Carbon-Oriented Expansion Planning of Integrated Electricity-Natural Gas Systems With EV Fast-Charging Stations

• Published in: IEEE transactions on transportation electrification 2022-06, Vol.8 (2), p.2797-2809
• Main Authors: Wu, Ting, Wei, Xiang, Zhang, Xian, Wang, Guibin, Qiu, Jing, Xia, Shiwei
• Format: Article
• Language: English
• Subjects: Carbon; Carbon dioxide; Carbon emission flow (CEF); Carbon sequestration; Climate change; Costs; Electric power grids; Electric vehicle charging; Electricity; Electricity distribution; expansion planning; fast-charging station (FCS); flexibility; Fuel cells; integrated electricity-natural gas systems; Natural gas; Optimization; Photovoltaic cells; Planning; Power plants; Renewable energy sources; Uncertainty
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2022.3151811

Under the pressure of climate change and the energy crisis, electric vehicles (EVs) have experienced a marked growth in many countries recently. However, the environmental benefits of EVs cannot be achieved if the charging power from the electricity grid is still mainly provided by fossil-fueled power plants. How to construct a low-carbon fast-charging system has become an emerging research topic. In this context, a novel carbon-oriented expansion planning model of the integrated electricity and natural gas system (IEGS) with EV fast-charging stations (FCSs) is proposed to determine the optimal alternatives, locations, and sizes for ecofriendly candidate assets, including roof-top photovoltaic (PV) panels and fuel cell (FC) units in each FCS, as well as renewable energy units and carbon capture and storage (CCS) systems in IEGS. To guarantee the carbon reduction performance of the planning scheme, the carbon emission cap of the EV charging system is specified by the planner and optimally allocated among multiple FCSs according to their carbon reduction abilities. Furthermore, the proposed model can not only consider the initial investment and operation costs but also take into account the expected adaptation cost to future scenarios that are generated in line with three uncertainties, namely, the traffic flow levels, conventional load levels, and renewable-based power generation levels. Finally, the proposed expansion planning method is illustrated by conducting numerical experiments in case studies.