Soft open points (SOPs) can perform load transferring and voltage support, helping to expand the range of service restoration in distribution networks (DNs) under fault scenarios. In addition, in the field of DN restoration, SOPs are usually controlled in coordination with switch actions (e.g., the network reconfiguration). However, in the planning level, the coordinated deployment of SOPs and remote-controlled switches (RCSs) has never been studied in the literature. To this end, for the first time, this paper proposes a co-deployment framework for SOPs and RCSs incorporating active management of flexible resources for resilience improvement in DNs, in which the coupled multiple recovery stages are also modeled and included. The objective is to minimize the investment cost of SOPs and RCSs, along with the cost caused by de-energized loads, with the consideration of exhaustive DN operation constraints over proactive islanding, degradation, isolation and restoration stages. Furthermore, resilience-cost tradeoff is analyzed to help DN managers determining equipment investment plans. The formulated model is further transformed into a mixed-integer second-order cone programming model that can be thus efficiently solved. Simulation results on modified IEEE 34-node and IEEE 123-node test systems verify the effectiveness of the proposed model.

This paper proposes a real-time coordinated scheduling method for active distribution networks (ADNs) with soft open points (SOPs) and plug-in electric vehicles (PEVs) via multi-timescale framework under uncertainties. Specifically, this method is achieved with day-ahead pre-scheduling and intra-day corrective control stages by coordinating various flexible resources at different timescales. The day-ahead stage is designed to reduce operational cost, regulate voltage profile and avoid risk exposure through joint scheduling of traditional devices, SOPs and PEVs on hourly basis. In intra-day corrective control stage, an hour is further divided into two timescales. The slow-timescale scheduling (STS) aims to corrective coordination of SOPs and across-time-and-space energy transmission of PEVs, and nested within the STS, the fast-timescale scheduling optimally coordinates the active & reactive power of SOPs and PV inverters to handle fast voltage fluctuations as well as against real-time uncertainties. The formulated three models are all transformed into second-order cone programming problems via sample weighted average approximation (SWAA), linearization and conic relaxation, which can be thus efficiently solved. Case studies based on three modified distribution systems (including two IEEE test systems and one actual distribution network) are performed to verify the effectiveness of the proposed method.

With the increasing data privacy concerns raised by not only organizations but also individuals in distribution systems, traditional centralized data-driven forecasting approaches for short-term load forecasting (STLF) in distribution transformer supply zones are confronted with the predicament of isolated data island. To this end, a federated model-agnostic meta learning (FMAML) based STLF method is proposed. On the basis of federated learning(FL), model agnostic meta learning (MAML) is employed to build high-quality personalized models for clients, thereby significantly enhancing the personalization and compatibility of the Federated Learning FL, while easing data privacy concerns leveraging the feature of FL. The stochastic controlled averaging (SCA) algorithm is integrated as the federated aggregation algorithm to mitigate the impacts of client-drift (CD) phenomenon that causes slow convergence and even divergence during the training process, especially when the data is highly heterogeneous. Finally, numerical results verify the high accuracy and strong robustness to data heterogeneity and packet dropout of the proposed method.

Coordinated Volt-Var control methods have demonstrated their techno-economic feasibility in voltage regulation of photovoltaic (PV) rich distribution systems. However, fast fluctuating PV power and imperfect communication networks may significantly challenge the effectiveness of these methods. In this paper, a revised dynamic consensus algorithm is proposed to coordinate distributed inverters for Volt-Var control in real time. With this proposed method, Var saturation and overvoltage issues which tend to occur at downstream buses of PV rich distribution systems are significantly mitigated. To quantitatively analyse the algorithm performance in imperfect communication environments, the information delivery between agents is modelled by stochastic state transition processes among finite numbers of virtual nodes so as to quantitatively depict the random time delay and packet dropout in a discrete way. On this basis, the state transition process of the whole system is further depicted by a series of row-stochastic matrices, and the ergodic theory is used to analytically derive the algorithm tracking error in an imperfect communication environment. Our proposed method can also be extended to more complex applications, where both Var compensation and PV curtailment (or EV dispatch) are available for system voltage control. Simulation results verify the superiority of our method over traditional ones.

Fault management is one of the crucial functions of Smart Grid to bring system back to normal operation from fault events. In this paper, we propose a multi-period cooperative solution for the jointly repair and restoration problem in distribution systems (DSs) to minimize the de-energized loads. In the proposed scheme, the novel fully-controlled power-electronic device SOP (soft open point) and fault isolation are included to accelerate system restoration. As the repair and restoration process goes forward, system topology changes frequently. How to determine the control mode of SOPs considering their characteristics during the entire restoration process is rarely studied in the literature. To this end, we present a new virtual power flow based automatic solution to adaptively perform control mode switching for the SOPs in faulted DS. Furthermore, a co-optimization model is formulated that coordinates SOP control, repair scheduling and DS restoration to obtain optimal repair and switch action sequences matching SOP control mode for DS resilience enhancement. The fault isolation is also conducted, since it has great impacts on repair and switch operations and can guarantee the safety of repair crews in practice. Numerical simulations on a modified IEEE 123-node test system and a large-scale actual distribution system verify the effectiveness of the proposed model.

This paper reviews recent advancements in predicting the temperature of lithium-ion batteries in electric vehicles. As environmental and energy concerns grow, the development of new energy vehicles, particularly electric vehicles, has become a significant trend. Lithium-ion batteries, as the core component of electric vehicles, have their performance and safety significantly impacted by temperature. This paper begins by introducing the fundamental components and operating principles of lithium-ion batteries, followed by an analysis of how temperature affects battery performance and safety. Next, the methods for measuring and predicting battery temperature are categorized and discussed, including model-based methods, data-driven methods, and hybrid approaches that combine both. Finally, the paper summarizes the application of temperature prediction in a BMS and provides an outlook on future research directions.