Heuristic-based programable controller for efficient energy management under renewable energy sources and energy storage system in smart grid

Imran, A., Hafeez, G., Khan, I., Usman, M., Shafiq, Z., Qazi, A.B., Khalid, A. ORCID: 0000-0001-5270-6599 and Thoben, K.-D., 2020. Heuristic-based programable controller for efficient energy management under renewable energy sources and energy storage system in smart grid. IEEE Access, 8, pp. 139587-139608. ISSN 2169-3536

[img]
Preview
Text
1351636_a963_Khalid.pdf - Published version

Download (3MB) | Preview

Abstract

An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

Item Type: Journal article
Publication Title: IEEE Access
Creators: Imran, A., Hafeez, G., Khan, I., Usman, M., Shafiq, Z., Qazi, A.B., Khalid, A. and Thoben, K.-D.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10 August 2020
Volume: 8
ISSN: 2169-3536
Identifiers:
NumberType
10.1109/access.2020.3012735DOI
1351636Other
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > School of Science and Technology
Record created by: Jill Tomkinson
Date Added: 11 Aug 2020 08:53
Last Modified: 31 May 2021 15:15
URI: https://irep.ntu.ac.uk/id/eprint/40420

Actions (login required)

Edit View Edit View

Views

Views per month over past year

Downloads

Downloads per month over past year