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The life cycle of a microgrid covers all the stages from idea to implementation, through exploitation until the end of its life, with a lifespan of around 25 years. Covering them usually requires several software tools, which can make the integration of results from different stages difficult and may imply costs being hard to estimate from the beginning of a project. This paper proposes a unified platform composed of four modules developed in MATLAB 2022b, designed to assist all the processes a microgrid passes through during its lifetime. This entire platform can be used by a user with low IT knowledge, because it is completed with fill-in-the-blank alone, as a major advantage. The authors detail the architecture, functions and development of the platform, either by highlighting the novel integration of existing MATLAB tools or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. By consolidating processes into a single platform, the proposed solution enhances integration, reduces complexity and provides better cost predictability throughout the project's duration. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting of a photovoltaic plant, and an office building as the consumer and energy storage units. This platform has also been developed by involving students within summer internships, as a process strengthening the cooperation between industry and academia. Being an open-source application, the platform will be used within the educational process, where the students will have the possibility to add functionalities, improve the graphical representation, create new reports, etc. [ABSTRACT FROM AUTHOR]

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency. [ABSTRACT FROM AUTHOR]

This study focuses on exploring the effects of geometrical imperfections and different analysis methods on the optimum design of Double-Layer Grids (DLGs), as used in the construction industry. A total of 12 notable metaheuristics are assessed and contrasted, and as a result, the Slime Mold Algorithm is identified as the most effective approach for size optimization of DLGs. To evaluate the influence of geometric imperfections and nonlinearity on the optimal design of real-size DLGs, the optimization process is carried out by considering and disregarding geometric nonlinearity while incorporating three distinct forms of geometrical imperfections, namely local imperfections, global imperfections, and combinations of both. In light of the uncertain nature of geometrical imperfections, probabilistic distributions are used to define these imperfections randomly in direction and magnitude. The results demonstrate that it is necessary to account for these imperfections to obtain an optimal solution. It's worth noting that structural imperfections can increase the maximum stress ratio by up to 70%. The analysis also reveals that the initial curvature of members has a more significant impact on the optimal design of structures than the nodal installation error, indicating the need for greater attention to local imperfection issues in space structure construction. [ABSTRACT FROM AUTHOR]

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm's competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study's insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices. [ABSTRACT FROM AUTHOR]

Renewable energy systems provide a dependable, environment-friendly, and cost-effective alternative for producing electricity in remote regions. The growing use of meta-heuristic algorithms is attributed to their ability to provide rapid, accurate, and optimal results for intricate optimization challenges. Therefore, in this work, an Evolved Opposition-based Mountain Gazelle Optimizer (EOBMGO) algorithm is explored to achieve the optimal design for the combination of off-gird hybrid renewable energy systems (HRES) that incorporate solar photovoltaic (PV) modules, wind turbines, and battery systems. The primary objective of the optimization process is to minimize the total net annual cost while maintaining an acceptable loss of power supply probability (LPSP), considering levelized energy costs and the generation of excess power. The designed EOBMGO technique has been evaluated for three distinct LPSP values (0%, 0.5%, and 1%), with each value tested across 25 independent runs and 50 iterations. The designed algorithm is then assessed against several established optimizers, including Grey Wolf Optimizer (GWO), Artificial Rabbit Optimization (ARO), Brown-bear Optimization Algorithm (BOA), and White Shark Optimizer (WSO). Statistical analysis has been performed to highlight the superiority of the EOBMGO algorithm over others, which included an evaluation of mean, standard deviation, variance, and crest values. The simulation outcomes revealed that the EOBMGO technique achieved a lower oscillation rate, a low standard deviation, and superior balancing of exploitation and exploration capabilities. Further, EOBMGO is found robust in the sensitivity analysis with variation in capital cost of the major components. These outcomes will provide researchers with a valuable reference for choosing the optimal technique for sizing problems.

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency.

