Optimal PV-BESS Systems Sizing Algorithm With a Novel Price-Driven Rule-Based Energy Management Strategy

In recent years, combined optimisation of Photovoltaic (PV) and Battery Energy Storage Systems (BESS) has attracted remarkable attention both in academic and industrial world, as it provides a twofold solution to increase prosumers self-consumption and mitigate issues related to high BESS capital costs. In this context, this work proposes a novel optimal sizing algorithm for PV-BESS systems, based on a two-level architecture. In the upper level, a Genetic Algorithm (GA) determines the optimal rated PV power, BESS power and storage capacity, with the objective of minimising the Levelised Cost of Energy (LCOE). In the lower level, a novel rule-based Energy Management System (EMS) is embedded, with rules being written for dynamic retailer and feed-in tariffs. In particular, electricity buy and sell price thresholds are also optimised by the GA. The proposed algorithm is eventually tested in a reality-based case study, with real data from a commercial prosumer located in Zaragoza, Spain, where the proposed solution is benchmarked against an optimal sizing algorithm composed of a GA in the upper level, and a Mixed Integer Linear Programming (MILP) EMS in the lower level.