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Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues

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JOURNAL OF ENERGY STORAGE 2021年 42卷

作者： Hannan, M. A. Wali, S. B. Ker, P. J. Abd Rahman, M. S. Mansor, M. Ramachandaramurthy, V. K. Muttaqi, K. M. Mahlia, T. M. I. Dong, Z. Y. Univ Tenaga Nas Dept Elect Power Engn Kajang 43000 Selangor Malaysia Univ Tenaga Nas Inst Sustainable Energy Kajang 43000 Selangor Malaysia Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia Univ Technol Sydney Sch Civil & Environm Engn Ctr Green Technol Sydney NSW 2007 Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Due to urbanization and the rapid growth of population, carbon emission is increasing, which leads to climate change and global warming. With an increased level of fossil fuel burning and scarcity of fossil fuel, the power industry is moving to alternative energy resources such as photovoltaic power (PV), wind power (WP), and battery energy-storage systems (BESS), among others. BESS has some advantages over conventional energy sources, which include fast and steady response, adaptability, controllability, environmental friendliness, and geographical independence, and it is considered as a potential solution to the global warming problem. This paper provides a comprehensive review of the battery energy-storage system concerning optimal sizing objectives, the system constraint, various optimization models, and approaches along with their advantages and weakness. Furthermore, for better understanding, the optimization objectives and methods have been classified into different categories. This paper also provides a detailed discussion on the BESS applications and explores the shortages of existing optimal BESS sizing algorithms to identify the gaps for future research. The issues and challenges are also highlighted to provide a clear idea to the researchers in the field of BESS. Overall, this paper conveys some significant recommendations that would be useful to the researchers and policymakers to structure a productive, powerful, efficient, and robust battery energy-storage system toward a future with a sustainable environment.

关键词： Battery energy-storage system Sizing Optimization algorithm objective functions Constraints

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The Dependence of Actual Laser Cutting Speed on CNC Sheet Equipment on Number of NC Program Commands for Metal Grades 1.0114 and AWAIMg3 4

The Dependence of Actual Laser Cutting Speed on CNC Sheet Eq...

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4th International Conference on Rheology and Modeling of Materials (IC-RMM) / 1st European Conference on Silicon and Silica Based Materials

作者： Petunin, Alexander Tavaeva, Anastasia Ural Fed Univ Ekaterinburg 620002 Russia Ubr RAS Inst Math & Mech Ekaterinburg 620990 Russia Prod Assoc Ural Opt & Mech Plant JSC Ekaterinburg Russia

One of the complex optimization problems arising in technical applications is the cutting path problem for CNC sheet metal cutting machines. This problem is to optimize the tool path for the CNC technological equipment when cutting a fixed set of parts allocated on sheet metal. The time of cutting process is one of objective functions for the optimization problem. This objective function depends on six parameters of cutting process: 1) cutting speed;2) lengths of the cutting tool path;3) speed of idling motion of tool;4) length of idling motion;5) number of piercings;6) time of one piercing. When programming NC program, speed of cutting is defined by the user of the CAM (Computer-Aided-Manufacturing) system and considered as a constant. However, this is actually not the case. Consequently, the problem of exact calculation of objective function is arisen. In order to solve this problem, the correction coefficients for the cutting speed value defined by the user must been calculated. Obviously, these coefficients will vary for different thicknesses and grades of metal. The paper presents the results of investigates, which made it possible to find the functional dependences of the cutting speed for the CNC laser cutting machine (CO2) on the number of NC program commands for the following metal grades: 1.0114 (thickness Delta = 1-10 mm) and AWAIMg3 (Delta = 1-5 mm).The statistical materials of experiments were processed by using software "Mathcad". The received results show that the actual average value of cutting speed is monotonically decreasing function depended on number of NC program commands. In addition, the calculation of the actual cutting speed provides not only an accurate calculation of the value of the objective function, but also, as a result, the correct finding of the optimal tool path

