Self-learning Control for Active Network Management

Abstract

Active network management (ANM) using power electronic devices will become an essential tool for distribution network operators to deal with the variability of a large number of low-carbon technologies. To enable ANM, this paper proposes a control scheme based on deep reinforcement learning, as an alternative to traditional optimisation. The algorithm uses only a small number of network measurements and can learn approximations of optimal control actions, identified in offline simulations, via a neural network. Once trained, the control scheme chooses power converter set-points that can, for instance, even out loadings on different substations in real-time without the computational burden of high-level optimisation. The performance of the proposed control algorithm is validated against the optimal power flow (OPF) using data from real low-voltage networks. The results show that the solution and benefits are comparable to those obtained by the OPF.