A Stochastic Multi-Objective Enhanced Energy Management System in Smart Microgrid Based on Improved Grey Wolf Optimizer and Reduced Unscented Transformation

Mohammad Heydari¹Taher Niknam¹Moslem Dehghani^2*Alvaro Hoffer Garces^2*Mokhtar Aly²Jose Rodriguez²

• ¹Shiraz University of Technology, Shiraz, Iran
• ²Universidad San Sebastian - Campus Bellavista, Santiago, Chile

The aim of this paper is to examine how energy management systems (EMSs) in smart microgrids (MGs) can be achieved with the increased use of renewable energy, energy storage systems (ESSs), and time-of-use tariffs, which introduce variability and uncertainty in the market, supply, and demand. As a result, operators will be able to decrease operation costs and pollutant emissions while improving the flexibility of energy in various conditions. In this regard, the mathematical model for EMS is first developed, which considers the real-time energy pricing, wind turbine (WT), photovoltaic (PV), fuel cell, microturbine, and ESS, as well as the energy sold to the grid by smart MG. Second, a stochastic optimization framework was examined that considers the uncertainty in the system using the reduced unscented transformation layout. Also, by applying the penalty function approach, the balance between generation and consumption, as well as grid limitations, is taken into account. Furthermore, to address the system's uncertainties, the stochastic optimization was used to model load demand, PV, WT, and market price uncertainties. To demonstrate the performance of the proposed framework, the suggested energy management is considered under different case studies, such as with and without ESS, and limitations on exchanged power with the grid. Different optimization algorithms, such as gray wolf optimizer (GWO), improved GWO (IGWO), whale optimization algorithm, particle swarm optimization (PSO), and improved PSO, are applied to solve the suggested stochastic multi-objective optimization problem by using the weighting factor ratios. To solve a stochastic multi-objective problem with grid limitations and ESS, it is shown that IGWO achieves the best rank in finding the best solution equal to 2366.88 with a low standard deviation of 72.43, whereas the best solutions of GWO, WOA, PSO, and IPSO are 2676.76, 2818.56, 2640.87, and 2439.42, respectively with the standard deviations equal to 15083.78, 146046.86, 4352.86, and 403.57. The total cost and emission of the best solution of the stochastic optimization problem for IGWO are 893.93 cents and 736.48 kg, respectively. This shows that the IGWO clearly outperforms GWO, WOA, PSO, and IPSO.