IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid
The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid sys...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Smart Cities |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6511/6/5/101 |
| _version_ | 1797572247706140672 |
|---|---|
| author | Faiza Qayyum Harun Jamil Naeem Iqbal Do-Hyeun Kim |
| author_facet | Faiza Qayyum Harun Jamil Naeem Iqbal Do-Hyeun Kim |
| author_sort | Faiza Qayyum |
| collection | DOAJ |
| description | The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance. |
| first_indexed | 2024-03-10T20:53:33Z |
| format | Article |
| id | doaj.art-44a351e17cdd41238f0b0e7f4bac37da |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-10T20:53:33Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-44a351e17cdd41238f0b0e7f4bac37da2023-11-19T18:06:46ZengMDPI AGSmart Cities2624-65112023-08-01652196222010.3390/smartcities6050101IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in NanogridFaiza Qayyum0Harun Jamil1Naeem Iqbal2Do-Hyeun Kim3Department of Computer Engineering, Jeju National University, Jeju-si 63243, Republic of KoreaBig Data Research Center, Jeju National University, Jeju-si 63243, Republic of KoreaBig Data Research Center, Jeju National University, Jeju-si 63243, Republic of KoreaDepartment of Computer Engineering, Jeju National University, Jeju-si 63243, Republic of KoreaThe Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance.https://www.mdpi.com/2624-6511/6/5/101Internet of Thingscomplex problem solvingcritical IoT systemsnanogridoptimizationtask modeling |
| spellingShingle | Faiza Qayyum Harun Jamil Naeem Iqbal Do-Hyeun Kim IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid Smart Cities Internet of Things complex problem solving critical IoT systems nanogrid optimization task modeling |
| title | IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid |
| title_full | IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid |
| title_fullStr | IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid |
| title_full_unstemmed | IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid |
| title_short | IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid |
| title_sort | iot orchestration based optimal energy cost decision mechanism with ess power optimization for peer to peer energy trading in nanogrid |
| topic | Internet of Things complex problem solving critical IoT systems nanogrid optimization task modeling |
| url | https://www.mdpi.com/2624-6511/6/5/101 |
| work_keys_str_mv | AT faizaqayyum iotorchestrationbasedoptimalenergycostdecisionmechanismwithesspoweroptimizationforpeertopeerenergytradinginnanogrid AT harunjamil iotorchestrationbasedoptimalenergycostdecisionmechanismwithesspoweroptimizationforpeertopeerenergytradinginnanogrid AT naeemiqbal iotorchestrationbasedoptimalenergycostdecisionmechanismwithesspoweroptimizationforpeertopeerenergytradinginnanogrid AT dohyeunkim iotorchestrationbasedoptimalenergycostdecisionmechanismwithesspoweroptimizationforpeertopeerenergytradinginnanogrid |