A novel modified artificial rabbit optimization for stochastic energy management of a grid-connected microgrid: A case study in China

The energy management (EM) solution of the microgrids (MGs) is a crucial task to attain most economic, reliable and sustainable operation state of the MGs. This paper aims to solve the optimal scheduling and stochastic EM problem of a smart MG without and with demand side response (DSR) including MT, FC, PV, WT, and a battery storage system (BSS). A study case in Wenzhou city of China is conducted to reduce the operation cost and maximize the utilization of renewable energy. The system uncertainties like loading, temperature, solar irradiance and wind speed are considered which were obtained from real meteorological data. The normal, lognormal, Weibull PDFs as well as Monte-Carlo and RBS methods are used for uncertainty modelling. A modified artificial rabbit optimization (MARO) is proposed for EM solution based on three strategies including fitness-distance balance, exploitation mechanism of PDO and quasi-opposite-based learning (QOBL) to boost exploration and exploitation phases of traditional ARO. The statistical and non-parametric tests are applied via benchmark functions to validate the performance of MARO. As per the obtained results, the MARO is superior for EM solution compared to other techniques and the operation cost is reduced from 252.0721<euro>ct/day to 184.8435<euro>ct/ day with saving of 26.86 % considerably with application of DSR.