Active Network Management for Electrical Distribution Systems
نویسندگان
چکیده
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. Résumé Avec la part croissante de production renouvelable et distribuée dans les réseaux électriques de distribution, la gestion active des réseaux de distribution devient une option crédible pour permettre aux gestionnaires de réseaux de distribution d’opérer leurs systèmes électriques. Les stratégies de gestion active sont des politiques de contrôle à court terme qui modulent la puissance injectée par les générateurs et/ou consommée par les charges afin d’éviter des problèmes de congestion ou de tension. Si les stratégies les plus simples se contentent de réduire les excès temporaires de production, d’autres plus complexes visent plutôt à anticiper les périodes de forte production renouvelable pour y déplacer la consommation des charges. De telles stratégies signifient que le gestionnaire de réseau doit résoudre des problèmes de prise de décisions séquentielles sous incertitude et de grande taille. Ces problèmes sont séquentiels pour plusieurs raisons. Par exemple, certaines décisions prises à un instant donné contraignent les décisions qui peuvent être prises dans le futur. Les décisions doivent également être communiquées suffisamment à l’avance aux acteurs du système pour leur laisser le temps de les implémenter. L’incertitude doit être explicitement prise en compte à cause de l’imprécision des prévisions de consommation et de production. Cette dissertation présente des contributions de recherche en gestion active des réseaux électriques de distribution. Ces contributions abordent notamment la motivation du cadre de décisions séquentielles sous incertitude et l’étude des méthodes de calcul qui implémentent les stratégies de gestion active. Une attention particulière est portée sur la formulation du problème, qui est finalement présenté comme un processus de décision markovien. Une approche originale reposant sur un modèle de mélange gaussien est décrite pour représenter l’incertitude. Des méthodes de calcul sont également considérées, en particuliers différentes relaxations et approximations d’extensions multi-périodes et multi-scénarios du problème d’écoulement de puissance optimal avec des variables entières.
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