Active Network Management for Electrical Distribution Systems

With the increasing share of renewable and distributed generation in electrical distribution systems, Active Network Management (ANM) has become 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. While simple ANM strategies consist of curtailing temporary excess generation, more advanced strategies instead attempt to move the consumption of loads to anticipated periods of high renewable generation.
Such advanced strategies mean that the system operator has to solve large-scale optimal sequential decision-making problems under uncertainty. The problems are sequential for several reasons. For example, decisions taken at a given moment constrain the future decisions that can be taken, and decisions should be communicated to the system’s actors sufficiently in advance to give them enough time for implementation. Uncertainty must be explicitly accounted for because neither demand nor generation can be accurately forecasted.
This dissertation presents various research contributions about ANM for distribution systems. These contributions range from the motivation of using a framework of sequential decision-making under uncertainty to the study of computational methods that implement ANM strategies. A particular emphasis is placed on the formulation of the problem, which ultimately falls within the class of Markov decision processes. The modeling of stochasticity is explored and a novel approach that relies on a Gaussian Mixture Model is presented. Computational methods including several relaxations and approximations of multi-period and multi-scenario extensions of the optimal power flow problem with discrete decision variables were considered.