A Novel Fractional Programming-Based Planning Model for 100% Renewable Poly-Generation of Electricity and Methanol

Engaging in long-term power purchase agreements (PPAs) with renewable energy producers (REPs) is a promising strategy for decarbonizing hard-to-abate sectors, such as internet data centers. However, due to the inherent volatility and intermittency of renewable energy sources (RES), REPs typically require substantial over-installation of capacity to meet fixed PPA delivery commitments, resulting in significant energy curtailment and increased supply costs. Power-to-methanol (P2M) offers a viable pathway for large-scale integration of RES by providing flexible chemical storage and demand. This study proposes a unified planning framework for the 100% renewable poly-generation of electricity and methanol, considering both flexible and inflexible loads. The objective is to maximize the internal rate of return (IRR), a key metric for investment decision-making. To this end, a mixed-integer linear fractional programming (MILFP) model is developed and solved in a computationally tractable manner. The model is validated using real-world data. Results show that the proposed approach significantly improves IRR, reduces RES curtailment, and lowers the levelized costs of both electricity and methanol. Furthermore, the model effectively coordinates electricity delivery under PPA constraints with methanol synthesis, offering an economically robust solution for renewable energy utilization and sector coupling.