Smart Grids for Smarter Cities

Bonetto, Riccardo; Sychev, Ilya; Zhdanenko, Oleksandr; Abdelkader, Abdelrahman; Fitzek, Frank H. P.

doi:10.1109/ccnc46108.2020.9045309

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…The development of hybrid vehicles has been a significant advance in energy efficiency and reduction of combustion emissions [7,12,13]. Typically, the hybrid vehicle operational profile is based on the speed reference: for low-speed operation, only the EM delivers the required power, for cruise speed only the ICE delivers the required power, and for high acceleration both the EM and the ICE supply power [4,14,15].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Vehicle CO2 Emissions Reduction Strategy Based on Model Predictive Control

2023

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This work proposes a hybrid drive controlled configuration, using a minimum emissions search algorithm, which ensures the operation of the Internal Combustion Engine (ICE) in its fuel efficiency range, minimizing CO2 emissions by controlling the power flow direction of the Electric Machine (EM). This action is achieved by means of Power Converters, in this case a bi-directional DC-DC Buck-Boost Converter in the DC-side and a DC-AC T-type Converter as the inverting stage. Power flow is controlled by means of a bi-directional Model Predictive Control (MPC) scheme, based on an emissions optimization algorithm. A novel drivetrain configuration is presented where both, the ICE and the EM are in tandem arrangement. The EM is driven depending on the traction requirements and the emissions of the ICE. The EM is capable of operates in motor and generator mode ensuring the Minimum Emission Operating Point (MEOP) of the ICE regardless of the mechanical demand at the drivetrain. Simulation and validation results using a Hardware in the Loop (HIL) virtual prototype under different operation conditions are presented in order to validate the proposed overall optimization strategy.

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Section: Introductionmentioning

confidence: 99%

Hybrid Vehicle CO2 Emissions Reduction Strategy Based on Model Predictive Control

2023

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“…In VPP the EVs and DERs serve as the energy delivery devices and form a self-sufficient island of the power distribution grid. In this paper, the application of 5Genabled technologies is shown for VPP that can detect the phase-to-ground faults in the presence of DG [14].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Real Time Energy Management of Virtual Power Plant Using PLC for Transactive Energy Framework

et al. 2021

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The high penetration of renewable sources owing to less environmental pollution creates challenges for the grid operators. Virtual Power Plant is a novel concept that will integrate the small distributed energy resources and will act as a single conventional power plant in the electricity market. As a core energy management system in VPP, the energy should be dispatched optimally for achieving the maximum profit. Therefore, smart energy management is developed in this article of VPP with PLC and IoT in a unified market environment that integrates the DA and RT market. The cost characteristics for the interruptible load, battery storage system are modelled individually. The proposed scheme can efficiently handle the energy demand for the VPP domain. Four different scenarios are considered with different loading conditions for validation of the concept of smart energy management. The profitability for each scenario is shown with the experimental results.

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Comparative study of ML models for IIoT intrusion detection: impact of data preprocessing and balancing

Eid,

Soudan,

Nassif

et al. 2024

Neural Comput & Applic

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Smart Grids for Smarter Cities

Cited by 10 publications

References 6 publications

Hybrid Vehicle CO2 Emissions Reduction Strategy Based on Model Predictive Control

Hybrid Vehicle CO2 Emissions Reduction Strategy Based on Model Predictive Control

IoT-Based Real Time Energy Management of Virtual Power Plant Using PLC for Transactive Energy Framework

Comparative study of ML models for IIoT intrusion detection: impact of data preprocessing and balancing

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