2014/05/12 by Quentin Gemine, Gemine, Quentin, Damien Ernst +3 · 1 citation
Engineering · #Smart Grid Energy Management #Optimal Power Flow Distribution #Electric Power System Optimization
paper · pdf · doi:10.48550/arxiv.1405.2806
With the increasing share of renewable and distributed generation in\nelectrical distribution systems, Active Network Management (ANM) becomes a\nvaluable option for a distribution system operator to operate his system in a\nsecure and cost-effective way without relying solely on network reinforcement.\nANM strategies are short-term policies that control the power injected by\ngenerators and/or taken off by loads in order to avoid congestion or voltage\nissues. Advanced ANM strategies imply that the system operator has to solve\nlarge-scale optimal sequential decision-making problems under uncertainty. For\nexample, decisions taken at a given moment constrain the future decisions that\ncan be taken and uncertainty must be explicitly accounted for because neither\ndemand nor generation can be accurately forecasted. We first formulate the ANM\nproblem, which in addition to be sequential and uncertain, has a nonlinear\nnature stemming from the power flow equations and a discrete nature arising\nfrom the activation of power modulation signals. This ANM problem is then cast\nas a stochastic mixed-integer nonlinear program, as well as second-order cone\nand linear counterparts, for which we provide quantitative results using state\nof the art solvers and perform a sensitivity analysis over the size of the\nsystem, the amount of available flexibility, and the number of scenarios\nconsidered in the deterministic equivalent of the stochastic program. To foster\nfurther research on this problem, we make available at\nhttp://www.montefiore.ulg.ac.be/~anm/ three test beds based on distribution\nnetworks of 5, 33, and 77 buses. These test beds contain a simulator of the\ndistribution system, with stochastic models for the generation and consumption\ndevices, and callbacks to implement and test various ANM strategies.\n