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In the context of modern power system development to support the evolution towards green energy and carbon-neutral emission goals, many existing problems and even challenges demand new technical solutions. In recent years, decentralized blockchain technology has been employed to address some problems in power systems, and many papers have been published. In this paper, the concept of blockchain is first introduced. A brief survey of the existing publications regarding the applications of blockchain in power systems, including power system dispatching, microgrid operation, energy trading, electricity trading settlement, transmission, and distribution system operation, is then carried out. In addition, several application scenarios of blockchain technology in power systems are also introduced. Through the discussion, we found that we still need to weigh the advantages and disadvantages, overcome its leakage, and bring its value into play if we apply blockchain technology in modern power systems in support of zero carbon goals. [ABSTRACT FROM AUTHOR]

This paper addresses the integrated planning problem of a power network and the charging infrastructure of electric vehicles (EVs) for enhancing power system resilience under various extreme weather scenarios. The planning methodology determines the optimal joint expansion decisions while modeling the benchmark system operation under the n − k resilience criterion. The proposed coordinated planning framework is a robust two-stage/tri-level mixed-integer optimization model. The proposed robust joint planning model includes the construction plan in the first level, identifying the worst-case scenario in the second level, and optimizing the operation cost and load shedding in the final level. To solve this model, a duality-based column and constraint generation (D-CCG) algorithm is developed. Using case studies, both the robust sole transmission planning and joint planning models are demonstrated on the IEEE 30-bus and IEEE 118-bus power systems. Numerical simulations of the benchmark systems validate the effectiveness of the developed framework and the efficiency of the proposed solution approach. Simulation results show the superiority of the proposed robust integrated planning over the sole transmission planning model. [ABSTRACT FROM AUTHOR]

The well-developed unified power flow controller (UPFC) has demonstrated its capability in providing voltage support and improving power system stability. The objective of this paper is to demonstrate the capability of the UPFC in mitigating oscillations in a wind farm integrated power system by employing eigenvalue analysis and dynamic time-domain simulation approaches. For this purpose, a power oscillation damping controller (PODC) of the UPFC is designed for damping oscillations caused by disturbances in a given interconnected power system, including the change in tie-line power, the changes of wind power outputs, and others. Simulations are carried out for two sample power systems, i.e., a four-machine system and an eight-machine system, for demonstration. Numerous eigenvalue analysis and dynamic time-domain simulation results confirm that the UPFC equipped with the designed PODC can effectively suppress oscillations of power systems under various disturbance scenarios. [ABSTRACT FROM AUTHOR]

The ever-increasing penetration of wind power generation and plug-in electric vehicles introduces stochastic continuous disturbances to the power system. This paper proposes an analytical approach to analyze the influence of stochastic continuous disturbances on power system small signal stability. The noise-to-state stability (NSS) and NSS Lyapunov function (NSS-LF) are adopted for stability analysis with respect to the magnitude of uncertainties in a power system. The power system is modeled as a set of stochastic differential equations (SDEs). The supremum of the norm of the covariance is employed to characterize the influence of magnitudes of uncertainties on the power system. Then the relationship between the magnitudes of stochastic variations and probabilistic stability is explicitly identified by NSS. The proposed method can assess the stochastic stability of the power system by checking some algebraic expressions. Hence, it has high computation efficiency compared with the well-established Monte Carlo based method. Besides, since the magnitudes of the stochastic variations are integrated into the definition of the stochastic stability, the proposed method provides theoretical explanations for the impacts of uncertainties. [ABSTRACT FROM AUTHOR]

When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems. [ABSTRACT FROM AUTHOR]