An Analysis of Multi Objective Energy Scheduling in PV-BESS System under Prediction Uncertainty

Energy storage systems (ESSs) are being considered to overcome issues in modern grids, caused by increasing penetration of renewable generation. Nevertheless, integration of ESS should also be supplemented with an optimal energy management framework to ensure maximum benefits from ESS. Conventional energy management of battery, used with PV system, maximises self-consumption but does not mitigate grid congestion or address battery degradation. Model predictive control (MPC) can alleviate congestion, degradation while maximizing self-consumption. As such, studies will be carried out, in this work, to highlight the improvement with MPC based energy management over conventional method using simulations of one-year system behaviour. As MPC uses forecast information in decision making, the impact of forecast uncertainties will be assessed and addressing the same through constraint tightening will be presented. It is concluded that MPC provides improvement in system behaviour over multiple performance criteria.This work is done as part of project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 675318 (INCITE). This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Projects CICYT RTI2018-094665-B-I00, DOVELAR ref. RTI2018-096001-B-C32 (MCIU/AEI/FEDER, UE) and Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656), and by the Generalitat de Catalunya through the Project 2017 SGR 48