Control of Battery Storage Systems in Residential Grids: Model-based vs. Data-Driven Approaches

Abstract

In this paper, control of Battery Storage Systems (BSS) in power distribution grids with residential consumers as well as prosumers equipped with rooftop photovoltaic (PV) solar panels and Electric Vehicles (EV) is addressed. Different features of these Distributed Energy Resources (DERs), such as intermittent behaviour and the difference between the maximum generation time and the maximum demand, have caused several issues for electricity distributors in delivering high quality power. Smart control and scheduling of ESS and EVs is a promising approach to protect the grid against extra power injection from prosumers during day times while the benefit of household owners from DERs are still achieved. In this context, the performance of model-based controllers such as model predictive controllers (MPC) is compared with model-free data driven controllers (DDC) considering different complex scenarios that may happen in a distribution grid. The control objective is to minimize the difference between the net power exchanged with the main grid from the estimated average net load of prosumers. Our study on the real consumption data of about 40 residential consumers/prosumers in Victoria, Australia, demonstrates the strength of data-driven control approaches to deal with the complex environment of power distribution grids in the presence of DERs.