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Dynamic Thermal Rating (DTR) of overhead transmission lines represents a significant improvement with respect to the traditional criteria used to assess the steady-stateampacity of conductors. In fact DTR uses actual operating conditions of the power line, rather than assumed conservative conditions. This is extremely promising for the secure operation of the power system: with DTR, TSOs can fully exploit the dynamic performances of conductors, i.e. currents significantly higher than the steady-state thermal limits, in the meantime that the system is redispatched. The present paper proposes a novel dynamic model for calculating sags and tensions in a multi-span power line, for purposes of DTR. The model considers not only the mechanical interaction between spans, due to rotation of strings, but also that the temperature of conductors can vary span by span, for different weather conditions. The problem was solved with a fast Newton-Raphson technique, rather than with conventional relaxation methods, in order to comply with the requirements of real-time operation. The developed tool is able to forecast the time trend of conductor temperatures, tensions, sags and clearances at each span, or to indicate which current can be carried for a given time, before a clearance or temperature onstraint is violated. A case study compares the results of this novel method with the outcomes of the traditional "ruling span" technique. [ABSTRACT FROM AUTHOR]

The security assessment of the working point set by the energy markets is one of the main tasks of any System Operator (SO). The experience of vertically-integrated utilities in the use of probabilistic methods has proved to be very useful also in the new deregulated environment: an increasing number of SOs is presently enforcing with probabilistic tools the deterministic criteria traditionally employed to validate the ex-ante dispatching. Very challenging, promising and discussed, but not yet well-established, is conversely the use of probabilistic tools also for real-time decision-support and very short-term dispatching. The computational complexity of many time-consuming algorithms has constituted for years a crucial barrier; nowadays, the availability of cheap and fast computers discloses new opportunities for probabilistic techniques also for purposes of on-line security assessment. In this paper a probabilistic technique for real-time security assessment and operational decision-making is proposed and described. The use of a novel power system simulator, based on a Sequential Monte Carlo technique, is applied to very short-term dispatching, in order to alert the SO if the system reliability is about to decrease in the next few hours, thus anticipating critical contingencies and supporting the control room to find the most cost-effective corrective action. A case study relevant to the IEEE Reliability Test System RTS-96 is shown and discussed. [ABSTRACT FROM AUTHOR]

Reliability test grids are often used by the analysts to simulate the effect of new possible operational procedures regarding the power system. One of the main problems affecting the use of test grids for reliability studies is that the result of the simulations is strongly dependent not only on the new procedure under investigation, but also on the working point preselected for the simulated grid, in particular in terms of dispatching scenario. In this paper, the use of a sensitivity analysis to separate the two effects is discussed; hence a novel Mixed-Integer Linear Programming (MILP) method is proposed and implemented for providing, with a single Optimal Power Flow, the Unit Commitment and the day-ahead production schedules of a hydro-thermal generating park, as a first stage to perform reliability studies on a given test grid. Operational requirements, like reserve margins and transmission constraints, can be easily tuned to perform sensitivity analyses. The dispatching scenarios are generated assuming a deregulated framework and implementing inside the OPF the rules of day-ahead markets for energy and ancillary services. A mixed deterministic-probabilistic tool for generating bidding strategies is used to simulate the behavior of market players. [ABSTRACT FROM AUTHOR]