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A robust-optimal design of multimodal shunt circuit for subsonic flutter control and energy harvesting

COMPOSITE STRUCTURES 2025年 360卷

作者： Diniz, Gutembergy Ferreira de Lima, Antonio Marcos Gonsalves Delgado Filho, Marcelo Araujo de Carvalho, Prince Azsembergh Nogueira Sena, Joao Pedro Bouhaddi, Noureddine Univ Fed Uberlandia Sch Mech Engn Campus Santa Mon BR-38400902 Uberlandia MG Brazil Univ Bourgogne Franche Comte FEMTO ST Inst Dept Appl Mech F-25000 Besancon France

Aerospace structures are becoming increasingly lightweight and flexible. At the same time, they are to operate at higher airspeeds, highlighting the need to control potentially dangerous aeroelastic phenomena. Numerous studies have reported the development of control techniques applied to composite structures embedded with smart materials. However, the application of these techniques combining aeroelastic control and energy harvesting features is not evident. The intended contribution here is the proposal of a robust-optimal device for passively control subsonic flutter and vibration in aeronautic composite panels through energy harvesting. This study performs a numerical analysis using a piezoceramic multimodal shunted circuit of parallel topology, which is embedded in the base composite structure. Finite element modeling combined with First-order Shear Deformation Theory (FSDT) describes the mechanical degrees of freedom, while the Doublet Lattice Method (DLM) represents the aerodynamic load. In turn, discrete layer theory depicts the electric potential. The circuit parameters were optimized to maximize the flutter boundary and the harvested power at the flutter point. The evaluation of the robustness of the optimized solutions demonstrates the practical interest of the presented methodology. The numerical simulations demonstrate the main capabilities of the proposed methodology.

关键词： Smart structures Flutter multi-mode shunt circuits Energy harvesting robust multi-objective optimization

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A robust optimal sizing of renewable-rich multi-source microgrid under uncertainties with multi-storage options

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ELECTRICAL ENGINEERING 2024年第5期106卷 6547-6563页

作者： Krishna, P. V. N. Mohan Sekhar, P. C. Behera, T. R. Indian Inst Technol Bhubaneswar Sch Elect Sci Argul 752050 Odisha India

Adapting the power and energy systems by integrating renewable sources is necessary to address climate change. On the other hand, microgrids are gaining prominence in meeting power and energy requirements, including in remote locations. Consequently, the power system's penetration of renewable energy-based microgrids is increasing. Planning an isolated microgrid necessitates cost-effective capacity sizing of energy sources and storage systems for maintaining continuity in power supply. Considering the variability and uncertainty of photovoltaic (PV), wind energies, and load variations, deciding the optimal size of renewable-rich, isolated microgrids is challenging. Batteries and fuel cells are potential storage solutions for managing variability. However, a more trustworthy sizing approach is necessary to maximize power availability at the lowest possible cost, even in the face of uncertainty. Moreover, providing the microgrid owner with the opportunity to choose from a range of optimal solutions is also essential. Therefore, incorporating the uncertainty handling feature with the help of robust assessment under worst-case scenarios in the multi-objective optimization method can provide a more trustworthy solution. In this connection, a novel algorithm is proposed that instills robustness in the solutions provided by traditional non-dominated sorting genetic algorithm-II (NSGA-II), which can offer multiple break-even solutions. The isolated microgrids with PV, wind as sources, and storage technologies such as Lithium-ion (Li-ion) batteries, fuel cells, and a combination of both, a less explored scenario, have been compared to determine the effective storage option over the long run while considering uncertainties in renewable energy and load variations. The NSGA-II benchmark solutions developed under these uncertainties and variations are used to validate the robustness of the solutions obtained from the proposed robust algorithm. With a good trade-off between the c

关键词： Capacity sizing Microgrid Energy storage robust multi-objective optimization Uncertainty

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robust multi-objective optimization based on NSGA-II for UCAV weapon delivery

Robust multi-objective optimization based on NSGA-II for UCA...

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4th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Gu, Xueqiang Zhang, Yu Wang, Nan Chen, Jing Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha Hunan Peoples R China

ISBN: (纸本)9780769547213

The problem of generating robust optimal air-toground weapon delivery planning (WDP) for UCAVs is studied. In order to deal with the uncertainties in combat operation, such as disturbing in battlefield, model imprecise, operating errors and so on, a strategy based on robust multi-objective optimization (RMO) approach is proposed. In this paper, several constraints include flight capability constraint, weapon constraint and battlefield constraint, are considered. And some robust optimal cost functions are built, using Monte Carlo method simulating criteria of weapon delivery, and then the weapon delivery planning problem is transformed into a robust multi-objective optimization problem. In order to improve the convergence performance, a combining robust multi-objective optimization algorithm based on an improved NSGA-II and Monte Carlo simulation is designed, and then a tactical basic flight maneuver (BFM) is presented to generate weapon delivery trajectory. The proposed approach is demonstrated on a typical air-to-ground attack mission scenario. The simulated results show that the proposed approach is capable of generating the robust and optimal weapon delivery trajectories efficiently.

