Application of multi-objective grey wolf algorithm on energy management of microgrids with techno-economic and environmental considerations

Abstract

Based on the prospect of the energy international agency, the penetration of renewable energies such as wind and solar will increase in smart electricity grids until 2050. Due to the intermittent nature of these energy forms, the use of a fully renewable-based grid is significantly undependable and may cause some problems in the grid's operation. The most applicable solution to battle with the mentioned problem is the employment of energy storage systems along with volatile generation sources. Thanks to monitoring and control approaches, smart protection systems, and energy storage technologies, it would be possible to connect a large capacity of distributed generation in form of renewable power generation to smart grids. The integration of storage units helps to balance the system while dealing with various sources of uncertainty in power grid. It also helps to decrease the vulnerability of the system against plausible fluctuations in generation or consumption and alleviate the generation cost at peak hours, and it increases the robustness and resiliency of the grid when it has collaborated operation with renewable resources. When the infrastructures of a smart electricity grid are provided, the renewable units can have better coordination with storage units due to being aware of instantaneous prices and condition of the grid, and they can increase their mutual profit. The reason is that they can have better decision making under uncertainty and have better cognition of grid condition, especially when they want to participate in a restructured electricity market. In this article, renewable energy sources and micro-turbines are integrated along, and the compressed air energy storage (CAES) is deployed to redress the imbalances. The proper Planning and precise control of renewable energies in coordination with electrical energy storage units as well as synchronizing them with load fluctuation will be led to the improvement of smart distribution grids' performance. In this study, the multi-objective grey wolf algorithm (MOGWO) is employed to simulate the short-term operation of the smart grid in the 70-bus test system to achieve the desired objectives (reduction in loss, voltage deviation, and pollution).