The aim of this article 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 an EMS is first developed, which considers the real-time energy pricing, wind turbines (WTs), photovoltaics (PVs), fuel cells, microturbines, and ESSs, as well as the energy sold to the grid by the smart MG. Second, a stochastic optimization framework was examined that accounts for system uncertainty using the reduced unscented transformation layout. In addition, by applying the penalty function approach, the balance between generation and consumption, as well as grid limitations, is taken into account. To address the system’s uncertainties, the stochastic optimization was used to model load demand, PVs, WTs, 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 grey wolf optimizer (GWO), improved GWO (IGWO), whale optimization algorithm, particle swarm optimization (PSO), and improved PSO (IPSO), 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 an ESS, IGWO achieves the best rank in finding the best solution of 2,366.88 with a low standard deviation of 72.43. In contrast, the best solutions of GWO, WOA, PSO, and IPSO are 2,676.76, 2,818.56, 2,640.87, and 2,439.42, respectively, with standard deviations of 15,083.78, 146,046.86, 4,352.86, and 403.57. The total cost and emission of the best solution of the stochastic optimization problem for IGWO are 893.93 cents/kWh and 736.48 kg/MWh, respectively. This shows that the IGWO clearly outperforms GWO, WOA, PSO, and IPSO. In addition, as shown in simulation results, this model reduces energy prices and environmental pollution, optimizes the MG operations, and demonstrates the effectiveness of IGWO. In addition, different weighting factor ratios are considered to assess the sensitivity of the results to the weighting factor ratios.
Heydari et al. (Mon,) studied this question.
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