Abstract:With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

Earlier calculations of geomagnetically induced currents (GIC) in Chinese power grids have been mainly concentrated on the highest voltage-level system whose geomagnetic risk is considered to be the largest, thus ignoring secondary voltage-level systems. With the 1 000 kV system being newly added to China's power system, it is significant to figure out the interaction between GIC in the 500 kV (EHV) system and GIC in the 1000 kV (UHV) system. Based on the North China-Central China-East China Power Grids, this paper establishes a single-voltage grid by only considering the 1 000 kV system and a dual-voltage grid by considering the 1 000 kV and 500 kV systems and investigates GIC in these two grids by developing their GIC "Full-node models". The impact of the 500 kV system on GIC in the 1000 kV system is analyzed. GIC risks in the UHV and EHV systems are assessed by comparing calculated GIC data with monitored values of GIC. The results show that the impact of the 500 kV system on GIC in the 1000 kV system is obvious, both the EHV and the UHV grid have a high GIC risk. So calculating GIC in the UHV system and in the EHV system should utilize GIC modeling methods for multi-voltage power grids.

Abstract:The increasing penetration of distributed generations (DGs) with intermittent and stochastic characteristics into current power distribution networks can lead to increased fault levels and degradation in network protection. As one of the key requirements of active network management (ANM), efficient power supply restoration solution to guarantee network self-healing capability with full consideration of DG uncertainties is demanded. This paper presents a joint power supply restoration through combining the DG local restoration and switcher operation-based restoration to enhance the self-healing capability in active distribution networks considering the availability of distributed generation. The restoration algorithmic solution is designed to be able to carry out power restoration in parallel upon multiple simultaneous faults to maximize the load restoration while additionally minimizing power loss, topology variation and power flow changes due to switcher operations. The performance of the proposed solution is validated based on a 53-bus distribution network with wind power generators through extensive simulation experiments for a range of fault cases and DG scenarios generated based on Heuristic Moment Matching (HMM) method to fully consider the DG randomness. The numerical result in comparison with the existing solutions demonstrates the effectiveness of the proposed power supply restoration solution.

The modular multilevel converter (MMC), as a new type of voltage source converter, is increasingly used because it is a distributed storage system. There are many advantages of using the topological structure of the MMC on a unified power quality controller (UPQC), and voltage sag mitigation is an important use of the MMC energy storage system for the power quality compensation process. In this paper, based on the analysis of the topology of the MMC, the essence of energy conversion in a UPQC of voltage sag compensation is analyzed; then, the energy storage characteristics are calculated and analyzed to determine the performance index of voltage sag compensation; in addition, the simulation method is used to verify the voltage sag compensation characteristics of the UPQC; finally, an industrial prototype of the UPQC based on an MMC for 10 kV of medium voltage distribution network has been developed, and the basic functions of UPQC have been tested.

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