Catalysts with optimal catalytic performance are typically structured with complicated morphology, such as high porosity and sophistic pore network, which makes it challenging for catalyst fabrication. Recent progress in utilizing 3D printing as the fabrication method has demonstrated great potential of handling the complex structure while improving catalytic performance; however, rare work has focused on biomass-bio-oil conversion (BBC). With the aim of developing the optimal structured catalysts for BBC applications, this paper particularly reviewed 1) typical catalysts used for biomass conversion applications, 2) 3D printed catalysts, and 3) design and optimization of structured catalysts. It is found that the BBC is often isolated from manufacturing and focused more on different catalyst composition with transition metals to provide the desired catalyst architecture and further to improve upgrading of the BBC product. Current work on 3D-printed catalysts also show deficiency in several aspects including formation of coke and catalyst deactivation, lack of a comprehensive design for the catalyst structure (e.g., mostly 2D structures), absence of manufacturing constraints in design operation, and insufficient resolution of 3D printing methods. To solve these issues, several potential research directions and opportunities are discussed at the end of the study. [ABSTRACT FROM AUTHOR]

This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed. [ABSTRACT FROM AUTHOR]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

The resources of the earth are being consumed day by day with the increasing population and necessities of humankind in many areas, such as industrial applications and basic needs in houses, workplaces and transportation. As a consequence, careful usage of the energy sources and the conversed energy is of great importance in order to obtain sustainable development. Machining operations have a large percentage of all manufacturing methods in terms of depleted energy which gives them a high potential for reducing the total energy consumption. The approaches handled in the literature for the minimization of the consumed energy in the machining industry were considered in this study. While several machinability characteristics under different machining processes were investigated broadly in the context of composites and superalloys, the comparison of these systems has been given cursory attention in the current literature, specifically for cutting energy saving. The overall performance of these group material systems utilizing widely in numerous significant industrial areas supplies important signs about manufacturing costs, service conditions and environmental impacts. It is highly crucial to monitor the indicators of energy-saving phenomena of the machined parts since the mechanisms behind the energy consumption of these systems is very complex and dynamic owing to different process-induced variables. This well-organized review paper distinguishes itself from previous studies in this field since the comprehensive literature survey paves the way for diverse approaches that regard energy saving, especially for composites and superalloys under different machining operations. This overview paper aims to contribute to the current literature by highlighting the effects of the state-of-the-art approaches in reducing energy consumption in the machining of industrially important materials. This study can also establish a framework in the context of the process-property interactions to comprehend the influence of energy-saving mechanisms through machining in a system of interest. [ABSTRACT FROM AUTHOR]

Cooperation among multiple connected vehicles (CVs) brings swarm intelligence to our transportation systems and helps improve their performance. This study proposes a fuel economy optimization approach for a platooning vehicle swarm via distributed economic model predictive control (DEMPC). Within the DEMPC framework, each CV shares its assumed trajectory in the predictive horizon with its neighboring CVs at each control loop. With its neighbors’ and its own assumed trajectories, each CV first solves an open-loop control optimization problem for platoon formation, and then solves an open-loop economic optimization problem for direct fuel economy improvement. In particular, the optimal cost of the former optimization problem is used in the latter one to build an upperbound constraint for stability guarantee. The asymptotic convergence of assumed terminal states is given in the analysis, and the recursive feasibility of the two optimization problems are proved with an explicit constraint on the weight matrices in the open-loop control optimization problem. Based on these analyses, the asymptotic stability of the closed-loop system is finally proved through Lyapunov analysis. Numerical simulation results validate the effectiveness of the proposed approach in terms of closed-loop stability and fuel economy improvement. Note to Practitioners—This work aims to optimize the fuel economy for a swarm of vehicles running in a platoon. Existing approaches on platoon control mainly focus on the accurate tracking of the following vehicles, but this may cause aggressive control and hence lead to poor fuel economy. Therefore, this paper proposes a distributed economic model predictive control approach to explicitly optimize the fuel consumption rate of each vehicle. The proposed method can improve fuel economy and reduce communication requirements and burdens for platooning vehicle swarms. The paper assumes no communication time delay and packet drops, so the impact of unreliable communication will be studied in the future work. [ABSTRACT FROM AUTHOR]