The life cycle of a microgrid covers all the stages from idea to implementation, through exploitation until the end of its life, with a lifespan of around 25 years. Covering them usually requires several software tools, which can make the integration of results from different stages difficult and may imply costs being hard to estimate from the beginning of a project. This paper proposes a unified platform composed of four modules developed in MATLAB 2022b, designed to assist all the processes a microgrid passes through during its lifetime. This entire platform can be used by a user with low IT knowledge, because it is completed with fill-in-the-blank alone, as a major advantage. The authors detail the architecture, functions and development of the platform, either by highlighting the novel integration of existing MATLAB tools or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. By consolidating processes into a single platform, the proposed solution enhances integration, reduces complexity and provides better cost predictability throughout the project’s duration. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting of a photovoltaic plant, and an office building as the consumer and energy storage units. This platform has also been developed by involving students within summer internships, as a process strengthening the cooperation between industry and academia. Being an open-source application, the platform will be used within the educational process, where the students will have the possibility to add functionalities, improve the graphical representation, create new reports, etc.

The pursuit of more efficient transport has led engineers to develop a wide variety of aircraft configurations with the aim of reducing fuel consumption and emissions. However, these innovative designs introduce significant aeroelastic couplings that can potentially lead to structural failure. Consequently, aeroelastic analysis and optimization have become an integral part of modern aircraft design. In addition, aeroelastic testing of scaled models is a critical phase in aircraft development, requiring the accurate prediction of aeroelastic behavior during scaled model construction to reduce costs and mitigate the risks associated with full-scale flight testing. Achieving a high degree of similarity between the stiffness, mass distribution and flow field characteristics of scaled models and their full-scale counterparts is of paramount importance. However, achieving similarity is not always straightforward due to the variety of configurations of modern lightweight aircraft, as identical geometry cannot always be directly scaled down. This configuration diversity has a direct impact on the aeroelastic response, necessitating the use of computational aeroelasticity tools and optimization algorithms. This paper presents the development of an aeroelastic scaling framework using multidisciplinary optimization. Specifically, a parametric Finite Element Model (FEM) of the wing is created, incorporating the parameterization of both thickness and geometry, primarily using shell elements. Aerodynamic loads are calculated using the Doublet Lattice Method (DLM) employing twist and camber correction factors, and aeroelastic coupling is established using infinite plate splines. The aeroelastic model is then integrated within an Ant Colony Optimization (ACO) algorithm to achieve static and dynamic similarity between the scaled model and the reference wing. A notable contribution of this work is the incorporation of internal geometry parameterization into the framework, increasing its versatility and effectiveness. [ABSTRACT FROM AUTHOR]

Integration of renewable energy systems can provide reliable, environmentally sustainable, and cost-effective alternatives for meeting the demand for electricity in remote locations. In this study, recently developed meta-heuristic techniques are explored to find the optimal design for two combinations of off-grid hybrid renewable energy systems. To evaluate the performance, the Tasmanian devil Optimization (TDO) was compared to three meta-heuristic algorithms, called the COOT bird optimization algorithm (COOT), the Grey wolf algorithm (GWO), and the Beluga whale optimization (BWO), and determined the optimal design of the proposed off-grid energy system in terms of best and worst-case solutions. The system consisting of a solar-battery is more cost-effective, with the lowest total annual cost (TAC) of 36,859 $ and the lowest levelized cost of electricity (LCOE) of 0.0930 $/kWh for 0% LPSPmax level as compared to the wind turbine-battery-diesel generator with the highest TAC (102580 $) and LCOE (0.2589 $/kWh). Hence, a solar-battery hybrid system is more viable for producing clean energy with effective storage and better power system reliability enhancement. Also, the obtained simulation results reveal the supremacy of the TDO compared to the other three meta-heuristic algorithms, where it achieved the optimal solution with a quick convergence time and fewer oscillations.

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm’s competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study’s insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.

This paper presents a very alarming forecasts about our future and particularly for a medium and long-term future. That is to say, several actions are being carried out by different civil and state parties to deal with these very concrete threats. And it is within this framework that this paper fits, and its objective is to highlight the hybrid systems and more precisely the photovoltaic-wind hybrid systems coupled with storage batteries, as an efficient alternative to the classical means of electricity production. This work will adopt a method of obtaining results called performance evaluation, so this manuscript will present firstly the mathematical model of this hybrid system to best conceive what it is about, then as second part will determine the exact figures of the largest amount of carbon dioxide (CO2) that can be avoided using this technology and following a precise methodology, this can be applied to our situation, i.e., a simple house or to any other type of installation. [ABSTRACT FROM AUTHOR]

