There is a growing interest in using dc power systems and microgrids for our electricity transmission and distribution, particularly with the increasing penetration of photovoltaic power systems. This paper presents an electric active suspension technology known as the dc electric springs (DC-ES) for voltage stabilization and power quality improvement. The basic operating modes and characteristic of a DC-ES with different types of serially connected non-critical loads will first be introduced. Then, the various power delivery issues of the dc power systems, namely bus voltage variation, voltage droop, system fault, and harmonics, are briefly described. The operating limits of a DC-ES in a dc power grid is studied. It is demonstrated that the aforementioned issues can be mitigated using the proposed DC-ES technology. Experiment results are provided to verify the feasibility of the proposed technology.

Regenerative braking energy (RBE) utilization plays a vital role in improving the energy efficiency of electrified railways. To date, various power flow control-based solutions have been developed to recycle the RBE for utilization within railway power systems (RPSs). In this paper, an overview of the state-of-the-art power flow control-based solutions for RBE utilization in AC electrified railways is presented. It provides a technical analysis of four primary power flow control-based solutions for RBE utilization, including power sharing-based, energy feedback-based, energy storage-based, and composite solutions. The critical architectures of power flow conditioners for each solution are analyzed in depth. Meanwhile, the power flow control strategies for these solutions are reviewed from the perspectives of power flow management and converter control. From the industrial point of view, the critical challenges associated with fault protection, economy, and environmental impact are discussed. In addition, future trends are comprehensively elaborated from internal and extended improvements. This comprehensive review provides an insightful understanding of the technology readiness, constraints, and perspectives regarding the power flow control-based RBE utilization in electrified railways, contributing to bridging the gaps between academic research and industry implementation.

Battery energy storage systems are often adopted to buffer the difference between the intermittent solar power and the load demand in power grids. The costs of such photovoltaic (PV) battery systems increase as the required energy storage increases. In this paper, a new configuration comprising the PV panels, a series dc electric spring (series ES) and a noncritical load is proposed to reduce the battery storage capacity of dc microgrids that have substantial PV installations. This arrangement forms a PV-embedded series dc ES (PVES). An optimization method considering the minimization of electricity bills of the dc microgrids is included to size the storage capacity and to determine the rating of the PV that are connected to the series ES. Experiments on a 48-V isolated dc grid and simulations on a 400-kVA grid-connected dc microgrid have been conducted to verify the storage reduction feature of the PVES. Both sets of results show that the PVES can tackle the intermittency of the solar power with a smaller storage capacity than that typically required in dc grids with PV installations.

It is energy-efficient and grid-friendly to utilize regenerative braking energy (RBE) in electrified railways. However, considering the segmented structure of the railway power system, it is challenging to utilize the RBE between adjacent traction substations (TSSs). To this end, an RBE utilization method involving power sharing and storage is proposed in this paper. The proposed method is implemented on a sectioning post-energy storage system (SPESS) to coordinate RBE utilization between adjacent TSSs. To provide real-time power management and control for the SPESS, a centralized power flow control strategy is developed. Specifically, a two-stage power dispatch algorithm-based power management strategy (PMS) is designed in the central controller. A nonlinear optimization function is formulated in the PMS to determine the active power commands to maximize the RBE utilization and shave the power demand. It also calculates the reactive power commands based on residual capacity to stabilize the catenary voltage. The power flow control is implemented in the local controllers as per the power commands from the central controller. The feasibility of the proposed method is verified through hardware-in-the-loop experiments. The techno-economic superiority of the proposed method over literature methods is validated based on field load data-based comparison analysis.

Timely perception of distribution system states is critical for the control and operation of power grids. Recently, it has been seriously challenged by the dramatic voltage fluctuations induced by high renewables. To address this issue, an Unrolled Spatiotemporal Graph Convolutional Network (USGCN) is proposed for distribution system state estimation (DSSE) and forecasting with augmented consideration of the underlying complex spatiotemporal correlations of renewable energy sources (RES). Specifically, the interconnection among individual spatial graphs of adjacent time steps will lead to an unrolled spatiotemporal graph and benefit the synchronous capture of spatial and temporal correlations to achieve enhanced accuracy. On top of this, the node-embedding technique is employed in the unrolled spatiotemporal convolutional layer to reveal the hidden nonlinear spatiotemporal correlations of RES outputs without relying on full prior knowledge. Moreover, the proposed USGCN stacks the unrolled spatiotemporal convolutional layers, leading to the perception of longtime correlations to obtain effective ahead-of-time state forecasting results robustly. The simulation results have been provided to verify the accuracy and efficiency of the proposed model in 118-node and 1746-node distribution systems.

This paper proposes a novel peer-to-peer (P2P) multi-grade energy trading design to encourage demand side flexibility to locally absorb the uncertainty of renewable distributed energy resources (DERs) in distribution networks. In particular, a reliability credit (RC) assignment method is developed for customers to differentiate the energy grades considering the heterogeneity in energy supplying reliability of DERs and the consumption preferences of customers. Later, an innovative P2P multi-grade energy trading model is introduced where different types of demand are matched up with the corresponding grades of energy. The market clearing is modeled as a social welfare maximization problem considering the net utilities of customers and the profits of DER producers. Furthermore, a fast decentralized pricing algorithm is developed that achieves the maximum of social welfare and also preserves the privacy of individual participants. To substantially improve the computational efficiency, a fast solution method is devised for customers to solve the optimal energy procurement combination problem, and closed-form solutions are derived for DER producers to allocate energy optimally. Numerical tests on a 69-node distribution feeder validate the superiority of the proposed P2P multi-grade energy trading scheme compared to the benchmark scheme. The high efficiency and scalability of the proposed fast pricing algorithm are corroborated by the simulation on the IEEE 8500-node distribution system.