Due to the environmental and economic advantages of combined heat and power (CHP) units, their use in power grids has expanded. The entry of CHP into power systems increases the complexity of the economic power flow problem. This complexity is due to the introduction of multiple constraints into problem. A mere electricity supply is not optimal in today's networks, and energies such as heat, power and gas must be planned and managed simultaneously as an energy hub. Therefore, in this paper, an intelligent multi-energy microgrid (MG) consisting of power generation units, CHP units and gas units is modeled for day-ahead energy management (DAEM). The economic distribution problem focuses on the amount of power generation, heat and gas of the units in the system. In contrast, the total generation cost of the system is minimized, and all the equality and inequality constraints of the problem are observed. The proposed microgrid includes various energy-dependent equipment such as CHP units, gas boilers, electricity-to-gas units, power and heat storage units and electric heat pumps. Also, price-based load management was included to reduce costs due to the transfer of information between the consumer and the generator in the context of smartization. Since the above problem is difficult to solve due to various constraints and decision parameters, a newly developed optimization method based on water flows was proposed. The simple movement of water flows on the ground is efficient and optimal and always follows the shortest and fastest path to reach the deepest point. In the proposed algorithm, simple movements of water in routing, a change of direction and even the creation of rapids and vortices were simulated as various mathematical operators. Finally, the proposed model and method were examined in different scenarios. The numerical outcomes demonstrated that, the proposed modeling framework is superior to hub-based multi-carrier microgrid models in terms of power system security. The sensitivity of operational expenses to changes in initial values of energy storage systems (ESS) and thermal storage system (TSS) is proved that the cost of operation reduces as the baseline values of ESS and TSS are reduced to 0.2% of the maximum capacity. Because DAEM performance is less flexible when the primary values are reduced by 0.2% of the maximum value, the system running expenses increase marginally. [Display omitted] • Introducing a novel developed algorithm based on water flows. • DAEM in the microgrid in the form of an energy hub. • Application of internal grids & energy flow of energy carriers in multi-energy MGs. [ABSTRACT FROM AUTHOR]

Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions. [ABSTRACT FROM AUTHOR]

This paper investigates conversion of a Nordic oil-heated townhouse into carbon-neutral by different energy efficiency (EE) improvements and an off-grid system including solar photovoltaics (PV), wind power, and battery and hydrogen energy storage systems (BESS and HESS). A heat-pump-based heating system including waste heat recovery (WHR) from the HESS and an off-grid electrical system are dimensioned for the building by applying models developed in MATLAB and Microsoft Excel to study the life cycle costs (LCC). The work uses a measured electrical load profile, and the heat generation of the new heating system and the power generation are simulated by commercial software. It is shown that the EE improvements and WHR from the HESS have a positive effect on the dimensioning of the off-grid system, and the LCC can be reduced by up to €2 million. With the EE improvements and WHR, the component dimensioning can be reduced by 22%–41% and 13%–51% on average, respectively. WHR can cover up to 57% of the building's annual heat demand, and full-power dimensioning of the heat pump is not reasonable when WHR is applied. Wind power was found to be very relevant in the Nordic conditions, reducing the LCC by 32%. [Display omitted] • Energy efficiency improvements notably reduce dimensioning of system components. • Waste heat from FC and electrolyzer covers up to 57% of the annual heat demand. • A full-power-dimensioned GSHP does not reduce the fuel cell dimensioning. • Seasonal storage on a small scale makes the system not economically viable. • Small-scale wind power is expensive, but it significantly reduces the system LCC. [ABSTRACT FROM AUTHOR]

Energy shortage has always been a problem that the world needs to face. The combined cooling, heating, and power (CCHP) system, as a multi-level energy utilization system that can provide cooling, heating, and electric energy simultaneously, is considered to have good development prospects in alleviating energy problems. In addition, because of the rapid development of electric vehicles (EVs), using EVs as power supply devices has become a hot topic of research. In this paper, EVs are combined with the CCHP system as new power supply equipment, and the influence of the season on the user's cooling, heating, and power demand is considered. Aiming at the minimum economic cost, the system is optimized by using the PSO algorithm in two operating modes: following electricity load (FEL) and following thermal load (FTL). The final results show that the participation of EVs can reduce costs in both operating modes, especially in FTL mode, which can reduce costs by 4.58%, 13.61%, 12.74%, and 3.57% in spring, summer, autumn, and winter, respectively. In addition, the FEL mode is more economical in spring and winter, and the FTL mode is more economical in summer and winter. In addition, the C O 2 emissions in FEL mode are always less than in FTL mode. [ABSTRACT FROM AUTHOR]

