Economic, environmental, and reliability assessment of distribution network with liquid carbon-based energy storage using multi-objective group teaching optimization algorithm

In this paper, the optimum operation of a distribution network in the presence of renewable resources, and a new combined system of liquid carbon dioxide energy storage is investigated. A comprehensive structure is proposed for modeling and optimizing the combination of wind turbine and solar sources with the new energy storage system by considering their converters. Then, a multipurpose structure in the presence of these resources and the new storage resource is presented to overcome the uncertainty of the output power of renewable resources and improve the reliability of the grid. To this end, first, by considering the probabilistic nature of wind and solar resources, a relatively comprehensive modeling of the system is presented. Economic costs, including the cost of setting up units, cost of generation, and emission of these units, are presented as optimization problem. The effects of the storage system on the load curve are investigated and discussed. The proposed objective function is determined based on optimal load distribution and technical constraints. The multi-objective group teaching optimization algorithm is used to solve the microgrid optimal operation problem. The proposed model consid-ering three cases, is successfully implemented on a 33-bus RBTS distribution network. The results indicated that the total cost of the proposed system in the first, second, and third case is decreased by about 2.88%, 21.78% and 21.2% compared to the base case study. The cost reduction in the first case is due to the use of renewable energy sources, and this reduction in the second case is more due to the use of new storage. Finally, in the third case, due to the consideration of uncertainty and reliability index, compared to the second case, the amount of cost has increased slightly, but compared to the main case, there is a significant decrease, which indicates the superiority of the proposed model.