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In the fashion industry, garment sizing is a challenging issue and involves different problems resulting in lost profits. The design of a sizing system is a complex task involving both anthropometric data and subjective data, such as comfort or fit. With the emergence of big data, companies can collect valuable information about their consumers and, thus, enhance their sizing systems. Although several methods have been proposed in the literature, they do not integrate consumer comfort and fit. The method proposed in this article aims to design a suitable sizing system, while taking into account both morphological features and consumer preferences. Consumer preferences are extracted from a satisfaction survey, and are then integrated into an optimization process. The main contribution of the proposed model is twofold: a comprehensive evaluation of sizing systems that are based on consumer preferences is proposed and an optimization algorithm is developed to perfectly fit the problem. The proposed system is implemented and evaluated on the measurement database of the CAESAR project and a satisfaction survey conducted by a French fashion retailer. A comparison with up to date techniques and the existing sizing systems demonstrates that our model provides significant improvements for consumer fit and coverage.

In the growing technology automobile industries plays an importance role. The tire industry is a part of automobile industry. The growing practice in the tire industry is to replace Initial wheel size machinery of a car of a greater wheel dia. and a smaller aspect tyre with the similar diameter is replaced by the pneumatics. This activity is usually synonymous with retailing, and automotive manufactures are facing an increasing inclination for new vehicles to suit the bigger tyres. This paper explores general practise and its influence on the performance characteristics of the automobile. This research is aimed at demonstrating how the plus-size pneumatic affects all vehicle performance as well as stability. Any complex tyre influence that could impact the performance of the system will be addressed and preliminary performance data will be provided on the dynamic influence of plus sizing.

In today’s time, photovoltaic systems are continuously gaining popularity and emerging due to their high sustainability, autonomy, and viability. It is necessary to assess the performance of these systems to understand various aspects related to their operation. The study analyses the usefulness of a PV system installation that supplies electricity to an academic institution. This paper aims to evaluate the performance of a grid-connected silicon-poly PV system with a peak power of 20.0 kW and voltage of 17v. The software used for analysis is PVsyst (7.1.7 version). PVsyst is a widely used simulation software for estimating the energy yield and for optimizing the system design. The PVsyst software has been used to design a grid-connected PV system for the Karunya Institute of technology. The simulated system has silicon-poly PV modules assembled in it. Each module consists of numerous photovoltaic cells interconnected. Each module has a power rating of 180wP and voltage sizing of Vmpp (60-degree Celsius) 17.5v Voc (−10degree) 28.9v. The photovoltaic modules are assembled in a total of 13 strings. Modules in a string are series-connected. Each string in the system consists of 10 PV modules connected in series with a power rating of 20.8 kW. The arrangement is grid-connected with a utility meter. The weather dataset used for evaluation is extracted from PVsyst’s database and has the attributes, solar radiation, and ambient temperature.

To meet fast growing in energy demand, all energy sources have to be exploited. Renewables energies are unlimited and clean but the most problem with them is their intermittent aspect. To overcome this problem a mixture of several energy sources is made to obtain what called hybrid renewable energy system. This paper aims at presenting and analyzing a deep literature review of the recent paper published in hybrid renewable energy field. This review focuses on four essential categories of hybrid renewable energy system which is sizing (using software or using traditional methods), optimization (classical, artificial and hybrid methods), control (centralized, distributed and hybrid control) and energy management (technical objective, economic objective and techno-economic objective). Furthermore, this paper compares between different methods used in each category.

Estimating the cost and schedule of agile software projects is critical at an early phase to establish baseline budgets and schedules for the selection of competitive bidders. The challenge is that common agile sizing measures such as story points and user stories are not practical for early estimation as these are often reported after contract award in DoD. This study provides a set of effort and schedule estimation models for agile projects using a sizing measure that is available before proposal evaluation based on data from 36 DoD agile projects. The results suggest that initial software requirements, defined as the sum of functions and external interfaces, is an effective sizing measure for early estimation of effort and schedule of agile projects. The models accuracy improves when application domain groups and peak staff are added as inputs.

This paper presents a planning model that utilizes mobile energy storage systems (MESSs) for increasing the connectivity of renewable energy sources (RESs) and fast charging stations (FCSs) in distribution systems (DSs). The proposed planning model aims at enabling high penetration levels of green technologies while minimizing the total DS cost that includes investment, operating, and emission costs. The proposed model determines the optimal MESS sizes and transportation schedules as well as the optimal sizes and locations of wind-based distributed generators (DGs), photovoltaic (PV) DGs, and FCSs. The model takes into account techno-economic and environmental factors in addition to the power variations of RESs, FCSs, and load demands. The proposed planning model is applied on two benchmark test systems (i.e., 33-bus and 69-bus DSs). To evaluate the efficacy of the proposed model, the results obtained from the model are compared to those obtained from a traditional planning technique. The comparison of the results demonstrates that the proposed model with MESSs successfully achieves a significant cost saving and increases the penetration levels of RESs and FCSs.

Machine learning-assisted global optimization methods for speeding up analog integrated circuit sizing is attracting much attention. However, often a few typical analog IC design specifications are considered in most relevant research. When considering the complete set of specifications, two main challenges are yet to be addressed: (1) The prediction error for some performances may be large and the prediction error is accumulated by many performances. This may mislead the optimization and fail the sizing, especially when the specifications are stringent. (2) The machine learning cost could be high considering the number of specifications, considerably canceling out the time saved. A new method, called Efficient Surrogate Model-assisted Sizing Method for High-performance Analog Building Blocks (ESSAB), is proposed in this paper to address the above challenges. The key innovations include a new candidate design ranking method and a new artificial neural network model construction method for analog circuit performances. Experiments using two amplifiers and a comparator with a complete set of stringent design specifications show the advantages of ESSAB.

