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Records with Subject: Optimization
Showing records 1 to 25 of 56. [First] Page: 1 2 3 Last
Simulation versus Optimisation: Theoretical Positions in Energy System Modelling
Henrik Lund, Finn Arler, Poul Alberg Østergaard, Frede Hvelplund, David Connolly, Brian Vad Mathiesen, Peter Karnøe
December 10, 2019 (v1)
Subject: Optimization
Keywords: energy system analysis, investment optimisation models, modelling theory, Renewable and Sustainable Energy, simulations models
In recent years, several tools and models have been developed and used for the design and analysis of future national energy systems. Many of these models focus on the integration of various renewable energy resources and the transformation of existing fossil-based energy systems into future sustainable energy systems. The models are diverse and often end up with different results and recommendations. This paper analyses this diversity of models and their implicit or explicit theoretical backgrounds. In particular, two archetypes are defined and compared. On the one hand, the prescriptive investment optimisation or optimal solutions approach. On the other hand the analytical simulation or alternatives assessment approach. Awareness of the dissimilar theoretical assumption behind the models clarifies differences between the models, explains dissimilarities in results, and provides a theoretical and methodological foundation for understanding and interpreting results from the two archety... [more]
Economic Optimization of Component Sizing for Residential Battery Storage Systems
Holger C. Hesse, Rodrigo Martins, Petr Musilek, Maik Naumann, Cong Nam Truong, Andreas Jossen
December 10, 2019 (v1)
Subject: Optimization
Keywords: battery aging, battery energy storage system, cost analysis, economic analysis, linear programming, Lithium-Ion battery, photovoltaic panel, residential battery, size optimization
Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system. In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on sola... [more]
Optimization of External Envelope Insulation Thickness: A Parametric Study
Eleftheria Touloupaki, Theodoros Theodosiou
December 10, 2019 (v1)
Subject: Optimization
Keywords: cost-optimal, Energy Efficiency, energy performance of buildings (EPBD) recast, energy policy, insulation thickness, nearly zero energy buildings (nZEBs)
Almost four years after the implementation deadline of the energy performance of buildings Directive recast (2010/31/EU) and after being referred to the Court of Justice of the EU by the European Commission, Greece has not yet proceeded with the necessary calculations and legislative measures on the minimum, cost-optimal energy performance requirements for buildings. This paper aims to identify the optimal thickness of insulation that is cost-effective to apply in urban multi-family domestic buildings in the four climate zones of Greece. A reference building is selected in order to perform calculations over ten scenarios of external insulation thickness for each climate zone on a basic and three sensitivity analysis calculations according to the EU comparative methodology framework. The resulting energy savings for each insulation scenario are calculated, and then the cost-effectiveness of the measure is examined in financial and macroeconomic perspective for an economic lifecycle of 3... [more]
Optimization of Microwave Coupled Hot Air Drying for Chinese Yam Using Response Surface Methodology
Hanyang Wang, Dan Liu, Haiming Yu, Donghai Wang, Jun Li
December 10, 2019 (v1)
Subject: Optimization
Keywords: Chinese yam, microwave coupled hot air, process optimization, rehydration ratio, total sugar content
The effect of microwave coupled hot air drying on rehydration ratio (RR) and total sugar content (TSC) of Chinese yam was investigated. Single factor test and response surface methodology were used for process parameter optimization with hot air temperature, hot air velocity, slice thickness, and microwave power density as variables and RR and TSC of dried products as responses. The effect of variables on RR followed the order: slice thickness > hot air temperature > microwave power density > hot air velocity. The effect of variables on TSC followed the order: slice thickness > microwave power density > hot air velocity > hot air temperature. The optimized process parameters were hot air velocity of 2.5 m/s, hot air temperature of 61.7 °C, slice thickness of 8.5 mm, and microwave power density of 5.9 W/g. Under the optimal conditions, the predicted values of RR and TSC were 1.90 g/g and 5.74 g/100 g, respectively, which is very close to corresponding actual values (1.83 g/g and 5.72 g/... [more]
Fine-Tuning Meta-Heuristic Algorithm for Global Optimization
Ziyad T. Allawi, Ibraheem Kasim Ibraheem, Amjad J. Humaidi
December 3, 2019 (v1)
Subject: Optimization
Keywords: benchmark functions, exploitation, exploration, global minimum, global optimization, local minimum, meta-heuristics, swarm intelligence
