Aiming at the problem of “1 hour to charge and 4 hours just to queue” during the National Day holiday in 2021, in order to reduce the problems of poor charging experience and reduced confidence in high-speed travel caused by too long charging queuing time, this paper proposes a charging path planning. Based on the state model of electric vehicles (EV), charging stations, traffic network and distribution network, this paper fully considers the charging resources around the expressway network when planning the charging path for users on the expressway network, and proposes a new method considering the “EV-pile-road-grid” state electric vehicle charging path planning. By calling the Baidu map API interface and the data of the charging piles of the Internet of Vehicles Platform, the optimized selection of charging stations is completed, and a feasible navigation path with the shortest travel (time) is finally formed.
Newly built EV charging stations are usually equipped with battery storage system and PV panels, which can maximize the usage of cleanable power energy, and provide great capabilities for meeting various control objectives of the power grid. Operated properly, the controllable load of such charging stations can be used to provide ancillary services in an electricity market. This paper presents a novel predictive optimization method that adopts deep neural network (DNN) for forecasting EV loads and electricity market prices and optimally operating storage systems. The DNN-based forecasting methods consist of key function modules of data collection/cleaning, feature engineering, model training with parameter tuning, and adaptive updates for ensuring long term effectiveness. The economic operation of an EV charging station is formulated as a nonlinear multi-objective optimization problem with multiple constraints. Detailed mathematical optimization models considering time-of-use electricity and peak shaving market are provided. Comprehensive case studies are conducted on an actual EV charging station using real operation data that verifies the effectiveness of the proposed models and methods.
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