This study introduces new probabilistic constraints and objective functions to manage the uncertain nature of the renewable energy sources in single-item capacitated lot sizing problem for flow-shop configurations by integrating the capacity contract selection problem with multiple energy sources. The aim of the probabilistic models built by considering the different probabilistic constraints and objective functions is to provide a decision-making tool and to promote the use of renewable energy sources in manufacturing industry despite of their stochastic nature. Mixed Integer Non-Linear Programming models are proposed by integrating the uncertainty of the renewable energy sources based on different features. The developed models are tested on a small-size instance and the results of the models are compared in terms of economical, ecological and reliability aspects.

For capacitated multi-item lot sizing problems, we propose a predictive search method that integrates machine learning/advanced analytics, mathematical programming, and heuristic search into a single framework. Advanced analytics can predict the probability that an event will happen and has been applied to pressing industry issues, such as credit scoring, risk management, and default management. Although little research has applied such technique for lot sizing problems, we observe that advanced analytics can uncover optimal patterns of setup variables given properties associated with the problems, such as problem attributes, and solution values yielded by linear programming relaxation, column generation, and Lagrangian relaxation. We, therefore, build advanced analytics models that yield information about how likely a solution pattern is the same as the optimum, which is insightful information used to partition the solution space into incumbent, superincumbent, and nonincumbent regions where an analytics-driven heuristic search procedure is applied to build restricted subproblems. These subproblems are solved by a combined mathematical programming technique to improve solution quality iteratively. We prove that the predictive search method can converge to the global optimal solution point. The discussion is followed by computational tests, where comparisons with other methods indicate that our approach can obtain better results for the benchmark problems than other state-of-the-art methods. Summary of Contribution: In this study, we propose a predictive search method that integrates machine learning/advanced analytics, mathematical programming, and heuristic search into a single framework for capacitated multi-item lot sizing problems. The advanced analytics models are used to yield information about how likely a solution pattern is the same as the optimum, which is insightful information used to divide the solution space into incumbent, superincumbent, and nonincumbent regions where an analytics-driven heuristic search procedure is applied to build restricted subproblems. These subproblems are solved by a combined mathematical programming technique to improve solution quality iteratively. We prove that the predictive search method can converge to the global optimal solution point. Through computational tests based on benchmark problems, we observe that the proposed approach can obtain better results than other state-of-the-art methods.

The world today is continuously tending toward clean energy technologies. Renewable energy sources are receiving more and more attention. Furthermore, there is an increasing interest in the development of energy storage systems which meet some specific design requirements such as structural rigidity, cost effectiveness, life-cycle impact, and increased energy capacity. Gravity energy storage (GES) is one of those innovative storage technologies that is still under development. Hence, this study proposes a new methodology which aims to optimally design and deploy a large-scale GES system in a hybrid PV-Wind plant to make it more competitive technically and economically. The objective of this study is to minimize GES construction cost restricted by handling mechanical load applied on the system's structure. The sizing methodology is based on genetic optimization algorithm which aims to determine the optimum dimensions of GES components. A case study has been used to verify the effectiveness of the proposed model. The findings of this study have shown that the optimal system's height is about 48 m with a diameter of 24 m and a wall thickness of 3m. The construction cost of the system reaches 6.7 M€ with the piston representing the largest cost share. The cost of electricity was found equals to 0.19 €/kWh. In addition, this work investigates the integration of GES with a hybrid wind-solar grid-connected power plant to properly dispatch renewable power by incorporating an efficient storage strategy preventing overcharging/discharging of GES. The findings demonstrate the ability of this new storage system to balance the energy supply and demand. A comparison between the obtained results with that of a battery energy storage has shown that GES performs better due to its high DOD and lifetime, as well as its good efficiency.

Investigates the optimal placement and sizing of shunt capacitor banks in the presence of harmonics. It includes the concept of reactive power compensation based on capacitors, and common types of shunt capacitor banks used in power systems. A survey of various optimization algorithms for the placement and sizing of shunt capacitor banks under sinusoidal operating conditions is listed, such as analytical methods, numerical programming methods, heuristic methods, artificial intelligence-based methods, genetic algorithms, expert systems, simulated annealing, artificial neural networks, fuzzy set theory, graph search algorithm, particle swarm algorithm, tabu search algorithm, and sequential quadratic programming. The analysis and comparison of various solution approaches for the optimal placement and sizing of fixed capacitor banks in the presence of voltage and current harmonics are outlined. This chapter includes 8 application examples with solutions.

Publisher Summary This chapter attempts to provide sufficient knowledge to specify burners, critically assess a burner design and optimize furnace performance and heat transfer. The performance of the burners has a huge effect on furnace operation and efficiency, yet it is relatively rarely that burner design is fully integrated with the furnace heat transfer requirements; little wonder then that poor performance is often the result. A burner is a transducer since it transforms one form of energy into another form of energy. In the case of the burner, it transforms the chemical energy in the fuel into heat energy within the furnace using the chemical reactions in a flame. The effectiveness with which this is achieved is a measure of its performance. Gas burners are generally classified as premixed or turbulent jet diffusion burners, such as premixed burners, where the fuel and most of the air are mixed together prior to passing through the nozzle and turbulent jet diffusion burners, where the fuel and most of the air are introduced separately and are mixed within the furnace. Burners and furnaces are also classified by the air delivery system as natural draught, forced draught, induced draught and balanced draught. Oil burners have the added complexity of two-phase flow resulting from the spray of fuel. Pulverized coal firing has many advantages over other forms of coal firing and makes burning coal similar to oil firing from a process viewpoint. Pulverized coal burners are similar to oil burners but the atomizer is replaced with a coal nozzle. The fine coal is air conveyed to the nozzle. Burners are often specified and selected from a catalogue based on little more than the estimated heat liberation required for the furnace, the number of burners needed and the price of the burner.

