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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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optimization of Regional Multimodal Traffic Structure on system Level 20

Optimization of Regional Multimodal Traffic Structure on Sys...

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20th COTA International conference of Transportation Professionals (CICTP) - Advanced Transportation, Enhanced Connection

作者： Wang, Shihan Ni, Ying Sun, Jian Hua, Xuedong Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai Peoples R China Southeast Univ Sch Transportat Jiangsu Key Lab Urban ITS Nanjing Peoples R China

ISBN: (纸本)9780784483053

the coordinated development of regional integration has become an inevitable trend of China's economic development, which needs to be supported by an efficient transportation system. Understanding travel demand and optimizing structure can promote the integration construction effectively. Taking into account the status quo of China's traffic development, this paper proposed a more accurate travel consumption calculation method and travel structure optimization model from the system level. the generalized consumption functions of travel modes are calculated, where the consumption includes the time, space, and money. this paper constructs a regional traffic structure optimization model which fully considers the supply, demand, and policy factors, and obtains the analytical solution of the model under two travel modes. the proposed model is tested by the travel structure from Nanjing to Beijing during the early peak period and obtains the optimization scheme of Nanjing travel structure based on two travel modes.

关键词： Regional traffic Generalized consumption Multimodal traffic system Regional travel structure optimization

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Real-Time Greenhouse Environmental Conditions optimization Using Neural Network and Image Processing 20

Real-Time Greenhouse Environmental Conditions Optimization U...

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20th International conference on Advances in ICT for Emerging Regions (ICTer)

作者： Wickramaarachchi, Piyumi Balasooriya, Niroshan Welipenne, Lakmal Gunasekara, Sachintha Jayakody, Anuradha Sri Lanka Inst Informat Technol Fac Comp Malabe Sri Lanka

ISBN: (纸本)9781728186535

Agricultural business is one of the biggest areas in world economy. With the growth of population losing agricultural lands is the major issue in world food production. therefore, controlled environment agricultural systems under vertical farming have been introduced with greenhouses. Within greenhouses there is not a mechanism to continuously monitor the growing community and change the climate conditions. Existing systems only predict the required conditions for the plant and once predicted that value is provided to the plants continuously or change the values from season to season. To address these issues, a working prototype of an IoT based smart hydroponic system is introduced, which uses computer vision to gain maximum profits by growing a specific cultivation by providing endemic environmental conditions and addressing the problems over its growing process. there, this research presents a way of external environmental condition optimization. Regression type Feed Forward Neural Network is considered for this research to optimize the required conditions for tomato plants. Based on the current height of the plant, expected height for next 24 hours, and growth date of the plants neural networks predict the CO2, temperature and humidity level for next 24 hours with the accuracy of 88.33%, 89.21% and 92.65% respectively. the objectives of the research can be achieved by this retrieved results. the successful implementation of neural networks results a cost-effective modern farming solution for growers. this research will be supportive to attain a fundamental comprehension on the concept of the research area.

关键词： vertical farming optimization growth detection condition prediction

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Fast and Reliable Stand-Up Motions for Humanoid Robots Using Spline Interpolation and Parameter optimization

Fast and Reliable Stand-Up Motions for Humanoid Robots Using...

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International conference on Advanced Robotics (ICAR)

作者： Sebastian Stelter Marc Bestmann Norman Hendrich Jianwei Zhang Universität Hamburg Hamburg Germany

ISBN: (纸本)9781665436854

Reliability and robustness to external influences are important characteristics for using humanoid robots outside of laboratory conditions. this paper proposes a closed-loop system to let the robots recover from falling over using stand-up motion trajectories based on quintic spline interpolation. By applying IMU-based PD controllers, faster and more reliable motions can be executed. the paper also explores the usage of parameter optimization methods, which can find suitable parameter sets quickly and are able to find solutions that are unlikely to be discovered via manual search.

关键词： Interpolation Torque Humanoid robots Manuals Stability analysis Robustness Trajectory

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A model for system integration of second life battery, renewable energy generation and mobile network station

A model for system integration of second life battery, renew...

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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： Berzi, Lorenzo Cultrera, Vincenzo Delogu, Massimo Dolfi, Marco Locorotondo, Edoardo Del Pero, Francesco Morosi, Simone Pugi, Luca Tanturli, Andrea Univ Florence DIEF Florence Italy Univ Florence DINFO Florence Italy

ISBN: (纸本)9781728174556

the use of lithium-based batteries in the automotive field is particularly demanding due to the need of significant installed capacity, high energy density and power-to-capacity ratio. For this reason, literature of the last decade suggested that even after being dismantled from vehicles, partially degraded battery would still be satisfactory for other "second life" uses. In parallel, the expansion of renewable energies such as photovoltaic panels both in civil and industrial energy market suggests that coupling such discontinuous generators with storage systems could improve system flexibility, while maintaining a low environmental impact. the present article describes the integration in a single simulation environment of a system including a second life battery, a photovoltaic generator and a grid-connected mobile network telecommunication station, which has been adopted as casestudy. Subsystem characteristics, energy management strategy and boundary conditions are described;results obtained from the defined scenario are presented and include energy balance expected costs, assumed on the basis of the Italian scenario.

关键词： second life battery cell aging mobile network energy impact modeling renewable energy

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Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling 20

Sample-Rank: Weak Multi-Objective Recommendations Using Reje...

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20th IEEE International conference on Data Mining (ICDM)

作者： Shukla, Abhay Sathyanarayana, Jairaj Banerjee, Dipyaman Swiggy Bengaluru India

ISBN: (纸本)9781728190129

Online food ordering marketplaces are multi-stakeholder systems where recommendations impact the experience and growth of each participant in the system. A recommender system in this setting has to encapsulate the objectives and constraints of different stakeholders in order to find utility of an item for recommendation. Constrained-optimization based approaches to this problem typically involve complex formulations and have high computational complexity in production settings involving millions of entities. Simplifications and relaxation techniques (for example, scalarization) help but introduce sub-optimality and can be time-consuming due to the amount of tuning needed. In this paper, we introduce a method involving multi-goal sampling followed by ranking for user-relevance (Sample-Rank), to nudge recommendations towards multi-objective (MO) goals of the marketplace. the proposed method's novelty is that it reduces the MO recommendation problem to sampling from a desired multi-goal distribution then using it to build a production-friendly learning-to-rank (LTR) model. In offline experiments we show that we are able to bias recommendations towards MO criteria with acceptable trade-offs in metrics like AUC and NDCG. We also show results from a large-scale online A/B experiment where this approach gave a statistically significant lift of 2.64% in average revenue per order (RPO) (objective #1) with no drop in conversion rate (CR) (objective #2) while holding the average last-mile traversed flat (objective #3), vs. the baseline ranking method. this method also significantly reduces time to model development and deployment in MO settings and allows for trivial extensions to more objectives.

关键词： multi-stakeholder recommendation multi-objective optimization learning to rank sampling

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