Integration of renewable energy systems can provide reliable, environmentally sustainable, and cost-effective alternatives for meeting the demand for electricity in remote locations. In this study, recently developed meta-heuristic techniques are explored to find the optimal design for two combinations of off-grid hybrid renewable energy systems. To evaluate the performance, the Tasmanian devil Optimization (TDO) was compared to three meta-heuristic algorithms, called the COOT bird optimization algorithm (COOT), the Grey wolf algorithm (GWO), and the Beluga whale optimization (BWO), and determined the optimal design of the proposed off-grid energy system in terms of best and worst-case solutions. The system consisting of a solar-battery is more cost-effective, with the lowest total annual cost (TAC) of 36,859 $ and the lowest levelized cost of electricity (LCOE) of 0.0930 $/kWh for 0% LPSP max level as compared to the wind turbine-battery-diesel generator with the highest TAC (102580 $) and LCOE (0.2589 $/kWh). Hence, a solar-battery hybrid system is more viable for producing clean energy with effective storage and better power system reliability enhancement. Also, the obtained simulation results reveal the supremacy of the TDO compared to the other three meta-heuristic algorithms, where it achieved the optimal solution with a quick convergence time and fewer oscillations. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) play extremely important roles in managing power supply and demand in a microgrid. The BESSs, on the other hand, have problems of high initial investment costs and limited lifetime. In this paper, a problem formulation and its solution are presented for sizing an aggregated BESS in a microgrid that minimizes the sum of initial costs of the aggregated BESS and operational costs of the microgrid. The target problem is originally categorized into a bilevel optimization because it comprises two optimization problems: the sizing of aggregated BESS and the operational scheduling of microgrid’s components. The authors, however, apply the Karush–Kuhn–Tucker approach and reformulate the target problem as a single-level optimization problem. This reformulation enables us to treat the problem as if it was a type of operation scheduling problems. That is, the solution techniques developed to obtain the optimal operation scheduling are applicable by the problem reformulation. As the basis for the solution method, an algorithm combining binary particle swarm optimization and quadratic programming is applied in this paper. To verify the validity of the proposal, numerical simulations are performed, and their results are discussed.

Sizing optimization and power smoothing methodology in highly fluctuating grid-connected renewable systems represent important challenges nowadays, the total cost and energy quality depend on the effective balance between source and load with certain restrictions to increase reliability and enhance the use of renewable sources at all scales. This article presents a novel power smoothing effect and sizing optimization methodology for a grid-connected photovoltaic-hydrokinetic system by pattern search method comparatively with three optimization algorithms encompassing a multi-objective optimization. In this context, the method considers the reduction of power fluctuations using supercapacitors, where the objective function considers technical and economic indexes. The main contribution of the research is that the objective function decreases the fluctuation suppression ratio to sizing optimization of renewable system through three pattern search size optimization algorithms. Experiments have shown that pattern search method can find the global optimum by reducing the computational effort; the Latin hypercube algorithm reduces the photovoltaic capacity by approximately 5 % and 2.3 % with respect to the Nelder Mead and genetic algorithm respectively. The influence of the supercapacitor successfully reduces power fluctuations and causes an increase in self-consumption of 32.32 %, this result represents an annual cost savings of 42 % on energy purchased from the grid. Furthermore, this paper goes further by calculating the optimal location of a hydrokinetic turbine in a river using the HEC-RAS software to determine with high precision the HKT power fluctuations as input signal to novel proposed method. [Display omitted] • Hybrid supercapacitor-integrated dispatch model with analysis of pattern search method. • Dispatch-coupled sizing renewable sources and energy storage with power smoothing, improving energy quality. • Numerical analysis on different size optimization methods comparatively considering experimental data. • An improved pattern search optimization that exceeds the standard one. • Validation of the effectiveness of the proposed framework in a laboratory microgrid test system. [ABSTRACT FROM AUTHOR]