This paper proposes a novel modeling approach for optimal sizing of the components of an islanded micro-grid subject to satisfying a reliability index for meeting the loads. The proposed micro-grid incorporates photovoltaic arrays, wind turbines, a battery bank, an inverter, and an electric vehicle (EV) charging station. A demand-side management mechanism based on a deferrable load program is implemented and a model reduction technique is also utilized to mitigate the computational cost. Three different optimization algorithms, namely the whale optimization algorithm (WOA), particle swarm optimization (PSO), and the genetic algorithm (GA) are considered in this study to minimize the total cost of the system. The simulation studies have shown that although the WOA reduces the computational burden and requires much lower iterations compared with PSO and GA, it converges to sub-optimal solutions; therefore, it is not a good option for micro-grid planning purposes. Moreover, the results demonstrate that by charging coordination of EVs and deferring a pre-determined portion of the residential loads, overloading can be avoided and available components can be utilized better, which in turn reduces the sizes of the components and total cost of the system.

[EN] This paper aims to propose a model for solving a capacitated lot-sizing problem with sequence-dependent setups (CLSD) and parallel machines in a bi-part injection moulding (BPIM) context that consists of injecting two different parts or products into the same mould in separate injection cavities. These two parts require the same sequence order and available capacity at the same instant, but generate different part inventories. The addressed problem should be considered a multi-machine CLSD-BPIM one. We provide a mixed integer linear programming (MILP) model for it. The proposed model is based on a real-world case study from a second-tier supplier of the automotive sector. In sequence setup, inventory and resource setup costs terms, the benefits of the proposed multi-machine CLSD-BPIM model are validated by comparing it with a single-machine CLSD-BPIM model that replicates the current planning scenario carried out in the company under study. Additionally, random instances based on the real problem have been generated and tested. The computational results exhibit optimal solutions for the small instances with an average of 0.2-second solution time, the medium instances present average gaps of 0.47% with 2.6 h and large instances present average gaps of 4% with 6 h solution time., The research leading to these results was supported in part by the European Commission under the Grant Agreement No. 825631 `Zero Defects Manufacturing Platform' (ZDMP) (www.zdmp.eu); and the Spanish Ministry of Science, Innovation and Universities project entitled `Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00).

Variability of renewables like solar and wind remains a major concern, despite a substantial decrease in the capital cost of their power conversion devices. One of the methods to improve the reliability of power is to combine more than one renewable power sources and storage systems together, as per the local renewable potential, is called a hybrid renewable energy system (HRES). The exact sizing of individual sources and storage systems to fulfill the respective load demand for the renewable potential of the site is not a straightforward task. Different sizing and optimization methodologies are developed by the research community. A comprehensive overview of the literature of these methodologies is carried out in this paper. Classical techniques were being applied to the HRES sizing, but their limitations led researchers to increasingly look for multi-objective heuristic techniques. This has led to a range of evolutionary algorithms being employed lately. The robustness of these techniques made them an effective tool for the search of global optimum, but their convergence speed may well be slower in comparison to gradient-based methods in the vicinity of the optimum point. It is needed to come up with novel hybrid techniques, which can carefully combine the conventional and evolutionary techniques by augmenting their advantages and at the same time avoiding their shortcomings. The literature survey proves that HRES from very small capacity of a few watts to many megawatts have been proposed for a variety of different locations throughout the world with varying degrees of cost of energy.

Particle-processing industries, such as those for manufacturing pharmaceuticals, ceramics and polymers, require accurate and reliable monitoring and controlling techniques for particle-size distribution to stabilize product quality and maximize productivity. Imaging is a highly valuable monitoring technique because it can measure precise particle-size distributions by detecting particles individually and also can measure particle shape. However, detecting submicron particles or smaller is difficult due to the diffraction of light in the optics. We propose an imaging-based monitoring system that extends the detectable particle-size range towards the submicron level by combining particle-shade and scattered-light imaging. The system consists of newly developed optics and an analysis algorithm. We evaluated the accuracy of the proposed system using polystyrene standard spheres.

The calls for actions to combat climate change have resulted in renewable energy adoption and, consequently, an emerging renewable energy industry. Of course, this has also precipitated the need for more renewable energy expert grounded in hybrid renewable energy system (HRES) software to support the growing industry. Hence, selecting particular software for teaching renewable energy modules for the training of students at a university level is essential. The appraisal and selection of HRES sizing and optimisation software have become crucial for instructors and learners being prepared for the industry. This paper's central contribution is to provide academics, tutors, and researchers in the field of renewable energy system sizing with an idea on how to proficiently and effectively deploy multi-criteria decision-making (MCDM) approaches in HRES sizing and optimisation software selection problems. A framework that combines fuzzy entropy method and fuzzy-VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) was used to rank HRES simulation software. The performance of the framework was compared with that of the complex proportional assessment (COPRAS) method. The article observed a difference between the fuzzy VIKOR and COPRAS solution for ranking the most preferred software; the most preferred option using the Fuzzy VIKOR is the BCHP screening tool, and HOMER for the COPRAS method. They ranked the least preferred software as RETScreen (VIKOR method) and ORCED (COPRAS method).

