Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes
In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in var...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7448 |
| _version_ | 1827653045753741312 |
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| author | Liu Fei Muhammad Shahzad Fazal Abbas Hafiz Abdul Muqeet Muhammad Majid Hussain Li Bin |
| author_facet | Liu Fei Muhammad Shahzad Fazal Abbas Hafiz Abdul Muqeet Muhammad Majid Hussain Li Bin |
| author_sort | Liu Fei |
| collection | DOAJ |
| description | In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in various studies, but the analysis of the impact of dynamic DR on operational cost has been ignored. So, in this paper, renewable energy resources and storages are integrated considering the demand response strategies such as real-time pricing (RTP), critical peak pricing (CPP), and time of use (ToU). The proposed system is mapped in a linear model and simulated in MATLAB using linear programming (LP). Different case studies are investigated considering the dynamic demand response schemes. Among different schemes, results based on real-time pricing (58% saving) show more saving as compared to the CPP and ToU. The obtained results reduced the operational cost and greenhouse gas (GHG) emissions, which shows the efficacy of the model. |
| first_indexed | 2024-03-09T21:10:34Z |
| format | Article |
| id | doaj.art-e6c4e047c34848e5a440ad081d61c86b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:34Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e6c4e047c34848e5a440ad081d61c86b2023-11-23T21:49:20ZengMDPI AGSensors1424-82202022-09-012219744810.3390/s22197448Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response SchemesLiu Fei0Muhammad Shahzad1Fazal Abbas2Hafiz Abdul Muqeet3Muhammad Majid Hussain4Li Bin5School of Electric and Information Engineering, Tianjin University, Tianjin 300072, ChinaDepartment of Electrical Engineering, Muhammad Nawaz Sharif University of Engineering and Technology, Multan 60000, Punjab, PakistanDepartment of Electrical Engineering, Muhammad Nawaz Sharif University of Engineering and Technology, Multan 60000, Punjab, PakistanDepartment of Electrical Engineering Technology, Punjab Tianjin University of Technology, Lahore 54770, Punjab, PakistanElectrical, Electronic & Computer Engineering, School of Engineering and Physical Sciences Heriot-Watt University, Edinburgh EH14 4AS, UKSchool of Electrical and Electronics Engineering, North China Electric Power University, Beijing 100096, ChinaIn the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in various studies, but the analysis of the impact of dynamic DR on operational cost has been ignored. So, in this paper, renewable energy resources and storages are integrated considering the demand response strategies such as real-time pricing (RTP), critical peak pricing (CPP), and time of use (ToU). The proposed system is mapped in a linear model and simulated in MATLAB using linear programming (LP). Different case studies are investigated considering the dynamic demand response schemes. Among different schemes, results based on real-time pricing (58% saving) show more saving as compared to the CPP and ToU. The obtained results reduced the operational cost and greenhouse gas (GHG) emissions, which shows the efficacy of the model.https://www.mdpi.com/1424-8220/22/19/7448demand responseelectric vehicleenergy storage systemenergy management systemmicrogridssmart grid |
| spellingShingle | Liu Fei Muhammad Shahzad Fazal Abbas Hafiz Abdul Muqeet Muhammad Majid Hussain Li Bin Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes Sensors demand response electric vehicle energy storage system energy management system microgrids smart grid |
| title | Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes |
| title_full | Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes |
| title_fullStr | Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes |
| title_full_unstemmed | Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes |
| title_short | Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes |
| title_sort | optimal energy management system of iot enabled large building considering electric vehicle scheduling distributed resources and demand response schemes |
| topic | demand response electric vehicle energy storage system energy management system microgrids smart grid |
| url | https://www.mdpi.com/1424-8220/22/19/7448 |
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