Battery/supercapacitor (SC) hybrid energy storage system (HESS) is an effective way to suppress the power fluctuation of photovoltaic (PV) power generation system during radiation change. This study focuses on the power sharing between different energy storage components with two optimisation objectives: energy loss and state of charge of SC. First, the topology of HESS and its connection with PV system are analysed. Second, the control targets of the HESS have been organised as optimisation objectives. A linear weighted summation algorithm based on variable weights is proposed to solve the multi‐objective optimisation problem to obtain current reference of SC and battery. Third, the energy management system of the HESS is designed based on proposed algorithms. Simulation and experiment results verified the proposed algorithms and control strategy.

Global changes in climate conditions and increasing number of weather-related disasters in the world have elevated the quest for better load restorations in power distribution systems. This paper proposes a distribution system restoration (DSR) model where non-exponential time-dependent cold load pickup (CLPU) characteristics and soft open point operations are simultaneously addressed. We develop a time-dependent CLPU model for attaining a more realistic and secure DSR in which we demonstrate that the traditional delayed exponential model (DEM) for a long outage duration is a special case of the adopted CLPU model. The current soft open point (SOP) models would typically relax the operation constraints without devising any tightening procedures, which might lead to inaccurate or even infeasible SOP states. In this paper, we analyze the reasons traditional SOP constraints would lead to unsecure operations and present a set of novel SOP operation constraints for recovering bindings. Also, we adopt the sequential second order cone programming (SSOCP) algorithm to establish the sequential framework of DSR and solve the proposed model effectively. Our presented case studies discuss the rationality of the proposed DSR model and algorithm.

In this paper, a fully modular control strategy for input-series output-parallel (ISOP) inverter system is proposed to ensure equal sharing of input voltage and output current among the constituent inverters. Compared with existing control strategies, such as centralized control that restricts the modularity of the system and distributed control, which realizes voltage and current sharing by means of connecting constituent inverter modules through sharing buses, the proposed control strategy can achieve fully modular design without any control signal interconnection among the constituent inverters, leading to superior modularity and reliability. First, the control strategy is deduced and the principle of the control strategy is analyzed. Then, the detailed implementation and stability analysis of the proposed strategy are presented. At last, a two-module ISOP inverter system is tested in the laboratory and the experimental results verify the effectiveness of the proposed control strategy.

This paper proposes a novel control method for an asymmetrical-cascaded-multilevel-converter-based hybrid energy storage system (HESS), which includes one battery and several electrical double-layer capacitors (EDLCs). Traditionally, it is challenging to control the power distribution between different cascaded H-bridges by using different energy storage components as dc sources. In this paper, a feedforward space-vector-modulation (FFSVM) technique is used to distribute the active power between the battery and EDLCs. With the proposed voltage feedforward mechanism, the HESS can flexibly operate in the normal operation mode, the high-power output mode, and the reverse power absorption mode. The principle and effectiveness of the improved FFSVM technique are analyzed. Meanwhile, the state-of-charge-balancing mechanisms are introduced. The simulation results obtained in MATLAB/Simulink as well as the experimental results of a hybrid energy storage test bed based on the proposed system are presented to verify its performance.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

The installation of distributed generations (DGs) and energy storages (ESs) in active distribution networks (DNs) could help to increase the resilience of the system. When large scale power outages of urban areas occur, these distributed resources could be used to recover partial, especially the most important loads. However, the determination of boundaries in the distribution networks and the strategy to recover loads are crucial to the overall DN reliability. This paper proposes a framework for resilient distribution network (RDN) to utilize all dispatchable resources to enhance the recovery ability under severe incidents like the complete failure of a substation. Firstly, the idea of restoration areas (RAs) planning with partial recovery is introduced. Each restoration area is schemed to utilize ES, DG and load transfer (LT) to recover loads as much as possible with considering the load importance. The combination weighting method is applied to evaluate the load priority. After that, a dynamic recovery strategy is designed that is valid regarding the actual operation condition and the reliability requirements in distribution system. The flexible boundaries of the RAs can expand or shrink to deal with the uncertainty of power sources output. Furthermore, the load regulation or demand response (DR) is performed to maintain power balance and enhance the overall recovery rate. Finally, illustrative results on IEEE RBTS test systems verify the effectiveness of the proposed model.