Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets
An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is dis...
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2024-11-01
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| author | Sina Parhoudeh Pablo Eguía López Abdollah Kavousi Fard |
| author_facet | Sina Parhoudeh Pablo Eguía López Abdollah Kavousi Fard |
| author_sort | Sina Parhoudeh |
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| description | An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction. |
| format | Article |
| id | doaj-art-dbff16333dd141fb8dc2ea5b05bc65bd |
| institution | DOAJ |
| issn | 2624-6511 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Smart Cities |
| spelling | doaj-art-dbff16333dd141fb8dc2ea5b05bc65bd2025-08-20T02:43:46ZengMDPI AGSmart Cities2624-65112024-11-01763587361510.3390/smartcities7060139Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve MarketsSina Parhoudeh0Pablo Eguía López1Abdollah Kavousi Fard2Department of Electrical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Plaza Ingeniero Torres Quevedo, 1, 48013 Bilbao, SpainDepartment of Electrical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Plaza Ingeniero Torres Quevedo, 1, 48013 Bilbao, SpainDepartment of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, IranAn Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction.https://www.mdpi.com/2624-6511/7/6/139smart energyenergy marketreserve marketreactive power marketsmart energy hubflexibility |
| spellingShingle | Sina Parhoudeh Pablo Eguía López Abdollah Kavousi Fard Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets Smart Cities smart energy energy market reserve market reactive power market smart energy hub flexibility |
| title | Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets |
| title_full | Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets |
| title_fullStr | Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets |
| title_full_unstemmed | Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets |
| title_short | Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets |
| title_sort | stochastic scheduling of grid connected smart energy hubs participating in the day ahead energy reactive power and reserve markets |
| topic | smart energy energy market reserve market reactive power market smart energy hub flexibility |
| url | https://www.mdpi.com/2624-6511/7/6/139 |
| work_keys_str_mv | AT sinaparhoudeh stochasticschedulingofgridconnectedsmartenergyhubsparticipatinginthedayaheadenergyreactivepowerandreservemarkets AT pabloeguialopez stochasticschedulingofgridconnectedsmartenergyhubsparticipatinginthedayaheadenergyreactivepowerandreservemarkets AT abdollahkavousifard stochasticschedulingofgridconnectedsmartenergyhubsparticipatinginthedayaheadenergyreactivepowerandreservemarkets |