Probabilistic optimization of coordinated fuel Cell-CHP and renewable energy policy in microgrid integrated with hydrogen storage for optimizing system profitability
With the growing adoption of renewable energy resources, microgrids (MGs) are increasingly relying on these sources to meet their electrical load demands. To ensure efficient operation, these units must be scheduled in a coordinated manner. This paper introduces a stochastic model for the joint sche...
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| Published in: | International journal of hydrogen energy Vol. 102; pp. 129 - 145 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
10.02.2025
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| ISSN: | 0360-3199 |
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| Abstract | With the growing adoption of renewable energy resources, microgrids (MGs) are increasingly relying on these sources to meet their electrical load demands. To ensure efficient operation, these units must be scheduled in a coordinated manner. This paper introduces a stochastic model for the joint scheduling of renewable and thermal energy units. The study focuses on integrating proton exchange membrane fuel cell-based combined heat and power (PEMFC-CHP) units, wind turbines (WTs), and photovoltaic (PV) systems within the scheduling framework. Additionally, hydrogen storage strategies are incorporated into the PEMFC-CHP operations. The model addresses uncertainties such as wind speed, solar irradiance, and market price fluctuations using a scenario-based approach. The proposed method formulates the scheduling problem as a mixed-integer nonlinear programming (MINP) model, incorporating hydrogen storage strategies. The inherent uncertainties in these parameters further transform the problem into a stochastic MINP. Coordinated scheduling of renewable and thermal units within microgrids not only optimizes resource utilization but also enhances the overall objective function value. These advancements align with current energy policy objectives aimed at improving the efficiency and sustainability of energy systems. To solve the complex scheduling problem, this research employs an Improved Salp Swarm Algorithm (ISSA), which has been tested on a modified 33-bus distribution network. Simulation results indicate that the ISSA achieves over 5% higher revenue compared to other optimization methods. Furthermore, incorporating combined heat and power (CHP) operations increases the total system profit by more than 15%. The algorithm achieved the optimal result in 28 out of 30 attempts. The ISSA algorithm, with a remarkable success rate of 95.28%, demonstrates its effectiveness in minimizing MG operating costs, a critical component of energy policy goals.
•A stochastic model is created for coordinated scheduling of renewable energy.•Including uncertainties in the model improves overall profitability.•The role of combined heat and power systems is analyzed in the model.•Hydrogen storage supports fuel cell operation in the proposed framework.•Profit maximization of the microgrid is the main objective. |
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| AbstractList | With the growing adoption of renewable energy resources, microgrids (MGs) are increasingly relying on these sources to meet their electrical load demands. To ensure efficient operation, these units must be scheduled in a coordinated manner. This paper introduces a stochastic model for the joint scheduling of renewable and thermal energy units. The study focuses on integrating proton exchange membrane fuel cell-based combined heat and power (PEMFC-CHP) units, wind turbines (WTs), and photovoltaic (PV) systems within the scheduling framework. Additionally, hydrogen storage strategies are incorporated into the PEMFC-CHP operations. The model addresses uncertainties such as wind speed, solar irradiance, and market price fluctuations using a scenario-based approach. The proposed method formulates the scheduling problem as a mixed-integer nonlinear programming (MINP) model, incorporating hydrogen storage strategies. The inherent uncertainties in these parameters further transform the problem into a stochastic MINP. Coordinated scheduling of renewable and thermal units within microgrids not only optimizes resource utilization but also enhances the overall objective function value. These advancements align with current energy policy objectives aimed at improving the efficiency and sustainability of energy systems. To solve the complex scheduling problem, this research employs an Improved Salp Swarm Algorithm (ISSA), which has been tested on a modified 33-bus distribution network. Simulation results indicate that the ISSA achieves over 5% higher revenue compared to other optimization methods. Furthermore, incorporating combined heat and power (CHP) operations increases the total system profit by more than 15%. The algorithm achieved the optimal result in 28 out of 30 attempts. The ISSA algorithm, with a remarkable success rate of 95.28%, demonstrates its effectiveness in minimizing MG operating costs, a critical component of energy policy goals.
•A stochastic model is created for coordinated scheduling of renewable energy.•Including uncertainties in the model improves overall profitability.•The role of combined heat and power systems is analyzed in the model.•Hydrogen storage supports fuel cell operation in the proposed framework.•Profit maximization of the microgrid is the main objective. |
| Author | Li, Siwei Tian, Congxiang Faraji, Hamid |
| Author_xml | – sequence: 1 givenname: Siwei surname: Li fullname: Li, Siwei organization: Yangtze University College of Arts and Sciences, Jingzhou, 434100, China – sequence: 2 givenname: Congxiang orcidid: 0000-0001-6372-7176 surname: Tian fullname: Tian, Congxiang email: 703216@yangtzeu.edu.cn organization: Yangtze University College of Arts and Sciences, Jingzhou, 434100, China – sequence: 3 givenname: Hamid surname: Faraji fullname: Faraji, Hamid organization: Department of Electrical Engineering, Institute of Science and Engineering, Istanbul University, Istanbul, Turkey |
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| Keywords | Hydrogen storage Microgrid Scheduling techniques Stochastic optimization Combined heat and power Optimization problem |
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