This paper addresses the main challenges to the security constrained optimal power flow (SCOPF) computations. We first discuss the issues related to the SCOPF problem formulation such as the use of a limited number of corrective actions in the post-contingency states and the modeling of voltage and transient stability constraints. Then we deal with the challenges to the techniques for solving the SCOPF, focusing mainly on: approaches to reduce the size of the problem by either efficiently identifying the binding contingencies and including only these contingencies in the SCOPF or by using approximate models for the post-contingency states, and the handling of discrete variables. We finally address the current trend of extending the SCOPF formulation to take into account the increasing levels of uncertainty in the operation planning. For each such topic we provide a review of the state of the art, we identify the advances that are needed, and we indicate ways to bridge the gap between the current state of the art and these needs. * Corresponding author Email addresses: capitane@montefiore.ulg.ac.be (F. Capitanescu), camel@us.es (J.L. Martinez Ramos), patrick.panciatici@rte-france.com (P. Panciatici), kirschen@uw.edu (D. Kirschen), alejandromm@us.es (A. Marano Marcolini), ludovic.platbrood@gdfsuez.com (L. Platbrood), l.wehenkel@ulg.ac.be (L. Wehenkel) Preprint submitted to Electric Power Systems ResearchMay 2, 2011Keywords: mixed integer linear programming, mixed integer nonlinear programming, nonlinear programming, optimal power flow, security constrained optimal power flow MotivationThe SCOPF [1,2] is an extension of the OPF problem [3,4] which takes into account constraints arising from the operation of the system under a set of postulated contingencies. The SCOPF problem is a nonlinear, nonconvex, large-scale optimization problem, with both continuous and discrete variables [1,2]. The SCOPF belongs therefore to the class of optimization problems called Mixed Integer Non-Linear Programming (MINLP).The SCOPF has become an essential tool for many Transmission System Operators (TSOs) for the planning, operational planning, and real time operation of their system [5,6, 7,8]. Furthermore, in several electricity markets (e.g. PJM, New-England, California, etc.) the locational marginal prices calculated using a DC SCOPF are used to price electricity. This approach is also under consideration in other systems [9,10,11].Several papers discussing the challenges to the OPF problem were published during the 90's [5,6, 7,8]. Since then several important changes have taken place not only in power systems operation and control but also in mathematical programming:• Power systems operate today in conditions that are more "stressed" and were not foreseen at the planning stage. In particular the increase in load has not been supported by an adequate upgrade of the generation and transmission systems. Furthermore the creation of electricity markets has led to the trading of significant amounts of electrical energy over lo...

As the amount of distributed generation (DG) is growing worldwide the need to increase the hosting capacity of distribution systems without reinforcements is becoming nowadays a major concern. This paper explores how the DG hosting capacity of active distribution systems can be increased by means of network reconfiguration, both static, i.e., grid reconfiguration at planning stage, and dynamic, i.e., grid reconfiguration using remotely controlled switches as an active network management (ANM) scheme. The problem is formulated as a mixed-integer, nonlinear, multi-period optimal power flow (MP-OPF) which aims to maximize the DG hosting capacity under thermal and voltage constraints. This work further proposes an algorithm to break-down the large problem size when many periods have to be considered. The effectiveness of the approach and the significant benefits obtained by static and dynamic reconfiguration options in terms of DG hosting capacity are demonstrated using a modified benchmark distribution system.

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Abstract-This paper deals with day-ahead security management with respect to a postulated set of contingencies, while taking into account uncertainties about the next day generation/load scenario. In order to help the system operator in decision making under uncertainty, we aim at ranking these contingencies into four clusters according to the type of control actions needed to cover the worst uncertainty pattern of each contingency with respect to branch overload. To this end we use a fixed point algorithm that loops over two main modules: a discrete bi-level program (BLV) that computes the worst-case scenario, and a special kind of security constrained optimal power flow (SCOPF) which computes optimal preventive/corrective actions to cover the worst-case. We rely on a DC grid model, as the large number of binary variables, the large size of the problem, and the stringent computational requirements preclude the use of existing mixed integer nonlinear programming (MINLP) solvers. Consequently we solve the SCOPF using a mixed integer linear programming (MILP) solver while the BLV is decomposed into a series of MILPs. We provide numerical results with our approach on a very large European system model with 9241 buses and 5126 contingencies. Index Terms-Bi-level programming, mixed integer linear programming, operation under uncertainty, optimal power flow, security-constrained optimal power flow, worst-case analysis.

Abstract-This paper focuses on contingency filtering to accelerate the iterative solution of preventive security-constrained optimal power flow (PSCOPF) problems. To this end, we propose two novel filtering techniques relying on the comparison at an intermediate PSCOPF solution of post-contingency constraint violations among postulated contingencies. We assess these techniques by comparing them with severity index-based filtering schemes, on a 60-and a 118-bus system. Our results show that the proposed contingency filtering techniques lead to faster solution of the PSCOPF, while being more robust and meaningful, than severity-index based ones.Index Terms-Contingency filtering, optimal power flow, security-constrained optimal power flow, static security control.