Characterization and Modeling Home Power Grid Topologies in PLC Broadband

Home power outlets can be connected in various topologies： derivation, star, cascade. This paper firstly describes the intrinsic behavior of the transfer function of these basic topologies. Secondly, the channel capacities of the three topologies are compared to check if there is a more favorable topology. Many deterministic modeling methods have been developed to compute the transfer function of power line networks but the given examples in the studies correspond only to the derivation topology with branches connected to the direct path between transceivers. Thirdly, this paper evaluates the ability of common modeling methods （multipath and chain matrices） to compute accurately the transfer function of any topology. Modeling the derivation topology with ＂secondary＂ branches and the star topology is shown to be inappropriate with chain matrices based method. Indeed, this method is very sensitive to the uncertainty of the second parameters of the power cables and this induces considerable fading shifts for those topologies. Multipath modeling method produces results agreeing with measurements for any topology.

Impact of power system network topology errors on real-time locational marginal price

This paper examines the impact of power transmission network topology change on locational marginal price(LMP) in real-time power markets. We consider the case where the false status of circuit breakers(CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch.The main goal of this paper is to assess the economic impact of this misconfigured network topology on realtime LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.

Dual-stage constructed random graph algorithm to generate random graphs featuring the same topological characteristics with power grids

It is a common practice to simulate some historical or test systems to validate the efficiency of new methods or concepts. However, there are only a small number of existing power system test cases, and validation and evaluation results, obtained using such a limited number of test cases, may not be deemed sufficient or convincing. In order to provide more available test cases, a new random graph generation algorithm, named ‘‘dualstage constructed random graph’’ algorithm, is proposed to effectively model the power grid topology. The algorithm generates a spanning tree to guarantee the connectivity of random graphs and is capable of controlling the number of lines precisely. No matter how much the average degree is,whether sparse or not, random graphs can be quickly formed to satisfy the requirements. An approach is developed to generate random graphs with prescribed numbers of connected components, in order to simulate the power grid topology under fault conditions. Our experimental study on several realistic power grid topologies proves that the proposed algorithm can quickly generate a large number of random graphs with the topology characteristics of real-world power grid.

Simplified Deep Reinforcement Learning Based Volt-var Control of Topologically Variable Power System

The high penetration and uncertainty of distributed energies force the upgrade of volt-var control(VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasingly incompetent for this task because of the slow online calculation speed. Deep reinforcement learning(DRL) has recently been recognized as an effective alternative as it transfers the computational pressure to the off-line training and the online calculation timescale reaches milliseconds. However, its slow offline training speed still limits its application to VVC. To overcome this issue, this paper proposes a simplified DRL method that simplifies and improves the training operations in DRL, avoiding invalid explorations and slow reward calculation speed. Given the problem that the DRL network parameters of original topology are not applicable to the other new topologies, side-tuning transfer learning(TL) is introduced to reduce the number of parameters needed to be updated in the TL process. Test results based on IEEE 30-bus and 118-bus systems prove the correctness and rapidity of the proposed method, as well as their strong applicability for large-scale control variables.

A Review of Isolated Bidirectional DC-DC Converters for Data Centers

In today’s fast-paced,information-driven world,data centers can offer high-speed,intricate capabilities on a larger scale owing to the ever-growing demand for networks and information systems.Because data centers process and transmit information,stability and reliability are important.Data center power supply architectures rely heavily on isolated bidirectional DC-DC converters to ensure safety and stability.For the smooth operation of a data center,the power supply must be reliable and uninterrupted.In this study,we summarize the basic principle,topology,switch conversion strategy,and control technology of the existing isolated bidirectional DC-DC converters.Subsequently,existing research results and problems with isolated bidirectional DC-DC converters are reviewed.Finally,future trends in the development of isolated bidirectional DC-DC converters for data centers are presented,which offer valuable insights for solving engineering obstacles and future research directions in the field.