Optimisation of photovoltaic and battery systems for cost-effective energy solutions in commercial buildings

This study investigates the optimisation of photovoltaic (PV) and battery energy storage systems (BESS) for commercial buildings in the UK, addressing the need for cost-effective energy solutions and the challenge of ensuring financial viability. A mixed-integer linear programming (MILP) model is developed to simultaneously optimise the design and operation of PV-BESS systems, focusing on minimising the 15-year net present cost. In doing so, the model comprehensively assesses a wide range of relevant factors, including electricity market dynamics, weather conditions, technology performance and costs, energy demand, and building-specific characteristics. The optimisation model is demonstrated through a case study informed by an actual commercial building in the UK. The results indicate that a combination of mono-crystalline silicon PV modules and lithium iron phosphate (LFP) batteries yields the optimal solution, providing about 46% of the building's annual energy demand. The optimised system successfully achieves a balanced trade-off between cost and technical performance, offering a sensible payback period of 5.5 years and a 15-year NPV of £303.8k, resulting in 20% cost savings compared to the business-as-usual (BaU) scenario. The sensitivity analysis shows that high electricity prices lead to better financial outcomes, while higher energy storage costs reduce system viability. This work provides a practical framework for evaluating the design, operation, and financial viability of PV-BESS systems, while delivering actionable insights to support the broader adoption of these technologies. • MILP model optimises PV-battery system design and operation. • PV and battery installation achieve 46% annual energy demand coverage. • Mono-Si PV and LFP battery yield 5.5-year payback, £303.8k NPV over 15 years. • Sensitivity analysis shows high electricity prices enhances financial outcomes. • Degradation has minimal impact on the optimal system performance.