Substation Automation Systems (SAS) provide reliable bedrock for future smart grid development in electric utilities. Implementation of high quality SAS system enables one to experience less outage rate using the state-of-the-art computerized functions of monitoring, control, and protection. As a result, it can immensely reinforce the reliability index of smart grid systems. However, the inextricable interdependency of cyber and power components in an automated substation creates more vulnerable operation process. In this sense, unlike the power component outage in a substation, a failure of the cyber components in an Ethernet fashion can interrupt the operation as well. Therefore the proper selection of SAS package that offers more reliable performance may hedge massive maloperation in the system. Since the introduction of multi-vendor SAS based IEC-61850 protocol, the interoperability of various SAS components with variety of manufacturer's brands is now possible. This paper surveys the most efficient and used SAS package and the configuration in a HV substation which leads to high reliable performance. Findings can pave the future smart grid development in an effective manner.
Considering system uncertainties in developing power systems, algorithms such as congestion management (CM) are vital in power system analysis and studies. This paper proposes a new model for power system CM by considering power system uncertainties based on chance-constrained programming (CCP). In the proposed approach, transmission constraints are taken into account by stochastic, instead of deterministic, models. The proposed approach considers network uncertainties with a specific level of probability in the optimization process, and then an analytical approach is used to solve the new model of stochastic congestion management. In this approach, the stochastic optimization problem is transformed into an equivalent deterministic problem. Moreover, an efficient numerical approach based on a real-coded genetic algorithm and Monte Carlo technique is proposed to solve the CCP-based congestion management problem in order to make a comparison to the analytical approach. The effectiveness of the proposed approach is evaluated by applying the method to the IEEE 30-bus test system. The results show that the proposed CCP model and the analytical solving approach outperform the existing models.
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