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

Superconducting magnetic energy storage-battery hybrid 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 HESS. However, considering that the relevant circuit parameters would change ambient temperature, interference ageing, effect of MPC may deteriorate inevitably. This article proposes an improved strategy for SMES-Battery HESS, taking moth-flame-optimisation (MFO) algorithm calculate real time. The actual are updated by MFO then sent model predictive controller minimise mismatches. advantages accuracy convergence speed is verified comparison grey wolf optimisation particle swarm optimisation. simulation shows proposed scheme, DC bus more stable superconducting can maintain than 95% capacity utilisation avoid over-discharge even if inconsistent ones circumstances alternating fault fluctuation new output.