The pursuit of more efficient transport has led engineers to develop a wide variety of aircraft configurations with the aim of reducing fuel consumption and emissions. However, these innovative designs introduce significant aeroelastic couplings that can potentially lead to structural failure. Consequently, aeroelastic analysis and optimization have become an integral part of modern aircraft design. In addition, aeroelastic testing of scaled models is a critical phase in aircraft development, requiring the accurate prediction of aeroelastic behavior during scaled model construction to reduce costs and mitigate the risks associated with full-scale flight testing. Achieving a high degree of similarity between the stiffness, mass distribution and flow field characteristics of scaled models and their full-scale counterparts is of paramount importance. However, achieving similarity is not always straightforward due to the variety of configurations of modern lightweight aircraft, as identical geometry cannot always be directly scaled down. This configuration diversity has a direct impact on the aeroelastic response, necessitating the use of computational aeroelasticity tools and optimization algorithms. This paper presents the development of an aeroelastic scaling framework using multidisciplinary optimization. Specifically, a parametric Finite Element Model (FEM) of the wing is created, incorporating the parameterization of both thickness and geometry, primarily using shell elements. Aerodynamic loads are calculated using the Doublet Lattice Method (DLM) employing twist and camber correction factors, and aeroelastic coupling is established using infinite plate splines. The aeroelastic model is then integrated within an Ant Colony Optimization (ACO) algorithm to achieve static and dynamic similarity between the scaled model and the reference wing. A notable contribution of this work is the incorporation of internal geometry parameterization into the framework, increasing its versatility and effectiveness.

Discrete sizing and topology optimization of truss structures subject to stress and displacement constraints has been formulated as a Mixed-Integer Linear Programming (MILP) problem. The computation time to solve a MILP problem to global optimality via a branch-and-cut solver highly depends on the problem size, the choice of design variables, and the quality of optimization constraint formulations. This paper presents a new formulation for discrete sizing and topology optimization of truss structures, which is benchmarked against two well-known existing formulations. Benchmarking is carried out through case studies to evaluate the influence of the number of structural members, candidate cross sections, load cases, and design constraints (e.g., stress and displacement limits) on computational performance. Results show that one of the existing formulations performs significantly worse than all other formulations. In most cases, the new formulation proposed in this work performs best to obtain near-optimal solutions and verify global optimality in the shortest computation time. [ABSTRACT FROM AUTHOR]

Large-scale tanks are widely used for storing chemicals and fuels. Their failure due to natural (e.g., earthquakes) and/or man-made hazards can lead to disastrous consequences. Nonetheless, they are often constructed in seismic-prone regions. For this reason, base isolation is often used for the seismic protection of large tanks, aiming to "decouple" the superstructure from the imposed ground motions. In this study, a combined optimization formulation is presented in order to further improve the seismic response of a base-isolated tank. The main aim is to optimize both the critical design parameters and the placement of the minimum number of isolators at the base of the tank. In particular, a Cuckoo Search (CS) optimizer is used to optimize the dynamic performance of liquid storage tanks, isolated either via single friction pendulum bearings (SFPB) or triple friction pendulum bearings (TFPB). The main objective is to minimize the eccentricity between the center of mass and the center of rigidity of the isolation system, while appropriate constraints are also imposed. Several cases are examined, while the results are compared with respect to isolator displacement fragility curves, as well as the reduced accelerations at the base of the tank. According to the findings of this study, the tank industry can significantly benefit from the proposed approach, as a more cost-efficient design of the base-isolation system of large-scale tanks can be achieved, i.e., using fewer isolators with optimal key parameters. [ABSTRACT FROM AUTHOR]

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

[Display omitted] • An electricity self-sufficient solar-driven rSOC-based polygeneration system is developed. • Operating strategy based on hourly electricity supply–demand matching is proposed. • Sizing of key energy-supplying components (rSOC, PV device and PTC) is optimized. • Optimized system achieves higher efficiency and lower cost than existing systems. • Hydrogen surplus during spring, summer, and autumn offsets the deficit in winter. Solar-driven polygeneration systems have the potential to significantly reduce global carbon emissions. However, the inherent variability of solar energy and the volatility of building energy demands result in extreme complexity in supply–demand matching of energy. Reversible solid oxide cells (rSOCs), which can store electricity as fuels in solid oxide electrolysis cell (SOEC) mode and generate electricity and heat using the stored fuels in solid oxide fuel cell (SOFC) mode, are well-suited to address this challenge. Previous relevant studies considering matching of energy supply and demand are relatively limited and are not able to achieve complete electricity self-sufficiency on solar energy. This study developed a completely electricity self-sufficient solar-driven rSOC-based polygeneration system composed of an rSOC, photovoltaic (PV) device, parabolic trough collector (PTC), and other balance of plants for residential buildings, without additional electricity supply and storage equipments. The system operation strategy based on hourly supply–demand matching of electricity was proposed: PV-SOEC-PTC mode for polygeneration of electricity, hydrogen, and heat in case of sufficient solar energy, and PV-SOFC or SOFC mode for cogeneration of electricity and heat in case of insufficient or no solar energy. Crucially, this study devised a method to optimize the sizing of the key energy-supplying components (PV device, rSOC, and PTC). The optimized system demonstrated an annual polygeneration efficiency of 68.61 % at a total cost rate of 0.7782 USD/h, which represents a 2.57 % increase and a 46.1 % decrease in comparison to a system in which the PV capacity was maximized to meet the peak demand. The hourly efficiencies of hydrogen production and power generation of rSOC, and polygeneration of the system were in the ranges of 89.00 %–99.70 %, 50.15 %–73.46 %, and 34.68 %–88.64 %, respectively. Furthermore, the total hydrogen surplus of 454 kg during spring, summer, and autumn was sufficient to offset the hydrogen deficit of 235 kg in winter, ensuring year-round self-sufficiency of electricity. [ABSTRACT FROM AUTHOR]

