1,539 research outputs found
Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution
With the increasing share of renewable and distributed generation in
electrical distribution systems, Active Network Management (ANM) becomes a
valuable option for a distribution system operator to operate his system in a
secure and cost-effective way without relying solely on network reinforcement.
ANM strategies are short-term policies that control the power injected by
generators and/or taken off by loads in order to avoid congestion or voltage
issues. Advanced ANM strategies imply that the system operator has to solve
large-scale optimal sequential decision-making problems under uncertainty. For
example, decisions taken at a given moment constrain the future decisions that
can be taken and uncertainty must be explicitly accounted for because neither
demand nor generation can be accurately forecasted. We first formulate the ANM
problem, which in addition to be sequential and uncertain, has a nonlinear
nature stemming from the power flow equations and a discrete nature arising
from the activation of power modulation signals. This ANM problem is then cast
as a stochastic mixed-integer nonlinear program, as well as second-order cone
and linear counterparts, for which we provide quantitative results using state
of the art solvers and perform a sensitivity analysis over the size of the
system, the amount of available flexibility, and the number of scenarios
considered in the deterministic equivalent of the stochastic program. To foster
further research on this problem, we make available at
http://www.montefiore.ulg.ac.be/~anm/ three test beds based on distribution
networks of 5, 33, and 77 buses. These test beds contain a simulator of the
distribution system, with stochastic models for the generation and consumption
devices, and callbacks to implement and test various ANM strategies
Reinforcement Learning for the Unit Commitment Problem
In this work we solve the day-ahead unit commitment (UC) problem, by
formulating it as a Markov decision process (MDP) and finding a low-cost policy
for generation scheduling. We present two reinforcement learning algorithms,
and devise a third one. We compare our results to previous work that uses
simulated annealing (SA), and show a 27% improvement in operation costs, with
running time of 2.5 minutes (compared to 2.5 hours of existing
state-of-the-art).Comment: Accepted and presented in IEEE PES PowerTech, Eindhoven 2015, paper
ID 46273
Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded
Decision trees usefully represent sparse, high dimensional and noisy data.
Having learned a function from this data, we may want to thereafter integrate
the function into a larger decision-making problem, e.g., for picking the best
chemical process catalyst. We study a large-scale, industrially-relevant
mixed-integer nonlinear nonconvex optimization problem involving both
gradient-boosted trees and penalty functions mitigating risk. This
mixed-integer optimization problem with convex penalty terms broadly applies to
optimizing pre-trained regression tree models. Decision makers may wish to
optimize discrete models to repurpose legacy predictive models, or they may
wish to optimize a discrete model that particularly well-represents a data set.
We develop several heuristic methods to find feasible solutions, and an exact,
branch-and-bound algorithm leveraging structural properties of the
gradient-boosted trees and penalty functions. We computationally test our
methods on concrete mixture design instance and a chemical catalysis industrial
instance
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