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检索条件"任意字段=20th Conference on System Modeling and Optimization"

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Optimisation of Prosumers' Participation in Energy Transactions

Optimisation of Prosumers' Participation in Energy Transacti...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Gough, Matthew Santos, Sergio F. Javadi, Mohammad Fitiwi, Desta Z. Osorio, Gerardo J. Castro, Rui Lotfi, Mohamed Catalao, Joao P. S. Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal ERSI Dublin Ireland C MAST UBI Covilha Portugal IST Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9781728174556

there is an ongoing paradigm shift occurring in the electricity sector. In particular, previously passive consumers are now becoming active prosumers and they can now offer important and cost-effective new forms of flexibility and demand response potential to the electricity sector and this can translate into system-wide operational and economic benefits. this work focuses on developing a model where prosumers participate in demand response programs through varying tariff schemes, and the model also quantifies the benefits of this flexibility and cost-reductions. this work includes transactive energy trading between various prosumers, the grid and the neighborhood. A stochastic tool is developed for this analysis, which also allows the quantification of the collective behavior so that the periods with the greatest demand response potential can be identified. Numerical results indicate that the optimization of energy transactions amongst the prosumers, and including the grid, leads to considerable cost reductions as well as introducing additional flexibility in the presence of demand response mechanisms.

关键词： prosumers flexibility demand response smart neighborhood stochastic optimization

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Aerodynamic Shape optimization for Delaying Dynamic Stall of Airfoils by Regression Kriging 20th

Aerodynamic Shape Optimization for Delaying Dynamic Stall of...

引用

20th Annual International conference on Computational Science (ICCS)

作者： Raul, Vishal Leifsson, Leifur Koziel, Slawomir Iowa State Univ Dept Aerosp Engn Ames IA 50011 USA Reykjavik Univ Engn Optimizat & Modeling Ctr Reykjavik Iceland

ISBN: (纸本)9783030867973;9783030504250

the phenomenon of dynamic stall produce adverse aerodynamic loading which can adversely affect the structural strength and life of aerodynamic systems. Aerodynamic shape optimization (ASO) provides an effective approach for delaying and mitigating dynamic stall characteristics without the addition of auxiliary system. ASO, however, requires multiple evaluations time-consuming computational fluid dynamics models. Metamodel-based optimization (MBO) provides an efficient approach to alleviate the computational burden. In this study, the MBO approach is utilized for the mitigation of dynamic stall characteristics while delaying dynamic stall angle of the flow past wind turbine airfoils. the regression Kriging metamodeling technique is used to approximate the objective and constrained functions. the airfoil shape design variables are described with six PARSEC parameters. A total of 60 initial samples are used to construct the metamodel, which is further refined with 20 infill points using expected improvement. the metamodel is validated with the normalized root mean square error based on 20 test data samples. the refined metamodel is used to search for the optimal design using a multi-start gradient-based method. the results show that an optimal design with a 3. delay in dynamic stall angle as well a reduction in the severity of pitching moment coefficients can be obtained.

关键词： Dynamic stall Unsteady CFD Surrogate-based optimization Regression Kriging Expected improvement

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Decomposition Model of Contract for Difference Considering Market Power Mitigation

Decomposition Model of Contract for Difference Considering M...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Xu, Chengwei Pang, Kaiyuan Wen, Fushuan Palu, Ivo Gong, Jianrong Xie, Yuzhe Chen, Cheng Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China Tallinn Univ Technol Dept Elect Power Engn & Mechatron Tallinn Estonia Zhejiang Power Exchange Ctr Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9781728174556

the Contract for Difference (CfD) is recognized as an efficient tool for hedging risks in electricity markets. the decomposition of a CfD aims to schedule the concerned contract energy over the contract period for future delivery and settlement, and could directly affects the amount and distribution of the excess capacity of a generator as well as its market power level. In this paper, a decomposition model of CfDs is proposed from the prospective of mitigating the potential market power in an electricity market. First, the residual demand is employed to analyze the impact of CfDs decomposition on market power. the well-established Cournot competition model and perfect competition model are next introduced for market power assessment. On this basis, a decomposition strategy with the objective of minimizing the market price markups is formulated as a bi-level optimization problem. Finally, a 6-unit test system is employed to demonstrate the proposed model, together with detailed analysis of numerical results.

