TY  - JOUR
AU  - Alfaverh, Khaldoon Iqtaish Khaled
AU  - Alfaverh, F.
AU  - Számel, László
TI  - Plugged-in electric vehicle-assisted demand response strategy for residential energy management
JF  - Energy Informatics
J2  - Energy Inform
VL  - 6
PY  - 2023
IS  - 1
PG  - 24
SN  - 2520-8942
DO  - 10.1186/s42162-023-00260-9
UR  - https://m2.mtmt.hu/api/publication/33742864
ID  - 33742864
N1  - Department of Electric Power Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, P.O.B. 91, Budapest, 1521, Hungary            
            School of Engineering and Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom            
            Export Date: 11 April 2023            
            Correspondence Address: Alfaverh, K.; Department of Electric Power Engineering, P.O.B. 91, Hungary; email: khaldoonalfaverh@edu.bme.hu
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Alfaverh, Khaldoon Iqtaish Khaled
AU  - Számel, László
ED  - Molnár, György
TI  - Battery Energy Storage System Impact Over Load Frequency Control In Microgrids
T2  - IEEE 5th International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE 2022)
PB  - IEEE
CY  - Budapest
SN  - 9798350346190
PY  - 2022
SP  - 61
EP  - 66
PG  - 6
DO  - 10.1109/CANDO-EPE57516.2022.10046390
UR  - https://m2.mtmt.hu/api/publication/33733405
ID  - 33733405
N1  - Correspondence Address: Alfaverh, K.; Budapest University of Technology and Economics, Hungary; email: khaldoonalfaverh@edu.bme.hu
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Alfaverh, Khaldoon Iqtaish Khaled
AU  - Számel, László
TI  - Plugged-in Electric Vehicle-Assisted Demand Response Strategy for Load Scheduling
JF  - PERIODICA POLYTECHNICA-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
J2  - PERIOD POLYTECH ELECTR ENG COMP SCI
PY  - 2020
SN  - 2064-5260
UR  - https://m2.mtmt.hu/api/publication/31311955
ID  - 31311955
AB  - In order to achieve a successful integrated future energy system, a smart home energy management system is critical for a prosumer. Thanks to advanced technology, it is applicable to monitor and control in real-time different smart home and microgrids applications (e.g. home appliances, renewable energies sources, energy storage systems, electric vehicles). In this paper, we propose a HEMS model including smart scheduling, photovoltaic management, and an optimally designed charging and discharging strategy for PEV to integrate it into the HEMS for helping reduce electricity costs and improve energy utilization. Using fuzzy logic controller on the proposed model, the energy profile of the smart home can ensure both cost reduction and user comfort. Different scenarios are employed here to manifest the performance of the proposed design.
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Alfaverh, Fayiz
AU  - Denai, Mouloud
AU  - Alfaverh, Khaldoon Iqtaish Khaled
ED  - Xin-She, Yang
ED  - Nilanjan, Dey
ED  - Amit, Joshi
TI  - Demand-Response Based Energy Advisor for Household Energy Management
T2  - 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4)
PB  - IEEE
CY  - London
SN  - 9781728137803
PY  - 2019
SP  - 153
EP  - 157
PG  - 5
DO  - 10.1109/WorldS4.2019.8904042
UR  - https://m2.mtmt.hu/api/publication/31297421
ID  - 31297421
AB  - Home energy management systems (HEMS) are set to play a key role in the future smart grid (SG). HEMS concept enables residential customers to actively participate in demand response programs (DR) to control their energy usage, reduce peak demand and therefore contribute to improve the performance and reliability of the grid. This paper proposes an energy management strategy for residential end-users. In this framework, a demand response strategy is developed to reduce home energy consumption. The proposed algorithm seeks to minimise peak load demands by scheduling the operation of electric appliances and shifting controllable loads during periods when electricity prices are high, to off-peak periods, when electricity prices are lower without compromising the customer's preferences. The overall system is simulated using MATLAB/Simulink and the results demonstrate the effectiveness of the proposed demand response strategy in managing the daily household energy consumption.
LA  - English
DB  - MTMT
ER  -