Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm
Coordinated energy scheduling and management strategies in the energy hub plan are essential to achieve optimal economic performance. In this paper, the scheduling and management framework of an energy hub (EH) is presented with the aim of energy profit maximization in partnership with electricity,...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/3526 |
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| author | Ali S. Alghamdi Mohana Alanazi Abdulaziz Alanazi Yazeed Qasaymeh Muhammad Zubair Ahmed Bilal Awan Muhammad Gul Bahar Ashiq |
| author_facet | Ali S. Alghamdi Mohana Alanazi Abdulaziz Alanazi Yazeed Qasaymeh Muhammad Zubair Ahmed Bilal Awan Muhammad Gul Bahar Ashiq |
| author_sort | Ali S. Alghamdi |
| collection | DOAJ |
| description | Coordinated energy scheduling and management strategies in the energy hub plan are essential to achieve optimal economic performance. In this paper, the scheduling and management framework of an energy hub (EH) is presented with the aim of energy profit maximization in partnership with electricity, natural gas, and district heating networks (EGHNs) considering the coordinated multi-energy management based on the day-ahead market. The optimum capacity of EH equipment, including photovoltaic and wind renewable energy sources, a combined heat and power system (CHP), a boiler, energy storage, and electric vehicles is determined in the day-ahead market using the improved Fick’s law algorithm (IFLA), considering the energy profit maximization and also satisfying the linear network and hub constraints. The conventional FLA is inspired by the concept of Fick’s diffusion law, and, in this study, its performance against premature convergence is improved by using Rosenbrock’s direct rotational method. The performance of the IFLA when applied to EH coordinated scheduling and management problems with the aim of profit maximization is compared with the conventional FLA, particle swarm optimization (PSO), and manta ray foraging optimization (MRFO) methods. The results show that the proposed scheduling and multi-energy management framework achieves more energy profit in the day-ahead electricity, gas, and heating markets by satisfying the operation and EH constraints compared to other methods. Furthermore, according to the findings, the increased (decreased) demand and the forced outage rate caused a decrease (increase) in the EH profit. The results show the effectiveness of the proposed framework to obtain the EH maximum energy profit in the day-ahead market. |
| first_indexed | 2024-03-11T06:59:14Z |
| format | Article |
| id | doaj.art-1983141226694fa1907c720173cd0d36 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:59:14Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1983141226694fa1907c720173cd0d362023-11-17T09:22:58ZengMDPI AGApplied Sciences2076-34172023-03-01136352610.3390/app13063526Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law AlgorithmAli S. Alghamdi0Mohana Alanazi1Abdulaziz Alanazi2Yazeed Qasaymeh3Muhammad Zubair4Ahmed Bilal Awan5Muhammad Gul Bahar Ashiq6Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72341, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Northern Border University, Ar’Ar 73222, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi ArabiaDepartment of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman 20550, United Arab EmiratesDepartment of Physics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaCoordinated energy scheduling and management strategies in the energy hub plan are essential to achieve optimal economic performance. In this paper, the scheduling and management framework of an energy hub (EH) is presented with the aim of energy profit maximization in partnership with electricity, natural gas, and district heating networks (EGHNs) considering the coordinated multi-energy management based on the day-ahead market. The optimum capacity of EH equipment, including photovoltaic and wind renewable energy sources, a combined heat and power system (CHP), a boiler, energy storage, and electric vehicles is determined in the day-ahead market using the improved Fick’s law algorithm (IFLA), considering the energy profit maximization and also satisfying the linear network and hub constraints. The conventional FLA is inspired by the concept of Fick’s diffusion law, and, in this study, its performance against premature convergence is improved by using Rosenbrock’s direct rotational method. The performance of the IFLA when applied to EH coordinated scheduling and management problems with the aim of profit maximization is compared with the conventional FLA, particle swarm optimization (PSO), and manta ray foraging optimization (MRFO) methods. The results show that the proposed scheduling and multi-energy management framework achieves more energy profit in the day-ahead electricity, gas, and heating markets by satisfying the operation and EH constraints compared to other methods. Furthermore, according to the findings, the increased (decreased) demand and the forced outage rate caused a decrease (increase) in the EH profit. The results show the effectiveness of the proposed framework to obtain the EH maximum energy profit in the day-ahead market.https://www.mdpi.com/2076-3417/13/6/3526optimal energy hub planningscheduled and multi-energy managementenergy profitimproved Fick’s law algorithmRosenbrock’s direct rotational method |
| spellingShingle | Ali S. Alghamdi Mohana Alanazi Abdulaziz Alanazi Yazeed Qasaymeh Muhammad Zubair Ahmed Bilal Awan Muhammad Gul Bahar Ashiq Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm Applied Sciences optimal energy hub planning scheduled and multi-energy management energy profit improved Fick’s law algorithm Rosenbrock’s direct rotational method |
| title | Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm |
| title_full | Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm |
| title_fullStr | Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm |
| title_full_unstemmed | Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm |
| title_short | Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm |
| title_sort | energy hub optimal scheduling and management in the day ahead market considering renewable energy sources chp electric vehicles and storage systems using improved fick s law algorithm |
| topic | optimal energy hub planning scheduled and multi-energy management energy profit improved Fick’s law algorithm Rosenbrock’s direct rotational method |
| url | https://www.mdpi.com/2076-3417/13/6/3526 |
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