Multi-Objective Random Model to Determine the Type, Capacity and Installation Location of Distributed Products in the New Supply Chain of the Electricity Industry

In recent years, with the high cost of building large and centralized power plants and the problems of long power transmission lines, the electricity industry has shifted to the use of small and distributed generation near the location of customers. On the other hand, due to environmental problems, some of these distributed products are based on renewable energy, which has a random behavior. Determining the location and capacity of these products at the distribution network level has a great impact on managing financial resources and improving supply chain parameters. In this research, a comprehensive multi-objective and probabilistic model is proposed to determine the installation location, type, and optimal capacity of distributed products at the level of the new electricity supply chain. The ultimate goal of this model is to minimize energy losses, investment and operation costs, unsupplied energy, and environmental pollutants. The proposed model is applied on a 33-region network by MATLAB software and solved in a multi-objective way by a genetic meta-heuristic algorithm with faulty sorting. The final results show the effectiveness of the proposed method in various economic, environmental, and social dimensions of the electricity supply chain.