Recently, renewable energy resources (RESs) have been utilized to supply electricity to remote areas, instead of the conventional methods of electrical energy production. In this paper, the optimal design of a standalone hybrid RES comprising photovoltaic (PV), wind turbine (WT), and biomass sources as well as an energy storage system, such as a hydro-pumped storage system, is studied. The problem of the optimal sizing of the generating units in the proposed energy system is formulated as an optimization problem and the algorithms heap-based optimizer (HBO), grey wolf optimizer (GWO), and particle swarm optimization (PSO) are applied to achieve the optimal sizing of each component of the proposed grid-independent hybrid system. The optimization problem is formulated depending on the real-time meteorological data of the Ataka region on the Red Sea in Egypt. The main goal of the optimization process is to minimize the cost of energy (COE) and the loss of power supply probability (LPSP), while satisfying the constraints of system operation. The results clarify that the HBO algorithm succeeded in obtaining the best design for the selected RE system with the minimum COE of 0.2750 USD/kWh and a net present cost (NPC) of USD 8,055,051. So, the HBO algorithm has the most promising performance over the GWO algorithm in addressing this optimization problem. [ABSTRACT FROM AUTHOR]

Optimization of the energy efficiency, fairness, and rates of the system is a vital part of communication systems. Multiple access techniques have a huge potential to enhance such performance parameters. This paper studies the performance of NOMA and OMA systems in a singular cell environment, where the cellular users are distributed randomly, and cooperative relays are considered for better system reliability. The relay nodes forward the signals to the cell-edge users. This paper considers a practical scenario where all the relay equipment is distributed with non-uniform battery power levels. The performance of OMA and NOMA schemes is compared based on the key performance indicators: sum rate, fairness, and energy efficiency. The fairness factor determines fairness in the allocation of resources to all the system's users. The performance of the two schemes is assessed in three deployment scenarios: urban, suburban, and rural scenarios. Through numerical results, it is proved that the performance of the NOMA dominates the OMA scheme. [ABSTRACT FROM AUTHOR]

The adverse impacts of using conventional batteries in the Internet of Things (IoT) devices, such as cost-effective maintenance, numerous battery replacements, and environmental hazards, have led to an interest in integrating energy harvesting technology into IoT devices to extend their lifetime and sustainably effectively. However, this requires improvements in different IoT protocol stack layers, especially in the MAC layer, due to its high level of energy consumption. These improvements are essential in critical applications such as IoT medical devices. In this paper, we simulated a dense solar-based energy harvesting Wi-Fi network in an e-Health environment, introducing a new algorithm for energy consumption mitigation while maintaining the required Quality of Service (QoS) for e-Health. In compliance with the upcoming Wi-Fi amendment 802.11be, the Access Point (AP) coordination-based optimization technique is proposed, where an AP can request dynamic resource rescheduling along with its nearby APs, to reduce the network energy consumption through adjustments within the standard MAC protocol. This paper shows that the proposed algorithm, alongside using solar energy harvesting technology, increases the energy efficiency by more than 40% while maintaining the e-Health QoS requirements. We believe this research will open new opportunities in IoT energy harvesting integration, especially in QoS-restricted environments. [ABSTRACT FROM AUTHOR]

Summary: Multiple‐input multiple‐output (MIMO) systems perform well from the energy efficiency (EE) and the spectral efficiency (SE) points of view in fifth generation (5G) communication systems. This paper considers the operation of a MIMO system with a relay. The optimization problem relates to the EE maximization. This problem has two types of limitations, which provide a maximum transmission power and a minimum data rate for users. The encountered objective function is in a fractional form and thus it is a non‐convex function. Besides, the problem is constrained. We utilize a lower bound analysis for the data rates, some properties of the linear programming, and the maximum ratio transmission (MRT) precoding scheme to obtain a convex objective function. Using the Lagrange dual function, we obliterate the constraints of the problem and then it is easy to solve. To improve system performance, users are divided into two groups based on their channel gains, and the maximum transmission power is reasonably divided between them. Two iterative algorithms are proposed to solve the optimization problem numerically, and finally we investigate performance of the proposed method. [ABSTRACT FROM AUTHOR]