Diagrids have received much attention in recent years as lateral-load resistant systems. Most of previous research works have modeled the structure on rigid foundations. It is while the present work takes into account the static soil-structure interaction in a planar 20-story example with a constant diagrid angle. A finite element program is provided for analyzing diagrid systems under a number of loading combinations including gravitational and lateral wind loadings. As the structural behavior depends on the distribution of section profiles among its members, sizing optimization is employed for screening the diagrid designs. Optimization is performed by a meta-heuristic algorithm inspired by hunting behavior of Falcons that samples the design space without need to any gradient calculation. The algorithm is recently developed in 2019 and applied for heat exchanger design. It simulates the hunting flight in: 1) logarithmic spiral flight in which the Falcon keeps its head straight looking sideways at the prey with maximum visual acuity, and 2) diving toward the prey by binocular vision. The first stage provides exploration while the second models search intensification in such a meta heuristic algorithm. In addition, the phenomena of klepto-parasitism between falcons is modeled to enhance the search. In order to apply both axial and flexural section properties in such a discrete problem, profile indices are taken as design variables. They are to be selected from the practical list of profiles during optimization. Member grouping is also employed to deserve symmetry and other practical considerations in the models. Consequently, diagrid design is fixed by minimizing the structural weight under code-based stress and deformation constraints. The designs are distinctly obtained for two cases of rigid and flexible soil-structure connections. By an external penalty approach, constraints are checked under simultaneous gravitational and wind loadings to be satisfied in the final optimal designs. Applying pushover analyses, various measures are studied and compared showing importance of soil-flexibility not only in the optimal costs but also in non-linear structural responses.

To elucidate the optimal techno-economic role of battery energy storage system (BESS), this study proposes optimal sizing of BESS in various scenarios based on BESS installation in existing photovoltaic systems. The proposed scenarios include different electricity market types (i.e., peer-to-grid, peer-to-peer, and energy storage sharing) considering utilization mechanism (i.e., individual-design or shared-design) and ownership (i.e., user-owned or developer-owned) of the BESS. Towards this end, this study develops comprehensive and systematic mathematical models for BESS sizing using mixed-integer linear programming algorithm, and the BESS sizing is optimized to maximize the total net present value. The developed models are validated in the Hong Kong context considering four peers, and the empirical data of electric load profile as well as the photovoltaic installed capacity are collected. As a result, it is indicated that the optimal BESS capacity in energy storage sharing scenario is the least. In terms of electricity bill saving, user-owned BESS is regarded as the model yielding the highest electricity bill savings. The breakdown of net present value exposes that under the same ownership, the optimal BESS capacity is determined by different utilization mechanisms. Regarding the total net present value, it is found that developer-owned BESS could achieve the maximum economic merits. Nonetheless, the distribution loss through energy storage sharing is also the highest. The policy implications of this study primarily emphasize incentivizing user-owned BESS, promoting energy storage sharing, supporting shared BESS infrastructure, and encouraging a diversity of electricity trading mechanisms to enhance the integration and efficiency of renewable energy systems. • Battery energy storage system (BESS) size is optimized in various scenarios. • Extensive mathematical models are developed for extended research contexts. • Mixed-integer linear programming is applied in the optimization of BESS sizing. • User-owned BESS is the most efficient model regarding electricity bill savings. • Developer-owned BESS enables maximized economic merits for developers and peers. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive power source sizing method for a fuel cell and battery electric ferry. The method incorporates a power management strategy based on optimization techniques aimed at reducing the voyage operation cost and increasing the fuel cell efficiency which is a primary energy source for propulsion. The proposed sizing strategy based on particle swarm optimization estimates the operation cost of various proton exchange membrane fuel cell stack configurations to determine the optimal structure for high-power applications. Furthermore, the integrated design and optimization scheme for the electric ferry provides further insights into sizing results and their implications on ferry operation by using MATLAB. The correlation between selecting various component parameters and their impacts on ferry operations is verified through dynamic programming. Data collected from a short-haul ferry is used to analyze the optimal power source size and the related cost factors. The thorough design process and the resulting optimal sizing parameters are effective for estimating the operational cost that increases fuel cell utilization and providing a further aspect of real-time ship operations. • An integrated design and management scheme for a fuel cell hybrid ferry is proposed. • Particle swarm optimization aims to improve fuel economy and source lifespan. • The process evaluates operational costs of various fuel cell unit combinations. • Cost analysis provides the effective use of fuel cells for high-power applications. • Dynamic programming ensures the reliable operation of a short-haul electric ferry. [ABSTRACT FROM AUTHOR]

