In this work we propose a data independent framework for the optimal integration of renewable sources of energy to produce fuels and power. A network is formulated using surrogate models for various technologies that use solar energy (photovoltaic, concentrated solar power or algae to produce oil), wind, biomass (to obtain ethanol, methanol, FT-liquids and thermal energy), hydroelectric, and waste (to produce power plant via biogas production). The optimization model is formulated as a mixed-integer linear programming model that evaluates the use of renewable resources and technologies and their integration to meet power and fuels demand; sustainability and CO₂ emissions are also considered. The network can be applied to evaluate process integration at different scales, county to country level, including uncertainty availability of resources. Spain and particular regions are used as a case study. The framework suggests that larger integration uses the resources more efficiently, while considering uncertainty in resource availability shows larger cost to ensure meeting the demand. For the particular case considered, hydropower is widely used while biofuels are produced close to large populated regions when larger areas are evaluated; otherwise a more distributed solution is proposed. Reaching large fuel substitution is difficult at current biomass yields and technology state of development.

在这项工作中，我们提出了一个独立于数据的框架，用于可再生能源生产燃料和电力的最佳整合。使用替代模型为使用太阳能（光伏，聚光太阳能或藻类生产石油），风能，生物质（以获得乙醇，甲醇，FT-液体和热能），水力发电和废物（多种能源）的各种技术制定网络模型。通过沼气生产发电厂）。优化模型被公式化为混合整数线性规划模型，该模型评估可再生资源和技术的使用以及它们的集成以满足电力和燃料需求；可持续性与CO ₂还考虑了排放。该网络可用于评估县到国家级别不同规模的流程集成，包括不确定的资源可用性。西班牙和特定地区用作案例研究。该框架建议更大的整合会更有效地利用资源，同时考虑到资源可用性的不确定性会显示出更大的成本以确保满足需求。对于所考虑的特殊情况，在评估较大面积时，水力发电被广泛使用，而生物燃料则在人口稠密的地区附近生产。否则，提出了一种更分布式的解决方案。在当前的生物质产量和技术发展状况下，难以实现大型燃料替代。