Optimizing the energy storage schedule of a battery in a PV grid-connected nZEB using linear programming

Abstract

Abstract Photovoltaic (PV) technology is highly adopted within buildings, as it is proven for reducing electricity bills. However, with the 2010/31/EU directive all new buildings shall be nearly Zero Energy Buildings (nZEB) from 2020 onward, with the requirement to maintain their energy consumption at low levels. For further embedding the nZEB concept in an integrated, holistic and efficient energy system, to overcome any application problems, one should not only focus on building energy efficiency designs, but also on smart and effective energy management techniques. For instance, as energy storage may contribute a key solution towards nZEB, a novel approach able to adapt to a given PV generation and load demand and individually control the battery and the net grid energy, is presented. This is achieved through Linear Programming (LP), a convex optimization tool, along with a weighted sum approach. Using real data, simulation results demonstrate that, choosing the right weight values based on the given generation and demand profiles, the LP model controls the building’s import energy, export energy and the battery accordingly. Hence, the net grid electrical energy is maintained to the minimum possible level. Finally, the LP model is crossed-checked with the freeware System Advisor Model (SAM) showing a normalized Root Mean Squared Error (nRMSE) of 2.10% for the annual battery dispatch. The analysis shows that the LP model combined with SAM, for addressing the non-linearity of the storage and to account for the power conversion losses, gives a lower annual net grid energy use than SAM’s automated target controller by 2.0%.