Summary: In this work, a combined ground structure topology–sizing optimization‐based methodology is presented, for supporting the design phases of moment‐resisting braced frames (MRBFs) for tall buildings, from the conceptual design phase to the final one. The mathematical problem is formulated as a minimum material volume problem subject to compliance and design check constraints imposed by the serviceability and ultimate limit states of the Eurocode design provisions, while the optimized designs achieved are composed of standardized section properties of the Euronorm. Most of the studies that rely on ground structure topology optimization formulation are limited to truss structures, while compliance is used as the objective to be minimized. Considering structural systems composed of elements that develop both axial and flexural stress, together with topology optimization problem formulation where the material volume is to be minimized, describe the major challenges of this study. The minimum material volume problem initially is dealt with by a frame structural topology optimization (FSTO) approach representing the conceptual design phase followed by a sizing structural design optimization procedure referring to the final design phase. In order to test the efficiency of the methodology, it is implemented for designing the structural system of MRBFs for mid‐ and high‐rise building structures. For the case of the real‐world structural systems examined, a variant of the methodology is used where its second design phase is implemented by means of the optimization computing platform (OCP) integrated with the commercial software ETABS v18. [ABSTRACT FROM AUTHOR]

In recent years, wind energy has gained extensive attention in the recent years in various countries due to the high energy demand of energy and shortage of traditional electric energy sources. Because wind energy constitutes a cost effective and environmentally friendly source, it can significantly contribute toward the reduction of the ever-increasing carbon emissions. It is one of the fastest growing green technologies worldwide, with a total generation share of 564 GW as of the end of 2018. In Malaysia, wind energy has been a hot topic in both academia and green energy industry. In this paper, the current status of wind energy research in Malaysia is reviewed. Different contributing factors such as potentiality and assessments, wind speed and direction modeling, wind prediction and spatial mapping, and optimal sizing of wind farms are extensively discussed. This paper discusses the progress of all studies related to wind energy and presents conclusions and recommendations for improving wind energy research in Malaysia.

Ever since the publication of the 99-line topology optimization MATLAB code (top99) by Sigmund in 2001, educational articles have emerged as a popular category of contributions within the structural and multidisciplinary optimization (SMO) community. The number of educational papers in the field of SMO has been growing rapidly in recent years. Some educational contributions have made a tremendous impact on both research and education. For example, top99 (Sigmund in Struct Multidisc Optim 21(2):120–127, 2001) has been downloaded over 13,000 times and cited over 2000 times in Google Scholar. In this paper, we attempt to provide a systematic and comprehensive review of educational articles and codes in SMO, including topology, sizing, and shape optimization and building blocks. We first assess the papers according to the adopted methods, which include density-based, level-set, ground structure, and more. We then provide comparisons and evaluations on the codes from several key aspects, including techniques, efficiency, usability, readability, environment, and compatibility. In addition, we conduct numerical experiments on the reviewed codes using the benchmark cantilever beam example to provide feedback on the overall user experience. With a systematic review and comparison, this paper aims to offer insights on the educational values and practicality for employing these codes. We try to provide not only guidance for beginners to approach various optimization methods, but also a dictionary to direct readers to effectively target the relevant codes and building blocks based on their demands. Finally, based on the findings in this review paper, we provide some perspectives and recommendations for future educational contributions. [ABSTRACT FROM AUTHOR]

