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With growing complexity and uncertainty from distributed generations and active customers, distribution networks are facing more unexpected events and topology changes; while very limited measurements may degrade the observability of distribution system states and endanger the security of the system. Meanwhile, massive data from various types of users (if they can be used), can be valuable for power utilities. This makes privacy and data security a more critical issue, while being endowed with operation and market flexibilities. Thus, we propose a blockchain-based privacy-protecting approach for Dynamic Topology Awareness (DTA) to distinguish topology changes from cyber anomalies, including physical, cyber and business layers. At the bottom layer (physical layer), the distribution network is partitioned into subareas and tie-line areas, coping with local data from measurement devices or users. Estimated border values from parallel local estimations would be examined through data consistency checking. And DTA consensus algorithm might be triggered, querying historical status stored as blockchain contracts for anomalies detections and new block formations. Voting mechanism is then adopted to verify new blocks from data tampering. The performance of the proposed DTA approach is validated through simulations to identify topology changes and cyber anomalies, with additional discussions on the trade-off between DTA sensitivity and estimation accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for the construction of a transient stability boundary (TSB) that overcomes the lack of TSB analytical description for multiple operating states and contingencies in power systems. The proposed approach models the TSB as a nonlinear mapping between the system parameters of interest and the corresponding critical clearing time (CCT). This compact mathematical expression is built based on an efficient discriminative network, broad learning system (BLS). While keeping a high accuracy and generalization ability, the newly introduced construction method provides an efficient update strategy that does not require an entire retraining cycle. These properties make this method suitable for online transient stability assessment (TSA). The prospects of the developed method are verified via case studies by employing the IEEE 9-bus, IEEE 39-bus (New England) test systems and a real system of China Southern Power Grid. In comparison with existing methods, the newly introduced algorithm has a higher prediction accuracy and improved robustness. The demonstrated characteristics indicate that the proposed method may serve as a valuable tool in the framework of online TSA. [ABSTRACT FROM AUTHOR]

The real-time simulation based on the field programmable gate array (FPGA) is receiving more and more attention. However, up to now, the simulation scale for the power electronic system is not so satisfactory due to the real-time requirement and the FPGA resource limitation. This paper proposes a sub-microsecond level real-time simulation method for microgrids. The power converters are modeled with fixed-admittance models and simulated with a compact electromagnetic transients program (EMTP) algorithm. In the meanwhile, the distribution lines/cables are modeled with π-circuit models and simulated with a distributed circuit solution method, called the latency insertion method (LIM). As a result, the distribution generation (DG) systems are decoupled with each other and can be simulated in parallel. The case study shows that the proposed simulation method consumes much fewer FPGA resources compared with the traditional one. Besides, the time step can be much smaller and doesn't need to increase with the simulation scale. With these advantages, the real-time simulation of a microgrid consisting of three three-phase converters, three boost circuits and 21 three-phase lines can be achieved on the Xilinx Kintex-7 410T FPGA with a minmum time-step of 380 ns. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) charging station is a critical part of the infrastructure for the wide adoption of EVs. Real-time simulation of an EV station plays an essential role in testing its operation under different operating modes. However, the large numbers of high-frequency power electronic switches contained in EV chargers pose great challenges for real-time simulation. This paper proposes a compact electromagnetic transient program (C-EMTP) algorithm for FPGA-based real-time simulation of an EV station with multiple high-frequency chargers. The C-EMTP algorithm transforms the traditional EMTP algorithm into two parallel sub-tasks only consisting of simple matrix operations, to fully utilize the high parallelism of FPGA. The simulation time step can be greatly reduced compared with that of the traditional EMTP algorithm, and so the simulation accuracy for high-frequency power electronics is improved. The EV chargers can be decoupled with each other and simulated in parallel. A CPU-FPGA-based real-time simulation platform is developed and the proposed simulation of the EV station is implemented. The control strategy is simulated in a CPU with 100 μs time-step, while the EV station circuit topology is simulated in a single FPGA with a 250 ns time-step. In the case studies, the EV station consists of a two-level rectifier and five dual-active bridge (DAB) EV chargers. It is tested under different scenarios, and the real-time simulation results are validated using PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

• Mechanical mapping model is used to evaluate resilience enhancement in distribution networks with SOPs. • Both normal operations and power restoration are considered to maximize resilience enhancement. • A bi-level programming algorithm is proposed to determine the siting and sizing of SOPs to boost resilience. Disturbances such as natural disasters or man-made attacks exert serious influences on power system, resulting in an increasing awareness of resilience enhancement strategies. As the integration of microgrids, distributed generators (DGs) and power electronic devices adds to the vulnerability of power distribution network to disruptions, it is highly required to enhance the resilience against failure. Soft open points (SOPs) are flexible power electronic devices, which can balance power distribution under normal operation, and supply restoration under abnormal conditions. Thus, the application of SOPs can boost resilience both in pre-failure prevention and post-failure recovery. In this paper, to maximize the effect of SOPs on the boost of resilience of distribution network, a mixed integer non-linear optimization problem is proposed to schedule the siting and sizing of SOPs based on multi-stage elastic mechanical model. A bi-level algorithm is used to tackle the SOP planning problem. The selection of location and capacity is solved by genetic algorithm, after which the control strategies of SOP are optimized by particle swarm algorithm with given planning scheme to obtain the maximum resilience. Finally, case studies on the IEEE 33-bus system and IEEE 123-node test feeder are used to verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]