Classifying pairs with trees for supervised biological network inference

Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the later for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.Comment: 22 page

Sensitivity-based approaches for handling discrete variables in optimal power flow computations

peer reviewedThis paper proposes and compares three iterative approaches for handling discrete variables in optimal power flow (OPF) computations. The first two approaches rely on the sensitivities of the objective and inequality constraints with respect to discrete variables. They set the discrete variables values either by solving a mixed-integer linear programming (MILP) problem or by using a simple procedure based on a merit function. The third approach relies on the use of Lagrange multipliers corresponding to the discrete variables bound constraints at the OPF solution. The classical round-off technique and a progressive round-off approach have been also used as a basis of comparison. We provide extensive numerical results with these approaches on four test systems with up to 1203 buses, and for two OPF problems: loss minimization and generation cost minimization, respectively. These results show that the sensitivity-based approach combined with the merit function clearly outperforms the other approaches in terms of: objective function quality, reliability, and computational times. Furthermore, the objective value obtained with this approach has been very close to that provided by the continuous relaxation OPF. This approach constitutes therefore a viable alternative to other methods dealing with discrete variables in an OPF

Optimal power flow computations with a limited number of controls allowed to move

This letter focuses on optimal power flow (OPF) computations in which no more than a pre-specified number of controls are allowed to move. To determine an efficient subset of controls satisfying this constraint we rely on the solution of a mixed integer linear programming (MILP) problem fed with sensitivity information of controls' impact on the objective and constraints. We illustrate this approach on a 60-bus system and for the OPF problem of minimum load curtailment cost to remove thermal congestion

Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates

Improving the statement of the corrective security-constrained optimal power flow problem

peer reviewedThis letter proposes a formulation of the corrective security-constrained optimal power-flow problem imposing, in addition to the classical post-contingency constraints, existence and viability constraints on the short-term equilibrium reached just after contingency. The rationale for doing so is discussed and supported by two examples

Redispatching active and reactive powers using a limited number of control actions

peer reviewedThis paper deals with some essential open questions in the field of optimal power flow (OPF) computations, namely: the limitation of the number of controls allowed to move, the trade-off between the objective function and the number of controls allowed to move, the computation of the minimum number of control actions needed to satisfy constraints, and the determination of the sequence of control actions to be taken by the system operator in order to achieve its operation goal. To address these questions, we propose approaches which rely on the computation of sensitivities of the objective function and inequality constraints with respect to control actions. We thus determine a subset of controls allowed to move in the OPF, by solving a sensitivity-based mixed integer linear programming (MILP) problem. We study the performances of these approaches on three test systems (of 60, 118, and 618 buses) and by considering three different OPF problems important for a system operator in emergency and/or in normal states, namely the removal of thermal congestions, the removal of bus voltage limits violation, and the reduction of the active power losses