Robust Expansion Planning of Distribution Network in Presence of Electric Vehicles to Improve Economic Parameters and Reduce Energy Consumption

First-world countries and underdeveloped countries share concerns regarding global warming, climate change, and climate migration. Hence, one of the greatest issues faced by governments in the present day is to effectively control the escalating pace of climate change and achieve equilibrium since it directly impacts the future. The fate of the human race hinges on this matter. One of the contributors to the production of pollutants and global warming is the use of cars based on nonrenewable fuel. Consequently, most governments must eliminate fossil fuel vehicles and substitute them with electric vehicles (EV) as part of their short-term and long-term goals. However, the growing adoption of EVs has made it crucial for operators and network designers to strategically plan for electric power consumption, adapt the distribution network structure, and accurately determine the optimal number and positioning of charging stations. Thus, this paper utilizes a convex solution method based on integer linear programming to effectively plan the expansion of the distribution network considering the availability of EV charging stations, compensators, substations, resources, and new lines. This article aims to minimize the long-term expenses associated with constructing network infrastructure, subtransmission substations, EV charging stations, building sources, capacitor banks, and procuring power from the upstream network. The YALMIP toolbox in MATLAB software was utilized to model and solve the objective function of this problem, which is expressed in both deterministic and nondeterministic versions. The MOSEK solver was employed as the problem-solving tool. Three scenarios were implemented to plan the growth of the network on the 18-bus bus system. The results indicate that having EV charging stations in the network can be advantageous as it improves reliability and reduces network costs.