Due to the uncertainty and fluctuation of distributed generation (DG) and load, the operation of active distribution network (ADN) is affected by multi-dimension factors which are described by massive operation scenarios. Efficient and accurate screening of severely restricted scenarios (SRSs) has become a new challenge in ADN planning. In this paper, a novel bi-level coordinated planning model which combines the short-time-scale operation problem with the long-time-scale planning problem is proposed. At the upper level, the demand response (DR) resource, an effective non-component planning resource characterized by low capacity price, high energy price, and short contract term, is co-optimized with the configuration of lines and energy storage systems (ESSs) to achieve the economic trade-off between the investment cost and the operation cost under SRSs. At the lower level, with the planning scheme obtained from the upper level, massive operation problems are optimized to minimize the daily operation cost; and the SRSs are provided to the upper level through a shadow-price-based scenario screening method, which simulates the planning information (i.e., the restricted degrees of operation scenarios) feedback process from ADN operators to ADN planners. Case studies on a 62-node distribution system in Jianshan New District, Zhejiang Province, China, illustrate the effectiveness of the proposed bi-level coordinated planning model considering DR resources and SRSs. Index Terms--Active distribution network, demand response resource, bi-level coordinated planning, severely restricted scenario screening, shadow price. Yixin Huang received the B.E. degree in electrical engineering from Zhejiang University, Hangzhou, China, in 2018, where she is currently pursuing the M.E. degree with the College of Electrical Engineering. Her research interests include power system planning and demand response. Zhenzhi Lin received the Ph. D. degree in electrical engineering from the

Along with the rapid increase in the number of electric vehicles, more and more EV charging stations tend to have charging infrastructure, rooftop photovoltaic and energy storage all together for energy saving and emission reduction. Compared with individual design for each of the components in such kind of systems, an integrated design can result in higher efficiency, increased reliability, and lower total capital cost. This paper mainly focuses on the tertiary control strategy for dynamic state operation, such as PV generation fluctuation and random arrival/leave of EVs. The tertiary control aims to achieve stable operation under dynamic states, as well as to optimize the energy flow in the station to realize maximal operational benefits with constraints such as peak/valley price of electricity, state of discharge limitation of battery, etc. In this paper, four energy management functions in tertiary control level are proposed, and their performance is verified by simulations. By using prediction of PV power and EV load in the following 72 h, a novel tertiary control logic is proposed to optimize PVC and ESC power flow by changing their droop characteristics, so that minimum operational cost for the station can be achieved. Furthermore, a sensitivity analysis is conducted for three parameters, including ES battery capacity, weather influence, and PV and EV load prediction error. The results from sensitivity analysis indicate that ES battery capacity and weather condition lead to a great impact on the operational cost of the integrated charging station, while a typical prediction error of PV power and EV load will not influence the optimization result significantly.