Abstract
This work proposes a hierarchical control based power management strategy exploiting an adaptive Power Pinch analysis algorithm. The power pinch analysis is aided via the insight-based graphical power grand composite curve (PGCC) of the hybrid energy storage system's (HESS) model. This results in the identification of an optimal power management strategy (PMS), effected at the beginning of a control horizon on the HESS. However, a recent study showed that weather and load uncertainty distorts the desired shaped PGCC and consequently leads to the violation of the energy storage's state of charge operating set points. In this work in order to negate the effect of uncertainty, the current output state is utilized as a feedback control. The PGCC is shaped within a receding horizon model predictive control framework. The PGCC re-computation ensues only if the error variance, due to uncertainty, between the real and the estimated battery's state of charge (SOAcc <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BAT</sub> ) is greater than 5%. The proposed method is evaluated against the standard or Day-Ahead pinch analysis open loop strategy and shows a reduction in over discharging and overcharging of the battery and fossil fuel emission impact by 15%, 44.97%, and 8.8% respectively.
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