This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown to check the performance of each algorithm, and the other test for 30 trials to measure the statistical results of the performance of the proposed algorithm against the others. Results confirm that the proposed FTMA global optimization algorithm has a competi... [more]
Pelletization of Sunflower Seed Husks: Evaluating and Optimizing Energy Consumption and Physical Properties by Response Surface Methodology (RSM)
Xuyang Cui, Junhong Yang, Xinyu Shi, Wanning Lei, Tao Huang, Chao Bai
November 24, 2019 (v1)
Subject: Optimization
Keywords: energy consumption, pelletization, performance evaluation, RSM optimization, sunflower seed husk
Pelletization is a significant approach for the efficient utilization of biomass energy. Sunflower seed husk is a common solid waste in the process of oil production. The novelty of this study was to determine the parameters during production of a novel pellet made from sunflower seed husk. The energy consumption (W) and physical properties (bulk density (BD) and mechanical durability (DU)) of the novel pellet were evaluated and optimized at the laboratory by using a pelletizer and response surface methodology (RSM) under a controlled moisture content (4%−14%), compression pressure (100−200 MPa), and die temperature (70−170 °C). The results show that the variables of temperature, pressure, and moisture content of raw material are positively correlated with BD and DU. Increasing the temperature and moisture content of raw materials can effectively reduce W, while increasing the pressure has an adverse effect on W. The optimum conditions of temperature (150 °C), pressure (180 MPa), and m... [more]
Towards Quality by Design to recover high-quality products from waste and wastewater streams
Céline Vaneeckhaute
November 2, 2019 (v1)
Subject: Optimization
Keywords: Mathematical modelling, Optimization, Process control, Product quality, Quality by Design, Resource Recovery
Recovering nutrients from wastewaters and wastes, such as sewage sludge and food waste, as sustainable bio-based products provides a key solution to major environmental problems. Classical technology development approaches for resource recovery largely ignore the real-world variability in raw waste materials, which currently hinders the successful implementation of recovery strategies. A major challenge is to create a consistent, sustainable and environmentally friendly supply of high-quality end-products that can compete with fossil-derived products currently on the market. There is urgent need for a paradigm shift from classical technology development approaches to sustainable integrated end-user focused strategies, supported by a reliable, competitive and repeatable quality assurance framework. An improved balance between efficiency and cost in bio-based production chains is needed, while continuously assuring product quality and safety. This
presentation suggests the use of a qual... [more]
Finite Time Thermodynamic Optimization of an Irreversible Proton Exchange Membrane Fuel Cell for Vehicle Use
Changjie Li, Ye Liu, Bing Xu, Zheshu Ma
September 13, 2019 (v1)
Subject: Optimization
Keywords: finite time thermodynamic model, irreversibility, operating parameters, optimal performance, PEMFC
A finite time thermodynamic model of an irreversible proton exchange membrane fuel cell (PEMFC) for vehicle use was established considering the effects of polarization losses and leakage current. Effects of operating parameters, including operating temperature, operating pressure, proton exchange membrane water content, and proton exchange membrane thickness, on the optimal performance of the irreversible PEMFC are numerically studied in detail. When the operating temperature of the PEMFC increases, the optimal performances of PEMFC including output power density, output efficiency, ecological objective function, and ecological coefficient of performance, will be improved. Among them, the optimal ecological objective function increased by 81%. The proton film thickness has little effect on the output efficiency and the ecological of coefficient performance. The maximum output power density increased by 58% as the water content of the proton exchange membrane increased from 50% to the s... [more]
Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization
Rashida Adeeb Khanum, Muhammad Asif Jan, Nasser Tairan, Wali Khan Mashwani, Muhammad Sulaiman, Hidayat Ullah Khan, Habib Shah
August 8, 2019 (v1)
Subject: Optimization
Keywords: adaptive differential evolution, evolutionary computation, external archives, global search, hybridization, local search, metaheuristics, Optimization, population minimization
Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon−Fletcher−Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the ar... [more]
Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis
Sergio Garcia, Cong T. Trinh
August 8, 2019 (v1)
Subject: Optimization
Keywords: constraint-based modeling, metabolic engineering, metabolic network modeling, modular cell, modular design, modularity, MOEA, multi-objective evolutionary algorithms, multi-objective optimization
A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has recently been proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. In our previous study, we mathematically formulated the modular cell design problem based on the multi-objective optimization framework. In this study, we evaluated a library of state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutio... [more]
Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving
Kexi Hou, Yaohui Li
August 7, 2019 (v1)
Subject: Optimization
Keywords: differential-algebraic equations, minimum solving graph, Modelica, multi-domain simulation, partial resolving, simulation optimization
The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-domain physical system based on Modelica is a typical simulation optimization problem. Traditionally, each simulation during each iterative step needs resolve all the variables in all the mass differential-algebraic equations (DAE) generated from the simulation model through constructing and traversing the solving dependency graph of the model. In order to improve the efficiency of the simulation optimization process, a new method named partial simulation resolving algorithm based on the set of input parameters and output variables for complex simulation model was proposed. By using this algorithm, a minimum solving graph (MSG) of the simulation model was built according to the set of paramet... [more]
Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization
Weiqin Ying, Hassan Jalil, Bingshen Wu, Yu Wu, Zhenyu Ying, Yucheng Luo, ZhenYu Wang
June 18, 2019 (v1)
Subject: Optimization
Keywords: community detection, complex networks, evolutionary algorithms, multi-objective optimization, parallel island models
Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In additio... [more]
Hybrid Approach for Optimisation and Analysis of Palm Oil Mill
Steve Z. Y. Foong, Viknesh Andiappan, Raymond R. Tan, Dominic C. Y. Foo, Denny K. S. Ng
June 10, 2019 (v1)
Subject: Optimization
Keywords: feasible operating range analysis, flexibility index, graphical approach, mathematical programming, utilisation index
A palm oil mill produces crude palm oil, crude palm kernel oil and other biomass from fresh fruit bunches. Although the milling process is well established in the industry, insufficient research and development reported in optimising and analysing the operations of a palm oil mill. The performance of a palm oil mill (e.g., costs, utilisation and flexibility) is affected by factors such as operating time, capacity and fruit availability. This paper presents a hybrid combined mathematical programming and graphical approach to solve and analyse a palm oil mill case study in Malaysia. The hybrid approach consists of two main steps: (1) optimising a palm oil milling process to achieve maximum economic performance via input-output optimisation model (IOM); and (2) performing a feasible operating range analysis (FORA) to study the utilisation and flexibility of the developed design. Based on the optimised results, the total equipment units needed is reduced from 39 to 26 unit, bringing down t... [more]
An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty
Daniel Laky, Shu Xu, Jose S. Rodriguez, Shankar Vaidyaraman, Salvador García Muñoz, Carl Laird
June 10, 2019 (v1)
Subject: Optimization
Keywords: flexibility analysis, global optimization, pharmaceutical processes, probabilistic design space
To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study.
Distilling Robust Design Principles of Biocircuits Using Mixed Integer Dynamic Optimization
Irene Otero-Muras, Julio R. Banga
June 10, 2019 (v1)
Subject: Optimization
Keywords: bio-design automation, biocircuits, computer-aided design, Optimization, robust design, synthetic biology
A major challenge in model-based design of synthetic biochemical circuits is how to address uncertainty in the parameters. A circuit whose behavior is robust to variations in the parameters will have more chances to behave as predicted when implemented in practice, and also to function reliably in presence of fluctuations and noise. Here, we extend our recent work on automated-design based on mixed-integer multi-criteria dynamic optimization to take into account parametric uncertainty. We exploit the intensive sampling of the design space performed by a global optimization algorithm to obtain the robustness of the topologies without significant additional computational effort. Our procedure provides automatically topologies that best trade-off performance and robustness against parameter fluctuations. We illustrate our approach considering the automated design of gene circuits achieving adaptation.