关键词： weapon delivery robust multi-objective optimization non-dominated sorting genetic algorithm basic flight maneuver

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A multi-objective formulation for the closed-loop plastic supply chain under uncertainty

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OPERATIONAL RESEARCH 2022年第5期22卷 4725-4768页

作者： Ebrahimi, Seyed Babak Bagheri, Ehsan KN Toosi Univ Technol Fac Ind Engn Financial Engn Grp Pardis St Molasadra AveVanak SqPOB 15875-4416 Tehran 193951999 Iran

Supply risk is one of the undeniable risks in a supply chain that can affect the supply chain process and lead to disruptions in customer satisfaction or distributor reliability. This research employs a closed-loop supply chain network for the plastic bottle industry, followed by formulating a multi-objective mathematical model by considering several assumptions. The model seeks to optimize total costs, supply risk, and customers' satisfaction (distributor's reliability). The revised multi-choice goal programming approach is also applied to solve the model and verify it through a case study. Moreover, the best-worst method as a robust multi-criteria decision-making tool is used to find the values of the parameters of supply risk. Afterward, the sensitivity analysis checks the proposed framework's robustness and shows its reaction under various conditions. Our results indicate the effectiveness of the proposed framework and offer insights that can be used to improve an organization's status.

关键词： robust multi-objective optimization Plastic industry Supply risk Reliability Revised multi-choice goal programming Best-worst method

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Community-level decentralized energy system planning under uncertainty: A comparison of mathematical models for strategy development

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APPLIED ENERGY 2021年 283卷 116304-116304页

作者： Prabatha, Tharindu Karunathilake, Hirushie Shotorbani, Amin Mohammadpour Sadiq, Rehan Hewage, Kasun Univ British Columbia Sch Engn Okanagan Campus EME42871137 Alumni Ave Kelowna BC V1V 1V7 Canada Univ Moratuwa Dept Mech Engn Katubedda 10400 Moratuwa Sri Lanka

Distributed energy systems renewable energy are one solution to the environmental and economic concerns of energy use. While energy planning and optimization have been conducted mainly as a mathematical exercise, practical approaches that incorporate the engineering realities and uncertainties are limited. Decision makers find challenges in community energy planning due to the lack of expertise, planning tools, and information. While a multitude of models and tools are currently available, there are no means of identifying the most appropriate or accurate methods, especially considering uncertainty. The main objective of this study is to compare and identify the strengths and limitations of various mathematical modelling techniques used in energy planning for grid connected renewable energy systems. As a case study demonstration, different multi-objective optimization techniques with and without uncertainty consideration (i.e. robust optimization, linear optimization, Taguchi Orthogonal Array method, and Monte Carlo simulation) were applied on a selected neighborhood in British Columbia. The optimization outcomes and the time and effort for evaluation were compared for the different methods. The findings indicate that robust optimization can be used to develop an uncertainty-based decision model. It significantly reduces evaluation time compared to the other methods. Although the presence of uncertainties can change the optimal configuration of a planned energy system, the assessment method itself does not significantly impact the outcomes. The findings of this study will enable the energy planners and researchers to compare different multi-objective optimization techniques, and to select the best for planning renewable energy projects, especially during the pre-project planning stage.

关键词： Community energy planning Renewable energy Uncertainty modelling Linear programming robust multi-objective optimization Monte Carlo simulation

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Sustainable microgrid design considering blockchain technology for real-time price-based demand response programs

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 125卷 106418-106418页

作者： Tsao, Yu-Chung Vo-Van Thanh Wu, Qiuwei Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan Tech Univ Denmark Dept Elect Engn Lyngby Denmark

In recent years, rapidly increasing electric power demand and organizational concerns regarding social and environmental issues have resulted in significant attention being given to microgrids. However, sustainable microgrids that simultaneously address economic benefits and environmental and social issues have not been broadly explored by researches. This study addresses the sustainable microgrid design problem by leveraging blockchain technology to provide the real time-based demand response programs. Three sustainable objectives (economy, environment, and society) are formulated by a multi-objective mixed integer-linear programming model. A robust fuzzy multi-objective optimization approach is proposed to determine the optimal number, location, and capacity of renewable distributed generation units as well as the equilibrium supply and dynamic pricing decisions under uncertain demand, capacity, and economic, environmental, and social parameters. The proposed model and solution approach are then applied to a case study in Vietnam. We find that blockchain technology-based sustainable microgrid can result in a 1.68% and 2.61% increase of profitability and consumer satisfaction, respectively, and a 0.97% reduction of environmental impacts.