In this paper, an economic analysis is conducted on standalone conventional solid oxide fuel cells (SOFCs) to determine their optimal economic operating conditions and to contribute to the commerci...

In the design procedure of a PV‐based microgrid, optimal sizing of its components plays a significant role, as it ensures optimum utilization of the available solar energy and associated storage devices. This in turn ensures efficient and economic operation of the microgrid. Various approaches have been reported in the literature in order to approach the sizing problem in PV‐based microgrids. However, the existing approaches are subjective to the requirements of the microgrid and there is no unique framework for approaching the microgrid sizing problem. In this study, a comprehensive review of the existing approaches used for sizing of PV‐based microgrids with a summary of the commonly adopted design considerations has been presented. Also, the optimization problem, including the usually considered designed objectives and constraints, for PV‐based microgrid sizing have been thoroughly reviewed in this study. Furthermore, the adopted approaches for solving the optimization problem associated with the sizing of a PV‐based microgrid system available in the literature have been reviewed comprehensively. With a view to present a generic framework for the optimal sizing of a PV‐based microgrid, this study further presents a framework based on the comprehensive review of the existing literature. The developed framework will be useful for both the practising engineers and academics to understand and apply the usual practices for optimal sizing of PV‐based microgrids.

OBJECTIVES Thoracic endovascular aortic repair (TEVAR) is the first-line therapy in acute complicated type B aortic dissections (cTBAD). Nevertheless, no evidence-based consensus on the optimal measurement technique and sizing for TEVAR in cTBAD exists. The aim was to evaluate how different measurement and sizing techniques for TEVAR affect long-term outcomes. METHODS Retrospective analysis investigating the association between sizing and postoperative results after TEVAR in patients with cTBAD, treated between January 2003 and December 2020. Diameter measurements were performed perpendicular to a centreline in pre-interventional Computed tomography angiographies. Oversizing was determined by measuring aortic diameter in zone 2 of the aortic arch in relation to the implanted stent graft, and categorized into 2 sizing groups (≤10% and >10%). The primary outcome was freedom from aortic-related events. Secondary outcomes included mortality and a comparison of 3 alternative measurement techniques considering the estimated pre-dissection diameter. RESULTS Fifty-seven patients (median age 69, interquartile range 59.6–78.2 years) were included. Stent graft oversizing by ≤10% showed a trend towards fewer aortic-related events hazard ratio 0.455 (95% confidence interval 0.128–1.624, P = 0.225). The 3 measurement techniques using the pre-dissection aortic diameter differed by a mean of 1.7–4.0 mm with a variability of up to 8.4 mm. In none of the 57 patients, the same stent graft would have been chosen based on the different measurement techniques using an oversizing ≤10%. CONCLUSIONS TEVAR oversizing of ≤10% in patients with cTBAD might reduce aortic-related events up to 50%. Consensus on measurement techniques of the pre-dissection aortic diameter and stent graft sizing is of paramount importance.

Copyright © The Author(s) 2021. This paper is concerned with the electric vehicle (EV) charging station planning problem based on the dynamic charging demand. Considering the dynamic charging behavior of EV users, a dynamic prediction method of EV charging demand is proposed by analyzing EV users’ travel law via the trip chain approach. In addition, a multi-objective charging station planing problem is formulated to achieve three objectives: (1) maximize the captured charging demands; (2) minimize the total cost of electricity and the time consumed for charging; and (3) minimize the load variance of the power grid. To solve such a problem, a novel method is proposed by combining the hybrid particle swarm optimization (HPSO) algorithm with the entropy-based technique for order preference by similarity to ideal solution (ETOPSIS) method. Specifically, the HPSO algorithm is used to obtain the Pareto solutions, and the ETOPSIS method is employed to determine the optimal scheme. Based on the proposed method, the siting and sizing of the EV charging station can be planned in an optimal way. Finally, the effectiveness of the proposed method is verified via the case study based on a test system composed of an IEEE 33-node distribution system and a 33-node traffic network system. National Natural Science Foundation of China under Grants 61703245, 61873148 and 61933007; Natural Science Foundation of Shandong Province of China under Grant ZR2020MF071; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA0y; Royal Society of the UK; Alexander von Humboldt Foundation of Germany.

Nowadays, the power systems face several environmental and economic challenges and Distributed Generations (DGs) will be an effectual solution for them. The integration of DGs may result in power system volatility and losses. The optimal allocation of DGs will resolve the aforesaid issues. This study aims to implement multi-objective firefly algorithm for siting and sizing of DGs by optimizing six dissimilar objective functions such as minimization of power losses, improvement of voltage profile, enhancement of Voltage Stability Index, reduction of pollutant emission and elimination of average voltage Total Harmonic Distortion. Besides, fuzzy decision-making methodology has been deployed to choose one of the Pareto-optimal solutions as the Best Compromise Solution. The studies have been conducted on standard IEEE 33-bus system and a practical 62 bus Indian Utility System namely Tamil Nadu Generation and Distribution Corporation Limited as a real-world distribution network. The outcomes of the proposed work have been compared with related past studies and prominent improvement has been experienced.

Capital cost limitation is a practical problem in a system installation and single optimal rating may lead to requirement of high capital cost investment. This calls for a need a need to develop a framework which provides alternate system ratings when confronted with capital cost limitation. A framework for sizing microgrid distributed energy resources (DER) considering capital cost limit and inclusion of manufacturer catalog ratings is introduced in this paper. This framework uses a system composite cost metric which combines system capital cost and operating cost and is calculated on monthly basis. The monthly optimization results are transformed to evaluate the feasible DER ratings and annual savings possible. The framework is demonstrated using a case study of sizing microgrid to perform peak shaving at industrial facilities located in the UK and USA. This paper also introduces development of Hardware-in-Loop (HiL) system as a tool to validate microgrid sizing results. The HiL simulation verifies the estimated annual savings possible through the installation of microgrid and verifies no violation of state of charge limits of battery energy storage system in the microgrid operation simulation, thus validating the sizing framework.

