Feasibility study of presenting a dynamic stochastic model based on mixed integer second-order conic programming to solve optimal distribution network reconfiguration in the presence of resources and demand-side management

Nowadays, with the use of devices such as fossil distributed generation and renewable energy resources and energy storage systems that are operated at the level of distribution networks, the problem of optimal reconfiguration has faced major challenges, so any change in the power of this resources can have different results in reconfiguration. Similarly, load changes during the day can lead to different results in reconfiguration, so it is necessary to provide a model that dynamically considers the daily load and models the uncertainties in renewable energy resources. The main issue is the reconfiguration that the distribution system operator must consider in the next 24 hours. This paper presents a mixed-integer second-order cone programming model to solve the problem of stochastic dynamic reconfiguration in distribution networks by including fossil and renewable energy resources, battery, electric vehicle parking lots, and demand-side management programs. The objective is to reduce the cost of load curtailment, reduce the cost of losses, reduce the cost of purchasing energy and reduce the cost of cutting off the power of renewable energy resources. The 33-bus distribution network is presented by defining different cases for the analysis of the proposed model, the results of which show the efficiency of the proposed model.