Sizing optimization and energy management strategy (EMS) are two key points for the application of the hybrid energy storage system (HESS) in electric vehicles. This article aims to conduct the sizing optimization of HESS and apply an adaptive real-time EMS for practice. First, considering the system cost and battery lifespan, the multiobjective grey wolf optimizer is used to obtain the Pareto front. Second, with optimal parameters, the offline optimal power splitting results by dynamic programming (DP) under different driving patterns are analyzed. Then, the random forests (RF) method is used to learn control rules from the DP results. Driving pattern recognition (DPR) is implemented by the support vector machine (SVM). The intelligent EMS is composed of RF to guide power distribution and SVM to realize DPR. Finally, a combined load cycle involving different driving patterns is used for verification. Results illustrate that the proposed adaptive RF-based EMS can demonstrate a notable superiority in terms of battery protection, ultra-capacitor utilization, and system efficiency. Compared with the ordinary RF-based EMS without DPR, the proposed method can reduce total energy loss by 0.74%–9.49%, and reduce the battery Ah-throughput by 0.5%–19.83% under unknown driving cycles. [ABSTRACT FROM AUTHOR]

The current evolution of technical and environmental regulations and the recent development of hydrogen mobility require technological breakthroughs in the industrial areas, such as in harbours. Thus, multi-energy microgrids integrating low-carbon energy sources, electrical and hydrogen loads and storage solutions have to be designed. To improve their economic viability, the microgrid components must be carefully sized and the energy must be distributed in the most cost-effective way at any time. So as to foster the expansion of multi-energy microgrids in industrial areas, we propose in this paper a two-level optimization for the energy management and the sizing, applied to an original multi-energy scenario considering electricity and hydrogen as energy vectors. The designed energy management optimization takes into account an objective of economic profitability and constraints related to the availability of the sources and storage solutions, their reliability and the costs. The sizing optimization aims to propose different solutions allowing the benefits to be maximized and the expenses to be minimized. One of the main contributions of the paper is to compare the possible ways of valorisation of the energy available in an industrial microgrid. The results show that the sale of hydrogen allows income to be increased, in addition to self-consumption and sale to the electrical grid, but the electrolyzer involves high investments costs. Finally, a sensitivity analysis is presented and shows that the investment costs of electrolyzer and battery and the hydrogen selling price are key points in the optimal design.

The inclusion of automation mechanisms in the intricate design of power management units (PMUs) is long pursued in industry. This paper addresses a mixed-signal design flow whose purpose is to automatically produce, from the system-level to the ready-for-tape-out layout, complex PMUs when given a set of specifications. Particularly, in that flow, at the IP level, the proper analysis of the design tradeoffs is tedious and impractical, as a large amount of conflicting performance figures obtained from transient analysis for typical and multiple process/temperature corners, must be considered simultaneously. Therefore, it is common for the design cycle of a single integrated charge pump required in a state-of-the-art PMU to surpass a full month, depending on the intended technology, devices' types and operation voltages. Additionally, by the end of this process, circuit designers often end up with a limited vision of the attainable performances. However, due to the recent improvements in computational power of modern workstations, optimization-based techniques with expensive evaluation mechanisms are now perceived as a potential solution to pursue optimal sizing and layout. Therefore, an academic electronic design automation tool is adapted and applied to explore the performance boundaries of a charge pump in a 180-nm technology node and determine the optimal tradeoffs between performance and the, severely constrained, layout area. In the last step, a worst-case corner optimization on a 45- and 99- dimensional design and performance spaces, respectively, derived from 9 time-consuming transient analysis of each candidate solution, produced 35 design solutions that provide a thorough analysis between worst-case efficiency, voltage drop, and rise and fall times, impossible to perform in the manual design. The obtained insights on the design space were used to speed-up the process of devising a solution for tape-out. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a damper design strategy for highly indeterminate 3D structures utilizing computational optimization and generalized response spectrum analysis, which is extended to incorporate non‐proportional damping. This enables a more efficient design process compared to trial‐and‐error with nonlinear response history analysis. The efficiency of the proposed design strategy is verified, analyzed and then applied to a series of illustrative examples including retrofit of an existing lattice tower structure and new build buckling‐restrained braced frames.

