Moth-flame-optimisation based parameter estimation for model-predictive-controlled superconducting magnetic energy storage-battery hybrid energy storage system

Abstract

Superconducting magnetic energy storage-battery hybrid energy storage system (HESS) has a broad application prospect in balancing direct current (DC) power grid voltage due to its fast dynamic response ability under low-frequency/high-frequency disturbances. Model-predictive-control (MPC) with characteristics such as high sampling rate and wide applicability could be applied to HESS. However, considering that the relevant circuit parameters would change with ambient temperature, interference and ageing, the effect of MPC may deteriorate inevitably. This article proposes an improved MPC strategy for SMES-Battery HESS, taking moth-flame-optimisation (MFO) algorithm to calculate the circuit parameters in real time. The actual parameters are updated by MFO and then sent to model predictive controller to minimise the model mismatches. The advantages of high accuracy and fast convergence speed is verified by comparison with grey wolf optimisation and particle swarm optimisation. The simulation shows that by taking the proposed scheme, DC bus voltage are more stable and the superconducting magnetic energy storage can maintain more than 95% capacity utilisation and avoid over-discharge even if the model parameters are inconsistent with the actual ones under circumstances of alternating current grid fault and fluctuation of new energy output.