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Open AccessFeature PaperArticle

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

1
Energy Research Institute, University of Kashan, Kashan 8731751167, Iran
2
Department of Electrical Engineering, College of Engineering, University of Tehran, Tehran 1193653471, Iran
3
Department of Electrical Engineering, Tarbiat Modares University, Tehran 1193653471, Iran
4
School of Electrical Engineering Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia
5
UMR CNRS 6026 IRDL, University of Brest, 29238 Brest, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2774; https://doi.org/10.3390/app10082774
Received: 21 March 2020 / Revised: 9 April 2020 / Accepted: 13 April 2020 / Published: 16 April 2020
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. View Full-Text
Keywords: prosumer; energy management system (EMS); energy storage system (ESS); plug-in hybrid electric vehicle (PHEV); day-ahead optimization; battery depreciation; feed-forward artificial neural network (FF-ANN); weather prediction prosumer; energy management system (EMS); energy storage system (ESS); plug-in hybrid electric vehicle (PHEV); day-ahead optimization; battery depreciation; feed-forward artificial neural network (FF-ANN); weather prediction
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Faraji, J.; Abazari, A.; Babaei, M.; Muyeen, S.M.; Benbouzid, M. Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources. Appl. Sci. 2020, 10, 2774.

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