Can a PV/wt/BES microgrid optimize a 33-bus network?

In this study, a multi-objective structure for a PV/WT/BES microgrid optimization in a 33-bus network was implemented for minimizing the annual energy losses, to minimize the network bus voltage oscillations, and minimize the cost of purchasing power from the microgrid by the network. The problem is implemented in three scenarios.

How often should a microgrid be optimized?

Using the MLP-ANN technique, this study offers a multi-objective optimization of the microgrid in an electrical network, producing the most accurate predicted layout for each parameter (irradiance, wind speed, ambient temperature, and load demand). It is believed that a pattern that repeats itself once every 24 h is the ideal one.

Does microgrid optimization reduce power losses in a 33-bus network?

As it can be seen, the microgrid optimization in the network to compute the optimum location and size of the equipment has decreased losses and also enhanced its voltage profile. Power losses of the 33-bus network via the MOIKOA for Scenario#1.

Can storage-based Hybrid microgrids improve network performance?

Consequently, without considering the comprehensive forecasted data, the optimization and detailed planning of storage-based hybrid microgrids fail to inform the network planning of the logical capacities of storage to enhance the network's performance by better compensating for fluctuations in renewable energy sources' power.

Does microgrid multi-objective optimization increase energy costs?

The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization.

Can multi-objective optimization improve PV/wt microgrid efficiency?

Robust multi-objective optimizing the PV/WT microgrid system incorporating multi-energy storage is suggested for future work using information gap decision theory considering efficiency, and reliability of hybrid microgrids and incorporating the adaptive real-time optimization.