Optimal sizing of behind-the-meter battery energy storage systems under optimal battery operation: A case study in Ireland

Abstract

The global shift toward a sustainable future has led to new policies to promote the use of renewable energy resources (RESs) in residential buildings. In Ireland, the number of installed roof-top photovoltaics (PVs) and home batteries, known as behind-the-meter (BTM) resources, has significantly increased in recent years, accelerated by grants administered by the Sustainable Energy Authority of Ireland (SEAI). In this work, an optimization tool that uses a combination of a Genetic Algorithm (GA) and a multi-objective mixed-integer linear programming (MOMILP) model is developed. This tool integrates an optimal battery control scheme from the MOMILP model aiming to minimize electricity bills and battery degradation to optimize the sizing of BTM batteries with minimum installation costs. Various scenarios have been simulated to assess the potential of the proposed model under different metering and billing systems, and optimization constraints. Compared to a case with a rule-based optimization model, results show that our optimization model can save 5.3 % more on annual bill costs while keeping battery degradation to a minimum of 2.1 % per year.