关键词： Sustainability Microgrid Blockchain technology Demand response robust multi-objective optimization Fuzzy programming

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robust multi-objective thermal and electrical energy hub management integrating hybrid battery-compressed air energy storage systems and plug-in-electric-vehicle-based demand response

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JOURNAL OF ENERGY STORAGE 2021年 35卷

作者： Zeynali, Saeed Rostami, Naghi Ahmadian, Ali Elkamel, Ali Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Univ Bonab Dept Elect Engn Bonab Iran Univ Waterloo Coll Engn Waterloo ON Canada

A compressed air energy storage (CAES) can operate together with a battery energy storage system (BESS) to enhance the economic and environmental features of the energy hubs (EH). In this regard, this paper investigates their mutual cooperation in a multi-objective thermal and electrical residential EH optimization problem, which aims to diminish the total operational cost and emission. The proposed formulation also includes a PEV-based demand response program (DRP), renewable energy production units, solar heat collectors (SHE), thermal energy storage (TES), and hot water storage. Additionally, the CAES is operated as a combined heat and power unit in discharging and simple cycle modes. A linearized battery degradation cost model is integrated into the cost objective function, which ensures global optimization. Moreover, the inherent uncertainties of wind speed, solar irradiation, residential loads, electricity market price and PEVs' load demand are handled by the computationally effective robust mixed-integer linear programming (RMILP) method. The PEVs' uncertain load demand is obtained from their corresponding arrival-departure time, daily traveled miles, and vehicle type. The optimal Pareto front of the multi-objective problem is obtained by epsilon-constrained method for different robustness adjustments. Different deterministic and robust case studies are designed to evaluate the functionality of the proposed formulation.

关键词： Energy hub robust multi-objective optimization Compressed air energy storage Demand response program Battery degradation Plug-in electric vehicles

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Exploring DC microgrid: Advanced applications and their control strategies

Energy 360

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Energy 360 2025年 3卷

作者： C. Elwely Salem A. Et-taleby Y. Chaibi N. Ayadi B. Elkari M. Benslimane Z. Chalh Sidi Mohamed Ben Abdellah University Fez Morocco ESME Research Lab Paris France IDEA School Euromed University Fez Morocco

The increasing reliance on microgrids (MG) as a power delivery system underscores the critical importance of advanced control strategies and application-specific solutions. With a focus on their technological advantages, possible uses and control mechanisms, this review evaluates the emerging role of DC microgrids as a viable substitute for conventional AC systems. Examining recent advancements, the paper explores key applications, including electric vehicle (EV) charging, smart buildings, hydrogen production, data centers and residential energy systems, highlighting the versatility of DC microgrids in optimizing renewable energy integration and enhancing energy efficiency. The review further categorizes and analyzes different control methods, including centralized, decentralized and distributed control, detailing their key features, limitations and mathematical representations. The findings emphasize that DC microgrids offer improved energy efficiency, reduced conversion losses and enhanced power reliability. Additionally, advanced control strategies play a crucial role in ensuring optimal energy management, system stability and operational resilience. By providing a critical analysis of these aspects, this review serves as a guide for future research and innovation in DC microgrid control and application optimization, contributing to the development of more sustainable and efficient energy systems.

关键词： AC, Alternative current AEMS, Automated energy management system BEMSs, Building energy management systems BESS, Battery energy storage system CAC, Centralized automated control CEMPC, Centralized economic model predictive control COS, Cooperative Online Scheduling CRAC, Computer room air conditioning CSWG, Climate Sustainability Work Group CVC, Clusters of vehicle control DC, Direct current DEMPC, Distributed economic model predictive control DERs, Distributed energy resources DGs, Distributed generators DMM, Direction method of multipliers DMS, Distributed management system DRC, Demand response control DTLR, Dynamic thermal line rating EMS, Energy management system ESS, Energy storage system EVCSs, Electric Vehicle Charging Stations EVs, Electric Vehicles FLC, Fuzzy logic control GA, Genetic algorithm GTBA, Game-theory based approach HDR, Holistic Demand Response HEMS, Hybrid energy management system HPWOA, Hybrid particle whale optimization algorithm HRSO, Hybrid robust-Stochastic optimization HV, High voltage HVAC, Heat, ventilation and air conditioning IRENA, International Renewable Energy Agency IT, Information technology LC, Local control LCOE, Levelized Cost of Energy LV, Low voltage LVAC, Low voltage alternative current LVDC, Low voltage direct current MCO, multiple criteria optimization MG, Microgrid MINLP, Mixed-integer non-linear programming MPPT, Maximum power point tracking MREAI, Microgrid renewable energy availability index MREEI, Microgrid renewable energy energy index MRI, Microgrid resiliency index MV, Medium voltage NPC, Net present cost NZB, Net zero building NZEB, Net-zero energy buildings OCO, Online Convex optimization P2P, Peer to peer PED, Peak energy demands PEVs, Plug-in electric vehicles PFF, Power fluctuation factor PHEVs, Plug-in hybrid electric vehicles PMS, Power management system PSO, Particle Swarm optimization PV, Photovoltaic RESs, Renewable energy sources RMOO, robust multi-objective optimization SIU, Smart interlinking unit SLA, Service legal agreement SMC, Sliding mode cont

