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http://bura.brunel.ac.uk/handle/2438/32814Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Alhasnawi, BN | - |
| dc.contributor.author | Jasim, BH | - |
| dc.contributor.author | Homod, RZ | - |
| dc.contributor.author | Bazooyar, B | - |
| dc.contributor.author | Zanker, M | - |
| dc.contributor.author | Bureš, V | - |
| dc.date.accessioned | 2026-02-16T15:44:00Z | - |
| dc.date.available | 2026-02-16T15:44:00Z | - |
| dc.date.issued | 2025-09-23 | - |
| dc.identifier | ORCiD: Bahamin Bazooyar https://orcid.org/0000-0002-7341-4509 | - |
| dc.identifier.citation | Alhasnawi, B.N. et al. (2025) 'A novel peer-to-peer energy trading strategy for multi-microgrid loads scheduling based on chance-constrained', Energy Nexus, 20, 100536, pp. 1–28. doi: 10.1016/j.nexus.2025.100536. | en_US |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/32814 | - |
| dc.description | Highlights: • Optimizing solar self-consumption, the proposed decentralized energy management system (EMS) accounts for load shifting and battery deterioration. It does this by combining a hierarchical management framework, an optimization-based real-time interactive algorithm, and an integrated P2P energy trading mechanism. • The suggested EMS is unique in that it differs from past research that describe peer-to-peer energy exchange in that: ○ Because distributed controllers are deployed in each microgrid to optimize the performance of the HBSS and the shiftable appliances located inside each microgrid, the suggested EMS is computationally efficient. As a result, although the approach in Naji Alhasnawi et al. (2024) cannot be applied to large-scale communities, the system can. ○ The proposed method synchronizes the day-ahead scheduling with the operating phases to appropriately accommodate the unpredictability of demand and the influence of renewable energy sources. ○ The proposed EMS's decentralized architecture guarantees excellent reliability; in the event of a malfunction or maintenance stoppage, the remaining part of the system can keep running without affecting the system's overall performance (a limitation of Belgioioso et al. (2020)). ○ Regarding handling changes in the energy system configuration, such as newly installed microgrids, DERs, increased loads, topology, etc., the suggested EMS does not require changing the network architecture or starting from scratch (a downside of Huang et al. (2023)). • The community's microgrids may trade energy more easily amongst themselves while accounting for the movement of money and energy between them thanks to the proposed EMS. Additionally, it ensures that the revenue generated by energy trading among microgrids is increased. The hierarchical EMS also has the advantage of precisely tracking and managing the energy and financial flows between microgrids, enabling the microgrids to exchange energy for a charge. This improves system efficiency by enabling the system to monitor and take into consideration the power losses related to the shared energy. • The sensitivity analysis provided for the peer-to-peer (P2P) operation of microgrids in the community is distinctive in that it looks at how changing certain parameters affects the percentage drop in the microgrids' annual energy costs (AEC) and how this change affects the other microgrids in the community. • A unique DR program under the TOU pricing and an P2P program under the direct load control are suggested in order to decrease electricity demand, slash rates, and benefit the community MG in order to save money. Every program stage participant will get a minimum incentive. • Simulations are conducted both with and without the use of the Improved Sparrow search algorithm (ISSA) in order to demonstrate the effectiveness of the suggested Improved Elephant Herding Optimization (IEHO) with regard to goals like energy cost, carbon emission, and PAR. | en_US |
| dc.description | Data availability: The data used for this research and preparation of this article can be accessed from Brunel University of London repository at: https://doi.org/10.17633/rd.brunel.25713888.v1. | - |
| dc.description | Supplementary materials are available online at: https://www.sciencedirect.com/science/article/pii/S2772427125001767?via%3Dihub#sec0031 . | - |
| dc.description.abstract | Facilitating producer-consumer P2P energy exchange is a viable paradigm in the era of decentralized energy. Energy trading requires the development of a fair pricing mechanism, but when numerous energy systems are involved in the transaction, the problem can get complicated. Through the decentralized coordination of distributed microgrid energy systems and shiftable microgrid appliances, this article introduces a decentralized EMS that facilitates P2P energy trading among prosumers in community. This lowers the energy costs per microgrid compared to operating each microgrid separately. A Chance-Constrained cooperative model connecting manufacturing, commercial, and residential prosumers with guaranteed trade fairness serves as foundation for suggested approach. The model is expanded to take into account several demand-side management strategies and widely utilized energy supply systems. This study offers a more succinct method for figuring out fair prices for multi-energy trading than earlier research. A comparison between chance-constrained optimization outcomes obtained results is implemented utilizing Improved Sparrow Search Algorithm (ISSA), and without optimization techniques. The results show that recommended strategy for microgrid demand control is appropriate and workable. Fair electricity pricing practices are used to minimize energy costs for prosumers in residential, commercial, and industrial sectors. The suggested solution improves overall electricity bills for the home, company, and factory by 80.34%, 61.429%, and 54.069%, respectively. | en_US |
| dc.description.sponsorship | The financial support of the project "Application of dynamic system models for ensuring power substation systems cyber security", n. TS01020105, granted by the Technology Agency of the Czech Republic within the Theta programme, is gratefully acknowledged. | en_US |
| dc.format.extent | 1–28 | - |
| dc.format.medium | Electronic | - |
| dc.language | English | - |
| dc.language.iso | en_US | en_US |
| dc.rights | Licence for published version: Creative Commons Attribution 4.0 International | - |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
| dc.subject | Sparrow search algorithm (SSA) | en_US |
| dc.subject | chance constrained optimization | en_US |
| dc.subject | photovoltaic energy | en_US |
| dc.subject | wind turbine energy | en_US |
| dc.subject | batteries | en_US |
| dc.title | A novel peer-to-peer energy trading strategy for multi-microgrid loads scheduling based on chance-constrained | en_US |
| dc.type | Article | en_US |
| dc.date.dateAccepted | 2025-09-22 | - |
| dc.identifier.doi | https://doi.org/10.1016/j.nexus.2025.100536 | - |
| dc.relation.isPartOf | Energy Nexus | - |
| pubs.publication-status | Published | - |
| pubs.volume | 20 | - |
| dc.identifier.eissn | 2772-4271 | - |
| dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
| dcterms.dateAccepted | 2025-09-22 | - |
| dc.rights.holder | The Authors | - |
| dc.contributor.orcid | Bazooyar, Bahamin [0000-0002-7341-4509] | - |
| dc.identifier.number | 100536 | - |
| Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 17.04 MB | Adobe PDF | View/Open |
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