Using the Optimization Algorithm to Evaluate the Energetic Industry: A Case Study in Thailand
Chia-Nan Wang, Tien-Muoi Le, Han-Khanh Nguyen, Hong Ngoc-Nguyen
June 10, 2019 (v1)
Subject: Optimization
Keywords: evaluation, Optimization, performance, Thai energy
Thailand’s economy is developing rapidly, with energy being a significant factor in this development. This study uses a variety of models to assess the performance of Thailand’s energy industry in two different phases, the first being from 2013 to 2017 and the second from 2018 to 2020. The Malmquist model-one of data envelopment required input and output data that showed Thailand’s productivity index and the rate-of-change ratio, which is used to assess technical changes, change efficiency, and productivity changes of the 12 listed companies in energetic generation and distribution in Thailand. To calculate future indicators, the forecast data are generated by applying the Grey model (1,1) GM(1,1). Accuracy prediction is determined by the mean absolute percentage error (MAPE). The results show that the magnitude of the change in efficiency during the first period is stable, and some major changes in the technical level of some companies may be observed. In the future, the performance o... [more]
Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation
Kumar Mahesh, Perumal Nallagownden, Irraivan Elamvazuthi
February 27, 2019 (v1)
Subject: Optimization
Keywords: distributed generation, distribution system, multi-objective particle swarm optimization (PSO), non-dominated sorting, placement and sizing, power loss reduction, voltage stability
This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. The optimization problem considers two multi-objective functions, i.e., power loss reduction and voltage stability improvements with voltage profile and power balance as constraints. First, the numerical output results of objective functions are obtained in the Pareto-optimal set. Later, fuzzy decision model is engendered for final selection of the compromised solution. The proposed method is employed and tested on standard IEEE 33 bus systems. Moreover, the results of proposed method are validated with other optimization algorithms as reported by others in the literature. The o... [more]
Analysis and Speed Ripple Mitigation of a Space Vector Pulse Width Modulation-Based Permanent Magnet Synchronous Motor with a Particle Swarm Optimization Algorithm
Xing Liu, Jinhua Du, Deliang Liang
February 5, 2019 (v1)
Subject: Optimization
Keywords: harmonic superposition, particle swarm optimization (PSO), speed ripple reduction
A method is proposed for reducing speed ripple of permanent magnet synchronous motors (PMSMs) controlled by space vector pulse width modulation (SVPWM). A flux graph and mathematics are used to analyze the speed ripple characteristics of the PMSM. Analysis indicates that the 6P (P refers to pole pairs of the PMSM) time harmonic of rotor mechanical speed is the main harmonic component in the SVPWM control PMSM system. To reduce PMSM speed ripple, harmonics are superposed on a SVPWM reference signal. A particle swarm optimization (PSO) algorithm is proposed to determine the optimal phase and multiplier coefficient of the superposed harmonics. The results of a Fourier decomposition and an optimized simulation model verified the accuracy of the analysis as well as the effectiveness of the speed ripple reduction methods, respectively.
A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings
Cristina Brunelli, Francesco Castellani, Alberto Garinei, Lorenzo Biondi, Marcello Marconi
February 5, 2019 (v1)
Subject: Optimization
Keywords: multi-objective optimization, sustainable buildings, uncertainty analysis
When dealing with sustainable design concepts in new construction or in retrofitting existing buildings, it is useful to define both economic and environmental performance indicators, in order to select the optimal technical solutions. In most of the cases, the definition of the optimal strategy is not trivial because it is necessary to solve a multi-objective problem with a high number of the variables subjected to nonlinear constraints. In this study, a powerful multi-objective optimization genetic algorithm, NSGAII (Non-dominated Sorting Genetic Algorithm-II), is used to derive the Pareto optimal solutions, which can illustrate the whole trade-off relationship between objectives. A method is then proposed, to introduce uncertainty evaluation in the optimization procedure. A new university building is taken as a case study to demonstrate how each step of the optimization process should be performed. The results achieved turn out to be reliable and show the suitableness of this proced... [more]
Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology
Youneng Huang, Chen Yang, Shaofeng Gong
January 30, 2019 (v1)
Subject: Optimization
Keywords: ant colony optimization, CBTC, discrete combination, optimization of energy-savings
More and more lines are using the Communication Based Train Control (CBTC) systems in urban rail transit. Trains are operated by tracking a pre-determined target speed curve in the CBTC system, so one of the most effective ways of reducing energy consumption is to fully understand the optimum curves that should prevail under varying operating conditions. Additionally, target speed curves need to be calculated with optimum real-time performance in order to cope with changed interstation planning running time. Therefore, this paper proposes a fast and effective algorithm for optimization, based on a two-stage method to find the optimal curve using a max-min ant colony optimization system, using approximate calculations of a discrete combination optimization model. The first stage unequally discretizes the line based on static gradient and speed limit in low-density and it could conduct a comprehensive search for viable energy saving target speed curves. The second stage unequally discret... [more]
