A Response-Function-Based Coordination Method for Transmission-Distribution-Coupled AC OPF

With distributed generation highly integrated into the grid, the transmission-distribution-coupled AC OPF (TDOPF) becomes increasingly important. This paper proposes a response-function-based coordination method to solve the TDOPF. Different from typical decomposition methods, this method employs approximate response functions of the power injections with respect to the bus voltage magnitude in the transmission-distribution (T-D) interface to reflect the "reaction" of the distribution to the transmission system control. By using the response functions, only one or two iterations between the transmission system operator (TSO) and the distribution system operator(s) (DSO(s)) are required to attain a nearly optimal TDOPF solution. Numerical tests confirm that, relative to a typical decomposition method, the proposed method does not only enjoy a cheaper computational cost but is workable even when the objectives of the TSO and the DSO(s) are in distinct scales.Comment: This paper will appear at 2018 IEEE PES Transmission and Distribution Conference and Expositio

Factor Analysis of the Aggregated Electric Vehicle Load Based on Data Mining

Electric vehicles (EVs) and the related infrastructure are being developed rapidly. In order to evaluate the impact of factors on the aggregated EV load and to coordinate charging, a model is established to capture the relationship between the charging load and important factors based on data mining. The factors can be categorized as internal and external. The internal factors include the EV battery size, charging rate at different places, penetration of the charging infrastructure, and charging habits. The external factor is the time-of-use pricing (TOU) policy. As a massive input data is necessary for data mining, an algorithm is implemented to generate a massive sample as input data which considers real-world travel patterns based on a historical travel dataset. With the input data, linear regression was used to build a linear model whose inputs were the internal factors. The impact of the internal factors on the EV load can be quantified by analyzing the sign, value, and temporal distribution of the model coefficients. The results showed that when no TOU policy is implemented, the rate of charging at home and range anxiety exerts the greatest influence on EV load. For the external factor, a support vector regression technique was used to build a relationship between the TOU policy and EV load. Then, an optimization model based on the relationship was proposed to devise a TOU policy that levels the load. The results suggest that implementing a TOU policy reduces the difference between the peak and valley loads remarkably