Version 1
: Received: 9 February 2022 / Approved: 10 February 2022 / Online: 10 February 2022 (07:55:02 CET)
How to cite:
Qayyum, F.; Jamil, H.; Jamil, F.; Kim, D. Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network. Preprints2022, 2022020145. https://doi.org/10.20944/preprints202202.0145.v1
Qayyum, F.; Jamil, H.; Jamil, F.; Kim, D. Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network. Preprints 2022, 2022020145. https://doi.org/10.20944/preprints202202.0145.v1
Qayyum, F.; Jamil, H.; Jamil, F.; Kim, D. Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network. Preprints2022, 2022020145. https://doi.org/10.20944/preprints202202.0145.v1
APA Style
Qayyum, F., Jamil, H., Jamil, F., & Kim, D. (2022). Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network. Preprints. https://doi.org/10.20944/preprints202202.0145.v1
Chicago/Turabian Style
Qayyum, F., Faisal Jamil and Do-Hyeun Kim. 2022 "Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network" Preprints. https://doi.org/10.20944/preprints202202.0145.v1
Abstract
The concept of distributed generation has made photovoltaic an integral source of energy in smart grid systems, especially in peer-to-peer energy trading frameworks that exploit excess power to fulfill the energy requirements of consumers in cost-efficient and eco-friendly manner. It is believed that P2P energy trading will dominate a significant portion of research in forthcoming power generation systems due to the excessive rise of energy demands across the globe. Despite a plethora of studies on energy optimization solutions in P2P trading, minimizing nanogrid energy trading cost and efficient energy sharing between consumers and prosumers are deemed among the challenging problems. This study overcomes essential issues overlooked by the contemporary P2P energy trading models by introducing a predictive optimization-oriented nanogrid energy trading model. The proposed study encompasses two stages: (1) predictive optimization model which harnesses BD-LSTM-based forecasted energy parameters (energy load, energy consumption, and PV generation) that are later incorporated in PSO-enabled objective function to reduce nanogrid trading cost, (2) optimal energy sharing plan is devised to decide the role of nanogrids as prosumers or consumers by emphasizing the use of PV-produced energy. The proposed model is validated on the case study containing nanogrid houses data. The simulation provides detailed experiments by comparing the energy demand and response using the proposed energy sharing model. The outcomes yield that the energy sharing plan holds a significant potential to fulfill maximum energy requirements of nanogrid house in P2P cluster and significantly reduces the energy cost compared to grid.
Keywords
Smart grids; Optimization; Prediction methods; Energy exchange
Subject
Computer Science and Mathematics, Analysis
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
