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

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

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

A new heuristic approach to deal with discrete variables in optimal power flow computations

peer reviewedThis paper proposes a new heuristic approach to deal with discrete variables in an optimal power flow (OPF). This approach relies on the first order sensitivity of the objective and inequality constraints with respect to the discrete variables. The impact of a discrete variable change on the objective and inequality constraints is aggregated into a merit function. The proposed approach searches iteratively for better discrete variable settings as long as the problem solution can be improved. We provide numerical results with the proposed approach on four test systems up to 1203 buses and for the OPF problem of active power loss minimization

Machine Learning in Systems Biology

This supplement contains extended versions of a selected subset of papers presented at the workshop MLSB 2007, Machine Learning in Systems Biology, Evry, France, from September 24 to 25, 2007