关键词： Electricity market Contract for Difference (CfD) market power mitigation game theory bi-level optimization

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Learning Periods from Incomplete Multivariate Time Series 20

Learning Periods from Incomplete Multivariate Time Series

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20th IEEE International conference on Data Mining (ICDM)

作者： Zhang, Lin Gorovits, Alexander Zhang, Wenyu Bogdanov, Petko SUNY Albany Albany NY 12222 USA Cornell Univ Ithaca NY USA

ISBN: (纸本)9781728183169

modeling and detection of seasonality in time series is essential for accurate analysis, prediction and anomaly detection. Examples of seasonal effects at different scales abound: the increase in consumer product sales during the holiday season recurs yearly, and similarly household electricity usage has daily, weekly and yearly cycles. the period in real-world time series, however, may be obfuscated by noise and missing values arising in data acquisition. How can one learn the natural periodicity from incomplete multivariate time series? We propose a robust framework for multivariate period detection, called LAPIS. It encodes incomplete and noisy data as a sparse summary via a Ramanujan periodic dictionary. LAPIS can accurately detect a mixture of multiple periods in the same time series even when 70% of the observations are missing. A key innovation of our framework is that it exploits shared periods across individual time series even when they are not correlated or in-phase. Beyond detecting periods, LAPIS enables improvements in downstream applications such as forecasting, missing value imputation and clustering. At the same time our approach scales to large real-world data executing within seconds on datasets of length up to half a million time points.

关键词： Period learning Multivariate time series Missing data imputation Alternating optimization

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Admission, Association, and Rate Maximization in a UAV Assisted User Clustering Based Downlink Hybrid Multiple Access HetNets

Admission, Association, and Rate Maximization in a UAV Assis...

引用

International Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Umar Ghafoor Mudassar Ali Adil Masood Siddiqui National University of Sciences and Technology Islamabad Pakistan University of Engineering and Technology Taxila Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

this paper addresses the future requirements of extensive coverage, high data rate, and support for highly interactive multimedia applications by exploring the benefits of fifth-generation (5G) networks. Existing studies have separately investigated unmanned aerial vehicles (UAVs), heterogeneous networks (HetNets), orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), and user clustering. However, the collective impact of these factors has not been explored together. HetNet is a collection of base stations (BSs) as macro BS (MBS) and small BS (SBS). A comprehensive approach by combining these elements, i.e., UAV-assisted user clustering based downlink hybrid-multiple access (hybrid-MA) in HetNet, is proposed to maximize sum rates, taking into account factors such as user admission in clusters, user association with BSs, power allocation, the distance of users from BSs, and minimum rate requirements. A novel network model is presented and the problem is formulated as mixed integer nonlinear programming (MINLP), which is solved using an outer approximation algorithm (OAA). Comparative evaluations with existing strategies, i.e., OMA-MBS-only, NOMA-MBS-only, hybrid-MA-MBS only, and hybrid-MA-HetNet, demonstrate the superior performance of the proposed technique in terms of user admission, user association, and sum rate.

关键词： NOMA system performance Programming Downlink Autonomous aerial vehicles Load management User experience Heterogeneous networks Resource management optimization

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Optimal Energy Bidding for the Italian Balancing Service by EVs: a Sensitivity Analysis

Optimal Energy Bidding for the Italian Balancing Service by ...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Canevese, Silvia Gatti, Antonio Ric Sul Sistema Energet RSE SpA Energy Syst Dev Dept Milan Italy

ISBN: (纸本)9781728174556

thanks to their flexible power response, Battery Energy Storage systems (BESSs) have gained attention as possible new suppliers of Ancillary Services (ASs) to the power system. BESSs in Electric Vehicles (EVs), in particular, could supply ASs during EV recharge. Here, some evaluations of how profitable the Balancing Service (BS) could be for the EV owner are carried out: an optimization problem is set up, returning the energy to be bid for the service on the Italian Balancing Market (BM) to minimize the cost of recharging the desired amount of energy;the bid prices and the bid acceptance/rejection status are assumed as known input values;hourly and quarter-of-an-hourly bids are considered, based, respectively, on the current product resolution on the market and on its foreseen evolution. the simulated case studies are for overnight recharge and involve two kinds of end user, with different values of the maximum recharging power, of the energy to be recharged and of taxes on energy exchanges;both one-directional (V1G) and bi-directional (V2G) recharge are analyzed. Overall annual results show that, if the energy to be recharged is small, moderate savings in the EV electricity bill can be achieved with respect to not supplying the BS;high savings can be achieved if such energy is larger (as in case of a non-domestic user) and V2G recharge is adopted, so that energy selling for the BS is enhanced.

关键词： Balancing Market balancing service battery cycling aging Electric Vehicle cost optimization mixed-integer linear programming V1G V2G

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Coordinated Operation of Electric Vehicle Parking Lots and Smart Homes as a Virtual Power Plant

Coordinated Operation of Electric Vehicle Parking Lots and S...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Lotfi, Mohamed Almeida, Tiago Javadi, Mohammad Osorio, Gerardo J. Catalao, Joao P. S. Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal Ontario Tech Univ Fac Energy Syst & Nucl Sci Oshawa ON L1G 8C4 Canada Univ Beira Interior C MAST P-6200358 Covilha Portugal

ISBN: (纸本)9781728174556

In recent years, virtual power plants (VPPs) rose as an effective framework to aggregate the collective potential of distributed energy resources (DERs), including distributed generation (DG) and energy storage systems (ESS), through demand response (DR) program implementation. In this work, the operation of two indispensable DER assets, electric vehicles (EVs) and photovoltaic-equipped parking lots (PVPLs), is coordinated in an optimal energy management framework, in order to study their possible aggregation as a VPP. the proposed energy management system (EMS) was developed using the optimization and simulation tools, namely GAMS and MATLAB, and is intended for use by grid operators to coordinate the operation of PVPLs and home energy management systems (HEMSs) in the context of smart cities. the developed model was validated and tested by considering real-life case studies in the city of Porto, Portugal.

