Enhancing renewable energy and electric mobility integration through strategic optimization planning of distributed energy resources in an agrifood farm

The urgent need to mitigate climate change requires transformative approaches in sectors such as agriculture, which contribute significantly to global greenhouse gas emissions. This study examines the integration of renewable energy technologies into a selected agri-food farm to improve decarbonisation, energy resilience and economic performance. This work's novel contribution is the development of a custom algorithm that reconstructs hourly electricity consumption profiles from monthly Time-of-Use (ToU) tariff band data. This enables accurate energy system sizing in contexts where high-resolution smart meter data are unavailable. The algorithm was validated against measured hourly consumption data for two users characterized by different consumption levels and patterns, including a residential user and a non-residential facility. Validation was performed under both summer and winter conditions using normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE). The resulting nRMSE values ranged from 3% to 10%, while nMAE values ranged from 1% to 8%, indicating a reasonable reconstruction accuracy across the investigated cases. Implemented in MATLAB, the algorithm is adaptable to various ToU schemes and supports detailed energy simulations for renewable energy planning. The most advanced configuration achieved up to 75% energy self-sufficiency and avoided approximately 160 tCO2 annually. However, the configuration including battery storage provided the greatest environmental benefits at a higher investment cost, whereas the configuration without storage achieved the best economic performance, with the lowest LCOSE (0.43 €/kWh). These results highlight the need to balance environmental performance and economic viability when selecting renewable energy configurations for agricultural systems.