Optimal Renewable Energy Integration into the Process Industry and Energy Infrastructure Using the Multi-Energy Hub Approach with Economic and Environmental Considerations

Renewable energy;Optimization;Modeling;Upstream Oil Supply Chain;Refinery;Electric Vehicle

Taqvi, Syedaffiliation1:Faculty of Engineering ; advisor:Elkamel, Ali ; Elkamel, Ali ;

University of Waterloo

关键词: Renewable energy;    Electric Vehicle;    Upstream Oil Supply Chain;    Refinery;    Modeling;    Doctoral Thesis;    Optimization;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/14325/3/Taqvi_Syed.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Fossil fuels are an integral part of the current energy infrastructure and the major contributor towards carbon emissions. Energy intensive industries and local energy framework, inclusive of the transport sector, can be integrated with distributed renewable energy technology (RET), to mitigate this problem. Cases exist in literature where the impact of a particular RET on system effectiveness is studied. In this work, however, a comprehensive model is developed, based on the multi-energy hub approach, to optimally integrate renewable energy into the process industry and the energy infrastructure in a systematic manner. MILP models are developed to evaluate optimal energy distribution within an upstream oil supply chain (USOSC) and a refinery, as well as the transport sector. Case studies are carried out on Abu Dhabi, where different scenarios, including varying EROI, implementation of EOR+ technology (i.e. carbon capture and re-injection) and employment of carbon cap-and-trade program (CC&T), are considered. On the other hand, the refinery is simulated using Aspen HYSYS. Various energy generation systems, with and without storage, are considered in order to meet effective demand. In the last phase, a study is conducted assessing rooftop area of structures within Abu Dhabi city, for the deployment of RET, designing an electric vehicle (EV) charging infrastructure. MATLAB image segmentation and region analyzing tools are employed. The optimal configuration of multi-energy systems is determined for both, minimum economic cost and CO2 emissions, using CPLEX 11.1.1 solver.