Demand-Profile Effects on PV Self-Consumption and Battery Storage Performance: Five Mediterranean Case Studies

This study evaluates photovoltaic (PV) self-production with and without battery energy storage for five contrasting consumer profiles under the meteorological conditions considered for Crete, Greece: a university campus, an aquarium, an industrial facility, a residential group and a travel agency. Annual chronological simulations at hourly resolution assess demand coverage, surplus generation and economic performance over a 25-year evaluation period, with no revenue assigned to surplus electricity. The model includes battery charging and discharging power limits, annual degradation rates of 0.5% for PV power capacity and 2% for battery energy capacity, and equipment replacement assumptions. Normalised generation and storage parameters facilitate comparison across cases. A one-at-a-time sensitivity analysis examines six technical and economic parameters for one selected configuration per consumer. Without storage, the tested configurations supply 36.1–45.8% of annual demand at the university, 36.5% at the aquarium, 47.7–67.3% at the industrial facility, 24.2–29.2% in the residential group and 50.2–55.1% at the travel agency. Among the reported configurations, those achieving the highest mean annual demand coverage in each case supply 89.5–94.6% of demand, while retaining surplus fractions of 20.1–77.1%. In the residential case, increasing PV capacity from 500 to 1000 kW at a fixed storage capacity of 1000 kWh raises coverage by only 2.6 percentage points, while surplus increases from 55.3% to 77.1%. Degradation affects useful supply differently from nominal asset performance: surplus generation buffers the decline in direct PV supply, whereas battery degradation generally drives the reduction in total coverage. Economic outcomes are particularly sensitive to electricity purchase price and demand level, with the residential and travel agency configurations showing greater dependence on favorable economic assumptions. The findings support joint PV–battery sizing based on demand timing and marginal useful energy gains, rather than annual energy matching or maximum coverage alone.