The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish-Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city.

In this paper, we introduce an innovative framework for the strategic planning of electric vehicle (EV) charging infrastructure within interconnected energy-transportation networks. By harnessing the small-world network model and the advanced optimization capabilities of the Non-dominated Sorting Genetic Algorithm III (NSGA-III), we address the complex challenges of station placement and network design. Our application of the small-world theory ensures that charging stations are optimally interconnected, fostering network resilience and ensuring consistent service availability. We approach the infrastructure planning as a multi-objective optimization task with NSGA-III, focusing on cost minimization and the enhancement of network resilience and connectivity. Through simulations and empirical case studies, we demonstrate the efficacy of our model, which markedly improves the reliability and operational efficiency of EV charging networks. The findings of this study significantly advance the integrated planning and operation of energy and transportation networks, offering insightful contributions to the domain of sustainable urban mobility.

As with rapid development of the electric vehicle (EV) industry, charging infrastructures are built fast. However, the unreasonable deployments with increasing EVs contribute to a long queuing time for charging demand of EVs, especially in the peak hours. How to navigate a specific EV to economically satisfy its charging demand, while relieve the traffic burden, is an urgent problem. To address that, a price incentive based charging navigation strategy for electric vehicles is proposed. Unlike previous charging navigation studies that mainly focus on the EVs-transportation-power systems modeling, it considers the spatial-temporal influence of EVs' charging decision, especially the simultaneous charging requests. Specifically, the charging navigation framework with the collaborative working mode of EV- charging station-information exchange center (IEC)-intelligent transportation system (ITS) is established first. Following this, spatio-temporal distribution of the charging demand is obtained through the origin-destination (OD) analysis. After this, an event-driven dynamic queue model is constructed. It contributes to the modeling of the charging strategy, together with the proposed reservation opportunity cost mechanism. Finally, the simulation results indicate that the presented charging navigation strategy cannot only reduce the EV's charging cost but also improve the utilization rate of charging facilities, which verify its effectiveness.