One feasible solution to reduce the cost of concentrated solar power is to employ a higher temperature and efficiency supercritical carbon dioxide Brayton power cycle. An alternative heat transfer medium and thermal energy storage (TES) system therefore needs to be explored. This work considered a variety of phase change materials (PCMs) and graphite as the storage media in four indirect shell-and-tube storage configurations, including a 3-PCM and 5-PCM cascade storage, a PCM-graphite-PCM hybrid storage and a single graphite storage. The sizing and design of the TES systems were performed by using a dynamic cycling methodology based on a transient 2D numerical model. The cost of those designs were then determined by using an economic model developed inhouse. This work also investigates the impact of some geometric parameters and cost assumptions on the techno-economic performance of the TES system. The analysis suggests a scenario exists whereby a low thermal efficiency storage system which therefore utilises less tube or storage material could be more cost-effective. Overall, the cost of hybrid TES is the lowest among all studied systems, at $26.96/kWht and $21.49/kWht for charging temperature of 720 °C and 750 °C, respectively, followed by the 5-PCM storage at $28.06/kWht and $21.82/kWht, respectively Refereed/Peer-reviewed

The optimum size of the prime mover (PM) for combined cooling, heating and power (CCHP) systems is determined taking account of economics, environmental impacts, energy, and exergy. The optimum size of the PM greatly increases the justification of establishing a CCHP system. Optimum sizing of the PM is conducted here considering exergy efficiency, exergy destruction rate, total energy efficiency, overall fuel energy consumption, carbon release, carbon release decrease, equivalent uniform annual benefit, and payback duration. To determine the optimum size, an objective function is presented in which some of the aforementioned criteria are minimized and some others are maximized. To solve the problem arisen from this objective function, the improved pathfinder optimization algorithm is proposed. To prove its superior performance, it is compared to six other popular optimization algorithms used in CCHP optimization using six benchmark functions. After obtaining the optimum size of the PM, sensitivity analyses are conducted on all of the eight assessment sub-criteria versus the PM capacity, and the results are given in figures. In addition, their values are given with the optimum size of the PM. The results indicate that the studied CCHP with the optimum PM size yields exergy efficiency of 55.53%, exergy destruction rate of 48.34%, total energy efficiency of 68.79%, and overall fuel energy consumption of 209 kWh. Also, this system will produce 36.82kg of carbon per hour which is 23.46% lower than the initial separated generation system. Moreover, with the equivalent uniform annual benefit of 26376$, it will pay back in 3.21 years.

To impart good application performance to chitosan (CS) sizes for high polyester content warp yarns, methyl acrylate (MA) and acrylamide (AM) monomers, with a variation in feed molar ratio from 1:9 to 4:6, were grafted onto the molecular chains of native CS to obtain CS- g-P(MA- co-AM) products, with similar grafting ratios through a K2S2O8-NaHSO3 redox system. Effects of monomer compatibility of MA and AM on sizing performance of the CS- g-P(MA- co-AM) for high polyester content warp were studied. Grafting MA and AM with rational compatibility onto the molecular chains of CS is an effective method to improve the application performance of CS sizes. In view of the overall performance of the CS- g-P(MA- co-AM) sizes, the appropriate feed molar ratio of MA/AM should be 3:7.

This work considers the aeroelastic optimization of transport wingbox structures under three different types of design variables: sizing (for example, skin thickness), shape (planform and airfoil t...

The optimal sizing of a hybrid energy system may be a difficult undertaking problem, because of the huge number of structure settings and the irregular nature of solar radiation and wind power sources. This issue has a place with the classification of combinatorial enhancement, and its solution dependent on the classical technique may be a waste of time. This paper proposes a Genetic Algorithm (GA) methodology to find the optimal sizing of a hybrid Photovoltaic, Wind Turbine, Battery Storage, and Diesel Generators (PV/WT/BA/DG) energy system based on the number of PV modules, the number of wind turbines, the number of batteries, the number of diesel generators, the slope angle of PV panel and a hub height of wind turbine as the design parameters and study its effect on the Levelized Cost of Energy (LCOE). The proposed method aims to minimize the LCOE with high reliability of load supplying by increasing the design variables gradually. The proposed method will be tested at different sites with various metrological data to ensure its robustness. The results show that the LCOE decreases as the design parameters increase, also that the average wind-speed is inversely proportional to the LCOE of the site under study.

DC distribution system (DCDS) has many preferable advantages to integrate renewable energy sources and native-DC loads than AC distribution system (ACDS), and the benefits of higher power density, greater energy conversion and wider supply radius can be obtained. In this paper, a methodology of sizing the techno-economic of a medium-low voltage DCDS is proposed. Firstly, the system framework and its equipment composition are stated. Then, considering the distribution capacity, supply ability, operation loss and reliability, the technical indicators of the demonstrated DCDS are analyzed and quantified. In regards to the DCDS's distribution-level and consumer-level, the calculation models of the annual costs are presented. Further, different scenarios are imitated to verify the efficacy of the proposed sizing approach, and the techno-economic comparison is implemented in the DCDS and ACDS with similar structure configurations. The calculation results reveal that the DCDS shows a visible economic performance at the consumer-level, and for the scenario of accessing high proportion DC loads, it is more cost-effective than the ACDS. Finally, the correctness of the cost models is proven, and this study can offer a reliable reference for building the medium-low voltage DCDS.

