A novel framework for optimization of size and control strategy of lithium-ion battery based off-grid renewable energy systems
•Novel optimization method for off-grid renewable installations is presented.•Results are compared with an installed PV-battery system.•At least 9.7% reduction in the lifetime cost of the system is achieved.•Sensitivity analysis is performed to consider Li-ion battery price uncertainties. This paper...
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Published in | Energy conversion and management Vol. 175; pp. 99 - 111 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Elsevier Ltd
01.11.2018
Elsevier Science Ltd |
Subjects | |
Online Access | Get full text |
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Summary: | •Novel optimization method for off-grid renewable installations is presented.•Results are compared with an installed PV-battery system.•At least 9.7% reduction in the lifetime cost of the system is achieved.•Sensitivity analysis is performed to consider Li-ion battery price uncertainties.
This paper proposes a new methodology to find the most economic system configuration and energy management strategy for Li-ion battery based off-grid renewable energy systems. A system level macroscopic model and a microscopic battery lifetime prediction model are incorporated into the optimization framework to simulate hourly performance of the system. Due to the computational efficiency of the model, optimization is carried out using enumerative method (evaluating all the possible combinations of components and control strategies) to ensure finding the global optimum solution of the problem. To investigate the effectiveness of the proposed methodology, the optimization results are compared with a baseline scenario which is an installed PV-battery system to provide electricity for an isolated house situated near Zaragoza, Spain. Results indicate that the optimized scenario leads to 9.7% reduction in the levelized cost of energy and 48.6% improvement in the battery service period in comparison with the baseline scenario. Moreover, by considering a 0.5% unmet load, the economic feasibility of the system and the battery longevity are enhanced to 14.6% and 78.4%, respectively. Finally, to evaluate the effect of battery unit price and future trends on the optimization results, sensitivity analysis is performed. |
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ISSN: | 0196-8904 1879-2227 |
DOI: | 10.1016/j.enconman.2018.08.107 |