This paper investigates the use of reinforcement learning in electric power system emergency control. The approach consists of using numerical simulations together with on-policy Monte Carlo control to determine a discrete switching control law to trip generators so as to avoid loss of synchronism. The proposed approach is tested on a model of a real large scale power system and results are compared with a quasioptimal control law designed by a brute force approach for this system.

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Abstmf-This paper proposes a methodology and a practical tool for the study of long-term network planning under uncertainties. In this approach the major external uncertainties during the planning horizon are modeled as macro-scenarios at different future time instants. On the other hand, the random nature of actual operating conditions is taken into account by using a probabilistic model of micro-scenarios based on past statistics. Massive Monte-Carlo simulatiuns are used to generate and simulate a large number of scenarios and store the detailed results in a relational database. Data mining techniques are then applied to extract information from the database so as to rank ~renario~ and network reinforcements according to different criteria.Index Terms-Data Mining, power systems planning, probabilistic methods, random sampling.

Abstract-This paper presents an approach for assessing, in operation planning studies, the security of a large-scale power system by decomposing it into elementary subproblems, each one corresponding to a structural weak-point of the system. We suppose that the structural weak-points are known a priori by the system operators, and are each one described by a set of constraints that are localized in some relatively small area of the system. The security analysis with respect to a given weakpoint thus reduces to the identification of the combinations of power system configurations and disturbances that could lead to the violation of some of its constraints. We propose an iterative rare-event simulation approach for identifying such combinations among the very large set of possible ones. The procedure is illustrated on a simplified version of this problem applied to the Belgian transmission system.