Distributed chance-constrained optimal power flow based on primary frequency control

Abstract : We propose a fully distributed algorithm to solve the Chance Constrained Optimal Power Flow (CCOPF), with the advantages of ensuring the privacy and autonomy of the different operators and actors of the system. We present, in this paper, a two-step algorithm that, first, carries out a distributed sensitivity analysis to obtain the generalized generation distribution factors. With these sensitivity factors, the second step solves a distributed CCOPF based on an analytical formulation relying on the Primary Frequency Control (PFC) of generators and on wind farms, whose forecast errors are assumed to be Gaussian. This algorithm allows us to schedule margins and reserves to ensure the security of the system regarding wind farms deviation from forecast with probabilistic guarantees, and to assess the cost of this uncertainty. The proposed method has been implemented and tested on a two-bus test system with one wind farm, and on the IEEE 14-bus test system, with two wind farms. Simulation results showed that the proposed algorithm can efficiently solve the CCOPF in a fully distributed manner.
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Submitted on : Tuesday, November 13, 2018 - 3:59:31 PM
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Maxime Velay, Meritxell Vinyals, Yvon Besanger, Nicolas Rétière. Distributed chance-constrained optimal power flow based on primary frequency control. 9th ACM International Conference on Future Energy Systems (ACM e-Energy), Jun 2018, Karlsruhe, Germany. pp.366-374, ⟨10.1145/3208903.3208921⟩. ⟨cea-01921099⟩



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