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Your search keyword '"Azevedo, A. M."' showing total 91 results
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91 results on '"Azevedo, A. M."'

6. A blockchain-based user-centric emission monitoring and trading system for multi-modal mobility

Author

Eckert, Johannes, Lopez, David, Lima Azevedo, Carlos M., Farooq, Bilal, Eckert, Johannes, Lopez, David, Lima Azevedo, Carlos M., and Farooq, Bilal

Abstract

Since the transport sector accounts for one of the highest shares of greenhouse gases (GHG) emissions, several existing proposals state the idea to control the by the transportation sector caused GHG emissions through an Emission Trading Systems (ETS). However, most existing approaches integrate GHG emissions through the fuel consumption and car registration, limiting the tracing of emissions in more complex modes e.g. shared vehicles, shared rides and even public transportation. This paper presents a new design of a user-centric ETS and its implementation as a carbon Blockchain framework for Smart Mobility Data-market (cBSMD). The cBSMD allows for the seamless transactions of token-equivalent GHG emissions when realizing a trip, or an emission trading action as well as the transaction of individual, service or system-wide emission performance data. We demonstrate an instance of the cBSMD implementation for the transactions of an ETS where all travellers receive a certain amount of emission credits in the form of tokens, linked to the GHG price and a total emission cap. Travellers use their tokens each time they emit GHG when travelling in a multimodal network, purchase tokens for a given trip when they have an insufficient token amount or sell when having a surplus of tokens due to a lower amount of emitted GHG. This instance of cBSMD is then applied to a case-study of 24hours of mobility of 3, 186 travellers from Oakville, Ontario, Canada, where we showcase different cBSMD transactions and analyze token usage and emission performance.

Published
2020

7. Hybrid Maximum Power Point Tracking Technique for PV Modules Based on a Double-Diode Model.

Author

Cavalcanti, Marcelo C., Bradaschia, Fabricio, do Nascimento, Aguinaldo J., Azevedo, Gustavo M. S., and Barbosa, Eduardo J.

Subjects
HYBRID power, MAXIMUM power point trackers, PARAMETER estimation, ALGORITHMS, MATHEMATICAL optimization
Abstract

Photovoltaic (PV) systems are more efficient when operated in its maximum power point (MPP). Even with the existence of high number of MPP tracking (MPPT) techniques, it is possible to improve the MPPT efficiency. One way to achieve this improvement is to use model-based MPPT techniques, since they can estimate the MPP with reasonable accuracy, accelerating the MPP search process. Based on this motivation, the main objective of this article is to propose an efficient double-diode model-based + perturb and observe (P&O) heuristic-based hybrid MPPT technique. The proposition has the following three unique features: the double-diode model used in the MPPT technique has the capability of extrapolating the I–V curves of the PV modules for any environmental condition, even for temperature and irradiance values not used during the model building process; a parameter estimation technique that uses a combination of analytical equations and pattern search optimization algorithm is also presented for this unique model; and an irradiance estimation algorithm is also proposed, avoiding the irradiance sensor usually needed in model-based and hybrid MPPT techniques. Experimental results demonstrate that the integration of the model, the irradiance estimator, and reduced step P&O algorithm is an effective hybrid MPPT solution for PV systems. [ABSTRACT FROM AUTHOR]

Published
2021
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12. Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

Author

Rodrigues, Filipe, Lima Azevedo, Carlos M., Rodrigues, Filipe, and Lima Azevedo, Carlos M.

Abstract

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.

Published
2019

16. Integrated simulation of activity-based demand and multi-modal dynamic supply for energy assessment

Author

Chen, Siyu, de Lima, Isabel Hemerly Viegas, Needell, Zach, Araldo, Andrea, Akkinepally, Arun, Trancik, Jessica, Ben-Akiva, Moshe, Fournier, Nicholas, Deliali, Aikaterini, Christofa, Eleni, Lima Azevedo, Carlos M., Chen, Siyu, de Lima, Isabel Hemerly Viegas, Needell, Zach, Araldo, Andrea, Akkinepally, Arun, Trancik, Jessica, Ben-Akiva, Moshe, Fournier, Nicholas, Deliali, Aikaterini, Christofa, Eleni, and Lima Azevedo, Carlos M.

Abstract

The development of a large scale agent-based simulation model for the Greater Boston Area is presented, closing the gap between state-of-the art integrated demandsupply modeling techniques (SimMobility) with advanced energy estimation models (TripEnergy) and shedding light on its practical application to large urban areas. This paper describes the technical details of its three key components (activity-based demand, multi-modal dynamic supply, and trajectory-based energy models), the used data, the model estimation, integration and calibration processes. The proposed model can simulate any day with and without congestion in order to capture changes in energy use across all dimensions of a mobility system, namely temporal, spatial, modal or functional. For an average 24h in the Greater Boston Area the simulated travel of 4.5-million people resulted in 15-million trips and a total vehicle energy consumption of 548 thousand equivalent gallons of gasoline. Our proposed platform allows for the comprehensive and consistent assessment of energy related policies, technologies and services affecting traveler behavior, the transportation system’s and vehicle energy performances

Published
2018

39. Unbalanced Grid Fault Ride-Through Control for Single-Stage Photovoltaic Inverters.

Author

Neves, Francisco A. S., Carrasco, Miguel, Mancilla-David, Fernando, Azevedo, Gustavo M. S., and Santos, Valeria S.

Subjects
ELECTRIC power distribution grids, ELECTRIC controllers, PHOTOVOLTAIC cells, ELECTRIC inverters, ELECTRIC potential
Abstract

Integration of distributed generation (DG) such as photovoltaics (PV) represents a challenge for the traditional operation of distribution power systems. As the installed power of DGs grows, grid codes are being modified to involve DGs in the provision of ancillary services. This includes the ability to ride-through large disturbances. In this paper, the behavior of a single-stage PV system under unbalanced voltage conditions is studied, and a fault ride-through control scheme is proposed which is able to support the grid through the injection of reactive power. Furthermore, adjustable power quality is enabled as a tradeoff between power ripple and current harmonics. The control scheme makes use of a current controller based on the space vector Fourier transform concept. No rotational transformation is required, and zero steady-state error is ensured when tracking distorted current references. The controller was tested in detailed PSCAD/EMTDC computer simulations, and implemented in a real grid-connected PV system to demonstrate its performance under unbalanced voltage conditions. [ABSTRACT FROM PUBLISHER]

Published
2016
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