چكيده به لاتين
Abstract:
These days, the energy crisis is one of the most important threats to the future of the world, not only in terms of reducing fossil fuel reserves and dependence on resources of the oil, but also on the environment and pollution of natural resources. Replacing fossil fuels with green fuels and renewable energy can help reduce pollution.
One of the issues that is being considered today is microgrids, microgrids are used to increase the use Therefore, new electricity such as wind, solar, geothermal, nuclear energy, wave energy, biomass, biodiesel and other renewable energies can be used to supply electricity.
of renewable energy potential of the region, reduce environmental pollution and prevent energy loss in the transmission path. Also , given the new attitude of supplying energy demand with regard to regional potentials that can help reduce the cost of generating electricity in a region and provide a more reliable and faster response to demand. Therefore, microgrids can be used to supply energy.
The microgrid designed in this study includes solar cells, wind turbines, gas generators, diesel generators, biodiesel generators and batteries for storing electricity. Due to the uncertainty and probability of energy demand and energy supply by renewable energy in a micro-grid system, possible planning in this research has been used. The model developed in this study is a two-level probabilistic model. The objective function of this model is to minimize the costs of installing and purchasing generators of electricity generation and installed batteries. In this possible model, demand and energy supply are considered as probable and associated with uncertainty, so the results obtained from the model solution In this research, the optimal investment and the number of optimal generators and batteries required on the grid has been obtained. The GAMS problem solving software is also used to solve the developed model in this research.
Keywords:
Energy management, microgrid design, uncertainty analysis, stochastic programming, biodiesel, renewable energy