Integrated solar combined cycle (ISCC) systems play a pivotal role in the utilization of non-fossil energy; however, the efficient application of solar energy has emerged as a primary issue in the study of ISCC systems. Therefore, it is extremely urgent to propose the best optimization scheme for ISCC under different operating conditions. In this paper, according to the idea of temperature matching and cascade utilization, the optimization of the ISCC system is carried out with the genetic algorithm for the whole working conditions, and the optimization schemes with the highest photoelectric efficiency and system efficiency under different working conditions are derived. In comparison with two optimization schemes with different objective functions, the conclusion can be drawn that: At 100% gas turbine load—30% DNI and 100% gas turbine load—100% DNI working conditions, respectively, the maximum system efficiency of 56.32% and the maximum solar photoelectric efficiency of 35.5% are attained. With the decreasing of gas turbine load, the solar energy integration position will gradually change from the topping cycle to the bottom cycle; with the gas turbine load variation from 100% to 75%, the optimal photoelectric efficiency model prefers two-stage integration, and up to 141.3 MW of solar energy could be integrated, which is greater than the maximum value of 127.1 MW for the optimal system efficiency model. Regarding the heat collection choice of bottom cycle, the optimal photoelectric efficiency model prefers the high-pressure boiler (HPB), while the optimal system efficiency model prefers the high-pressure superheater (HPS). The comparison between the optimal solution and the actual cases confirms the correctness of the optimization results and provides guidance for the subsequent ISCC study. [ABSTRACT FROM AUTHOR]

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality. The power sector accounts for approximately 40% of China's total CO2 emissions. Accordingly, collaborative optimization in power generation expansion planning (GEP) simultaneously considering economic, environmental, and technological concerns as carbon emissions is necessary. This paper proposes a collaborative mixedinteger linear programming optimization approach for GEP. This minimizes the power system's operating cost to resolve emission concerns considering energy development strategies, flexible generation, and resource limitations constraints. This research further analyzes the advantages and disadvantages of current GEP techniques. Results show that the main determinants of new investment decisions are carbon emissions, reserve margins, resource availability, fuel consumption, and fuel price. The proposed optimization method is simulated and validated based on China's power system data. Finally, this study provides policy recommendations on the flexible management of traditional power sources, the market-oriented mechanism of new energy sources, and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process. [ABSTRACT FROM AUTHOR]

Energy-absorbing structures with anti-climbers for railway vehicles rarely experience pure axial loading in real crash events, but rather a combination of axial and off-axial loads. In this perspective, it is critical to understand the non-centric collision process of energy absorbers. To address this issue, this paper aims to investigate the crashworthiness of shrink circular tube energy absorbers with anti-climbers under vertical, horizontal eccentric and oblique loads comprehensively. Finite element (FE) models of the shrink circular tube energy absorber with anti-climbers are then developed and validated by a physical impact experiment. The numerical results show that the different eccentric distances and oblique angles affect the crashworthiness of the proposed energy absorber significantly. The energy-absorbing structure exhibits a stable and controlled deformation pattern as well as a very high energy absorption efficiency in offset collisions at different vertical heights. However, when the horizontal offset distance exceeds half of the anti-climber width, the energy-absorbing circular tube suffers an overall destabilizing deformation, failing in the energy-absorbing capacity. Additionally, the proposed energy absorber is suitable for oblique load collision scenarios that the oblique angle is no more than 10°. Finally, the response surface modeling technique and non-dominated sorting genetic algorithm are employed to optimize the key structural parameters of shrink circular tube energy absorbers under multiple loading cases. The results exhibit that an optimized shrink circular tube energy absorber is more competent in crashworthiness for multiple load cases. [ABSTRACT FROM AUTHOR]

