A stochastic method for behind-the-meter PV-battery energy storage systems sizing with degradation minimization by limiting battery cycling
Mostafa Rezaeimozafar, Enda Barrett, Rory F.D. Monaghan, Maeve Duffy
Abstract
Global desire for a sustainable future has led to the implementation of new policies to promote the use of behind-the-meter (BTM) photovoltaic (PV)-battery energy storage systems (BESSs) for power system end-users. The PV-BESS control problem has received a lot of attention so far, whereas the optimal system design – which is vital for achieving efficient performance – has not received as much. In this work, a multi-objective optimization tool using the scenario-based two-stage stochastic optimization technique is proposed for optimal sizing of residential BTM PV-battery systems under optimal operational planning. An objective function is formulated to simultaneously minimize the cost of system installation, expected annual electricity bill cost and the frequency of damaging deep cycles for the battery. Battery cycle control is achieved using a novel model that effectively controls the battery capacity over the optimization period so that the number of deep cycles is minimized. This model is not limited to a particular battery technology and can be used for any type of energy storage system with the cyclic ageing feature. The presented study provides a system design that is comparable to that from optimization with deterministic data when compared to two different case studies.