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This paper develops a novel solution to integrate electric vehicles and optimally determine the siting and sizing of charging stations (CSs), considering the interactions between power and transportation industries. Firstly, the origin-destination (OD) traffic flow data is optimally assigned to the transportation network, which is then utilized to determine the capacity of charging stations. Secondly, the charging demand of charging infrastructures is integrated into a cost-based model to evaluate the economics of candidate plans. Furthermore, load capability constraints are proposed to evaluate whether the candidate CSs deployment and tie line plans could be adopted. Different scenarios generated by load profile templates are innovatively integrated into the economic planning model to deal with uncertain operational states. The models and framework are demonstrated and verified by a test case, which offers a perspective for effectively realizing optimal planning of the CSs considering the constraints from both transportation and distribution networks.

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Energy storage and demand response resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the capability of reducing their electricity consumption. This paper highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers' electricity bills without intruding on people's daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of demand-response (DR) interventions, leading to different levels of customer weariness and consumption patterns. Thus, DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with PV generation, a chanceconstrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimization (DRO), the ambiguity set is accurately built for this distributionally robust chance-constrained (DRCC) problem without the need of any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this paper, the proposed DRCC-HEMS is proven to be optimally effective and computationally efficient while considering uncertainty. Nomenclature A. Sets T Set of time slots. A Set of appliances. B. Parameters F Fuse capacity. , , , Minimum charging and discharging power of ESS. , , , Maximum charging and discharging power of ESS. , Minimum and maximum state of charge of ESS. , , , Charging and discharging efficiency of ESS. , Degradation cost coefficient of ESS. , , , , Capital cost, life cycle and total capacity of ESS. 0 Statistical mean of . 0 Statistical covariance of .