Large-scale tanks are widely used for storing chemicals and fuels. Their failure due to natural (e.g., earthquakes) and/or man-made hazards can lead to disastrous consequences. Nonetheless, they are often constructed in seismic-prone regions. For this reason, base isolation is often used for the seismic protection of large tanks, aiming to “decouple” the superstructure from the imposed ground motions. In this study, a combined optimization formulation is presented in order to further improve the seismic response of a base-isolated tank. The main aim is to optimize both the critical design parameters and the placement of the minimum number of isolators at the base of the tank. In particular, a Cuckoo Search (CS) optimizer is used to optimize the dynamic performance of liquid storage tanks, isolated either via single friction pendulum bearings (SFPB) or triple friction pendulum bearings (TFPB). The main objective is to minimize the eccentricity between the center of mass and the center of rigidity of the isolation system, while appropriate constraints are also imposed. Several cases are examined, while the results are compared with respect to isolator displacement fragility curves, as well as the reduced accelerations at the base of the tank. According to the findings of this study, the tank industry can significantly benefit from the proposed approach, as a more cost-efficient design of the base-isolation system of large-scale tanks can be achieved, i.e., using fewer isolators with optimal key parameters.

This brief note presents some observations about the modelling of stress constraints in fail-safe structural optimization considering local degradation of member properties. An analytical study of a three-bar example demonstrates the impact of the constraint modelling choices on the optimal design. One notable finding is that degradation of one arbitrary member may cause a worse objective function than complete removal of the member. The observation is similar to the singular optimum issue in topology optimization with stress constraints, but applies also to sizing problems. This deserves special attention and should be considered when modelling fail-safe structural optimization with stress constraints. [ABSTRACT FROM AUTHOR]

It is an undeniable fact that there are main challenges in the use of metaheuristics for optimal design of real-size steel frames in practice. In general, steel frame optimization problems usually require an inordinate amount of processing time where the main portion of computational effort is devoted to myriad structural response computations during the optimization iterations. Moreover, the inherent complexity of steel frame optimization problems may result in poor performance of even contemporary or advanced metaheuristics. Beside the challenging nature of such problems, significant difference in geometrical properties of two adjacent steel sections in a list of available profiles can also mislead the optimization algorithm and may result in trapping the algorithm in a poor local optimum. Consequently, akin to other challenging engineering optimization instances, significant fluctuations could be observed in the final results of steel frame optimization problems over multiple runs even using contemporary metaheuristics. Accordingly, the main focus of this study is to improve the solution quality as well as the stability of results in metaheuristic optimization of real-size steel frames using a recently developed framework so-called monitored convergence curve (MCC). Two enhanced variants of the well-known big bang-big crunch algorithm are adopted as typical contemporary metaheuristic algorithms to evaluate the usefulness of the MCC framework in steel frame optimization problems. The numerical experiments using challenging test examples of real-size steel frames confirm the efficiency of the MCC integrated metaheuristics versus their standard counterparts. [ABSTRACT FROM AUTHOR]

In this paper the effects of adding buckling constraints to truss sizing optimization for minimizing mass are investigated. Introduction of buckling testing increases the complexity of the optimization process as Euler buckling criteria changes with each iteration of the optimization process due to the changes in element cross section dimensions. The resulting models which consider this criteria are practically applicable. For the purposes of showing the effects of dynamic constraints for buckling, optimal parametric standard test models of 10 bar, 17 bar, and 25 bar trusses from the literature are tested for buckling and compared to the models with the added constraint. Models which do not consider buckling criteria have a considerable number of elements which do not meet buckling criteria. The masses of these models are substantially smaller than their counterparts which consider buckling.

In this paper, a recently developed swarm based metaheuristic algorithm called weighted superposition attraction (WSA) is implemented for sizing optimization of truss structures first time in literature. The WSA algorithm based on superposition and attracted movement of agents that are observable in many natural systems. The efficiency and robustness of the WSA are investigated by solving five classic 2D and 3D truss-weight minimization problems with fixed-geometry and up to 200 elements. Optimization results demonstrated that WSA is able to generate the best results in terms of optimized weight, standard deviation and number of structural analyses in comparison to all other compared state-of-the-art metaheuristic algorithms. [ABSTRACT FROM AUTHOR]