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Generation and selection of Pareto-optimal solution for the sorption enhanced steam biomass gasification system with solid oxide fuel cell

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ENERGY CONVERSION AND MANAGEMENT 2019年 196卷 1420-1432页

作者： Detchusananard, Thanaphorn Sharma, Shivom Marechal, Francois Arpornwichanop, Amornchai Chulalongkorn Univ Fac Engn Dept Chem Engn Computat Proc Engn Res Unit Bangkok 10330 Thailand EPFL Ind Proc & Energy Syst Engn CH-1951 Sion Switzerland

The biomass gasification coupled with a solid oxide fuel cell (SOFC) system is one of the most efficient and environmentally friendly technologies for combined heat and power generations. For the development and improvement of the integrated process systems, the optimization problem has more than one conflicting objective functions to be optimized (i.e., thermodynamic performance, environmental impacts, annual profit, capital and operating costs) simultaneously. multi-objective optimization (MOO) methods are used to find a set of optimal (or non-dominated) solutions. In this work, MOO of a sorption enhanced steam biomass gasification (SEG) integrated with an SOFC and gas turbine (GT) system, for combined heat and power production from Eucalyptus wood chips as biomass feedstock, is investigated. Firstly, the model of this integrated plant is developed in Aspen Plus that can be divided into five parts: (1) SEG, (2) hot gas cleaning and steam reforming, (3) SOFC, (4) catalytic burning, GT and CO2 compression, and (5) Portland cement production. As the annual profit demonstrates the economic viability of the plant and annualized capital cost (ACC) indicates availability of investments, the MOO of the integrated plant is performed to obtain Pareto-optimal solutions based on the minimization of ACC and maximization of annual profit with five important decision variables. After that, ten selection methods are used to recommend practical solutions for implementing in the integrated plant. In order to explore the effect of decision variables uncertainty on obtained Pareto-optimal solutions, random variations in decision variables are used to quantify deviations in objective functions. The Pareto-optimal solutions are ranked based on the normalized variations for decision variables uncertainty. At the end of this study, robust MOO of the integrated plant is performed, with respect to uncertainties in the market and operating parameters.

关键词： Sorption enhanced steam biomass gasifier Solid oxide fuel cell robust multi-objective optimization Non-dominated solution selection Uncertainty analysis

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robust optimization of valve management to improve water quality in WDNs under demand uncertainty

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URBAN WATER JOURNAL 2018年第10期15卷 943-952页

作者： Calvo, Oscar Osvaldo Marquez Quintiliani, Claudia Alfonso, Leonardo Di Cristo, Cristiana Leopardi, Angelo Solomatine, Dimitri de Marinis, Giovanni IHE Delft Inst Water Educ Dept Integrated Water Syst & Governance Delft Netherlands Univ Cassino & Southern Lazio Dept Civil & Mech Engn Cassino Italy Univ Naples Federico II Dept Civil Architectural & Environm Engn Naples Italy

Water quality deterioration in water distribution networks can be associated with high water residence time in the network. To this end, some previous studies have proposed optimization procedures for valve management. However, these studies generally come up with operational configurations assuming deterministic user demand patterns that may never occur in reality. In consequence, the proposed solutions may not be effective for improving water quality or do not comply with pressure constraints if different demand patterns are observed. This study proposes a methodology to determine robust configurations of the valves to keep water residence time at acceptable levels regardless of the variability in demand patterns. The methodology is tested on four different distribution systems of varying topology and size. Results show the importance of executing robust - instead of deterministic, optimization to find valve configurations that guarantee the performance of the networks in terms of hydraulics and water quality.

关键词： robust multi-objective optimization water age demand uncertainty

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