Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources
In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying econom...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/8/2774 |
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| author | Jamal Faraji Ahmadreza Abazari Masoud Babaei S. M. Muyeen Mohamed Benbouzid |
| author_facet | Jamal Faraji Ahmadreza Abazari Masoud Babaei S. M. Muyeen Mohamed Benbouzid |
| author_sort | Jamal Faraji |
| collection | DOAJ |
| description | In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647. |
| first_indexed | 2024-03-10T20:25:39Z |
| format | Article |
| id | doaj.art-e6317c57050c4bba8d6d4418c0c6990c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:25:39Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e6317c57050c4bba8d6d4418c0c6990c2023-11-19T21:51:08ZengMDPI AGApplied Sciences2076-34172020-04-01108277410.3390/app10082774Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy SourcesJamal Faraji0Ahmadreza Abazari1Masoud Babaei2S. M. Muyeen3Mohamed Benbouzid4Energy Research Institute, University of Kashan, Kashan 8731751167, IranDepartment of Electrical Engineering, College of Engineering, University of Tehran, Tehran 1193653471, IranDepartment of Electrical Engineering, Tarbiat Modares University, Tehran 1193653471, IranSchool of Electrical Engineering Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, AustraliaUMR CNRS 6026 IRDL, University of Brest, 29238 Brest, FranceIn recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.https://www.mdpi.com/2076-3417/10/8/2774prosumerenergy management system (EMS)energy storage system (ESS)plug-in hybrid electric vehicle (PHEV)day-ahead optimizationbattery depreciation |
| spellingShingle | Jamal Faraji Ahmadreza Abazari Masoud Babaei S. M. Muyeen Mohamed Benbouzid Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources Applied Sciences prosumer energy management system (EMS) energy storage system (ESS) plug-in hybrid electric vehicle (PHEV) day-ahead optimization battery depreciation |
| title | Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources |
| title_full | Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources |
| title_fullStr | Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources |
| title_full_unstemmed | Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources |
| title_short | Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources |
| title_sort | day ahead optimization of prosumer considering battery depreciation and weather prediction for renewable energy sources |
| topic | prosumer energy management system (EMS) energy storage system (ESS) plug-in hybrid electric vehicle (PHEV) day-ahead optimization battery depreciation |
| url | https://www.mdpi.com/2076-3417/10/8/2774 |
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