In this paper, determination of optimized regenerative braking-torque function and application in energy efficient driving strategies is presented. The study investigates a lightweight electric vehicle developed for the Shell Eco-Marathon. The measurement-based simulation model was implemented in the MATLAB/Simulink environment and used to establish the optimization. The optimization of braking-torque function was performed to maximize the recuperated energy. The determined braking-torque function was applied in a driving strategy optimization framework. The extended driving strategy optimization model is suitable for energy consumption minimization in a designated track. The driving strategy optimization was created for the TT Circuit Assen, where the 2022 Shell Eco-Marathon competition was hosted. The extended optimization resulted in a 2.97% improvement in energy consumption when compared to the result previously achieved, which shows the feasibility of the proposed methodology and optimization model. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is a well‐developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error‐free data and run an energy‐efficient network. In this paper, we propose a sling‐shot spider optimization (S2SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)‐based networks. The S2SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1‐hop, 2‐hop, and multi‐hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio‐inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%. [ABSTRACT FROM AUTHOR]

Economic, technological, and environmental causes are moving energy toward smart distribution networks. The change from fossil fuels to renewable energy sources is extremely environmentally friendly in both the building and transport sectors. Microgrids could be efficiently used in remote areas utilizing renewable energy resources for supplying different energy demands namely power, heat and cold. This paper presents a mixed-integer linear programming (MILP) model for optimizing planning and sizing decisions in microgrids connected to main grid. Planning decisions the amount of generation of each distributed generation (DG) technology and the amount of power transmission to other nodes. Also, sizing of different DGs including photovoltaic and combined heat and power facilities are performed by the proposed model. According to the results, the model is able to optimize sizing and planning decisions in grid-connected microgrids. [ABSTRACT FROM AUTHOR]

Energy‐efficient train operation (EETO) in high‐speed railways (HSRs) is an extra cost‐effective and flexible means to promote energy‐saving. This paper first examines the energy consumption sources and energy‐saving measures of high‐speed trains (HSTs). Then presents the EETO in HSRs, including three categories: energy‐efficient train control, energy‐efficient train timetabling, and EETO considering train timetabling and driving strategy. Next, the current research status and progress on three aspects of EETO in HSRs, namely optimization algorithm, constraint condition, and oriented scenario, are sorted out. Finally, the research on EETO in HSRs has prospected and proposed directions that can study in depth in the future. By summarizing and reviewing the research on EETO in HSRs, it is hoped to furnish a reference for the railway sector to enhance quality, efficiency, and green, low‐carbon development. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the gradual depletion of fossil energy sources and the improvement in environmental protection attention, efficient use of energy and reduction in carbon emissions have become urgent issues. The integrated electricity and heating energy system (IEHS) is a significant solution to reduce the proportion of fossil fuel and carbon emissions. In this paper, a stochastic optimization model of the IEHS considering the uncertainty of wind power (WP) output and carbon capture power plants (CCPs) is proposed. The WP output in the IEHS is represented by stochastic scenarios, and the scenarios are reduced by fast scenario reduction to obtain typical scenarios. Then, the conventional thermal power plants are modified with CCPs, and the CCPs are equipped with flue gas bypass systems and solution storage to form the integrated and flexible operation mode of CCPs. Furthermore, based on the different load demand responses (DRs) in the IEHS, the optimization model of the IEHS with a CCP is constructed. Finally, the results show that with the proposed optimization model and shunt-type CCP, the integrated operation approach allows for a better reduction in carbon capture costs and carbon emissions. [ABSTRACT FROM AUTHOR]

The energy efficiency of material extrusion additive manufacturing has a significant impact on the economics and environmental footprint of the process. Control parameters that ensure 3D-printed functional products of premium quality and mechanical strength are an established market-driven requirement. To accomplish multiple objectives is challenging, especially for multi-purpose industrial polymers, such as the Poly[methyl methacrylate]. The current paper explores the contribution of six generic control factors (infill density, raster deposition angle, nozzle temperature, print speed, layer thickness, and bed temperature) to the energy performance of Poly[methyl methacrylate] over its mechanical performance. A five-level L25 Taguchi orthogonal array was composed, with five replicas, involving 135 experiments. The 3D printing time and the electrical consumption were documented with the stopwatch approach. The tensile strength, modulus, and toughness were experimentally obtained. The raster deposition angle and the printing speed were the first and second most influential control parameters on tensile strength. Layer thickness and printing speed were the corresponding ones for the energy consumption. Quadratic regression model equations for each response metric over the six control parameters were compiled and validated. Thus, the best compromise between energy efficiency and mechanical strength is achievable, and a tool creates significant value for engineering applications. [ABSTRACT FROM AUTHOR]