Transportation electrification is a pivotal factor in accelerating the transition to sustainable energy. Electric vehicles (EVs) can operate either as loads or distributed power resources in vehicle-to-grid (V2G) or vehicle-to-vehicle (V2V) linkage. This paper reviews the status quo and the implications of transportation electrification in regard to environmental benefits, consumer side impacts, battery technologies, sustainability of batteries, technology trends, utility side impacts, self-driving technologies, and socio-economic benefits. These are crucial subject matters that have not received appropriate research focus in the relevant literature and this review paper aims to explore them. Our findings suggest that transitioning toward cleaner sources of electricity generation should be considered along with transportation electrification. In addition, the lower cost of EV ownership is correlated with higher EV adoption and increased social justice. It is also found that EVs suffer from a higher mile-per-hour charging rate than conventional vehicles, which is an open technological challenge. Literature indicates that electric vehicle penetration will not affect the power grid in short term but charging management is required for higher vehicle penetration in the long-term scenario. The bi-directional power flow in a V2G linkage enhances the efficiency, security, reliability, scalability, and sustainability of the electricity grid. Vehicle-to-Vehicle (V2V) charging/discharging has also been found to be effective to offload the distribution system in presence of high EV loads.
This paper presents a reformulation for the automatic generation control (AGC) in a decomposed convex relaxation algorithm. It finds an optimal solution to the AC optimal power flow (ACOPF) problem that is secure against a large set of contingencies. The original ACOPF problem which represents the system without contingency constraints, is convexified by applying the second-order cone relaxation method. The contingencies are filtered to distinguish those that will be treated with preventive actions from those that will be left for corrective actions. The selected contingencies for preventive action are included in the set of security constraints. Benders decomposition is employed to decompose the convexified Security-Constrained ACOPF problem into a master problem and several security check subproblems. Subproblems are evaluated in a parallel computing process with enhanced computational efficiency. AGC within each subproblem is modeled by a set of proposed valid constraints, so the procured solution is the physical response of each generation unit during a contingency. Benders optimality cuts are generated for the subproblems having mismatches and the cuts are passed to the master problem to encounter the security-constraints. The accuracy of the relaxation results is verified using the presented tightness measure. The effectiveness of the presented valid AGC constraints and scalability of the proposed algorithm is demonstrated in several case studies.
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