关键词： CNC laser cutting machines NC Program cutting speed tool path problem optimization objective functions Metal Grades 1.0114 and AWAIMg3

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Propagation of CMORPH rainfall errors to REW streamflow simulation mismatch in the upper Zambezi Basin

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JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2021年 38卷

作者： Gumindoga, W. Rientjes, T. H. M. Haile, A. T. Reggiani, P. Makurira, H. Univ Twente Fac ITC Enschede Netherlands Univ Zimbabwe Dept Construct & Civil Engn Harare Zimbabwe Int Water Management Inst IWMI Addis Ababa Ethiopia Univ Siegen Dept Civil Engn Siegen Germany

Study region: This research is carried out in the Kabompo Basin, a headwater catchment of the Zambezi Basin which has an area of 67,261 km(2). Kabompo River originates in North-Western Province of Zambia between the Zambezi and Congo River Basins. Study focus: This study focuses on error propagation of the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center-MORPHing (CMORPH) rainfall product on simulated streamflow. Assessments are based on automated multi-objective calibration of the Representative Elementary Watershed (REW) model (2006-2012). Parameters of the model were optimized using the epsilon-NSGAII algorithm. Assessments on error propagation targeted streamflow modelling for hydrograph shape and volume, specific hydrograph characteristics, and water balance composition. New hydrological insights for the region: By use of multiple objective functions, this study shows that uncorrected CMORPH results in substantial augmentation of rainfall error to streamflow simulation mismatch whereas bias corrected estimates result in attenuation of error. This study shows that analysis for water balance composition has great potential to improve application of satellite precipitation products in water management and decision making in the Zambezi basin. This study advises optimization of model parameters for each respective rainfall input data source so to identify outcomes and effects of respective rainfall data sources on the simulated water balance. Optimization of parameter sets by epsilon -NSGAII results in optimal parameter sets that attributes to findings on the water balance composition and closure.

关键词： Bias correction Error Model calibration objective functions Water balance Zambezi basin

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Impact of FFC distributed generations in a DNR in the presence of renewable and load uncertainties by mixed-discrete particle swarm-based point estimation method

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IET RENEWABLE POWER GENERATION 2019年第9期13卷 1431-1445页

作者： Barik, Soumyabrata Das, Debapriya Indian Inst Technol Kharagpur Elect Engn Dept Kharagpur W Bengal India

This study presents the uncertainty analysis of a distribution network (DNR) caused by unit power output control (UPC) distributed generations (DGs) (solar, wind) and load demand by point estimate method (PEM) based on mixed-discrete-based particle swarm optimisation (MDPSO) technique. The uncertainties are taken care by the feeder flow control (FFC) DGs which make the DNR power independent of the main grid, which means the DNR does not exchange any power with the main grid at any load level. To analyse the situation, the load flow technique is modified with introducing $PQV\delta $PQV delta and zero bus in the system. $2m + 1$2m+1 and the higher-order PEM methods are applied in this study for uncertainty analysis. The FFC and the UPC DGs are placed and sized by the MDPSO algorithm. The uncertainty analysis of the system is done based on different objective functions and test cases which are the combinations of active power loss, voltage deviation, and the DG operation cost. The proposed method is applied to the 69-bus DNR, and the results are compared with teaching learning-based meta-heuristic optimisation method. The cumulative distribution function and probability density function of the output random variable are approximate with Gram-Charlier expansion method.

关键词： distributed power generation particle swarm optimisation probability load flow control power distribution control mixed-discrete particle swarm-based point estimation method uncertainty analysis distribution network unit power output control DGs load demand point estimate method mixed-discrete-based particle swarm optimisation technique feeder flow control DGs DNR power main grid load level load flow technique higher-order PEM methods UPC DGs active power loss 69-bus DNR teaching learning-based meta-heuristic optimisation method cumulative distribution function probability density function Gram-Charlier expansion method renewable uncertainties objective functions FFC distributed generations load uncertainties MDPSO technique

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Stochastic Collection and Replenishment (SCAR): objective functions

