Monte-Carlo, real time thermal rating, risk. 22 NOMENCLATURE 23The symbols used throughout this paper are defined below.

Abstract-The paper proposes a probabilistic methodology for minimizing wind spillage and maximizing capacity of the deployed wind generation, whilst improving system reliability. Capacities of the connected wind units are initially determined by using a method developed by the industry. A probabilistic approach is applied for the day-ahead planning to find maximum deployable wind sources so that the prescribed wind spillage is not exceeded. This is done using the optimum power flow, where wind spillages are prioritised with the probabilistic 'cost coefficients'. Further improvement of wind energy utilization is achieved by installing FACTS devices and making use of realtime thermal ratings (RTTR). Two ranking lists are developed to prioritize location of SVCs and TCSCs, and they are then combined into a unified method for best FACTS placement. The entire methodology is realized in two sequential Monte Carlo procedures, and the probabilistic results are compared with the state enumeration ones. Results show improved wind utilization, network reliability and economic aspects.

Abstract-Contemporary transmission networks are not fully utilized due to increased uncertainty and security buffers resulting mainly from the inefficient operation and planning, currently based on conservative predetermined thermal ratings. However, in a smart operation scheme, which considers time varying thermal ratings of transmission assets, operation and planning could be optimized to facilitate the proliferation of generation expansion projects while maintaining required levels of reliability. This paper presents a methodology which enhances the current methods of network element ratings by incorporating a more detailed modelling of the overhead line (OHL) properties. Three thermal rating models, static (STR), seasonal (SeTR) and time varying (TVTR), are implemented for comparative studies, under both deterministic and probabilistic frameworks with an aim to identify the most cost-effective and optimal flexible network operation plan in today's congestion-driven and competitive power markets. In addition, the effects of line outages on transmission losses in the electric power networks are presented, quantifying the transmission losses in a realistic manner due to the incorporation of real thermal ratings. The IEEE 24-bus RTS is used under sequential modeling to validate the methodological enhancements and to evaluate network performance. The system annual operating costs are reduced when using the proposed TVTR model.

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Abstract-Traditional transmission operation is mainly driven by passive consumer behaviour and predetermined static or seasonal overhead lines (OHLs) thermal ratings. However, in a smart operation scheme, which considers demand side management (DSM) regimes as well as time varying thermal ratings (TVTR) of transmission assets, network operation could be updated and optimally accommodate novel transmission reinforcement concepts. In this paper a reliability evaluation with incentive-based demand response (IBDR) is performed considering peak shaving and valley shifting (PSVS) in conjunction with emergency demand response (EDR). The novelty of the proposed model is the inclusion of customer interruption frequency and interruption duration, based on the different customer types (residential, commercial, industrial, and large users), using probabilistic metrics to capture the total cost of network operation. The methodology is applied to the IEEE 24-bus reliability test system with a more advanced modelling of OHLs to facilitate the inclusion of TVTR. From this analysis increased benefits from DSM are evident when TVTR is implemented and considerably improves network performance.