Two-Stage Planning of Network-Constrained Hybrid Energy Supply Stations for Electric and Natural Gas Vehicles

• Published in: IEEE transactions on smart grid Vol. 12; no. 3; pp. 2013 - 2026
• Main Authors: Gan, Wei, Shahidehpour, Mohammad, Guo, Jianbo, Yao, Wei, Paaso, Aleksi, Zhang, Liuxi, Wen, Jinyu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Capacity planning; Constraints; Electric vehicle charging; Electric vehicles; Electrical networks; Energy conversion; Energy storage; Investment; Natural gas; Natural gas vehicles; natural gas-fueled vehicles; nested column and constraint generation; Photovoltaic cells; Planning; Roads; Robust planning of hybrid energy supply stations; Robustness (mathematics); solar photovoltaic; Stations; System effectiveness; Transportation
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2020.3039493

The increasing penetration of electric vehicles (EVs) and natural gas-fueled vehicles (NGVs) has introduced higher potentials for strengthening the coordination of transportation, natural gas, and electric networks. This article proposes the concept and the formulation of a planning model for hybrid energy supply stations (HESSes) to supply EVs and NGVs. In addition, this article coordinates the three networks for attaining a higher operational flexibility and lower investment cost for HESS. The proposed planning model incorporates EV charging, NGV refueling, solar photovoltaic (PV) units, energy conversion devices for power-to-gas and gas-to-electricity, and battery storage (electric and natural gas) units. The proposed HESS planning model is formulated as a two-stage robust problem to accommodate variable solar PV power and loads, where the first stage determines HESS investment decisions and the second stage determines HESS operation variables subject to the three network constraints and the availability of solar PV power. The original nonlinear HESS planning model is reformulated in a linear form, then the nested column and constraint generation algorithm is adopted to solve the planning model. Several enhancement techniques are applied to improve the computational performance of the proposed solution technique. Numerical results are presented for two systems to validate the effectiveness of the proposed HESS planning model and its solution technique.