Stochastic Collection and Replenishment (SCAR): Objective Fu...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Andrew W Palmer Andrew J Hill Steven J Scheding Australian Centre for Field Robotics University of Sydney Australia

ISBN: (纸本)9781467363563

This paper introduces two objective functions for computing the expected cost in the Stochastic Collection and Replenishment (SCAR) scenario. In the SCAR scenario, multiple user agents have a limited supply of a resource that they either use or collect, depending on the scenario. To enable persistent autonomy, dedicated replenishment agents travel to the user agents and replenish or collect their supply of the resource, thus allowing them to operate indefinitely in the field. Of the two objective functions, one uses a Monte Carlo method, while the other uses a significantly faster analytical method. Approximations to multiplication, division and inversion of Gaussian distributed variables are used to facilitate propagation of probability distributions in the analytical method when Gaussian distributed parameters are used. The analytical objective function is shown to have greater than 99% comparison accuracy when compared with the Monte Carlo objective function while achieving speed gains of several orders of magnitude.

关键词： Stochastic Collection and Replenishment (SCAR) objective functions Monte Carlo method

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Multi-objective differential evolution-based ensemble method for brain tumour diagnosis

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IET IMAGE PROCESSING 2019年第9期13卷 1421-1430页

作者： Rezaei, Kimia Agahi, Hamed Mahmoodzadeh, Azar Islamic Azad Univ Shiraz Branch Dept Elect Engn Shiraz Iran

Accurate diagnosis of tumour type is a paramount issue in the appropriate treatment of cancer. In this study, a novel multi-step method, recognising benign and malignant tumour slices in real brain computed tomography (CT) images is proposed. First, image reconstruction is conducted for mixed noise removal using 'weighted encoding with sparse non-local regularisation' technique. For the segmentation purpose, support vector machine (SVM) is performed on CT images. Afterwards, 17 features are extracted, among which multiple important features are selected by the genetic algorithm. These selected features are used as the input to the multilayer perceptron neural network, the weighted kernel width SVM and the k-nearest neighbours models in tumour classification phase. Following this, the outcomes of mentioned classifiers are combined by means of multi-objective differential evolution-based ensemble technique, in order to enhance the classification performance indices. The parameters of this weighted linear combination are found by solving an optimisation problem containing precision and recall as the objective functions. The performance of the implemented approach is compared with the experienced radiologist ground truth and some state-of-the-art methods. The results demonstrate that the ensemble technique achieves high classification rates including the accuracy of 98.65%.

关键词： medical image processing brain cancer genetic algorithms image classification image texture feature extraction statistical analysis tumours multilayer perceptrons computerised tomography support vector machines image segmentation image denoising image reconstruction multiobjective differential evolution-based ensemble method brain tumour diagnosis tumour type paramount issue benign tumour slices malignant tumour slices tomography images image reconstruction mixed noise removal weighted encoding nonlocal regularisation technique segmentation purpose support vector machine CT images genetic algorithm multilayer perceptron neural network weighted kernel width SVM nearest neighbours models tumour classification phase classification performance indices weighted linear combination objective functions multistep method

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Finding the Best System Nested Defense Under Budget Uncertainty

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MILITARY OPERATIONS RESEARCH 2019年第1期24卷 5-22页

作者： Lazzaro, Gary Carlyle, W. Matthew US Naval Acad Math Dept Annapolis MD 21402 USA Naval Postgrad Sch Operat Res Dept Monterey CA USA

We extend a defender-attacker-defender (DAD) trilevel optimization model to investigate system defense planning under uncertain budget scenarios, and how that uncertainty impacts optimality. A nested defense is a monotonic sequence of sets, where the set of defenses for a particular budget scenario contains the set of defenses for all smaller budget scenarios. The use of nested defenses is attractive to decision makers because it simplifies the choices of the best defenses under uncertain budgetary scenarios into a single list. However, a set of nested defenses over many budget scenarios is almost always suboptimal when compared to the optimal defense for each individual budget. We investigate both heuristic and parametric programming approaches to identify the set of nested defenses to a system that is as close as possible to the optimal defenses over all possible budget scenarios.

