A dynamic optimization approach for preventive control in a DSA environment

Summary form only given. As power systems become more stressed due to limited resources and economic pressure such as competitive market of energy, there is an increasing interest in improving dynamic performances. Power system performances depend upon a large number of decisions and a typical problem is to choose the "best" set of decisions to achieve a particular objective. So the essential optimization problem is to find the set of decisions which minimize the cost function. Both the model building and the optimization phases require large amounts of calculation and these calculations increase dramatically as the order of the problem increases. Starting from an overview of the dynamic optimization in the continuous time domain, this paper aims to show how a new methodology, based on discretized dynamic optimization, can be applied for assessing preventive control actions to guarantee dynamic security of power systems. The idea is to discretize from the very beginning the differential problem and just solve it through nonlinear programming techniques or gradient-based methods used for static optimization problems. The proposed approach entails the ability to force the system trajectories in an acceptable state space domain under a set of severe but credible contingencies and gives indications about preventive actions when necessary. The approach is sufficiently general to improve the transient behavior of power system with regard to different objectives. In the paper, numerical results are provided to show the feasibility of the approach for an actual Italian power grid.