The increasing share of the distributed renewable energy in power generation is an important development direction in the electrical power system. However, its intermittent and nonprogrammable nature is a major challenge. Battery storage is providing an effective solution to solve these issues. In the paper, the PV/battery/grid (PVBG) system is established for residential buildings, and the optimal combination of PV size and battery size was obtained by techno-economic analysis. Firstly, self-sufficiency ratio (SSR) and self-consumption ratio (SCR) as the technical indicators were applied to evaluate and analyze the performance of different PV/battery/grid systems. Secondly, the levelized cost of energy (LCOE) as an important economic indicator was applied to optimize PVBG system configuration by using the particle swarm optimization (PSO) technique. PVBG systems were simulated under the actual electricity price and feed-in-tariff of a typical power market. Lastly, the optimal PV and battery system combination was obtained by maximizing SCR and SSR and minimizing LCOE. The results showed that integrating battery energy storage can improve SCR and SSR. The PVBG systems with a higher SSR resulted in more PV production to sell back to the grid and reduced the usage rate of the battery. Eight cases with different technical indicators were compared and discussed. The results showed that the LCOE of the PVBG system ranged from 0.351 to 0.769 RMB/kWh for the residential building studied. According to the technical and economic indicators, the optimal sizes of PV and battery were respectively determined to be 6696 Wp and 2366 Wh for the case studied.

This paper proposes a hierarchical sizing method and a power distribution strategy of a hybrid energy storage system for plug-in hybrid electric vehicles (PHEVs), aiming to reduce both the energy consumption and battery degradation cost. As the optimal size matching is significant to multi-energy systems like PHEV with both battery and supercapacitor (SC), this hybrid system is adopted herein. First, the hierarchical optimization is conducted, when the optimal power of the internal combustion engine is calculated based on dynamic programming, and a wavelet transformer is introduced to distribute the power between the battery and the SC. Then, the fuel economy and battery degradation are evaluated to return feedback value to each sizing point within the hybrid energy storage system sizing space, obtaining the optimal sizes for the battery and the SC by comparing all the values in the whole sizing space. Finally, an all-hardware test platform is established with a fully active power conversion topology, on which the real-time control capability of the wavelet transformer method and the size matching between the battery and the SC are verified in both short and long time spans.

Stand-alone hybrid energy systems are an enticing option for electrification in remote areas in several aspects such as grid extension difficulty, economic feasibility and reliability. The use of existing micro-hydropower (MHP) with other renewable resources in rural areas has not been well studied. Moreover, it is challenging to use mathematical optimization algorithms for these kinds of real-world problems, so the derivative-free algorithm is highly sought. In this paper, a methodology has been proposed to perform the optimal sizing, financial and generation uncertainty analysis of solar photovoltaic (SPV) based on an MHP that is proposed to handle the intermittent power output of the SPV. The analysis is performed in two cases: using storage and without storage. The optimal sizing is performed using the least present value cost and reliability constraint using different derivative-free algorithms. The storage-based hybrid system has been found to generate reliable electricity at minimal cost than without a storage-based one. This study would be helpful to propose electrification and existing micro-hydro reinforcement policies to provide reliable electricity in rural areas.

Solar district heating systems have shown significant promise to facilitate the large scale adoption of solar energy technologies and thus substantially reduce greenhouse gas emissions. Given the mismatch between solar energy and district heating demand, energy storage devices play a critical role given their capacity to stockpile solar energy in both the short-term (hours to days) and long-term (months). However, the integration, sizing and control of energy storage technologies is far from simple. This paper investigates sizing and controlling thermal energy storage from the perspective of its performance within a district heating system, highlighting the close link between design and control. A 52-house Canadian solar district heating system, the Drake Landing Solar Community (DLSC), was used as a case study. This system uses solar collectors as main energy supply, borehole thermal energy storage (BTES) for seasonal storage and two 120-m3 water tanks for short-term thermal storage (STTS). The effect of (a) storage sizing (STTS volume) and (b) storage control (rate at which energy is either injected or extracted from the BTES) was evaluated. A control-oriented model, calibrated and validated with operational data at 10-min intervals, was used along with an optimal rule-based control to gauge system primary energy use. Different scenarios were tested, with STTS volumes ranging from 120 m3 to 480 m3, and BTES loop nominal flow rates between 2.5 and 4.5 L/s. An optimization routine was developed to calculate the optimum parameters of the rule-based control strategy. Results show that, in comparison with the design and control in place, primary energy savings of 13%–30% (with BTES flow rates of 2.5–4.5 L/s) could have been obtained with the proposed rule-based control strategy. By decreasing the STTS volume to 120-m3, energy savings up to 6% could still be achieved; savings could reach 27%–36% by increasing the STTS size to 360-m3.

District heating and cooling are considered effective solutions to decarbonise the energy use in the building sector. The latest generation of district heating and cooling also increases the potential of integrating heat pumps and chillers in each building substation. The benefits of such integration are the reduction of network temperature and distribution losses; the recovery of waste heat through a bidirectional network; and the decentralised production of heating and cooling. Sizing the network depends mainly on the heat flows between connected buildings. The substation performance and technical installations determine these heat flows. We present in this paper Modelica-based simulations of two design cases for substations. The first design case involves installed heat pump, chiller, and circulation pumps. Alternatively, the second design enables the heat pump to provide direct cooling through a heat exchanger. The models for these installations were developed using the Modelica language to perform continuous-time simulations. The performance in each design case was evaluated in terms of seasonal coefficient of performance and total electric energy use. An analysis on a cluster of 11 buildings suggests that the addition of the direct cooling heat exchanger can save up to 10% of the total annual electric energy use. Additional savings can be achieved by optimising the building supply temperatures and the district network temperature.

