This paper presents a novel battery degradation cost (BDC) model for lithium-ion batteries (LIBs) based on accurately estimating the battery lifetime. For this purpose, a linear cycle counting algorithm is devised to estimate the battery cycle aging. In this algorithm, the local maximum and minimum values of the profile of the battery state of charge are identified by the proposed linear formulations. Then, the battery cycle aging due to the complete and incomplete cycles is determined. In this step, the battery cycle aging during an incomplete cycle is calculated by converting it to two complete cycles. After that, the calendar aging process of the LIB is linearly formulated based on the semi-empirical model to estimate the BDC accurately. After linearizing the LIB degradation process, a mechanism for computing the BDC during the scheduling horizon is designed by modeling the BDC as a series of equal payments over the LIB lifetime. In order to incorporate the BDC in the battery energy management problem, an iterative algorithm is presented for efficiently calculating the BDC associated with the adopted charging/discharging strategy. The numerical simulation results indicate that integrating the battery degradation process into the battery scheduling problem can reduce the amount of the battery capacity fading by 32.81%, as well as increase the profit of battery owners by 1.21%. Moreover, the conducted analyses highlight the importance of considering the LIB calendar aging process in determining the optimal LIB capacity.INDEX TERMS Battery degradation cost (BDC), cycle aging process, calendar aging process, battery scheduling problem, wind energy.
This study proposes a novel predictive energy management strategy to integrate the battery energy storage (BES) degradation cost into the BES scheduling problem and address the uncertainty in the energy management problem. As the first step, the factors affecting the BES calendar aging and cycle aging are linearly modelled. Furthermore, a linear algorithm is provided to calculate the BES cycle aging due to the BES complete and incomplete cycles. Subsequently, a novel approach to estimating the BES degradation cost function according to the BES specifications and degradation process is presented. Finally, taking into account the BES degradation cost model, the proposed predictive energy management strategy framework is implemented on an integrated photovoltaic and BES system to evaluate the applicability and efficiency of the proposed scheme in integrating the BES degradation cost in the energy management problem. The numerical simulation results indicate that integrating the BES degradation cost into the energy management problem significantly affects the BES charge/discharge strategy. Moreover, comparing the proposed predictive energy management strategy with a simple one, it is verified that the provided approach could decrease the BES capacity fade and degradation cost by 5.06% and 4.67%, respectively, and increase the photovoltaic farm profit by 1.10%.
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