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With the increasing number of electric vehicles, a large number of charging loads connected to the power system will have an impact on the economic and safe operation of the power system. In this paper a day-ahead optimal dispatching method for distribution network (DN) with fast charging station (FCS) integrated with photovoltaic (PV) and energy storage (ES) is proposed to deal with the negative impact of FCS on DN. By adjusting the load distribution of DN through the optimization decision of ES and soft open points (SOP), the operation level of DN is improved. Firstly, based on the historical vehicle travel data, Monte Carlo simulation method (MCSM) is applied to realize the simulation of fast charging load. Secondly, the uncertainties of PV power is addressed via a robust optimization model of the economic operation level of DN. Based on the second order cone relaxation and duality theory, a two-stage optimal dispatching model of DN is proposed. The optimization model is divided into main problem (MP) and sub problem (SP). For MP, the access position of FCS is adjusted based on SOP. And the charging and discharging power of ES is adjusted. The load distribution is optimized. For SP, based on the uncertainty of PV, the worst scenario of DN is calculated. The robustness of the proposed strategy is guaranteed. Finally, the proposed is verified based on the IEEE 33 bus system and a road network with 34 nodes. The simulation results show that the proposed method can effectively relieve line congestion of DN. The operating range of the voltage is better optimized. And the operation cost of DN is reduced significantly.

Accurate estimations of the temperature and the state-of-charge (SOC) are of extreme importance for the safety of lithium-ion battery operation. Traditional battery temperature and SOC estimation methods often omit the relation between battery temperature and SOC, which may lead to significant errors in the estimations. This study presents a coupled electrothermal battery model and a coestimation method for simultaneously estimating the temperature and SOC of lithium-ion batteries. The coestimation method is performed by a coupled model-based dual extended Kalman filter (DEKF). The coupled estimators utilizing electrochemical impedance spectroscopy (EIS) measurements, rather than utilizing direct battery surface measurements, are adopted to estimate the battery temperature and SOC, respectively. The information being exchanged between the temperature estimator and the SOC estimator effectively improves the estimation accuracy. Extensive experiments show that, in contrast with the EKF-based separate estimation method, the DEKF-based coestimation method is more favorable in reducing errors for estimating both the temperature and SOC even if the battery core temperature has increased by 17°C or more during the process of test.

With the high penetrated photovoltaics (PVs) accessing distribution networks (DNs) in the future, the overvoltage of distribution network will be more serious, and the voltage control will be more complex. To solve this problem, a cluster partition-based zonal voltage control method for DN is proposed in this study. First, a cluster partition method using a comprehensive performance index is proposed: in terms of DN structure, a global density quality function index is introduced to measure the coupling degree of nodes in clusters. In terms of cluster function, the inconsistency coefficient index is presented to reflect the PV output characteristics, and a node membership function index is presented to limit the scale of the cluster. Then, the DN cluster partition is carried out under the fast Newman (FN) algorithm. Second, a second-order cone programming (SOCP) model of voltage control is established to maximize the PV consumption in each cluster. To achieve the coordinated optimization of clusters, an iterative optimization strategy among clusters is proposed. Finally, the effectiveness and rationality of the proposed method are verified in a 10 kV actual feeder system in Zhejiang Province, China.