This paper proposes a new and comprehensive approach for optimal sizing and placement of a wind farm (WF) in a radial distribution network (RDN) while considering several factors associated with its reliability, operation, economics and environment impact. The consideration of the reliability aspect is essential to correctly assess the impacts of forced outages of wind turbines in the field. In this context, a sequential Monte Carlo simulation-based technique has been formulated to evaluate the performance of the WF with varying capacities at different locations in the RDN. Additionally, the capability of the WF to perform Volt-Var and Volt-Watt control has been capitalized on to provide voltage support to the grid in compliance with international standards (IEEE 1547-2018). Finally, a combination of Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution has been used to assess and determine the optimal size and location of the WF, based on a selected set of performance criteria, namely cumulative violations of the steady-state voltage range, cumulative losses in RDN, cumulative violations of the line thermal rating, levelized cost of energy, and reduction in carbon emission. The proposed approach has been validated considering different modes of voltage control on the standard 33-bus RDN system.

A novel optimized sizing and management strategy of a grid-connected hybrid photovoltaic (PV)-solar-battery-group system were proposed for the electrification of residential consumers in Northwest Africa (a case of Mauritania), and the influence of the state of the sky (clear, moderately overcast, and overcast) was analyzed according to the load flowing (LF) and the cycle charging (CC) strategies. In order to mitigate the pressure on the national grid, consolidate the consumer autonomy, minimize the cost of medium- and long-term consumption bills, and CO2-related emissions, a cartographic approach was conducted as the first attempt to map the electricity consumption potential for buildings in the city of Nouakchott (Mauritania) using a geo-referenced database. ArcGIS®, HOMER Pro®, and MATLAB® softwares were used for the establishment of the load profile, optimized sizing of the PV-batteries-group-grid system, and calculation of the lightness index, respectively. The LF strategy provided the best monitoring of the load throughout the day by minimizing the generator uptime. The techno-economic analysis revealed the values of cost of energy (COE) and net present cost (NPC) as follows: COE = $0.0549/kWh and NPC = $24,796 for the LF PV-batteries-grid strategy, COE = 0.0646 $/kWh and NPC = $23,380 for the CC PV-batteries-grid strategy, and COE = $0.17/kWh and NPC = $23,262 for the case of the grid on its own.

District cooling (DC) is gaining interest with global warming and rising demands on indoor comfort. As DC grid expansions are capital intensive, cost effective alternatives to meet these rising cooling demands are desired. Integrating cold storage (CS) into the grids is one attractive choice, allowing peak shaving, load shifting, and renewable electricity recovery via power-to-cold. To analyze the impact of new CSs, the DC distribution grid dynamics must be investigated. This work evaluates the implementation of several new CSs into an existing DC system (called the base case-BC), to find the optimal solution. This is performed considering the case study DC system of Norrenergi AB, Sweden, catering to Solna and Sundbyberg via three production plants Solnaverket, Sundbybergsverket and Frosundaverket, and a 10 MW CS (in Solnaverket). The software tool Netsim is used for distribution grid dynamics analysis of the BC and three scenarios with additional cold storages, for the optimization of differential pressure (dP) of the grids to be within 100–800 kPa. These scenarios include: one additional 15 MW CS in Sundbybergsverket (Scenario 1), one additional 15 MW CS in Frosunda (Scenario 2) and two additional 3 MW CSs in Sundbybergsverket and Frosunda (Scenario 3). The CSs in Sundbybergsverket are centrally placed, whereas, those in Frosunda were positioned in a grid loop experiencing low differential pressure, identified in Netsim simulations of BC. The simulations were done for 24 h at 1-hour resolution, on a chosen historically highest demand day (02 August 2018). The results indicate that the optimal solution is implementing two additional CSs in Sundbybergsverket (centralized) and Frosunda (distributed), each with a capacity between 3–7.5 MW (6–15 MW total capacities). Further evaluations to determine the optimal sizing of these CSs is the next step in this study.

This article proposes a supply modulator (SM) that uses high switching frequency gallium-nitride (GaN) devices to achieve a four-phase fast step-down converter for the fast-tracking ability of the envelope tracking (ET). The ET signal is reshaped to generate an average power tracking (APT)-based signal to control the size of GaN switches to further increase light-load efficiency due to reduced switching loss. Moreover, a high-bandwidth and low-loss linear amplifier (LA) is used to supply the power amplifier (PA) to sufficiently provide fast-tracking speed for 5th generation (5G) NR ET. At a power of 3.5 W for 5G mobile phones, the peak efficiency that is tested with NR −130 MHz in the ET mode is as high as 85.7% over a wide load range.

Energy storage unit (ESU) is playing an increasingly important role in load shifting and uncertainty mitigation. This paper aims to quantify the value of ESU in the unit commitment (UC) with renewable generation. By treating the power and energy capacities of ESU as continuous parameters, the stochastic UC problem is cast as a multi-parametric mixed-integer linear program (mp-MILP), whose optimal value function (OVF) gives the relation between storage capacity and the daily UC cost in an analytical manner. It encompasses abundant sensitivity information, and its surface can be easily visualized. The reduced cost compared with the benchmark case without storage can be regarded as the value of ESU. As a potential application, the OVF is used to formulated an optimal storage sizing problem that maximizes the ratio between the reduced operation cost and the investment cost, ensuring the minimum time of cost recovery. The solution consists of two steps: the first step constructs the OVF parameterized in storage capacity; the second step reformulates the fractional storage sizing program into an MILP, leveraging the expression of the OVF and variable transformations. Case studies conducted on the IEEE 9-bus system and IEEE 118-bus system illustrate how the OVF offers useful reference for power system operation and planning. The proposed optimal sizing model shows advantages in efficient utilization of investment in terms of payback time and rate of return.