关键词： Military defense Defense budgets objective functions Mathematical vectors Heuristics Optimal solutions Military operations Operations research Fens Unit costs

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Energy management of microgrid based on day-ahead and short-term optimisation

Energy management of microgrid based on day-ahead and short-...

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International Journal of Simulation and Process Modelling

作者： Wang, Xiaohui Zheng, Yiming School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

Microgrid is an effective way to accept distributed renewable energy. However, due to the uncontrollable and intermittent nature of renewable energy, coupled with the complex operational modes of microgrid, the scheduling of microgrid is more difficult to optimise and control. So it has become a key issue to optimise the coordinated operation of distributed generation units, energy storage devices and loads in energy management of microgrid. In order to guarantee the stable operation, an optimisation scheduling strategy with multiple time scales is proposed in this paper. Based on an independent microgrid, the output power of the equipment units in the microgrid is modelled and the objective functions and constraint conditions are determined. A day-ahead and short-term optimisation scheme is carried out and the scheduling program and combination of each distributed generator are optimised by the particle swarm optimisation (PSO) algorithm. The simulation results are analysed and summarised to verify the feasibility and validity of the proposed optimisation strategy. © 2020 Inderscience Enterprises Ltd.

关键词： Particle swarm optimization (PSO) Distributed power generation Energy management Energy storage Microgrids Scheduling Constraint conditions Coordinated operation Distributed generation units Distributed generators Multiple time scale objective functions Particle swarm optimisation Scheduling strategies

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Parameter design method for capacitive power and signal transfer system based on multi-objective optimisation algorithm

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IET POWER ELECTRONICS 2019年第10期12卷 2650-2659页

作者： Su, Yu-Gang Zhou, Wei Wang, Zhi-Hui Wu, Xue-Ying Qing, Xiao-Dong Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Sch Automat 174 Shazheng St Chongqing 400044 Peoples R China

For most capacitive power transfer applications, a reliable signal transfer link between the primary side and the secondary side plays an essential role. The parameters design of the system has a key effect on the operating performance, however, the existing parameter design method can only get an acceptable solution, instead of a globally optimal solution. This study has proposed a general and global optimal parameter design method based on the constraint multi-objective algorithm. The mathematical models of the power and signal channels as long as the power noise is established and all the objective functions are given according to the models. Besides, the constraints considering the reasonable region of the parameter values and the limitation of the voltage and current on the elements are given. Then the multi-objective optimisation problem with the objective functions and constraints are solved by the non-dominated sorting genetic algorithm II, and the proper global optimal parameters are selected from the Pareto set. Finally, a simulation model and an experimental setup with 180 W power and 200 kbps signal transfer capability are constructed, and the results verified the effectiveness and correctness of this method.

关键词： genetic algorithms Pareto optimisation sorting inductive power transmission signal transfer system multiobjective optimisation algorithm capacitive power transfer applications reliable signal transfer link globally optimal solution general parameter design method global optimal parameter design method constraint multiobjective algorithm mathematical models signal channels power noise objective functions parameter values multiobjective optimisation problem nondominated sorting genetic algorithm II global optimal parameters signal transfer capability Pareto set power 180 0 W bit rate 200 kbit s

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Methods for Solving of the Aircraft Landing Problem. I. Exact Solution Methods

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AUTOMATION AND REMOTE CONTROL 2019年第7期80卷 1317-1334页

作者： Veresnikov, G. S. Egorov, N. A. Kulida, E. L. Lebedev, V. G. Russian Acad Sci Trapeznikov Inst Control Sci Moscow Russia

It is noted that the aircraft landing problem is one of the main tasks in the planning and regulation of air traffic flows, which requires the use of a sufficiently serious mathematical apparatus. A brief overview of ... 详细信息

关键词： air traffic objective functions the aircraft landing problem linear programming branch and bound method

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