Large-scale deployment of electric vehicles (EVs) is anticipated in the foreseeable future. Heavy intermittent charging load of EVs will create bottlenecks in supplying capacity and expose power system to severe security risks. In this paper, we propose an intelligent method to control EV charging loads in response to time-of-use (TOU) price in a regulated market. First, an optimized charging model is formulated to minimize the charging cost. Then, a heuristic method is implemented to minimize the charging cost considering the relation between the acceptable charging power of EV battery and the state of charge (SOC). Finally, the charging cost and energy demand in different time intervals are compared for both typical charging pattern and optimized charging pattern. Results show that the optimized charging pattern has great benefit in reducing cost and flatting the load curve if the peak and valley time periods are partitioned appropriately.

This paper proposes a distributed multi-period multi-energy operational model for the multi-carrier energy system. In this model, energy hubs function as distributed decision-makers and feature the synergistic interactions of generation, delivery, and consumption of coupled electrical, heating, and natural gas energy networks. The multi-period multi-energy scheduling is a challenging optimization problem due to its strong couplings and inherent nonconvexities within the multi-energy networks. The original problem is thus reformulated as a mixed integer second-order cone programming (MISOCP) and subsequently solved with a sequential second-order cone programming (SOCP) approach to guarantee a satisfactory convergence performance. Furthermore, a fully-distributed consensus-based alternating direction method of multipliers (ADMM) approach with only neighboring information exchange required is developed to optimize the multi-energy flows while considering the local energy-autonomy of heterogeneous energy hubs. The proposed methodology is performed and benchmarked on a four-hub urban multi-energy system over a 24 hourly scheduling periods. Simulation results demonstrated the superiority of the proposed scheme in system operational economy and renewable energy utilization, and also verify the effectiveness of the proposed distributed approach.

This paper proposes a multi-source multi-product framework for coupled multi-carrier energy supplies with a biogas-solar-wind hybrid renewable system. In this framework, the biogas-solar-wind complementarities are fully exploited based on digesting thermodynamic effects for the synergetic interactions of electricity, gas and heating energy flows, and a coupling matrix is formulated for the modeling of production, conversion, storage, and consumption of different energy carriers. The multi-energy complementarity of biogas-solar-wind renewable portfolio can be utilized to facilitate the mitigation of renewable intermittency and the efficient utilization of batteries, and a multi-carrier generation scheduling scheme is further presented to dynamically optimize dispatch factors in the coupling matrix for energy-efficient con-version and storage, while different energy demands of end-users are satisfied. The proposed methodology has been fully tested and benchmarked on a stand-alone Microgrid over a 24-hour scheduling horizon. Comparative results demonstrate that the proposed scheme can lower the battery charging/discharging actions as well as the degradation cost, and also confirm its capability to accommodate high penetration of variable renewables.

Electric vehicles (EVs) are promising to provide frequency regulation services due to their fast regulating characteristics. However, when EVs participate in supplementary frequency regulation, it is challenging to simultaneously achieve the dispatch of the control center and the expected state of charge (SOC) levels of EV batteries. To solve this problem, in this paper we propose a vehicle-to-grid (V2G) control strategy, in which an uncertain dispatch is implemented in the control center without detailed EV charging/discharging information. The regulation from the control center is achieved by allocating the regulation task within the frequency regulation capacity of EVs. The expected SOC levels of EV batteries are guaranteed by a real-time correction of their scheduled V2G power in EV charging stations. Simulations on an interconnected two-area power system validate the effectiveness of the proposed V2G control in achieving both the frequency regulation and the expected SOC levels of EVs.