Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response
Abstract Multi-Energy Microgrids (ME-MGs) represent an integrated and advanced energy system, playing a vital role in delivering optimal and sustainable energy solutions in modern societies. These systems combine various energy sources, such as electricity, heat, and storage systems, to ensure effic...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88776-w |
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| author | Genfu Xiao Huan Liu Javad Nabatalizadeh |
| author_facet | Genfu Xiao Huan Liu Javad Nabatalizadeh |
| author_sort | Genfu Xiao |
| collection | DOAJ |
| description | Abstract Multi-Energy Microgrids (ME-MGs) represent an integrated and advanced energy system, playing a vital role in delivering optimal and sustainable energy solutions in modern societies. These systems combine various energy sources, such as electricity, heat, and storage systems, to ensure efficient resource management and operation. One of the primary challenges in managing ME-MGs is reducing operational costs and emissions while addressing uncertainties. This study investigates the optimization and energy management (EM) in ME-MGs through the application of the Multi-Objective Walrus Optimization Algorithm (MOWaOA) combined with fuzzy decision-making techniques. The main objective of the research is to minimize operational costs and emissions in the face of uncertain conditions. To achieve this goal, multiple scenarios were analyzed, including EM without considering demand response and electric vehicles, EM with the inclusion of these factors, and EM under uncertain conditions. The results demonstrated that integrating electric vehicles and demand response into microgrid EM led to a 15.6% reduction in operational costs and a 12.8% decrease in emissions compared to scenarios where these factors were excluded. Furthermore, when uncertainties were accounted for, operational costs increased by 2.1% and emissions rose by 1.2%. This increase emphasizes the significance of employing more precise management techniques and advanced strategies to effectively address uncertainties in ME-MGs. |
| format | Article |
| id | doaj-art-0e08b3183a5a483787a71c1bbd7681ef |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0e08b3183a5a483787a71c1bbd7681ef2025-08-20T03:00:59ZengNature PortfolioScientific Reports2045-23222025-02-0115112910.1038/s41598-025-88776-wOptimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand responseGenfu Xiao0Huan Liu1Javad Nabatalizadeh2College of Mechanical and Electronic, Jinggangshan UniversityCollege of Electronic and Information Engineering, Jinggangshan UniversityDepartment of Electrical Engineering, Islamic Azad University, Ardabil branchAbstract Multi-Energy Microgrids (ME-MGs) represent an integrated and advanced energy system, playing a vital role in delivering optimal and sustainable energy solutions in modern societies. These systems combine various energy sources, such as electricity, heat, and storage systems, to ensure efficient resource management and operation. One of the primary challenges in managing ME-MGs is reducing operational costs and emissions while addressing uncertainties. This study investigates the optimization and energy management (EM) in ME-MGs through the application of the Multi-Objective Walrus Optimization Algorithm (MOWaOA) combined with fuzzy decision-making techniques. The main objective of the research is to minimize operational costs and emissions in the face of uncertain conditions. To achieve this goal, multiple scenarios were analyzed, including EM without considering demand response and electric vehicles, EM with the inclusion of these factors, and EM under uncertain conditions. The results demonstrated that integrating electric vehicles and demand response into microgrid EM led to a 15.6% reduction in operational costs and a 12.8% decrease in emissions compared to scenarios where these factors were excluded. Furthermore, when uncertainties were accounted for, operational costs increased by 2.1% and emissions rose by 1.2%. This increase emphasizes the significance of employing more precise management techniques and advanced strategies to effectively address uncertainties in ME-MGs.https://doi.org/10.1038/s41598-025-88776-wMulti-energy MicrogridEnergy ManagementWalrus optimization AlgorithmFuzzy decision-makingElectric vehiclesDemand response |
| spellingShingle | Genfu Xiao Huan Liu Javad Nabatalizadeh Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response Scientific Reports Multi-energy Microgrid Energy Management Walrus optimization Algorithm Fuzzy decision-making Electric vehicles Demand response |
| title | Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response |
| title_full | Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response |
| title_fullStr | Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response |
| title_full_unstemmed | Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response |
| title_short | Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response |
| title_sort | optimal scheduling and energy management of a multi energy microgrid with electric vehicles incorporating decision making approach and demand response |
| topic | Multi-energy Microgrid Energy Management Walrus optimization Algorithm Fuzzy decision-making Electric vehicles Demand response |
| url | https://doi.org/10.1038/s41598-025-88776-w |
| work_keys_str_mv | AT genfuxiao optimalschedulingandenergymanagementofamultienergymicrogridwithelectricvehiclesincorporatingdecisionmakingapproachanddemandresponse AT huanliu optimalschedulingandenergymanagementofamultienergymicrogridwithelectricvehiclesincorporatingdecisionmakingapproachanddemandresponse AT javadnabatalizadeh optimalschedulingandenergymanagementofamultienergymicrogridwithelectricvehiclesincorporatingdecisionmakingapproachanddemandresponse |