关键词： Electric Vehicles Smart Grid Power system Operation Virtual Power Plant optimization Methods

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A Predictive Maintenance Model of Equipment system Based on GRU Model

A Predictive Maintenance Model of Equipment System Based on ...

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Virtual Reality, Human-Computer Interaction and Artificial Intelligence (VRHCIAI), International conference on

作者： Jiarui Zhang Fan Zhang Xiaowei Guo Xibing Yang Panlong Mu The 20th Research Institute of China Electronics Technology Group Corporation Xi'an China CRRC YONGJI Electric Co. LTD Xi'an China

the equipment system's status seriously affects the calculation's reliability and the availability of industrial equipment. system status is usually determined by the operating characteristics of the equipment. Criteria of abnormal status are determined by expert review, which is not objective. Due to the lack of objective, comprehensive health assessments of equipment systems, staff cannot perform predictive maintenance of the system. In this paper, an anomaly detection algorithm is proposed to evaluate the operating state data of the detection equipment system, which is used to solve the predictive maintenance problem of the system. We adopt the DBSCAN algorithm for detecting the operating status of the equipment system and marking out the historical data operating status to replace the assessment criteria created by experts. the abnormal status is applied to the GRU model as a train data label to predict the abnormal status of the equipment system. Compared with other mainstream algorithms, the universality and accuracy of the abnormal status prediction model we proposed are verified in multi-dimensional time series.

关键词： Human computer interaction Solid modeling Time series analysis Clustering algorithms Virtual reality Predictive models Prediction algorithms

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A Collision-Free Framework for Navigation of Nonholonomic Vehicle systems 20

A Collision-Free Framework for Navigation of Nonholonomic Ve...

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20th International conference on Control, Automation and systems (ICCAS)

作者： Zhang, Xiaoxue Ma, Jun Cheng, Zilong Lee, Tong Heng Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Singapore 119077 Singapore Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

ISBN: (纸本)9788993215199

the task of highly effective navigation is certainly one of the critical technologies of autonomous vehicles that have been widely investigated by researchers. In this paper, a navigation framework with path planning and tracking is presented, where a feasible and collision-free trajectory is generated for nonholonomic vehicle systems. In terms of path planning, an LQR-RRT* algorithm is applied to plan a path without collision. It is an optimal sampling-based planning algorithm with linear quadratic regulation-based heuristics, and able to tackle some rather complicated or under-actuated dynamic systems. In order to improve the feasibility of the planned path and compensate for the deterioration of optimality due to sampling times, the approach is taken where a cubic spline path is additionally generated. the path tracking problem is formulated as a constrained optimization problem for the nonholonomic vehicle system, and then the required steering angle and acceleration is computed to avoid potential collisions. the results of the simulation experiments clearly demonstrate the effectiveness of the proposed navigation framework.

关键词： Nonholonomic system model predictive control autonomous vehicle navigation path planning

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Optimizing Multi-Stage Hydraulic Fracturing Treatments for Economical Production in Permian Basin Using Machine Learning

Optimizing Multi-Stage Hydraulic Fracturing Treatments for E...

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International conference on Machine Learning and Applications (ICMLA)

作者： Yanfang Wang Jianhua Chen Seung Kam Anqi Bao Louisiana State University Baton Rouge United States Texas A&M University College Station United States

ISBN: (纸本)9781665443388

Due to the heavy computational costs of pure simulation-based reservoir models when used for hydraulic fracturing design, machine learning (ML) approaches become promising in petroleum industry to provide effective and efficient well completion optimization solutions. In this work, we propose a framework of using ML approaches to optimize multi-stage hydraulic fracturing design for economical development of unconventional wells in Permian basin, West Texas. Since the goals of maximizing oil production and minimizing completion cost are generally conflicting, combining both as a bi-objective optimization for maximal profit is the main objective in this study. Two ML regression models are selected to predict the $1^{\mathrm{s}\mathrm{t}}$-year oil production and completion cost, respectively. the bi-objective optimization is shown to help gain more profit than the single-objective optimizations of oil production or completion cost only. From the case study of an exam well, we predict the profit as ${\$}$12.5 millions when optimizing oil production only, ${\$}$7.9 millions when optimizing completion cost only, and ${\$}$13.4 millions when optimizing both. this bi-objective optimization approach in this study can help to increase profit up to ${\$}$1 million. Moreover, the framework can help the decision makers to select solutions of optimal completion design parameters based on their preferences.

关键词： Costs Oils Biological system modeling Production Machine learning Hydraulic systems Predictive models

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