A Selection Method for Power Generation Plants Used for Enhanced Geothermal Systems (EGS)
Kaiyong Hu, Jialing Zhu, Wei Zhang, Xinli Lu
January 7, 2019 (v1)
Subject: Optimization
Keywords: enhanced geothermal systems, geothermal energy, optimization method, power cycle’s selection method
As a promising and advanced technology, enhanced geothermal systems (EGS) can be used to generate electricity using deep geothermal energy. In order to better utilize the EGS to produce electricity, power cycles’ selection maps are generated for people to choose the best system based on the geofluids’ temperature and dryness conditions. Optimizations on double-flash system (DF), flash-organic Rankine cycle system (FORC), and double-flash-organic Rankine cycle system (DFORC) are carried out, and the single-flash (SF) system is set as a reference system. The results indicate that each upgraded system (DF, FORC, and DFORC) can produce more net power output compared with the SF system and can reach a maximum net power output under a given geofluid condition. For an organic Rankine cycle (ORC) using R245fa as working fluid, the generated selection maps indicate that using the FORC system can produce more power than using other power cycles when the heat source temperature is below 170 °C. E... [more]
An Integer Linear Programming Model for an Ecovat Buffer
Gijs J. H. de Goeijen, Gerard J. M. Smit, Johann L. Hurink
January 7, 2019 (v1)
Subject: Optimization
Keywords: integer linear programming, Modelling, seasonal thermal storage, smart grids
An increase in the number of volatile renewables in the electricity grid enhances the imbalance of supply and demand. One promising candidate to solve this problem is to improve the energy storage. The Ecovat system is a new seasonal thermal energy storage system currently under development. In this paper, an integer linear programming model is developed to describe the behaviour and potential of this system. Furthermore, it is compared with a previously developed model, which is simplifying the behaviour of the Ecovat system much more, but is much less computationally expensive. It is shown that the new approach performs significantly better for several cases. For controlling a real Ecovat system in the future we may incorporate a number of improvements identified by our comparison analysis into the previously developed approach, which may help increase the quality of the obtained results without increasing the computational effort too much.
A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs
Athanasios Kolios, Varvara Mytilinou, Estivaliz Lozano-Minguez, Konstantinos Salonitis
January 7, 2019 (v1)
Subject: Optimization
Keywords: analytical hierarchy process (AHP), elimination et choix traduisant la realité (ELECTRE), multi-criteria decision methods, preference ranking organization method for enrichment evaluation (PROMETHEE), stochastic inputs, support structures, technique for the order of preference by similarity to the ideal solution (TOPSIS), weighted product method (WPM), weighted sum method (WSM), wind turbine
This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated me... [more]
Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization
Xiang Yu, Hui Sun, Hui Wang, Zuhan Liu, Jia Zhao, Tianhui Zhou, Hui Qin
November 28, 2018 (v1)
Subject: Optimization
Keywords: comprehensive learning, hydropower reservoir system, multi-objective optimal operation, multi-swarm, Particle Swarm Optimization
Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing h... [more]
A Multi-Point Method Considering the Maximum Power Point Tracking Dynamic Process for Aerodynamic Optimization of Variable-Speed Wind Turbine Blades
Zhiqiang Yang, Minghui Yin, Yan Xu, Zhengyang Zhang, Yun Zou, Zhao Yang Dong
November 28, 2018 (v1)
Subject: Optimization
Keywords: aerodynamic optimization, closed-loop system, maximum power point tracking (MPPT) control, multi-point method, variable-speed wind turbine (VSWT)
Due to the dynamic process of maximum power point tracking (MPPT) caused by turbulence and large rotor inertia, variable-speed wind turbines (VSWTs) cannot maintain the optimal tip speed ratio (TSR) from cut-in wind speed up to the rated speed. Therefore, in order to increase the total captured wind energy, the existing aerodynamic design for VSWT blades, which only focuses on performance improvement at a single TSR, needs to be improved to a multi-point design. In this paper, based on a closed-loop system of VSWTs, including turbulent wind, rotor, drive train and MPPT controller, the distribution of operational TSR and its description based on inflow wind energy are investigated. Moreover, a multi-point method considering the MPPT dynamic process for the aerodynamic optimization of VSWT blades is proposed. In the proposed method, the distribution of operational TSR is obtained through a dynamic simulation of the closed-loop system under a specific turbulent wind, and accordingly the m... [more]
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