The optimal placement and the sizing of various distribution system components have gained extra significance in the recent past as the concepts of smarter distribution systems, distributed generation, and microgrids blossomed. This paper presents two novel techniques for sizing the Distributed Generators (DGs) in a large-sized radial distribution network. The primary strategy is proposed by considering the bi-directional progression of current in the parts of the circuit that occurs because of the presence of newly installed DGs. The subsequent strategy utilizes the active power sensitivity calculations for sizing the DGs. These strategies are applied on the chosen radial distribution systems to size numerous DGs all at once, with the main objective being the improvement of efficiency, by accomplishing minimization of losses and improvement in nodal voltages. Then, the maximum number of self-adequate and highly reliable microgrids is formed in the distribution network utilizing the “Reverse Current Flow” procedure cited in the literature, for the proposed sizing methods. Finally, a sensitivity analysis is performed by varying the rates of infiltration of the DGs to throw light on the suitability of the newly introduced methods of sizing. This analysis gives a set of operational rules for the controller of the smart grid.

There are several profits of distributed generator (DG) units which are believed for improving the safety of the distribution power grids. However, these profits can be maximized by ensuring optimum sizing and positioning of DG units because an arbitrary location of DG units may adversely affect and jeopardize power grids which could contribute to maximising of power loss and degradation of the voltage profile. Therefore, several approaches were suggested to ensure optimum position and size of DGs. The primary aim of this article is for establishing technique for optimum scheduling and operating of DG to lessen power loss, revamp voltage profile and overall network reliability. Artificial intelligence method called particle swarm optimization (PSO) is utilized for finding the best site and size of DG to lessen power loss and boost the voltage profile. In this paper, IEEE 33 distribution system is utilized to display applicability of PSO. The results of the PSO are compared with the results gotten by other methods in the literature. Finally, the results show that the PSO is superior than the other methods.

The motivation of this work is to propose a shared balance of plant (BoP) and power supply (PS) design for industrial scale alkaline electrolyzer plant that has reduced CAPEX with a minimum loss in OPEX for variable load operation. Three important aspects are: a) flowsheet - either shared or individual BoP and PS per stack, b) variable or constant lye flowrate per stack and c) sizing of cooling duty in the lye circulation loop. Steady-state optimization shows that individual BoP per stack (with higher CAPEX) is optimal when the plant is expected to operate at high capacity. For shared BoP and PS, the hydrogen production is higher by 8–12% when operated with variable lye flowrate compared to fixed lye flowrate. Our results further suggest that lye cooling duty should be designed based on the cooling requirements of the degraded electrolyzer stacks at end of life.

In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply’s reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are[Formula: see text] with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

This research study presents the design of a mobile Android application which provides efficient PV sizing recommendations for residential users. The paper aims to promote the adoption of solar ene...

This work includes a complete optimization, design, sizing and validation application of density-based topology optimization for a single stiffened panel from the upper skin of a commercial aircraft wing. In this work, topology optimization is carried with Optistruct from Altair. The initial values of the density variables are shown to have the biggest impact on the optimized material distribution. This effect is used in a random restart approach to explore multiple local minima, and thus to find different material distributions. Different material distribution obtained with topology optimization have shown the same buckling resistance within the implicit model used for optimization. However, the stiffened panel models created from the different material distributions show very different strength when they are sized with a subsequent optimization and validated using quasi-static non-linear analysis. Besides, the optimization results are almost all simple orthogrid solutions. Finding these layouts with topology optimization has proven to be difficult and highlights the fact that the technology is not mature for the design of new layouts for stiffened panels.

Through the development of photovoltaic technologies, the market related to this type of renewable energy grows constantly. In Brazil, the generated power through photovoltaic (PV) power stations approximates to 2.3 GW representing 1.3% of the total Brazilian energy mix. To supply the public it is necessary to offer tools that facilitate consumer decision making or even the work of a solar farm designer. By analyzing the user’s necessity, it is possible to determine that a great parcel needs a quick and simple response to help them with their questions, and programs that are already well established in the marketplace are complex for inexperienced users. For this reason, a software that calculates the best inclination for PV modules using the method proposed by Liu and Jordan in 1962 is developed. It also determines the number of modules that can be installed without any shading between arrays within a user-defined area. This calculation is possible through the standard dimensions provided by the module manufacturers and the solar height and declination study. Through a simple interface, the user quickly receives the optimum tilt angle of installation, the number of PV modules and the maximum generated power.

The objective of this article is to determine the location and size of the renewable energy sources (RESs) to be installed in microgrid networks that have multiple distributed generators. Since RESs have faster dynamics and low inertia, microgrid sources could encounter large disturbances in their power production when RESs are deployed. When the disturbance magnitude exceeds a certain limit, the stable operation of the microgrid can be compromised. In this study, the impact of installing a certain RES capacity on a microgrid is numerically evaluated. A novel methodology is developed to accurately determine the location and size of a newly installed RES in a microgrid. The proposed methodology considers the complete system dynamics in the process of locating and sizing the installed RES. The stability of the microgrid is examined using a modified microgrid load flow model. The proposed method automatically checks for the optimum RES placement to ensure minimal effect on microgrid stability. The effectiveness of the proposed methodology is verified experimentally on a microgrid testbed.