Multi-faceted procurement with mixed integer linear programming for corporate 100 % renewable energy goal

To achieve net-zero emissions, wind and solar power are projected to provide over half of global electricity by 2050. These sources, along with storage systems and Renewable Energy Certificates, are crucial for companies aiming for carbon neutrality. This study applies a mixed integer linear programming model to minimize procurement costs, considering regional cost differences and capacity factors. It focuses on the corporate sector, examining two cases based on different Renewable Energy 100 % Initiative accounting methods: the "Non- Circulation Case" and the "Circulation Case." Each case includes "Aggressive" and "Normal" scenarios based on progress, outlining strategies, including capacity, electricity, storage, and cost. Key findings reveal that in the "Non-Circulation Case with Aggressive Scenario," relying solely on a single energy source proves insufficient, necessitating investment in diverse sources such as secondary renewables (like wind), storage systems, and Renewable Energy Certificates. This strategy enhances system resilience but may pose financial challenges for smaller companies. In the "Non-Circulation Case with Normal Scenario," companies can gradually invest in solar photovoltaics, supported by storage systems and Renewable Energy Certificates, to balance flexibility, efficiency, and affordability. The most significant contribution is demonstrated in the "Circulation Case with Aggressive Scenario," where the model identifies a solar-dominated system emerges as the optimal strategy for achieving corporate 100 % renewable goals, driven by solar energy's superior cost-effectiveness and capacity factor in southern regions. In the "Circulation Case with Normal Scenario," companies shift towards a solar-only energy system to leverage the high capacity factor in southern regions, ensuring a streamlined, scalable procurement strategy.