The increasing integration of variable wind generation has aggravated the imbalance between electricity supply and demand. Power-to-hydrogen (P2H) is a promising solution to balance supply and demand in a variable power grid, in which excess wind power is converted into hydrogen via electrolysis and stored for later use. In this study, an energy hub (EH) with both a P2H facility (electrolyzer) and a gas-to-power (G2P) facility (hydrogen gas turbine) is proposed to accommodate a high penetration of wind power. The EH is modeled and integrated into a security-constrained unit commitment (SCUC) problem, and this optimization problem is solved by a mixed-integer linear programming (MILP) method with the Benders decomposition technique. Case studies are presented to validate the proposed model and elaborate on the technological potential of integrating P2H into a power system with a high level of wind penetration (HWP).

As an emerging energy carrier, hydrogen presents a potential opportunity to increase the flexibility and resiliency of the integrated power and transportation system having ultrahigh penetration of renewable generation. This article studies a conceptual integrated energy-mobility system (IEMS) based on renewable-to-hydrogen stations (RHSs) and tank truck fleets. In an RHS, the renewables power the grid while also charge a hydrogen energy storage unit that can perform power-to-hydrogen (P2H) and hydrogen-to-power (H2P) operations. Besides, using tank truck fleets, the RHS will distribute its surplus hydrogen products to local consumers, e.g., refueling stations serving hydrogen fuel cell vehicles. The operation framework of the IEMS is analyzed. And some preprocessing technologies are developed to reduce modeling complexity. Then, a joint optimal scheduling model, involving renewable generationcontract, P2H, and H2P of the RHS, hydrogen product demands of the local consumers, and vehicle routing problem (VRP) of the truck fleets, is established. Afterwards, the formulated problem is solved by a heuristic method based on the exhaustive search and genetic algorithm. Numerical results verify the effectiveness of the joint optimal scheduling model and illustrate that the IEMS has the potentials to promote hydrogen-integrated transportation and power systems.

This paper considers networked nanogrids with an electric vehicle battery swapping station (BSS) as a cyber-physical energy management system (CPEMS) for enhancing the energy supply reliability, resilience, and economics. This paper discusses the architecture and control framework of the proposed CPEMS. A capacity optimization problem is formulated based on the mixed-integer linear programming (MILP) and solved with a scenario-based approach to schedule the CPRMS generation and storage. Individual nanogrids and BSS share scheduled generation and storage resources and exploit diversities in supply and demand profiles to achieve mutual economic and security benefits. The paper considers the following three issues: 1) Optimal sizing and scheduling of nanogrids and BSS can supply a sufficient storage capacity for smoothing out volatile renewable energy sources (RESs). 2) Surplus RES generation can be aggregated and stored in the BSS for supplying electric vehicles at peak hours. 3) A networked nanogrid structure can share BSS resources for attaining considerable savings and higher resilience, without compromising each nanogrid's cyber-physical security. Numerical case studies demonstrate the applications of the proposed CPEMS to power system operation and control.

Nanogrids are expected to play a significant role in managing the ever-increasing distributed renewable energy sources. If an off-grid nanogrid can supply fully-charged batteries to a battery swapping station (BSS) serving regional electric vehicles (EVs), it will help establish a structure for implementing renewable-energy-to-vehicle systems. A capacity planning problem is formulated to determine the optimal sizing of photovoltaic (PV) generation and battery-based energy storage system (BESS) in such a nanogrid. The problem is formulated based on the mixed-integer linear programming (MILP) and then solved by a robust optimization approach. Flexible uncertainty sets are employed to adjust the conservativeness of the robust optimization, and Monte Carlo simulations are carried out to compare the performance of the solutions. Case studies demonstrate the merits of the proposed applications and verify our approach.

Transportation electrification is regarded as one of the most significant opportunities to address the social and environmental challenges posed by mass motorization and rapid urbanization. Electric vehicles (EVs) are playing an essential role in realizing transportation electrification, with benefits of oil conservation, local air-quality improvement, global climate change mitigation, and transit mode upgrading. In principle, EVs include battery electric vehicles and plug-in hybrid electric vehicles, with service types representing passenger vehicles, light-duty commercial vehicles, buses, and trucks. For brevity, EVs as referred to in this article are primarily passenger vehicles and light-duty commercial vehicles.

The temperature rise in wireless charging systems (WCS) limits their reliability and material lifespan, hindering the development of wireless charging technology. This article proposes a thermal design and optimization method for the magnetic coupler in WCSs. The proposed method uses electromagnetic–thermal simulation to predict the steady-state temperature of the magnetic coupler and employs multiobjective optimization to select suitable parameters. First, the losses in the WCS are analyzed, and a thermal model is established. The necessary electrical and thermal parameters for the modeling process are derived. Second, the nondominated sorting genetic algorithm II performs the multiobjective Pareto optimization on the established optimization model. Finally, a set of parameters with excellent performance is selected from the optimization results, and an experimental prototype is constructed to validate the proposed method's correctness.