Optimal day-ahead economic/emission scheduling of renewable energy resources based microgrid considering demand side management

This research attempts to provide a multi-objective energy management system for optimizing the functioning of micro-grids (MGs) in a short period of time, incorporating the availability of naturally stochastic Renewable Energy Sources (RESs) such as solar and wind energies. All market sectors, including households, enterprises, and industries, can engage in demand response ac-tivities. To aid central micro-grid management in optimizing micro-grid performance as well as addressing energy production uncertainties from renewables, consumers may state their inter-ruptible/curtailable demand rate or choose from a variety of available rates. Schedules for De-mand Response (DR) operations are developed in this study by applying motivation payments that take the shape of predetermined price bundles in addition to DR volume acquired through Demand Response Providers (DRPs). Several power sources, including Wind Turbines (WTs), Solar Panels (PVs), Micro-Turbines (MTs), Full Cells (FCs), an integrated system with battery power, and responsive loads, are employed to establish a standard microgrid. The proposed problem is non-linear and complicated, hence a search algorithm known as the Modified manta ray foraging optimization (MMRFO) algorithm is employed to solve it. Three various case studies are considered here for optimal operation with DR and addressing the uncertainty of WT and PV. The results suggest that where the price of energy is low in the early hours, it is more cost-effective that the storage begins to charge. Instead, in the situation that energy prices are high from 9 to 16, the MG sells its generated power to the main grid. Comparison of the results with the aim of minimizing cost show that considering DR leads to a reduction of WT from 8.50 kW to 7.85 kW and PV system from 4.81 kW to 3.32 kW. In addition, the results of this case study with the aim of minimizing emissions show that considering DR leads to a reduction of WT from 50.21 kW to 47.19 kW and PV system from 91.31 kW to 89.48 kW.

Automated summary

This research aims to optimize the functioning of micro-grids by providing a multi-objective energy management system that incorporates the availability of renewable energy sources and allows for demand response activities. The study develops schedules for demand response operations and employs various power sources to establish a standard microgrid. A modified manta ray foraging optimization algorithm is used to solve the complex problem. The results show that considering demand response leads to a reduction in the power capacity of wind turbines and PV systems.